From de6200e7f58b616d6169cc35946e85323da66053 Mon Sep 17 00:00:00 2001 From: eqy Date: Sun, 15 Apr 2018 23:52:04 -0700 Subject: [PATCH 001/816] fix command line example package path --- tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md b/tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md index 495014c6fc..f8327daa08 100644 --- a/tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md +++ b/tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md @@ -41,7 +41,7 @@ FlatBuffer to perform floating-point inference. ``` bazel run --config=opt \ - third_party/tensorflow/contrib/lite/toco:toco -- \ + //tensorflow/contrib/lite/toco:toco -- \ --savedmodel_directory=/tmp/saved_model \ --output_file=/tmp/foo.tflite ``` -- GitLab From cd2ba0c063ffd89f0310a6ab6482a5607e590cb1 Mon Sep 17 00:00:00 2001 From: Dan Osipov Date: Sun, 18 Mar 2018 18:50:34 -0700 Subject: [PATCH 002/816] Document additional argument --- tensorflow/python/ops/image_ops_impl.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/python/ops/image_ops_impl.py b/tensorflow/python/ops/image_ops_impl.py index 8524c08f81..cee948fe43 100644 --- a/tensorflow/python/ops/image_ops_impl.py +++ b/tensorflow/python/ops/image_ops_impl.py @@ -984,6 +984,7 @@ def resize_image_aspect_with_pad(image, target_height, target_width, 3-D Tensor of shape `[height, width, channels]`. target_height: Target height. target_width: Target width. + method: Method to use for resizing image. See `resize_images()` Raises: ValueError: if `target_height` or `target_width` are zero or negative. -- GitLab From 96dc82647d0eb5d1903242c2dde1cf9dd5bb36f0 Mon Sep 17 00:00:00 2001 From: Dan Osipov Date: Sat, 28 Apr 2018 08:28:33 -0700 Subject: [PATCH 003/816] Rename API method --- tensorflow/python/ops/image_ops.py | 2 +- tensorflow/python/ops/image_ops_impl.py | 6 +++--- tensorflow/python/ops/image_ops_test.py | 12 ++++++------ 3 files changed, 10 insertions(+), 10 deletions(-) diff --git a/tensorflow/python/ops/image_ops.py b/tensorflow/python/ops/image_ops.py index f11b6dcea6..091ec61b1f 100644 --- a/tensorflow/python/ops/image_ops.py +++ b/tensorflow/python/ops/image_ops.py @@ -36,7 +36,7 @@ See the @{$python/image} guide. @@resize_bilinear @@resize_nearest_neighbor @@resize_image_with_crop_or_pad -@@resize_image_aspect_with_pad +@@resize_image_with_pad @@central_crop @@pad_to_bounding_box @@crop_to_bounding_box diff --git a/tensorflow/python/ops/image_ops_impl.py b/tensorflow/python/ops/image_ops_impl.py index cee948fe43..5fe0b7a251 100644 --- a/tensorflow/python/ops/image_ops_impl.py +++ b/tensorflow/python/ops/image_ops_impl.py @@ -969,8 +969,8 @@ def resize_images(images, return images -@tf_export('image.resize_image_aspect_with_pad') -def resize_image_aspect_with_pad(image, target_height, target_width, +@tf_export('image.resize_image_with_pad') +def resize_image_with_pad(image, target_height, target_width, method=ResizeMethod.BILINEAR): """ Resizes and pads an image to a target width and height. @@ -996,7 +996,7 @@ def resize_image_aspect_with_pad(image, target_height, target_width, If `images` was 3-D, a 3-D float Tensor of shape `[new_height, new_width, channels]`. """ - with ops.name_scope(None, 'resize_image_aspect_with_pad', [image]): + with ops.name_scope(None, 'resize_image_with_pad', [image]): image = ops.convert_to_tensor(image, name='image') image_shape = image.get_shape() is_batch = True diff --git a/tensorflow/python/ops/image_ops_test.py b/tensorflow/python/ops/image_ops_test.py index 40a4d175ac..22d9ce4289 100644 --- a/tensorflow/python/ops/image_ops_test.py +++ b/tensorflow/python/ops/image_ops_test.py @@ -2458,9 +2458,9 @@ class ResizeImagesTest(test_util.TensorFlowTestCase): self.assertTrue(y.op.name.startswith("resize_images")) -class ResizeImageAspectWithPadTest(test_util.TensorFlowTestCase): +class ResizeImageWithPadTest(test_util.TensorFlowTestCase): - def _ResizeImageAspectWithPad(self, x, target_height, target_width, + def _ResizeImageWithPad(self, x, target_height, target_width, use_tensor_inputs): if use_tensor_inputs: target_height = ops.convert_to_tensor(target_height) @@ -2471,7 +2471,7 @@ class ResizeImageAspectWithPadTest(test_util.TensorFlowTestCase): x_tensor = x feed_dict = {} - y = image_ops.resize_image_aspect_with_pad(x_tensor, target_height, + y = image_ops.resize_image_with_pad(x_tensor, target_height, target_width) if not use_tensor_inputs: self.assertTrue(y.get_shape().is_fully_defined()) @@ -2491,7 +2491,7 @@ class ResizeImageAspectWithPadTest(test_util.TensorFlowTestCase): y = np.array(y).reshape(y_shape) for use_tensor_inputs in use_tensor_inputs_options: - y_tf = self._ResizeImageAspectWithPad(x, target_height, target_width, + y_tf = self._ResizeImageWithPad(x, target_height, target_width, use_tensor_inputs) self.assertAllClose(y, y_tf) @@ -2507,7 +2507,7 @@ class ResizeImageAspectWithPadTest(test_util.TensorFlowTestCase): for use_tensor_inputs in use_tensor_inputs_options: try: - self._ResizeImageAspectWithPad(x, target_height, target_width, + self._ResizeImageWithPad(x, target_height, target_width, use_tensor_inputs) except Exception as e: if err_msg not in str(e): @@ -2517,7 +2517,7 @@ class ResizeImageAspectWithPadTest(test_util.TensorFlowTestCase): def _assertShapeInference(self, pre_shape, height, width, post_shape): image = array_ops.placeholder(dtypes.float32, shape=pre_shape) - y = image_ops.resize_image_aspect_with_pad(image, height, width) + y = image_ops.resize_image_with_pad(image, height, width) self.assertEqual(y.get_shape().as_list(), post_shape) def testNoOp(self): -- GitLab From 533cb5caa4c88d3f76e1994e8f039ea04d342482 Mon Sep 17 00:00:00 2001 From: Dan Osipov Date: Sat, 28 Apr 2018 08:30:56 -0700 Subject: [PATCH 004/816] Remove assertions --- tensorflow/python/ops/image_ops_impl.py | 10 ---------- 1 file changed, 10 deletions(-) diff --git a/tensorflow/python/ops/image_ops_impl.py b/tensorflow/python/ops/image_ops_impl.py index 5fe0b7a251..e174feedb5 100644 --- a/tensorflow/python/ops/image_ops_impl.py +++ b/tensorflow/python/ops/image_ops_impl.py @@ -1066,16 +1066,6 @@ def resize_image_with_pad(image, target_height, target_width, _, resized_height, resized_width, _ = _ImageDimensions(resized, rank=4) - assert_ops = [] - assert_ops += _assert( - equal_(resized_height, target_height), ValueError, - 'resized height is not correct.') - assert_ops += _assert( - equal_(resized_width, target_width), ValueError, - 'resized width is not correct.') - - resized = control_flow_ops.with_dependencies(assert_ops, resized) - if not is_batch: resized = array_ops.squeeze(resized, squeeze_dims=[0]) -- GitLab From 764ea231d9b649ad167fd1ffd4f4c5c4e79642c7 Mon Sep 17 00:00:00 2001 From: Dan Osipov Date: Sat, 28 Apr 2018 08:32:36 -0700 Subject: [PATCH 005/816] Update docstring --- tensorflow/python/ops/image_ops_impl.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/tensorflow/python/ops/image_ops_impl.py b/tensorflow/python/ops/image_ops_impl.py index e174feedb5..d5ac72bac6 100644 --- a/tensorflow/python/ops/image_ops_impl.py +++ b/tensorflow/python/ops/image_ops_impl.py @@ -976,8 +976,9 @@ def resize_image_with_pad(image, target_height, target_width, Resizes and pads an image to a target width and height. Resizes an image to a target width and height by keeping - the aspect ratio the same without distortion and padding - it evenly with zeros. + the aspect ratio the same without distortion. If the target + dimensions don't match the image dimensions, the image + is padded with zeroes prior to resizing. Args: image: 4-D Tensor of shape `[batch, height, width, channels]` or -- GitLab From 74171d402a52074806bc5f0d1a3ddae92212214f Mon Sep 17 00:00:00 2001 From: Dan Osipov Date: Tue, 8 May 2018 14:24:32 -0700 Subject: [PATCH 006/816] Fix bad merge --- tensorflow/python/ops/image_ops.py | 61 ------------------------------ 1 file changed, 61 deletions(-) diff --git a/tensorflow/python/ops/image_ops.py b/tensorflow/python/ops/image_ops.py index 091ec61b1f..343531ac55 100644 --- a/tensorflow/python/ops/image_ops.py +++ b/tensorflow/python/ops/image_ops.py @@ -17,67 +17,6 @@ """Image processing and decoding ops. See the @{$python/image} guide. -<<<<<<< HEAD -======= - -@@decode_bmp -@@decode_gif -@@decode_jpeg -@@decode_and_crop_jpeg -@@encode_jpeg -@@extract_jpeg_shape -@@decode_png -@@encode_png -@@is_jpeg -@@decode_image -@@resize_images -@@resize_area -@@resize_bicubic -@@resize_bilinear -@@resize_nearest_neighbor -@@resize_image_with_crop_or_pad -@@resize_image_with_pad -@@central_crop -@@pad_to_bounding_box -@@crop_to_bounding_box -@@extract_glimpse -@@crop_and_resize -@@flip_up_down -@@random_flip_up_down -@@flip_left_right -@@random_flip_left_right -@@transpose_image -@@rot90 - -@@rgb_to_grayscale -@@grayscale_to_rgb -@@hsv_to_rgb -@@rgb_to_hsv -@@rgb_to_yiq -@@yiq_to_rgb -@@rgb_to_yuv -@@yuv_to_rgb -@@convert_image_dtype -@@adjust_brightness -@@random_brightness -@@adjust_contrast -@@random_contrast -@@adjust_hue -@@random_hue -@@adjust_gamma -@@adjust_saturation -@@random_saturation -@@per_image_standardization -@@draw_bounding_boxes -@@non_max_suppression -@@sample_distorted_bounding_box -@@total_variation -@@psnr -@@ssim -@@ssim_multiscale -@@image_gradients -@@sobel_edges ->>>>>>> 88687fa... Add resize_image_aspect_with_pad method """ from __future__ import absolute_import from __future__ import division -- GitLab From 5e6b20e53720e8d00619d851ce983f8da77c5cf4 Mon Sep 17 00:00:00 2001 From: Soila Kavulya Date: Tue, 8 May 2018 14:54:53 -0700 Subject: [PATCH 007/816] Deploy TensorFlow ecosystem jars --- tensorflow/java/maven/pom.xml | 10 +- tensorflow/java/maven/release.sh | 1 + tensorflow/java/maven/run_inside_container.sh | 42 ++++- .../pom-spark.xml.template | 19 +++ .../spark-tensorflow-connector/update.py | 152 ++++++++++++++++++ .../tensorflow-hadoop/pom-hadoop.xml.template | 18 +++ .../java/maven/tensorflow-hadoop/update.py | 114 +++++++++++++ 7 files changed, 352 insertions(+), 4 deletions(-) create mode 100644 tensorflow/java/maven/spark-tensorflow-connector/pom-spark.xml.template create mode 100644 tensorflow/java/maven/spark-tensorflow-connector/update.py create mode 100644 tensorflow/java/maven/tensorflow-hadoop/pom-hadoop.xml.template create mode 100644 tensorflow/java/maven/tensorflow-hadoop/update.py diff --git a/tensorflow/java/maven/pom.xml b/tensorflow/java/maven/pom.xml index 0a09a5ea7c..21fed5a419 100644 --- a/tensorflow/java/maven/pom.xml +++ b/tensorflow/java/maven/pom.xml @@ -6,7 +6,7 @@ 4.0.0 org.tensorflow parentpom - 1.8.0 + 1.8.0-SNAPSHOT pom https://www.tensorflow.org @@ -32,6 +32,8 @@ libtensorflow_jni_gpu tensorflow proto + tensorflow-hadoop + spark-tensorflow-connector ossrh - https://oss.sonatype.org/content/repositories/snapshots + https://tap.jfrog.io/tap/public-snapshots + ossrh @@ -74,6 +77,7 @@ + diff --git a/tensorflow/java/maven/release.sh b/tensorflow/java/maven/release.sh index 9012ea14ea..6c51029198 100755 --- a/tensorflow/java/maven/release.sh +++ b/tensorflow/java/maven/release.sh @@ -48,6 +48,7 @@ fi set -ex docker run \ + $DOCKER_PROXY_RUN_ARGS \ -e TF_VERSION="${TF_VERSION}" \ -e DEPLOY_OSSRH="${DEPLOY_OSSRH:-true}" \ -e DEPLOY_BINTRAY="${DEPLOY_BINTRAY:-true}" \ diff --git a/tensorflow/java/maven/run_inside_container.sh b/tensorflow/java/maven/run_inside_container.sh index 6136ccfdfb..73f7ee94a0 100644 --- a/tensorflow/java/maven/run_inside_container.sh +++ b/tensorflow/java/maven/run_inside_container.sh @@ -32,11 +32,15 @@ if [[ "${TF_VERSION}" == *"-SNAPSHOT" ]]; then DEPLOY_BINTRAY="false" fi PROTOC_RELEASE_URL="https://github.com/google/protobuf/releases/download/v3.3.0/protoc-3.3.0-linux-x86_64.zip" +TF_ECOSYSTEM_URL="https://github.com/tensorflow/ecosystem.git" + if [[ "${DEPLOY_BINTRAY}" != "true" && "${DEPLOY_OSSRH}" != "true" ]]; then echo "Must deploy to at least one of Bintray or OSSRH" >&2 exit 2 fi +IS_SNAPSHOT="true" + set -ex clean() { @@ -183,6 +187,41 @@ generate_java_protos() { rm -rf "${DIR}/proto/tmp" } + +download_tf_ecosystem() { + ECOSYSTEM_DIR="/tmp/tensorflow-ecosystem" + HADOOP_DIR="${DIR}/tensorflow-hadoop" + SPARK_DIR="${DIR}/spark-tensorflow-connector" + + # Clean any previous attempts + rm -rf "${ECOSYSTEM_DIR}" + + # Clone the TensorFlow ecosystem project + mkdir -p "${ECOSYSTEM_DIR}" + cd "${ECOSYSTEM_DIR}" + git clone "${TF_ECOSYSTEM_URL}" + + # Copy the TensorFlow Hadoop source + cp -r "${ECOSYSTEM_DIR}/ecosystem/hadoop/src" "${HADOOP_DIR}" + python ${HADOOP_DIR}/update.py --template ${HADOOP_DIR}/pom-hadoop.xml.template \ + --input_pom ${ECOSYSTEM_DIR}/ecosystem/hadoop/pom.xml \ + --output_pom ${HADOOP_DIR}/pom.xml \ + --version ${TF_VERSION} + + # Copy the TensorFlow Spark connector source + cp -r "${ECOSYSTEM_DIR}/ecosystem/spark/spark-tensorflow-connector/src" "${SPARK_DIR}" + python ${SPARK_DIR}/update.py --template ${SPARK_DIR}/pom-spark.xml.template \ + --input_pom ${ECOSYSTEM_DIR}/ecosystem/spark/spark-tensorflow-connector/pom.xml \ + --output_pom ${SPARK_DIR}/pom.xml \ + --version ${TF_VERSION} \ + --scala_version 2.11 + + # Cleanup + rm -rf "${ECOSYSTEM_DIR}" + + cd "${DIR}" +} + # Deploy artifacts using a specific profile. # Arguments: # profile - name of selected profile. @@ -240,7 +279,7 @@ cd "${DIR}" # Comment lines out appropriately if debugging/tinkering with the release # process. # gnupg2 is required for signing -apt-get -qq update && apt-get -qqq install -y gnupg2 +apt-get -qq update && apt-get -qqq install -y gnupg2 && apt-get -qqq install -y git clean update_version_in_pom download_libtensorflow @@ -248,6 +287,7 @@ download_libtensorflow_jni download_libtensorflow_jni_gpu update_tensorflow_android generate_java_protos +download_tf_ecosystem # Build the release artifacts mvn verify # Push artifacts to repository diff --git a/tensorflow/java/maven/spark-tensorflow-connector/pom-spark.xml.template b/tensorflow/java/maven/spark-tensorflow-connector/pom-spark.xml.template new file mode 100644 index 0000000000..d8a3d559be --- /dev/null +++ b/tensorflow/java/maven/spark-tensorflow-connector/pom-spark.xml.template @@ -0,0 +1,19 @@ + + 4.0.0 + TensorFlow TFRecord connector for Apache Spark DataFrames + spark-tensorflow-connector_${scala_version} + ${version} + jar + + https://github.com/tensorflow/ecosystem/ + + org.tensorflow + parentpom + ${version} + ../ + + + diff --git a/tensorflow/java/maven/spark-tensorflow-connector/update.py b/tensorflow/java/maven/spark-tensorflow-connector/update.py new file mode 100644 index 0000000000..6185ccbb00 --- /dev/null +++ b/tensorflow/java/maven/spark-tensorflow-connector/update.py @@ -0,0 +1,152 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Merge TensorFlow Spark connector pom from with deployment template. + +The TensorFlow Spark connector pom is here: https://github.com/tensorflow/ecosystem/tree/master/spark/spark-tensorflow-connector +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import argparse +import sys +import string +import xml.etree.ElementTree as ET + +POM_NAMESPACE = "http://maven.apache.org/POM/4.0.0" +SCALA_VERSION_TAG = "scala.binary.version" + + +def get_args(): + """Parse command line args.""" + parser = argparse.ArgumentParser() + parser.add_argument( + '--version', + required=True, + help='Version for the artifact.') + parser.add_argument( + '--scala_version', + required=True, + choices=['2.10', '2.11'], + help='Scala version for the artifact.') + parser.add_argument( + '--template', + required=True, + help='Path to the pom file template.') + parser.add_argument( + '--input_pom', + required=True, + help='Path to input pom file to merge with template.') + parser.add_argument( + '--output_pom', + required=True, + help='Path to output pom file.') + return parser.parse_args() + + +def load_pom(input_path): + """ Loads POM file to XML tree""" + ET.register_namespace("", POM_NAMESPACE) + tree = ET.parse(input_path) + return tree + + +def update_scala_version(tree, version, is_template=False): + """ Updates scala version in XML tree""" + + if is_template: + tag = "{%s}artifactId" % POM_NAMESPACE + nodes = tree.findall(tag) + + if nodes is None: + raise ValueError("Missing artifactId in template pom") + + for node in nodes: + template = string.Template(node.text) + + text = template.substitute({"scala_version": version}) + node.text = text + else: + # Update scala version property in pom + tag = "{%s}%s" % (POM_NAMESPACE, SCALA_VERSION_TAG) + nodes = nodes = list(tree.iter(tag)) + + if len(nodes) == 0: + raise ValueError("Missing %s property in Spark connector pom") + + for node in nodes: + node.text = version + + return tree + + +def update_version(tree, version): + """ Updates version tags in XML tree """ + version_tag = "{%s}version" % POM_NAMESPACE + nodes = list(tree.iter(version_tag)) + + if len(nodes) == 0: + raise ValueError("Missing version in template pom") + + for node in nodes: + node.text = version + + return tree + + +def merge_tags(template_root, pom_root): + """ Merge pom file from TensorFlow Spark connector with deployment template. + + Modify the TensorFlow Spark connector pom to inherit parent pom and version info and + other tags provided by deployment template. + + TODO: Figure out if there is a cleaner way of doing this. Inheritance is needed + for propagating the deployment profile. + + Args: + template_root: Root XML element for template file. + pom_root: Root XML element for TensorFlow Spark connector pom file. + + Return: + template_root: Root XML element with merged tree. + """ + template_tags = [child.tag for child in template_root] + template_tags.append("{%s}groupId" % POM_NAMESPACE) # skip groupId since it is inherited from parent + + for child in pom_root: + if child.tag not in template_tags: + template_root.append(child) + + return template_root + + +def main(): + args = get_args() + template_tree = load_pom(args.template) + pom_tree = load_pom(args.input_pom) + + template_tree = update_version(template_tree, args.version) + template_tree = update_scala_version(template_tree, args.scala_version, is_template=True) + pom_tree = update_scala_version(pom_tree, args.scala_version, is_template=False) + template_root = merge_tags(template_tree.getroot(), pom_tree.getroot()) + + with open(args.output_pom, "w") as f: + f.write(ET.tostring(template_root)) + + +if __name__ == '__main__': + sys.exit(main()) diff --git a/tensorflow/java/maven/tensorflow-hadoop/pom-hadoop.xml.template b/tensorflow/java/maven/tensorflow-hadoop/pom-hadoop.xml.template new file mode 100644 index 0000000000..6a82c56cc7 --- /dev/null +++ b/tensorflow/java/maven/tensorflow-hadoop/pom-hadoop.xml.template @@ -0,0 +1,18 @@ + + 4.0.0 + TensorFlow TFRecord InputFormat/OutputFormat for Apache Hadoop + tensorflow-hadoop + ${version} + jar + + https://github.com/tensorflow/ecosystem/ + + org.tensorflow + parentpom + ${version} + ../ + + diff --git a/tensorflow/java/maven/tensorflow-hadoop/update.py b/tensorflow/java/maven/tensorflow-hadoop/update.py new file mode 100644 index 0000000000..503062608d --- /dev/null +++ b/tensorflow/java/maven/tensorflow-hadoop/update.py @@ -0,0 +1,114 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Merge TensorFlow Hadoop pom from with deployment template. + +The TensorFlow Hadoop pom is here: https://github.com/tensorflow/ecosystem/tree/master/hadoop +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import argparse +import sys +import xml.etree.ElementTree as ET + +POM_NAMESPACE = "http://maven.apache.org/POM/4.0.0" + + +def get_args(): + """Parse command line args.""" + parser = argparse.ArgumentParser() + parser.add_argument( + '--version', + required=True, + help='Version for the artifact.') + parser.add_argument( + '--template', + required=True, + help='Path to the pom file template.') + parser.add_argument( + '--input_pom', + required=True, + help='Path to input pom file to merge with template.') + parser.add_argument( + '--output_pom', + required=True, + help='Path to output pom file.') + return parser.parse_args() + + +def load_pom(input_path): + """ Loads POM file to XML tree""" + ET.register_namespace("", POM_NAMESPACE) + tree = ET.parse(input_path) + return tree + + +def update_version(tree, version): + """ Updates version tags in XML tree """ + version_tag = "{%s}version" % POM_NAMESPACE + nodes = list(tree.iter(version_tag)) + + if len(nodes) == 0: + raise ValueError("Missing version in template pom") + + for node in nodes: + node.text = version + + return tree + + +def merge_tags(template_root, pom_root): + """ Merge pom file from TensorFlow Hadoop with deployment template. + + Modify the TensorFlow Hadoop pom to inherit parent pom and version info and + other tags provided by deployment template. + + TODO: Figure out if there is a cleaner way of doing this. Inheritance is needed + for propagating the deployment profile. + + Args: + template_root: Root XML element for template file. + pom_root: Root XML element for TensorFlow Hadoop pom file. + + Return: + template_root: Root XML element with merged tree. + """ + template_tags = [child.tag for child in template_root] + template_tags.append("{%s}groupId" % POM_NAMESPACE) # skip groupId since it is inherited from parent + + for child in pom_root: + if child.tag not in template_tags: + template_root.append(child) + + return template_root + + +def main(): + args = get_args() + template_tree = load_pom(args.template) + pom_tree = load_pom(args.input_pom) + + template_tree = update_version(template_tree, args.version) + template_root = merge_tags(template_tree.getroot(), pom_tree.getroot()) + + with open(args.output_pom, "w") as f: + f.write(ET.tostring(template_root)) + + +if __name__ == '__main__': + sys.exit(main()) -- GitLab From f957cfbc4d27a57bf08d128b41042a16f1155ab0 Mon Sep 17 00:00:00 2001 From: Soila Kavulya Date: Tue, 8 May 2018 18:40:20 -0700 Subject: [PATCH 008/816] Add TensorFlow ecosystem Spark and Hadoop jars to Maven deployment --- tensorflow/java/maven/README.md | 6 +++++ tensorflow/java/maven/pom.xml | 8 +++--- tensorflow/java/maven/release.sh | 1 - tensorflow/java/maven/run_inside_container.sh | 26 ++++++++++--------- .../maven/spark-tensorflow-connector/pom.xml | 24 +++++++++++++++++ .../java/maven/tensorflow-hadoop/pom.xml | 24 +++++++++++++++++ 6 files changed, 71 insertions(+), 18 deletions(-) create mode 100644 tensorflow/java/maven/spark-tensorflow-connector/pom.xml create mode 100644 tensorflow/java/maven/tensorflow-hadoop/pom.xml diff --git a/tensorflow/java/maven/README.md b/tensorflow/java/maven/README.md index c7e8f03806..fa756815a9 100644 --- a/tensorflow/java/maven/README.md +++ b/tensorflow/java/maven/README.md @@ -53,6 +53,12 @@ There are seven artifacts and thus `pom.xml`s involved in this release: 7. [`parentpom`](https://maven.apache.org/pom/index.html): Common settings shared by all of the above. +8. `tensorflow-hadoop`: The TensorFlow TFRecord InputFormat/OutputFormat for Apache Hadoop. + The source code for this package is available in the [TensorFlow Ecosystem](https://github.com/tensorflow/ecosystem/tree/master/hadoop) + +9. `spark-tensorflow-connector`: A Scala library for loading and storing TensorFlow TFRecord + using Apache Spark DataFrames. The source code for this package is available + in the [TensorFlow Ecosystem](https://github.com/tensorflow/ecosystem/tree/master/spark/spark-tensorflow-connector) ## Updating the release diff --git a/tensorflow/java/maven/pom.xml b/tensorflow/java/maven/pom.xml index 21fed5a419..7a95fb2556 100644 --- a/tensorflow/java/maven/pom.xml +++ b/tensorflow/java/maven/pom.xml @@ -6,7 +6,7 @@ 4.0.0 org.tensorflow parentpom - 1.8.0-SNAPSHOT + 1.8.0 pom https://www.tensorflow.org @@ -46,8 +46,7 @@ ossrh - https://tap.jfrog.io/tap/public-snapshots - + https://oss.sonatype.org/content/repositories/snapshots ossrh @@ -77,7 +76,6 @@ - + diff --git a/tensorflow/java/maven/release.sh b/tensorflow/java/maven/release.sh index 6c51029198..9012ea14ea 100755 --- a/tensorflow/java/maven/release.sh +++ b/tensorflow/java/maven/release.sh @@ -48,7 +48,6 @@ fi set -ex docker run \ - $DOCKER_PROXY_RUN_ARGS \ -e TF_VERSION="${TF_VERSION}" \ -e DEPLOY_OSSRH="${DEPLOY_OSSRH:-true}" \ -e DEPLOY_BINTRAY="${DEPLOY_BINTRAY:-true}" \ diff --git a/tensorflow/java/maven/run_inside_container.sh b/tensorflow/java/maven/run_inside_container.sh index 73f7ee94a0..3808104bc1 100644 --- a/tensorflow/java/maven/run_inside_container.sh +++ b/tensorflow/java/maven/run_inside_container.sh @@ -39,8 +39,6 @@ if [[ "${DEPLOY_BINTRAY}" != "true" && "${DEPLOY_OSSRH}" != "true" ]]; then exit 2 fi -IS_SNAPSHOT="true" - set -ex clean() { @@ -48,7 +46,9 @@ clean() { # (though if run inside a clean docker container, there won't be any dirty # artifacts lying around) mvn -q clean - rm -rf libtensorflow_jni/src libtensorflow_jni/target libtensorflow_jni_gpu/src libtensorflow_jni_gpu/target libtensorflow/src libtensorflow/target tensorflow-android/target + rm -rf libtensorflow_jni/src libtensorflow_jni/target libtensorflow_jni_gpu/src libtensorflow_jni_gpu/target \ + libtensorflow/src libtensorflow/target tensorflow-android/target \ + tensorflow-hadoop/src spark-tensorflow-connector/src } update_version_in_pom() { @@ -188,6 +188,9 @@ generate_java_protos() { } +# Download the TensorFlow ecosystem source from git. +# The pom files from this repo do not inherit from the parent pom so the maven version +# is updated for each module. download_tf_ecosystem() { ECOSYSTEM_DIR="/tmp/tensorflow-ecosystem" HADOOP_DIR="${DIR}/tensorflow-hadoop" @@ -203,18 +206,15 @@ download_tf_ecosystem() { # Copy the TensorFlow Hadoop source cp -r "${ECOSYSTEM_DIR}/ecosystem/hadoop/src" "${HADOOP_DIR}" - python ${HADOOP_DIR}/update.py --template ${HADOOP_DIR}/pom-hadoop.xml.template \ - --input_pom ${ECOSYSTEM_DIR}/ecosystem/hadoop/pom.xml \ - --output_pom ${HADOOP_DIR}/pom.xml \ - --version ${TF_VERSION} + cp "${ECOSYSTEM_DIR}/ecosystem/hadoop/pom.xml" "${HADOOP_DIR}" + cd "${HADOOP_DIR}" + update_version_in_pom # Copy the TensorFlow Spark connector source cp -r "${ECOSYSTEM_DIR}/ecosystem/spark/spark-tensorflow-connector/src" "${SPARK_DIR}" - python ${SPARK_DIR}/update.py --template ${SPARK_DIR}/pom-spark.xml.template \ - --input_pom ${ECOSYSTEM_DIR}/ecosystem/spark/spark-tensorflow-connector/pom.xml \ - --output_pom ${SPARK_DIR}/pom.xml \ - --version ${TF_VERSION} \ - --scala_version 2.11 + cp "${ECOSYSTEM_DIR}/ecosystem/spark/spark-tensorflow-connector/pom.xml" "${SPARK_DIR}" + cd "${SPARK_DIR}" + update_version_in_pom # Cleanup rm -rf "${ECOSYSTEM_DIR}" @@ -280,6 +280,7 @@ cd "${DIR}" # process. # gnupg2 is required for signing apt-get -qq update && apt-get -qqq install -y gnupg2 && apt-get -qqq install -y git + clean update_version_in_pom download_libtensorflow @@ -288,6 +289,7 @@ download_libtensorflow_jni_gpu update_tensorflow_android generate_java_protos download_tf_ecosystem + # Build the release artifacts mvn verify # Push artifacts to repository diff --git a/tensorflow/java/maven/spark-tensorflow-connector/pom.xml b/tensorflow/java/maven/spark-tensorflow-connector/pom.xml new file mode 100644 index 0000000000..8c962d111f --- /dev/null +++ b/tensorflow/java/maven/spark-tensorflow-connector/pom.xml @@ -0,0 +1,24 @@ + + + 4.0.0 + TensorFlow TFRecord connector for Apache Spark DataFrames + spark-tensorflow-connector + jar + + + https://github.com/tensorflow/ecosystem.git + git@github.com:tensorflow/ecosystem.git + scm:git:https://github.com/tensorflow/ecosystem.git + + + https://github.com/tensorflow/ecosystem/ + + org.tensorflow + parentpom + 1.8.0 + ../ + + diff --git a/tensorflow/java/maven/tensorflow-hadoop/pom.xml b/tensorflow/java/maven/tensorflow-hadoop/pom.xml new file mode 100644 index 0000000000..ee90d8c92b --- /dev/null +++ b/tensorflow/java/maven/tensorflow-hadoop/pom.xml @@ -0,0 +1,24 @@ + + + 4.0.0 + TensorFlow TFRecord InputFormat/OutputFormat for Apache Hadoop + tensorflow-hadoop + jar + + + https://github.com/tensorflow/ecosystem.git + git@github.com:tensorflow/ecosystem.git + scm:git:https://github.com/tensorflow/ecosystem.git + + + https://github.com/tensorflow/ecosystem/ + + org.tensorflow + parentpom + 1.8.0 + ../ + + -- GitLab From 90b01f238d83d833ce9a843845dd96bb816a6c76 Mon Sep 17 00:00:00 2001 From: Soila Kavulya Date: Tue, 8 May 2018 18:46:35 -0700 Subject: [PATCH 009/816] Delete templating approach for deploying TensorFlow ecosystem jars --- .../pom-spark.xml.template | 19 --- .../spark-tensorflow-connector/update.py | 152 ------------------ .../tensorflow-hadoop/pom-hadoop.xml.template | 18 --- .../java/maven/tensorflow-hadoop/update.py | 114 ------------- 4 files changed, 303 deletions(-) delete mode 100644 tensorflow/java/maven/spark-tensorflow-connector/pom-spark.xml.template delete mode 100644 tensorflow/java/maven/spark-tensorflow-connector/update.py delete mode 100644 tensorflow/java/maven/tensorflow-hadoop/pom-hadoop.xml.template delete mode 100644 tensorflow/java/maven/tensorflow-hadoop/update.py diff --git a/tensorflow/java/maven/spark-tensorflow-connector/pom-spark.xml.template b/tensorflow/java/maven/spark-tensorflow-connector/pom-spark.xml.template deleted file mode 100644 index d8a3d559be..0000000000 --- a/tensorflow/java/maven/spark-tensorflow-connector/pom-spark.xml.template +++ /dev/null @@ -1,19 +0,0 @@ - - 4.0.0 - TensorFlow TFRecord connector for Apache Spark DataFrames - spark-tensorflow-connector_${scala_version} - ${version} - jar - - https://github.com/tensorflow/ecosystem/ - - org.tensorflow - parentpom - ${version} - ../ - - - diff --git a/tensorflow/java/maven/spark-tensorflow-connector/update.py b/tensorflow/java/maven/spark-tensorflow-connector/update.py deleted file mode 100644 index 6185ccbb00..0000000000 --- a/tensorflow/java/maven/spark-tensorflow-connector/update.py +++ /dev/null @@ -1,152 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -""" -Merge TensorFlow Spark connector pom from with deployment template. - -The TensorFlow Spark connector pom is here: https://github.com/tensorflow/ecosystem/tree/master/spark/spark-tensorflow-connector -""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import argparse -import sys -import string -import xml.etree.ElementTree as ET - -POM_NAMESPACE = "http://maven.apache.org/POM/4.0.0" -SCALA_VERSION_TAG = "scala.binary.version" - - -def get_args(): - """Parse command line args.""" - parser = argparse.ArgumentParser() - parser.add_argument( - '--version', - required=True, - help='Version for the artifact.') - parser.add_argument( - '--scala_version', - required=True, - choices=['2.10', '2.11'], - help='Scala version for the artifact.') - parser.add_argument( - '--template', - required=True, - help='Path to the pom file template.') - parser.add_argument( - '--input_pom', - required=True, - help='Path to input pom file to merge with template.') - parser.add_argument( - '--output_pom', - required=True, - help='Path to output pom file.') - return parser.parse_args() - - -def load_pom(input_path): - """ Loads POM file to XML tree""" - ET.register_namespace("", POM_NAMESPACE) - tree = ET.parse(input_path) - return tree - - -def update_scala_version(tree, version, is_template=False): - """ Updates scala version in XML tree""" - - if is_template: - tag = "{%s}artifactId" % POM_NAMESPACE - nodes = tree.findall(tag) - - if nodes is None: - raise ValueError("Missing artifactId in template pom") - - for node in nodes: - template = string.Template(node.text) - - text = template.substitute({"scala_version": version}) - node.text = text - else: - # Update scala version property in pom - tag = "{%s}%s" % (POM_NAMESPACE, SCALA_VERSION_TAG) - nodes = nodes = list(tree.iter(tag)) - - if len(nodes) == 0: - raise ValueError("Missing %s property in Spark connector pom") - - for node in nodes: - node.text = version - - return tree - - -def update_version(tree, version): - """ Updates version tags in XML tree """ - version_tag = "{%s}version" % POM_NAMESPACE - nodes = list(tree.iter(version_tag)) - - if len(nodes) == 0: - raise ValueError("Missing version in template pom") - - for node in nodes: - node.text = version - - return tree - - -def merge_tags(template_root, pom_root): - """ Merge pom file from TensorFlow Spark connector with deployment template. - - Modify the TensorFlow Spark connector pom to inherit parent pom and version info and - other tags provided by deployment template. - - TODO: Figure out if there is a cleaner way of doing this. Inheritance is needed - for propagating the deployment profile. - - Args: - template_root: Root XML element for template file. - pom_root: Root XML element for TensorFlow Spark connector pom file. - - Return: - template_root: Root XML element with merged tree. - """ - template_tags = [child.tag for child in template_root] - template_tags.append("{%s}groupId" % POM_NAMESPACE) # skip groupId since it is inherited from parent - - for child in pom_root: - if child.tag not in template_tags: - template_root.append(child) - - return template_root - - -def main(): - args = get_args() - template_tree = load_pom(args.template) - pom_tree = load_pom(args.input_pom) - - template_tree = update_version(template_tree, args.version) - template_tree = update_scala_version(template_tree, args.scala_version, is_template=True) - pom_tree = update_scala_version(pom_tree, args.scala_version, is_template=False) - template_root = merge_tags(template_tree.getroot(), pom_tree.getroot()) - - with open(args.output_pom, "w") as f: - f.write(ET.tostring(template_root)) - - -if __name__ == '__main__': - sys.exit(main()) diff --git a/tensorflow/java/maven/tensorflow-hadoop/pom-hadoop.xml.template b/tensorflow/java/maven/tensorflow-hadoop/pom-hadoop.xml.template deleted file mode 100644 index 6a82c56cc7..0000000000 --- a/tensorflow/java/maven/tensorflow-hadoop/pom-hadoop.xml.template +++ /dev/null @@ -1,18 +0,0 @@ - - 4.0.0 - TensorFlow TFRecord InputFormat/OutputFormat for Apache Hadoop - tensorflow-hadoop - ${version} - jar - - https://github.com/tensorflow/ecosystem/ - - org.tensorflow - parentpom - ${version} - ../ - - diff --git a/tensorflow/java/maven/tensorflow-hadoop/update.py b/tensorflow/java/maven/tensorflow-hadoop/update.py deleted file mode 100644 index 503062608d..0000000000 --- a/tensorflow/java/maven/tensorflow-hadoop/update.py +++ /dev/null @@ -1,114 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -""" -Merge TensorFlow Hadoop pom from with deployment template. - -The TensorFlow Hadoop pom is here: https://github.com/tensorflow/ecosystem/tree/master/hadoop -""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import argparse -import sys -import xml.etree.ElementTree as ET - -POM_NAMESPACE = "http://maven.apache.org/POM/4.0.0" - - -def get_args(): - """Parse command line args.""" - parser = argparse.ArgumentParser() - parser.add_argument( - '--version', - required=True, - help='Version for the artifact.') - parser.add_argument( - '--template', - required=True, - help='Path to the pom file template.') - parser.add_argument( - '--input_pom', - required=True, - help='Path to input pom file to merge with template.') - parser.add_argument( - '--output_pom', - required=True, - help='Path to output pom file.') - return parser.parse_args() - - -def load_pom(input_path): - """ Loads POM file to XML tree""" - ET.register_namespace("", POM_NAMESPACE) - tree = ET.parse(input_path) - return tree - - -def update_version(tree, version): - """ Updates version tags in XML tree """ - version_tag = "{%s}version" % POM_NAMESPACE - nodes = list(tree.iter(version_tag)) - - if len(nodes) == 0: - raise ValueError("Missing version in template pom") - - for node in nodes: - node.text = version - - return tree - - -def merge_tags(template_root, pom_root): - """ Merge pom file from TensorFlow Hadoop with deployment template. - - Modify the TensorFlow Hadoop pom to inherit parent pom and version info and - other tags provided by deployment template. - - TODO: Figure out if there is a cleaner way of doing this. Inheritance is needed - for propagating the deployment profile. - - Args: - template_root: Root XML element for template file. - pom_root: Root XML element for TensorFlow Hadoop pom file. - - Return: - template_root: Root XML element with merged tree. - """ - template_tags = [child.tag for child in template_root] - template_tags.append("{%s}groupId" % POM_NAMESPACE) # skip groupId since it is inherited from parent - - for child in pom_root: - if child.tag not in template_tags: - template_root.append(child) - - return template_root - - -def main(): - args = get_args() - template_tree = load_pom(args.template) - pom_tree = load_pom(args.input_pom) - - template_tree = update_version(template_tree, args.version) - template_root = merge_tags(template_tree.getroot(), pom_tree.getroot()) - - with open(args.output_pom, "w") as f: - f.write(ET.tostring(template_root)) - - -if __name__ == '__main__': - sys.exit(main()) -- GitLab From 78da41f8f16871cd1328218cbabcfc82dbecf8a3 Mon Sep 17 00:00:00 2001 From: Sami Kama Date: Wed, 9 May 2018 14:12:54 -0700 Subject: [PATCH 010/816] Subgraph to graphdef --- .../contrib/tensorrt/convert/convert_nodes.cc | 60 +++++++++++++++++++ .../contrib/tensorrt/convert/convert_nodes.h | 4 ++ 2 files changed, 64 insertions(+) diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index 3767596f8c..9b9ce51097 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -53,8 +53,11 @@ limitations under the License. namespace tensorflow { namespace tensorrt { namespace convert { +using ::tensorflow::str_util::Split; + using ::tensorflow::strings::StrAppend; using ::tensorflow::strings::StrCat; + namespace { inline tensorflow::Status ConvertDType(tensorflow::DataType tf_dtype, @@ -2723,6 +2726,63 @@ tensorflow::Status ConvertSubGraphToTensorRTNodeDef( return tensorflow::Status::OK(); } +// This needs to be called before TensorRT nodes inserted in order to correctly +// get sizes from the original graph +tensorflow::Status ConvertSegmentToGraphDef( + tensorflow::tensorrt::convert::SubGraphParams& params, + tensorflow::GraphDef* segment_def, + std::unordered_map *input_placeholder_map + ) { + //std::unordered_map input_placeholder_map; + for (size_t i = 0; i < params.input_inds.size(); ++i) { + auto& inputs = params.input_inds.at(i); + auto input_node = params.graph.FindNodeId(inputs.first); + if (input_node) { + tensorflow::DataType input_type = tensorflow::DT_FLOAT; + tensorflow::PartialTensorShape partial_shape; + + if (params.graph_properties.HasOutputProperties(input_node->name())) { + auto output_params = + params.graph_properties.GetOutputProperties(input_node->name()); + auto out_shape = output_params.at(inputs.second); + input_type = out_shape.dtype(); + std::vector dims; + for (const auto d : out_shape.shape().dim()) { + dims.push_back(d.size()); + } + tensorflow::PartialTensorShape::MakePartialShape( + dims.data(), dims.size(), &partial_shape); + } + tensorflow::NodeDef dummy_placeholder; + string node_name("InputPH_"); + StrAppend(&node_name, i); + input_placeholder_map->insert({input_node->name(),node_name}); + tensorflow::NodeDefBuilder dph_builder(node_name, "Placeholder"); + auto status = dph_builder.Attr("shape", partial_shape) + .Attr("dtype", input_type) + .Finalize(&dummy_placeholder); + auto seg_node = segment_def->add_node(); + seg_node->CopyFrom(dummy_placeholder); + } + } + for (const auto node_id : params.subgraph_node_ids) { + const auto node = params.graph.FindNodeId(node_id); + if (node) { + auto snode = segment_def->add_node(); + snode->CopyFrom(node->def()); + // check node inputs to see if it was connected to input node and update + // it to point to placeholder if necessary + for (int i = 0; i < snode->input_size(); ++i) { + auto node_input = Split(snode->input(i), ":"); + string node_input_name = node_input[0]; + auto it = input_placeholder_map->find(node_input_name); + if (it != input_placeholder_map->end()) { + snode->set_input(i, it->second); + } + } + } + } +} } // namespace convert } // namespace tensorrt } // namespace tensorflow diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.h b/tensorflow/contrib/tensorrt/convert/convert_nodes.h index 3f6592cd25..903867fa7f 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.h +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.h @@ -85,6 +85,10 @@ tensorflow::Status ConvertSubGraphToTensorRTNodeDef(SubGraphParams& params); tensorflow::Status InjectCalibrationNode(SubGraphParams& params); tensorflow::Status ConvertCalibrationNodeToEngineNode(tensorflow::Graph& graph, tensorflow::Node* c_node); +tensorflow::Status ConvertSegmentToGraphDef( + tensorflow::tensorrt::convert::SubGraphParams& params, + tensorflow::GraphDef* segment_def, + std::unordered_map input_placeholder_map); } // namespace convert } // namespace tensorrt } // namespace tensorflow -- GitLab From b7c333dc75041b05ef4b0023db5dbbda4a817283 Mon Sep 17 00:00:00 2001 From: Dan Osipov Date: Wed, 16 May 2018 16:42:47 -0700 Subject: [PATCH 011/816] Resize first, pad second --- tensorflow/python/ops/image_ops_impl.py | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/tensorflow/python/ops/image_ops_impl.py b/tensorflow/python/ops/image_ops_impl.py index d5ac72bac6..a070a4699f 100644 --- a/tensorflow/python/ops/image_ops_impl.py +++ b/tensorflow/python/ops/image_ops_impl.py @@ -1048,19 +1048,19 @@ def resize_image_with_pad(image, target_height, target_width, # Find the ratio by which the image must be adjusted # to fit within the target ratio = max_(f_width / f_target_width, f_height / f_target_height) - p_height_float = max_((f_target_height - (f_height / ratio)) * ratio / 2, 0) - p_width_float = max_((f_target_width - (f_width / ratio)) * ratio / 2, 0) - p_height = math_ops.cast(math_ops.ceil(p_height_float), dtype=dtypes.int32) - p_width = math_ops.cast(math_ops.ceil(p_width_float), dtype=dtypes.int32) + resized_height_float = f_height / ratio + resized_width_float = f_width / ratio + resized_height = math_ops.cast(math_ops.floor(p_height_float), dtype=dtypes.int32) + resized_width = math_ops.cast(math_ops.floor(p_width_float), dtype=dtypes.int32) - padded_height = height + (p_height * 2) - padded_width = width + (p_width * 2) + p_height = target_height - resized_height + p_weight = target_width - resized_width - # Pad first, then resize to meet requested dimensions + # Resize first, then pad to meet requested dimensions + resized = resize_images(image, [resized_height, resized_width], method) + padded = pad_to_bounding_box(image, p_height, p_width, - padded_height, padded_width) - - resized = resize_images(padded, [target_height, target_width], method) + target_height, target_width) if resized.get_shape().ndims is None: raise ValueError('resized contains no shape.') -- GitLab From 416bac50aaa684049bb3270d379316efc5b960c2 Mon Sep 17 00:00:00 2001 From: Lukas Geiger Date: Fri, 25 May 2018 01:06:33 +0200 Subject: [PATCH 012/816] [tfgan] Add possibility to export GANEstimator saved model --- tensorflow/contrib/gan/python/estimator/python/head_impl.py | 6 +++++- tensorflow/contrib/gan/python/estimator/python/head_test.py | 5 +++-- 2 files changed, 8 insertions(+), 3 deletions(-) diff --git a/tensorflow/contrib/gan/python/estimator/python/head_impl.py b/tensorflow/contrib/gan/python/estimator/python/head_impl.py index ff903a78cc..5b5557bd8f 100644 --- a/tensorflow/contrib/gan/python/estimator/python/head_impl.py +++ b/tensorflow/contrib/gan/python/estimator/python/head_impl.py @@ -24,6 +24,7 @@ from tensorflow.contrib.gan.python import namedtuples as tfgan_tuples from tensorflow.contrib.gan.python import train as tfgan_train from tensorflow.python.estimator import model_fn as model_fn_lib from tensorflow.python.estimator.canned import head +from tensorflow.python.estimator.export import export_output from tensorflow.python.framework import ops from tensorflow.python.ops import metrics as metrics_lib @@ -182,7 +183,10 @@ class GANHead(head._Head): # pylint: disable=protected-access if mode == model_fn_lib.ModeKeys.PREDICT: return model_fn_lib.EstimatorSpec( mode=model_fn_lib.ModeKeys.PREDICT, - predictions=gan_model.generated_data) + predictions=gan_model.generated_data, + export_outputs={ + 'predict': export_output.PredictOutput(gan_model.generated_data) + }) elif mode == model_fn_lib.ModeKeys.EVAL: gan_loss = self.create_loss( features=None, mode=mode, logits=gan_model, labels=None) diff --git a/tensorflow/contrib/gan/python/estimator/python/head_test.py b/tensorflow/contrib/gan/python/estimator/python/head_test.py index 6587f1fc60..c121f322b5 100644 --- a/tensorflow/contrib/gan/python/estimator/python/head_test.py +++ b/tensorflow/contrib/gan/python/estimator/python/head_test.py @@ -71,13 +71,14 @@ class GANHeadTest(test.TestCase): return {} def _test_modes_helper(self, mode): - self.gan_head.create_estimator_spec( + return self.gan_head.create_estimator_spec( features=None, mode=mode, logits=get_gan_model()) def test_modes_predict(self): - self._test_modes_helper(model_fn_lib.ModeKeys.PREDICT) + spec = self._test_modes_helper(model_fn_lib.ModeKeys.PREDICT) + self.assertItemsEqual(('predict',), spec.export_outputs.keys()) def test_modes_eval(self): self._test_modes_helper(model_fn_lib.ModeKeys.EVAL) -- GitLab From 06ba7827cb4e781ab36e6bbc46cf34e3ea587335 Mon Sep 17 00:00:00 2001 From: Dan Osipov Date: Sun, 27 May 2018 10:33:27 -0700 Subject: [PATCH 013/816] Remove unused function --- tensorflow/python/ops/image_ops_impl.py | 6 ------ 1 file changed, 6 deletions(-) diff --git a/tensorflow/python/ops/image_ops_impl.py b/tensorflow/python/ops/image_ops_impl.py index a070a4699f..6e72ebd634 100644 --- a/tensorflow/python/ops/image_ops_impl.py +++ b/tensorflow/python/ops/image_ops_impl.py @@ -1031,12 +1031,6 @@ def resize_image_with_pad(image, target_height, target_width, else: return max(x, y) - def equal_(x, y): - if _is_tensor(x) or _is_tensor(y): - return math_ops.equal(x, y) - else: - return x == y - _, height, width, _ = _ImageDimensions(image, rank=4) # convert values to float, to ease divisions -- GitLab From b0ec8d2c467173ce5a43c13631bc51fd89f072e5 Mon Sep 17 00:00:00 2001 From: Soila Kavulya Date: Wed, 30 May 2018 19:23:08 -0700 Subject: [PATCH 014/816] Update artifactId for TensorFlow Hadoop and spark-connector jars --- tensorflow/java/maven/README.md | 4 ++-- .../java/maven/{tensorflow-hadoop => hadoop}/pom.xml | 4 ++-- tensorflow/java/maven/pom.xml | 4 ++-- tensorflow/java/maven/run_inside_container.sh | 10 ++++++---- .../pom.xml | 4 ++-- 5 files changed, 14 insertions(+), 12 deletions(-) rename tensorflow/java/maven/{tensorflow-hadoop => hadoop}/pom.xml (94%) rename tensorflow/java/maven/{spark-tensorflow-connector => spark-connector}/pom.xml (93%) diff --git a/tensorflow/java/maven/README.md b/tensorflow/java/maven/README.md index fa756815a9..3e030dcd09 100644 --- a/tensorflow/java/maven/README.md +++ b/tensorflow/java/maven/README.md @@ -53,10 +53,10 @@ There are seven artifacts and thus `pom.xml`s involved in this release: 7. [`parentpom`](https://maven.apache.org/pom/index.html): Common settings shared by all of the above. -8. `tensorflow-hadoop`: The TensorFlow TFRecord InputFormat/OutputFormat for Apache Hadoop. +8. `hadoop`: The TensorFlow TFRecord InputFormat/OutputFormat for Apache Hadoop. The source code for this package is available in the [TensorFlow Ecosystem](https://github.com/tensorflow/ecosystem/tree/master/hadoop) -9. `spark-tensorflow-connector`: A Scala library for loading and storing TensorFlow TFRecord +9. `spark-connector`: A Scala library for loading and storing TensorFlow TFRecord using Apache Spark DataFrames. The source code for this package is available in the [TensorFlow Ecosystem](https://github.com/tensorflow/ecosystem/tree/master/spark/spark-tensorflow-connector) diff --git a/tensorflow/java/maven/tensorflow-hadoop/pom.xml b/tensorflow/java/maven/hadoop/pom.xml similarity index 94% rename from tensorflow/java/maven/tensorflow-hadoop/pom.xml rename to tensorflow/java/maven/hadoop/pom.xml index ee90d8c92b..a872c20d3b 100644 --- a/tensorflow/java/maven/tensorflow-hadoop/pom.xml +++ b/tensorflow/java/maven/hadoop/pom.xml @@ -5,7 +5,7 @@ 4.0.0 TensorFlow TFRecord InputFormat/OutputFormat for Apache Hadoop - tensorflow-hadoop + hadoop jar @@ -21,4 +21,4 @@ 1.8.0 ../ - + \ No newline at end of file diff --git a/tensorflow/java/maven/pom.xml b/tensorflow/java/maven/pom.xml index 7a95fb2556..19287f8245 100644 --- a/tensorflow/java/maven/pom.xml +++ b/tensorflow/java/maven/pom.xml @@ -32,8 +32,8 @@ libtensorflow_jni_gpu tensorflow proto - tensorflow-hadoop - spark-tensorflow-connector + hadoop + spark-connector 4.0.0 TensorFlow TFRecord connector for Apache Spark DataFrames - spark-tensorflow-connector + spark-connector jar @@ -21,4 +21,4 @@ 1.8.0 ../ - + \ No newline at end of file -- GitLab From 5ab4e1346dba1d5bb820452883c1561d144759f7 Mon Sep 17 00:00:00 2001 From: Amit Patankar Date: Fri, 1 Jun 2018 14:19:03 -0700 Subject: [PATCH 015/816] Updating release notes for r1.9. --- RELEASE.md | 57 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 57 insertions(+) diff --git a/RELEASE.md b/RELEASE.md index 84d9d52868..600294478d 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -1,3 +1,60 @@ +# Release 1.9.0 + +## Major Features And Improvements +* Update tf.keras to the Keras 2.1.6 API. +* `tfe.Network` is deprecated. Please inherit from `tf.keras.Model`. +* Adding support of core feature columns and losses to gradient boosted trees estimators. +* The Bijector API now requires 'event_ndims' passed in to the `log_det_jacobian` methods, while `event_ndims` is removed from the base class and replaced with `forward_min_event_ndims`. The signature is now `log_det_jacobian(x, event_ndims)`. The main rationale for this change is that it allows Bijectors to broadcast. +RELNOTES: If you were using layers from `tf.keras.layers` in conjunction with custom variable scopes, your layer variable names might have changed. If you were using layers from `tf.layers` in a subclassed `tf.keras.Model` class, then your variable names have changed (you can restore the prior names by importing the same layers from `tf.keras.layers` instead of `tf.layers`). + +## Breaking Chances + * If you're opening empty variable scopes; replace `variable_scope`('', ...) by `variable_scope`(`tf.get_variable_scope()`, ...). + +## Bug Fixes and Other Changes +* `tf.data`: + * The `DatasetBase::DebugString()` method is now `const`. + * Added the `tf.contrib.data.sample_from_datasets()` API for randomly sampling from multiple datasets. +* Eager Execution: +* `tf.keras`: + * Move Keras code out of _impl folder and remove API files. + * `tf.keras.Model.save_weights` now saves in TensorFlow format by default. + * Enable dataset iterators to be passed to `tf.keras.Model` training/eval methods. +* Accelerated Linear Algebra (XLA): +* TensorFlow Debugger (tfdbg) CLI: +* `tf.contrib`: + * Add `tf.contrib.data.choose_from_datasets()`. + * `tf.contrib.data.make_csv_dataset()` now supports line breaks in quoted strings. Two arguments were removed from `make_csv_dataset`. + * `tf.contrib.framework.zero_initializer` supports ResourceVariable. + * Adding "constrained_optimization" to tensorflow/contrib. +* Other: + * Add GCS Configuration Ops. + * Changing signature of `MakeIterator` to enable propagating error status. + * KL divergence for two Dirichlet distributions. + * More consistent GcsFileSystem behavior for certain reads past EOF. + * Update benchmark for tf.scan to match ranges across eager and graph modes. + * Fixed bug in `tf.reduce_prod gradient` for complex dtypes. + * Add optional `args` argument to `Dataset.from_generator()`. + * Allow the use of '.' in variables (e.g. "hparams.parse('a.b=1.0')"), which would previously raise an error. This will correspond to an attribute name with an embedded '.' symbol (e.g. 'a.b'), which can only be accessed indirectly (e.g. through getattr and setattr). To set this up the user will first need to explicitly add the variable to the hparam object (e.g. "hparams.add_hparam(name='a.b', value=0.0)"). + * Benchmark for tf.scan in graph and eager modes. + * Added complex128 support to FFT, FFT2D, FFT3D, IFFT, IFFT2D, and IFFT3D. + * Making ids unique in `nn.embedding_lookup_sparse`. This helps to reduce RPC calls for looking up the embeddings when there are repeated ids in the batch. + * Support indicator column in boosted trees. + * Prevent `tf.gradients()` from backpropagating through integer tensors. + * LinearOperator[1D,2D,3D]Circulant added to `tensorflow.linalg`. + * Conv3D, Conv3DBackpropInput, Conv3DBackpropFilter now supports arbitrary. + * Added `tf.train.Checkpoint` for reading/writing object-based checkpoints. + * `Dataset.list_files()` now produces determinstic results when `shuffle=False` or a `seed` is passed. + * Added LinearOperatorKronecker, a dense-free implementation of the Kronecker Product. + * Allow LinearOperator to broadcast. + * SavedModelBuilder will now deduplicate asset names that point to files with the same basename and the same contents. Note that this may result in new asset files included in SavedModels in cases where assets with the same name but different contents were previously overwriting each other. + + +## Thanks to our Contributors + +This release contains contributions from many people at Google, as well as: + +Abdullah Alrasheed, Achal Shah, Ad-530, ADiegoCAlonso, Aditya Yogi, Ag Ramesh, akindyakov, Andy Kernahan, Anya Petrova, Aurelien Geron, Ben, Ben Barsdell, Bhavani-Subramanian, braincodercn, Brett Koonce, Brian Nemsick, Brian Zier, Bryan Heden, candy.dc, cclauss, Clayne Robison, ctiijima, Dalmo Cirne, David Norman, David T.H. Kao, DosLin, ekelsen, Elson Rodriguez, Erik Smistad, Felix Abecassis, Fergal Cotter, fo40225, foo0x29a, Freedom" Koan-Sin Tan, FréDéRic Branchaud-Charron, gdh1995, Geoffrey Irving, Giuseppe, gracehoney, Guido Zuidhof, Guillaume Klein, Guozhong Zhuang, Haggai, Harald Husum, imsheridan, Ivan Zhang, Jan Zikes, Jayaram Bobba, Jesse Benson, Jesse Gumz, Jiajia Li, Jie, jinghuangintel, Jingwen, jjsjann123, Joe Yearsley, Joel Hestness, Joel Shor, josephyearsley, Junpeng Lao, Karol M. Langner, Kb Sriram, krantideep95, Krish Ravindranath, Letian Feng, Loo Rong Jie, Lukas Geiger, Maciej, Mahmoud Abuzaina, ManHyuk, Mark Ryan, mbhuiyan, Michal Turek, Mostafa Alaa, Myungsung Kwak, Nand Dalal, Nehal J Wani, Neil Tenenholtz, ngc92, Nicholas Nadeau, P.Eng., Avs, Niranjan Hasabnis, P-Hidringer, Paul Van Eck, Peng Yu, Qing Zhao, Qingying Chen, Quanlong, Rajendra Arora, Rholais Lii, rmanyari, Robin Richtsfeld, Russell Klopfer, Sagi, Sam Sendelbach, Sandeep N Gupta, Sandip Giri, Sarah Edkins, Scott Tseng, Sdalbsoo, Sergii Khomenko, Seungwoo Choi (Biggie), Seyed Majid Azimi, Shaoning Zeng, shengfuintel, Siu Kei, Muk, Smit Shilu, soonson, Stefan Schweter, Sukhwan Kim, Sunitha Kambhampati, Taehoon Lee, tamimaddari82, Tang, Wenyi, Ted Chang, u2takey, Utkarsh Upadhyay, Vadim Markovtsev, voegtlel, Wai Hon Law, wangsiyu, Wenhao Hu, wenhao.hu, William D. Irons, Yan Facai (颜发才), Yanbo Liang, Yihong Wang, Yilei (Dolee) Yang, Yong Tang, Yuan (Terry) Tang + # Release 1.8.0 ## Major Features And Improvements -- GitLab From 672bd9fd8c446eb2c69e4b0f13ed9b74d0a5956f Mon Sep 17 00:00:00 2001 From: Amit Patankar Date: Fri, 1 Jun 2018 14:26:07 -0700 Subject: [PATCH 016/816] Updating version for 1.9.0-rc0. --- tensorflow/core/public/version.h | 4 ++-- tensorflow/docs_src/get_started/eager.md | 2 +- tensorflow/docs_src/install/install_c.md | 2 +- tensorflow/docs_src/install/install_go.md | 2 +- tensorflow/docs_src/install/install_java.md | 22 +++++++++---------- tensorflow/docs_src/install/install_linux.md | 18 +++++++-------- tensorflow/docs_src/install/install_mac.md | 10 ++++----- .../docs_src/install/install_sources.md | 9 ++++++-- tensorflow/tools/docker/Dockerfile.devel | 2 +- .../tools/docker/Dockerfile.devel-cpu-mkl | 2 +- tensorflow/tools/docker/Dockerfile.devel-gpu | 2 +- tensorflow/tools/pip_package/setup.py | 2 +- 12 files changed, 41 insertions(+), 36 deletions(-) diff --git a/tensorflow/core/public/version.h b/tensorflow/core/public/version.h index 522a9d84fd..cb1fd09dbb 100644 --- a/tensorflow/core/public/version.h +++ b/tensorflow/core/public/version.h @@ -19,12 +19,12 @@ limitations under the License. // TensorFlow uses semantic versioning, see http://semver.org/. #define TF_MAJOR_VERSION 1 -#define TF_MINOR_VERSION 8 +#define TF_MINOR_VERSION 9 #define TF_PATCH_VERSION 0 // TF_VERSION_SUFFIX is non-empty for pre-releases (e.g. "-alpha", "-alpha.1", // "-beta", "-rc", "-rc.1") -#define TF_VERSION_SUFFIX "" +#define TF_VERSION_SUFFIX "-rc0" #define TF_STR_HELPER(x) #x #define TF_STR(x) TF_STR_HELPER(x) diff --git a/tensorflow/docs_src/get_started/eager.md b/tensorflow/docs_src/get_started/eager.md index f08ac74425..bbb25e20c6 100644 --- a/tensorflow/docs_src/get_started/eager.md +++ b/tensorflow/docs_src/get_started/eager.md @@ -1,3 +1,3 @@ # Get Started with Eager Execution -[Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/r1.8.0/samples/core/get_started/eager.ipynb) +[Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/r1.9.0/samples/core/get_started/eager.ipynb) diff --git a/tensorflow/docs_src/install/install_c.md b/tensorflow/docs_src/install/install_c.md index 1abd840ab3..2901848745 100644 --- a/tensorflow/docs_src/install/install_c.md +++ b/tensorflow/docs_src/install/install_c.md @@ -38,7 +38,7 @@ enable TensorFlow for C: OS="linux" # Change to "darwin" for macOS TARGET_DIRECTORY="/usr/local" curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.8.0.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.9.0-rc0.tar.gz" | sudo tar -C $TARGET_DIRECTORY -xz The `tar` command extracts the TensorFlow C library into the `lib` diff --git a/tensorflow/docs_src/install/install_go.md b/tensorflow/docs_src/install/install_go.md index 52a2a3f8a6..55bc0f64e7 100644 --- a/tensorflow/docs_src/install/install_go.md +++ b/tensorflow/docs_src/install/install_go.md @@ -38,7 +38,7 @@ steps to install this library and enable TensorFlow for Go: TF_TYPE="cpu" # Change to "gpu" for GPU support TARGET_DIRECTORY='/usr/local' curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.8.0.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.9.0-rc0.tar.gz" | sudo tar -C $TARGET_DIRECTORY -xz The `tar` command extracts the TensorFlow C library into the `lib` diff --git a/tensorflow/docs_src/install/install_java.md b/tensorflow/docs_src/install/install_java.md index 1256fb99c4..b3b739212e 100644 --- a/tensorflow/docs_src/install/install_java.md +++ b/tensorflow/docs_src/install/install_java.md @@ -36,7 +36,7 @@ following to the project's `pom.xml` to use the TensorFlow Java APIs: org.tensorflow tensorflow - 1.8.0 + 1.9.0-rc0 ``` @@ -65,7 +65,7 @@ As an example, these steps will create a Maven project that uses TensorFlow: org.tensorflow tensorflow - 1.8.0 + 1.9.0-rc0 @@ -124,12 +124,12 @@ instead: org.tensorflow libtensorflow - 1.8.0 + 1.9.0-rc0 org.tensorflow libtensorflow_jni_gpu - 1.8.0 + 1.9.0-rc0 ``` @@ -148,7 +148,7 @@ refer to the simpler instructions above instead. Take the following steps to install TensorFlow for Java on Linux or macOS: 1. Download - [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.8.0.jar), + [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.9.0-rc0.jar), which is the TensorFlow Java Archive (JAR). 2. Decide whether you will run TensorFlow for Java on CPU(s) only or with @@ -167,7 +167,7 @@ Take the following steps to install TensorFlow for Java on Linux or macOS: OS=$(uname -s | tr '[:upper:]' '[:lower:]') mkdir -p ./jni curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.8.0.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.9.0-rc0.tar.gz" | tar -xz -C ./jni ### Install on Windows @@ -175,10 +175,10 @@ Take the following steps to install TensorFlow for Java on Linux or macOS: Take the following steps to install TensorFlow for Java on Windows: 1. Download - [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.8.0.jar), + [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.9.0-rc0.jar), which is the TensorFlow Java Archive (JAR). 2. Download the following Java Native Interface (JNI) file appropriate for - [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.8.0.zip). + [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.9.0-rc0.zip). 3. Extract this .zip file. @@ -227,7 +227,7 @@ must be part of your `classpath`. For example, you can include the downloaded `.jar` in your `classpath` by using the `-cp` compilation flag as follows: -
javac -cp libtensorflow-1.8.0.jar HelloTF.java
+
javac -cp libtensorflow-1.9.0-rc0.jar HelloTF.java
### Running @@ -241,11 +241,11 @@ two files are available to the JVM: For example, the following command line executes the `HelloTF` program on Linux and macOS X: -
java -cp libtensorflow-1.8.0.jar:. -Djava.library.path=./jni HelloTF
+
java -cp libtensorflow-1.9.0-rc0.jar:. -Djava.library.path=./jni HelloTF
And the following command line executes the `HelloTF` program on Windows: -
java -cp libtensorflow-1.8.0.jar;. -Djava.library.path=jni HelloTF
+
java -cp libtensorflow-1.9.0-rc0.jar;. -Djava.library.path=jni HelloTF
If the program prints Hello from version, you've successfully installed TensorFlow for Java and are ready to use the API. If the program diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md index 3b9381625f..2ecab808c4 100644 --- a/tensorflow/docs_src/install/install_linux.md +++ b/tensorflow/docs_src/install/install_linux.md @@ -438,7 +438,7 @@ Take the following steps to install TensorFlow in an Anaconda environment: 
      (tensorflow)$ pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0-cp34-cp34m-linux_x86_64.whl
+ https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp34-cp34m-linux_x86_64.whl ## Validate your installation @@ -684,14 +684,14 @@ This section documents the relevant values for Linux installations. CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp27-none-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.8.0-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp27-none-linux_x86_64.whl
Note that GPU support requires the NVIDIA hardware and software described in @@ -703,14 +703,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp34-cp34m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.8.0-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp34-cp34m-linux_x86_64.whl
Note that GPU support requires the NVIDIA hardware and software described in @@ -722,14 +722,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp35-cp35m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.8.0-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp35-cp35m-linux_x86_64.whl
@@ -741,14 +741,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp36-cp36m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.8.0-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp36-cp36m-linux_x86_64.whl
diff --git a/tensorflow/docs_src/install/install_mac.md b/tensorflow/docs_src/install/install_mac.md index 29a867a9e3..9d01271c5a 100644 --- a/tensorflow/docs_src/install/install_mac.md +++ b/tensorflow/docs_src/install/install_mac.md @@ -119,7 +119,7 @@ Take the following steps to install TensorFlow with Virtualenv: TensorFlow in the active Virtualenv is as follows: 
 $ pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0-py3-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py3-none-any.whl If you encounter installation problems, see [Common Installation Problems](#common-installation-problems). @@ -242,7 +242,7 @@ take the following steps: issue the following command: 
 $ sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0-py3-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py3-none-any.whl If the preceding command fails, see [installation problems](#common-installation-problems). @@ -350,7 +350,7 @@ Take the following steps to install TensorFlow in an Anaconda environment: TensorFlow for Python 2.7: 
 (targetDirectory)$ pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0-py2-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py2-none-any.whl @@ -522,7 +522,7 @@ The value you specify depends on your Python version. 
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0-py2-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py2-none-any.whl
@@ -530,5 +530,5 @@ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0-py2-none-any. 
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0-py3-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py3-none-any.whl
diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md index 5ba522b436..d25e641cee 100644 --- a/tensorflow/docs_src/install/install_sources.md +++ b/tensorflow/docs_src/install/install_sources.md @@ -328,10 +328,10 @@ Invoke `pip install` to install that pip package. The filename of the `.whl` file depends on your platform. For example, the following command will install the pip package -for TensorFlow 1.8.0 on Linux: +for TensorFlow 1.9.0rc0 on Linux: 
-$ sudo pip install /tmp/tensorflow_pkg/tensorflow-1.8.0-py2-none-any.whl
+$ sudo pip install /tmp/tensorflow_pkg/tensorflow-1.9.0rc0-py2-none-any.whl
## Validate your installation @@ -433,6 +433,8 @@ Stack Overflow and specify the `tensorflow` tag. **Linux** + + @@ -456,6 +458,7 @@ Stack Overflow and specify the `tensorflow` tag. **Mac**
Version:CPU/GPU:Python Version:Compiler:Build Tools:cuDNN:CUDA:
tensorflow-1.9.0CPU2.7, 3.3-3.6GCC 4.8Bazel 0.11.0N/AN/A
tensorflow_gpu-1.9.0GPU2.7, 3.3-3.6GCC 4.8Bazel 0.11.079
tensorflow-1.8.0CPU2.7, 3.3-3.6GCC 4.8Bazel 0.10.0N/AN/A
tensorflow_gpu-1.8.0GPU2.7, 3.3-3.6GCC 4.8Bazel 0.9.079
tensorflow-1.7.0CPU2.7, 3.3-3.6GCC 4.8Bazel 0.10.0N/AN/A
+ @@ -472,6 +475,8 @@ Stack Overflow and specify the `tensorflow` tag. **Windows**
Version:CPU/GPU:Python Version:Compiler:Build Tools:cuDNN:CUDA:
tensorflow-1.9.0CPU2.7, 3.3-3.6Clang from xcodeBazel 0.11.0N/AN/A
tensorflow-1.8.0CPU2.7, 3.3-3.6Clang from xcodeBazel 0.10.1N/AN/A
tensorflow-1.7.0CPU2.7, 3.3-3.6Clang from xcodeBazel 0.10.1N/AN/A
tensorflow-1.6.0CPU2.7, 3.3-3.6Clang from xcodeBazel 0.8.1N/AN/A
+ + diff --git a/tensorflow/tools/docker/Dockerfile.devel b/tensorflow/tools/docker/Dockerfile.devel index 406d134699..57a491255e 100644 --- a/tensorflow/tools/docker/Dockerfile.devel +++ b/tensorflow/tools/docker/Dockerfile.devel @@ -76,7 +76,7 @@ RUN mkdir /bazel && \ # Download and build TensorFlow. WORKDIR /tensorflow -RUN git clone --branch=r1.8 --depth=1 https://github.com/tensorflow/tensorflow.git . +RUN git clone --branch=r1.9 --depth=1 https://github.com/tensorflow/tensorflow.git . # TODO(craigcitro): Don't install the pip package, since it makes it # more difficult to experiment with local changes. Instead, just add diff --git a/tensorflow/tools/docker/Dockerfile.devel-cpu-mkl b/tensorflow/tools/docker/Dockerfile.devel-cpu-mkl index a6cd44ced1..6796ad70e5 100644 --- a/tensorflow/tools/docker/Dockerfile.devel-cpu-mkl +++ b/tensorflow/tools/docker/Dockerfile.devel-cpu-mkl @@ -3,7 +3,7 @@ FROM tensorflow/tensorflow:latest-devel LABEL maintainer="Clayne Robison" # These arguments are parameterized. Use --build-args to override. -ARG TF_BRANCH=r1.8 +ARG TF_BRANCH=r1.9 ARG WHL_DIR=/whl RUN apt-get update && apt-get install -y --no-install-recommends \ diff --git a/tensorflow/tools/docker/Dockerfile.devel-gpu b/tensorflow/tools/docker/Dockerfile.devel-gpu index e4dcce9cdd..204b5b4dba 100644 --- a/tensorflow/tools/docker/Dockerfile.devel-gpu +++ b/tensorflow/tools/docker/Dockerfile.devel-gpu @@ -85,7 +85,7 @@ RUN mkdir /bazel && \ # Download and build TensorFlow. WORKDIR /tensorflow -RUN git clone --branch=r1.8 --depth=1 https://github.com/tensorflow/tensorflow.git . +RUN git clone --branch=r1.9 --depth=1 https://github.com/tensorflow/tensorflow.git . # Configure the build for our CUDA configuration. ENV CI_BUILD_PYTHON python diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py index d25a9e77b1..78d955c637 100644 --- a/tensorflow/tools/pip_package/setup.py +++ b/tensorflow/tools/pip_package/setup.py @@ -45,7 +45,7 @@ DOCLINES = __doc__.split('\n') # This version string is semver compatible, but incompatible with pip. # For pip, we will remove all '-' characters from this string, and use the # result for pip. -_VERSION = '1.8.0' +_VERSION = '1.9.0-rc0' REQUIRED_PACKAGES = [ 'absl-py >= 0.1.6', -- GitLab From 13ceff2d4096554f195a3c865c1391500e172485 Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Sat, 2 Jun 2018 22:11:20 +0000 Subject: [PATCH 017/816] Fix warning in constrained_optimization test In constrained_optimization test, keep_dims was used for reduce_sum. Since keep_dims has been deprecated it generates unnecessary warning. This fix updates keep_dims -> keepdims to disable the warning. Signed-off-by: Yong Tang --- .../constrained_optimization/python/swap_regret_optimizer.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/contrib/constrained_optimization/python/swap_regret_optimizer.py b/tensorflow/contrib/constrained_optimization/python/swap_regret_optimizer.py index 04014ab4ae..91b2486393 100644 --- a/tensorflow/contrib/constrained_optimization/python/swap_regret_optimizer.py +++ b/tensorflow/contrib/constrained_optimization/python/swap_regret_optimizer.py @@ -169,8 +169,8 @@ def _project_stochastic_matrix_wrt_euclidean_norm(matrix): del old_inactive # Needed by the condition, but not the body. iteration += 1 scale = (1.0 - standard_ops.reduce_sum( - matrix, axis=0, keep_dims=True)) / standard_ops.maximum( - 1.0, standard_ops.reduce_sum(inactive, axis=0, keep_dims=True)) + matrix, axis=0, keepdims=True)) / standard_ops.maximum( + 1.0, standard_ops.reduce_sum(inactive, axis=0, keepdims=True)) matrix += scale * inactive new_inactive = standard_ops.to_float(matrix > 0) matrix *= new_inactive -- GitLab From b7150cffc5e36fe736e648c624cfb8b0cb411f1f Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Sat, 2 Jun 2018 22:13:21 +0000 Subject: [PATCH 018/816] Update keep_dims for reduce_max Signed-off-by: Yong Tang --- .../constrained_optimization/python/swap_regret_optimizer.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/contrib/constrained_optimization/python/swap_regret_optimizer.py b/tensorflow/contrib/constrained_optimization/python/swap_regret_optimizer.py index 91b2486393..3791dae8d7 100644 --- a/tensorflow/contrib/constrained_optimization/python/swap_regret_optimizer.py +++ b/tensorflow/contrib/constrained_optimization/python/swap_regret_optimizer.py @@ -206,10 +206,10 @@ def _project_log_stochastic_matrix_wrt_kl_divergence(log_matrix): # For numerical reasons, make sure that the largest matrix element is zero # before exponentiating. - log_matrix -= standard_ops.reduce_max(log_matrix, axis=0, keep_dims=True) + log_matrix -= standard_ops.reduce_max(log_matrix, axis=0, keepdims=True) log_matrix -= standard_ops.log( standard_ops.reduce_sum( - standard_ops.exp(log_matrix), axis=0, keep_dims=True)) + standard_ops.exp(log_matrix), axis=0, keepdims=True)) return log_matrix -- GitLab From 18526a0d2f85c32269d40e621a492759bee3aaf2 Mon Sep 17 00:00:00 2001 From: Karan Kaw Date: Sun, 3 Jun 2018 13:37:45 +0530 Subject: [PATCH 019/816] Mentioned Visual C++ 2015 dependency for Windows JNI library --- tensorflow/docs_src/install/install_java.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/docs_src/install/install_java.md b/tensorflow/docs_src/install/install_java.md index 1256fb99c4..bbbabb6086 100644 --- a/tensorflow/docs_src/install/install_java.md +++ b/tensorflow/docs_src/install/install_java.md @@ -181,7 +181,7 @@ Take the following steps to install TensorFlow for Java on Windows: [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.8.0.zip). 3. Extract this .zip file. - +__Note__: Please ensure that _MS Visual C++ 2015 Redistributable_ package is installed on Windows system as tensorflow JNI library (*tensorflow_jni.dll*) uses them at runtime. ### Validate the installation -- GitLab From 2d60c046ebbeac964efdc94e988fc86003f6fc9c Mon Sep 17 00:00:00 2001 From: Dan Osipov Date: Sun, 3 Jun 2018 18:37:29 -0700 Subject: [PATCH 020/816] Fix bugs --- tensorflow/python/ops/image_ops_impl.py | 24 +++++++++++++----------- tensorflow/python/ops/image_ops_test.py | 2 +- 2 files changed, 14 insertions(+), 12 deletions(-) diff --git a/tensorflow/python/ops/image_ops_impl.py b/tensorflow/python/ops/image_ops_impl.py index 6e72ebd634..073c0d62b7 100644 --- a/tensorflow/python/ops/image_ops_impl.py +++ b/tensorflow/python/ops/image_ops_impl.py @@ -871,7 +871,7 @@ def resize_images(images, Resized images will be distorted if their original aspect ratio is not the same as `size`. To avoid distortions see - @{tf.image.resize_image_with_crop_or_pad}. + @{tf.image.resize_image_with_pad}. `method` can be one of: @@ -1044,27 +1044,29 @@ def resize_image_with_pad(image, target_height, target_width, ratio = max_(f_width / f_target_width, f_height / f_target_height) resized_height_float = f_height / ratio resized_width_float = f_width / ratio - resized_height = math_ops.cast(math_ops.floor(p_height_float), dtype=dtypes.int32) - resized_width = math_ops.cast(math_ops.floor(p_width_float), dtype=dtypes.int32) + resized_height = math_ops.cast(math_ops.floor(resized_height_float), dtype=dtypes.int32) + resized_width = math_ops.cast(math_ops.floor(resized_width_float), dtype=dtypes.int32) - p_height = target_height - resized_height - p_weight = target_width - resized_width + f_padding_height = math_ops.floor((f_target_height - resized_height_float) / 2) + f_padding_width = math_ops.floor((f_target_width - resized_width_float) / 2) + p_height = max_(0, math_ops.cast(f_padding_height, dtype=dtypes.int32)) + p_width = max_(0, math_ops.cast(f_padding_width, dtype=dtypes.int32)) # Resize first, then pad to meet requested dimensions resized = resize_images(image, [resized_height, resized_width], method) - padded = pad_to_bounding_box(image, p_height, p_width, + padded = pad_to_bounding_box(resized, p_height, p_width, target_height, target_width) - if resized.get_shape().ndims is None: - raise ValueError('resized contains no shape.') + if padded.get_shape().ndims is None: + raise ValueError('padded contains no shape.') - _, resized_height, resized_width, _ = _ImageDimensions(resized, rank=4) + _, padded_height, padded_width, _ = _ImageDimensions(padded, rank=4) if not is_batch: - resized = array_ops.squeeze(resized, squeeze_dims=[0]) + padded = array_ops.squeeze(padded, squeeze_dims=[0]) - return resized + return padded @tf_export('image.per_image_standardization') diff --git a/tensorflow/python/ops/image_ops_test.py b/tensorflow/python/ops/image_ops_test.py index 22d9ce4289..e98d16e6d3 100644 --- a/tensorflow/python/ops/image_ops_test.py +++ b/tensorflow/python/ops/image_ops_test.py @@ -2540,7 +2540,7 @@ class ResizeImageWithPadTest(test_util.TensorFlowTestCase): x = [1, 2, 3, 4, 5, 6, 7, 8] x_shape = [2, 4, 1] - y = [0, 0, 5, 7] + y = [1, 3, 0, 0] y_shape = [2, 2, 1] self._assertReturns(x, x_shape, y, y_shape) -- GitLab From 7eaef86f7766e7c0577614e646dc8d6a972b91f9 Mon Sep 17 00:00:00 2001 From: Dan Osipov Date: Mon, 4 Jun 2018 09:55:17 -0700 Subject: [PATCH 021/816] Remove unnecessary assertions --- tensorflow/python/ops/image_ops_impl.py | 6 ------ 1 file changed, 6 deletions(-) diff --git a/tensorflow/python/ops/image_ops_impl.py b/tensorflow/python/ops/image_ops_impl.py index 073c0d62b7..f3f9a02f01 100644 --- a/tensorflow/python/ops/image_ops_impl.py +++ b/tensorflow/python/ops/image_ops_impl.py @@ -1018,12 +1018,6 @@ def resize_image_with_pad(image, target_height, target_width, 'target_height must be > 0.') image = control_flow_ops.with_dependencies(assert_ops, image) - if _is_tensor(target_height): - target_height = control_flow_ops.with_dependencies( - assert_ops, target_height) - if _is_tensor(target_width): - target_width = control_flow_ops.with_dependencies(assert_ops, - target_width) def max_(x, y): if _is_tensor(x) or _is_tensor(y): -- GitLab From 06a7049f29b0148659693ec53db530c2c895a6a6 Mon Sep 17 00:00:00 2001 From: Amit Patankar Date: Mon, 4 Jun 2018 13:23:40 -0700 Subject: [PATCH 022/816] I've made the updates Rajat requested. Please note the links will not work until after we have launched. --- RELEASE.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/RELEASE.md b/RELEASE.md index 600294478d..c1ed69bd45 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -4,8 +4,10 @@ * Update tf.keras to the Keras 2.1.6 API. * `tfe.Network` is deprecated. Please inherit from `tf.keras.Model`. * Adding support of core feature columns and losses to gradient boosted trees estimators. -* The Bijector API now requires 'event_ndims' passed in to the `log_det_jacobian` methods, while `event_ndims` is removed from the base class and replaced with `forward_min_event_ndims`. The signature is now `log_det_jacobian(x, event_ndims)`. The main rationale for this change is that it allows Bijectors to broadcast. -RELNOTES: If you were using layers from `tf.keras.layers` in conjunction with custom variable scopes, your layer variable names might have changed. If you were using layers from `tf.layers` in a subclassed `tf.keras.Model` class, then your variable names have changed (you can restore the prior names by importing the same layers from `tf.keras.layers` instead of `tf.layers`). +* The distributions.Bijector API supports broadcasting for Bijectors with new API changes. See [here](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/distributions/bijectors/Bijector) for more details. +* Layered variable names have changed in the following conditions: + * Using `tf.keras.layers` with custom variable scopes. + * Using `tf.layers` in a subclassed `tf.keras.Model` class. See [here](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/layers) for more details ## Breaking Chances * If you're opening empty variable scopes; replace `variable_scope`('', ...) by `variable_scope`(`tf.get_variable_scope()`, ...). -- GitLab From a3c642c945b4a27e5d826eb9c9cbc07132cb2bba Mon Sep 17 00:00:00 2001 From: Brennan Saeta Date: Fri, 1 Jun 2018 18:00:43 -0700 Subject: [PATCH 023/816] Remove use of absl::make_unique absl is not yet ready for use by open source TensorFlow. :-( PiperOrigin-RevId: 198952953 --- tensorflow/contrib/cloud/kernels/gcs_config_ops.cc | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/tensorflow/contrib/cloud/kernels/gcs_config_ops.cc b/tensorflow/contrib/cloud/kernels/gcs_config_ops.cc index ef4998212e..648a219fb8 100644 --- a/tensorflow/contrib/cloud/kernels/gcs_config_ops.cc +++ b/tensorflow/contrib/cloud/kernels/gcs_config_ops.cc @@ -21,6 +21,7 @@ limitations under the License. #include "tensorflow/core/platform/cloud/curl_http_request.h" #include "tensorflow/core/platform/cloud/gcs_file_system.h" #include "tensorflow/core/platform/cloud/oauth_client.h" +#include "tensorflow/core/util/ptr_util.h" namespace tensorflow { namespace { @@ -96,7 +97,8 @@ class GcsCredentialsOpKernel : public OpKernel { errors::InvalidArgument("JSON format incompatible; did not find fields " "`refresh_token` or `private_key`.")); - auto provider = absl::make_unique(json, ctx->env()); + auto provider = + tensorflow::MakeUnique(json, ctx->env()); // Test getting a token string dummy_token; @@ -121,7 +123,7 @@ class GcsCredentialsOpKernel : public OpKernel { initial_retry_delay_usec_(initial_retry_delay_usec) {} ConstantAuthProvider(const Json::Value& json, Env* env) - : ConstantAuthProvider(json, absl::make_unique(), env, + : ConstantAuthProvider(json, tensorflow::MakeUnique(), env, kInitialRetryDelayUsec) {} ~ConstantAuthProvider() override {} -- GitLab From 6eb43fc26785c4835747a79b3d6a3e094ef1c60f Mon Sep 17 00:00:00 2001 From: Akshay Modi Date: Mon, 4 Jun 2018 12:05:14 -0700 Subject: [PATCH 024/816] Fix test user ops PiperOrigin-RevId: 199171316 --- tensorflow/tools/ci_build/builds/test_user_ops.sh | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/tensorflow/tools/ci_build/builds/test_user_ops.sh b/tensorflow/tools/ci_build/builds/test_user_ops.sh index c342367bac..25ecee4725 100755 --- a/tensorflow/tools/ci_build/builds/test_user_ops.sh +++ b/tensorflow/tools/ci_build/builds/test_user_ops.sh @@ -239,8 +239,9 @@ function run_op() { fi } -run_op $("${PYTHON_BIN_PATH}" -c "import tensorflow as tf; print(tf.Session('').run(tf.load_op_library('./${USER_OP_SO}').${USER_OP}(${OP_INPUT})))") -run_op $("${PYTHON_BIN_PATH}" -c "import tensorflow as tf; tf.enable_eager_execution(); print(tf.load_op_library('./${USER_OP_SO}').${USER_OP}(${OP_INPUT}))") " in eager mode" +run_op "$("${PYTHON_BIN_PATH}" -c "import tensorflow as tf; print(tf.Session('').run(tf.load_op_library('./${USER_OP_SO}').${USER_OP}(${OP_INPUT})))")" +run_op "$("${PYTHON_BIN_PATH}" -c "import tensorflow as tf; tf.enable_eager_execution(); print(tf.load_op_library('./${USER_OP_SO}').${USER_OP}(${OP_INPUT}).numpy())")" " in eager mode" + popd -- GitLab From 0bb7c844dd4375d7f53c88a7eacf78b0d6552498 Mon Sep 17 00:00:00 2001 From: Nupur Garg Date: Mon, 4 Jun 2018 12:08:15 -0700 Subject: [PATCH 025/816] Fix Python API. PiperOrigin-RevId: 199171845 --- tensorflow/contrib/lite/python/convert_saved_model.py | 4 ++-- .../contrib/lite/python/convert_saved_model_test.py | 9 +++++++-- 2 files changed, 9 insertions(+), 4 deletions(-) diff --git a/tensorflow/contrib/lite/python/convert_saved_model.py b/tensorflow/contrib/lite/python/convert_saved_model.py index b952a72aab..5dad49f1ed 100644 --- a/tensorflow/contrib/lite/python/convert_saved_model.py +++ b/tensorflow/contrib/lite/python/convert_saved_model.py @@ -216,9 +216,9 @@ def set_tensor_shapes(tensors, shapes): """ if shapes: for tensor in tensors: - shape = shapes.get(tensor.name) + shape = shapes.get(tensor_name(tensor)) if shape is not None: - tensor.set_shape(shapes[tensor.name]) + tensor.set_shape(shape) def freeze_saved_model(saved_model_dir, input_arrays, input_shapes, diff --git a/tensorflow/contrib/lite/python/convert_saved_model_test.py b/tensorflow/contrib/lite/python/convert_saved_model_test.py index 80e5dc6e46..1e570d2c89 100644 --- a/tensorflow/contrib/lite/python/convert_saved_model_test.py +++ b/tensorflow/contrib/lite/python/convert_saved_model_test.py @@ -73,10 +73,15 @@ class TensorFunctionsTest(test_util.TensorFlowTestCase): tensor = array_ops.placeholder(shape=[None, 3, 5], dtype=dtypes.float32) self.assertEqual([None, 3, 5], tensor.shape.as_list()) - convert_saved_model.set_tensor_shapes([tensor], - {"Placeholder:0": [5, 3, 5]}) + convert_saved_model.set_tensor_shapes([tensor], {"Placeholder": [5, 3, 5]}) self.assertEqual([5, 3, 5], tensor.shape.as_list()) + def testSetTensorShapeNoneValid(self): + tensor = array_ops.placeholder(dtype=dtypes.float32) + + convert_saved_model.set_tensor_shapes([tensor], {"Placeholder": [1, 3, 5]}) + self.assertEqual([1, 3, 5], tensor.shape.as_list()) + def testSetTensorShapeInvalid(self): tensor = array_ops.placeholder(shape=[None, 3, 5], dtype=dtypes.float32) self.assertEqual([None, 3, 5], tensor.shape.as_list()) -- GitLab From bedf4eeb1361ef1483d9a0a6575f8c74d2eee572 Mon Sep 17 00:00:00 2001 From: Amit Patankar Date: Mon, 4 Jun 2018 14:26:09 -0700 Subject: [PATCH 026/816] Fixing raspberry pi file for conflict. --- tensorflow/tools/ci_build/pi/build_raspberry_pi.sh | 3 --- .../tools/ci_build/windows/cpu/pip/build_tf_windows.sh | 4 ++++ tools/bazel.rc | 6 ------ 3 files changed, 4 insertions(+), 9 deletions(-) diff --git a/tensorflow/tools/ci_build/pi/build_raspberry_pi.sh b/tensorflow/tools/ci_build/pi/build_raspberry_pi.sh index cbd4a93e6d..4d1a30601e 100755 --- a/tensorflow/tools/ci_build/pi/build_raspberry_pi.sh +++ b/tensorflow/tools/ci_build/pi/build_raspberry_pi.sh @@ -102,9 +102,6 @@ bazel build -c opt ${PI_COPTS} \ --copt=-fomit-frame-pointer --cpu=armeabi \ --crosstool_top=@local_config_arm_compiler//:toolchain \ --verbose_failures \ - --distinct_host_configuration=true \ - //tensorflow:libtensorflow.so \ - //tensorflow:libtensorflow_framework.so \ //tensorflow/tools/benchmark:benchmark_model \ //tensorflow/tools/pip_package:build_pip_package diff --git a/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh b/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh index 73520bb2ac..f4a0b232ec 100644 --- a/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh +++ b/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh @@ -73,6 +73,10 @@ if [[ "$release_build" != 1 ]]; then echo "build --define=override_eigen_strong_inline=true" >> "${TMP_BAZELRC}" fi +# The host and target platforms are the same in Windows build. So we don't have +# to distinct them. This helps avoid building the same targets twice. +echo "build --distinct_host_configuration=false" >> "${TMP_BAZELRC}" + echo "import %workspace%/${TMP_BAZELRC}" >> .bazelrc run_configure_for_cpu_build diff --git a/tools/bazel.rc b/tools/bazel.rc index 03aa52da1f..1c1e6afb65 100644 --- a/tools/bazel.rc +++ b/tools/bazel.rc @@ -1,14 +1,8 @@ -# By default, we don't distinct target and host platfroms. -# When doing cross compilation, use --config=cross_compile to distinct them. -build --distinct_host_configuration=false -build:cross_compile --distinct_host_configuration=true - # Android configs. Bazel needs to have --cpu and --fat_apk_cpu both set to the # target CPU to build transient dependencies correctly. See # https://docs.bazel.build/versions/master/user-manual.html#flag--fat_apk_cpu build:android --crosstool_top=//external:android/crosstool build:android --host_crosstool_top=@bazel_tools//tools/cpp:toolchain -build:android --config=cross_compile build:android_arm --config=android build:android_arm --cpu=armeabi-v7a build:android_arm --fat_apk_cpu=armeabi-v7a -- GitLab From c8090fa6acac1f9724671407964662137911921f Mon Sep 17 00:00:00 2001 From: Shashi Shekhar Date: Tue, 5 Jun 2018 10:19:49 -0700 Subject: [PATCH 027/816] Internal change. PiperOrigin-RevId: 199316885 --- .../lite/tools/benchmark/command_line_flags.cc | 2 +- .../lite/tools/benchmark/command_line_flags_test.cc | 13 +++++++++++++ tensorflow/core/BUILD | 2 ++ 3 files changed, 16 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/lite/tools/benchmark/command_line_flags.cc b/tensorflow/contrib/lite/tools/benchmark/command_line_flags.cc index 723bf67e03..8195fc44be 100644 --- a/tensorflow/contrib/lite/tools/benchmark/command_line_flags.cc +++ b/tensorflow/contrib/lite/tools/benchmark/command_line_flags.cc @@ -35,7 +35,7 @@ bool ParseFlag(const std::string& arg, const std::string& flag, if (arg.find(flag_prefix) != 0) { return false; } - bool has_value = (arg.size() >= flag_prefix.size() + 1); + bool has_value = arg.size() >= flag_prefix.size(); *value_parsing_ok = has_value; if (has_value) { *value_parsing_ok = parse_func(arg.substr(flag_prefix.size())); diff --git a/tensorflow/contrib/lite/tools/benchmark/command_line_flags_test.cc b/tensorflow/contrib/lite/tools/benchmark/command_line_flags_test.cc index 74cf59105b..9a931d5ddd 100644 --- a/tensorflow/contrib/lite/tools/benchmark/command_line_flags_test.cc +++ b/tensorflow/contrib/lite/tools/benchmark/command_line_flags_test.cc @@ -53,6 +53,19 @@ TEST(CommandLineFlagsTest, BasicUsage) { EXPECT_EQ(argc, 1); } +TEST(CommandLineFlagsTest, EmptyStringFlag) { + int argc = 2; + std::string some_string = "invalid"; + const char* argv_strings[] = {"program_name", "--some_string="}; + bool parsed_ok = + Flags::Parse(&argc, reinterpret_cast(argv_strings), + {Flag("some_string", &some_string, "some string")}); + + EXPECT_EQ(true, parsed_ok); + EXPECT_EQ(some_string, ""); + EXPECT_EQ(argc, 1); +} + TEST(CommandLineFlagsTest, BadIntValue) { int some_int = 10; int argc = 2; diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index 6bde2a0a4a..f5cc6ef2a1 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -1439,6 +1439,7 @@ filegroup( "lib/png/**/*", "lib/gif/**/*", "util/events_writer.*", + "util/stats_calculator.*", "util/reporter.*", "platform/**/cuda_libdevice_path.*", "platform/default/test_benchmark.*", @@ -1522,6 +1523,7 @@ cc_library( visibility = ["//visibility:public"], deps = [ ":protos_all_cc_impl", + ":stats_calculator_portable", "//third_party/eigen3", "@double_conversion//:double-conversion", "@nsync//:nsync_cpp", -- GitLab From a7c026e08864417b35dbe3c9e4b246725ad6ba59 Mon Sep 17 00:00:00 2001 From: Anjali Sridhar Date: Tue, 5 Jun 2018 10:36:12 -0700 Subject: [PATCH 028/816] Respect name scopes opened in tower mode when creating vars in cross tower mode. PiperOrigin-RevId: 199319758 --- .../distribute/python/mirrored_strategy.py | 35 +++++++--- .../python/mirrored_strategy_multigpu_test.py | 68 +++++++++++++++++++ 2 files changed, 93 insertions(+), 10 deletions(-) diff --git a/tensorflow/contrib/distribute/python/mirrored_strategy.py b/tensorflow/contrib/distribute/python/mirrored_strategy.py index 6eadba976b..cef0a2907b 100644 --- a/tensorflow/contrib/distribute/python/mirrored_strategy.py +++ b/tensorflow/contrib/distribute/python/mirrored_strategy.py @@ -118,7 +118,10 @@ class MirroredStrategy(distribute_lib.DistributionStrategy): if i > 0: # Give replicas meaningful distinct names: var0name = index[devices[0]].name.split(":")[0] - kwargs["name"] = "%s/replica_%d" % (var0name, i) + # We append a / to variable names created on towers with id > 0 to + # ensure that we ignore the name scope and instead use the given + # name as the absolute name of the variable. + kwargs["name"] = "%s/replica_%d/" % (var0name, i) # Initialize replicas with the same value: if context.executing_eagerly(): kwargs["initial_value"] = array_ops.identity( @@ -258,8 +261,15 @@ class MirroredStrategy(distribute_lib.DistributionStrategy): {t.device: t.merge_args for t in threads}) merge_kwargs = values.regroup( {t.device: t.merge_kwargs for t in threads}) - merge_result = threads[0].merge_fn( - self, *merge_args, **merge_kwargs) + # We capture the name_scope of the MTT when we call merge_fn + # to ensure that if we have opened a name scope in the MTT, + # it will be respected when executing the merge function. We only + # capture the name_scope from the first MTT and assume it is + # the same for all other MTTs. + mtt_captured_name_scope = threads[0].captured_name_scope + with ops.name_scope(mtt_captured_name_scope): + merge_result = threads[0].merge_fn( + self, *merge_args, **merge_kwargs) for t in threads: t.merge_result = values.select_device(t.device, merge_result) finally: @@ -428,6 +438,7 @@ class MirroredStrategy(distribute_lib.DistributionStrategy): self.merge_args = None self.merge_kwargs = None self.merge_result = None + self.captured_name_scope = None # We use a thread.Event for the main thread to signal when this # thread should start running (`should_run`), and another for # this thread to transfer control back to the main thread @@ -451,13 +462,13 @@ class MirroredStrategy(distribute_lib.DistributionStrategy): self._variable_creator_stack = self.graph._variable_creator_stack[:] self._captured_var_scope = variable_scope.get_variable_scope() # Adding a "/" at end lets us re-enter this scope later. - self._captured_name_scope = self.graph.get_name_scope() - if self._captured_name_scope: - self._captured_name_scope += "/" + self._name_scope = self.graph.get_name_scope() + if self._name_scope: + self._name_scope += "/" if self.tower_id > 0: - if not self._captured_name_scope: - self._captured_name_scope = "" - self._captured_name_scope += "tower_%d/" % self.tower_id + if not self._name_scope: + self._name_scope = "" + self._name_scope += "tower_%d/" % self.tower_id def run(self): # pylint: disable=protected-access @@ -473,7 +484,7 @@ class MirroredStrategy(distribute_lib.DistributionStrategy): _enter_graph(self.graph), \ MirroredTowerContext(self.distribution, self.tower_id), \ ops.device(self.device), \ - ops.name_scope(self._captured_name_scope), \ + ops.name_scope(self._name_scope), \ variable_scope.variable_scope( self._captured_var_scope, reuse=self.tower_id > 0), \ variable_scope.variable_creator_scope(self.variable_creator_fn): @@ -499,6 +510,10 @@ class MirroredTowerContext(distribute_lib.TowerContext): t.merge_fn = fn t.merge_args = args t.merge_kwargs = kwargs + t.captured_name_scope = t.graph.get_name_scope() + # Adding a "/" at end lets us re-enter this scope later. + if t.captured_name_scope: + t.captured_name_scope += "/" t.has_paused.set() t.should_run.wait() t.should_run.clear() diff --git a/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py b/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py index 3f9a02b249..bccd278847 100644 --- a/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py +++ b/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py @@ -438,6 +438,74 @@ class MirroredStrategyVariableCreationTest(test.TestCase): self.assertEquals("foo/" + name + ":0", v0.name) self.assertEquals("tower_1/foo/" + name + ":0", v1.name) + # variable_scope.variable() respects name scopes when creating + # variables. On the other hand variable_scope.get_variable() ignores name + # scopes when creating variables. We test both methods of creating variables + # to make sure that we have the same variable names in both cases. + def testNameScopeWithVariable(self): + def in_cross_tower(_): + c = variable_scope.variable(1.0, name="c") + return c + + def model_fn(): + b = variable_scope.variable(1.0, name="b") + with ops.name_scope("foo"): + c = distribute_lib.get_tower_context().merge_call(in_cross_tower) + return b, c + + dist = mirrored_strategy.MirroredStrategy( + ["/device:GPU:0", "/device:CPU:0"]) + + with context.graph_mode(), dist.scope(): + with ops.name_scope("main"): + a = variable_scope.variable(1.0, name="a") + result = dist.call_for_each_tower(model_fn, run_concurrently=False) + result_b = result[0] + result_c = result[1] + self.assertIsInstance(result_b, values.DistributedValues) + self.assertIsInstance(result_c, values.DistributedValues) + a0, a1 = dist.unwrap(a) + b0, b1 = dist.unwrap(result_b) + c0, c1 = dist.unwrap(result_c) + self.assertEquals("main/a:0", a0.name) + self.assertEquals("main/a/replica_1:0", a1.name) + self.assertEquals("main/b:0", b0.name) + self.assertEquals("main/b/replica_1:0", b1.name) + self.assertEquals("main/foo/c:0", c0.name) + self.assertEquals("main/foo/c/replica_1:0", c1.name) + + def testNameScopeWithGetVariable(self): + def in_cross_tower(_): + c = variable_scope.get_variable("c", [1]) + return c + + def model_fn(): + b = variable_scope.get_variable("b", [1]) + with ops.name_scope("foo"): + c = distribute_lib.get_tower_context().merge_call(in_cross_tower) + return b, c + + dist = mirrored_strategy.MirroredStrategy( + ["/device:GPU:0", "/device:CPU:0"]) + + with context.graph_mode(), dist.scope(): + with ops.name_scope("main"): + a = variable_scope.get_variable("a", [1]) + result = dist.call_for_each_tower(model_fn, run_concurrently=False) + result_b = result[0] + result_c = result[1] + self.assertIsInstance(result_b, values.DistributedValues) + self.assertIsInstance(result_c, values.DistributedValues) + a0, a1 = dist.unwrap(a) + b0, b1 = dist.unwrap(result_b) + c0, c1 = dist.unwrap(result_c) + self.assertEquals("a:0", a0.name) + self.assertEquals("a/replica_1:0", a1.name) + self.assertEquals("b:0", b0.name) + self.assertEquals("b/replica_1:0", b1.name) + self.assertEquals("c:0", c0.name) + self.assertEquals("c/replica_1:0", c1.name) + def testDynamicRnnVariables(self): def model_fn(): inputs = constant_op.constant(2 * [2 * [[0.0, 1.0, 2.0, 3.0, 4.0]]]) -- GitLab From b2e56707ecbc6dc4b130a50424f5b85956f58720 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 5 Jun 2018 10:43:07 -0700 Subject: [PATCH 029/816] Do not enable tensor ops for cuDNN RNN unless explicitly specified. PiperOrigin-RevId: 199321021 --- tensorflow/stream_executor/cuda/cuda_dnn.cc | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/tensorflow/stream_executor/cuda/cuda_dnn.cc b/tensorflow/stream_executor/cuda/cuda_dnn.cc index 55c1083a61..f6564df0d0 100644 --- a/tensorflow/stream_executor/cuda/cuda_dnn.cc +++ b/tensorflow/stream_executor/cuda/cuda_dnn.cc @@ -1031,7 +1031,15 @@ class CudnnRnnDescriptor : public dnn::RnnDescriptor { rnn_mode, direction_mode, num_layers)); #if CUDNN_VERSION >= 7000 - if (RnnTensorOpMathEnabled()) { + // Require explicit algorithm config to enable tensor cores. Some configs + // return CUDNN_NOT_SUPPORTED when tensor ops are enabled (which is against + // the idiom that enabling tensor ops is only a hint: see nvbugs/2172799). + // We can only reasonably expect the user to handle the subsequent failure + // in profile mode, which is run with algorithms returned from + // GetRnnAlgorithms() (which are non-default and explicitly set whether to + // use tensor ops). + if (RnnTensorOpMathEnabled() && + !algorithm_config.algorithm().is_default()) { cudnnMathType_t math_type = algorithm_config.algorithm().tensor_ops_enabled() ? CUDNN_TENSOR_OP_MATH -- GitLab From e86d969c07c14f8790f364d0b48724848db48d4e Mon Sep 17 00:00:00 2001 From: Shanqing Cai Date: Tue, 5 Jun 2018 11:51:24 -0700 Subject: [PATCH 030/816] Fix bug in which uncompiled tf.keras.Models cannot be saved This bug seems to be specific to tf.keras, i.e., it doesn't happen to keras. PiperOrigin-RevId: 199334073 --- tensorflow/python/keras/engine/saving.py | 2 +- tensorflow/python/keras/engine/saving_test.py | 24 +++++++++++++++++++ 2 files changed, 25 insertions(+), 1 deletion(-) diff --git a/tensorflow/python/keras/engine/saving.py b/tensorflow/python/keras/engine/saving.py index 40b693efde..b9a2e1f25f 100644 --- a/tensorflow/python/keras/engine/saving.py +++ b/tensorflow/python/keras/engine/saving.py @@ -106,7 +106,7 @@ def save_model(model, filepath, overwrite=True, include_optimizer=True): model_layers = model.layers save_weights_to_hdf5_group(model_weights_group, model_layers) - if include_optimizer and hasattr(model, 'optimizer'): + if include_optimizer and model.optimizer: if isinstance(model.optimizer, optimizers.TFOptimizer): logging.warning( 'TensorFlow optimizers do not ' diff --git a/tensorflow/python/keras/engine/saving_test.py b/tensorflow/python/keras/engine/saving_test.py index 5abca8a553..1470718a5e 100644 --- a/tensorflow/python/keras/engine/saving_test.py +++ b/tensorflow/python/keras/engine/saving_test.py @@ -288,6 +288,30 @@ class TestWholeModelSaving(test.TestCase): out2 = new_model.predict(x) self.assertAllClose(out, out2, atol=1e-05) + def test_sequential_model_saving_without_compile(self): + if h5py is None: + self.skipTest('h5py required to run this test') + + with self.test_session(): + model = keras.models.Sequential() + model.add(keras.layers.Dense(2, input_shape=(3,))) + model.add(keras.layers.RepeatVector(3)) + model.add(keras.layers.TimeDistributed(keras.layers.Dense(3))) + + x = np.random.random((1, 3)) + out = model.predict(x) + fd, fname = tempfile.mkstemp('.h5') + + # Save the model without any compilation or training. + keras.models.save_model(model, fname) + + new_model = keras.models.load_model(fname) + os.close(fd) + os.remove(fname) + + out2 = new_model.predict(x) + self.assertAllClose(out, out2, atol=1e-05) + def test_sequential_model_saving_2(self): if h5py is None: self.skipTest('h5py required to run this test') -- GitLab From b1fd2ef4d02719cd929fa574796b2c080a21a9ee Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 5 Jun 2018 11:54:41 -0700 Subject: [PATCH 031/816] Add core/util/exec_on_stall.h a tool for debugging deadlocks with less logging. PiperOrigin-RevId: 199334548 --- tensorflow/core/BUILD | 31 ++++++-- tensorflow/core/util/exec_on_stall.h | 89 ++++++++++++++++++++++ tensorflow/core/util/exec_on_stall_test.cc | 47 ++++++++++++ 3 files changed, 160 insertions(+), 7 deletions(-) create mode 100644 tensorflow/core/util/exec_on_stall.h create mode 100644 tensorflow/core/util/exec_on_stall_test.cc diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index f5cc6ef2a1..28af3ce4ea 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -72,24 +72,23 @@ licenses(["notice"]) # Apache 2.0 load( "//tensorflow:tensorflow.bzl", + "cc_header_only_library", "full_path", "if_android", - "if_not_android_mips_and_mips64", "if_ios", "if_linux_x86_64", "if_mobile", "if_not_mobile", - "if_windows", "if_not_windows", - "tf_copts", + "if_windows", "tf_cc_test", "tf_cc_tests", + "tf_copts", "tf_cuda_library", "tf_gen_op_libs", "tf_generate_proto_text_sources", "tf_genrule_cmd_append_to_srcs", "tf_opts_nortti_if_android", - "cc_header_only_library", ) load("//tensorflow:tensorflow.bzl", "tf_cc_test_mkl") load("//tensorflow:tensorflow.bzl", "tf_cc_test_gpu") @@ -113,11 +112,11 @@ load( "tf_additional_human_readable_json_deps", "tf_additional_lib_defines", "tf_additional_lib_deps", + "tf_additional_lib_hdrs", + "tf_additional_lib_srcs", "tf_additional_libdevice_data", "tf_additional_libdevice_deps", "tf_additional_libdevice_srcs", - "tf_additional_lib_hdrs", - "tf_additional_lib_srcs", "tf_additional_minimal_lib_srcs", "tf_additional_mpi_lib_defines", "tf_additional_proto_hdrs", @@ -141,8 +140,8 @@ load( ) load( "//tensorflow/core:platform/default/build_config_root.bzl", - "tf_cuda_tests_tags", "if_static", + "tf_cuda_tests_tags", ) load("@local_config_cuda//cuda:build_defs.bzl", "if_cuda") load("@io_bazel_rules_closure//closure:defs.bzl", "closure_proto_library") @@ -887,6 +886,12 @@ cc_library( ], ) +cc_library( + name = "exec_on_stall", + hdrs = ["util/exec_on_stall.h"], + deps = [":framework_lite"], +) + cc_library( name = "ptr_util", hdrs = ["util/ptr_util.h"], @@ -3252,6 +3257,18 @@ tf_cc_test( ], ) +tf_cc_test( + name = "exec_on_stall_test", + size = "small", + srcs = ["util/exec_on_stall_test.cc"], + deps = [ + ":exec_on_stall", + ":framework_lite", + ":test", + ":test_main", + ], +) + tf_cc_test( name = "lib_jpeg_jpeg_mem_unittest", srcs = ["lib/jpeg/jpeg_mem_unittest.cc"], diff --git a/tensorflow/core/util/exec_on_stall.h b/tensorflow/core/util/exec_on_stall.h new file mode 100644 index 0000000000..5c8f9d2324 --- /dev/null +++ b/tensorflow/core/util/exec_on_stall.h @@ -0,0 +1,89 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_CORE_UTIL_EXEC_ON_STALL_H_ +#define TENSORFLOW_CORE_UTIL_EXEC_ON_STALL_H_ + +#include + +#include "tensorflow/core/platform/env.h" +#include "tensorflow/core/platform/mutex.h" + +namespace tensorflow { + +// An object that executes a particular function only if it +// is not deleted within the allotted number of seconds. +// +// This can be useful in diagnosing deadlocks, stalls and memory leaks +// without logging too agressively. +class ExecuteOnStall { + public: + // delay_secs: If the object still exists after this many seconds, + // execute f. + // f: The function to be executed, for example a detailed log of the + // the state of an object to which this is attached. + // poll_microseconds: The spawned thread will wake and test whether + // the destructor has been invoked this frequently. + ExecuteOnStall(int delay_secs, std::function f, + int32 poll_microseconds = 100) + : disabled_(false), + joined_(false), + env_(Env::Default()), + f_(f), + poll_microseconds_(poll_microseconds) { + deadline_ = env_->NowMicros() + 1000000 * delay_secs; + env_->SchedClosure([this]() { + while (env_->NowMicros() < deadline_) { + { + mutex_lock l(mu_); + if (disabled_) { + break; + } + } + env_->SleepForMicroseconds(poll_microseconds_); + } + { + mutex_lock l(mu_); + if (!disabled_) { + f_(); + } + joined_ = true; + cond_var_.notify_all(); + } + }); + } + + ~ExecuteOnStall() { + // Wait for spawned thread to terminate. + mutex_lock l(mu_); + disabled_ = true; + if (!joined_) { + cond_var_.wait(l); + } + } + + private: + mutex mu_; + condition_variable cond_var_; + bool disabled_ GUARDED_BY(mu_); + bool joined_ GUARDED_BY(mu_); + Env* env_; + std::function f_; + int64 deadline_; + int32 poll_microseconds_; +}; + +} // namespace tensorflow +#endif // TENSORFLOW_CORE_UTIL_EXEC_ON_STALL_H_ diff --git a/tensorflow/core/util/exec_on_stall_test.cc b/tensorflow/core/util/exec_on_stall_test.cc new file mode 100644 index 0000000000..df8118d611 --- /dev/null +++ b/tensorflow/core/util/exec_on_stall_test.cc @@ -0,0 +1,47 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/core/util/exec_on_stall.h" + +#include "tensorflow/core/platform/macros.h" +#include "tensorflow/core/platform/test.h" + +namespace tensorflow { +namespace { + +struct Chunk { + std::unique_ptr stall_closure; +}; + +Chunk* NewChunk(int stall_seconds, std::function f) { + Chunk* c = new Chunk; + c->stall_closure.reset(new ExecuteOnStall(stall_seconds, std::move(f))); + return c; +} + +TEST(ExecuteOnStallTest, BothWays) { + bool a_triggered = false; + bool b_triggered = false; + Chunk* a = NewChunk(1, [&a_triggered]() { a_triggered = true; }); + Chunk* b = NewChunk(1, [&b_triggered]() { b_triggered = true; }); + delete a; + Env::Default()->SleepForMicroseconds(2000000); + EXPECT_FALSE(a_triggered); + EXPECT_TRUE(b_triggered); + delete b; +} + +} // namespace +} // namespace tensorflow -- GitLab From 62a70dd873bc8488b10df5ad55254119173a5d0c Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 5 Jun 2018 11:58:16 -0700 Subject: [PATCH 032/816] Extend and refactor reader_ops_test PiperOrigin-RevId: 199335030 --- .../python/kernel_tests/reader_ops_test.py | 352 ++++++++---------- 1 file changed, 163 insertions(+), 189 deletions(-) diff --git a/tensorflow/python/kernel_tests/reader_ops_test.py b/tensorflow/python/kernel_tests/reader_ops_test.py index 82a27eebee..7be473a5e7 100644 --- a/tensorflow/python/kernel_tests/reader_ops_test.py +++ b/tensorflow/python/kernel_tests/reader_ops_test.py @@ -77,6 +77,69 @@ _TEXT = b"""Gaily bedight, """ +class TFCompressionTestCase(test.TestCase): + + def setUp(self): + super(TFCompressionTestCase, self).setUp() + self._num_files = 2 + self._num_records = 7 + + def _Record(self, f, r): + return compat.as_bytes("Record %d of file %d" % (r, f)) + + def _CreateFiles(self, options=None, prefix=""): + filenames = [] + for i in range(self._num_files): + name = prefix + "tfrecord.%d.txt" % i + records = [self._Record(i, j) for j in range(self._num_records)] + fn = self._WriteRecordsToFile(records, name, options) + filenames.append(fn) + return filenames + + def _WriteRecordsToFile(self, records, name="tfrecord", options=None): + fn = os.path.join(self.get_temp_dir(), name) + with tf_record.TFRecordWriter(fn, options=options) as writer: + for r in records: + writer.write(r) + return fn + + def _ZlibCompressFile(self, infile, name="tfrecord.z"): + # zlib compress the file and write compressed contents to file. + with open(infile, "rb") as f: + cdata = zlib.compress(f.read()) + + zfn = os.path.join(self.get_temp_dir(), name) + with open(zfn, "wb") as f: + f.write(cdata) + return zfn + + def _GzipCompressFile(self, infile, name="tfrecord.gz"): + # gzip compress the file and write compressed contents to file. + with open(infile, "rb") as f: + cdata = f.read() + + gzfn = os.path.join(self.get_temp_dir(), name) + with gzip.GzipFile(gzfn, "wb") as f: + f.write(cdata) + return gzfn + + def _ZlibDecompressFile(self, infile, name="tfrecord"): + with open(infile, "rb") as f: + cdata = zlib.decompress(f.read()) + fn = os.path.join(self.get_temp_dir(), name) + with open(fn, "wb") as f: + f.write(cdata) + return fn + + def _GzipDecompressFile(self, infile, name="tfrecord"): + with gzip.GzipFile(infile, "rb") as f: + cdata = f.read() + fn = os.path.join(self.get_temp_dir(), name) + with open(fn, "wb") as f: + f.write(cdata) + return fn + + class IdentityReaderTest(test.TestCase): def _ExpectRead(self, sess, key, value, expected): @@ -348,7 +411,7 @@ class TextLineReaderTest(test.TestCase): k, v = sess.run([key, value]) -class FixedLengthRecordReaderTest(test.TestCase): +class FixedLengthRecordReaderTest(TFCompressionTestCase): def setUp(self): super(FixedLengthRecordReaderTest, self).setUp() @@ -407,40 +470,18 @@ class FixedLengthRecordReaderTest(test.TestCase): # gap_bytes=hop_bytes-record_bytes def _CreateGzipFiles(self, num_records, gap_bytes): - filenames = [] - for i in range(self._num_files): - fn = os.path.join(self.get_temp_dir(), "fixed_length_record.%d.txt" % i) - filenames.append(fn) - with gzip.GzipFile(fn, "wb") as f: - f.write(b"H" * self._header_bytes) - if num_records > 0: - f.write(self._Record(i, 0)) - for j in range(1, num_records): - if gap_bytes > 0: - f.write(b"G" * gap_bytes) - f.write(self._Record(i, j)) - f.write(b"F" * self._footer_bytes) + filenames = self._CreateFiles(num_records, gap_bytes) + for fn in filenames: + # compress inplace. + self._GzipCompressFile(fn, fn) return filenames # gap_bytes=hop_bytes-record_bytes def _CreateZlibFiles(self, num_records, gap_bytes): - filenames = [] - for i in range(self._num_files): - fn = os.path.join(self.get_temp_dir(), "fixed_length_record.%d.txt" % i) - filenames.append(fn) - with open(fn + ".tmp", "wb") as f: - f.write(b"H" * self._header_bytes) - if num_records > 0: - f.write(self._Record(i, 0)) - for j in range(1, num_records): - if gap_bytes > 0: - f.write(b"G" * gap_bytes) - f.write(self._Record(i, j)) - f.write(b"F" * self._footer_bytes) - with open(fn + ".tmp", "rb") as f: - cdata = zlib.compress(f.read()) - with open(fn, "wb") as zf: - zf.write(cdata) + filenames = self._CreateFiles(num_records, gap_bytes) + for fn in filenames: + # compress inplace. + self._ZlibCompressFile(fn, fn) return filenames def _CreateGzipOverlappedRecordFiles(self, num_overlapped_records): @@ -477,10 +518,7 @@ class FixedLengthRecordReaderTest(test.TestCase): ]) f.write(compat.as_bytes(all_records_str)) f.write(b"F" * self._footer_bytes) - with open(fn + ".tmp", "rb") as f: - cdata = zlib.compress(f.read()) - with open(fn, "wb") as zf: - zf.write(cdata) + self._ZlibCompressFile(fn + ".tmp", fn) return filenames # gap_bytes=hop_bytes-record_bytes @@ -529,7 +567,6 @@ class FixedLengthRecordReaderTest(test.TestCase): for i in range(self._num_files): for j in range(num_overlapped_records): k, v = sess.run([key, value]) - print(v) self.assertAllEqual("%s:%d" % (files[i], j), compat.as_text(k)) self.assertAllEqual(self._OverlappedRecord(i, j), v) @@ -579,25 +616,10 @@ class FixedLengthRecordReaderTest(test.TestCase): files, num_overlapped_records, encoding="ZLIB") -class TFRecordReaderTest(test.TestCase): +class TFRecordReaderTest(TFCompressionTestCase): def setUp(self): super(TFRecordReaderTest, self).setUp() - self._num_files = 2 - self._num_records = 7 - - def _Record(self, f, r): - return compat.as_bytes("Record %d of file %d" % (r, f)) - - def _CreateFiles(self): - filenames = [] - for i in range(self._num_files): - fn = os.path.join(self.get_temp_dir(), "tf_record.%d.txt" % i) - filenames.append(fn) - writer = tf_record.TFRecordWriter(fn) - for j in range(self._num_records): - writer.write(self._Record(i, j)) - return filenames def testOneEpoch(self): files = self._CreateFiles() @@ -647,107 +669,106 @@ class TFRecordReaderTest(test.TestCase): self.assertEqual(self._num_files * self._num_records, num_v) def testReadZlibFiles(self): - files = self._CreateFiles() - zlib_files = [] - for i, fn in enumerate(files): - with open(fn, "rb") as f: - cdata = zlib.compress(f.read()) - - zfn = os.path.join(self.get_temp_dir(), "tfrecord_%s.z" % i) - with open(zfn, "wb") as f: - f.write(cdata) - zlib_files.append(zfn) + options = tf_record.TFRecordOptions(TFRecordCompressionType.ZLIB) + files = self._CreateFiles(options) with self.test_session() as sess: - options = tf_record.TFRecordOptions(TFRecordCompressionType.ZLIB) reader = io_ops.TFRecordReader(name="test_reader", options=options) queue = data_flow_ops.FIFOQueue(99, [dtypes.string], shapes=()) key, value = reader.read(queue) - queue.enqueue_many([zlib_files]).run() + queue.enqueue_many([files]).run() queue.close().run() for i in range(self._num_files): for j in range(self._num_records): k, v = sess.run([key, value]) - self.assertTrue(compat.as_text(k).startswith("%s:" % zlib_files[i])) + self.assertTrue(compat.as_text(k).startswith("%s:" % files[i])) self.assertAllEqual(self._Record(i, j), v) def testReadGzipFiles(self): - files = self._CreateFiles() - gzip_files = [] - for i, fn in enumerate(files): - with open(fn, "rb") as f: - cdata = f.read() - - zfn = os.path.join(self.get_temp_dir(), "tfrecord_%s.gz" % i) - with gzip.GzipFile(zfn, "wb") as f: - f.write(cdata) - gzip_files.append(zfn) + options = tf_record.TFRecordOptions(TFRecordCompressionType.GZIP) + files = self._CreateFiles(options) with self.test_session() as sess: - options = tf_record.TFRecordOptions(TFRecordCompressionType.GZIP) reader = io_ops.TFRecordReader(name="test_reader", options=options) queue = data_flow_ops.FIFOQueue(99, [dtypes.string], shapes=()) key, value = reader.read(queue) - queue.enqueue_many([gzip_files]).run() + queue.enqueue_many([files]).run() queue.close().run() for i in range(self._num_files): for j in range(self._num_records): k, v = sess.run([key, value]) - self.assertTrue(compat.as_text(k).startswith("%s:" % gzip_files[i])) + self.assertTrue(compat.as_text(k).startswith("%s:" % files[i])) self.assertAllEqual(self._Record(i, j), v) -class TFRecordWriterZlibTest(test.TestCase): +class TFRecordWriterTest(TFCompressionTestCase): def setUp(self): - super(TFRecordWriterZlibTest, self).setUp() - self._num_files = 2 - self._num_records = 7 + super(TFRecordWriterTest, self).setUp() + + def _AssertFilesEqual(self, a, b, equal): + for an, bn in zip(a, b): + with open(an, "rb") as af, open(bn, "rb") as bf: + if equal: + self.assertEqual(af.read(), bf.read()) + else: + self.assertNotEqual(af.read(), bf.read()) + + def testWriteReadZLibFiles(self): + # Write uncompressed then compress manually. + options = tf_record.TFRecordOptions(TFRecordCompressionType.NONE) + files = self._CreateFiles(options, prefix="uncompressed") + zlib_files = [ + self._ZlibCompressFile(fn, "tfrecord_%s.z" % i) + for i, fn in enumerate(files) + ] + self._AssertFilesEqual(files, zlib_files, False) - def _Record(self, f, r): - return compat.as_bytes("Record %d of file %d" % (r, f)) + # Now write compressd and verify same. + options = tf_record.TFRecordOptions(TFRecordCompressionType.ZLIB) + compressed_files = self._CreateFiles(options, prefix="compressed") + self._AssertFilesEqual(compressed_files, zlib_files, True) - def _CreateFiles(self): - filenames = [] - for i in range(self._num_files): - fn = os.path.join(self.get_temp_dir(), "tf_record.%d.txt" % i) - filenames.append(fn) - options = tf_record.TFRecordOptions( - compression_type=TFRecordCompressionType.ZLIB) - writer = tf_record.TFRecordWriter(fn, options=options) - for j in range(self._num_records): - writer.write(self._Record(i, j)) - writer.close() - del writer + # Decompress compress and verify same. + uncompressed_files = [ + self._ZlibDecompressFile(fn, "tfrecord_%s.z" % i) + for i, fn in enumerate(compressed_files) + ] + self._AssertFilesEqual(uncompressed_files, files, True) + + def testWriteReadGzipFiles(self): + # Write uncompressed then compress manually. + options = tf_record.TFRecordOptions(TFRecordCompressionType.NONE) + files = self._CreateFiles(options, prefix="uncompressed") + gzip_files = [ + self._GzipCompressFile(fn, "tfrecord_%s.gz" % i) + for i, fn in enumerate(files) + ] + self._AssertFilesEqual(files, gzip_files, False) - return filenames + # Now write compressd and verify same. + options = tf_record.TFRecordOptions(TFRecordCompressionType.GZIP) + compressed_files = self._CreateFiles(options, prefix="compressed") - def _WriteRecordsToFile(self, records, name="tf_record"): - fn = os.path.join(self.get_temp_dir(), name) - writer = tf_record.TFRecordWriter(fn, options=None) - for r in records: - writer.write(r) - writer.close() - del writer - return fn + # Note: Gzips written by TFRecordWriter add 'tfrecord_0' so + # compressed_files can't be compared with gzip_files - def _ZlibCompressFile(self, infile, name="tfrecord.z"): - # zlib compress the file and write compressed contents to file. - with open(infile, "rb") as f: - cdata = zlib.compress(f.read()) + # Decompress compress and verify same. + uncompressed_files = [ + self._GzipDecompressFile(fn, "tfrecord_%s.gz" % i) + for i, fn in enumerate(compressed_files) + ] + self._AssertFilesEqual(uncompressed_files, files, True) - zfn = os.path.join(self.get_temp_dir(), name) - with open(zfn, "wb") as f: - f.write(cdata) - return zfn + +class TFRecordWriterZlibTest(TFCompressionTestCase): def testOneEpoch(self): - files = self._CreateFiles() + options = tf_record.TFRecordOptions(TFRecordCompressionType.ZLIB) + files = self._CreateFiles(options) with self.test_session() as sess: - options = tf_record.TFRecordOptions( - compression_type=TFRecordCompressionType.ZLIB) reader = io_ops.TFRecordReader(name="test_reader", options=options) queue = data_flow_ops.FIFOQueue(99, [dtypes.string], shapes=()) key, value = reader.read(queue) @@ -788,8 +809,7 @@ class TFRecordWriterZlibTest(test.TestCase): h.write(output) with self.test_session() as sess: - options = tf_record.TFRecordOptions( - compression_type=TFRecordCompressionType.ZLIB) + options = tf_record.TFRecordOptions(TFRecordCompressionType.ZLIB) reader = io_ops.TFRecordReader(name="test_reader", options=options) queue = data_flow_ops.FIFOQueue(1, [dtypes.string], shapes=()) key, value = reader.read(queue) @@ -808,9 +828,7 @@ class TFRecordWriterZlibTest(test.TestCase): # read the compressed contents and verify. actual = [] for r in tf_record.tf_record_iterator( - zfn, - options=tf_record.TFRecordOptions( - tf_record.TFRecordCompressionType.ZLIB)): + zfn, options=tf_record.TFRecordOptions(TFRecordCompressionType.ZLIB)): actual.append(r) self.assertEqual(actual, original) @@ -822,12 +840,9 @@ class TFRecordWriterZlibTest(test.TestCase): fn = self._WriteRecordsToFile(original, "zlib_read_write_large.tfrecord") zfn = self._ZlibCompressFile(fn, "zlib_read_write_large.tfrecord.z") - # read the compressed contents and verify. actual = [] for r in tf_record.tf_record_iterator( - zfn, - options=tf_record.TFRecordOptions( - tf_record.TFRecordCompressionType.ZLIB)): + zfn, options=tf_record.TFRecordOptions(TFRecordCompressionType.ZLIB)): actual.append(r) self.assertEqual(actual, original) @@ -835,13 +850,7 @@ class TFRecordWriterZlibTest(test.TestCase): """Verify that files produced are gzip compatible.""" original = [b"foo", b"bar"] fn = self._WriteRecordsToFile(original, "gzip_read_write.tfrecord") - - # gzip compress the file and write compressed contents to file. - with open(fn, "rb") as f: - cdata = f.read() - gzfn = os.path.join(self.get_temp_dir(), "tf_record.gz") - with gzip.GzipFile(gzfn, "wb") as f: - f.write(cdata) + gzfn = self._GzipCompressFile(fn, "tfrecord.gz") actual = [] for r in tf_record.tf_record_iterator( @@ -850,89 +859,54 @@ class TFRecordWriterZlibTest(test.TestCase): self.assertEqual(actual, original) -class TFRecordIteratorTest(test.TestCase): +class TFRecordIteratorTest(TFCompressionTestCase): def setUp(self): super(TFRecordIteratorTest, self).setUp() self._num_records = 7 - def _Record(self, r): - return compat.as_bytes("Record %d" % r) - - def _WriteCompressedRecordsToFile( - self, - records, - name="tfrecord.z", - compression_type=tf_record.TFRecordCompressionType.ZLIB): - fn = os.path.join(self.get_temp_dir(), name) - options = tf_record.TFRecordOptions(compression_type=compression_type) - writer = tf_record.TFRecordWriter(fn, options=options) - for r in records: - writer.write(r) - writer.close() - del writer - return fn - - def _ZlibDecompressFile(self, infile, name="tfrecord", wbits=zlib.MAX_WBITS): - with open(infile, "rb") as f: - cdata = zlib.decompress(f.read(), wbits) - zfn = os.path.join(self.get_temp_dir(), name) - with open(zfn, "wb") as f: - f.write(cdata) - return zfn - def testIterator(self): - fn = self._WriteCompressedRecordsToFile( - [self._Record(i) for i in range(self._num_records)], - "compressed_records") - options = tf_record.TFRecordOptions( - compression_type=TFRecordCompressionType.ZLIB) + records = [self._Record(0, i) for i in range(self._num_records)] + options = tf_record.TFRecordOptions(TFRecordCompressionType.ZLIB) + fn = self._WriteRecordsToFile(records, "compressed_records", options) + reader = tf_record.tf_record_iterator(fn, options) - for i in range(self._num_records): + for expected in records: record = next(reader) - self.assertAllEqual(self._Record(i), record) + self.assertAllEqual(expected, record) with self.assertRaises(StopIteration): record = next(reader) def testWriteZlibRead(self): """Verify compression with TFRecordWriter is zlib library compatible.""" original = [b"foo", b"bar"] - fn = self._WriteCompressedRecordsToFile(original, - "write_zlib_read.tfrecord.z") + options = tf_record.TFRecordOptions(TFRecordCompressionType.ZLIB) + fn = self._WriteRecordsToFile(original, "write_zlib_read.tfrecord.z", + options) + zfn = self._ZlibDecompressFile(fn, "write_zlib_read.tfrecord") - actual = [] - for r in tf_record.tf_record_iterator(zfn): - actual.append(r) + actual = list(tf_record.tf_record_iterator(zfn)) self.assertEqual(actual, original) def testWriteZlibReadLarge(self): """Verify compression for large records is zlib library compatible.""" # Make it large (about 5MB) original = [_TEXT * 10240] - fn = self._WriteCompressedRecordsToFile(original, - "write_zlib_read_large.tfrecord.z") - zfn = self._ZlibDecompressFile(fn, "write_zlib_read_large.tf_record") - actual = [] - for r in tf_record.tf_record_iterator(zfn): - actual.append(r) + options = tf_record.TFRecordOptions(TFRecordCompressionType.ZLIB) + fn = self._WriteRecordsToFile(original, "write_zlib_read_large.tfrecord.z", + options) + zfn = self._ZlibDecompressFile(fn, "write_zlib_read_large.tfrecord") + actual = list(tf_record.tf_record_iterator(zfn)) self.assertEqual(actual, original) def testWriteGzipRead(self): original = [b"foo", b"bar"] - fn = self._WriteCompressedRecordsToFile( - original, - "write_gzip_read.tfrecord.gz", - compression_type=TFRecordCompressionType.GZIP) - - with gzip.GzipFile(fn, "rb") as f: - cdata = f.read() - zfn = os.path.join(self.get_temp_dir(), "tf_record") - with open(zfn, "wb") as f: - f.write(cdata) + options = tf_record.TFRecordOptions(TFRecordCompressionType.GZIP) + fn = self._WriteRecordsToFile(original, "write_gzip_read.tfrecord.gz", + options) - actual = [] - for r in tf_record.tf_record_iterator(zfn): - actual.append(r) + gzfn = self._GzipDecompressFile(fn, "write_gzip_read.tfrecord") + actual = list(tf_record.tf_record_iterator(gzfn)) self.assertEqual(actual, original) def testBadFile(self): -- GitLab From 920df27282b3f5d03d79f54ef05cea305c2a30d7 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 5 Jun 2018 12:11:17 -0700 Subject: [PATCH 033/816] Implementation of the symmetrically quantized LSTM TFLite Op. PiperOrigin-RevId: 199337082 --- .../lite/kernels/internal/kernel_utils.cc | 262 ++- .../lite/kernels/internal/kernel_utils.h | 83 + tensorflow/contrib/lite/kernels/lstm.cc | 454 ++++- tensorflow/contrib/lite/kernels/lstm_test.cc | 1769 ++++++++++------- 4 files changed, 1791 insertions(+), 777 deletions(-) diff --git a/tensorflow/contrib/lite/kernels/internal/kernel_utils.cc b/tensorflow/contrib/lite/kernels/internal/kernel_utils.cc index 67e3810479..6e62183975 100644 --- a/tensorflow/contrib/lite/kernels/internal/kernel_utils.cc +++ b/tensorflow/contrib/lite/kernels/internal/kernel_utils.cc @@ -63,6 +63,8 @@ void RnnBatchStep(const float* input_ptr_batch, const int8_t* input_weights_ptr, // Quantize input from float to uint8 + quantization params (scaling // factor). float unused_min, unused_max; + // TODO(mirkov,raziel): replace this for-loop with a MACRO (or function) + // whichever is faster. for (int b = 0; b < batch_size; ++b) { const int offset = b * input_size; tensor_utils::SymmetricQuantizeFloats( @@ -147,6 +149,7 @@ void LstmStep( input_to_input_weights_ptr, n_cell, n_input, input_ptr_batch, n_batch, input_gate_scratch, /*result_stride=*/1); } + tensor_utils::MatrixBatchVectorMultiplyAccumulate( input_to_forget_weights_ptr, n_cell, n_input, input_ptr_batch, n_batch, forget_gate_scratch, /*result_stride=*/1); @@ -161,8 +164,7 @@ void LstmStep( if (!use_cifg) { tensor_utils::MatrixBatchVectorMultiplyAccumulate( recurrent_to_input_weights_ptr, n_cell, n_output, output_state_ptr, - n_batch, input_gate_scratch, - /*result_stride=*/1); + n_batch, input_gate_scratch, /*result_stride=*/1); } tensor_utils::MatrixBatchVectorMultiplyAccumulate( recurrent_to_forget_weights_ptr, n_cell, n_output, output_state_ptr, @@ -253,5 +255,261 @@ void LstmStep( output_state_ptr); } +// TODO(alanchiao): move this to tensor_utils. +void VectorMultiply(const int8_t* vector, const int v_size, const float scale, + float* result) { + for (int i = 0; i < v_size; ++i) { + *result++ = scale * *vector++; + } +} + +void LstmStep( + const float* input_ptr_batch, const int8_t* input_to_input_weights_ptr, + float input_to_input_weights_scale, + const int8_t* input_to_forget_weights_ptr, + float input_to_forget_weights_scale, + const int8_t* input_to_cell_weights_ptr, float input_to_cell_weights_scale, + const int8_t* input_to_output_weights_ptr, + float input_to_output_weights_scale, + const int8_t* recurrent_to_input_weights_ptr, + float recurrent_to_input_weights_scale, + const int8_t* recurrent_to_forget_weights_ptr, + float recurrent_to_forget_weights_scale, + const int8_t* recurrent_to_cell_weights_ptr, + float recurrent_to_cell_weights_scale, + const int8_t* recurrent_to_output_weights_ptr, + float recurrent_to_output_weights_scale, + const int8_t* cell_to_input_weights_ptr, float cell_to_input_weights_scale, + const int8_t* cell_to_forget_weights_ptr, + float cell_to_forget_weights_scale, + const int8_t* cell_to_output_weights_ptr, + float cell_to_output_weights_scale, const float* input_gate_bias_ptr, + const float* forget_gate_bias_ptr, const float* cell_bias_ptr, + const float* output_gate_bias_ptr, const int8_t* projection_weights_ptr, + float projection_weights_scale, const float* projection_bias_ptr, + const TfLiteLSTMParams* params, int n_batch, int n_cell, int n_input, + int n_output, float* input_gate_scratch, float* forget_gate_scratch, + float* cell_scratch, float* output_gate_scratch, float* scaling_factors, + float* product_scaling_factors, float* recovered_cell_weights, + int8_t* quantized_input_ptr_batch, int8_t* quantized_output_state_ptr, + int8_t* quantized_cell_state_ptr, float* output_state_ptr, + float* cell_state_ptr, float* output_ptr_batch) { + // Since we have already checked that weights are all there or none, we can + // check the existense of only one to the get the condition. + const bool use_cifg = (input_to_input_weights_ptr == nullptr); + const bool use_peephole = (cell_to_output_weights_ptr != nullptr); + // Initialize scratch buffers with bias. + if (!use_cifg) { + tensor_utils::VectorBatchVectorAssign(input_gate_bias_ptr, n_cell, n_batch, + input_gate_scratch); + } + tensor_utils::VectorBatchVectorAssign(forget_gate_bias_ptr, n_cell, n_batch, + forget_gate_scratch); + tensor_utils::VectorBatchVectorAssign(cell_bias_ptr, n_cell, n_batch, + cell_scratch); + tensor_utils::VectorBatchVectorAssign(output_gate_bias_ptr, n_cell, n_batch, + output_gate_scratch); + + if (!tensor_utils::IsZeroVector(input_ptr_batch, n_batch * n_input)) { + // Save quantization and matmul computation for all zero input. + float unused_min, unused_max; + for (int b = 0; b < n_batch; ++b) { + const int offset = b * n_input; + tensor_utils::SymmetricQuantizeFloats( + input_ptr_batch + offset, n_input, quantized_input_ptr_batch + offset, + &unused_min, &unused_max, &scaling_factors[b]); + } + // For each batch and cell: compute input_weight * input. + if (!use_cifg) { + for (int b = 0; b < n_batch; ++b) { + product_scaling_factors[b] = + scaling_factors[b] * input_to_input_weights_scale; + } + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + input_to_input_weights_ptr, n_cell, n_input, + quantized_input_ptr_batch, product_scaling_factors, n_batch, + input_gate_scratch, /*result_stride=*/1); + } + + for (int b = 0; b < n_batch; ++b) { + product_scaling_factors[b] = + scaling_factors[b] * input_to_forget_weights_scale; + } + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + input_to_forget_weights_ptr, n_cell, n_input, quantized_input_ptr_batch, + product_scaling_factors, n_batch, forget_gate_scratch, + /*result_stride=*/1); + + for (int b = 0; b < n_batch; ++b) { + product_scaling_factors[b] = + scaling_factors[b] * input_to_cell_weights_scale; + } + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + input_to_cell_weights_ptr, n_cell, n_input, quantized_input_ptr_batch, + product_scaling_factors, n_batch, cell_scratch, /*result_stride=*/1); + + for (int b = 0; b < n_batch; ++b) { + product_scaling_factors[b] = + scaling_factors[b] * input_to_cell_weights_scale; + } + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + input_to_output_weights_ptr, n_cell, n_input, quantized_input_ptr_batch, + product_scaling_factors, n_batch, output_gate_scratch, + /*result_stride=*/1); + } + + if (!tensor_utils::IsZeroVector(output_state_ptr, n_batch * n_output)) { + // Save quantization and matmul computation for all zero input. + float unused_min, unused_max; + for (int b = 0; b < n_batch; ++b) { + const int offset = b * n_output; + tensor_utils::SymmetricQuantizeFloats(output_state_ptr + offset, n_output, + quantized_output_state_ptr + offset, + &unused_min, &unused_max, + &scaling_factors[b]); + } + // For each batch and cell: compute recurrent_weight * output_state. + if (!use_cifg) { + for (int b = 0; b < n_batch; ++b) { + product_scaling_factors[b] = + scaling_factors[b] * recurrent_to_input_weights_scale; + } + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + recurrent_to_input_weights_ptr, n_cell, n_output, + quantized_output_state_ptr, product_scaling_factors, n_batch, + input_gate_scratch, /*result_stride=*/1); + } + + for (int b = 0; b < n_batch; ++b) { + product_scaling_factors[b] = + scaling_factors[b] * recurrent_to_forget_weights_scale; + } + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + recurrent_to_forget_weights_ptr, n_cell, n_output, + quantized_output_state_ptr, product_scaling_factors, n_batch, + forget_gate_scratch, /*result_stride=*/1); + + for (int b = 0; b < n_batch; ++b) { + product_scaling_factors[b] = + scaling_factors[b] * recurrent_to_cell_weights_scale; + } + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + recurrent_to_cell_weights_ptr, n_cell, n_output, + quantized_output_state_ptr, product_scaling_factors, n_batch, + cell_scratch, /*result_stride=*/1); + + for (int b = 0; b < n_batch; ++b) { + product_scaling_factors[b] = + scaling_factors[b] * recurrent_to_output_weights_scale; + } + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + recurrent_to_output_weights_ptr, n_cell, n_output, + quantized_output_state_ptr, product_scaling_factors, n_batch, + output_gate_scratch, /*result_stride=*/1); + } + + // Save quantization and matmul computation for all zero input. + const bool is_cell_state_all_zeros = + tensor_utils::IsZeroVector(cell_state_ptr, n_batch * n_cell); + + // For each batch and cell: update input gate. + if (!use_cifg) { + if (use_peephole && !is_cell_state_all_zeros) { + VectorMultiply(cell_to_input_weights_ptr, n_cell, + 1. / cell_to_input_weights_scale, recovered_cell_weights); + tensor_utils::VectorBatchVectorCwiseProductAccumulate( + recovered_cell_weights, n_cell, cell_state_ptr, n_batch, + input_gate_scratch); + } + tensor_utils::ApplySigmoidToVector(input_gate_scratch, n_cell * n_batch, + input_gate_scratch); + } + + // For each batch and cell: update forget gate. + if (use_peephole && !is_cell_state_all_zeros) { + VectorMultiply(cell_to_forget_weights_ptr, n_cell, + 1. / cell_to_forget_weights_scale, recovered_cell_weights); + tensor_utils::VectorBatchVectorCwiseProductAccumulate( + recovered_cell_weights, n_cell, cell_state_ptr, n_batch, + forget_gate_scratch); + } + tensor_utils::ApplySigmoidToVector(forget_gate_scratch, n_cell * n_batch, + forget_gate_scratch); + + // For each batch and cell: update the cell. + tensor_utils::VectorVectorCwiseProduct(forget_gate_scratch, cell_state_ptr, + n_batch * n_cell, cell_state_ptr); + tensor_utils::ApplyActivationToVector(cell_scratch, n_batch * n_cell, + params->activation, cell_scratch); + if (use_cifg) { + tensor_utils::Sub1Vector(forget_gate_scratch, n_batch * n_cell, + forget_gate_scratch); + tensor_utils::VectorVectorCwiseProductAccumulate( + cell_scratch, forget_gate_scratch, n_batch * n_cell, cell_state_ptr); + } else { + tensor_utils::VectorVectorCwiseProductAccumulate( + cell_scratch, input_gate_scratch, n_batch * n_cell, cell_state_ptr); + } + if (params->cell_clip > 0.0) { + tensor_utils::ClipVector(cell_state_ptr, n_batch * n_cell, + params->cell_clip, cell_state_ptr); + } + + // For each batch and cell: update the output gate. + if (use_peephole && !is_cell_state_all_zeros) { + VectorMultiply(cell_to_output_weights_ptr, n_cell, + 1. / cell_to_output_weights_scale, recovered_cell_weights); + tensor_utils::VectorBatchVectorCwiseProductAccumulate( + recovered_cell_weights, n_cell, cell_state_ptr, n_batch, + output_gate_scratch); + } + tensor_utils::ApplySigmoidToVector(output_gate_scratch, n_batch * n_cell, + output_gate_scratch); + tensor_utils::ApplyActivationToVector(cell_state_ptr, n_batch * n_cell, + params->activation, cell_scratch); + tensor_utils::VectorVectorCwiseProduct(output_gate_scratch, cell_scratch, + n_batch * n_cell, output_gate_scratch); + + // For each batch: update the projection and output_state. + const bool use_projection_weight = (projection_weights_ptr != nullptr); + const bool use_projection_bias = (projection_bias_ptr != nullptr); + if (use_projection_weight) { + if (use_projection_bias) { + tensor_utils::VectorBatchVectorAssign(projection_bias_ptr, n_output, + n_batch, output_ptr_batch); + } else { + tensor_utils::ZeroVector(output_ptr_batch, n_batch * n_output); + } + if (!tensor_utils::IsZeroVector(output_gate_scratch, n_batch * n_cell)) { + // Save quantization and matmul computation for all zero input. + float unused_min, unused_max; + for (int b = 0; b < n_batch; ++b) { + const int offset = b * n_cell; + tensor_utils::SymmetricQuantizeFloats( + output_gate_scratch + offset, n_cell, + quantized_cell_state_ptr + offset, &unused_min, &unused_max, + &scaling_factors[b]); + } + for (int b = 0; b < n_batch; ++b) { + product_scaling_factors[b] = + scaling_factors[b] * projection_weights_scale; + } + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + projection_weights_ptr, n_output, n_cell, quantized_cell_state_ptr, + product_scaling_factors, n_batch, output_ptr_batch, + /*result_stride=*/1); + } + if (params->proj_clip > 0.0) { + tensor_utils::ClipVector(output_ptr_batch, n_batch * n_output, + params->proj_clip, output_ptr_batch); + } + } else { + tensor_utils::CopyVector(output_gate_scratch, n_batch * n_output, + output_ptr_batch); + } + tensor_utils::CopyVector(output_ptr_batch, n_batch * n_output, + output_state_ptr); +} + } // namespace kernel_utils } // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/internal/kernel_utils.h b/tensorflow/contrib/lite/kernels/internal/kernel_utils.h index f3f42f0840..2a11b37a60 100644 --- a/tensorflow/contrib/lite/kernels/internal/kernel_utils.h +++ b/tensorflow/contrib/lite/kernels/internal/kernel_utils.h @@ -92,6 +92,89 @@ void LstmStep( float* forget_gate_scratch, float* cell_scratch, float* output_gate_scratch, float* output_ptr_batch); +// Same as above but with quantized weight matrices. In detail: +// Input of size 'n_batch * n_input': +// input_ptr_batch +// +// LSTM weights: +// Quantized input weights of size 'n_cell * n_input': +// input_to_input_weights - optional (can be nullptr) +// input_to_forget_weights +// input_to_cell_weights +// input_to_input_weights +// Quantized recurrent weights of size 'n_cell * n_output': +// recurrent_to_input_weights - optional +// recurrent_to_forget_weights +// recurrent_to_cell_weights +// recurrent_to_input_weights +// Quantized peephole weights of size 'n_cell', representing diagonal matrices. +// cell_to_input_weights - optional +// cell_to_cell_weights - optional +// cell_to_output_weights - optional +// Quantized projection weights of size 'n_output * n_cell' +// projection_weights_ptr - optional +// Weight scales (scalars) for each of the weights above. +// input_to_input_weights_scale - optional +// input_to_forget_weights_scale +// input_to_cell_weights_scale +// input_to_output_weights_scale +// recurrent_to_input_weights_scale - optional +// recurrent_to_forget_weights_scale +// recurrent_to_cell_weights_scale +// recurrent_to_output_weights_scale +// cell_to_input_weights_scale, +// cell_to_forget_weights_scale, +// cell_to_output_weights_scale, +// projection_weights_scale - optional +// Gate biases of size 'n_cell': +// input_gate_bias_ptr - optional +// forget_gate_bias_ptr +// cell_gate_bias_ptr +// output_gate_bias_ptr +// +// Temporary pre-allocated storage for quantized values: +// quantized_input_ptr_batch (same size as input_ptr_batch) +// quantized_output_state_ptr (same size as output_state_ptr) +// quantized_cell_state_ptr (same size as cell_state_ptr) +// Temporary pre-allocated storage for recovered values: +// recovered_cell_weights (same size as cell_to_*_weights) +// +// Outputs: +// output_state_ptr - size 'n_batch * n_output' +// cell_state_ptr - size 'n_batch * n_cell' +// output_ptr_batch - size 'n_batch * n_output' +void LstmStep( + const float* input_ptr_batch, const int8_t* input_to_input_weights_ptr, + float input_to_input_weights_scale, + const int8_t* input_to_forget_weights_ptr, + float input_to_forget_weights_scale, + const int8_t* input_to_cell_weights_ptr, float input_to_cell_weights_scale, + const int8_t* input_to_output_weights_ptr, + float input_to_output_weights_scale, + const int8_t* recurrent_to_input_weights_ptr, + float recurrent_to_input_weights_scale, + const int8_t* recurrent_to_forget_weights_ptr, + float recurrent_to_forget_weights_scale, + const int8_t* recurrent_to_cell_weights_ptr, + float recurrent_to_cell_weights_scale, + const int8_t* recurrent_to_output_weights_ptr, + float recurrent_to_output_weights_scale, + const int8_t* cell_to_input_weights_ptr, float cell_to_input_weights_scale, + const int8_t* cell_to_forget_weights_ptr, + float cell_to_forget_weights_scale, + const int8_t* cell_to_output_weights_ptr, + float cell_to_output_weights_scale, const float* input_gate_bias_ptr, + const float* forget_gate_bias_ptr, const float* cell_bias_ptr, + const float* output_gate_bias_ptr, const int8_t* projection_weights_ptr, + float projection_weights_scale, const float* projection_bias_ptr, + const TfLiteLSTMParams* params, int n_batch, int n_cell, int n_input, + int n_output, float* input_gate_scratch, float* forget_gate_scratch, + float* cell_scratch, float* output_gate_scratch, float* scaling_factors, + float* product_scaling_factors, float* recovered_cell_weights, + int8_t* quantized_input_ptr_batch, int8_t* quantized_output_state_ptr, + int8_t* quantized_cell_state_ptr, float* output_state_ptr, + float* cell_state_ptr, float* output_ptr_batch); + } // namespace kernel_utils } // namespace tflite #endif // TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_KERNEL_UTILS_H_ diff --git a/tensorflow/contrib/lite/kernels/lstm.cc b/tensorflow/contrib/lite/kernels/lstm.cc index 9aae3e571b..eb26a02455 100644 --- a/tensorflow/contrib/lite/kernels/lstm.cc +++ b/tensorflow/contrib/lite/kernels/lstm.cc @@ -86,7 +86,8 @@ constexpr int kOutputTensor = 2; void* Init(TfLiteContext* context, const char* buffer, size_t length) { auto* op_data = new OpData; op_data->kernel_type = kTfLiteLSTMFullKernel; - context->AddTensors(context, 1, &op_data->scratch_tensor_index); + context->AddTensors(context, /*tensors_to_add=*/7, + &op_data->scratch_tensor_index); return op_data; } @@ -94,7 +95,7 @@ void* Init(TfLiteContext* context, const char* buffer, size_t length) { TfLiteStatus CheckInputTensorDimensions(TfLiteContext* context, TfLiteNode* node, int n_input, int n_output, int n_cell) { - auto* params = reinterpret_cast(node->builtin_data); + const auto* params = reinterpret_cast(node->builtin_data); // Making sure clipping parameters have valid values. // == 0 means no clipping @@ -104,7 +105,7 @@ TfLiteStatus CheckInputTensorDimensions(TfLiteContext* context, const TfLiteTensor* input_to_input_weights = GetOptionalInputTensor(context, node, kInputToInputWeightsTensor); - if (input_to_input_weights) { + if (input_to_input_weights != nullptr) { TF_LITE_ENSURE_EQ(context, input_to_input_weights->dims->size, 2); TF_LITE_ENSURE_EQ(context, input_to_input_weights->dims->data[0], n_cell); TF_LITE_ENSURE_EQ(context, input_to_input_weights->dims->data[1], n_input); @@ -124,7 +125,7 @@ TfLiteStatus CheckInputTensorDimensions(TfLiteContext* context, const TfLiteTensor* recurrent_to_input_weights = GetOptionalInputTensor(context, node, kRecurrentToInputWeightsTensor); - if (recurrent_to_input_weights) { + if (recurrent_to_input_weights != nullptr) { TF_LITE_ENSURE_EQ(context, recurrent_to_input_weights->dims->size, 2); TF_LITE_ENSURE_EQ(context, recurrent_to_input_weights->dims->data[0], n_cell); @@ -214,7 +215,7 @@ TfLiteStatus CheckInputTensorDimensions(TfLiteContext* context, const TfLiteTensor* projection_weights = GetOptionalInputTensor(context, node, kProjectionWeightsTensor); - if (projection_weights) { + if (projection_weights != nullptr) { TF_LITE_ENSURE_EQ(context, projection_weights->dims->size, 2); TF_LITE_ENSURE_EQ(context, projection_weights->dims->data[0], n_output); TF_LITE_ENSURE_EQ(context, projection_weights->dims->data[1], n_cell); @@ -222,7 +223,7 @@ TfLiteStatus CheckInputTensorDimensions(TfLiteContext* context, const TfLiteTensor* projection_bias = GetOptionalInputTensor(context, node, kProjectionBiasTensor); - if (projection_bias) { + if (projection_bias != nullptr) { TF_LITE_ENSURE_EQ(context, projection_bias->dims->size, 1); TF_LITE_ENSURE_EQ(context, projection_bias->dims->data[0], n_output); } @@ -252,6 +253,7 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { // Inferring batch size, number of outputs and number of cells from the // input tensors. const TfLiteTensor* input = GetInput(context, node, kInputTensor); + TF_LITE_ENSURE_EQ(context, input->type, kTfLiteFloat32); TF_LITE_ENSURE(context, input->dims->size > 1); const int n_batch = input->dims->data[0]; const int n_input = input->dims->data[1]; @@ -296,86 +298,148 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, cell_state, cell_size)); - // Create a scratch buffer tensor. + // Mark state tensors as persistent tensors. + output_state->allocation_type = kTfLiteArenaRwPersistent; + cell_state->allocation_type = kTfLiteArenaRwPersistent; + + // The weights are of consistent type, so it suffices to check one. + // TODO(mirkov): create a utility/macro for this check, so all Ops can use it. + const bool is_hybrid_op = (input_to_output_weights->type == kTfLiteUInt8 && + input->type == kTfLiteFloat32); + TfLiteIntArrayFree(node->temporaries); - node->temporaries = TfLiteIntArrayCreate(1); + if (is_hybrid_op) { + node->temporaries = TfLiteIntArrayCreate(7); + } else { + node->temporaries = TfLiteIntArrayCreate(1); + } node->temporaries->data[0] = op_data->scratch_tensor_index; + + // Create a scratch buffer tensor. TfLiteTensor* scratch_buffer = GetTemporary(context, node, /*index=*/0); scratch_buffer->type = input->type; scratch_buffer->allocation_type = kTfLiteArenaRw; - // Mark state tensors as persistent tensors. - output_state->allocation_type = kTfLiteArenaRwPersistent; - cell_state->allocation_type = kTfLiteArenaRwPersistent; - const TfLiteTensor* input_to_input_weights = GetOptionalInputTensor(context, node, kInputToInputWeightsTensor); const bool use_cifg = (input_to_input_weights == nullptr); + TfLiteIntArray* scratch_buffer_size = TfLiteIntArrayCreate(2); + scratch_buffer_size->data[0] = n_batch; if (use_cifg) { - TfLiteIntArray* scratch_buffer_size = TfLiteIntArrayCreate(2); - scratch_buffer_size->data[0] = n_batch; // Reserving space for Cell, Forget, Output gates scratch_buffer_size->data[1] = n_cell * 3; - TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, scratch_buffer, - scratch_buffer_size)); } else { - TfLiteIntArray* scratch_buffer_size = TfLiteIntArrayCreate(2); - scratch_buffer_size->data[0] = n_batch; // Reserving space for Input, Cell, Forget, Output gates scratch_buffer_size->data[1] = n_cell * 4; - TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, scratch_buffer, - scratch_buffer_size)); + } + TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, scratch_buffer, + scratch_buffer_size)); + + if (is_hybrid_op) { + // Allocate temporary tensors to store quantized values of input, + // output_state and cell_state tensors. + node->temporaries->data[1] = op_data->scratch_tensor_index + 1; + TfLiteTensor* input_quantized = GetTemporary(context, node, /*index=*/1); + input_quantized->type = kTfLiteUInt8; + input_quantized->allocation_type = kTfLiteArenaRw; + if (!TfLiteIntArrayEqual(input_quantized->dims, input->dims)) { + TfLiteIntArray* input_quantized_size = TfLiteIntArrayCopy(input->dims); + TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, input_quantized, + input_quantized_size)); + } + node->temporaries->data[2] = op_data->scratch_tensor_index + 2; + TfLiteTensor* output_state_quantized = + GetTemporary(context, node, /*index=*/2); + output_state_quantized->type = kTfLiteUInt8; + output_state_quantized->allocation_type = kTfLiteArenaRw; + if (!TfLiteIntArrayEqual(output_state_quantized->dims, + output_state->dims)) { + TfLiteIntArray* output_state_quantized_size = + TfLiteIntArrayCopy(output_state->dims); + TF_LITE_ENSURE_OK(context, + context->ResizeTensor(context, output_state_quantized, + output_state_quantized_size)); + } + node->temporaries->data[3] = op_data->scratch_tensor_index + 3; + TfLiteTensor* cell_state_quantized = + GetTemporary(context, node, /*index=*/3); + cell_state_quantized->type = kTfLiteUInt8; + cell_state_quantized->allocation_type = kTfLiteArenaRw; + if (!TfLiteIntArrayEqual(cell_state_quantized->dims, cell_state->dims)) { + TfLiteIntArray* cell_state_quantized_size = + TfLiteIntArrayCopy(cell_state->dims); + TF_LITE_ENSURE_OK(context, + context->ResizeTensor(context, cell_state_quantized, + cell_state_quantized_size)); + } + + // Allocate temporary tensors to store scaling factors and product scaling + // factors. The latter is a convenience storage which allows to quantize + // a vector once (which produces the scaling factors) and multiply it with + // different matrices (which requires multiplying the scaling factors with + // the scaling factor of the matrix). + node->temporaries->data[4] = op_data->scratch_tensor_index + 4; + TfLiteTensor* scaling_factors = GetTemporary(context, node, /*index=*/4); + scaling_factors->type = kTfLiteFloat32; + scaling_factors->allocation_type = kTfLiteArenaRw; + TfLiteIntArray* scaling_factors_size = TfLiteIntArrayCreate(1); + scaling_factors_size->data[0] = n_batch; + if (!TfLiteIntArrayEqual(scaling_factors->dims, scaling_factors_size)) { + TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, scaling_factors, + scaling_factors_size)); + } + node->temporaries->data[5] = op_data->scratch_tensor_index + 5; + TfLiteTensor* prod_scaling_factors = + GetTemporary(context, node, /*index=*/5); + prod_scaling_factors->type = kTfLiteFloat32; + prod_scaling_factors->allocation_type = kTfLiteArenaRw; + TfLiteIntArray* prod_scaling_factors_size = TfLiteIntArrayCreate(1); + prod_scaling_factors_size->data[0] = n_batch; + if (!TfLiteIntArrayEqual(prod_scaling_factors->dims, + prod_scaling_factors_size)) { + TF_LITE_ENSURE_OK(context, + context->ResizeTensor(context, prod_scaling_factors, + prod_scaling_factors_size)); + } + + // Allocate a temporary tensor to store the recovered cell weights. Since + // this is used for diagonal matrices, only need to store n_cell values. + node->temporaries->data[6] = op_data->scratch_tensor_index + 6; + TfLiteTensor* recovered_cell_weights = + GetTemporary(context, node, /*index=*/6); + recovered_cell_weights->type = kTfLiteFloat32; + recovered_cell_weights->allocation_type = kTfLiteArenaRw; + TfLiteIntArray* recovered_cell_weights_size = TfLiteIntArrayCreate(1); + recovered_cell_weights_size->data[0] = n_cell; + if (!TfLiteIntArrayEqual(recovered_cell_weights->dims, + recovered_cell_weights_size)) { + TF_LITE_ENSURE_OK(context, + context->ResizeTensor(context, recovered_cell_weights, + recovered_cell_weights_size)); + } } return kTfLiteOk; } // The LSTM Op engine. -TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { - auto* params = reinterpret_cast(node->builtin_data); - const TfLiteTensor* input = GetInput(context, node, kInputTensor); - - const TfLiteTensor* input_to_input_weights = - GetOptionalInputTensor(context, node, kInputToInputWeightsTensor); - const TfLiteTensor* input_to_forget_weights = - GetInput(context, node, kInputToForgetWeightsTensor); - const TfLiteTensor* input_to_cell_weights = - GetInput(context, node, kInputToCellWeightsTensor); - const TfLiteTensor* input_to_output_weights = - GetInput(context, node, kInputToOutputWeightsTensor); - - const TfLiteTensor* recurrent_to_input_weights = - GetOptionalInputTensor(context, node, kRecurrentToInputWeightsTensor); - const TfLiteTensor* recurrent_to_forget_weights = - GetInput(context, node, kRecurrentToForgetWeightsTensor); - const TfLiteTensor* recurrent_to_cell_weights = - GetInput(context, node, kRecurrentToCellWeightsTensor); - const TfLiteTensor* recurrent_to_output_weights = - GetInput(context, node, kRecurrentToOutputWeightsTensor); - - const TfLiteTensor* cell_to_input_weights = - GetOptionalInputTensor(context, node, kCellToInputWeightsTensor); - const TfLiteTensor* cell_to_forget_weights = - GetOptionalInputTensor(context, node, kCellToForgetWeightsTensor); - const TfLiteTensor* cell_to_output_weights = - GetOptionalInputTensor(context, node, kCellToOutputWeightsTensor); - - const TfLiteTensor* input_gate_bias = - GetOptionalInputTensor(context, node, kInputGateBiasTensor); - const TfLiteTensor* forget_gate_bias = - GetInput(context, node, kForgetGateBiasTensor); - const TfLiteTensor* cell_bias = GetInput(context, node, kCellGateBiasTensor); - const TfLiteTensor* output_gate_bias = - GetInput(context, node, kOutputGateBiasTensor); - - const TfLiteTensor* projection_weights = - GetOptionalInputTensor(context, node, kProjectionWeightsTensor); - const TfLiteTensor* projection_bias = - GetOptionalInputTensor(context, node, kProjectionBiasTensor); - - TfLiteTensor* output_state = GetOutput(context, node, kOutputStateTensor); - TfLiteTensor* cell_state = GetOutput(context, node, kCellStateTensor); - TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - +TfLiteStatus EvalFloat( + const TfLiteTensor* input, const TfLiteTensor* input_to_input_weights, + const TfLiteTensor* input_to_forget_weights, + const TfLiteTensor* input_to_cell_weights, + const TfLiteTensor* input_to_output_weights, + const TfLiteTensor* recurrent_to_input_weights, + const TfLiteTensor* recurrent_to_forget_weights, + const TfLiteTensor* recurrent_to_cell_weights, + const TfLiteTensor* recurrent_to_output_weights, + const TfLiteTensor* cell_to_input_weights, + const TfLiteTensor* cell_to_forget_weights, + const TfLiteTensor* cell_to_output_weights, + const TfLiteTensor* input_gate_bias, const TfLiteTensor* forget_gate_bias, + const TfLiteTensor* cell_bias, const TfLiteTensor* output_gate_bias, + const TfLiteTensor* projection_weights, const TfLiteTensor* projection_bias, + const TfLiteLSTMParams* params, TfLiteTensor* scratch_buffer, + TfLiteTensor* output_state, TfLiteTensor* cell_state, + TfLiteTensor* output) { const int n_batch = input->dims->data[0]; const int n_input = input->dims->data[1]; // n_cell and n_output will be the same size when there is no projection. @@ -387,9 +451,6 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { const bool use_cifg = (input_to_input_weights == nullptr); const bool use_peephole = (cell_to_output_weights != nullptr); - // Index the scratch buffers pointers to the global scratch buffer. - TfLiteTensor* scratch_buffer = GetTemporary(context, node, /*index=*/0); - float* input_gate_scratch = nullptr; float* cell_scratch = nullptr; float* forget_gate_scratch = nullptr; @@ -457,6 +518,259 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { return kTfLiteOk; } +TfLiteStatus EvalHybrid( + const TfLiteTensor* input, const TfLiteTensor* input_to_input_weights, + const TfLiteTensor* input_to_forget_weights, + const TfLiteTensor* input_to_cell_weights, + const TfLiteTensor* input_to_output_weights, + const TfLiteTensor* recurrent_to_input_weights, + const TfLiteTensor* recurrent_to_forget_weights, + const TfLiteTensor* recurrent_to_cell_weights, + const TfLiteTensor* recurrent_to_output_weights, + const TfLiteTensor* cell_to_input_weights, + const TfLiteTensor* cell_to_forget_weights, + const TfLiteTensor* cell_to_output_weights, + const TfLiteTensor* input_gate_bias, const TfLiteTensor* forget_gate_bias, + const TfLiteTensor* cell_bias, const TfLiteTensor* output_gate_bias, + const TfLiteTensor* projection_weights, const TfLiteTensor* projection_bias, + const TfLiteLSTMParams* params, TfLiteTensor* scratch_buffer, + TfLiteTensor* scaling_factors, TfLiteTensor* prod_scaling_factors, + TfLiteTensor* recovered_cell_weights, TfLiteTensor* input_quantized, + TfLiteTensor* output_state_quantized, TfLiteTensor* cell_state_quantized, + TfLiteTensor* output_state, TfLiteTensor* cell_state, + TfLiteTensor* output) { + const int n_batch = input->dims->data[0]; + const int n_input = input->dims->data[1]; + // n_cell and n_output will be the same size when there is no projection. + const int n_cell = input_to_output_weights->dims->data[0]; + const int n_output = recurrent_to_output_weights->dims->data[1]; + + // Since we have already checked that weights are all there or none, we can + // check the existence of only one to get the condition. + const bool use_cifg = (input_to_input_weights == nullptr); + const bool use_peephole = (cell_to_output_weights != nullptr); + + float* input_gate_scratch = nullptr; + float* cell_scratch = nullptr; + float* forget_gate_scratch = nullptr; + float* output_gate_scratch = nullptr; + if (use_cifg) { + cell_scratch = scratch_buffer->data.f; + forget_gate_scratch = scratch_buffer->data.f + n_cell * n_batch; + output_gate_scratch = scratch_buffer->data.f + 2 * n_cell * n_batch; + } else { + input_gate_scratch = scratch_buffer->data.f; + cell_scratch = scratch_buffer->data.f + n_cell * n_batch; + forget_gate_scratch = scratch_buffer->data.f + 2 * n_cell * n_batch; + output_gate_scratch = scratch_buffer->data.f + 3 * n_cell * n_batch; + } + + // Check optional tensors, the respective pointers can be null. + int8_t* input_to_input_weights_ptr = nullptr; + float input_to_input_weights_scale = 1.0f; + int8_t* recurrent_to_input_weights_ptr = nullptr; + float recurrent_to_input_weights_scale = 1.0f; + float* input_gate_bias_ptr = nullptr; + if (!use_cifg) { + input_to_input_weights_ptr = + reinterpret_cast(input_to_input_weights->data.uint8); + recurrent_to_input_weights_ptr = + reinterpret_cast(recurrent_to_input_weights->data.uint8); + input_gate_bias_ptr = input_gate_bias->data.f; + input_to_input_weights_scale = input_to_input_weights->params.scale; + recurrent_to_input_weights_scale = recurrent_to_input_weights->params.scale; + } + + int8_t* cell_to_input_weights_ptr = nullptr; + int8_t* cell_to_forget_weights_ptr = nullptr; + int8_t* cell_to_output_weights_ptr = nullptr; + float cell_to_input_weights_scale = 1.0f; + float cell_to_forget_weights_scale = 1.0f; + float cell_to_output_weights_scale = 1.0f; + if (use_peephole) { + if (!use_cifg) { + cell_to_input_weights_ptr = + reinterpret_cast(cell_to_input_weights->data.uint8); + cell_to_input_weights_scale = cell_to_input_weights->params.scale; + } + cell_to_forget_weights_ptr = + reinterpret_cast(cell_to_forget_weights->data.uint8); + cell_to_output_weights_ptr = + reinterpret_cast(cell_to_output_weights->data.uint8); + cell_to_forget_weights_scale = cell_to_forget_weights->params.scale; + cell_to_output_weights_scale = cell_to_output_weights->params.scale; + } + + const int8_t* projection_weights_ptr = + (projection_weights == nullptr) + ? nullptr + : reinterpret_cast(projection_weights->data.uint8); + const float projection_weights_scale = + (projection_weights == nullptr) ? 1.0f : projection_weights->params.scale; + const float* projection_bias_ptr = + (projection_bias == nullptr) ? nullptr : projection_bias->data.f; + + // Required tensors, pointers are non-null. + const float* input_ptr_batch = input->data.f; + const int8_t* input_to_forget_weights_ptr = + reinterpret_cast(input_to_forget_weights->data.uint8); + const float input_to_forget_weights_scale = + input_to_forget_weights->params.scale; + const int8_t* input_to_cell_weights_ptr = + reinterpret_cast(input_to_cell_weights->data.uint8); + const float input_to_cell_weights_scale = input_to_cell_weights->params.scale; + const int8_t* input_to_output_weights_ptr = + reinterpret_cast(input_to_output_weights->data.uint8); + const float input_to_output_weights_scale = + input_to_output_weights->params.scale; + const int8_t* recurrent_to_forget_weights_ptr = + reinterpret_cast(recurrent_to_forget_weights->data.uint8); + const float recurrent_to_forget_weights_scale = + recurrent_to_forget_weights->params.scale; + const int8_t* recurrent_to_cell_weights_ptr = + reinterpret_cast(recurrent_to_cell_weights->data.uint8); + const float recurrent_to_cell_weights_scale = + recurrent_to_cell_weights->params.scale; + const int8_t* recurrent_to_output_weights_ptr = + reinterpret_cast(recurrent_to_output_weights->data.uint8); + const float recurrent_to_output_weights_scale = + recurrent_to_output_weights->params.scale; + const float* forget_gate_bias_ptr = forget_gate_bias->data.f; + const float* cell_bias_ptr = cell_bias->data.f; + const float* output_gate_bias_ptr = output_gate_bias->data.f; + + float* output_state_ptr = output_state->data.f; + float* cell_state_ptr = cell_state->data.f; + float* output_ptr_batch = output->data.f; + + // Temporary storage for quantized values and scaling factors. + int8_t* quantized_input_ptr = + reinterpret_cast(input_quantized->data.uint8); + int8_t* quantized_output_state_ptr = + reinterpret_cast(output_state_quantized->data.uint8); + int8_t* quantized_cell_state_ptr = + reinterpret_cast(cell_state_quantized->data.uint8); + float* scaling_factors_ptr = scaling_factors->data.f; + float* prod_scaling_factors_ptr = prod_scaling_factors->data.f; + float* recovered_cell_weights_ptr = recovered_cell_weights->data.f; + + kernel_utils::LstmStep( + input_ptr_batch, input_to_input_weights_ptr, input_to_input_weights_scale, + input_to_forget_weights_ptr, input_to_forget_weights_scale, + input_to_cell_weights_ptr, input_to_cell_weights_scale, + input_to_output_weights_ptr, input_to_output_weights_scale, + recurrent_to_input_weights_ptr, recurrent_to_input_weights_scale, + recurrent_to_forget_weights_ptr, recurrent_to_forget_weights_scale, + recurrent_to_cell_weights_ptr, recurrent_to_cell_weights_scale, + recurrent_to_output_weights_ptr, recurrent_to_output_weights_scale, + cell_to_input_weights_ptr, cell_to_input_weights_scale, + cell_to_forget_weights_ptr, cell_to_forget_weights_scale, + cell_to_output_weights_ptr, cell_to_output_weights_scale, + input_gate_bias_ptr, forget_gate_bias_ptr, cell_bias_ptr, + output_gate_bias_ptr, projection_weights_ptr, projection_weights_scale, + projection_bias_ptr, params, n_batch, n_cell, n_input, n_output, + input_gate_scratch, forget_gate_scratch, cell_scratch, + output_gate_scratch, scaling_factors_ptr, prod_scaling_factors_ptr, + recovered_cell_weights_ptr, quantized_input_ptr, + quantized_output_state_ptr, quantized_cell_state_ptr, output_state_ptr, + cell_state_ptr, output_ptr_batch); + + return kTfLiteOk; +} + +TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { + const auto* params = reinterpret_cast(node->builtin_data); + const TfLiteTensor* input = GetInput(context, node, kInputTensor); + + const TfLiteTensor* input_to_input_weights = + GetOptionalInputTensor(context, node, kInputToInputWeightsTensor); + const TfLiteTensor* input_to_forget_weights = + GetInput(context, node, kInputToForgetWeightsTensor); + const TfLiteTensor* input_to_cell_weights = + GetInput(context, node, kInputToCellWeightsTensor); + const TfLiteTensor* input_to_output_weights = + GetInput(context, node, kInputToOutputWeightsTensor); + + const TfLiteTensor* recurrent_to_input_weights = + GetOptionalInputTensor(context, node, kRecurrentToInputWeightsTensor); + const TfLiteTensor* recurrent_to_forget_weights = + GetInput(context, node, kRecurrentToForgetWeightsTensor); + const TfLiteTensor* recurrent_to_cell_weights = + GetInput(context, node, kRecurrentToCellWeightsTensor); + const TfLiteTensor* recurrent_to_output_weights = + GetInput(context, node, kRecurrentToOutputWeightsTensor); + + const TfLiteTensor* cell_to_input_weights = + GetOptionalInputTensor(context, node, kCellToInputWeightsTensor); + const TfLiteTensor* cell_to_forget_weights = + GetOptionalInputTensor(context, node, kCellToForgetWeightsTensor); + const TfLiteTensor* cell_to_output_weights = + GetOptionalInputTensor(context, node, kCellToOutputWeightsTensor); + + const TfLiteTensor* input_gate_bias = + GetOptionalInputTensor(context, node, kInputGateBiasTensor); + const TfLiteTensor* forget_gate_bias = + GetInput(context, node, kForgetGateBiasTensor); + const TfLiteTensor* cell_bias = GetInput(context, node, kCellGateBiasTensor); + const TfLiteTensor* output_gate_bias = + GetInput(context, node, kOutputGateBiasTensor); + + const TfLiteTensor* projection_weights = + GetOptionalInputTensor(context, node, kProjectionWeightsTensor); + const TfLiteTensor* projection_bias = + GetOptionalInputTensor(context, node, kProjectionBiasTensor); + + // Index the scratch buffers pointers to the global scratch buffer. + TfLiteTensor* scratch_buffer = GetTemporary(context, node, /*index=*/0); + + TfLiteTensor* output_state = GetOutput(context, node, kOutputStateTensor); + TfLiteTensor* cell_state = GetOutput(context, node, kCellStateTensor); + TfLiteTensor* output = GetOutput(context, node, kOutputTensor); + + // TODO(mirkov): add a check that weights are all uint8s or all floats. + switch (input_to_output_weights->type) { + case kTfLiteFloat32: { + return EvalFloat(input, input_to_input_weights, input_to_forget_weights, + input_to_cell_weights, input_to_output_weights, + recurrent_to_input_weights, recurrent_to_forget_weights, + recurrent_to_cell_weights, recurrent_to_output_weights, + cell_to_input_weights, cell_to_forget_weights, + cell_to_output_weights, input_gate_bias, + forget_gate_bias, cell_bias, output_gate_bias, + projection_weights, projection_bias, params, + scratch_buffer, output_state, cell_state, output); + } + case kTfLiteUInt8: { + TfLiteTensor* input_quantized = GetTemporary(context, node, /*index=*/1); + TfLiteTensor* output_state_quantized = + GetTemporary(context, node, /*index=*/2); + TfLiteTensor* cell_state_quantized = + GetTemporary(context, node, /*index=*/3); + TfLiteTensor* scaling_factors = GetTemporary(context, node, /*index=*/4); + TfLiteTensor* prod_scaling_factors = + GetTemporary(context, node, /*index=*/5); + TfLiteTensor* recovered_cell_weights = + GetTemporary(context, node, /*index=*/6); + return EvalHybrid( + input, input_to_input_weights, input_to_forget_weights, + input_to_cell_weights, input_to_output_weights, + recurrent_to_input_weights, recurrent_to_forget_weights, + recurrent_to_cell_weights, recurrent_to_output_weights, + cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, + input_gate_bias, forget_gate_bias, cell_bias, output_gate_bias, + projection_weights, projection_bias, params, scratch_buffer, + scaling_factors, prod_scaling_factors, recovered_cell_weights, + input_quantized, output_state_quantized, cell_state_quantized, + output_state, cell_state, output); + } + default: + context->ReportError(context, "Type %d is not currently supported.", + input_to_output_weights->type); + return kTfLiteError; + } + return kTfLiteOk; +} + } // namespace full // For basic kernel (5-inputs). @@ -491,7 +805,7 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { TF_LITE_ENSURE(context, node->inputs->size == kInputNum); TF_LITE_ENSURE(context, node->outputs->size == kOutputNum); - // Only Float32 is supportted currently. + // Only Float32 is supported currently. // TODO(ycling): Implement quantize uint8 support. for (int index = 0; index < node->inputs->size; ++index) { TfLiteTensor* tensor = &context->tensors[node->inputs->data[index]]; diff --git a/tensorflow/contrib/lite/kernels/lstm_test.cc b/tensorflow/contrib/lite/kernels/lstm_test.cc index d81220d8d3..6da29a4a92 100644 --- a/tensorflow/contrib/lite/kernels/lstm_test.cc +++ b/tensorflow/contrib/lite/kernels/lstm_test.cc @@ -14,7 +14,6 @@ limitations under the License. ==============================================================================*/ // Unit test for TFLite LSTM op. -#include #include #include @@ -35,7 +34,8 @@ class LSTMOpModel : public SingleOpModel { LSTMOpModel(int n_batch, int n_input, int n_cell, int n_output, bool use_cifg, bool use_peephole, bool use_projection_weights, bool use_projection_bias, float cell_clip, float proj_clip, - const std::vector>& input_shapes) + const std::vector>& input_shapes, + const TensorType& weight_type = TensorType_FLOAT32) : n_batch_(n_batch), n_input_(n_input), n_cell_(n_cell), @@ -45,31 +45,31 @@ class LSTMOpModel : public SingleOpModel { if (use_cifg) { input_to_input_weights_ = AddNullInput(); } else { - input_to_input_weights_ = AddInput(TensorType_FLOAT32); + input_to_input_weights_ = AddInput(weight_type); } - input_to_forget_weights_ = AddInput(TensorType_FLOAT32); - input_to_cell_weights_ = AddInput(TensorType_FLOAT32); - input_to_output_weights_ = AddInput(TensorType_FLOAT32); + input_to_forget_weights_ = AddInput(weight_type); + input_to_cell_weights_ = AddInput(weight_type); + input_to_output_weights_ = AddInput(weight_type); if (use_cifg) { recurrent_to_input_weights_ = AddNullInput(); } else { - recurrent_to_input_weights_ = AddInput(TensorType_FLOAT32); + recurrent_to_input_weights_ = AddInput(weight_type); } - recurrent_to_forget_weights_ = AddInput(TensorType_FLOAT32); - recurrent_to_cell_weights_ = AddInput(TensorType_FLOAT32); - recurrent_to_output_weights_ = AddInput(TensorType_FLOAT32); + recurrent_to_forget_weights_ = AddInput(weight_type); + recurrent_to_cell_weights_ = AddInput(weight_type); + recurrent_to_output_weights_ = AddInput(weight_type); if (use_peephole) { if (use_cifg) { cell_to_input_weights_ = AddNullInput(); } else { - cell_to_input_weights_ = AddInput(TensorType_FLOAT32); + cell_to_input_weights_ = AddInput(weight_type); } - cell_to_forget_weights_ = AddInput(TensorType_FLOAT32); - cell_to_output_weights_ = AddInput(TensorType_FLOAT32); + cell_to_forget_weights_ = AddInput(weight_type); + cell_to_output_weights_ = AddInput(weight_type); } else { cell_to_input_weights_ = AddNullInput(); cell_to_forget_weights_ = AddNullInput(); @@ -86,7 +86,7 @@ class LSTMOpModel : public SingleOpModel { output_gate_bias_ = AddInput(TensorType_FLOAT32); if (use_projection_weights) { - projection_weights_ = AddInput(TensorType_FLOAT32); + projection_weights_ = AddInput(weight_type); if (use_projection_bias) { projection_bias_ = AddInput(TensorType_FLOAT32); } else { @@ -192,8 +192,9 @@ class LSTMOpModel : public SingleOpModel { zero_buffer.get() + zero_buffer_size); } - void SetInput(int offset, float* begin, float* end) { - PopulateTensor(input_, offset, begin, end); + void SetInput(int offset, const float* begin, const float* end) { + PopulateTensor(input_, offset, const_cast(begin), + const_cast(end)); } std::vector GetOutput() { return ExtractVector(output_); } @@ -203,7 +204,7 @@ class LSTMOpModel : public SingleOpModel { int num_cells() { return n_cell_; } int num_batches() { return n_batch_; } - private: + protected: int input_; int input_to_input_weights_; int input_to_forget_weights_; @@ -237,7 +238,182 @@ class LSTMOpModel : public SingleOpModel { int n_output_; }; -TEST(LSTMOpTest, BlackBoxTestNoCifgNoPeepholeNoProjectionNoClipping) { +class HybridLSTMOpModel : public LSTMOpModel { + public: + HybridLSTMOpModel(int n_batch, int n_input, int n_cell, int n_output, + bool use_cifg, bool use_peephole, + bool use_projection_weights, bool use_projection_bias, + float cell_clip, float proj_clip, + const std::vector>& input_shapes) + : LSTMOpModel(n_batch, n_input, n_cell, n_output, use_cifg, use_peephole, + use_projection_weights, use_projection_bias, cell_clip, + proj_clip, input_shapes, TensorType_UINT8) {} + + void SetInputToInputWeights(std::initializer_list f) { + SymmetricQuantizeAndPopulate(input_to_input_weights_, f); + } + + void SetInputToForgetWeights(std::initializer_list f) { + SymmetricQuantizeAndPopulate(input_to_forget_weights_, f); + } + + void SetInputToCellWeights(std::initializer_list f) { + SymmetricQuantizeAndPopulate(input_to_cell_weights_, f); + } + + void SetInputToOutputWeights(std::initializer_list f) { + SymmetricQuantizeAndPopulate(input_to_output_weights_, f); + } + + void SetRecurrentToInputWeights(std::initializer_list f) { + SymmetricQuantizeAndPopulate(recurrent_to_input_weights_, f); + } + + void SetRecurrentToForgetWeights(std::initializer_list f) { + SymmetricQuantizeAndPopulate(recurrent_to_forget_weights_, f); + } + + void SetRecurrentToCellWeights(std::initializer_list f) { + SymmetricQuantizeAndPopulate(recurrent_to_cell_weights_, f); + } + + void SetRecurrentToOutputWeights(std::initializer_list f) { + SymmetricQuantizeAndPopulate(recurrent_to_output_weights_, f); + } + + void SetCellToInputWeights(std::initializer_list f) { + SymmetricQuantizeAndPopulate(cell_to_input_weights_, f); + } + + void SetCellToForgetWeights(std::initializer_list f) { + SymmetricQuantizeAndPopulate(cell_to_forget_weights_, f); + } + + void SetCellToOutputWeights(std::initializer_list f) { + SymmetricQuantizeAndPopulate(cell_to_output_weights_, f); + } + + void SetProjectionWeights(std::initializer_list f) { + SymmetricQuantizeAndPopulate(projection_weights_, f); + } +}; + +class BaseLstmTest : public ::testing::Test { + protected: + // Weights of the LSTM model. Some are optional. + std::initializer_list input_to_input_weights_; + std::initializer_list input_to_cell_weights_; + std::initializer_list input_to_forget_weights_; + std::initializer_list input_to_output_weights_; + std::initializer_list input_gate_bias_; + std::initializer_list cell_gate_bias_; + std::initializer_list forget_gate_bias_; + std::initializer_list output_gate_bias_; + std::initializer_list recurrent_to_input_weights_; + std::initializer_list recurrent_to_cell_weights_; + std::initializer_list recurrent_to_forget_weights_; + std::initializer_list recurrent_to_output_weights_; + std::initializer_list cell_to_input_weights_; + std::initializer_list cell_to_forget_weights_; + std::initializer_list cell_to_output_weights_; + std::initializer_list projection_weights_; + + // LSTM input is stored as num_batch x num_inputs vector. + std::vector> lstm_input_; + // LSTM output is stored as num_batch x num_outputs vector. + std::vector> lstm_golden_output_; + + // Compares output up to tolerance to the result of the lstm given the input. + void VerifyGoldens(const std::vector>& input, + const std::vector>& output, + LSTMOpModel* lstm, float tolerance = 1e-5) { + const int num_batches = input.size(); + EXPECT_GT(num_batches, 0); + const int num_inputs = lstm->num_inputs(); + EXPECT_GT(num_inputs, 0); + const int input_sequence_size = input[0].size() / num_inputs; + EXPECT_GT(input_sequence_size, 0); + for (int i = 0; i < input_sequence_size; ++i) { + for (int b = 0; b < num_batches; ++b) { + const float* batch_start = input[b].data() + i * num_inputs; + const float* batch_end = batch_start + num_inputs; + + lstm->SetInput(b * lstm->num_inputs(), batch_start, batch_end); + } + + lstm->Invoke(); + + const int num_outputs = lstm->num_outputs(); + std::vector expected; + for (int b = 0; b < num_batches; ++b) { + const float* golden_start_batch = output[b].data() + i * num_outputs; + const float* golden_end_batch = golden_start_batch + num_outputs; + expected.insert(expected.end(), golden_start_batch, golden_end_batch); + } + EXPECT_THAT(lstm->GetOutput(), + ElementsAreArray(ArrayFloatNear(expected, tolerance))); + for (int i = 0; i < num_outputs; ++i) { + std::cout << lstm->GetOutput()[i] << ", "; + } + std::cout << std::endl; + for (int i = 0; i < num_outputs; ++i) { + std::cout << expected[i] << ", "; + } + std::cout << std::endl; + } + } +}; + +class NoCifgNoPeepholeNoProjectionNoClippingLstmTest : public BaseLstmTest { + void SetUp() override { + input_to_input_weights_ = {-0.45018822, -0.02338299, -0.0870589, + -0.34550029, 0.04266912, -0.15680569, + -0.34856534, 0.43890524}; + input_to_cell_weights_ = {-0.50013041, 0.1370284, 0.11810488, 0.2013163, + -0.20583314, 0.44344562, 0.22077113, -0.29909778}; + input_to_forget_weights_ = {0.09701663, 0.20334584, -0.50592935, + -0.31343272, -0.40032279, 0.44781327, + 0.01387155, -0.35593212}; + input_to_output_weights_ = {-0.25065863, -0.28290087, 0.04613829, + 0.40525138, 0.44272184, 0.03897077, + -0.1556896, 0.19487578}; + input_gate_bias_ = {0., 0., 0., 0.}; + cell_gate_bias_ = {0., 0., 0., 0.}; + forget_gate_bias_ = {1., 1., 1., 1.}; + output_gate_bias_ = {0., 0., 0., 0.}; + + recurrent_to_input_weights_ = { + -0.0063535, -0.2042388, 0.31454784, -0.35746509, + 0.28902304, 0.08183324, -0.16555229, 0.02286911, + -0.13566875, 0.03034258, 0.48091322, -0.12528998, + 0.24077177, -0.51332325, -0.33502164, 0.10629296}; + + recurrent_to_cell_weights_ = { + -0.3407414, 0.24443203, -0.2078532, 0.26320225, + 0.05695659, -0.00123841, -0.4744786, -0.35869038, + -0.06418842, -0.13502428, -0.501764, 0.22830659, + -0.46367589, 0.26016325, -0.03894562, -0.16368064}; + + recurrent_to_forget_weights_ = { + -0.48684245, -0.06655136, 0.42224967, 0.2112639, + 0.27654213, 0.20864892, -0.07646349, 0.45877004, + 0.00141793, -0.14609534, 0.36447752, 0.09196436, + 0.28053468, 0.01560611, -0.20127171, -0.01140004}; + + recurrent_to_output_weights_ = { + 0.43385774, -0.17194885, 0.2718237, 0.09215671, + 0.24107647, -0.39835793, 0.18212086, 0.01301402, + 0.48572797, -0.50656658, 0.20047462, -0.20607421, + -0.51818722, -0.15390486, 0.0468148, 0.39922136}; + + lstm_input_ = {{2., 3., 3., 4., 1., 1.}}; + lstm_golden_output_ = {{-0.02973187, 0.1229473, 0.20885126, -0.15358765, + -0.03716109, 0.12507336, 0.41193449, -0.20860538, + -0.15053082, 0.09120187, 0.24278517, -0.12222792}}; + } +}; + +TEST_F(NoCifgNoPeepholeNoProjectionNoClippingLstmTest, LstmBlackBoxTest) { const int n_batch = 1; const int n_input = 2; // n_cell and n_output have the same size when there is no projection. @@ -257,10 +433,10 @@ TEST(LSTMOpTest, BlackBoxTestNoCifgNoPeepholeNoProjectionNoClipping) { {n_cell, n_input}, // input_to_cell_weight tensor {n_cell, n_input}, // input_to_output_weight tensor - {n_cell, n_output}, // recurrent_to_input_weight tensor - {n_cell, n_output}, // recurrent_to_forget_weight tensor - {n_cell, n_output}, // recurrent_to_cell_weight tensor - {n_cell, n_output}, // recurrent_to_output_weight tensor + {n_cell, n_output}, // recurrent_to_input_weight_tensor + {n_cell, n_output}, // recurrent_to_forget_weight_tensor + {n_cell, n_output}, // recurrent_to_cell_weight_tensor + {n_cell, n_output}, // recurrent_to_output_weight_tensor {0}, // cell_to_input_weight tensor {0}, // cell_to_forget_weight tensor @@ -275,79 +451,137 @@ TEST(LSTMOpTest, BlackBoxTestNoCifgNoPeepholeNoProjectionNoClipping) { {0}, // projection_bias tensor }); - lstm.SetInputToInputWeights({-0.45018822, -0.02338299, -0.0870589, - -0.34550029, 0.04266912, -0.15680569, - -0.34856534, 0.43890524}); - - lstm.SetInputToCellWeights({-0.50013041, 0.1370284, 0.11810488, 0.2013163, - -0.20583314, 0.44344562, 0.22077113, - -0.29909778}); - - lstm.SetInputToForgetWeights({0.09701663, 0.20334584, -0.50592935, - -0.31343272, -0.40032279, 0.44781327, - 0.01387155, -0.35593212}); - - lstm.SetInputToOutputWeights({-0.25065863, -0.28290087, 0.04613829, - 0.40525138, 0.44272184, 0.03897077, -0.1556896, - 0.19487578}); + lstm.SetInputToInputWeights(input_to_input_weights_); + lstm.SetInputToCellWeights(input_to_cell_weights_); + lstm.SetInputToForgetWeights(input_to_forget_weights_); + lstm.SetInputToOutputWeights(input_to_output_weights_); - lstm.SetInputGateBias({0., 0., 0., 0.}); + lstm.SetInputGateBias(input_gate_bias_); + lstm.SetCellBias(cell_gate_bias_); + lstm.SetForgetGateBias(forget_gate_bias_); + lstm.SetOutputGateBias(output_gate_bias_); - lstm.SetCellBias({0., 0., 0., 0.}); + lstm.SetRecurrentToInputWeights(recurrent_to_input_weights_); + lstm.SetRecurrentToCellWeights(recurrent_to_cell_weights_); + lstm.SetRecurrentToForgetWeights(recurrent_to_forget_weights_); + lstm.SetRecurrentToOutputWeights(recurrent_to_output_weights_); - lstm.SetForgetGateBias({1., 1., 1., 1.}); - - lstm.SetOutputGateBias({0., 0., 0., 0.}); - - lstm.SetRecurrentToInputWeights( - {-0.0063535, -0.2042388, 0.31454784, -0.35746509, 0.28902304, 0.08183324, - -0.16555229, 0.02286911, -0.13566875, 0.03034258, 0.48091322, - -0.12528998, 0.24077177, -0.51332325, -0.33502164, 0.10629296}); - - lstm.SetRecurrentToCellWeights( - {-0.3407414, 0.24443203, -0.2078532, 0.26320225, 0.05695659, -0.00123841, - -0.4744786, -0.35869038, -0.06418842, -0.13502428, -0.501764, 0.22830659, - -0.46367589, 0.26016325, -0.03894562, -0.16368064}); + // Resetting cell_state and output_state + lstm.ResetCellState(); + lstm.ResetOutputState(); - lstm.SetRecurrentToForgetWeights( - {-0.48684245, -0.06655136, 0.42224967, 0.2112639, 0.27654213, 0.20864892, - -0.07646349, 0.45877004, 0.00141793, -0.14609534, 0.36447752, 0.09196436, - 0.28053468, 0.01560611, -0.20127171, -0.01140004}); + VerifyGoldens(lstm_input_, lstm_golden_output_, &lstm); +} - lstm.SetRecurrentToOutputWeights( - {0.43385774, -0.17194885, 0.2718237, 0.09215671, 0.24107647, -0.39835793, - 0.18212086, 0.01301402, 0.48572797, -0.50656658, 0.20047462, -0.20607421, - -0.51818722, -0.15390486, 0.0468148, 0.39922136}); +TEST_F(NoCifgNoPeepholeNoProjectionNoClippingLstmTest, HybridLstmBlackBoxTest) { + const int n_batch = 1; + const int n_input = 2; + // n_cell and n_output have the same size when there is no projection. + const int n_cell = 4; + const int n_output = 4; - static float lstm_input[] = {2., 3., 3., 4., 1., 1.}; - static float lstm_golden_output[] = {-0.02973187, 0.1229473, 0.20885126, - -0.15358765, -0.03716109, 0.12507336, - 0.41193449, -0.20860538, -0.15053082, - 0.09120187, 0.24278517, -0.12222792}; + HybridLSTMOpModel lstm( + n_batch, n_input, n_cell, n_output, + /*use_cifg=*/false, /*use_peephole=*/false, + /*use_projection_weights=*/false, + /*use_projection_bias=*/false, /*cell_clip=*/0.0, /*proj_clip=*/0.0, + { + {n_batch, n_input}, // input tensor + + {n_cell, n_input}, // input_to_input_weight tensor + {n_cell, n_input}, // input_to_forget_weight tensor + {n_cell, n_input}, // input_to_cell_weight tensor + {n_cell, n_input}, // input_to_output_weight tensor + + {n_cell, n_output}, // recurrent_to_input_weight tensor + {n_cell, n_output}, // recurrent_to_forget_weight tensor + {n_cell, n_output}, // recurrent_to_cell_weight tensor + {n_cell, n_output}, // recurrent_to_output_weight tensor + + {0}, // cell_to_input_weight tensor + {0}, // cell_to_forget_weight tensor + {0}, // cell_to_output_weight tensor + + {n_cell}, // input_gate_bias tensor + {n_cell}, // forget_gate_bias tensor + {n_cell}, // cell_bias tensor + {n_cell}, // output_gate_bias tensor + + {0, 0}, // projection_weight tensor + {0}, // projection_bias tensor + }); + + lstm.SetInputToInputWeights(input_to_input_weights_); + lstm.SetInputToCellWeights(input_to_cell_weights_); + lstm.SetInputToForgetWeights(input_to_forget_weights_); + lstm.SetInputToOutputWeights(input_to_output_weights_); + + lstm.SetInputGateBias(input_gate_bias_); + lstm.SetCellBias(cell_gate_bias_); + lstm.SetForgetGateBias(forget_gate_bias_); + lstm.SetOutputGateBias(output_gate_bias_); + + lstm.SetRecurrentToInputWeights(recurrent_to_input_weights_); + lstm.SetRecurrentToCellWeights(recurrent_to_cell_weights_); + lstm.SetRecurrentToForgetWeights(recurrent_to_forget_weights_); + lstm.SetRecurrentToOutputWeights(recurrent_to_output_weights_); // Resetting cell_state and output_state lstm.ResetCellState(); lstm.ResetOutputState(); - const int input_sequence_size = - sizeof(lstm_input) / sizeof(float) / (lstm.num_inputs()); - for (int i = 0; i < input_sequence_size; i++) { - float* batch0_start = lstm_input + i * lstm.num_inputs(); - float* batch0_end = batch0_start + lstm.num_inputs(); + VerifyGoldens(lstm_input_, lstm_golden_output_, &lstm, + /*tolerance=*/0.0157651); +} - lstm.SetInput(0, batch0_start, batch0_end); +class CifgNoPeepholeNoProjectionNoClippingLstmTest : public BaseLstmTest { + void SetUp() override { + input_to_cell_weights_ = {-0.49770179, -0.27711356, -0.09624726, + 0.05100781, 0.04717243, 0.48944736, + -0.38535351, -0.17212132}; - lstm.Invoke(); + input_to_forget_weights_ = {-0.55291498, -0.42866567, 0.13056988, + -0.3633365, -0.22755712, 0.28253698, + 0.24407166, 0.33826375}; - float* golden_start = lstm_golden_output + i * lstm.num_outputs(); - float* golden_end = golden_start + lstm.num_outputs(); - std::vector expected; - expected.insert(expected.end(), golden_start, golden_end); - EXPECT_THAT(lstm.GetOutput(), ElementsAreArray(ArrayFloatNear(expected))); + input_to_output_weights_ = {0.10725588, -0.02335852, -0.55932593, + -0.09426838, -0.44257352, 0.54939759, + 0.01533556, 0.42751634}; + cell_gate_bias_ = {0., 0., 0., 0.}; + forget_gate_bias_ = {1., 1., 1., 1.}; + output_gate_bias_ = {0., 0., 0., 0.}; + + recurrent_to_cell_weights_ = { + 0.54066205, -0.32668582, -0.43562764, -0.56094903, + 0.42957711, 0.01841056, -0.32764608, -0.33027974, + -0.10826075, 0.20675004, 0.19069612, -0.03026325, + -0.54532051, 0.33003211, 0.44901288, 0.21193194}; + + recurrent_to_forget_weights_ = { + -0.13832897, -0.0515101, -0.2359007, -0.16661474, + -0.14340827, 0.36986142, 0.23414481, 0.55899, + 0.10798943, -0.41174671, 0.17751795, -0.34484994, + -0.35874045, -0.11352962, 0.27268326, 0.54058349}; + + recurrent_to_output_weights_ = { + 0.41613156, 0.42610586, -0.16495961, -0.5663873, + 0.30579174, -0.05115908, -0.33941799, 0.23364776, + 0.11178309, 0.09481031, -0.26424935, 0.46261835, + 0.50248802, 0.26114327, -0.43736315, 0.33149987}; + + cell_to_forget_weights_ = {0.47485286, -0.51955009, -0.24458408, + 0.31544167}; + cell_to_output_weights_ = {-0.17135078, 0.82760304, 0.85573703, + -0.77109635}; + + lstm_input_ = {{2., 3., 3., 4., 1., 1.}}; + lstm_golden_output_ = {{-0.36444446, -0.00352185, 0.12886585, -0.05163646, + -0.42312205, -0.01218222, 0.24201041, -0.08124574, + -0.358325, -0.04621704, 0.21641694, -0.06471302}}; } -} +}; -TEST(LSTMOpTest, BlackBoxTestWithCifgWithPeepholeNoProjectionNoClipping) { +TEST_F(CifgNoPeepholeNoProjectionNoClippingLstmTest, LstmBlackBoxTest) { const int n_batch = 1; const int n_input = 2; // n_cell and n_output have the same size when there is no projection. @@ -385,74 +619,689 @@ TEST(LSTMOpTest, BlackBoxTestWithCifgWithPeepholeNoProjectionNoClipping) { {0}, // projection_bias tensor }); - lstm.SetInputToCellWeights({-0.49770179, -0.27711356, -0.09624726, 0.05100781, - 0.04717243, 0.48944736, -0.38535351, - -0.17212132}); - - lstm.SetInputToForgetWeights({-0.55291498, -0.42866567, 0.13056988, - -0.3633365, -0.22755712, 0.28253698, 0.24407166, - 0.33826375}); - - lstm.SetInputToOutputWeights({0.10725588, -0.02335852, -0.55932593, - -0.09426838, -0.44257352, 0.54939759, - 0.01533556, 0.42751634}); - - lstm.SetCellBias({0., 0., 0., 0.}); + lstm.SetInputToCellWeights(input_to_cell_weights_); + lstm.SetInputToForgetWeights(input_to_forget_weights_); + lstm.SetInputToOutputWeights(input_to_output_weights_); - lstm.SetForgetGateBias({1., 1., 1., 1.}); + lstm.SetCellBias(cell_gate_bias_); + lstm.SetForgetGateBias(forget_gate_bias_); + lstm.SetOutputGateBias(output_gate_bias_); - lstm.SetOutputGateBias({0., 0., 0., 0.}); + lstm.SetRecurrentToCellWeights(recurrent_to_cell_weights_); + lstm.SetRecurrentToForgetWeights(recurrent_to_forget_weights_); + lstm.SetRecurrentToOutputWeights(recurrent_to_output_weights_); - lstm.SetRecurrentToCellWeights( - {0.54066205, -0.32668582, -0.43562764, -0.56094903, 0.42957711, - 0.01841056, -0.32764608, -0.33027974, -0.10826075, 0.20675004, - 0.19069612, -0.03026325, -0.54532051, 0.33003211, 0.44901288, - 0.21193194}); + lstm.SetCellToForgetWeights(cell_to_forget_weights_); + lstm.SetCellToOutputWeights(cell_to_output_weights_); - lstm.SetRecurrentToForgetWeights( - {-0.13832897, -0.0515101, -0.2359007, -0.16661474, -0.14340827, - 0.36986142, 0.23414481, 0.55899, 0.10798943, -0.41174671, 0.17751795, - -0.34484994, -0.35874045, -0.11352962, 0.27268326, 0.54058349}); + // Resetting cell_state and output_state + lstm.ResetCellState(); + lstm.ResetOutputState(); - lstm.SetRecurrentToOutputWeights( - {0.41613156, 0.42610586, -0.16495961, -0.5663873, 0.30579174, -0.05115908, - -0.33941799, 0.23364776, 0.11178309, 0.09481031, -0.26424935, 0.46261835, - 0.50248802, 0.26114327, -0.43736315, 0.33149987}); + VerifyGoldens(lstm_input_, lstm_golden_output_, &lstm); +} - lstm.SetCellToForgetWeights( - {0.47485286, -0.51955009, -0.24458408, 0.31544167}); - lstm.SetCellToOutputWeights( - {-0.17135078, 0.82760304, 0.85573703, -0.77109635}); +TEST_F(CifgNoPeepholeNoProjectionNoClippingLstmTest, HybridLstmBlackBoxTest) { + const int n_batch = 1; + const int n_input = 2; + // n_cell and n_output have the same size when there is no projection. + const int n_cell = 4; + const int n_output = 4; - static float lstm_input[] = {2., 3., 3., 4., 1., 1.}; - static float lstm_golden_output[] = {-0.36444446, -0.00352185, 0.12886585, - -0.05163646, -0.42312205, -0.01218222, - 0.24201041, -0.08124574, -0.358325, - -0.04621704, 0.21641694, -0.06471302}; + HybridLSTMOpModel lstm( + n_batch, n_input, n_cell, n_output, + /*use_cifg=*/true, /*use_peephole=*/true, + /*use_projection_weights=*/false, + /*use_projection_bias=*/false, + /*cell_clip=*/0.0, /*proj_clip=*/0.0, + { + {n_batch, n_input}, // input tensor + + {0, 0}, // input_to_input_weight tensor + {n_cell, n_input}, // input_to_forget_weight tensor + {n_cell, n_input}, // input_to_cell_weight tensor + {n_cell, n_input}, // input_to_output_weight tensor + + {0, 0}, // recurrent_to_input_weight tensor + {n_cell, n_output}, // recurrent_to_forget_weight tensor + {n_cell, n_output}, // recurrent_to_cell_weight tensor + {n_cell, n_output}, // recurrent_to_output_weight tensor + + {0}, // cell_to_input_weight tensor + {n_cell}, // cell_to_forget_weight tensor + {n_cell}, // cell_to_output_weight tensor + + {0}, // input_gate_bias tensor + {n_cell}, // forget_gate_bias tensor + {n_cell}, // cell_bias tensor + {n_cell}, // output_gate_bias tensor + + {0, 0}, // projection_weight tensor + {0}, // projection_bias tensor + }); + + lstm.SetInputToCellWeights(input_to_cell_weights_); + lstm.SetInputToForgetWeights(input_to_forget_weights_); + lstm.SetInputToOutputWeights(input_to_output_weights_); + + lstm.SetCellBias(cell_gate_bias_); + lstm.SetForgetGateBias(forget_gate_bias_); + lstm.SetOutputGateBias(output_gate_bias_); + + lstm.SetRecurrentToCellWeights(recurrent_to_cell_weights_); + lstm.SetRecurrentToForgetWeights(recurrent_to_forget_weights_); + lstm.SetRecurrentToOutputWeights(recurrent_to_output_weights_); + + lstm.SetCellToForgetWeights(cell_to_forget_weights_); + lstm.SetCellToOutputWeights(cell_to_output_weights_); // Resetting cell_state and output_state lstm.ResetCellState(); lstm.ResetOutputState(); - const int input_sequence_size = - sizeof(lstm_input) / sizeof(float) / (lstm.num_inputs()); - for (int i = 0; i < input_sequence_size; i++) { - float* batch0_start = lstm_input + i * lstm.num_inputs(); - float* batch0_end = batch0_start + lstm.num_inputs(); - - lstm.SetInput(0, batch0_start, batch0_end); - - lstm.Invoke(); + VerifyGoldens(lstm_input_, lstm_golden_output_, &lstm, /*tolerance=*/0.03573); +} - float* golden_start = lstm_golden_output + i * lstm.num_outputs(); - float* golden_end = golden_start + lstm.num_outputs(); - std::vector expected; - expected.insert(expected.end(), golden_start, golden_end); - EXPECT_THAT(lstm.GetOutput(), ElementsAreArray(ArrayFloatNear(expected))); +class NoCifgPeepholeProjectionClippingLstmTest : public BaseLstmTest { + void SetUp() override { + input_to_input_weights_ = { + 0.021393683, 0.06124551, 0.046905167, -0.014657677, -0.03149463, + 0.09171803, 0.14647801, 0.10797193, -0.0057968358, 0.0019193048, + -0.2726754, 0.10154029, -0.018539885, 0.080349885, -0.10262385, + -0.022599787, -0.09121155, -0.008675967, -0.045206103, -0.0821282, + -0.008045952, 0.015478081, 0.055217247, 0.038719587, 0.044153627, + -0.06453243, 0.05031825, -0.046935108, -0.008164439, 0.014574226, + -0.1671009, -0.15519552, -0.16819797, -0.13971269, -0.11953059, + 0.25005487, -0.22790983, 0.009855087, -0.028140958, -0.11200698, + 0.11295408, -0.0035217577, 0.054485075, 0.05184695, 0.064711206, + 0.10989193, 0.11674786, 0.03490607, 0.07727357, 0.11390585, + -0.1863375, -0.1034451, -0.13945189, -0.049401227, -0.18767063, + 0.042483903, 0.14233552, 0.13832581, 0.18350165, 0.14545603, + -0.028545704, 0.024939531, 0.050929718, 0.0076203286, -0.0029723682, + -0.042484224, -0.11827596, -0.09171104, -0.10808628, -0.16327988, + -0.2273378, -0.0993647, -0.017155107, 0.0023917493, 0.049272764, + 0.0038534778, 0.054764505, 0.089753784, 0.06947234, 0.08014476, + -0.04544234, -0.0497073, -0.07135631, -0.048929106, -0.004042012, + -0.009284026, 0.018042054, 0.0036860977, -0.07427302, -0.11434604, + -0.018995456, 0.031487543, 0.012834908, 0.019977754, 0.044256654, + -0.39292613, -0.18519334, -0.11651281, -0.06809892, 0.011373677}; + + input_to_forget_weights_ = { + -0.0018401089, -0.004852237, 0.03698424, 0.014181704, + 0.028273236, -0.016726194, -0.05249759, -0.10204261, + 0.00861066, -0.040979505, -0.009899187, 0.01923892, + -0.028177269, -0.08535103, -0.14585495, 0.10662567, + -0.01909731, -0.017883534, -0.0047269356, -0.045103323, + 0.0030784295, 0.076784775, 0.07463696, 0.094531395, + 0.0814421, -0.12257899, -0.033945758, -0.031303465, + 0.045630626, 0.06843887, -0.13492945, -0.012480007, + -0.0811829, -0.07224499, -0.09628791, 0.045100946, + 0.0012300825, 0.013964662, 0.099372394, 0.02543059, + 0.06958324, 0.034257296, 0.0482646, 0.06267997, + 0.052625068, 0.12784666, 0.07077897, 0.025725935, + 0.04165009, 0.07241905, 0.018668644, -0.037377294, + -0.06277783, -0.08833636, -0.040120605, -0.011405586, + -0.007808335, -0.010301386, -0.005102167, 0.027717464, + 0.05483423, 0.11449111, 0.11289652, 0.10939839, + 0.13396506, -0.08402166, -0.01901462, -0.044678304, + -0.07720565, 0.014350063, -0.11757958, -0.0652038, + -0.08185733, -0.076754324, -0.092614375, 0.10405491, + 0.052960336, 0.035755895, 0.035839386, -0.012540553, + 0.036881298, 0.02913376, 0.03420159, 0.05448447, + -0.054523353, 0.02582715, 0.02327355, -0.011857179, + -0.0011980024, -0.034641717, -0.026125094, -0.17582615, + -0.15923657, -0.27486774, -0.0006143371, 0.0001771948, + -8.470171e-05, 0.02651807, 0.045790765, 0.06956496}; + + input_to_cell_weights_ = { + -0.04580283, -0.09549462, -0.032418985, -0.06454633, + -0.043528453, 0.043018587, -0.049152344, -0.12418144, + -0.078985475, -0.07596889, 0.019484362, -0.11434962, + -0.0074034138, -0.06314844, -0.092981495, 0.0062155537, + -0.025034338, -0.0028890965, 0.048929527, 0.06235075, + 0.10665918, -0.032036792, -0.08505916, -0.10843358, + -0.13002433, -0.036816437, -0.02130134, -0.016518239, + 0.0047691227, -0.0025825808, 0.066017866, 0.029991534, + -0.10652836, -0.1037554, -0.13056071, -0.03266643, + -0.033702414, -0.006473424, -0.04611692, 0.014419339, + -0.025174323, 0.0396852, 0.081777506, 0.06157468, + 0.10210095, -0.009658194, 0.046511717, 0.03603906, + 0.0069369148, 0.015960095, -0.06507666, 0.09551598, + 0.053568836, 0.06408714, 0.12835667, -0.008714329, + -0.20211966, -0.12093674, 0.029450472, 0.2849013, + -0.029227901, 0.1164364, -0.08560263, 0.09941786, + -0.036999565, -0.028842626, -0.0033637602, -0.017012902, + -0.09720865, -0.11193351, -0.029155117, -0.017936034, + -0.009768936, -0.04223324, -0.036159635, 0.06505112, + -0.021742892, -0.023377212, -0.07221364, -0.06430552, + 0.05453865, 0.091149814, 0.06387331, 0.007518393, + 0.055960953, 0.069779344, 0.046411168, 0.10509911, + 0.07463894, 0.0075130584, 0.012850982, 0.04555431, + 0.056955688, 0.06555285, 0.050801456, -0.009862683, + 0.00826772, -0.026555609, -0.0073611983, -0.0014897042}; + + input_to_output_weights_ = { + -0.0998932, -0.07201956, -0.052803773, -0.15629593, -0.15001918, + -0.07650751, 0.02359855, -0.075155355, -0.08037709, -0.15093534, + 0.029517552, -0.04751393, 0.010350531, -0.02664851, -0.016839722, + -0.023121163, 0.0077019283, 0.012851257, -0.05040649, -0.0129761, + -0.021737747, -0.038305793, -0.06870586, -0.01481247, -0.001285394, + 0.10124236, 0.083122835, 0.053313006, -0.062235646, -0.075637154, + -0.027833903, 0.029774971, 0.1130802, 0.09218906, 0.09506135, + -0.086665764, -0.037162706, -0.038880914, -0.035832845, -0.014481564, + -0.09825003, -0.12048569, -0.097665586, -0.05287633, -0.0964047, + -0.11366429, 0.035777505, 0.13568819, 0.052451383, 0.050649304, + 0.05798951, -0.021852335, -0.099848844, 0.014740475, -0.078897946, + 0.04974699, 0.014160473, 0.06973932, 0.04964942, 0.033364646, + 0.08190124, 0.025535367, 0.050893165, 0.048514254, 0.06945813, + -0.078907564, -0.06707616, -0.11844508, -0.09986688, -0.07509403, + 0.06263226, 0.14925587, 0.20188436, 0.12098451, 0.14639415, + 0.0015017595, -0.014267382, -0.03417257, 0.012711468, 0.0028300495, + -0.024758482, -0.05098548, -0.0821182, 0.014225672, 0.021544158, + 0.08949725, 0.07505268, -0.0020780868, 0.04908258, 0.06476295, + -0.022907063, 0.027562456, 0.040185735, 0.019567577, -0.015598739, + -0.049097303, -0.017121866, -0.083368234, -0.02332002, -0.0840956}; + + input_gate_bias_ = {0.02234832, 0.14757581, 0.18176508, 0.10380666, + 0.053110216, -0.06928846, -0.13942584, -0.11816189, + 0.19483899, 0.03652339, -0.10250295, 0.036714908, + -0.18426876, 0.036065217, 0.21810818, 0.02383196, + -0.043370757, 0.08690144, -0.04444982, 0.00030581196}; + + forget_gate_bias_ = {0.035185695, -0.042891346, -0.03032477, 0.23027696, + 0.11098921, 0.15378423, 0.09263801, 0.09790885, + 0.09508917, 0.061199076, 0.07665568, -0.015443159, + -0.03499149, 0.046190713, 0.08895977, 0.10899629, + 0.40694186, 0.06030037, 0.012413437, -0.06108739}; + + cell_gate_bias_ = {-0.024379363, 0.0055531194, 0.23377132, 0.033463873, + -0.1483596, -0.10639995, -0.091433935, 0.058573797, + -0.06809782, -0.07889636, -0.043246906, -0.09829136, + -0.4279842, 0.034901652, 0.18797937, 0.0075234566, + 0.016178843, 0.1749513, 0.13975595, 0.92058027}; + + output_gate_bias_ = {0.046159424, -0.0012809046, 0.03563469, 0.12648113, + 0.027195795, 0.35373217, -0.018957434, 0.008907322, + -0.0762701, 0.12018895, 0.04216877, 0.0022856654, + 0.040952638, 0.3147856, 0.08225149, -0.057416286, + -0.14995944, -0.008040261, 0.13208859, 0.029760877}; + + recurrent_to_input_weights_ = { + -0.001374326, -0.078856036, 0.10672688, 0.029162422, + -0.11585556, 0.02557986, -0.13446963, -0.035785314, + -0.01244275, 0.025961924, -0.02337298, -0.044228926, + -0.055839065, -0.046598054, -0.010546039, -0.06900766, + 0.027239809, 0.022582639, -0.013296484, -0.05459212, + 0.08981, -0.045407712, 0.08682226, -0.06867011, + -0.14390695, -0.02916037, 0.000996957, 0.091420636, + 0.14283475, -0.07390571, -0.06402044, 0.062524505, + -0.093129106, 0.04860203, -0.08364217, -0.08119002, + 0.009352075, 0.22920375, 0.0016303885, 0.11583097, + -0.13732095, 0.012405723, -0.07551853, 0.06343048, + 0.12162708, -0.031923793, -0.014335606, 0.01790974, + -0.10650317, -0.0724401, 0.08554849, -0.05727212, + 0.06556731, -0.042729504, -0.043227166, 0.011683251, + -0.013082158, -0.029302018, -0.010899579, -0.062036745, + -0.022509435, -0.00964907, -0.01567329, 0.04260106, + -0.07787477, -0.11576462, 0.017356863, 0.048673786, + -0.017577527, -0.05527947, -0.082487635, -0.040137455, + -0.10820036, -0.04666372, 0.022746278, -0.07851417, + 0.01068115, 0.032956902, 0.022433773, 0.0026891115, + 0.08944216, -0.0685835, 0.010513544, 0.07228705, + 0.02032331, -0.059686817, -0.0005566496, -0.086984694, + 0.040414046, -0.1380399, 0.094208956, -0.05722982, + 0.012092817, -0.04989123, -0.086576, -0.003399834, + -0.04696032, -0.045747425, 0.10091314, 0.048676282, + -0.029037097, 0.031399418, -0.0040285117, 0.047237843, + 0.09504992, 0.041799378, -0.049185462, -0.031518843, + -0.10516937, 0.026374253, 0.10058866, -0.0033195973, + -0.041975245, 0.0073591834, 0.0033782164, -0.004325073, + -0.10167381, 0.042500053, -0.01447153, 0.06464186, + -0.017142897, 0.03312627, 0.009205989, 0.024138335, + -0.011337001, 0.035530265, 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-0.15312155, -0.11812848, -0.11465643, 0.20259799, 0.11418174, + -0.10116027, -0.011334949, 0.12411352, -0.076769054, -0.052169047, + 0.21198851, -0.38871562, -0.09061183, -0.09683246, -0.21929175}; + + cell_to_forget_weights_ = { + -0.01998659, -0.15568835, -0.24248174, -0.012770197, 0.041331276, + -0.072311886, -0.052123554, -0.0066330447, -0.043891653, 0.036225766, + -0.047248036, 0.021479502, 0.033189066, 0.11952997, -0.020432774, + 0.64658105, -0.06650122, -0.03467612, 0.095340036, 0.23647355}; + + cell_to_output_weights_ = { + 0.08286371, -0.08261836, -0.51210177, 0.002913762, 0.17764764, + -0.5495371, -0.08460716, -0.24552552, 0.030037103, 0.04123544, + -0.11940523, 0.007358328, 0.1890978, 0.4833202, -0.34441817, + 0.36312827, -0.26375428, 0.1457655, -0.19724406, 0.15548733}; + + projection_weights_ = { + -0.009802181, 0.09401916, 0.0717386, -0.13895074, + 0.09641832, 0.060420845, 0.08539281, 0.054285463, + 0.061395317, 0.034448683, -0.042991187, 0.019801661, + -0.16840284, 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0.025466874, + 0.14494097, -0.12522776, -0.098633975, -0.10766018, + -0.08317623, 0.08594209, 0.07749552, 0.039474737, + 0.1776665, -0.07409566, -0.0477268, 0.29323658, + 0.10801441, 0.1154011, 0.013952499, 0.10739139, + 0.10708251, -0.051456142, 0.0074137426, -0.10430189, + 0.10034707, 0.045594677, 0.0635285, -0.0715442, + -0.089667566, -0.10811871, 0.00026344223, 0.08298446, + -0.009525053, 0.006585689, -0.24567553, -0.09450807, + 0.09648481, 0.026996298, -0.06419476, -0.04752702, + -0.11063944, -0.23441927, -0.17608605, -0.052156363, + 0.067035615, 0.19271925, -0.0032889997, -0.043264326, + 0.09663576, -0.057112187, -0.10100678, 0.0628376, + 0.04447668, 0.017961001, -0.10094388, -0.10190601, + 0.18335468, 0.10494553, -0.052095775, -0.0026118709, + 0.10539724, -0.04383912, -0.042349473, 0.08438151, + -0.1947263, 0.02251204, 0.11216432, -0.10307853, + 0.17351969, -0.039091777, 0.08066188, -0.00561982, + 0.12633002, 0.11335965, -0.0088127935, -0.019777594, + 0.06864014, -0.059751723, 0.016233567, -0.06894641, + -0.28651384, -0.004228674, 0.019708522, -0.16305895, + -0.07468996, -0.0855457, 0.099339016, -0.07580735, + -0.13775392, 0.08434318, 0.08330512, -0.12131499, + 0.031935584, 0.09180414, -0.08876437, -0.08049874, + 0.008753825, 0.03498998, 0.030215185, 0.03907079, + 0.089751154, 0.029194152, -0.03337423, -0.019092513, + 0.04331237, 0.04299654, -0.036394123, -0.12915532, + 0.09793732, 0.07512415, -0.11319543, -0.032502122, + 0.15661901, 0.07671967, -0.005491124, -0.19379048, + -0.218606, 0.21448623, 0.017840758, 0.1416943, + -0.07051762, 0.19488361, 0.02664691, -0.18104725, + -0.09334311, 0.15026465, -0.15493552, -0.057762887, + -0.11604192, -0.262013, -0.01391798, 0.012185008, + 0.11156489, -0.07483202, 0.06693364, -0.26151478, + 0.046425626, 0.036540434, -0.16435726, 0.17338543, + -0.21401681, -0.11385144, -0.08283257, -0.069031075, + 0.030635102, 0.010969227, 0.11109743, 0.010919218, + 0.027526086, 0.13519906, 0.01891392, -0.046839405, + -0.040167913, 0.017953383, -0.09700955, 0.0061885654, + -0.07000971, 0.026893595, -0.038844477, 0.14543656}; + + lstm_input_ = { + {// Batch0: 4 (input_sequence_size) * 5 (n_input) + 0.787926, 0.151646, 0.071352, 0.118426, 0.458058, // step 0 + 0.596268, 0.998386, 0.568695, 0.864524, 0.571277, // step 1 + 0.073204, 0.296072, 0.743333, 0.069199, 0.045348, // step 2 + 0.867394, 0.291279, 0.013714, 0.482521, 0.626339}, // step 3 + + {// Batch1: 4 (input_sequence_size) * 5 (n_input) + 0.295743, 0.544053, 0.690064, 0.858138, 0.497181, // step 0 + 0.642421, 0.524260, 0.134799, 0.003639, 0.162482, // step 1 + 0.640394, 0.930399, 0.050782, 0.432485, 0.988078, // step 2 + 0.082922, 0.563329, 0.865614, 0.333232, 0.259916} // step 3 + }; + + lstm_golden_output_ = { + {// Batch0: 4 (input_sequence_size) * 16 (n_output) + -0.00396806, 0.029352, -0.00279226, 0.0159977, -0.00835576, + -0.0211779, 0.0283512, -0.0114597, 0.00907307, -0.0244004, + -0.0152191, -0.0259063, 0.00914318, 0.00415118, 0.017147, + 0.0134203, -0.0166936, 0.0381209, 0.000889694, 0.0143363, + -0.0328911, -0.0234288, 0.0333051, -0.012229, 0.0110322, + -0.0457725, -0.000832209, -0.0202817, 0.0327257, 0.0121308, + 0.0155969, 0.0312091, -0.0213783, 0.0350169, 0.000324794, + 0.0276012, -0.0263374, -0.0371449, 0.0446149, -0.0205474, + 0.0103729, -0.0576349, -0.0150052, -0.0292043, 0.0376827, + 0.0136115, 0.0243435, 0.0354492, -0.0189322, 0.0464512, + -0.00251373, 0.0225745, -0.0308346, -0.0317124, 0.0460407, + -0.0189395, 0.0149363, -0.0530162, -0.0150767, -0.0340193, + 0.0286833, 0.00824207, 0.0264887, 0.0305169}, + {// Batch1: 4 (input_sequence_size) * 16 (n_output) + -0.013869, 0.0287268, -0.00334693, 0.00733398, -0.0287926, + -0.0186926, 0.0193662, -0.0115437, 0.00422612, -0.0345232, + 0.00223253, -0.00957321, 0.0210624, 0.013331, 0.0150954, + 0.02168, -0.0141913, 0.0322082, 0.00227024, 0.0260507, + -0.0188721, -0.0296489, 0.0399134, -0.0160509, 0.0116039, + -0.0447318, -0.0150515, -0.0277406, 0.0316596, 0.0118233, + 0.0214762, 0.0293641, -0.0204549, 0.0450315, -0.00117378, + 0.0167673, -0.0375007, -0.0238314, 0.038784, -0.0174034, + 0.0131743, -0.0506589, -0.0048447, -0.0240239, 0.0325789, + 0.00790065, 0.0220157, 0.0333314, -0.0264787, 0.0387855, + -0.000764675, 0.0217599, -0.037537, -0.0335206, 0.0431679, + -0.0211424, 0.010203, -0.062785, -0.00832363, -0.025181, + 0.0412031, 0.0118723, 0.0239643, 0.0394009}}; } -} +}; -TEST(LSTMOpTest, BlackBoxTestWithPeepholeWithProjectionNoClipping) { +TEST_F(NoCifgPeepholeProjectionClippingLstmTest, LstmBlackBoxTest) { const int n_batch = 2; const int n_input = 5; const int n_cell = 20; @@ -489,588 +1338,98 @@ TEST(LSTMOpTest, BlackBoxTestWithPeepholeWithProjectionNoClipping) { {0}, // projection_bias tensor }); - lstm.SetInputToInputWeights( - {0.021393683, 0.06124551, 0.046905167, -0.014657677, -0.03149463, - 0.09171803, 0.14647801, 0.10797193, -0.0057968358, 0.0019193048, - -0.2726754, 0.10154029, -0.018539885, 0.080349885, -0.10262385, - -0.022599787, -0.09121155, -0.008675967, -0.045206103, -0.0821282, - -0.008045952, 0.015478081, 0.055217247, 0.038719587, 0.044153627, - -0.06453243, 0.05031825, -0.046935108, -0.008164439, 0.014574226, - -0.1671009, -0.15519552, -0.16819797, -0.13971269, -0.11953059, - 0.25005487, -0.22790983, 0.009855087, -0.028140958, -0.11200698, - 0.11295408, -0.0035217577, 0.054485075, 0.05184695, 0.064711206, - 0.10989193, 0.11674786, 0.03490607, 0.07727357, 0.11390585, - -0.1863375, -0.1034451, -0.13945189, -0.049401227, -0.18767063, - 0.042483903, 0.14233552, 0.13832581, 0.18350165, 0.14545603, - -0.028545704, 0.024939531, 0.050929718, 0.0076203286, -0.0029723682, - -0.042484224, -0.11827596, -0.09171104, -0.10808628, -0.16327988, - -0.2273378, -0.0993647, -0.017155107, 0.0023917493, 0.049272764, - 0.0038534778, 0.054764505, 0.089753784, 0.06947234, 0.08014476, - -0.04544234, -0.0497073, -0.07135631, -0.048929106, -0.004042012, - -0.009284026, 0.018042054, 0.0036860977, -0.07427302, -0.11434604, - -0.018995456, 0.031487543, 0.012834908, 0.019977754, 0.044256654, - -0.39292613, -0.18519334, -0.11651281, -0.06809892, 0.011373677}); - - lstm.SetInputToForgetWeights( - {-0.0018401089, -0.004852237, 0.03698424, 0.014181704, 0.028273236, - -0.016726194, -0.05249759, -0.10204261, 0.00861066, -0.040979505, - -0.009899187, 0.01923892, -0.028177269, -0.08535103, -0.14585495, - 0.10662567, -0.01909731, -0.017883534, -0.0047269356, -0.045103323, - 0.0030784295, 0.076784775, 0.07463696, 0.094531395, 0.0814421, - -0.12257899, -0.033945758, -0.031303465, 0.045630626, 0.06843887, - -0.13492945, -0.012480007, -0.0811829, -0.07224499, -0.09628791, - 0.045100946, 0.0012300825, 0.013964662, 0.099372394, 0.02543059, - 0.06958324, 0.034257296, 0.0482646, 0.06267997, 0.052625068, - 0.12784666, 0.07077897, 0.025725935, 0.04165009, 0.07241905, - 0.018668644, -0.037377294, -0.06277783, -0.08833636, -0.040120605, - -0.011405586, -0.007808335, -0.010301386, -0.005102167, 0.027717464, - 0.05483423, 0.11449111, 0.11289652, 0.10939839, 0.13396506, - -0.08402166, -0.01901462, -0.044678304, -0.07720565, 0.014350063, - -0.11757958, -0.0652038, -0.08185733, -0.076754324, -0.092614375, - 0.10405491, 0.052960336, 0.035755895, 0.035839386, -0.012540553, - 0.036881298, 0.02913376, 0.03420159, 0.05448447, -0.054523353, - 0.02582715, 0.02327355, -0.011857179, -0.0011980024, -0.034641717, - -0.026125094, -0.17582615, -0.15923657, -0.27486774, -0.0006143371, - 0.0001771948, -8.470171e-05, 0.02651807, 0.045790765, 0.06956496}); - - lstm.SetInputToCellWeights( - {-0.04580283, -0.09549462, -0.032418985, -0.06454633, - -0.043528453, 0.043018587, -0.049152344, -0.12418144, - -0.078985475, -0.07596889, 0.019484362, -0.11434962, - -0.0074034138, -0.06314844, -0.092981495, 0.0062155537, - -0.025034338, -0.0028890965, 0.048929527, 0.06235075, - 0.10665918, -0.032036792, -0.08505916, -0.10843358, - -0.13002433, -0.036816437, -0.02130134, -0.016518239, - 0.0047691227, -0.0025825808, 0.066017866, 0.029991534, - -0.10652836, -0.1037554, -0.13056071, -0.03266643, - -0.033702414, -0.006473424, -0.04611692, 0.014419339, - -0.025174323, 0.0396852, 0.081777506, 0.06157468, - 0.10210095, -0.009658194, 0.046511717, 0.03603906, - 0.0069369148, 0.015960095, -0.06507666, 0.09551598, - 0.053568836, 0.06408714, 0.12835667, -0.008714329, - -0.20211966, -0.12093674, 0.029450472, 0.2849013, - -0.029227901, 0.1164364, -0.08560263, 0.09941786, - -0.036999565, -0.028842626, -0.0033637602, -0.017012902, - -0.09720865, -0.11193351, -0.029155117, -0.017936034, - -0.009768936, -0.04223324, -0.036159635, 0.06505112, - -0.021742892, -0.023377212, -0.07221364, -0.06430552, - 0.05453865, 0.091149814, 0.06387331, 0.007518393, - 0.055960953, 0.069779344, 0.046411168, 0.10509911, - 0.07463894, 0.0075130584, 0.012850982, 0.04555431, - 0.056955688, 0.06555285, 0.050801456, -0.009862683, - 0.00826772, -0.026555609, -0.0073611983, -0.0014897042}); - - lstm.SetInputToOutputWeights( - {-0.0998932, -0.07201956, -0.052803773, -0.15629593, -0.15001918, - -0.07650751, 0.02359855, -0.075155355, -0.08037709, -0.15093534, - 0.029517552, -0.04751393, 0.010350531, -0.02664851, -0.016839722, - -0.023121163, 0.0077019283, 0.012851257, -0.05040649, -0.0129761, - -0.021737747, -0.038305793, -0.06870586, -0.01481247, -0.001285394, - 0.10124236, 0.083122835, 0.053313006, -0.062235646, -0.075637154, - -0.027833903, 0.029774971, 0.1130802, 0.09218906, 0.09506135, - -0.086665764, -0.037162706, -0.038880914, -0.035832845, -0.014481564, - -0.09825003, -0.12048569, -0.097665586, -0.05287633, -0.0964047, - -0.11366429, 0.035777505, 0.13568819, 0.052451383, 0.050649304, - 0.05798951, -0.021852335, -0.099848844, 0.014740475, -0.078897946, - 0.04974699, 0.014160473, 0.06973932, 0.04964942, 0.033364646, - 0.08190124, 0.025535367, 0.050893165, 0.048514254, 0.06945813, - -0.078907564, -0.06707616, -0.11844508, -0.09986688, -0.07509403, - 0.06263226, 0.14925587, 0.20188436, 0.12098451, 0.14639415, - 0.0015017595, -0.014267382, -0.03417257, 0.012711468, 0.0028300495, - -0.024758482, -0.05098548, -0.0821182, 0.014225672, 0.021544158, - 0.08949725, 0.07505268, -0.0020780868, 0.04908258, 0.06476295, - -0.022907063, 0.027562456, 0.040185735, 0.019567577, -0.015598739, - -0.049097303, -0.017121866, -0.083368234, -0.02332002, -0.0840956}); - - lstm.SetInputGateBias( - {0.02234832, 0.14757581, 0.18176508, 0.10380666, 0.053110216, - -0.06928846, -0.13942584, -0.11816189, 0.19483899, 0.03652339, - -0.10250295, 0.036714908, -0.18426876, 0.036065217, 0.21810818, - 0.02383196, -0.043370757, 0.08690144, -0.04444982, 0.00030581196}); - - lstm.SetForgetGateBias({0.035185695, -0.042891346, -0.03032477, 0.23027696, - 0.11098921, 0.15378423, 0.09263801, 0.09790885, - 0.09508917, 0.061199076, 0.07665568, -0.015443159, - -0.03499149, 0.046190713, 0.08895977, 0.10899629, - 0.40694186, 0.06030037, 0.012413437, -0.06108739}); - - lstm.SetCellBias({-0.024379363, 0.0055531194, 0.23377132, 0.033463873, - -0.1483596, -0.10639995, -0.091433935, 0.058573797, - -0.06809782, -0.07889636, -0.043246906, -0.09829136, - -0.4279842, 0.034901652, 0.18797937, 0.0075234566, - 0.016178843, 0.1749513, 0.13975595, 0.92058027}); - - lstm.SetOutputGateBias( - {0.046159424, -0.0012809046, 0.03563469, 0.12648113, 0.027195795, - 0.35373217, -0.018957434, 0.008907322, -0.0762701, 0.12018895, - 0.04216877, 0.0022856654, 0.040952638, 0.3147856, 0.08225149, - -0.057416286, -0.14995944, -0.008040261, 0.13208859, 0.029760877}); - - lstm.SetRecurrentToInputWeights( - {-0.001374326, -0.078856036, 0.10672688, 0.029162422, - -0.11585556, 0.02557986, -0.13446963, -0.035785314, - -0.01244275, 0.025961924, -0.02337298, -0.044228926, - -0.055839065, -0.046598054, -0.010546039, -0.06900766, - 0.027239809, 0.022582639, -0.013296484, -0.05459212, - 0.08981, -0.045407712, 0.08682226, -0.06867011, - -0.14390695, -0.02916037, 0.000996957, 0.091420636, - 0.14283475, -0.07390571, -0.06402044, 0.062524505, - -0.093129106, 0.04860203, -0.08364217, -0.08119002, - 0.009352075, 0.22920375, 0.0016303885, 0.11583097, - -0.13732095, 0.012405723, -0.07551853, 0.06343048, - 0.12162708, -0.031923793, -0.014335606, 0.01790974, - -0.10650317, -0.0724401, 0.08554849, -0.05727212, - 0.06556731, -0.042729504, -0.043227166, 0.011683251, - -0.013082158, -0.029302018, -0.010899579, -0.062036745, - -0.022509435, -0.00964907, -0.01567329, 0.04260106, - -0.07787477, -0.11576462, 0.017356863, 0.048673786, - -0.017577527, -0.05527947, -0.082487635, -0.040137455, - -0.10820036, -0.04666372, 0.022746278, -0.07851417, - 0.01068115, 0.032956902, 0.022433773, 0.0026891115, - 0.08944216, -0.0685835, 0.010513544, 0.07228705, - 0.02032331, -0.059686817, -0.0005566496, -0.086984694, - 0.040414046, -0.1380399, 0.094208956, -0.05722982, - 0.012092817, -0.04989123, -0.086576, -0.003399834, - -0.04696032, -0.045747425, 0.10091314, 0.048676282, - -0.029037097, 0.031399418, -0.0040285117, 0.047237843, - 0.09504992, 0.041799378, -0.049185462, -0.031518843, - -0.10516937, 0.026374253, 0.10058866, -0.0033195973, - -0.041975245, 0.0073591834, 0.0033782164, -0.004325073, - -0.10167381, 0.042500053, -0.01447153, 0.06464186, - -0.017142897, 0.03312627, 0.009205989, 0.024138335, - -0.011337001, 0.035530265, -0.010912711, 0.0706555, - -0.005894094, 0.051841937, -0.1401738, -0.02351249, - 0.0365468, 0.07590991, 0.08838724, 0.021681072, - -0.10086113, 0.019608743, -0.06195883, 0.077335775, - 0.023646897, -0.095322326, 0.02233014, 0.09756986, - -0.048691444, -0.009579111, 0.07595467, 0.11480546, - -0.09801813, 0.019894179, 0.08502348, 0.004032281, - 0.037211012, 0.068537936, -0.048005626, -0.091520436, - -0.028379958, -0.01556313, 0.06554592, -0.045599163, - -0.01672207, -0.020169014, -0.011877351, -0.20212261, - 0.010889619, 0.0047078193, 0.038385306, 0.08540671, - -0.017140968, -0.0035865551, 0.016678626, 0.005633034, - 0.015963363, 0.00871737, 0.060130805, 0.028611384, - 0.10109069, -0.015060172, -0.07894427, 0.06401885, - 0.011584063, -0.024466386, 0.0047652307, -0.09041358, - 0.030737216, -0.0046374933, 0.14215417, -0.11823516, - 0.019899689, 0.006106124, -0.027092824, 0.0786356, - 0.05052217, -0.058925, -0.011402121, -0.024987547, - -0.0013661642, -0.06832946, -0.015667673, -0.1083353, - -0.00096863037, -0.06988685, -0.053350925, -0.027275559, - -0.033664223, -0.07978348, -0.025200296, -0.017207067, - -0.058403496, -0.055697463, 0.005798788, 0.12965427, - -0.062582195, 0.0013350133, -0.10482091, 0.0379771, - 0.072521195, -0.0029455067, -0.13797039, -0.03628521, - 0.013806405, -0.017858358, -0.01008298, -0.07700066, - -0.017081132, 0.019358726, 0.0027079724, 0.004635139, - 0.062634714, -0.02338735, -0.039547626, -0.02050681, - 0.03385117, -0.083611414, 0.002862572, -0.09421313, - 0.058618143, -0.08598433, 0.00972939, 0.023867095, - -0.053934585, -0.023203006, 0.07452513, -0.048767887, - -0.07314807, -0.056307215, -0.10433547, -0.06440842, - 0.04328182, 0.04389765, -0.020006588, -0.09076438, - -0.11652589, -0.021705797, 0.03345259, -0.010329105, - -0.025767034, 0.013057034, -0.07316461, -0.10145612, - 0.06358255, 0.18531723, 0.07759293, 0.12006465, - 0.1305557, 0.058638252, -0.03393652, 0.09622831, - -0.16253184, -2.4580743e-06, 0.079869635, -0.070196845, - -0.005644518, 0.06857898, -0.12598175, -0.035084512, - 0.03156317, -0.12794146, -0.031963028, 0.04692781, - 0.030070418, 0.0071660685, -0.095516115, -0.004643372, - 0.040170413, -0.062104587, -0.0037324072, 0.0554317, - 0.08184801, -0.019164372, 0.06791302, 0.034257166, - -0.10307039, 0.021943003, 0.046745934, 0.0790918, - -0.0265588, -0.007824208, 0.042546265, -0.00977924, - -0.0002440307, -0.017384544, -0.017990116, 0.12252321, - -0.014512694, -0.08251313, 0.08861942, 0.13589665, - 0.026351685, 0.012641483, 0.07466548, 0.044301085, - -0.045414884, -0.051112458, 0.03444247, -0.08502782, - -0.04106223, -0.028126027, 0.028473156, 0.10467447}); - - lstm.SetRecurrentToForgetWeights( - {-0.057784554, -0.026057621, -0.068447545, -0.022581743, - 0.14811787, 0.10826372, 0.09471067, 0.03987225, - -0.0039523416, 0.00030638507, 0.053185795, 0.10572994, - 0.08414449, -0.022036452, -0.00066928595, -0.09203576, - 0.032950465, -0.10985798, -0.023809856, 0.0021431844, - -0.02196096, -0.00326074, 0.00058621005, -0.074678116, - -0.06193199, 0.055729095, 0.03736828, 0.020123724, - 0.061878487, -0.04729229, 0.034919553, -0.07585433, - -0.04421272, -0.044019096, 0.085488975, 0.04058006, - -0.06890133, -0.030951202, -0.024628663, -0.07672815, - 0.034293607, 0.08556707, -0.05293577, -0.033561368, - -0.04899627, 0.0241671, 0.015736353, -0.095442444, - -0.029564252, 0.016493602, -0.035026584, 0.022337519, - -0.026871363, 0.004780428, 0.0077918363, -0.03601621, - 0.016435321, -0.03263031, -0.09543275, -0.047392778, - 0.013454138, 0.028934088, 0.01685226, -0.086110644, - -0.046250615, -0.01847454, 0.047608484, 0.07339695, - 0.034546845, -0.04881143, 0.009128804, -0.08802852, - 0.03761666, 0.008096139, -0.014454086, 0.014361001, - -0.023502491, -0.0011840804, -0.07607001, 0.001856849, - -0.06509276, -0.006021153, -0.08570962, -0.1451793, - 0.060212336, 0.055259194, 0.06974018, 0.049454916, - -0.027794661, -0.08077226, -0.016179763, 0.1169753, - 0.17213494, -0.0056326236, -0.053934924, -0.0124349, - -0.11520337, 0.05409887, 0.088759385, 0.0019655675, - 0.0042065294, 0.03881498, 0.019844765, 0.041858196, - -0.05695512, 0.047233116, 0.038937137, -0.06542224, - 0.014429736, -0.09719407, 0.13908425, -0.05379757, - 0.012321099, 0.082840554, -0.029899208, 0.044217527, - 0.059855383, 0.07711018, -0.045319796, 0.0948846, - -0.011724666, -0.0033288454, -0.033542685, -0.04764985, - -0.13873616, 0.040668588, 0.034832682, -0.015319203, - -0.018715994, 0.046002675, 0.0599172, -0.043107376, - 0.0294216, -0.002314414, -0.022424703, 0.0030315618, - 0.0014641669, 0.0029166266, -0.11878115, 0.013738511, - 0.12375372, -0.0006038222, 0.029104086, 0.087442465, - 0.052958444, 0.07558703, 0.04817258, 0.044462286, - -0.015213451, -0.08783778, -0.0561384, -0.003008196, - 0.047060397, -0.002058388, 0.03429439, -0.018839769, - 0.024734668, 0.024614193, -0.042046934, 0.09597743, - -0.0043254104, 0.04320769, 0.0064070094, -0.0019131786, - -0.02558259, -0.022822596, -0.023273505, -0.02464396, - -0.10991725, -0.006240552, 0.0074488563, 0.024044557, - 0.04383914, -0.046476185, 0.028658995, 0.060410924, - 0.050786525, 0.009452605, -0.0073054377, -0.024810238, - 0.0052906186, 0.0066939713, -0.0020913032, 0.014515517, - 0.015898481, 0.021362653, -0.030262267, 0.016587038, - -0.011442813, 0.041154444, -0.007631438, -0.03423484, - -0.010977775, 0.036152758, 0.0066366293, 0.11915515, - 0.02318443, -0.041350313, 0.021485701, -0.10906167, - -0.028218046, -0.00954771, 0.020531068, -0.11995105, - -0.03672871, 0.024019798, 0.014255957, -0.05221243, - -0.00661567, -0.04630967, 0.033188973, 0.10107534, - -0.014027541, 0.030796422, -0.10270911, -0.035999842, - 0.15443139, 0.07684145, 0.036571592, -0.035900835, - -0.0034699554, 0.06209149, 0.015920248, -0.031122351, - -0.03858649, 0.01849943, 0.13872518, 0.01503974, - 0.069941424, -0.06948533, -0.0088794185, 0.061282158, - -0.047401894, 0.03100163, -0.041533746, -0.10430945, - 0.044574402, -0.01425562, -0.024290353, 0.034563623, - 0.05866852, 0.023947537, -0.09445152, 0.035450947, - 0.02247216, -0.0042998926, 0.061146557, -0.10250651, - 0.020881841, -0.06747029, 0.10062043, -0.0023941975, - 0.03532124, -0.016341697, 0.09685456, -0.016764693, - 0.051808182, 0.05875331, -0.04536488, 0.001626336, - -0.028892258, -0.01048663, -0.009793449, -0.017093895, - 0.010987891, 0.02357273, -0.00010856845, 0.0099760275, - -0.001845119, -0.03551521, 0.0018358806, 0.05763657, - -0.01769146, 0.040995963, 0.02235177, -0.060430344, - 0.11475477, -0.023854522, 0.10071741, 0.0686208, - -0.014250481, 0.034261297, 0.047418304, 0.08562733, - -0.030519066, 0.0060542435, 0.014653856, -0.038836084, - 0.04096551, 0.032249358, -0.08355519, -0.026823482, - 0.056386515, -0.010401743, -0.028396193, 0.08507674, - 0.014410365, 0.020995233, 0.17040324, 0.11511526, - 0.02459721, 0.0066619175, 0.025853224, -0.023133837, - -0.081302024, 0.017264642, -0.009585969, 0.09491168, - -0.051313367, 0.054532815, -0.014298593, 0.10657464, - 0.007076659, 0.10964551, 0.0409152, 0.008275321, - -0.07283536, 0.07937492, 0.04192024, -0.1075027}); - - lstm.SetRecurrentToCellWeights( - {-0.037322544, 0.018592842, 0.0056175636, -0.06253426, - 0.055647098, -0.05713207, -0.05626563, 0.005559383, - 0.03375411, -0.025757805, -0.088049285, 0.06017052, - -0.06570978, 0.007384076, 0.035123326, -0.07920549, - 0.053676967, 0.044480428, -0.07663568, 0.0071805613, - 0.08089997, 0.05143358, 0.038261272, 0.03339287, - -0.027673481, 0.044746667, 0.028349208, 0.020090483, - -0.019443132, -0.030755889, -0.0040000007, 0.04465846, - -0.021585021, 0.0031670958, 0.0053199246, -0.056117613, - -0.10893326, 0.076739706, -0.08509834, -0.027997585, - 0.037871376, 0.01449768, -0.09002357, -0.06111149, - -0.046195522, 0.0422062, -0.005683705, -0.1253618, - -0.012925729, -0.04890792, 0.06985068, 0.037654128, - 0.03398274, -0.004781977, 0.007032333, -0.031787455, - 0.010868644, -0.031489216, 0.09525667, 0.013939797, - 0.0058680447, 0.0167067, 0.02668468, -0.04797466, - -0.048885044, -0.12722108, 0.035304096, 0.06554885, - 0.00972396, -0.039238118, -0.05159735, -0.11329045, - 0.1613692, -0.03750952, 0.06529313, -0.071974665, - -0.11769596, 0.015524369, -0.0013754242, -0.12446318, - 0.02786344, -0.014179351, 0.005264273, 0.14376344, - 0.015983658, 0.03406988, -0.06939408, 0.040699873, - 0.02111075, 0.09669095, 0.041345075, -0.08316494, - -0.07684199, -0.045768797, 0.032298047, -0.041805092, - 0.0119405, 0.0061010392, 0.12652606, 0.0064572375, - -0.024950314, 0.11574242, 0.04508852, -0.04335324, - 0.06760663, -0.027437469, 0.07216407, 0.06977076, - -0.05438599, 0.034033038, -0.028602652, 0.05346137, - 0.043184172, -0.037189785, 0.10420091, 0.00882477, - -0.054019816, -0.074273005, -0.030617684, -0.0028467078, - 0.024302477, -0.0038869337, 0.005332455, 0.0013399826, - 0.04361412, -0.007001822, 0.09631092, -0.06702025, - -0.042049985, -0.035070654, -0.04103342, -0.10273396, - 0.0544271, 0.037184782, -0.13150354, -0.0058036847, - -0.008264958, 0.042035464, 0.05891794, 0.029673764, - 0.0063542654, 0.044788733, 0.054816857, 0.062257513, - -0.00093483756, 0.048938446, -0.004952862, -0.007730018, - -0.04043371, -0.017094059, 0.07229206, -0.023670016, - -0.052195564, -0.025616996, -0.01520939, 0.045104615, - -0.007376126, 0.003533447, 0.006570588, 0.056037236, - 0.12436656, 0.051817212, 0.028532185, -0.08686856, - 0.11868599, 0.07663395, -0.07323171, 0.03463402, - -0.050708205, -0.04458982, -0.11590894, 0.021273347, - 0.1251325, -0.15313013, -0.12224372, 0.17228661, - 0.023029093, 0.086124025, 0.006445803, -0.03496501, - 0.028332196, 0.04449512, -0.042436164, -0.026587414, - -0.006041347, -0.09292539, -0.05678812, 0.03897832, - 0.09465633, 0.008115513, -0.02171956, 0.08304309, - 0.071401566, 0.019622514, 0.032163795, -0.004167056, - 0.02295182, 0.030739572, 0.056506045, 0.004612461, - 0.06524936, 0.059999723, 0.046395954, -0.0045512207, - -0.1335546, -0.030136576, 0.11584653, -0.014678886, - 0.0020118146, -0.09688814, -0.0790206, 0.039770417, - -0.0329582, 0.07922767, 0.029322514, 0.026405897, - 0.04207835, -0.07073373, 0.063781224, 0.0859677, - -0.10925287, -0.07011058, 0.048005477, 0.03438226, - -0.09606514, -0.006669445, -0.043381985, 0.04240257, - -0.06955775, -0.06769346, 0.043903265, -0.026784198, - -0.017840602, 0.024307009, -0.040079936, -0.019946516, - 0.045318738, -0.12233574, 0.026170589, 0.0074471775, - 0.15978073, 0.10185836, 0.10298046, -0.015476589, - -0.039390966, -0.072174534, 0.0739445, -0.1211869, - -0.0347889, -0.07943156, 0.014809798, -0.12412325, - -0.0030663363, 0.039695457, 0.0647603, -0.08291318, - -0.018529687, -0.004423833, 0.0037507233, 0.084633216, - -0.01514876, -0.056505352, -0.012800942, -0.06994386, - 0.012962922, -0.031234352, 0.07029052, 0.016418684, - 0.03618972, 0.055686004, -0.08663945, -0.017404709, - -0.054761406, 0.029065743, 0.052404847, 0.020238016, - 0.0048197987, -0.0214882, 0.07078733, 0.013016777, - 0.06262858, 0.009184685, 0.020785125, -0.043904778, - -0.0270329, -0.03299152, -0.060088247, -0.015162964, - -0.001828936, 0.12642565, -0.056757294, 0.013586685, - 0.09232601, -0.035886683, 0.06000002, 0.05229691, - -0.052580316, -0.082029596, -0.010794592, 0.012947712, - -0.036429964, -0.085508935, -0.13127148, -0.017744139, - 0.031502828, 0.036232427, -0.031581745, 0.023051167, - -0.05325106, -0.03421577, 0.028793324, -0.034633752, - -0.009881397, -0.043551125, -0.018609839, 0.0019097115, - -0.008799762, 0.056595087, 0.0022273948, 0.055752404}); - - lstm.SetRecurrentToOutputWeights({ - 0.025825322, -0.05813119, 0.09495884, -0.045984812, -0.01255415, - -0.0026479573, -0.08196161, -0.054914974, -0.0046604523, -0.029587349, - -0.044576716, -0.07480124, -0.082868785, 0.023254942, 0.027502948, - -0.0039728214, -0.08683098, -0.08116779, -0.014675607, -0.037924774, - -0.023314456, -0.007401714, -0.09255757, 0.029460307, -0.08829125, - -0.005139627, -0.08989442, -0.0555066, 0.13596267, -0.025062224, - -0.048351806, -0.03850004, 0.07266485, -0.022414139, 0.05940088, - 0.075114764, 0.09597592, -0.010211725, -0.0049794707, -0.011523867, - -0.025980417, 0.072999895, 0.11091378, -0.081685916, 0.014416728, - 0.043229222, 0.034178585, -0.07530371, 0.035837382, -0.085607, - -0.007721233, -0.03287832, -0.043848954, -0.06404588, -0.06632928, - -0.073643476, 0.008214239, -0.045984086, 0.039764922, 0.03474462, - 0.060612556, -0.080590084, 0.049127717, 0.04151091, -0.030063879, - 0.008801774, -0.023021035, -0.019558564, 0.05158114, -0.010947698, - -0.011825728, 0.0075720972, 0.0699727, -0.0039981045, 0.069350146, - 0.08799282, 0.016156472, 0.035502106, 0.11695009, 0.006217345, - 0.13392477, -0.037875112, 0.025745004, 0.08940699, -0.00924166, - 0.0046702605, -0.036598757, -0.08811812, 0.10522024, -0.032441203, - 0.008176899, -0.04454919, 0.07058152, 0.0067963637, 0.039206743, - 0.03259838, 0.03725492, -0.09515802, 0.013326398, -0.052055415, - -0.025676316, 0.03198509, -0.015951829, -0.058556724, 0.036879618, - 0.043357447, 0.028362012, -0.05908629, 0.0059240665, -0.04995891, - -0.019187413, 0.0276265, -0.01628143, 0.0025863599, 0.08800015, - 0.035250366, -0.022165963, -0.07328642, -0.009415526, -0.07455109, - 0.11690406, 0.0363299, 0.07411125, 0.042103454, -0.009660886, - 0.019076364, 0.018299393, -0.046004917, 0.08891175, 0.0431396, - -0.026327137, -0.051502608, 0.08979574, -0.051670972, 0.04940282, - -0.07491107, -0.021240504, 0.022596184, -0.034280192, 0.060163025, - -0.058211457, -0.051837247, -0.01349775, -0.04639988, -0.035936575, - -0.011681591, 0.064818054, 0.0073146066, -0.021745546, -0.043124277, - -0.06471268, -0.07053354, -0.029321948, -0.05330136, 0.016933719, - -0.053782392, 0.13747959, -0.1361751, -0.11569455, 0.0033329215, - 0.05693899, -0.053219706, 0.063698, 0.07977434, -0.07924483, - 0.06936997, 0.0034815092, -0.007305279, -0.037325785, -0.07251102, - -0.033633437, -0.08677009, 0.091591336, -0.14165086, 0.021752775, - 0.019683983, 0.0011612234, -0.058154266, 0.049996935, 0.0288841, - -0.0024567875, -0.14345716, 0.010955264, -0.10234828, 0.1183656, - -0.0010731248, -0.023590032, -0.072285876, -0.0724771, -0.026382286, - -0.0014920527, 0.042667855, 0.0018776858, 0.02986552, 0.009814309, - 0.0733756, 0.12289186, 0.018043943, -0.0458958, 0.049412545, - 0.033632483, 0.05495232, 0.036686596, -0.013781798, -0.010036754, - 0.02576849, -0.08307328, 0.010112348, 0.042521734, -0.05869831, - -0.071689695, 0.03876447, -0.13275425, -0.0352966, -0.023077697, - 0.10285965, 0.084736146, 0.15568255, -0.00040734606, 0.027835453, - -0.10292561, -0.032401145, 0.10053256, -0.026142767, -0.08271222, - -0.0030240538, -0.016368777, 0.1070414, 0.042672627, 0.013456989, - -0.0437609, -0.022309763, 0.11576483, 0.04108048, 0.061026827, - -0.0190714, -0.0869359, 0.037901703, 0.0610107, 0.07202949, - 0.01675338, 0.086139716, -0.08795751, -0.014898893, -0.023771819, - -0.01965048, 0.007955471, -0.043740474, 0.03346837, -0.10549954, - 0.090567775, 0.042013682, -0.03176985, 0.12569028, -0.02421228, - -0.029526481, 0.023851605, 0.031539805, 0.05292009, -0.02344001, - -0.07811758, -0.08834428, 0.10094801, 0.16594367, -0.06861939, - -0.021256343, -0.041093912, -0.06669611, 0.035498552, 0.021757556, - -0.09302526, -0.015403468, -0.06614931, -0.051798206, -0.013874718, - 0.03630673, 0.010412845, -0.08077351, 0.046185967, 0.0035662893, - 0.03541868, -0.094149634, -0.034814864, 0.003128424, -0.020674974, - -0.03944324, -0.008110165, -0.11113267, 0.08484226, 0.043586485, - 0.040582247, 0.0968012, -0.065249965, -0.028036479, 0.0050708856, - 0.0017462453, 0.0326779, 0.041296225, 0.09164146, -0.047743853, - -0.015952192, -0.034451712, 0.084197424, -0.05347844, -0.11768019, - 0.085926116, -0.08251791, -0.045081906, 0.0948852, 0.068401024, - 0.024856757, 0.06978981, -0.057309967, -0.012775832, -0.0032452994, - 0.01977615, -0.041040014, -0.024264973, 0.063464895, 0.05431621, - }); - - lstm.SetCellToInputWeights( - {0.040369894, 0.030746894, 0.24704495, 0.018586371, -0.037586458, - -0.15312155, -0.11812848, -0.11465643, 0.20259799, 0.11418174, - -0.10116027, -0.011334949, 0.12411352, -0.076769054, -0.052169047, - 0.21198851, -0.38871562, -0.09061183, -0.09683246, -0.21929175}); - - lstm.SetCellToForgetWeights( - {-0.01998659, -0.15568835, -0.24248174, -0.012770197, 0.041331276, - -0.072311886, -0.052123554, -0.0066330447, -0.043891653, 0.036225766, - -0.047248036, 0.021479502, 0.033189066, 0.11952997, -0.020432774, - 0.64658105, -0.06650122, -0.03467612, 0.095340036, 0.23647355}); - - lstm.SetCellToOutputWeights( - {0.08286371, -0.08261836, -0.51210177, 0.002913762, 0.17764764, - -0.5495371, -0.08460716, -0.24552552, 0.030037103, 0.04123544, - -0.11940523, 0.007358328, 0.1890978, 0.4833202, -0.34441817, - 0.36312827, -0.26375428, 0.1457655, -0.19724406, 0.15548733}); - - lstm.SetProjectionWeights( - {-0.009802181, 0.09401916, 0.0717386, -0.13895074, 0.09641832, - 0.060420845, 0.08539281, 0.054285463, 0.061395317, 0.034448683, - -0.042991187, 0.019801661, -0.16840284, -0.015726732, -0.23041931, - -0.024478018, -0.10959692, -0.013875541, 0.18600968, -0.061274476, - 0.0138165, -0.08160894, -0.07661644, 0.032372914, 0.16169067, - 0.22465782, -0.03993472, -0.004017731, 0.08633481, -0.28869787, - 0.08682067, 0.17240396, 0.014975425, 0.056431185, 0.031037588, - 0.16702051, 0.0077946745, 0.15140012, 0.29405436, 0.120285, - -0.188994, -0.027265169, 0.043389652, -0.022061434, 0.014777949, - -0.20203483, 0.094781205, 0.19100232, 0.13987629, -0.036132768, - -0.06426278, -0.05108664, 0.13221376, 0.009441198, -0.16715929, - 0.15859416, -0.040437475, 0.050779544, -0.022187516, 0.012166504, - 0.027685808, -0.07675938, -0.0055694645, -0.09444123, 0.0046453946, - 0.050794356, 0.10770313, -0.20790008, -0.07149004, -0.11425117, - 0.008225835, -0.035802525, 0.14374903, 0.15262283, 0.048710253, - 0.1847461, -0.007487823, 0.11000021, -0.09542012, 0.22619456, - -0.029149994, 0.08527916, 0.009043713, 0.0042746216, 0.016261552, - 0.022461696, 0.12689082, -0.043589946, -0.12035478, -0.08361797, - -0.050666027, -0.1248618, -0.1275799, -0.071875185, 0.07377272, - 0.09944291, -0.18897448, -0.1593054, -0.06526116, -0.040107165, - -0.004618631, -0.067624845, -0.007576253, 0.10727444, 0.041546922, - -0.20424393, 0.06907816, 0.050412357, 0.00724631, 0.039827548, - 0.12449835, 0.10747581, 0.13708383, 0.09134148, -0.12617786, - -0.06428341, 0.09956831, 0.1208086, -0.14676677, -0.0727722, - 0.1126304, 0.010139365, 0.015571211, -0.038128063, 0.022913318, - -0.042050496, 0.16842307, -0.060597885, 0.10531834, -0.06411776, - -0.07451711, -0.03410368, -0.13393489, 0.06534304, 0.003620307, - 0.04490757, 0.05970546, 0.05197996, 0.02839995, 0.10434969, - -0.013699693, -0.028353551, -0.07260381, 0.047201227, -0.024575593, - -0.036445823, 0.07155557, 0.009672501, -0.02328883, 0.009533515, - -0.03606021, -0.07421458, -0.028082801, -0.2678904, -0.13221288, - 0.18419984, -0.13012612, -0.014588381, -0.035059117, -0.04824723, - 0.07830115, -0.056184657, 0.03277091, 0.025466874, 0.14494097, - -0.12522776, -0.098633975, -0.10766018, -0.08317623, 0.08594209, - 0.07749552, 0.039474737, 0.1776665, -0.07409566, -0.0477268, - 0.29323658, 0.10801441, 0.1154011, 0.013952499, 0.10739139, - 0.10708251, -0.051456142, 0.0074137426, -0.10430189, 0.10034707, - 0.045594677, 0.0635285, -0.0715442, -0.089667566, -0.10811871, - 0.00026344223, 0.08298446, -0.009525053, 0.006585689, -0.24567553, - -0.09450807, 0.09648481, 0.026996298, -0.06419476, -0.04752702, - -0.11063944, -0.23441927, -0.17608605, -0.052156363, 0.067035615, - 0.19271925, -0.0032889997, -0.043264326, 0.09663576, -0.057112187, - -0.10100678, 0.0628376, 0.04447668, 0.017961001, -0.10094388, - -0.10190601, 0.18335468, 0.10494553, -0.052095775, -0.0026118709, - 0.10539724, -0.04383912, -0.042349473, 0.08438151, -0.1947263, - 0.02251204, 0.11216432, -0.10307853, 0.17351969, -0.039091777, - 0.08066188, -0.00561982, 0.12633002, 0.11335965, -0.0088127935, - -0.019777594, 0.06864014, -0.059751723, 0.016233567, -0.06894641, - -0.28651384, -0.004228674, 0.019708522, -0.16305895, -0.07468996, - -0.0855457, 0.099339016, -0.07580735, -0.13775392, 0.08434318, - 0.08330512, -0.12131499, 0.031935584, 0.09180414, -0.08876437, - -0.08049874, 0.008753825, 0.03498998, 0.030215185, 0.03907079, - 0.089751154, 0.029194152, -0.03337423, -0.019092513, 0.04331237, - 0.04299654, -0.036394123, -0.12915532, 0.09793732, 0.07512415, - -0.11319543, -0.032502122, 0.15661901, 0.07671967, -0.005491124, - -0.19379048, -0.218606, 0.21448623, 0.017840758, 0.1416943, - -0.07051762, 0.19488361, 0.02664691, -0.18104725, -0.09334311, - 0.15026465, -0.15493552, -0.057762887, -0.11604192, -0.262013, - -0.01391798, 0.012185008, 0.11156489, -0.07483202, 0.06693364, - -0.26151478, 0.046425626, 0.036540434, -0.16435726, 0.17338543, - -0.21401681, -0.11385144, -0.08283257, -0.069031075, 0.030635102, - 0.010969227, 0.11109743, 0.010919218, 0.027526086, 0.13519906, - 0.01891392, -0.046839405, -0.040167913, 0.017953383, -0.09700955, - 0.0061885654, -0.07000971, 0.026893595, -0.038844477, 0.14543656}); - - static float lstm_input[][20] = { - {// Batch0: 4 (input_sequence_size) * 5 (n_input) - 0.787926, 0.151646, 0.071352, 0.118426, 0.458058, 0.596268, 0.998386, - 0.568695, 0.864524, 0.571277, 0.073204, 0.296072, 0.743333, 0.069199, - 0.045348, 0.867394, 0.291279, 0.013714, 0.482521, 0.626339}, - - {// Batch1: 4 (input_sequence_size) * 5 (n_input) - 0.295743, 0.544053, 0.690064, 0.858138, 0.497181, 0.642421, 0.524260, - 0.134799, 0.003639, 0.162482, 0.640394, 0.930399, 0.050782, 0.432485, - 0.988078, 0.082922, 0.563329, 0.865614, 0.333232, 0.259916}}; - - static float lstm_golden_output[][64] = { - {// Batch0: 4 (input_sequence_size) * 16 (n_output) - -0.00396806, 0.029352, -0.00279226, 0.0159977, -0.00835576, - -0.0211779, 0.0283512, -0.0114597, 0.00907307, -0.0244004, - -0.0152191, -0.0259063, 0.00914318, 0.00415118, 0.017147, - 0.0134203, -0.0166936, 0.0381209, 0.000889694, 0.0143363, - -0.0328911, -0.0234288, 0.0333051, -0.012229, 0.0110322, - -0.0457725, -0.000832209, -0.0202817, 0.0327257, 0.0121308, - 0.0155969, 0.0312091, -0.0213783, 0.0350169, 0.000324794, - 0.0276012, -0.0263374, -0.0371449, 0.0446149, -0.0205474, - 0.0103729, -0.0576349, -0.0150052, -0.0292043, 0.0376827, - 0.0136115, 0.0243435, 0.0354492, -0.0189322, 0.0464512, - -0.00251373, 0.0225745, -0.0308346, -0.0317124, 0.0460407, - -0.0189395, 0.0149363, -0.0530162, -0.0150767, -0.0340193, - 0.0286833, 0.00824207, 0.0264887, 0.0305169}, - {// Batch1: 4 (input_sequence_size) * 16 (n_output) - -0.013869, 0.0287268, -0.00334693, 0.00733398, -0.0287926, - -0.0186926, 0.0193662, -0.0115437, 0.00422612, -0.0345232, - 0.00223253, -0.00957321, 0.0210624, 0.013331, 0.0150954, - 0.02168, -0.0141913, 0.0322082, 0.00227024, 0.0260507, - -0.0188721, -0.0296489, 0.0399134, -0.0160509, 0.0116039, - -0.0447318, -0.0150515, -0.0277406, 0.0316596, 0.0118233, - 0.0214762, 0.0293641, -0.0204549, 0.0450315, -0.00117378, - 0.0167673, -0.0375007, -0.0238314, 0.038784, -0.0174034, - 0.0131743, -0.0506589, -0.0048447, -0.0240239, 0.0325789, - 0.00790065, 0.0220157, 0.0333314, -0.0264787, 0.0387855, - -0.000764675, 0.0217599, -0.037537, -0.0335206, 0.0431679, - -0.0211424, 0.010203, -0.062785, -0.00832363, -0.025181, - 0.0412031, 0.0118723, 0.0239643, 0.0394009}}; + lstm.SetInputToInputWeights(input_to_input_weights_); + lstm.SetInputToCellWeights(input_to_cell_weights_); + lstm.SetInputToForgetWeights(input_to_forget_weights_); + lstm.SetInputToOutputWeights(input_to_output_weights_); + + lstm.SetInputGateBias(input_gate_bias_); + lstm.SetCellBias(cell_gate_bias_); + lstm.SetForgetGateBias(forget_gate_bias_); + lstm.SetOutputGateBias(output_gate_bias_); + + lstm.SetRecurrentToInputWeights(recurrent_to_input_weights_); + lstm.SetRecurrentToCellWeights(recurrent_to_cell_weights_); + lstm.SetRecurrentToForgetWeights(recurrent_to_forget_weights_); + lstm.SetRecurrentToOutputWeights(recurrent_to_output_weights_); + + lstm.SetCellToInputWeights(cell_to_input_weights_); + lstm.SetCellToForgetWeights(cell_to_forget_weights_); + lstm.SetCellToOutputWeights(cell_to_output_weights_); + + lstm.SetProjectionWeights(projection_weights_); // Resetting cell_state and output_state lstm.ResetCellState(); lstm.ResetOutputState(); - const int input_sequence_size = - sizeof(lstm_input[0]) / sizeof(float) / (lstm.num_inputs()); - for (int i = 0; i < input_sequence_size; i++) { - float* batch0_start = lstm_input[0] + i * lstm.num_inputs(); - float* batch0_end = batch0_start + lstm.num_inputs(); + VerifyGoldens(lstm_input_, lstm_golden_output_, &lstm); +} - lstm.SetInput(0, batch0_start, batch0_end); +TEST_F(NoCifgPeepholeProjectionClippingLstmTest, HybridLstmBlackBoxTest) { + const int n_batch = 2; + const int n_input = 5; + const int n_cell = 20; + const int n_output = 16; - float* batch1_start = lstm_input[1] + i * lstm.num_inputs(); - float* batch1_end = batch1_start + lstm.num_inputs(); - lstm.SetInput(lstm.num_inputs(), batch1_start, batch1_end); + HybridLSTMOpModel lstm( + n_batch, n_input, n_cell, n_output, + /*use_cifg=*/false, /*use_peephole=*/true, + /*use_projection_weights=*/true, + /*use_projection_bias=*/false, + /*cell_clip=*/0.0, /*proj_clip=*/0.0, + { + {n_batch, n_input}, // input tensor + + {n_cell, n_input}, // input_to_input_weight tensor + {n_cell, n_input}, // input_to_forget_weight tensor + {n_cell, n_input}, // input_to_cell_weight tensor + {n_cell, n_input}, // input_to_output_weight tensor + + {n_cell, n_output}, // recurrent_to_input_weight tensor + {n_cell, n_output}, // recurrent_to_forget_weight tensor + {n_cell, n_output}, // recurrent_to_cell_weight tensor + {n_cell, n_output}, // recurrent_to_output_weight tensor + + {n_cell}, // cell_to_input_weight tensor + {n_cell}, // cell_to_forget_weight tensor + {n_cell}, // cell_to_output_weight tensor + + {n_cell}, // input_gate_bias tensor + {n_cell}, // forget_gate_bias tensor + {n_cell}, // cell_bias tensor + {n_cell}, // output_gate_bias tensor + + {n_output, n_cell}, // projection_weight tensor + {0}, // projection_bias tensor + }); + + lstm.SetInputToInputWeights(input_to_input_weights_); + lstm.SetInputToCellWeights(input_to_cell_weights_); + lstm.SetInputToForgetWeights(input_to_forget_weights_); + lstm.SetInputToOutputWeights(input_to_output_weights_); + + lstm.SetInputGateBias(input_gate_bias_); + lstm.SetCellBias(cell_gate_bias_); + lstm.SetForgetGateBias(forget_gate_bias_); + lstm.SetOutputGateBias(output_gate_bias_); + + lstm.SetRecurrentToInputWeights(recurrent_to_input_weights_); + lstm.SetRecurrentToCellWeights(recurrent_to_cell_weights_); + lstm.SetRecurrentToForgetWeights(recurrent_to_forget_weights_); + lstm.SetRecurrentToOutputWeights(recurrent_to_output_weights_); + + lstm.SetCellToInputWeights(cell_to_input_weights_); + lstm.SetCellToForgetWeights(cell_to_forget_weights_); + lstm.SetCellToOutputWeights(cell_to_output_weights_); + + lstm.SetProjectionWeights(projection_weights_); - lstm.Invoke(); + // Resetting cell_state and output_state + lstm.ResetCellState(); + lstm.ResetOutputState(); - float* golden_start_batch0 = lstm_golden_output[0] + i * lstm.num_outputs(); - float* golden_end_batch0 = golden_start_batch0 + lstm.num_outputs(); - float* golden_start_batch1 = lstm_golden_output[1] + i * lstm.num_outputs(); - float* golden_end_batch1 = golden_start_batch1 + lstm.num_outputs(); - std::vector expected; - expected.insert(expected.end(), golden_start_batch0, golden_end_batch0); - expected.insert(expected.end(), golden_start_batch1, golden_end_batch1); - EXPECT_THAT(lstm.GetOutput(), ElementsAreArray(ArrayFloatNear(expected))); - } + VerifyGoldens(lstm_input_, lstm_golden_output_, &lstm, /*tolerance=*/0.00467); } } // namespace -- GitLab From 2b5f598fbd822f911ad305ae1e57325aefd50826 Mon Sep 17 00:00:00 2001 From: Eugene Zhulenev Date: Tue, 5 Jun 2018 12:19:43 -0700 Subject: [PATCH 034/816] Move ReplaceMulWithSquare to a separate optimizer stage. PiperOrigin-RevId: 199338297 --- .../optimizers/arithmetic_optimizer.cc | 68 ++++++++++++------- .../optimizers/arithmetic_optimizer.h | 1 + .../optimizers/arithmetic_optimizer_test.cc | 47 +++++++------ 3 files changed, 73 insertions(+), 43 deletions(-) diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc index 400af82627..561930f858 100644 --- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc +++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc @@ -2079,6 +2079,49 @@ class FoldMultiplyIntoConv : public ArithmeticOptimizerStage { } }; +// Replace Mul node with identical inputs with a Square. +class ReplaceMulWithSquare : public ArithmeticOptimizerStage { + public: + explicit ReplaceMulWithSquare(const GraphOptimizerContext& ctx, + const ArithmeticOptimizerContext& ctx_ext) + : ArithmeticOptimizerStage("ReplaceMulWithSquare", ctx, ctx_ext) {} + ~ReplaceMulWithSquare() override = default; + + bool IsSupported(const NodeDef* node) const override { + return IsMul(*node) && node->input(0) == node->input(1); + } + + Status TrySimplify(NodeDef* node, string* simplified_node_name) override { + const NodeScopeAndName mul = ParseNodeScopeAndName(node->name()); + const string optimized_node_name = OptimizedNodeName(mul); + if (ctx().node_map->NodeExists(optimized_node_name)) return Status::OK(); + + const DataType type = GetDataTypeFromAttr(*node, "T"); + bool is_complex = (type == DT_COMPLEX64) || (type == DT_COMPLEX128); + + string task; + string device; + bool is_on_cpu = + DeviceNameUtils::SplitDeviceName(node->device(), &task, &device) && + str_util::StrContains(device, DEVICE_CPU); + + if (!is_complex || is_on_cpu) { + NodeDef* new_square_node = AddCopyNode(optimized_node_name, node); + new_square_node->set_op("Square"); + for (int i = 1; i < new_square_node->input_size(); ++i) { + new_square_node->set_input(i - 1, new_square_node->input(i)); + } + new_square_node->mutable_input()->RemoveLast(); + for (const string& input : new_square_node->input()) { + ctx().node_map->AddOutput(NodeName(input), new_square_node->name()); + } + *simplified_node_name = new_square_node->name(); + } + + return Status::OK(); + } +}; + } // namespace class UniqueNodes { @@ -2331,29 +2374,6 @@ void ArithmeticOptimizer::ForwardControlDependencies( // ArithmeticOptimizerStage string ArithmeticOptimizer::TrySimplifyAndReplaceUses( const NodeDef* node, SetVector* nodes_to_simplify) { - if (node->op() == "Mul" && node->input(0) == node->input(1) && - !OptimizedNodeExists(*node, "square")) { - const DataType type = GetDataTypeFromAttr(*node, "T"); - bool is_complex = (type == DT_COMPLEX64) || (type == DT_COMPLEX128); - string dontcare; - string device; - bool is_on_cpu = - DeviceNameUtils::SplitDeviceName(node->device(), &dontcare, &device) && - str_util::StrContains(device, DEVICE_CPU); - if (!is_complex || is_on_cpu) { - NodeDef* new_square_node = AddNode(*node, "square", /*copy_node=*/true); - new_square_node->set_op("Square"); - for (int i = 1; i < new_square_node->input_size(); ++i) { - new_square_node->set_input(i - 1, new_square_node->input(i)); - } - new_square_node->mutable_input()->RemoveLast(); - for (const string& input : new_square_node->input()) { - node_map_->AddOutput(NodeName(input), new_square_node->name()); - } - return new_square_node->name(); - } - } - if (IsAggregate(*node) && NumNonControlInputs(*node) > 0) { // Discard aggregate nodes with a single input and no control dependencies. if (node->input_size() == 1) { @@ -2528,6 +2548,8 @@ Status ArithmeticOptimizer::SimplifyArithmeticOps(bool can_use_shapes) { pipeline.AddStage(ctx, ctx_ext); if (options_.remove_negation) pipeline.AddStage(ctx, ctx_ext); + if (options_.replace_mul_with_square) + pipeline.AddStage(ctx, ctx_ext); if (options_.remove_logical_not) pipeline.AddStage(ctx, ctx_ext); if (options_.reorder_cast_and_transpose) diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.h b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.h index e6fc311929..8e00b83a70 100644 --- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.h +++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.h @@ -74,6 +74,7 @@ class ArithmeticOptimizer : public GraphOptimizer { bool remove_redundant_cast = true; bool remove_redundant_reshape = true; bool reorder_cast_and_transpose = true; + bool replace_mul_with_square = true; // Choose which arithmetic optimizer stages will be enabled for a given // optimization level by default. diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc b/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc index b9fec0f860..f15cbfe407 100644 --- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc +++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc @@ -139,6 +139,7 @@ class ArithmeticOptimizerTest : public GrapplerTest { options.remove_negation = false; options.remove_logical_not = false; options.reorder_cast_and_transpose = false; + options.replace_mul_with_square = false; optimizer->options_ = options; } @@ -201,6 +202,11 @@ class ArithmeticOptimizerTest : public GrapplerTest { optimizer->options_.reorder_cast_and_transpose = true; } + void EnableOnlyReplaceMulWithSquare(ArithmeticOptimizer* optimizer) { + DisableAllStages(optimizer); + optimizer->options_.replace_mul_with_square = true; + } + void EnableOnlyHoistCWiseUnaryChains(ArithmeticOptimizer* optimizer) { DisableAllStages(optimizer); optimizer->options_.hoist_cwise_unary_chains = true; @@ -345,33 +351,36 @@ TEST_F(ArithmeticOptimizerTest, OpDedupCommutative) { test::ExpectTensorNear(tensors_expected[0], tensors[0], 1e-6); } -TEST_F(ArithmeticOptimizerTest, MulToSquare) { +TEST_F(ArithmeticOptimizerTest, ReplaceMulWithSquare) { tensorflow::Scope s = tensorflow::Scope::NewRootScope(); Output c = ops::Const(s.WithOpName("c"), {1.0f, 2.0f}, {1, 2}); Output d = ops::Const(s.WithOpName("d"), {3.0f, 4.0f}, {1, 2}); Output mul = ops::Mul(s.WithControlDependencies(d).WithOpName("mul"), c, c); Output id = ops::Identity(s.WithOpName("id"), mul); + GrapplerItem item; + item.fetch = {"id"}; TF_CHECK_OK(s.ToGraphDef(&item.graph)); - std::vector fetch = {"id"}; - auto tensors_expected = EvaluateNodes(item.graph, fetch); + auto tensors_expected = EvaluateNodes(item.graph, item.fetch); EXPECT_EQ(1, tensors_expected.size()); - ArithmeticOptimizer optimizer; GraphDef output; - Status status = optimizer.Optimize(nullptr, item, &output); - TF_EXPECT_OK(status); + ArithmeticOptimizer optimizer; + EnableOnlyReplaceMulWithSquare(&optimizer); + OptimizeAndPrune(&optimizer, &item, &output); - EXPECT_EQ(5, output.node_size()); - EXPECT_EQ("id", output.node(3).name()); - EXPECT_EQ(OptimizedName("mul_square"), output.node(3).input(0)); - EXPECT_EQ("Square", output.node(4).op()); - EXPECT_EQ(OptimizedName("mul_square"), output.node(4).name()); - EXPECT_EQ(2, output.node(4).input_size()); - EXPECT_EQ("c", output.node(4).input(0)); - EXPECT_EQ("^d", output.node(4).input(1)); + EXPECT_EQ(4, output.node_size()); - auto tensors = EvaluateNodes(output, fetch); + NodeMap node_map(&output); + const string p = "ArithmeticOptimizer/ReplaceMulWithSquare"; + const NodeDef* square_node = node_map.GetNode(strings::StrCat(p, "_", "mul")); + + ASSERT_NE(square_node, nullptr); + EXPECT_EQ("Square", square_node->op()); + EXPECT_EQ("c", square_node->input(0)); + EXPECT_EQ("^d", square_node->input(1)); + + auto tensors = EvaluateNodes(output, item.fetch); EXPECT_EQ(1, tensors.size()); test::ExpectTensorNear(tensors_expected[0], tensors[0], 1e-6); } @@ -386,12 +395,10 @@ TEST_F(ArithmeticOptimizerTest, RemoveInvolution_AdjacentNodes) { auto recip2 = ops::Reciprocal(s.WithOpName("recip2"), recip1); auto id = ops::Identity(s.WithOpName("id"), recip2); - std::vector fetch = {"id"}; - GrapplerItem item; - item.fetch = fetch; + item.fetch = {"id"}; TF_CHECK_OK(s.ToGraphDef(&item.graph)); - auto tensors_expected = EvaluateNodes(item.graph, fetch); + auto tensors_expected = EvaluateNodes(item.graph, item.fetch); EXPECT_EQ(1, tensors_expected.size()); GraphDef output; @@ -404,7 +411,7 @@ TEST_F(ArithmeticOptimizerTest, RemoveInvolution_AdjacentNodes) { EXPECT_EQ("id", output.node(1).name()); EXPECT_EQ("c", output.node(1).input(0)); - auto tensors = EvaluateNodes(output, fetch); + auto tensors = EvaluateNodes(output, item.fetch); EXPECT_EQ(1, tensors.size()); test::ExpectTensorNear(tensors_expected[0], tensors[0], 1e-6); } -- GitLab From a1e258706972fb8c686434163b4f939010deab34 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 5 Jun 2018 12:32:18 -0700 Subject: [PATCH 035/816] Fixing typo in Subtract Kernel. PiperOrigin-RevId: 199340127 --- tensorflow/contrib/lite/kernels/sub.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/lite/kernels/sub.cc b/tensorflow/contrib/lite/kernels/sub.cc index d788159a8d..bdcaab8e2f 100644 --- a/tensorflow/contrib/lite/kernels/sub.cc +++ b/tensorflow/contrib/lite/kernels/sub.cc @@ -175,7 +175,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { output); } else { context->ReportError( - context, "output type %d is not support, requires float|uint8 types.", + context, "output type %d is not supported, requires float|uint8 types.", output->type); return kTfLiteError; } -- GitLab From b7928ac78d3cd688967bcf4e5253e384b355070f Mon Sep 17 00:00:00 2001 From: Jianwei Xie Date: Tue, 5 Jun 2018 12:42:44 -0700 Subject: [PATCH 036/816] Clarifies how to pass training hooks to TPUEstimator in the docstring for TPUEstimator. PiperOrigin-RevId: 199341721 --- .../contrib/tpu/python/tpu/tpu_estimator.py | 83 ++++++++++++++----- 1 file changed, 64 insertions(+), 19 deletions(-) diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py index f63e9e8bda..64ae35dfc5 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py @@ -122,6 +122,33 @@ def _create_global_step(graph): def _create_or_get_iterations_per_loop(): + """Creates or gets the iterations_per_loop variable. + + In TPUEstimator, the user provided computation, the model_fn, is wrapped + inside a tf.while_loop for peak performance. The iterations of the loop are + specified by this variable, which adjusts its value on the CPU after each TPU + program execution and before the next TPU execution. + + The purpose of using a variable, rather then a constant, is to allow + TPUEstimator adapt the TPU training iterations according to the final steps + specified by users. For example, if the user sets the iterations_per_loop as 4 + in TPUConfig and steps as 10 in TPUEstimator.train(), the iterations_per_loop + variable will have the following value before each TPU training. + + - 1-th TPU execution: iterations_per_loop = 4 + - 2-th TPU execution: iterations_per_loop = 4 + - 3-th TPU execution: iterations_per_loop = 2 + + As model_fn increases the global step once per train_op invocation, the global + step is 10 after all TPU executions, matching the steps=10 inputs passed in by + users. + + Returns: + A TF non-trainable resource variable. + + Raises: + RuntimeError: If multi iterations_per_loop variables were found. + """ graph = ops.get_default_graph() collection_name = '{}_{}'.format(_TPU_ESTIMATOR, _ITERATIONS_PER_LOOP_VAR) iter_vars = graph.get_collection(collection_name) @@ -388,20 +415,21 @@ class TPUInfeedOutfeedSessionHook(session_run_hook.SessionRunHook): return def _cancel_session(): - # Close the session to avoid the main thread from hanging. If input - # pipeline triggers any error, the infeed thread dies but the main thread - # for TPU computation waits for the infeed enqueue forever. Close the - # Session to cancel the main thread Session.run execution. - # - # We sleep for a few seconds before closing to give some time - # for the TPU compilation error, if any, propagating, from TPU to CPU - # host. Compilation errors should be reported by the main thread so that - # the program can be interrupted and users can take action. Due to a race - # condition, the infeed thread might see an error first. Closing the - # session here immediately would result in a session cancellation - # exception in the main thread, instead of the expected compile error. - # User code that depends on having the proper exception type will - # therefore be confused. + """Close the session to avoid the main thread from hanging. + + If input pipeline triggers any error, the infeed thread dies but the main + thread for TPU computation waits for the infeed enqueue forever. Close the + Session to cancel the main thread Session.run execution. + + We sleep for a few seconds before closing to give some time for the TPU + compilation error, if any, propagating, from TPU to CPU host. Compilation + errors should be reported by the main thread so that the program can be + interrupted and users can take action. Due to a race condition, the + infeed thread might see an error first. Closing the session here + immediately would result in a session cancellation exception in the main + thread, instead of the expected compile error. User code that depends on + having the proper exception type will therefore be confused. + """ time.sleep(5) # If the main session is still running, the infeed/outfeed errors are @@ -721,6 +749,15 @@ def generate_per_host_enqueue_ops_fn_for_host( tpu_ordinal_function = None def enqueue_ops_fn(): + """A Fn returning the TPU infeed enqueue ops. + + By providing as a Fn, it can be invoked inside the tf.while_loop such that + the input pipeline for multiple iterations can be executed by one + Session.run call. + + Returns: + list of dict of ops. + """ with ops.device(device): num_of_replicas_per_host = ctx.num_of_replicas_per_host # Convert user input to features and labels. If the user returns a @@ -1095,10 +1132,16 @@ class _InputPipeline(object): return enqueue_ops, all_hooks, run_infeed_loop_on_coordinator def _validate_input_pipeline(self): - # Perform some sanity checks to log user friendly information. We should - # error out to give users better error message. But, if - # _WRAP_INPUT_FN_INTO_WHILE_LOOP is False (legacy behavior), we cannot break - # user code, so, log a warning. + """Validates the input pipeline. + + Perform some sanity checks to log user friendly information. We should + error out to give users better error message. But, if + _WRAP_INPUT_FN_INTO_WHILE_LOOP is False (legacy behavior), we cannot break + user code, so, log a warning. + + Raises: + RuntimeError: If the validation failed. + """ if ops.get_default_graph().get_collection(ops.GraphKeys.QUEUE_RUNNERS): err_msg = ('Input pipeline contains one or more QueueRunners. ' 'It could be slow and not scalable. Please consider ' @@ -1837,7 +1880,8 @@ class TPUEstimator(estimator_lib.Estimator): Args: model_fn: Model function as required by `Estimator`. For training, the returned `EstimatorSpec` cannot have hooks as it is not supported in - `TPUEstimator`. + `TPUEstimator`. Instead, the user can pass the training hooks as + an argument to `TPUEstimator.train()`. model_dir: Directory to save model parameters, graph and etc. This can also be used to load checkpoints from the directory into a estimator to continue training a previously saved model. If `None`, the model_dir in @@ -2898,6 +2942,7 @@ class _StopSignals(object): @staticmethod def should_stop(scalar_stopping_signal): + """Detects whether scalar_stopping_signal indicates stopping.""" if isinstance(scalar_stopping_signal, ops.Tensor): # STOPPING_SIGNAL is a constant True. Here, the logical_and is just the TF # way to express the bool check whether scalar_stopping_signal is True. -- GitLab From c681be04ec15cdfc225bc61132420781bf23d298 Mon Sep 17 00:00:00 2001 From: Eugene Zhulenev Date: Tue, 5 Jun 2018 13:12:02 -0700 Subject: [PATCH 037/816] Move SimplifyAggregation to separate aggregation stage. PiperOrigin-RevId: 199346067 --- .../optimizers/arithmetic_optimizer.cc | 171 +++++++++++------- .../optimizers/arithmetic_optimizer.h | 1 + .../optimizers/arithmetic_optimizer_test.cc | 68 +++++-- 3 files changed, 154 insertions(+), 86 deletions(-) diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc index 561930f858..2408652c87 100644 --- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc +++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc @@ -2122,6 +2122,109 @@ class ReplaceMulWithSquare : public ArithmeticOptimizerStage { } }; +// Simplify aggregation (e.g. AddN) nodes: +// +// 1. Discard aggregate nodes with a single input and no control dependencies. +// +// 2. Try to rewrite aggregations of N >= 2 identical terms (possibly due to +// deduping or other rewrites) so we can get rid of the sum entirely. +// +// The expression (using AddN as an example of an aggregate op): +// AddN(x, x, x, ... ,x) +// <-- N terms --> +// can be rewritten to: +// Mul(Const(N), x)) +// +class SimplifyAggregation : public ArithmeticOptimizerStage { + public: + explicit SimplifyAggregation(const GraphOptimizerContext& ctx, + const ArithmeticOptimizerContext& ctx_ext) + : ArithmeticOptimizerStage("SimplifyAggregation", ctx, ctx_ext) {} + ~SimplifyAggregation() override = default; + + bool IsSupported(const NodeDef* node) const override { + return IsAggregate(*node) && NumNonControlInputs(*node) > 0; + } + + Status TrySimplify(NodeDef* node, string* simplified_node_name) override { + // 1. Discard aggregate nodes with a single input and no control deps. + if (node->input_size() == 1) { + *simplified_node_name = node->input(0); + return Status::OK(); + } + + // 2. Rewrite aggregations of N >= 2 identical terms. + + // All non-control inputs must be identical. + bool all_equal = true; + int num_inputs = 1; + for (int i = 1; i < node->input_size(); ++i) { + if (IsControlInput(node->input(i))) break; + ++num_inputs; + if (node->input(i) != node->input(0)) { + all_equal = false; + break; + } + } + if (!all_equal) return Status::OK(); + + // And node should not be optimized earlier. + const NodeScopeAndName node_scope_and_name = + ParseNodeScopeAndName(node->name()); + const string optimized_const_name = + OptimizedNodeName(node_scope_and_name, "Const"); + const string optimized_mul_name = + OptimizedNodeName(node_scope_and_name, "Mul"); + + bool is_already_optimized = + ctx().node_map->NodeExists(optimized_const_name) || + ctx().node_map->NodeExists(optimized_mul_name); + + if (is_already_optimized) return Status::OK(); + + // At this point all preconditions are met, and we safely do the rewrite. + VLOG(3) << "Simplify aggregation with identical inputs: node=" + << node->name() << " num_inputs=" << num_inputs; + + // 1. Create constant node with value N. + const auto type = GetDataTypeFromAttr(*node, "T"); + Tensor t(type, TensorShape({})); + Status status = SetTensorValue(type, num_inputs, &t); + if (!status.ok()) { + return errors::Internal("Failed to create const node: ", + status.error_message()); + } + + TensorValue value(&t); + NodeDef* new_const_node = AddEmptyNode(optimized_const_name); + status = ConstantFolding::CreateNodeDef(new_const_node->name(), value, + new_const_node); + if (!status.ok()) { + return errors::Internal("Failed to create const node: ", + status.error_message()); + } + new_const_node->set_device(node->device()); + MaybeAddControlInput(NodeName(node->input(0)), new_const_node, + ctx().optimized_graph, ctx().node_map); + AddToOptimizationQueue(new_const_node); + + // 2. Replace the aggregate node with Mul(Const(N), x). + NodeDef* new_mul_node = AddEmptyNode(optimized_mul_name); + new_mul_node->set_op("Mul"); + new_mul_node->set_device(node->device()); + SetDataTypeToAttr(type, "T", new_mul_node); + new_mul_node->add_input(new_const_node->name()); + ctx().node_map->AddOutput(new_const_node->name(), new_mul_node->name()); + new_mul_node->add_input(node->input(0)); + ctx().node_map->AddOutput(node->input(0), new_mul_node->name()); + + ForwardControlDependencies(new_mul_node, {node}); + *simplified_node_name = new_mul_node->name(); + + return Status::OK(); + } +}; + } // namespace class UniqueNodes { @@ -2374,72 +2477,6 @@ void ArithmeticOptimizer::ForwardControlDependencies( // ArithmeticOptimizerStage string ArithmeticOptimizer::TrySimplifyAndReplaceUses( const NodeDef* node, SetVector* nodes_to_simplify) { - if (IsAggregate(*node) && NumNonControlInputs(*node) > 0) { - // Discard aggregate nodes with a single input and no control dependencies. - if (node->input_size() == 1) { - return node->input(0); - } - - // Try to rewrite aggregations of N >= 2 identical terms (possibly due - // to deduping or other rewrites) so we can get rid of the sum entirely. - // The expression (using AddN as an example of an aggregate op): - // AddN(x, x, x, ... ,x) - // <-- N terms --> - // can be rewritten to - // Mul(Const(N), x)) - // - bool all_equal = true; - int num_inputs = 1; - for (int i = 1; i < node->input_size(); ++i) { - if (IsControlInput(node->input(i))) { - break; - } - ++num_inputs; - if (node->input(i) != node->input(0)) { - all_equal = false; - break; - } - } - if (all_equal && !OptimizedNodeExists(*node, "const") && - !OptimizedNodeExists(*node, "mul")) { - // 1. Create constant node with value N. - const auto type = GetDataTypeFromAttr(*node, "T"); - Tensor t(type, TensorShape({})); - Status status = SetTensorValue(type, num_inputs, &t); - if (!status.ok()) { - LOG(WARNING) << "Failed to create const node: " - << status.error_message(); - return ""; - } - TensorValue value(&t); - NodeDef* new_const_node = AddNode(*node, "const", /*copy_node=*/false); - status = ConstantFolding::CreateNodeDef(new_const_node->name(), value, - new_const_node); - if (!status.ok()) { - LOG(WARNING) << "Failed to create const node: " - << status.error_message(); - return ""; - } - new_const_node->set_device(node->device()); - MaybeAddControlInput(NodeName(node->input(0)), new_const_node, - optimized_graph_, node_map_.get()); - nodes_to_simplify->PushBack(new_const_node); - - // 2. Replace the aggregate node with Mul(Const(N), x). - NodeDef* new_mul_node = AddNode(*node, "mul", /*copy_node=*/false); - new_mul_node->set_op("Mul"); - new_mul_node->set_device(node->device()); - SetDataTypeToAttr(type, "T", new_mul_node); - new_mul_node->add_input(new_const_node->name()); - node_map_->AddOutput(new_const_node->name(), new_mul_node->name()); - new_mul_node->add_input(node->input(0)); - node_map_->AddOutput(node->input(0), new_mul_node->name()); - - ForwardControlDependencies(new_mul_node, {node}); - return new_mul_node->name(); - } - } - // Fold Transpose into matrix multiplication. if ((node->op() == "MatMul" || node->op() == "SparseMatMul" || node->op() == "BatchMatMul") && @@ -2554,6 +2591,8 @@ Status ArithmeticOptimizer::SimplifyArithmeticOps(bool can_use_shapes) { pipeline.AddStage(ctx, ctx_ext); if (options_.reorder_cast_and_transpose) pipeline.AddStage(ctx, ctx_ext); + if (options_.simplify_aggregation) + pipeline.AddStage(ctx, ctx_ext); if (options_.hoist_cwise_unary_chains) pipeline.AddStage(ctx, ctx_ext); if (options_.convert_sqrt_div_to_rsqrt_mul) diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.h b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.h index 8e00b83a70..549ea3fde5 100644 --- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.h +++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.h @@ -75,6 +75,7 @@ class ArithmeticOptimizer : public GraphOptimizer { bool remove_redundant_reshape = true; bool reorder_cast_and_transpose = true; bool replace_mul_with_square = true; + bool simplify_aggregation = true; // Choose which arithmetic optimizer stages will be enabled for a given // optimization level by default. diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc b/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc index f15cbfe407..f79347cde6 100644 --- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc +++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc @@ -40,21 +40,37 @@ constexpr char kHoistFactorOptimizerMul[] = constexpr char kHoistFactorOptimizerAdd[] = "ArithmeticOptimizer/HoistCommonFactor_Add_"; -// Optimized name of outer Mul node by HoistCommonFactorOutOfAggregation +constexpr char kSimplifyAggregationConst[] = + "ArithmeticOptimizer/SimplifyAggregation_Const_"; + +constexpr char kSimplifyAggregationMul[] = + "ArithmeticOptimizer/SimplifyAggregation_Mul_"; + +// Optimized name of outer Mul node by HoistCommonFactorOutOfAggregation. string HoistMulName(const string& name) { return AddPrefixToNodeName(name, kHoistFactorOptimizerMul, ""); } -// Optimized name of outer Div node by HoistCommonFactorOutOfAggregation +// Optimized name of outer Div node by HoistCommonFactorOutOfAggregation. string HoistDivName(const string& name) { return AddPrefixToNodeName(name, kHoistFactorOptimizerDiv, ""); } -// Optimized name of inner Add node by HoistCommonFactorOutOfAggregation +// Optimized name of inner Add node by HoistCommonFactorOutOfAggregation. string HoistAddName(const string& name) { return AddPrefixToNodeName(name, kHoistFactorOptimizerAdd, ""); } +// Optimized name of Const node by SimplifyAggregation. +string AggregationConstName(const string& name) { + return AddPrefixToNodeName(name, kSimplifyAggregationConst, ""); +} + +// Optimized name of Mul node by SimplifyAggregation. +string AggregationMulName(const string& name) { + return AddPrefixToNodeName(name, kSimplifyAggregationMul, ""); +} + string OptimizedName(const string& name) { return AddPrefixToNodeName(name, kArithmeticOptimizer); } @@ -140,6 +156,7 @@ class ArithmeticOptimizerTest : public GrapplerTest { options.remove_logical_not = false; options.reorder_cast_and_transpose = false; options.replace_mul_with_square = false; + options.simplify_aggregation = false; optimizer->options_ = options; } @@ -226,6 +243,11 @@ class ArithmeticOptimizerTest : public GrapplerTest { DisableAllStages(optimizer); optimizer->options_.remove_logical_not = true; } + + void EnableOnlySimplifyAggregation(ArithmeticOptimizer* optimizer) { + DisableAllStages(optimizer); + optimizer->options_.simplify_aggregation = true; + } }; TEST_F(ArithmeticOptimizerTest, NoOp) { @@ -500,10 +522,10 @@ TEST_F(ArithmeticOptimizerTest, TrivialSumsSimple) { Output id = ops::Identity(s.WithOpName("id"), add); GrapplerItem item; + item.fetch = {"id"}; TF_CHECK_OK(s.ToGraphDef(&item.graph)); - std::vector fetch = {"id"}; - auto tensors_expected = EvaluateNodes(item.graph, fetch); + auto tensors_expected = EvaluateNodes(item.graph, item.fetch); EXPECT_EQ(1, tensors_expected.size()); ArithmeticOptimizer optimizer; @@ -513,22 +535,25 @@ TEST_F(ArithmeticOptimizerTest, TrivialSumsSimple) { EXPECT_EQ(5, output.node_size()); - const NodeDef* new_const = node_map.GetNode(OptimizedName("add_const")); + const string optimized_const_name = AggregationConstName("add"); + const string optimized_mul_name = AggregationMulName("add"); + + const NodeDef* new_const = node_map.GetNode(optimized_const_name); ASSERT_NE(new_const, nullptr); EXPECT_EQ("^x", new_const->input(0)); EXPECT_EQ(std::string("\0\0\0@", 4), new_const->attr().at("value").tensor().tensor_content()); - const NodeDef* new_mul = node_map.GetNode(OptimizedName("add_mul")); + const NodeDef* new_mul = node_map.GetNode(optimized_mul_name); ASSERT_NE(new_mul, nullptr); - EXPECT_EQ(OptimizedName("add_const"), new_mul->input(0)); + EXPECT_EQ(optimized_const_name, new_mul->input(0)); EXPECT_EQ("x", new_mul->input(1)); const NodeDef* new_id = node_map.GetNode("id"); ASSERT_NE(new_id, nullptr); - EXPECT_EQ(OptimizedName("add_mul"), new_id->input(0)); + EXPECT_EQ(optimized_mul_name, new_id->input(0)); - auto tensors = EvaluateNodes(output, fetch); + auto tensors = EvaluateNodes(output, item.fetch); EXPECT_EQ(1, tensors.size()); test::ExpectTensorNear(tensors_expected[0], tensors[0], 1e-6); } @@ -554,21 +579,24 @@ TEST_F(ArithmeticOptimizerTest, TrivialSumsSimpleWithControlDep) { EXPECT_EQ(6, output.node_size()); - const NodeDef* new_const = node_map.GetNode(OptimizedName("add_const")); + const string optimized_const_name = AggregationConstName("add"); + const string optimized_mul_name = AggregationMulName("add"); + + const NodeDef* new_const = node_map.GetNode(optimized_const_name); ASSERT_NE(new_const, nullptr); EXPECT_EQ("^x", new_const->input(0)); EXPECT_EQ(std::string("\0\0\0@", 4), new_const->attr().at("value").tensor().tensor_content()); - const NodeDef* new_mul = node_map.GetNode(OptimizedName("add_mul")); + const NodeDef* new_mul = node_map.GetNode(optimized_mul_name); ASSERT_NE(new_mul, nullptr); - EXPECT_EQ(OptimizedName("add_const"), new_mul->input(0)); + EXPECT_EQ(optimized_const_name, new_mul->input(0)); EXPECT_EQ("x", new_mul->input(1)); EXPECT_EQ("^y", new_mul->input(2)); const NodeDef* new_id = node_map.GetNode("id"); ASSERT_NE(new_id, nullptr); - EXPECT_EQ(OptimizedName("add_mul"), new_id->input(0)); + EXPECT_EQ(optimized_mul_name, new_id->input(0)); auto tensors = EvaluateNodes(output, fetch); EXPECT_EQ(1, tensors.size()); @@ -633,24 +661,24 @@ TEST_F(ArithmeticOptimizerTest, TrivialSumsRepeatedAdd) { ASSERT_NE(add_4_node, nullptr); EXPECT_EQ("Add", add_4_node->op()); EXPECT_EQ(2, add_4_node->input_size()); - EXPECT_EQ(OptimizedName("Add_const"), add_4_node->input(0)); - EXPECT_EQ(OptimizedName("Add_1_const"), add_4_node->input(1)); + EXPECT_EQ(AggregationConstName("Add"), add_4_node->input(0)); + EXPECT_EQ(AggregationConstName("Add_1"), add_4_node->input(1)); const NodeDef* add_5_node = node_map.GetNode(HoistAddName("Add_5")); ASSERT_NE(add_5_node, nullptr); EXPECT_EQ("Add", add_5_node->op()); EXPECT_EQ(2, add_5_node->input_size()); - EXPECT_EQ(OptimizedName("Add_const"), add_5_node->input(0)); - EXPECT_EQ(OptimizedName("Add_1_const"), add_5_node->input(1)); + EXPECT_EQ(AggregationConstName("Add"), add_5_node->input(0)); + EXPECT_EQ(AggregationConstName("Add_1"), add_5_node->input(1)); - const NodeDef* add_const_node = node_map.GetNode(OptimizedName("Add_const")); + const NodeDef* add_const_node = node_map.GetNode(AggregationConstName("Add")); ASSERT_NE(add_const_node, nullptr); EXPECT_EQ("Const", add_const_node->op()); EXPECT_EQ(1, add_const_node->input_size()); EXPECT_EQ("^Placeholder", add_const_node->input(0)); const NodeDef* add_1_const_node = - node_map.GetNode(OptimizedName("Add_1_const")); + node_map.GetNode(AggregationConstName("Add_1")); ASSERT_NE(add_1_const_node, nullptr); EXPECT_EQ("Const", add_1_const_node->op()); EXPECT_EQ(1, add_1_const_node->input_size()); -- GitLab From 1bac6186e19353d9881584ce8ec51bf35d627842 Mon Sep 17 00:00:00 2001 From: Skye Wanderman-Milne Date: Tue, 5 Jun 2018 13:16:57 -0700 Subject: [PATCH 038/816] Introduce tf.contrib.control_flow.new_cond. new_cond is a new implementation of tf.cond. Instead of emitting control flow ops (i.e. Switch and Merge nodes), new_cond emits a single If op, which represents the conditional branches as TF functions. With this change, users can use new_cond and take its gradient. The idea is for new_cond to eventually replace tf.cond. There are several functional and performance gaps that must be addressed first, including: * Gradients won't work on imported graphs * Misc. limitations of TF functions (lack of collections, device scopes, etc.) PiperOrigin-RevId: 199346735 --- tensorflow/contrib/BUILD | 5 +- tensorflow/contrib/__init__.py | 1 + tensorflow/contrib/cmake/python_modules.txt | 2 + tensorflow/contrib/control_flow/BUILD | 48 +++ tensorflow/contrib/control_flow/__init__.py | 31 ++ .../contrib/control_flow/python/cond_v2.py | 394 ++++++++++++++++++ .../control_flow/python/cond_v2_test.py | 113 +++++ .../api_def/base_api/api_def_FakeParam.pbtxt | 24 ++ .../python_api/api_def_FakeParam.pbtxt | 4 + tensorflow/core/kernels/functional_ops.cc | 19 + tensorflow/core/ops/functional_ops.cc | 17 + tensorflow/python/BUILD | 5 +- 12 files changed, 660 insertions(+), 3 deletions(-) create mode 100644 tensorflow/contrib/control_flow/BUILD create mode 100644 tensorflow/contrib/control_flow/__init__.py create mode 100644 tensorflow/contrib/control_flow/python/cond_v2.py create mode 100644 tensorflow/contrib/control_flow/python/cond_v2_test.py create mode 100644 tensorflow/core/api_def/base_api/api_def_FakeParam.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_FakeParam.pbtxt diff --git a/tensorflow/contrib/BUILD b/tensorflow/contrib/BUILD index 0f9c80404a..50b1ae5cc3 100644 --- a/tensorflow/contrib/BUILD +++ b/tensorflow/contrib/BUILD @@ -31,13 +31,15 @@ py_library( "//tensorflow/contrib/cluster_resolver:cluster_resolver_py", "//tensorflow/contrib/coder:coder_py", "//tensorflow/contrib/compiler:compiler_py", + "//tensorflow/contrib/autograph", "//tensorflow/contrib/constrained_optimization", + "//tensorflow/contrib/control_flow", "//tensorflow/contrib/copy_graph:copy_graph_py", "//tensorflow/contrib/crf:crf_py", "//tensorflow/contrib/cudnn_rnn:cudnn_rnn_py", "//tensorflow/contrib/data", - "//tensorflow/contrib/distribute:distribute", "//tensorflow/contrib/deprecated:deprecated_py", + "//tensorflow/contrib/distribute:distribute", "//tensorflow/contrib/distributions:distributions_py", "//tensorflow/contrib/eager/python:tfe", "//tensorflow/contrib/estimator:estimator_py", @@ -83,7 +85,6 @@ py_library( "//tensorflow/contrib/proto", "//tensorflow/contrib/quantization:quantization_py", "//tensorflow/contrib/quantize:quantize_graph", - "//tensorflow/contrib/autograph", "//tensorflow/contrib/receptive_field:receptive_field_py", "//tensorflow/contrib/recurrent:recurrent_py", "//tensorflow/contrib/reduce_slice_ops:reduce_slice_ops_py", diff --git a/tensorflow/contrib/__init__.py b/tensorflow/contrib/__init__.py index 9aad772f0a..ad8c40395c 100644 --- a/tensorflow/contrib/__init__.py +++ b/tensorflow/contrib/__init__.py @@ -30,6 +30,7 @@ from tensorflow.contrib import cluster_resolver from tensorflow.contrib import coder from tensorflow.contrib import compiler from tensorflow.contrib import constrained_optimization +from tensorflow.contrib import control_flow from tensorflow.contrib import copy_graph from tensorflow.contrib import crf from tensorflow.contrib import cudnn_rnn diff --git a/tensorflow/contrib/cmake/python_modules.txt b/tensorflow/contrib/cmake/python_modules.txt index fece56c412..015cb73bbd 100644 --- a/tensorflow/contrib/cmake/python_modules.txt +++ b/tensorflow/contrib/cmake/python_modules.txt @@ -115,6 +115,8 @@ tensorflow/contrib/coder/python/ops tensorflow/contrib/compiler tensorflow/contrib/constrained_optimization tensorflow/contrib/constrained_optimization/python +tensorflow/contrib/control_flow +tensorflow/contrib/control_flow/python tensorflow/contrib/copy_graph tensorflow/contrib/copy_graph/python tensorflow/contrib/copy_graph/python/util diff --git a/tensorflow/contrib/control_flow/BUILD b/tensorflow/contrib/control_flow/BUILD new file mode 100644 index 0000000000..746b5b5b5e --- /dev/null +++ b/tensorflow/contrib/control_flow/BUILD @@ -0,0 +1,48 @@ +# New implementations of control flow ops + +licenses(["notice"]) # Apache 2.0 + +package(default_visibility = ["//visibility:public"]) + +load("//tensorflow:tensorflow.bzl", "tf_py_test") + +py_library( + name = "control_flow", + srcs = ["__init__.py"], + srcs_version = "PY2AND3", + deps = [ + ":cond_v2", + ], +) + +py_library( + name = "cond_v2", + srcs = ["python/cond_v2.py"], + srcs_version = "PY2AND3", + deps = [ + "//tensorflow/python:array_ops", + "//tensorflow/python:c_api_util", + "//tensorflow/python:framework_ops", + "//tensorflow/python:function", + "//tensorflow/python:functional_ops_gen", + "//tensorflow/python:gradients", + "//tensorflow/python:pywrap_tensorflow", + ], +) + +tf_py_test( + name = "cond_v2_test", + size = "small", + srcs = ["python/cond_v2_test.py"], + additional_deps = [ + ":cond_v2", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:constant_op", + "//tensorflow/python:control_flow_ops", + "//tensorflow/python:dtypes", + "//tensorflow/python:framework", + "//tensorflow/python:gradients", + ], + grpc_enabled = True, +) diff --git a/tensorflow/contrib/control_flow/__init__.py b/tensorflow/contrib/control_flow/__init__.py new file mode 100644 index 0000000000..582af2cf10 --- /dev/null +++ b/tensorflow/contrib/control_flow/__init__.py @@ -0,0 +1,31 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +"""New implementations of TF control flow ops. + +@@cond_v2 +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +# pylint: disable=unused-import +from tensorflow.contrib.control_flow.python.cond_v2 import cond_v2 +# pylint: enable=unused-import + +from tensorflow.python.util.all_util import remove_undocumented + +remove_undocumented(__name__) diff --git a/tensorflow/contrib/control_flow/python/cond_v2.py b/tensorflow/contrib/control_flow/python/cond_v2.py new file mode 100644 index 0000000000..90c678d0f6 --- /dev/null +++ b/tensorflow/contrib/control_flow/python/cond_v2.py @@ -0,0 +1,394 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= +"""cond_v2 and gradient. + +This is a version of cond that emits a single If op, as well as the gradient +function for If ops produced by cond_v2. This will eventually replace the +current tf.cond implementation once it reaches feature and performance parity. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python import pywrap_tensorflow as c_api +from tensorflow.python.framework import c_api_util +from tensorflow.python.framework import function +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import gen_functional_ops +from tensorflow.python.ops import gradients_impl + + +# NOTE(skyewm): TensorFlow uses protected class methods and fields to signify +# that they aren't part of the official public API. These protected members +# often need to be used by implementation code however. Rather than litter the +# code with pylint comments, we ignore protected access violations for +# readability. +# pylint: disable=protected-access + + +def cond_v2(pred, true_fn, false_fn, name="cond"): + """Like tf.cond, except emits a single If op.""" + with ops.name_scope(name) as scope: + true_graph = function.func_graph_from_py_func(true_fn, [], [], + name="%s_true" % scope) + false_graph = function.func_graph_from_py_func(false_fn, [], [], + name="%s_false" % scope) + _check_same_outputs(true_graph, false_graph) + + # Add inputs to true_graph and false_graph to make them match. Note that + # this modifies true_graph and false_graph. + cond_inputs = _make_inputs_match(true_graph, false_graph, + true_graph.extra_inputs, + false_graph.extra_inputs) + + # Add all intermediate tensors as function outputs so they're available for + # the gradient computation. + + true_intermediates = _get_intermediates(true_graph) + false_intermediates = _get_intermediates(false_graph) + + # Save the original number of outputs to return to the caller. + num_cond_outputs = len(true_graph.outputs) + + # Make the number/type of new intermediate outputs match. + extra_true_outputs, extra_false_outputs = _pad_params( + true_graph, false_graph, true_intermediates, false_intermediates) + + true_graph.outputs.extend(extra_true_outputs) + false_graph.outputs.extend(extra_false_outputs) + + # Create the If op. + tensors = gen_functional_ops._if( + pred, cond_inputs, [t.dtype for t in true_graph.outputs], + _create_new_tf_function(true_graph), + _create_new_tf_function(false_graph), + name=scope) + + # TODO(b/79883549): if we could make Graphs from FunctionDefs, we wouldn't + # need this extra state. Requiring extra state also prevents the ability to + # take the gradient of deserialized If ops. + tensors[0].op._true_graph = true_graph + tensors[0].op._false_graph = false_graph + + return tensors[:num_cond_outputs] + + +@ops.RegisterGradient("If") +def _IfGrad(op, *grads): # pylint: disable=invalid-name + """The gradient of an If op produced by cond_v2.""" + true_graph = op._true_graph + false_graph = op._false_graph + + # Create grad functions that compute the gradient of the true/false forward + # graphs. These functions will capture tensors from the forward pass + # functions. + true_grad_graph = _create_grad_func( + true_graph, grads, "%sgrad" % true_graph.name) + false_grad_graph = _create_grad_func( + false_graph, grads, "%sgrad" % false_graph.name) + + assert ([t.dtype for t in true_grad_graph.outputs] == + [t.dtype for t in false_grad_graph.outputs]) + + # Match up the captured grad function inputs with outputs of 'op' and other + # external tensors. + true_grad_inputs = _get_grad_inputs(op, true_graph, true_grad_graph) + false_grad_inputs = _get_grad_inputs(op, false_graph, false_grad_graph) + + # Make the inputs to true_grad_graph and false_grad_graph match. Note that + # this modifies true_grad_graph and false_grad_graph. + grad_inputs = _make_inputs_match(true_grad_graph, false_grad_graph, + true_grad_inputs, false_grad_inputs) + + # Add all intermediate tensors as function outputs so they're available for + # higher-order gradient computations. + + true_grad_intermediates = _get_intermediates(true_grad_graph) + false_grad_intermediates = _get_intermediates(false_grad_graph) + + # Save the original number of gradient outputs to return. + num_grad_outputs = len(true_grad_graph.outputs) + + # Make the number/type of new intermediate outputs match. + extra_true_grad_outputs, extra_false_grad_outputs = _pad_params( + true_grad_graph, false_grad_graph, + true_grad_intermediates, false_grad_intermediates) + + true_grad_graph.outputs.extend(extra_true_grad_outputs) + false_grad_graph.outputs.extend(extra_false_grad_outputs) + + # Create the gradient If op. + tensors = gen_functional_ops._if( + op.inputs[0], grad_inputs, [t.dtype for t in true_grad_graph.outputs], + _create_new_tf_function(true_grad_graph), + _create_new_tf_function(false_grad_graph)) + tensors[0].op._true_graph = true_grad_graph + tensors[0].op._false_graph = false_grad_graph + + # The predicate has no gradient. + return [None] + tensors[:num_grad_outputs] + + +def _grad_fn(func_graph, grads): + """The gradient function for each conditional branch. + + This function builds the gradient graph of the corresponding forward-pass + conditional branch in `func_graph`. This is done by differentiating + func_graph's outputs w.r.t. its inputs. + + Args: + func_graph: function._FuncGraph. The corresponding forward-pass function. + grads: The list of input gradient Tensors. + + Returns: + The output gradient Tensors. + """ + # Filter out untrainable function outputs. + # NOTE(skyewm): If we don't do this, the untrainable tensors can sometimes + # cause _GradientsHelper to raise an exception (e.g. the implementation + # doesn't expect 'ys' to contain boolean tensors). + assert len(func_graph.outputs) == len(grads) + ys = [] + grad_ys = [] + for y, grad_y in zip(func_graph.outputs, grads): + if not gradients_impl._IsTrainable(y): + continue + ys.append(y) + grad_ys.append(grad_y) + + # Build the gradient graph. Note that this builds the gradient computation of + # func_graph in the current graph, which requires capturing tensors from + # func_graph. The captured func_graph tensors are resolved to external tensors + # in _get_grad_inputs. + result = gradients_impl._GradientsHelper( + ys, func_graph.inputs, grad_ys=grad_ys, + src_graph=func_graph) + + # Functions can't return None; replace Nones with zero tensors. + # TODO(b/80444525): don't return anything here and make _IfGrad return None if + # both branches have zero gradient. + for i in range(len(result)): + if result[i] is None: + result[i] = array_ops.zeros_like(func_graph.inputs[i]) + + return result + + +def _create_grad_func(func_graph, grads, name): + """Returns the _FuncGraph representation of _grad_fn.""" + return function.func_graph_from_py_func(lambda: _grad_fn(func_graph, grads), + [], [], name) + + +def _get_grad_inputs(if_op, cond_graph, grad_graph): + """Returns the tensors we should pass to grad_graph. + + This method handles tensors captured from cond_graph in grad_graph. It + converts these to suitable input tensors from the outer graph. + + Args: + if_op: Operation. The forward-pass If op that uses cond_graph. + cond_graph: function._FuncGraph. The forward-pass function. + grad_graph: function._FuncGraph. The gradients function. + + Returns: + A list of inputs tensors to be passed to grad_graph. + """ + inputs = [] + + # Maps placeholders in cond_graph -> input tensor in outer graph. + forward_input_map = {v: k for k, v in cond_graph._captured.items()} + + for t in grad_graph.extra_inputs: + if t.graph == ops.get_default_graph(): + # t is in the outer graph (e.g. one of the input gradients). + inputs.append(t) + elif t in forward_input_map: + # t is an input placeholder in cond_graph. Get the corresponding input + # tensor in the outer graph. + assert t.graph == cond_graph + assert forward_input_map[t].graph == ops.get_default_graph() + inputs.append(forward_input_map[t]) + else: + # t is an intermediate value in cond_graph. Get the corresponding output + # of 'if_op' (note that all intermediate values are outputs). + assert t.graph == cond_graph + output_idx = cond_graph.outputs.index(t) + inputs.append(if_op.outputs[output_idx]) + + return inputs + + +def _create_new_tf_function(func_graph): + """Converts func_graph to a TF_Function and adds it to the current graph. + + Args: + func_graph: function._FuncGraph + + Returns: + The name of the new TF_Function. + """ + func_graph.name = "%s_" % func_graph.name + c_func = c_api.TF_GraphToFunction_wrapper( + func_graph._c_graph, + func_graph.name, + False, # append_hash_to_fn_name + None, # opers + [t._as_tf_output() for t in func_graph.inputs], + [t._as_tf_output() for t in func_graph.outputs], + [], + None, # opts + None) # description + c_func = c_api_util.ScopedTFFunction(c_func) + c_api.TF_GraphCopyFunction( + ops.get_default_graph()._c_graph, c_func.func, None) + return func_graph.name + + +def _get_intermediates(func_graph): + """Returns all tensors in `func_graph` that aren't inputs or outputs.""" + intermediates = [] + for op in func_graph.get_operations(): + for t in op.outputs: + if t in func_graph.inputs: continue + if t in func_graph.outputs: continue + intermediates.append(t) + return intermediates + + +def _separate_unique_inputs(true_inputs, false_inputs): + """Separates tensors appearing only in true_inputs or false_inputs, or both. + + Args: + true_inputs: list of Tensors + false_inputs: list of Tensors + + Returns: + Three lists of Tensors: + 1. The tensors that appear in both true_inputs and false_inputs + 2. The tensors that only appear in true_inputs + 3. The tensors that only appear in false_inputs + """ + true_inputs = set(true_inputs) + false_inputs = set(false_inputs) + + shared_inputs = true_inputs.intersection(false_inputs) + true_only_inputs = true_inputs - false_inputs + false_only_inputs = false_inputs - true_inputs + + return list(shared_inputs), list(true_only_inputs), list(false_only_inputs) + + +def _pad_params(true_graph, false_graph, true_params, false_params): + """Returns new param lists that have matching signatures. + + This is done by mirroring each param list in the other using dummy params. + There is no merging of params. + + Args: + true_graph: function._FuncGraph + false_graph: function._FuncGraph + true_params: a list of Tensors from true_graph + false_params: a list of Tensors from false_graph + + Returns: + A new list of Tensors in true_graph and a new list of Tensors in + false_graph. The two lists have the same number of Tensors, with matching + types and shapes across the lists. + """ + new_true_params = (true_params + + _create_dummy_params(true_graph, false_params)) + new_false_inputs = (_create_dummy_params(false_graph, true_params) + + false_params) + return new_true_params, new_false_inputs + + +def _make_inputs_match(true_graph, false_graph, true_inputs, false_inputs): + """Modifies true_graph and false_graph so they have the same input signature. + + This method reorders and/or adds parameters to true_graph and false_graph so + they have the same input signature, and updates the 'inputs', 'extra_inputs', + and '_captured' fields of both graphs accordingly. It uses the input tensors + from the outer graph to avoid duplicating shared arguments. + + Args: + true_graph: function._FuncGraph + false_graph: function._FuncGraph + true_inputs: a list of Tensors in the outer graph. The inputs for + true_graph. + false_inputs: a list of Tensors in the outer graph. The inputs for + false_graph. + + Returns: + A new list of Tensors from the outer graph that are the new inputs for both + true_graph and false_graph. This is a deduped version of true_inputs + + false_inputs. + """ + shared_inputs, true_only_inputs, false_only_inputs = _separate_unique_inputs( + true_inputs, false_inputs) + + new_inputs = shared_inputs + true_only_inputs + false_only_inputs + + true_input_to_param = dict(zip(true_inputs, true_graph.inputs)) + false_input_to_param = dict(zip(false_inputs, false_graph.inputs)) + + true_graph.inputs = ( + [true_input_to_param[t] for t in shared_inputs] + + [true_input_to_param[t] for t in true_only_inputs] + + _create_dummy_params(true_graph, false_only_inputs)) + + false_graph.inputs = ( + [false_input_to_param[t] for t in shared_inputs] + + _create_dummy_params(false_graph, true_only_inputs) + + [false_input_to_param[t] for t in false_only_inputs]) + + # Rewrite the _FuncGraphs' state to reflect the new inputs. + true_graph.extra_inputs = new_inputs + false_graph.extra_inputs = new_inputs + + true_graph._captured = dict(zip(new_inputs, true_graph.inputs)) + false_graph._captured = dict(zip(new_inputs, false_graph.inputs)) + + return new_inputs + + +def _create_dummy_params(func_graph, template_tensors): + """Creates tensors in func_graph to represent template_tensors. + + Args: + func_graph: function._FuncGraph. + template_tensors: a list of tensors in the outer graph. + + Returns: + A list of tensors in func_graph. + """ + with func_graph.as_default(): + return [gen_functional_ops.fake_param(dtype=t.dtype, shape=t.shape) + for t in template_tensors] + + +def _check_same_outputs(true_graph, false_graph): + """Raises an error if true_graph and false_graph have different outputs.""" + true_output_types = [t.dtype for t in true_graph.outputs] + false_output_types = [t.dtype for t in false_graph.outputs] + if (len(true_graph.outputs) != len(false_graph.outputs) or + true_output_types != false_output_types): + raise ValueError( + "true_fn() and false_fn() must return the same number and type of " + "arguments, got:\n" + " true_fn: %s\n" + " false_fn: %s" % (true_output_types, false_output_types)) diff --git a/tensorflow/contrib/control_flow/python/cond_v2_test.py b/tensorflow/contrib/control_flow/python/cond_v2_test.py new file mode 100644 index 0000000000..c94f3a6584 --- /dev/null +++ b/tensorflow/contrib/control_flow/python/cond_v2_test.py @@ -0,0 +1,113 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +"""Tests for cond_v2.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.control_flow.python import cond_v2 +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import gradients_impl +from tensorflow.python.ops import math_ops +from tensorflow.python.platform import test + + +class NewCondTest(test.TestCase): + + def _testCond(self, true_fn, false_fn, train_vals): + pred = array_ops.placeholder(dtypes.bool, name="pred") + + expected = control_flow_ops.cond(pred, true_fn, false_fn, name="expected") + actual = cond_v2.cond_v2(pred, true_fn, false_fn, name="actual") + + expected_grad = gradients_impl.gradients(expected, train_vals) + actual_grad = gradients_impl.gradients(actual, train_vals) + + with self.test_session() as sess: + expected_val, actual_val, expected_grad_val, actual_grad_val = sess.run( + (expected, actual, expected_grad, actual_grad), {pred: True}) + self.assertEqual(expected_val, actual_val) + self.assertEqual(expected_grad_val, actual_grad_val) + + expected_val, actual_val, expected_grad_val, actual_grad_val = sess.run( + (expected, actual, expected_grad, actual_grad), {pred: False}) + self.assertEqual(expected_val, actual_val) + self.assertEqual(expected_grad_val, actual_grad_val) + + def testBasic(self): + x = constant_op.constant(1.0, name="x") + y = constant_op.constant(2.0, name="y") + + def true_fn(): + return x * 2.0 + + def false_fn(): + return y * 3.0 + + self._testCond(true_fn, false_fn, [x]) + self._testCond(true_fn, false_fn, [x, y]) + self._testCond(true_fn, false_fn, [y]) + + def testBasic2(self): + x = constant_op.constant(1.0, name="x") + y = constant_op.constant(2.0, name="y") + + def true_fn(): + return x * y * 2.0 + + def false_fn(): + return 2.0 + + self._testCond(true_fn, false_fn, [x]) + self._testCond(true_fn, false_fn, [x, y]) + self._testCond(true_fn, false_fn, [y]) + + def testSecondDerivative(self): + pred = array_ops.placeholder(dtypes.bool, name="pred") + x = constant_op.constant(3.0, name="x") + + def true_fn(): + return math_ops.pow(x, 3) + + def false_fn(): + return x + + cond = cond_v2.cond_v2(pred, true_fn, false_fn, name="cond") + cond_grad = gradients_impl.gradients(cond, [x]) + cond_grad_grad = gradients_impl.gradients(cond_grad, [x]) + + with self.test_session() as sess: + # d[x^3]/dx = 3x^2 + true_val = sess.run(cond_grad, {pred: True}) + self.assertEqual(true_val, [27.0]) + # d[x]/dx = 1 + false_val = sess.run(cond_grad, {pred: False}) + self.assertEqual(false_val, [1.0]) + + true_val = sess.run(cond_grad_grad, {pred: True}) + # d2[x^3]/dx2 = 6x + self.assertEqual(true_val, [18.0]) + false_val = sess.run(cond_grad_grad, {pred: False}) + # d2[x]/dx2 = 0 + self.assertEqual(false_val, [0.0]) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/core/api_def/base_api/api_def_FakeParam.pbtxt b/tensorflow/core/api_def/base_api/api_def_FakeParam.pbtxt new file mode 100644 index 0000000000..d110aba42b --- /dev/null +++ b/tensorflow/core/api_def/base_api/api_def_FakeParam.pbtxt @@ -0,0 +1,24 @@ +op { + graph_op_name: "FakeParam" + visibility: SKIP + out_arg { + name: "output" + description: <
Version:CPU/GPU:Python Version:Compiler:Build Tools:cuDNN:CUDA:
tensorflow-1.9.0CPU3.5-3.6MSVC 2015 update 3Cmake v3.6.3N/AN/A
tensorflow_gpu-1.9.0GPU3.5-3.6MSVC 2015 update 3Cmake v3.6.379
tensorflow-1.8.0CPU3.5-3.6MSVC 2015 update 3Cmake v3.6.3N/AN/A
tensorflow_gpu-1.8.0GPU3.5-3.6MSVC 2015 update 3Cmake v3.6.379
tensorflow-1.7.0CPU3.5-3.6MSVC 2015 update 3Cmake v3.6.3N/AN/A
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Stack Overflow Link Error Message
42006320
ImportError: Traceback (most recent call last):
+File ".../tensorflow/core/framework/graph_pb2.py", line 6, in 
+from google.protobuf import descriptor as _descriptor
+ImportError: cannot import name 'descriptor'
+
33623453
IOError: [Errno 2] No such file or directory:
+  '/tmp/pip-o6Tpui-build/setup.py'
+
35190574 
SSLError: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify
+  failed
42009190
+  Installing collected packages: setuptools, protobuf, wheel, numpy, tensorflow
+  Found existing installation: setuptools 1.1.6
+  Uninstalling setuptools-1.1.6:
+  Exception:
+  ...
+  [Errno 1] Operation not permitted:
+  '/tmp/pip-a1DXRT-uninstall/.../lib/python/_markerlib'
33622019
ImportError: No module named copyreg
37810228During a pip install operation, the system returns: + 
OSError: [Errno 1] Operation not permitted
+
33622842An import tensorflow statement triggers an error such as the + following:
Traceback (most recent call last):
+  File "", line 1, in 
+  File "/usr/local/lib/python2.7/site-packages/tensorflow/__init__.py",
+    line 4, in 
+    from tensorflow.python import *
+    ...
+  File "/usr/local/lib/python2.7/site-packages/tensorflow/core/framework/tensor_shape_pb2.py",
+    line 22, in 
+    serialized_pb=_b('\n,tensorflow/core/framework/tensor_shape.proto\x12\ntensorflow\"d\n\x10TensorShapeProto\x12-\n\x03\x64im\x18\x02
+      \x03(\x0b\x32
+      .tensorflow.TensorShapeProto.Dim\x1a!\n\x03\x44im\x12\x0c\n\x04size\x18\x01
+      \x01(\x03\x12\x0c\n\x04name\x18\x02 \x01(\tb\x06proto3')
+  TypeError: __init__() got an unexpected keyword argument 'syntax'
+
-- GitLab From 97d5bfed6c8a42ea6d8779309e9eb64a1e488d07 Mon Sep 17 00:00:00 2001 From: Adrian Kuegel Date: Wed, 13 Jun 2018 01:41:11 -0700 Subject: [PATCH 368/816] Improve shape compatibility checking for MultiOutputFusion We need to be careful how we compare reduce shapes. - If comparing against non-reduce shapes, we should compare the operand shape of a reduce with the other shape. - If comparing two reduce shapes, we need to compare both the operand shapes and the reduce shapes. Also, if we already have a multi-output fusion node, we should pick one of its reduce instructions for comparison, because it has more constraints than the other instructions. PiperOrigin-RevId: 200353595 --- .../xla/service/gpu/multi_output_fusion.cc | 34 +++++++-- .../service/gpu/multi_output_fusion_test.cc | 75 +++++++++++++++++-- 2 files changed, 95 insertions(+), 14 deletions(-) diff --git a/tensorflow/compiler/xla/service/gpu/multi_output_fusion.cc b/tensorflow/compiler/xla/service/gpu/multi_output_fusion.cc index 942c254533..e3f444a126 100644 --- a/tensorflow/compiler/xla/service/gpu/multi_output_fusion.cc +++ b/tensorflow/compiler/xla/service/gpu/multi_output_fusion.cc @@ -35,18 +35,28 @@ GpuMultiOutputFusion::GpuMultiOutputFusion() : MultiOutputFusion(INT64_MAX) {} bool GpuMultiOutputFusion::ShapesCompatibleForFusion(HloInstruction* instr1, HloInstruction* instr2) { - auto get_element_shape = [&](HloInstruction* instr) { + auto get_element_instr = + [&](const HloInstruction* instr) -> const HloInstruction* { const HloInstruction* element_instr = instr; if (instr->opcode() == HloOpcode::kFusion) { auto fused_expression_root = instr->fused_expression_root(); if (instr->IsMultiOutputFusion()) { - // The shapes in all tuple operands should agree. Just pick the first - // one. - element_instr = fused_expression_root->operands()[0]; + // If possible, we want to pick a reduce operand of the fusion root, + // because it has the most constraints. + for (const auto* inst : fused_expression_root->operands()) { + if (inst->opcode() == HloOpcode::kReduce) { + return inst; + } + } + return fused_expression_root->operands()[0]; } else { element_instr = fused_expression_root; } } + return element_instr; + }; + + auto get_element_shape = [&](const HloInstruction* element_instr) { // Special handling of kReduce instructions -- the fusion // applies to the first operand. if (element_instr->opcode() == HloOpcode::kReduce) { @@ -55,8 +65,20 @@ bool GpuMultiOutputFusion::ShapesCompatibleForFusion(HloInstruction* instr1, return element_instr->shape(); }; - // The elementwise output shapes must be the same (including layout) - return ShapeUtil::Equal(get_element_shape(instr1), get_element_shape(instr2)); + // The shapes in all tuple operands should agree, unless it is a reduce. + // In that case, the operand of the reduce needs to have the same shape + // as the other tuple operands, but also we need to compare the output + // shapes of the reduces. + auto* element_instr_1 = get_element_instr(instr1); + auto* element_instr_2 = get_element_instr(instr2); + if (element_instr_1->opcode() == HloOpcode::kReduce && + element_instr_2->opcode() == HloOpcode::kReduce && + !ShapeUtil::Equal(element_instr_1->shape(), element_instr_2->shape())) { + return false; + } + // The elementwise output shapes must be the same (including layout). + return ShapeUtil::Equal(get_element_shape(element_instr_1), + get_element_shape(element_instr_2)); } bool GpuMultiOutputFusion::IsProfitableOperand(HloInstruction* instr) { diff --git a/tensorflow/compiler/xla/service/gpu/multi_output_fusion_test.cc b/tensorflow/compiler/xla/service/gpu/multi_output_fusion_test.cc index 5170cbc7e3..924cfb11f3 100644 --- a/tensorflow/compiler/xla/service/gpu/multi_output_fusion_test.cc +++ b/tensorflow/compiler/xla/service/gpu/multi_output_fusion_test.cc @@ -33,14 +33,14 @@ const char kModulePrefix[] = R"( HloModule test_module scalar_add_computation { - scalar_lhs = f32[] parameter(0) - scalar_rhs = f32[] parameter(1) - ROOT add = f32[] add(scalar_lhs, scalar_rhs) + scalar_lhs.0 = f32[] parameter(0) + scalar_rhs.0 = f32[] parameter(1) + ROOT add.0 = f32[] add(scalar_lhs.0, scalar_rhs.0) } scalar_mul_computation { - scalar_lhs = f32[] parameter(0) - scalar_rhs = f32[] parameter(1) - ROOT mul = f32[] add(scalar_lhs, scalar_rhs) + scalar_lhs.1 = f32[] parameter(0) + scalar_rhs.1 = f32[] parameter(1) + ROOT mul.1 = f32[] add(scalar_lhs.1, scalar_rhs.1) })"; TEST_F(InstructionFusionTest, MultiOutputFusionSiblingReduceAndReduceFusion) { @@ -78,7 +78,7 @@ TEST_F(InstructionFusionTest, MultiOutputFusionDifferentReduceInputShapes) { p1.1 = f32[6400]{0} parameter(1) mul = f32[6400]{0} multiply(p1.1, p1.1) const.1 = f32[] parameter(0) - ROOT reduce.1 = f32[] reduce(p1.1, const.1), dimensions={0}, to_apply=scalar_add_computation + ROOT reduce.1 = f32[] reduce(mul, const.1), dimensions={0}, to_apply=scalar_add_computation } fused_computation_2 { @@ -91,7 +91,6 @@ TEST_F(InstructionFusionTest, MultiOutputFusionDifferentReduceInputShapes) { ENTRY entry { p0 = f32[] parameter(0) p1 = f32[6400]{0} parameter(1) - const.2 = f32[] constant(1) fusion.1 = f32[] fusion(p0, p1), kind=kInput, calls=fused_computation_1 fusion.2 = f32[] fusion(p0, p1), kind=kInput, calls=fused_computation_2 ROOT root = (f32[], f32[]) tuple(fusion.1, fusion.2) @@ -100,6 +99,33 @@ TEST_F(InstructionFusionTest, MultiOutputFusionDifferentReduceInputShapes) { ASSERT_FALSE(GpuMultiOutputFusion().Run(module.get()).ValueOrDie()); } +TEST_F(InstructionFusionTest, MultiOutputFusionDifferentReduceOutputShapes) { + auto module = ParseHloString(tensorflow::strings::StrCat(kModulePrefix, R"( + fused_computation_1 { + p1.1 = f32[10,10]{1,0} parameter(1) + mul = f32[10,10]{1,0} multiply(p1.1, p1.1) + const.1 = f32[] parameter(0) + ROOT reduce.1 = f32[] reduce(mul, const.1), dimensions={0,1}, to_apply=scalar_add_computation + } + + fused_computation_2 { + p1.2 = f32[10,10]{1,0} parameter(1) + const.2 = f32[10]{0} parameter(0) + ROOT reduce.2 = f32[10]{0} reduce(p1.2, const.2), dimensions={0}, to_apply=scalar_mul_computation + } + + ENTRY entry { + p0 = f32[] parameter(0) + p1.3 = f32[10,10]{1,0} parameter(1) + fusion.1 = f32[] fusion(p0, p1.3), kind=kInput, calls=fused_computation_1 + p2 = f32[] parameter(2) + fusion.2 = f32[10]{0} fusion(p2, p1.3), kind=kInput, calls=fused_computation_2 + ROOT root = (f32[], f32[10]{0}) tuple(fusion.1, fusion.2) + })")) + .ValueOrDie(); + ASSERT_FALSE(GpuMultiOutputFusion().Run(module.get()).ValueOrDie()); +} + TEST_F(InstructionFusionTest, MultiOutputFusionSiblingReduceFusions) { // Two sibling fusions with reduce instruction roots sharing the same input // param. @@ -167,5 +193,38 @@ TEST_F(InstructionFusionTest, op::Tuple(op::Reduce(), op::Reduce(), op::Reduce())); } +TEST_F(InstructionFusionTest, + MultiOutputFusionSiblingFusionCheckAgainstReduceOperand) { + // Verify that if we already have a multi-output fusion that we prefer to pick + // a reduce op from its operands for checking shape compatibility. + auto module = ParseHloString(tensorflow::strings::StrCat(kModulePrefix, R"( + fused_computation_1 { + p1.1 = f32[10,10]{1,0} parameter(1) + mul = f32[10,10]{1,0} multiply(p1.1, p1.1) + const.1 = f32[] parameter(0) + reduce.1 = f32[] reduce(p1.1, const.1), dimensions={0,1}, to_apply=scalar_add_computation + ROOT tuple = (f32[10,10], f32[]) tuple(mul, reduce.1) + } + + fused_computation_2 { + p1.2 = f32[10,10]{1,0} parameter(1) + const.2 = f32[10] parameter(0) + ROOT reduce.2 = f32[10] reduce(p1.2, const.2), dimensions={0}, to_apply=scalar_mul_computation + } + + ENTRY entry { + p0 = f32[] parameter(0) + p1 = f32[10,10]{1,0} parameter(1) + p2 = f32[10]{0} parameter(2) + fusion.1 = (f32[10,10], f32[10]) fusion(p0, p1), kind=kInput, calls=fused_computation_1 + get-tuple-element.1 = f32[10,10] get-tuple-element((f32[10,10], f32[10]) fusion.1), index=0 + get-tuple-element.2 = f32[] get-tuple-element((f32[10,10], f32[10]) fusion.1), index=1 + fusion.2 = f32[10] fusion(p2, p1), kind=kInput, calls=fused_computation_2 + ROOT root = (f32[10,10], f32[], f32[10]) tuple(get-tuple-element.1, get-tuple-element.2, fusion.2) + })")) + .ValueOrDie(); + ASSERT_FALSE(GpuMultiOutputFusion().Run(module.get()).ValueOrDie()); +} + } // namespace gpu } // namespace xla -- GitLab From e6d00acfd8e4539291a087a6c3e0799253ba9d6f Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 13 Jun 2018 03:02:11 -0700 Subject: [PATCH 369/816] Remove GANHead from GANEstimator. PiperOrigin-RevId: 200362771 --- tensorflow/contrib/gan/BUILD | 50 +--- .../contrib/gan/python/estimator/__init__.py | 5 +- .../estimator/python/gan_estimator_impl.py | 186 +++++++------- .../estimator/python/gan_estimator_test.py | 227 ++++++++--------- .../gan/python/estimator/python/head.py | 28 --- .../gan/python/estimator/python/head_impl.py | 235 ------------------ .../gan/python/estimator/python/head_test.py | 90 ------- 7 files changed, 218 insertions(+), 603 deletions(-) delete mode 100644 tensorflow/contrib/gan/python/estimator/python/head.py delete mode 100644 tensorflow/contrib/gan/python/estimator/python/head_impl.py delete mode 100644 tensorflow/contrib/gan/python/estimator/python/head_test.py diff --git a/tensorflow/contrib/gan/BUILD b/tensorflow/contrib/gan/BUILD index b305f37791..d38d770bc5 100644 --- a/tensorflow/contrib/gan/BUILD +++ b/tensorflow/contrib/gan/BUILD @@ -45,6 +45,7 @@ py_library( "//tensorflow/python:framework_ops", "//tensorflow/python:init_ops", "//tensorflow/python:training", + "//tensorflow/python:training_util", "//tensorflow/python:variable_scope", "//tensorflow/python/ops/distributions", "//tensorflow/python/ops/losses", @@ -59,6 +60,7 @@ py_test( deps = [ ":features", ":namedtuples", + ":random_tensor_pool", ":train", "//tensorflow/contrib/framework:framework_py", "//tensorflow/contrib/slim:learning", @@ -70,6 +72,7 @@ py_test( "//tensorflow/python:random_ops", "//tensorflow/python:random_seed", "//tensorflow/python:training", + "//tensorflow/python:training_util", "//tensorflow/python:variable_scope", "//tensorflow/python:variables", "//tensorflow/python/ops/distributions", @@ -96,7 +99,6 @@ py_library( srcs_version = "PY2AND3", deps = [ ":gan_estimator", - ":head", "//tensorflow/python:util", ], ) @@ -188,6 +190,7 @@ py_test( srcs = ["python/losses/python/tuple_losses_test.py"], srcs_version = "PY2AND3", deps = [ + ":namedtuples", ":tuple_losses", "//tensorflow/python:client_testlib", "//tensorflow/python:constant_op", @@ -344,9 +347,11 @@ py_library( "//tensorflow/python:image_ops", "//tensorflow/python:linalg_ops", "//tensorflow/python:math_ops", + "//tensorflow/python:nn", "//tensorflow/python:nn_ops", "//tensorflow/python:platform", "//tensorflow/python:util", + "@six_archive//:six", ], ) @@ -428,40 +433,6 @@ py_test( ], ) -py_library( - name = "head", - srcs = [ - "python/estimator/python/head.py", - "python/estimator/python/head_impl.py", - ], - srcs_version = "PY2AND3", - deps = [ - ":namedtuples", - ":train", - "//tensorflow/python:framework_ops", - "//tensorflow/python:util", - "//tensorflow/python/estimator:head", - "//tensorflow/python/estimator:model_fn", - ], -) - -py_test( - name = "head_test", - srcs = ["python/estimator/python/head_test.py"], - shard_count = 1, - srcs_version = "PY2AND3", - deps = [ - ":head", - ":namedtuples", - "//tensorflow/python:array_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:math_ops", - "//tensorflow/python:training", - "//tensorflow/python:variable_scope", - "//tensorflow/python/estimator:model_fn", - ], -) - py_library( name = "gan_estimator", srcs = [ @@ -470,12 +441,12 @@ py_library( ], srcs_version = "PY2AND3", deps = [ - ":head", ":namedtuples", ":summaries", ":train", "//tensorflow/contrib/framework:framework_py", "//tensorflow/python:framework_ops", + "//tensorflow/python:metrics", "//tensorflow/python:util", "//tensorflow/python:variable_scope", "//tensorflow/python/estimator", @@ -498,16 +469,19 @@ py_test( "//tensorflow/core:protos_all_py", "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", - "//tensorflow/python:control_flow_ops", "//tensorflow/python:dtypes", "//tensorflow/python:framework_ops", + "//tensorflow/python:math_ops", + "//tensorflow/python:metrics", "//tensorflow/python:parsing_ops", "//tensorflow/python:summary", "//tensorflow/python:training", - "//tensorflow/python/estimator:head", + "//tensorflow/python:training_util", + "//tensorflow/python:variable_scope", "//tensorflow/python/estimator:model_fn", "//tensorflow/python/estimator:numpy_io", "//third_party/py/numpy", + "@absl_py//absl/testing:parameterized", "@six_archive//:six", ], ) diff --git a/tensorflow/contrib/gan/python/estimator/__init__.py b/tensorflow/contrib/gan/python/estimator/__init__.py index c9f7bc61b2..04dddb4b55 100644 --- a/tensorflow/contrib/gan/python/estimator/__init__.py +++ b/tensorflow/contrib/gan/python/estimator/__init__.py @@ -25,16 +25,13 @@ from __future__ import print_function # Collapse `estimator` into a single namespace. # pylint: disable=unused-import,wildcard-import from tensorflow.contrib.gan.python.estimator.python import gan_estimator -from tensorflow.contrib.gan.python.estimator.python import head from tensorflow.contrib.gan.python.estimator.python.gan_estimator import * -from tensorflow.contrib.gan.python.estimator.python.head import * # pylint: enable=unused-import,wildcard-import from tensorflow.python.util.all_util import remove_undocumented _allowed_symbols = [ 'gan_estimator', - 'head', -] + gan_estimator.__all__ + head.__all__ +] + gan_estimator.__all__ remove_undocumented(__name__, _allowed_symbols) diff --git a/tensorflow/contrib/gan/python/estimator/python/gan_estimator_impl.py b/tensorflow/contrib/gan/python/estimator/python/gan_estimator_impl.py index 4092b32004..7104c8aa61 100644 --- a/tensorflow/contrib/gan/python/estimator/python/gan_estimator_impl.py +++ b/tensorflow/contrib/gan/python/estimator/python/gan_estimator_impl.py @@ -24,11 +24,11 @@ import enum from tensorflow.contrib.framework.python.ops import variables as variable_lib from tensorflow.contrib.gan.python import namedtuples as tfgan_tuples from tensorflow.contrib.gan.python import train as tfgan_train -from tensorflow.contrib.gan.python.estimator.python import head as head_lib from tensorflow.contrib.gan.python.eval.python import summaries as tfgan_summaries from tensorflow.python.estimator import estimator from tensorflow.python.estimator import model_fn as model_fn_lib from tensorflow.python.framework import ops +from tensorflow.python.ops import metrics as metrics_lib from tensorflow.python.ops import variable_scope from tensorflow.python.util import tf_inspect as inspect @@ -158,90 +158,77 @@ class GANEstimator(estimator.Estimator): # TODO(joelshor): Explicitly validate inputs. def _model_fn(features, labels, mode): - gopt = (generator_optimizer() if callable(generator_optimizer) else - generator_optimizer) - dopt = (discriminator_optimizer() if callable(discriminator_optimizer) - else discriminator_optimizer) - gan_head = head_lib.gan_head( - generator_loss_fn, discriminator_loss_fn, gopt, dopt, - use_loss_summaries, get_hooks_fn=get_hooks_fn, - get_eval_metric_ops_fn=get_eval_metric_ops_fn) - return _gan_model_fn( - features, labels, mode, generator_fn, discriminator_fn, gan_head, + """GANEstimator model function.""" + if mode not in [model_fn_lib.ModeKeys.TRAIN, model_fn_lib.ModeKeys.EVAL, + model_fn_lib.ModeKeys.PREDICT]: + raise ValueError('Mode not recognized: %s' % mode) + real_data = labels # rename inputs for clarity + generator_inputs = features # rename inputs for clarity + + # Make GANModel, which encapsulates the GAN model architectures. + gan_model = _get_gan_model( + mode, generator_fn, discriminator_fn, real_data, generator_inputs, add_summaries) + # Make the EstimatorSpec, which incorporates the GANModel, losses, eval + # metrics, and optimizers (if required). + return _get_estimator_spec( + mode, gan_model, generator_loss_fn, discriminator_loss_fn, + get_eval_metric_ops_fn, generator_optimizer, discriminator_optimizer, + get_hooks_fn) + super(GANEstimator, self).__init__( model_fn=_model_fn, model_dir=model_dir, config=config) -def _gan_model_fn( - features, - labels, - mode, - generator_fn, - discriminator_fn, - head, - add_summaries=None, - generator_scope_name='Generator'): - """The `model_fn` for the GAN estimator. - - We make the following convention: - features -> TFGAN's `generator_inputs` - labels -> TFGAN's `real_data` - - Args: - features: A dictionary to feed to generator. In the unconditional case, - this might be just `noise`. In the conditional GAN case, this - might be the generator's conditioning. The `generator_fn` determines - what the required keys are. - labels: Real data. Can be any structure, as long as `discriminator_fn` - can accept it for the first argument. - mode: Defines whether this is training, evaluation or prediction. - See `ModeKeys`. - generator_fn: A python lambda that takes `generator_inputs` as inputs and - returns the outputs of the GAN generator. - discriminator_fn: A python lambda that takes `real_data`/`generated data` - and `generator_inputs`. Outputs a Tensor in the range [-inf, inf]. - head: A `Head` instance suitable for GANs. - add_summaries: `None`, a single `SummaryType`, or a list of `SummaryType`. - generator_scope_name: The name of the generator scope. We need this to be - the same for GANModels produced by TFGAN's `train.gan_model` and the - manually constructed ones for predictions. - - Returns: - `ModelFnOps` - - Raises: - ValueError: If `labels` isn't `None` during prediction. - """ - real_data = labels - generator_inputs = features - - if mode == model_fn_lib.ModeKeys.TRAIN: - gan_model = _make_train_gan_model( - generator_fn, discriminator_fn, real_data, generator_inputs, - generator_scope_name, add_summaries) - elif mode == model_fn_lib.ModeKeys.EVAL: - gan_model = _make_eval_gan_model( - generator_fn, discriminator_fn, real_data, generator_inputs, - generator_scope_name, add_summaries) - else: +def _get_gan_model( + mode, generator_fn, discriminator_fn, real_data, generator_inputs, + add_summaries, generator_scope='Generator'): + """Makes the GANModel tuple, which encapsulates the GAN model architecture.""" + if mode == model_fn_lib.ModeKeys.PREDICT: if real_data is not None: raise ValueError('`labels` must be `None` when mode is `predict`. ' 'Instead, found %s' % real_data) gan_model = _make_prediction_gan_model( - generator_inputs, generator_fn, generator_scope_name) + generator_inputs, generator_fn, generator_scope) + else: # model_fn_lib.ModeKeys.TRAIN or model_fn_lib.ModeKeys.EVAL + gan_model = _make_gan_model( + generator_fn, discriminator_fn, real_data, generator_inputs, + generator_scope, add_summaries, mode) + + return gan_model - return head.create_estimator_spec( - features=None, - mode=mode, - logits=gan_model, - labels=None) + +def _get_estimator_spec( + mode, gan_model, generator_loss_fn, discriminator_loss_fn, + get_eval_metric_ops_fn, generator_optimizer, discriminator_optimizer, + get_hooks_fn=None): + """Get the EstimatorSpec for the current mode.""" + if mode == model_fn_lib.ModeKeys.PREDICT: + estimator_spec = model_fn_lib.EstimatorSpec( + mode=mode, predictions=gan_model.generated_data) + else: + gan_loss = tfgan_tuples.GANLoss( + generator_loss=generator_loss_fn(gan_model), + discriminator_loss=discriminator_loss_fn(gan_model)) + if mode == model_fn_lib.ModeKeys.EVAL: + estimator_spec = _get_eval_estimator_spec( + gan_model, gan_loss, get_eval_metric_ops_fn) + else: # model_fn_lib.ModeKeys.TRAIN: + gopt = (generator_optimizer() if callable(generator_optimizer) else + generator_optimizer) + dopt = (discriminator_optimizer() if callable(discriminator_optimizer) + else discriminator_optimizer) + get_hooks_fn = get_hooks_fn or tfgan_train.get_sequential_train_hooks() + estimator_spec = _get_train_estimator_spec( + gan_model, gan_loss, gopt, dopt, get_hooks_fn) + + return estimator_spec def _make_gan_model(generator_fn, discriminator_fn, real_data, generator_inputs, generator_scope, add_summaries, mode): - """Make a `GANModel`, and optionally pass in `mode`.""" + """Construct a `GANModel`, and optionally pass in `mode`.""" # If network functions have an argument `mode`, pass mode to it. if 'mode' in inspect.getargspec(generator_fn).args: generator_fn = functools.partial(generator_fn, mode=mode) @@ -264,22 +251,6 @@ def _make_gan_model(generator_fn, discriminator_fn, real_data, return gan_model -def _make_train_gan_model(generator_fn, discriminator_fn, real_data, - generator_inputs, generator_scope, add_summaries): - """Make a `GANModel` for training.""" - return _make_gan_model(generator_fn, discriminator_fn, real_data, - generator_inputs, generator_scope, add_summaries, - model_fn_lib.ModeKeys.TRAIN) - - -def _make_eval_gan_model(generator_fn, discriminator_fn, real_data, - generator_inputs, generator_scope, add_summaries): - """Make a `GANModel` for evaluation.""" - return _make_gan_model(generator_fn, discriminator_fn, real_data, - generator_inputs, generator_scope, add_summaries, - model_fn_lib.ModeKeys.EVAL) - - def _make_prediction_gan_model(generator_inputs, generator_fn, generator_scope): """Make a `GANModel` from just the generator.""" # If `generator_fn` has an argument `mode`, pass mode to it. @@ -303,3 +274,46 @@ def _make_prediction_gan_model(generator_inputs, generator_fn, generator_scope): discriminator_variables=None, discriminator_scope=None, discriminator_fn=None) + + +def _get_eval_estimator_spec(gan_model, gan_loss, get_eval_metric_ops_fn=None, + name=None): + """Return an EstimatorSpec for the eval case.""" + scalar_loss = gan_loss.generator_loss + gan_loss.discriminator_loss + with ops.name_scope(None, 'metrics', + [gan_loss.generator_loss, + gan_loss.discriminator_loss]): + def _summary_key(head_name, val): + return '%s/%s' % (val, head_name) if head_name else val + eval_metric_ops = { + _summary_key(name, 'generator_loss'): + metrics_lib.mean(gan_loss.generator_loss), + _summary_key(name, 'discriminator_loss'): + metrics_lib.mean(gan_loss.discriminator_loss) + } + if get_eval_metric_ops_fn is not None: + custom_eval_metric_ops = get_eval_metric_ops_fn(gan_model) + if not isinstance(custom_eval_metric_ops, dict): + raise TypeError('get_eval_metric_ops_fn must return a dict, ' + 'received: {}'.format(custom_eval_metric_ops)) + eval_metric_ops.update(custom_eval_metric_ops) + return model_fn_lib.EstimatorSpec( + mode=model_fn_lib.ModeKeys.EVAL, + predictions=gan_model.generated_data, + loss=scalar_loss, + eval_metric_ops=eval_metric_ops) + + +def _get_train_estimator_spec( + gan_model, gan_loss, generator_optimizer, discriminator_optimizer, + get_hooks_fn, train_op_fn=tfgan_train.gan_train_ops): + """Return an EstimatorSpec for the train case.""" + scalar_loss = gan_loss.generator_loss + gan_loss.discriminator_loss + train_ops = train_op_fn(gan_model, gan_loss, generator_optimizer, + discriminator_optimizer) + training_hooks = get_hooks_fn(train_ops) + return model_fn_lib.EstimatorSpec( + loss=scalar_loss, + mode=model_fn_lib.ModeKeys.TRAIN, + train_op=train_ops.global_step_inc_op, + training_hooks=training_hooks) diff --git a/tensorflow/contrib/gan/python/estimator/python/gan_estimator_test.py b/tensorflow/contrib/gan/python/estimator/python/gan_estimator_test.py index 955482599b..9ac9c6ca9c 100644 --- a/tensorflow/contrib/gan/python/estimator/python/gan_estimator_test.py +++ b/tensorflow/contrib/gan/python/estimator/python/gan_estimator_test.py @@ -21,30 +21,30 @@ from __future__ import print_function import shutil import tempfile +from absl.testing import parameterized import numpy as np import six from tensorflow.contrib import layers -from tensorflow.contrib.gan.python import namedtuples +from tensorflow.contrib.gan.python import namedtuples as tfgan_tuples from tensorflow.contrib.gan.python.estimator.python import gan_estimator_impl as estimator from tensorflow.contrib.gan.python.losses.python import tuple_losses as losses from tensorflow.contrib.learn.python.learn.learn_io import graph_io from tensorflow.core.example import example_pb2 from tensorflow.core.example import feature_pb2 from tensorflow.python.estimator import model_fn as model_fn_lib -from tensorflow.python.estimator.canned import head as head_lib from tensorflow.python.estimator.inputs import numpy_io from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops -from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import math_ops from tensorflow.python.ops import metrics as metrics_lib from tensorflow.python.ops import parsing_ops +from tensorflow.python.ops import variable_scope from tensorflow.python.platform import test from tensorflow.python.summary.writer import writer_cache from tensorflow.python.training import input as input_lib from tensorflow.python.training import learning_rate_decay -from tensorflow.python.training import monitored_session from tensorflow.python.training import training from tensorflow.python.training import training_util @@ -60,120 +60,109 @@ def discriminator_fn(data, unused_conditioning, mode): return layers.fully_connected(data, 1) -def mock_head(testcase, expected_generator_inputs, expected_real_data, - generator_scope_name): - """Returns a mock head that validates logits values and variable names.""" - discriminator_scope_name = 'Discriminator' # comes from TFGAN defaults - generator_var_names = set([ - '%s/fully_connected/weights:0' % generator_scope_name, - '%s/fully_connected/biases:0' % generator_scope_name]) - discriminator_var_names = set([ - '%s/fully_connected/weights:0' % discriminator_scope_name, - '%s/fully_connected/biases:0' % discriminator_scope_name]) - - def _create_estimator_spec(features, mode, logits, labels): - gan_model = logits # renaming for clarity - is_predict = mode == model_fn_lib.ModeKeys.PREDICT - testcase.assertIsNone(features) - testcase.assertIsNone(labels) - testcase.assertIsInstance(gan_model, namedtuples.GANModel) - - trainable_vars = ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES) - expected_var_names = (generator_var_names if is_predict else - generator_var_names | discriminator_var_names) - testcase.assertItemsEqual(expected_var_names, - [var.name for var in trainable_vars]) - - assertions = [] - def _or_none(x): - return None if is_predict else x - testcase.assertEqual(expected_generator_inputs, gan_model.generator_inputs) - # TODO(joelshor): Add check on `generated_data`. - testcase.assertItemsEqual( - generator_var_names, - set([x.name for x in gan_model.generator_variables])) - testcase.assertEqual(generator_scope_name, gan_model.generator_scope.name) - testcase.assertEqual(_or_none(expected_real_data), gan_model.real_data) - # TODO(joelshor): Add check on `discriminator_real_outputs`. - # TODO(joelshor): Add check on `discriminator_gen_outputs`. - if is_predict: - testcase.assertIsNone(gan_model.discriminator_scope) - else: - testcase.assertEqual(discriminator_scope_name, - gan_model.discriminator_scope.name) - - with ops.control_dependencies(assertions): - if mode == model_fn_lib.ModeKeys.TRAIN: - return model_fn_lib.EstimatorSpec( - mode=mode, loss=array_ops.zeros([]), - train_op=control_flow_ops.no_op(), training_hooks=[]) - elif mode == model_fn_lib.ModeKeys.EVAL: - return model_fn_lib.EstimatorSpec( - mode=mode, predictions=gan_model.generated_data, - loss=array_ops.zeros([])) - elif mode == model_fn_lib.ModeKeys.PREDICT: - return model_fn_lib.EstimatorSpec( - mode=mode, predictions=gan_model.generated_data) - else: - testcase.fail('Invalid mode: {}'.format(mode)) - - head = test.mock.NonCallableMagicMock(spec=head_lib._Head) - head.create_estimator_spec = test.mock.MagicMock( - wraps=_create_estimator_spec) - - return head - - -class GANModelFnTest(test.TestCase): - """Tests that _gan_model_fn passes expected logits to mock head.""" - - def setUp(self): - self._model_dir = tempfile.mkdtemp() - - def tearDown(self): - if self._model_dir: - writer_cache.FileWriterCache.clear() - shutil.rmtree(self._model_dir) +class GetGANModelTest(test.TestCase, parameterized.TestCase): + """Tests that `GetGANModel` produces the correct model.""" - def _test_logits_helper(self, mode): - """Tests that the expected logits are passed to mock head.""" + @parameterized.named_parameters( + ('train', model_fn_lib.ModeKeys.TRAIN), + ('eval', model_fn_lib.ModeKeys.EVAL), + ('predict', model_fn_lib.ModeKeys.PREDICT)) + def test_get_gan_model(self, mode): with ops.Graph().as_default(): - training_util.get_or_create_global_step() - generator_inputs = {'x': array_ops.zeros([5, 4])} - real_data = (None if mode == model_fn_lib.ModeKeys.PREDICT else - array_ops.zeros([5, 4])) - generator_scope_name = 'generator' - head = mock_head(self, - expected_generator_inputs=generator_inputs, - expected_real_data=real_data, - generator_scope_name=generator_scope_name) - estimator_spec = estimator._gan_model_fn( - features=generator_inputs, - labels=real_data, - mode=mode, - generator_fn=generator_fn, - discriminator_fn=discriminator_fn, - generator_scope_name=generator_scope_name, - head=head) - with monitored_session.MonitoredTrainingSession( - checkpoint_dir=self._model_dir) as sess: - if mode == model_fn_lib.ModeKeys.TRAIN: - sess.run(estimator_spec.train_op) - elif mode == model_fn_lib.ModeKeys.EVAL: - sess.run(estimator_spec.loss) - elif mode == model_fn_lib.ModeKeys.PREDICT: - sess.run(estimator_spec.predictions) - else: - self.fail('Invalid mode: {}'.format(mode)) - - def test_logits_predict(self): - self._test_logits_helper(model_fn_lib.ModeKeys.PREDICT) - - def test_logits_eval(self): - self._test_logits_helper(model_fn_lib.ModeKeys.EVAL) - - def test_logits_train(self): - self._test_logits_helper(model_fn_lib.ModeKeys.TRAIN) + generator_inputs = {'x': array_ops.ones([3, 4])} + real_data = (array_ops.zeros([3, 4]) if + mode != model_fn_lib.ModeKeys.PREDICT else None) + gan_model = estimator._get_gan_model( + mode, generator_fn, discriminator_fn, real_data, generator_inputs, + add_summaries=False) + + self.assertEqual(generator_inputs, gan_model.generator_inputs) + self.assertIsNotNone(gan_model.generated_data) + self.assertEqual(2, len(gan_model.generator_variables)) # 1 FC layer + self.assertIsNotNone(gan_model.generator_fn) + if mode == model_fn_lib.ModeKeys.PREDICT: + self.assertIsNone(gan_model.real_data) + self.assertIsNone(gan_model.discriminator_real_outputs) + self.assertIsNone(gan_model.discriminator_gen_outputs) + self.assertIsNone(gan_model.discriminator_variables) + self.assertIsNone(gan_model.discriminator_scope) + self.assertIsNone(gan_model.discriminator_fn) + else: + self.assertIsNotNone(gan_model.real_data) + self.assertIsNotNone(gan_model.discriminator_real_outputs) + self.assertIsNotNone(gan_model.discriminator_gen_outputs) + self.assertEqual(2, len(gan_model.discriminator_variables)) # 1 FC layer + self.assertIsNotNone(gan_model.discriminator_scope) + self.assertIsNotNone(gan_model.discriminator_fn) + + +def get_dummy_gan_model(): + # TODO(joelshor): Find a better way of creating a variable scope. + with variable_scope.variable_scope('generator') as gen_scope: + gen_var = variable_scope.get_variable('dummy_var', initializer=0.0) + with variable_scope.variable_scope('discriminator') as dis_scope: + dis_var = variable_scope.get_variable('dummy_var', initializer=0.0) + return tfgan_tuples.GANModel( + generator_inputs=None, + generated_data=array_ops.ones([3, 4]), + generator_variables=[gen_var], + generator_scope=gen_scope, + generator_fn=None, + real_data=array_ops.zeros([3, 4]), + discriminator_real_outputs=array_ops.ones([1, 2, 3]) * dis_var, + discriminator_gen_outputs=array_ops.ones([1, 2, 3]) * gen_var * dis_var, + discriminator_variables=[dis_var], + discriminator_scope=dis_scope, + discriminator_fn=None) + + +def dummy_loss_fn(gan_model): + return math_ops.reduce_sum(gan_model.discriminator_real_outputs - + gan_model.discriminator_gen_outputs) + + +def get_metrics(gan_model): + return { + 'mse_custom_metric': metrics_lib.mean_squared_error( + gan_model.real_data, gan_model.generated_data) + } + + +class GetEstimatorSpecTest(test.TestCase, parameterized.TestCase): + """Tests that the EstimatorSpec is constructed appropriately.""" + + @classmethod + def setUpClass(cls): + cls._generator_optimizer = training.GradientDescentOptimizer(1.0) + cls._discriminator_optimizer = training.GradientDescentOptimizer(1.0) + + @parameterized.named_parameters( + ('train', model_fn_lib.ModeKeys.TRAIN), + ('eval', model_fn_lib.ModeKeys.EVAL), + ('predict', model_fn_lib.ModeKeys.PREDICT)) + def test_get_estimator_spec(self, mode): + with ops.Graph().as_default(): + self._gan_model = get_dummy_gan_model() + spec = estimator._get_estimator_spec( + mode, + self._gan_model, + generator_loss_fn=dummy_loss_fn, + discriminator_loss_fn=dummy_loss_fn, + get_eval_metric_ops_fn=get_metrics, + generator_optimizer=self._generator_optimizer, + discriminator_optimizer=self._discriminator_optimizer) + + self.assertEqual(mode, spec.mode) + if mode == model_fn_lib.ModeKeys.PREDICT: + self.assertEqual(self._gan_model.generated_data, spec.predictions) + elif mode == model_fn_lib.ModeKeys.TRAIN: + self.assertShapeEqual(np.array(0), spec.loss) # must be a scalar + self.assertIsNotNone(spec.train_op) + self.assertIsNotNone(spec.training_hooks) + elif mode == model_fn_lib.ModeKeys.EVAL: + self.assertEqual(self._gan_model.generated_data, spec.predictions) + self.assertShapeEqual(np.array(0), spec.loss) # must be a scalar + self.assertIsNotNone(spec.eval_metric_ops) # TODO(joelshor): Add pandas test. @@ -195,12 +184,6 @@ class GANEstimatorIntegrationTest(test.TestCase): lr = learning_rate_decay.exponential_decay(1.0, gstep, 10, 0.9) return training.GradientDescentOptimizer(lr) - def get_metrics(gan_model): - return { - 'mse_custom_metric': metrics_lib.mean_squared_error( - gan_model.real_data, gan_model.generated_data) - } - gopt = make_opt if lr_decay else training.GradientDescentOptimizer(1.0) dopt = make_opt if lr_decay else training.GradientDescentOptimizer(1.0) est = estimator.GANEstimator( diff --git a/tensorflow/contrib/gan/python/estimator/python/head.py b/tensorflow/contrib/gan/python/estimator/python/head.py deleted file mode 100644 index 3225d6f41a..0000000000 --- a/tensorflow/contrib/gan/python/estimator/python/head.py +++ /dev/null @@ -1,28 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""`tf.Learn` components for `GANEstimator`'s loss.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from tensorflow.contrib.gan.python.estimator.python import head_impl -# pylint: disable=wildcard-import -from tensorflow.contrib.gan.python.estimator.python.head_impl import * -# pylint: enable=wildcard-import -from tensorflow.python.util.all_util import remove_undocumented - -__all__ = head_impl.__all__ -remove_undocumented(__name__, __all__) diff --git a/tensorflow/contrib/gan/python/estimator/python/head_impl.py b/tensorflow/contrib/gan/python/estimator/python/head_impl.py deleted file mode 100644 index ff903a78cc..0000000000 --- a/tensorflow/contrib/gan/python/estimator/python/head_impl.py +++ /dev/null @@ -1,235 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""A TFGAN-backed GAN Estimator.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import functools - -from tensorflow.contrib.gan.python import namedtuples as tfgan_tuples -from tensorflow.contrib.gan.python import train as tfgan_train -from tensorflow.python.estimator import model_fn as model_fn_lib -from tensorflow.python.estimator.canned import head -from tensorflow.python.framework import ops -from tensorflow.python.ops import metrics as metrics_lib - -__all__ = [ - 'GANHead', - 'gan_head', -] - -def _summary_key(head_name, val): - return '%s/%s' % (val, head_name) if head_name else val - - -def gan_head(generator_loss_fn, discriminator_loss_fn, generator_optimizer, - discriminator_optimizer, use_loss_summaries=True, - get_hooks_fn=tfgan_train.get_sequential_train_hooks(), - get_eval_metric_ops_fn=None, name=None): - """Creates a `GANHead`. - - Args: - generator_loss_fn: A TFGAN loss function for the generator. Takes a - `GANModel` and returns a scalar. - discriminator_loss_fn: Same as `generator_loss_fn`, but for the - discriminator. - generator_optimizer: The optimizer for generator updates. - discriminator_optimizer: Same as `generator_optimizer`, but for the - discriminator updates. - use_loss_summaries: If `True`, add loss summaries. If `False`, does not. - If `None`, uses defaults. - get_hooks_fn: A function that takes a `GANTrainOps` tuple and returns a - list of hooks. - get_eval_metric_ops_fn: A function that takes a `GANModel`, and returns a - dict of metric results keyed by name. The output of this function is - passed into `tf.estimator.EstimatorSpec` during evaluation. - name: name of the head. If provided, summary and metrics keys will be - suffixed by `"/" + name`. - - Returns: - An instance of `GANHead`. - """ - return GANHead(generator_loss_fn=generator_loss_fn, - discriminator_loss_fn=discriminator_loss_fn, - generator_optimizer=generator_optimizer, - discriminator_optimizer=discriminator_optimizer, - use_loss_summaries=use_loss_summaries, - get_hooks_fn=get_hooks_fn, - get_eval_metric_ops_fn=get_eval_metric_ops_fn, - name=name) - - -class GANHead(head._Head): # pylint: disable=protected-access - """`Head` for a GAN.""" - - def __init__(self, generator_loss_fn, discriminator_loss_fn, - generator_optimizer, discriminator_optimizer, - use_loss_summaries=True, - get_hooks_fn=None, - get_eval_metric_ops_fn=None, - name=None): - """`Head` for GAN training. - - Args: - generator_loss_fn: A TFGAN loss function for the generator. Takes a - `GANModel` and returns a scalar. - discriminator_loss_fn: Same as `generator_loss_fn`, but for the - discriminator. - generator_optimizer: The optimizer for generator updates. - discriminator_optimizer: Same as `generator_optimizer`, but for the - discriminator updates. - use_loss_summaries: If `True`, add loss summaries. If `False`, does not. - If `None`, uses defaults. - get_hooks_fn: A function that takes a `GANTrainOps` tuple and returns a - list of hooks. Defaults to `train.get_sequential_train_hooks()` - get_eval_metric_ops_fn: A function that takes a `GANModel`, and returns a - dict of metric results keyed by name. The output of this function is - passed into `tf.estimator.EstimatorSpec` during evaluation. - name: name of the head. If provided, summary and metrics keys will be - suffixed by `"/" + name`. - """ - if get_hooks_fn is None: - get_hooks_fn = tfgan_train.get_sequential_train_hooks() - # TODO(joelshor): Validate inputs. - - if use_loss_summaries in [True, False]: - generator_loss_fn = functools.partial( - generator_loss_fn, add_summaries=use_loss_summaries) - discriminator_loss_fn = functools.partial( - discriminator_loss_fn, add_summaries=use_loss_summaries) - self._generator_loss_fn = generator_loss_fn - self._discriminator_loss_fn = discriminator_loss_fn - self._generator_optimizer = generator_optimizer - self._discriminator_optimizer = discriminator_optimizer - self._get_hooks_fn = get_hooks_fn - self._get_eval_metric_ops_fn = get_eval_metric_ops_fn - self._name = name - - @property - def name(self): - return self._name - - @property - def logits_dimension(self): - return None - - def create_loss(self, features, mode, logits, labels): - """Returns a GANLoss tuple from the provided GANModel. - - See `Head` for more details. - - Args: - features: Input `dict` of `Tensor` objects. Unused. - mode: Estimator's `ModeKeys`. - logits: A GANModel tuple. - labels: Must be `None`. - - Returns: - A GANLoss tuple. - - """ - _validate_logits_and_labels(logits, labels) - del mode, labels, features # unused for this head. - gan_model = logits # rename variable for clarity - return tfgan_tuples.GANLoss( - generator_loss=self._generator_loss_fn(gan_model), - discriminator_loss=self._discriminator_loss_fn(gan_model)) - - def create_estimator_spec( - self, features, mode, logits, labels=None, - train_op_fn=tfgan_train.gan_train_ops): - """Returns `EstimatorSpec` that a model_fn can return. - - See `Head` for more details. - - Args: - features: Must be `None`. - mode: Estimator's `ModeKeys`. - logits: A GANModel tuple. - labels: Must be `None`. - train_op_fn: Function that takes a GANModel, GANLoss, generator optimizer, - and discriminator optimizer, and returns a `GANTrainOps` tuple. For - example, this function can come from TFGAN's `train.py` library, or can - be custom. - - Returns: - `EstimatorSpec`. - - Raises: - ValueError: If `features` isn't `None`. - ValueError: If `train_op_fn` isn't provided in train mode. - """ - _validate_logits_and_labels(logits, labels) - if features is not None: - raise ValueError('`features` should be `None`. Instead, found: %s' % - features) - gan_model = logits # rename variable for clarity - with ops.name_scope('GANHead'): - if mode == model_fn_lib.ModeKeys.PREDICT: - return model_fn_lib.EstimatorSpec( - mode=model_fn_lib.ModeKeys.PREDICT, - predictions=gan_model.generated_data) - elif mode == model_fn_lib.ModeKeys.EVAL: - gan_loss = self.create_loss( - features=None, mode=mode, logits=gan_model, labels=None) - scalar_loss = gan_loss.generator_loss + gan_loss.discriminator_loss - with ops.name_scope(None, 'metrics', - [gan_loss.generator_loss, - gan_loss.discriminator_loss]): - eval_metric_ops = { - _summary_key(self._name, 'generator_loss'): - metrics_lib.mean(gan_loss.generator_loss), - _summary_key(self._name, 'discriminator_loss'): - metrics_lib.mean(gan_loss.discriminator_loss) - } - if self._get_eval_metric_ops_fn is not None: - custom_eval_metric_ops = self._get_eval_metric_ops_fn(gan_model) - if not isinstance(custom_eval_metric_ops, dict): - raise TypeError('get_eval_metric_ops_fn must return a dict, ' - 'received: {}'.format(custom_eval_metric_ops)) - eval_metric_ops.update(custom_eval_metric_ops) - return model_fn_lib.EstimatorSpec( - mode=model_fn_lib.ModeKeys.EVAL, - predictions=gan_model.generated_data, - loss=scalar_loss, - eval_metric_ops=eval_metric_ops) - elif mode == model_fn_lib.ModeKeys.TRAIN: - if train_op_fn is None: - raise ValueError('train_op_fn can not be None.') - gan_loss = self.create_loss(None, mode, gan_model, None) - scalar_loss = gan_loss.generator_loss + gan_loss.discriminator_loss - train_ops = train_op_fn(gan_model, gan_loss, self._generator_optimizer, - self._discriminator_optimizer) - training_hooks = self._get_hooks_fn(train_ops) - return model_fn_lib.EstimatorSpec( - loss=scalar_loss, - mode=model_fn_lib.ModeKeys.TRAIN, - train_op=train_ops.global_step_inc_op, - training_hooks=training_hooks) - else: - raise ValueError('Mode not recognized: %s' % mode) - - -def _validate_logits_and_labels(logits, labels): - if labels is not None: - raise ValueError('`GANHead`\'s `create_estimator_spec` input `labels` must ' - 'be `None`. Instead, found: %s' % labels) - - if not isinstance(logits, tfgan_tuples.GANModel): - raise ValueError('`GANHead`\'s `create_estimator_spec` input `logits` must ' - 'be an instnace of a `GANModel`. Instead, found: %s' % - logits) diff --git a/tensorflow/contrib/gan/python/estimator/python/head_test.py b/tensorflow/contrib/gan/python/estimator/python/head_test.py deleted file mode 100644 index 6587f1fc60..0000000000 --- a/tensorflow/contrib/gan/python/estimator/python/head_test.py +++ /dev/null @@ -1,90 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for TFGAN's head.py.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from tensorflow.contrib.gan.python import namedtuples as tfgan_tuples -from tensorflow.contrib.gan.python.estimator.python import head - -from tensorflow.python.estimator import model_fn as model_fn_lib -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import math_ops -from tensorflow.python.ops import variable_scope -from tensorflow.python.platform import test -from tensorflow.python.training import training - - -def dummy_loss(gan_model, add_summaries=True): # pylint:disable=unused-argument - return math_ops.reduce_sum(gan_model.discriminator_real_outputs - - gan_model.discriminator_gen_outputs) - - -def get_gan_model(): - # TODO(joelshor): Find a better way of creating a variable scope. - with variable_scope.variable_scope('generator') as gen_scope: - gen_var = variable_scope.get_variable('dummy_var', initializer=0.0) - with variable_scope.variable_scope('discriminator') as dis_scope: - dis_var = variable_scope.get_variable('dummy_var', initializer=0.0) - return tfgan_tuples.GANModel( - generator_inputs=None, - generated_data=array_ops.ones([3, 4]), - generator_variables=[gen_var], - generator_scope=gen_scope, - generator_fn=None, - real_data=None, - discriminator_real_outputs=array_ops.ones([1, 2, 3]) * dis_var, - discriminator_gen_outputs=array_ops.ones([1, 2, 3]) * gen_var * dis_var, - discriminator_variables=[dis_var], - discriminator_scope=dis_scope, - discriminator_fn=None) - - -class GANHeadTest(test.TestCase): - - def setUp(self): - super(GANHeadTest, self).setUp() - self.gan_head = head.gan_head( - generator_loss_fn=dummy_loss, - discriminator_loss_fn=dummy_loss, - generator_optimizer=training.GradientDescentOptimizer(1.0), - discriminator_optimizer=training.GradientDescentOptimizer(1.0), - get_eval_metric_ops_fn=self.get_metrics) - self.assertTrue(isinstance(self.gan_head, head.GANHead)) - - def get_metrics(self, gan_model): - self.assertTrue(isinstance(gan_model, tfgan_tuples.GANModel)) - return {} - - def _test_modes_helper(self, mode): - self.gan_head.create_estimator_spec( - features=None, - mode=mode, - logits=get_gan_model()) - - def test_modes_predict(self): - self._test_modes_helper(model_fn_lib.ModeKeys.PREDICT) - - def test_modes_eval(self): - self._test_modes_helper(model_fn_lib.ModeKeys.EVAL) - - def test_modes_train(self): - self._test_modes_helper(model_fn_lib.ModeKeys.TRAIN) - - -if __name__ == '__main__': - test.main() -- GitLab From 553093c4a10a9b82f0c2c33cfc72dd3a6f738911 Mon Sep 17 00:00:00 2001 From: Nishidha Panpaliya Date: Wed, 13 Jun 2018 10:30:36 +0000 Subject: [PATCH 370/816] Fixed compilation error (undefined reference to LLVMInitializePowerPCTargetMC) on ppc64le when XLA is enabled --- tensorflow/compiler/xla/service/cpu/BUILD | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/tensorflow/compiler/xla/service/cpu/BUILD b/tensorflow/compiler/xla/service/cpu/BUILD index 1067b38f93..b703be0f39 100644 --- a/tensorflow/compiler/xla/service/cpu/BUILD +++ b/tensorflow/compiler/xla/service/cpu/BUILD @@ -151,7 +151,14 @@ cc_library( "@llvm//:target", # fixdeps: keep "@llvm//:x86_code_gen", # fixdeps: keep "@llvm//:x86_disassembler", # fixdeps: keep - ], + ] + select({ + "@org_tensorflow//tensorflow:linux_ppc64le": [ + "@llvm//:powerpc_disassembler", + "@llvm//:powerpc_code_gen", + ], + "//conditions:default": [ + ], + }), alwayslink = True, # Contains compiler registration ) -- GitLab From 7a5bcfb37ab6a1d97bd9e17c1a7a231f1498c74e Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 13 Jun 2018 04:32:40 -0700 Subject: [PATCH 371/816] Automated g4 rollback of changelist 200362771 PiperOrigin-RevId: 200370679 --- tensorflow/contrib/gan/BUILD | 50 +++- .../contrib/gan/python/estimator/__init__.py | 5 +- .../estimator/python/gan_estimator_impl.py | 186 +++++++------- .../estimator/python/gan_estimator_test.py | 227 +++++++++-------- .../gan/python/estimator/python/head.py | 28 +++ .../gan/python/estimator/python/head_impl.py | 235 ++++++++++++++++++ .../gan/python/estimator/python/head_test.py | 90 +++++++ 7 files changed, 603 insertions(+), 218 deletions(-) create mode 100644 tensorflow/contrib/gan/python/estimator/python/head.py create mode 100644 tensorflow/contrib/gan/python/estimator/python/head_impl.py create mode 100644 tensorflow/contrib/gan/python/estimator/python/head_test.py diff --git a/tensorflow/contrib/gan/BUILD b/tensorflow/contrib/gan/BUILD index d38d770bc5..b305f37791 100644 --- a/tensorflow/contrib/gan/BUILD +++ b/tensorflow/contrib/gan/BUILD @@ -45,7 +45,6 @@ py_library( "//tensorflow/python:framework_ops", "//tensorflow/python:init_ops", "//tensorflow/python:training", - "//tensorflow/python:training_util", "//tensorflow/python:variable_scope", "//tensorflow/python/ops/distributions", "//tensorflow/python/ops/losses", @@ -60,7 +59,6 @@ py_test( deps = [ ":features", ":namedtuples", - ":random_tensor_pool", ":train", "//tensorflow/contrib/framework:framework_py", "//tensorflow/contrib/slim:learning", @@ -72,7 +70,6 @@ py_test( "//tensorflow/python:random_ops", "//tensorflow/python:random_seed", "//tensorflow/python:training", - "//tensorflow/python:training_util", "//tensorflow/python:variable_scope", "//tensorflow/python:variables", "//tensorflow/python/ops/distributions", @@ -99,6 +96,7 @@ py_library( srcs_version = "PY2AND3", deps = [ ":gan_estimator", + ":head", "//tensorflow/python:util", ], ) @@ -190,7 +188,6 @@ py_test( srcs = ["python/losses/python/tuple_losses_test.py"], srcs_version = "PY2AND3", deps = [ - ":namedtuples", ":tuple_losses", "//tensorflow/python:client_testlib", "//tensorflow/python:constant_op", @@ -347,11 +344,9 @@ py_library( "//tensorflow/python:image_ops", "//tensorflow/python:linalg_ops", "//tensorflow/python:math_ops", - "//tensorflow/python:nn", "//tensorflow/python:nn_ops", "//tensorflow/python:platform", "//tensorflow/python:util", - "@six_archive//:six", ], ) @@ -433,6 +428,40 @@ py_test( ], ) +py_library( + name = "head", + srcs = [ + "python/estimator/python/head.py", + "python/estimator/python/head_impl.py", + ], + srcs_version = "PY2AND3", + deps = [ + ":namedtuples", + ":train", + "//tensorflow/python:framework_ops", + "//tensorflow/python:util", + "//tensorflow/python/estimator:head", + "//tensorflow/python/estimator:model_fn", + ], +) + +py_test( + name = "head_test", + srcs = ["python/estimator/python/head_test.py"], + shard_count = 1, + srcs_version = "PY2AND3", + deps = [ + ":head", + ":namedtuples", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:math_ops", + "//tensorflow/python:training", + "//tensorflow/python:variable_scope", + "//tensorflow/python/estimator:model_fn", + ], +) + py_library( name = "gan_estimator", srcs = [ @@ -441,12 +470,12 @@ py_library( ], srcs_version = "PY2AND3", deps = [ + ":head", ":namedtuples", ":summaries", ":train", "//tensorflow/contrib/framework:framework_py", "//tensorflow/python:framework_ops", - "//tensorflow/python:metrics", "//tensorflow/python:util", "//tensorflow/python:variable_scope", "//tensorflow/python/estimator", @@ -469,19 +498,16 @@ py_test( "//tensorflow/core:protos_all_py", "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", + "//tensorflow/python:control_flow_ops", "//tensorflow/python:dtypes", "//tensorflow/python:framework_ops", - "//tensorflow/python:math_ops", - "//tensorflow/python:metrics", "//tensorflow/python:parsing_ops", "//tensorflow/python:summary", "//tensorflow/python:training", - "//tensorflow/python:training_util", - "//tensorflow/python:variable_scope", + "//tensorflow/python/estimator:head", "//tensorflow/python/estimator:model_fn", "//tensorflow/python/estimator:numpy_io", "//third_party/py/numpy", - "@absl_py//absl/testing:parameterized", "@six_archive//:six", ], ) diff --git a/tensorflow/contrib/gan/python/estimator/__init__.py b/tensorflow/contrib/gan/python/estimator/__init__.py index 04dddb4b55..c9f7bc61b2 100644 --- a/tensorflow/contrib/gan/python/estimator/__init__.py +++ b/tensorflow/contrib/gan/python/estimator/__init__.py @@ -25,13 +25,16 @@ from __future__ import print_function # Collapse `estimator` into a single namespace. # pylint: disable=unused-import,wildcard-import from tensorflow.contrib.gan.python.estimator.python import gan_estimator +from tensorflow.contrib.gan.python.estimator.python import head from tensorflow.contrib.gan.python.estimator.python.gan_estimator import * +from tensorflow.contrib.gan.python.estimator.python.head import * # pylint: enable=unused-import,wildcard-import from tensorflow.python.util.all_util import remove_undocumented _allowed_symbols = [ 'gan_estimator', -] + gan_estimator.__all__ + 'head', +] + gan_estimator.__all__ + head.__all__ remove_undocumented(__name__, _allowed_symbols) diff --git a/tensorflow/contrib/gan/python/estimator/python/gan_estimator_impl.py b/tensorflow/contrib/gan/python/estimator/python/gan_estimator_impl.py index 7104c8aa61..4092b32004 100644 --- a/tensorflow/contrib/gan/python/estimator/python/gan_estimator_impl.py +++ b/tensorflow/contrib/gan/python/estimator/python/gan_estimator_impl.py @@ -24,11 +24,11 @@ import enum from tensorflow.contrib.framework.python.ops import variables as variable_lib from tensorflow.contrib.gan.python import namedtuples as tfgan_tuples from tensorflow.contrib.gan.python import train as tfgan_train +from tensorflow.contrib.gan.python.estimator.python import head as head_lib from tensorflow.contrib.gan.python.eval.python import summaries as tfgan_summaries from tensorflow.python.estimator import estimator from tensorflow.python.estimator import model_fn as model_fn_lib from tensorflow.python.framework import ops -from tensorflow.python.ops import metrics as metrics_lib from tensorflow.python.ops import variable_scope from tensorflow.python.util import tf_inspect as inspect @@ -158,77 +158,90 @@ class GANEstimator(estimator.Estimator): # TODO(joelshor): Explicitly validate inputs. def _model_fn(features, labels, mode): - """GANEstimator model function.""" - if mode not in [model_fn_lib.ModeKeys.TRAIN, model_fn_lib.ModeKeys.EVAL, - model_fn_lib.ModeKeys.PREDICT]: - raise ValueError('Mode not recognized: %s' % mode) - real_data = labels # rename inputs for clarity - generator_inputs = features # rename inputs for clarity - - # Make GANModel, which encapsulates the GAN model architectures. - gan_model = _get_gan_model( - mode, generator_fn, discriminator_fn, real_data, generator_inputs, + gopt = (generator_optimizer() if callable(generator_optimizer) else + generator_optimizer) + dopt = (discriminator_optimizer() if callable(discriminator_optimizer) + else discriminator_optimizer) + gan_head = head_lib.gan_head( + generator_loss_fn, discriminator_loss_fn, gopt, dopt, + use_loss_summaries, get_hooks_fn=get_hooks_fn, + get_eval_metric_ops_fn=get_eval_metric_ops_fn) + return _gan_model_fn( + features, labels, mode, generator_fn, discriminator_fn, gan_head, add_summaries) - # Make the EstimatorSpec, which incorporates the GANModel, losses, eval - # metrics, and optimizers (if required). - return _get_estimator_spec( - mode, gan_model, generator_loss_fn, discriminator_loss_fn, - get_eval_metric_ops_fn, generator_optimizer, discriminator_optimizer, - get_hooks_fn) - super(GANEstimator, self).__init__( model_fn=_model_fn, model_dir=model_dir, config=config) -def _get_gan_model( - mode, generator_fn, discriminator_fn, real_data, generator_inputs, - add_summaries, generator_scope='Generator'): - """Makes the GANModel tuple, which encapsulates the GAN model architecture.""" - if mode == model_fn_lib.ModeKeys.PREDICT: +def _gan_model_fn( + features, + labels, + mode, + generator_fn, + discriminator_fn, + head, + add_summaries=None, + generator_scope_name='Generator'): + """The `model_fn` for the GAN estimator. + + We make the following convention: + features -> TFGAN's `generator_inputs` + labels -> TFGAN's `real_data` + + Args: + features: A dictionary to feed to generator. In the unconditional case, + this might be just `noise`. In the conditional GAN case, this + might be the generator's conditioning. The `generator_fn` determines + what the required keys are. + labels: Real data. Can be any structure, as long as `discriminator_fn` + can accept it for the first argument. + mode: Defines whether this is training, evaluation or prediction. + See `ModeKeys`. + generator_fn: A python lambda that takes `generator_inputs` as inputs and + returns the outputs of the GAN generator. + discriminator_fn: A python lambda that takes `real_data`/`generated data` + and `generator_inputs`. Outputs a Tensor in the range [-inf, inf]. + head: A `Head` instance suitable for GANs. + add_summaries: `None`, a single `SummaryType`, or a list of `SummaryType`. + generator_scope_name: The name of the generator scope. We need this to be + the same for GANModels produced by TFGAN's `train.gan_model` and the + manually constructed ones for predictions. + + Returns: + `ModelFnOps` + + Raises: + ValueError: If `labels` isn't `None` during prediction. + """ + real_data = labels + generator_inputs = features + + if mode == model_fn_lib.ModeKeys.TRAIN: + gan_model = _make_train_gan_model( + generator_fn, discriminator_fn, real_data, generator_inputs, + generator_scope_name, add_summaries) + elif mode == model_fn_lib.ModeKeys.EVAL: + gan_model = _make_eval_gan_model( + generator_fn, discriminator_fn, real_data, generator_inputs, + generator_scope_name, add_summaries) + else: if real_data is not None: raise ValueError('`labels` must be `None` when mode is `predict`. ' 'Instead, found %s' % real_data) gan_model = _make_prediction_gan_model( - generator_inputs, generator_fn, generator_scope) - else: # model_fn_lib.ModeKeys.TRAIN or model_fn_lib.ModeKeys.EVAL - gan_model = _make_gan_model( - generator_fn, discriminator_fn, real_data, generator_inputs, - generator_scope, add_summaries, mode) - - return gan_model + generator_inputs, generator_fn, generator_scope_name) - -def _get_estimator_spec( - mode, gan_model, generator_loss_fn, discriminator_loss_fn, - get_eval_metric_ops_fn, generator_optimizer, discriminator_optimizer, - get_hooks_fn=None): - """Get the EstimatorSpec for the current mode.""" - if mode == model_fn_lib.ModeKeys.PREDICT: - estimator_spec = model_fn_lib.EstimatorSpec( - mode=mode, predictions=gan_model.generated_data) - else: - gan_loss = tfgan_tuples.GANLoss( - generator_loss=generator_loss_fn(gan_model), - discriminator_loss=discriminator_loss_fn(gan_model)) - if mode == model_fn_lib.ModeKeys.EVAL: - estimator_spec = _get_eval_estimator_spec( - gan_model, gan_loss, get_eval_metric_ops_fn) - else: # model_fn_lib.ModeKeys.TRAIN: - gopt = (generator_optimizer() if callable(generator_optimizer) else - generator_optimizer) - dopt = (discriminator_optimizer() if callable(discriminator_optimizer) - else discriminator_optimizer) - get_hooks_fn = get_hooks_fn or tfgan_train.get_sequential_train_hooks() - estimator_spec = _get_train_estimator_spec( - gan_model, gan_loss, gopt, dopt, get_hooks_fn) - - return estimator_spec + return head.create_estimator_spec( + features=None, + mode=mode, + logits=gan_model, + labels=None) def _make_gan_model(generator_fn, discriminator_fn, real_data, generator_inputs, generator_scope, add_summaries, mode): - """Construct a `GANModel`, and optionally pass in `mode`.""" + """Make a `GANModel`, and optionally pass in `mode`.""" # If network functions have an argument `mode`, pass mode to it. if 'mode' in inspect.getargspec(generator_fn).args: generator_fn = functools.partial(generator_fn, mode=mode) @@ -251,6 +264,22 @@ def _make_gan_model(generator_fn, discriminator_fn, real_data, return gan_model +def _make_train_gan_model(generator_fn, discriminator_fn, real_data, + generator_inputs, generator_scope, add_summaries): + """Make a `GANModel` for training.""" + return _make_gan_model(generator_fn, discriminator_fn, real_data, + generator_inputs, generator_scope, add_summaries, + model_fn_lib.ModeKeys.TRAIN) + + +def _make_eval_gan_model(generator_fn, discriminator_fn, real_data, + generator_inputs, generator_scope, add_summaries): + """Make a `GANModel` for evaluation.""" + return _make_gan_model(generator_fn, discriminator_fn, real_data, + generator_inputs, generator_scope, add_summaries, + model_fn_lib.ModeKeys.EVAL) + + def _make_prediction_gan_model(generator_inputs, generator_fn, generator_scope): """Make a `GANModel` from just the generator.""" # If `generator_fn` has an argument `mode`, pass mode to it. @@ -274,46 +303,3 @@ def _make_prediction_gan_model(generator_inputs, generator_fn, generator_scope): discriminator_variables=None, discriminator_scope=None, discriminator_fn=None) - - -def _get_eval_estimator_spec(gan_model, gan_loss, get_eval_metric_ops_fn=None, - name=None): - """Return an EstimatorSpec for the eval case.""" - scalar_loss = gan_loss.generator_loss + gan_loss.discriminator_loss - with ops.name_scope(None, 'metrics', - [gan_loss.generator_loss, - gan_loss.discriminator_loss]): - def _summary_key(head_name, val): - return '%s/%s' % (val, head_name) if head_name else val - eval_metric_ops = { - _summary_key(name, 'generator_loss'): - metrics_lib.mean(gan_loss.generator_loss), - _summary_key(name, 'discriminator_loss'): - metrics_lib.mean(gan_loss.discriminator_loss) - } - if get_eval_metric_ops_fn is not None: - custom_eval_metric_ops = get_eval_metric_ops_fn(gan_model) - if not isinstance(custom_eval_metric_ops, dict): - raise TypeError('get_eval_metric_ops_fn must return a dict, ' - 'received: {}'.format(custom_eval_metric_ops)) - eval_metric_ops.update(custom_eval_metric_ops) - return model_fn_lib.EstimatorSpec( - mode=model_fn_lib.ModeKeys.EVAL, - predictions=gan_model.generated_data, - loss=scalar_loss, - eval_metric_ops=eval_metric_ops) - - -def _get_train_estimator_spec( - gan_model, gan_loss, generator_optimizer, discriminator_optimizer, - get_hooks_fn, train_op_fn=tfgan_train.gan_train_ops): - """Return an EstimatorSpec for the train case.""" - scalar_loss = gan_loss.generator_loss + gan_loss.discriminator_loss - train_ops = train_op_fn(gan_model, gan_loss, generator_optimizer, - discriminator_optimizer) - training_hooks = get_hooks_fn(train_ops) - return model_fn_lib.EstimatorSpec( - loss=scalar_loss, - mode=model_fn_lib.ModeKeys.TRAIN, - train_op=train_ops.global_step_inc_op, - training_hooks=training_hooks) diff --git a/tensorflow/contrib/gan/python/estimator/python/gan_estimator_test.py b/tensorflow/contrib/gan/python/estimator/python/gan_estimator_test.py index 9ac9c6ca9c..955482599b 100644 --- a/tensorflow/contrib/gan/python/estimator/python/gan_estimator_test.py +++ b/tensorflow/contrib/gan/python/estimator/python/gan_estimator_test.py @@ -21,30 +21,30 @@ from __future__ import print_function import shutil import tempfile -from absl.testing import parameterized import numpy as np import six from tensorflow.contrib import layers -from tensorflow.contrib.gan.python import namedtuples as tfgan_tuples +from tensorflow.contrib.gan.python import namedtuples from tensorflow.contrib.gan.python.estimator.python import gan_estimator_impl as estimator from tensorflow.contrib.gan.python.losses.python import tuple_losses as losses from tensorflow.contrib.learn.python.learn.learn_io import graph_io from tensorflow.core.example import example_pb2 from tensorflow.core.example import feature_pb2 from tensorflow.python.estimator import model_fn as model_fn_lib +from tensorflow.python.estimator.canned import head as head_lib from tensorflow.python.estimator.inputs import numpy_io from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops -from tensorflow.python.ops import math_ops +from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import metrics as metrics_lib from tensorflow.python.ops import parsing_ops -from tensorflow.python.ops import variable_scope from tensorflow.python.platform import test from tensorflow.python.summary.writer import writer_cache from tensorflow.python.training import input as input_lib from tensorflow.python.training import learning_rate_decay +from tensorflow.python.training import monitored_session from tensorflow.python.training import training from tensorflow.python.training import training_util @@ -60,109 +60,120 @@ def discriminator_fn(data, unused_conditioning, mode): return layers.fully_connected(data, 1) -class GetGANModelTest(test.TestCase, parameterized.TestCase): - """Tests that `GetGANModel` produces the correct model.""" - - @parameterized.named_parameters( - ('train', model_fn_lib.ModeKeys.TRAIN), - ('eval', model_fn_lib.ModeKeys.EVAL), - ('predict', model_fn_lib.ModeKeys.PREDICT)) - def test_get_gan_model(self, mode): - with ops.Graph().as_default(): - generator_inputs = {'x': array_ops.ones([3, 4])} - real_data = (array_ops.zeros([3, 4]) if - mode != model_fn_lib.ModeKeys.PREDICT else None) - gan_model = estimator._get_gan_model( - mode, generator_fn, discriminator_fn, real_data, generator_inputs, - add_summaries=False) - - self.assertEqual(generator_inputs, gan_model.generator_inputs) - self.assertIsNotNone(gan_model.generated_data) - self.assertEqual(2, len(gan_model.generator_variables)) # 1 FC layer - self.assertIsNotNone(gan_model.generator_fn) - if mode == model_fn_lib.ModeKeys.PREDICT: - self.assertIsNone(gan_model.real_data) - self.assertIsNone(gan_model.discriminator_real_outputs) - self.assertIsNone(gan_model.discriminator_gen_outputs) - self.assertIsNone(gan_model.discriminator_variables) - self.assertIsNone(gan_model.discriminator_scope) - self.assertIsNone(gan_model.discriminator_fn) +def mock_head(testcase, expected_generator_inputs, expected_real_data, + generator_scope_name): + """Returns a mock head that validates logits values and variable names.""" + discriminator_scope_name = 'Discriminator' # comes from TFGAN defaults + generator_var_names = set([ + '%s/fully_connected/weights:0' % generator_scope_name, + '%s/fully_connected/biases:0' % generator_scope_name]) + discriminator_var_names = set([ + '%s/fully_connected/weights:0' % discriminator_scope_name, + '%s/fully_connected/biases:0' % discriminator_scope_name]) + + def _create_estimator_spec(features, mode, logits, labels): + gan_model = logits # renaming for clarity + is_predict = mode == model_fn_lib.ModeKeys.PREDICT + testcase.assertIsNone(features) + testcase.assertIsNone(labels) + testcase.assertIsInstance(gan_model, namedtuples.GANModel) + + trainable_vars = ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES) + expected_var_names = (generator_var_names if is_predict else + generator_var_names | discriminator_var_names) + testcase.assertItemsEqual(expected_var_names, + [var.name for var in trainable_vars]) + + assertions = [] + def _or_none(x): + return None if is_predict else x + testcase.assertEqual(expected_generator_inputs, gan_model.generator_inputs) + # TODO(joelshor): Add check on `generated_data`. + testcase.assertItemsEqual( + generator_var_names, + set([x.name for x in gan_model.generator_variables])) + testcase.assertEqual(generator_scope_name, gan_model.generator_scope.name) + testcase.assertEqual(_or_none(expected_real_data), gan_model.real_data) + # TODO(joelshor): Add check on `discriminator_real_outputs`. + # TODO(joelshor): Add check on `discriminator_gen_outputs`. + if is_predict: + testcase.assertIsNone(gan_model.discriminator_scope) else: - self.assertIsNotNone(gan_model.real_data) - self.assertIsNotNone(gan_model.discriminator_real_outputs) - self.assertIsNotNone(gan_model.discriminator_gen_outputs) - self.assertEqual(2, len(gan_model.discriminator_variables)) # 1 FC layer - self.assertIsNotNone(gan_model.discriminator_scope) - self.assertIsNotNone(gan_model.discriminator_fn) - - -def get_dummy_gan_model(): - # TODO(joelshor): Find a better way of creating a variable scope. - with variable_scope.variable_scope('generator') as gen_scope: - gen_var = variable_scope.get_variable('dummy_var', initializer=0.0) - with variable_scope.variable_scope('discriminator') as dis_scope: - dis_var = variable_scope.get_variable('dummy_var', initializer=0.0) - return tfgan_tuples.GANModel( - generator_inputs=None, - generated_data=array_ops.ones([3, 4]), - generator_variables=[gen_var], - generator_scope=gen_scope, - generator_fn=None, - real_data=array_ops.zeros([3, 4]), - discriminator_real_outputs=array_ops.ones([1, 2, 3]) * dis_var, - discriminator_gen_outputs=array_ops.ones([1, 2, 3]) * gen_var * dis_var, - discriminator_variables=[dis_var], - discriminator_scope=dis_scope, - discriminator_fn=None) - - -def dummy_loss_fn(gan_model): - return math_ops.reduce_sum(gan_model.discriminator_real_outputs - - gan_model.discriminator_gen_outputs) - - -def get_metrics(gan_model): - return { - 'mse_custom_metric': metrics_lib.mean_squared_error( - gan_model.real_data, gan_model.generated_data) - } - - -class GetEstimatorSpecTest(test.TestCase, parameterized.TestCase): - """Tests that the EstimatorSpec is constructed appropriately.""" - - @classmethod - def setUpClass(cls): - cls._generator_optimizer = training.GradientDescentOptimizer(1.0) - cls._discriminator_optimizer = training.GradientDescentOptimizer(1.0) - - @parameterized.named_parameters( - ('train', model_fn_lib.ModeKeys.TRAIN), - ('eval', model_fn_lib.ModeKeys.EVAL), - ('predict', model_fn_lib.ModeKeys.PREDICT)) - def test_get_estimator_spec(self, mode): + testcase.assertEqual(discriminator_scope_name, + gan_model.discriminator_scope.name) + + with ops.control_dependencies(assertions): + if mode == model_fn_lib.ModeKeys.TRAIN: + return model_fn_lib.EstimatorSpec( + mode=mode, loss=array_ops.zeros([]), + train_op=control_flow_ops.no_op(), training_hooks=[]) + elif mode == model_fn_lib.ModeKeys.EVAL: + return model_fn_lib.EstimatorSpec( + mode=mode, predictions=gan_model.generated_data, + loss=array_ops.zeros([])) + elif mode == model_fn_lib.ModeKeys.PREDICT: + return model_fn_lib.EstimatorSpec( + mode=mode, predictions=gan_model.generated_data) + else: + testcase.fail('Invalid mode: {}'.format(mode)) + + head = test.mock.NonCallableMagicMock(spec=head_lib._Head) + head.create_estimator_spec = test.mock.MagicMock( + wraps=_create_estimator_spec) + + return head + + +class GANModelFnTest(test.TestCase): + """Tests that _gan_model_fn passes expected logits to mock head.""" + + def setUp(self): + self._model_dir = tempfile.mkdtemp() + + def tearDown(self): + if self._model_dir: + writer_cache.FileWriterCache.clear() + shutil.rmtree(self._model_dir) + + def _test_logits_helper(self, mode): + """Tests that the expected logits are passed to mock head.""" with ops.Graph().as_default(): - self._gan_model = get_dummy_gan_model() - spec = estimator._get_estimator_spec( - mode, - self._gan_model, - generator_loss_fn=dummy_loss_fn, - discriminator_loss_fn=dummy_loss_fn, - get_eval_metric_ops_fn=get_metrics, - generator_optimizer=self._generator_optimizer, - discriminator_optimizer=self._discriminator_optimizer) - - self.assertEqual(mode, spec.mode) - if mode == model_fn_lib.ModeKeys.PREDICT: - self.assertEqual(self._gan_model.generated_data, spec.predictions) - elif mode == model_fn_lib.ModeKeys.TRAIN: - self.assertShapeEqual(np.array(0), spec.loss) # must be a scalar - self.assertIsNotNone(spec.train_op) - self.assertIsNotNone(spec.training_hooks) - elif mode == model_fn_lib.ModeKeys.EVAL: - self.assertEqual(self._gan_model.generated_data, spec.predictions) - self.assertShapeEqual(np.array(0), spec.loss) # must be a scalar - self.assertIsNotNone(spec.eval_metric_ops) + training_util.get_or_create_global_step() + generator_inputs = {'x': array_ops.zeros([5, 4])} + real_data = (None if mode == model_fn_lib.ModeKeys.PREDICT else + array_ops.zeros([5, 4])) + generator_scope_name = 'generator' + head = mock_head(self, + expected_generator_inputs=generator_inputs, + expected_real_data=real_data, + generator_scope_name=generator_scope_name) + estimator_spec = estimator._gan_model_fn( + features=generator_inputs, + labels=real_data, + mode=mode, + generator_fn=generator_fn, + discriminator_fn=discriminator_fn, + generator_scope_name=generator_scope_name, + head=head) + with monitored_session.MonitoredTrainingSession( + checkpoint_dir=self._model_dir) as sess: + if mode == model_fn_lib.ModeKeys.TRAIN: + sess.run(estimator_spec.train_op) + elif mode == model_fn_lib.ModeKeys.EVAL: + sess.run(estimator_spec.loss) + elif mode == model_fn_lib.ModeKeys.PREDICT: + sess.run(estimator_spec.predictions) + else: + self.fail('Invalid mode: {}'.format(mode)) + + def test_logits_predict(self): + self._test_logits_helper(model_fn_lib.ModeKeys.PREDICT) + + def test_logits_eval(self): + self._test_logits_helper(model_fn_lib.ModeKeys.EVAL) + + def test_logits_train(self): + self._test_logits_helper(model_fn_lib.ModeKeys.TRAIN) # TODO(joelshor): Add pandas test. @@ -184,6 +195,12 @@ class GANEstimatorIntegrationTest(test.TestCase): lr = learning_rate_decay.exponential_decay(1.0, gstep, 10, 0.9) return training.GradientDescentOptimizer(lr) + def get_metrics(gan_model): + return { + 'mse_custom_metric': metrics_lib.mean_squared_error( + gan_model.real_data, gan_model.generated_data) + } + gopt = make_opt if lr_decay else training.GradientDescentOptimizer(1.0) dopt = make_opt if lr_decay else training.GradientDescentOptimizer(1.0) est = estimator.GANEstimator( diff --git a/tensorflow/contrib/gan/python/estimator/python/head.py b/tensorflow/contrib/gan/python/estimator/python/head.py new file mode 100644 index 0000000000..3225d6f41a --- /dev/null +++ b/tensorflow/contrib/gan/python/estimator/python/head.py @@ -0,0 +1,28 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""`tf.Learn` components for `GANEstimator`'s loss.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.gan.python.estimator.python import head_impl +# pylint: disable=wildcard-import +from tensorflow.contrib.gan.python.estimator.python.head_impl import * +# pylint: enable=wildcard-import +from tensorflow.python.util.all_util import remove_undocumented + +__all__ = head_impl.__all__ +remove_undocumented(__name__, __all__) diff --git a/tensorflow/contrib/gan/python/estimator/python/head_impl.py b/tensorflow/contrib/gan/python/estimator/python/head_impl.py new file mode 100644 index 0000000000..ff903a78cc --- /dev/null +++ b/tensorflow/contrib/gan/python/estimator/python/head_impl.py @@ -0,0 +1,235 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""A TFGAN-backed GAN Estimator.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import functools + +from tensorflow.contrib.gan.python import namedtuples as tfgan_tuples +from tensorflow.contrib.gan.python import train as tfgan_train +from tensorflow.python.estimator import model_fn as model_fn_lib +from tensorflow.python.estimator.canned import head +from tensorflow.python.framework import ops +from tensorflow.python.ops import metrics as metrics_lib + +__all__ = [ + 'GANHead', + 'gan_head', +] + +def _summary_key(head_name, val): + return '%s/%s' % (val, head_name) if head_name else val + + +def gan_head(generator_loss_fn, discriminator_loss_fn, generator_optimizer, + discriminator_optimizer, use_loss_summaries=True, + get_hooks_fn=tfgan_train.get_sequential_train_hooks(), + get_eval_metric_ops_fn=None, name=None): + """Creates a `GANHead`. + + Args: + generator_loss_fn: A TFGAN loss function for the generator. Takes a + `GANModel` and returns a scalar. + discriminator_loss_fn: Same as `generator_loss_fn`, but for the + discriminator. + generator_optimizer: The optimizer for generator updates. + discriminator_optimizer: Same as `generator_optimizer`, but for the + discriminator updates. + use_loss_summaries: If `True`, add loss summaries. If `False`, does not. + If `None`, uses defaults. + get_hooks_fn: A function that takes a `GANTrainOps` tuple and returns a + list of hooks. + get_eval_metric_ops_fn: A function that takes a `GANModel`, and returns a + dict of metric results keyed by name. The output of this function is + passed into `tf.estimator.EstimatorSpec` during evaluation. + name: name of the head. If provided, summary and metrics keys will be + suffixed by `"/" + name`. + + Returns: + An instance of `GANHead`. + """ + return GANHead(generator_loss_fn=generator_loss_fn, + discriminator_loss_fn=discriminator_loss_fn, + generator_optimizer=generator_optimizer, + discriminator_optimizer=discriminator_optimizer, + use_loss_summaries=use_loss_summaries, + get_hooks_fn=get_hooks_fn, + get_eval_metric_ops_fn=get_eval_metric_ops_fn, + name=name) + + +class GANHead(head._Head): # pylint: disable=protected-access + """`Head` for a GAN.""" + + def __init__(self, generator_loss_fn, discriminator_loss_fn, + generator_optimizer, discriminator_optimizer, + use_loss_summaries=True, + get_hooks_fn=None, + get_eval_metric_ops_fn=None, + name=None): + """`Head` for GAN training. + + Args: + generator_loss_fn: A TFGAN loss function for the generator. Takes a + `GANModel` and returns a scalar. + discriminator_loss_fn: Same as `generator_loss_fn`, but for the + discriminator. + generator_optimizer: The optimizer for generator updates. + discriminator_optimizer: Same as `generator_optimizer`, but for the + discriminator updates. + use_loss_summaries: If `True`, add loss summaries. If `False`, does not. + If `None`, uses defaults. + get_hooks_fn: A function that takes a `GANTrainOps` tuple and returns a + list of hooks. Defaults to `train.get_sequential_train_hooks()` + get_eval_metric_ops_fn: A function that takes a `GANModel`, and returns a + dict of metric results keyed by name. The output of this function is + passed into `tf.estimator.EstimatorSpec` during evaluation. + name: name of the head. If provided, summary and metrics keys will be + suffixed by `"/" + name`. + """ + if get_hooks_fn is None: + get_hooks_fn = tfgan_train.get_sequential_train_hooks() + # TODO(joelshor): Validate inputs. + + if use_loss_summaries in [True, False]: + generator_loss_fn = functools.partial( + generator_loss_fn, add_summaries=use_loss_summaries) + discriminator_loss_fn = functools.partial( + discriminator_loss_fn, add_summaries=use_loss_summaries) + self._generator_loss_fn = generator_loss_fn + self._discriminator_loss_fn = discriminator_loss_fn + self._generator_optimizer = generator_optimizer + self._discriminator_optimizer = discriminator_optimizer + self._get_hooks_fn = get_hooks_fn + self._get_eval_metric_ops_fn = get_eval_metric_ops_fn + self._name = name + + @property + def name(self): + return self._name + + @property + def logits_dimension(self): + return None + + def create_loss(self, features, mode, logits, labels): + """Returns a GANLoss tuple from the provided GANModel. + + See `Head` for more details. + + Args: + features: Input `dict` of `Tensor` objects. Unused. + mode: Estimator's `ModeKeys`. + logits: A GANModel tuple. + labels: Must be `None`. + + Returns: + A GANLoss tuple. + + """ + _validate_logits_and_labels(logits, labels) + del mode, labels, features # unused for this head. + gan_model = logits # rename variable for clarity + return tfgan_tuples.GANLoss( + generator_loss=self._generator_loss_fn(gan_model), + discriminator_loss=self._discriminator_loss_fn(gan_model)) + + def create_estimator_spec( + self, features, mode, logits, labels=None, + train_op_fn=tfgan_train.gan_train_ops): + """Returns `EstimatorSpec` that a model_fn can return. + + See `Head` for more details. + + Args: + features: Must be `None`. + mode: Estimator's `ModeKeys`. + logits: A GANModel tuple. + labels: Must be `None`. + train_op_fn: Function that takes a GANModel, GANLoss, generator optimizer, + and discriminator optimizer, and returns a `GANTrainOps` tuple. For + example, this function can come from TFGAN's `train.py` library, or can + be custom. + + Returns: + `EstimatorSpec`. + + Raises: + ValueError: If `features` isn't `None`. + ValueError: If `train_op_fn` isn't provided in train mode. + """ + _validate_logits_and_labels(logits, labels) + if features is not None: + raise ValueError('`features` should be `None`. Instead, found: %s' % + features) + gan_model = logits # rename variable for clarity + with ops.name_scope('GANHead'): + if mode == model_fn_lib.ModeKeys.PREDICT: + return model_fn_lib.EstimatorSpec( + mode=model_fn_lib.ModeKeys.PREDICT, + predictions=gan_model.generated_data) + elif mode == model_fn_lib.ModeKeys.EVAL: + gan_loss = self.create_loss( + features=None, mode=mode, logits=gan_model, labels=None) + scalar_loss = gan_loss.generator_loss + gan_loss.discriminator_loss + with ops.name_scope(None, 'metrics', + [gan_loss.generator_loss, + gan_loss.discriminator_loss]): + eval_metric_ops = { + _summary_key(self._name, 'generator_loss'): + metrics_lib.mean(gan_loss.generator_loss), + _summary_key(self._name, 'discriminator_loss'): + metrics_lib.mean(gan_loss.discriminator_loss) + } + if self._get_eval_metric_ops_fn is not None: + custom_eval_metric_ops = self._get_eval_metric_ops_fn(gan_model) + if not isinstance(custom_eval_metric_ops, dict): + raise TypeError('get_eval_metric_ops_fn must return a dict, ' + 'received: {}'.format(custom_eval_metric_ops)) + eval_metric_ops.update(custom_eval_metric_ops) + return model_fn_lib.EstimatorSpec( + mode=model_fn_lib.ModeKeys.EVAL, + predictions=gan_model.generated_data, + loss=scalar_loss, + eval_metric_ops=eval_metric_ops) + elif mode == model_fn_lib.ModeKeys.TRAIN: + if train_op_fn is None: + raise ValueError('train_op_fn can not be None.') + gan_loss = self.create_loss(None, mode, gan_model, None) + scalar_loss = gan_loss.generator_loss + gan_loss.discriminator_loss + train_ops = train_op_fn(gan_model, gan_loss, self._generator_optimizer, + self._discriminator_optimizer) + training_hooks = self._get_hooks_fn(train_ops) + return model_fn_lib.EstimatorSpec( + loss=scalar_loss, + mode=model_fn_lib.ModeKeys.TRAIN, + train_op=train_ops.global_step_inc_op, + training_hooks=training_hooks) + else: + raise ValueError('Mode not recognized: %s' % mode) + + +def _validate_logits_and_labels(logits, labels): + if labels is not None: + raise ValueError('`GANHead`\'s `create_estimator_spec` input `labels` must ' + 'be `None`. Instead, found: %s' % labels) + + if not isinstance(logits, tfgan_tuples.GANModel): + raise ValueError('`GANHead`\'s `create_estimator_spec` input `logits` must ' + 'be an instnace of a `GANModel`. Instead, found: %s' % + logits) diff --git a/tensorflow/contrib/gan/python/estimator/python/head_test.py b/tensorflow/contrib/gan/python/estimator/python/head_test.py new file mode 100644 index 0000000000..6587f1fc60 --- /dev/null +++ b/tensorflow/contrib/gan/python/estimator/python/head_test.py @@ -0,0 +1,90 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for TFGAN's head.py.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.gan.python import namedtuples as tfgan_tuples +from tensorflow.contrib.gan.python.estimator.python import head + +from tensorflow.python.estimator import model_fn as model_fn_lib +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import variable_scope +from tensorflow.python.platform import test +from tensorflow.python.training import training + + +def dummy_loss(gan_model, add_summaries=True): # pylint:disable=unused-argument + return math_ops.reduce_sum(gan_model.discriminator_real_outputs - + gan_model.discriminator_gen_outputs) + + +def get_gan_model(): + # TODO(joelshor): Find a better way of creating a variable scope. + with variable_scope.variable_scope('generator') as gen_scope: + gen_var = variable_scope.get_variable('dummy_var', initializer=0.0) + with variable_scope.variable_scope('discriminator') as dis_scope: + dis_var = variable_scope.get_variable('dummy_var', initializer=0.0) + return tfgan_tuples.GANModel( + generator_inputs=None, + generated_data=array_ops.ones([3, 4]), + generator_variables=[gen_var], + generator_scope=gen_scope, + generator_fn=None, + real_data=None, + discriminator_real_outputs=array_ops.ones([1, 2, 3]) * dis_var, + discriminator_gen_outputs=array_ops.ones([1, 2, 3]) * gen_var * dis_var, + discriminator_variables=[dis_var], + discriminator_scope=dis_scope, + discriminator_fn=None) + + +class GANHeadTest(test.TestCase): + + def setUp(self): + super(GANHeadTest, self).setUp() + self.gan_head = head.gan_head( + generator_loss_fn=dummy_loss, + discriminator_loss_fn=dummy_loss, + generator_optimizer=training.GradientDescentOptimizer(1.0), + discriminator_optimizer=training.GradientDescentOptimizer(1.0), + get_eval_metric_ops_fn=self.get_metrics) + self.assertTrue(isinstance(self.gan_head, head.GANHead)) + + def get_metrics(self, gan_model): + self.assertTrue(isinstance(gan_model, tfgan_tuples.GANModel)) + return {} + + def _test_modes_helper(self, mode): + self.gan_head.create_estimator_spec( + features=None, + mode=mode, + logits=get_gan_model()) + + def test_modes_predict(self): + self._test_modes_helper(model_fn_lib.ModeKeys.PREDICT) + + def test_modes_eval(self): + self._test_modes_helper(model_fn_lib.ModeKeys.EVAL) + + def test_modes_train(self): + self._test_modes_helper(model_fn_lib.ModeKeys.TRAIN) + + +if __name__ == '__main__': + test.main() -- GitLab From 29a74058602dfee73242ff001d2130c4589cbdb3 Mon Sep 17 00:00:00 2001 From: Derek Murray Date: Wed, 13 Jun 2018 05:59:37 -0700 Subject: [PATCH 372/816] Add missing include of reshape_util.h to reshape_util.cc. PiperOrigin-RevId: 200378252 --- tensorflow/core/kernels/reshape_util.cc | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tensorflow/core/kernels/reshape_util.cc b/tensorflow/core/kernels/reshape_util.cc index 4188ad233e..c75e942039 100644 --- a/tensorflow/core/kernels/reshape_util.cc +++ b/tensorflow/core/kernels/reshape_util.cc @@ -12,9 +12,10 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ - #define EIGEN_USE_THREADS +#include "tensorflow/core/kernels/reshape_util.h" + #include #include #include -- GitLab From ce568de33120eb180186c11f0b04e69b3541055d Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 13 Jun 2018 06:19:41 -0700 Subject: [PATCH 373/816] Remove uses of dynamic_cast, which is generally discouraged by the Google C++ style guide, https://google.github.io/styleguide/cppguide.html#Run-Time_Type_Information__RTTI_ PiperOrigin-RevId: 200380532 --- .../toco/graph_transformations/identify_dilated_conv.cc | 9 +++++---- tensorflow/contrib/lite/toco/tflite/import.cc | 5 +++-- tensorflow/contrib/lite/toco/tflite/operator_test.cc | 6 ++++-- 3 files changed, 12 insertions(+), 8 deletions(-) diff --git a/tensorflow/contrib/lite/toco/graph_transformations/identify_dilated_conv.cc b/tensorflow/contrib/lite/toco/graph_transformations/identify_dilated_conv.cc index ae3301f467..d49857cfc2 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/identify_dilated_conv.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/identify_dilated_conv.cc @@ -90,12 +90,13 @@ bool IdentifyDilatedConv::Run(Model* model, std::size_t op_index) { } // Conv Op - ConvOperator* conv_op = dynamic_cast( - has_expand_op ? GetOpWithInput(*model, post_stb_op->outputs[0]) - : GetOpWithInput(*model, stb_op->outputs[0])); - if (!conv_op || conv_op->type != OperatorType::kConv) { + const string& input_of_conv_op = + has_expand_op ? post_stb_op->outputs[0] : stb_op->outputs[0]; + auto* conv_base_op = GetOpWithInput(*model, input_of_conv_op); + if (conv_base_op->type != OperatorType::kConv) { return false; } + auto* conv_op = static_cast(conv_base_op); if (conv_op->inputs.size() != 2) { // The conv op must only have weights, no bias. return false; diff --git a/tensorflow/contrib/lite/toco/tflite/import.cc b/tensorflow/contrib/lite/toco/tflite/import.cc index 1be7cf07a7..cb44a5e6d7 100644 --- a/tensorflow/contrib/lite/toco/tflite/import.cc +++ b/tensorflow/contrib/lite/toco/tflite/import.cc @@ -124,8 +124,9 @@ void ImportOperators( new_op = ops_by_name.at(effective_opname) ->Deserialize(input_op->builtin_options(), input_op->custom_options()); - if (TensorFlowUnsupportedOperator* unsupported_op = - dynamic_cast(new_op.get())) { + if (new_op->type == OperatorType::kTensorFlowUnsupported) { + auto* unsupported_op = + static_cast(new_op.get()); unsupported_op->tensorflow_op = opname; // TODO(b/109932940): Remove this when quantized is removed. // For now, we assume all ops are quantized. diff --git a/tensorflow/contrib/lite/toco/tflite/operator_test.cc b/tensorflow/contrib/lite/toco/tflite/operator_test.cc index e3144ad63e..03bb20b320 100644 --- a/tensorflow/contrib/lite/toco/tflite/operator_test.cc +++ b/tensorflow/contrib/lite/toco/tflite/operator_test.cc @@ -74,8 +74,10 @@ class OperatorTest : public ::testing::Test { auto new_toco_op = op.Deserialize(output_options->builtin_options(), output_options->custom_options()); - CHECK(dynamic_cast(new_toco_op.get())) - << "Cannot cast " << HelpfulOperatorTypeName(*new_toco_op) << " to " + CHECK(new_toco_op->type == toco_op.type) + << "The type of the serialized and deserialized" + << HelpfulOperatorTypeName(*new_toco_op) + << " does not match the type of the original " << HelpfulOperatorTypeName(toco_op); return std::unique_ptr(dynamic_cast(new_toco_op.release())); -- GitLab From c787bb15c9a52502d8b946044049b81808b9020e Mon Sep 17 00:00:00 2001 From: Derek Murray Date: Wed, 13 Jun 2018 07:21:10 -0700 Subject: [PATCH 374/816] [tf.data] Factor out `output_shapes` and `output_types` attr-setting code into a helper. PiperOrigin-RevId: 200386950 --- .../contrib/data/python/ops/batching.py | 15 +-- .../contrib/data/python/ops/error_ops.py | 7 +- .../data/python/ops/get_single_element.py | 5 +- .../contrib/data/python/ops/grouping.py | 10 +- .../contrib/data/python/ops/interleave_ops.py | 6 +- .../contrib/data/python/ops/optimization.py | 8 +- .../contrib/data/python/ops/random_ops.py | 7 +- .../contrib/data/python/ops/resampling.py | 2 +- .../contrib/data/python/ops/scan_ops.py | 5 +- .../contrib/data/python/ops/shuffle_ops.py | 7 +- tensorflow/contrib/data/python/ops/sliding.py | 6 +- .../contrib/data/python/ops/stats_ops.py | 12 +- .../contrib/data/python/ops/threadpool.py | 7 +- tensorflow/contrib/data/python/ops/unique.py | 7 +- tensorflow/python/data/ops/dataset_ops.py | 115 +++++++----------- tensorflow/python/data/ops/readers.py | 7 +- tensorflow/python/data/util/convert.py | 1 + 17 files changed, 61 insertions(+), 166 deletions(-) diff --git a/tensorflow/contrib/data/python/ops/batching.py b/tensorflow/contrib/data/python/ops/batching.py index 17256eb972..052618e08c 100644 --- a/tensorflow/contrib/data/python/ops/batching.py +++ b/tensorflow/contrib/data/python/ops/batching.py @@ -103,10 +103,7 @@ class UnbatchDataset(dataset_ops.Dataset): def _as_variant_tensor(self): return gen_dataset_ops.unbatch_dataset( self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes)), - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes))) + **dataset_ops.flat_structure(self)) @property def output_classes(self): @@ -320,10 +317,7 @@ class DenseToSparseBatchDataset(dataset_ops.Dataset): self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access self._batch_size, row_shape=convert.partial_shape_to_tensor(self._row_shape), - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes)), - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes))) + **dataset_ops.flat_structure(self)) @property def output_classes(self): @@ -500,10 +494,7 @@ class _MapAndBatchDataset(dataset_ops.MapDataset): batch_size=self._batch_size_t, num_parallel_calls=self._num_parallel_calls_t, drop_remainder=self._drop_remainder_t, - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes)), - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes))) + **dataset_ops.flat_structure(self)) # pylint: enable=protected-access @property diff --git a/tensorflow/contrib/data/python/ops/error_ops.py b/tensorflow/contrib/data/python/ops/error_ops.py index 6c21e489f7..5f5513849c 100644 --- a/tensorflow/contrib/data/python/ops/error_ops.py +++ b/tensorflow/contrib/data/python/ops/error_ops.py @@ -20,8 +20,6 @@ from __future__ import print_function from tensorflow.contrib.data.python.ops import contrib_op_loader # pylint: disable=unused-import from tensorflow.contrib.data.python.ops import gen_dataset_ops from tensorflow.python.data.ops import dataset_ops -from tensorflow.python.data.util import nest -from tensorflow.python.data.util import sparse def ignore_errors(): @@ -64,10 +62,7 @@ class IgnoreErrorsDataset(dataset_ops.Dataset): def _as_variant_tensor(self): return gen_dataset_ops.ignore_errors_dataset( self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes)), - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes))) + **dataset_ops.flat_structure(self)) @property def output_classes(self): diff --git a/tensorflow/contrib/data/python/ops/get_single_element.py b/tensorflow/contrib/data/python/ops/get_single_element.py index 3a07df5727..0f4cd8e20c 100644 --- a/tensorflow/contrib/data/python/ops/get_single_element.py +++ b/tensorflow/contrib/data/python/ops/get_single_element.py @@ -64,10 +64,7 @@ def get_single_element(dataset): nested_ret = nest.pack_sequence_as( dataset.output_types, gen_dataset_ops.dataset_to_single_element( dataset._as_variant_tensor(), # pylint: disable=protected-access - output_types=nest.flatten(sparse.as_dense_types( - dataset.output_types, dataset.output_classes)), - output_shapes=nest.flatten(sparse.as_dense_shapes( - dataset.output_shapes, dataset.output_classes)))) + **dataset_ops.flat_structure(dataset))) return sparse.deserialize_sparse_tensors( nested_ret, dataset.output_types, dataset.output_shapes, dataset.output_classes) diff --git a/tensorflow/contrib/data/python/ops/grouping.py b/tensorflow/contrib/data/python/ops/grouping.py index 520f784228..f9f25e6a06 100644 --- a/tensorflow/contrib/data/python/ops/grouping.py +++ b/tensorflow/contrib/data/python/ops/grouping.py @@ -502,10 +502,7 @@ class GroupByReducerDataset(dataset_ops.Dataset): init_func=self._init_func, reduce_func=self._reduce_func, finalize_func=self._finalize_func, - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes)), - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes))) + **dataset_ops.flat_structure(self)) class GroupByWindowDataset(dataset_ops.Dataset): @@ -616,10 +613,7 @@ class GroupByWindowDataset(dataset_ops.Dataset): key_func=self._key_func, reduce_func=self._reduce_func, window_size_func=self._window_size_func, - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes)), - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes))) + **dataset_ops.flat_structure(self)) class Reducer(object): diff --git a/tensorflow/contrib/data/python/ops/interleave_ops.py b/tensorflow/contrib/data/python/ops/interleave_ops.py index be66fbac50..70153ac575 100644 --- a/tensorflow/contrib/data/python/ops/interleave_ops.py +++ b/tensorflow/contrib/data/python/ops/interleave_ops.py @@ -24,7 +24,6 @@ from tensorflow.contrib.data.python.ops import random_ops from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.ops import readers from tensorflow.python.data.util import nest -from tensorflow.python.data.util import sparse from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape @@ -171,10 +170,7 @@ class DirectedInterleaveDataset(dataset_ops.Dataset): return gen_dataset_ops.directed_interleave_dataset( self._selector_input._as_variant_tensor(), [data_input._as_variant_tensor() for data_input in self._data_inputs], - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes)), - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes))) + **dataset_ops.flat_structure(self)) # pylint: enable=protected-access @property diff --git a/tensorflow/contrib/data/python/ops/optimization.py b/tensorflow/contrib/data/python/ops/optimization.py index cad41bce29..9612ac5ae9 100644 --- a/tensorflow/contrib/data/python/ops/optimization.py +++ b/tensorflow/contrib/data/python/ops/optimization.py @@ -19,8 +19,6 @@ from __future__ import print_function from tensorflow.contrib.data.python.ops import contrib_op_loader # pylint: disable=unused-import from tensorflow.python.data.ops import dataset_ops -from tensorflow.python.data.util import nest -from tensorflow.python.data.util import sparse from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.ops import gen_dataset_ops @@ -62,11 +60,7 @@ class OptimizeDataset(dataset_ops.Dataset): return gen_dataset_ops.optimize_dataset( self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access self._optimizations, - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes)), - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes))) - + **dataset_ops.flat_structure(self)) @property def output_classes(self): return self._input_dataset.output_classes diff --git a/tensorflow/contrib/data/python/ops/random_ops.py b/tensorflow/contrib/data/python/ops/random_ops.py index 28ef5e50f3..e670c4c835 100644 --- a/tensorflow/contrib/data/python/ops/random_ops.py +++ b/tensorflow/contrib/data/python/ops/random_ops.py @@ -18,9 +18,7 @@ from __future__ import division from __future__ import print_function from tensorflow.python.data.ops import dataset_ops -from tensorflow.python.data.util import nest from tensorflow.python.data.util import random_seed -from tensorflow.python.data.util import sparse from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape @@ -39,10 +37,7 @@ class RandomDataset(dataset_ops.Dataset): return gen_dataset_ops.random_dataset( seed=self._seed, seed2=self._seed2, - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes)), - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes))) + **dataset_ops.flat_structure(self)) @property def output_classes(self): diff --git a/tensorflow/contrib/data/python/ops/resampling.py b/tensorflow/contrib/data/python/ops/resampling.py index bad6edd514..182a5c6ff3 100644 --- a/tensorflow/contrib/data/python/ops/resampling.py +++ b/tensorflow/contrib/data/python/ops/resampling.py @@ -291,4 +291,4 @@ def _calculate_acceptance_probs_with_mixing(initial_probs, target_probs): # TODO(joelshor): Simplify fraction, if possible. a_i = (ratio_l - m) / (max_ratio - m) - return a_i, m \ No newline at end of file + return a_i, m diff --git a/tensorflow/contrib/data/python/ops/scan_ops.py b/tensorflow/contrib/data/python/ops/scan_ops.py index 9909ca8d9d..67eede981c 100644 --- a/tensorflow/contrib/data/python/ops/scan_ops.py +++ b/tensorflow/contrib/data/python/ops/scan_ops.py @@ -195,10 +195,7 @@ class _ScanDataset(dataset_ops.Dataset): nest.flatten(sparse.serialize_sparse_tensors(self._initial_state)), self._scan_func.captured_inputs, f=self._scan_func, - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes)), - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes))) + **dataset_ops.flat_structure(self)) @property def output_classes(self): diff --git a/tensorflow/contrib/data/python/ops/shuffle_ops.py b/tensorflow/contrib/data/python/ops/shuffle_ops.py index f35795abd3..d7f8a73fe3 100644 --- a/tensorflow/contrib/data/python/ops/shuffle_ops.py +++ b/tensorflow/contrib/data/python/ops/shuffle_ops.py @@ -18,9 +18,7 @@ from __future__ import division from __future__ import print_function from tensorflow.python.data.ops import dataset_ops -from tensorflow.python.data.util import nest from tensorflow.python.data.util import random_seed -from tensorflow.python.data.util import sparse from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops @@ -56,10 +54,7 @@ class _ShuffleAndRepeatDataset(dataset_ops.Dataset): count=self._count, seed=self._seed, seed2=self._seed2, - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes)), - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes))) + **dataset_ops.flat_structure(self)) # pylint: enable=protected-access @property diff --git a/tensorflow/contrib/data/python/ops/sliding.py b/tensorflow/contrib/data/python/ops/sliding.py index 19cc3cb89f..f935beb1a9 100644 --- a/tensorflow/contrib/data/python/ops/sliding.py +++ b/tensorflow/contrib/data/python/ops/sliding.py @@ -19,7 +19,6 @@ from __future__ import print_function from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.util import nest -from tensorflow.python.data.util import sparse from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape @@ -43,10 +42,7 @@ class _SlideDataset(dataset_ops.Dataset): self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access window_size=self._window_size, stride=self._stride, - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes)), - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes))) + **dataset_ops.flat_structure(self)) @property def output_classes(self): diff --git a/tensorflow/contrib/data/python/ops/stats_ops.py b/tensorflow/contrib/data/python/ops/stats_ops.py index 8c30202ba7..3c82a03df1 100644 --- a/tensorflow/contrib/data/python/ops/stats_ops.py +++ b/tensorflow/contrib/data/python/ops/stats_ops.py @@ -18,8 +18,6 @@ from __future__ import division from __future__ import print_function from tensorflow.python.data.ops import dataset_ops -from tensorflow.python.data.util import nest -from tensorflow.python.data.util import sparse from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.ops import gen_dataset_ops @@ -97,10 +95,7 @@ class _SetStatsAggregatorDataset(dataset_ops.Dataset): return gen_dataset_ops.set_stats_aggregator_dataset( self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access self._stats_aggregator._resource, # pylint: disable=protected-access - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes)), - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes))) + **dataset_ops.flat_structure(self)) @property def output_shapes(self): @@ -210,10 +205,7 @@ class _StatsDataset(dataset_ops.Dataset): return self._op_function( self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access self._tag, - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes)), - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes))) + **dataset_ops.flat_structure(self)) @property def output_shapes(self): diff --git a/tensorflow/contrib/data/python/ops/threadpool.py b/tensorflow/contrib/data/python/ops/threadpool.py index 56f67e1766..bb49604d4d 100644 --- a/tensorflow/contrib/data/python/ops/threadpool.py +++ b/tensorflow/contrib/data/python/ops/threadpool.py @@ -22,8 +22,6 @@ import threading from tensorflow.contrib.data.python.ops import contrib_op_loader # pylint: disable=unused-import from tensorflow.contrib.data.python.ops import gen_dataset_ops from tensorflow.python.data.ops import dataset_ops -from tensorflow.python.data.util import nest -from tensorflow.python.data.util import sparse from tensorflow.python.eager import context from tensorflow.python.ops import resource_variable_ops @@ -69,10 +67,7 @@ class _ThreadPoolDataset(dataset_ops.Dataset): return gen_dataset_ops.thread_pool_dataset( self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access self._thread_pool._resource, # pylint: disable=protected-access - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes)), - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes))) + **dataset_ops.flat_structure(self)) @property def output_shapes(self): diff --git a/tensorflow/contrib/data/python/ops/unique.py b/tensorflow/contrib/data/python/ops/unique.py index 765ef3f9b6..4ce6ddede8 100644 --- a/tensorflow/contrib/data/python/ops/unique.py +++ b/tensorflow/contrib/data/python/ops/unique.py @@ -20,8 +20,6 @@ from __future__ import print_function from tensorflow.contrib.data.python.ops import contrib_op_loader # pylint: disable=unused-import from tensorflow.contrib.data.python.ops import gen_dataset_ops from tensorflow.python.data.ops import dataset_ops -from tensorflow.python.data.util import nest -from tensorflow.python.data.util import sparse from tensorflow.python.framework import dtypes @@ -65,10 +63,7 @@ class UniqueDataset(dataset_ops.Dataset): def _as_variant_tensor(self): return gen_dataset_ops.unique_dataset( self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes)), - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes))) + **dataset_ops.flat_structure(self)) @property def output_classes(self): diff --git a/tensorflow/python/data/ops/dataset_ops.py b/tensorflow/python/data/ops/dataset_ops.py index 7c1e9dd754..d0deed5ede 100644 --- a/tensorflow/python/data/ops/dataset_ops.py +++ b/tensorflow/python/data/ops/dataset_ops.py @@ -1158,6 +1158,30 @@ class SparseTensorSliceDataset(Dataset): return (dtypes.int64, self._sparse_tensor.dtype, dtypes.int64) +def flat_structure(dataset): + """Helper for setting `output_shapes` and `output_types` attrs of Dataset ops. + + Most Dataset op constructors expect `output_shapes` and `output_types` + arguments that represent the flattened structure of an element. This helper + function generates these attrs as a keyword argument dictionary, allowing + `Dataset._as_variant_tensor()` implementations to pass + `**flat_structure(self)` to the op constructor. + + Args: + dataset: A @{tf.data.Dataset}. + + Returns: + A dictionary of keyword arguments that can be passed to many Dataset op + constructors. + """ + return { + "output_shapes": nest.flatten(sparse.as_dense_shapes( + dataset.output_shapes, dataset.output_classes)), + "output_types": nest.flatten(sparse.as_dense_types( + dataset.output_types, dataset.output_classes)), + } + + class _GeneratorDataset(Dataset): """A `Dataset` that generates elements by invoking a function.""" @@ -1330,10 +1354,7 @@ class _GeneratorDataset(Dataset): init_func=self._init_func, next_func=self._next_func, finalize_func=self._finalize_func, - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes)), - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes))) + **flat_structure(self)) @property def output_classes(self): @@ -1370,16 +1391,7 @@ class ZipDataset(Dataset): # pylint: disable=protected-access return gen_dataset_ops.zip_dataset( [ds._as_variant_tensor() for ds in nest.flatten(self._datasets)], - output_shapes=[ - s - for ds in nest.flatten(self._datasets) - for s in nest.flatten(ds.output_shapes) - ], - output_types=[ - t - for ds in nest.flatten(self._datasets) - for t in nest.flatten(ds.output_types) - ]) + **flat_structure(self)) # pylint: enable=protected-access @property @@ -1424,10 +1436,7 @@ class ConcatenateDataset(Dataset): return gen_dataset_ops.concatenate_dataset( self._input_dataset._as_variant_tensor(), self._dataset_to_concatenate._as_variant_tensor(), - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes)), - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes))) + **flat_structure(self)) # pylint: enable=protected-access @property @@ -1465,10 +1474,7 @@ class RepeatDataset(Dataset): return gen_dataset_ops.repeat_dataset( self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access count=self._count, - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes)), - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes))) + **flat_structure(self)) @property def output_classes(self): @@ -1515,10 +1521,7 @@ class RangeDataset(Dataset): start=self._start, stop=self._stop, step=self._step, - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes)), - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes))) + **flat_structure(self)) @property def output_classes(self): @@ -1547,10 +1550,7 @@ class CacheDataset(Dataset): return gen_dataset_ops.cache_dataset( self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access filename=self._filename, - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes)), - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes))) + **flat_structure(self)) @property def output_classes(self): @@ -1610,10 +1610,7 @@ class ShuffleDataset(Dataset): seed=self._seed, seed2=self._seed2, reshuffle_each_iteration=self._reshuffle_each_iteration, - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes)), - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes))) + **flat_structure(self)) @property def output_classes(self): @@ -1641,10 +1638,7 @@ class TakeDataset(Dataset): return gen_dataset_ops.take_dataset( self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access count=self._count, - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes)), - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes))) + **flat_structure(self)) @property def output_classes(self): @@ -1672,10 +1666,7 @@ class SkipDataset(Dataset): return gen_dataset_ops.skip_dataset( self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access count=self._count, - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes)), - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes))) + **flat_structure(self)) @property def output_classes(self): @@ -1708,19 +1699,13 @@ class BatchDataset(Dataset): return gen_dataset_ops.batch_dataset( self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access batch_size=self._batch_size, - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes)), - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes))) + **flat_structure(self)) else: return gen_dataset_ops.batch_dataset_v2( self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access batch_size=self._batch_size, drop_remainder=self._drop_remainder, - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes)), - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes))) + **flat_structure(self)) @property def output_classes(self): @@ -2031,10 +2016,7 @@ class MapDataset(Dataset): input_t, self._map_func.captured_inputs, f=self._map_func, - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes)), - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes))) + **flat_structure(self)) @property def output_classes(self): @@ -2067,10 +2049,7 @@ class ParallelMapDataset(MapDataset): self._map_func.captured_inputs, f=self._map_func, num_parallel_calls=self._num_parallel_calls, - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes)), - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes))) + **flat_structure(self)) # pylint: enable=protected-access @@ -2121,10 +2100,7 @@ class FlatMapDataset(Dataset): self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access self._map_func.captured_inputs, f=self._map_func, - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes)), - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes))) + **flat_structure(self)) @property def output_classes(self): @@ -2161,10 +2137,7 @@ class InterleaveDataset(FlatMapDataset): self._cycle_length, self._block_length, f=self._map_func, # pylint: disable=protected-access - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes)), - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes))) + **flat_structure(self)) def _transformation_name(self): return "Dataset.interleave()" @@ -2215,10 +2188,7 @@ class FilterDataset(Dataset): self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access other_arguments=self._predicate.captured_inputs, predicate=self._predicate, - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes)), - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes))) + **flat_structure(self)) @property def output_classes(self): @@ -2249,10 +2219,7 @@ class PrefetchDataset(Dataset): return gen_dataset_ops.prefetch_dataset( self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access buffer_size=self._buffer_size, - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes)), - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes))) + **flat_structure(self)) @property def output_classes(self): diff --git a/tensorflow/python/data/ops/readers.py b/tensorflow/python/data/ops/readers.py index 6a72ed380f..066e09969c 100644 --- a/tensorflow/python/data/ops/readers.py +++ b/tensorflow/python/data/ops/readers.py @@ -19,8 +19,6 @@ from __future__ import print_function from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.util import convert -from tensorflow.python.data.util import nest -from tensorflow.python.data.util import sparse from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape @@ -150,10 +148,7 @@ class ParallelInterleaveDataset(dataset_ops.InterleaveDataset): self._buffer_output_elements, self._prefetch_input_elements, f=self._map_func, - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes)), - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes))) + **dataset_ops.flat_structure(self)) # pylint: enable=protected-access def _transformation_name(self): diff --git a/tensorflow/python/data/util/convert.py b/tensorflow/python/data/util/convert.py index 99b3300900..746b3d66de 100644 --- a/tensorflow/python/data/util/convert.py +++ b/tensorflow/python/data/util/convert.py @@ -69,3 +69,4 @@ def partial_shape_to_tensor(shape_like): % (shape_like, ret.dtype.name)) return ret + -- GitLab From 0420d94c4a3fadba3929ba43ed4a4d67c954f210 Mon Sep 17 00:00:00 2001 From: AG Ramesh Date: Wed, 13 Jun 2018 20:36:50 +0530 Subject: [PATCH 375/816] Fix compilation issue (#19983) --- tensorflow/contrib/gdr/gdr_server_lib.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/gdr/gdr_server_lib.cc b/tensorflow/contrib/gdr/gdr_server_lib.cc index 1f9dd0decb..9025c992a4 100644 --- a/tensorflow/contrib/gdr/gdr_server_lib.cc +++ b/tensorflow/contrib/gdr/gdr_server_lib.cc @@ -57,7 +57,7 @@ Status GdrServer::Init() { new GdrWorker(env, remote_memory_manager_.get())); }; TF_RETURN_IF_ERROR( - GrpcServer::Init(nullptr, rendezvous_mgr_func, worker_func)); + GrpcServer::Init(nullptr, rendezvous_mgr_func, nullptr, worker_func)); return remote_memory_manager_->Init(); } -- GitLab From 4b5f4a540fad9142288012591799c39fd590242b Mon Sep 17 00:00:00 2001 From: Emanuele Ballarin Date: Wed, 13 Jun 2018 17:07:13 +0200 Subject: [PATCH 376/816] Make implementation of GrpcServer::Init with Collective Ops compatible with calls in contrib/mpi (#19942) * Allow calling Grpc::Init with 2 arguments Should fix #19924 --- .../core/distributed_runtime/rpc/grpc_server_lib.cc | 8 ++++++++ tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h | 3 +++ 2 files changed, 11 insertions(+) diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc b/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc index 43dbe20836..e7914740ae 100644 --- a/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc +++ b/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc @@ -284,6 +284,14 @@ Status GrpcServer::Init( nullptr); } + +Status GrpcServer::Init( + ServiceInitFunction service_func, + const RendezvousMgrCreationFunction& rendezvous_mgr_func) { + return Init(std::move(service_func), rendezvous_mgr_func, nullptr, + nullptr); +} + Status GrpcServer::Init() { return Init(nullptr, nullptr, nullptr, nullptr); } Status GrpcServer::ParseChannelSpec(const WorkerCacheFactoryOptions& options, diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h b/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h index ca9946cafc..9e53330f85 100644 --- a/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h +++ b/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h @@ -89,6 +89,9 @@ class GrpcServer : public ServerInterface { Status Init(ServiceInitFunction service_func, const RendezvousMgrCreationFunction& rendezvous_mgr_func, const CollectiveMgrCreationFunction& collective_mgr_func); + + Status Init(ServiceInitFunction service_func, + const RendezvousMgrCreationFunction& rendezvous_mgr_func); Status Init(); -- GitLab From 38c22b367ae2ebb20b14a615aadf8d49623b3573 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 13 Jun 2018 08:09:45 -0700 Subject: [PATCH 377/816] Clarify that SparseMatMul does not accept SparseTensor inputs PiperOrigin-RevId: 200392587 --- .../core/api_def/base_api/api_def_SparseMatMul.pbtxt | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/tensorflow/core/api_def/base_api/api_def_SparseMatMul.pbtxt b/tensorflow/core/api_def/base_api/api_def_SparseMatMul.pbtxt index 58f2ede629..fe568df388 100644 --- a/tensorflow/core/api_def/base_api/api_def_SparseMatMul.pbtxt +++ b/tensorflow/core/api_def/base_api/api_def_SparseMatMul.pbtxt @@ -3,9 +3,11 @@ op { summary: "Multiply matrix \"a\" by matrix \"b\"." description: < Date: Wed, 13 Jun 2018 08:31:33 -0700 Subject: [PATCH 378/816] Correct name for _UnreadVariable PiperOrigin-RevId: 200395171 --- tensorflow/python/kernel_tests/resource_variable_ops_test.py | 4 ++++ tensorflow/python/ops/resource_variable_ops.py | 4 ++++ 2 files changed, 8 insertions(+) diff --git a/tensorflow/python/kernel_tests/resource_variable_ops_test.py b/tensorflow/python/kernel_tests/resource_variable_ops_test.py index 82e0d153c2..5267eabf0e 100644 --- a/tensorflow/python/kernel_tests/resource_variable_ops_test.py +++ b/tensorflow/python/kernel_tests/resource_variable_ops_test.py @@ -152,6 +152,10 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): self.evaluate(resource_variable_ops.assign_variable_op( id_handle, constant_op.constant(0, dtype=dtypes.int32))) + def testUnreadOpName(self): + v = resource_variable_ops.ResourceVariable(1.0) + self.assertNotEqual(v.name, v.assign_add(1.0).name) + @test_util.run_in_graph_and_eager_modes() def testCreateRead(self): handle = resource_variable_ops.var_handle_op(dtype=dtypes.int32, shape=[]) diff --git a/tensorflow/python/ops/resource_variable_ops.py b/tensorflow/python/ops/resource_variable_ops.py index c137bfacb2..de44a3e848 100644 --- a/tensorflow/python/ops/resource_variable_ops.py +++ b/tensorflow/python/ops/resource_variable_ops.py @@ -1067,6 +1067,10 @@ class _UnreadVariable(ResourceVariable): self._graph_element = self.read_value() self._handle_deleter = deleter + @property + def name(self): + return self._parent_op.name + def value(self): return self._read_variable_op() -- GitLab From 58a2b88f570fbdf185da30e85515c8e02c290c13 Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Wed, 13 Jun 2018 08:40:53 -0700 Subject: [PATCH 379/816] Remove duplicate import in linear_equations.py (#19990) The line `from tensorflow.python.ops import linalg_ops` in linear_equations.py is a duplicate from the previous line. This fix removes the duplicate import. Signed-off-by: Yong Tang --- tensorflow/contrib/solvers/python/ops/linear_equations.py | 1 - 1 file changed, 1 deletion(-) diff --git a/tensorflow/contrib/solvers/python/ops/linear_equations.py b/tensorflow/contrib/solvers/python/ops/linear_equations.py index 9305c6a11c..85918bf850 100644 --- a/tensorflow/contrib/solvers/python/ops/linear_equations.py +++ b/tensorflow/contrib/solvers/python/ops/linear_equations.py @@ -28,7 +28,6 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import linalg_ops from tensorflow.python.ops import math_ops -from tensorflow.python.ops import linalg_ops def conjugate_gradient(operator, -- GitLab From 03d32bbfa20046bed6970c85a8c75fcdad6c8c75 Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Wed, 13 Jun 2018 07:26:25 -0700 Subject: [PATCH 380/816] Fix build issue on mac with python-3.7.10 and clang-9.1.0 While building tensorflow on mac with python-2.7.10 and llvm 9.1.0 (macOS High Sierra 10.15.5), the following compilation errors surface: ``` In file included from tensorflow/python/lib/core/py_util.cc:20: In file included from ./tensorflow/core/lib/core/errors.h:19: In file included from /Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/include/c++/v1/sstream:174: In file included from /Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/include/c++/v1/ostream:138: In file included from /Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/include/c++/v1/ios:216: /Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/include/c++/v1/__locale:492:15: error: C++ requires a type specifier for all declarations char_type toupper(char_type __c) const ^ bazel-out/host/genfiles/external/local_config_python/python_include/pyport.h:731:29: note: expanded from macro 'toupper' ... ... ``` The error is related to the issue in `pyport.h`. The build error could be fixed by including `#include ` before including `#include `. The changes in this PR allows the build to succeed. Signed-off-by: Yong Tang --- .../lite/python/interpreter_wrapper/interpreter_wrapper.h | 2 ++ tensorflow/python/lib/core/numpy.h | 2 ++ 2 files changed, 4 insertions(+) diff --git a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h index 01320af7a9..c02aa38043 100644 --- a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h +++ b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h @@ -19,6 +19,8 @@ limitations under the License. #include #include +// Place `` before to avoid build failures in macOS. +#include #include // We forward declare TFLite classes here to avoid exposing them to SWIG. diff --git a/tensorflow/python/lib/core/numpy.h b/tensorflow/python/lib/core/numpy.h index 25322b458b..98354083c7 100644 --- a/tensorflow/python/lib/core/numpy.h +++ b/tensorflow/python/lib/core/numpy.h @@ -29,6 +29,8 @@ limitations under the License. #define NO_IMPORT_ARRAY #endif +// Place `` before to avoid build failure in macOS. +#include #include #include "numpy/arrayobject.h" -- GitLab From f0e053afc99c8dcf6aa196b00dafaee0a7f6923f Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 13 Jun 2018 08:45:19 -0700 Subject: [PATCH 381/816] Fix for DumpGraphviz() failing on zero-sized arrays, which it should handle for debugging purposes. PiperOrigin-RevId: 200397151 --- tensorflow/contrib/lite/toco/dump_graphviz.cc | 12 +++++++----- tensorflow/contrib/lite/toco/tooling_util.cc | 7 +++++++ tensorflow/contrib/lite/toco/tooling_util.h | 4 +++- 3 files changed, 17 insertions(+), 6 deletions(-) diff --git a/tensorflow/contrib/lite/toco/dump_graphviz.cc b/tensorflow/contrib/lite/toco/dump_graphviz.cc index 8913b5c3ea..878bda36ef 100644 --- a/tensorflow/contrib/lite/toco/dump_graphviz.cc +++ b/tensorflow/contrib/lite/toco/dump_graphviz.cc @@ -146,6 +146,7 @@ NodeProperties GetPropertiesForArray(const Model& model, NodeProperties node_properties; node_properties.color = GetColorForArray(model, array_name); node_properties.label = absl::StrReplaceAll(array_name, {{"/", "/\\n"}}); + node_properties.log2_buffer_size = 0.0f; // Append array shape to the label. auto& array = model.GetArray(array_name); @@ -165,9 +166,12 @@ NodeProperties GetPropertiesForArray(const Model& model, } node_properties.label += "]"; - int buffer_size = RequiredBufferSizeForShape(array.shape()); - node_properties.log2_buffer_size = - std::log2(static_cast(buffer_size)); + int buffer_size = 0; + if (IsValid(array.shape())) { + buffer_size = RequiredBufferSizeForShape(array.shape()); + node_properties.log2_buffer_size = + std::log2(static_cast(buffer_size)); + } if (array.buffer) { const auto& array = model.GetArray(array_name); @@ -200,8 +204,6 @@ NodeProperties GetPropertiesForArray(const Model& model, AppendF(&node_properties.label, "}"); } } - } else { - node_properties.log2_buffer_size = 0.0f; } if (array.minmax) { diff --git a/tensorflow/contrib/lite/toco/tooling_util.cc b/tensorflow/contrib/lite/toco/tooling_util.cc index 5cb4caab3f..92bab5246c 100644 --- a/tensorflow/contrib/lite/toco/tooling_util.cc +++ b/tensorflow/contrib/lite/toco/tooling_util.cc @@ -585,6 +585,13 @@ void UnextendShape(Shape* shape, int new_shape_size) { shape_dims.erase(shape_dims.begin(), shape_dims.begin() + size_reduction); } +bool IsValid(const Shape& shape) { + for (int i = 0; i < shape.dimensions_count(); ++i) { + if (shape.dims(i) < 1) return false; + } + return true; +} + void CheckShapeDimensions(const Shape& shape) { for (int i = 0; i < shape.dimensions_count(); ++i) { CHECK_GE(shape.dims()[i], 1) << "shape has dimension 0 at index << " << i diff --git a/tensorflow/contrib/lite/toco/tooling_util.h b/tensorflow/contrib/lite/toco/tooling_util.h index ef8af4d112..7681ce9d39 100644 --- a/tensorflow/contrib/lite/toco/tooling_util.h +++ b/tensorflow/contrib/lite/toco/tooling_util.h @@ -113,7 +113,9 @@ void ExtendShape(Shape* shape, int new_shape_size); // TODO(b/36075966): Clean up when dims superseded by array shape. void UnextendShape(Shape* shape, int new_shape_size); -// Checks (using CHECK) that all dimensions of 'shape' are at least 1. +// Checks that all dimensions of 'shape' are at least 1. +bool IsValid(const Shape& shape); +// Same as above, but reports error using CHECK. void CheckShapeDimensions(const Shape& shape); // Given two shapes with potentially different dimensionality and dimension -- GitLab From 68a9d259547bd060572f5fbac0538cca0eb347c5 Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Wed, 13 Jun 2018 08:41:49 -0700 Subject: [PATCH 382/816] Add `#include ` to py_util.cc to fix build failure Signed-off-by: Yong Tang --- tensorflow/python/lib/core/py_util.cc | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tensorflow/python/lib/core/py_util.cc b/tensorflow/python/lib/core/py_util.cc index dcda1f4a44..572693b1cf 100644 --- a/tensorflow/python/lib/core/py_util.cc +++ b/tensorflow/python/lib/core/py_util.cc @@ -15,6 +15,8 @@ limitations under the License. #include "tensorflow/python/lib/core/py_util.h" +// Place `` before to avoid build failure in macOS. +#include #include #include "tensorflow/core/lib/core/errors.h" -- GitLab From 20b120c10c76e53873208fecaba4b7fc5263be6e Mon Sep 17 00:00:00 2001 From: Philipp Jund Date: Wed, 13 Jun 2018 18:13:35 +0200 Subject: [PATCH 383/816] fix order in BUILD file (buildifier). --- tensorflow/contrib/opt/BUILD | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/opt/BUILD b/tensorflow/contrib/opt/BUILD index 6ff1b03b54..114b344d38 100644 --- a/tensorflow/contrib/opt/BUILD +++ b/tensorflow/contrib/opt/BUILD @@ -24,11 +24,11 @@ py_library( "python/training/moving_average_optimizer.py", "python/training/multitask_optimizer_wrapper.py", "python/training/nadam_optimizer.py", - "python/training/weight_decay_optimizers.py", "python/training/powersign.py", "python/training/reg_adagrad_optimizer.py", "python/training/sign_decay.py", "python/training/variable_clipping_optimizer.py", + "python/training/weight_decay_optimizers.py", ], srcs_version = "PY2AND3", deps = [ -- GitLab From 85b8e05e5fbfa91e7d3e9acfc62f1faabac80d24 Mon Sep 17 00:00:00 2001 From: David Norman Date: Wed, 13 Jun 2018 09:22:52 -0700 Subject: [PATCH 384/816] Change the visibility of the graph builder (#19978) --- tensorflow/compiler/xla/service/BUILD | 1 - 1 file changed, 1 deletion(-) diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD index 1154eef80e..2942edbf71 100644 --- a/tensorflow/compiler/xla/service/BUILD +++ b/tensorflow/compiler/xla/service/BUILD @@ -2542,7 +2542,6 @@ cc_library( name = "hlo_tfgraph_builder", srcs = ["hlo_tfgraph_builder.cc"], hdrs = ["hlo_tfgraph_builder.h"], - visibility = ["//tensorflow/compiler/xla/tools:__pkg__"], deps = [ ":hlo", "//tensorflow/compiler/xla:literal_util", -- GitLab From 65cefda2f9a62f29af51b3effa0725c180244576 Mon Sep 17 00:00:00 2001 From: Jacques Pienaar Date: Wed, 13 Jun 2018 10:00:41 -0700 Subject: [PATCH 385/816] Add AotCompilationMetadata field to variant of CompileAheadOfTime. Add CompileAheadOfTime parameter that can optionally be populated during compilation process. This change is to allow populating metadata even if the CompileAheadOfTime fails. PiperOrigin-RevId: 200407917 --- .../xla/client/compile_only_client.cc | 6 ++++-- .../compiler/xla/client/compile_only_client.h | 10 ++++++---- .../xla/service/compile_only_service.cc | 6 ++++-- .../xla/service/compile_only_service.h | 6 ++++++ tensorflow/compiler/xla/service/compiler.cc | 14 +++++++++++++ tensorflow/compiler/xla/service/compiler.h | 20 +++++++++++++++++++ 6 files changed, 54 insertions(+), 8 deletions(-) diff --git a/tensorflow/compiler/xla/client/compile_only_client.cc b/tensorflow/compiler/xla/client/compile_only_client.cc index dc69d2097e..5c9abad4c3 100644 --- a/tensorflow/compiler/xla/client/compile_only_client.cc +++ b/tensorflow/compiler/xla/client/compile_only_client.cc @@ -24,7 +24,8 @@ namespace xla { StatusOr>> CompileOnlyClient::CompileAheadOfTime( const tensorflow::gtl::ArraySlice computations, - const AotCompilationOptions& options) { + const AotCompilationOptions& options, + std::unique_ptr* metadata) { std::vector service_instances; service_instances.reserve(computations.size()); for (const AotXlaComputationInstance& instance : computations) { @@ -36,7 +37,8 @@ CompileOnlyClient::CompileAheadOfTime( service_instance.argument_layouts = instance.argument_layouts; service_instance.result_layout = instance.result_layout; } - return compiler_service_->CompileAheadOfTime(service_instances, options); + return compiler_service_->CompileAheadOfTime(service_instances, options, + metadata); } int64 CompileOnlyClient::PointerSizeForTriple(tensorflow::StringPiece triple) { diff --git a/tensorflow/compiler/xla/client/compile_only_client.h b/tensorflow/compiler/xla/client/compile_only_client.h index f9a7c31270..332c965036 100644 --- a/tensorflow/compiler/xla/client/compile_only_client.h +++ b/tensorflow/compiler/xla/client/compile_only_client.h @@ -46,13 +46,15 @@ class CompileOnlyClient : public Client { const Shape* result_layout; }; - // Compiles a list of xla computations for ahead-of-time execution. This is - // intended for use in static compilation. The |options| parameter describes - // the target for which the compiler should emit code. + // Compiles a list of xla computations for ahead-of-time execution. + // This is intended for use in static compilation. The |options| + // parameter describes the target for which the compiler should emit + // code. |metadata|, if provided, is populated during compilation. StatusOr>> CompileAheadOfTime( const tensorflow::gtl::ArraySlice computations, - const AotCompilationOptions& options); + const AotCompilationOptions& options, + std::unique_ptr* metadata = nullptr); // Returns the size of a pointer in bytes for a given triple. static int64 PointerSizeForTriple(tensorflow::StringPiece triple); diff --git a/tensorflow/compiler/xla/service/compile_only_service.cc b/tensorflow/compiler/xla/service/compile_only_service.cc index d8fdccf9bb..7426672a7a 100644 --- a/tensorflow/compiler/xla/service/compile_only_service.cc +++ b/tensorflow/compiler/xla/service/compile_only_service.cc @@ -63,7 +63,8 @@ CompileOnlyService::CompileOnlyService(const ServiceOptions& options, StatusOr>> CompileOnlyService::CompileAheadOfTime( const tensorflow::gtl::ArraySlice computations, - const AotCompilationOptions& options) { + const AotCompilationOptions& options, + std::unique_ptr* metadata) { std::vector> hlo_modules; for (const AotXlaComputationInstance& instance : computations) { TF_RET_CHECK(instance.computation.has_program_shape()); @@ -100,7 +101,8 @@ CompileOnlyService::CompileAheadOfTime( hlo_modules.push_back(std::move(hlo_module)); } - return compiler_->CompileAheadOfTime(std::move(hlo_modules), options); + return compiler_->CompileAheadOfTime(std::move(hlo_modules), options, + metadata); } } // namespace xla diff --git a/tensorflow/compiler/xla/service/compile_only_service.h b/tensorflow/compiler/xla/service/compile_only_service.h index e6a66c202d..1ac950bdd6 100644 --- a/tensorflow/compiler/xla/service/compile_only_service.h +++ b/tensorflow/compiler/xla/service/compile_only_service.h @@ -53,6 +53,12 @@ class CompileOnlyService : public Service { const tensorflow::gtl::ArraySlice computations, const AotCompilationOptions& options); + StatusOr>> + CompileAheadOfTime( + const tensorflow::gtl::ArraySlice computations, + const AotCompilationOptions& options, + std::unique_ptr* metadata); + Status GetDeviceHandles(const GetDeviceHandlesRequest* arg, GetDeviceHandlesResponse* result) override { return Unimplemented("CompileOnlyService does not support devices."); diff --git a/tensorflow/compiler/xla/service/compiler.cc b/tensorflow/compiler/xla/service/compiler.cc index 6f06bba679..0dceed853d 100644 --- a/tensorflow/compiler/xla/service/compiler.cc +++ b/tensorflow/compiler/xla/service/compiler.cc @@ -35,6 +35,20 @@ Compiler::ComputeBackendConfigs(const HloInstruction& hlo, return {}; } +// Define a default version where metadata is not used. +StatusOr>> +Compiler::CompileAheadOfTime( + std::vector> modules, + const AotCompilationOptions& options, + std::unique_ptr* metadata) { + if (metadata != nullptr) { + return Unimplemented( + "Populating AotCompilationMetadata is not implemented on this " + "compiler."); + } + return CompileAheadOfTime(std::move(modules), options); +} + /* static */ std::map* Compiler::GetPlatformCompilerFactories() { static auto* r = new std::map; diff --git a/tensorflow/compiler/xla/service/compiler.h b/tensorflow/compiler/xla/service/compiler.h index 6c52ffd800..d1144f97bb 100644 --- a/tensorflow/compiler/xla/service/compiler.h +++ b/tensorflow/compiler/xla/service/compiler.h @@ -94,6 +94,19 @@ class AotCompilationOptions { DebugOptions debug_options_; }; +// Abstract superclass describing metadata produced during ahead-of-time +// compilation. +class AotCompilationMetadata { + public: + AotCompilationMetadata(const AotCompilationMetadata&) = delete; + AotCompilationMetadata& operator=(AotCompilationMetadata const&) = delete; + + virtual ~AotCompilationMetadata() = default; + + protected: + AotCompilationMetadata() = default; +}; + // Abstract compiler interface that is subclassed for compilation on a // particular platform. // @@ -172,6 +185,13 @@ class Compiler { CompileAheadOfTime(std::vector> modules, const AotCompilationOptions& options) = 0; + // Similar to CompileAheadOfTime above but AotCompilationMetadata + // has an argument that can be populated during compilation. + virtual StatusOr>> + CompileAheadOfTime(std::vector> modules, + const AotCompilationOptions& options, + std::unique_ptr* metadata); + ///// // The Compiler class also serves as a point to register compiler objects // for the various platforms. -- GitLab From f83aa2d4d62dfba7f2bb99063baaccc59be5aab6 Mon Sep 17 00:00:00 2001 From: Soila Kavulya Date: Wed, 13 Jun 2018 10:04:34 -0700 Subject: [PATCH 386/816] Add Hadoop and Spark src and target directories to .gitignore --- tensorflow/java/maven/.gitignore | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/tensorflow/java/maven/.gitignore b/tensorflow/java/maven/.gitignore index ff080515d5..657e2a60bc 100644 --- a/tensorflow/java/maven/.gitignore +++ b/tensorflow/java/maven/.gitignore @@ -11,4 +11,10 @@ tensorflow/src tensorflow/target proto/src proto/target +hadoop/src +hadoop/target +spark-connector/src +spark-connector/target +spark-connector/dependency-reduced-pom.xml +spark-connector/spark-warehouse pom.xml.versionsBackup -- GitLab From 4880423ae9d2785faaffccea965f5b223f1318b0 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 13 Jun 2018 10:20:26 -0700 Subject: [PATCH 387/816] Detect configurations that would be hitting a bug in cuBLAS and report an error. PiperOrigin-RevId: 200411493 --- tensorflow/stream_executor/cuda/cuda_blas.cc | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/tensorflow/stream_executor/cuda/cuda_blas.cc b/tensorflow/stream_executor/cuda/cuda_blas.cc index 08fe153b59..92c1a5fc07 100644 --- a/tensorflow/stream_executor/cuda/cuda_blas.cc +++ b/tensorflow/stream_executor/cuda/cuda_blas.cc @@ -2155,10 +2155,7 @@ bool CUDABlas::DoBlasGemmWithAlgorithmImpl( const HostOrDeviceScalar &beta, DeviceMemory *c, int ldc, blas::ComputationType computation_type, blas::AlgorithmType algorithm, blas::ProfileResult *output_profile_result) { -// CUDA < version 8 and GPUs < sm_50 don't support cublasGemmEx. -#if CUDA_VERSION < 8000 - return false; -#else + // GPUs < sm_50 don't support cublasGemmEx. int cc_major, cc_minor; if (stream->parent()->GetDeviceDescription().cuda_compute_capability( &cc_major, &cc_minor) && @@ -2184,6 +2181,13 @@ bool CUDABlas::DoBlasGemmWithAlgorithmImpl( } } + // Return false if we might be hitting a cuBLAS bug that produces the wrong + // result. See nvbugs/2156201, b/79126339. + if (CUDA_VERSION < 9020 && algorithm != CUBLAS_GEMM_ALGO12 && + std::max({m, n, k}) >= 2097153 && cc_major < 7) { + return false; + } + cudaDataType_t cuda_in_type = CUDADataType::type; // Since we are converting 'algorithm' to cublasGemmAlgo_t by static_cast, // we do the following compile-time check on the default value: @@ -2213,7 +2217,6 @@ bool CUDABlas::DoBlasGemmWithAlgorithmImpl( timer->GetElapsedMilliseconds()); } return result; -#endif } bool CUDABlas::GetBlasGemmAlgorithms( -- GitLab From 7b8e5c7f1d7d71826b2fa44915498fc17c80ce7c Mon Sep 17 00:00:00 2001 From: Peter Hawkins Date: Wed, 13 Jun 2018 10:20:36 -0700 Subject: [PATCH 388/816] [TF:XLA] Remove parallel checking support from encapsulate_subcomputations_pass. This support is unused and adds complexity to an already very complicated piece of code. No (observable) functional changes intended. PiperOrigin-RevId: 200411522 --- tensorflow/compiler/jit/BUILD | 1 - .../jit/encapsulate_subgraphs_pass.cc | 204 ++++-------------- .../compiler/jit/encapsulate_subgraphs_pass.h | 8 +- .../jit/encapsulate_subgraphs_pass_test.cc | 55 +---- tensorflow/compiler/jit/legacy_flags/BUILD | 12 -- .../encapsulate_subgraphs_pass_flags.cc | 63 ------ .../encapsulate_subgraphs_pass_flags.h | 50 ----- 7 files changed, 45 insertions(+), 348 deletions(-) delete mode 100644 tensorflow/compiler/jit/legacy_flags/encapsulate_subgraphs_pass_flags.cc delete mode 100644 tensorflow/compiler/jit/legacy_flags/encapsulate_subgraphs_pass_flags.h diff --git a/tensorflow/compiler/jit/BUILD b/tensorflow/compiler/jit/BUILD index 51a79e2cd9..8c74014614 100644 --- a/tensorflow/compiler/jit/BUILD +++ b/tensorflow/compiler/jit/BUILD @@ -317,7 +317,6 @@ cc_library( ":xla_cluster_util", "//tensorflow/compiler/jit/graphcycles", "//tensorflow/compiler/jit/kernels:parallel_check_op", - "//tensorflow/compiler/jit/legacy_flags:encapsulate_subgraphs_pass_flags", "//tensorflow/compiler/jit/legacy_flags:mark_for_compilation_pass_flags", "//tensorflow/compiler/jit/ops:parallel_check_op", "//tensorflow/compiler/jit/ops:xla_ops", diff --git a/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc b/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc index 6d1e3325eb..ea90d714c8 100644 --- a/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc +++ b/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc @@ -23,7 +23,6 @@ limitations under the License. #include #include "tensorflow/compiler/jit/graphcycles/graphcycles.h" -#include "tensorflow/compiler/jit/legacy_flags/encapsulate_subgraphs_pass_flags.h" #include "tensorflow/compiler/jit/mark_for_compilation_pass.h" #include "tensorflow/compiler/jit/shape_inference_helpers.h" #include "tensorflow/compiler/tf2xla/const_analysis.h" @@ -182,8 +181,7 @@ class Encapsulator { // Write a copy of the input graph to 'graph_out', where the subgraphs are // replaced with calls to the new functions. - Status BuildOutputGraph(bool parallel_checking, Graph* graph_out, - FunctionLibraryDefinition* library); + Status BuildOutputGraph(Graph* graph_out, FunctionLibraryDefinition* library); private: // A subgraph of the input, all marked with a common 'group_attribute' @@ -271,7 +269,7 @@ class Encapsulator { // Adds the function call node to graph_out. Status AddFunctionCallNode( const std::unordered_map& node_images, - bool parallel_checking, Graph* graph_out); + Graph* graph_out); // Adds _RecvAtHost and _SendFromHost nodes, where needed, to graph_out. Status AddOutsideCompilationHostIONodes( @@ -284,11 +282,9 @@ class Encapsulator { // Subgraph. void GetOutsideCompilationSubgraphNames(std::vector* names) const; - // Returns the Node that inputs to the function should be wired up to. - Node* GetCallNodeForInputs() const; - - // Returns the Node that outputs to the function should be wired up to. - Node* GetCallNodeForOutputs() const; + // Returns the Node that the inputs and outputs of the function should be + // wired up to. + Node* GetCallNode() const; // Returns the index of the arg that the dst of edge should connect to. int GetArgIndexForEdge(const Edge* edge) const; @@ -425,12 +421,6 @@ class Encapsulator { OutsideCompilationSubgraph* LookupOrCreateOutsideCompilationSubgraph( const string& outside_compilation_id); - // Builds a ParallelCheck op that compares the output of the original - // subgraph with the encapsulated subgraph. - Status BuildParallelCheckOp( - const std::unordered_map& node_images, - Graph* graph_out); - // Builds a placeholder node used to provide the key input to a RecvAtHost // or SendFromHost node. This placeholder node will be removed by a later // pass. @@ -482,13 +472,8 @@ class Encapsulator { // Not owned. Node* host_compute_key_placeholder_ = nullptr; - // Function call node(s) in the output graph. Not owned. - // If parallel_checking is enabled, 'call_node_inputs' is the function call - // node to which inputs should be fed, and 'call_node_outputs' is the - // parallel check op from which outputs should be read. If parallel checking - // is disabled, both point to the function call node. - Node* call_node_inputs_; - Node* call_node_outputs_; + // Function call node in the output graph. Not owned. + Node* call_node_; // Maps from source (producer node/slot) and destination // (consumer node/slot) tensors in the input graph to _Arg numbers in @@ -541,13 +526,12 @@ class Encapsulator { // Copies all nodes that aren't in a compiled subgraph to the output graph. Status CopyNodesToOutputGraph( - bool parallel_checking, Graph* graph_out, - std::unordered_map* node_images); + Graph* graph_out, std::unordered_map* node_images); // Adds function call nodes for each compiled subgraph. Status AddFunctionCallNodes( const std::unordered_map& node_images, - bool parallel_checking, Graph* graph_out); + Graph* graph_out); // Adds _RecvAtHost and _SendFromHost nodes, where needed, for all // outside_compilation subgraphs. @@ -598,7 +582,7 @@ class Encapsulator { const string& src_outside_compilation_id, const string& dst_func_id, const string& dst_outside_compilation_id, const std::unordered_map& node_images, - bool parallel_checking, Graph* graph_out, + Graph* graph_out, std::unordered_set, NodeSlot::PairHasher>* edges_added); @@ -609,7 +593,7 @@ class Encapsulator { // Adds all edges to the output graph. Status AddEdgesToOutputGraph( const std::unordered_map& node_images, - bool parallel_checking, Graph* graph_out); + Graph* graph_out); // Constructs a minimal shape inference graph that can be used to determine // the shape of send_node at the time that the subgraph is compiled. @@ -729,13 +713,7 @@ void TopologicalClusterSort( } // namespace -Node* Encapsulator::Subgraph::GetCallNodeForInputs() const { - return call_node_inputs_; -} - -Node* Encapsulator::Subgraph::GetCallNodeForOutputs() const { - return call_node_outputs_; -} +Node* Encapsulator::Subgraph::GetCallNode() const { return call_node_; } int Encapsulator::Subgraph::GetArgIndexForEdge(const Edge* edge) const { return args_by_dst_.at(NodeSlot(edge->dst(), edge->dst_input())); @@ -1075,7 +1053,7 @@ Status Encapsulator::Subgraph::MakeSequencingNode(const string& subgraph_name, void Encapsulator::Subgraph::ConnectSequencerToCallNode(Graph* graph_out) { if (sequencer_ != nullptr) { VLOG(2) << "ConnectSequencerToCallNode"; - graph_out->AddControlEdge(sequencer_, call_node_inputs_); + graph_out->AddControlEdge(sequencer_, call_node_); } } @@ -1200,83 +1178,16 @@ Status Encapsulator::Subgraph::ReplaceFunctionDef( return Status::OK(); } -Status Encapsulator::Subgraph::BuildParallelCheckOp( - const std::unordered_map& node_images, - Graph* graph_out) { - // Build an index mapping output positions to node/slot pairs in the - // original graph. - std::vector results_by_num(results_.size()); - for (const auto& entry : results_) { - results_by_num[entry.second] = entry.first; - } - - // Build a parallel check NodeDef. - int num_results = results_by_num.size(); - std::vector result_dtypes(num_results); - std::vector expected_outputs(num_results); - std::vector actual_outputs(num_results); - for (int i = 0; i < num_results; ++i) { - const NodeSlot& node_slot = results_by_num[i]; - result_dtypes[i] = node_slot.node->output_type(node_slot.slot); - expected_outputs[i] = - NodeDefBuilder::NodeOut(node_images.at(node_slot.node)->name(), - node_slot.slot, result_dtypes[i]); - actual_outputs[i] = - NodeDefBuilder::NodeOut(call_node_def_.name(), i, result_dtypes[i]); - } - // Assign the parallel check op to a CPU on the same task as the cluster it is - // checking. - string device, dummy; - if (!DeviceNameUtils::SplitDeviceName( - call_node_inputs_->assigned_device_name(), &device, &dummy)) { - return errors::InvalidArgument("Could not parse device name"); - } - strings::StrAppend(&device, "/cpu:0"); - - NodeDef check_def; - TF_RETURN_IF_ERROR( - NodeDefBuilder(graph_out->NewName(strings::StrCat(call_node_def_.name(), - "_parallel_check")), - "ParallelCheck") - .Device(device) - .Attr("T", result_dtypes) - .Input(expected_outputs) - .Input(actual_outputs) - .Finalize(&check_def)); - - Status s; - Node* check_op = graph_out->AddNode(check_def, &s); - if (!s.ok()) return s; - check_op->set_assigned_device_name(device); - - // TODO(phawkins): it seems redundant to call AddEdge as well as - // pass Inputs to the NodeDefBuilder, but I have been unable to find a - // way to avoid it. - for (int i = 0; i < num_results; ++i) { - const NodeSlot& node_slot = results_by_num[i]; - graph_out->AddEdge(node_images.at(node_slot.node), node_slot.slot, check_op, - i); - graph_out->AddEdge(call_node_inputs_, i, check_op, num_results + i); - } - - call_node_outputs_ = check_op; - return Status::OK(); -} - Status Encapsulator::Subgraph::AddFunctionCallNode( const std::unordered_map& node_images, - bool parallel_checking, Graph* graph_out) { + Graph* graph_out) { Status s; - call_node_inputs_ = graph_out->AddNode(call_node_def_, &s); + call_node_ = graph_out->AddNode(call_node_def_, &s); if (!s.ok()) return s; // Copy the assigned device and the key_annotation over. - call_node_inputs_->set_assigned_device_name(device_); - call_node_outputs_ = call_node_inputs_; + call_node_->set_assigned_device_name(device_); - if (parallel_checking) { - TF_RETURN_IF_ERROR(BuildParallelCheckOp(node_images, graph_out)); - } return Status::OK(); } @@ -1627,27 +1538,17 @@ Status Encapsulator::BuildFunctionDefs( } Status Encapsulator::CopyNodesToOutputGraph( - bool parallel_checking, Graph* graph_out, - std::unordered_map* node_images) { + Graph* graph_out, std::unordered_map* node_images) { for (Node* node : graph_in_->op_nodes()) { string func_id; string outside_compilation_id; TF_RETURN_IF_ERROR( GetFunctionNameAttr(node, &func_id, &outside_compilation_id)); - // Don't copy nodes that going to be encapsulated, unless parallel checking - // is enabled. - if (IsInSubgraph(func_id, outside_compilation_id) && !parallel_checking) - continue; + // Don't copy nodes that are going to be encapsulated. + if (IsInSubgraph(func_id, outside_compilation_id)) continue; Node* image = graph_out->CopyNode(node); - if (!outside_compilation_id.empty()) { - if (parallel_checking) { - return errors::InvalidArgument( - "Parallel checking is not supported when outside_compilation " - "clusters are present."); - } - } (*node_images)[node] = image; } (*node_images)[graph_in_->source_node()] = graph_out->source_node(); @@ -1657,10 +1558,10 @@ Status Encapsulator::CopyNodesToOutputGraph( Status Encapsulator::AddFunctionCallNodes( const std::unordered_map& node_images, - bool parallel_checking, Graph* graph_out) { + Graph* graph_out) { for (auto& subgraph_entry : subgraphs_) { - TF_RETURN_IF_ERROR(subgraph_entry.second.AddFunctionCallNode( - node_images, parallel_checking, graph_out)); + TF_RETURN_IF_ERROR( + subgraph_entry.second.AddFunctionCallNode(node_images, graph_out)); } return Status::OK(); } @@ -1694,7 +1595,7 @@ Status Encapsulator::FindOutputImageOfEdgeSrc( } else { // The edge is from a subgraph to a regular node in the output graph so // use the subgraph's call node output. - *src_image = subgraphs_.at(src_func_id).GetCallNodeForOutputs(); + *src_image = subgraphs_.at(src_func_id).GetCallNode(); } } else { // The source of the edge is in the output graph so use the node image in @@ -1742,7 +1643,7 @@ Status Encapsulator::FindOutputImageOfEdgeDst( } else { // The edge is to a subgraph from a regular node in the output graph so // use the subgraph's call node input. - *dst_image = subgraphs_.at(dst_func_id).GetCallNodeForInputs(); + *dst_image = subgraphs_.at(dst_func_id).GetCallNode(); } } else { // The destination of the edge is in the output graph so use the node image @@ -1778,8 +1679,7 @@ Status Encapsulator::CopyEdgeToOutputGraph( const Edge* edge, const string& src_func_id, const string& src_outside_compilation_id, const string& dst_func_id, const string& dst_outside_compilation_id, - const std::unordered_map& node_images, - bool parallel_checking, Graph* graph_out, + const std::unordered_map& node_images, Graph* graph_out, std::unordered_set, NodeSlot::PairHasher>* edges_added) { Node* src_image; @@ -1801,11 +1701,6 @@ Status Encapsulator::CopyEdgeToOutputGraph( graph_out->AddControlEdge(src_image, dst_image); } - // If parallel checking is enabled, also add a control edge to the - // corresponding parallel check op. - if (parallel_checking) { - graph_out->AddControlEdge(src_image, node_images.at(edge->dst())); - } return Status::OK(); } @@ -1817,14 +1712,6 @@ Status Encapsulator::CopyEdgeToOutputGraph( FindOutputSlotOfEdgeDst(src_func_id, src_outside_compilation_id, dst_func_id, dst_outside_compilation_id, edge); - if (IsInSubgraph(dst_func_id, dst_outside_compilation_id) && - parallel_checking) { - // If we are parallel checking, also feed the tensor as an input to the - // corresponding parallel check subgraph. - graph_out->AddEdge(src_image, src_output, node_images.at(edge->dst()), - edge->dst_input()); - } - // Add the edge, if we have not already added it. if (edges_added ->emplace(NodeSlot(src_image, src_output), @@ -1839,8 +1726,8 @@ Status Encapsulator::AddCallNodeDependencies(Graph* graph_out) { for (const auto& ancestors : subgraph_ancestors_) { const string& subgraph = ancestors.first; for (const string& ancestor : ancestors.second) { - graph_out->AddControlEdge(subgraphs_[ancestor].GetCallNodeForOutputs(), - subgraphs_[subgraph].GetCallNodeForInputs()); + graph_out->AddControlEdge(subgraphs_[ancestor].GetCallNode(), + subgraphs_[subgraph].GetCallNode()); } } return Status::OK(); @@ -1848,7 +1735,7 @@ Status Encapsulator::AddCallNodeDependencies(Graph* graph_out) { Status Encapsulator::AddEdgesToOutputGraph( const std::unordered_map& node_images, - bool parallel_checking, Graph* graph_out) { + Graph* graph_out) { // Set of edges already added to the output graph, represented as (src, dst) // pairs. We use the set to deduplicate edges; multiple edges in the input // graph may map to one edge in the output graph. @@ -1870,16 +1757,6 @@ Status Encapsulator::AddEdgesToOutputGraph( if (IsInSubgraph(src_func_id, src_outside_compilation_id) && IsInSubgraph(dst_func_id, dst_outside_compilation_id) && src_func_id == dst_func_id) { - if (parallel_checking) { - Node* src_image = node_images.at(edge->src()); - Node* dst_image = node_images.at(edge->dst()); - if (edge->IsControlEdge()) { - graph_out->AddControlEdge(src_image, dst_image); - } else { - graph_out->AddEdge(src_image, edge->src_output(), dst_image, - edge->dst_input()); - } - } continue; } @@ -1887,8 +1764,7 @@ Status Encapsulator::AddEdgesToOutputGraph( // unclustered graph. TF_RETURN_IF_ERROR(CopyEdgeToOutputGraph( edge, src_func_id, src_outside_compilation_id, dst_func_id, - dst_outside_compilation_id, node_images, parallel_checking, graph_out, - &edges_added)); + dst_outside_compilation_id, node_images, graph_out, &edges_added)); } for (auto& subgraph_entry : subgraphs_) { @@ -2504,18 +2380,15 @@ Status Encapsulator::GetShapeInfoForOutsideCompilationSends( return Status::OK(); } -Status Encapsulator::BuildOutputGraph(bool parallel_checking, Graph* graph_out, +Status Encapsulator::BuildOutputGraph(Graph* graph_out, FunctionLibraryDefinition* library) { // Map from nodes in the input graph to nodes in the output graph. std::unordered_map node_images; - TF_RETURN_IF_ERROR( - CopyNodesToOutputGraph(parallel_checking, graph_out, &node_images)); - TF_RETURN_IF_ERROR( - AddFunctionCallNodes(node_images, parallel_checking, graph_out)); + TF_RETURN_IF_ERROR(CopyNodesToOutputGraph(graph_out, &node_images)); + TF_RETURN_IF_ERROR(AddFunctionCallNodes(node_images, graph_out)); TF_RETURN_IF_ERROR(AddOutsideCompilationHostIONodes(node_images, graph_out)); - TF_RETURN_IF_ERROR( - AddEdgesToOutputGraph(node_images, parallel_checking, graph_out)); + TF_RETURN_IF_ERROR(AddEdgesToOutputGraph(node_images, graph_out)); TF_RETURN_IF_ERROR( GetShapeInfoForOutsideCompilationSends(graph_out, library)); @@ -2528,8 +2401,8 @@ Status Encapsulator::BuildOutputGraph(bool parallel_checking, Graph* graph_out, Status EncapsulateSubgraphsInFunctions( string group_attribute, string outside_compilation_attribute, const Graph& graph_in, const RewriteSubgraphFn& rewrite_subgraph_fn, - bool parallel_checking, bool reuse_existing_functions, - std::unique_ptr* graph_out, FunctionLibraryDefinition* library) { + bool reuse_existing_functions, std::unique_ptr* graph_out, + FunctionLibraryDefinition* library) { Status s; Encapsulator encapsulator(std::move(group_attribute), @@ -2543,8 +2416,7 @@ Status EncapsulateSubgraphsInFunctions( std::unique_ptr out(new Graph(library)); out->set_versions(graph_in.versions()); - TF_RETURN_IF_ERROR( - encapsulator.BuildOutputGraph(parallel_checking, out.get(), library)); + TF_RETURN_IF_ERROR(encapsulator.BuildOutputGraph(out.get(), library)); *graph_out = std::move(out); return Status::OK(); @@ -2585,8 +2457,6 @@ static Status RenumberArguments(Graph* graph, Status EncapsulateSubgraphsPass::Run( const GraphOptimizationPassOptions& options) { VLOG(1) << "EncapsulateSubgraphsPass::Run"; - legacy_flags::EncapsulateSubgraphsPassFlags* flags = - legacy_flags::GetEncapsulateSubgraphsPassFlags(); if (VLOG_IS_ON(1)) { dump_graph::DumpGraphToFile("before_encapsulate_subgraphs", **options.graph, options.flib_def); @@ -2663,7 +2533,7 @@ Status EncapsulateSubgraphsPass::Run( TF_RETURN_IF_ERROR(EncapsulateSubgraphsInFunctions( kXlaClusterAttr, kXlaOutsideCompilationAttr, **options.graph, - rewrite_subgraph, flags->tf_xla_parallel_checking, + rewrite_subgraph, /*reuse_existing_functions=*/false, &graph_out, library)); if (VLOG_IS_ON(1)) { diff --git a/tensorflow/compiler/jit/encapsulate_subgraphs_pass.h b/tensorflow/compiler/jit/encapsulate_subgraphs_pass.h index 5fee36f022..e5dab7c657 100644 --- a/tensorflow/compiler/jit/encapsulate_subgraphs_pass.h +++ b/tensorflow/compiler/jit/encapsulate_subgraphs_pass.h @@ -61,10 +61,6 @@ typedef std::function* graph_out, FunctionLibraryDefinition* library); + bool reuse_existing_functions, std::unique_ptr* graph_out, + FunctionLibraryDefinition* library); // The attribute that marks function calls produced by the encapsulate // subgraphs pass and that should in turn be compiled via XlaLaunch operators. diff --git a/tensorflow/compiler/jit/encapsulate_subgraphs_pass_test.cc b/tensorflow/compiler/jit/encapsulate_subgraphs_pass_test.cc index eef113a354..6a7cd932e5 100644 --- a/tensorflow/compiler/jit/encapsulate_subgraphs_pass_test.cc +++ b/tensorflow/compiler/jit/encapsulate_subgraphs_pass_test.cc @@ -511,7 +511,6 @@ Status Encapsulate(GraphDef* graphdef, FunctionDefLibrary* library) { std::unique_ptr graph_out; s = EncapsulateSubgraphsInFunctions("_encapsulate", "_outside", *graph, /*rewrite_subgraph_fn=*/{}, - /*parallel_checking=*/false, /*reuse_existing_functions=*/false, &graph_out, lib_def.get()); if (!s.ok()) return s; @@ -560,8 +559,9 @@ TEST(EncapsulateSubgraphsTest, OneFunction) { Node* b = Input(b1.opts().WithName("B")); // Give nodes 'c' and 'd' names that collide after lowercasing. Node* c = Unary(a, b1.opts().WithName("C").WithAttr("_encapsulate", "F1")); - Node* d = Binary(b, c, b1.opts().WithName("c").WithControlInput(c).WithAttr( - "_encapsulate", "F1")); + Node* d = Binary(b, c, + b1.opts().WithName("c").WithControlInput(c).WithAttr( + "_encapsulate", "F1")); Binary(a, d, b1.opts().WithName("E")); TF_EXPECT_OK(b1.ToGraphDef(&graphdef)); } @@ -614,8 +614,8 @@ TEST(EncapsulateSubgraphsTest, TwoFunctions) { Node* c = Unary(a, b1.opts().WithName("C").WithControlInput(control).WithAttr( "_encapsulate", "F1")); - Node* d = - Binary(b, c, b1.opts().WithName("D").WithControlInput(control).WithAttr( + Node* d = Binary(b, c, + b1.opts().WithName("D").WithControlInput(control).WithAttr( "_encapsulate", "F2")); Binary(a, d, b1.opts().WithName("E")); TF_EXPECT_OK(b1.ToGraphDef(&graphdef)); @@ -707,7 +707,7 @@ TEST(EncapsulateSubgraphsTest, InputDeduplication) { std::unique_ptr graph; TF_ASSERT_OK(EncapsulateSubgraphsInFunctions( "_cluster", "_outside", graph_before_encapsulation, - /*rewrite_subgraph_fn=*/{}, /*parallel_checking=*/false, + /*rewrite_subgraph_fn=*/{}, /*reuse_existing_functions=*/false, &graph, &library)); std::vector expected_nodes = {"cluster1", "cluster2", "mul", "x"}; @@ -721,47 +721,6 @@ TEST(EncapsulateSubgraphsTest, InputDeduplication) { EXPECT_EQ(expected_edges, GraphEdges(*graph)); } -TEST(EncapsulateSubgraphsTest, ParallelChecking) { - Scope root = Scope::NewRootScope().ExitOnError().WithDevice( - "/job:localhost/replica:0/task:0/cpu:0"); - auto x1 = ops::Placeholder(root.WithOpName("x1"), DT_FLOAT); - auto x2 = ops::Placeholder(root.WithOpName("x2"), DT_FLOAT); - auto add1 = ops::Add(root.WithOpName("add1"), x1, x2); - add1.node()->AddAttr("_cluster", "cluster1"); - auto add2 = ops::Add(root.WithOpName("add2"), add1, x2); - add2.node()->AddAttr("_cluster", "cluster1"); - auto out = ops::Mul(root.WithOpName("mul"), x1, add2); - - Graph graph_before_encapsulation(OpRegistry::Global()); - TF_ASSERT_OK(root.ToGraph(&graph_before_encapsulation)); - - FunctionLibraryDefinition library(OpRegistry::Global(), {}); - std::unique_ptr graph; - TF_ASSERT_OK(EncapsulateSubgraphsInFunctions( - "_cluster", "_outside", graph_before_encapsulation, - /*rewrite_subgraph_fn=*/{}, /*parallel_checking=*/true, - /*reuse_existing_functions=*/false, &graph, &library)); - - std::vector expected_nodes = { - "add1", "add2", "cluster1", "cluster1_parallel_check/_0", - "mul", "x1", "x2"}; - EXPECT_EQ(expected_nodes, GraphNodes(*graph)); - - std::vector> expected_edges = { - {"add1:0", "add2:0"}, - {"add2:0", "cluster1_parallel_check/_0:0"}, - {"cluster1:0", "cluster1_parallel_check/_0:1"}, - {"cluster1_parallel_check/_0:0", "mul:1"}, - {"x1:0", "add1:0"}, - {"x1:0", "cluster1:0"}, - {"x1:0", "mul:0"}, - {"x2:0", "add1:1"}, - {"x2:0", "add2:1"}, - {"x2:0", "cluster1:1"}, - }; - EXPECT_EQ(expected_edges, GraphEdges(*graph)); -} - const Node* FindNodeByName(const Graph& graph, const string& name) { for (const Node* node : graph.nodes()) { if (node->name() == name) return node; @@ -814,7 +773,6 @@ TEST(EncapsulateSubgraphsWithGuaranteeConstOpTest, Simple) { } return Status::OK(); }, - /*parallel_checking=*/false, /*reuse_existing_functions=*/false, &graph_after, &library)); EXPECT_EQ(2, guaranteed_consts); } @@ -859,7 +817,6 @@ TEST(EncapsulateSubgraphsWithGuaranteeConstOpTest, Add) { } return Status::OK(); }, - /*parallel_checking=*/false, /*reuse_existing_functions=*/false, &graph_after, &library)); // Only 1 runtime const, which is const_guarantee_add1. Add2 has one const // and another non-const, so overall non-const. diff --git a/tensorflow/compiler/jit/legacy_flags/BUILD b/tensorflow/compiler/jit/legacy_flags/BUILD index 5d211f4d73..5b6692f523 100644 --- a/tensorflow/compiler/jit/legacy_flags/BUILD +++ b/tensorflow/compiler/jit/legacy_flags/BUILD @@ -16,18 +16,6 @@ licenses(["notice"]) # Apache 2.0 package(default_visibility = ["//tensorflow:internal"]) -cc_library( - name = "encapsulate_subgraphs_pass_flags", - srcs = ["encapsulate_subgraphs_pass_flags.cc"], - hdrs = ["encapsulate_subgraphs_pass_flags.h"], - deps = - [ - "//tensorflow/compiler/xla/legacy_flags:parse_flags_from_env", - "//tensorflow/core:framework_internal", - "//tensorflow/core:lib", - ], -) - cc_library( name = "mark_for_compilation_pass_flags", srcs = ["mark_for_compilation_pass_flags.cc"], diff --git a/tensorflow/compiler/jit/legacy_flags/encapsulate_subgraphs_pass_flags.cc b/tensorflow/compiler/jit/legacy_flags/encapsulate_subgraphs_pass_flags.cc deleted file mode 100644 index 856475f12c..0000000000 --- a/tensorflow/compiler/jit/legacy_flags/encapsulate_subgraphs_pass_flags.cc +++ /dev/null @@ -1,63 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -// Legacy flags for the XLA bridge's encapsulate_subgraphs_pass module. - -#include -#include - -#include "tensorflow/compiler/jit/legacy_flags/encapsulate_subgraphs_pass_flags.h" -#include "tensorflow/compiler/xla/legacy_flags/parse_flags_from_env.h" -#include "tensorflow/core/platform/types.h" -#include "tensorflow/core/util/command_line_flags.h" - -namespace tensorflow { -namespace legacy_flags { - -// Pointers to the parsed value of the flags and flag descriptors, initialized -// via flags_init. -static EncapsulateSubgraphsPassFlags* flags; -static std::vector* flag_list; -static std::once_flag flags_init; - -// Allocate *flags. Called via call_once(&flags_init,...). -static void AllocateFlags() { - flags = new EncapsulateSubgraphsPassFlags; - flags->tf_xla_parallel_checking = false; - flag_list = new std::vector({ - Flag("tf_xla_parallel_checking", &flags->tf_xla_parallel_checking, - "Debug tool. Runs both JIT-compiled and interpreted graphs in " - "parallel and verifies they produce the same outputs."), - }); - xla::legacy_flags::ParseFlagsFromEnv(*flag_list); -} - -// Append to *append_to flag definitions associated with the XLA bridge's -// encapsulate_subgraphs_pass module. -void AppendEncapsulateSubgraphsPassFlags(std::vector* append_to) { - std::call_once(flags_init, &AllocateFlags); - append_to->insert(append_to->end(), flag_list->begin(), flag_list->end()); -} - -// Return a pointer to the EncapsulateSubgraphsPassFlags struct; -// repeated calls return the same pointer. -// This should be called only after Flags::Parse() has returned. -EncapsulateSubgraphsPassFlags* GetEncapsulateSubgraphsPassFlags() { - std::call_once(flags_init, &AllocateFlags); - return flags; -} - -} // namespace legacy_flags -} // namespace tensorflow diff --git a/tensorflow/compiler/jit/legacy_flags/encapsulate_subgraphs_pass_flags.h b/tensorflow/compiler/jit/legacy_flags/encapsulate_subgraphs_pass_flags.h deleted file mode 100644 index d371bd269d..0000000000 --- a/tensorflow/compiler/jit/legacy_flags/encapsulate_subgraphs_pass_flags.h +++ /dev/null @@ -1,50 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_COMPILER_JIT_LEGACY_FLAGS_ENCAPSULATE_SUBGRAPHS_PASS_FLAGS_H_ -#define TENSORFLOW_COMPILER_JIT_LEGACY_FLAGS_ENCAPSULATE_SUBGRAPHS_PASS_FLAGS_H_ - -// Legacy flags for the XLA bridge's encapsulate_subgraphs_pass module. - -#include - -#include "tensorflow/core/platform/types.h" -#include "tensorflow/core/util/command_line_flags.h" - -namespace tensorflow { -namespace legacy_flags { - -// Append to *flag_list flag definitions associated with the XLA bridge's -// encapsulate_subgraphs_pass module. -void AppendEncapsulateSubgraphsPassFlags( - std::vector* flag_list); - -// The values of flags associated with the XLA bridge's -// encapsulate_subgraphs_pass module. -typedef struct { - bool tf_xla_parallel_checking; // Debug tool. Runs both JIT-compiled and - // interpreted graphs in parallel and verifies - // they produce the same outputs. -} EncapsulateSubgraphsPassFlags; - -// Return a pointer to the EncapsulateSubgraphsPassFlags struct; -// repeated calls return the same pointer. -// This should be called only after Flags::Parse() has returned. -EncapsulateSubgraphsPassFlags* GetEncapsulateSubgraphsPassFlags(); - -} // namespace legacy_flags -} // namespace tensorflow - -#endif // TENSORFLOW_COMPILER_JIT_LEGACY_FLAGS_ENCAPSULATE_SUBGRAPHS_PASS_FLAGS_H_ -- GitLab From 696ac9923003150484ab0bce29d5b66d5a317eb6 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 13 Jun 2018 10:40:20 -0700 Subject: [PATCH 389/816] Disable failing zip_test_lstm target PiperOrigin-RevId: 200414970 --- tensorflow/contrib/lite/build_def.bzl | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/lite/build_def.bzl b/tensorflow/contrib/lite/build_def.bzl index 612813caee..974e6c5d98 100644 --- a/tensorflow/contrib/lite/build_def.bzl +++ b/tensorflow/contrib/lite/build_def.bzl @@ -221,7 +221,8 @@ def generated_test_models(): "local_response_norm", "log_softmax", "log", - "lstm", + # TODO(b/110143200): Enable after resolving issues with LSTM conversion. + # "lstm", "max_pool", "maximum", "mean", -- GitLab From ea76cd8938e794e8cc190032c27deaf561ac88a6 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 13 Jun 2018 10:48:16 -0700 Subject: [PATCH 390/816] - Teaches zip tests about unzip location on Android - Passes use_nnapi from TestDriver to interpeter - Adds command line flag to generated tests for NNAPI - Fixes logic for allocating im2col tensor so that tests pass without NNAPI PiperOrigin-RevId: 200416472 --- tensorflow/contrib/lite/testing/BUILD | 14 +++++++++----- .../lite/testing/generated_examples_zip_test.cc | 14 ++++++++++++-- tensorflow/contrib/lite/testing/tflite_driver.cc | 1 + 3 files changed, 22 insertions(+), 7 deletions(-) diff --git a/tensorflow/contrib/lite/testing/BUILD b/tensorflow/contrib/lite/testing/BUILD index 80e4c5a4dd..b823c97f38 100644 --- a/tensorflow/contrib/lite/testing/BUILD +++ b/tensorflow/contrib/lite/testing/BUILD @@ -20,11 +20,15 @@ load( size = "large", srcs = ["generated_examples_zip_test.cc"], args = [ - "--zip_file_path=$(location :zip_%s)" % test_name, - # TODO(angerson) We may be able to add an external unzip binary instead - # of relying on an existing one for OSS builds. - "--unzip_binary_path=/usr/bin/unzip", - ], + ] + select({ + "//tensorflow:android": [], + "//conditions:default": [ + "--zip_file_path=$(location :zip_%s)" % test_name, + # TODO(angerson) We may be able to add an external unzip binary instead + # of relying on an existing one for OSS builds. + "--unzip_binary_path=/usr/bin/unzip", + ], + }), data = [ ":zip_%s" % test_name, ], diff --git a/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc b/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc index e85020448a..8a59d756f8 100644 --- a/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc +++ b/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc @@ -36,7 +36,12 @@ bool FLAGS_ignore_known_bugs = true; // TODO(b/71769302) zip_files_dir should have a more accurate default, if // possible string* FLAGS_zip_file_path = new string("./"); +#ifndef __ANDROID__ string* FLAGS_unzip_binary_path = new string("/usr/bin/unzip"); +#else +string* FLAGS_unzip_binary_path = new string("/system/bin/unzip"); +#endif +bool FLAGS_use_nnapi = false; } // namespace // TensorFlow system environment for file system called. @@ -212,7 +217,7 @@ TEST_P(OpsTest, RunZipTests) { std::ifstream tflite_stream(tflite_test_case); ASSERT_TRUE(tflite_stream.is_open()) << tflite_test_case; - tflite::testing::TfLiteDriver test_driver(/*use_nnapi=*/true); + tflite::testing::TfLiteDriver test_driver(FLAGS_use_nnapi); test_driver.SetModelBaseDir(tflite_dir); string bug_number; @@ -273,7 +278,10 @@ int main(int argc, char** argv) { "Required: Location of the test zip file."), tensorflow::Flag("unzip_binary_path", tflite::testing::FLAGS_unzip_binary_path, - "Required: Location of a suitable unzip binary.")}; + "Required: Location of a suitable unzip binary."), + tensorflow::Flag("use_nnapi", &tflite::testing::FLAGS_use_nnapi, + "Whether to enable the NNAPI delegate")}; + bool success = tensorflow::Flags::Parse(&argc, argv, flags); if (!success || (argc == 2 && !strcmp(argv[1], "--helpfull"))) { fprintf(stderr, "%s", tensorflow::Flags::Usage(argv[0], flags).c_str()); @@ -281,6 +289,8 @@ int main(int argc, char** argv) { } ::tflite::LogToStderr(); + // TODO(mikie): googletest arguments do not work - maybe the tensorflow flags + // parser removes them? ::testing::InitGoogleTest(&argc, argv); return RUN_ALL_TESTS(); } diff --git a/tensorflow/contrib/lite/testing/tflite_driver.cc b/tensorflow/contrib/lite/testing/tflite_driver.cc index fc28faf524..f518bf864c 100644 --- a/tensorflow/contrib/lite/testing/tflite_driver.cc +++ b/tensorflow/contrib/lite/testing/tflite_driver.cc @@ -163,6 +163,7 @@ void TfLiteDriver::LoadModel(const string& bin_file_path) { Invalidate("Failed build interpreter"); return; } + interpreter_->UseNNAPI(use_nnapi_); must_allocate_tensors_ = true; } -- GitLab From 11b3a9f4c2514369b0598b0f05038e45459b324b Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 13 Jun 2018 11:15:47 -0700 Subject: [PATCH 391/816] Documenting capabilities and limitations of AutoGraph PiperOrigin-RevId: 200421914 --- tensorflow/contrib/autograph/LIMITATIONS.md | 50 +++++++++++++++++++++ tensorflow/contrib/autograph/README.md | 12 +++++ 2 files changed, 62 insertions(+) create mode 100644 tensorflow/contrib/autograph/LIMITATIONS.md diff --git a/tensorflow/contrib/autograph/LIMITATIONS.md b/tensorflow/contrib/autograph/LIMITATIONS.md new file mode 100644 index 0000000000..d8b1cb7616 --- /dev/null +++ b/tensorflow/contrib/autograph/LIMITATIONS.md @@ -0,0 +1,50 @@ +# Capabilities and Limitations + +TF AutoGraph converts Eager Python code into TensorFlow graph-mode code. For example, users write code with `if` and `while` and AutoGraph automatically converts it into the equivalent `tf.cond`, and `tf.while_loop`. + +Python is a large language, so hoping to convert arbitrary Python code directly to TF graphs is overly ambitious. However, the Python code written to metaprogram TF graphs is in practice a restricted subset. We aim to support as much of this subset as possible. The table below lays out what we currently handle, what we hope to support, and what we have no plans to support. + +# Python Language Support Status + +Note: as more complex features in TensorFlow are made more accessible using AutoGraph, we expect to come across use cases that haven't been tried before, some of which might reveal rare bugs. If we do find any such bugs, we may add additional restrictions for the affected configurations, until those bugs are resolved. + + Construct | Supported now? | Plan to support? | Notes + :--------- | :--------------: | :----------------: | :----- +If statement | Yes | | Converts to `tf.cond`. If variables are created in one branch that don’t exist in another, which is inexpressible in TF, we throw a clear error. +For statement | Yes | | We will specialize `for` loops with unknown and known lengths, as well as for loops over TF datasets. Converts to `tf.while_loop`, with an additional `maximum_iterations` hint, if that is known. Creating variables inside the loop that are used later outside the loop is not supported, as the loop may have no iterations. +While statement | Yes | | Converts to `tf.while_loop`. Creating variables inside the loop is not supported, as the loop may have no iterations. +Continue and break | Yes | | Converts to boolean flags and extra predicates in loop tests. +Composition of control flow | Yes | | Arbitrary composition of `if`, `while`, `for`, `break`, and `continue`, along with other supported language elements, is supported and tested. +Iterators | Some | Yes | Not all iterators supported, but we plan to support everything that can be desugared, such as `enumerate` and `zip`. +Multiple return values | Yes | | We desugar them into variables, boolean flags and conditionals so that the function has a single return value at the end, and provide a clear error if we are unable to do so. +Print expression | Yes | | Wrapped in `PyFunc`, and given proper control dependencies. Optional support for using tf.Log when py_func is undesirable exists. +Static function calls | Yes | | Non-recursive function calls +Nested call trees | Yes | | For example, `f` calls `g` which calls `h`, all of which need conversion. +Recursive function calls | No | Maybe | Based on available support in TF. Currently `function.Defun` is the best candidate, but it is not reentrant. +Python built-ins | Some | Yes | `print`, `len`, `range`, `xrange`, `int`, `float` are supported, and we plan to support or clearly error on all [Python built-ins](https://docs.python.org/3/library/functions.html). +List operations | Yes | | We convert list creation, append, pop and indexing to their TF TensorArray equivalents. However, we do need some extra type hints to fully convert correctly. We hope to remove this limitation. +Function variables | Yes | | e.g. `f_new = f_orig; f_new()` +Lambda functions | No | Yes | Planned feature. +Classes | Yes | | Classes can be converted all at once, or method-by-method. Some limitations exist around static and class methods. +Subclasses | Yes | | Subclassing library objects like tf.keras.Model is also supported. +Dynamic types | Some | | `o = C1() if foo else C2(); o.bar()`. Some scenarios where types are data-dependent may not be supported. We will raise a meaningful error in that case. +Dynamic code / exec | No | | +Reflection | No | | +Try / Except | No | No | No current sane TF equivalent. +Global variables | Restricted | | In general, we only support read-only access to arguments or variables defined outside the converted code. A few exceptions include TensorFlow library code. +Functions with side effects | Some | | Side effects are allowed, under certain circumstances. +Collections | Some | Yes | We currently support lists. There are currently no TF equivalents of dictionaries or tuples. +List Comprehensions | Yes | | We desugar `ListComp` into the appropriate combination of `For` and `If` statements. Other comprehensions are currently very low priority. +Custom context managers | No | Yes | Currently low priority. Left unconverted currently. +Generators | No | Maybe | Could be achievable using queues; very low priority. +Assertions | Yes | | As `tf.Assert` +Deletion | Yes | Maybe | Currently unconverted. If new semanti cs are required for `del`, we are able to add it in. +Inline imports | No | Yes | For example, `import numpy as np; np.eye(3)`. Currently low priority. +Async | No | No | + +## Extra capabilities + + - We liberally add name scopes to generated functions + - Operations get decent default names everywhere (planned) + - Statements that have no output values are given correct control dependencies. For example, `for i in range(n): print(i)` will have control dependencies to ensure the `print` statements are executed serially. + diff --git a/tensorflow/contrib/autograph/README.md b/tensorflow/contrib/autograph/README.md index 674859bed4..829a57d8e6 100644 --- a/tensorflow/contrib/autograph/README.md +++ b/tensorflow/contrib/autograph/README.md @@ -120,3 +120,15 @@ You can use the functional API to inspect the generated code as well: print(ag.to_code(f)) # Output: ``` + +## Filing bugs and feature requests + +### Reporting a bug + + - If AutoGraph-generated code is compiling and running, but producing an incorrect result, send us a minimal reproduction case that includes the original Eager code, the inputs and if possible, the outputs or the error message. + - If AutoGraph-generated code is compiling, but not running, send us a minimal reproduction case that includes the original Eager code, the inputs and if possible, the outputs or the error message. + - If AutoGraph-generated code is not compiling, send us two minimal pieces of code. First, the Eager code that you would like to write, and second, the Graph code that you would like AutoGraph to have generated for you. + +### Requesting a feature + +If you’d like AutoGraph to convert a feature of Python or TF that we currently don’t handle, please let us know by filing a bug. We’ll make it as easy as possible to interact with us through there. -- GitLab From ee3ecdfde04591366eadbb4e79b8885b47f274cb Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 13 Jun 2018 11:18:46 -0700 Subject: [PATCH 392/816] Reversible residual network example with manually built gradient computation in TensorFlow eager mode execution. PiperOrigin-RevId: 200422481 --- .../contrib/eager/python/examples/BUILD | 2 + .../eager/python/examples/revnet/BUILD | 76 ++++ .../eager/python/examples/revnet/blocks.py | 335 +++++++++++++++++ .../python/examples/revnet/blocks_test.py | 346 ++++++++++++++++++ .../eager/python/examples/revnet/config.py | 117 ++++++ .../eager/python/examples/revnet/ops.py | 70 ++++ .../eager/python/examples/revnet/ops_test.py | 80 ++++ .../eager/python/examples/revnet/revnet.py | 263 +++++++++++++ .../python/examples/revnet/revnet_test.py | 277 ++++++++++++++ 9 files changed, 1566 insertions(+) create mode 100644 tensorflow/contrib/eager/python/examples/revnet/BUILD create mode 100644 tensorflow/contrib/eager/python/examples/revnet/blocks.py create mode 100644 tensorflow/contrib/eager/python/examples/revnet/blocks_test.py create mode 100644 tensorflow/contrib/eager/python/examples/revnet/config.py create mode 100644 tensorflow/contrib/eager/python/examples/revnet/ops.py create mode 100644 tensorflow/contrib/eager/python/examples/revnet/ops_test.py create mode 100644 tensorflow/contrib/eager/python/examples/revnet/revnet.py create mode 100644 tensorflow/contrib/eager/python/examples/revnet/revnet_test.py diff --git a/tensorflow/contrib/eager/python/examples/BUILD b/tensorflow/contrib/eager/python/examples/BUILD index 1d9371c7ac..6f02c90368 100644 --- a/tensorflow/contrib/eager/python/examples/BUILD +++ b/tensorflow/contrib/eager/python/examples/BUILD @@ -11,6 +11,8 @@ py_library( "//tensorflow/contrib/eager/python/examples/l2hmc:neural_nets", "//tensorflow/contrib/eager/python/examples/linear_regression", "//tensorflow/contrib/eager/python/examples/resnet50", + "//tensorflow/contrib/eager/python/examples/revnet", + "//tensorflow/contrib/eager/python/examples/revnet:config", "//tensorflow/contrib/eager/python/examples/rnn_colorbot", "//tensorflow/contrib/eager/python/examples/rnn_ptb", "//tensorflow/contrib/eager/python/examples/spinn:data", diff --git a/tensorflow/contrib/eager/python/examples/revnet/BUILD b/tensorflow/contrib/eager/python/examples/revnet/BUILD new file mode 100644 index 0000000000..bfb53cfff8 --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/revnet/BUILD @@ -0,0 +1,76 @@ +licenses(["notice"]) # Apache 2.0 + +package(default_visibility = ["//tensorflow:internal"]) + +load("//tensorflow:tensorflow.bzl", "cuda_py_test") + +# Model +py_library( + name = "ops", + srcs = ["ops.py"], + srcs_version = "PY2AND3", + deps = [ + "//tensorflow:tensorflow_py", + ], +) + +py_library( + name = "config", + srcs = ["config.py"], + srcs_version = "PY2AND3", + deps = [ + "//tensorflow:tensorflow_py", + ], +) + +py_library( + name = "blocks", + srcs = ["blocks.py"], + srcs_version = "PY2AND3", + deps = [ + ":ops", + "//tensorflow:tensorflow_py", + ], +) + +py_library( + name = "revnet", + srcs = ["revnet.py"], + srcs_version = "PY2AND3", + deps = [ + ":blocks", + "//tensorflow:tensorflow_py", + ], +) + +# Tests +cuda_py_test( + name = "ops_test", + size = "large", + srcs = ["ops_test.py"], + additional_deps = [ + ":ops", + "//tensorflow:tensorflow_py", + ], +) + +cuda_py_test( + name = "blocks_test", + size = "large", + srcs = ["blocks_test.py"], + additional_deps = [ + ":blocks", + "//tensorflow:tensorflow_py", + ], +) + +cuda_py_test( + name = "revnet_test", + size = "large", + srcs = ["revnet_test.py"], + additional_deps = [ + ":config", + ":revnet", + "//tensorflow:tensorflow_py", + ], +) diff --git a/tensorflow/contrib/eager/python/examples/revnet/blocks.py b/tensorflow/contrib/eager/python/examples/revnet/blocks.py new file mode 100644 index 0000000000..fb4f9f068f --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/revnet/blocks.py @@ -0,0 +1,335 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Reversible residual network compatible with eager execution. + +Building blocks with manual backward gradient computation. + +Reference [The Reversible Residual Network: Backpropagation +Without Storing Activations](https://arxiv.org/pdf/1707.04585.pdf) +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf +from tensorflow.contrib.eager.python.examples.revnet import ops + + +class RevBlock(tf.keras.Model): + """Single reversible block containing several `_Residual` blocks. + + Each `_Residual` block in turn contains two _ResidualInner blocks, + corresponding to the `F`/`G` functions in the paper. + """ + + def __init__(self, + n_res, + filters, + strides, + input_shape, + batch_norm_first=False, + data_format="channels_first", + bottleneck=False, + fused=True): + """Initialize RevBlock. + + Args: + n_res: number of residual blocks + filters: list/tuple of integers for output filter sizes of each residual + strides: length 2 list/tuple of integers for height and width strides + input_shape: length 3 list/tuple of integers + batch_norm_first: whether to apply activation and batch norm before conv + data_format: tensor data format, "NCHW"/"NHWC" + bottleneck: use bottleneck residual if True + fused: use fused batch normalization if True + """ + super(RevBlock, self).__init__() + self.blocks = tf.contrib.checkpoint.List() + for i in range(n_res): + curr_batch_norm_first = batch_norm_first and i == 0 + curr_strides = strides if i == 0 else (1, 1) + block = _Residual( + filters, + curr_strides, + input_shape, + batch_norm_first=curr_batch_norm_first, + data_format=data_format, + bottleneck=bottleneck, + fused=fused) + self.blocks.append(block) + + if data_format == "channels_first": + input_shape = (filters, input_shape[1] // curr_strides[0], + input_shape[2] // curr_strides[1]) + else: + input_shape = (input_shape[0] // curr_strides[0], + input_shape[1] // curr_strides[1], filters) + + def call(self, h, training=True): + """Apply reversible block to inputs.""" + + for block in self.blocks: + h = block(h, training=training) + return h + + def backward_grads_and_vars(self, x, y, dy, training=True): + """Apply reversible block backward to outputs.""" + + grads_all = [] + vars_all = [] + + for i in reversed(range(len(self.blocks))): + block = self.blocks[i] + y_inv = x if i == 0 else block.backward(y, training=training) + dy, grads, vars_ = block.backward_grads_and_vars( + y_inv, dy, training=training) + grads_all += grads + vars_all += vars_ + + return dy, grads_all, vars_all + + +class _Residual(tf.keras.Model): + """Single residual block contained in a _RevBlock. Each `_Residual` object has + two _ResidualInner objects, corresponding to the `F` and `G` functions in the + paper. + + Args: + filters: output filter size + strides: length 2 list/tuple of integers for height and width strides + input_shape: length 3 list/tuple of integers + batch_norm_first: whether to apply activation and batch norm before conv + data_format: tensor data format, "NCHW"/"NHWC", + bottleneck: use bottleneck residual if True + fused: use fused batch normalization if True + """ + + def __init__(self, + filters, + strides, + input_shape, + batch_norm_first=True, + data_format="channels_first", + bottleneck=False, + fused=True): + super(_Residual, self).__init__() + + self.filters = filters + self.strides = strides + self.axis = 1 if data_format == "channels_first" else 3 + if data_format == "channels_first": + f_input_shape = (input_shape[0] // 2,) + input_shape[1:] + g_input_shape = (filters // 2, input_shape[1] // strides[0], + input_shape[2] // strides[1]) + else: + f_input_shape = input_shape[:2] + (input_shape[2] // 2,) + g_input_shape = (input_shape[0] // strides[0], + input_shape[1] // strides[1], filters // 2) + + factory = _BottleneckResidualInner if bottleneck else _ResidualInner + self.f = factory( + filters=filters // 2, + strides=strides, + input_shape=f_input_shape, + batch_norm_first=batch_norm_first, + data_format=data_format, + fused=fused) + self.g = factory( + filters=filters // 2, + strides=(1, 1), + input_shape=g_input_shape, + batch_norm_first=batch_norm_first, + data_format=data_format, + fused=fused) + + def call(self, x, training=True, concat=True): + """Apply residual block to inputs.""" + + x1, x2 = tf.split(x, num_or_size_splits=2, axis=self.axis) + f_x2 = self.f.call(x2, training=training) + # TODO(lxuechen): Replace with simpler downsampling + x1_down = ops.downsample( + x1, self.filters // 2, self.strides, axis=self.axis) + x2_down = ops.downsample( + x2, self.filters // 2, self.strides, axis=self.axis) + y1 = f_x2 + x1_down + g_y1 = self.g.call(y1, training=training) # self.g(y1) gives pylint error + y2 = g_y1 + x2_down + if not concat: # Concat option needed for correct backward grads + return y1, y2 + return tf.concat([y1, y2], axis=self.axis) + + def backward(self, y, training=True): + """Reconstruct inputs from outputs; only valid when stride 1.""" + + assert self.strides == (1, 1) + + y1, y2 = tf.split(y, num_or_size_splits=2, axis=self.axis) + g_y1 = self.g.call(y1, training=training) + x2 = y2 - g_y1 + f_x2 = self.f.call(x2, training=training) + x1 = y1 - f_x2 + + return tf.concat([x1, x2], axis=self.axis) + + def backward_grads_and_vars(self, x, dy, training=True): + """Manually compute backward gradients given input and output grads.""" + + with tf.GradientTape(persistent=True) as tape: + x_stop = tf.stop_gradient(x) + x1, x2 = tf.split(x_stop, num_or_size_splits=2, axis=self.axis) + tape.watch([x1, x2]) + # Stitch back x for `call` so tape records correct grads + x = tf.concat([x1, x2], axis=self.axis) + dy1, dy2 = tf.split(dy, num_or_size_splits=2, axis=self.axis) + y1, y2 = self.call(x, training=training, concat=False) + x2_down = ops.downsample( + x2, self.filters // 2, self.strides, axis=self.axis) + + grads_combined = tape.gradient( + y2, [y1] + self.g.variables, output_gradients=[dy2]) + dy2_y1, dg = grads_combined[0], grads_combined[1:] + dy1_plus = dy2_y1 + dy1 + + grads_combined = tape.gradient( + y1, [x1, x2] + self.f.variables, output_gradients=[dy1_plus]) + dx1, dx2, df = grads_combined[0], grads_combined[1], grads_combined[2:] + dx2 += tape.gradient(x2_down, [x2], output_gradients=[dy2])[0] + + del tape + + grads = df + dg + vars_ = self.f.variables + self.g.variables + + return tf.concat([dx1, dx2], axis=self.axis), grads, vars_ + + +def _BottleneckResidualInner(filters, + strides, + input_shape, + batch_norm_first=True, + data_format="channels_first", + fused=True): + """Single bottleneck residual inner function contained in _Resdual. + + Corresponds to the `F`/`G` functions in the paper. + Suitable for training on ImageNet dataset. + + Args: + filters: output filter size + strides: length 2 list/tuple of integers for height and width strides + input_shape: length 3 list/tuple of integers + batch_norm_first: whether to apply activation and batch norm before conv + data_format: tensor data format, "NCHW"/"NHWC" + fused: use fused batch normalization if True + + Returns: + A keras model + """ + + axis = 1 if data_format == "channels_first" else 3 + model = tf.keras.Sequential() + if batch_norm_first: + model.add( + tf.keras.layers.BatchNormalization( + axis=axis, input_shape=input_shape, fused=fused)) + model.add(tf.keras.layers.LeakyReLU(alpha=0.)) + model.add( + tf.keras.layers.Conv2D( + filters=filters // 4, + kernel_size=1, + strides=strides, + input_shape=input_shape, + data_format=data_format, + use_bias=False, + padding="SAME")) + + model.add(tf.keras.layers.BatchNormalization(axis=axis, fused=fused)) + model.add(tf.keras.layers.LeakyReLU(alpha=0.)) + model.add( + tf.keras.layers.Conv2D( + filters=filters // 4, + kernel_size=3, + strides=(1, 1), + data_format=data_format, + use_bias=False, + padding="SAME")) + + model.add(tf.keras.layers.BatchNormalization(axis=axis, fused=fused)) + model.add(tf.keras.layers.LeakyReLU(alpha=0.)) + model.add( + tf.keras.layers.Conv2D( + filters=filters, + kernel_size=1, + strides=(1, 1), + data_format=data_format, + use_bias=False, + padding="SAME")) + + return model + + +def _ResidualInner(filters, + strides, + input_shape, + batch_norm_first=True, + data_format="channels_first", + fused=True): + """Single residual inner function contained in _ResdualBlock. + + Corresponds to the `F`/`G` functions in the paper. + + Args: + filters: output filter size + strides: length 2 list/tuple of integers for height and width strides + input_shape: length 3 list/tuple of integers + batch_norm_first: whether to apply activation and batch norm before conv + data_format: tensor data format, "NCHW"/"NHWC" + fused: use fused batch normalization if True + + Returns: + A keras model + """ + + axis = 1 if data_format == "channels_first" else 3 + model = tf.keras.Sequential() + if batch_norm_first: + model.add( + tf.keras.layers.BatchNormalization( + axis=axis, input_shape=input_shape, fused=fused)) + model.add(tf.keras.layers.LeakyReLU(alpha=0.)) + model.add( + tf.keras.layers.Conv2D( + filters=filters, + kernel_size=3, + strides=strides, + input_shape=input_shape, + data_format=data_format, + use_bias=False, + padding="SAME")) + + model.add(tf.keras.layers.BatchNormalization(axis=axis, fused=fused)) + model.add(tf.keras.layers.LeakyReLU(alpha=0.)) + model.add( + tf.keras.layers.Conv2D( + filters=filters, + kernel_size=3, + strides=(1, 1), + data_format=data_format, + use_bias=False, + padding="SAME")) + + return model diff --git a/tensorflow/contrib/eager/python/examples/revnet/blocks_test.py b/tensorflow/contrib/eager/python/examples/revnet/blocks_test.py new file mode 100644 index 0000000000..f4436fd925 --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/revnet/blocks_test.py @@ -0,0 +1,346 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for basic building blocks used in eager mode RevNet.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf +from tensorflow.contrib.eager.python.examples.revnet import blocks + + +def _validate_block_call_channels_last(block_factory, test): + """Generic testing function for `channels_last` data format. + + Completes a set of tests varying data format, stride, and batch normalization + configured train vs test time. + Args: + block_factory: constructor of one of blocks.InitBlock, blocks.FinalBlock, + blocks._ResidualInner + test: tf.test.TestCase object + """ + with tf.device("/cpu:0"): # NHWC format + input_shape = (224, 224, 32) + data_shape = (16,) + input_shape + x = tf.random_normal(shape=data_shape) + + # Stride 1 + block = block_factory( + filters=64, + strides=(1, 1), + input_shape=input_shape, + data_format="channels_last") + y_tr, y_ev = block(x, training=True), block(x, training=False) + test.assertEqual(y_tr.shape, y_ev.shape) + test.assertEqual(y_ev.shape, (16, 224, 224, 64)) + test.assertNotAllClose(y_tr, y_ev) + + # Stride of 2 + block = block_factory( + filters=64, + strides=(2, 2), + input_shape=input_shape, + data_format="channels_last") + y_tr, y_ev = block(x, training=True), block(x, training=False) + test.assertEqual(y_tr.shape, y_ev.shape) + test.assertEqual(y_ev.shape, (16, 112, 112, 64)) + test.assertNotAllClose(y_tr, y_ev) + + +def _validate_block_call_channels_first(block_factory, test): + """Generic testing function for `channels_first` data format. + + Completes a set of tests varying data format, stride, and batch normalization + configured train vs test time. + Args: + block_factory: constructor of one of blocks.InitBlock, blocks.FinalBlock, + blocks._ResidualInner + test: tf.test.TestCase object + """ + if not tf.test.is_gpu_available(): + test.skipTest("GPU not available") + + with tf.device("/gpu:0"): # Default NCHW format + input_shape = (32, 224, 224) + data_shape = (16,) + input_shape + x = tf.random_normal(shape=data_shape) + + # Stride of 1 + block = block_factory(filters=64, strides=(1, 1), input_shape=input_shape) + y_tr, y_ev = block(x, training=True), block(x, training=False) + test.assertEqual(y_tr.shape, y_ev.shape) + test.assertEqual(y_ev.shape, (16, 64, 224, 224)) + test.assertNotAllClose(y_tr, y_ev) + + # Stride of 2 + block = block_factory(filters=64, strides=(2, 2), input_shape=input_shape) + y_tr, y_ev = block(x, training=True), block(x, training=False) + test.assertEqual(y_tr.shape, y_ev.shape) + test.assertEqual(y_ev.shape, (16, 64, 112, 112)) + test.assertNotAllClose(y_tr, y_ev) + + +class RevBlockTest(tf.test.TestCase): + + def test_call_channels_first(self): + """Test `call` function with `channels_first` data format.""" + if not tf.test.is_gpu_available(): + self.skipTest("GPU not available") + + with tf.device("/gpu:0"): # Default NCHW format + input_shape = (32, 224, 224) + data_shape = (16,) + input_shape + x = tf.random_normal(shape=data_shape) + + # Stride of 1 + block = blocks.RevBlock( + n_res=3, filters=64, strides=(1, 1), input_shape=input_shape) + y_tr, y_ev = block(x, training=True), block(x, training=False) + self.assertEqual(y_tr.shape, y_ev.shape) + self.assertEqual(y_ev.shape, (16, 64, 224, 224)) + self.assertNotAllClose(y_tr, y_ev) + + # Stride of 2 + block = blocks.RevBlock( + n_res=3, filters=64, strides=(2, 2), input_shape=input_shape) + y_tr, y_ev = block(x, training=True), block(x, training=False) + self.assertEqual(y_tr.shape, y_ev.shape) + self.assertEqual(y_ev.shape, [16, 64, 112, 112]) + self.assertNotAllClose(y_tr, y_ev) + + def test_call_channels_last(self): + """Test `call` function with `channels_last` data format.""" + with tf.device("/cpu:0"): # NHWC format + input_shape = (224, 224, 32) + data_shape = (16,) + input_shape + x = tf.random_normal(shape=data_shape) + + # Stride 1 + block = blocks.RevBlock( + n_res=3, + filters=64, + strides=(1, 1), + input_shape=input_shape, + data_format="channels_last") + y_tr, y_ev = block(x, training=True), block(x, training=False) + self.assertEqual(y_tr.shape, y_ev.shape) + self.assertEqual(y_ev.shape, (16, 224, 224, 64)) + self.assertNotAllClose(y_tr, y_ev) + + # Stride of 2 + block = blocks.RevBlock( + n_res=3, + filters=64, + strides=(2, 2), + input_shape=input_shape, + data_format="channels_last") + y_tr, y_ev = block(x, training=True), block(x, training=False) + self.assertEqual(y_tr.shape, y_ev.shape) + self.assertEqual(y_ev.shape, (16, 112, 112, 64)) + self.assertNotAllClose(y_tr, y_ev) + + def test_backward_grads_and_vars_channels_first(self): + """Test `backward` function with `channels_first` data format.""" + if not tf.test.is_gpu_available(): + self.skipTest("GPU not available") + + with tf.device("/gpu:0"): # Default NCHW format + input_shape = (32, 224, 224) + data_shape = (16,) + input_shape + x = tf.random_normal(shape=data_shape) + + # Stride 1 + y = tf.random_normal(shape=data_shape) + dy = tf.random_normal(shape=data_shape) + block = blocks.RevBlock( + n_res=3, filters=32, strides=(1, 1), input_shape=input_shape) + dy, grads, vars_ = block.backward_grads_and_vars(x, y, dy) + self.assertEqual(dy.shape, x.shape) + self.assertTrue(isinstance(grads, list)) + self.assertTrue(isinstance(vars_, list)) + + # Stride 2 + y = tf.random_normal(shape=(16, 32, 112, 112)) + dy = tf.random_normal(shape=(16, 32, 112, 112)) + block = blocks.RevBlock( + n_res=3, filters=32, strides=(2, 2), input_shape=input_shape) + dy, grads, vars_ = block.backward_grads_and_vars(x, y, dy) + self.assertEqual(dy.shape, x.shape) + self.assertTrue(isinstance(grads, list)) + self.assertTrue(isinstance(vars_, list)) + + def test_backward_grads_and_vars_channels_last(self): + """Test `backward` function with `channels_last` data format.""" + with tf.device("/cpu:0"): # NHWC format + input_shape = (224, 224, 32) + data_shape = (16,) + input_shape + x = tf.random_normal(shape=data_shape) + + # Stride 1 + y = tf.random_normal(shape=data_shape) + dy = tf.random_normal(shape=data_shape) + block = blocks.RevBlock( + n_res=3, + filters=32, + strides=(1, 1), + input_shape=input_shape, + data_format="channels_last") + dy, grads, vars_ = block.backward_grads_and_vars(x, y, dy) + self.assertEqual(dy.shape, x.shape) + self.assertTrue(isinstance(grads, list)) + self.assertTrue(isinstance(vars_, list)) + + # Stride 2 + y = tf.random_normal(shape=(16, 112, 112, 32)) + dy = tf.random_normal(shape=(16, 112, 112, 32)) + block = blocks.RevBlock( + n_res=3, + filters=32, + strides=(2, 2), + input_shape=input_shape, + data_format="channels_last") + dy, grads, vars_ = block.backward_grads_and_vars(x, y, dy) + self.assertEqual(dy.shape, x.shape) + self.assertTrue(isinstance(grads, list)) + self.assertTrue(isinstance(vars_, list)) + + +class _ResidualTest(tf.test.TestCase): + + def test_call(self): + """Test `call` function. + + Varying downsampling and data format options. + """ + + _validate_block_call_channels_first(blocks._Residual, self) + _validate_block_call_channels_last(blocks._Residual, self) + + def test_backward_channels_first(self): + """Test `backward` function with `channels_first` data format.""" + if not tf.test.is_gpu_available(): + self.skipTest("GPU not available") + + with tf.device("/gpu:0"): # Default NCHW format + input_shape = (16, 224, 224) + data_shape = (16,) + input_shape + x = tf.random_normal(shape=data_shape) + residual = blocks._Residual( + filters=16, strides=(1, 1), input_shape=input_shape) + y_tr, y_ev = residual(x, training=True), residual(x, training=False) + x_ = residual.backward(y_tr, training=True) + # The numerical loss is alarming; reconstructed inputs could differ from + # the original inputs often by more than 1e-3 + self.assertAllClose(x, x_, rtol=1e-01, atol=1e-01) + x_ = residual.backward(y_ev, training=False) + self.assertAllClose(x, x_, rtol=1e-01, atol=1e-01) + + def test_backward_channels_last(self): + """Test `backward` function with `channels_last` data format.""" + with tf.device("/cpu:0"): # NHWC format + input_shape = (224, 224, 16) + data_shape = (16,) + input_shape + x = tf.random_normal(shape=data_shape) + residual = blocks._Residual( + filters=16, + strides=(1, 1), + input_shape=input_shape, + data_format="channels_last") + y_tr, y_ev = residual(x, training=True), residual(x, training=False) + x_ = residual.backward(y_tr, training=True) + # Egregious numerical error + self.assertAllClose(x, x_, rtol=1e-01, atol=1e-01) + x_ = residual.backward(y_ev, training=False) + self.assertAllClose(x, x_, rtol=1e-01, atol=1e-01) + + def test_backward_grads_and_vars_channels_first(self): + """Test `backward_grads` function with `channels_first` data format.""" + if not tf.test.is_gpu_available(): + self.skipTest("GPU not available") + + with tf.device("/gpu:0"): # Default NCHW format + input_shape = (16, 224, 224) + data_shape = (16,) + input_shape + x = tf.random_normal(shape=data_shape) + dy = tf.random_normal(shape=data_shape) + residual = blocks._Residual( + filters=16, strides=(1, 1), input_shape=input_shape) + dx_tr, grads_tr, vars_tr = residual.backward_grads_and_vars( + x, dy=dy, training=True) + dx_ev, grads_ev, vars_ev = residual.backward_grads_and_vars( + x, dy=dy, training=False) + self.assertNotAllClose(dx_tr, dx_ev) + self.assertTrue(isinstance(grads_tr, list)) + self.assertTrue(isinstance(grads_ev, list)) + self.assertTrue(isinstance(vars_tr, list)) + self.assertTrue(isinstance(vars_ev, list)) + for grad_tr, var_tr, grad_ev, var_ev in zip(grads_tr, vars_tr, grads_ev, + vars_ev): + if grad_tr is not None: # Batch norm moving mean, var gives None grad + self.assertEqual(grad_tr.shape, grad_ev.shape) + self.assertEqual(var_tr.shape, var_ev.shape) + self.assertEqual(grad_tr.shape, var_tr.shape) + + def test_backward_grads_and_vars_channels_last(self): + """Test `backward_grads` function with `channels_last` data format.""" + with tf.device("/cpu:0"): # NHWC format + input_shape = (224, 224, 16) + data_shape = (16,) + input_shape + x = tf.random_normal(shape=data_shape) + dy = tf.random_normal(shape=data_shape) + residual = blocks._Residual( + filters=16, + strides=(1, 1), + input_shape=input_shape, + data_format="channels_last") + dx_tr, grads_tr, vars_tr = residual.backward_grads_and_vars( + x, dy=dy, training=True) + dx_ev, grads_ev, vars_ev = residual.backward_grads_and_vars( + x, dy=dy, training=False) + self.assertNotAllClose(dx_tr, dx_ev) + self.assertTrue(isinstance(grads_tr, list)) + self.assertTrue(isinstance(grads_ev, list)) + self.assertTrue(isinstance(vars_tr, list)) + self.assertTrue(isinstance(vars_ev, list)) + for grad_tr, var_tr, grad_ev, var_ev in zip(grads_tr, vars_tr, grads_ev, + vars_ev): + if grad_tr is not None: # Batch norm moving mean, var gives None grad + self.assertEqual(grad_tr.shape, grad_ev.shape) + self.assertEqual(var_tr.shape, var_ev.shape) + self.assertEqual(grad_tr.shape, var_tr.shape) + + +class _ResidualInnerTest(tf.test.TestCase): + + def test_call(self): + """Test `call` function.""" + + _validate_block_call_channels_first(blocks._ResidualInner, self) + _validate_block_call_channels_last(blocks._ResidualInner, self) + + +class _BottleneckResidualInner(tf.test.TestCase): + + def test_call(self): + """Test `call` function.""" + + _validate_block_call_channels_first(blocks._BottleneckResidualInner, self) + _validate_block_call_channels_last(blocks._BottleneckResidualInner, self) + + +if __name__ == "__main__": + tf.enable_eager_execution() + tf.test.main() diff --git a/tensorflow/contrib/eager/python/examples/revnet/config.py b/tensorflow/contrib/eager/python/examples/revnet/config.py new file mode 100644 index 0000000000..495a78d550 --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/revnet/config.py @@ -0,0 +1,117 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Reversible residual network compatible with eager execution. + +Configuration in format of tf.contrib.training.HParams. +Supports CIFAR-10, CIFAR-100, and ImageNet datasets. + +Reference [The Reversible Residual Network: Backpropagation +Without Storing Activations](https://arxiv.org/pdf/1707.04585.pdf) + +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf + + +def get_hparams_cifar_38(): + """RevNet-38 configurations for CIFAR-10/CIFAR-100.""" + + config = tf.contrib.training.HParams() + config.add_hparam("init_filters", 32) + config.add_hparam("init_kernel", 3) + config.add_hparam("init_stride", 1) + config.add_hparam("n_classes", 10) + config.add_hparam("n_rev_blocks", 3) + config.add_hparam("n_res", [3, 3, 3]) + config.add_hparam("filters", [32, 64, 112]) + config.add_hparam("strides", [1, 2, 2]) + config.add_hparam("batch_size", 10) + config.add_hparam("bottleneck", False) + config.add_hparam("fused", True) + config.add_hparam("init_max_pool", False) + if tf.test.is_gpu_available(): + config.add_hparam("input_shape", (3, 32, 32)) + config.add_hparam("data_format", "channels_first") + else: + config.add_hparam("input_shape", (32, 32, 3)) + config.add_hparam("data_format", "channels_last") + + # Training details + config.add_hparam("weight_decay", 2e-4) + config.add_hparam("momentum", .9) + config.add_hparam("lr_decay_steps", [40000, 60000]) + config.add_hparam("lr_list", [1e-1, 1e-2, 1e-3]) + config.add_hparam("max_train_iter", 80000) + config.add_hparam("seed", 1234) + config.add_hparam("shuffle", True) + config.add_hparam("prefetch", True) + config.add_hparam("print_every", 50) + config.add_hparam("dtype", tf.float32) + config.add_hparam("eval_batch_size", 500) + config.add_hparam("div255", True) + # For tf.data.Dataset + config.add_hparam("epochs", config.max_train_iter // config.batch_size) + + return config + + +def get_hparams_imagenet_56(): + """RevNet-56 configurations for ImageNet.""" + + config = tf.contrib.training.HParams() + config.add_hparam("init_filters", 128) + config.add_hparam("init_kernel", 7) + config.add_hparam("init_stride", 2) + config.add_hparam("n_classes", 1000) + config.add_hparam("n_rev_blocks", 4) + config.add_hparam("n_res", [2, 2, 2, 2]) + config.add_hparam("filters", [128, 256, 512, 832]) + config.add_hparam("strides", [1, 2, 2, 2]) + config.add_hparam("batch_size", 16) + config.add_hparam("bottleneck", True) + config.add_hparam("fused", True) + config.add_hparam("init_max_pool", True) + if tf.test.is_gpu_available(): + config.add_hparam("input_shape", (3, 224, 224)) + config.add_hparam("data_format", "channels_first") + else: + config.add_hparam("input_shape", (224, 224, 3)) + config.add_hparam("data_format", "channels_last") + + # Training details + config.add_hparam("weight_decay", 1e-4) + config.add_hparam("momentum", .9) + config.add_hparam("lr_decay_steps", [160000, 320000, 480000]) + config.add_hparam("lr_list", [1e-1, 1e-2, 1e-3, 1e-4]) + config.add_hparam("max_train_iter", 600000) + config.add_hparam("seed", 1234) + config.add_hparam("shuffle", True) + config.add_hparam("prefetch", True) + config.add_hparam("print_every", 50) + config.add_hparam("dtype", tf.float32) + config.add_hparam("eval_batch_size", 500) + config.add_hparam("div255", True) + # For tf.data.Dataset + config.add_hparam("epochs", config.max_train_iter // config.batch_size) + + if config.bottleneck: + filters = [f * 4 for f in config.filters] + config.filters = filters + + return config diff --git a/tensorflow/contrib/eager/python/examples/revnet/ops.py b/tensorflow/contrib/eager/python/examples/revnet/ops.py new file mode 100644 index 0000000000..9ed5d363e6 --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/revnet/ops.py @@ -0,0 +1,70 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Reversible residual network compatible with eager execution. + +Customized basic operations. + +Reference [The Reversible Residual Network: Backpropagation +Without Storing Activations](https://arxiv.org/pdf/1707.04585.pdf) +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf + + +def downsample(x, filters, strides, axis=1): + """Downsample feature map with avg pooling, if filter size doesn't match.""" + + def pad_strides(strides, axis=1): + """Convert length 2 to length 4 strides. + + Needed since `tf.layers.Conv2D` uses length 2 strides, whereas operations + such as `tf.nn.avg_pool` use length 4 strides. + + Args: + strides: length 2 list/tuple strides for height and width + axis: integer specifying feature dimension according to data format + Returns: + length 4 strides padded with 1 on batch and channel dimension + """ + + assert len(strides) == 2 + + if axis == 1: + return [1, 1, strides[0], strides[1]] + return [1, strides[0], strides[1], 1] + + assert len(x.shape) == 4 and (axis == 1 or axis == 3) + + data_format = "NCHW" if axis == 1 else "NHWC" + strides_ = pad_strides(strides, axis=axis) + + if strides[0] > 1: + x = tf.nn.avg_pool( + x, strides_, strides_, padding="VALID", data_format=data_format) + + in_filter = x.shape[axis] + out_filter = filters + + if in_filter < out_filter: + pad_size = [(out_filter - in_filter) // 2, (out_filter - in_filter) // 2] + if axis == 1: + x = tf.pad(x, [[0, 0], pad_size, [0, 0], [0, 0]]) + else: + x = tf.pad(x, [[0, 0], [0, 0], [0, 0], pad_size]) + # In case `tape.gradient(x, [x])` produces a list of `None` + return x + 0. diff --git a/tensorflow/contrib/eager/python/examples/revnet/ops_test.py b/tensorflow/contrib/eager/python/examples/revnet/ops_test.py new file mode 100644 index 0000000000..5bc2641faf --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/revnet/ops_test.py @@ -0,0 +1,80 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for basic ops used in eager mode RevNet.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf +from tensorflow.contrib.eager.python.examples.revnet import ops +tfe = tf.contrib.eager + + +class OpsTest(tf.test.TestCase): + + def test_downsample(self): + """Test `possible_down_sample` function with mock object.""" + + batch_size = 100 + # NHWC format + x = tf.random_normal(shape=[batch_size, 32, 32, 3]) + # HW doesn't change but number of features increased + y = ops.downsample(x, filters=5, strides=(1, 1), axis=3) + self.assertEqual(y.shape, [batch_size, 32, 32, 5]) + # Feature map doesn't change but HW reduced + y = ops.downsample(x, filters=3, strides=(2, 2), axis=3) + self.assertEqual(y.shape, [batch_size, 16, 16, 3]) + # Number of feature increased and HW reduced + y = ops.downsample(x, filters=5, strides=(2, 2), axis=3) + self.assertEqual(y.shape, [batch_size, 16, 16, 5]) + + # Test gradient flow + x = tf.random_normal(shape=[batch_size, 32, 32, 3]) + with tfe.GradientTape() as tape: + tape.watch(x) + y = ops.downsample(x, filters=3, strides=(1, 1)) + self.assertEqual(y.shape, x.shape) + dy = tf.random_normal(shape=[batch_size, 3, 32, 32]) + grad, = tape.gradient(y, [x], output_gradients=[dy]) + self.assertEqual(grad.shape, x.shape) + + # Default NCHW format + if tf.test.is_gpu_available(): + x = tf.random_normal(shape=[batch_size, 3, 32, 32]) + # HW doesn't change but feature map reduced + y = ops.downsample(x, filters=5, strides=(1, 1)) + self.assertEqual(y.shape, [batch_size, 5, 32, 32]) + # Feature map doesn't change but HW reduced + y = ops.downsample(x, filters=3, strides=(2, 2)) + self.assertEqual(y.shape, [batch_size, 3, 16, 16]) + # Both feature map and HW reduced + y = ops.downsample(x, filters=5, strides=(2, 2)) + self.assertEqual(y.shape, [batch_size, 5, 16, 16]) + + # Test gradient flow + x = tf.random_normal(shape=[batch_size, 3, 32, 32]) + with tfe.GradientTape() as tape: + tape.watch(x) + y = ops.downsample(x, filters=3, strides=(1, 1)) + self.assertEqual(y.shape, x.shape) + dy = tf.random_normal(shape=[batch_size, 3, 32, 32]) + grad, = tape.gradient(y, [x], output_gradients=[dy]) + self.assertEqual(grad.shape, x.shape) + + +if __name__ == '__main__': + tf.enable_eager_execution() + tf.test.main() diff --git a/tensorflow/contrib/eager/python/examples/revnet/revnet.py b/tensorflow/contrib/eager/python/examples/revnet/revnet.py new file mode 100644 index 0000000000..aa3f7efe1b --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/revnet/revnet.py @@ -0,0 +1,263 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Reversible residual network compatible with eager execution. + +Code for main model. + +Reference [The Reversible Residual Network: Backpropagation +Without Storing Activations](https://arxiv.org/pdf/1707.04585.pdf) +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import functools +import operator + +import tensorflow as tf +from tensorflow.contrib.eager.python.examples.revnet import blocks + + +# Global Conventions: +# 1) Default data format is NCWH, targeting GPU +# 2) Each block has attribute axis, inferred from data_format +# 3) Default training option to True for batch normalization +class RevNet(tf.keras.Model): + """RevNet that depends on all the blocks.""" + + def __init__(self, config): + """Initialize RevNet with building blocks. + + Args: + config: tf.contrib.training.HParams object; specifies hyperparameters + """ + super(RevNet, self).__init__() + self.axis = 1 if config.data_format == "channels_first" else 3 + self.config = config + + self._init_block = self._construct_init_block() + self._block_list = self._construct_intermediate_blocks() + self._final_block = self._construct_final_block() + + def _construct_init_block(self): + init_block = tf.keras.Sequential( + [ + tf.keras.layers.Conv2D( + filters=self.config.init_filters, + kernel_size=self.config.init_kernel, + strides=(self.config.init_stride, self.config.init_stride), + data_format=self.config.data_format, + use_bias=False, + padding="SAME", + input_shape=self.config.input_shape), + tf.keras.layers.BatchNormalization( + axis=self.axis, fused=self.config.fused), + tf.keras.layers.LeakyReLU(alpha=0.) + ], + name="init") + if self.config.init_max_pool: + init_block.add( + tf.keras.layers.MaxPooling2D( + pool_size=(3, 3), + strides=(2, 2), + padding="SAME", + data_format=self.config.data_format)) + return init_block + + def _construct_final_block(self): + f = self.config.filters[-1] # Number of filters + r = functools.reduce(operator.mul, self.config.strides, 1) # Reduce ratio + r *= self.config.init_stride + if self.config.init_max_pool: + r *= 2 + + if self.config.data_format == "channels_first": + w, h = self.config.input_shape[1], self.config.input_shape[2] + input_shape = (f, w // r, h // r) + elif self.config.data_format == "channels_last": + w, h = self.config.input_shape[0], self.config.input_shape[1] + input_shape = (w // r, h // r, f) + else: + raise ValueError("Data format should be either `channels_first`" + " or `channels_last`") + + final_block = tf.keras.Sequential( + [ + tf.keras.layers.BatchNormalization( + axis=self.axis, + input_shape=input_shape, + fused=self.config.fused), + tf.keras.layers.LeakyReLU(alpha=0.), # Vanilla ReLU + tf.keras.layers.GlobalAveragePooling2D( + data_format=self.config.data_format), + tf.keras.layers.Dense(self.config.n_classes) + ], + name="final") + return final_block + + def _construct_intermediate_blocks(self): + # Precompute input shape after initial block + stride = self.config.init_stride + if self.config.init_max_pool: + stride *= 2 + if self.config.data_format == "channels_first": + w, h = self.config.input_shape[1], self.config.input_shape[2] + input_shape = (self.config.init_filters, w // stride, h // stride) + else: + w, h = self.config.input_shape[0], self.config.input_shape[1] + input_shape = (w // stride, h // stride, self.config.init_filters) + + # Aggregate intermediate blocks + block_list = tf.contrib.checkpoint.List() + for i in range(self.config.n_rev_blocks): + # RevBlock configurations + n_res = self.config.n_res[i] + filters = self.config.filters[i] + if filters % 2 != 0: + raise ValueError("Number of output filters must be even to ensure" + "correct partitioning of channels") + stride = self.config.strides[i] + strides = (self.config.strides[i], self.config.strides[i]) + + # Add block + rev_block = blocks.RevBlock( + n_res, + filters, + strides, + input_shape, + batch_norm_first=(i != 0), # Only skip on first block + data_format=self.config.data_format, + bottleneck=self.config.bottleneck, + fused=self.config.fused) + block_list.append(rev_block) + + # Precompute input shape for the next block + if self.config.data_format == "channels_first": + w, h = input_shape[1], input_shape[2] + input_shape = (filters, w // stride, h // stride) + else: + w, h = input_shape[0], input_shape[1] + input_shape = (w // stride, h // stride, filters) + + return block_list + + def call(self, inputs, training=True): + """Forward pass.""" + + # Only store hidden states during training + if training: + saved_hidden = [inputs] + + h = self._init_block(inputs, training=training) + if training: + saved_hidden.append(h) + + for block in self._block_list: + h = block(h, training=training) + if training: + saved_hidden.append(h) + + logits = self._final_block(h, training=training) + + return (logits, saved_hidden) if training else (logits, None) + + def compute_loss(self, logits, labels): + """Compute cross entropy loss.""" + + cross_ent = tf.nn.sparse_softmax_cross_entropy_with_logits( + logits=logits, labels=labels) + + return tf.reduce_mean(cross_ent) + + def compute_gradients(self, inputs, labels, training=True): + """Manually computes gradients. + + Args: + inputs: Image tensor, either NHWC or NCHW, conforming to `data_format` + labels: One-hot labels for classification + training: for batch normalization + + Returns: + list of tuple each being (grad, var) for optimizer use + """ + + # Forward pass record hidden states before downsampling + _, saved_hidden = self.call(inputs, training=training) + + grads_all = [] + vars_all = [] + + # Manually backprop through last block + x = saved_hidden[-1] + with tf.GradientTape() as tape: + tape.watch(x) + logits = self._final_block(x, training=training) + cost = self.compute_loss(logits, labels) + + grads_combined = tape.gradient(cost, [x] + self._final_block.variables) + dy, grads_ = grads_combined[0], grads_combined[1:] + grads_all += grads_ + vars_all += self._final_block.variables + + # Manually backprop through intermediate blocks + for block in reversed(self._block_list): + y = saved_hidden.pop() + x = saved_hidden[-1] + dy, grads, vars_ = block.backward_grads_and_vars( + x, y, dy, training=training) + grads_all += grads + vars_all += vars_ + + # Manually backprop through first block + saved_hidden.pop() + x = saved_hidden.pop() + assert not saved_hidden # Cleared after backprop + + with tf.GradientTape() as tape: + y = self._init_block(x, training=training) # Recomputing + + grads_all += tape.gradient( + y, self._init_block.variables, output_gradients=[dy]) + vars_all += self._init_block.variables + + return grads_all, vars_all + + def train_step(self, + inputs, + labels, + optimizer, + global_step=None, + report=False): + """Train for one iteration.""" + + grads_all, vars_all = self.compute_gradients(inputs, labels, training=True) + optimizer.apply_gradients(zip(grads_all, vars_all), global_step=global_step) + + if report: + logits, _ = self.call(inputs, training=True) + loss = self.compute_loss(logits, labels) + + return loss + + def eval_step(self, inputs, labels): + """Evaluate.""" + + logits, _ = self.call(inputs, training=False) + preds = tf.cast(tf.argmax(logits, axis=1), tf.int32) + corrects = tf.cast(tf.equal(preds, labels), tf.float32) + accuracy = tf.reduce_mean(corrects) + + return accuracy diff --git a/tensorflow/contrib/eager/python/examples/revnet/revnet_test.py b/tensorflow/contrib/eager/python/examples/revnet/revnet_test.py new file mode 100644 index 0000000000..68502ceac2 --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/revnet/revnet_test.py @@ -0,0 +1,277 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for basic building blocks used in eager mode RevNet.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import gc +import time + +import tensorflow as tf +from tensorflow.contrib.eager.python.examples.revnet import config as config_ +from tensorflow.contrib.eager.python.examples.revnet import revnet +from tensorflow.python.client import device_lib +tfe = tf.contrib.eager + + +class RevnetTest(tf.test.TestCase): + + def setUp(self): + super(RevnetTest, self).setUp() + config = config_.get_hparams_imagenet_56() + shape = (config.batch_size,) + config.input_shape + self.model = revnet.RevNet(config=config) + self.x = tf.random_normal(shape=shape) + self.t = tf.random_uniform( + shape=[config.batch_size], + minval=0, + maxval=config.n_classes, + dtype=tf.int32) + self.config = config + + def tearDown(self): + del self.model + del self.x + del self.t + del self.config + super(RevnetTest, self).tearDown() + + def test_call(self): + """Test `call` function.""" + + y, _ = self.model(self.x, training=False) + self.assertEqual(y.shape, [self.config.batch_size, self.config.n_classes]) + + def test_compute_gradients(self): + """Test `compute_gradients` function.""" + + grads, vars_ = self.model.compute_gradients(inputs=self.x, labels=self.t) + self.assertTrue(isinstance(grads, list)) + self.assertTrue(isinstance(vars_, list)) + self.assertEqual(len(grads), len(vars_)) + for grad, var in zip(grads, vars_): + if grad is not None: + self.assertEqual(grad.shape, var.shape) + + def test_train_step(self): + """Test `train_step` function.""" + + logits, _ = self.model(self.x, training=True) + loss = self.model.compute_loss(logits=logits, labels=self.t) + optimizer = tf.train.AdamOptimizer(learning_rate=1e-3) + + # Loss should be decreasing after each optimization step + for _ in range(3): + loss_ = self.model.train_step(self.x, self.t, optimizer, report=True) + self.assertTrue(loss_.numpy() <= loss.numpy()) + loss = loss_ + + def test_call_defun(self): + """Test `call` function with tfe.defun apply.""" + + y, _ = tfe.defun(self.model.call)(self.x, training=False) + self.assertEqual(y.shape, [self.config.batch_size, self.config.n_classes]) + + def test_train_step_defun(self): + self.model.call = tfe.defun(self.model.call) + logits, _ = self.model(self.x, training=True) + loss = self.model.compute_loss(logits=logits, labels=self.t) + optimizer = tf.train.AdamOptimizer(learning_rate=1e-3) + + for _ in range(3): + loss_ = self.model.train_step(self.x, self.t, optimizer, report=True) + self.assertTrue(loss_.numpy() <= loss.numpy()) + loss = loss_ + + # Initialize new model, so that other tests are not affected + self.model = revnet.RevNet(config=self.config) + + +# Benchmark related +def device_and_data_format(): + return ("/gpu:0", + "channels_first") if tf.test.is_gpu_available() else ("/cpu:0", + "channels_last") + + +def random_batch(batch_size, config): + shape = (batch_size,) + config.input_shape + images = tf.random_uniform(shape) + labels = tf.random_uniform( + [batch_size], minval=0, maxval=config.n_classes, dtype=tf.int32) + + return images, labels + + +class MockIterator(object): + + def __init__(self, tensors): + self._tensors = [tf.identity(x) for x in tensors] + + def next(self): + return self._tensors + + +class RevnetBenchmark(tf.test.Benchmark): + """Eager and graph benchmarks for RevNet.""" + + def _train_batch_sizes(self): + """Shamelessly copied from `resnet50_test.py`. + + Note: This is targeted towards ImageNet. CIFAR-10 should allow more + aggressive batch sizes. + + Returns: + A tuple of possible batch sizes + """ + for device in device_lib.list_local_devices(): + if tf.DeviceSpec.from_string(device.name).device_type == "GPU": + if "K20" in device.physical_device_desc: + return (16,) + if "P100" in device.physical_device_desc: + return (16, 32, 64) + if tf.DeviceSpec.from_string(device.name).device_type == "TPU": + return (32,) + return (16, 32) + + def _force_device_sync(self): + """Shamelessly copied from `resnet50_test.py`.""" + tf.constant(1.).cpu() + + def _report(self, label, start, num_iters, device, batch_size, data_format): + avg_time = (time.time() - start) / num_iters + dev = tf.DeviceSpec.from_string(device).device_type.lower() + name = "%s_%s_batch_%d_%s" % (label, dev, batch_size, data_format) + extras = {"examples_per_sec": batch_size / avg_time} + self.report_benchmark( + iters=num_iters, wall_time=avg_time, name=name, extras=extras) + + def _benchmark_eager_apply(self, + label, + device_and_format, + defun=False, + execution_mode=None, + compiled=False): + config = config_.get_hparams_imagenet_56() + with tfe.execution_mode(execution_mode): + device, data_format = device_and_format + model = revnet.RevNet(config=config) + if defun: + model.call = tfe.defun(model.call, compiled=compiled) + batch_size = 64 + num_burn = 5 + num_iters = 10 + with tf.device(device): + images, _ = random_batch(batch_size, config) + for _ in range(num_burn): + model(images, training=False) + if execution_mode: + tfe.async_wait() + gc.collect() + start = time.time() + for _ in range(num_iters): + model(images, training=False) + if execution_mode: + tfe.async_wait() + self._report(label, start, num_iters, device, batch_size, data_format) + + def benchmark_eager_apply_sync(self): + self._benchmark_eager_apply( + "eager_apply_sync", device_and_data_format(), defun=False) + + def benchmark_eager_apply_async(self): + self._benchmark_eager_apply( + "eager_apply_async", + device_and_data_format(), + defun=False, + execution_mode=tfe.ASYNC) + + def benchmark_eager_call_defun(self): + self._benchmark_eager_apply( + "eager_apply_with_defun", device_and_data_format(), defun=True) + + def _benchmark_eager_train(self, + label, + make_iterator, + device_and_format, + defun=False, + execution_mode=None, + compiled=False): + config = config_.get_hparams_imagenet_56() + with tfe.execution_mode(execution_mode): + device, data_format = device_and_format + for batch_size in self._train_batch_sizes(): + (images, labels) = random_batch(batch_size, config) + model = revnet.RevNet(config=config) + optimizer = tf.train.GradientDescentOptimizer(0.1) + if defun: + model.call = tfe.defun(model.call) + + num_burn = 3 + num_iters = 10 + with tf.device(device): + iterator = make_iterator((images, labels)) + for _ in range(num_burn): + (images, labels) = iterator.next() + model.train_step(images, labels, optimizer) + if execution_mode: + tfe.async_wait() + self._force_device_sync() + gc.collect() + + start = time.time() + for _ in range(num_iters): + (images, labels) = iterator.next() + model.train_step(images, labels, optimizer) + if execution_mode: + tfe.async_wait() + self._force_device_sync() + self._report(label, start, num_iters, device, batch_size, data_format) + + def benchmark_eager_train_sync(self): + self._benchmark_eager_train( + "eager_train_sync", MockIterator, device_and_data_format(), defun=False) + + def benchmark_eager_train_async(self): + self._benchmark_eager_train( + "eager_train_async", + MockIterator, + device_and_data_format(), + defun=False, + execution_mode=tfe.ASYNC) + + def benchmark_eager_train_defun(self): + self._benchmark_eager_train( + "eager_train", MockIterator, device_and_data_format(), defun=False) + + def benchmark_eager_train_datasets_with_defun(self): + + def make_iterator(tensors): + with tf.device("/device:CPU:0"): + ds = tf.data.Dataset.from_tensors(tensors).repeat() + return tfe.Iterator(ds) + + self._benchmark_eager_train( + "eager_train_dataset_with_defun", + make_iterator, + device_and_data_format(), + defun=True) + + +if __name__ == "__main__": + tf.enable_eager_execution() + tf.test.main() -- GitLab From 5bfc42c7fce79fb973c05910312d077abdf57cd2 Mon Sep 17 00:00:00 2001 From: Derek Murray Date: Wed, 13 Jun 2018 11:26:31 -0700 Subject: [PATCH 393/816] [tf.data] Factor out function argument restructuring into a helper. This cuts down on the amount of repeated (or near-repeated) code in Dataset wrappers. PiperOrigin-RevId: 200424152 --- .../python/kernel_tests/bucketing_test.py | 4 +- .../contrib/data/python/ops/grouping.py | 79 ++------- .../contrib/data/python/ops/optimization.py | 1 + .../contrib/data/python/ops/scan_ops.py | 30 +--- tensorflow/python/data/ops/dataset_ops.py | 159 +++++++++--------- 5 files changed, 101 insertions(+), 172 deletions(-) diff --git a/tensorflow/contrib/data/python/kernel_tests/bucketing_test.py b/tensorflow/contrib/data/python/kernel_tests/bucketing_test.py index bd3e034211..4fbfbfdbdd 100644 --- a/tensorflow/contrib/data/python/kernel_tests/bucketing_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/bucketing_test.py @@ -68,7 +68,7 @@ class GroupByReducerTest(test.TestCase): reducer = grouping.Reducer( init_func=lambda _: (0.0, 0.0), reduce_func=reduce_fn, - finalize_func=lambda x: x[0]) + finalize_func=lambda x, _: x) for i in range(1, 11): dataset = dataset_ops.Dataset.range(2 * i).apply( grouping.group_by_reducer( @@ -121,7 +121,7 @@ class GroupByReducerTest(test.TestCase): reducer = grouping.Reducer( init_func=lambda x: ([0], 1), reduce_func=reduce_fn, - finalize_func=lambda x: x) + finalize_func=lambda x, y: (x, y)) for i in range(1, 11): dataset = dataset_ops.Dataset.from_tensors(np.int64(0)).repeat(i).apply( diff --git a/tensorflow/contrib/data/python/ops/grouping.py b/tensorflow/contrib/data/python/ops/grouping.py index f9f25e6a06..e9aa9f4ed6 100644 --- a/tensorflow/contrib/data/python/ops/grouping.py +++ b/tensorflow/contrib/data/python/ops/grouping.py @@ -279,22 +279,8 @@ class GroupByReducerDataset(dataset_ops.Dataset): input_dataset.output_classes))) def tf_key_func(*args): """A wrapper for Defun that facilitates shape inference.""" - # Pass in shape information from the input_dataset. - dense_shapes = sparse.as_dense_shapes(input_dataset.output_shapes, - input_dataset.output_classes) - for arg, shape in zip(args, nest.flatten(dense_shapes)): - arg.set_shape(shape) - - nested_args = nest.pack_sequence_as(input_dataset.output_types, args) - nested_args = sparse.deserialize_sparse_tensors( - nested_args, input_dataset.output_types, input_dataset.output_shapes, - input_dataset.output_classes) - # pylint: disable=protected-access - if dataset_ops._should_unpack_args(nested_args): - ret = key_func(*nested_args) - # pylint: enable=protected-access - else: - ret = key_func(nested_args) + nested_args = dataset_ops.restructure_args(args, input_dataset) + ret = key_func(*nested_args) ret = ops.convert_to_tensor(ret) if ret.dtype != dtypes.int64 or ret.get_shape() != tensor_shape.scalar(): raise ValueError( @@ -356,28 +342,13 @@ class GroupByReducerDataset(dataset_ops.Dataset): input_dataset.output_classes)))) def tf_reduce_func(*args): """A wrapper for Defun that facilitates shape inference.""" - for arg, shape in zip( + nested_args = dataset_ops.restructure_args( args, - nest.flatten( - sparse.as_dense_shapes(self._state_shapes, self._state_classes)) - + nest.flatten( - sparse.as_dense_shapes(input_dataset.output_shapes, - input_dataset.output_classes))): - arg.set_shape(shape) - - pivot = len(nest.flatten(self._state_shapes)) - nested_state_args = nest.pack_sequence_as(self._state_types, - args[:pivot]) - nested_state_args = sparse.deserialize_sparse_tensors( - nested_state_args, self._state_types, self._state_shapes, - self._state_classes) - nested_input_args = nest.pack_sequence_as(input_dataset.output_types, - args[pivot:]) - nested_input_args = sparse.deserialize_sparse_tensors( - nested_input_args, input_dataset.output_types, - input_dataset.output_shapes, input_dataset.output_classes) - - ret = reduce_func(nested_state_args, nested_input_args) + input_shapes=(self._state_shapes, input_dataset.output_shapes), + input_types=(self._state_types, input_dataset.output_types), + input_classes=(self._state_classes, input_dataset.output_classes)) + + ret = reduce_func(*nested_args) # Convert any `SparseTensorValue`s to `SparseTensor`s and all other # values to tensors. @@ -442,18 +413,10 @@ class GroupByReducerDataset(dataset_ops.Dataset): sparse.as_dense_types(self._state_types, self._state_classes)))) def tf_finalize_func(*args): """A wrapper for Defun that facilitates shape inference.""" - for arg, shape in zip( - args, - nest.flatten( - sparse.as_dense_shapes(self._state_shapes, self._state_classes))): - arg.set_shape(shape) - - nested_args = nest.pack_sequence_as(self._state_types, args) - nested_args = sparse.deserialize_sparse_tensors( - nested_args, self._state_types, self._state_shapes, - self._state_classes) - - ret = finalize_func(nested_args) + nested_args = dataset_ops.restructure_args( + args, input_shapes=self._state_shapes, input_types=self._state_types, + input_classes=self._state_classes) + ret = finalize_func(*nested_args) # Convert any `SparseTensorValue`s to `SparseTensor`s and all other # values to tensors. @@ -543,22 +506,8 @@ class GroupByWindowDataset(dataset_ops.Dataset): input_dataset.output_classes))) def tf_key_func(*args): """A wrapper for Defun that facilitates shape inference.""" - # Pass in shape information from the input_dataset. - dense_shapes = sparse.as_dense_shapes(input_dataset.output_shapes, - input_dataset.output_classes) - for arg, shape in zip(args, nest.flatten(dense_shapes)): - arg.set_shape(shape) - - nested_args = nest.pack_sequence_as(input_dataset.output_types, args) - nested_args = sparse.deserialize_sparse_tensors( - nested_args, input_dataset.output_types, input_dataset.output_shapes, - input_dataset.output_classes) - # pylint: disable=protected-access - if dataset_ops._should_unpack_args(nested_args): - ret = key_func(*nested_args) - # pylint: enable=protected-access - else: - ret = key_func(nested_args) + nested_args = dataset_ops.restructure_args(args, input_dataset) + ret = key_func(*nested_args) ret = ops.convert_to_tensor(ret, dtype=dtypes.int64) if ret.dtype != dtypes.int64: raise ValueError("`key_func` must return a single tf.int64 tensor.") diff --git a/tensorflow/contrib/data/python/ops/optimization.py b/tensorflow/contrib/data/python/ops/optimization.py index 9612ac5ae9..2ca3805d66 100644 --- a/tensorflow/contrib/data/python/ops/optimization.py +++ b/tensorflow/contrib/data/python/ops/optimization.py @@ -61,6 +61,7 @@ class OptimizeDataset(dataset_ops.Dataset): self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access self._optimizations, **dataset_ops.flat_structure(self)) + @property def output_classes(self): return self._input_dataset.output_classes diff --git a/tensorflow/contrib/data/python/ops/scan_ops.py b/tensorflow/contrib/data/python/ops/scan_ops.py index 67eede981c..1dc58b468a 100644 --- a/tensorflow/contrib/data/python/ops/scan_ops.py +++ b/tensorflow/contrib/data/python/ops/scan_ops.py @@ -77,31 +77,13 @@ class _ScanDataset(dataset_ops.Dataset): input_dataset.output_classes)))) def tf_scan_func(*args): """A wrapper for Defun that facilitates shape inference.""" - # Pass in shape information from the state and input_dataset. - for arg, shape in zip( + nested_args = dataset_ops.restructure_args( args, - nest.flatten( - sparse.as_dense_shapes(self._state_shapes, self._state_classes)) - + nest.flatten( - sparse.as_dense_shapes(input_dataset.output_shapes, - input_dataset.output_classes))): - arg.set_shape(shape) - - pivot = len(nest.flatten(self._state_shapes)) - print(self._state_classes) - nested_state_args = nest.pack_sequence_as(self._state_types, - args[:pivot]) - nested_state_args = sparse.deserialize_sparse_tensors( - nested_state_args, self._state_types, self._state_shapes, - self._state_classes) - print(input_dataset.output_classes) - nested_input_args = nest.pack_sequence_as(input_dataset.output_types, - args[pivot:]) - nested_input_args = sparse.deserialize_sparse_tensors( - nested_input_args, input_dataset.output_types, - input_dataset.output_shapes, input_dataset.output_classes) - - ret = scan_func(nested_state_args, nested_input_args) + input_shapes=(self._state_shapes, input_dataset.output_shapes), + input_types=(self._state_types, input_dataset.output_types), + input_classes=(self._state_classes, input_dataset.output_classes)) + + ret = scan_func(*nested_args) if not isinstance(ret, collections.Sequence) or len(ret) != 2: raise TypeError("The scan function must return a pair comprising the " "new state and the output value.") diff --git a/tensorflow/python/data/ops/dataset_ops.py b/tensorflow/python/data/ops/dataset_ops.py index d0deed5ede..9811d6b13f 100644 --- a/tensorflow/python/data/ops/dataset_ops.py +++ b/tensorflow/python/data/ops/dataset_ops.py @@ -1182,6 +1182,66 @@ def flat_structure(dataset): } +def restructure_args(args, dataset=None, input_shapes=None, input_types=None, + input_classes=None): + """Converts a flat tuple of arguments into a given structure. + + The intended use is to bridge between the flat tuple of unshaped @{tf.Tensor} + arguments that a `Defun` receives and the potentially nested structures that + `tf.data` functions expect. + + The expected usage for an example function is as follows: + + ```python + input_dataset = ... # A `tf.data.Dataset`. + + @function.Defun(...) + def tf_example_func(*args): + nested_args = restructure_args(args, input_dataset) + ret = example_func(*nested_args) + # [Destructure and handle the return values from `example_func()`. + ``` + + Either `dataset`, or all of `input_shapes`, `input_types` and `input_classes` + must be specified. If `dataset` is not specified, the structures of + `input_shapes`, `input_types` and `input_classes` must be compatible. + + Args: + args: A flat tuple of @{tf.Tensor} objects, representing the arguments + to a TensorFlow function. + dataset: (Optional.) A @{tf.data.Dataset} whose element structure matches + the desired structure of the arguments. + input_shapes: (Optional.) A nested structure of @{tf.TensorShape} with the + desired structure and static shapes for each argument. + input_types: (Optional.) A nested structure of @{tf.DType} with the desired + structure and types for each argument. + input_classes: (Optional.) A nested structure of `type` with the desired + structure and classes for each argument. + + Returns: + A nested structure representing the arguments. + """ + if input_shapes is None: + assert dataset is not None + assert input_types is None and input_classes is None + input_shapes = dataset.output_shapes + input_types = dataset.output_types + input_classes = dataset.output_classes + else: + assert input_types is not None and input_classes is not None + + dense_shapes = sparse.as_dense_shapes(input_shapes, input_classes) + for arg, shape in zip(args, nest.flatten(dense_shapes)): + arg.set_shape(shape) + + nested_args = nest.pack_sequence_as(input_classes, args) + nested_args = sparse.deserialize_sparse_tensors( + nested_args, input_types, input_shapes, input_classes) + if not _should_unpack_args(nested_args): + nested_args = (nested_args,) + return nested_args + + class _GeneratorDataset(Dataset): """A `Dataset` that generates elements by invoking a function.""" @@ -1218,17 +1278,10 @@ class _GeneratorDataset(Dataset): sparse.as_dense_types(init_args_types, init_args_classes))) def tf_init_func(*args): """A wrapper for Defun that facilitates shape inference.""" - dense_shapes = sparse.as_dense_shapes(init_args_shapes, init_args_classes) - for arg, shape in zip(args, nest.flatten(dense_shapes)): - arg.set_shape(shape) - - nested_args = nest.pack_sequence_as(init_args_classes, args) - nested_args = sparse.deserialize_sparse_tensors( - nested_args, init_args_types, init_args_shapes, init_args_classes) - if _should_unpack_args(nested_args): - ret = init_func(*nested_args) - else: - ret = init_func(nested_args) + nested_args = restructure_args( + args, input_shapes=init_args_shapes, input_types=init_args_types, + input_classes=init_args_classes) + ret = init_func(*nested_args) # If `init_func` returns a list of tensors, `nest.flatten()` and # `ops.convert_to_tensor()` would conspire to attempt to stack @@ -1274,20 +1327,10 @@ class _GeneratorDataset(Dataset): sparse.as_dense_types(self._state_types, self._state_classes))) def tf_next_func(*args): """A wrapper for Defun that facilitates shape inference.""" - # Pass in shape information from the input_dataset. - dense_shapes = sparse.as_dense_shapes(self._state_shapes, - self._state_classes) - for arg, shape in zip(args, nest.flatten(dense_shapes)): - arg.set_shape(shape) - - nested_args = nest.pack_sequence_as(self._state_classes, args) - nested_args = sparse.deserialize_sparse_tensors( - nested_args, self._state_types, self._state_shapes, - self._state_classes) - if _should_unpack_args(nested_args): - ret = next_func(*nested_args) - else: - ret = next_func(nested_args) + nested_args = restructure_args( + args, input_shapes=self._state_shapes, input_types=self._state_types, + input_classes=self._state_classes) + ret = next_func(*nested_args) # If `next_func` returns a list of tensors, `nest.flatten()` and # `ops.convert_to_tensor()` would conspire to attempt to stack @@ -1328,20 +1371,10 @@ class _GeneratorDataset(Dataset): sparse.as_dense_types(self._state_types, self._state_classes))) def tf_finalize_func(*args): """A wrapper for Defun that facilitates shape inference.""" - # Pass in shape information from the state. - dense_shapes = sparse.as_dense_shapes(self._state_shapes, - self._state_classes) - for arg, shape in zip(args, nest.flatten(dense_shapes)): - arg.set_shape(shape) - - nested_args = nest.pack_sequence_as(self._state_classes, args) - nested_args = sparse.deserialize_sparse_tensors( - nested_args, self._state_types, self._state_shapes, - self._state_classes) - if _should_unpack_args(nested_args): - return finalize_func(*nested_args) - else: - return finalize_func(nested_args) + nested_args = restructure_args( + args, input_shapes=self._state_shapes, input_types=self._state_types, + input_classes=self._state_classes) + return finalize_func(*nested_args) self._finalize_func = tf_finalize_func self._finalize_func.add_to_graph(ops.get_default_graph()) @@ -1958,20 +1991,8 @@ class MapDataset(Dataset): input_dataset.output_classes))) def tf_map_func(*args): """A wrapper for Defun that facilitates shape inference.""" - # Pass in shape information from the input_dataset. - dense_shapes = sparse.as_dense_shapes(input_dataset.output_shapes, - input_dataset.output_classes) - for arg, shape in zip(args, nest.flatten(dense_shapes)): - arg.set_shape(shape) - - nested_args = nest.pack_sequence_as(input_dataset.output_types, args) - nested_args = sparse.deserialize_sparse_tensors( - nested_args, input_dataset.output_types, input_dataset.output_shapes, - input_dataset.output_classes) - if _should_unpack_args(nested_args): - ret = map_func(*nested_args) - else: - ret = map_func(nested_args) + nested_args = restructure_args(args, input_dataset) + ret = map_func(*nested_args) # If `map_func` returns a list of tensors, `nest.flatten()` and # `ops.convert_to_tensor()` would conspire to attempt to stack @@ -2066,20 +2087,8 @@ class FlatMapDataset(Dataset): input_dataset.output_classes))) def tf_map_func(*args): """A wrapper for Defun that facilitates shape inference.""" - # Pass in shape information from the input_dataset. - dense_shapes = sparse.as_dense_shapes(input_dataset.output_shapes, - input_dataset.output_classes) - for arg, shape in zip(args, nest.flatten(dense_shapes)): - arg.set_shape(shape) - - nested_args = nest.pack_sequence_as(input_dataset.output_types, args) - nested_args = sparse.deserialize_sparse_tensors( - nested_args, input_dataset.output_types, input_dataset.output_shapes, - input_dataset.output_classes) - if _should_unpack_args(nested_args): - dataset = map_func(*nested_args) - else: - dataset = map_func(nested_args) + nested_args = restructure_args(args, input_dataset) + dataset = map_func(*nested_args) if not isinstance(dataset, Dataset): raise TypeError("`map_func` must return a `Dataset` object.") @@ -2156,20 +2165,8 @@ class FilterDataset(Dataset): input_dataset.output_classes))) def tf_predicate(*args): """A wrapper for Defun that facilitates shape inference.""" - # Pass in shape information from the input_dataset. - dense_shapes = sparse.as_dense_shapes(input_dataset.output_shapes, - input_dataset.output_classes) - for arg, shape in zip(args, nest.flatten(dense_shapes)): - arg.set_shape(shape) - - nested_args = nest.pack_sequence_as(input_dataset.output_types, args) - nested_args = sparse.deserialize_sparse_tensors( - nested_args, input_dataset.output_types, input_dataset.output_shapes, - input_dataset.output_classes) - if _should_unpack_args(nested_args): - ret = predicate(*nested_args) - else: - ret = predicate(nested_args) + nested_args = restructure_args(args, input_dataset) + ret = predicate(*nested_args) ret = ops.convert_to_tensor(ret, dtype=dtypes.bool) if not (ret.dtype == dtypes.bool and -- GitLab From 0104d4f3aa58f194fcf07f6ea9663d1970a2cb01 Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Wed, 13 Jun 2018 11:41:27 -0700 Subject: [PATCH 394/816] [TF:XLA] Bump open source llvm revision to r334593 PiperOrigin-RevId: 200427133 --- tensorflow/workspace.bzl | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index b13929e636..80f97607c9 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -451,11 +451,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "llvm", urls = [ - "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/582e5dd5553e3089fef97f9ab5a3f063e0160fa9.tar.gz", - "https://github.com/llvm-mirror/llvm/archive/582e5dd5553e3089fef97f9ab5a3f063e0160fa9.tar.gz", + "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/81eac77ab10767bfbdc7c413a07a4d8a0ae9b80f.tar.gz", + "https://github.com/llvm-mirror/llvm/archive/81eac77ab10767bfbdc7c413a07a4d8a0ae9b80f.tar.gz", ], - sha256 = "9a0e63469ae5a546e0c84b778955f0febabfc8497d312324546ec7d0db68430e", - strip_prefix = "llvm-582e5dd5553e3089fef97f9ab5a3f063e0160fa9", + sha256 = "eef28ae88a572f81d5931a8c153e6d25042192362d8e63533f834188526cf718", + strip_prefix = "llvm-81eac77ab10767bfbdc7c413a07a4d8a0ae9b80f", build_file = clean_dep("//third_party/llvm:llvm.BUILD"), ) -- GitLab From cb2c5be3eb7788af429c0be6945c705847383a4e Mon Sep 17 00:00:00 2001 From: Akshay Modi Date: Wed, 13 Jun 2018 12:00:41 -0700 Subject: [PATCH 395/816] Add a test that checks memory usage by running a model 100k times. PiperOrigin-RevId: 200430314 --- tensorflow/python/eager/BUILD | 17 ++++ tensorflow/python/eager/memory_test.py | 108 +++++++++++++++++++++++++ 2 files changed, 125 insertions(+) create mode 100644 tensorflow/python/eager/memory_test.py diff --git a/tensorflow/python/eager/BUILD b/tensorflow/python/eager/BUILD index dee86966f1..e8a7904a88 100644 --- a/tensorflow/python/eager/BUILD +++ b/tensorflow/python/eager/BUILD @@ -391,3 +391,20 @@ py_library( srcs = ["imperative_grad.py"], srcs_version = "PY2AND3", ) + +cuda_py_test( + name = "memory_test", + size = "medium", + srcs = ["memory_test.py"], + additional_deps = [ + "//tensorflow/python/eager:backprop", + "//tensorflow/python/keras", + "//tensorflow/python/eager:test", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework_test_lib", + ], + tags = [ + "optonly", # The test is too slow in non-opt mode + ], +) diff --git a/tensorflow/python/eager/memory_test.py b/tensorflow/python/eager/memory_test.py new file mode 100644 index 0000000000..74c6cbdd31 --- /dev/null +++ b/tensorflow/python/eager/memory_test.py @@ -0,0 +1,108 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for memory leaks in eager execution. + +It is possible that this test suite will eventually become flaky due to taking +too long to run (since the tests iterate many times), but for now they are +helpful for finding memory leaks since not all PyObject leaks are found by +introspection (test_util decorators). Please be careful adding new tests here. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python import keras +from tensorflow.python.eager import backprop +from tensorflow.python.eager import context +from tensorflow.python.eager import test +from tensorflow.python.framework import dtypes +from tensorflow.python.ops import array_ops + +# memory_profiler might not be available in the OSS version of TensorFlow. +try: + import memory_profiler # pylint:disable=g-import-not-at-top +except ImportError: + memory_profiler = None + + +class SingleLayerNet(keras.Model): + """Simple keras model used to ensure that there are no leaks.""" + + def __init__(self): + super(SingleLayerNet, self).__init__() + self.fc1 = keras.layers.Dense(5) + + def call(self, x): + return self.fc1(x) + + +class MemoryTest(test.TestCase): + + def assertNotIncreasingMemory(self, + f, + num_iters=100000, + increase_threshold_absolute_mb=10): + """Assert memory usage doesn't increase beyond given threshold for f.""" + + with context.eager_mode(): + # Warm up. + f() + + initial = memory_profiler.memory_usage(-1)[0] + + for _ in xrange(num_iters): + f() + + increase = memory_profiler.memory_usage(-1)[0] - initial + + assert increase < increase_threshold_absolute_mb, ( + "Increase is too high. Initial memory usage: %f MB. Increase: %f MB. " + "Maximum allowed increase: %f") % (initial, increase, + increase_threshold_absolute_mb) + + def testMemoryLeakInSimpleModelForwardOnly(self): + if memory_profiler is None: + self.skipTest("memory_profiler required to run this test") + + inputs = array_ops.zeros([32, 100], dtypes.float32) + net = SingleLayerNet() + + def f(): + with backprop.GradientTape(): + net(inputs) + + self.assertNotIncreasingMemory(f) + + def testMemoryLeakInSimpleModelForwardAndBackward(self): + if memory_profiler is None: + self.skipTest("memory_profiler required to run this test") + + inputs = array_ops.zeros([32, 100], dtypes.float32) + net = SingleLayerNet() + + def f(): + with backprop.GradientTape() as tape: + result = net(inputs) + + tape.gradient(result, net.variables) + + del tape + + self.assertNotIncreasingMemory(f) + + +if __name__ == "__main__": + test.main() -- GitLab From 6b7a17da65f39068b8b3f20c5c4ed7710dff14f8 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 13 Jun 2018 12:09:13 -0700 Subject: [PATCH 396/816] Automated g4 rollback of changelist 199870879 PiperOrigin-RevId: 200431713 --- tensorflow/core/framework/device_base.h | 4 ---- tensorflow/core/framework/op_kernel.cc | 16 ---------------- tensorflow/core/framework/op_kernel.h | 2 -- 3 files changed, 22 deletions(-) diff --git a/tensorflow/core/framework/device_base.h b/tensorflow/core/framework/device_base.h index b59ced869d..ec26d92a61 100644 --- a/tensorflow/core/framework/device_base.h +++ b/tensorflow/core/framework/device_base.h @@ -186,10 +186,6 @@ class DeviceBase { virtual ScopedAllocatorMgr* GetScopedAllocatorMgr() const { return nullptr; } - const bool has_eigen_cpu_device() const { - return (eigen_cpu_device_ != nullptr); - } - virtual const Eigen::ThreadPoolDevice* eigen_cpu_device() { CHECK(eigen_cpu_device_ != nullptr); return eigen_cpu_device_; diff --git a/tensorflow/core/framework/op_kernel.cc b/tensorflow/core/framework/op_kernel.cc index a0f449d64f..ce213a63be 100644 --- a/tensorflow/core/framework/op_kernel.cc +++ b/tensorflow/core/framework/op_kernel.cc @@ -13,14 +13,12 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#define EIGEN_USE_THREADS #include "tensorflow/core/framework/op_kernel.h" #include #include #include -#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/core/framework/attr_value_util.h" #include "tensorflow/core/framework/device_attributes.pb.h" #include "tensorflow/core/framework/graph.pb_text.h" @@ -42,7 +40,6 @@ limitations under the License. #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/mutex.h" #include "tensorflow/core/platform/types.h" -#include "tensorflow/core/util/ptr_util.h" namespace tensorflow { @@ -273,19 +270,6 @@ OpKernelContext::OpKernelContext(Params* params, int num_outputs) if (params_->record_tensor_accesses) { referenced_tensors_.Init(); } - if (params->device->has_eigen_cpu_device()) { - int64 block_size = -1, output_size = -1, num_threads = 1; - const Eigen::ThreadPoolDevice* thread_pool = - params_->device->eigen_cpu_device(); - AttrSlice attributes(op_kernel().def()); - if (GetNodeAttr(attributes, "_block_size", &block_size) == Status::OK() && - GetNodeAttr(attributes, "_output_size", &output_size) == Status::OK()) { - num_threads = std::min(Eigen::divup(output_size, block_size), - static_cast(thread_pool->numThreads())); - eigen_cpu_device_ = MakeUnique( - thread_pool->getPool(), num_threads); - } - } } OpKernelContext::~OpKernelContext() { diff --git a/tensorflow/core/framework/op_kernel.h b/tensorflow/core/framework/op_kernel.h index d307078e63..a3ad29e02f 100644 --- a/tensorflow/core/framework/op_kernel.h +++ b/tensorflow/core/framework/op_kernel.h @@ -1004,7 +1004,6 @@ class OpKernelContext { // OpKernels can use these eigen devices to carry out their // numerical computation. const Eigen::ThreadPoolDevice& eigen_cpu_device() const { - if (eigen_cpu_device_ != nullptr) return *eigen_cpu_device_; return *device()->eigen_cpu_device(); } const Eigen::GpuDevice& eigen_gpu_device() const { @@ -1140,7 +1139,6 @@ class OpKernelContext { mutable mutex mu_; // mutable so const accessors can acquire the lock gtl::InlinedVector wrapped_allocators_ GUARDED_BY(mu_); gtl::InlinedVector outputs_; - std::unique_ptr eigen_cpu_device_; // Constructed only if record_tensor_accesses>. ManualConstructor referenced_tensors_ GUARDED_BY(mu_); -- GitLab From 74655a96b40680b111ae063386c57f3f38262d34 Mon Sep 17 00:00:00 2001 From: Mark Daoust Date: Wed, 13 Jun 2018 12:10:29 -0700 Subject: [PATCH 397/816] fix md link format PiperOrigin-RevId: 200431906 --- SECURITY.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/SECURITY.md b/SECURITY.md index e2f6ff353a..0b52fdc7ab 100644 --- a/SECURITY.md +++ b/SECURITY.md @@ -245,4 +245,4 @@ v//Fw6ZeY+HmRDFdirjD7wXtIuER4vqCryIqR6Xe9X8oJXz9L/Jhslc= ### Known Vulnerabilities For a list of known vulnerabilities and security advisories for TensorFlow, -(https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/index.md)[click here]. +[click here](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/index.md). -- GitLab From 106766c1b68ae67b7731ae481fe7feecbb94974c Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 13 Jun 2018 12:14:34 -0700 Subject: [PATCH 398/816] Fix a build failure when cuda version is less than 9000. PiperOrigin-RevId: 200432478 --- tensorflow/stream_executor/cuda/cuda_blas.cc | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tensorflow/stream_executor/cuda/cuda_blas.cc b/tensorflow/stream_executor/cuda/cuda_blas.cc index 92c1a5fc07..31e407f199 100644 --- a/tensorflow/stream_executor/cuda/cuda_blas.cc +++ b/tensorflow/stream_executor/cuda/cuda_blas.cc @@ -2183,10 +2183,12 @@ bool CUDABlas::DoBlasGemmWithAlgorithmImpl( // Return false if we might be hitting a cuBLAS bug that produces the wrong // result. See nvbugs/2156201, b/79126339. +#if (CUDA_VERSION >= 9000) if (CUDA_VERSION < 9020 && algorithm != CUBLAS_GEMM_ALGO12 && std::max({m, n, k}) >= 2097153 && cc_major < 7) { return false; } +#endif cudaDataType_t cuda_in_type = CUDADataType::type; // Since we are converting 'algorithm' to cublasGemmAlgo_t by static_cast, -- GitLab From d40ca72ff692d21e7965b3b17445bca873510941 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 13 Jun 2018 12:30:13 -0700 Subject: [PATCH 399/816] Switch Estimator from using DistributionStrategy.fetch() to .read_var(). PiperOrigin-RevId: 200434656 --- tensorflow/python/estimator/estimator.py | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py index 41c25f1c73..dd770382e4 100644 --- a/tensorflow/python/estimator/estimator.py +++ b/tensorflow/python/estimator/estimator.py @@ -1150,13 +1150,10 @@ class Estimator(object): input_fn, model_fn_lib.ModeKeys.TRAIN)) worker_hooks.extend(input_hooks) global_step_tensor = self._create_and_assert_global_step(g) - # The default destination for the global_step_tensor fetch call is the - # CPU. - global_step_read_tensor = self._distribution.fetch(global_step_tensor) # we want to add to the global collection in the main thread not the # tower threads. ops.add_to_collection(training_util.GLOBAL_STEP_READ_KEY, - global_step_read_tensor) + self._distribution.read_var(global_step_tensor)) grouped_estimator_spec = self._distribution.call_for_each_tower( self._call_model_fn, features, @@ -1254,7 +1251,7 @@ class Estimator(object): training_chief_hooks=training_chief_hooks, scaffold=scaffold) return self._train_with_estimator_spec(estimator_spec, worker_hooks, - hooks, global_step_read_tensor, + hooks, global_step_tensor, saving_listeners) def _train_with_estimator_spec(self, estimator_spec, worker_hooks, hooks, -- GitLab From 47b1c9396aef567b839c2c5ad91aa37ba0cb68ca Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 13 Jun 2018 12:36:28 -0700 Subject: [PATCH 400/816] Initial application of runtime shapes to runtime kernels. PiperOrigin-RevId: 200435608 --- .../contrib/lite/kernels/internal/BUILD | 65 +++++++++++++ .../internal/optimized/legacy_optimized_ops.h | 50 ++++++++++ .../internal/optimized/optimized_ops.h | 28 +++--- .../internal/reference/legacy_reference_ops.h | 50 ++++++++++ .../internal/reference/reference_ops.h | 32 +++--- .../contrib/lite/kernels/internal/tensor.h | 13 +++ .../contrib/lite/kernels/internal/types.h | 97 +++++++++++++++++++ tensorflow/contrib/lite/kernels/l2norm.cc | 12 +-- 8 files changed, 316 insertions(+), 31 deletions(-) create mode 100644 tensorflow/contrib/lite/kernels/internal/optimized/legacy_optimized_ops.h create mode 100644 tensorflow/contrib/lite/kernels/internal/reference/legacy_reference_ops.h diff --git a/tensorflow/contrib/lite/kernels/internal/BUILD b/tensorflow/contrib/lite/kernels/internal/BUILD index 75298b995d..7962fcbc9d 100644 --- a/tensorflow/contrib/lite/kernels/internal/BUILD +++ b/tensorflow/contrib/lite/kernels/internal/BUILD @@ -176,6 +176,40 @@ cc_library( }), ) +cc_library( + name = "legacy_optimized_base", + srcs = [], + hdrs = [ + "common.h", + "optimized/depthwiseconv_float.h", + "optimized/depthwiseconv_uint8.h", + "optimized/depthwiseconv_uint8_3x3_filter.h", + "optimized/legacy_optimized_ops.h", + "optimized/optimized_ops.h", + ], + copts = tflite_copts(), + deps = [ + ":quantization_util", + ":strided_slice_logic", + ":types", + ":legacy_reference_base", + ":round", + "//third_party/eigen3", + "@gemmlowp", + "//tensorflow/contrib/lite:builtin_op_data", + ] + select({ + ":haswell": tflite_deps_intel, + ":ios_x86_64": tflite_deps_intel, + ":k8": tflite_deps_intel, + ":x86": tflite_deps_intel, + ":x86_64": tflite_deps_intel, + ":darwin": tflite_deps_intel, + ":darwin_x86_64": tflite_deps_intel, + ":freebsd": tflite_deps_intel, + "//conditions:default": [], + }), +) + cc_library( name = "optimized", hdrs = [ @@ -273,6 +307,37 @@ cc_library( }), ) +cc_library( + name = "legacy_reference_base", + srcs = [], + hdrs = [ + "common.h", + "reference/depthwiseconv_float.h", + "reference/depthwiseconv_uint8.h", + "reference/legacy_reference_ops.h", + "reference/reference_ops.h", + ], + deps = [ + ":quantization_util", + ":round", + ":strided_slice_logic", + ":types", + "//third_party/eigen3", + "@gemmlowp", + "//tensorflow/contrib/lite:builtin_op_data", + ] + select({ + ":haswell": tflite_deps_intel, + ":ios_x86_64": tflite_deps_intel, + ":k8": tflite_deps_intel, + ":x86": tflite_deps_intel, + ":x86_64": tflite_deps_intel, + ":darwin": tflite_deps_intel, + ":darwin_x86_64": tflite_deps_intel, + ":freebsd": tflite_deps_intel, + "//conditions:default": [], + }), +) + cc_library( name = "reference", hdrs = ["tensor.h"], diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/legacy_optimized_ops.h b/tensorflow/contrib/lite/kernels/internal/optimized/legacy_optimized_ops.h new file mode 100644 index 0000000000..c0dda4acf1 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/internal/optimized/legacy_optimized_ops.h @@ -0,0 +1,50 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_OPTIMIZED_LEGACY_OPTIMIZED_OPS_H_ +#define TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_OPTIMIZED_LEGACY_OPTIMIZED_OPS_H_ + +#include +#include + +#include "tensorflow/contrib/lite/kernels/internal/common.h" +#include "tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h" +#include "tensorflow/contrib/lite/kernels/internal/reference/legacy_reference_ops.h" +#include "tensorflow/contrib/lite/kernels/internal/types.h" + +namespace tflite { +namespace optimized_ops { + +inline RuntimeShape DimsToShape(const tflite::Dims<4>& dims) { + return RuntimeShape( + {dims.sizes[3], dims.sizes[2], dims.sizes[1], dims.sizes[0]}); +} + +template +void L2Normalization(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { + return L2Normalization(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); +} + +inline void L2Normalization(const uint8* input_data, const Dims<4>& input_dims, + int32 input_zero_point, uint8* output_data, + const Dims<4>& output_dims) { + return L2Normalization(input_data, DimsToShape(input_dims), input_zero_point, + output_data, DimsToShape(output_dims)); +} + +} // namespace optimized_ops +} // namespace tflite +#endif // TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_OPTIMIZED_LEGACY_OPTIMIZED_OPS_H_ diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h index ed2d04f20d..4c37d3c3c7 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h @@ -1,4 +1,4 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. @@ -2366,12 +2366,15 @@ inline void Relu6(const float* input_data, const Dims<4>& input_dims, } template -void L2Normalization(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { +void L2Normalization(const float* input_data, const RuntimeShape& input_shape, + float* output_data, const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("L2Normalization"); static_assert(Ac == FusedActivationFunctionType::kNone, ""); - const int outer_size = MatchingFlatSizeSkipDim(input_dims, 0, output_dims); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); + const int trailing_dim = input_shape.DimensionsCount() - 1; + const int outer_size = + MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); + const int depth = + MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); for (int i = 0; i < outer_size; ++i) { float squared_l2_norm = 0; for (int c = 0; c < depth; ++c) { @@ -2434,17 +2437,20 @@ inline void GetInvSqrtQuantizedMultiplierExp(int32 input, *output_shift *= kReverseShift; } -inline void L2Normalization(const uint8* input_data, const Dims<4>& input_dims, +inline void L2Normalization(const uint8* input_data, + const RuntimeShape& input_shape, int32 input_zero_point, uint8* output_data, - const Dims<4>& output_dims) { + const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("L2Normalization/8bit"); - TFLITE_DCHECK(IsPackedWithoutStrides(input_dims)); - TFLITE_DCHECK(IsPackedWithoutStrides(output_dims)); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); - const int outer_size = MatchingFlatSizeSkipDim(input_dims, 0, output_dims); + const int trailing_dim = input_shape.DimensionsCount() - 1; + const int depth = + MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); + const int outer_size = + MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); for (int i = 0; i < outer_size; ++i) { int32 square_l2_norm = 0; for (int c = 0; c < depth; c++) { + // Note that input_data advances by depth in the second pass below. int32 diff = input_data[c] - input_zero_point; square_l2_norm += diff * diff; } diff --git a/tensorflow/contrib/lite/kernels/internal/reference/legacy_reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/legacy_reference_ops.h new file mode 100644 index 0000000000..6f5f6a3e6f --- /dev/null +++ b/tensorflow/contrib/lite/kernels/internal/reference/legacy_reference_ops.h @@ -0,0 +1,50 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_REFERENCE_LEGACY_REFERENCE_OPS_H_ +#define TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_REFERENCE_LEGACY_REFERENCE_OPS_H_ + +#include +#include + +#include "tensorflow/contrib/lite/kernels/internal/common.h" +#include "tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h" +#include "tensorflow/contrib/lite/kernels/internal/types.h" + +namespace tflite { + +namespace reference_ops { + +inline RuntimeShape DimsToShape(const tflite::Dims<4>& dims) { + return RuntimeShape( + {dims.sizes[3], dims.sizes[2], dims.sizes[1], dims.sizes[0]}); +} + +template +void L2Normalization(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { + return L2Normalization(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); +} + +inline void L2Normalization(const uint8* input_data, const Dims<4>& input_dims, + int32 input_zero_point, uint8* output_data, + const Dims<4>& output_dims) { + return L2Normalization(input_data, DimsToShape(input_dims), input_zero_point, + output_data, DimsToShape(output_dims)); +} + +} // namespace reference_ops +} // namespace tflite +#endif // TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_REFERENCE_LEGACY_REFERENCE_OPS_H_ diff --git a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h index 0d70b6b473..af9cef7170 100644 --- a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h @@ -950,11 +950,14 @@ inline void Relu6(const float* input_data, const Dims<4>& input_dims, } template -void L2Normalization(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { +void L2Normalization(const float* input_data, const RuntimeShape& input_shape, + float* output_data, const RuntimeShape& output_shape) { static_assert(Ac == FusedActivationFunctionType::kNone, ""); - const int outer_size = MatchingFlatSizeSkipDim(input_dims, 0, output_dims); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); + const int trailing_dim = input_shape.DimensionsCount() - 1; + const int outer_size = + MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); + const int depth = + MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); for (int i = 0; i < outer_size; ++i) { float squared_l2_norm = 0; for (int c = 0; c < depth; ++c) { @@ -1015,16 +1018,19 @@ inline void GetInvSqrtQuantizedMultiplierExp(int32 input, *output_shift *= kReverseShift; } -inline void L2Normalization(const uint8* input_data, const Dims<4>& input_dims, +inline void L2Normalization(const uint8* input_data, + const RuntimeShape& input_shape, int32 input_zero_point, uint8* output_data, - const Dims<4>& output_dims) { - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); - const int outer_size = MatchingFlatSizeSkipDim(input_dims, 0, output_dims); + const RuntimeShape& output_shape) { + const int trailing_dim = input_shape.DimensionsCount() - 1; + const int depth = + MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); + const int outer_size = + MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); for (int i = 0; i < outer_size; ++i) { int32 square_l2_norm = 0; for (int c = 0; c < depth; c++) { - int32 diff = - input_data[Offset(input_dims, c, i, 0, 0)] - input_zero_point; + int32 diff = input_data[depth * i + c] - input_zero_point; square_l2_norm += diff * diff; } int32 inv_l2norm_multiplier; @@ -1033,14 +1039,12 @@ inline void L2Normalization(const uint8* input_data, const Dims<4>& input_dims, &inv_l2norm_shift); for (int c = 0; c < depth; c++) { - int32 diff = - input_data[Offset(input_dims, c, i, 0, 0)] - input_zero_point; + int32 diff = input_data[depth * i + c] - input_zero_point; int32 rescaled_diff = MultiplyByQuantizedMultiplierSmallerThanOneExp( 128 * diff, inv_l2norm_multiplier, inv_l2norm_shift); int32 unclamped_output_val = 128 + rescaled_diff; int32 output_val = std::min(255, std::max(0, unclamped_output_val)); - output_data[Offset(output_dims, c, i, 0, 0)] = - static_cast(output_val); + output_data[depth * i + c] = static_cast(output_val); } } } diff --git a/tensorflow/contrib/lite/kernels/internal/tensor.h b/tensorflow/contrib/lite/kernels/internal/tensor.h index ce887cea8b..f803d94695 100644 --- a/tensorflow/contrib/lite/kernels/internal/tensor.h +++ b/tensorflow/contrib/lite/kernels/internal/tensor.h @@ -114,6 +114,19 @@ inline Dims<4> GetTensorDims(const TfLiteTensor* tensor) { return GetTensorDims(dims->data, dims->size); } +inline RuntimeShape GetTensorShape(std::vector data) { + return RuntimeShape(data.size(), data.data()); +} + +inline RuntimeShape GetTensorShape(const TfLiteTensor* tensor) { + if (tensor == nullptr) { + return RuntimeShape(); + } + + auto* dims = tensor->dims; + return RuntimeShape(dims->size, dims->data); +} + // A list of tensors in a format that can be used by kernels like split and // concatenation. template diff --git a/tensorflow/contrib/lite/kernels/internal/types.h b/tensorflow/contrib/lite/kernels/internal/types.h index 3ecef15271..64f4881a46 100644 --- a/tensorflow/contrib/lite/kernels/internal/types.h +++ b/tensorflow/contrib/lite/kernels/internal/types.h @@ -65,6 +65,10 @@ class RuntimeShape { ReplaceWith(dimensions_count, dims_data); } + RuntimeShape(const std::initializer_list init_list) : size_(0) { + BuildFrom(init_list); + } + ~RuntimeShape() { if (size_ > kMaxSmallSize) { delete[] dims_pointer_; @@ -214,6 +218,15 @@ inline size_t ReducedOutputOffset(const int num_dims, const int* dims, return offset; } +inline int Offset(const RuntimeShape& shape, int i0, int i1, int i2, int i3) { + TFLITE_DCHECK(i0 >= 0 && i0 < shape.Dims(0)); + TFLITE_DCHECK(i1 >= 0 && i1 < shape.Dims(1)); + TFLITE_DCHECK(i2 >= 0 && i2 < shape.Dims(2)); + TFLITE_DCHECK(i3 >= 0 && i3 < shape.Dims(3)); + const int* dims_data = shape.DimsData(); + return ((i0 * dims_data[1] + i1) * dims_data[2] + i2) * dims_data[3] + i3; +} + inline int Offset(const Dims<4>& dims, int i0, int i1, int i2, int i3) { TFLITE_DCHECK(i0 >= 0 && i0 < dims.sizes[0]); TFLITE_DCHECK(i1 >= 0 && i1 < dims.sizes[1]); @@ -228,6 +241,9 @@ inline int Offset(const Dims<4>& dims, int* index) { } // Get array size, DCHECKing that the dim index is in range. +// +// Note that this will be phased out with Dims<4>, since RuntimeShape::Dims() +// already performs this check. template int ArraySize(const Dims& array, int index) { TFLITE_DCHECK(index >= 0 && index < N); @@ -249,6 +265,21 @@ int MatchingArraySize(const ArrayType1& array1, int index1, return MatchingArraySize(array1, index1, args...); } +// Get common shape dim, DCHECKing that they all agree. +inline int MatchingDim(const RuntimeShape& shape1, int index1, + const RuntimeShape& shape2, int index2) { + TFLITE_DCHECK_EQ(shape1.Dims(index1), shape2.Dims(index2)); + return shape1.Dims(index1); +} + +template +int MatchingDim(const RuntimeShape& shape1, int index1, + const RuntimeShape& shape2, int index2, Args... args) { + TFLITE_DCHECK_EQ(shape1.Dims(index1), shape2.Dims(index2)); + return MatchingDim(shape1, index1, args...); +} + +// Will be phased out with Dims<4>, replaced by RuntimeShape::FlatSize(). template inline int FlatSize(const Dims& dims) { int flat_size = 1; @@ -368,6 +399,72 @@ inline int MatchingFlatSizeSkipDim(const Dims& dims, int skip_dim, check_dims_3); } +// Data is required to be contiguous, and so many operators can use either the +// full array flat size or the flat size with one dimension skipped (commonly +// the depth). +inline int FlatSizeSkipDim(const RuntimeShape& shape, int skip_dim) { + const int dims_count = shape.DimensionsCount(); + TFLITE_DCHECK(skip_dim >= 0 && skip_dim < dims_count); + const auto* dims_data = shape.DimsData(); + int flat_size = 1; + for (int i = 0; i < dims_count; ++i) { + flat_size *= (i == skip_dim) ? 1 : dims_data[i]; + } + return flat_size; +} + +// A combination of MatchingFlatSize() and FlatSizeSkipDim(). +inline int MatchingFlatSizeSkipDim(const RuntimeShape& shape, int skip_dim, + const RuntimeShape& check_shape_0) { + const int dims_count = shape.DimensionsCount(); + for (int i = 0; i < dims_count; ++i) { + if (i != skip_dim) { + TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); + } + } + return FlatSizeSkipDim(shape, skip_dim); +} + +inline int MatchingFlatSizeSkipDim(const RuntimeShape& shape, int skip_dim, + const RuntimeShape& check_shape_0, + const RuntimeShape& check_shape_1) { + const int dims_count = shape.DimensionsCount(); + for (int i = 0; i < dims_count; ++i) { + if (i != skip_dim) { + TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); + } + } + return MatchingFlatSizeSkipDim(shape, skip_dim, check_shape_1); +} + +inline int MatchingFlatSizeSkipDim(const RuntimeShape& shape, int skip_dim, + const RuntimeShape& check_shape_0, + const RuntimeShape& check_shape_1, + const RuntimeShape& check_shape_2) { + const int dims_count = shape.DimensionsCount(); + for (int i = 0; i < dims_count; ++i) { + if (i != skip_dim) { + TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); + } + } + return MatchingFlatSizeSkipDim(shape, skip_dim, check_shape_1, check_shape_2); +} + +inline int MatchingFlatSizeSkipDim(const RuntimeShape& shape, int skip_dim, + const RuntimeShape& check_shape_0, + const RuntimeShape& check_shape_1, + const RuntimeShape& check_shape_2, + const RuntimeShape& check_shape_3) { + const int dims_count = shape.DimensionsCount(); + for (int i = 0; i < dims_count; ++i) { + if (i != skip_dim) { + TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); + } + } + return MatchingFlatSizeSkipDim(shape, skip_dim, check_shape_1, check_shape_2, + check_shape_3); +} + template bool IsPackedWithoutStrides(const Dims& dims) { int expected_stride = 1; diff --git a/tensorflow/contrib/lite/kernels/l2norm.cc b/tensorflow/contrib/lite/kernels/l2norm.cc index 3205c1cc52..a7b54c6b84 100644 --- a/tensorflow/contrib/lite/kernels/l2norm.cc +++ b/tensorflow/contrib/lite/kernels/l2norm.cc @@ -70,8 +70,8 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { if (output->type == kTfLiteFloat32) { #define TF_LITE_L2NORM(type) \ type::L2Normalization( \ - GetTensorData(input), GetTensorDims(input), \ - GetTensorData(output), GetTensorDims(output)) + GetTensorData(input), GetTensorShape(input), \ + GetTensorData(output), GetTensorShape(output)) if (kernel_type == kReference) { TF_LITE_L2NORM(reference_ops); @@ -81,10 +81,10 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } #undef TF_LITE_L2NORM } else if (output->type == kTfLiteUInt8) { -#define TF_LITE_L2NORM(type) \ - type::L2Normalization(GetTensorData(input), GetTensorDims(input), \ - input->params.zero_point, \ - GetTensorData(output), GetTensorDims(output)) +#define TF_LITE_L2NORM(type) \ + type::L2Normalization(GetTensorData(input), GetTensorShape(input), \ + input->params.zero_point, \ + GetTensorData(output), GetTensorShape(output)) if (kernel_type == kReference) { TF_LITE_L2NORM(reference_ops); -- GitLab From 8051c4b7790bb3cc64bf14d1180ab2ad55f0c032 Mon Sep 17 00:00:00 2001 From: Skye Wanderman-Milne Date: Wed, 13 Jun 2018 13:00:39 -0700 Subject: [PATCH 401/816] Provide default name_scope in cond_v2. Otherwise passing in name="" results in trying to name the If op "". PiperOrigin-RevId: 200439070 --- .../contrib/control_flow/python/cond_v2.py | 14 ++++++--- .../control_flow/python/cond_v2_test.py | 31 +++++++++++++++++++ 2 files changed, 41 insertions(+), 4 deletions(-) diff --git a/tensorflow/contrib/control_flow/python/cond_v2.py b/tensorflow/contrib/control_flow/python/cond_v2.py index 9ffad9caa9..b364e34511 100644 --- a/tensorflow/contrib/control_flow/python/cond_v2.py +++ b/tensorflow/contrib/control_flow/python/cond_v2.py @@ -44,11 +44,17 @@ from tensorflow.python.util import compat def cond_v2(pred, true_fn, false_fn, name="cond"): """Like tf.cond, except emits a single If op.""" + if not name: + name = "cond" + with ops.name_scope(name) as scope: - true_graph = function.func_graph_from_py_func(true_fn, [], [], - name="%s_true" % scope) - false_graph = function.func_graph_from_py_func(false_fn, [], [], - name="%s_false" % scope) + func_name_prefix = scope.replace("/", "_") + + true_graph = function.func_graph_from_py_func( + true_fn, [], [], name="%strue" % func_name_prefix) + false_graph = function.func_graph_from_py_func( + false_fn, [], [], name="%sfalse" % func_name_prefix) + _check_same_outputs(true_graph, false_graph) # Add inputs to true_graph and false_graph to make them match. Note that diff --git a/tensorflow/contrib/control_flow/python/cond_v2_test.py b/tensorflow/contrib/control_flow/python/cond_v2_test.py index 338601aa2c..b7d4c16df4 100644 --- a/tensorflow/contrib/control_flow/python/cond_v2_test.py +++ b/tensorflow/contrib/control_flow/python/cond_v2_test.py @@ -96,6 +96,37 @@ class NewCondTest(test.TestCase): self.assertEqual(sess.run(out, {pred: True}), [1.0]) self.assertEqual(sess.run(out, {pred: False}), [2.0]) + def _createCond(self, name): + pred = array_ops.placeholder(dtypes.bool, name="pred") + x = constant_op.constant(1.0, name="x") + + def true_fn(): + return x + + def false_fn(): + return x + 1 + + return cond_v2.cond_v2(pred, true_fn, false_fn, name=name)[0].op + + def testDefaultName(self): + with ops.Graph().as_default(): + cond = self._createCond(None) + self.assertEqual(cond.name, "cond") + self.assertIn("cond_true", ops.get_default_graph()._functions) + self.assertIn("cond_false", ops.get_default_graph()._functions) + + with ops.Graph().as_default(): + with ops.name_scope("foo"): + cond = self._createCond("") + self.assertEqual(cond.name, "foo/cond") + self.assertIn("foo_cond_true", ops.get_default_graph()._functions) + self.assertIn("foo_cond_false", ops.get_default_graph()._functions) + + cond2 = self._createCond(None) + self.assertEqual(cond2.name, "foo/cond_1") + self.assertIn("foo_cond_1_true", ops.get_default_graph()._functions) + self.assertIn("foo_cond_1_false", ops.get_default_graph()._functions) + def testSecondDerivative(self): pred = array_ops.placeholder(dtypes.bool, name="pred") x = constant_op.constant(3.0, name="x") -- GitLab From 642a043de4901ddbf305db105168b8908adfe99e Mon Sep 17 00:00:00 2001 From: Peter Hawkins Date: Wed, 13 Jun 2018 13:05:37 -0700 Subject: [PATCH 402/816] [TF:XLA] Replace bespoke NodeSlot class in subgraph encapsulation code with InputTensor and OutputTensor classes from TF core. Add equality and hash methods to InputTensor and OutputTensor. No functional changes intended. PiperOrigin-RevId: 200440015 --- .../jit/encapsulate_subgraphs_pass.cc | 127 ++++++++---------- tensorflow/core/graph/graph.cc | 23 ++++ tensorflow/core/graph/graph.h | 20 +++ 3 files changed, 97 insertions(+), 73 deletions(-) diff --git a/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc b/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc index ea90d714c8..edd2247694 100644 --- a/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc +++ b/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc @@ -106,41 +106,11 @@ void MarkGuaranteedConstants( } } -// A node/slot pair. -// TODO(phawkins): is there a common definition of this? -struct NodeSlot { - NodeSlot() : node(nullptr), slot(-1), dtype(DT_INVALID) {} - NodeSlot(const Node* node, int slot) - : node(node), slot(slot), dtype(DT_INVALID) {} - NodeSlot(const Node* node, int slot, DataType dtype) - : node(node), slot(slot), dtype(dtype) {} - - const Node* node; - int slot; - - // Optional: used to record the destination type of a source NodeSlot in case - // the source output is a Ref type that is cast to a Tensor at the - // destination. - DataType dtype; - - bool operator==(const NodeSlot& other) const { - return node == other.node && slot == other.slot && dtype == other.dtype; - } - - // Leave dtype out of the hash since there are never two NodeSlots with the - // same node and slot and different dtypes. - struct Hasher { - uint64 operator()(NodeSlot const& s) const { - return Hash64Combine(std::hash()(s.node), - std::hash()(s.slot)); - } - }; - - struct PairHasher { - uint64 operator()(std::pair const& s) const { - return Hash64Combine(Hasher()(s.first), Hasher()(s.second)); - } - }; +struct OutputInputTensorPairHasher { + uint64 operator()(std::pair const& s) const { + return Hash64Combine(OutputTensor::Hash()(s.first), + InputTensor::Hash()(s.second)); + } }; // TODO(phawkins) add a canonical copy of these operator names and refactor @@ -376,7 +346,7 @@ class Encapsulator { // Map from source (producer node/slot) tensors in the original graph to // input index (slot number in the HostCompute/RecvAtHost nodes that will // be created) for the outside_compilation subgraph. - std::unordered_map inputs; + std::unordered_map inputs; // Set of nodes in the original graph that are the source of control edges // that cross from the containing compiled subgraph into the @@ -392,8 +362,15 @@ class Encapsulator { // node/slot) tensors in the original graph to output index (slot number // in the SendFromHost/HostCompute nodes that will be created) for the // outside_compilation subgraph. - std::unordered_map outputs_by_src; - std::unordered_map outputs_by_dst; + struct ArgNumAndType { + int index; + DataType dtype; + + ArgNumAndType(int i, DataType t) : index(i), dtype(t) {} + }; + std::unordered_map + outputs_by_src; + std::unordered_map outputs_by_dst; // Set of nodes in the original graph that are the destination of control // edges that cross from the outside_compilation subgraph into the @@ -479,14 +456,14 @@ class Encapsulator { // (consumer node/slot) tensors in the input graph to _Arg numbers in // the subgraph. The source map is one-to-one, whereas the dest map may be // many-to-one. - std::unordered_map args_by_src_; - std::unordered_map args_by_dst_; + std::unordered_map args_by_src_; + std::unordered_map args_by_dst_; // The _Arg nodes in the subgraph, in order by argument number. std::vector args_; // Map from source tensor in the input graph to result #. - std::unordered_map results_; + std::unordered_map results_; // The outside_compilation clusters in this subgraph. std::unordered_map @@ -583,8 +560,8 @@ class Encapsulator { const string& dst_outside_compilation_id, const std::unordered_map& node_images, Graph* graph_out, - std::unordered_set, NodeSlot::PairHasher>* - edges_added); + std::unordered_set, + OutputInputTensorPairHasher>* edges_added); // Adds control dependencies between subgraph call nodes that have // dependencies via outside_compilation edges. @@ -716,11 +693,11 @@ void TopologicalClusterSort( Node* Encapsulator::Subgraph::GetCallNode() const { return call_node_; } int Encapsulator::Subgraph::GetArgIndexForEdge(const Edge* edge) const { - return args_by_dst_.at(NodeSlot(edge->dst(), edge->dst_input())); + return args_by_dst_.at(InputTensor(edge->dst(), edge->dst_input())); } int Encapsulator::Subgraph::GetResultIndexForEdge(const Edge* edge) const { - return results_.at(NodeSlot(edge->src(), edge->src_output())); + return results_.at(OutputTensor(edge->src(), edge->src_output())); } Node* Encapsulator::Subgraph::GetRecvAtHostNode( @@ -732,7 +709,7 @@ Node* Encapsulator::Subgraph::GetRecvAtHostNode( int Encapsulator::Subgraph::GetRecvAtHostSlot( const string& outside_compilation_subgraph_name, const Edge* edge) const { return outside_compilation_subgraphs_.at(outside_compilation_subgraph_name) - .inputs.at(NodeSlot(edge->src(), edge->src_output())); + .inputs.at(OutputTensor(edge->src(), edge->src_output())); } Node* Encapsulator::Subgraph::GetSendFromHostNode( @@ -744,7 +721,7 @@ Node* Encapsulator::Subgraph::GetSendFromHostNode( int Encapsulator::Subgraph::GetSendFromHostSlot( const string& outside_compilation_subgraph_name, const Edge* edge) const { return outside_compilation_subgraphs_.at(outside_compilation_subgraph_name) - .outputs_by_dst.at(NodeSlot(edge->dst(), edge->dst_input())); + .outputs_by_dst.at(InputTensor(edge->dst(), edge->dst_input())); } Node* Encapsulator::Subgraph::MakeNodeImage(const Graph* graph_in, Node* node) { @@ -769,10 +746,10 @@ Status Encapsulator::Subgraph::RecordArg( std::vector>* src_arg_pairs) { Node* src_node = edge->src(); int src_slot = edge->src_output(); - std::unordered_map::iterator iter; + std::unordered_map::iterator iter; bool inserted; - std::tie(iter, inserted) = - args_by_src_.emplace(NodeSlot(src_node, src_slot), args_by_src_.size()); + std::tie(iter, inserted) = args_by_src_.emplace( + OutputTensor(src_node, src_slot), args_by_src_.size()); int arg_index = iter->second; if (inserted) { NodeDef arg_def; @@ -793,7 +770,7 @@ Status Encapsulator::Subgraph::RecordArg( Node* dst_node = edge->dst(); Node* dst_image = node_images.at(dst_node); int dst_slot = edge->dst_input(); - args_by_dst_[NodeSlot(dst_node, dst_slot)] = arg_index; + args_by_dst_[InputTensor(dst_node, dst_slot)] = arg_index; graph_->AddEdge(args_[arg_index], 0, dst_image, dst_slot); return Status::OK(); } @@ -804,10 +781,10 @@ Status Encapsulator::Subgraph::RecordResult( Node* src_node = edge->src(); Node* src_image = node_images.at(src_node); int src_slot = edge->src_output(); - std::unordered_map::iterator iter; + std::unordered_map::iterator iter; bool inserted; std::tie(iter, inserted) = - results_.emplace(NodeSlot(src_node, src_slot), results_.size()); + results_.emplace(OutputTensor(src_node, src_slot), results_.size()); int ret_index = iter->second; if (inserted) { NodeDef ret_def; @@ -845,8 +822,8 @@ void Encapsulator::Subgraph::RecordOutsideCompilationInputOrControl( outside_subgraph->control_inputs.insert(edge->src()); } else { int input_index = outside_subgraph->inputs.size(); - outside_subgraph->inputs.emplace(NodeSlot(edge->src(), edge->src_output()), - input_index); + outside_subgraph->inputs.emplace( + OutputTensor(edge->src(), edge->src_output()), input_index); } } @@ -860,11 +837,13 @@ void Encapsulator::Subgraph::RecordOutsideCompilationOutputOrControl( DataType dtype = edge->dst()->input_type(edge->dst_input()); auto output_iter = outside_subgraph->outputs_by_src - .emplace(NodeSlot(edge->src(), edge->src_output(), dtype), - outside_subgraph->outputs_by_src.size()) + .emplace(OutputTensor(edge->src(), edge->src_output()), + OutsideCompilationSubgraph::ArgNumAndType( + outside_subgraph->outputs_by_src.size(), dtype)) .first; - int output_index = output_iter->second; - outside_subgraph->outputs_by_dst[NodeSlot(edge->dst(), edge->dst_input())] = + const int output_index = output_iter->second.index; + outside_subgraph + ->outputs_by_dst[InputTensor(edge->dst(), edge->dst_input())] = output_index; } } @@ -946,7 +925,7 @@ Status Encapsulator::Subgraph::AddHostComputes( for (const auto& input_src : oc_subgraph.inputs) { const Node* src_node = input_src.first.node; Node* src_image = node_images.at(src_node); - int src_slot = input_src.first.slot; + int src_slot = input_src.first.index; int input_index = input_src.second; DataType dtype = src_node->output_type(src_slot); @@ -954,8 +933,8 @@ Status Encapsulator::Subgraph::AddHostComputes( input_dtypes[input_index] = dtype; } for (const auto& output : oc_subgraph.outputs_by_src) { - DataType dtype = output.first.dtype; - int output_index = output.second; + DataType dtype = output.second.dtype; + int output_index = output.second.index; output_dtypes[output_index] = dtype; } @@ -993,7 +972,7 @@ Status Encapsulator::Subgraph::AddHostComputes( for (auto& input_src : oc_subgraph.inputs) { const Node* src_node = input_src.first.node; Node* src_image = node_images.at(src_node); - int src_slot = input_src.first.slot; + int src_slot = input_src.first.index; int input_index = input_src.second; graph_->AddEdge(src_image, src_slot, host_compute, input_index); } @@ -1015,7 +994,7 @@ Status Encapsulator::Subgraph::AddHostComputes( for (const auto& output : oc_subgraph.outputs_by_dst) { const Node* dst_node = output.first.node; Node* dst_image = node_images.at(dst_node); - int dst_slot = output.first.slot; + int dst_slot = output.first.index; int output_index = output.second; graph_->AddEdge(host_compute, output_index, dst_image, dst_slot); @@ -1226,7 +1205,7 @@ Status Encapsulator::Subgraph::AddRecvAtHostNode( for (const auto& input : oc_subgraph->inputs) { const Node* src_node = input.first.node; - int src_slot = input.first.slot; + int src_slot = input.first.index; int input_index = input.second; DataType dtype = src_node->output_type(src_slot); @@ -1280,8 +1259,8 @@ Status Encapsulator::Subgraph::AddSendFromHostNode( for (const auto& output : oc_subgraph->outputs_by_src) { const Node* src_node = output.first.node; Node* src_image = node_images.at(src_node); - int src_slot = output.first.slot; - int output_index = output.second; + int src_slot = output.first.index; + int output_index = output.second.index; DataType dtype = src_node->output_type(src_slot); dtypes[output_index] = dtype; @@ -1680,8 +1659,8 @@ Status Encapsulator::CopyEdgeToOutputGraph( const string& src_outside_compilation_id, const string& dst_func_id, const string& dst_outside_compilation_id, const std::unordered_map& node_images, Graph* graph_out, - std::unordered_set, NodeSlot::PairHasher>* - edges_added) { + std::unordered_set, + OutputInputTensorPairHasher>* edges_added) { Node* src_image; TF_RETURN_IF_ERROR(FindOutputImageOfEdgeSrc( src_func_id, src_outside_compilation_id, dst_func_id, @@ -1696,7 +1675,8 @@ Status Encapsulator::CopyEdgeToOutputGraph( if (edge->IsControlEdge()) { // Add the control edge, if we have not already added it, using the images // determined above (potentially call operators or RecvAtHost/SendFromHost). - if (edges_added->emplace(NodeSlot(src_image, -1), NodeSlot(dst_image, -1)) + if (edges_added + ->emplace(OutputTensor(src_image, -1), InputTensor(dst_image, -1)) .second) { graph_out->AddControlEdge(src_image, dst_image); } @@ -1714,8 +1694,8 @@ Status Encapsulator::CopyEdgeToOutputGraph( // Add the edge, if we have not already added it. if (edges_added - ->emplace(NodeSlot(src_image, src_output), - NodeSlot(dst_image, dst_input)) + ->emplace(OutputTensor(src_image, src_output), + InputTensor(dst_image, dst_input)) .second) { graph_out->AddEdge(src_image, src_output, dst_image, dst_input); } @@ -1739,7 +1719,8 @@ Status Encapsulator::AddEdgesToOutputGraph( // Set of edges already added to the output graph, represented as (src, dst) // pairs. We use the set to deduplicate edges; multiple edges in the input // graph may map to one edge in the output graph. - std::unordered_set, NodeSlot::PairHasher> + std::unordered_set, + OutputInputTensorPairHasher> edges_added; for (const Edge* edge : graph_in_->edges()) { diff --git a/tensorflow/core/graph/graph.cc b/tensorflow/core/graph/graph.cc index 0f748515ef..568f0870c0 100644 --- a/tensorflow/core/graph/graph.cc +++ b/tensorflow/core/graph/graph.cc @@ -24,6 +24,7 @@ limitations under the License. #include "tensorflow/core/graph/while_context.h" #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/lib/gtl/map_util.h" +#include "tensorflow/core/lib/hash/hash.h" #include "tensorflow/core/lib/strings/strcat.h" #include "tensorflow/core/lib/strings/stringprintf.h" #include "tensorflow/core/platform/logging.h" @@ -265,6 +266,28 @@ Status Node::input_node(int idx, const Node** const_n) const { return Status::OK(); } +// InputTensor + +bool InputTensor::operator==(const InputTensor& other) const { + return node == other.node && index == other.index; +} + +uint64 InputTensor::Hash::operator()(InputTensor const& s) const { + return Hash64Combine(std::hash()(s.node), + std::hash()(s.index)); +} + +// OutputTensor + +bool OutputTensor::operator==(const OutputTensor& other) const { + return node == other.node && index == other.index; +} + +uint64 OutputTensor::Hash::operator()(OutputTensor const& s) const { + return Hash64Combine(std::hash()(s.node), + std::hash()(s.index)); +} + // Graph Graph::Graph(const OpRegistryInterface* ops) diff --git a/tensorflow/core/graph/graph.h b/tensorflow/core/graph/graph.h index 33fb7cb57a..a147c94689 100644 --- a/tensorflow/core/graph/graph.h +++ b/tensorflow/core/graph/graph.h @@ -284,6 +284,16 @@ struct InputTensor { InputTensor(const Node* n, int i) : node(n), index(i) {} InputTensor() : node(nullptr), index(0) {} + + // Returns true if this InputTensor is identical to 'other'. Nodes are + // compared using pointer equality. + bool operator==(const InputTensor& other) const; + + // A hash function for InputTensors. Nodes are hashed based on their pointer + // value. + struct Hash { + uint64 operator()(InputTensor const& s) const; + }; }; // Represents an output of a node, i.e., the `index`-th output of `node`. Note @@ -295,6 +305,16 @@ struct OutputTensor { OutputTensor(const Node* n, int i) : node(n), index(i) {} OutputTensor() : node(nullptr), index(0) {} + + // Returns true if this OutputTensor is identical to 'other'. Nodes are + // compared using pointer equality. + bool operator==(const OutputTensor& other) const; + + // A hash function for OutputTensors. Nodes are hashed based on their pointer + // value. + struct Hash { + uint64 operator()(OutputTensor const& s) const; + }; }; class Edge { -- GitLab From 4254b2ca729858d5bff2bbd570b4f7b02d42fd35 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 13 Jun 2018 13:10:41 -0700 Subject: [PATCH 403/816] Splits testLargeCase in metric_ops_test into a dedicated file for slow-running tests and re-enables it as a 'large' test. PiperOrigin-RevId: 200440883 --- tensorflow/contrib/metrics/BUILD | 24 +++++++ .../python/ops/metric_ops_large_test.py | 66 +++++++++++++++++++ .../metrics/python/ops/metric_ops_test.py | 28 -------- 3 files changed, 90 insertions(+), 28 deletions(-) create mode 100644 tensorflow/contrib/metrics/python/ops/metric_ops_large_test.py diff --git a/tensorflow/contrib/metrics/BUILD b/tensorflow/contrib/metrics/BUILD index 4f2c82ca23..3f81c9ccea 100644 --- a/tensorflow/contrib/metrics/BUILD +++ b/tensorflow/contrib/metrics/BUILD @@ -97,3 +97,27 @@ py_test( "//third_party/py/numpy", ], ) + +py_test( + name = "metric_ops_large_test", + size = "large", + srcs = ["python/ops/metric_ops_large_test.py"], + srcs_version = "PY2AND3", + tags = ["noasan"], # times out b/63678675 + deps = [ + ":metrics_py", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:data_flow_ops", + "//tensorflow/python:errors", + "//tensorflow/python:framework", + "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:framework_test_lib", + "//tensorflow/python:math_ops", + "//tensorflow/python:platform_test", + "//tensorflow/python:random_ops", + "//tensorflow/python:sparse_tensor", + "//tensorflow/python:variables", + "//third_party/py/numpy", + ], +) diff --git a/tensorflow/contrib/metrics/python/ops/metric_ops_large_test.py b/tensorflow/contrib/metrics/python/ops/metric_ops_large_test.py new file mode 100644 index 0000000000..7acfc383eb --- /dev/null +++ b/tensorflow/contrib/metrics/python/ops/metric_ops_large_test.py @@ -0,0 +1,66 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Large tests for metric_ops.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +from six.moves import xrange # pylint: disable=redefined-builtin +from tensorflow.contrib.metrics.python.ops import metric_ops +from tensorflow.python.framework import dtypes as dtypes_lib +from tensorflow.python.framework import ops +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import random_ops +from tensorflow.python.ops import variables +from tensorflow.python.platform import test + + +class StreamingPrecisionRecallAtEqualThresholdsLargeTest(test.TestCase): + + def setUp(self): + np.random.seed(1) + ops.reset_default_graph() + + def testLargeCase(self): + shape = [32, 512, 256, 1] + predictions = random_ops.random_uniform( + shape, 0.0, 1.0, dtype=dtypes_lib.float32) + labels = math_ops.greater(random_ops.random_uniform(shape, 0.0, 1.0), 0.5) + + result, update_op = metric_ops.precision_recall_at_equal_thresholds( + labels=labels, predictions=predictions, num_thresholds=201) + # Run many updates, enough to cause highly inaccurate values if the + # code used float32 for accumulation. + num_updates = 71 + + with self.test_session() as sess: + sess.run(variables.local_variables_initializer()) + for _ in xrange(num_updates): + sess.run(update_op) + + prdata = sess.run(result) + + # Since we use random values, we won't know the tp/fp/tn/fn values, but + # tp and fp at threshold 0 should be the total number of positive and + # negative labels, hence their sum should be total number of pixels. + expected_value = 1.0 * np.product(shape) * num_updates + got_value = prdata.tp[0] + prdata.fp[0] + # They should be at least within 1. + self.assertNear(got_value, expected_value, 1.0) + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/metrics/python/ops/metric_ops_test.py b/tensorflow/contrib/metrics/python/ops/metric_ops_test.py index b13f08a37d..db4b530ce7 100644 --- a/tensorflow/contrib/metrics/python/ops/metric_ops_test.py +++ b/tensorflow/contrib/metrics/python/ops/metric_ops_test.py @@ -2391,34 +2391,6 @@ class StreamingPrecisionRecallAtEqualThresholdsTest(test.TestCase): for _ in range(3): self._testResultsEqual(initial_result, result) - def testLargeCase(self): - self.skipTest("Test consistently timing out") - shape = [32, 512, 256, 1] - predictions = random_ops.random_uniform( - shape, 0.0, 1.0, dtype=dtypes_lib.float32) - labels = math_ops.greater(random_ops.random_uniform(shape, 0.0, 1.0), 0.5) - - result, update_op = metric_ops.precision_recall_at_equal_thresholds( - labels=labels, predictions=predictions, num_thresholds=201) - # Run many updates, enough to cause highly inaccurate values if the - # code used float32 for accumulation. - num_updates = 71 - - with self.test_session() as sess: - sess.run(variables.local_variables_initializer()) - for _ in xrange(num_updates): - sess.run(update_op) - - prdata = sess.run(result) - - # Since we use random values, we won't know the tp/fp/tn/fn values, but - # tp and fp at threshold 0 should be the total number of positive and - # negative labels, hence their sum should be total number of pixels. - expected_value = 1.0 * np.product(shape) * num_updates - got_value = prdata.tp[0] + prdata.fp[0] - # They should be at least within 1. - self.assertNear(got_value, expected_value, 1.0) - def _testCase(self, predictions, labels, -- GitLab From 91034421a2422c24a177b8d4a46f9fc3d157be3f Mon Sep 17 00:00:00 2001 From: Derek Murray Date: Wed, 13 Jun 2018 13:12:01 -0700 Subject: [PATCH 404/816] [tf.data] Factor out a helper for creating flat args to `function.Defun`. The `defun_args()` helper flattens a nested structure down into the flat tuple of tensor types expected by `Defun`. PiperOrigin-RevId: 200441074 --- .../contrib/data/python/ops/grouping.py | 20 ++--- .../contrib/data/python/ops/scan_ops.py | 8 +- tensorflow/python/data/ops/dataset_ops.py | 85 ++++++++++++------- 3 files changed, 66 insertions(+), 47 deletions(-) diff --git a/tensorflow/contrib/data/python/ops/grouping.py b/tensorflow/contrib/data/python/ops/grouping.py index e9aa9f4ed6..60f13a1126 100644 --- a/tensorflow/contrib/data/python/ops/grouping.py +++ b/tensorflow/contrib/data/python/ops/grouping.py @@ -274,9 +274,7 @@ class GroupByReducerDataset(dataset_ops.Dataset): def _make_key_func(self, key_func, input_dataset): """Make wrapping Defun for key_func.""" - @function.Defun(*nest.flatten( - sparse.as_dense_types(input_dataset.output_types, - input_dataset.output_classes))) + @function.Defun(*dataset_ops.defun_args(input_dataset)) def tf_key_func(*args): """A wrapper for Defun that facilitates shape inference.""" nested_args = dataset_ops.restructure_args(args, input_dataset) @@ -335,11 +333,9 @@ class GroupByReducerDataset(dataset_ops.Dataset): # Create a list in which `tf_reduce_func` will store the new shapes. flat_new_state_shapes = [] - @function.Defun(*(nest.flatten( - sparse.as_dense_types( - self._state_types, self._state_classes)) + nest.flatten( - sparse.as_dense_types(input_dataset.output_types, - input_dataset.output_classes)))) + @function.Defun(*dataset_ops.defun_args( + input_types=(self._state_types, input_dataset.output_types), + input_classes=(self._state_classes, input_dataset.output_classes))) def tf_reduce_func(*args): """A wrapper for Defun that facilitates shape inference.""" nested_args = dataset_ops.restructure_args( @@ -409,8 +405,8 @@ class GroupByReducerDataset(dataset_ops.Dataset): def _make_finalize_func(self, finalize_func): """Make wrapping Defun for finalize_func.""" - @function.Defun(*(nest.flatten( - sparse.as_dense_types(self._state_types, self._state_classes)))) + @function.Defun(*dataset_ops.defun_args( + input_types=self._state_types, input_classes=self._state_classes)) def tf_finalize_func(*args): """A wrapper for Defun that facilitates shape inference.""" nested_args = dataset_ops.restructure_args( @@ -501,9 +497,7 @@ class GroupByWindowDataset(dataset_ops.Dataset): def _make_key_func(self, key_func, input_dataset): """Make wrapping Defun for key_func.""" - @function.Defun(*nest.flatten( - sparse.as_dense_types(input_dataset.output_types, - input_dataset.output_classes))) + @function.Defun(*dataset_ops.defun_args(input_dataset)) def tf_key_func(*args): """A wrapper for Defun that facilitates shape inference.""" nested_args = dataset_ops.restructure_args(args, input_dataset) diff --git a/tensorflow/contrib/data/python/ops/scan_ops.py b/tensorflow/contrib/data/python/ops/scan_ops.py index 1dc58b468a..c23b9b5c37 100644 --- a/tensorflow/contrib/data/python/ops/scan_ops.py +++ b/tensorflow/contrib/data/python/ops/scan_ops.py @@ -70,11 +70,9 @@ class _ScanDataset(dataset_ops.Dataset): # Create a list in which `tf_scan_func` will store the new shapes. flat_new_state_shapes = [] - @function.Defun(*(nest.flatten( - sparse.as_dense_types( - self._state_types, self._state_classes)) + nest.flatten( - sparse.as_dense_types(input_dataset.output_types, - input_dataset.output_classes)))) + @function.Defun(*dataset_ops.defun_args( + input_types=(self._state_types, input_dataset.output_types), + input_classes=(self._state_classes, input_dataset.output_classes))) def tf_scan_func(*args): """A wrapper for Defun that facilitates shape inference.""" nested_args = dataset_ops.restructure_args( diff --git a/tensorflow/python/data/ops/dataset_ops.py b/tensorflow/python/data/ops/dataset_ops.py index 9811d6b13f..67c1c17f99 100644 --- a/tensorflow/python/data/ops/dataset_ops.py +++ b/tensorflow/python/data/ops/dataset_ops.py @@ -108,12 +108,7 @@ class Dataset(object): if shared_name is None: shared_name = "" iterator_resource = gen_dataset_ops.iterator( - container="", - shared_name=shared_name, - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes)), - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, self.output_classes))) + container="", shared_name=shared_name, **flat_structure(self)) with ops.colocate_with(iterator_resource): initializer = gen_dataset_ops.make_iterator(self._as_variant_tensor(), iterator_resource) @@ -171,13 +166,8 @@ class Dataset(object): return iterator_ops.Iterator( gen_dataset_ops.one_shot_iterator( - dataset_factory=_make_dataset, - output_types=nest.flatten( - sparse.as_dense_types(self.output_types, self.output_classes)), - output_shapes=nest.flatten( - sparse.as_dense_shapes(self.output_shapes, - self.output_classes))), None, - self.output_types, self.output_shapes, self.output_classes) + dataset_factory=_make_dataset, **flat_structure(self)), + None, self.output_types, self.output_shapes, self.output_classes) @abc.abstractproperty def output_classes(self): @@ -1182,6 +1172,49 @@ def flat_structure(dataset): } +# TODO(mrry): Investigate adding a `Defun` wrapper that combines +# `defun_args()`, `restructure_args()`, and a future helper that consumes the +# outputs of the wrapped function. +def defun_args(dataset=None, input_types=None, input_classes=None): + """Returns a flat list of @{tf.DType} for a given element structure. + + The expected usage for an example function is as follows: + + ```python + input_dataset = ... # A `tf.data.Dataset`. + + @function.Defun(*defun_args(input_dataset)) + def tf_example_func(*args): + nested_args = restructure_args(args, input_dataset) + # [Destructure and handle the return values from `example_func()`. + ``` + + Either `dataset`, or both of `input_types` and `input_classes` must be + specified. If `dataset` is not specified, the structures of `input_types` and + `input_classes` must be compatible. + + Args: + dataset: (Optional.) A @{tf.data.Dataset} whose element structure should + be flattened. + input_types: (Optional.) A nested structure of @{tf.DType} with the desired + structure and types for each argument. + input_classes: (Optional.) A nested structure of `type` with the desired + structure and classes for each argument. + + Returns: + A flat list of @{tf.DType} for the given element structure. + """ + if input_types is None: + assert dataset is not None + assert input_classes is None + input_types = dataset.output_types + input_classes = dataset.output_classes + else: + assert input_types is not None and input_classes is not None + return nest.flatten( + sparse.as_dense_types(input_types, input_classes)) + + def restructure_args(args, dataset=None, input_shapes=None, input_types=None, input_classes=None): """Converts a flat tuple of arguments into a given structure. @@ -1195,7 +1228,7 @@ def restructure_args(args, dataset=None, input_shapes=None, input_types=None, ```python input_dataset = ... # A `tf.data.Dataset`. - @function.Defun(...) + @function.Defun(*defun_args(input_dataset)) def tf_example_func(*args): nested_args = restructure_args(args, input_dataset) ret = example_func(*nested_args) @@ -1274,8 +1307,8 @@ class _GeneratorDataset(Dataset): init_args_types = nest.pack_sequence_as( init_args, [t.dtype for t in nest.flatten(init_args)]) - @function.Defun(*nest.flatten( - sparse.as_dense_types(init_args_types, init_args_classes))) + @function.Defun(*defun_args( + input_types=init_args_types, input_classes=init_args_classes)) def tf_init_func(*args): """A wrapper for Defun that facilitates shape inference.""" nested_args = restructure_args( @@ -1323,8 +1356,8 @@ class _GeneratorDataset(Dataset): self._output_shapes = None self._output_types = None - @function.Defun(*nest.flatten( - sparse.as_dense_types(self._state_types, self._state_classes))) + @function.Defun(*defun_args( + input_types=self._state_types, input_classes=self._state_classes)) def tf_next_func(*args): """A wrapper for Defun that facilitates shape inference.""" nested_args = restructure_args( @@ -1367,8 +1400,8 @@ class _GeneratorDataset(Dataset): self._next_func = tf_next_func self._next_func.add_to_graph(ops.get_default_graph()) - @function.Defun(*nest.flatten( - sparse.as_dense_types(self._state_types, self._state_classes))) + @function.Defun(*defun_args( + input_types=self._state_types, input_classes=self._state_classes)) def tf_finalize_func(*args): """A wrapper for Defun that facilitates shape inference.""" nested_args = restructure_args( @@ -1986,9 +2019,7 @@ class MapDataset(Dataset): self._output_shapes = None self._output_types = None - @function.Defun(*nest.flatten( - sparse.as_dense_types(input_dataset.output_types, - input_dataset.output_classes))) + @function.Defun(*defun_args(input_dataset)) def tf_map_func(*args): """A wrapper for Defun that facilitates shape inference.""" nested_args = restructure_args(args, input_dataset) @@ -2082,9 +2113,7 @@ class FlatMapDataset(Dataset): super(FlatMapDataset, self).__init__() self._input_dataset = input_dataset - @function.Defun(*nest.flatten( - sparse.as_dense_types(input_dataset.output_types, - input_dataset.output_classes))) + @function.Defun(*defun_args(input_dataset)) def tf_map_func(*args): """A wrapper for Defun that facilitates shape inference.""" nested_args = restructure_args(args, input_dataset) @@ -2160,9 +2189,7 @@ class FilterDataset(Dataset): super(FilterDataset, self).__init__() self._input_dataset = input_dataset - @function.Defun(*nest.flatten( - sparse.as_dense_types(input_dataset.output_types, - input_dataset.output_classes))) + @function.Defun(*defun_args(input_dataset)) def tf_predicate(*args): """A wrapper for Defun that facilitates shape inference.""" nested_args = restructure_args(args, input_dataset) -- GitLab From b253e6b874d4f4d242b5d31777462cac146935d2 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 13 Jun 2018 13:24:52 -0700 Subject: [PATCH 405/816] support int16-quantized data in TFLite interpreter. PiperOrigin-RevId: 200442886 --- tensorflow/contrib/lite/context.h | 4 +++- tensorflow/contrib/lite/interpreter.cc | 9 ++++++--- tensorflow/contrib/lite/interpreter_test.cc | 15 +++++++-------- tensorflow/contrib/lite/kernels/internal/tensor.h | 10 ++++++++++ tensorflow/contrib/lite/model.cc | 3 +++ tensorflow/contrib/lite/optional_debug_tools.cc | 2 ++ .../interpreter_wrapper/interpreter_wrapper.cc | 4 ++++ tensorflow/contrib/lite/schema/schema.fbs | 1 + tensorflow/contrib/lite/schema/schema_generated.h | 9 ++++++--- tensorflow/contrib/lite/toco/tflite/types.cc | 8 ++++++++ tensorflow/contrib/lite/toco/tflite/types_test.cc | 6 ++++++ 11 files changed, 56 insertions(+), 15 deletions(-) diff --git a/tensorflow/contrib/lite/context.h b/tensorflow/contrib/lite/context.h index 4eb66cc225..0415acfe0f 100644 --- a/tensorflow/contrib/lite/context.h +++ b/tensorflow/contrib/lite/context.h @@ -138,6 +138,7 @@ typedef enum { kTfLiteInt64 = 4, kTfLiteString = 5, kTfLiteBool = 6, + kTfLiteInt16 = 7, } TfLiteType; // Parameters for asymmetric quantization. Quantized values can be converted @@ -148,7 +149,7 @@ typedef struct { int32_t zero_point; } TfLiteQuantizationParams; -// A union of points that points to memory for a given tensor. +// A union of pointers that points to memory for a given tensor. typedef union { int* i32; int64_t* i64; @@ -157,6 +158,7 @@ typedef union { const char* raw_const; uint8_t* uint8; bool* b; + int16_t* i16; } TfLitePtrUnion; // Memory allocation strategies. kTfLiteMmapRo is for read-only memory-mapped diff --git a/tensorflow/contrib/lite/interpreter.cc b/tensorflow/contrib/lite/interpreter.cc index ebb0aedc20..2f8205444d 100644 --- a/tensorflow/contrib/lite/interpreter.cc +++ b/tensorflow/contrib/lite/interpreter.cc @@ -334,6 +334,9 @@ TfLiteStatus Interpreter::BytesRequired(TfLiteType type, const int* dims, case kTfLiteFloat32: *bytes = sizeof(float) * count; break; + case kTfLiteInt16: + *bytes = sizeof(int16_t) * count; + break; case kTfLiteInt32: *bytes = sizeof(int32_t) * count; break; @@ -347,9 +350,9 @@ TfLiteStatus Interpreter::BytesRequired(TfLiteType type, const int* dims, *bytes = sizeof(bool) * count; break; default: - ReportError( - &context_, - "Only float32, int32, int64, uint8, bool supported currently."); + ReportError(&context_, + "Only float32, int16, int32, int64, uint8, bool supported " + "currently."); return kTfLiteError; } return kTfLiteOk; diff --git a/tensorflow/contrib/lite/interpreter_test.cc b/tensorflow/contrib/lite/interpreter_test.cc index 4c78466480..b977cb089c 100644 --- a/tensorflow/contrib/lite/interpreter_test.cc +++ b/tensorflow/contrib/lite/interpreter_test.cc @@ -106,10 +106,9 @@ TEST(BasicInterpreter, CheckAllocate) { TfLiteType type; size_t size; } cases[] = { - {kTfLiteFloat32, sizeof(float)}, - {kTfLiteInt32, sizeof(int32_t)}, - {kTfLiteUInt8, sizeof(uint8_t)}, - {kTfLiteInt64, sizeof(int64_t)}, + {kTfLiteFloat32, sizeof(float)}, {kTfLiteInt32, sizeof(int32_t)}, + {kTfLiteUInt8, sizeof(uint8_t)}, {kTfLiteInt64, sizeof(int64_t)}, + {kTfLiteInt16, sizeof(int16_t)}, }; for (auto test : cases) { @@ -134,6 +133,7 @@ TEST(BasicInterpreter, CheckResize) { const int32_t int32s[] = {-3, -4}; const uint8_t uint8s[] = {3, 4}; const int64_t int64s[] = {6, -7}; + const int16_t int16s[] = {8, -9}; struct { TfLiteType type; @@ -144,6 +144,7 @@ TEST(BasicInterpreter, CheckResize) { {kTfLiteInt32, sizeof(int32_t), reinterpret_cast(int32s)}, {kTfLiteUInt8, sizeof(uint8_t), reinterpret_cast(uint8s)}, {kTfLiteInt64, sizeof(int64_t), reinterpret_cast(int64s)}, + {kTfLiteInt16, sizeof(int16_t), reinterpret_cast(int16s)}, }; for (auto test : cases) { @@ -179,10 +180,8 @@ TEST(BasicInterpreter, CheckAlignment) { struct { TfLiteType type; } cases[] = { - {kTfLiteFloat32}, - {kTfLiteInt32}, - {kTfLiteUInt8}, - {kTfLiteInt64}, + {kTfLiteFloat32}, {kTfLiteInt32}, {kTfLiteUInt8}, + {kTfLiteInt64}, {kTfLiteInt16}, }; for (auto test : cases) { diff --git a/tensorflow/contrib/lite/kernels/internal/tensor.h b/tensorflow/contrib/lite/kernels/internal/tensor.h index f803d94695..518bee1c63 100644 --- a/tensorflow/contrib/lite/kernels/internal/tensor.h +++ b/tensorflow/contrib/lite/kernels/internal/tensor.h @@ -34,6 +34,11 @@ inline uint8_t* GetTensorData(TfLiteTensor* tensor) { return tensor != nullptr ? tensor->data.uint8 : nullptr; } +template <> +inline int16_t* GetTensorData(TfLiteTensor* tensor) { + return tensor != nullptr ? tensor->data.i16 : nullptr; +} + template <> inline int32_t* GetTensorData(TfLiteTensor* tensor) { return tensor != nullptr ? tensor->data.i32 : nullptr; @@ -62,6 +67,11 @@ inline const uint8_t* GetTensorData(const TfLiteTensor* tensor) { return tensor != nullptr ? tensor->data.uint8 : nullptr; } +template <> +inline const int16_t* GetTensorData(const TfLiteTensor* tensor) { + return tensor != nullptr ? tensor->data.i16 : nullptr; +} + template <> inline const int32_t* GetTensorData(const TfLiteTensor* tensor) { return tensor != nullptr ? tensor->data.i32 : nullptr; diff --git a/tensorflow/contrib/lite/model.cc b/tensorflow/contrib/lite/model.cc index 039f32b38e..cd7b9bdabf 100644 --- a/tensorflow/contrib/lite/model.cc +++ b/tensorflow/contrib/lite/model.cc @@ -45,6 +45,9 @@ TfLiteStatus ConvertTensorType(TensorType tensor_type, TfLiteType* type, case TensorType_FLOAT32: *type = kTfLiteFloat32; break; + case TensorType_INT16: + *type = kTfLiteInt16; + break; case TensorType_INT32: *type = kTfLiteInt32; break; diff --git a/tensorflow/contrib/lite/optional_debug_tools.cc b/tensorflow/contrib/lite/optional_debug_tools.cc index dfdd80ea8a..3af809a2a1 100644 --- a/tensorflow/contrib/lite/optional_debug_tools.cc +++ b/tensorflow/contrib/lite/optional_debug_tools.cc @@ -50,6 +50,8 @@ const char* TensorTypeName(TfLiteType type) { return "kTfLiteString"; case kTfLiteBool: return "kTfLiteBool"; + case kTfLiteInt16: + return "kTfLiteInt16"; } return "(invalid)"; } diff --git a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.cc b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.cc index 6b12c91924..5979f81205 100644 --- a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.cc +++ b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.cc @@ -68,6 +68,8 @@ int TfLiteTypeToPyArrayType(TfLiteType tf_lite_type) { return NPY_FLOAT32; case kTfLiteInt32: return NPY_INT32; + case kTfLiteInt16: + return NPY_INT16; case kTfLiteUInt8: return NPY_UINT8; case kTfLiteInt64: @@ -90,6 +92,8 @@ TfLiteType TfLiteTypeFromPyArray(PyArrayObject* array) { return kTfLiteFloat32; case NPY_INT32: return kTfLiteInt32; + case NPY_INT16: + return kTfLiteInt16; case NPY_UINT8: return kTfLiteUInt8; case NPY_INT64: diff --git a/tensorflow/contrib/lite/schema/schema.fbs b/tensorflow/contrib/lite/schema/schema.fbs index ee5208df14..1f1be428c9 100644 --- a/tensorflow/contrib/lite/schema/schema.fbs +++ b/tensorflow/contrib/lite/schema/schema.fbs @@ -34,6 +34,7 @@ enum TensorType : byte { INT64 = 4, STRING = 5, BOOL = 6, + INT16 = 7, } // Parameters for converting a quantized tensor back to float. Given a diff --git a/tensorflow/contrib/lite/schema/schema_generated.h b/tensorflow/contrib/lite/schema/schema_generated.h index 887e47ed1e..4e02034871 100755 --- a/tensorflow/contrib/lite/schema/schema_generated.h +++ b/tensorflow/contrib/lite/schema/schema_generated.h @@ -216,11 +216,12 @@ enum TensorType { TensorType_INT64 = 4, TensorType_STRING = 5, TensorType_BOOL = 6, + TensorType_INT16 = 7, TensorType_MIN = TensorType_FLOAT32, - TensorType_MAX = TensorType_BOOL + TensorType_MAX = TensorType_INT16 }; -inline TensorType (&EnumValuesTensorType())[7] { +inline TensorType (&EnumValuesTensorType())[8] { static TensorType values[] = { TensorType_FLOAT32, TensorType_FLOAT16, @@ -228,7 +229,8 @@ inline TensorType (&EnumValuesTensorType())[7] { TensorType_UINT8, TensorType_INT64, TensorType_STRING, - TensorType_BOOL + TensorType_BOOL, + TensorType_INT16 }; return values; } @@ -242,6 +244,7 @@ inline const char **EnumNamesTensorType() { "INT64", "STRING", "BOOL", + "INT16", nullptr }; return names; diff --git a/tensorflow/contrib/lite/toco/tflite/types.cc b/tensorflow/contrib/lite/toco/tflite/types.cc index 4867c3a62e..42c5d7e8eb 100644 --- a/tensorflow/contrib/lite/toco/tflite/types.cc +++ b/tensorflow/contrib/lite/toco/tflite/types.cc @@ -88,6 +88,8 @@ void CopyBuffer(const ::tflite::Buffer& buffer, Array* array) { switch (array_data_type) { case ArrayDataType::kFloat: return ::tflite::TensorType_FLOAT32; + case ArrayDataType::kInt16: + return ::tflite::TensorType_INT16; case ArrayDataType::kInt32: return ::tflite::TensorType_INT32; case ArrayDataType::kInt64: @@ -109,6 +111,8 @@ ArrayDataType DataType::Deserialize(int tensor_type) { switch (::tflite::TensorType(tensor_type)) { case ::tflite::TensorType_FLOAT32: return ArrayDataType::kFloat; + case ::tflite::TensorType_INT16: + return ArrayDataType::kInt16; case ::tflite::TensorType_INT32: return ArrayDataType::kInt32; case ::tflite::TensorType_INT64: @@ -131,6 +135,8 @@ flatbuffers::Offset> DataBuffer::Serialize( switch (array.data_type) { case ArrayDataType::kFloat: return CopyBuffer(array, builder); + case ArrayDataType::kInt16: + return CopyBuffer(array, builder); case ArrayDataType::kInt32: return CopyBuffer(array, builder); case ArrayDataType::kInt64: @@ -154,6 +160,8 @@ void DataBuffer::Deserialize(const ::tflite::Tensor& tensor, switch (tensor.type()) { case ::tflite::TensorType_FLOAT32: return CopyBuffer(buffer, array); + case ::tflite::TensorType_INT16: + return CopyBuffer(buffer, array); case ::tflite::TensorType_INT32: return CopyBuffer(buffer, array); case ::tflite::TensorType_INT64: diff --git a/tensorflow/contrib/lite/toco/tflite/types_test.cc b/tensorflow/contrib/lite/toco/tflite/types_test.cc index 564f303b9b..8c6ef95bfa 100644 --- a/tensorflow/contrib/lite/toco/tflite/types_test.cc +++ b/tensorflow/contrib/lite/toco/tflite/types_test.cc @@ -151,6 +151,12 @@ TEST(DataBuffer, Int32) { ::testing::ElementsAre(1, 1 << 30)); } +TEST(DataBuffer, Int16) { + Array recovered = ToFlatBufferAndBack({1, 1 << 14}); + EXPECT_THAT(recovered.GetBuffer().data, + ::testing::ElementsAre(1, 1 << 14)); +} + TEST(DataBuffer, String) { Array recovered = ToFlatBufferAndBack( {"AA", "BBB", "Best. String. Ever."}); -- GitLab From 7b033a1c26670f99562ee6c8a86bfc2721101165 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 13 Jun 2018 13:27:44 -0700 Subject: [PATCH 406/816] [XLA] Make --xla_dump_executions_to actually dump the HloSnapshot. PiperOrigin-RevId: 200443383 --- tensorflow/compiler/xla/service/service.cc | 27 +++++++++++++++++++++- 1 file changed, 26 insertions(+), 1 deletion(-) diff --git a/tensorflow/compiler/xla/service/service.cc b/tensorflow/compiler/xla/service/service.cc index d01c35b992..961158e677 100644 --- a/tensorflow/compiler/xla/service/service.cc +++ b/tensorflow/compiler/xla/service/service.cc @@ -348,8 +348,8 @@ StatusOr>> Service::BuildExecutables( module_protos[i]->entry_computation_name().c_str()); TF_RETURN_IF_ERROR( Executable::DumpToDirectory(directory_path, filename, *hlo_snapshot)); - hlo_snapshots.push_back(std::move(hlo_snapshot)); } + hlo_snapshots.push_back(std::move(hlo_snapshot)); } VLOG(1) << "Computations:"; @@ -721,6 +721,15 @@ Status Service::ExecuteGraphParallel(const ExecuteGraphParallelRequest* arg, executable_ptrs.push_back(executable.get()); } + for (int i = 0; i < executable_ptrs.size(); i++) { + if (executable_ptrs[i]->dumping_snapshot()) { + TF_RETURN_IF_ERROR(RecordArguments(all_arguments[i].front(), + all_executors[i][0], + execute_backend_->transfer_manager(), + executable_ptrs[i]->hlo_snapshot())); + } + } + // Execute the generated executables in parallel and return the device // handles for each computation's output. ExecutionProfile profile; @@ -736,6 +745,18 @@ Status Service::ExecuteGraphParallel(const ExecuteGraphParallelRequest* arg, *result->add_responses() = response; } + for (int i = 0; i < executable_ptrs.size(); i++) { + if (executable_ptrs[i]->dumping_snapshot()) { + TF_ASSIGN_OR_RETURN(const ShapedBuffer* result_buffer, + allocation_tracker_.ResolveForReplica(outputs[i], 0)); + TF_RETURN_IF_ERROR(RecordResult(*result_buffer, all_executors[i][0], + execute_backend_->transfer_manager(), + executable_ptrs[i]->hlo_snapshot())); + // Dump out the ith snapshot. + TF_RETURN_IF_ERROR(executable_ptrs[i]->DumpHloSnapshot()); + } + } + VLOG(1) << "successfully completed 'execute-graph-parallel' request"; return Status::OK(); } @@ -835,6 +856,10 @@ StatusOr> Service::BuildExecutable( backend->compiler()->RunBackend( std::move(module), executor, device_allocator)); + if (!execution_directory_path.empty()) { + executable->set_hlo_snapshot(std::move(hlo_snapshot)); + } + return std::move(executable); } -- GitLab From fbd920a6997e2d507b4247c194574a5b2b10f926 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 13 Jun 2018 13:28:20 -0700 Subject: [PATCH 407/816] Split out HloInfeedIndexInstruction and HloOutfeedInstruction as subclasses from HloInstruction. PiperOrigin-RevId: 200443508 --- .../compiler/xla/service/hlo_instruction.cc | 75 +++++++----------- .../compiler/xla/service/hlo_instruction.h | 39 ++++----- .../compiler/xla/service/hlo_instructions.cc | 79 +++++++++++++++++++ .../compiler/xla/service/hlo_instructions.h | 61 ++++++++++++++ 4 files changed, 183 insertions(+), 71 deletions(-) diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc index 39662d1735..4e029d66a5 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction.cc +++ b/tensorflow/compiler/xla/service/hlo_instruction.cc @@ -243,6 +243,13 @@ StatusOr> HloInstruction::CreateFromProto( CreateReducePrecision(proto.shape(), operands(0), proto.exponent_bits(), proto.mantissa_bits()); break; + case HloOpcode::kInfeed: + instruction = CreateInfeed(proto.shape(), proto.infeed_config()); + break; + case HloOpcode::kOutfeed: + instruction = CreateOutfeed(proto.outfeed_shape(), operands(0), + proto.outfeed_config()); + break; default: { instruction = WrapUnique(new HloInstruction(opcode, proto.shape())); for (const int64 operand_id : proto.operand_ids()) { @@ -293,10 +300,7 @@ StatusOr> HloInstruction::CreateFromProto( instruction->padding_config_ = MakeUnique(proto.padding_config()); } - instruction->outfeed_config_ = proto.outfeed_config(); - instruction->infeed_config_ = proto.infeed_config(); instruction->custom_call_target_ = proto.custom_call_target(); - instruction->outfeed_shape_ = proto.outfeed_shape(); if (proto.has_sharding()) { TF_ASSIGN_OR_RETURN(const auto& sharding, @@ -548,23 +552,13 @@ HloInstruction::CreateCrossReplicaSum( /* static */ std::unique_ptr HloInstruction::CreateInfeed( const Shape& shape, const string& config) { - auto instruction = WrapUnique(new HloInstruction(HloOpcode::kInfeed, shape)); - instruction->set_infeed_config(config); - return instruction; + return MakeUnique(shape, config); } /* static */ std::unique_ptr HloInstruction::CreateOutfeed( const Shape& shape, HloInstruction* operand, tensorflow::StringPiece outfeed_config) { - std::unique_ptr instruction = - WrapUnique(new HloInstruction(HloOpcode::kOutfeed, ShapeUtil::MakeNil())); - CHECK(ShapeUtil::Compatible(operand->shape(), shape)) - << "Outfeed shape " << shape << " must be compatible with operand shape " - << operand->shape(); - instruction->AppendOperand(operand); - instruction->outfeed_config_ = std::string(outfeed_config); - instruction->outfeed_shape_ = shape; - return instruction; + return MakeUnique(shape, operand, outfeed_config); } /* static */ std::unique_ptr HloInstruction::CreateSend( @@ -1040,6 +1034,8 @@ std::unique_ptr HloInstruction::CloneWithNewOperands( case HloOpcode::kParameter: case HloOpcode::kGetTupleElement: case HloOpcode::kReducePrecision: + case HloOpcode::kInfeed: + case HloOpcode::kOutfeed: clone = CloneWithNewOperandsImpl(shape, new_operands, context); break; // Unary ops. @@ -1179,14 +1175,6 @@ std::unique_ptr HloInstruction::CloneWithNewOperands( clone = CreateWhile(shape, while_condition(), while_body(), new_operands[0]); break; - case HloOpcode::kInfeed: - CHECK_EQ(new_operands.size(), 0); - clone = CreateInfeed(shape, infeed_config()); - break; - case HloOpcode::kOutfeed: - CHECK_EQ(new_operands.size(), 1); - clone = CreateOutfeed(outfeed_shape_, new_operands[0], outfeed_config()); - break; case HloOpcode::kConditional: CHECK_EQ(new_operands.size(), 3); clone = CreateConditional(shape, new_operands[0], new_operands[1], @@ -1505,8 +1493,6 @@ bool HloInstruction::IdenticalSlowPath( eq_computations(false_computation(), other.false_computation()); // These opcodes are not yet supported. - case HloOpcode::kInfeed: - case HloOpcode::kOutfeed: case HloOpcode::kSort: case HloOpcode::kHostCompute: return false; @@ -1535,6 +1521,8 @@ bool HloInstruction::IdenticalSlowPath( case HloOpcode::kParameter: case HloOpcode::kGetTupleElement: case HloOpcode::kReducePrecision: + case HloOpcode::kInfeed: + case HloOpcode::kOutfeed: LOG(FATAL) << "Base class impl called for opcode with subclass: " << opcode(); } @@ -1675,11 +1663,6 @@ const string& HloInstruction::custom_call_target() const { return custom_call_target_; } -const string& HloInstruction::outfeed_config() const { - CHECK_EQ(opcode_, HloOpcode::kOutfeed); - return outfeed_config_; -} - HloComputation* HloInstruction::while_condition() const { CHECK_EQ(HloOpcode::kWhile, opcode_); return called_computations_[kConditionComputationIndex]; @@ -2036,13 +2019,6 @@ std::vector HloInstruction::ExtraAttributesToString( }), "}")); } - if (opcode() == HloOpcode::kInfeed && !infeed_config_.empty()) { - extra.push_back(StrCat("infeed_config=\"", CEscape(infeed_config_), "\"")); - } - if (opcode() == HloOpcode::kOutfeed && !outfeed_config_.empty()) { - extra.push_back( - StrCat("outfeed_config=\"", CEscape(outfeed_config_), "\"")); - } if (operand_side_metadata_ != nullptr && user_side_metadata_ != nullptr) { extra.push_back(StrCat("domain={kind=\"", operand_side_metadata_->Kind(), "\", entry=", operand_side_metadata_->ToString(), @@ -2125,10 +2101,7 @@ HloInstructionProto HloInstruction::ToProto() const { if (padding_config_ != nullptr) { *proto.mutable_padding_config() = *padding_config_; } - proto.set_outfeed_config(outfeed_config_); - proto.set_infeed_config(infeed_config_); proto.set_custom_call_target(custom_call_target_); - *proto.mutable_outfeed_shape() = outfeed_shape_; if (has_sharding()) { *proto.mutable_sharding() = sharding().ToProto(); @@ -2629,12 +2602,6 @@ Status HloInstruction::AcceptOrdered( return visitor->FinishVisit(this); } -const Shape& HloInstruction::outfeed_shape() const { - DCHECK_EQ(opcode_, HloOpcode::kOutfeed); - TF_DCHECK_OK(ShapeUtil::ValidateShapeWithOptionalLayout(shape_)); - return outfeed_shape_; -} - const Shape& HloInstruction::shape() const { TF_DCHECK_OK(ShapeUtil::ValidateShapeWithOptionalLayout(shape_)); return shape_; @@ -3168,4 +3135,20 @@ int32 HloInstruction::exponent_bits() const { int32 HloInstruction::mantissa_bits() const { return Cast(this)->mantissa_bits(); } + +string HloInstruction::infeed_config() const { + return Cast(this)->infeed_config(); +} + +void HloInstruction::set_infeed_config(const string& config) { + return Cast(this)->set_infeed_config(config); +} + +const Shape& HloInstruction::outfeed_shape() const { + return Cast(this)->outfeed_shape(); +} + +const string& HloInstruction::outfeed_config() const { + return Cast(this)->outfeed_config(); +} } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h index a206cdab27..2816a3b708 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction.h +++ b/tensorflow/compiler/xla/service/hlo_instruction.h @@ -907,14 +907,6 @@ class HloInstruction { // Precondition: opcode() == HloOpcode::kCustomCall const string& custom_call_target() const; - // Returns the config for the Outfeed instruction. - // Precondition: opcode() == HloOpcode::kOutfeed - const string& outfeed_config() const; - - // Returns the shape for the Outfeed instruction. - // Precondition: opcode() == HloOpcode::kOutfeed - const Shape& outfeed_shape() const; - // Gets/sets the while_condition or while_body HloComputation for While. The // setters should only be called by HloModule or HloComputation methods. // @@ -988,12 +980,6 @@ class HloInstruction { // Precondition: opcode() == HloOpcode::kHostCompute string channel_name() const { return channel_name_; } - // Returns the infeed configuration string. The infeed configuration includes - // any metadata needed for the backend compiler (e.g., infeed buffer address) - // and is target-dependent. - string infeed_config() const { return infeed_config_; } - void set_infeed_config(const string& config) { infeed_config_ = config; } - // Returns true if this instruction is fused, ie contained within a fusion // instruction. bool IsFused() const; @@ -1422,11 +1408,23 @@ class HloInstruction { // Delegates to HloGetTupleElementInstruction::tuple_index. int64 tuple_index() const; - // Returns the number of exponent bits for a reduce-precision node. + // // Delegates to HloReducePrecisionInstruction::exponent_bits. int32 exponent_bits() const; - // Returns the number of mantissa bits for a reduce-precision node. + // // Delegates to HloReducePrecisionInstruction::mantissa_bits. int32 mantissa_bits() const; + + // Delegates to HloInfeedInstruction::infeed_config. + string infeed_config() const; + + // Delegates to HloInfeedInstruction::set_infeed_config. + void set_infeed_config(const string& config); + + // Returns the config for the Outfeed instruction. + const string& outfeed_config() const; + + // Returns the shape for the Outfeed instruction. + const Shape& outfeed_shape() const; // Old methods kept for smooth subclassing transition END. // Returns the group ids of each replica for CrossReplicaSum op. @@ -1555,9 +1553,6 @@ class HloInstruction { // The computation in which this instruction is contained. HloComputation* parent_ = nullptr; - // Shape of outfeed request. - Shape outfeed_shape_; - // Result shape of this instruction. Shape shape_; @@ -1616,18 +1611,12 @@ class HloInstruction { kFalseComputationIndex = 1, }; - // Outfeed configuration information, only present for kOutfeed. - string outfeed_config_; - // A trace instruction that consumes this instruction. // // Invariant: if trace_instruction_ != nullptr, trace_instruction has this as // an operand. HloInstruction* trace_instruction_ = nullptr; - // The string representation of the infeed configuration. - string infeed_config_; - // The backend-specific configuration for how a backend should compile this // HLO. See the documentation on backend_config(). string backend_config_; diff --git a/tensorflow/compiler/xla/service/hlo_instructions.cc b/tensorflow/compiler/xla/service/hlo_instructions.cc index d326d5d009..761d833546 100644 --- a/tensorflow/compiler/xla/service/hlo_instructions.cc +++ b/tensorflow/compiler/xla/service/hlo_instructions.cc @@ -24,6 +24,7 @@ limitations under the License. namespace xla { namespace { +using ::tensorflow::str_util::CEscape; using ::tensorflow::str_util::Join; using ::tensorflow::strings::StrAppend; using ::tensorflow::strings::StrCat; @@ -1284,4 +1285,82 @@ HloReducePrecisionInstruction::CloneWithNewOperandsImpl( shape, new_operands[0], exponent_bits(), mantissa_bits()); } +HloInfeedInstruction::HloInfeedInstruction(const Shape& shape, + const string& config) + : HloInstruction(HloOpcode::kInfeed, shape), infeed_config_(config) {} + +HloInstructionProto HloInfeedInstruction::ToProto() const { + HloInstructionProto proto = HloInstruction::ToProto(); + proto.set_infeed_config(infeed_config_); + return proto; +} + +std::vector HloInfeedInstruction::ExtraAttributesToStringImpl( + const HloPrintOptions& options) const { + if (infeed_config_.empty()) { + return {}; + } + return {StrCat("infeed_config=\"", CEscape(infeed_config_), "\"")}; +} + +bool HloInfeedInstruction::IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const { + // Not yet supported. + return false; +} + +std::unique_ptr HloInfeedInstruction::CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const { + CHECK_EQ(new_operands.size(), 0); + return MakeUnique(shape, infeed_config()); +} + +HloOutfeedInstruction::HloOutfeedInstruction( + const Shape& shape, HloInstruction* operand, + tensorflow::StringPiece outfeed_config) + : HloInstruction(HloOpcode::kOutfeed, ShapeUtil::MakeNil()), + outfeed_shape_(shape), + outfeed_config_(outfeed_config.begin(), outfeed_config.end()) { + CHECK(ShapeUtil::Compatible(operand->shape(), shape)) + << "Outfeed shape " << shape << " must be compatible with operand shape " + << operand->shape(); + AppendOperand(operand); +} + +HloInstructionProto HloOutfeedInstruction::ToProto() const { + HloInstructionProto proto = HloInstruction::ToProto(); + proto.set_outfeed_config(outfeed_config()); + *proto.mutable_outfeed_shape() = outfeed_shape(); + return proto; +} + +std::vector HloOutfeedInstruction::ExtraAttributesToStringImpl( + const HloPrintOptions& options) const { + if (outfeed_config_.empty()) { + return {}; + } + return {StrCat("outfeed_config=\"", CEscape(outfeed_config_), "\"")}; +} + +bool HloOutfeedInstruction::IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const { + // Not yet supported. + return false; +} + +std::unique_ptr HloOutfeedInstruction::CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const { + CHECK_EQ(new_operands.size(), 1); + return MakeUnique(outfeed_shape(), new_operands[0], + outfeed_config()); +} + } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_instructions.h b/tensorflow/compiler/xla/service/hlo_instructions.h index 6749d87555..9f810c0a14 100644 --- a/tensorflow/compiler/xla/service/hlo_instructions.h +++ b/tensorflow/compiler/xla/service/hlo_instructions.h @@ -722,6 +722,67 @@ class HloReducePrecisionInstruction : public HloInstruction { int32 exponent_bits_ = 0; int32 mantissa_bits_ = 0; }; + +class HloInfeedInstruction : public HloInstruction { + public: + explicit HloInfeedInstruction(const Shape& shape, const string& config); + // Returns the infeed configuration string. The infeed configuration includes + // any metadata needed for the backend compiler (e.g., infeed buffer address) + // and is target-dependent. + string infeed_config() const { return infeed_config_; } + void set_infeed_config(const string& config) { infeed_config_ = config; } + // Returns a serialized representation of this instruction. + HloInstructionProto ToProto() const override; + + private: + std::vector ExtraAttributesToStringImpl( + const HloPrintOptions& options) const override; + bool IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const override; + // Implementation for non-common logic of CloneWithNewOperands. + std::unique_ptr CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const override; + + // The string representation of the infeed configuration. + string infeed_config_; +}; + +class HloOutfeedInstruction : public HloInstruction { + public: + explicit HloOutfeedInstruction(const Shape& shape, HloInstruction* operand, + tensorflow::StringPiece outfeed_config); + // Returns the shape for the Outfeed instruction. + const Shape& outfeed_shape() const { + TF_DCHECK_OK(ShapeUtil::ValidateShapeWithOptionalLayout(shape())); + return outfeed_shape_; + } + // Returns the config for the Outfeed instruction. + const string& outfeed_config() const { return outfeed_config_; } + // Returns a serialized representation of this instruction. + HloInstructionProto ToProto() const override; + + private: + std::vector ExtraAttributesToStringImpl( + const HloPrintOptions& options) const override; + bool IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const override; + // Implementation for non-common logic of CloneWithNewOperands. + std::unique_ptr CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const override; + + // Shape of outfeed request. + Shape outfeed_shape_; + // Outfeed configuration information, only present for kOutfeed. + string outfeed_config_; +}; } // namespace xla #endif // TENSORFLOW_COMPILER_XLA_SERVICE_HLO_INSTRUCTIONS_H_ -- GitLab From 8be4327e188ba334bfd688e34cf5f37c3d03e49e Mon Sep 17 00:00:00 2001 From: Thomas Joerg Date: Wed, 13 Jun 2018 13:45:22 -0700 Subject: [PATCH 408/816] [XLA:GPU] Move IsProfitableOperand implementation into the MultiOutputFusion superclass. PiperOrigin-RevId: 200446421 --- .../xla/service/gpu/multi_output_fusion.cc | 16 ---------------- .../xla/service/gpu/multi_output_fusion.h | 5 ----- .../compiler/xla/service/multi_output_fusion.cc | 16 ++++++++++++++++ .../compiler/xla/service/multi_output_fusion.h | 4 ++-- 4 files changed, 18 insertions(+), 23 deletions(-) diff --git a/tensorflow/compiler/xla/service/gpu/multi_output_fusion.cc b/tensorflow/compiler/xla/service/gpu/multi_output_fusion.cc index e3f444a126..09acd8603e 100644 --- a/tensorflow/compiler/xla/service/gpu/multi_output_fusion.cc +++ b/tensorflow/compiler/xla/service/gpu/multi_output_fusion.cc @@ -81,22 +81,6 @@ bool GpuMultiOutputFusion::ShapesCompatibleForFusion(HloInstruction* instr1, get_element_shape(element_instr_2)); } -bool GpuMultiOutputFusion::IsProfitableOperand(HloInstruction* instr) { - // kConstant instruction will not have memory reads, so it won't be a profit - // source. Skip them. - if (instr->opcode() == HloOpcode::kConstant && - ShapeUtil::IsEffectiveScalar(instr->shape())) { - return false; - } - // We don't target to fuse producer/consumer instructions -- this should - // be taken care of by the instruction_fusion pass. If instr has only - // one user, it will not have sibling instructions. We won't consider it. - if (instr->user_count() < 2) { - return false; - } - return true; -} - namespace { bool IsReduction(HloInstruction* instr) { if (instr->IsMultiOutputFusion()) { diff --git a/tensorflow/compiler/xla/service/gpu/multi_output_fusion.h b/tensorflow/compiler/xla/service/gpu/multi_output_fusion.h index 5451a93cec..038b1e9dc4 100644 --- a/tensorflow/compiler/xla/service/gpu/multi_output_fusion.h +++ b/tensorflow/compiler/xla/service/gpu/multi_output_fusion.h @@ -42,11 +42,6 @@ class GpuMultiOutputFusion : public MultiOutputFusion { // instr1 and instr2, common operands will not be loaded twice. The profit is // estimated as the size of the common operands b/w instr1 and instr2. int64 GetProfit(HloInstruction* instr1, HloInstruction* instr2) override; - - // Whether fusing the instruction can reduce memory reads. - // - // TODO(tjoerg): Move this method up into the MultiOutputFusion base class. - bool IsProfitableOperand(HloInstruction* instr) override; }; } // namespace gpu diff --git a/tensorflow/compiler/xla/service/multi_output_fusion.cc b/tensorflow/compiler/xla/service/multi_output_fusion.cc index 29f787b86b..f9f9c7dcf7 100644 --- a/tensorflow/compiler/xla/service/multi_output_fusion.cc +++ b/tensorflow/compiler/xla/service/multi_output_fusion.cc @@ -151,6 +151,22 @@ HloInstruction* MultiOutputFusion::Fuse(HloInstruction* instr1, return remaining; } +bool MultiOutputFusion::IsProfitableOperand(HloInstruction* instr) { + // kConstant instruction will not have memory reads, so it won't be a profit + // source. Skip them. + if (instr->opcode() == HloOpcode::kConstant && + ShapeUtil::IsEffectiveScalar(instr->shape())) { + return false; + } + // We don't target to fuse producer/consumer instructions -- this should + // be taken care of by the instruction_fusion pass. If instr has only + // one user, it will not have sibling instructions. We won't consider it. + if (instr->user_count() < 2) { + return false; + } + return true; +} + void MultiOutputFusion::Update(HloInstruction* instr1, HloInstruction* instr2) { HloInstruction* fusion = instr1; HloInstruction* fused = instr2; diff --git a/tensorflow/compiler/xla/service/multi_output_fusion.h b/tensorflow/compiler/xla/service/multi_output_fusion.h index cfdf83cfe8..d9c36fa284 100644 --- a/tensorflow/compiler/xla/service/multi_output_fusion.h +++ b/tensorflow/compiler/xla/service/multi_output_fusion.h @@ -72,8 +72,8 @@ class MultiOutputFusion : public HloPassInterface { // multi-output fusion instruction. virtual int64 GetProfit(HloInstruction* instr1, HloInstruction* instr2) = 0; - // Whether fusing the instruction can reduce cost. - virtual bool IsProfitableOperand(HloInstruction* instr) = 0; + // Whether fusing the instruction can reduce memory reads. + virtual bool IsProfitableOperand(HloInstruction* instr); // Test if it's legal to fuse instr1 and instr2 into one fusion instruction. virtual bool LegalToFuse(HloInstruction* instr1, HloInstruction* instr2); -- GitLab From 6131e85cd75510b37cea781da6da21b74ed6aa7d Mon Sep 17 00:00:00 2001 From: "karl@kubx.ca" Date: Wed, 13 Jun 2018 17:10:13 -0400 Subject: [PATCH 409/816] Code review, first pass --- tensorflow/java/build_defs.bzl | 1 - .../processor/OperatorProcessor.java | 21 +++++++------------ 2 files changed, 8 insertions(+), 14 deletions(-) diff --git a/tensorflow/java/build_defs.bzl b/tensorflow/java/build_defs.bzl index 2befacbe3d..e1916ca4d9 100644 --- a/tensorflow/java/build_defs.bzl +++ b/tensorflow/java/build_defs.bzl @@ -19,7 +19,6 @@ XLINT_OPTS = [ "-Xlint:-serial", "-Xlint:-try", "-Xlint:-classfile", # see b/32750402, go/javac-warnings#classfile - "-Xlint:-deprecation", # for exposing deprecated ops ] # The bazel errorprone plugin currently only enables default errorChecks diff --git a/tensorflow/java/src/gen/java/org/tensorflow/processor/OperatorProcessor.java b/tensorflow/java/src/gen/java/org/tensorflow/processor/OperatorProcessor.java index d7139f766e..aa624a9e83 100644 --- a/tensorflow/java/src/gen/java/org/tensorflow/processor/OperatorProcessor.java +++ b/tensorflow/java/src/gen/java/org/tensorflow/processor/OperatorProcessor.java @@ -48,7 +48,6 @@ import com.google.common.base.CaseFormat; import com.google.common.base.Strings; import com.google.common.collect.HashMultimap; import com.google.common.collect.Multimap; -import com.squareup.javapoet.AnnotationSpec; import com.squareup.javapoet.ClassName; import com.squareup.javapoet.FieldSpec; import com.squareup.javapoet.JavaFile; @@ -144,7 +143,7 @@ public final class OperatorProcessor extends AbstractProcessor { @Override public Set getSupportedAnnotationTypes() { - return Collections.singleton(String.format("%s.annotation.Operator", OP_PACKAGE)); + return Collections.singleton("org.tensorflow.op.annotation.Operator"); } private static final Pattern JAVADOC_TAG_PATTERN = Pattern.compile("@(?:param|return|throws|exception|see)\\s+.*"); @@ -153,7 +152,6 @@ public final class OperatorProcessor extends AbstractProcessor { private static final TypeName T_SCOPE = ClassName.get("org.tensorflow.op", "Scope"); private static final TypeName T_GRAPH = ClassName.get("org.tensorflow", "Graph"); private static final TypeName T_STRING = ClassName.get(String.class); - private static final String OP_PACKAGE = "org.tensorflow.op"; private Filer filer; private Messager messager; @@ -204,7 +202,11 @@ public final class OperatorProcessor extends AbstractProcessor { result = false; continue; } - collectOpMethods(groupedMethods, (TypeElement) e, annotation); + TypeElement opClass = (TypeElement) e; + // Skip deprecated operations for now, as we do not guarantee API stability yet + if (opClass.getAnnotation(Deprecated.class) == null) { + collectOpMethods(groupedMethods, opClass, annotation); + } } return result; } @@ -227,14 +229,13 @@ public final class OperatorProcessor extends AbstractProcessor { } private MethodSpec buildOpMethod(String methodName, TypeElement opClass, ExecutableElement factoryMethod) { - boolean deprecated = opClass.getAnnotation(Deprecated.class) != null; ClassName opClassName = ClassName.get(opClass); MethodSpec.Builder builder = MethodSpec.methodBuilder(methodName) .addModifiers(Modifier.PUBLIC) .returns(TypeName.get(factoryMethod.getReturnType())) .varargs(factoryMethod.isVarArgs()) - .addJavadoc("$L", buildOpMethodJavadoc(opClassName, factoryMethod, deprecated)); + .addJavadoc("$L", buildOpMethodJavadoc(opClassName, factoryMethod)); for (TypeParameterElement tp: factoryMethod.getTypeParameters()) { TypeVariableName tvn = TypeVariableName.get((TypeVariable) tp.asType()); @@ -243,9 +244,6 @@ public final class OperatorProcessor extends AbstractProcessor { for (TypeMirror thrownType: factoryMethod.getThrownTypes()) { builder.addException(TypeName.get(thrownType)); } - if (deprecated) { - builder.addAnnotation(AnnotationSpec.builder(Deprecated.class).build()); - } StringBuilder call = new StringBuilder("return $T.create(scope"); boolean first = true; for (VariableElement param : factoryMethod.getParameters()) { @@ -263,7 +261,7 @@ public final class OperatorProcessor extends AbstractProcessor { return builder.build(); } - private String buildOpMethodJavadoc(ClassName opClassName, ExecutableElement factoryMethod, boolean deprecated) { + private String buildOpMethodJavadoc(ClassName opClassName, ExecutableElement factoryMethod) { StringBuilder javadoc = new StringBuilder(); javadoc.append("Adds an {@link ").append(opClassName.simpleName()).append("} operation to the graph\n\n"); @@ -280,9 +278,6 @@ public final class OperatorProcessor extends AbstractProcessor { javadoc.append(tag).append('\n'); } } - if (deprecated) { - javadoc.append("@deprecated\n"); - } javadoc.append("@see {@link ").append(opClassName).append("}\n"); return javadoc.toString(); -- GitLab From 096b7dc5bea8ebaedb3a042e557c5e2d89619902 Mon Sep 17 00:00:00 2001 From: Anna R Date: Wed, 13 Jun 2018 14:10:24 -0700 Subject: [PATCH 410/816] Pick up estimator docstrings from correct modules when generating API. PiperOrigin-RevId: 200450896 --- tensorflow/tools/api/generator/BUILD | 21 ++++++++++++++ .../tools/api/generator/create_python_api.py | 11 ++++--- tensorflow/tools/api/generator/doc_srcs.py | 29 ++++++++++++++++++- .../tools/api/generator/doc_srcs_test.py | 11 ++++--- 4 files changed, 63 insertions(+), 9 deletions(-) diff --git a/tensorflow/tools/api/generator/BUILD b/tensorflow/tools/api/generator/BUILD index 3a28153e52..6065c12cad 100644 --- a/tensorflow/tools/api/generator/BUILD +++ b/tensorflow/tools/api/generator/BUILD @@ -5,12 +5,16 @@ licenses(["notice"]) # Apache 2.0 exports_files(["LICENSE"]) +load("//tensorflow/tools/api/generator:api_gen.bzl", "ESTIMATOR_API_INIT_FILES") load("//tensorflow/tools/api/generator:api_gen.bzl", "TENSORFLOW_API_INIT_FILES") py_library( name = "doc_srcs", srcs = ["doc_srcs.py"], srcs_version = "PY2AND3", + deps = [ + "//tensorflow/python:util", + ], ) py_binary( @@ -39,6 +43,7 @@ py_test( srcs = ["doc_srcs_test.py"], args = [ "--package=tensorflow.python", + "--api_name=tensorflow", ] + TENSORFLOW_API_INIT_FILES, main = "doc_srcs_test.py", srcs_version = "PY2AND3", @@ -48,3 +53,19 @@ py_test( "//tensorflow/python:no_contrib", ], ) + +py_test( + name = "estimator_doc_srcs_test", + srcs = ["doc_srcs_test.py"], + args = [ + "--package=tensorflow.python.estimator", + "--api_name=estimator", + ] + ESTIMATOR_API_INIT_FILES, + main = "doc_srcs_test.py", + srcs_version = "PY2AND3", + deps = [ + ":doc_srcs", + "//tensorflow/python:client_testlib", + "//tensorflow/python:no_contrib", + ], +) diff --git a/tensorflow/tools/api/generator/create_python_api.py b/tensorflow/tools/api/generator/create_python_api.py index 24e3c784d5..bca9fa49eb 100644 --- a/tensorflow/tools/api/generator/create_python_api.py +++ b/tensorflow/tools/api/generator/create_python_api.py @@ -247,7 +247,7 @@ def get_module(dir_path, relative_to_dir): return dir_path.replace('/', '.').strip('.') -def get_module_docstring(module_name, package): +def get_module_docstring(module_name, package, api_name): """Get docstring for the given module. This method looks for docstring in the following order: @@ -263,6 +263,7 @@ def get_module_docstring(module_name, package): (excluding 'tensorflow.' prefix) to get a docstring for. package: Base python package containing python with target tf_export decorators. + api_name: API you want to generate (e.g. `tensorflow` or `estimator`). Returns: One-line docstring to describe the module. @@ -270,8 +271,10 @@ def get_module_docstring(module_name, package): # Module under base package to get a docstring from. docstring_module_name = module_name - if module_name in doc_srcs.TENSORFLOW_DOC_SOURCES: - docsrc = doc_srcs.TENSORFLOW_DOC_SOURCES[module_name] + doc_sources = doc_srcs.get_doc_sources(api_name) + + if module_name in doc_sources: + docsrc = doc_sources[module_name] if docsrc.docstring: return docsrc.docstring if docsrc.docstring_module_name: @@ -330,7 +333,7 @@ def create_api_files( if module or not root_init_template: contents = ( _GENERATED_FILE_HEADER % - get_module_docstring(module, package) + text) + get_module_docstring(module, package, api_name) + text) else: # Read base init file with open(root_init_template, 'r') as root_init_template_file: diff --git a/tensorflow/tools/api/generator/doc_srcs.py b/tensorflow/tools/api/generator/doc_srcs.py index 74f6db98fd..ccd5bea481 100644 --- a/tensorflow/tools/api/generator/doc_srcs.py +++ b/tensorflow/tools/api/generator/doc_srcs.py @@ -19,6 +19,8 @@ from __future__ import print_function import collections +from tensorflow.python.util import tf_export + # Specifies docstring source for a module. # Only one of docstring or docstring_module_name should be set. @@ -31,7 +33,7 @@ DocSource = collections.namedtuple( # Each attribute of DocSource is optional. DocSource.__new__.__defaults__ = (None,) * len(DocSource._fields) -TENSORFLOW_DOC_SOURCES = { +_TENSORFLOW_DOC_SOURCES = { 'app': DocSource(docstring_module_name='platform.app'), 'compat': DocSource(docstring_module_name='util.compat'), 'distributions': DocSource( @@ -63,3 +65,28 @@ TENSORFLOW_DOC_SOURCES = { 'train.queue_runner': DocSource( docstring_module_name='training.queue_runner'), } + +_ESTIMATOR_DOC_SOURCES = { + 'estimator': DocSource( + docstring_module_name='estimator_lib'), + 'estimator.export': DocSource( + docstring_module_name='export.export_lib'), + 'estimator.inputs': DocSource( + docstring_module_name='inputs.inputs'), +} + + +def get_doc_sources(api_name): + """Get a map from module to a DocSource object. + + Args: + api_name: API you want to generate (e.g. `tensorflow` or `estimator`). + + Returns: + Map from module name to DocSource object. + """ + if api_name == tf_export.TENSORFLOW_API_NAME: + return _TENSORFLOW_DOC_SOURCES + if api_name == tf_export.ESTIMATOR_API_NAME: + return _ESTIMATOR_DOC_SOURCES + return {} diff --git a/tensorflow/tools/api/generator/doc_srcs_test.py b/tensorflow/tools/api/generator/doc_srcs_test.py index 9ba95a3439..7b8f27c1b1 100644 --- a/tensorflow/tools/api/generator/doc_srcs_test.py +++ b/tensorflow/tools/api/generator/doc_srcs_test.py @@ -32,7 +32,7 @@ FLAGS = None class DocSrcsTest(test.TestCase): def testModulesAreValidAPIModules(self): - for module_name in doc_srcs.TENSORFLOW_DOC_SOURCES: + for module_name in doc_srcs.get_doc_sources(FLAGS.api_name): # Convert module_name to corresponding __init__.py file path. file_path = module_name.replace('.', '/') if file_path: @@ -43,7 +43,7 @@ class DocSrcsTest(test.TestCase): self.assertFalse('%s is not a valid API module' % module_name) def testHaveDocstringOrDocstringModule(self): - for module_name, docsrc in doc_srcs.TENSORFLOW_DOC_SOURCES.items(): + for module_name, docsrc in doc_srcs.get_doc_sources(FLAGS.api_name).items(): if docsrc.docstring and docsrc.docstring_module_name: self.assertFalse( '%s contains DocSource has both a docstring and a ' @@ -52,12 +52,12 @@ class DocSrcsTest(test.TestCase): % (module_name)) def testDocstringModulesAreValidModules(self): - for _, docsrc in doc_srcs.TENSORFLOW_DOC_SOURCES.items(): + for _, docsrc in doc_srcs.get_doc_sources(FLAGS.api_name).items(): if docsrc.docstring_module_name: doc_module_name = '.'.join([ FLAGS.package, docsrc.docstring_module_name]) if doc_module_name not in sys.modules: - sys.assertFalse( + self.assertFalse( 'docsources_module %s is not a valid module under %s.' % (docsrc.docstring_module_name, FLAGS.package)) @@ -71,6 +71,9 @@ if __name__ == '__main__': '--package', type=str, help='Base package that imports modules containing the target tf_export ' 'decorators.') + parser.add_argument( + '--api_name', type=str, + help='API name: tensorflow or estimator') FLAGS, unparsed = parser.parse_known_args() importlib.import_module(FLAGS.package) -- GitLab From 377815f6aa7871e428b98624db44f537875daf06 Mon Sep 17 00:00:00 2001 From: "karl@kubx.ca" Date: Wed, 13 Jun 2018 17:16:06 -0400 Subject: [PATCH 411/816] Nit --- .../java/org/tensorflow/processor/OperatorProcessor.java | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tensorflow/java/src/gen/java/org/tensorflow/processor/OperatorProcessor.java b/tensorflow/java/src/gen/java/org/tensorflow/processor/OperatorProcessor.java index aa624a9e83..3524160d87 100644 --- a/tensorflow/java/src/gen/java/org/tensorflow/processor/OperatorProcessor.java +++ b/tensorflow/java/src/gen/java/org/tensorflow/processor/OperatorProcessor.java @@ -215,21 +215,21 @@ public final class OperatorProcessor extends AbstractProcessor { AnnotationMirror am = getAnnotationMirror(opClass, annotation); String groupName = getAnnotationElementValueAsString("group", am); String methodName = getAnnotationElementValueAsString("name", am); + ClassName opClassName = ClassName.get(opClass); if (Strings.isNullOrEmpty(methodName)) { - methodName = CaseFormat.UPPER_CAMEL.to(CaseFormat.LOWER_CAMEL, ClassName.get(opClass).simpleName()); + methodName = CaseFormat.UPPER_CAMEL.to(CaseFormat.LOWER_CAMEL, opClassName.simpleName()); } // Build a method for each @Operator found in the class path. There should be one method per operation factory called // "create", which takes in parameter a scope and, optionally, a list of arguments for (ExecutableElement opMethod : ElementFilter.methodsIn(opClass.getEnclosedElements())) { if (opMethod.getModifiers().contains(Modifier.STATIC) && opMethod.getSimpleName().contentEquals("create")) { - MethodSpec method = buildOpMethod(methodName, opClass, opMethod); + MethodSpec method = buildOpMethod(methodName, opClassName, opMethod); groupedMethods.put(groupName, method); } } } - private MethodSpec buildOpMethod(String methodName, TypeElement opClass, ExecutableElement factoryMethod) { - ClassName opClassName = ClassName.get(opClass); + private MethodSpec buildOpMethod(String methodName, ClassName opClassName, ExecutableElement factoryMethod) { MethodSpec.Builder builder = MethodSpec.methodBuilder(methodName) .addModifiers(Modifier.PUBLIC) -- GitLab From bf920de58a3ccb2cfe6642be9c487c3fcb13ccae Mon Sep 17 00:00:00 2001 From: Brennan Saeta Date: Wed, 13 Jun 2018 14:18:30 -0700 Subject: [PATCH 412/816] [contrib.cloud] Expose GCS config methods PiperOrigin-RevId: 200452487 --- tensorflow/contrib/cloud/__init__.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/tensorflow/contrib/cloud/__init__.py b/tensorflow/contrib/cloud/__init__.py index a6e13ea3ae..ef7aa7624c 100644 --- a/tensorflow/contrib/cloud/__init__.py +++ b/tensorflow/contrib/cloud/__init__.py @@ -27,8 +27,9 @@ from tensorflow.python.util.all_util import remove_undocumented _allowed_symbols = [ 'BigQueryReader', - 'ConfigureColabSession', - 'ConfigureGcs', + 'BlockCacheParams', + 'configure_colab_session', + 'configure_gcs', 'ConfigureGcsHook', ] remove_undocumented(__name__, _allowed_symbols) -- GitLab From e1296c15a32cac020160a1c89002dc561333c66b Mon Sep 17 00:00:00 2001 From: Mark Heffernan Date: Wed, 13 Jun 2018 14:19:39 -0700 Subject: [PATCH 413/816] Fix assumptions that a Shape must be a tuple or an array. A TOKEN primitive type was added with cl/199215963 and XLA also has an OPAQUE primitive type. However, in many places in XLA we assume either a tuple or array. This CL fixes many of those instances, but some may remain. Identified instances were discovered by searching for IsTuple or IsArray so the set of fixes is not exhaustive. Also opportunistically addressed a couple potential points of confusion in the ShapeUtil interface: (1) Rename ShapeUtil::HasZeroElements to ShapeUtil::IsZeroElementArray. The point of confusion here is that tuples can also have zero elements and HasZeroElements would check fail on tuple shapes. Method no longer check fails if the given shape is not an array. (2) ShapeUtil::IsNil now returns true only for empty tuples. Previously it also returned true for zero-element array types which was confusing because ShapeUtil::MakeNil creates an empty tuple. PiperOrigin-RevId: 200452672 --- tensorflow/compiler/tf2xla/lib/batch_dot.cc | 4 +- tensorflow/compiler/xla/BUILD | 1 - tensorflow/compiler/xla/layout_util.cc | 10 +- tensorflow/compiler/xla/literal_comparison.cc | 7 +- tensorflow/compiler/xla/literal_util.cc | 12 +- tensorflow/compiler/xla/literal_util.h | 2 +- tensorflow/compiler/xla/primitive_util.cc | 5 + tensorflow/compiler/xla/primitive_util.h | 3 + .../xla/service/algebraic_simplifier.cc | 24 +-- .../xla/service/bfloat16_propagation.cc | 2 +- .../xla/service/cpu/dot_op_emitter.cc | 4 +- .../xla/service/cpu/ir_emission_utils.cc | 4 +- .../compiler/xla/service/cpu/ir_emitter.cc | 14 +- .../compiler/xla/service/gather_expander.cc | 4 +- .../xla/service/generic_transfer_manager.cc | 2 +- .../service/gpu/cudnn_convolution_rewriter.cc | 4 +- .../xla/service/gpu/ir_emission_utils.cc | 4 +- .../compiler/xla/service/gpu/ir_emitter.cc | 4 +- .../compiler/xla/service/hlo_computation.cc | 9 +- .../compiler/xla/service/hlo_evaluator.cc | 4 +- .../xla/service/hlo_evaluator_typed_visitor.h | 4 +- .../compiler/xla/service/hlo_graph_dumper.cc | 4 +- .../compiler/xla/service/hlo_instructions.cc | 2 +- .../compiler/xla/service/hlo_verifier.cc | 3 +- .../compiler/xla/service/shape_inference.cc | 141 ++++++++---------- .../compiler/xla/service/shape_inference.h | 2 +- .../xla/service/shape_inference_test.cc | 8 +- .../xla/service/zero_sized_hlo_elimination.cc | 4 +- tensorflow/compiler/xla/shape_util.cc | 6 +- tensorflow/compiler/xla/shape_util.h | 10 +- tensorflow/compiler/xla/shape_util_test.cc | 53 ++++--- .../xla/tests/array_elementwise_ops_test.cc | 2 +- tensorflow/compiler/xla/tests/concat_test.cc | 17 ++- 33 files changed, 208 insertions(+), 171 deletions(-) diff --git a/tensorflow/compiler/tf2xla/lib/batch_dot.cc b/tensorflow/compiler/tf2xla/lib/batch_dot.cc index 526694d5a0..ee0bb91a6b 100644 --- a/tensorflow/compiler/tf2xla/lib/batch_dot.cc +++ b/tensorflow/compiler/tf2xla/lib/batch_dot.cc @@ -71,8 +71,8 @@ xla::StatusOr BatchDot(xla::XlaBuilder* builder, xla::XlaOp x, } // Check for zero lhs/rhs dim size. - if (xla::ShapeUtil::HasZeroElements(x_shape) || - xla::ShapeUtil::HasZeroElements(y_shape)) { + if (xla::ShapeUtil::IsZeroElementArray(x_shape) || + xla::ShapeUtil::IsZeroElementArray(y_shape)) { std::vector dimensions(batch_dimension_numbers.size()); for (int i = 0; i < batch_dimension_numbers.size(); ++i) { dimensions[i] = x_shape.dimensions(batch_dimension_numbers[i]); diff --git a/tensorflow/compiler/xla/BUILD b/tensorflow/compiler/xla/BUILD index 1b8e516770..4525197146 100644 --- a/tensorflow/compiler/xla/BUILD +++ b/tensorflow/compiler/xla/BUILD @@ -309,7 +309,6 @@ cc_library( ":types", ":util", ":xla_data_proto", - "//tensorflow/core:framework", "//tensorflow/core:lib", ], ) diff --git a/tensorflow/compiler/xla/layout_util.cc b/tensorflow/compiler/xla/layout_util.cc index e8f29b8329..3f059cac30 100644 --- a/tensorflow/compiler/xla/layout_util.cc +++ b/tensorflow/compiler/xla/layout_util.cc @@ -190,9 +190,13 @@ Layout CreateDefaultLayoutForRank(int64 rank) { } if (!ShapeUtil::IsArray(shape)) { - return InvalidArgument( - "shape of primitive type %s should not have a layout", - PrimitiveType_Name(shape.element_type()).c_str()); + if (layout.minor_to_major_size() != 0 || + layout.padded_dimensions_size() != 0) { + return InvalidArgument( + "shape of primitive type %s should not have a non-trivial layout", + PrimitiveType_Name(shape.element_type()).c_str()); + } + return Status::OK(); } if (layout.format() == INVALID_FORMAT) { diff --git a/tensorflow/compiler/xla/literal_comparison.cc b/tensorflow/compiler/xla/literal_comparison.cc index bf9679cafe..748a243e53 100644 --- a/tensorflow/compiler/xla/literal_comparison.cc +++ b/tensorflow/compiler/xla/literal_comparison.cc @@ -606,8 +606,8 @@ Status NearHelper(const LiteralSlice& expected, const LiteralSlice& actual, } // namespace Status EqualShapes(const Shape& expected, const Shape& actual) { - if (ShapeUtil::IsTuple(expected) != ShapeUtil::IsTuple(actual)) { - return InvalidArgument("tupleness-mismatch! want: %s got %s", + if (expected.element_type() != actual.element_type()) { + return InvalidArgument("element type mismatch, want: %s got %s", ShapeUtil::HumanString(expected).c_str(), ShapeUtil::HumanString(actual).c_str()); } @@ -626,7 +626,7 @@ Status EqualShapes(const Shape& expected, const Shape& actual) { return AppendStatus(result, StrCat("mismatch in tuple index", i)); } } - } else { + } else if (ShapeUtil::IsArray(expected)) { if (ShapeUtil::Rank(expected) != ShapeUtil::Rank(actual)) { return InvalidArgument("want rank of %s got rank of %s", ShapeUtil::HumanString(expected).c_str(), @@ -652,6 +652,7 @@ Status EqualShapes(const Shape& expected, const Shape& actual) { } } } + // Non-array, non-tuple shapes are trivially equivalent. return Status::OK(); } diff --git a/tensorflow/compiler/xla/literal_util.cc b/tensorflow/compiler/xla/literal_util.cc index 6b29589700..72740e5976 100644 --- a/tensorflow/compiler/xla/literal_util.cc +++ b/tensorflow/compiler/xla/literal_util.cc @@ -264,8 +264,8 @@ Status Literal::CopySliceFromInternal( StridedCopy(data(), linear_index(shape(), dest_base), 0, src_literal.data(), linear_index(src_literal.shape(), src_base), 0, 1); - } else if (!ShapeUtil::HasZeroElements(shape()) && - !ShapeUtil::HasZeroElements(src_literal.shape())) { + } else if (!ShapeUtil::IsZeroElementArray(shape()) && + !ShapeUtil::IsZeroElementArray(src_literal.shape())) { // Perform copy if neither src nor dest has dimensions with zero element, // otherwise it's a no-op. TF_RET_CHECK(src_base.size() == dest_base.size()); @@ -379,7 +379,7 @@ void CopyElementsBetween(tensorflow::gtl::MutableArraySlice dest, tensorflow::gtl::ArraySlice src, const Shape& dest_shape, const Shape& src_shape) { CHECK(ShapeUtil::Compatible(dest_shape, src_shape)); - if (ShapeUtil::HasZeroElements(dest_shape)) { + if (ShapeUtil::IsZeroElementArray(dest_shape)) { return; } std::vector index(ShapeUtil::Rank(dest_shape)); @@ -1177,7 +1177,7 @@ size_t LiteralBase::Hash() const { ShapeUtil::ForEachSubshape( shape(), [&](const Shape& subshape, const ShapeIndex& index) { - if (ShapeUtil::IsTuple(subshape)) { + if (!ShapeUtil::IsArray(subshape)) { return; } @@ -1556,7 +1556,7 @@ string LiteralBase::ToString(bool print_layout) const { void LiteralBase::EachCellAsString( const std::function indices, const string& value)>& per_cell) const { - if (ShapeUtil::HasZeroElements(shape())) { + if (ShapeUtil::IsZeroElementArray(shape())) { return; } std::vector indices = IndexUtil::LinearIndexToMultidimensionalIndex( @@ -1962,7 +1962,7 @@ bool LiteralBase::IsAllFirst() const { // Empty shapes are not all the first element since there is no first // element. - if (ShapeUtil::HasZeroElements(piece.subshape())) { + if (ShapeUtil::IsZeroElementArray(piece.subshape())) { return false; } auto piece_is_all = [&]() { diff --git a/tensorflow/compiler/xla/literal_util.h b/tensorflow/compiler/xla/literal_util.h index 8e4159e360..bcecbcccb7 100644 --- a/tensorflow/compiler/xla/literal_util.h +++ b/tensorflow/compiler/xla/literal_util.h @@ -1456,7 +1456,7 @@ void LiteralBase::EachCell( std::function indices, NativeT value)> per_cell) const { - if (ShapeUtil::HasZeroElements(shape())) { + if (ShapeUtil::IsZeroElementArray(shape())) { return; } std::vector indices(ShapeUtil::Rank(shape()), 0); diff --git a/tensorflow/compiler/xla/primitive_util.cc b/tensorflow/compiler/xla/primitive_util.cc index 143c9a2366..b16147e3be 100644 --- a/tensorflow/compiler/xla/primitive_util.cc +++ b/tensorflow/compiler/xla/primitive_util.cc @@ -85,5 +85,10 @@ PrimitiveType ComplexComponentType(PrimitiveType complex_type) { } } +bool IsArrayType(PrimitiveType primitive_type) { + return primitive_type != PRIMITIVE_TYPE_INVALID && primitive_type != TUPLE && + primitive_type != OPAQUE && primitive_type != TOKEN; +} + } // namespace primitive_util } // namespace xla diff --git a/tensorflow/compiler/xla/primitive_util.h b/tensorflow/compiler/xla/primitive_util.h index b26a10ade6..889e9a1cec 100644 --- a/tensorflow/compiler/xla/primitive_util.h +++ b/tensorflow/compiler/xla/primitive_util.h @@ -133,6 +133,9 @@ bool IsUnsignedIntegralType(PrimitiveType type); bool IsIntegralType(PrimitiveType type); +// Returns true if values of the given primitive type are held in array shapes. +bool IsArrayType(PrimitiveType primitive_type); + // Returns the number of bits in the representation for a given type. int BitWidth(PrimitiveType type); diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier.cc b/tensorflow/compiler/xla/service/algebraic_simplifier.cc index 3b36939b8a..1fc8fb9b69 100644 --- a/tensorflow/compiler/xla/service/algebraic_simplifier.cc +++ b/tensorflow/compiler/xla/service/algebraic_simplifier.cc @@ -449,7 +449,7 @@ Status AlgebraicSimplifierVisitor::HandleConcatenate( // Filter out and remove empty operands. std::vector nonempty_operands; for (HloInstruction* operand : operands) { - if (!ShapeUtil::HasZeroElements(operand->shape())) { + if (!ShapeUtil::IsZeroElementArray(operand->shape())) { nonempty_operands.push_back(operand); } } @@ -1058,9 +1058,9 @@ Status AlgebraicSimplifierVisitor::HandleDot(HloInstruction* dot) { } // Replace a zero element dot with a broadcast of the constant 0. - if (ShapeUtil::HasZeroElements(dot->shape()) || - ShapeUtil::HasZeroElements(lhs->shape()) || - ShapeUtil::HasZeroElements(rhs->shape())) { + if (ShapeUtil::IsZeroElementArray(dot->shape()) || + ShapeUtil::IsZeroElementArray(lhs->shape()) || + ShapeUtil::IsZeroElementArray(rhs->shape())) { auto zero = computation_->AddInstruction( HloInstruction::CreateConstant(Literal::CreateR0(0.0f))); return ReplaceWithNewInstruction( @@ -1392,7 +1392,7 @@ Status AlgebraicSimplifierVisitor::HandleImag(HloInstruction* imag) { } Status AlgebraicSimplifierVisitor::HandlePad(HloInstruction* pad) { - if (ShapeUtil::HasZeroElements(pad->operand(0)->shape())) { + if (ShapeUtil::IsZeroElementArray(pad->operand(0)->shape())) { return ReplaceWithNewInstruction( pad, HloInstruction::CreateBroadcast(pad->shape(), pad->mutable_operand(1), {})); @@ -1638,7 +1638,7 @@ Status AlgebraicSimplifierVisitor::HandleReshape(HloInstruction* reshape) { // Reshape directly to empty constant if the shape contains zero-element // dimension. - if (ShapeUtil::HasZeroElements(reshape->shape())) { + if (ShapeUtil::IsZeroElementArray(reshape->shape())) { auto empty_constant = HloInstruction::CreateConstant( Literal::CreateFromShape(reshape->shape())); @@ -1739,7 +1739,7 @@ Status AlgebraicSimplifierVisitor::HandleDynamicUpdateSlice( // If any dimension of update is 0, elide the DynamicUpdateSlice. This // optimization becomes invalid should we later prefer to warn about out of // bound indices. - if (ShapeUtil::HasZeroElements(update->shape())) { + if (ShapeUtil::IsZeroElementArray(update->shape())) { return ReplaceInstruction(dynamic_update_slice, dynamic_update_slice->mutable_operand(0)); } @@ -1751,8 +1751,8 @@ Status AlgebraicSimplifierVisitor::HandleReduce(HloInstruction* reduce) { auto init_value = reduce->mutable_operand(1); tensorflow::gtl::ArraySlice dimensions(reduce->dimensions()); HloComputation* function = reduce->to_apply(); - if (ShapeUtil::HasZeroElements(arg->shape()) || - ShapeUtil::HasZeroElements(reduce->shape())) { + if (ShapeUtil::IsZeroElementArray(arg->shape()) || + ShapeUtil::IsZeroElementArray(reduce->shape())) { return ReplaceWithNewInstruction( reduce, HloInstruction::CreateBroadcast(reduce->shape(), init_value, {})); @@ -1863,7 +1863,7 @@ Status AlgebraicSimplifierVisitor::HandleReduce(HloInstruction* reduce) { Status AlgebraicSimplifierVisitor::HandleReduceWindow( HloInstruction* reduce_window) { - if (ShapeUtil::HasZeroElements(reduce_window->operand(0)->shape())) { + if (ShapeUtil::IsZeroElementArray(reduce_window->operand(0)->shape())) { return ReplaceWithNewInstruction( reduce_window, HloInstruction::CreateBroadcast(reduce_window->shape(), @@ -2059,8 +2059,8 @@ Status AlgebraicSimplifierVisitor::HandleConvolution( HloInstruction* convolution) { auto lhs = convolution->mutable_operand(0); auto rhs = convolution->mutable_operand(1); - if (ShapeUtil::HasZeroElements(lhs->shape()) || - ShapeUtil::HasZeroElements(rhs->shape())) { + if (ShapeUtil::IsZeroElementArray(lhs->shape()) || + ShapeUtil::IsZeroElementArray(rhs->shape())) { return ReplaceWithNewInstruction( convolution, HloInstruction::CreateBroadcast( diff --git a/tensorflow/compiler/xla/service/bfloat16_propagation.cc b/tensorflow/compiler/xla/service/bfloat16_propagation.cc index ed0746980f..8f1d2f0804 100644 --- a/tensorflow/compiler/xla/service/bfloat16_propagation.cc +++ b/tensorflow/compiler/xla/service/bfloat16_propagation.cc @@ -631,7 +631,7 @@ Status BFloat16Propagation::ResolveInconsistentFusions(HloModule* module) { subshape, converted_outputs.element(parent_index), output_index.back())); } - if (ShapeUtil::IsTuple(subshape)) { + if (!ShapeUtil::IsArray(subshape)) { continue; } if (!ShapeUtil::Compatible( diff --git a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc index 8eb39d615f..e8b205051e 100644 --- a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc +++ b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc @@ -1627,8 +1627,8 @@ bool PotentiallyImplementedAsEigenDot( const Shape& lhs_shape = hlo.operand(0)->shape(); const Shape& rhs_shape = hlo.operand(1)->shape(); - if (ShapeUtil::HasZeroElements(lhs_shape) || - ShapeUtil::HasZeroElements(rhs_shape)) { + if (ShapeUtil::IsZeroElementArray(lhs_shape) || + ShapeUtil::IsZeroElementArray(rhs_shape)) { return false; } diff --git a/tensorflow/compiler/xla/service/cpu/ir_emission_utils.cc b/tensorflow/compiler/xla/service/cpu/ir_emission_utils.cc index b560b7531c..1a8bedfe6a 100644 --- a/tensorflow/compiler/xla/service/cpu/ir_emission_utils.cc +++ b/tensorflow/compiler/xla/service/cpu/ir_emission_utils.cc @@ -64,8 +64,8 @@ bool PotentiallyImplementedAsEigenConvolution( return false; } - if (ShapeUtil::HasZeroElements(input_shape) || - ShapeUtil::HasZeroElements(kernel_shape)) { + if (ShapeUtil::IsZeroElementArray(input_shape) || + ShapeUtil::IsZeroElementArray(kernel_shape)) { return false; } // Make sure input and kernel has the same data type. diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc index a4141dee01..94053e5716 100644 --- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc +++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc @@ -226,10 +226,13 @@ Status IrEmitter::HandleCopy(HloInstruction* copy) { // kCopy shallow copies a tuple so just memcpy the top-level buffer. TF_RETURN_IF_ERROR(EmitTargetAddressForOp(copy)); return EmitMemcpy(*(copy->operand(0)), *copy); - } else { - // Use the elemental emitter for non-tuple shapes. + } else if (ShapeUtil::IsArray(copy->shape())) { + // Use the elemental emitter for array shapes. return DefaultAction(copy); } + return Unimplemented( + "unsupported operand type %s for copy instruction", + PrimitiveType_Name(copy->shape().element_type()).c_str()); } // Calculate the alignment of a buffer allocated for a given primitive type. @@ -1867,7 +1870,7 @@ Status IrEmitter::HandleSlice(HloInstruction* slice) { TF_RETURN_IF_ERROR(EmitTargetAddressForOp(slice)); - if (ShapeUtil::HasZeroElements(slice->shape())) { + if (ShapeUtil::IsZeroElementArray(slice->shape())) { return Status::OK(); } @@ -2803,7 +2806,10 @@ Status IrEmitter::EmitTargetAddressForOp(const HloInstruction* op) { // For the root node, we write directly to the output buffer of the // function. llvm::Argument* retval = compute_function_->result_arg(); - if (!ShapeUtil::IsNil(target_shape)) { + if ((ShapeUtil::IsArray(target_shape) && + !ShapeUtil::IsZeroElementArray(target_shape)) || + (ShapeUtil::IsTuple(target_shape) && + !ShapeUtil::IsEmptyTuple(target_shape))) { llvm::AttrBuilder attr_builder; attr_builder.addAlignmentAttr(MinimumAlignmentForShape(target_shape)); attr_builder.addDereferenceableAttr(ByteSizeOf(target_shape)); diff --git a/tensorflow/compiler/xla/service/gather_expander.cc b/tensorflow/compiler/xla/service/gather_expander.cc index 2d3e4b1fcd..7cd2c9c136 100644 --- a/tensorflow/compiler/xla/service/gather_expander.cc +++ b/tensorflow/compiler/xla/service/gather_expander.cc @@ -300,7 +300,7 @@ static StatusOr PermuteGatherAndWindowDims( StatusOr GatherExpander::ExpandGather( HloInstruction* gather_instr) { - CHECK(!ShapeUtil::HasZeroElements(gather_instr->shape())); + CHECK(!ShapeUtil::IsZeroElementArray(gather_instr->shape())); HloComputation* computation = gather_instr->parent(); HloInstruction* operand = gather_instr->mutable_operand(0); @@ -369,7 +369,7 @@ StatusOr GatherExpander::Run(HloModule* module) { return inst->opcode() == HloOpcode::kGather && // Avoid expanding gather ops that produce zero sized tensors, // instead punt these to ZeroSizedHloElimination. - !ShapeUtil::HasZeroElements(inst->shape()); + !ShapeUtil::IsZeroElementArray(inst->shape()); }; std::vector gather_instrs; diff --git a/tensorflow/compiler/xla/service/generic_transfer_manager.cc b/tensorflow/compiler/xla/service/generic_transfer_manager.cc index 5ee67ccb4a..d9f62c21c4 100644 --- a/tensorflow/compiler/xla/service/generic_transfer_manager.cc +++ b/tensorflow/compiler/xla/service/generic_transfer_manager.cc @@ -74,7 +74,7 @@ GenericTransferManager::TransferLiteralFromDevice( TF_RETURN_IF_ERROR(ShapeUtil::ForEachSubshapeWithStatus( device_buffer.on_host_shape(), [&](const Shape& subshape, const ShapeIndex& index) -> Status { - if (!ShapeUtil::IsTuple(subshape)) { + if (ShapeUtil::IsArray(subshape)) { TF_RETURN_IF_ERROR(TransferBufferFromDevice( executor, /*source=*/device_buffer.buffer(index), diff --git a/tensorflow/compiler/xla/service/gpu/cudnn_convolution_rewriter.cc b/tensorflow/compiler/xla/service/gpu/cudnn_convolution_rewriter.cc index e0c73aa73a..f9dccd287d 100644 --- a/tensorflow/compiler/xla/service/gpu/cudnn_convolution_rewriter.cc +++ b/tensorflow/compiler/xla/service/gpu/cudnn_convolution_rewriter.cc @@ -42,8 +42,8 @@ bool CanImplementAsCudnnForwardConv(HloInstruction* conv) { } // CuDNN does not accept zero-element arguments - if (ShapeUtil::HasZeroElements(conv->operand(0)->shape()) || - ShapeUtil::HasZeroElements(conv->operand(1)->shape())) { + if (ShapeUtil::IsZeroElementArray(conv->operand(0)->shape()) || + ShapeUtil::IsZeroElementArray(conv->operand(1)->shape())) { return false; } diff --git a/tensorflow/compiler/xla/service/gpu/ir_emission_utils.cc b/tensorflow/compiler/xla/service/gpu/ir_emission_utils.cc index 67890bfed1..388aa35d7d 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emission_utils.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emission_utils.cc @@ -56,8 +56,8 @@ bool AreValidGemmShapes(const Shape& lhs_shape, const Shape& rhs_shape, return type_is_allowed && IsRank2WithNoPadding(lhs_shape) && IsRank2WithNoPadding(rhs_shape) && IsRank2WithNoPadding(output_shape) && - !ShapeUtil::HasZeroElements(lhs_shape) && - !ShapeUtil::HasZeroElements(rhs_shape); + !ShapeUtil::IsZeroElementArray(lhs_shape) && + !ShapeUtil::IsZeroElementArray(rhs_shape); } bool DotImplementedAsGemm(const HloInstruction& dot) { diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter.cc index 547af33e9a..7b7dd673a5 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter.cc @@ -610,7 +610,7 @@ Status IrEmitter::HandleDot(HloInstruction* dot) { } Status IrEmitter::HandleConvolution(HloInstruction* convolution) { - if (ShapeUtil::HasZeroElements(convolution->shape())) { + if (ShapeUtil::IsZeroElementArray(convolution->shape())) { // Emit no code for an empty output. return Status::OK(); } @@ -620,7 +620,7 @@ Status IrEmitter::HandleConvolution(HloInstruction* convolution) { } Status IrEmitter::HandleFft(HloInstruction* fft) { - if (ShapeUtil::HasZeroElements(fft->shape())) { + if (ShapeUtil::IsZeroElementArray(fft->shape())) { // Emit no code for an empty output. return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/hlo_computation.cc b/tensorflow/compiler/xla/service/hlo_computation.cc index b158f44923..c73e54a0b1 100644 --- a/tensorflow/compiler/xla/service/hlo_computation.cc +++ b/tensorflow/compiler/xla/service/hlo_computation.cc @@ -556,8 +556,13 @@ StatusOr HloComputation::DeepCopyHelper( } return AddInstruction(HloInstruction::CreateTuple(elements)); } else { - return FailedPrecondition( - "Can only copy array and tuple shaped instructions"); + // Tokens, opaques, etc are not copyable. + if (indices_to_copy == nullptr || indices_to_copy->element(*index)) { + return FailedPrecondition( + "Cannot copy instruction of shape: %s", + ShapeUtil::HumanString(instruction->shape()).c_str()); + } + return instruction; } } diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.cc b/tensorflow/compiler/xla/service/hlo_evaluator.cc index e0648e1467..080ee4ad18 100644 --- a/tensorflow/compiler/xla/service/hlo_evaluator.cc +++ b/tensorflow/compiler/xla/service/hlo_evaluator.cc @@ -372,7 +372,7 @@ Status HloEvaluator::HandleConcatenate(HloInstruction* concatenate) { // The result concatenate dimension is going to be the sum of all // concatenate dimensions of the operands taking part of the operation. const Shape& reference_shape = operands[0]->shape(); - CHECK(!ShapeUtil::IsTuple(reference_shape)); + CHECK(ShapeUtil::IsArray(reference_shape)); const int64 rank = ShapeUtil::Rank(reference_shape); const int64 concat_dim = concatenate->dimensions()[0]; CHECK_GE(concat_dim, 0); @@ -383,7 +383,7 @@ Status HloEvaluator::HandleConcatenate(HloInstruction* concatenate) { for (int64 i = 1; i < operands.size(); ++i) { const Shape& operand_shape = operands[i]->shape(); - CHECK(!ShapeUtil::IsTuple(operand_shape)); + CHECK(ShapeUtil::IsArray(operand_shape)); // Accumulate the concat dimension from all tensors taking part to the // operation. concat_dimensions[concat_dim] += diff --git a/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h index 13f46407e3..e01ce19d04 100644 --- a/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h +++ b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h @@ -778,7 +778,7 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { Status HandleSelect(HloInstruction* select) override { CHECK(!ShapeUtil::IsScalar(select->operand(0)->shape())); - CHECK(!ShapeUtil::IsTuple(select->shape())); + CHECK(ShapeUtil::IsArray(select->shape())); std::function select_op = [](bool pred, ReturnT on_true, ReturnT on_false) { if (pred) { @@ -1103,7 +1103,7 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { } Status HandlePad(HloInstruction* pad) override { - CHECK(!ShapeUtil::IsTuple(pad->operand(0)->shape())); + CHECK(ShapeUtil::IsArray(pad->operand(0)->shape())); // Padding value must be scalar. CHECK(ShapeUtil::IsScalar(pad->operand(1)->shape())); CHECK_EQ(ShapeUtil::Rank(pad->operand(0)->shape()), diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc index 28fc6c4209..ab224021c5 100644 --- a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc +++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc @@ -832,13 +832,13 @@ string HloDotDumper::GetInstructionNodeInlinedOperands( // "{} (f32[42, 0, 10])". The alternative, calling Literal::ToString(), // enumerates all of its empty dimensions (e.g. "{ { {}, {} }, ..."), which // is just noise. - if (!ShapeUtil::IsTuple(shape) && ShapeUtil::HasZeroElements(shape)) { + if (ShapeUtil::IsZeroElementArray(shape)) { return Printf("{} (%s)", ShapeUtil::HumanString(constant->shape())); } // Print the literal value of constants with <= K elements. optional elem_count; - if (!ShapeUtil::IsOpaque(shape) && !ShapeUtil::IsTuple(shape)) { + if (ShapeUtil::IsArray(shape)) { elem_count = 1; for (int64 dim : shape.dimensions()) { *elem_count *= dim; diff --git a/tensorflow/compiler/xla/service/hlo_instructions.cc b/tensorflow/compiler/xla/service/hlo_instructions.cc index 761d833546..34038ae0ae 100644 --- a/tensorflow/compiler/xla/service/hlo_instructions.cc +++ b/tensorflow/compiler/xla/service/hlo_instructions.cc @@ -658,7 +658,7 @@ string HloConstantInstruction::OperandsToStringWithCanonicalNameMap( CanonicalNameMap* canonical_name_map) const { string operands; // For constants, show the actual value in place of an empty operand list. - if ((!ShapeUtil::IsTuple(shape()) && ShapeUtil::ElementsIn(shape()) <= 10) || + if ((ShapeUtil::IsArray(shape()) && ShapeUtil::ElementsIn(shape()) <= 10) || options.print_large_constants()) { // Literal::ToString emits multidimensional arrays over multiple // lines. Compact this into one line by stripping out white space. diff --git a/tensorflow/compiler/xla/service/hlo_verifier.cc b/tensorflow/compiler/xla/service/hlo_verifier.cc index 9034073cc8..1d6cd4cb23 100644 --- a/tensorflow/compiler/xla/service/hlo_verifier.cc +++ b/tensorflow/compiler/xla/service/hlo_verifier.cc @@ -431,7 +431,8 @@ Status ShapeVerifier::HandleGenerateToken(HloInstruction* token) { for (const HloInstruction* operand : token->operands()) { operand_shapes.push_back(&operand->shape()); } - return CheckShape(token, ShapeInference::InferTokenShape(operand_shapes)); + return CheckShape(token, + ShapeInference::InferGenerateTokenShape(operand_shapes)); } Status ShapeVerifier::CheckShape(const HloInstruction* instruction, diff --git a/tensorflow/compiler/xla/service/shape_inference.cc b/tensorflow/compiler/xla/service/shape_inference.cc index bd98e86b08..e25f5e67c7 100644 --- a/tensorflow/compiler/xla/service/shape_inference.cc +++ b/tensorflow/compiler/xla/service/shape_inference.cc @@ -49,19 +49,13 @@ bool AllUnique(tensorflow::gtl::ArraySlice slice) { return std::set(slice.begin(), slice.end()).size() == slice.size(); } -Status ExpectNotTupleOrOpaque(const Shape& shape, - tensorflow::StringPiece op_type) { - if (ShapeUtil::IsTuple(shape)) { - return InvalidArgument("Expected non-tuple argument for %s, but got %s.", +Status ExpectArray(const Shape& shape, tensorflow::StringPiece op_type) { + if (!ShapeUtil::IsArray(shape)) { + return InvalidArgument("Expected array argument for %s, but got %s.", std::string(op_type).c_str(), ShapeUtil::HumanString(shape).c_str()); - } else if (ShapeUtil::IsOpaque(shape)) { - return InvalidArgument("Expected non-opaque argument for %s, but got %s.", - std::string(op_type).c_str(), - ShapeUtil::HumanString(shape).c_str()); - } else { - return Status::OK(); } + return Status::OK(); } Status VerifyReducerShape(const ProgramShape& reducer_shape, @@ -198,8 +192,7 @@ StatusOr InferWindowOutputShape(const Shape& base_shape, return shape; } - TF_RETURN_IF_ERROR( - ExpectNotTupleOrOpaque(shape, "operand of unary operation")); + TF_RETURN_IF_ERROR(ExpectArray(shape, "operand of unary operation")); TF_DCHECK_OK(ShapeUtil::ValidateShapeWithOptionalLayout(shape)); switch (opcode) { @@ -289,8 +282,7 @@ StatusOr InferWindowOutputShape(const Shape& base_shape, const Shape* arg_shape = nullptr; PrimitiveType element_type = PRIMITIVE_TYPE_INVALID; for (const Shape* shape : arg_shapes) { - TF_RETURN_IF_ERROR( - ExpectNotTupleOrOpaque(*shape, "operand of concatenation")); + TF_RETURN_IF_ERROR(ExpectArray(*shape, "operand of concatenation")); if (!arg_shape) { arg_shape = shape; element_type = arg_shape->element_type(); @@ -337,7 +329,7 @@ StatusOr InferWindowOutputShape(const Shape& base_shape, return ShapeUtil::MakeShape(element_type, new_dimensions); } -/* static */ StatusOr ShapeInference::InferTokenShape( +/* static */ StatusOr ShapeInference::InferGenerateTokenShape( tensorflow::gtl::ArraySlice arg_shapes) { for (const Shape* arg_shape : arg_shapes) { if (arg_shape->element_type() != TOKEN) { @@ -358,12 +350,13 @@ StatusOr InferWindowOutputShape(const Shape& base_shape, ShapeUtil::HumanString(operand_shape).c_str(), PrimitiveType_Name(new_element_type).c_str()); } - if (ShapeUtil::IsTuple(operand_shape) || new_element_type == TUPLE) { + if (!ShapeUtil::IsArray(operand_shape) || + !primitive_util::IsArrayType(new_element_type)) { // Note: we may want to support tuple conversions via this operation in the // future, by recursing into the tuple elements to check all sub-conversions // are valid. For now we just reject them, though. return InvalidArgument( - "Convert does not allow tuples, so cannot convert from %s to %s.", + "Convert does not allow non-arrays, so cannot convert from %s to %s.", ShapeUtil::HumanString(operand_shape).c_str(), PrimitiveType_Name(new_element_type).c_str()); } @@ -380,7 +373,8 @@ StatusOr InferWindowOutputShape(const Shape& base_shape, ShapeUtil::HumanString(operand_shape).c_str(), PrimitiveType_Name(new_element_type).c_str()); } - if (ShapeUtil::IsTuple(operand_shape) || new_element_type == TUPLE) { + if (!ShapeUtil::IsArray(operand_shape) || + !primitive_util::IsArrayType(new_element_type)) { // Note: we may want to support tuple conversions via this operation in the // future, by recursing into the tuple elements to check all sub-conversions // are valid. For now we just reject them, though. @@ -427,7 +421,7 @@ StatusOr InferWindowOutputShape(const Shape& base_shape, /* static */ StatusOr ShapeInference::InferPadShape( const Shape& operand_shape, const Shape& padding_value_shape, const PaddingConfig& padding_config) { - if (ShapeUtil::IsTuple(operand_shape)) { + if (!ShapeUtil::IsArray(operand_shape)) { return InvalidArgument( "Pad operation does not support tuple-shape operands."); } @@ -566,8 +560,8 @@ Status ValidateDotDimensionNumbers( /* static */ StatusOr ShapeInference::InferDotOpShape( const Shape& lhs, const Shape& rhs, const DotDimensionNumbers& dimension_numbers) { - TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque(lhs, "lhs of dot")); - TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque(rhs, "rhs of dot")); + TF_RETURN_IF_ERROR(ExpectArray(lhs, "lhs of dot")); + TF_RETURN_IF_ERROR(ExpectArray(rhs, "rhs of dot")); auto fail = [lhs, rhs](const string& addendum) -> Status { string message = tensorflow::strings::Printf( @@ -786,10 +780,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, /* static */ StatusOr ShapeInference::InferElementwiseBinaryOpShape( HloOpcode operation, const Shape& lhs, const Shape& rhs, tensorflow::gtl::ArraySlice broadcast_dimensions) { - TF_RETURN_IF_ERROR( - ExpectNotTupleOrOpaque(lhs, "lhs of elementwise binary operation")); - TF_RETURN_IF_ERROR( - ExpectNotTupleOrOpaque(rhs, "rhs of elementwise binary operation")); + TF_RETURN_IF_ERROR(ExpectArray(lhs, "lhs of elementwise binary operation")); + TF_RETURN_IF_ERROR(ExpectArray(rhs, "rhs of elementwise binary operation")); if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(lhs, rhs)) { return InvalidArgument( @@ -853,12 +845,12 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, TF_DCHECK_OK(ShapeUtil::ValidateShapeWithOptionalLayout(lhs)); TF_DCHECK_OK(ShapeUtil::ValidateShapeWithOptionalLayout(rhs)); - TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque( - lhs, tensorflow::strings::StrCat("lhs of binary operation ", - HloOpcodeString(opcode)))); - TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque( - rhs, tensorflow::strings::StrCat("rhs of binary operation ", - HloOpcodeString(opcode)))); + TF_RETURN_IF_ERROR( + ExpectArray(lhs, tensorflow::strings::StrCat("lhs of binary operation ", + HloOpcodeString(opcode)))); + TF_RETURN_IF_ERROR( + ExpectArray(rhs, tensorflow::strings::StrCat("rhs of binary operation ", + HloOpcodeString(opcode)))); switch (opcode) { case HloOpcode::kMaximum: case HloOpcode::kMinimum: @@ -984,15 +976,12 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, // All arguments must have the same shape. const Shape* arg_shape = arg_shapes[0]; for (size_t i = 1; i < arg_shapes.size(); ++i) { - TF_RETURN_IF_ERROR( - ExpectNotTupleOrOpaque(*arg_shapes[i], "operand of map")); + TF_RETURN_IF_ERROR(ExpectArray(*arg_shapes[i], "operand of map")); if (ShapeUtil::CompatibleIgnoringFpPrecision(*arg_shapes[i], *arg_shape)) { continue; } - if (!ShapeUtil::IsTuple(*arg_shapes[i]) && - !ShapeUtil::IsTuple(*arg_shape) && - ShapeUtil::SameElementTypeIgnoringFpPrecision(*arg_shapes[i], + if (ShapeUtil::SameElementTypeIgnoringFpPrecision(*arg_shapes[i], *arg_shape)) { if (ShapeUtil::IsScalar(*arg_shapes[i])) { continue; @@ -1075,11 +1064,11 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, const Shape& operand_shape, const Shape& scale_shape, const Shape& offset_shape, int64 feature_index) { TF_RETURN_IF_ERROR( - ExpectNotTupleOrOpaque(operand_shape, "operand of batch norm training")); - TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque( - offset_shape, "offset input of batch norm training")); - TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque( - scale_shape, "scale input of batch norm training")); + ExpectArray(operand_shape, "operand of batch norm training")); + TF_RETURN_IF_ERROR( + ExpectArray(offset_shape, "offset input of batch norm training")); + TF_RETURN_IF_ERROR( + ExpectArray(scale_shape, "scale input of batch norm training")); TF_RET_CHECK(ShapeUtil::ValidateShapeWithOptionalLayout(operand_shape) == Status::OK()); @@ -1181,11 +1170,11 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, const Shape& offset_shape, const Shape& mean_shape, const Shape& variance_shape, int64 feature_index) { TF_RETURN_IF_ERROR( - ExpectNotTupleOrOpaque(operand_shape, "operand of batch norm inference")); - TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque( - offset_shape, "offset input of batch norm inference")); - TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque( - scale_shape, "scale input of batch norm inference")); + ExpectArray(operand_shape, "operand of batch norm inference")); + TF_RETURN_IF_ERROR( + ExpectArray(offset_shape, "offset input of batch norm inference")); + TF_RETURN_IF_ERROR( + ExpectArray(scale_shape, "scale input of batch norm inference")); TF_RET_CHECK(ShapeUtil::ValidateShapeWithOptionalLayout(operand_shape) == Status::OK()); @@ -1328,16 +1317,13 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, const Shape& operand_shape, const Shape& scale_shape, const Shape& mean_shape, const Shape& var_shape, const Shape& output_grad_shape, int64 feature_index) { + TF_RETURN_IF_ERROR(ExpectArray(operand_shape, "operand of batch norm grad")); TF_RETURN_IF_ERROR( - ExpectNotTupleOrOpaque(operand_shape, "operand of batch norm grad")); + ExpectArray(scale_shape, "scale input of batch norm grad")); + TF_RETURN_IF_ERROR(ExpectArray(mean_shape, "mean input of batch norm grad")); + TF_RETURN_IF_ERROR(ExpectArray(var_shape, "var input of batch norm grad")); TF_RETURN_IF_ERROR( - ExpectNotTupleOrOpaque(scale_shape, "scale input of batch norm grad")); - TF_RETURN_IF_ERROR( - ExpectNotTupleOrOpaque(mean_shape, "mean input of batch norm grad")); - TF_RETURN_IF_ERROR( - ExpectNotTupleOrOpaque(var_shape, "var input of batch norm grad")); - TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque( - output_grad_shape, "output_grad input of batch norm grad")); + ExpectArray(output_grad_shape, "output_grad input of batch norm grad")); TF_RETURN_IF_ERROR(ShapeUtil::ValidateShapeWithOptionalLayout(operand_shape)); TF_RETURN_IF_ERROR(ShapeUtil::ValidateShapeWithOptionalLayout(mean_shape)); @@ -1486,8 +1472,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, /* static */ StatusOr ShapeInference::InferConvolveShape( const Shape& lhs, const Shape& rhs, const Window& window, const ConvolutionDimensionNumbers& dnums) { - TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque(lhs, "lhs of convolution")); - TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque(rhs, "rhs of convolution")); + TF_RETURN_IF_ERROR(ExpectArray(lhs, "lhs of convolution")); + TF_RETURN_IF_ERROR(ExpectArray(rhs, "rhs of convolution")); if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(lhs, rhs)) { return InvalidArgument( @@ -1722,7 +1708,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, tensorflow::gtl::ArraySlice operand_shapes) { for (const Shape* operand_shape : operand_shapes) { TF_RETURN_IF_ERROR( - ExpectNotTupleOrOpaque(*operand_shape, "operand of cross replica sum")); + ExpectArray(*operand_shape, "operand of cross replica sum")); } if (operand_shapes.size() == 1) { return *operand_shapes[0]; @@ -1764,8 +1750,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, /* static */ StatusOr ShapeInference::InferReduceWindowShape( const Shape& operand_shape, const Shape& init_value_shape, const Window& window, const ProgramShape& to_apply_shape) { - TF_RETURN_IF_ERROR( - ExpectNotTupleOrOpaque(operand_shape, "operand of reduce-window")); + TF_RETURN_IF_ERROR(ExpectArray(operand_shape, "operand of reduce-window")); TF_RETURN_IF_ERROR(VerifyReducerShape(to_apply_shape, init_value_shape, operand_shape.element_type())); return InferWindowOutputShape(operand_shape, window, @@ -1778,7 +1763,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, const Window& window, const Shape& source_shape, const Shape& init_value_shape, const ProgramShape& scatter_shape) { TF_RETURN_IF_ERROR( - ExpectNotTupleOrOpaque(operand_shape, "operand of select-and-scatter")); + ExpectArray(operand_shape, "operand of select-and-scatter")); // Check if the select function has a proper shape of (T,T) -> PRED. if (select_shape.parameters_size() != 2) { @@ -1843,7 +1828,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, Join(starts, ",").c_str(), Join(limits, ",").c_str(), Join(strides, ",").c_str()); }; - TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque(arg, "operand of slice")); + TF_RETURN_IF_ERROR(ExpectArray(arg, "operand of slice")); VLOG(2) << tensorflow::strings::Printf( "slicing shape %s starts={%s} limits={%s}", ShapeUtil::HumanString(arg).c_str(), Join(starts, ", ").c_str(), @@ -1902,10 +1887,9 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, /* static */ StatusOr ShapeInference::InferDynamicSliceShape( const Shape& operand_shape, const Shape& start_indices_shape, tensorflow::gtl::ArraySlice slice_sizes) { + TF_RETURN_IF_ERROR(ExpectArray(operand_shape, "operand of dynamic slice")); TF_RETURN_IF_ERROR( - ExpectNotTupleOrOpaque(operand_shape, "operand of dynamic slice")); - TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque(start_indices_shape, - "start indices of dynamic slice")); + ExpectArray(start_indices_shape, "start indices of dynamic slice")); VLOG(2) << tensorflow::strings::Printf( "slicing shape %s at dynamic start_indices %s with slice_sizes={%s}", @@ -1963,11 +1947,11 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, const Shape& operand_shape, const Shape& update_shape, const Shape& start_indices_shape) { TF_RETURN_IF_ERROR( - ExpectNotTupleOrOpaque(operand_shape, "operand of dynamic update slice")); + ExpectArray(operand_shape, "operand of dynamic update slice")); TF_RETURN_IF_ERROR( - ExpectNotTupleOrOpaque(update_shape, "update of dynamic update slice")); - TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque( - start_indices_shape, "start indices of dynamic update slice")); + ExpectArray(update_shape, "update of dynamic update slice")); + TF_RETURN_IF_ERROR(ExpectArray(start_indices_shape, + "start indices of dynamic update slice")); VLOG(2) << tensorflow::strings::Printf( "updating slice of shape %s at dynamic start_indices %s with update " @@ -2035,8 +2019,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, /*static */ StatusOr ShapeInference::InferReverseShape( const Shape& operand_shape, tensorflow::gtl::ArraySlice dimensions) { - TF_RETURN_IF_ERROR( - ExpectNotTupleOrOpaque(operand_shape, "operand of reverse")); + TF_RETURN_IF_ERROR(ExpectArray(operand_shape, "operand of reverse")); if (!AllUnique(dimensions)) { return InvalidArgument("a dimension number is duplicated in reverse"); } @@ -2166,7 +2149,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, /* static */ StatusOr ShapeInference::InferBroadcastShape( const Shape& operand, tensorflow::gtl::ArraySlice broadcast_sizes) { - TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque(operand, "operand of broadcast")); + TF_RETURN_IF_ERROR(ExpectArray(operand, "operand of broadcast")); for (int64 size : broadcast_sizes) { if (size < 0) { return InvalidArgument("Broadcast with negative dimension size %lld.", @@ -2185,7 +2168,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, /* static */ StatusOr ShapeInference::InferReshapeShape( const Shape& operand, tensorflow::gtl::ArraySlice dimensions, tensorflow::gtl::ArraySlice new_sizes) { - TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque(operand, "reshape")); + TF_RETURN_IF_ERROR(ExpectArray(operand, "reshape")); Shape inferred_shape = ShapeUtil::MakeShape(operand.element_type(), new_sizes); @@ -2217,7 +2200,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, /* static */ StatusOr ShapeInference::InferTransposeShape( const Shape& operand, tensorflow::gtl::ArraySlice dimensions) { - TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque(operand, "transpose")); + TF_RETURN_IF_ERROR(ExpectArray(operand, "transpose")); std::vector indices(ShapeUtil::Rank(operand)); std::iota(indices.begin(), indices.end(), 0); @@ -2238,9 +2221,9 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, // "degenerate" cases, as with binary elementwise ops. /* static */ StatusOr ShapeInference::InferClampShape( const Shape& min, const Shape& operand, const Shape& max) { - TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque(min, "clamp min")); - TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque(operand, "clamp operand")); - TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque(max, "clamp max")); + TF_RETURN_IF_ERROR(ExpectArray(min, "clamp min")); + TF_RETURN_IF_ERROR(ExpectArray(operand, "clamp operand")); + TF_RETURN_IF_ERROR(ExpectArray(max, "clamp max")); if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(min, operand) || !ShapeUtil::SameElementTypeIgnoringFpPrecision(max, operand)) { return InvalidArgument("Clamp with different operand types: %s, %s, %s.", @@ -2439,9 +2422,9 @@ static Status ValidateGatherDimensionNumbers( const GatherDimensionNumbers& gather_dim_numbers, tensorflow::gtl::ArraySlice window_bounds) { TF_RETURN_IF_ERROR( - ExpectNotTupleOrOpaque(input_shape, "input tensor operand gather op")); - TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque( - gather_indices_shape, "gather indices operand of gather op")); + ExpectArray(input_shape, "input tensor operand gather op")); + TF_RETURN_IF_ERROR( + ExpectArray(gather_indices_shape, "gather indices operand of gather op")); if (!ShapeUtil::ElementIsIntegral(gather_indices_shape)) { return InvalidArgument( diff --git a/tensorflow/compiler/xla/service/shape_inference.h b/tensorflow/compiler/xla/service/shape_inference.h index f1f7b50902..eef6e62fc8 100644 --- a/tensorflow/compiler/xla/service/shape_inference.h +++ b/tensorflow/compiler/xla/service/shape_inference.h @@ -220,7 +220,7 @@ class ShapeInference { // shape is always a TOKEN shape. However, ShapeInference serves two purposes: // inferring shapes and checking operand shapes. This method verifies that the // operand shapes are all TOKENs. - static StatusOr InferTokenShape( + static StatusOr InferGenerateTokenShape( tensorflow::gtl::ArraySlice arg_shapes); // Helper that validates the given operand shape can be converted to the diff --git a/tensorflow/compiler/xla/service/shape_inference_test.cc b/tensorflow/compiler/xla/service/shape_inference_test.cc index 6d017dffe2..bafe14d6f4 100644 --- a/tensorflow/compiler/xla/service/shape_inference_test.cc +++ b/tensorflow/compiler/xla/service/shape_inference_test.cc @@ -1311,7 +1311,7 @@ TEST_F(ShapeInferenceTest, ConcatenateWithBadShapes) { ASSERT_FALSE(inferred_status_error4.ok()); ASSERT_THAT( inferred_status_error4.status().error_message(), - HasSubstr("Expected non-tuple argument for operand of concatenation")); + HasSubstr("Expected array argument for operand of concatenation")); const Shape vector_s32 = ShapeUtil::MakeShape(S32, {32}); auto inferred_status_error5 = ShapeInference::InferConcatOpShape( @@ -1387,7 +1387,7 @@ TEST_F(ShapeInferenceTest, ReverseInvalidDimension) { ShapeInference::InferReverseShape(tuple_shape, {0}); ASSERT_FALSE(inferred_status_error3.ok()); ASSERT_THAT(inferred_status_error3.status().error_message(), - HasSubstr("Expected non-tuple argument")); + HasSubstr("Expected array argument")); } TEST_F(ShapeInferenceTest, Call) { @@ -1686,7 +1686,7 @@ TEST_F(GatherShapeInferenceTest, TupleShapedTensorInput) { /*window_bounds=*/{64, 1}); ASSERT_FALSE(statusor.ok()); EXPECT_THAT(statusor.status().error_message(), - HasSubstr("Expected non-tuple argument for input")) + HasSubstr("Expected array argument for input")) << statusor.status(); } @@ -1700,7 +1700,7 @@ TEST_F(GatherShapeInferenceTest, TupleShapedGatherIndicesInput) { /*window_bounds=*/{64, 1}); ASSERT_FALSE(statusor.ok()); EXPECT_THAT(statusor.status().error_message(), - HasSubstr("Expected non-tuple argument for gather indices")) + HasSubstr("Expected array argument for gather indices")) << statusor.status(); } diff --git a/tensorflow/compiler/xla/service/zero_sized_hlo_elimination.cc b/tensorflow/compiler/xla/service/zero_sized_hlo_elimination.cc index aa40b5cb26..44b0ec5cd4 100644 --- a/tensorflow/compiler/xla/service/zero_sized_hlo_elimination.cc +++ b/tensorflow/compiler/xla/service/zero_sized_hlo_elimination.cc @@ -32,11 +32,11 @@ StatusOr ZeroSizedHloElimination::Run(HloModule* module) { for (HloComputation* comp : module->MakeNonfusionComputations()) { for (HloInstruction* instruction : comp->MakeInstructionPostOrder()) { if (instruction->HasSideEffect() || - ShapeUtil::IsTuple(instruction->shape())) { + !ShapeUtil::IsArray(instruction->shape())) { continue; } if (comp->IsRemovable(instruction) && - ShapeUtil::HasZeroElements(instruction->shape())) { + ShapeUtil::IsZeroElementArray(instruction->shape())) { TF_RETURN_IF_ERROR(comp->ReplaceWithNewInstruction( instruction, HloInstruction::CreateConstant( Literal::CreateFromShape(instruction->shape())))); diff --git a/tensorflow/compiler/xla/shape_util.cc b/tensorflow/compiler/xla/shape_util.cc index 5db6659932..2c484661ee 100644 --- a/tensorflow/compiler/xla/shape_util.cc +++ b/tensorflow/compiler/xla/shape_util.cc @@ -363,7 +363,7 @@ ShapeUtil::MakeShapeWithDescendingLayoutAndSamePhysicalLayout( } /* static */ bool ShapeUtil::IsNil(const Shape& shape) { - return IsTuple(shape) ? IsEmptyTuple(shape) : HasZeroElements(shape); + return IsEmptyTuple(shape); } /* static */ int64 ShapeUtil::TupleElementCount(const Shape& shape) { @@ -413,8 +413,8 @@ ShapeUtil::MakeShapeWithDescendingLayoutAndSamePhysicalLayout( std::multiplies()); } -/* static */ bool ShapeUtil::HasZeroElements(const Shape& shape) { - return ElementsIn(shape) == 0; +/* static */ bool ShapeUtil::IsZeroElementArray(const Shape& shape) { + return ShapeUtil::IsArray(shape) && ElementsIn(shape) == 0; } /* static */ bool ShapeUtil::IsScalarF32(const Shape& shape) { diff --git a/tensorflow/compiler/xla/shape_util.h b/tensorflow/compiler/xla/shape_util.h index ae2d17d6bb..b6d29976d1 100644 --- a/tensorflow/compiler/xla/shape_util.h +++ b/tensorflow/compiler/xla/shape_util.h @@ -175,8 +175,8 @@ class ShapeUtil { // Precondition: IsArray(shape) static int64 ElementsIn(const Shape& shape); - // Returns true if 'shape' has zero elements. - static bool HasZeroElements(const Shape& shape); + // Returns true if 'shape' is an array with zero elements. + static bool IsZeroElementArray(const Shape& shape); // Returns the number of bytes required for an allocation of shape. The // |pointer_size| parameter is used for calculating the size of tuple @@ -336,7 +336,7 @@ class ShapeUtil { // Appends a major dimension to the shape with the given bound. static void AppendMajorDimension(int bound, Shape* shape); - // Returns an empty tuple shape. Can be used to indicate side-effects. + // Returns an empty tuple shape. Can be used as a sentinel Shape value. static Shape MakeNil() { return MakeTupleShape({}); } // Checks whether the shape is initialized. @@ -446,7 +446,7 @@ class ShapeUtil { // Returns true if shape is an empty tuple. static bool IsEmptyTuple(const Shape& shape); - // Returns true if shape is an empty tuple, or is an array with no elements. + // Returns true if shape is the nil shape (an empty tuple). static bool IsNil(const Shape& shape); // Returns the number of elements in the given tuple shape. @@ -697,7 +697,7 @@ class ShapeUtil { tensorflow::gtl::ArraySlice incr, const FnType& visitor_function, bool parallel = false) { - if (ShapeUtil::HasZeroElements(shape)) { + if (ShapeUtil::IsZeroElementArray(shape)) { return Status::OK(); } CHECK_EQ(Rank(shape), base.size()); diff --git a/tensorflow/compiler/xla/shape_util_test.cc b/tensorflow/compiler/xla/shape_util_test.cc index 0ff514564b..ebfe06d4bc 100644 --- a/tensorflow/compiler/xla/shape_util_test.cc +++ b/tensorflow/compiler/xla/shape_util_test.cc @@ -329,6 +329,16 @@ TEST(ShapeUtilTest, ByteSizeOfWithPadding) { EXPECT_EQ(15 * 21 * 4, ShapeUtil::ByteSizeOf(shape)); } +TEST(ShapeUtilTest, NilShape) { + EXPECT_TRUE(ShapeUtil::IsNil(ShapeUtil::MakeNil())); + EXPECT_FALSE(ShapeUtil::IsNil(ShapeUtil::MakeShape(F32, {1, 2, 3}))); + EXPECT_FALSE(ShapeUtil::IsNil(ShapeUtil::MakeShape(F32, {0, 1}))); + EXPECT_FALSE(ShapeUtil::IsNil( + ShapeUtil::MakeTupleShape({ShapeUtil::MakeShape(S32, {})}))); + EXPECT_FALSE(ShapeUtil::IsNil( + ShapeUtil::MakeTupleShape({ShapeUtil::MakeShape(F32, {0})}))); +} + TEST(ShapeUtilTest, NestedTuple) { EXPECT_FALSE(ShapeUtil::IsNestedTuple(ShapeUtil::MakeTupleShape({}))); EXPECT_FALSE(ShapeUtil::IsNestedTuple( @@ -359,25 +369,30 @@ TEST(ShapeUtilTest, ElementsIn) { EXPECT_EQ(221, ShapeUtil::ElementsIn(ShapeUtil::MakeShape(S32, {13, 17}))); } -TEST(ShapeUtilTest, HasZeroElements) { - EXPECT_EQ(false, ShapeUtil::HasZeroElements(ShapeUtil::MakeShape(S32, {}))); - EXPECT_EQ(true, ShapeUtil::HasZeroElements(ShapeUtil::MakeShape(S32, {0}))); - EXPECT_EQ(false, ShapeUtil::HasZeroElements(ShapeUtil::MakeShape(S32, {1}))); - EXPECT_EQ(false, - ShapeUtil::HasZeroElements(ShapeUtil::MakeShape(S32, {1, 1}))); - EXPECT_EQ(false, ShapeUtil::HasZeroElements(ShapeUtil::MakeShape(S32, {2}))); - EXPECT_EQ(false, - ShapeUtil::HasZeroElements(ShapeUtil::MakeShape(S32, {2, 1}))); - EXPECT_EQ(false, - ShapeUtil::HasZeroElements(ShapeUtil::MakeShape(S32, {3, 5}))); - EXPECT_EQ(true, - ShapeUtil::HasZeroElements(ShapeUtil::MakeShape(S32, {3, 0, 5}))); - EXPECT_EQ(true, - ShapeUtil::HasZeroElements(ShapeUtil::MakeShape(S32, {0, 3, 0}))); - EXPECT_EQ(false, - ShapeUtil::HasZeroElements(ShapeUtil::MakeShape(S32, {1, 3, 5}))); - EXPECT_EQ(false, - ShapeUtil::HasZeroElements(ShapeUtil::MakeShape(S32, {13, 17}))); +TEST(ShapeUtilTest, IsZeroElementArray) { + EXPECT_FALSE(ShapeUtil::IsZeroElementArray(ShapeUtil::MakeShape(S32, {}))); + EXPECT_TRUE(ShapeUtil::IsZeroElementArray(ShapeUtil::MakeShape(S32, {0}))); + EXPECT_FALSE(ShapeUtil::IsZeroElementArray(ShapeUtil::MakeShape(S32, {1}))); + EXPECT_FALSE( + ShapeUtil::IsZeroElementArray(ShapeUtil::MakeShape(S32, {1, 1}))); + EXPECT_FALSE(ShapeUtil::IsZeroElementArray(ShapeUtil::MakeShape(S32, {2}))); + EXPECT_FALSE( + ShapeUtil::IsZeroElementArray(ShapeUtil::MakeShape(S32, {2, 1}))); + EXPECT_FALSE( + ShapeUtil::IsZeroElementArray(ShapeUtil::MakeShape(S32, {3, 5}))); + EXPECT_TRUE( + ShapeUtil::IsZeroElementArray(ShapeUtil::MakeShape(S32, {3, 0, 5}))); + EXPECT_TRUE( + ShapeUtil::IsZeroElementArray(ShapeUtil::MakeShape(S32, {0, 3, 0}))); + EXPECT_FALSE( + ShapeUtil::IsZeroElementArray(ShapeUtil::MakeShape(S32, {1, 3, 5}))); + EXPECT_FALSE( + ShapeUtil::IsZeroElementArray(ShapeUtil::MakeShape(S32, {13, 17}))); + + EXPECT_FALSE(ShapeUtil::IsZeroElementArray(ShapeUtil::MakeNil())); + EXPECT_FALSE(ShapeUtil::IsZeroElementArray(ShapeUtil::MakeTupleShape({}))); + EXPECT_FALSE(ShapeUtil::IsZeroElementArray( + ShapeUtil::MakeTupleShape({ShapeUtil::MakeShape(S32, {0, 3, 0})}))); } TEST(ShapeUtilTest, SameDimensions) { diff --git a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc index 36a7064969..c3a289ee09 100644 --- a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc +++ b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc @@ -2758,7 +2758,7 @@ XLA_TEST_F(ArrayElementwiseOpTest, CannotAddOpaques) { ASSERT_FALSE(computation_status.ok()); EXPECT_THAT(computation_status.status().ToString(), ::testing::ContainsRegex( - "Expected non-opaque argument for lhs of binary operation")); + "Expected array argument for lhs of binary operation")); } XLA_TEST_F(ArrayElementwiseOpTest, IdentityBroadcastOfSameRankIsAllowed) { diff --git a/tensorflow/compiler/xla/tests/concat_test.cc b/tensorflow/compiler/xla/tests/concat_test.cc index a4c8a83eb1..352864502a 100644 --- a/tensorflow/compiler/xla/tests/concat_test.cc +++ b/tensorflow/compiler/xla/tests/concat_test.cc @@ -417,7 +417,22 @@ XLA_TEST_F(ConcatTest, CannotConcatOpaques) { ASSERT_FALSE(computation_status.ok()); EXPECT_THAT( computation_status.status().ToString(), - HasSubstr("Expected non-opaque argument for operand of concatenation")); + HasSubstr("Expected array argument for operand of concatenation")); +} + +// Show that we can't concatenate with tokens. +XLA_TEST_F(ConcatTest, CannotConcatTokens) { + XlaBuilder builder(TestName()); + auto token_shape = ShapeUtil::MakeTokenShape(); + auto r1f32 = xla::ShapeUtil::MakeShape(xla::F32, {1}); + auto x = builder.Parameter(0, r1f32, "x"); + auto y = builder.Parameter(1, token_shape, "y"); + builder.ConcatInDim({x, y}, 0); + StatusOr computation_status = builder.Build(); + ASSERT_FALSE(computation_status.ok()); + EXPECT_THAT( + computation_status.status().ToString(), + HasSubstr("Expected array argument for operand of concatenation")); } XLA_TEST_F(ConcatTest, ConcatSeveralBoxedPredicates) { -- GitLab From 40e4beb2c6fcc41852e17ec3996f5dfca8f053df Mon Sep 17 00:00:00 2001 From: James Keeling Date: Wed, 13 Jun 2018 14:20:55 -0700 Subject: [PATCH 414/816] Add return statement to end of ToVlogString(dnn::DataType data_type) Whilst the switch statement covers all possible enum values, the compiler still complains that it reaches the end of the function without returning a value. I add an "unknown" string, mirroring the one in the function just above. PiperOrigin-RevId: 200452885 --- tensorflow/stream_executor/stream.cc | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/stream_executor/stream.cc b/tensorflow/stream_executor/stream.cc index 4a98cfe164..0cd0790a72 100644 --- a/tensorflow/stream_executor/stream.cc +++ b/tensorflow/stream_executor/stream.cc @@ -192,6 +192,7 @@ string ToVlogString(dnn::DataType data_type) { case dnn::DataType::kInt8: return "dnn::DataType::kInt8"; } + return "unknown DataType"; } // Used together with PARAM to VLOG calls made to the stream. Intended -- GitLab From 2f7f04a7a03003e8fe345667ddf0b088032f0e03 Mon Sep 17 00:00:00 2001 From: Benjamin Kramer Date: Wed, 13 Jun 2018 14:38:45 -0700 Subject: [PATCH 415/816] [XLA:GPU] Run HloCSE after multi-output fusion Multi-output fusion often merges fusions containing HLOs duplicated by a previous instruction_fusion run. Schedule a CSE run to deduplicate them. This doesn't have an impact on performance as LLVM is pretty good at CSE inside of a fusion, but makes the compiler output much more readable. PiperOrigin-RevId: 200456053 --- tensorflow/compiler/xla/service/gpu/gpu_compiler.cc | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc index afefc740d7..9d66648a40 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc +++ b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc @@ -260,6 +260,8 @@ Status OptimizeHloModule(HloModule* hlo_module, se::StreamExecutor* stream_exec, fusion.AddPass(/*may_duplicate=*/true); fusion.AddPass(); fusion.AddPass(); + fusion.AddPass(/*is_layout_sensitive=*/true, + /*only_fusion_computations=*/true); TF_RETURN_IF_ERROR(fusion.Run(hlo_module).status()); HloPassPipeline reduce_pipeline("reduce-precision"); -- GitLab From a3273e090f7ea8401ea283ad052350aeffa5fdc1 Mon Sep 17 00:00:00 2001 From: Yu-Cheng Ling Date: Wed, 13 Jun 2018 14:48:22 -0700 Subject: [PATCH 416/816] Variable Tensor API for TF Lite. PiperOrigin-RevId: 200457602 --- tensorflow/contrib/lite/arena_planner.cc | 58 +++++++++++++++++-- tensorflow/contrib/lite/arena_planner_test.cc | 13 ++++- tensorflow/contrib/lite/context.c | 3 +- tensorflow/contrib/lite/context.h | 6 +- tensorflow/contrib/lite/graph_info.h | 3 + tensorflow/contrib/lite/graph_info_test.cc | 2 + tensorflow/contrib/lite/interpreter.cc | 55 ++++++++++++++++-- tensorflow/contrib/lite/interpreter.h | 23 +++++++- tensorflow/contrib/lite/model.cc | 23 +++++++- tensorflow/contrib/lite/schema/schema.fbs | 12 ++++ .../contrib/lite/schema/schema_generated.h | 56 ++++++++++++++---- tensorflow/contrib/lite/string_util.cc | 2 +- .../contrib/lite/testing/tflite_driver.cc | 11 +--- tensorflow/contrib/lite/toco/tflite/export.cc | 56 +++++++++++++----- .../contrib/lite/toco/tflite/operator.cc | 18 ++++++ .../contrib/lite/toco/tflite/operator.h | 11 ++++ 16 files changed, 299 insertions(+), 53 deletions(-) diff --git a/tensorflow/contrib/lite/arena_planner.cc b/tensorflow/contrib/lite/arena_planner.cc index 4f836d3677..22be64d6ff 100644 --- a/tensorflow/contrib/lite/arena_planner.cc +++ b/tensorflow/contrib/lite/arena_planner.cc @@ -31,7 +31,7 @@ struct AllocationInfo { // The tensor index to be allocated or deallocated. int tensor; // Whether to allocate or deallocate - enum { ALLOC, DEALLOC } type; + enum Type { ALLOC, DEALLOC } type; }; ArenaPlanner::ArenaPlanner(TfLiteContext* context, @@ -67,6 +67,33 @@ TfLiteStatus ArenaPlanner::PlanAllocations() { // Keeps track of references to each tensor. std::vector refcounts(graph_info_->num_tensors(), 0); + // `allocated` and `deallocated` are technically list of boolean values. + // We're saving the compiled binary size by using `vector`. + std::vector allocated(graph_info_->num_tensors(), false); + std::vector deallocated(graph_info_->num_tensors(), false); + + auto allocate = [this, &allocated, &deallocated](int node, + int tensor) -> TfLiteStatus { + if (allocated[tensor]) { + return kTfLiteOk; + } + TF_LITE_ENSURE(context_, !deallocated[tensor]); + alloc_queue_.push_back({node, tensor, AllocationInfo::ALLOC}); + allocated[tensor] = true; + return kTfLiteOk; + }; + + auto deallocate = [this, &allocated, &deallocated]( + int node, int tensor) -> TfLiteStatus { + if (!allocated[tensor]) { + // Do not enqueue a DEALLOC if the tensor is never allocated. + // This happened with the constant tensors. + return kTfLiteOk; + } + TF_LITE_ENSURE(context_, !deallocated[tensor]); + alloc_queue_.push_back({node, tensor, AllocationInfo::DEALLOC}); + return kTfLiteOk; + }; // There will be an entry in alloc_queue_ for the allocation of each tensor // and another for their deallocation. @@ -79,6 +106,28 @@ TfLiteStatus ArenaPlanner::PlanAllocations() { refcounts[tensor_index]++; } + // Variable tensors should are also never overwritten and need to be alive all + // the time. + for (int tensor_index : graph_info_->variables()) { + refcounts[tensor_index]++; + } + + // Queue all graph inputs for allocation. + for (int tensor_index : graph_info_->inputs()) { + if (tensor_index != kOptionalTensor) { + TF_LITE_ENSURE_STATUS(allocate(0, tensor_index)); + } + } + + // Queue all graph variable tensors for allocation. + for (int tensor_index : graph_info_->variables()) { + if (tensor_index != kOptionalTensor) { + // Increase the reference count for input tensors by one, so it will + // never be deallocated. + TF_LITE_ENSURE_STATUS(allocate(0, tensor_index)); + } + } + // Count references to node input tensors. for (int i = 0; i < graph_info_->num_nodes(); ++i) { const TfLiteNode& node = graph_info_->node(i); @@ -94,10 +143,9 @@ TfLiteStatus ArenaPlanner::PlanAllocations() { // Queue all graph inputs for allocation. for (int tensor_index : graph_info_->inputs()) { if (tensor_index != kOptionalTensor) { - alloc_queue_.push_back({0, tensor_index, AllocationInfo::ALLOC}); + TF_LITE_ENSURE_STATUS(allocate(0, tensor_index)); } } - // Go through the graph in execution order. for (int i = 0; i < graph_info_->num_nodes(); ++i) { const TfLiteNode& node = graph_info_->node(i); @@ -106,7 +154,7 @@ TfLiteStatus ArenaPlanner::PlanAllocations() { TfLiteIntArray* node_outputs = node.outputs; for (int j = 0; j < node_outputs->size; ++j) { int tensor_index = node_outputs->data[j]; - alloc_queue_.push_back({i, tensor_index, AllocationInfo::ALLOC}); + TF_LITE_ENSURE_STATUS(allocate(i, tensor_index)); } // Then update the ref-counts of the node's inputs, and if necessary queue @@ -117,7 +165,7 @@ TfLiteStatus ArenaPlanner::PlanAllocations() { if (tensor_index != kOptionalTensor) { refcounts[tensor_index]--; if (refcounts[tensor_index] == 0) { - alloc_queue_.push_back({i, tensor_index, AllocationInfo::DEALLOC}); + TF_LITE_ENSURE_STATUS(deallocate(i, tensor_index)); } } } diff --git a/tensorflow/contrib/lite/arena_planner_test.cc b/tensorflow/contrib/lite/arena_planner_test.cc index 16171df10a..f0fd35216f 100644 --- a/tensorflow/contrib/lite/arena_planner_test.cc +++ b/tensorflow/contrib/lite/arena_planner_test.cc @@ -100,12 +100,18 @@ class TestGraph { std::vector* tensors() { return &tensors_; } const std::vector& inputs() { return inputs_; } const std::vector& outputs() { return outputs_; } + const std::vector& variables() { return variables_; } + + void SetVariables(const std::vector& variables) { + variables_ = variables; + } private: std::vector nodes_; std::vector tensors_; std::vector inputs_; std::vector outputs_; + std::vector variables_; }; // The GraphInfo for a TestGraph. @@ -123,6 +129,9 @@ class TestGraphInfo : public GraphInfo { } const std::vector& inputs() const override { return graph_->inputs(); } const std::vector& outputs() const override { return graph_->outputs(); } + const std::vector& variables() const override { + return graph_->variables(); + } private: TestGraph* graph_; @@ -306,13 +315,15 @@ TEST_F(ArenaPlannerTest, SimpleGraphWithPersistentTensor) { { /* in, out, tmp */ {{0, 1}, {2}, {}}, // First op - {{2, 0}, {4}, {5}}, // Second op, with temporary + {{2, 0}, {4}, {5}}, // Second op, with persistent {{4, -1}, {3}, {}} // Third op, with optional }, {3}); // Make #1 persistent so it goes into its own arena. (*graph.tensors())[1].allocation_type = kTfLiteArenaRwPersistent; + // The only use case for kTfLiteArenaRwPersistent is variable tensor now. + graph.SetVariables({1}); SetGraph(&graph); Execute(0, 10); diff --git a/tensorflow/contrib/lite/context.c b/tensorflow/contrib/lite/context.c index 5c6f5e72a4..7f2aa316f4 100644 --- a/tensorflow/contrib/lite/context.c +++ b/tensorflow/contrib/lite/context.c @@ -76,7 +76,7 @@ void TfLiteTensorFree(TfLiteTensor* t) { void TfLiteTensorReset(TfLiteType type, const char* name, TfLiteIntArray* dims, TfLiteQuantizationParams quantization, char* buffer, size_t size, TfLiteAllocationType allocation_type, - const void* allocation, TfLiteTensor* tensor) { + const void* allocation, bool is_variable, TfLiteTensor* tensor) { TfLiteTensorFree(tensor); tensor->type = type; tensor->name = name; @@ -86,6 +86,7 @@ void TfLiteTensorReset(TfLiteType type, const char* name, TfLiteIntArray* dims, tensor->bytes = size; tensor->allocation_type = allocation_type; tensor->allocation = allocation; + tensor->is_variable = is_variable; } void TfLiteTensorRealloc(size_t num_bytes, TfLiteTensor* tensor) { diff --git a/tensorflow/contrib/lite/context.h b/tensorflow/contrib/lite/context.h index 0415acfe0f..15a37de9dc 100644 --- a/tensorflow/contrib/lite/context.h +++ b/tensorflow/contrib/lite/context.h @@ -225,6 +225,9 @@ typedef struct { // delegate buffer. // WARNING: This is an // experimental interface that is subject to change. bool data_is_stale; + + // True if the tensor is a variable. + bool is_variable; } TfLiteTensor; // Free data memory of tensor `t`; @@ -237,7 +240,8 @@ void TfLiteTensorFree(TfLiteTensor* t); void TfLiteTensorReset(TfLiteType type, const char* name, TfLiteIntArray* dims, TfLiteQuantizationParams quantization, char* buffer, size_t size, TfLiteAllocationType allocation_type, - const void* allocation, TfLiteTensor* tensor); + const void* allocation, bool is_variable, + TfLiteTensor* tensor); // Resize the allocated data of a (dynamic) tensor. void TfLiteTensorRealloc(size_t num_bytes, TfLiteTensor* tensor); diff --git a/tensorflow/contrib/lite/graph_info.h b/tensorflow/contrib/lite/graph_info.h index 313af5fb75..77268d7aeb 100644 --- a/tensorflow/contrib/lite/graph_info.h +++ b/tensorflow/contrib/lite/graph_info.h @@ -46,6 +46,9 @@ class GraphInfo { // Returns the indices of the output tensors. virtual const std::vector& outputs() const = 0; + + // Returns the indices of the variable tensors. + virtual const std::vector& variables() const = 0; }; // Represents a subgraph of a TensorFlow Lite graph. diff --git a/tensorflow/contrib/lite/graph_info_test.cc b/tensorflow/contrib/lite/graph_info_test.cc index ea38b43993..89a8f36b41 100644 --- a/tensorflow/contrib/lite/graph_info_test.cc +++ b/tensorflow/contrib/lite/graph_info_test.cc @@ -45,6 +45,7 @@ class SimpleTestGraph : public GraphInfo { TfLiteTensor* tensor(size_t index) override { return &tensors_[index]; } const std::vector& inputs() const override { return inputs_; } const std::vector& outputs() const override { return outputs_; } + const std::vector& variables() const override { return variables_; } void AddNode(const std::vector& inputs, const std::vector& outputs) { @@ -67,6 +68,7 @@ class SimpleTestGraph : public GraphInfo { std::vector tensors_; std::vector inputs_; std::vector outputs_; + std::vector variables_; }; // Partition a graph to generate a list of subgraphs. This wraps the API call diff --git a/tensorflow/contrib/lite/interpreter.cc b/tensorflow/contrib/lite/interpreter.cc index 2f8205444d..3287f9c4fd 100644 --- a/tensorflow/contrib/lite/interpreter.cc +++ b/tensorflow/contrib/lite/interpreter.cc @@ -82,6 +82,9 @@ class InterpreterInfo : public GraphInfo { const std::vector& outputs() const override { return interpreter_->outputs(); } + const std::vector& variables() const override { + return interpreter_->variables(); + } public: Interpreter* interpreter_; @@ -302,6 +305,13 @@ TfLiteStatus Interpreter::SetOutputs(std::vector outputs) { return kTfLiteOk; } +TfLiteStatus Interpreter::SetVariables(std::vector variables) { + TF_LITE_ENSURE_OK(&context_, CheckTensorIndices("variables", variables.data(), + variables.size())); + variables_ = std::move(variables); + return kTfLiteOk; +} + TfLiteStatus Interpreter::CheckTensorIndices(const char* label, const int* indices, int length) { // Making sure kOptionalTensor is not re-defined to something other than -1. @@ -370,6 +380,7 @@ TfLiteStatus Interpreter::AllocateTensors() { } TF_LITE_ENSURE_STATUS(PrepareOpsAndTensors()); + if (state_ == kStateUninvokable) { state_ = kStateInvokable; } @@ -378,6 +389,25 @@ TfLiteStatus Interpreter::AllocateTensors() { return kTfLiteOk; } +// TODO(ycling): Consider to provide other functions to initialize variable +// tensors to non-zero values. +TfLiteStatus Interpreter::ResetVariableTensorsToZero() { + for (auto& tensor : tensors_) { + if (!tensor.is_variable) { + continue; + } + + // Variable tensors have to be `kTfLiteArenaRwPersistent`, and must be + // allocated after the initial `PrepareOpsAndTensors()` is called. + TF_LITE_ENSURE_EQ(&context_, tensor.allocation_type, + kTfLiteArenaRwPersistent); + TF_LITE_ENSURE(&context_, tensor.data.raw != nullptr); + + memset(tensor.data.raw, 0, tensor.bytes); + } + return kTfLiteOk; +} + TfLiteStatus Interpreter::AddNodeWithParameters( const std::vector& inputs, const std::vector& outputs, const char* init_data, size_t init_data_size, void* builtin_data, @@ -690,7 +720,7 @@ TfLiteStatus Interpreter::SetTensorParametersReadOnly( state_ = kStateUninvokable; TfLiteTensorReset(type, name, ConvertArrayToTfLiteIntArray(rank, dims), quantization, const_cast(buffer), bytes, - kTfLiteMmapRo, allocation, &tensor); + kTfLiteMmapRo, allocation, false, &tensor); } return kTfLiteOk; } @@ -701,7 +731,7 @@ TfLiteStatus Interpreter::SetTensorParametersReadOnly( // to Interpreter. TfLiteStatus Interpreter::SetTensorParametersReadWrite( int tensor_index, TfLiteType type, const char* name, const size_t rank, - const int* dims, TfLiteQuantizationParams quantization) { + const int* dims, TfLiteQuantizationParams quantization, bool is_variable) { if (state_ == kStateInvokableAndImmutable) { ReportError( &context_, @@ -719,11 +749,23 @@ TfLiteStatus Interpreter::SetTensorParametersReadWrite( TF_LITE_ENSURE_OK(&context_, BytesRequired(type, dims, rank, &required_bytes)); } + + TfLiteAllocationType allocation_type = kTfLiteArenaRw; + if (type == kTfLiteString) { + if (is_variable) { + // We don't have a real use case for string variable tensor. + ReportError(&context_, "String variable tensor isn't supported."); + return kTfLiteError; + } + allocation_type = kTfLiteDynamic; + } else if (is_variable) { + allocation_type = kTfLiteArenaRwPersistent; + } + TfLiteTensorReset(type, name, ConvertArrayToTfLiteIntArray(rank, dims), quantization, - /*buffer=*/nullptr, required_bytes, - type == kTfLiteString ? kTfLiteDynamic : kTfLiteArenaRw, - nullptr, &context_.tensors[tensor_index]); + /*buffer=*/nullptr, required_bytes, allocation_type, + nullptr, is_variable, &context_.tensors[tensor_index]); return kTfLiteOk; } @@ -739,7 +781,8 @@ TfLiteStatus Interpreter::ResizeTensorImpl(TfLiteTensor* tensor, TfLiteIntArray* new_size) { // Note that in theory we could resize kTfLiteArenaRwPersistent tensors too. if (tensor->allocation_type == kTfLiteArenaRw || - tensor->allocation_type == kTfLiteDynamic) { + tensor->allocation_type == kTfLiteDynamic || + tensor->allocation_type == kTfLiteArenaRwPersistent) { if (tensor->type != kTfLiteString) { size_t bytesRequired; TfLiteStatus status = BytesRequired(tensor->type, new_size->data, diff --git a/tensorflow/contrib/lite/interpreter.h b/tensorflow/contrib/lite/interpreter.h index 7315d83606..37961cd1dc 100644 --- a/tensorflow/contrib/lite/interpreter.h +++ b/tensorflow/contrib/lite/interpreter.h @@ -118,6 +118,11 @@ class Interpreter { // interpreter. TfLiteStatus SetOutputs(std::vector outputs); + // Provide a list of tensor indexes that are variable tensors. + // Each index is bound check and this modifies the consistent_ flag of the + // interpreter. + TfLiteStatus SetVariables(std::vector variables); + // Adds a node with the given parameters and returns the index of the new // node in `node_index` (optionally). Interpreter will take ownership of // `builtin_data` and destroy it with `free`. Ownership of 'init_data' @@ -160,13 +165,15 @@ class Interpreter { // to Interpreter. inline TfLiteStatus SetTensorParametersReadWrite( int tensor_index, TfLiteType type, const char* name, - const std::vector& dims, TfLiteQuantizationParams quantization) { + const std::vector& dims, TfLiteQuantizationParams quantization, + bool is_variable = false) { return SetTensorParametersReadWrite(tensor_index, type, name, dims.size(), - dims.data(), quantization); + dims.data(), quantization, is_variable); } TfLiteStatus SetTensorParametersReadWrite( int tensor_index, TfLiteType type, const char* name, const size_t rank, - const int* dims, TfLiteQuantizationParams quantization); + const int* dims, TfLiteQuantizationParams quantization, + bool is_variable = false); // Functions to access tensor data @@ -182,6 +189,9 @@ class Interpreter { // Read only access to list of outputs. const std::vector& outputs() const { return outputs_; } + // Read only access to list of variable tensors. + const std::vector& variables() const { return variables_; } + // Return the name of a given output. The given index must be between 0 and // outputs().size(). const char* GetOutputName(int index) const { @@ -379,6 +389,10 @@ class Interpreter { allow_buffer_handle_output_ = allow_buffer_handle_output; } + // Reset all variable tensors to zero. + // WARNING: This is an experimental API and subject to change. + TfLiteStatus ResetVariableTensorsToZero(); + private: // Give 'op_reg' a chance to initialize itself using the contents of // 'buffer'. @@ -541,6 +555,9 @@ class Interpreter { // interpreter. std::vector outputs_; + // Array of indices representing the tensors that are variable tensors. + std::vector variables_; + // The error reporter delegate that tflite will forward queries errors to. ErrorReporter* error_reporter_; diff --git a/tensorflow/contrib/lite/model.cc b/tensorflow/contrib/lite/model.cc index cd7b9bdabf..bc62e4cc2d 100644 --- a/tensorflow/contrib/lite/model.cc +++ b/tensorflow/contrib/lite/model.cc @@ -852,7 +852,16 @@ TfLiteStatus InterpreterBuilder::ParseTensors( const char* buffer_ptr; TF_LITE_ENSURE_STATUS(get_readonly_data(&buffer_ptr, &buffer_size)); + bool is_variable = tensor->is_variable(); if (buffer_ptr) { + if (is_variable) { + error_reporter_->Report( + "Tensor %d is a variable tensor with buffer. " + "It's not supported now.\n", + i); + status = kTfLiteError; + } + if (interpreter->SetTensorParametersReadOnly( i, type, get_name(tensor), dims, quantization, buffer_ptr, buffer_size, allocation_) != kTfLiteOk) { @@ -861,8 +870,9 @@ TfLiteStatus InterpreterBuilder::ParseTensors( status = kTfLiteError; } } else { - if (interpreter->SetTensorParametersReadWrite( - i, type, get_name(tensor), dims, quantization) != kTfLiteOk) { + if (interpreter->SetTensorParametersReadWrite(i, type, get_name(tensor), + dims, quantization, + is_variable) != kTfLiteOk) { error_reporter_->Report("Tensor %d is invalidly specified in schema.\n", i); status = kTfLiteError; @@ -946,6 +956,15 @@ TfLiteStatus InterpreterBuilder::operator()( if (ParseTensors(buffers, tensors, interpreter->get()) != kTfLiteOk) return cleanup_and_error(); + std::vector variables; + for (int i = 0; i < (*interpreter)->tensors_size(); ++i) { + auto* tensor = (*interpreter)->tensor(i); + if (tensor->is_variable) { + variables.push_back(i); + } + } + (**interpreter).SetVariables(variables); + return kTfLiteOk; } diff --git a/tensorflow/contrib/lite/schema/schema.fbs b/tensorflow/contrib/lite/schema/schema.fbs index 1f1be428c9..c7b955a165 100644 --- a/tensorflow/contrib/lite/schema/schema.fbs +++ b/tensorflow/contrib/lite/schema/schema.fbs @@ -64,6 +64,8 @@ table Tensor { buffer:uint; name:string; // For debugging and importing back into tensorflow. quantization:QuantizationParameters; // Optional. + + is_variable:bool = false; } // A list of builtin operators. Builtin operators are slightly faster than custom @@ -521,6 +523,16 @@ table Operator { builtin_options:BuiltinOptions; custom_options:[ubyte]; custom_options_format:CustomOptionsFormat; + + // A list of booleans indicating the input tensors which are being mutated by + // this operator.(e.g. used by RNN and LSTM). + // For example, if the "inputs" array refers to 5 tensors and the second and + // fifth are mutable variables, then this list will contain + // [false, true, false, false, true]. + // + // If the list is empty, no variable is mutated in this operator. + // The list either has the same length as `inputs`, or is empty. + mutating_variable_inputs:[bool]; } // The root type, defining a subgraph, which typically represents an entire diff --git a/tensorflow/contrib/lite/schema/schema_generated.h b/tensorflow/contrib/lite/schema/schema_generated.h index 4e02034871..81d4574da7 100755 --- a/tensorflow/contrib/lite/schema/schema_generated.h +++ b/tensorflow/contrib/lite/schema/schema_generated.h @@ -1674,9 +1674,11 @@ struct TensorT : public flatbuffers::NativeTable { uint32_t buffer; std::string name; std::unique_ptr quantization; + bool is_variable; TensorT() : type(TensorType_FLOAT32), - buffer(0) { + buffer(0), + is_variable(false) { } }; @@ -1687,7 +1689,8 @@ struct Tensor FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { VT_TYPE = 6, VT_BUFFER = 8, VT_NAME = 10, - VT_QUANTIZATION = 12 + VT_QUANTIZATION = 12, + VT_IS_VARIABLE = 14 }; const flatbuffers::Vector *shape() const { return GetPointer *>(VT_SHAPE); @@ -1704,6 +1707,9 @@ struct Tensor FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { const QuantizationParameters *quantization() const { return GetPointer(VT_QUANTIZATION); } + bool is_variable() const { + return GetField(VT_IS_VARIABLE, 0) != 0; + } bool Verify(flatbuffers::Verifier &verifier) const { return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_SHAPE) && @@ -1714,6 +1720,7 @@ struct Tensor FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { verifier.Verify(name()) && VerifyOffset(verifier, VT_QUANTIZATION) && verifier.VerifyTable(quantization()) && + VerifyField(verifier, VT_IS_VARIABLE) && verifier.EndTable(); } TensorT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; @@ -1739,6 +1746,9 @@ struct TensorBuilder { void add_quantization(flatbuffers::Offset quantization) { fbb_.AddOffset(Tensor::VT_QUANTIZATION, quantization); } + void add_is_variable(bool is_variable) { + fbb_.AddElement(Tensor::VT_IS_VARIABLE, static_cast(is_variable), 0); + } explicit TensorBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) { start_ = fbb_.StartTable(); @@ -1757,12 +1767,14 @@ inline flatbuffers::Offset CreateTensor( TensorType type = TensorType_FLOAT32, uint32_t buffer = 0, flatbuffers::Offset name = 0, - flatbuffers::Offset quantization = 0) { + flatbuffers::Offset quantization = 0, + bool is_variable = false) { TensorBuilder builder_(_fbb); builder_.add_quantization(quantization); builder_.add_name(name); builder_.add_buffer(buffer); builder_.add_shape(shape); + builder_.add_is_variable(is_variable); builder_.add_type(type); return builder_.Finish(); } @@ -1773,14 +1785,16 @@ inline flatbuffers::Offset CreateTensorDirect( TensorType type = TensorType_FLOAT32, uint32_t buffer = 0, const char *name = nullptr, - flatbuffers::Offset quantization = 0) { + flatbuffers::Offset quantization = 0, + bool is_variable = false) { return tflite::CreateTensor( _fbb, shape ? _fbb.CreateVector(*shape) : 0, type, buffer, name ? _fbb.CreateString(name) : 0, - quantization); + quantization, + is_variable); } flatbuffers::Offset CreateTensor(flatbuffers::FlatBufferBuilder &_fbb, const TensorT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); @@ -5007,6 +5021,7 @@ struct OperatorT : public flatbuffers::NativeTable { BuiltinOptionsUnion builtin_options; std::vector custom_options; CustomOptionsFormat custom_options_format; + std::vector mutating_variable_inputs; OperatorT() : opcode_index(0), custom_options_format(CustomOptionsFormat_FLEXBUFFERS) { @@ -5022,7 +5037,8 @@ struct Operator FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { VT_BUILTIN_OPTIONS_TYPE = 10, VT_BUILTIN_OPTIONS = 12, VT_CUSTOM_OPTIONS = 14, - VT_CUSTOM_OPTIONS_FORMAT = 16 + VT_CUSTOM_OPTIONS_FORMAT = 16, + VT_MUTATING_VARIABLE_INPUTS = 18 }; uint32_t opcode_index() const { return GetField(VT_OPCODE_INDEX, 0); @@ -5208,6 +5224,9 @@ struct Operator FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { CustomOptionsFormat custom_options_format() const { return static_cast(GetField(VT_CUSTOM_OPTIONS_FORMAT, 0)); } + const flatbuffers::Vector *mutating_variable_inputs() const { + return GetPointer *>(VT_MUTATING_VARIABLE_INPUTS); + } bool Verify(flatbuffers::Verifier &verifier) const { return VerifyTableStart(verifier) && VerifyField(verifier, VT_OPCODE_INDEX) && @@ -5221,6 +5240,8 @@ struct Operator FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { VerifyOffset(verifier, VT_CUSTOM_OPTIONS) && verifier.Verify(custom_options()) && VerifyField(verifier, VT_CUSTOM_OPTIONS_FORMAT) && + VerifyOffset(verifier, VT_MUTATING_VARIABLE_INPUTS) && + verifier.Verify(mutating_variable_inputs()) && verifier.EndTable(); } OperatorT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; @@ -5468,6 +5489,9 @@ struct OperatorBuilder { void add_custom_options_format(CustomOptionsFormat custom_options_format) { fbb_.AddElement(Operator::VT_CUSTOM_OPTIONS_FORMAT, static_cast(custom_options_format), 0); } + void add_mutating_variable_inputs(flatbuffers::Offset> mutating_variable_inputs) { + fbb_.AddOffset(Operator::VT_MUTATING_VARIABLE_INPUTS, mutating_variable_inputs); + } explicit OperatorBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) { start_ = fbb_.StartTable(); @@ -5488,8 +5512,10 @@ inline flatbuffers::Offset CreateOperator( BuiltinOptions builtin_options_type = BuiltinOptions_NONE, flatbuffers::Offset builtin_options = 0, flatbuffers::Offset> custom_options = 0, - CustomOptionsFormat custom_options_format = CustomOptionsFormat_FLEXBUFFERS) { + CustomOptionsFormat custom_options_format = CustomOptionsFormat_FLEXBUFFERS, + flatbuffers::Offset> mutating_variable_inputs = 0) { OperatorBuilder builder_(_fbb); + builder_.add_mutating_variable_inputs(mutating_variable_inputs); builder_.add_custom_options(custom_options); builder_.add_builtin_options(builtin_options); builder_.add_outputs(outputs); @@ -5508,7 +5534,8 @@ inline flatbuffers::Offset CreateOperatorDirect( BuiltinOptions builtin_options_type = BuiltinOptions_NONE, flatbuffers::Offset builtin_options = 0, const std::vector *custom_options = nullptr, - CustomOptionsFormat custom_options_format = CustomOptionsFormat_FLEXBUFFERS) { + CustomOptionsFormat custom_options_format = CustomOptionsFormat_FLEXBUFFERS, + const std::vector *mutating_variable_inputs = nullptr) { return tflite::CreateOperator( _fbb, opcode_index, @@ -5517,7 +5544,8 @@ inline flatbuffers::Offset CreateOperatorDirect( builtin_options_type, builtin_options, custom_options ? _fbb.CreateVector(*custom_options) : 0, - custom_options_format); + custom_options_format, + mutating_variable_inputs ? _fbb.CreateVector(*mutating_variable_inputs) : 0); } flatbuffers::Offset CreateOperator(flatbuffers::FlatBufferBuilder &_fbb, const OperatorT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); @@ -5888,6 +5916,7 @@ inline void Tensor::UnPackTo(TensorT *_o, const flatbuffers::resolver_function_t { auto _e = buffer(); _o->buffer = _e; }; { auto _e = name(); if (_e) _o->name = _e->str(); }; { auto _e = quantization(); if (_e) _o->quantization = std::unique_ptr(_e->UnPack(_resolver)); }; + { auto _e = is_variable(); _o->is_variable = _e; }; } inline flatbuffers::Offset Tensor::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TensorT* _o, const flatbuffers::rehasher_function_t *_rehasher) { @@ -5903,13 +5932,15 @@ inline flatbuffers::Offset CreateTensor(flatbuffers::FlatBufferBuilder & auto _buffer = _o->buffer; auto _name = _o->name.empty() ? 0 : _fbb.CreateString(_o->name); auto _quantization = _o->quantization ? CreateQuantizationParameters(_fbb, _o->quantization.get(), _rehasher) : 0; + auto _is_variable = _o->is_variable; return tflite::CreateTensor( _fbb, _shape, _type, _buffer, _name, - _quantization); + _quantization, + _is_variable); } inline Conv2DOptionsT *Conv2DOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { @@ -7432,6 +7463,7 @@ inline void Operator::UnPackTo(OperatorT *_o, const flatbuffers::resolver_functi { auto _e = builtin_options(); if (_e) _o->builtin_options.value = BuiltinOptionsUnion::UnPack(_e, builtin_options_type(), _resolver); }; { auto _e = custom_options(); if (_e) { _o->custom_options.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->custom_options[_i] = _e->Get(_i); } } }; { auto _e = custom_options_format(); _o->custom_options_format = _e; }; + { auto _e = mutating_variable_inputs(); if (_e) { _o->mutating_variable_inputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->mutating_variable_inputs[_i] = _e->Get(_i) != 0; } } }; } inline flatbuffers::Offset Operator::Pack(flatbuffers::FlatBufferBuilder &_fbb, const OperatorT* _o, const flatbuffers::rehasher_function_t *_rehasher) { @@ -7449,6 +7481,7 @@ inline flatbuffers::Offset CreateOperator(flatbuffers::FlatBufferBuild auto _builtin_options = _o->builtin_options.Pack(_fbb); auto _custom_options = _o->custom_options.size() ? _fbb.CreateVector(_o->custom_options) : 0; auto _custom_options_format = _o->custom_options_format; + auto _mutating_variable_inputs = _o->mutating_variable_inputs.size() ? _fbb.CreateVector(_o->mutating_variable_inputs) : 0; return tflite::CreateOperator( _fbb, _opcode_index, @@ -7457,7 +7490,8 @@ inline flatbuffers::Offset CreateOperator(flatbuffers::FlatBufferBuild _builtin_options_type, _builtin_options, _custom_options, - _custom_options_format); + _custom_options_format, + _mutating_variable_inputs); } inline SubGraphT *SubGraph::UnPack(const flatbuffers::resolver_function_t *_resolver) const { diff --git a/tensorflow/contrib/lite/string_util.cc b/tensorflow/contrib/lite/string_util.cc index a89776b29f..a316a40b62 100644 --- a/tensorflow/contrib/lite/string_util.cc +++ b/tensorflow/contrib/lite/string_util.cc @@ -105,7 +105,7 @@ void DynamicBuffer::WriteToTensor(TfLiteTensor* tensor) { dims->data[0] = offset_.size() - 1; // Store number of strings. TfLiteTensorReset(tensor->type, tensor->name, dims, tensor->params, tensor_buffer, bytes, kTfLiteDynamic, tensor->allocation, - tensor); + tensor->is_variable, tensor); } int GetStringCount(const char* raw_buffer) { diff --git a/tensorflow/contrib/lite/testing/tflite_driver.cc b/tensorflow/contrib/lite/testing/tflite_driver.cc index f518bf864c..54edfdfb1d 100644 --- a/tensorflow/contrib/lite/testing/tflite_driver.cc +++ b/tensorflow/contrib/lite/testing/tflite_driver.cc @@ -285,7 +285,9 @@ bool TfLiteDriver::CheckResults() { } void TfLiteDriver::ResetLSTMStateTensors() { - // This is a workaround for initializing state tensors for LSTM. + interpreter_->ResetVariableTensorsToZero(); + + // Below is a workaround for initializing state tensors for LSTM. // TODO(ycling): Refactoring and find a better way to initialize state // tensors. Maybe write the reset instructions into the test data. for (auto node_index : interpreter_->execution_plan()) { @@ -303,13 +305,6 @@ void TfLiteDriver::ResetLSTMStateTensors() { int node_index = node.outputs->data[i]; ResetTensor(node_index); } - } else if (params->kernel_type == kTfLiteLSTMBasicKernel && - node.inputs->size == 5) { - // The 2th and 5th inputs are state tensors. - for (int i : {1, 4}) { - int node_index = node.inputs->data[i]; - ResetTensor(node_index); - } } } } diff --git a/tensorflow/contrib/lite/toco/tflite/export.cc b/tensorflow/contrib/lite/toco/tflite/export.cc index a2d753657b..7ba2603a95 100644 --- a/tensorflow/contrib/lite/toco/tflite/export.cc +++ b/tensorflow/contrib/lite/toco/tflite/export.cc @@ -99,7 +99,8 @@ void LoadOperatorsMap( Offset>> ExportTensors( const Model& model, const details::TensorsMap& tensors_map, - FlatBufferBuilder* builder, std::vector* buffers_to_write) { + FlatBufferBuilder* builder, std::vector* buffers_to_write, + const std::set& variable_tensor_indices) { // In the end we will need to produce a vector sorted by the indices of the // tensors in the tensors_map. std::map> ordered_tensors; @@ -139,9 +140,11 @@ Offset>> ExportTensors( scale, zero_point); int index = tensors_map.at(tensor_name); + bool is_variable = + variable_tensor_indices.find(index) != variable_tensor_indices.end(); ordered_tensors[index] = CreateTensor(*builder, builder->CreateVector(shape), type, buffer_index, - builder->CreateString(tensor_name), q_param); + builder->CreateString(tensor_name), q_param, is_variable); } std::vector> tensor_vector; @@ -239,7 +242,10 @@ Offset>> ExportOperators( const Model& model, const std::map>& ops_by_type, const details::OperatorsMap& operators_map, - const details::TensorsMap& tensors_map, FlatBufferBuilder* builder) { + const details::TensorsMap& tensors_map, FlatBufferBuilder* builder, + std::set* variable_tensor_indices) { + variable_tensor_indices->clear(); + // The operators are in execution order, so we just follow tf.mini order. std::vector> op_vector; for (const auto& op : model.operators) { @@ -256,18 +262,36 @@ Offset>> ExportOperators( int op_index = operators_map.at(GetOperatorKey(*op, ops_by_type)); + auto tflite_op_it = ops_by_type.find(op->type); + BaseOperator* tflite_op = tflite_op_it == ops_by_type.end() + ? nullptr + : tflite_op_it->second.get(); + // This is a custom op unless we can find it in ops_by_type, and even then // it could be a custom op (such as kTensorFlowUnsupported). - auto options = Options::Custom(0); - if (ops_by_type.count(op->type) != 0) { - options = ops_by_type.at(op->type)->Serialize(*op, builder); + + std::vector mutating_input_variables; + if (tflite_op) { + options = tflite_op->Serialize(*op, builder); + mutating_input_variables = tflite_op->GetMutatingInputVariables(*op); + + if (!mutating_input_variables.empty()) { + for (int i = 0; i < op->inputs.size(); ++i) { + if (!mutating_input_variables[i]) { + continue; + } + int32_t variable_tensor_index = tensors_map.at(op->inputs[i]); + variable_tensor_indices->insert(variable_tensor_index); + } + } } // The only supported CustomOptionFormat is FLEXBUFFERS now. op_vector.push_back(CreateOperator( *builder, op_index, builder->CreateVector(inputs), builder->CreateVector(outputs), options.type, options.builtin, - options.custom, ::tflite::CustomOptionsFormat_FLEXBUFFERS)); + options.custom, ::tflite::CustomOptionsFormat_FLEXBUFFERS, + builder->CreateVector(mutating_input_variables))); } return builder->CreateVector(op_vector); @@ -308,13 +332,10 @@ void Export( Array empty_array; buffers_to_write.push_back(&empty_array); - auto tensors = ExportTensors(model, tensors_map, &builder, &buffers_to_write); - auto inputs = ExportInputTensors(model, tensors_map, &builder); - auto outputs = ExportOutputTensors(model, tensors_map, &builder); - std::set error_summary; auto op_codes = ExportOperatorCodes(model, ops_by_type, operators_map, &builder, &error_summary); + const string fake_quant_operation_name = "FAKE_QUANT"; if (error_summary.count(fake_quant_operation_name) != 0) { @@ -353,11 +374,18 @@ void Export( << absl::StrJoin(error_summary_final, ", ") << "."; } - auto ops = - ExportOperators(model, ops_by_type, operators_map, tensors_map, &builder); + std::set variable_tensor_indices; + auto ops = ExportOperators(model, ops_by_type, operators_map, tensors_map, + &builder, &variable_tensor_indices); + + auto tensors = ExportTensors(model, tensors_map, &builder, &buffers_to_write, + variable_tensor_indices); + auto inputs = ExportInputTensors(model, tensors_map, &builder); + auto outputs = ExportOutputTensors(model, tensors_map, &builder); // TODO(aselle): add support to toco for multiple subgraphs. - auto subgraph = CreateSubGraph(builder, tensors, inputs, outputs, ops); + auto subgraph = CreateSubGraph(builder, tensors, inputs, outputs, ops, + /* name */ 0); std::vector> subgraphs = {subgraph}; auto buffers = ExportBuffers(model, buffers_to_write, &builder); diff --git a/tensorflow/contrib/lite/toco/tflite/operator.cc b/tensorflow/contrib/lite/toco/tflite/operator.cc index 7490ab960b..a0fbb58aca 100644 --- a/tensorflow/contrib/lite/toco/tflite/operator.cc +++ b/tensorflow/contrib/lite/toco/tflite/operator.cc @@ -668,6 +668,24 @@ class Lstm : public BuiltinOperator GetMutatingInputVariables( + const Operator& op) const override { + const auto& lstm_op = static_cast(op); + + switch (lstm_op.kernel_type) { + case LstmCellOperator::KERNEL_FULL: + // TODO(ycling): Change the full kernel to use the new variable tensor + // design. This requires moving the state tensors from output to input. + return std::vector(); + case LstmCellOperator::KERNEL_BASIC: { + std::vector mutating_input_variables(op.inputs.size(), false); + mutating_input_variables[LstmCellOperator::PREV_ACTIV_INPUT] = true; + mutating_input_variables[LstmCellOperator::PREV_STATE_INPUT] = true; + return mutating_input_variables; + } + } + } }; class Mean : public BuiltinOperator GetMutatingInputVariables( + const Operator& op) const { + // Most ops don't have variable tensors. This function can be overridden. + return std::vector(); + } + private: string name_; OperatorType type_; -- GitLab From e2213af0f25d17c5d91337aaf1ad5815ed5d2871 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 13 Jun 2018 14:56:58 -0700 Subject: [PATCH 417/816] [XLA] Update the error message for AllReduce. PiperOrigin-RevId: 200459250 --- tensorflow/compiler/xla/client/xla_client/xla_builder.cc | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/tensorflow/compiler/xla/client/xla_client/xla_builder.cc b/tensorflow/compiler/xla/client/xla_client/xla_builder.cc index ae8fbdb2dc..d7ebcf8beb 100644 --- a/tensorflow/compiler/xla/client/xla_client/xla_builder.cc +++ b/tensorflow/compiler/xla/client/xla_client/xla_builder.cc @@ -1632,8 +1632,7 @@ XlaOp XlaBuilder::CrossReplicaSum( const tensorflow::gtl::optional& channel_id) { return NoteErrorOrReturn([&]() -> StatusOr { if (channel_id.has_value()) { - return Unimplemented( - "replica_group_ids and channel_id and is not supported in AllReduce"); + return Unimplemented("channel_id is not supported in AllReduce"); } HloInstructionProto instr; -- GitLab From cb2da309d3ae973158d15c337c011131eab9eb4f Mon Sep 17 00:00:00 2001 From: Vincent Date: Thu, 14 Jun 2018 00:31:07 +0200 Subject: [PATCH 418/816] Space handling in equation parameter of tf.einsum (#19980) * Fixes #19858 Adds space handling in the equation parameter similar to the np.einsum function to tf.einsum * Add tests for space handling Adds tests for the space handling in the equation parameter of tf.einsum and adjusts the `run_test` method to ignore the spaces when assigning dimensionality to the random input tensors for the tests. --- tensorflow/python/ops/special_math_ops.py | 2 ++ tensorflow/python/ops/special_math_ops_test.py | 10 +++++++++- 2 files changed, 11 insertions(+), 1 deletion(-) diff --git a/tensorflow/python/ops/special_math_ops.py b/tensorflow/python/ops/special_math_ops.py index 6d3a85e3fd..1508873b75 100644 --- a/tensorflow/python/ops/special_math_ops.py +++ b/tensorflow/python/ops/special_math_ops.py @@ -201,6 +201,8 @@ def einsum(equation, *inputs, **kwargs): indices in its subscript, or - the input shapes are inconsistent along a particular axis. """ + equation = equation.replace(" ", "") + name = kwargs.pop('name', None) if kwargs: raise TypeError('invalid keyword arguments for this function: ' + ', '.join( diff --git a/tensorflow/python/ops/special_math_ops_test.py b/tensorflow/python/ops/special_math_ops_test.py index 19a566166a..b7e164f149 100644 --- a/tensorflow/python/ops/special_math_ops_test.py +++ b/tensorflow/python/ops/special_math_ops_test.py @@ -223,6 +223,12 @@ class EinsumTest(test.TestCase): 'iJ,Jk->ik', 'iJ,Ki->JK', 'iJk,Jklm->Jk' + 'ij, jk, kl -> il', + 'a, ab, abc -> abc', + 'ab, ab, cd, cd, ef, ef -> ', + 'abc, bac', + 'iJ, Ki -> JK', + 'iJk, Jklm -> Jk' ] long_cases = [ @@ -231,6 +237,8 @@ class EinsumTest(test.TestCase): 'ea,fb,gc,hd,abcd->efgh', 'ea,fb,abcd,gc,hd->efgh', 'abhe,hidj,jgba,hiab,gab', + 'efc, dbc, acf, fd -> abe', + 'abhe, hidj, jgba, hiab, gab', ] invalid_cases = [ @@ -301,7 +309,7 @@ class EinsumTest(test.TestCase): input_axes, _, _ = axes.partition('->') for idx in input_axes.split(','): - shape = [all_axes[ax] for ax in idx] + shape = [all_axes[ax] for ax in idx if ax.isalpha()] input_vals.append(np.random.random(shape)) input_tensors = [constant_op.constant(val) for val in input_vals] -- GitLab From 88ad9949ef4ea6e07105a326a1d21c108cb2883a Mon Sep 17 00:00:00 2001 From: Yuefeng Zhou Date: Wed, 13 Jun 2018 15:48:32 -0700 Subject: [PATCH 419/816] Make ops.colocate_with work with tower-local variables as well. PiperOrigin-RevId: 200467472 --- .../contrib/distribute/python/values.py | 36 +++++++++++++------ .../contrib/distribute/python/values_test.py | 12 +++++++ .../python/keras/layers/normalization.py | 11 +++--- 3 files changed, 43 insertions(+), 16 deletions(-) diff --git a/tensorflow/contrib/distribute/python/values.py b/tensorflow/contrib/distribute/python/values.py index 9572ade8e4..aca544b7e7 100644 --- a/tensorflow/contrib/distribute/python/values.py +++ b/tensorflow/contrib/distribute/python/values.py @@ -238,17 +238,6 @@ class DistributedVariable(DistributedDelegate): pass -# Register a conversion function which reads the value of the variable, -# allowing instances of the class to be used as tensors. -def _tensor_conversion(var, dtype=None, name=None, as_ref=False): - # Try to avoid assignments to and other mutations of MirroredVariable - # state except through a DistributionStrategy.update() call. - assert not as_ref - return ops.internal_convert_to_tensor( - var.get(), dtype=dtype, name=name, as_ref=as_ref) - - -ops.register_tensor_conversion_function(DistributedVariable, _tensor_conversion) ops.register_dense_tensor_like_type(DistributedVariable) @@ -342,6 +331,20 @@ class MirroredVariable(DistributedVariable, Mirrored, return {checkpointable.VARIABLE_VALUE_KEY: _saveable_factory} +# Register a conversion function which reads the value of the variable, +# allowing instances of the class to be used as tensors. +def _tensor_conversion_mirrored(var, dtype=None, name=None, as_ref=False): + # Try to avoid assignments to and other mutations of MirroredVariable + # state except through a DistributionStrategy.update() call. + assert not as_ref + return ops.internal_convert_to_tensor( + var.get(), dtype=dtype, name=name, as_ref=as_ref) + + +ops.register_tensor_conversion_function(MirroredVariable, + _tensor_conversion_mirrored) + + class _TowerLocalSaveable(saver.BaseSaverBuilder.SaveableObject): """Class for defining how to restore a TowerLocalVariable.""" @@ -431,6 +434,17 @@ class TowerLocalVariable(DistributedVariable, PerDevice, return {checkpointable.VARIABLE_VALUE_KEY: _saveable_factory} +# Register a conversion function for TowerLocalVariable which allows as_ref to +# be true. +def _tensor_conversion_tower_local(var, dtype=None, name=None, as_ref=False): + return ops.internal_convert_to_tensor( + var.get(), dtype=dtype, name=name, as_ref=as_ref) + + +ops.register_tensor_conversion_function(TowerLocalVariable, + _tensor_conversion_tower_local) + + def _devices_match(d1, d2): return device_util.canonicalize(d1) == device_util.canonicalize(d2) diff --git a/tensorflow/contrib/distribute/python/values_test.py b/tensorflow/contrib/distribute/python/values_test.py index 1c95758d96..b0bd92c7b0 100644 --- a/tensorflow/contrib/distribute/python/values_test.py +++ b/tensorflow/contrib/distribute/python/values_test.py @@ -966,6 +966,18 @@ class TowerLocalVariableTest(test.TestCase): save_path = self._save_normal() self._restore_tower_local_sum(save_path) + def testTensorConversion(self): + with context.graph_mode(): + _, tower_local = _make_tower_local("sum") + converted = ops.internal_convert_to_tensor(tower_local, as_ref=False) + self.assertIsInstance(converted, ops.Tensor) + self.assertEqual(converted.dtype, tower_local.dtype) + + converted = ops.internal_convert_to_tensor(tower_local, as_ref=True) + # Resources variable are converted to tensors as well when as_ref is True. + self.assertIsInstance(converted, ops.Tensor) + self.assertEqual(converted.dtype, tower_local.dtype) + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/keras/layers/normalization.py b/tensorflow/python/keras/layers/normalization.py index ff51eadee9..28cedec338 100644 --- a/tensorflow/python/keras/layers/normalization.py +++ b/tensorflow/python/keras/layers/normalization.py @@ -364,11 +364,12 @@ class BatchNormalization(Layer): def _assign_moving_average(self, variable, value, momentum): with ops.name_scope(None, 'AssignMovingAvg', [variable, value, momentum]) as scope: - decay = ops.convert_to_tensor(1.0 - momentum, name='decay') - if decay.dtype != variable.dtype.base_dtype: - decay = math_ops.cast(decay, variable.dtype.base_dtype) - update_delta = (variable - value) * decay - return state_ops.assign_sub(variable, update_delta, name=scope) + with ops.colocate_with(variable): + decay = ops.convert_to_tensor(1.0 - momentum, name='decay') + if decay.dtype != variable.dtype.base_dtype: + decay = math_ops.cast(decay, variable.dtype.base_dtype) + update_delta = (variable - value) * decay + return state_ops.assign_sub(variable, update_delta, name=scope) def _fused_batch_norm(self, inputs, training): """Returns the output of fused batch norm.""" -- GitLab From 02c74ef9bf6108440c31332a9116eb6c0340e06e Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 13 Jun 2018 15:49:06 -0700 Subject: [PATCH 420/816] Add xla::ShapeUtil::TryGetSubshape that doesn't CHECK fail on invalid input. PiperOrigin-RevId: 200467533 --- tensorflow/compiler/xla/shape_util.cc | 15 +++++++++++++++ tensorflow/compiler/xla/shape_util.h | 5 ++++- 2 files changed, 19 insertions(+), 1 deletion(-) diff --git a/tensorflow/compiler/xla/shape_util.cc b/tensorflow/compiler/xla/shape_util.cc index 2c484661ee..fe844ea2b1 100644 --- a/tensorflow/compiler/xla/shape_util.cc +++ b/tensorflow/compiler/xla/shape_util.cc @@ -903,6 +903,21 @@ StatusOr ParseShapeStringInternal(tensorflow::StringPiece* s) { return *return_shape; } +/* static */ StatusOr ShapeUtil::TryGetSubshape( + const Shape& shape, ShapeIndexView index) { + const Shape* return_shape = &shape; + for (auto i : index) { + if (!IsTuple(*return_shape) || i < 0 || + i >= return_shape->tuple_shapes_size()) { + return InvalidArgument( + "Shape index %s not a valid subshape index for tuple with shape %s", + index.ToString().c_str(), shape.DebugString().c_str()); + } + return_shape = &return_shape->tuple_shapes(i); + } + return return_shape; +} + /* static */ Shape* ShapeUtil::GetMutableSubshape(Shape* shape, ShapeIndexView index) { Shape* return_shape = shape; diff --git a/tensorflow/compiler/xla/shape_util.h b/tensorflow/compiler/xla/shape_util.h index b6d29976d1..8ee3f490a0 100644 --- a/tensorflow/compiler/xla/shape_util.h +++ b/tensorflow/compiler/xla/shape_util.h @@ -476,8 +476,11 @@ class ShapeUtil { static bool IndexIsValid(const Shape& shape, ShapeIndexView index); // GetSubshape and GetMutableSubshape return a particular nested Shape within - // the given Shape argument. + // the given Shape argument. The non-Try variants check fail if index is + // invalid. static const Shape& GetSubshape(const Shape& shape, ShapeIndexView index); + static StatusOr TryGetSubshape(const Shape& shape, + ShapeIndexView index); static Shape* GetMutableSubshape(Shape* shape, ShapeIndexView index); // Returns whether the given index in the given shape is a leaf element of the -- GitLab From 31ea26d15004a3b5ac5b87e598cd6dfdc71f6012 Mon Sep 17 00:00:00 2001 From: Mark Daoust Date: Wed, 13 Jun 2018 15:49:08 -0700 Subject: [PATCH 421/816] Fix `Input` to allow scalar shape. The primary use-case is for models that include their pre-processing, and expect a batch of strings as input (like most of the tensorflow_hub text modules). In python the empty tuple (a scalar-shape) is Falsey. This change avoids the "ValueError please provide a `tensor` or `shape`" error when the user provides an empty shape. PiperOrigin-RevId: 200467536 --- tensorflow/python/keras/engine/input_layer.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/python/keras/engine/input_layer.py b/tensorflow/python/keras/engine/input_layer.py index 7996110829..8a4018a0df 100644 --- a/tensorflow/python/keras/engine/input_layer.py +++ b/tensorflow/python/keras/engine/input_layer.py @@ -215,7 +215,7 @@ def Input( # pylint: disable=invalid-name if dtype is None: dtype = K.floatx() - if not shape and tensor is None: + if shape is None and tensor is None: raise ValueError('Please provide to Input either a `shape`' ' or a `tensor` argument. Note that ' '`shape` does not include the batch ' -- GitLab From 4d48d1dc5a1a6010132988e4afe1e70e1f01be03 Mon Sep 17 00:00:00 2001 From: Alexandre Passos Date: Wed, 13 Jun 2018 15:49:22 -0700 Subject: [PATCH 422/816] Uses a resource variable by default for the global step. PiperOrigin-RevId: 200467580 --- tensorflow/contrib/data/python/ops/iterator_ops_test.py | 2 +- .../contrib/estimator/python/estimator/hooks_test.py | 4 ++-- tensorflow/contrib/kfac/examples/tests/BUILD | 1 + .../learn/python/learn/estimators/composable_model_test.py | 2 +- .../python/learn/estimators/dnn_linear_combined_test.py | 2 +- tensorflow/contrib/learn/python/learn/monitors_test.py | 6 ------ tensorflow/contrib/linear_optimizer/python/ops/sdca_ops.py | 2 +- .../opt/python/training/drop_stale_gradient_optimizer.py | 7 ++++--- tensorflow/contrib/slim/python/slim/learning_test.py | 4 +--- tensorflow/python/estimator/model_fn.py | 3 ++- tensorflow/python/saved_model/builder_impl.py | 7 ++++--- tensorflow/python/training/training_util.py | 7 +++++-- 12 files changed, 23 insertions(+), 24 deletions(-) diff --git a/tensorflow/contrib/data/python/ops/iterator_ops_test.py b/tensorflow/contrib/data/python/ops/iterator_ops_test.py index 30a993b1f7..628d983137 100644 --- a/tensorflow/contrib/data/python/ops/iterator_ops_test.py +++ b/tensorflow/contrib/data/python/ops/iterator_ops_test.py @@ -44,7 +44,7 @@ class CheckpointInputPipelineHookTest(test.TestCase): latest_feature = variables.Variable( 0, name='latest_feature', dtype=dtypes.int64) store_latest_feature_op = latest_feature.assign(features) - ops.add_to_collection('my_vars', global_step) + ops.add_to_collection('my_vars', global_step.read_value()) ops.add_to_collection('my_vars', latest_feature) return model_fn.EstimatorSpec( mode='train', diff --git a/tensorflow/contrib/estimator/python/estimator/hooks_test.py b/tensorflow/contrib/estimator/python/estimator/hooks_test.py index 95ae971852..685ca473bd 100644 --- a/tensorflow/contrib/estimator/python/estimator/hooks_test.py +++ b/tensorflow/contrib/estimator/python/estimator/hooks_test.py @@ -156,8 +156,8 @@ class InMemoryEvaluatorHookTest(test.TestCase): estimator.eval_dir()) # w = 0 if step==0 else step+2 self.assertEqual(0, step_keyword_to_value[0]['mean_of_const']) - self.assertEqual(6, step_keyword_to_value[4]['mean_of_const']) - self.assertEqual(12, step_keyword_to_value[10]['mean_of_const']) + self.assertEqual(5, step_keyword_to_value[4]['mean_of_const']) + self.assertEqual(11, step_keyword_to_value[10]['mean_of_const']) def test_dnn_classifier(self): embedding = feature_column_lib.embedding_column( diff --git a/tensorflow/contrib/kfac/examples/tests/BUILD b/tensorflow/contrib/kfac/examples/tests/BUILD index ede7f183fe..72e623185b 100644 --- a/tensorflow/contrib/kfac/examples/tests/BUILD +++ b/tensorflow/contrib/kfac/examples/tests/BUILD @@ -28,6 +28,7 @@ py_test( srcs = ["convnet_test.py"], srcs_version = "PY2AND3", tags = [ + "no_oss", "no_pip", "notsan", ], diff --git a/tensorflow/contrib/learn/python/learn/estimators/composable_model_test.py b/tensorflow/contrib/learn/python/learn/estimators/composable_model_test.py index ef5e620e8f..d84f9ad2be 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/composable_model_test.py +++ b/tensorflow/contrib/learn/python/learn/estimators/composable_model_test.py @@ -56,7 +56,7 @@ def _base_model_fn(features, labels, mode, params): def _train_op_fn(loss): global_step = training_util.get_global_step() - assert global_step + assert global_step is not None train_step = model.get_train_step(loss) with ops.control_dependencies(train_step): diff --git a/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined_test.py b/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined_test.py index 4e65c180d8..a3d6f1efb0 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined_test.py +++ b/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined_test.py @@ -1811,7 +1811,7 @@ class FeatureEngineeringFunctionTest(test.TestCase): prediction_without_fe_fn = next( estimator_without_fe_fn.predict_scores( input_fn=input_fn, as_iterable=True)) - self.assertAlmostEqual(100., prediction_without_fe_fn, delta=1.0) + self.assertAlmostEqual(100., prediction_without_fe_fn, delta=3.0) if __name__ == '__main__': diff --git a/tensorflow/contrib/learn/python/learn/monitors_test.py b/tensorflow/contrib/learn/python/learn/monitors_test.py index 5c34d0ddb0..8750f62299 100644 --- a/tensorflow/contrib/learn/python/learn/monitors_test.py +++ b/tensorflow/contrib/learn/python/learn/monitors_test.py @@ -802,9 +802,6 @@ class RunHookAdapterForMonitorsTest(test.TestCase): mon_sess.run(inc_5) for mon in [mock_mon, mock_mon2]: self.assertEqual(mon.output, {}) - self.assertEqual(mon.last_begin_step, 11) - self.assertEqual(mon.last_end_step, 11) - self.assertEqual(mon.last_post_step, 11) self.assertEqual(mon.call_counter['step_end'], 1) self.assertEqual(mon.call_counter['step_begin'], 1) self.assertEqual(mon.call_counter['post_step'], 1) @@ -812,9 +809,6 @@ class RunHookAdapterForMonitorsTest(test.TestCase): mon_sess.run(inc_5) for mon in [mock_mon, mock_mon2]: self.assertEqual(mon.output, {}) - self.assertEqual(mon.last_begin_step, 16) - self.assertEqual(mon.last_end_step, 16) - self.assertEqual(mon.last_post_step, 16) self.assertEqual(mon.call_counter['step_end'], 2) self.assertEqual(mon.call_counter['step_begin'], 2) self.assertEqual(mon.call_counter['post_step'], 2) diff --git a/tensorflow/contrib/linear_optimizer/python/ops/sdca_ops.py b/tensorflow/contrib/linear_optimizer/python/ops/sdca_ops.py index 0047d5753a..2b5058e47d 100644 --- a/tensorflow/contrib/linear_optimizer/python/ops/sdca_ops.py +++ b/tensorflow/contrib/linear_optimizer/python/ops/sdca_ops.py @@ -518,7 +518,7 @@ class SdcaModel(object): update_ops.append(state_ops.assign_add(v, split_update)) else: update_ops.append(state_ops.assign_add(w, u)) - if not global_step: + if global_step is None: return control_flow_ops.group(*update_ops) with ops.control_dependencies(update_ops): return state_ops.assign_add(global_step, 1, name=name).op diff --git a/tensorflow/contrib/opt/python/training/drop_stale_gradient_optimizer.py b/tensorflow/contrib/opt/python/training/drop_stale_gradient_optimizer.py index 4a905b1b2a..918165bc6a 100644 --- a/tensorflow/contrib/opt/python/training/drop_stale_gradient_optimizer.py +++ b/tensorflow/contrib/opt/python/training/drop_stale_gradient_optimizer.py @@ -63,7 +63,7 @@ class DropStaleGradientOptimizer(optimizer.Optimizer): def compute_gradients(self, loss, *args, **kwargs): # Record current global step for worker. with ops.colocate_with(loss): - self._local_step = training_util.get_global_step() + 0 + self._local_step = training_util.get_global_step().read_value() + 0 with ops.control_dependencies([self._local_step]): loss = gen_array_ops.identity(loss) @@ -102,7 +102,7 @@ class DropStaleGradientOptimizer(optimizer.Optimizer): with ops.control_dependencies(gradients), ops.colocate_with(global_step): staleness = gen_array_ops.reshape( - global_step - self._local_step, shape=()) + global_step.read_value() - self._local_step, shape=()) conditional_update = stale_counter.assign_add(control_flow_ops.cond( gen_math_ops.less_equal(staleness, self._staleness), @@ -110,5 +110,6 @@ class DropStaleGradientOptimizer(optimizer.Optimizer): summary.scalar( "Gradient staleness percentage", - stale_counter / (math_ops.cast(global_step + 1, dtypes.float32))) + stale_counter / (math_ops.cast(global_step.read_value() + 1, + dtypes.float32))) return conditional_update diff --git a/tensorflow/contrib/slim/python/slim/learning_test.py b/tensorflow/contrib/slim/python/slim/learning_test.py index 831c6e427a..6bd55e7a24 100644 --- a/tensorflow/contrib/slim/python/slim/learning_test.py +++ b/tensorflow/contrib/slim/python/slim/learning_test.py @@ -520,8 +520,6 @@ class TrainTest(test.TestCase): run_root = glob.glob(os.path.join(dump_root, 'run_*'))[-1] dump = debug_data.DebugDumpDir(run_root) - self.assertAllEqual(0, - dump.get_tensors('global_step', 0, 'DebugIdentity')[0]) def testTrainWithTrace(self): logdir = os.path.join( @@ -547,7 +545,7 @@ class TrainTest(test.TestCase): log_every_n_steps=10, trace_every_n_steps=100) self.assertIsNotNone(loss) - for trace_step in [1, 101, 201]: + for trace_step in [0, 100, 200]: trace_filename = 'tf_trace-%d.json' % trace_step self.assertTrue(os.path.isfile(os.path.join(logdir, trace_filename))) diff --git a/tensorflow/python/estimator/model_fn.py b/tensorflow/python/estimator/model_fn.py index c60c7f63ba..d8bdd35bdc 100644 --- a/tensorflow/python/estimator/model_fn.py +++ b/tensorflow/python/estimator/model_fn.py @@ -26,6 +26,7 @@ import six from tensorflow.python.estimator.export.export_output import ExportOutput from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape +from tensorflow.python.framework import tensor_util from tensorflow.python.ops import array_ops from tensorflow.python.saved_model import signature_constants from tensorflow.python.saved_model import tag_constants @@ -386,7 +387,7 @@ class _TPUEstimatorSpec(collections.namedtuple('TPUEstimatorSpec', [ def _check_is_tensor_or_operation(x, name): - if not (isinstance(x, ops.Operation) or isinstance(x, ops.Tensor)): + if not (isinstance(x, ops.Operation) or tensor_util.is_tensor(x)): raise TypeError('{} must be Operation or Tensor, given: {}'.format(name, x)) diff --git a/tensorflow/python/saved_model/builder_impl.py b/tensorflow/python/saved_model/builder_impl.py index e58be804c2..531da052ac 100644 --- a/tensorflow/python/saved_model/builder_impl.py +++ b/tensorflow/python/saved_model/builder_impl.py @@ -28,6 +28,7 @@ from tensorflow.core.protobuf import saved_model_pb2 from tensorflow.core.protobuf import saver_pb2 from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops +from tensorflow.python.framework import tensor_util from tensorflow.python.lib.io import file_io from tensorflow.python.ops import variables from tensorflow.python.platform import tf_logging @@ -178,11 +179,11 @@ class SavedModelBuilder(object): stored as a collection with key TRAIN_OP_KEY, but not executed. Raises: - TypeError if Train op is not of type `Operation`. + TypeError if Train op is not of type `Operation` or a Tensor. """ if train_op is not None: - if (not isinstance(train_op, ops.Tensor) and - not isinstance(train_op, ops.Operation)): + if not (tensor_util.is_tensor(train_op) or + isinstance(train_op, ops.Operation)): raise TypeError("train_op needs to be a Tensor or Op: %r" % train_op) ops.add_to_collection(constants.TRAIN_OP_KEY, train_op) diff --git a/tensorflow/python/training/training_util.py b/tensorflow/python/training/training_util.py index 0877b2a8a2..59ba7d3c23 100644 --- a/tensorflow/python/training/training_util.py +++ b/tensorflow/python/training/training_util.py @@ -128,7 +128,8 @@ def create_global_step(graph=None): initializer=init_ops.zeros_initializer(), trainable=False, collections=[ops.GraphKeys.GLOBAL_VARIABLES, - ops.GraphKeys.GLOBAL_STEP]) + ops.GraphKeys.GLOBAL_STEP], + use_resource=True) # Create in proper graph and base name_scope. with graph.as_default() as g, g.name_scope(None): return variable_scope.get_variable( @@ -138,7 +139,9 @@ def create_global_step(graph=None): initializer=init_ops.zeros_initializer(), trainable=False, collections=[ops.GraphKeys.GLOBAL_VARIABLES, - ops.GraphKeys.GLOBAL_STEP]) + ops.GraphKeys.GLOBAL_STEP], + caching_device='cpu:0', + use_resource=True) @tf_export('train.get_or_create_global_step') -- GitLab From ec927becf175474a3892e5e07557fffa1e5bc198 Mon Sep 17 00:00:00 2001 From: Youlong Cheng Date: Wed, 13 Jun 2018 16:02:21 -0700 Subject: [PATCH 423/816] Subgroup CrossReplicaSum and change in TpuOptimizer. PiperOrigin-RevId: 200469639 --- .../contrib/tpu/ops/cross_replica_ops.cc | 12 ++++- tensorflow/contrib/tpu/python/ops/tpu_ops.py | 3 +- .../contrib/tpu/python/tpu/tpu_optimizer.py | 52 +++++++++++++++++-- 3 files changed, 59 insertions(+), 8 deletions(-) diff --git a/tensorflow/contrib/tpu/ops/cross_replica_ops.cc b/tensorflow/contrib/tpu/ops/cross_replica_ops.cc index d389050e67..06553929dc 100644 --- a/tensorflow/contrib/tpu/ops/cross_replica_ops.cc +++ b/tensorflow/contrib/tpu/ops/cross_replica_ops.cc @@ -23,15 +23,23 @@ REGISTER_OP("CrossReplicaSum") .Input("input: T") .Output("output: T") .Attr("T: {bfloat16, float}") + .Attr("group_assignment: list(int) = []") .SetShapeFn(shape_inference::UnchangedShape) .Doc(R"doc( An Op to sum inputs across replicated TPU instances. Each -instance supplies its own input, and the output of each is the sum of -all the inputs. +instance supplies its own input. If group_assignment is empty, the output of +each is the sum of all the inputs, otherwise the output of each is the sum of +the inputs belonging to the same group. + +For example, suppose there are 4 TPU instances: `[A, B, C, D]`. Passing +group_assignment=`[0,1,0,1]` sets `A, C` as group 0, and `B, D` as group 1. +Thus we get the outputs: `[A+C, B+D, A+C, B+D]`. input: The local input to the sum. output: The sum of all the distributed inputs. T: The type of elements to be summed. +group_assignment: The list of group ids. `group_assignment[i]` represents the + group id of replica i. )doc"); } // namespace tensorflow diff --git a/tensorflow/contrib/tpu/python/ops/tpu_ops.py b/tensorflow/contrib/tpu/python/ops/tpu_ops.py index 14c63a7976..bf442d9116 100644 --- a/tensorflow/contrib/tpu/python/ops/tpu_ops.py +++ b/tensorflow/contrib/tpu/python/ops/tpu_ops.py @@ -38,9 +38,8 @@ if platform.system() != "Windows": @ops.RegisterGradient("CrossReplicaSum") def _cross_replica_sum_grad(op, grad): - del op # Unused # The gradient of a cross replica sum is also a cross-replica sum. - return gen_tpu_ops.cross_replica_sum(grad) + return gen_tpu_ops.cross_replica_sum(grad, op.get_attr("group_assignment")) # This extra type checking exists to give a more helpful error message in # the common case that uint8 and int64 values are infed. Remove when both diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_optimizer.py b/tensorflow/contrib/tpu/python/tpu/tpu_optimizer.py index e76cf83e4d..15f99d7eeb 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_optimizer.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_optimizer.py @@ -19,6 +19,8 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import collections + from tensorflow.contrib.tpu.python.ops import tpu_ops from tensorflow.contrib.tpu.python.tpu import tpu_function from tensorflow.python.ops.losses import losses @@ -32,7 +34,8 @@ class CrossShardOptimizer(optimizer.Optimizer): def __init__(self, opt, reduction=losses.Reduction.MEAN, - name="CrossShardOptimizer"): + name="CrossShardOptimizer", + group_assignment=None): """Construct a new cross-shard optimizer. Args: @@ -40,6 +43,8 @@ class CrossShardOptimizer(optimizer.Optimizer): reduction: The reduction to apply to the shard losses. name: Optional name prefix for the operations created when applying gradients. Defaults to "CrossShardOptimizer". + group_assignment: Optional list of group ids for applying the optimizer + to subgroups. Raises: ValueError: If reduction is not a valid cross-shard reduction. @@ -50,6 +55,35 @@ class CrossShardOptimizer(optimizer.Optimizer): super(CrossShardOptimizer, self).__init__(False, name) self._opt = opt self._reduction = reduction + self._group_assignment = group_assignment + + def _verify_and_get_subgroup_size(self, group_assignment, num_shards): + """Verify group_assignment and get the subgroup size". + + Args: + group_assignment: list of group ids for applying the optimizer + to subgroups. + num_shards: The number of TPU shards. + + Returns: + The size of one subgroup in group_assignment. + + Raises: + ValueError: If group_assignment is invalid. + """ + if not group_assignment: + return None + if len(group_assignment) != num_shards: + raise ValueError("The size of group_assignment does not equal to " + "num_shard({0}). Got group_assignment={1}".format( + num_shards, self._group_assignment)) + subgroup_size_list = dict(collections.Counter(group_assignment)).values() + if all(subgroup_size_list[0] == size for size in subgroup_size_list): + return subgroup_size_list[0] + else: + raise ValueError("The size of each subgroup in group_assignment must " + "be equal. Got group_assignment={}".format( + self._group_assignment)) def compute_gradients(self, loss, var_list=None, **kwargs): """Compute gradients of "loss" for the variables in "var_list". @@ -71,7 +105,8 @@ class CrossShardOptimizer(optimizer.Optimizer): A list of (gradient, variable) pairs. Raises: - ValueError: If not within a tpu_shard_context. + ValueError: If not within a tpu_shard_context or group_assignment is + invalid. """ num_shards = tpu_function.get_tpu_context().number_of_shards if num_shards is None: @@ -79,9 +114,17 @@ class CrossShardOptimizer(optimizer.Optimizer): "CrossShardOptimizer should be used within a tpu_shard_context, but " "got unset number_of_shards. Assuming 1.") num_shards = 1 + + subgroup_size = self._verify_and_get_subgroup_size(self._group_assignment, + num_shards) + if num_shards > 1 and self._reduction == losses.Reduction.MEAN: - scale = 1.0 / num_shards + if self._group_assignment: + scale = 1.0 / subgroup_size + else: + scale = 1.0 / num_shards loss *= scale + return self._opt.compute_gradients(loss, var_list=var_list, **kwargs) def apply_gradients(self, grads_and_vars, global_step=None, name=None): @@ -110,7 +153,8 @@ class CrossShardOptimizer(optimizer.Optimizer): if grad is None: summed_grads_and_vars.append((grad, var)) else: - summed_grads_and_vars.append((tpu_ops.cross_replica_sum(grad), var)) + summed_grads_and_vars.append((tpu_ops.cross_replica_sum( + grad, self._group_assignment), var)) return self._opt.apply_gradients(summed_grads_and_vars, global_step, name) def get_slot(self, *args, **kwargs): -- GitLab From b74197c6cba3e11deaff553c280933afa3e5a075 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 13 Jun 2018 16:05:30 -0700 Subject: [PATCH 424/816] Upgrade the tpu profiler version to 1.7.0. Change to use --tpu to specify where to launch the TPU profile service. PiperOrigin-RevId: 200470382 --- .../pip_package/cloud_tpu_profiler/main.py | 20 +++++++++---------- .../contrib/tpu/profiler/pip_package/setup.py | 4 ++-- tensorflow/contrib/tpu/profiler/version.h | 2 +- 3 files changed, 13 insertions(+), 13 deletions(-) diff --git a/tensorflow/contrib/tpu/profiler/pip_package/cloud_tpu_profiler/main.py b/tensorflow/contrib/tpu/profiler/pip_package/cloud_tpu_profiler/main.py index 508c7a842f..7f1d25732e 100644 --- a/tensorflow/contrib/tpu/profiler/pip_package/cloud_tpu_profiler/main.py +++ b/tensorflow/contrib/tpu/profiler/pip_package/cloud_tpu_profiler/main.py @@ -35,19 +35,19 @@ flags.DEFINE_string( None, help='GCE zone where the Cloud TPU is located in. If not specified, we ' 'will attempt to automatically detect the GCE project from metadata.') -flags.DEFINE_string('tpu_name', None, +flags.DEFINE_string('tpu', None, 'Name of the Cloud TPU for Cluster Resolvers. You must ' 'specify either this flag or --service_addr.') # Tool specific parameters flags.DEFINE_string( 'service_addr', None, 'Address of TPU profiler service e.g. ' - 'localhost:8466, you must specify either this flag or --tpu_name.') + 'localhost:8466, you must specify either this flag or --tpu.') flags.DEFINE_string( 'workers_list', None, 'The list of worker TPUs that we are about to profile' - ' e.g. 10.0.1.2, 10.0.1.3. You can specify this flag with --tpu_name or ' + ' e.g. 10.0.1.2, 10.0.1.3. You can specify this flag with --tpu or ' '--service_addr to profile a subset of tpu nodes. You can also use only' - '--tpu_name and leave this flag unspecified to profile all the tpus.') + '--tpu and leave this flag unspecified to profile all the tpus.') flags.DEFINE_string('logdir', None, 'Path of TensorBoard log directory e.g. /tmp/tb_log, ' 'gs://tb_bucket') @@ -76,19 +76,19 @@ def run_main(): def main(unused_argv=None): tf.logging.set_verbosity(tf.logging.INFO) - if FLAGS.service_addr is None and FLAGS.tpu_name is None: - sys.exit('You must specify either --service_addr or --tpu_name.') + if FLAGS.service_addr is None and FLAGS.tpu is None: + sys.exit('You must specify either --service_addr or --tpu.') tpu_cluster_resolver = None if FLAGS.service_addr is not None: - if FLAGS.tpu_name is not None: - tf.logging.warn('Both --service_addr and --tpu_name are set. Ignoring ' - '--tpu_name and using --service_addr.') + if FLAGS.tpu is not None: + tf.logging.warn('Both --service_addr and --tpu are set. Ignoring ' + '--tpu and using --service_addr.') service_addr = FLAGS.service_addr else: tpu_cluster_resolver = ( tf.contrib.cluster_resolver.TPUClusterResolver( - [FLAGS.tpu_name], + [FLAGS.tpu], zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)) service_addr = tpu_cluster_resolver.get_master() diff --git a/tensorflow/contrib/tpu/profiler/pip_package/setup.py b/tensorflow/contrib/tpu/profiler/pip_package/setup.py index ebd478fd02..f97a972f01 100644 --- a/tensorflow/contrib/tpu/profiler/pip_package/setup.py +++ b/tensorflow/contrib/tpu/profiler/pip_package/setup.py @@ -20,7 +20,7 @@ from __future__ import print_function from setuptools import setup -_VERSION = '1.6.0' +_VERSION = '1.7.0' CONSOLE_SCRIPTS = [ 'capture_tpu_profile=cloud_tpu_profiler.main:run_main', @@ -46,7 +46,7 @@ setup( # 3 - Alpha # 4 - Beta # 5 - Production/Stable - 'Development Status :: 4 - Beta', + 'Development Status :: 5 - Production/Stable', 'Intended Audience :: Developers', 'Intended Audience :: Education', 'Intended Audience :: Science/Research', diff --git a/tensorflow/contrib/tpu/profiler/version.h b/tensorflow/contrib/tpu/profiler/version.h index 618479e1a6..bd9ba6697e 100644 --- a/tensorflow/contrib/tpu/profiler/version.h +++ b/tensorflow/contrib/tpu/profiler/version.h @@ -16,6 +16,6 @@ limitations under the License. #ifndef TENSORFLOW_CONTRIB_TPU_PROFILER_VERSION_H_ #define TENSORFLOW_CONTRIB_TPU_PROFILER_VERSION_H_ -#define TPU_PROFILER_VERSION "1.6.0" +#define TPU_PROFILER_VERSION "1.7.0" #endif // TENSORFLOW_CONTRIB_TPU_PROFILER_VERSION_H_ -- GitLab From 11e1a45229b9f758a143b5fcf121ba689eca74e2 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 13 Jun 2018 16:21:09 -0700 Subject: [PATCH 425/816] Automated g4 rollback of changelist 200309129 PiperOrigin-RevId: 200472722 --- tensorflow/compiler/xla/service/BUILD | 3 + .../compiler/xla/service/copy_insertion.cc | 68 +++++++++++-------- .../compiler/xla/service/copy_insertion.h | 7 ++ .../compiler/xla/service/hlo_instruction.h | 16 +++++ .../compiler/xla/service/hlo_ordering.cc | 5 ++ .../xla/service/hlo_rematerialization.cc | 18 ++++- .../xla/service/hlo_rematerialization.h | 11 ++- .../xla/service/hlo_rematerialization_test.cc | 2 +- 8 files changed, 96 insertions(+), 34 deletions(-) diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD index 1154eef80e..cb2e159a38 100644 --- a/tensorflow/compiler/xla/service/BUILD +++ b/tensorflow/compiler/xla/service/BUILD @@ -2123,6 +2123,7 @@ cc_library( ":buffer_liveness", ":buffer_value", ":call_graph", + ":copy_insertion", ":flatten_call_graph", ":hlo", ":hlo_dce", @@ -2130,6 +2131,7 @@ cc_library( ":hlo_scheduling", ":logical_buffer", ":tuple_points_to_analysis", + ":tuple_simplifier", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", @@ -2143,6 +2145,7 @@ tf_cc_test( name = "hlo_rematerialization_test", srcs = ["hlo_rematerialization_test.cc"], deps = [ + ":flatten_call_graph", ":hlo", ":hlo_matchers", ":hlo_ordering", diff --git a/tensorflow/compiler/xla/service/copy_insertion.cc b/tensorflow/compiler/xla/service/copy_insertion.cc index 33d8338809..3625891b4f 100644 --- a/tensorflow/compiler/xla/service/copy_insertion.cc +++ b/tensorflow/compiler/xla/service/copy_insertion.cc @@ -613,7 +613,10 @@ class CopyRemover { VLOG(2) << copy->name() << " is not removable"; return false; } - + if (!ShapeUtil::Equal(copy->shape(), copy->operand(0)->shape())) { + VLOG(2) << copy->name() << " is not removable (shape mismatch)"; + return false; + } const CopyNodes& copy_node = copy_map_.at(copy); ValueNode* src = copy_node.src; ValueNode* dest = copy_node.dest; @@ -947,28 +950,6 @@ class CopyRemover { BufferValueTracker buffer_value_tracker_; }; -// Try to remove as many copies from the module as possible without introducing -// live range interference. Copy instructions (identified by their unique id) in -// the set copies_to_exclude are not considered for removal. -Status RemoveUnnecessaryCopies( - const HloOrdering& ordering, - const tensorflow::gtl::FlatSet& copies_to_exclude, HloModule* module) { - TF_ASSIGN_OR_RETURN(std::unique_ptr alias_analysis, - HloAliasAnalysis::Run(module)); - CopyRemover copy_remover(*alias_analysis, ordering, module); - XLA_VLOG_LINES(3, copy_remover.ToString()); - - for (HloComputation* computation : module->computations()) { - for (HloInstruction* instruction : computation->instructions()) { - if (instruction->opcode() == HloOpcode::kCopy && - !ContainsKey(copies_to_exclude, instruction->unique_id())) { - TF_RETURN_IF_ERROR(copy_remover.TryElideCopy(instruction).status()); - } - } - } - return Status::OK(); -} - // Add copies to address special constraints on the roots of computations not // related to live range interference: // @@ -1065,13 +1046,23 @@ Status AddSpecialCaseCopies(const CallGraph& call_graph, HloModule* module) { HloInstruction* instruction = pair.first; const ShapeTree& indices_to_copy = pair.second; + ShapeTree copies_added(indices_to_copy.shape()); std::vector users = instruction->users(); TF_ASSIGN_OR_RETURN(HloInstruction * deep_copy, instruction->parent()->DeepCopyInstruction( - instruction, &indices_to_copy)); + instruction, &indices_to_copy, &copies_added)); for (HloInstruction* user : users) { TF_RETURN_IF_ERROR(instruction->ReplaceUseWith(user, deep_copy)); } + // Special case copies are not eligible for later copy elision passes. + indices_to_copy.ForEachElement([&](const ShapeIndex& index, bool has_copy) { + if (has_copy) { + HloInstruction* copy = *copies_added.mutable_element(index); + if (copy != nullptr) { + copy->SetCopyElisionAllowed(false); + } + } + }); if (instruction == instruction->parent()->root_instruction()) { instruction->parent()->set_root_instruction(deep_copy); } @@ -1097,6 +1088,31 @@ void MaybeDumpModule(const string& message, const HloModule& module) { } // namespace +Status RemoveUnnecessaryCopies( + const HloOrdering& ordering, + const tensorflow::gtl::FlatSet& copies_to_exclude, HloModule* module) { + MaybeDumpModule("after adding copies to resolve interference", *module); + + TF_ASSIGN_OR_RETURN(std::unique_ptr alias_analysis, + HloAliasAnalysis::Run(module)); + CopyRemover copy_remover(*alias_analysis, ordering, module); + XLA_VLOG_LINES(3, copy_remover.ToString()); + + std::unique_ptr call_graph = CallGraph::Build(module); + for (HloComputation* computation : module->computations()) { + for (HloInstruction* instruction : computation->instructions()) { + if (instruction->opcode() == HloOpcode::kCopy && + !ContainsKey(copies_to_exclude, instruction->unique_id()) && + instruction->CopyElisionAllowed()) { + TF_RETURN_IF_ERROR(copy_remover.TryElideCopy(instruction).status()); + } + } + } + MaybeDumpModule("after removing unnecessary copies", *module); + + return Status::OK(); +} + StatusOr CopyInsertion::Run(HloModule* module) { // Copy insertion is performed in three steps: // @@ -1158,14 +1174,10 @@ StatusOr CopyInsertion::Run(HloModule* module) { TF_DCHECK_OK(VerifyNoLiveRangeInterference(module)); - MaybeDumpModule("after adding copies to resolve interference", *module); - DependencyHloOrdering ordering(module); TF_RETURN_IF_ERROR( RemoveUnnecessaryCopies(ordering, existing_copies, module)); - MaybeDumpModule("after removing unnecessary copies", *module); - TF_RETURN_IF_ERROR(AddSpecialCaseCopies(*call_graph, module)); MaybeDumpModule("after adding special-case copies", *module); diff --git a/tensorflow/compiler/xla/service/copy_insertion.h b/tensorflow/compiler/xla/service/copy_insertion.h index 65e3d31e34..0d7b3c20f9 100644 --- a/tensorflow/compiler/xla/service/copy_insertion.h +++ b/tensorflow/compiler/xla/service/copy_insertion.h @@ -64,6 +64,13 @@ class CopyInsertion : public HloPassInterface { static StatusOr AddCopiesForBufferAssignment(HloModule* module); }; +// Try to remove as many copies from the module as possible without introducing +// live range interference. Copy instructions (identified by their unique id) in +// the set copies_to_exclude are not considered for removal. +Status RemoveUnnecessaryCopies( + const HloOrdering& ordering, + const tensorflow::gtl::FlatSet& copies_to_exclude, HloModule* module); + } // namespace xla #endif // TENSORFLOW_COMPILER_XLA_SERVICE_COPY_INSERTION_H_ diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h index 2816a3b708..2a38e2b063 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction.h +++ b/tensorflow/compiler/xla/service/hlo_instruction.h @@ -1046,6 +1046,19 @@ class HloInstruction { // instruction. void SetupDerivedInstruction(HloInstruction* derived_instruction) const; + // TODO(b/80249101): Remove these methods once HLO scheduling and copy + // insertion are integrated, and we don't need to run a separate pass + // of copy elision anymore. + bool CopyElisionAllowed() const { + CHECK_EQ(HloOpcode::kCopy, opcode_); + return copy_elision_allowed_; + } + + void SetCopyElisionAllowed(bool value) { + CHECK_EQ(HloOpcode::kCopy, opcode_); + copy_elision_allowed_ = value; + } + // Returns the size of the slice in the given dimension for a dynamic // slice node. // @@ -1568,6 +1581,9 @@ class HloInstruction { std::unique_ptr gather_dimension_numbers_; std::vector gather_window_bounds_; + // Used to tag kCopy instructions that are eligible for copy elision. + bool copy_elision_allowed_ = true; + // Describes the [start, start + size) range size for a dynamic slice // ('start' is specified dynamically in the second operand of the operation). std::vector dynamic_slice_sizes_; diff --git a/tensorflow/compiler/xla/service/hlo_ordering.cc b/tensorflow/compiler/xla/service/hlo_ordering.cc index dcd4725fe7..6c1e015f77 100644 --- a/tensorflow/compiler/xla/service/hlo_ordering.cc +++ b/tensorflow/compiler/xla/service/hlo_ordering.cc @@ -232,6 +232,11 @@ bool HloOrdering::UseIsBeforeValueDefinition( << " and def is in FALSE computation"; return true; } + if (value.defining_instruction() == use.instruction) { + VLOG(4) << " use is conditional " << use << " and def is " + << value.ToShortString(); + return true; + } } VLOG(4) << " use is not before value"; diff --git a/tensorflow/compiler/xla/service/hlo_rematerialization.cc b/tensorflow/compiler/xla/service/hlo_rematerialization.cc index 9c7bc7a5ea..62c07d7fac 100644 --- a/tensorflow/compiler/xla/service/hlo_rematerialization.cc +++ b/tensorflow/compiler/xla/service/hlo_rematerialization.cc @@ -23,6 +23,7 @@ limitations under the License. #include "tensorflow/compiler/xla/map_util.h" #include "tensorflow/compiler/xla/primitive_util.h" #include "tensorflow/compiler/xla/service/buffer_value.h" +#include "tensorflow/compiler/xla/service/copy_insertion.h" #include "tensorflow/compiler/xla/service/flatten_call_graph.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_dce.h" @@ -1201,7 +1202,8 @@ StatusOr HloRematerialization::RematerializeComputation( StatusOr HloRematerialization::Run( HloModule* module, SequentialHloOrdering::HloModuleSequence* sequence, - int64 memory_limit_bytes, RematerializationSizes* sizes) { + int64 memory_limit_bytes, RematerializationSizes* sizes, + bool run_copy_elision) { // The sequence is constructed entirely by this method. TF_RET_CHECK(sequence->empty()); @@ -1236,6 +1238,15 @@ StatusOr HloRematerialization::Run( return size_function_(buffer.shape()); }, scheduler_algorithm_)); + if (run_copy_elision) { + // We run a separate pass of copy elision here because the sequential + // ordering from the HLO schedule allows for more copies to be eliminated. + // TODO(b/80249101): Instead of a separate copy elision pass, use the + // ordering from the HLO schedule directly for copy insertion. + SequentialHloOrdering ordering(module, *sequence); + TF_RETURN_IF_ERROR(RemoveUnnecessaryCopies(ordering, {}, module)); + } + // Compute peak memory usage of all computations in the module called in a // sequential context. call_graph_ = CallGraph::Build(module); @@ -1338,9 +1349,10 @@ StatusOr HloRematerialization::Run( int64 memory_limit_bytes, HloModule* hlo_module, MemorySchedulerAlgorithm scheduler_algorithm, SequentialHloOrdering::HloModuleSequence* sequence, - RematerializationSizes* sizes) { + RematerializationSizes* sizes, bool run_copy_elision) { HloRematerialization remat(scheduler_algorithm, size_function); - return remat.Run(hlo_module, sequence, memory_limit_bytes, sizes); + return remat.Run(hlo_module, sequence, memory_limit_bytes, sizes, + run_copy_elision); } } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_rematerialization.h b/tensorflow/compiler/xla/service/hlo_rematerialization.h index 2ee2dd0571..59b4cf5dcc 100644 --- a/tensorflow/compiler/xla/service/hlo_rematerialization.h +++ b/tensorflow/compiler/xla/service/hlo_rematerialization.h @@ -57,6 +57,12 @@ class HloRematerialization { // sizes: Optional outparam that indicates the peak memory usage of the HLO // module before/after rematerialization. // + // run_copy_elision: Enable copy elision. This pass is used to eliminate + // copies that were inserted before HLO scheduling. + // + // TODO(b/80249101): Remove the 'run_copy_elision' parameter when copy + // insertion is integrated with HLO scheduling. + // // Returns whether any instructions were rematerialized. If memory use is // already below the given limit then no instructions are rematerialized and // false is returned. @@ -68,7 +74,7 @@ class HloRematerialization { const ShapeSizeFunction& size_function, int64 memory_limit_bytes, HloModule* hlo_module, MemorySchedulerAlgorithm scheduler_algorithm, SequentialHloOrdering::HloModuleSequence* sequence, - RematerializationSizes* sizes = nullptr); + RematerializationSizes* sizes, bool run_copy_elision = true); protected: HloRematerialization(MemorySchedulerAlgorithm scheduler_algorithm, @@ -83,7 +89,8 @@ class HloRematerialization { // contains the memory-minimizing order in which to emit the HLO instructions. StatusOr Run(HloModule* module, SequentialHloOrdering::HloModuleSequence* sequence, - int64 memory_limit, RematerializationSizes* sizes); + int64 memory_limit, RematerializationSizes* sizes, + bool run_copy_elision); // Rematerializes instructions within the given computation. 'order' is the // order in which the computation's instructions will be emitted in the diff --git a/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc b/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc index e81334d5a8..7a46da6efe 100644 --- a/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc +++ b/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc @@ -147,7 +147,7 @@ class HloRematerializationTest : public HloTestBase { TF_EXPECT_OK(verifier().Run(module).status()); return HloRematerialization::RematerializeAndSchedule( ByteSizeOf, memory_limit_bytes, module, DefaultMemoryScheduler, - sequence); + sequence, /*sizes=*/nullptr, /*run_copy_elision=*/false); } // Various shapes used in the canned computations. -- GitLab From a6cccdcc5eb6e0a7915856467c97ac4acc8f624a Mon Sep 17 00:00:00 2001 From: Michael Kuperstein Date: Wed, 13 Jun 2018 16:33:48 -0700 Subject: [PATCH 426/816] [XLA] Add missing space in evaluator error message. PiperOrigin-RevId: 200474564 --- .../compiler/xla/service/hlo_evaluator_typed_visitor.h | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h index e01ce19d04..bc7340aa03 100644 --- a/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h +++ b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h @@ -1116,7 +1116,7 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { /*padding_config=*/pad->padding_config())); CHECK(ShapeUtil::Compatible(pad->shape(), inferred_return_shape)) << "return shape is set to: " << ShapeUtil::HumanString(pad->shape()) - << "but is inferred to be: " + << " but is inferred to be: " << ShapeUtil::HumanString(inferred_return_shape); // Create new HLO of padded shape with padding value. @@ -1182,7 +1182,7 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { dynamic_slice->dynamic_slice_sizes())); TF_RET_CHECK(ShapeUtil::Compatible(result_shape, inferred_return_shape)) << "return shape is set to: " << ShapeUtil::HumanString(result_shape) - << "but is inferred to be: " + << " but is inferred to be: " << ShapeUtil::HumanString(inferred_return_shape); TF_RET_CHECK( primitive_util::IsIntegralType(start_indices->shape().element_type())); @@ -1237,7 +1237,7 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { operand->shape(), update->shape(), start_indices->shape())); TF_RET_CHECK(ShapeUtil::Compatible(result_shape, inferred_return_shape)) << "return shape is set to: " << ShapeUtil::HumanString(result_shape) - << "but is inferred to be: " + << " but is inferred to be: " << ShapeUtil::HumanString(inferred_return_shape); TF_RET_CHECK( primitive_util::IsIntegralType(start_indices->shape().element_type())); @@ -1393,7 +1393,7 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { /*to_apply=*/function->ComputeProgramShape())); TF_RET_CHECK(ShapeUtil::Compatible(reduce->shape(), inferred_return_shape)) << "return shape is set to: " << ShapeUtil::HumanString(reduce->shape()) - << "but is inferred to be: " + << " but is inferred to be: " << ShapeUtil::HumanString(inferred_return_shape); const Literal& arg_literal = parent_->GetEvaluatedLiteralFor(arg); @@ -1613,7 +1613,7 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { ShapeUtil::Compatible(reduce_window->shape(), inferred_return_shape)) << "return shape is set to: " << ShapeUtil::HumanStringWithLayout(reduce_window->shape()) - << "but is inferred to be: " + << " but is inferred to be: " << ShapeUtil::HumanStringWithLayout(inferred_return_shape); const Literal& operand_literal = -- GitLab From d1ff8bc9b84b15c8e12c1cfab6585911fdac39db Mon Sep 17 00:00:00 2001 From: Stanley Bileschi Date: Wed, 13 Jun 2018 16:55:55 -0700 Subject: [PATCH 427/816] Documentation style fix. PiperOrigin-RevId: 200477609 --- .../layers/python/layers/feature_column_ops.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/tensorflow/contrib/layers/python/layers/feature_column_ops.py b/tensorflow/contrib/layers/python/layers/feature_column_ops.py index 06060b99e7..a85cff4f70 100644 --- a/tensorflow/contrib/layers/python/layers/feature_column_ops.py +++ b/tensorflow/contrib/layers/python/layers/feature_column_ops.py @@ -683,11 +683,12 @@ def parse_feature_columns_from_sequence_examples( the serialized proto. Returns: - A tuple consisting of: - context_features: a dict mapping `FeatureColumns` from - `context_feature_columns` to their parsed `Tensors`/`SparseTensor`s. - sequence_features: a dict mapping `FeatureColumns` from - `sequence_feature_columns` to their parsed `Tensors`/`SparseTensor`s. + A tuple consisting of (context_features, sequence_features) + + * context_features: a dict mapping `FeatureColumns` from + `context_feature_columns` to their parsed `Tensors`/`SparseTensor`s. + * sequence_features: a dict mapping `FeatureColumns` from + `sequence_feature_columns` to their parsed `Tensors`/`SparseTensor`s. """ # Sequence example parsing requires a single (scalar) example. try: -- GitLab From 49861688cb516ec0ad63a653a2cd8fbf37228009 Mon Sep 17 00:00:00 2001 From: Justin Lebar Date: Wed, 13 Jun 2018 16:57:55 -0700 Subject: [PATCH 428/816] [XLA] Fix indentation in comment in EmitRowReduction. PiperOrigin-RevId: 200477884 --- .../xla/service/gpu/ir_emitter_unnested.cc | 19 +++++++++---------- 1 file changed, 9 insertions(+), 10 deletions(-) diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc index 726434c3df..9c704e525e 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc @@ -1493,21 +1493,21 @@ Status IrEmitterUnnested::EmitRowReduction( // x + (x_tile_size - 1) * warpSize < width) { // // The entire x_tile is in bounds. // for (int element_id_in_z_tile = 0; element_id_in_z_tile < z_tile_size; - // ++element_id_in_z_tile) { + // ++element_id_in_z_tile) { // z = z_in_tiles * z_tile_size + element_id_in_z_tile; - // for (int element_id_in_x_tile = 0;element_id_in_x_tile < x_tile_size; - // ++element_id_in_x_tile, x += warpSize) { + // for (int element_id_in_x_tile = 0; + // element_id_in_x_tile < x_tile_size; + // ++element_id_in_x_tile, x += warpSize) { // partial_result = Reducer(partial_result, input[z][y][x]); // } // } // } else { // // The tile is partially in bounds. // for (int element_id_in_z_tile = 0; element_id_in_z_tile < z_tile_size; - // ++element_id_in_z_tile) { + // ++element_id_in_z_tile) { // z = z_in_tiles * z_tile_size + element_id_in_z_tile; // for (int element_id_in_x_tile = 0; element_id_in_x_tile < - // x_tile_size; - // ++element_id_in_tile, x += warpSize) { + // x_tile_size; ++element_id_in_tile, x += warpSize) { // if (x < width) // partial_result = Reducer(partial_result, input[z][y][x]); // } @@ -1558,8 +1558,7 @@ Status IrEmitterUnnested::EmitRowReduction( x_tile, ir_builder_.getInt64(kWarpSize), "lane_id"); // The x-location of the last element in this z-x-tile. - // last_x = lane_id + warpSize * (x_tile_size - 1 + warp_id * - // x_tile_size); + // last_x = lane_id + warpSize * (x_tile_size - 1 + warp_id * x_tile_size); llvm::Value* last_x = ir_builder_.CreateNSWAdd( lane_id, ir_builder_.CreateNSWMul( ir_builder_.getInt64(kWarpSize), @@ -1586,8 +1585,8 @@ Status IrEmitterUnnested::EmitRowReduction( "x_tile", /*start=*/0, /*end=*/x_tile_loop_bound, /*step=*/1, [&](llvm::Value* x_indvar) -> Status { - // x = lane_id + warpSize * (element_id_in_x_tile + warp_id * - // x_tile_size); + // x = lane_id + + // warpSize * (element_id_in_x_tile + warp_id * x_tile_size); llvm::Value* x = ir_builder_.CreateNSWAdd( lane_id, ir_builder_.CreateNSWMul( -- GitLab From 1babacb30c63e7a5231c3aaaac79bc56f68bf3ec Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 13 Jun 2018 17:40:06 -0700 Subject: [PATCH 429/816] Minor fix for lt.map_fn, handling a case where Tensor type inference can fail. PiperOrigin-RevId: 200483619 --- tensorflow/contrib/labeled_tensor/python/ops/ops.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/labeled_tensor/python/ops/ops.py b/tensorflow/contrib/labeled_tensor/python/ops/ops.py index 3ba1026383..2ede5daee7 100644 --- a/tensorflow/contrib/labeled_tensor/python/ops/ops.py +++ b/tensorflow/contrib/labeled_tensor/python/ops/ops.py @@ -652,7 +652,8 @@ def map_fn(fn, labeled_tensor, name=None): tensor_lt = core.LabeledTensor(tensor, original_axes) return fn(tensor_lt).tensor - map_op = functional_ops.map_fn(tf_fn, labeled_tensor.tensor) + map_op = functional_ops.map_fn( + tf_fn, labeled_tensor.tensor, dtype=first_map_lt.dtype) map_lt = core.LabeledTensor(map_op, final_axes) return core.identity(map_lt, name=scope) -- GitLab From 462a7e063169010899ce0fa9534f6d7c980f1116 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 13 Jun 2018 18:01:03 -0700 Subject: [PATCH 430/816] Add sequential functionality to _SharedEmbeddingColumn. PiperOrigin-RevId: 200485876 --- .../sequence_feature_column_test.py | 279 +++++++++++++++++- .../python/feature_column/feature_column.py | 46 ++- 2 files changed, 322 insertions(+), 3 deletions(-) diff --git a/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py index 88f5d53516..ee74cf56dc 100644 --- a/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py +++ b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py @@ -109,7 +109,7 @@ class SequenceInputLayerTest(test.TestCase): expected_sequence_length, sequence_length.eval(session=sess)) def test_embedding_column_with_non_sequence_categorical(self): - """Tests that error is raised for non-sequence categorical column.""" + """Tests that error is raised for non-sequence embedding column.""" vocabulary_size = 3 sparse_input = sparse_tensor.SparseTensorValue( # example 0, ids [2] @@ -131,6 +131,107 @@ class SequenceInputLayerTest(test.TestCase): features={'aaa': sparse_input}, feature_columns=[embedding_column_a]) + def test_shared_embedding_column(self): + vocabulary_size = 3 + sparse_input_a = sparse_tensor.SparseTensorValue( + # example 0, ids [2] + # example 1, ids [0, 1] + indices=((0, 0), (1, 0), (1, 1)), + values=(2, 0, 1), + dense_shape=(2, 2)) + sparse_input_b = sparse_tensor.SparseTensorValue( + # example 0, ids [1] + # example 1, ids [2, 0] + indices=((0, 0), (1, 0), (1, 1)), + values=(1, 2, 0), + dense_shape=(2, 2)) + + embedding_dimension = 2 + embedding_values = ( + (1., 2.), # id 0 + (3., 4.), # id 1 + (5., 6.) # id 2 + ) + + def _get_initializer(embedding_dimension, embedding_values): + + def _initializer(shape, dtype, partition_info): + self.assertAllEqual((vocabulary_size, embedding_dimension), shape) + self.assertEqual(dtypes.float32, dtype) + self.assertIsNone(partition_info) + return embedding_values + + return _initializer + + expected_input_layer = [ + # example 0, ids_a [2], ids_b [1] + [[5., 6., 3., 4.], [0., 0., 0., 0.]], + # example 1, ids_a [0, 1], ids_b [2, 0] + [[1., 2., 5., 6.], [3., 4., 1., 2.]], + ] + expected_sequence_length = [1, 2] + + categorical_column_a = sfc.sequence_categorical_column_with_identity( + key='aaa', num_buckets=vocabulary_size) + categorical_column_b = sfc.sequence_categorical_column_with_identity( + key='bbb', num_buckets=vocabulary_size) + # Test that columns are reordered alphabetically. + shared_embedding_columns = fc.shared_embedding_columns( + [categorical_column_b, categorical_column_a], + dimension=embedding_dimension, + initializer=_get_initializer(embedding_dimension, embedding_values)) + + input_layer, sequence_length = sfc.sequence_input_layer( + features={ + 'aaa': sparse_input_a, + 'bbb': sparse_input_b, + }, + feature_columns=shared_embedding_columns) + + global_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES) + self.assertItemsEqual( + ('sequence_input_layer/aaa_bbb_shared_embedding/embedding_weights:0',), + tuple([v.name for v in global_vars])) + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual(embedding_values, global_vars[0].eval(session=sess)) + self.assertAllEqual(expected_input_layer, input_layer.eval(session=sess)) + self.assertAllEqual( + expected_sequence_length, sequence_length.eval(session=sess)) + + def test_shared_embedding_column_with_non_sequence_categorical(self): + """Tests that error is raised for non-sequence shared embedding column.""" + vocabulary_size = 3 + sparse_input_a = sparse_tensor.SparseTensorValue( + # example 0, ids [2] + # example 1, ids [0, 1] + indices=((0, 0), (1, 0), (1, 1)), + values=(2, 0, 1), + dense_shape=(2, 2)) + sparse_input_b = sparse_tensor.SparseTensorValue( + # example 0, ids [2] + # example 1, ids [0, 1] + indices=((0, 0), (1, 0), (1, 1)), + values=(2, 0, 1), + dense_shape=(2, 2)) + + categorical_column_a = fc.categorical_column_with_identity( + key='aaa', num_buckets=vocabulary_size) + categorical_column_b = fc.categorical_column_with_identity( + key='bbb', num_buckets=vocabulary_size) + shared_embedding_columns = fc.shared_embedding_columns( + [categorical_column_a, categorical_column_b], dimension=2) + + with self.assertRaisesRegexp( + ValueError, + r'In embedding_column: aaa_shared_embedding\. categorical_column must ' + r'be of type _SequenceCategoricalColumn to use sequence_input_layer\.'): + _, _ = sfc.sequence_input_layer( + features={ + 'aaa': sparse_input_a, + 'bbb': sparse_input_b + }, + feature_columns=shared_embedding_columns) + def test_indicator_column(self): vocabulary_size_a = 3 sparse_input_a = sparse_tensor.SparseTensorValue( @@ -577,6 +678,182 @@ class SequenceEmbeddingColumnTest(test.TestCase): expected_sequence_length, sequence_length.eval(session=sess)) +class SequenceSharedEmbeddingColumnTest(test.TestCase): + + def test_get_sequence_dense_tensor(self): + vocabulary_size = 3 + embedding_dimension = 2 + embedding_values = ( + (1., 2.), # id 0 + (3., 5.), # id 1 + (7., 11.) # id 2 + ) + + def _initializer(shape, dtype, partition_info): + self.assertAllEqual((vocabulary_size, embedding_dimension), shape) + self.assertEqual(dtypes.float32, dtype) + self.assertIsNone(partition_info) + return embedding_values + + sparse_input_a = sparse_tensor.SparseTensorValue( + # example 0, ids [2] + # example 1, ids [0, 1] + # example 2, ids [] + # example 3, ids [1] + indices=((0, 0), (1, 0), (1, 1), (3, 0)), + values=(2, 0, 1, 1), + dense_shape=(4, 2)) + sparse_input_b = sparse_tensor.SparseTensorValue( + # example 0, ids [1] + # example 1, ids [0, 2] + # example 2, ids [0] + # example 3, ids [] + indices=((0, 0), (1, 0), (1, 1), (2, 0)), + values=(1, 0, 2, 0), + dense_shape=(4, 2)) + + expected_lookups_a = [ + # example 0, ids [2] + [[7., 11.], [0., 0.]], + # example 1, ids [0, 1] + [[1., 2.], [3., 5.]], + # example 2, ids [] + [[0., 0.], [0., 0.]], + # example 3, ids [1] + [[3., 5.], [0., 0.]], + ] + + expected_lookups_b = [ + # example 0, ids [1] + [[3., 5.], [0., 0.]], + # example 1, ids [0, 2] + [[1., 2.], [7., 11.]], + # example 2, ids [0] + [[1., 2.], [0., 0.]], + # example 3, ids [] + [[0., 0.], [0., 0.]], + ] + + categorical_column_a = sfc.sequence_categorical_column_with_identity( + key='aaa', num_buckets=vocabulary_size) + categorical_column_b = sfc.sequence_categorical_column_with_identity( + key='bbb', num_buckets=vocabulary_size) + shared_embedding_columns = fc.shared_embedding_columns( + [categorical_column_a, categorical_column_b], + dimension=embedding_dimension, + initializer=_initializer) + + embedding_lookup_a = shared_embedding_columns[0]._get_sequence_dense_tensor( + _LazyBuilder({ + 'aaa': sparse_input_a + }))[0] + embedding_lookup_b = shared_embedding_columns[1]._get_sequence_dense_tensor( + _LazyBuilder({ + 'bbb': sparse_input_b + }))[0] + + global_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES) + self.assertItemsEqual(('embedding_weights:0',), + tuple([v.name for v in global_vars])) + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual(embedding_values, global_vars[0].eval(session=sess)) + self.assertAllEqual( + expected_lookups_a, embedding_lookup_a.eval(session=sess)) + self.assertAllEqual( + expected_lookups_b, embedding_lookup_b.eval(session=sess)) + + def test_sequence_length(self): + vocabulary_size = 3 + + sparse_input_a = sparse_tensor.SparseTensorValue( + # example 0, ids [2] + # example 1, ids [0, 1] + indices=((0, 0), (1, 0), (1, 1)), + values=(2, 0, 1), + dense_shape=(2, 2)) + expected_sequence_length_a = [1, 2] + categorical_column_a = sfc.sequence_categorical_column_with_identity( + key='aaa', num_buckets=vocabulary_size) + + sparse_input_b = sparse_tensor.SparseTensorValue( + # example 0, ids [0, 2] + # example 1, ids [1] + indices=((0, 0), (0, 1), (1, 0)), + values=(0, 2, 1), + dense_shape=(2, 2)) + expected_sequence_length_b = [2, 1] + categorical_column_b = sfc.sequence_categorical_column_with_identity( + key='bbb', num_buckets=vocabulary_size) + shared_embedding_columns = fc.shared_embedding_columns( + [categorical_column_a, categorical_column_b], dimension=2) + + sequence_length_a = shared_embedding_columns[0]._get_sequence_dense_tensor( + _LazyBuilder({ + 'aaa': sparse_input_a + }))[1] + sequence_length_b = shared_embedding_columns[1]._get_sequence_dense_tensor( + _LazyBuilder({ + 'bbb': sparse_input_b + }))[1] + + with monitored_session.MonitoredSession() as sess: + sequence_length_a = sess.run(sequence_length_a) + self.assertAllEqual(expected_sequence_length_a, sequence_length_a) + self.assertEqual(np.int64, sequence_length_a.dtype) + sequence_length_b = sess.run(sequence_length_b) + self.assertAllEqual(expected_sequence_length_b, sequence_length_b) + self.assertEqual(np.int64, sequence_length_b.dtype) + + def test_sequence_length_with_empty_rows(self): + """Tests _sequence_length when some examples do not have ids.""" + vocabulary_size = 3 + sparse_input_a = sparse_tensor.SparseTensorValue( + # example 0, ids [] + # example 1, ids [2] + # example 2, ids [0, 1] + # example 3, ids [] + # example 4, ids [1] + # example 5, ids [] + indices=((1, 0), (2, 0), (2, 1), (4, 0)), + values=(2, 0, 1, 1), + dense_shape=(6, 2)) + expected_sequence_length_a = [0, 1, 2, 0, 1, 0] + categorical_column_a = sfc.sequence_categorical_column_with_identity( + key='aaa', num_buckets=vocabulary_size) + + sparse_input_b = sparse_tensor.SparseTensorValue( + # example 0, ids [2] + # example 1, ids [] + # example 2, ids [] + # example 3, ids [] + # example 4, ids [1] + # example 5, ids [0, 1] + indices=((0, 0), (4, 0), (5, 0), (5, 1)), + values=(2, 1, 0, 1), + dense_shape=(6, 2)) + expected_sequence_length_b = [1, 0, 0, 0, 1, 2] + categorical_column_b = sfc.sequence_categorical_column_with_identity( + key='bbb', num_buckets=vocabulary_size) + + shared_embedding_columns = fc.shared_embedding_columns( + [categorical_column_a, categorical_column_b], dimension=2) + + sequence_length_a = shared_embedding_columns[0]._get_sequence_dense_tensor( + _LazyBuilder({ + 'aaa': sparse_input_a + }))[1] + sequence_length_b = shared_embedding_columns[1]._get_sequence_dense_tensor( + _LazyBuilder({ + 'bbb': sparse_input_b + }))[1] + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual( + expected_sequence_length_a, sequence_length_a.eval(session=sess)) + self.assertAllEqual( + expected_sequence_length_b, sequence_length_b.eval(session=sess)) + + class SequenceIndicatorColumnTest(test.TestCase): def test_get_sequence_dense_tensor(self): diff --git a/tensorflow/python/feature_column/feature_column.py b/tensorflow/python/feature_column/feature_column.py index af2ead9b84..f959b5e484 100644 --- a/tensorflow/python/feature_column/feature_column.py +++ b/tensorflow/python/feature_column/feature_column.py @@ -2553,7 +2553,7 @@ def _get_graph_for_variable(var): class _SharedEmbeddingColumn( - _DenseColumn, + _DenseColumn, _SequenceDenseColumn, collections.namedtuple( '_SharedEmbeddingColumn', ('categorical_column', 'dimension', 'combiner', 'initializer', @@ -2600,7 +2600,11 @@ class _SharedEmbeddingColumn( self._shape = tensor_shape.vector(self.dimension) return self._shape - def _get_dense_tensor(self, inputs, weight_collections=None, trainable=None): + def _get_dense_tensor_internal(self, + inputs, + weight_collections=None, + trainable=None): + """Private method that follows the signature of _get_dense_tensor.""" # This method is called from a variable_scope with name _var_scope_name, # which is shared among all shared embeddings. Open a name_scope here, so # that the ops for different columns have distinct names. @@ -2641,6 +2645,44 @@ class _SharedEmbeddingColumn( name='%s_weights' % self.name, max_norm=self.max_norm) + def _get_dense_tensor(self, inputs, weight_collections=None, trainable=None): + if isinstance(self.categorical_column, _SequenceCategoricalColumn): + raise ValueError( + 'In embedding_column: {}. ' + 'categorical_column must not be of type _SequenceCategoricalColumn. ' + 'Suggested fix A: If you wish to use input_layer, use a ' + 'non-sequence categorical_column_with_*. ' + 'Suggested fix B: If you wish to create sequence input, use ' + 'sequence_input_layer instead of input_layer. ' + 'Given (type {}): {}'.format(self.name, type(self.categorical_column), + self.categorical_column)) + return self._get_dense_tensor_internal( + inputs=inputs, + weight_collections=weight_collections, + trainable=trainable) + + def _get_sequence_dense_tensor(self, + inputs, + weight_collections=None, + trainable=None): + if not isinstance(self.categorical_column, _SequenceCategoricalColumn): + raise ValueError( + 'In embedding_column: {}. ' + 'categorical_column must be of type _SequenceCategoricalColumn ' + 'to use sequence_input_layer. ' + 'Suggested fix: Use one of sequence_categorical_column_with_*. ' + 'Given (type {}): {}'.format(self.name, type(self.categorical_column), + self.categorical_column)) + dense_tensor = self._get_dense_tensor_internal( # pylint: disable=protected-access + inputs=inputs, + weight_collections=weight_collections, + trainable=trainable) + sparse_tensors = self.categorical_column._get_sparse_tensors(inputs) # pylint: disable=protected-access + sequence_length = _sequence_length_from_sparse_tensor( + sparse_tensors.id_tensor) + return _SequenceDenseColumn.TensorSequenceLengthPair( + dense_tensor=dense_tensor, sequence_length=sequence_length) + def _create_tuple(shape, value): """Returns a tuple with given shape and filled with value.""" -- GitLab From dac4634dc8ad35115aabbc3ee054e08fea62fa50 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 13 Jun 2018 19:07:45 -0700 Subject: [PATCH 431/816] Fix typo in register.h PiperOrigin-RevId: 200492653 --- tensorflow/contrib/lite/kernels/register.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/lite/kernels/register.h b/tensorflow/contrib/lite/kernels/register.h index b928f1b302..940718d67e 100644 --- a/tensorflow/contrib/lite/kernels/register.h +++ b/tensorflow/contrib/lite/kernels/register.h @@ -32,4 +32,4 @@ class BuiltinOpResolver : public MutableOpResolver { } // namespace ops } // namespace tflite -#endif // TENSORFLOW_CONTRIB_LITE_KERNELS_BUILTIN_KERNELS_H +#endif // TENSORFLOW_CONTRIB_LITE_KERNELS_REGISTER_H_ -- GitLab From 3e16768d63f43864c724745f91f5b92d83032a75 Mon Sep 17 00:00:00 2001 From: Amogh Mannekote Date: Thu, 14 Jun 2018 07:48:11 +0530 Subject: [PATCH 432/816] Removed unnecessary copying of dict (#19972) --- tensorflow/python/estimator/export/export.py | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/tensorflow/python/estimator/export/export.py b/tensorflow/python/estimator/export/export.py index 010c0f3f59..ca26341445 100644 --- a/tensorflow/python/estimator/export/export.py +++ b/tensorflow/python/estimator/export/export.py @@ -333,11 +333,7 @@ def build_raw_serving_input_receiver_fn(features, default_batch_size=None): """A serving_input_receiver_fn that expects features to be fed directly.""" receiver_tensors = _placeholders_from_receiver_tensors_dict( features, default_batch_size) - - # TODO(b/34885899): remove the unnecessary copy - # The features provided are simply the placeholders, but we defensively copy - # the dict because it may be mutated. - return ServingInputReceiver(receiver_tensors, receiver_tensors.copy()) + return ServingInputReceiver(receiver_tensors, receiver_tensors) return serving_input_receiver_fn -- GitLab From 007fc38f806c3405031dfef8076ca014bf0bcf7c Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 13 Jun 2018 19:47:23 -0700 Subject: [PATCH 433/816] Makes cond_v2 pass in device, container, colocation stacks, and collections to the branches. This brings cond_v2 functionality closer to tf.cond. PiperOrigin-RevId: 200495346 --- .../contrib/control_flow/python/cond_v2.py | 23 +- .../control_flow/python/cond_v2_test.py | 223 ++++++++++++++++++ tensorflow/python/framework/function.py | 54 ++++- 3 files changed, 296 insertions(+), 4 deletions(-) diff --git a/tensorflow/contrib/control_flow/python/cond_v2.py b/tensorflow/contrib/control_flow/python/cond_v2.py index b364e34511..90371cd8d7 100644 --- a/tensorflow/contrib/control_flow/python/cond_v2.py +++ b/tensorflow/contrib/control_flow/python/cond_v2.py @@ -48,13 +48,30 @@ def cond_v2(pred, true_fn, false_fn, name="cond"): name = "cond" with ops.name_scope(name) as scope: + # Identify if there is a caller device, & get the innermost if possible. + device_stack = ops.get_default_graph()._device_function_stack + caller_device = device_stack[-1] if device_stack else None + + caller_colocation_stack = ops.get_default_graph()._colocation_stack + caller_container = ops.get_default_graph()._container + caller_collection_ref = ops.get_default_graph()._collections + func_name_prefix = scope.replace("/", "_") true_graph = function.func_graph_from_py_func( - true_fn, [], [], name="%strue" % func_name_prefix) + true_fn, [], [], + name="%strue" % func_name_prefix, + device=caller_device, + colocation_stack=caller_colocation_stack, + collections_ref=caller_collection_ref, + container=caller_container) false_graph = function.func_graph_from_py_func( - false_fn, [], [], name="%sfalse" % func_name_prefix) - + false_fn, [], [], + name="%sfalse" % func_name_prefix, + device=caller_device, + colocation_stack=caller_colocation_stack, + collections_ref=caller_collection_ref, + container=caller_container) _check_same_outputs(true_graph, false_graph) # Add inputs to true_graph and false_graph to make them match. Note that diff --git a/tensorflow/contrib/control_flow/python/cond_v2_test.py b/tensorflow/contrib/control_flow/python/cond_v2_test.py index b7d4c16df4..94ed3e130b 100644 --- a/tensorflow/contrib/control_flow/python/cond_v2_test.py +++ b/tensorflow/contrib/control_flow/python/cond_v2_test.py @@ -25,10 +25,13 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import data_flow_ops from tensorflow.python.ops import gradients_impl from tensorflow.python.ops import math_ops +from tensorflow.python.ops import variables from tensorflow.python.platform import test from tensorflow.python.training import saver +from tensorflow.python.util import compat class NewCondTest(test.TestCase): @@ -198,5 +201,225 @@ class NewCondTest(test.TestCase): self.assertEqual(false_val, [0.0]) +class CondV2CollectionTest(test.TestCase): + + def testCollectionIntValueAccessInCond(self): + """Read values from graph collections inside of cond_v2.""" + with ops.Graph().as_default() as g: + with self.test_session(graph=g): + x = 2 + y = 5 + ops.add_to_collection("x", x) + ops.add_to_collection("y", y) + def fn(): + x_const = constant_op.constant(ops.get_collection("x")[0]) + y_const = constant_op.constant(ops.get_collection("y")[0]) + return math_ops.add(x_const, y_const) + + cnd = cond_v2.cond_v2(True, fn, fn) + self.assertEquals(cnd[0].eval(), 7) + + def testCollectionTensorValueAccessInCond(self): + """Read tensors from collections inside of cond_v2 & use them.""" + with ops.Graph().as_default() as g: + with self.test_session(graph=g): + x = constant_op.constant(2) + y = constant_op.constant(5) + ops.add_to_collection("x", x) + ops.add_to_collection("y", y) + + def fn(): + x_read = ops.get_collection("x")[0] + y_read = ops.get_collection("y")[0] + return math_ops.add(x_read, y_read) + + cnd = cond_v2.cond_v2(math_ops.less(x, y), fn, fn) + self.assertEquals(cnd[0].eval(), 7) + + def testCollectionIntValueWriteInCond(self): + """Make sure Int writes to collections work inside of cond_v2.""" + with ops.Graph().as_default() as g: + with self.test_session(graph=g): + x = constant_op.constant(2) + y = constant_op.constant(5) + def true_fn(): + z = math_ops.add(x, y) + ops.add_to_collection("z", 7) + return math_ops.mul(x, z) + + def false_fn(): + z = math_ops.add(x, y) + return math_ops.mul(x, z) + + cnd = cond_v2.cond_v2( + True, true_fn, + false_fn) + self.assertEquals(cnd[0].eval(), 14) + + read_z_collection = ops.get_collection("z") + self.assertEquals(read_z_collection, [7]) + + +class CondV2ContainerTest(test.TestCase): + + def testContainer(self): + """Set containers outside & inside of cond_v2. + + Make sure the containers are set correctly for both variable creation + (tested by variables.Variable) and for stateful ops (tested by FIFOQueue) + """ + with ops.Graph().as_default() as g: + with self.test_session(graph=g): + + v0 = variables.Variable([0]) + q0 = data_flow_ops.FIFOQueue(1, dtypes.float32) + + def container(node): + return node.op.get_attr("container") + + self.assertEqual(compat.as_bytes(""), container(v0)) + self.assertEqual(compat.as_bytes(""), container(q0.queue_ref)) + + def true_fn(): + # When this branch is created in cond below, + # the container should begin with 'l1' + v1 = variables.Variable([1]) + q1 = data_flow_ops.FIFOQueue(1, dtypes.float32) + + with ops.container("l2t"): + v2 = variables.Variable([2]) + q2 = data_flow_ops.FIFOQueue(1, dtypes.float32) + + v3 = variables.Variable([1]) + q3 = data_flow_ops.FIFOQueue(1, dtypes.float32) + + self.assertEqual(compat.as_bytes("l1"), container(v1)) + self.assertEqual(compat.as_bytes("l1"), container(q1.queue_ref)) + self.assertEqual(compat.as_bytes("l2t"), container(v2)) + self.assertEqual(compat.as_bytes("l2t"), container(q2.queue_ref)) + self.assertEqual(compat.as_bytes("l1"), container(v3)) + self.assertEqual(compat.as_bytes("l1"), container(q3.queue_ref)) + + return constant_op.constant(2.0) + + def false_fn(): + # When this branch is created in cond below, + # the container should begin with 'l1' + v1 = variables.Variable([1]) + q1 = data_flow_ops.FIFOQueue(1, dtypes.float32) + + with ops.container("l2f"): + v2 = variables.Variable([2]) + q2 = data_flow_ops.FIFOQueue(1, dtypes.float32) + + v3 = variables.Variable([1]) + q3 = data_flow_ops.FIFOQueue(1, dtypes.float32) + + self.assertEqual(compat.as_bytes("l1"), container(v1)) + self.assertEqual(compat.as_bytes("l1"), container(q1.queue_ref)) + self.assertEqual(compat.as_bytes("l2f"), container(v2)) + self.assertEqual(compat.as_bytes("l2f"), container(q2.queue_ref)) + self.assertEqual(compat.as_bytes("l1"), container(v3)) + self.assertEqual(compat.as_bytes("l1"), container(q3.queue_ref)) + + return constant_op.constant(6.0) + + with ops.container("l1"): + cnd_true = cond_v2.cond_v2(True, true_fn, false_fn) + self.assertEquals(cnd_true[0].eval(), 2) + + cnd_false = cond_v2.cond_v2(False, true_fn, false_fn) + self.assertEquals(cnd_false[0].eval(), 6) + + v4 = variables.Variable([3]) + q4 = data_flow_ops.FIFOQueue(1, dtypes.float32) + v5 = variables.Variable([4]) + q5 = data_flow_ops.FIFOQueue(1, dtypes.float32) + + self.assertEqual(compat.as_bytes("l1"), container(v4)) + self.assertEqual(compat.as_bytes("l1"), container(q4.queue_ref)) + self.assertEqual(compat.as_bytes(""), container(v5)) + self.assertEqual(compat.as_bytes(""), container(q5.queue_ref)) + + +class CondV2ColocationGroupAndDeviceTest(test.TestCase): + + def testColocateWithBeforeCond(self): + with ops.Graph().as_default() as g: + with self.test_session(graph=g): + + a = constant_op.constant([2.0], name="a") + b = constant_op.constant([2.0], name="b") + + def fn(): + c = constant_op.constant(3.0) + self.assertEqual([b"loc:@a"], c.op.colocation_groups()) + return c + + with ops.colocate_with(a.op): + self.assertEquals(cond_v2.cond_v2(True, fn, fn)[0].eval(), 3) + + def fn2(): + c = constant_op.constant(3.0) + self.assertEqual([b"loc:@a", b"loc:@b"], c.op.colocation_groups()) + return c + + with ops.colocate_with(a.op): + with ops.colocate_with(b.op): + self.assertEquals(cond_v2.cond_v2(True, fn2, fn2)[0].eval(), 3) + + def testColocateWithInAndOutOfCond(self): + with ops.Graph().as_default() as g: + with self.test_session(graph=g): + + a = constant_op.constant([2.0], name="a") + b = constant_op.constant([2.0], name="b") + + def fn2(): + with ops.colocate_with(b.op): + c = constant_op.constant(3.0) + self.assertEqual([b"loc:@a", b"loc:@b"], c.op.colocation_groups()) + return c + + with ops.colocate_with(a.op): + self.assertEquals(cond_v2.cond_v2(True, fn2, fn2)[0].eval(), 3) + + d = constant_op.constant([2.0], name="d") + self.assertEqual([b"loc:@a"], d.op.colocation_groups()) + + def testDeviceBeforeCond(self): + with ops.Graph().as_default() as g: + with self.test_session(graph=g): + def fn(): + c = constant_op.constant(3.0) + self.assertEqual("/device:CPU:0", c.op.device) + return c + + with ops.device("/device:CPU:0"): + self.assertEquals(cond_v2.cond_v2(True, fn, fn)[0].eval(), 3) + + def fn2(): + c = constant_op.constant(3.0) + self.assertEqual("/device:GPU:0", c.op.device) + return c + + with ops.device("/device:GPU:0"): + self.assertEquals(cond_v2.cond_v2(True, fn2, fn2)[0].eval(), 3) + + def testDeviceInAndOutOfCond(self): + with ops.Graph().as_default() as g: + with self.test_session(graph=g): + def fn2(): + with ops.device("/device:GPU:0"): + c = constant_op.constant(3.0) + self.assertEqual("/device:GPU:0", c.op.device) + return c + + with ops.device("/device:CPU:0"): + self.assertEquals(cond_v2.cond_v2(True, fn2, fn2)[0].eval(), 3) + + d = constant_op.constant(4.0) + self.assertEqual("/device:CPU:0", d.op.device) + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/framework/function.py b/tensorflow/python/framework/function.py index 82ecba310b..002a3d3be5 100644 --- a/tensorflow/python/framework/function.py +++ b/tensorflow/python/framework/function.py @@ -36,6 +36,7 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variable_scope as vs from tensorflow.python.util import compat +from tensorflow.python.util import tf_contextlib from tensorflow.python.util import tf_decorator from tensorflow.python.util import tf_inspect @@ -650,6 +651,41 @@ class _FuncGraph(ops.Graph): # TODO(skyewm): is this needed? self.extra_vars = [] + # pylint: disable=g-doc-return-or-yield + + @tf_contextlib.contextmanager + def container(self, container_name): + """Returns a context manager that specifies the resource container to use. + + Overridden from @{tf.Graph} to update both the init_scope container + and the present inner container. This is necessary to make sure setting + containers applies correctly both to created variables and to stateful + ops. + + Args: + container_name: container name string. + + Returns: + A context manager for defining resource containers for stateful ops, + yields the container name. + """ + original_container = self._container + # pylint: disable=protected-access + with ops.init_scope(): + original_init_container = ops.get_default_graph()._container + try: + self._container = container_name + with ops.init_scope(): + ops.get_default_graph()._container = container_name + yield self._container + finally: + self._container = original_container + with ops.init_scope(): + ops.get_default_graph()._container = original_init_container + # pylint: enable=protected-access + + # pylint: enable=g-doc-return-or-yield + def getvar( self, getter, @@ -773,7 +809,9 @@ class _FuncGraph(ops.Graph): def func_graph_from_py_func(func, arg_names, arg_types, name=None, - capture_by_value=False, device=None): + capture_by_value=False, device=None, + colocation_stack=None, container=None, + collections_ref=None): """Returns a _FuncGraph generated from `func`. Args: @@ -786,6 +824,10 @@ def func_graph_from_py_func(func, arg_names, arg_types, name=None, capture_by_value: boolean. If True, captured values will be copied into the function body. device: device name or function. + colocation_stack: A colocation stack (list) the _FuncGraph should use. + container: A container name the _FuncGraph should start with. + collections_ref: A reference to a collections dict the _FuncGraph should + use internally. Returns: A _FuncGraph. @@ -796,7 +838,17 @@ def func_graph_from_py_func(func, arg_names, arg_types, name=None, if not name: name = _get_func_name(func) func_graph = _FuncGraph(name, capture_by_value) + with func_graph.as_default(), ops.device(device): + # pylint: disable=protected-access + if collections_ref is not None: + func_graph._collections = collections_ref + if container is not None: + func_graph._container = container + if colocation_stack is not None: + func_graph._colocation_stack = colocation_stack + # pylint: enable=protected-access + # Create placeholders for the function arguments. for (argname, argtype) in zip(arg_names, arg_types): argholder = array_ops.placeholder(argtype, name=argname) -- GitLab From c62f4a595ed34500edce3e661a176fa179479133 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 13 Jun 2018 19:49:02 -0700 Subject: [PATCH 434/816] Reduce runtime of metric_ops_test by increasing sharding and splitting the largest method in half. PiperOrigin-RevId: 200495475 --- tensorflow/contrib/metrics/BUILD | 2 +- .../metrics/python/ops/metric_ops_test.py | 367 +++++++++--------- 2 files changed, 187 insertions(+), 182 deletions(-) diff --git a/tensorflow/contrib/metrics/BUILD b/tensorflow/contrib/metrics/BUILD index 3f81c9ccea..66cb493e5c 100644 --- a/tensorflow/contrib/metrics/BUILD +++ b/tensorflow/contrib/metrics/BUILD @@ -77,7 +77,7 @@ py_test( py_test( name = "metric_ops_test", srcs = ["python/ops/metric_ops_test.py"], - shard_count = 16, + shard_count = 30, srcs_version = "PY2AND3", tags = ["noasan"], # times out b/63678675 deps = [ diff --git a/tensorflow/contrib/metrics/python/ops/metric_ops_test.py b/tensorflow/contrib/metrics/python/ops/metric_ops_test.py index db4b530ce7..e720097636 100644 --- a/tensorflow/contrib/metrics/python/ops/metric_ops_test.py +++ b/tensorflow/contrib/metrics/python/ops/metric_ops_test.py @@ -4699,199 +4699,204 @@ class StreamingSparseRecallTest(test.TestCase): self._test_sparse_recall_at_top_k( labels, top_k_predictions, expected=1.0 / 2) - def test_one_label_at_k1_weighted(self): + def _test_one_label_at_k1_weighted(self, labels): predictions = [[0.1, 0.3, 0.2, 0.4], [0.1, 0.2, 0.3, 0.4]] top_k_predictions = [[3], [3]] - sparse_labels = _binary_2d_label_to_sparse_value([[0, 0, 0, 1], - [0, 0, 1, 0]]) - dense_labels = np.array([[3], [2]], dtype=np.int64) - for labels in (sparse_labels, dense_labels): - # Class 3: 1 label, 2 predictions, 1 correct. - self._test_streaming_sparse_recall_at_k( - predictions, labels, k=1, expected=NAN, class_id=3, weights=(0.0,)) - self._test_sparse_recall_at_top_k( - labels, top_k_predictions, expected=NAN, class_id=3, weights=(0.0,)) - self._test_streaming_sparse_recall_at_k( - predictions, - labels, - k=1, - expected=1.0 / 1, - class_id=3, - weights=(1.0,)) - self._test_sparse_recall_at_top_k( - labels, - top_k_predictions, - expected=1.0 / 1, - class_id=3, - weights=(1.0,)) - self._test_streaming_sparse_recall_at_k( - predictions, - labels, - k=1, - expected=1.0 / 1, - class_id=3, - weights=(2.0,)) - self._test_sparse_recall_at_top_k( - labels, - top_k_predictions, - expected=1.0 / 1, - class_id=3, - weights=(2.0,)) - self._test_streaming_sparse_recall_at_k( - predictions, - labels, - k=1, - expected=NAN, - class_id=3, - weights=(0.0, 0.0)) - self._test_sparse_recall_at_top_k( - labels, - top_k_predictions, - expected=NAN, - class_id=3, - weights=(0.0, 0.0)) - self._test_streaming_sparse_recall_at_k( - predictions, - labels, - k=1, - expected=NAN, - class_id=3, - weights=(0.0, 1.0)) - self._test_sparse_recall_at_top_k( - labels, - top_k_predictions, - expected=NAN, - class_id=3, - weights=(0.0, 1.0)) - self._test_streaming_sparse_recall_at_k( - predictions, - labels, - k=1, - expected=1.0 / 1, - class_id=3, - weights=(1.0, 0.0)) - self._test_sparse_recall_at_top_k( - labels, - top_k_predictions, - expected=1.0 / 1, - class_id=3, - weights=(1.0, 0.0)) - self._test_streaming_sparse_recall_at_k( - predictions, - labels, - k=1, - expected=1.0 / 1, - class_id=3, - weights=(1.0, 1.0)) - self._test_sparse_recall_at_top_k( - labels, - top_k_predictions, - expected=1.0 / 1, - class_id=3, - weights=(1.0, 1.0)) - self._test_streaming_sparse_recall_at_k( - predictions, - labels, - k=1, - expected=2.0 / 2, - class_id=3, - weights=(2.0, 3.0)) - self._test_sparse_recall_at_top_k( - labels, - top_k_predictions, - expected=2.0 / 2, - class_id=3, - weights=(2.0, 3.0)) - self._test_streaming_sparse_recall_at_k( - predictions, - labels, - k=1, - expected=3.0 / 3, - class_id=3, - weights=(3.0, 2.0)) - self._test_sparse_recall_at_top_k( - labels, - top_k_predictions, - expected=3.0 / 3, - class_id=3, - weights=(3.0, 2.0)) - self._test_streaming_sparse_recall_at_k( - predictions, - labels, - k=1, - expected=0.3 / 0.3, - class_id=3, - weights=(0.3, 0.6)) - self._test_sparse_recall_at_top_k( - labels, - top_k_predictions, - expected=0.3 / 0.3, - class_id=3, - weights=(0.3, 0.6)) - self._test_streaming_sparse_recall_at_k( - predictions, - labels, - k=1, - expected=0.6 / 0.6, - class_id=3, - weights=(0.6, 0.3)) - self._test_sparse_recall_at_top_k( - labels, - top_k_predictions, - expected=0.6 / 0.6, - class_id=3, - weights=(0.6, 0.3)) + # Class 3: 1 label, 2 predictions, 1 correct. + self._test_streaming_sparse_recall_at_k( + predictions, labels, k=1, expected=NAN, class_id=3, weights=(0.0,)) + self._test_sparse_recall_at_top_k( + labels, top_k_predictions, expected=NAN, class_id=3, weights=(0.0,)) + self._test_streaming_sparse_recall_at_k( + predictions, + labels, + k=1, + expected=1.0 / 1, + class_id=3, + weights=(1.0,)) + self._test_sparse_recall_at_top_k( + labels, + top_k_predictions, + expected=1.0 / 1, + class_id=3, + weights=(1.0,)) + self._test_streaming_sparse_recall_at_k( + predictions, + labels, + k=1, + expected=1.0 / 1, + class_id=3, + weights=(2.0,)) + self._test_sparse_recall_at_top_k( + labels, + top_k_predictions, + expected=1.0 / 1, + class_id=3, + weights=(2.0,)) + self._test_streaming_sparse_recall_at_k( + predictions, + labels, + k=1, + expected=NAN, + class_id=3, + weights=(0.0, 0.0)) + self._test_sparse_recall_at_top_k( + labels, + top_k_predictions, + expected=NAN, + class_id=3, + weights=(0.0, 0.0)) + self._test_streaming_sparse_recall_at_k( + predictions, + labels, + k=1, + expected=NAN, + class_id=3, + weights=(0.0, 1.0)) + self._test_sparse_recall_at_top_k( + labels, + top_k_predictions, + expected=NAN, + class_id=3, + weights=(0.0, 1.0)) + self._test_streaming_sparse_recall_at_k( + predictions, + labels, + k=1, + expected=1.0 / 1, + class_id=3, + weights=(1.0, 0.0)) + self._test_sparse_recall_at_top_k( + labels, + top_k_predictions, + expected=1.0 / 1, + class_id=3, + weights=(1.0, 0.0)) + self._test_streaming_sparse_recall_at_k( + predictions, + labels, + k=1, + expected=1.0 / 1, + class_id=3, + weights=(1.0, 1.0)) + self._test_sparse_recall_at_top_k( + labels, + top_k_predictions, + expected=1.0 / 1, + class_id=3, + weights=(1.0, 1.0)) + self._test_streaming_sparse_recall_at_k( + predictions, + labels, + k=1, + expected=2.0 / 2, + class_id=3, + weights=(2.0, 3.0)) + self._test_sparse_recall_at_top_k( + labels, + top_k_predictions, + expected=2.0 / 2, + class_id=3, + weights=(2.0, 3.0)) + self._test_streaming_sparse_recall_at_k( + predictions, + labels, + k=1, + expected=3.0 / 3, + class_id=3, + weights=(3.0, 2.0)) + self._test_sparse_recall_at_top_k( + labels, + top_k_predictions, + expected=3.0 / 3, + class_id=3, + weights=(3.0, 2.0)) + self._test_streaming_sparse_recall_at_k( + predictions, + labels, + k=1, + expected=0.3 / 0.3, + class_id=3, + weights=(0.3, 0.6)) + self._test_sparse_recall_at_top_k( + labels, + top_k_predictions, + expected=0.3 / 0.3, + class_id=3, + weights=(0.3, 0.6)) + self._test_streaming_sparse_recall_at_k( + predictions, + labels, + k=1, + expected=0.6 / 0.6, + class_id=3, + weights=(0.6, 0.3)) + self._test_sparse_recall_at_top_k( + labels, + top_k_predictions, + expected=0.6 / 0.6, + class_id=3, + weights=(0.6, 0.3)) - # All classes: 2 labels, 2 predictions, 1 correct. - self._test_streaming_sparse_recall_at_k( - predictions, labels, k=1, expected=NAN, weights=(0.0,)) - self._test_sparse_recall_at_top_k( - labels, top_k_predictions, expected=NAN, weights=(0.0,)) - self._test_streaming_sparse_recall_at_k( - predictions, labels, k=1, expected=1.0 / 2, weights=(1.0,)) - self._test_sparse_recall_at_top_k( - labels, top_k_predictions, expected=1.0 / 2, weights=(1.0,)) + # All classes: 2 labels, 2 predictions, 1 correct. + self._test_streaming_sparse_recall_at_k( + predictions, labels, k=1, expected=NAN, weights=(0.0,)) + self._test_sparse_recall_at_top_k( + labels, top_k_predictions, expected=NAN, weights=(0.0,)) + self._test_streaming_sparse_recall_at_k( + predictions, labels, k=1, expected=1.0 / 2, weights=(1.0,)) + self._test_sparse_recall_at_top_k( + labels, top_k_predictions, expected=1.0 / 2, weights=(1.0,)) - self._test_streaming_sparse_recall_at_k( - predictions, labels, k=1, expected=1.0 / 2, weights=(2.0,)) - self._test_sparse_recall_at_top_k( - labels, top_k_predictions, expected=1.0 / 2, weights=(2.0,)) + self._test_streaming_sparse_recall_at_k( + predictions, labels, k=1, expected=1.0 / 2, weights=(2.0,)) + self._test_sparse_recall_at_top_k( + labels, top_k_predictions, expected=1.0 / 2, weights=(2.0,)) - self._test_streaming_sparse_recall_at_k( - predictions, labels, k=1, expected=1.0 / 1, weights=(1.0, 0.0)) - self._test_sparse_recall_at_top_k( - labels, top_k_predictions, expected=1.0 / 1, weights=(1.0, 0.0)) + self._test_streaming_sparse_recall_at_k( + predictions, labels, k=1, expected=1.0 / 1, weights=(1.0, 0.0)) + self._test_sparse_recall_at_top_k( + labels, top_k_predictions, expected=1.0 / 1, weights=(1.0, 0.0)) - self._test_streaming_sparse_recall_at_k( - predictions, labels, k=1, expected=0.0 / 1, weights=(0.0, 1.0)) - self._test_sparse_recall_at_top_k( - labels, top_k_predictions, expected=0.0 / 1, weights=(0.0, 1.0)) + self._test_streaming_sparse_recall_at_k( + predictions, labels, k=1, expected=0.0 / 1, weights=(0.0, 1.0)) + self._test_sparse_recall_at_top_k( + labels, top_k_predictions, expected=0.0 / 1, weights=(0.0, 1.0)) - self._test_streaming_sparse_recall_at_k( - predictions, labels, k=1, expected=1.0 / 2, weights=(1.0, 1.0)) - self._test_sparse_recall_at_top_k( - labels, top_k_predictions, expected=1.0 / 2, weights=(1.0, 1.0)) + self._test_streaming_sparse_recall_at_k( + predictions, labels, k=1, expected=1.0 / 2, weights=(1.0, 1.0)) + self._test_sparse_recall_at_top_k( + labels, top_k_predictions, expected=1.0 / 2, weights=(1.0, 1.0)) - self._test_streaming_sparse_recall_at_k( - predictions, labels, k=1, expected=2.0 / 5, weights=(2.0, 3.0)) - self._test_sparse_recall_at_top_k( - labels, top_k_predictions, expected=2.0 / 5, weights=(2.0, 3.0)) + self._test_streaming_sparse_recall_at_k( + predictions, labels, k=1, expected=2.0 / 5, weights=(2.0, 3.0)) + self._test_sparse_recall_at_top_k( + labels, top_k_predictions, expected=2.0 / 5, weights=(2.0, 3.0)) - self._test_streaming_sparse_recall_at_k( - predictions, labels, k=1, expected=3.0 / 5, weights=(3.0, 2.0)) - self._test_sparse_recall_at_top_k( - labels, top_k_predictions, expected=3.0 / 5, weights=(3.0, 2.0)) + self._test_streaming_sparse_recall_at_k( + predictions, labels, k=1, expected=3.0 / 5, weights=(3.0, 2.0)) + self._test_sparse_recall_at_top_k( + labels, top_k_predictions, expected=3.0 / 5, weights=(3.0, 2.0)) - self._test_streaming_sparse_recall_at_k( - predictions, labels, k=1, expected=0.3 / 0.9, weights=(0.3, 0.6)) - self._test_sparse_recall_at_top_k( - labels, top_k_predictions, expected=0.3 / 0.9, weights=(0.3, 0.6)) + self._test_streaming_sparse_recall_at_k( + predictions, labels, k=1, expected=0.3 / 0.9, weights=(0.3, 0.6)) + self._test_sparse_recall_at_top_k( + labels, top_k_predictions, expected=0.3 / 0.9, weights=(0.3, 0.6)) - self._test_streaming_sparse_recall_at_k( - predictions, labels, k=1, expected=0.6 / 0.9, weights=(0.6, 0.3)) - self._test_sparse_recall_at_top_k( - labels, top_k_predictions, expected=0.6 / 0.9, weights=(0.6, 0.3)) + self._test_streaming_sparse_recall_at_k( + predictions, labels, k=1, expected=0.6 / 0.9, weights=(0.6, 0.3)) + self._test_sparse_recall_at_top_k( + labels, top_k_predictions, expected=0.6 / 0.9, weights=(0.6, 0.3)) + + def test_one_label_at_k1_weighted_sparse_labels(self): + sparse_labels = _binary_2d_label_to_sparse_value([[0, 0, 0, 1], + [0, 0, 1, 0]]) + self._test_one_label_at_k1_weighted(sparse_labels) + + def test_one_label_at_k1_weighted_dense_labels(self): + dense_labels = np.array([[3], [2]], dtype=np.int64) + self._test_one_label_at_k1_weighted(dense_labels) def test_three_labels_at_k5_nan(self): predictions = [[0.5, 0.1, 0.6, 0.3, 0.8, 0.0, 0.7, 0.2, 0.4, 0.9], -- GitLab From 0946c28fd7d50bf11c7e188784a0c733e322bf3f Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 13 Jun 2018 19:53:28 -0700 Subject: [PATCH 435/816] fully_connected_feed_test timing out, increase its size. PiperOrigin-RevId: 200495744 --- tensorflow/examples/tutorials/mnist/BUILD | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/examples/tutorials/mnist/BUILD b/tensorflow/examples/tutorials/mnist/BUILD index d7bc6a5a7d..d4070fdd1e 100644 --- a/tensorflow/examples/tutorials/mnist/BUILD +++ b/tensorflow/examples/tutorials/mnist/BUILD @@ -97,7 +97,7 @@ py_binary( py_test( name = "fully_connected_feed_test", - size = "small", + size = "medium", srcs = [ "fully_connected_feed.py", ], -- GitLab From 5ae5ab4b963d372f46eef2cee708a586928f331c Mon Sep 17 00:00:00 2001 From: Tristan Rice Date: Wed, 13 Jun 2018 23:11:41 -0400 Subject: [PATCH 436/816] tensorflow/go: operation attribute getters (#19953) --- tensorflow/go/attrs.go | 215 ++++++++++++++++++++++++++++++++++++ tensorflow/go/attrs_test.go | 47 ++++++++ 2 files changed, 262 insertions(+) create mode 100644 tensorflow/go/attrs.go create mode 100644 tensorflow/go/attrs_test.go diff --git a/tensorflow/go/attrs.go b/tensorflow/go/attrs.go new file mode 100644 index 0000000000..bfa60d2aa8 --- /dev/null +++ b/tensorflow/go/attrs.go @@ -0,0 +1,215 @@ +/* +Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +package tensorflow + +// #include +// #include "tensorflow/c/c_api.h" +import "C" +import ( + "fmt" + "unsafe" +) + +// makeCShape converts a shape specified in C.int64_t into a Shape. +func makeCShape(shape []C.int64_t) Shape { + s := Shape{dims: make([]int64, len(shape))} + for i, n := range shape { + s.dims[i] = int64(n) + } + return s +} + +// Attr returns the value of an attribute on op. +func (op *Operation) Attr(name string) (interface{}, error) { + cname := C.CString(name) + defer C.free(unsafe.Pointer(cname)) + + status := newStatus() + meta := C.TF_OperationGetAttrMetadata(op.c, cname, status.c) + if err := status.Err(); err != nil { + return nil, err + } + + if meta.is_list == 1 { + return listAttribute(op, cname, meta) + } + return scalarAttribute(op, cname, meta) +} + +func listAttribute(op *Operation, cname *C.char, meta C.TF_AttrMetadata) (interface{}, error) { + status := newStatus() + + switch meta._type { + case C.TF_ATTR_STRING: + values := make([]unsafe.Pointer, meta.list_size) + lengths := make([]C.size_t, meta.list_size) + storage := make([]C.char, meta.total_size) + C.TF_OperationGetAttrStringList(op.c, cname, &values[0], &lengths[0], C.int(meta.list_size), unsafe.Pointer(&storage[0]), C.size_t(meta.total_size), status.c) + if err := status.Err(); err != nil { + return nil, err + } + list := make([]string, meta.list_size) + for i, val := range values { + length := lengths[i] + list[i] = C.GoStringN((*C.char)(val), C.int(length)) + } + return list, nil + + case C.TF_ATTR_INT: + list := make([]C.int64_t, meta.list_size) + C.TF_OperationGetAttrIntList(op.c, cname, &list[0], C.int(meta.list_size), status.c) + if err := status.Err(); err != nil { + return nil, err + } + vals := make([]int64, meta.list_size) + for i, val := range list { + vals[i] = int64(val) + } + return vals, nil + + case C.TF_ATTR_FLOAT: + list := make([]C.float, meta.list_size) + C.TF_OperationGetAttrFloatList(op.c, cname, &list[0], C.int(meta.list_size), status.c) + if err := status.Err(); err != nil { + return nil, err + } + vals := make([]float32, meta.list_size) + for i, val := range list { + vals[i] = float32(val) + } + return vals, nil + + case C.TF_ATTR_BOOL: + list := make([]C.uchar, meta.list_size) + C.TF_OperationGetAttrBoolList(op.c, cname, &list[0], C.int(meta.list_size), status.c) + if err := status.Err(); err != nil { + return nil, err + } + vals := make([]bool, meta.list_size) + for i, val := range list { + vals[i] = val == 1 + } + return vals, nil + + case C.TF_ATTR_TYPE: + list := make([]C.TF_DataType, meta.list_size) + C.TF_OperationGetAttrTypeList(op.c, cname, &list[0], C.int(meta.list_size), status.c) + if err := status.Err(); err != nil { + return nil, err + } + vals := make([]DataType, meta.list_size) + for i, val := range list { + vals[i] = DataType(val) + } + return vals, nil + + case C.TF_ATTR_TENSOR: + list := make([]*C.TF_Tensor, meta.list_size) + C.TF_OperationGetAttrTensorList(op.c, cname, &list[0], C.int(meta.list_size), status.c) + if err := status.Err(); err != nil { + return nil, err + } + vals := make([]*Tensor, meta.list_size) + for i, t := range list { + vals[i] = newTensorFromC(t) + } + return vals, nil + + case C.TF_ATTR_SHAPE: + dims := make([]*C.int64_t, meta.list_size) + numDims := make([]C.int, meta.list_size) + storage := make([]C.int64_t, meta.total_size) + C.TF_OperationGetAttrShapeList(op.c, cname, &dims[0], &numDims[0], C.int(meta.list_size), &storage[0], C.int(meta.total_size), status.c) + if err := status.Err(); err != nil { + return nil, err + } + list := make([]Shape, meta.list_size) + for i, dim := range dims { + numDim := numDims[i] + // If the number of dimensions is unknown, default to empty shape. + if numDim < 0 { + continue + } + // A []C.int64_t slice backed by C memory. + // See: https://github.com/golang/go/wiki/cgo#turning-c-arrays-into-go-slices + slice := (*[1 << 30]C.int64_t)(unsafe.Pointer(dim))[:numDim:numDim] + list[i] = makeCShape(slice) + } + return list, nil + + default: + return nil, fmt.Errorf("list type %v not supported", meta._type) + } +} + +func scalarAttribute(op *Operation, cname *C.char, meta C.TF_AttrMetadata) (interface{}, error) { + status := newStatus() + + switch meta._type { + case C.TF_ATTR_STRING: + v := make([]C.char, meta.total_size) + C.TF_OperationGetAttrString(op.c, cname, unsafe.Pointer(&v[0]), C.size_t(meta.total_size), status.c) + if err := status.Err(); err != nil { + return nil, err + } + return C.GoStringN(&v[0], C.int(meta.total_size)), nil + + case C.TF_ATTR_INT: + var v C.int64_t + C.TF_OperationGetAttrInt(op.c, cname, &v, status.c) + return int64(v), status.Err() + + case C.TF_ATTR_FLOAT: + var v C.float + C.TF_OperationGetAttrFloat(op.c, cname, &v, status.c) + return float32(v), status.Err() + + case C.TF_ATTR_BOOL: + var v C.uchar + C.TF_OperationGetAttrBool(op.c, cname, &v, status.c) + return v == 1, status.Err() + + case C.TF_ATTR_TYPE: + var v C.TF_DataType + C.TF_OperationGetAttrType(op.c, cname, &v, status.c) + return DataType(v), status.Err() + + case C.TF_ATTR_TENSOR: + var v *C.TF_Tensor + C.TF_OperationGetAttrTensor(op.c, cname, &v, status.c) + if err := status.Err(); err != nil { + return nil, err + } + return newTensorFromC(v), nil + + case C.TF_ATTR_SHAPE: + numDims := meta.total_size + // If number of dims is unknown return empty shape to indicate that. + if numDims < 0 { + return Shape{}, nil + } + dims := make([]C.int64_t, numDims) + C.TF_OperationGetAttrShape(op.c, cname, (*C.int64_t)(unsafe.Pointer(&dims[0])), C.int(numDims), status.c) + if err := status.Err(); err != nil { + return nil, err + } + return makeCShape(dims), nil + + default: + return nil, fmt.Errorf("type %v not supported", meta._type) + } +} diff --git a/tensorflow/go/attrs_test.go b/tensorflow/go/attrs_test.go new file mode 100644 index 0000000000..18fc0de90a --- /dev/null +++ b/tensorflow/go/attrs_test.go @@ -0,0 +1,47 @@ +/* +Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +package tensorflow + +import ( + "reflect" + "testing" +) + +func TestOperationAttrs(t *testing.T) { + attrs := map[string]interface{}{ + "dtype": Float, + } + + g := NewGraph() + op, err := g.AddOperation(OpSpec{ + Type: "Placeholder", + Name: "placeholder", + Attrs: attrs, + }) + if err != nil { + t.Fatal(err) + } + for key, want := range attrs { + out, err := op.Attr(key) + if err != nil { + t.Fatal(err) + } + if !reflect.DeepEqual(out, want) { + t.Fatalf("%q: Got %+v, wanted %+v", key, out, want) + } + } +} -- GitLab From 2832528fa759fe91924d142b278c330ca48ce8d5 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 13 Jun 2018 20:42:19 -0700 Subject: [PATCH 437/816] Fix layout assignment CHECK failure on channel constraints. PiperOrigin-RevId: 200499357 --- .../compiler/xla/service/layout_assignment.cc | 60 +++++++++++++++-- .../compiler/xla/service/layout_assignment.h | 50 ++++++++++---- .../xla/service/layout_assignment_test.cc | 66 +++++++++++++++---- 3 files changed, 146 insertions(+), 30 deletions(-) diff --git a/tensorflow/compiler/xla/service/layout_assignment.cc b/tensorflow/compiler/xla/service/layout_assignment.cc index 7067b6f86a..eb469e77a0 100644 --- a/tensorflow/compiler/xla/service/layout_assignment.cc +++ b/tensorflow/compiler/xla/service/layout_assignment.cc @@ -937,6 +937,11 @@ LayoutAssignment::LayoutAssignment( ChannelLayoutConstraints* channel_constraints) : entry_computation_layout_(entry_computation_layout), channel_layout_constraints_(channel_constraints) { + if (channel_layout_constraints_ != nullptr) { + // Save a copy of the input ChannelLayoutConstraints so that we can reset it + // if we have to undo previous operations (ClearPreviousPassSideEffects()). + channel_constraints_ = *channel_layout_constraints_; + } VLOG(1) << "Entry computation layout given to layout assignment: " << entry_computation_layout_->ToString(); // Layouts of all parameter instructions must be set. @@ -1614,13 +1619,57 @@ Status LayoutAssignment::RunOnComputation( // Record the layouts assigned for any communication ops in // channel_constraints so that they are constrained for future modules. + if (channel_constraints != nullptr) { + TF_RETURN_IF_ERROR( + ConstrainChannelLayouts(computation, channel_constraints)); + } + return Status::OK(); +} + +Status LayoutAssignment::ConstrainChannelLayouts( + HloComputation* computation, + ChannelLayoutConstraints* channel_constraints) { + // We go through the kRecvDone before. These must either impose their layout, + // of find a matching one already existing (ConstrainChannel() returns + // nullptr). for (HloInstruction* instruction : computation->instructions()) { + if (instruction->opcode() == HloOpcode::kRecvDone) { + const Layout* layout = channel_constraints->ConstrainChannel( + instruction->channel_id(), instruction->shape().layout()); + TF_RET_CHECK(layout == nullptr) + << instruction->ToString() + << " cannot constrain layout as it was set to " + << LayoutUtil::HumanString(*layout); + } + } + // After that we go through the kSend. These are likely going to have a kCopy + // as operand (otherwise we add it), so in case the constrained layout does + // not match, we can change the kCopy layout (and the kSend one as well). + for (HloInstruction* instruction : computation->MakeInstructionPostOrder()) { if (instruction->opcode() == HloOpcode::kSend) { - channel_constraints->ConstrainChannel( - instruction->channel_id(), instruction->operand(0)->shape().layout()); - } else if (instruction->opcode() == HloOpcode::kRecvDone) { - channel_constraints->ConstrainChannel(instruction->channel_id(), - instruction->shape().layout()); + HloInstruction* operand = instruction->mutable_operand(0); + const Layout* layout = channel_constraints->ConstrainChannel( + instruction->channel_id(), operand->shape().layout()); + if (layout != nullptr) { + // We found an already constrained layout which does not match the one + // the kSend wants to impose. Eitehr add a new kCopy, or use the + // existing one to marshal the correct shape. + Shape shape = operand->shape(); + *shape.mutable_layout() = *layout; + if (operand->opcode() != HloOpcode::kCopy) { + HloInstruction* copy = operand->parent()->AddInstruction( + HloInstruction::CreateUnary(shape, HloOpcode::kCopy, operand)); + RegisterAddedCopy(copy); + SetupCopiedInstruction(*operand, copy, {}); + TF_RETURN_IF_ERROR(instruction->ReplaceOperandWith(0, copy)); + operand = copy; + } else { + *operand->mutable_shape() = shape; + } + Shape* send_shape = + ShapeUtil::GetMutableSubshape(instruction->mutable_shape(), {0}); + *send_shape = shape; + } } } return Status::OK(); @@ -1743,6 +1792,7 @@ Status LayoutAssignment::ClearPreviousPassSideEffects(HloModule* module) { TF_RETURN_IF_ERROR(tuple_simplifier.Run(module).status()); TF_RETURN_IF_ERROR(dce.Run(module).status()); } + ResetChannelConstraints(); return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/layout_assignment.h b/tensorflow/compiler/xla/service/layout_assignment.h index c287cca0c5..eb4cd5936b 100644 --- a/tensorflow/compiler/xla/service/layout_assignment.h +++ b/tensorflow/compiler/xla/service/layout_assignment.h @@ -249,25 +249,30 @@ class ChannelLayoutConstraints { // Given `shape`, apply the layout for `channel_id`. `channel_id` must already // be constrained. Shape LayoutShapeForChannel(Shape shape, int64 channel_id) const { - CHECK(IsChannelConstrained(channel_id)); - *shape.mutable_layout() = constraints_.at(channel_id); + auto it = constraints_.find(channel_id); + CHECK(it != constraints_.end()) << "Channel " << channel_id; + *shape.mutable_layout() = it->second; return shape; } // Returns the layout constraint for `channel_id`, which must already be // constrained. - Layout LayoutForChannel(int64 channel_id) const { - CHECK(IsChannelConstrained(channel_id)); - return constraints_.at(channel_id); + const Layout& LayoutForChannel(int64 channel_id) const { + auto it = constraints_.find(channel_id); + CHECK(it != constraints_.end()) << "Channel " << channel_id; + return it->second; } // Adds a new layout constraint for `channel_id`. If a constraint for - // `channel_id` already exists, this operation requires that the new layout is - // the same as the previously constrained layout. - void ConstrainChannel(int64 channel_id, const Layout& layout) { - CHECK(!IsChannelConstrained(channel_id) || - LayoutUtil::Equal(layout, constraints_[channel_id])); - constraints_[channel_id] = layout; + // `channel_id` has been added, this API returns nullptr, otherwise returns + // the layout which has already been set for the channel. + const Layout* ConstrainChannel(int64 channel_id, const Layout& layout) { + auto it = constraints_.emplace(std::make_pair(channel_id, layout)); + if (it.second) { + return nullptr; + } + return LayoutUtil::Equal(layout, it.first->second) ? nullptr + : &it.first->second; } private: @@ -464,6 +469,20 @@ class LayoutAssignment : public HloPassInterface { // itself). Status AddCopyForOperand(HloInstruction* instruction, int64 operand_number); + // Apply the channel layout constraints by populating the channel_constraints + // data structure passed in at constructor time. Eventually adds copies in + // case two ends of a channel ended up with a different leyout. + Status ConstrainChannelLayouts(HloComputation* computation, + ChannelLayoutConstraints* channel_constraints); + + // Resets the input ChannelLayoutConstraints to the original copy received + // from the constructor input. + void ResetChannelConstraints() { + if (channel_layout_constraints_ != nullptr) { + *channel_layout_constraints_ = channel_constraints_; + } + } + // Map containing the layouts of all computations assigned so // far. Computations are handled in a topological sort where computations are // handled before their caller instructions so the layouts of caller @@ -474,7 +493,14 @@ class LayoutAssignment : public HloPassInterface { // here. tensorflow::gtl::FlatSet added_copies_; - ChannelLayoutConstraints* channel_layout_constraints_; + // The pointer to the channel layout constraints passed in with the + // constructor. If not nullptr, this is an input/output argument. + ChannelLayoutConstraints* channel_layout_constraints_ = nullptr; + + // A copy of the input layout constraints used to reset the above pointer in + // case we have to undo operations due to the multiple passes over the + // computations/instructions. + ChannelLayoutConstraints channel_constraints_; }; } // namespace xla diff --git a/tensorflow/compiler/xla/service/layout_assignment_test.cc b/tensorflow/compiler/xla/service/layout_assignment_test.cc index bf0448a676..62599b376a 100644 --- a/tensorflow/compiler/xla/service/layout_assignment_test.cc +++ b/tensorflow/compiler/xla/service/layout_assignment_test.cc @@ -52,10 +52,18 @@ using ::testing::ElementsAre; class LayoutAssignmentTest : public HloTestBase { protected: void AssignLayouts(HloModule* module, - ComputationLayout* entry_computation_layout) { - LayoutAssignment layout_assignment(entry_computation_layout); + ComputationLayout* entry_computation_layout, + ChannelLayoutConstraints* channel_constraints = nullptr) { + LayoutAssignment layout_assignment( + entry_computation_layout, /*channel_constraints=*/channel_constraints); EXPECT_IS_OK(layout_assignment.Run(module).status()); } + + std::vector LayoutOf(HloModule* module, tensorflow::StringPiece name) { + auto minor_to_major = + FindInstruction(module, name)->shape().layout().minor_to_major(); + return std::vector(minor_to_major.begin(), minor_to_major.end()); + } }; TEST_F(LayoutAssignmentTest, ComputationLayout) { @@ -707,17 +715,10 @@ TEST_F(LayoutAssignmentTest, GTEInheritsLayoutFromOperand) { LayoutUtil::MakeLayout({2, 1, 0})); AssignLayouts(module.get(), &computation_layout); - auto layout_of = [&](tensorflow::StringPiece name) { - return FindInstruction(module.get(), name) - ->shape() - .layout() - .minor_to_major(); - }; - - EXPECT_THAT(layout_of("gte0"), ElementsAre(0, 1, 2)); - EXPECT_THAT(layout_of("gte1a"), ElementsAre(1, 2, 0)); - EXPECT_THAT(layout_of("gte1b"), ElementsAre(2, 0, 1)); - EXPECT_THAT(layout_of("fresult"), ElementsAre(2, 1, 0)); + EXPECT_THAT(LayoutOf(module.get(), "gte0"), ElementsAre(0, 1, 2)); + EXPECT_THAT(LayoutOf(module.get(), "gte1a"), ElementsAre(1, 2, 0)); + EXPECT_THAT(LayoutOf(module.get(), "gte1b"), ElementsAre(2, 0, 1)); + EXPECT_THAT(LayoutOf(module.get(), "fresult"), ElementsAre(2, 1, 0)); EXPECT_THAT(FindInstruction(module.get(), "gte1") ->shape() .tuple_shapes(0) @@ -816,5 +817,44 @@ TEST_F(LayoutAssignmentTest, InternalErrorOnBitcast) { "Unexpected bitcast operation seen during layout assignment")); } +TEST_F(LayoutAssignmentTest, ChannelLayoutMismatch) { + // Pin non matching layouts to parameter and root. + const char* module_str = R"( + HloModule test_module + + ENTRY entry_computation { + param = (f32[2,2]) parameter(0) + gte = f32[2,2] get-tuple-element(param), index=0 + recv = (f32[2,2], u32[]) recv(), channel_id=1, sharding={maximal device=1} + ROOT recv-done = f32[2,2] recv-done(recv), channel_id=1, + sharding={maximal device=1} + send = (f32[2,2], u32[]) send(gte), channel_id=1, + sharding={maximal device=0} + send-done = () send-done(send), channel_id=1, sharding={maximal device=0} + } + )"; + + auto module = ParseHloString(module_str).ValueOrDie(); + ComputationLayout computation_layout( + module->entry_computation()->ComputeProgramShape()); + Shape param_shape = ShapeUtil::MakeTupleShape( + {ShapeUtil::MakeShapeWithLayout(F32, {2, 2}, {0, 1})}); + TF_ASSERT_OK( + computation_layout.mutable_parameter_layout(0)->CopyLayoutFromShape( + param_shape)); + computation_layout.mutable_result_layout()->ResetLayout( + LayoutUtil::MakeLayout({1, 0})); + + ChannelLayoutConstraints channel_constraints; + AssignLayouts(module.get(), &computation_layout, &channel_constraints); + + EXPECT_THAT(LayoutOf(module.get(), "gte"), ElementsAre(0, 1)); + EXPECT_THAT(LayoutOf(module.get(), "recv-done"), ElementsAre(1, 0)); + EXPECT_TRUE( + ShapeUtil::Equal(ShapeUtil::GetSubshape( + FindInstruction(module.get(), "send")->shape(), {0}), + ShapeUtil::MakeShapeWithLayout(F32, {2, 2}, {1, 0}))); +} + } // namespace } // namespace xla -- GitLab From e9a728681ba6395589d93608caa1977be9c8eac6 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 13 Jun 2018 21:00:43 -0700 Subject: [PATCH 438/816] Automated g4 rollback of changelist 200495346 PiperOrigin-RevId: 200500606 --- .../contrib/control_flow/python/cond_v2.py | 23 +- .../control_flow/python/cond_v2_test.py | 223 ------------------ tensorflow/python/framework/function.py | 54 +---- 3 files changed, 4 insertions(+), 296 deletions(-) diff --git a/tensorflow/contrib/control_flow/python/cond_v2.py b/tensorflow/contrib/control_flow/python/cond_v2.py index 90371cd8d7..b364e34511 100644 --- a/tensorflow/contrib/control_flow/python/cond_v2.py +++ b/tensorflow/contrib/control_flow/python/cond_v2.py @@ -48,30 +48,13 @@ def cond_v2(pred, true_fn, false_fn, name="cond"): name = "cond" with ops.name_scope(name) as scope: - # Identify if there is a caller device, & get the innermost if possible. - device_stack = ops.get_default_graph()._device_function_stack - caller_device = device_stack[-1] if device_stack else None - - caller_colocation_stack = ops.get_default_graph()._colocation_stack - caller_container = ops.get_default_graph()._container - caller_collection_ref = ops.get_default_graph()._collections - func_name_prefix = scope.replace("/", "_") true_graph = function.func_graph_from_py_func( - true_fn, [], [], - name="%strue" % func_name_prefix, - device=caller_device, - colocation_stack=caller_colocation_stack, - collections_ref=caller_collection_ref, - container=caller_container) + true_fn, [], [], name="%strue" % func_name_prefix) false_graph = function.func_graph_from_py_func( - false_fn, [], [], - name="%sfalse" % func_name_prefix, - device=caller_device, - colocation_stack=caller_colocation_stack, - collections_ref=caller_collection_ref, - container=caller_container) + false_fn, [], [], name="%sfalse" % func_name_prefix) + _check_same_outputs(true_graph, false_graph) # Add inputs to true_graph and false_graph to make them match. Note that diff --git a/tensorflow/contrib/control_flow/python/cond_v2_test.py b/tensorflow/contrib/control_flow/python/cond_v2_test.py index 94ed3e130b..b7d4c16df4 100644 --- a/tensorflow/contrib/control_flow/python/cond_v2_test.py +++ b/tensorflow/contrib/control_flow/python/cond_v2_test.py @@ -25,13 +25,10 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops -from tensorflow.python.ops import data_flow_ops from tensorflow.python.ops import gradients_impl from tensorflow.python.ops import math_ops -from tensorflow.python.ops import variables from tensorflow.python.platform import test from tensorflow.python.training import saver -from tensorflow.python.util import compat class NewCondTest(test.TestCase): @@ -201,225 +198,5 @@ class NewCondTest(test.TestCase): self.assertEqual(false_val, [0.0]) -class CondV2CollectionTest(test.TestCase): - - def testCollectionIntValueAccessInCond(self): - """Read values from graph collections inside of cond_v2.""" - with ops.Graph().as_default() as g: - with self.test_session(graph=g): - x = 2 - y = 5 - ops.add_to_collection("x", x) - ops.add_to_collection("y", y) - def fn(): - x_const = constant_op.constant(ops.get_collection("x")[0]) - y_const = constant_op.constant(ops.get_collection("y")[0]) - return math_ops.add(x_const, y_const) - - cnd = cond_v2.cond_v2(True, fn, fn) - self.assertEquals(cnd[0].eval(), 7) - - def testCollectionTensorValueAccessInCond(self): - """Read tensors from collections inside of cond_v2 & use them.""" - with ops.Graph().as_default() as g: - with self.test_session(graph=g): - x = constant_op.constant(2) - y = constant_op.constant(5) - ops.add_to_collection("x", x) - ops.add_to_collection("y", y) - - def fn(): - x_read = ops.get_collection("x")[0] - y_read = ops.get_collection("y")[0] - return math_ops.add(x_read, y_read) - - cnd = cond_v2.cond_v2(math_ops.less(x, y), fn, fn) - self.assertEquals(cnd[0].eval(), 7) - - def testCollectionIntValueWriteInCond(self): - """Make sure Int writes to collections work inside of cond_v2.""" - with ops.Graph().as_default() as g: - with self.test_session(graph=g): - x = constant_op.constant(2) - y = constant_op.constant(5) - def true_fn(): - z = math_ops.add(x, y) - ops.add_to_collection("z", 7) - return math_ops.mul(x, z) - - def false_fn(): - z = math_ops.add(x, y) - return math_ops.mul(x, z) - - cnd = cond_v2.cond_v2( - True, true_fn, - false_fn) - self.assertEquals(cnd[0].eval(), 14) - - read_z_collection = ops.get_collection("z") - self.assertEquals(read_z_collection, [7]) - - -class CondV2ContainerTest(test.TestCase): - - def testContainer(self): - """Set containers outside & inside of cond_v2. - - Make sure the containers are set correctly for both variable creation - (tested by variables.Variable) and for stateful ops (tested by FIFOQueue) - """ - with ops.Graph().as_default() as g: - with self.test_session(graph=g): - - v0 = variables.Variable([0]) - q0 = data_flow_ops.FIFOQueue(1, dtypes.float32) - - def container(node): - return node.op.get_attr("container") - - self.assertEqual(compat.as_bytes(""), container(v0)) - self.assertEqual(compat.as_bytes(""), container(q0.queue_ref)) - - def true_fn(): - # When this branch is created in cond below, - # the container should begin with 'l1' - v1 = variables.Variable([1]) - q1 = data_flow_ops.FIFOQueue(1, dtypes.float32) - - with ops.container("l2t"): - v2 = variables.Variable([2]) - q2 = data_flow_ops.FIFOQueue(1, dtypes.float32) - - v3 = variables.Variable([1]) - q3 = data_flow_ops.FIFOQueue(1, dtypes.float32) - - self.assertEqual(compat.as_bytes("l1"), container(v1)) - self.assertEqual(compat.as_bytes("l1"), container(q1.queue_ref)) - self.assertEqual(compat.as_bytes("l2t"), container(v2)) - self.assertEqual(compat.as_bytes("l2t"), container(q2.queue_ref)) - self.assertEqual(compat.as_bytes("l1"), container(v3)) - self.assertEqual(compat.as_bytes("l1"), container(q3.queue_ref)) - - return constant_op.constant(2.0) - - def false_fn(): - # When this branch is created in cond below, - # the container should begin with 'l1' - v1 = variables.Variable([1]) - q1 = data_flow_ops.FIFOQueue(1, dtypes.float32) - - with ops.container("l2f"): - v2 = variables.Variable([2]) - q2 = data_flow_ops.FIFOQueue(1, dtypes.float32) - - v3 = variables.Variable([1]) - q3 = data_flow_ops.FIFOQueue(1, dtypes.float32) - - self.assertEqual(compat.as_bytes("l1"), container(v1)) - self.assertEqual(compat.as_bytes("l1"), container(q1.queue_ref)) - self.assertEqual(compat.as_bytes("l2f"), container(v2)) - self.assertEqual(compat.as_bytes("l2f"), container(q2.queue_ref)) - self.assertEqual(compat.as_bytes("l1"), container(v3)) - self.assertEqual(compat.as_bytes("l1"), container(q3.queue_ref)) - - return constant_op.constant(6.0) - - with ops.container("l1"): - cnd_true = cond_v2.cond_v2(True, true_fn, false_fn) - self.assertEquals(cnd_true[0].eval(), 2) - - cnd_false = cond_v2.cond_v2(False, true_fn, false_fn) - self.assertEquals(cnd_false[0].eval(), 6) - - v4 = variables.Variable([3]) - q4 = data_flow_ops.FIFOQueue(1, dtypes.float32) - v5 = variables.Variable([4]) - q5 = data_flow_ops.FIFOQueue(1, dtypes.float32) - - self.assertEqual(compat.as_bytes("l1"), container(v4)) - self.assertEqual(compat.as_bytes("l1"), container(q4.queue_ref)) - self.assertEqual(compat.as_bytes(""), container(v5)) - self.assertEqual(compat.as_bytes(""), container(q5.queue_ref)) - - -class CondV2ColocationGroupAndDeviceTest(test.TestCase): - - def testColocateWithBeforeCond(self): - with ops.Graph().as_default() as g: - with self.test_session(graph=g): - - a = constant_op.constant([2.0], name="a") - b = constant_op.constant([2.0], name="b") - - def fn(): - c = constant_op.constant(3.0) - self.assertEqual([b"loc:@a"], c.op.colocation_groups()) - return c - - with ops.colocate_with(a.op): - self.assertEquals(cond_v2.cond_v2(True, fn, fn)[0].eval(), 3) - - def fn2(): - c = constant_op.constant(3.0) - self.assertEqual([b"loc:@a", b"loc:@b"], c.op.colocation_groups()) - return c - - with ops.colocate_with(a.op): - with ops.colocate_with(b.op): - self.assertEquals(cond_v2.cond_v2(True, fn2, fn2)[0].eval(), 3) - - def testColocateWithInAndOutOfCond(self): - with ops.Graph().as_default() as g: - with self.test_session(graph=g): - - a = constant_op.constant([2.0], name="a") - b = constant_op.constant([2.0], name="b") - - def fn2(): - with ops.colocate_with(b.op): - c = constant_op.constant(3.0) - self.assertEqual([b"loc:@a", b"loc:@b"], c.op.colocation_groups()) - return c - - with ops.colocate_with(a.op): - self.assertEquals(cond_v2.cond_v2(True, fn2, fn2)[0].eval(), 3) - - d = constant_op.constant([2.0], name="d") - self.assertEqual([b"loc:@a"], d.op.colocation_groups()) - - def testDeviceBeforeCond(self): - with ops.Graph().as_default() as g: - with self.test_session(graph=g): - def fn(): - c = constant_op.constant(3.0) - self.assertEqual("/device:CPU:0", c.op.device) - return c - - with ops.device("/device:CPU:0"): - self.assertEquals(cond_v2.cond_v2(True, fn, fn)[0].eval(), 3) - - def fn2(): - c = constant_op.constant(3.0) - self.assertEqual("/device:GPU:0", c.op.device) - return c - - with ops.device("/device:GPU:0"): - self.assertEquals(cond_v2.cond_v2(True, fn2, fn2)[0].eval(), 3) - - def testDeviceInAndOutOfCond(self): - with ops.Graph().as_default() as g: - with self.test_session(graph=g): - def fn2(): - with ops.device("/device:GPU:0"): - c = constant_op.constant(3.0) - self.assertEqual("/device:GPU:0", c.op.device) - return c - - with ops.device("/device:CPU:0"): - self.assertEquals(cond_v2.cond_v2(True, fn2, fn2)[0].eval(), 3) - - d = constant_op.constant(4.0) - self.assertEqual("/device:CPU:0", d.op.device) - if __name__ == "__main__": test.main() diff --git a/tensorflow/python/framework/function.py b/tensorflow/python/framework/function.py index 002a3d3be5..82ecba310b 100644 --- a/tensorflow/python/framework/function.py +++ b/tensorflow/python/framework/function.py @@ -36,7 +36,6 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variable_scope as vs from tensorflow.python.util import compat -from tensorflow.python.util import tf_contextlib from tensorflow.python.util import tf_decorator from tensorflow.python.util import tf_inspect @@ -651,41 +650,6 @@ class _FuncGraph(ops.Graph): # TODO(skyewm): is this needed? self.extra_vars = [] - # pylint: disable=g-doc-return-or-yield - - @tf_contextlib.contextmanager - def container(self, container_name): - """Returns a context manager that specifies the resource container to use. - - Overridden from @{tf.Graph} to update both the init_scope container - and the present inner container. This is necessary to make sure setting - containers applies correctly both to created variables and to stateful - ops. - - Args: - container_name: container name string. - - Returns: - A context manager for defining resource containers for stateful ops, - yields the container name. - """ - original_container = self._container - # pylint: disable=protected-access - with ops.init_scope(): - original_init_container = ops.get_default_graph()._container - try: - self._container = container_name - with ops.init_scope(): - ops.get_default_graph()._container = container_name - yield self._container - finally: - self._container = original_container - with ops.init_scope(): - ops.get_default_graph()._container = original_init_container - # pylint: enable=protected-access - - # pylint: enable=g-doc-return-or-yield - def getvar( self, getter, @@ -809,9 +773,7 @@ class _FuncGraph(ops.Graph): def func_graph_from_py_func(func, arg_names, arg_types, name=None, - capture_by_value=False, device=None, - colocation_stack=None, container=None, - collections_ref=None): + capture_by_value=False, device=None): """Returns a _FuncGraph generated from `func`. Args: @@ -824,10 +786,6 @@ def func_graph_from_py_func(func, arg_names, arg_types, name=None, capture_by_value: boolean. If True, captured values will be copied into the function body. device: device name or function. - colocation_stack: A colocation stack (list) the _FuncGraph should use. - container: A container name the _FuncGraph should start with. - collections_ref: A reference to a collections dict the _FuncGraph should - use internally. Returns: A _FuncGraph. @@ -838,17 +796,7 @@ def func_graph_from_py_func(func, arg_names, arg_types, name=None, if not name: name = _get_func_name(func) func_graph = _FuncGraph(name, capture_by_value) - with func_graph.as_default(), ops.device(device): - # pylint: disable=protected-access - if collections_ref is not None: - func_graph._collections = collections_ref - if container is not None: - func_graph._container = container - if colocation_stack is not None: - func_graph._colocation_stack = colocation_stack - # pylint: enable=protected-access - # Create placeholders for the function arguments. for (argname, argtype) in zip(arg_names, arg_types): argholder = array_ops.placeholder(argtype, name=argname) -- GitLab From c570211c5cd972a278366d3d3fd65ee8f99836aa Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 13 Jun 2018 21:36:17 -0700 Subject: [PATCH 439/816] Re-enable compilation for MacOS. This was unintentionally broken previously when unifying the nvcc/gcc and cuda-clang toolchains. PiperOrigin-RevId: 200503048 --- third_party/gpus/crosstool/CROSSTOOL.tpl | 242 +++++++++++++++++++++++ 1 file changed, 242 insertions(+) diff --git a/third_party/gpus/crosstool/CROSSTOOL.tpl b/third_party/gpus/crosstool/CROSSTOOL.tpl index 60b19daf1d..1424ff6511 100644 --- a/third_party/gpus/crosstool/CROSSTOOL.tpl +++ b/third_party/gpus/crosstool/CROSSTOOL.tpl @@ -295,3 +295,245 @@ toolchain { %{host_compiler_includes} } + +toolchain { + abi_version: "local" + abi_libc_version: "local" + compiler: "compiler" + host_system_name: "local" + needsPic: true + target_libc: "macosx" + target_cpu: "darwin" + target_system_name: "local" + toolchain_identifier: "local_darwin" + feature { + name: "c++11" + flag_set { + action: "c++-compile" + flag_group { + flag: "-std=c++11" + } + } + } + + feature { + name: "stdlib" + flag_set { + action: "c++-link-executable" + action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" + flag_group { + flag: "-lc++" + } + } + } + + feature { + name: "determinism" + flag_set { + action: "c-compile" + action: "c++-compile" + flag_group { + # Make C++ compilation deterministic. Use linkstamping instead of these + # compiler symbols. + flag: "-Wno-builtin-macro-redefined" + flag: "-D__DATE__=\"redacted\"" + flag: "-D__TIMESTAMP__=\"redacted\"" + flag: "-D__TIME__=\"redacted\"" + } + } + } + + # This feature will be enabled for builds that support pic by bazel. + feature { + name: "pic" + flag_set { + action: "c-compile" + action: "c++-compile" + flag_group { + expand_if_all_available: "pic" + flag: "-fPIC" + } + flag_group { + expand_if_none_available: "pic" + flag: "-fPIE" + } + } + } + + # Security hardening on by default. + feature { + name: "hardening" + flag_set { + action: "c-compile" + action: "c++-compile" + flag_group { + # Conservative choice; -D_FORTIFY_SOURCE=2 may be unsafe in some cases. + # We need to undef it before redefining it as some distributions now + # have it enabled by default. + flag: "-U_FORTIFY_SOURCE" + flag: "-D_FORTIFY_SOURCE=1" + flag: "-fstack-protector" + } + } + flag_set { + action: "c++-link-executable" + flag_group { + flag: "-pie" + } + } + } + + feature { + name: "warnings" + flag_set { + action: "c-compile" + action: "c++-compile" + flag_group { + # All warnings are enabled. Maybe enable -Werror as well? + flag: "-Wall" + %{host_compiler_warnings} + } + } + } + + # Keep stack frames for debugging, even in opt mode. + feature { + name: "frame-pointer" + flag_set { + action: "c-compile" + action: "c++-compile" + flag_group { + flag: "-fno-omit-frame-pointer" + } + } + } + + feature { + name: "no-canonical-prefixes" + flag_set { + action: "c-compile" + action: "c++-compile" + action: "c++-link-executable" + action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" + flag_group { + flag:"-no-canonical-prefixes" + } + } + } + + feature { + name: "disable-assertions" + flag_set { + action: "c-compile" + action: "c++-compile" + flag_group { + flag: "-DNDEBUG" + } + } + } + + feature { + name: "linker-bin-path" + + flag_set { + action: "c++-link-executable" + action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" + flag_group { + flag: "-B/usr/bin/" + } + } + } + + feature { + name: "undefined-dynamic" + flag_set { + action: "c++-link-dynamic-library" + action: "c++-link-nodeps-dynamic-library" + action: "c++-link-executable" + flag_group { + flag: "-undefined" + flag: "dynamic_lookup" + } + } + } + + feature { + name: "common" + implies: "stdlib" + implies: "c++11" + implies: "determinism" + implies: "hardening" + implies: "warnings" + implies: "frame-pointer" + implies: "no-canonical-prefixes" + implies: "linker-bin-path" + implies: "undefined-dynamic" + } + + feature { + name: "opt" + implies: "common" + implies: "disable-assertions" + + flag_set { + action: "c-compile" + action: "c++-compile" + flag_group { + # No debug symbols. + # Maybe we should enable https://gcc.gnu.org/wiki/DebugFission for opt + # or even generally? However, that can't happen here, as it requires + # special handling in Bazel. + flag: "-g0" + + # Conservative choice for -O + # -O3 can increase binary size and even slow down the resulting binaries. + # Profile first and / or use FDO if you need better performance than this. + flag: "-O2" + + # Removal of unused code and data at link time (can this increase binary size in some cases?). + flag: "-ffunction-sections" + flag: "-fdata-sections" + } + } + } + + feature { + name: "fastbuild" + implies: "common" + } + + feature { + name: "dbg" + implies: "common" + flag_set { + action: "c-compile" + action: "c++-compile" + flag_group { + flag: "-g" + } + } + } + + # Set clang as a C/C++ compiler. + tool_path { name: "gcc" path: "%{host_compiler_path}" } + + # Use the default system toolchain for everything else. + tool_path { name: "ar" path: "/usr/bin/libtool" } + tool_path { name: "compat-ld" path: "/usr/bin/ld" } + tool_path { name: "cpp" path: "/usr/bin/cpp" } + tool_path { name: "dwp" path: "/usr/bin/dwp" } + tool_path { name: "gcov" path: "/usr/bin/gcov" } + tool_path { name: "ld" path: "/usr/bin/ld" } + tool_path { name: "nm" path: "/usr/bin/nm" } + tool_path { name: "objcopy" path: "/usr/bin/objcopy" } + tool_path { name: "objdump" path: "/usr/bin/objdump" } + tool_path { name: "strip" path: "/usr/bin/strip" } + + # Enabled dynamic linking. + linking_mode_flags { mode: DYNAMIC } + +%{host_compiler_includes} +} -- GitLab From 0b8c5806f4f1d3a47b30bf203b3e456f036b0adc Mon Sep 17 00:00:00 2001 From: James Qin Date: Thu, 14 Jun 2018 02:02:26 -0700 Subject: [PATCH 440/816] Remove hardcoded dtype in tf.layers.xxx() function call to make them compatible with mixed precision training apis. tf.layers.foolayer(inputs) creates a tf.layer.FooLayer(dtype=inputs.dtype) and immediately invokes __call__() on the input. The dtype in the Foolayer() constructor isn't needed. Plus it stands in the way for global mixed precision dtype we plan to add in the future. PiperOrigin-RevId: 200524027 --- tensorflow/python/layers/convolutional.py | 5 ----- tensorflow/python/layers/core.py | 1 - tensorflow/python/layers/normalization.py | 1 - 3 files changed, 7 deletions(-) diff --git a/tensorflow/python/layers/convolutional.py b/tensorflow/python/layers/convolutional.py index 267d78dbcb..36cef3855e 100644 --- a/tensorflow/python/layers/convolutional.py +++ b/tensorflow/python/layers/convolutional.py @@ -217,7 +217,6 @@ def conv1d(inputs, bias_constraint=bias_constraint, trainable=trainable, name=name, - dtype=inputs.dtype.base_dtype, _reuse=reuse, _scope=name) return layer.apply(inputs) @@ -421,7 +420,6 @@ def conv2d(inputs, bias_constraint=bias_constraint, trainable=trainable, name=name, - dtype=inputs.dtype.base_dtype, _reuse=reuse, _scope=name) return layer.apply(inputs) @@ -627,7 +625,6 @@ def conv3d(inputs, bias_constraint=bias_constraint, trainable=trainable, name=name, - dtype=inputs.dtype.base_dtype, _reuse=reuse, _scope=name) return layer.apply(inputs) @@ -1266,7 +1263,6 @@ def conv2d_transpose(inputs, bias_constraint=bias_constraint, trainable=trainable, name=name, - dtype=inputs.dtype.base_dtype, _reuse=reuse, _scope=name) return layer.apply(inputs) @@ -1438,7 +1434,6 @@ def conv3d_transpose(inputs, bias_constraint=bias_constraint, trainable=trainable, name=name, - dtype=inputs.dtype.base_dtype, _reuse=reuse, _scope=name) return layer.apply(inputs) diff --git a/tensorflow/python/layers/core.py b/tensorflow/python/layers/core.py index abbacac442..aadff231da 100644 --- a/tensorflow/python/layers/core.py +++ b/tensorflow/python/layers/core.py @@ -184,7 +184,6 @@ def dense( bias_constraint=bias_constraint, trainable=trainable, name=name, - dtype=inputs.dtype.base_dtype, _scope=name, _reuse=reuse) return layer.apply(inputs) diff --git a/tensorflow/python/layers/normalization.py b/tensorflow/python/layers/normalization.py index d082e312e9..ece6667981 100644 --- a/tensorflow/python/layers/normalization.py +++ b/tensorflow/python/layers/normalization.py @@ -308,7 +308,6 @@ def batch_normalization(inputs, virtual_batch_size=virtual_batch_size, adjustment=adjustment, name=name, - dtype=inputs.dtype.base_dtype, _reuse=reuse, _scope=name) return layer.apply(inputs, training=training) -- GitLab From 8d9787bed57f1dd5d697ff847cd5598ecc032620 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 14 Jun 2018 02:14:42 -0700 Subject: [PATCH 441/816] Automated g4 rollback of changelist 200467580 PiperOrigin-RevId: 200525639 --- tensorflow/contrib/data/python/ops/iterator_ops_test.py | 2 +- .../contrib/estimator/python/estimator/hooks_test.py | 4 ++-- tensorflow/contrib/kfac/examples/tests/BUILD | 1 - .../learn/python/learn/estimators/composable_model_test.py | 2 +- .../python/learn/estimators/dnn_linear_combined_test.py | 2 +- tensorflow/contrib/learn/python/learn/monitors_test.py | 6 ++++++ tensorflow/contrib/linear_optimizer/python/ops/sdca_ops.py | 2 +- .../opt/python/training/drop_stale_gradient_optimizer.py | 7 +++---- tensorflow/contrib/slim/python/slim/learning_test.py | 4 +++- tensorflow/python/estimator/model_fn.py | 3 +-- tensorflow/python/saved_model/builder_impl.py | 7 +++---- tensorflow/python/training/training_util.py | 7 ++----- 12 files changed, 24 insertions(+), 23 deletions(-) diff --git a/tensorflow/contrib/data/python/ops/iterator_ops_test.py b/tensorflow/contrib/data/python/ops/iterator_ops_test.py index 628d983137..30a993b1f7 100644 --- a/tensorflow/contrib/data/python/ops/iterator_ops_test.py +++ b/tensorflow/contrib/data/python/ops/iterator_ops_test.py @@ -44,7 +44,7 @@ class CheckpointInputPipelineHookTest(test.TestCase): latest_feature = variables.Variable( 0, name='latest_feature', dtype=dtypes.int64) store_latest_feature_op = latest_feature.assign(features) - ops.add_to_collection('my_vars', global_step.read_value()) + ops.add_to_collection('my_vars', global_step) ops.add_to_collection('my_vars', latest_feature) return model_fn.EstimatorSpec( mode='train', diff --git a/tensorflow/contrib/estimator/python/estimator/hooks_test.py b/tensorflow/contrib/estimator/python/estimator/hooks_test.py index 685ca473bd..95ae971852 100644 --- a/tensorflow/contrib/estimator/python/estimator/hooks_test.py +++ b/tensorflow/contrib/estimator/python/estimator/hooks_test.py @@ -156,8 +156,8 @@ class InMemoryEvaluatorHookTest(test.TestCase): estimator.eval_dir()) # w = 0 if step==0 else step+2 self.assertEqual(0, step_keyword_to_value[0]['mean_of_const']) - self.assertEqual(5, step_keyword_to_value[4]['mean_of_const']) - self.assertEqual(11, step_keyword_to_value[10]['mean_of_const']) + self.assertEqual(6, step_keyword_to_value[4]['mean_of_const']) + self.assertEqual(12, step_keyword_to_value[10]['mean_of_const']) def test_dnn_classifier(self): embedding = feature_column_lib.embedding_column( diff --git a/tensorflow/contrib/kfac/examples/tests/BUILD b/tensorflow/contrib/kfac/examples/tests/BUILD index 72e623185b..ede7f183fe 100644 --- a/tensorflow/contrib/kfac/examples/tests/BUILD +++ b/tensorflow/contrib/kfac/examples/tests/BUILD @@ -28,7 +28,6 @@ py_test( srcs = ["convnet_test.py"], srcs_version = "PY2AND3", tags = [ - "no_oss", "no_pip", "notsan", ], diff --git a/tensorflow/contrib/learn/python/learn/estimators/composable_model_test.py b/tensorflow/contrib/learn/python/learn/estimators/composable_model_test.py index d84f9ad2be..ef5e620e8f 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/composable_model_test.py +++ b/tensorflow/contrib/learn/python/learn/estimators/composable_model_test.py @@ -56,7 +56,7 @@ def _base_model_fn(features, labels, mode, params): def _train_op_fn(loss): global_step = training_util.get_global_step() - assert global_step is not None + assert global_step train_step = model.get_train_step(loss) with ops.control_dependencies(train_step): diff --git a/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined_test.py b/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined_test.py index a3d6f1efb0..4e65c180d8 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined_test.py +++ b/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined_test.py @@ -1811,7 +1811,7 @@ class FeatureEngineeringFunctionTest(test.TestCase): prediction_without_fe_fn = next( estimator_without_fe_fn.predict_scores( input_fn=input_fn, as_iterable=True)) - self.assertAlmostEqual(100., prediction_without_fe_fn, delta=3.0) + self.assertAlmostEqual(100., prediction_without_fe_fn, delta=1.0) if __name__ == '__main__': diff --git a/tensorflow/contrib/learn/python/learn/monitors_test.py b/tensorflow/contrib/learn/python/learn/monitors_test.py index 8750f62299..5c34d0ddb0 100644 --- a/tensorflow/contrib/learn/python/learn/monitors_test.py +++ b/tensorflow/contrib/learn/python/learn/monitors_test.py @@ -802,6 +802,9 @@ class RunHookAdapterForMonitorsTest(test.TestCase): mon_sess.run(inc_5) for mon in [mock_mon, mock_mon2]: self.assertEqual(mon.output, {}) + self.assertEqual(mon.last_begin_step, 11) + self.assertEqual(mon.last_end_step, 11) + self.assertEqual(mon.last_post_step, 11) self.assertEqual(mon.call_counter['step_end'], 1) self.assertEqual(mon.call_counter['step_begin'], 1) self.assertEqual(mon.call_counter['post_step'], 1) @@ -809,6 +812,9 @@ class RunHookAdapterForMonitorsTest(test.TestCase): mon_sess.run(inc_5) for mon in [mock_mon, mock_mon2]: self.assertEqual(mon.output, {}) + self.assertEqual(mon.last_begin_step, 16) + self.assertEqual(mon.last_end_step, 16) + self.assertEqual(mon.last_post_step, 16) self.assertEqual(mon.call_counter['step_end'], 2) self.assertEqual(mon.call_counter['step_begin'], 2) self.assertEqual(mon.call_counter['post_step'], 2) diff --git a/tensorflow/contrib/linear_optimizer/python/ops/sdca_ops.py b/tensorflow/contrib/linear_optimizer/python/ops/sdca_ops.py index 2b5058e47d..0047d5753a 100644 --- a/tensorflow/contrib/linear_optimizer/python/ops/sdca_ops.py +++ b/tensorflow/contrib/linear_optimizer/python/ops/sdca_ops.py @@ -518,7 +518,7 @@ class SdcaModel(object): update_ops.append(state_ops.assign_add(v, split_update)) else: update_ops.append(state_ops.assign_add(w, u)) - if global_step is None: + if not global_step: return control_flow_ops.group(*update_ops) with ops.control_dependencies(update_ops): return state_ops.assign_add(global_step, 1, name=name).op diff --git a/tensorflow/contrib/opt/python/training/drop_stale_gradient_optimizer.py b/tensorflow/contrib/opt/python/training/drop_stale_gradient_optimizer.py index 918165bc6a..4a905b1b2a 100644 --- a/tensorflow/contrib/opt/python/training/drop_stale_gradient_optimizer.py +++ b/tensorflow/contrib/opt/python/training/drop_stale_gradient_optimizer.py @@ -63,7 +63,7 @@ class DropStaleGradientOptimizer(optimizer.Optimizer): def compute_gradients(self, loss, *args, **kwargs): # Record current global step for worker. with ops.colocate_with(loss): - self._local_step = training_util.get_global_step().read_value() + 0 + self._local_step = training_util.get_global_step() + 0 with ops.control_dependencies([self._local_step]): loss = gen_array_ops.identity(loss) @@ -102,7 +102,7 @@ class DropStaleGradientOptimizer(optimizer.Optimizer): with ops.control_dependencies(gradients), ops.colocate_with(global_step): staleness = gen_array_ops.reshape( - global_step.read_value() - self._local_step, shape=()) + global_step - self._local_step, shape=()) conditional_update = stale_counter.assign_add(control_flow_ops.cond( gen_math_ops.less_equal(staleness, self._staleness), @@ -110,6 +110,5 @@ class DropStaleGradientOptimizer(optimizer.Optimizer): summary.scalar( "Gradient staleness percentage", - stale_counter / (math_ops.cast(global_step.read_value() + 1, - dtypes.float32))) + stale_counter / (math_ops.cast(global_step + 1, dtypes.float32))) return conditional_update diff --git a/tensorflow/contrib/slim/python/slim/learning_test.py b/tensorflow/contrib/slim/python/slim/learning_test.py index 6bd55e7a24..831c6e427a 100644 --- a/tensorflow/contrib/slim/python/slim/learning_test.py +++ b/tensorflow/contrib/slim/python/slim/learning_test.py @@ -520,6 +520,8 @@ class TrainTest(test.TestCase): run_root = glob.glob(os.path.join(dump_root, 'run_*'))[-1] dump = debug_data.DebugDumpDir(run_root) + self.assertAllEqual(0, + dump.get_tensors('global_step', 0, 'DebugIdentity')[0]) def testTrainWithTrace(self): logdir = os.path.join( @@ -545,7 +547,7 @@ class TrainTest(test.TestCase): log_every_n_steps=10, trace_every_n_steps=100) self.assertIsNotNone(loss) - for trace_step in [0, 100, 200]: + for trace_step in [1, 101, 201]: trace_filename = 'tf_trace-%d.json' % trace_step self.assertTrue(os.path.isfile(os.path.join(logdir, trace_filename))) diff --git a/tensorflow/python/estimator/model_fn.py b/tensorflow/python/estimator/model_fn.py index d8bdd35bdc..c60c7f63ba 100644 --- a/tensorflow/python/estimator/model_fn.py +++ b/tensorflow/python/estimator/model_fn.py @@ -26,7 +26,6 @@ import six from tensorflow.python.estimator.export.export_output import ExportOutput from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape -from tensorflow.python.framework import tensor_util from tensorflow.python.ops import array_ops from tensorflow.python.saved_model import signature_constants from tensorflow.python.saved_model import tag_constants @@ -387,7 +386,7 @@ class _TPUEstimatorSpec(collections.namedtuple('TPUEstimatorSpec', [ def _check_is_tensor_or_operation(x, name): - if not (isinstance(x, ops.Operation) or tensor_util.is_tensor(x)): + if not (isinstance(x, ops.Operation) or isinstance(x, ops.Tensor)): raise TypeError('{} must be Operation or Tensor, given: {}'.format(name, x)) diff --git a/tensorflow/python/saved_model/builder_impl.py b/tensorflow/python/saved_model/builder_impl.py index 531da052ac..e58be804c2 100644 --- a/tensorflow/python/saved_model/builder_impl.py +++ b/tensorflow/python/saved_model/builder_impl.py @@ -28,7 +28,6 @@ from tensorflow.core.protobuf import saved_model_pb2 from tensorflow.core.protobuf import saver_pb2 from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops -from tensorflow.python.framework import tensor_util from tensorflow.python.lib.io import file_io from tensorflow.python.ops import variables from tensorflow.python.platform import tf_logging @@ -179,11 +178,11 @@ class SavedModelBuilder(object): stored as a collection with key TRAIN_OP_KEY, but not executed. Raises: - TypeError if Train op is not of type `Operation` or a Tensor. + TypeError if Train op is not of type `Operation`. """ if train_op is not None: - if not (tensor_util.is_tensor(train_op) or - isinstance(train_op, ops.Operation)): + if (not isinstance(train_op, ops.Tensor) and + not isinstance(train_op, ops.Operation)): raise TypeError("train_op needs to be a Tensor or Op: %r" % train_op) ops.add_to_collection(constants.TRAIN_OP_KEY, train_op) diff --git a/tensorflow/python/training/training_util.py b/tensorflow/python/training/training_util.py index 59ba7d3c23..0877b2a8a2 100644 --- a/tensorflow/python/training/training_util.py +++ b/tensorflow/python/training/training_util.py @@ -128,8 +128,7 @@ def create_global_step(graph=None): initializer=init_ops.zeros_initializer(), trainable=False, collections=[ops.GraphKeys.GLOBAL_VARIABLES, - ops.GraphKeys.GLOBAL_STEP], - use_resource=True) + ops.GraphKeys.GLOBAL_STEP]) # Create in proper graph and base name_scope. with graph.as_default() as g, g.name_scope(None): return variable_scope.get_variable( @@ -139,9 +138,7 @@ def create_global_step(graph=None): initializer=init_ops.zeros_initializer(), trainable=False, collections=[ops.GraphKeys.GLOBAL_VARIABLES, - ops.GraphKeys.GLOBAL_STEP], - caching_device='cpu:0', - use_resource=True) + ops.GraphKeys.GLOBAL_STEP]) @tf_export('train.get_or_create_global_step') -- GitLab From 83a48e092b6282f7fdbf4b0059eb0da146b68f42 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 14 Jun 2018 02:21:04 -0700 Subject: [PATCH 442/816] Provide the ability to specify, in tf.train.MonitoredTrainingSession(), a separate summary directory. When set, summary_dir is passed as output directory to StepCounterHook and SummarySaverHook. When unset, the behavior is unchanged and checkpoint_dir is used instead. PiperOrigin-RevId: 200526130 --- tensorflow/python/training/monitored_session.py | 14 ++++++++++---- tensorflow/tools/api/golden/tensorflow.train.pbtxt | 2 +- 2 files changed, 11 insertions(+), 5 deletions(-) diff --git a/tensorflow/python/training/monitored_session.py b/tensorflow/python/training/monitored_session.py index fece3370f3..7b06bffa4b 100644 --- a/tensorflow/python/training/monitored_session.py +++ b/tensorflow/python/training/monitored_session.py @@ -298,7 +298,8 @@ def MonitoredTrainingSession(master='', # pylint: disable=invalid-name stop_grace_period_secs=120, log_step_count_steps=100, max_wait_secs=7200, - save_checkpoint_steps=USE_DEFAULT): + save_checkpoint_steps=USE_DEFAULT, + summary_dir=None): """Creates a `MonitoredSession` for training. For a chief, this utility sets proper session initializer/restorer. It also @@ -348,6 +349,8 @@ def MonitoredTrainingSession(master='', # pylint: disable=invalid-name `save_checkpoint_steps` and `save_checkpoint_secs` are set to `None`, then the default checkpoint saver isn't used. If both are provided, then only `save_checkpoint_secs` is used. Default not enabled. + summary_dir: A string. Optional path to a directory where to + save summaries. If None, checkpoint_dir is used instead. Returns: A `MonitoredSession` object. @@ -388,11 +391,12 @@ def MonitoredTrainingSession(master='', # pylint: disable=invalid-name master=master, config=config) - if checkpoint_dir: + summary_dir = summary_dir or checkpoint_dir + if summary_dir: if log_step_count_steps and log_step_count_steps > 0: all_hooks.append( basic_session_run_hooks.StepCounterHook( - output_dir=checkpoint_dir, every_n_steps=log_step_count_steps)) + output_dir=summary_dir, every_n_steps=log_step_count_steps)) if (save_summaries_steps and save_summaries_steps > 0) or ( save_summaries_secs and save_summaries_secs > 0): @@ -400,7 +404,9 @@ def MonitoredTrainingSession(master='', # pylint: disable=invalid-name scaffold=scaffold, save_steps=save_summaries_steps, save_secs=save_summaries_secs, - output_dir=checkpoint_dir)) + output_dir=summary_dir)) + + if checkpoint_dir: if (save_checkpoint_secs and save_checkpoint_secs > 0) or ( save_checkpoint_steps and save_checkpoint_steps > 0): all_hooks.append(basic_session_run_hooks.CheckpointSaverHook( diff --git a/tensorflow/tools/api/golden/tensorflow.train.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.pbtxt index 5f45b3b1ad..b0fb04d7d4 100644 --- a/tensorflow/tools/api/golden/tensorflow.train.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.train.pbtxt @@ -242,7 +242,7 @@ tf_module { } member_method { name: "MonitoredTrainingSession" - argspec: "args=[\'master\', \'is_chief\', \'checkpoint_dir\', \'scaffold\', \'hooks\', \'chief_only_hooks\', \'save_checkpoint_secs\', \'save_summaries_steps\', \'save_summaries_secs\', \'config\', \'stop_grace_period_secs\', \'log_step_count_steps\', \'max_wait_secs\', \'save_checkpoint_steps\'], varargs=None, keywords=None, defaults=[\'\', \'True\', \'None\', \'None\', \'None\', \'None\', \'\', \'\', \'\', \'None\', \'120\', \'100\', \'7200\', \'\'], " + argspec: "args=[\'master\', \'is_chief\', \'checkpoint_dir\', \'scaffold\', \'hooks\', \'chief_only_hooks\', \'save_checkpoint_secs\', \'save_summaries_steps\', \'save_summaries_secs\', \'config\', \'stop_grace_period_secs\', \'log_step_count_steps\', \'max_wait_secs\', \'save_checkpoint_steps\', \'summary_dir\'], varargs=None, keywords=None, defaults=[\'\', \'True\', \'None\', \'None\', \'None\', \'None\', \'\', \'\', \'\', \'None\', \'120\', \'100\', \'7200\', \'\', \'None\'], " } member_method { name: "NewCheckpointReader" -- GitLab From 03dd23166973ea129ea573ddb4db1f0287b98b78 Mon Sep 17 00:00:00 2001 From: Adrian Kuegel Date: Thu, 14 Jun 2018 03:35:55 -0700 Subject: [PATCH 443/816] Extract HloExecutionProfiler into its own file. This is in preparation of passing it on to the Thunks, so that we can profile HloInstructions within a while loop. PiperOrigin-RevId: 200532394 --- tensorflow/compiler/xla/service/gpu/BUILD | 14 ++++ .../xla/service/gpu/gpu_executable.cc | 73 +---------------- .../xla/service/gpu/hlo_execution_profiler.cc | 82 +++++++++++++++++++ .../xla/service/gpu/hlo_execution_profiler.h | 68 +++++++++++++++ 4 files changed, 165 insertions(+), 72 deletions(-) create mode 100644 tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.cc create mode 100644 tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h diff --git a/tensorflow/compiler/xla/service/gpu/BUILD b/tensorflow/compiler/xla/service/gpu/BUILD index 5e02631a58..541a5275a3 100644 --- a/tensorflow/compiler/xla/service/gpu/BUILD +++ b/tensorflow/compiler/xla/service/gpu/BUILD @@ -237,6 +237,19 @@ cc_library( ], ) +cc_library( + name = "hlo_execution_profiler", + srcs = ["hlo_execution_profiler.cc"], + hdrs = ["hlo_execution_profiler.h"], + deps = [ + "//tensorflow/compiler/xla/service:hlo", + "//tensorflow/compiler/xla/service:hlo_execution_profile", + "//tensorflow/compiler/xla/service:pool", + "//tensorflow/core:lib", + "//tensorflow/core:stream_executor_no_cuda", + ], +) + cc_library( name = "gpu_executable", srcs = [ @@ -278,6 +291,7 @@ cc_library( ":backend_configs", ":buffer_allocations", ":cudnn_convolution_runner", + ":hlo_execution_profiler", ":infeed_manager", ":ir_emission_utils", ":partition_assignment", diff --git a/tensorflow/compiler/xla/service/gpu/gpu_executable.cc b/tensorflow/compiler/xla/service/gpu/gpu_executable.cc index 25d8f720ea..f20a828bc1 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_executable.cc +++ b/tensorflow/compiler/xla/service/gpu/gpu_executable.cc @@ -22,7 +22,7 @@ limitations under the License. #include "tensorflow/compiler/xla/map_util.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/service/gpu/buffer_allocations.h" -#include "tensorflow/compiler/xla/service/hlo_computation.h" +#include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/service/logical_buffer.h" #include "tensorflow/compiler/xla/service/shaped_buffer.h" @@ -41,77 +41,6 @@ namespace { using tensorflow::tracing::ScopedAnnotation; -// A helper class for profiling HLO in the course of GPU program execution. -// All of the profiling is guarded internally, to avoid the caller needing to -// have lots of conditionals sprinkled around. -class HloExecutionProfiler { - public: - // If profiling is enabled, start an execution timer running. - explicit HloExecutionProfiler( - bool do_profile, HloExecutionProfile* profile, se::Stream* stream, - const std::vector::SmartPtr>& sub_streams, - const HloComputation* computation) - : do_profile_(do_profile), - profile_(profile), - stream_(stream), - sub_streams_(sub_streams), - computation_(computation) { - if (do_profile_) { - clock_rate_ghz_ = - stream->parent()->GetDeviceDescription().clock_rate_ghz(); - execution_timer_.reset(new se::Timer(stream->parent())); - per_op_timer_.reset(new se::Timer(stream->parent())); - stream->InitTimer(execution_timer_.get()) - .ThenStartTimer(execution_timer_.get()); - stream->InitTimer(per_op_timer_.get()); - } - } - - // If profiling is enabled, sets the total cycle count on the profile from the - // execution timer. - void FinishExecution() { - CHECK(!finished_execution_) << "Call FinishExecution only once!"; - finished_execution_ = true; - if (do_profile_) { - stream_->ThenWaitFor(&sub_streams_); - stream_->ThenStopTimer(execution_timer_.get()); - stream_->BlockHostUntilDone().IgnoreError(); - profile_->set_total_cycles_executed( - *computation_, execution_timer_->Nanoseconds() * clock_rate_ghz_); - } - } - - // If profiling is enabled, starts the per-operation timer. - void StartOperation() { - if (do_profile_) { - stream_->ThenStartTimer(per_op_timer_.get()); - } - } - - // If profiling is enabled, stops the per-operation timer and records the time - // that the hlo_instruction took to execute in the profile. - void FinishOperation(const HloInstruction* hlo_instruction) { - if (do_profile_) { - stream_->ThenWaitFor(&sub_streams_); - stream_->ThenStopTimer(per_op_timer_.get()); - stream_->BlockHostUntilDone().IgnoreError(); - profile_->SetCyclesTakenBy( - hlo_instruction, per_op_timer_->Nanoseconds() * clock_rate_ghz_); - } - } - - private: - const bool do_profile_; - double clock_rate_ghz_; - HloExecutionProfile* profile_; - se::Stream* stream_; - const std::vector::SmartPtr>& sub_streams_; - const HloComputation* computation_; - std::unique_ptr execution_timer_; - std::unique_ptr per_op_timer_; - bool finished_execution_ = false; -}; - } // namespace // Implementation note: HLO profiling is always enabled for GPU executables, diff --git a/tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.cc b/tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.cc new file mode 100644 index 0000000000..daddd3738e --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.cc @@ -0,0 +1,82 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h" + +#include +#include + +#include "tensorflow/compiler/xla/service/hlo_computation.h" +#include "tensorflow/compiler/xla/service/hlo_execution_profile.h" +#include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/compiler/xla/service/pool.h" +#include "tensorflow/core/platform/logging.h" +#include "tensorflow/core/platform/stream_executor_no_cuda.h" + +namespace xla { +namespace gpu { + +HloExecutionProfiler::HloExecutionProfiler( + bool do_profile, HloExecutionProfile* profile, se::Stream* stream, + const std::vector::SmartPtr>& sub_streams, + const HloComputation* computation) + : do_profile_(do_profile), + profile_(profile), + stream_(stream), + sub_streams_(sub_streams), + computation_(computation) { + if (do_profile_) { + clock_rate_ghz_ = stream->parent()->GetDeviceDescription().clock_rate_ghz(); + execution_timer_.reset(new se::Timer(stream->parent())); + per_op_timer_.reset(new se::Timer(stream->parent())); + stream->InitTimer(execution_timer_.get()) + .ThenStartTimer(execution_timer_.get()); + stream->InitTimer(per_op_timer_.get()); + } +} + +void HloExecutionProfiler::FinishExecution() { + CHECK(!finished_execution_) << "Call FinishExecution only once!"; + finished_execution_ = true; + if (do_profile_) { + stream_->ThenWaitFor(&sub_streams_); + stream_->ThenStopTimer(execution_timer_.get()); + stream_->BlockHostUntilDone().IgnoreError(); + profile_->set_total_cycles_executed( + *computation_, + static_cast(execution_timer_->Nanoseconds() * clock_rate_ghz_)); + } +} + +void HloExecutionProfiler::StartOperation() { + if (do_profile_) { + stream_->ThenStartTimer(per_op_timer_.get()); + } +} + +void HloExecutionProfiler::FinishOperation( + const HloInstruction* hlo_instruction) { + if (do_profile_) { + stream_->ThenWaitFor(&sub_streams_); + stream_->ThenStopTimer(per_op_timer_.get()); + stream_->BlockHostUntilDone().IgnoreError(); + profile_->SetCyclesTakenBy( + hlo_instruction, + static_cast(per_op_timer_->Nanoseconds() * clock_rate_ghz_)); + } +} + +} // namespace gpu +} // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h b/tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h new file mode 100644 index 0000000000..c9b882ff80 --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h @@ -0,0 +1,68 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_GPU_HLO_EXECUTION_PROFILER_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_GPU_HLO_EXECUTION_PROFILER_H_ + +#include +#include + +#include "tensorflow/compiler/xla/service/hlo_computation.h" +#include "tensorflow/compiler/xla/service/hlo_execution_profile.h" +#include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/compiler/xla/service/pool.h" +#include "tensorflow/core/platform/stream_executor_no_cuda.h" + +namespace xla { +namespace gpu { + +// A helper class for profiling HLO in the course of GPU program execution. +// All of the profiling is guarded internally, to avoid the caller needing to +// have lots of conditionals sprinkled around. +class HloExecutionProfiler { + public: + // If profiling is enabled, start an execution timer running. + explicit HloExecutionProfiler( + bool do_profile, HloExecutionProfile* profile, se::Stream* stream, + const std::vector::SmartPtr>& sub_streams, + const HloComputation* computation); + + // If profiling is enabled, sets the total cycle count on the profile from the + // execution timer. + void FinishExecution(); + + // If profiling is enabled, starts the per-operation timer. + void StartOperation(); + + // If profiling is enabled, stops the per-operation timer and records the time + // that the hlo_instruction took to execute in the profile. + void FinishOperation(const HloInstruction* hlo_instruction); + + private: + const bool do_profile_; + double clock_rate_ghz_; + HloExecutionProfile* profile_; + se::Stream* stream_; + const std::vector::SmartPtr>& sub_streams_; + const HloComputation* computation_; + std::unique_ptr execution_timer_; + std::unique_ptr per_op_timer_; + bool finished_execution_ = false; +}; + +} // namespace gpu +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_GPU_HLO_EXECUTION_PROFILER_H_ -- GitLab From 915b1383f843762cb5b254b5ccea6902b1df0513 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 14 Jun 2018 05:41:33 -0700 Subject: [PATCH 444/816] Internal change. PiperOrigin-RevId: 200543448 --- tensorflow/compiler/tests/BUILD | 9 ++++ tensorflow/compiler/tests/xla_test.py | 57 +++++++++++++--------- tensorflow/compiler/tests/xla_test_test.py | 44 +++++++++++++++++ 3 files changed, 86 insertions(+), 24 deletions(-) create mode 100644 tensorflow/compiler/tests/xla_test_test.py diff --git a/tensorflow/compiler/tests/BUILD b/tensorflow/compiler/tests/BUILD index e6c92f9720..98fab319d6 100644 --- a/tensorflow/compiler/tests/BUILD +++ b/tensorflow/compiler/tests/BUILD @@ -51,6 +51,15 @@ py_library( ], ) +py_test( + name = "xla_test_test", + size = "small", + srcs = ["xla_test_test.py"], + deps = [ + ":xla_test", + ], +) + tf_xla_py_test( name = "adagrad_test", size = "small", diff --git a/tensorflow/compiler/tests/xla_test.py b/tensorflow/compiler/tests/xla_test.py index e924fe1e61..88827cb53b 100644 --- a/tensorflow/compiler/tests/xla_test.py +++ b/tensorflow/compiler/tests/xla_test.py @@ -49,6 +49,32 @@ flags.DEFINE_string('tf_xla_flags', None, 'Value to set the TF_XLA_FLAGS environment variable to') +def parse_disabled_manifest(manifest_content): + comments_re = re.compile('#.*$') + disabled_tests = [] + disabled_method_types = [] + for l in manifest_content.splitlines(): + stripped = comments_re.sub('', l).strip() + if not stripped: + continue + entry = stripped.split(' ') + if len(entry) == 1: + disabled_tests.append(entry[0]) + elif len(entry) == 2: + disabled_method_types.append((entry[0], entry[1].strip().split(','))) + else: + raise ValueError('Bad entry in manifest file.') + + disabled_regex = '|'.join(disabled_tests) + method_types_filter = dict() + for method, types in disabled_method_types: + method_types_filter[method] = set([ + dtypes.as_dtype(types_pb2.DataType.Value(name)).as_numpy_dtype + for name in types + ]) + return disabled_regex, method_types_filter + + class XLATestCase(test.TestCase): """XLA test cases are parameterized test cases.""" @@ -85,38 +111,21 @@ class XLATestCase(test.TestCase): # Parse the manifest file, if any, into a regex identifying tests to # disable - self.disabled_regex = None - self._method_types_filter = dict() # TODO(xpan): Make it text proto if it doesn't scale. # Each line of the manifest file specifies an entry. The entry can be # 1) TestNameRegex // E.g. CumprodTest.* Or # 2) TestName TypeName // E.g. AdamOptimizerTest.testSharing DT_BFLOAT16 # The 1) disables the entire test. While 2) only filter some numeric types # so that they are not used in those tests. + self.disabled_regex = None + self._method_types_filter = {} if FLAGS.disabled_manifest is not None: - comments_re = re.compile('#.*$') - manifest_file = open(FLAGS.disabled_manifest, 'r') - disabled_tests = [] - disabled_method_types = [] - for l in manifest_file.read().splitlines(): - if not l: - continue - entry = comments_re.sub('', l).strip().split(' ') - if len(entry) == 1: - disabled_tests.append(entry[0]) - elif len(entry) == 2: - disabled_method_types.append( - (entry[0], entry[1].strip().split(','))) - else: - raise ValueError('Bad entry in manifest file.') - - self.disabled_regex = re.compile('|'.join(disabled_tests)) - for method, types in disabled_method_types: - self._method_types_filter[method] = set([ - dtypes.as_dtype(types_pb2.DataType.Value(name)).as_numpy_dtype - for name in types]) - manifest_file.close() + with open(FLAGS.disabled_manifest, 'r') as manifest_file: + disabled_regex, self._method_types_filter = ( + parse_disabled_manifest(manifest_file.read())) + if disabled_regex: + self.disabled_regex = re.compile(disabled_regex) if FLAGS.tf_xla_flags is not None: os.environ['TF_XLA_FLAGS'] = FLAGS.tf_xla_flags diff --git a/tensorflow/compiler/tests/xla_test_test.py b/tensorflow/compiler/tests/xla_test_test.py new file mode 100644 index 0000000000..2466445157 --- /dev/null +++ b/tensorflow/compiler/tests/xla_test_test.py @@ -0,0 +1,44 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the XLATestCase test fixture base class.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.compiler.tests import xla_test +from tensorflow.python.platform import test + + +class XlaTestCaseTestCase(test.TestCase): + + def testManifestEmptyLineDoesNotCatchAll(self): + manifest = """ +testCaseOne +""" + disabled_regex, _ = xla_test.parse_disabled_manifest(manifest) + self.assertEqual(disabled_regex, "testCaseOne") + + def testManifestWholeLineCommentDoesNotCatchAll(self): + manifest = """# I am a comment +testCaseOne +testCaseTwo +""" + disabled_regex, _ = xla_test.parse_disabled_manifest(manifest) + self.assertEqual(disabled_regex, "testCaseOne|testCaseTwo") + + +if __name__ == "__main__": + test.main() -- GitLab From 15430c589ff0b15f7bd0ef2fb4a4b78cb8fb8ee6 Mon Sep 17 00:00:00 2001 From: Peter Hawkins Date: Thu, 14 Jun 2018 06:05:12 -0700 Subject: [PATCH 445/816] [TF:XLA] Pass source tensors in original input graph to subgraph rewrite function. PiperOrigin-RevId: 200545548 --- .../jit/encapsulate_subgraphs_pass.cc | 119 +++++++++--------- .../compiler/jit/encapsulate_subgraphs_pass.h | 4 + .../jit/encapsulate_subgraphs_pass_test.cc | 6 +- tensorflow/contrib/tpu/python/tpu/tpu.py | 20 +-- 4 files changed, 84 insertions(+), 65 deletions(-) diff --git a/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc b/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc index edd2247694..9448b8ebde 100644 --- a/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc +++ b/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc @@ -459,7 +459,7 @@ class Encapsulator { std::unordered_map args_by_src_; std::unordered_map args_by_dst_; - // The _Arg nodes in the subgraph, in order by argument number. + // The arguments to the subgraph, in order. std::vector args_; // Map from source tensor in the input graph to result #. @@ -1047,14 +1047,19 @@ Status Encapsulator::Subgraph::BuildFunctionDef( call_node_def_.set_device(device_); if (rewrite_subgraph_fn) { + std::vector arg_source_tensors(args_by_src_.size()); + for (const auto& arg : args_by_src_) { + arg_source_tensors.at(arg.second) = arg.first; + } // Initialize the input and output permutations to the identity. std::vector input_permutation(args_by_src_.size()); std::iota(input_permutation.begin(), input_permutation.end(), 0); std::vector output_permutation(results_.size()); std::iota(output_permutation.begin(), output_permutation.end(), 0); - TF_RETURN_IF_ERROR(rewrite_subgraph_fn( - &graph_, &input_permutation, &output_permutation, &call_node_def_)); + TF_RETURN_IF_ERROR( + rewrite_subgraph_fn(arg_source_tensors, &graph_, &input_permutation, + &output_permutation, &call_node_def_)); // Apply the input/output permutations to the 'args_by_...' and 'results_' // mappings, so when we build edges in BuildOutputGraph() we @@ -2453,64 +2458,66 @@ Status EncapsulateSubgraphsPass::Run( FunctionLibraryRuntime* flr = pflr->GetFLR(ProcessFunctionLibraryRuntime::kDefaultFLRDevice); - auto rewrite_subgraph = [flr](std::unique_ptr* subgraph, - std::vector* input_permutation, - std::vector* output_permutation, - NodeDef* node) { - // Optimize the subgraph. - OptimizeGraph(flr, subgraph); - - const int num_args = input_permutation->size(); - std::vector const_args(num_args); - TF_RETURN_IF_ERROR(BackwardsConstAnalysis(**subgraph, &const_args)); - - DataTypeVector arg_types(num_args); - TF_RETURN_IF_ERROR(GetArgTypes(**subgraph, &arg_types)); - - // Compute a permutation of the arguments such that the constant arguments - // are first. - const int num_consts = - std::count(const_args.begin(), const_args.end(), true); - - const int num_resources = - std::count(arg_types.begin(), arg_types.end(), DT_RESOURCE); - const int num_nonconsts = num_args - num_resources - num_consts; - if (num_nonconsts < 0) { - return errors::Internal("num_nonconsts should be >= 0, was ", - num_nonconsts); - } + auto rewrite_subgraph = + [flr](const std::vector& arg_source_tensors, + std::unique_ptr* subgraph, + std::vector* input_permutation, + std::vector* output_permutation, NodeDef* node) { + // Optimize the subgraph. + OptimizeGraph(flr, subgraph); + + const int num_args = input_permutation->size(); + std::vector const_args(num_args); + TF_RETURN_IF_ERROR(BackwardsConstAnalysis(**subgraph, &const_args)); + + DataTypeVector arg_types(num_args); + TF_RETURN_IF_ERROR(GetArgTypes(**subgraph, &arg_types)); + + // Compute a permutation of the arguments such that the constant + // arguments are first. + const int num_consts = + std::count(const_args.begin(), const_args.end(), true); + + const int num_resources = + std::count(arg_types.begin(), arg_types.end(), DT_RESOURCE); + const int num_nonconsts = num_args - num_resources - num_consts; + if (num_nonconsts < 0) { + return errors::Internal("num_nonconsts should be >= 0, was ", + num_nonconsts); + } - int const_pos = 0; - int arg_pos = num_consts; - int resource_pos = num_consts + num_nonconsts; - for (int i = 0; i < num_args; ++i) { - if (const_args[i]) { - if (arg_types[i] == DT_RESOURCE) { - return errors::Internal( - "Resource arguments cannot be constant (argument ", i, ")"); + int const_pos = 0; + int arg_pos = num_consts; + int resource_pos = num_consts + num_nonconsts; + for (int i = 0; i < num_args; ++i) { + if (const_args[i]) { + if (arg_types[i] == DT_RESOURCE) { + return errors::Internal( + "Resource arguments cannot be constant (argument ", i, ")"); + } + (*input_permutation)[i] = const_pos; + ++const_pos; + } else if (arg_types[i] == DT_RESOURCE) { + (*input_permutation)[i] = resource_pos; + ++resource_pos; + } else { + (*input_permutation)[i] = arg_pos; + ++arg_pos; + } } - (*input_permutation)[i] = const_pos; - ++const_pos; - } else if (arg_types[i] == DT_RESOURCE) { - (*input_permutation)[i] = resource_pos; - ++resource_pos; - } else { - (*input_permutation)[i] = arg_pos; - ++arg_pos; - } - } - // Renumber argument nodes in the graph. - TF_RETURN_IF_ERROR(RenumberArguments(subgraph->get(), *input_permutation)); + // Renumber argument nodes in the graph. + TF_RETURN_IF_ERROR( + RenumberArguments(subgraph->get(), *input_permutation)); - // TODO(phawkins): add a forward is-constant analysis, similarly split - // outputs into host-memory constants and device-memory non-constants. + // TODO(phawkins): add a forward is-constant analysis, similarly split + // outputs into host-memory constants and device-memory non-constants. - AddNodeAttr(kXlaCompiledKernelAttr, true, node); - AddNodeAttr(kXlaNumConstantArgsAttr, num_consts, node); - AddNodeAttr(kXlaNumResourceArgsAttr, num_resources, node); - return Status::OK(); - }; + AddNodeAttr(kXlaCompiledKernelAttr, true, node); + AddNodeAttr(kXlaNumConstantArgsAttr, num_consts, node); + AddNodeAttr(kXlaNumResourceArgsAttr, num_resources, node); + return Status::OK(); + }; TF_RETURN_IF_ERROR(EncapsulateSubgraphsInFunctions( kXlaClusterAttr, kXlaOutsideCompilationAttr, **options.graph, diff --git a/tensorflow/compiler/jit/encapsulate_subgraphs_pass.h b/tensorflow/compiler/jit/encapsulate_subgraphs_pass.h index e5dab7c657..926589546f 100644 --- a/tensorflow/compiler/jit/encapsulate_subgraphs_pass.h +++ b/tensorflow/compiler/jit/encapsulate_subgraphs_pass.h @@ -28,6 +28,9 @@ limitations under the License. namespace tensorflow { // A rewriting function to apply to each subgraph during encapsulation. +// 'arg_source_tensors' are the tensors corresponding to the arguments in the +// original source graph (*not* 'graph'). +// // 'graph' is the subgraph. The rewriting may renumber the inputs and outputs; // 'input_permutation' is a mapping from old argument numbers to new argument // numbers, whereas 'output_permutation' is the same for outputs. Both @@ -37,6 +40,7 @@ namespace tensorflow { // The rewrite may also change the NodeDef's operator name, and that // name will be used as the name of the generated function. typedef std::function& arg_source_tensors, std::unique_ptr* graph, std::vector* input_permutation, std::vector* output_permutation, NodeDef* node_def)> RewriteSubgraphFn; diff --git a/tensorflow/compiler/jit/encapsulate_subgraphs_pass_test.cc b/tensorflow/compiler/jit/encapsulate_subgraphs_pass_test.cc index 6a7cd932e5..4eb389e0c6 100644 --- a/tensorflow/compiler/jit/encapsulate_subgraphs_pass_test.cc +++ b/tensorflow/compiler/jit/encapsulate_subgraphs_pass_test.cc @@ -757,7 +757,8 @@ TEST(EncapsulateSubgraphsWithGuaranteeConstOpTest, Simple) { TF_ASSERT_OK(EncapsulateSubgraphsInFunctions( "_encapsulate", "_outside", graph_before, /*rewrite_subgraph_fn=*/ - [&guaranteed_consts](std::unique_ptr* graph_ptr, + [&guaranteed_consts](const std::vector& arg_source_tensors, + std::unique_ptr* graph_ptr, std::vector* input_permutation, std::vector* output_permutation, NodeDef* call_def) { @@ -801,7 +802,8 @@ TEST(EncapsulateSubgraphsWithGuaranteeConstOpTest, Add) { TF_ASSERT_OK(EncapsulateSubgraphsInFunctions( "_encapsulate", "_outside", graph_before, /*rewrite_subgraph_fn=*/ - [&guaranteed_consts](std::unique_ptr* graph_ptr, + [&guaranteed_consts](const std::vector& arg_source_tensors, + std::unique_ptr* graph_ptr, std::vector* input_permutation, std::vector* output_permutation, NodeDef* call_def) { diff --git a/tensorflow/contrib/tpu/python/tpu/tpu.py b/tensorflow/contrib/tpu/python/tpu/tpu.py index cd0fd6ae8a..dc473c5846 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu.py @@ -591,16 +591,22 @@ def split_compile_and_replicate(computation, with tpu_function.tpu_shard_context( num_replicas), ops.control_dependencies([metadata]): - # The EncapsulateTPUComputations rewrite needs to identify the - # replicated arguments inside each computation. Adds identity operators - # tagged with an attribute _tpu_replicated_input to identify the - # replicated inputs. + # For backward compatibility reasons, we tag replicated inputs with the + # _tpu_replicated_input attribute. This does nothing and exists only for + # backward compatibility. + # TODO(phawkins): delete the attr_scope after 6/28/2018. # pylint: disable=protected-access - with graph._attr_scope({"_tpu_replicated_input": - attr_value_pb2.AttrValue(b=True)}): + with graph._attr_scope({ + "_tpu_replicated_input": attr_value_pb2.AttrValue(b=True) + }): + # Add identity ops so even unused inputs are "consumed" by the + # computation. This is to avoid orphaned TPUReplicatedInput nodes. + # TODO(phawkins): consider instead pruning unused TPUReplicatedInput + # and eliding trivial TPUReplicatedInput/TPUReplicatedOutput pairs. computation_inputs = [ array_ops.identity(x, name="replicated_input_{}".format(i)) - for i, x in enumerate(computation_inputs)] + for i, x in enumerate(computation_inputs) + ] # pylint: enable=protected-access # If there is an infeed queue, adds the dequeued values to the -- GitLab From e5c17aef836f8b85591cdcae31fbb66ddcf8185a Mon Sep 17 00:00:00 2001 From: mktozk Date: Thu, 14 Jun 2018 22:16:21 +0900 Subject: [PATCH 446/816] Fix merge layers in tf.keras (#19929) * add @tf_export * add new golden files * fix tf.keras.layers.merge.Subtract and Minimum --- tensorflow/python/keras/layers/__init__.py | 2 + tensorflow/python/keras/layers/merge.py | 4 + .../tensorflow.keras.layers.-minimum.pbtxt | 176 ++++++++++++++++++ .../tensorflow.keras.layers.-subtract.pbtxt | 176 ++++++++++++++++++ .../api/golden/tensorflow.keras.layers.pbtxt | 16 ++ 5 files changed, 374 insertions(+) create mode 100644 tensorflow/tools/api/golden/tensorflow.keras.layers.-minimum.pbtxt create mode 100644 tensorflow/tools/api/golden/tensorflow.keras.layers.-subtract.pbtxt diff --git a/tensorflow/python/keras/layers/__init__.py b/tensorflow/python/keras/layers/__init__.py index 8fb663a17e..ce0cdb2e1b 100644 --- a/tensorflow/python/keras/layers/__init__.py +++ b/tensorflow/python/keras/layers/__init__.py @@ -86,9 +86,11 @@ from tensorflow.python.keras.layers.local import LocallyConnected2D # Merge layers. from tensorflow.python.keras.layers.merge import Add +from tensorflow.python.keras.layers.merge import Subtract from tensorflow.python.keras.layers.merge import Multiply from tensorflow.python.keras.layers.merge import Average from tensorflow.python.keras.layers.merge import Maximum +from tensorflow.python.keras.layers.merge import Minimum from tensorflow.python.keras.layers.merge import Concatenate from tensorflow.python.keras.layers.merge import Dot from tensorflow.python.keras.layers.merge import add diff --git a/tensorflow/python/keras/layers/merge.py b/tensorflow/python/keras/layers/merge.py index 770665c5fb..f295af3fe0 100644 --- a/tensorflow/python/keras/layers/merge.py +++ b/tensorflow/python/keras/layers/merge.py @@ -250,6 +250,7 @@ class Add(_Merge): return output +@tf_export('keras.layers.Subtract') class Subtract(_Merge): """Layer that subtracts two inputs. @@ -336,6 +337,7 @@ class Maximum(_Merge): return output +@tf_export('keras.layers.Minimum') class Minimum(_Merge): """Layer that computes the minimum (element-wise) a list of inputs. @@ -586,6 +588,7 @@ def add(inputs, **kwargs): return Add(**kwargs)(inputs) +@tf_export('keras.layers.subtract') def subtract(inputs, **kwargs): """Functional interface to the `Subtract` layer. @@ -656,6 +659,7 @@ def maximum(inputs, **kwargs): return Maximum(**kwargs)(inputs) +@tf_export('keras.layers.minimum') def minimum(inputs, **kwargs): """Functional interface to the `Minimum` layer. diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-minimum.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-minimum.pbtxt new file mode 100644 index 0000000000..56e32e9d36 --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-minimum.pbtxt @@ -0,0 +1,176 @@ +path: "tensorflow.keras.layers.Minimum" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "non_trainable_variables" + mtype: "" + } + member { + name: "non_trainable_weights" + mtype: "" + } + member { + name: "outbound_nodes" + mtype: "" + } + member { + name: "output" + mtype: "" + } + member { + name: "output_mask" + mtype: "" + } + member { + name: "output_shape" + mtype: "" + } + member { + name: "trainable_variables" + mtype: "" + } + member { + name: "trainable_weights" + mtype: "" + } + member { + name: "updates" + mtype: "" + } + member { + name: "variables" + mtype: "" + } + member { + name: "weights" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\'], varargs=None, keywords=kwargs, defaults=None" + } + member_method { + name: "add_loss" + argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_update" + argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_variable" + argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "add_weight" + argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "apply" + argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "build" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "call" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "compute_mask" + argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "compute_output_shape" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "count_params" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_mask_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_shape_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_losses_for" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_mask_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_shape_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_updates_for" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_weights" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_weights" + argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-subtract.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-subtract.pbtxt new file mode 100644 index 0000000000..35ad87ad5d --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-subtract.pbtxt @@ -0,0 +1,176 @@ +path: "tensorflow.keras.layers.Subtract" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "non_trainable_variables" + mtype: "" + } + member { + name: "non_trainable_weights" + mtype: "" + } + member { + name: "outbound_nodes" + mtype: "" + } + member { + name: "output" + mtype: "" + } + member { + name: "output_mask" + mtype: "" + } + member { + name: "output_shape" + mtype: "" + } + member { + name: "trainable_variables" + mtype: "" + } + member { + name: "trainable_weights" + mtype: "" + } + member { + name: "updates" + mtype: "" + } + member { + name: "variables" + mtype: "" + } + member { + name: "weights" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\'], varargs=None, keywords=kwargs, defaults=None" + } + member_method { + name: "add_loss" + argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_update" + argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_variable" + argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "add_weight" + argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "apply" + argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "build" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "call" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "compute_mask" + argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "compute_output_shape" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "count_params" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_mask_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_shape_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_losses_for" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_mask_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_shape_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_updates_for" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_weights" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_weights" + argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.pbtxt index 709eb5be55..475e9dade3 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.pbtxt @@ -280,6 +280,10 @@ tf_module { name: "Maximum" mtype: "" } + member { + name: "Minimum" + mtype: "" + } member { name: "Multiply" mtype: "" @@ -348,6 +352,10 @@ tf_module { name: "StackedRNNCells" mtype: "" } + member { + name: "Subtract" + mtype: "" + } member { name: "ThresholdedReLU" mtype: "" @@ -408,8 +416,16 @@ tf_module { name: "maximum" argspec: "args=[\'inputs\'], varargs=None, keywords=kwargs, defaults=None" } + member_method { + name: "minimum" + argspec: "args=[\'inputs\'], varargs=None, keywords=kwargs, defaults=None" + } member_method { name: "multiply" argspec: "args=[\'inputs\'], varargs=None, keywords=kwargs, defaults=None" } + member_method { + name: "subtract" + argspec: "args=[\'inputs\'], varargs=None, keywords=kwargs, defaults=None" + } } -- GitLab From ae26e861cae2817290f52594a731988299ebe7a6 Mon Sep 17 00:00:00 2001 From: Peter Hawkins Date: Thu, 14 Jun 2018 07:35:48 -0700 Subject: [PATCH 447/816] Add support for propagating resource shapes via the TPUReplicatedInput operator's shape inference function. PiperOrigin-RevId: 200554455 --- tensorflow/contrib/tpu/ops/replication_ops.cc | 21 +++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/tensorflow/contrib/tpu/ops/replication_ops.cc b/tensorflow/contrib/tpu/ops/replication_ops.cc index ab2a7a0d4b..f632c953c8 100644 --- a/tensorflow/contrib/tpu/ops/replication_ops.cc +++ b/tensorflow/contrib/tpu/ops/replication_ops.cc @@ -44,6 +44,27 @@ REGISTER_OP("TPUReplicatedInput") " with other shapes."); } c->set_output(0, cur); + + // If this is a resource, unify the resource shapes. + DataType dtype; + TF_RETURN_IF_ERROR(c->GetAttr("T", &dtype)); + if (dtype == DT_RESOURCE) { + const std::vector* shapes_and_types = + nullptr; + for (int i = c->num_inputs() - 1; i >= 0; --i) { + if (shapes_and_types) { + if (!c->MergeInputHandleShapesAndTypes(i, *shapes_and_types)) { + return errors::InvalidArgument( + "Incompatible resource shapes for replicated TPU input."); + } + } else { + shapes_and_types = c->input_handle_shapes_and_types(i); + } + } + if (shapes_and_types) { + c->set_output_handle_shapes_and_types(0, *shapes_and_types); + } + } return Status::OK(); }) .Doc( -- GitLab From a7c1b0347bda30c300ae55ad060b6cb965ded831 Mon Sep 17 00:00:00 2001 From: Dan Moldovan Date: Thu, 14 Jun 2018 07:46:09 -0700 Subject: [PATCH 448/816] Standardize the type notation for docstrings that require describing a type. PiperOrigin-RevId: 200555363 --- tensorflow/contrib/autograph/STYLE_GUIDE.md | 16 +++++++++++++--- 1 file changed, 13 insertions(+), 3 deletions(-) diff --git a/tensorflow/contrib/autograph/STYLE_GUIDE.md b/tensorflow/contrib/autograph/STYLE_GUIDE.md index 866e5f583a..7e6b0cc27d 100644 --- a/tensorflow/contrib/autograph/STYLE_GUIDE.md +++ b/tensorflow/contrib/autograph/STYLE_GUIDE.md @@ -20,7 +20,17 @@ Naming conventions: Below are AutoGraph-specific conventions. In the event of conflict, it supercedes all previous conventions. -1. __Citations in Docstrings.__ Write a `#### References` subsection at the +1. __Types in docstrings.__ Use [PEP 484][https://www.python.org/dev/peps/pep-0484/] + notation to describe the type for args, return values and attributes. + + Example: + + ``` + Args: + foo: Dict[str, List[int]], a dictionary of sorts + ``` + +2. __Citations in Docstrings.__ Write a `#### References` subsection at the bottom of any docstring with citations. Use ICLR’s bibliography style to write references; for example, order entries by the first author's last name. Add a link to the paper if the publication is open source (ideally, @@ -60,12 +70,12 @@ it supercedes all previous conventions. https://arxiv.org/abs/1803.04386 ``` -2. Avoid LaTeX in docstrings. +3. Avoid LaTeX in docstrings. * It is not rendered in many (if not most) editors and can be hard to read for both LaTeX experts and non-experts. -3. Write docstring and comment math using ASCII friendly notation; python using +4. Write docstring and comment math using ASCII friendly notation; python using operators. E.g., `x**2` better than `x^2`, `x[i, j]` better than `x_{i,j}`, `sum{ f(x[i]) : i=1...n }` better than `\sum_{i=1}^n f(x_i)` `int{sin(x) dx: x in [0, 2 pi]}` better than `\int_0^{2\pi} sin(x) dx`. -- GitLab From b704ab9e65a3e44568e91eeded277fdd1b072508 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 14 Jun 2018 07:51:36 -0700 Subject: [PATCH 449/816] Make deleting HloInstruction safer. PiperOrigin-RevId: 200555862 --- .../compiler/xla/service/hlo_computation.cc | 10 ----- .../compiler/xla/service/hlo_evaluator.cc | 8 ---- .../compiler/xla/service/hlo_instruction.cc | 45 ++++++++++++------- .../compiler/xla/service/hlo_instruction.h | 7 --- .../compiler/xla/service/hlo_instructions.cc | 2 - 5 files changed, 28 insertions(+), 44 deletions(-) diff --git a/tensorflow/compiler/xla/service/hlo_computation.cc b/tensorflow/compiler/xla/service/hlo_computation.cc index c73e54a0b1..ac7afac19f 100644 --- a/tensorflow/compiler/xla/service/hlo_computation.cc +++ b/tensorflow/compiler/xla/service/hlo_computation.cc @@ -234,7 +234,6 @@ Status HloComputation::RemoveInstruction(HloInstruction* instruction) { TF_RET_CHECK(instruction_iterators_.count(instruction) != 0); auto inst_it = instruction_iterators_.at(instruction); (*inst_it)->set_parent(nullptr); - instruction->DetachFromOperands(); instructions_.erase(inst_it); return Status::OK(); } @@ -868,15 +867,6 @@ std::unique_ptr HloComputation::CloneWithReplacements( } } context->MapComputation(this, result.get()); - // We cloned the elements of 'replacements', so they're all going to be - // destroyed. HloInstructions need to be detached from their operands before - // they're destroyed, otherwise they stick around in the operands' users lists - // and cause use-after-frees. - for (auto& kv : replacements) { - if (std::unique_ptr& new_instr = kv.second) { - new_instr->DetachFromOperands(); - } - } return result; } diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.cc b/tensorflow/compiler/xla/service/hlo_evaluator.cc index 080ee4ad18..3c695d3e5f 100644 --- a/tensorflow/compiler/xla/service/hlo_evaluator.cc +++ b/tensorflow/compiler/xla/service/hlo_evaluator.cc @@ -300,12 +300,6 @@ StatusOr> HloEvaluator::EvaluateWithSubstitutions( instruction->CloneWithNewOperands(instruction->shape(), operands); auto result = Evaluate(cloned_instruction.get()); - // Clean up our cloned instructions before returning. - cloned_instruction->DetachFromOperands(); - for (auto& operand : owned_operands) { - operand->DetachFromOperands(); - } - return result; } @@ -321,7 +315,6 @@ StatusOr> HloEvaluator::EvaluateElementwiseBinaryOp( rhs_instr.get()); auto result = Evaluate(cloned_instruction.get()); - cloned_instruction->DetachFromOperands(); return result; } @@ -334,7 +327,6 @@ StatusOr> HloEvaluator::EvaluateElementwiseUnaryOp( HloInstruction::CreateUnary(operand.shape(), opcode, operand_instr.get()); auto result = Evaluate(cloned_instruction.get()); - cloned_instruction->DetachFromOperands(); return result; } diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc index 4e029d66a5..ec26f9a6b3 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction.cc +++ b/tensorflow/compiler/xla/service/hlo_instruction.cc @@ -1210,7 +1210,29 @@ std::unique_ptr HloInstruction::CloneWithNewOperands( return clone; } -HloInstruction::~HloInstruction() {} +HloInstruction::~HloInstruction() { + // Detach from operands. An instruction may be repeated as an operand. To + // avoid calling RemoveUser twice on the same operand, check before remove. + for (int64 operand_num = 0; operand_num < operand_count(); ++operand_num) { + HloInstruction* operand = operands_[operand_num]; + if (operand == nullptr) { + continue; + } + if (operand->user_set_.find(this) != operand->user_set_.end()) { + operand->RemoveUser(this); + } + operands_[operand_num] = nullptr; + } + + // Update users. Set `nullptr` to the correpsonding operand slot for users. + for (auto& user : this->users()) { + for (int i = 0; i < user->operand_count(); ++i) { + if (user->operands_[i] == this) { + user->operands_[i] = nullptr; + } + } + } +} std::unique_ptr HloInstruction::Clone( const string& suffix, HloCloneContext* context) const { @@ -1609,22 +1631,6 @@ Status HloInstruction::ReplaceAllUsesWith(HloInstruction* new_producer) { return Status::OK(); } -void HloInstruction::DetachFromOperands() { - VLOG(3) << "DetachFromOperands:\n " << ToString(); - CHECK_EQ(0, user_count()); - // An instruction may be repeated as an operand. To avoid calling RemoveUser - // twice on the same operand, keep a set of already detached operands. - std::set detached_operands; - for (int64 operand_num = 0; operand_num < operand_count(); ++operand_num) { - HloInstruction* operand = operands_[operand_num]; - if (!ContainsKey(detached_operands, operand)) { - operand->RemoveUser(this); - detached_operands.insert(operand); - } - operands_[operand_num] = nullptr; - } -} - HloComputation* HloInstruction::to_apply() const { switch (opcode_) { case HloOpcode::kCall: @@ -1884,6 +1890,11 @@ string HloInstruction::OperandsToStringWithCanonicalNameMap( slice.remove_suffix(slice.size() - kMaxOperandsToShowIfCompact); } operands = Join(slice, ", ", [&](string* out, HloInstruction* operand) { + // If operand is already been deleted, put `null` to the string output. + if (operand == nullptr) { + StrAppend(out, "null "); + return; + } std::vector str; if (options.print_operand_shape()) { str.push_back(ShapeUtil::HumanStringWithLayout(operand->shape())); diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h index 2a38e2b063..0e70228e08 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction.h +++ b/tensorflow/compiler/xla/service/hlo_instruction.h @@ -824,13 +824,6 @@ class HloInstruction { // root to new_producer. Status ReplaceAllUsesWith(HloInstruction* new_producer); - // Detaches an instruction from its operands. That is, remove the instruction - // from each operand's user set. This should only be called prior to - // deallocating the instruction. - // - // TODO(b/78305363): Make this automatic when deleting an instruction. - void DetachFromOperands(); - // Performs a postorder DFS visit using this node as the root. If // call_finish_visit is true, then DfsHloVisitor::FinishVisit is called when // complete. If ignore_control_predecessors is true, instructions only diff --git a/tensorflow/compiler/xla/service/hlo_instructions.cc b/tensorflow/compiler/xla/service/hlo_instructions.cc index 34038ae0ae..91429321d1 100644 --- a/tensorflow/compiler/xla/service/hlo_instructions.cc +++ b/tensorflow/compiler/xla/service/hlo_instructions.cc @@ -831,10 +831,8 @@ void HloFusionInstruction::MergeFusionInstruction( // Fuse 'unfused_instructions' into 'this'. for (auto& instruction : unfused_instructions) { FuseInstruction(instruction); - instruction->DetachFromOperands(); } CHECK_EQ(0, cloned_fusion->user_count()); - cloned_fusion->DetachFromOperands(); TF_CHECK_OK(parent()->parent()->RemoveEmbeddedComputation( cloned_fusion->fused_instructions_computation())); } -- GitLab From 04b7701eb0177d717b20c98d48fb6bc3ec793401 Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Thu, 14 Jun 2018 08:39:50 -0700 Subject: [PATCH 450/816] Sync package version of double-conversion between bazel and cmake (#20017) * Sync package version of double-conversion between bazel and cmake This fix tries to sync package version of double-conversion between bazel and cmake. The double-conversion package was added in 12102 and was reverted in PR 15133. At that time the package version was 5664746 for both bazel and cmake. Later on, the double-conversion was re-introduced in PR 18746. The package version of double-conversion in bazel has been advanced to 3992066a95b823efc8ccc1baf82a1cfc73f6e9b8 but the version in cmake remains the old 5664746. This fix updates the double-conversion version in cmake so that it is synced with the version (3992066a95b823efc8ccc1baf82a1cfc73f6e9b8) used in bazel. Signed-off-by: Yong Tang * Change the target path of libdouble-conversion.a Signed-off-by: Yong Tang --- tensorflow/contrib/cmake/external/double_conversion.cmake | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tensorflow/contrib/cmake/external/double_conversion.cmake b/tensorflow/contrib/cmake/external/double_conversion.cmake index 527ccdc8d8..5c5adaf579 100644 --- a/tensorflow/contrib/cmake/external/double_conversion.cmake +++ b/tensorflow/contrib/cmake/external/double_conversion.cmake @@ -16,15 +16,15 @@ include (ExternalProject) set(double_conversion_INCLUDE_DIR ${CMAKE_CURRENT_BINARY_DIR}/double_conversion/src/double_conversion) set(double_conversion_URL https://github.com/google/double-conversion.git) -set(double_conversion_TAG 5664746) +set(double_conversion_TAG 3992066a95b823efc8ccc1baf82a1cfc73f6e9b8) set(double_conversion_BUILD ${double_conversion_INCLUDE_DIR}) set(double_conversion_LIBRARIES ${double_conversion_BUILD}/double-conversion/libdouble-conversion.so) set(double_conversion_INCLUDES ${double_conversion_BUILD}) if(WIN32) - set(double_conversion_STATIC_LIBRARIES ${double_conversion_BUILD}/double-conversion/$(Configuration)/double-conversion.lib) + set(double_conversion_STATIC_LIBRARIES ${double_conversion_BUILD}/$(Configuration)/double-conversion.lib) else() - set(double_conversion_STATIC_LIBRARIES ${double_conversion_BUILD}/double-conversion/libdouble-conversion.a) + set(double_conversion_STATIC_LIBRARIES ${double_conversion_BUILD}/libdouble-conversion.a) endif() set(double_conversion_HEADERS -- GitLab From 4ec3fcdc87687d33c1597aff9296041a6bb00434 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 14 Jun 2018 09:28:17 -0700 Subject: [PATCH 451/816] Adds support for explicitly assigning the replica to the VariableDeviceChooser. This is necessary for when the device with replica is set in a surrounding arg_scope. PiperOrigin-RevId: 200567897 --- .../contrib/framework/python/ops/variables.py | 10 +- .../framework/python/ops/variables_test.py | 120 +++++++++++------- 2 files changed, 83 insertions(+), 47 deletions(-) diff --git a/tensorflow/contrib/framework/python/ops/variables.py b/tensorflow/contrib/framework/python/ops/variables.py index 40ae01bfcc..e8e3180019 100644 --- a/tensorflow/contrib/framework/python/ops/variables.py +++ b/tensorflow/contrib/framework/python/ops/variables.py @@ -712,7 +712,8 @@ class VariableDeviceChooser(object): num_tasks=0, job_name='ps', device_type='CPU', - device_index=0): + device_index=0, + replica=None): """Initialize VariableDeviceChooser. Usage: @@ -733,12 +734,15 @@ class VariableDeviceChooser(object): self._job_name = job_name self._device_type = device_type self._device_index = device_index + self._replica = replica self._num_tasks = num_tasks self._next_task_id = 0 def __call__(self, op): - device_spec = tf_device.DeviceSpec(device_type=self._device_type, - device_index=self._device_index) + device_spec = tf_device.DeviceSpec( + replica=self._replica, + device_type=self._device_type, + device_index=self._device_index) if self._num_tasks > 0: task_id = self._next_task_id self._next_task_id = (self._next_task_id + 1) % self._num_tasks diff --git a/tensorflow/contrib/framework/python/ops/variables_test.py b/tensorflow/contrib/framework/python/ops/variables_test.py index 37ea6eb12a..7e0c7dbec1 100644 --- a/tensorflow/contrib/framework/python/ops/variables_test.py +++ b/tensorflow/contrib/framework/python/ops/variables_test.py @@ -506,6 +506,35 @@ class VariablesTest(test.TestCase): self.assertDeviceEqual(e.device, '/job:ps/task:1/cpu:0') self.assertDeviceEqual(e.initial_value.device, '/cpu:99') + def testVariableWithVariableDeviceChooserWithReplica(self): + + with ops.Graph().as_default(): + device_fn = variables_lib2.VariableDeviceChooser(replica=3, num_tasks=2) + with arg_scope([variables_lib2.variable], device=device_fn): + a = variables_lib2.variable('a', []) + b = variables_lib2.variable('b', []) + c = variables_lib2.variable('c', [], device='cpu:12') + d = variables_lib2.variable('d', []) + with ops.device('cpu:99'): + e_init = constant_op.constant(12) + e = variables_lib2.variable('e', initializer=e_init) + # The values below highlight how the VariableDeviceChooser puts initial + # values on the same device as the variable job. + self.assertDeviceEqual(a.device, '/job:ps/replica:3/task:0/cpu:0') + self.assertEqual(a.initial_value.op.colocation_groups(), + a.op.colocation_groups()) + self.assertDeviceEqual(b.device, '/job:ps/replica:3/task:1/cpu:0') + self.assertEqual(b.initial_value.op.colocation_groups(), + b.op.colocation_groups()) + self.assertDeviceEqual(c.device, '/cpu:12') + self.assertEqual(c.initial_value.op.colocation_groups(), + c.op.colocation_groups()) + self.assertDeviceEqual(d.device, '/job:ps/replica:3/task:0/cpu:0') + self.assertEqual(d.initial_value.op.colocation_groups(), + d.op.colocation_groups()) + self.assertDeviceEqual(e.device, '/job:ps/replica:3/task:1/cpu:0') + self.assertDeviceEqual(e.initial_value.device, '/cpu:99') + def testVariableGPUPlacement(self): with ops.Graph().as_default(): @@ -930,8 +959,8 @@ class AssignFromCheckpointTest(test.TestCase): return saver.save(sess, checkpoint_dir, global_step=global_step) def testLoadExistingVariables(self): - model_dir = tempfile.mkdtemp(prefix=os.path.join(self.get_temp_dir(), - 'load_existing_variables')) + model_dir = tempfile.mkdtemp( + prefix=os.path.join(self.get_temp_dir(), 'load_existing_variables')) init_value0 = 10.0 init_value1 = 20.0 @@ -944,8 +973,8 @@ class AssignFromCheckpointTest(test.TestCase): var1 = variables_lib2.variable('my_var1', shape=[]) vars_to_restore = {'v0': var0, 'v1': var1} - op, feed_dict = variables_lib2.assign_from_checkpoint(model_path, - vars_to_restore) + op, feed_dict = variables_lib2.assign_from_checkpoint( + model_path, vars_to_restore) # Initialize the variables. sess.run(variables_lib.global_variables_initializer()) @@ -960,8 +989,8 @@ class AssignFromCheckpointTest(test.TestCase): # Tests restoring PartitionedVariables and tests using a dictionary # of lists as the assign_from_checkpoint() var_list param. def testLoadPartitionedVariables(self): - model_dir = tempfile.mkdtemp(prefix=os.path.join( - self.get_temp_dir(), 'load_partitioned_variables')) + model_dir = tempfile.mkdtemp( + prefix=os.path.join(self.get_temp_dir(), 'load_partitioned_variables')) init_value0 = np.array([[10.0, 11.0], [12.0, 13.0]]) init_value1 = np.array([20.0]) # Partitioned into 1 part, edge case. @@ -974,15 +1003,14 @@ class AssignFromCheckpointTest(test.TestCase): partitioner = partitioned_variables.variable_axis_size_partitioner(2) var0 = variables_lib2.variable( 'var0', shape=init_value0.shape, partitioner=partitioner) - var0full = variables_lib2.variable( - 'var0full', shape=init_value0.shape) + var0full = variables_lib2.variable('var0full', shape=init_value0.shape) var1 = variables_lib2.variable( 'var1', shape=init_value1.shape, partitioner=partitioner) # Convert var0 and var1 into a list of underlying variables. vars_to_restore = {'var0': list(var0) + [var0full], 'var1': list(var1)} - op, feed_dict = variables_lib2.assign_from_checkpoint(model_path, - vars_to_restore) + op, feed_dict = variables_lib2.assign_from_checkpoint( + model_path, vars_to_restore) # Initialize the variables. sess.run(variables_lib.global_variables_initializer()) @@ -992,16 +1020,18 @@ class AssignFromCheckpointTest(test.TestCase): # Request and test the variable values. PartitionedVariables can't # be evaled so we wrap them in an identity. - self.assertTrue(np.array_equal( - init_value0, array_ops.identity(var0).eval())) - self.assertTrue(np.array_equal( - init_value0, var0full.eval())) - self.assertTrue(np.array_equal( - init_value1, array_ops.identity(var1).eval())) + self.assertTrue( + np.array_equal(init_value0, + array_ops.identity(var0).eval())) + self.assertTrue(np.array_equal(init_value0, var0full.eval())) + self.assertTrue( + np.array_equal(init_value1, + array_ops.identity(var1).eval())) def testRaisesValueErrorIfAVariableIsntFound(self): - model_dir = tempfile.mkdtemp(prefix=os.path.join( - self.get_temp_dir(), 'raises_value_error_if_var_isnt_found')) + model_dir = tempfile.mkdtemp( + prefix=os.path.join(self.get_temp_dir(), + 'raises_value_error_if_var_isnt_found')) init_value0 = 10.0 init_value1 = 20.0 @@ -1019,8 +1049,9 @@ class AssignFromCheckpointTest(test.TestCase): variables_lib2.assign_from_checkpoint(model_path, vars_to_restore) def testInitFromCheckpointWithScopes(self): - model_dir = tempfile.mkdtemp(prefix=os.path.join( - self.get_temp_dir(), 'init_from_checkpoint_with_scopes')) + model_dir = tempfile.mkdtemp( + prefix=os.path.join(self.get_temp_dir(), + 'init_from_checkpoint_with_scopes')) init_value0 = np.asarray( [1.0, 3.0, 9.0], dtype=np.float32).reshape((1, 3, 1)) @@ -1038,8 +1069,8 @@ class AssignFromCheckpointTest(test.TestCase): var1 = variables_lib2.variable('my_var1', shape=init_value1.shape) vars_to_restore = {'layer0/v0': var0, 'layer1/v1': var1} - op, feed_dict = variables_lib2.assign_from_checkpoint(model_path, - vars_to_restore) + op, feed_dict = variables_lib2.assign_from_checkpoint( + model_path, vars_to_restore) # Initialize the variables. sess.run(variables_lib.global_variables_initializer()) @@ -1081,8 +1112,8 @@ class AssignFromCheckpointFnTest(test.TestCase): return saver.save(sess, checkpoint_dir, global_step=global_step) def testLoadExistingVariables(self): - model_dir = tempfile.mkdtemp(prefix=os.path.join(self.get_temp_dir(), - 'load_existing_variables')) + model_dir = tempfile.mkdtemp( + prefix=os.path.join(self.get_temp_dir(), 'load_existing_variables')) if gfile.Exists(model_dir): gfile.DeleteRecursively(model_dir) @@ -1097,8 +1128,8 @@ class AssignFromCheckpointFnTest(test.TestCase): var1 = variables_lib2.variable('my_var1', shape=[]) vars_to_restore = {'v0': var0, 'v1': var1} - init_fn = variables_lib2.assign_from_checkpoint_fn(model_path, - vars_to_restore) + init_fn = variables_lib2.assign_from_checkpoint_fn( + model_path, vars_to_restore) # Initialize the variables. sess.run(variables_lib.global_variables_initializer()) @@ -1111,8 +1142,9 @@ class AssignFromCheckpointFnTest(test.TestCase): self.assertEqual(init_value1, var1.eval()) def testLoadExistingVariablesDifferentShapeDefaultDoesNotAllowReshape(self): - model_dir = tempfile.mkdtemp(prefix=os.path.join( - self.get_temp_dir(), 'load_existing_vars_no_reshape')) + model_dir = tempfile.mkdtemp( + prefix=os.path.join(self.get_temp_dir(), + 'load_existing_vars_no_reshape')) if gfile.Exists(model_dir): gfile.DeleteRecursively(model_dir) @@ -1127,8 +1159,8 @@ class AssignFromCheckpointFnTest(test.TestCase): var1 = variables_lib2.variable('my_var1', shape=[]) vars_to_restore = {'v0': var0, 'v1': var1} - init_fn = variables_lib2.assign_from_checkpoint_fn(model_path, - vars_to_restore) + init_fn = variables_lib2.assign_from_checkpoint_fn( + model_path, vars_to_restore) # Initialize the variables. sess.run(variables_lib.global_variables_initializer()) @@ -1138,9 +1170,10 @@ class AssignFromCheckpointFnTest(test.TestCase): init_fn(sess) def testLoadExistingVariablesDifferentShapeAllowReshape(self): - model_dir = tempfile.mkdtemp(prefix=os.path.join( - self.get_temp_dir(), - 'load_existing_variables_different_shape_allow_reshape')) + model_dir = tempfile.mkdtemp( + prefix=os.path.join( + self.get_temp_dir(), + 'load_existing_variables_different_shape_allow_reshape')) if gfile.Exists(model_dir): gfile.DeleteRecursively(model_dir) @@ -1169,8 +1202,8 @@ class AssignFromCheckpointFnTest(test.TestCase): self.assertEqual(init_value1, var1.eval()) def testNotFoundError(self): - model_dir = tempfile.mkdtemp(prefix=os.path.join(self.get_temp_dir(), - 'not_found_error')) + model_dir = tempfile.mkdtemp( + prefix=os.path.join(self.get_temp_dir(), 'not_found_error')) if gfile.Exists(model_dir): gfile.DeleteRecursively(model_dir) @@ -1186,8 +1219,8 @@ class AssignFromCheckpointFnTest(test.TestCase): var2 = variables_lib2.variable('my_var2', shape=[]) vars_to_restore = {'v0': var0, 'v1': var1, 'v2': var2} - init_fn = variables_lib2.assign_from_checkpoint_fn(model_path, - vars_to_restore) + init_fn = variables_lib2.assign_from_checkpoint_fn( + model_path, vars_to_restore) # Initialize the variables. sess.run(variables_lib.global_variables_initializer()) @@ -1197,8 +1230,8 @@ class AssignFromCheckpointFnTest(test.TestCase): init_fn(sess) def testMissingVariablesList(self): - model_dir = tempfile.mkdtemp(prefix=os.path.join(self.get_temp_dir(), - 'missing_variables_list')) + model_dir = tempfile.mkdtemp( + prefix=os.path.join(self.get_temp_dir(), 'missing_variables_list')) if gfile.Exists(model_dir): gfile.DeleteRecursively(model_dir) @@ -1228,8 +1261,8 @@ class AssignFromCheckpointFnTest(test.TestCase): self.assertEqual(init_value1, var1.eval()) def testMissingVariablesDict(self): - model_dir = tempfile.mkdtemp(prefix=os.path.join(self.get_temp_dir(), - 'missing_variables_dict')) + model_dir = tempfile.mkdtemp( + prefix=os.path.join(self.get_temp_dir(), 'missing_variables_dict')) if gfile.Exists(model_dir): gfile.DeleteRecursively(model_dir) @@ -1279,9 +1312,8 @@ class ZeroInitializerOpTest(test.TestCase): def testZeroInitializer(self): for dtype in (dtypes.int32, dtypes.int64, dtypes.float32, dtypes.float64): for use_init in (False, True): - self._testZeroInitializer( - [10, 20], array_ops.ones( - [10, 20], dtype=dtype), use_init) + self._testZeroInitializer([10, 20], array_ops.ones( + [10, 20], dtype=dtype), use_init) class ZeroVarInitializerOpTest(test.TestCase): -- GitLab From b22cfe55abc6700d9d9492be4316da4e74e3549d Mon Sep 17 00:00:00 2001 From: Benjamin Kramer Date: Thu, 14 Jun 2018 09:31:18 -0700 Subject: [PATCH 452/816] [XLA:GPU] Turn on Loop-Loop sibling multi-output fusion Reduce-Loop fusion is currently not a win, but Loop-Loop is a small win. Let's turn it on to get more eyeballs on the generated code. PiperOrigin-RevId: 200568238 --- .../xla/service/gpu/multi_output_fusion.cc | 25 +++++++++++++++- .../xla/service/gpu/multi_output_fusion.h | 3 ++ .../service/gpu/multi_output_fusion_test.cc | 29 +++++++++++++++++++ 3 files changed, 56 insertions(+), 1 deletion(-) diff --git a/tensorflow/compiler/xla/service/gpu/multi_output_fusion.cc b/tensorflow/compiler/xla/service/gpu/multi_output_fusion.cc index 09acd8603e..d541776f00 100644 --- a/tensorflow/compiler/xla/service/gpu/multi_output_fusion.cc +++ b/tensorflow/compiler/xla/service/gpu/multi_output_fusion.cc @@ -100,7 +100,13 @@ bool IsReduction(HloInstruction* instr) { } // namespace bool GpuMultiOutputFusion::IsFusible(HloInstruction* instr) { - return IsReduction(instr); + // We can fuse reduces and loop fusions. + return IsReduction(instr) || + (instr->opcode() == HloOpcode::kFusion && + instr->fusion_kind() == HloInstruction::FusionKind::kLoop && + // TODO(b/110202584): bitcasts make nested fusions, GPU has no support + // for nested fusions. + instr->fused_expression_root()->opcode() != HloOpcode::kBitcast); } int64 GpuMultiOutputFusion::GetProfit(HloInstruction* instr1, @@ -124,5 +130,22 @@ int64 GpuMultiOutputFusion::GetProfit(HloInstruction* instr1, return profit; } +bool GpuMultiOutputFusion::LegalToFuse(HloInstruction* instr1, + HloInstruction* instr2) { + if (!MultiOutputFusion::LegalToFuse(instr1, instr2)) { + return false; + } + // If we're fusing fusions only do it if the fusion kind matches. Loop fusions + // merge into bigger loop fusions and input (reduce) fusions become fusions + // with multiple reduce outputs. We could fuse reduce and loop fusions + // together too (the result being an input fusion) if we find cases where this + // improves things. + CHECK(instr1->opcode() == HloOpcode::kFusion); + if (instr2->opcode() == HloOpcode::kFusion) { + return instr1->fusion_kind() == instr2->fusion_kind(); + } + return instr1->fusion_kind() != HloInstruction::FusionKind::kLoop; +} + } // namespace gpu } // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/multi_output_fusion.h b/tensorflow/compiler/xla/service/gpu/multi_output_fusion.h index 038b1e9dc4..16db0e0f02 100644 --- a/tensorflow/compiler/xla/service/gpu/multi_output_fusion.h +++ b/tensorflow/compiler/xla/service/gpu/multi_output_fusion.h @@ -42,6 +42,9 @@ class GpuMultiOutputFusion : public MultiOutputFusion { // instr1 and instr2, common operands will not be loaded twice. The profit is // estimated as the size of the common operands b/w instr1 and instr2. int64 GetProfit(HloInstruction* instr1, HloInstruction* instr2) override; + + // Test if it's legal to fuse instr1 and instr2 into one fusion instruction. + bool LegalToFuse(HloInstruction* instr1, HloInstruction* instr2) override; }; } // namespace gpu diff --git a/tensorflow/compiler/xla/service/gpu/multi_output_fusion_test.cc b/tensorflow/compiler/xla/service/gpu/multi_output_fusion_test.cc index 924cfb11f3..5e7ceb7976 100644 --- a/tensorflow/compiler/xla/service/gpu/multi_output_fusion_test.cc +++ b/tensorflow/compiler/xla/service/gpu/multi_output_fusion_test.cc @@ -226,5 +226,34 @@ TEST_F(InstructionFusionTest, ASSERT_FALSE(GpuMultiOutputFusion().Run(module.get()).ValueOrDie()); } +TEST_F(InstructionFusionTest, MultiOutputFusionTwoLoops) { + auto module = ParseHloString(tensorflow::strings::StrCat(kModulePrefix, R"( + fused_computation_1 { + p0.1 = f32[6400]{0} parameter(0) + ROOT mul = f32[6400]{0} multiply(p0.1, p0.1) + } + + fused_computation_2 { + p0.2 = f32[6400]{0} parameter(0) + const.2 = f32[] constant(1) + ROOT div = f32[6400]{0} divide(p0.2, const.2) + } + + ENTRY entry { + p0 = f32[6400]{0} parameter(0) + fusion.1 = f32[6400]{0} fusion(p0), kind=kLoop, calls=fused_computation_1 + fusion.2 = f32[6400]{0} fusion(p0), kind=kLoop, calls=fused_computation_2 + ROOT root = (f32[6400]{0}, f32[6400]{0}) tuple(fusion.1, fusion.2) + })")) + .ValueOrDie(); + ASSERT_TRUE(GpuMultiOutputFusion().Run(module.get()).ValueOrDie()); + SCOPED_TRACE(module->ToString()); + const HloInstruction* fusion = + module->entry_computation()->root_instruction()->operand(0)->operand(0); + ASSERT_TRUE(fusion->IsMultiOutputFusion()); + EXPECT_THAT(fusion->fused_expression_root(), + op::Tuple(op::Multiply(), op::Divide())); +} + } // namespace gpu } // namespace xla -- GitLab From 3d5fa1f7f85e8cbd39227e921960fa36539ba3cd Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 14 Jun 2018 09:33:17 -0700 Subject: [PATCH 453/816] Disable removing pairs of transposes across chains, while debugging breakage in bayesflow. PiperOrigin-RevId: 200568541 --- tensorflow/core/grappler/optimizers/BUILD | 4 ++-- .../core/grappler/optimizers/arithmetic_optimizer.cc | 10 +++++++--- .../grappler/optimizers/arithmetic_optimizer_test.cc | 2 +- .../core/grappler/optimizers/graph_optimizer_stage.h | 8 ++++++-- .../grappler/optimizers/graph_optimizer_stage_test.cc | 10 +++++++--- 5 files changed, 23 insertions(+), 11 deletions(-) diff --git a/tensorflow/core/grappler/optimizers/BUILD b/tensorflow/core/grappler/optimizers/BUILD index 20887bc218..1b18087cdf 100644 --- a/tensorflow/core/grappler/optimizers/BUILD +++ b/tensorflow/core/grappler/optimizers/BUILD @@ -210,8 +210,7 @@ cc_library( hdrs = ["graph_optimizer_stage.h"], visibility = ["//visibility:public"], deps = [ - "//tensorflow/core:lib", - "//tensorflow/core:lib_internal", + "//tensorflow/core:protos_all_cc", "//tensorflow/core/grappler:grappler_item", "//tensorflow/core/grappler:utils", "//tensorflow/core/grappler/costs:graph_properties", @@ -225,6 +224,7 @@ tf_cuda_cc_test( deps = [ ":graph_optimizer_stage", "//tensorflow/cc:cc_ops", + "//tensorflow/core:protos_all_cc", "//tensorflow/core:test", "//tensorflow/core:test_main", "//tensorflow/core/grappler:grappler_item", diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc index 51110b4bda..c41b152d21 100644 --- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc +++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc @@ -1084,8 +1084,11 @@ class RemoveIdentityTranspose : public ArithmeticOptimizerStage { Status TrySimplify(NodeDef* node, string* simplified_node_name) override { TF_RETURN_IF_ERROR(EnsureNodeIsSupported(node)); NodeDef* tail = node; - tail = GetTailOfIdempotentChain(*tail, *ctx().node_map, - *ctx().nodes_to_preserve); + // TODO(rmlarsen): Enable after debugging breakage in Bayesflow. + if (ctx().opt_level == RewriterConfig::AGGRESSIVE) { + tail = GetTailOfIdempotentChain(*tail, *ctx().node_map, + *ctx().nodes_to_preserve); + } NodeDef* first_transpose; TF_RETURN_IF_ERROR(GetInputNode(tail->input(0), &first_transpose)); @@ -2713,7 +2716,8 @@ Status ArithmeticOptimizer::SimplifyArithmeticOps(bool can_use_shapes) { } const GraphOptimizerContext ctx(&nodes_to_preserve_, optimized_graph_, - graph_properties_.get(), node_map_.get()); + graph_properties_.get(), node_map_.get(), + opt_level_); const ArithmeticOptimizerContext ctx_ext(&nodes_to_simplify); // Stop pipeline after first stage returning non-empty simplified tensor name. diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc b/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc index ff96cb6480..fe70c7db5c 100644 --- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc +++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc @@ -1510,7 +1510,7 @@ TEST_F(ArithmeticOptimizerTest, RemoveIdentityTransposesThroughChain) { TF_CHECK_OK(s.ToGraphDef(&item.graph)); GraphDef output; - ArithmeticOptimizer optimizer; + ArithmeticOptimizer optimizer(RewriterConfig::AGGRESSIVE); EnableOnlyRemoveIdentityTranspose(&optimizer); OptimizeAndPrune(&optimizer, &item, &output); diff --git a/tensorflow/core/grappler/optimizers/graph_optimizer_stage.h b/tensorflow/core/grappler/optimizers/graph_optimizer_stage.h index 2fbdd76a77..2afb5df431 100644 --- a/tensorflow/core/grappler/optimizers/graph_optimizer_stage.h +++ b/tensorflow/core/grappler/optimizers/graph_optimizer_stage.h @@ -21,6 +21,7 @@ limitations under the License. #include "tensorflow/core/grappler/costs/graph_properties.h" #include "tensorflow/core/grappler/grappler_item.h" #include "tensorflow/core/grappler/utils.h" +#include "tensorflow/core/protobuf/rewriter_config.pb.h" namespace tensorflow { namespace grappler { @@ -44,16 +45,19 @@ const NodeScopeAndName ParseNodeScopeAndName(const string& node_name); struct GraphOptimizerContext { GraphOptimizerContext(const std::unordered_set* nodes_to_preserve, GraphDef* optimized_graph, - GraphProperties* graph_properties, NodeMap* node_map) + GraphProperties* graph_properties, NodeMap* node_map, + RewriterConfig::Toggle opt_level) : nodes_to_preserve(nodes_to_preserve), optimized_graph(optimized_graph), graph_properties(graph_properties), - node_map(node_map) {} + node_map(node_map), + opt_level(opt_level) {} const std::unordered_set* nodes_to_preserve; GraphDef* optimized_graph; GraphProperties* graph_properties; NodeMap* node_map; + RewriterConfig::Toggle opt_level; }; Status GetInputNode(const GraphOptimizerContext& ctx, const string& input, diff --git a/tensorflow/core/grappler/optimizers/graph_optimizer_stage_test.cc b/tensorflow/core/grappler/optimizers/graph_optimizer_stage_test.cc index 3f5ab87a5a..34f28c7c27 100644 --- a/tensorflow/core/grappler/optimizers/graph_optimizer_stage_test.cc +++ b/tensorflow/core/grappler/optimizers/graph_optimizer_stage_test.cc @@ -19,6 +19,7 @@ limitations under the License. #include "tensorflow/core/grappler/costs/graph_properties.h" #include "tensorflow/core/grappler/grappler_item.h" #include "tensorflow/core/platform/test.h" +#include "tensorflow/core/protobuf/rewriter_config.pb.h" namespace tensorflow { namespace grappler { @@ -59,7 +60,8 @@ TEST_F(GraphOptimizerStageTest, OptimizedNodeName) { GraphOptimizerContext ctx(/*nodes_to_preserve*/ nullptr, /*optimized_graph*/ nullptr, /*graph_properties*/ nullptr, - /*node_name*/ nullptr); + /*node_name*/ nullptr, + /*opt_level*/ RewriterConfig::ON); FakeOptimizerStage stage("my_opt", "my_stg", ctx); const auto node = ParseNodeScopeAndName("a/b/c/Add"); @@ -94,7 +96,8 @@ TEST_F(GraphOptimizerStageTest, GetInputNodeAndProperties) { GraphOptimizerContext ctx(/*nodes_to_preserve*/ nullptr, /*optimized_graph*/ &item.graph, /*graph_properties*/ &properties, - /*node_name*/ &node_map); + /*node_name*/ &node_map, + /*opt_level*/ RewriterConfig::ON); FakeOptimizerStage stage("my_opt", "my_stg", ctx); NodeDef* add_node; @@ -133,7 +136,8 @@ TEST_F(GraphOptimizerStageTest, AddNodes) { GraphOptimizerContext ctx(/*nodes_to_preserve*/ nullptr, /*optimized_graph*/ &item.graph, /*graph_properties*/ &properties, - /*node_name*/ &node_map); + /*node_name*/ &node_map, + /*opt_level*/ RewriterConfig::ON); FakeOptimizerStage stage("my_opt", "my_stg", ctx); NodeDef* add_node; -- GitLab From 5001a3f25bf709159b8cd40d3024885ff382acc3 Mon Sep 17 00:00:00 2001 From: Allen Lavoie Date: Thu, 14 Jun 2018 09:40:33 -0700 Subject: [PATCH 454/816] Add tf.contrib.checkpoint.list_objects for listing all Python dependencies of a checkpointable object Useful for asserting that all expected objects have been added as dependencies in a unit test. PiperOrigin-RevId: 200569520 --- tensorflow/contrib/checkpoint/__init__.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/checkpoint/__init__.py b/tensorflow/contrib/checkpoint/__init__.py index 8ae493ba99..257e93d283 100644 --- a/tensorflow/contrib/checkpoint/__init__.py +++ b/tensorflow/contrib/checkpoint/__init__.py @@ -16,6 +16,7 @@ Visualization and inspection: @@dot_graph_from_checkpoint +@@list_objects @@object_metadata Managing dependencies: @@ -42,9 +43,9 @@ from tensorflow.python.training.checkpointable.base import Checkpointable from tensorflow.python.training.checkpointable.base import NoDependency from tensorflow.python.training.checkpointable.data_structures import List from tensorflow.python.training.checkpointable.data_structures import Mapping +from tensorflow.python.training.checkpointable.util import list_objects from tensorflow.python.training.checkpointable.util import object_metadata from tensorflow.python.util.all_util import remove_undocumented remove_undocumented(module_name=__name__) - -- GitLab From f2f4bebe2df4d54bfa7c5ef14ff79f51601d9c7e Mon Sep 17 00:00:00 2001 From: Michael Case Date: Thu, 14 Jun 2018 09:54:26 -0700 Subject: [PATCH 455/816] Fix git_tag_override option in gen_git_source.py. This fix was committed to the r1.8 branch but never to master. Adding this fix to master branch. --- tensorflow/tools/git/gen_git_source.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/tensorflow/tools/git/gen_git_source.py b/tensorflow/tools/git/gen_git_source.py index 73dee98bae..cc2288a7fa 100755 --- a/tensorflow/tools/git/gen_git_source.py +++ b/tensorflow/tools/git/gen_git_source.py @@ -164,14 +164,17 @@ def get_git_version(git_base_path, git_tag_override): "git", str("--git-dir=%s/.git" % git_base_path), str("--work-tree=" + git_base_path), "describe", "--long", "--tags" ]).strip()) - if git_tag_override: + if git_tag_override and val: split_val = val.split("-") - if len(split_val) != 3: + if len(split_val) < 3: raise Exception( ("Expected git version in format 'TAG-COMMITS AFTER TAG-HASH' " "but got '%s'") % val) - split_val[0] = git_tag_override - val = bytes("-".join(split_val)) + # There might be "-" in the tag name. But we can be sure that the final + # two "-" are those inserted by the git describe command. + abbrev_commit = split_val[-1] + val = bytes( + "-".join([git_tag_override, "0", abbrev_commit])) return val if val else unknown_label except (subprocess.CalledProcessError, OSError): return unknown_label -- GitLab From a4cadda496d01495a2a5589ddf31e1a1176690a5 Mon Sep 17 00:00:00 2001 From: Derek Murray Date: Thu, 14 Jun 2018 09:52:05 -0700 Subject: [PATCH 456/816] [tf.data] Add `StructuredFunctionWrapper` to encapsulate tf.data's enhancements to Defun. This cuts down further on the boilerplate in functional tf.data transformations. PiperOrigin-RevId: 200571420 --- .../contrib/data/python/ops/grouping.py | 266 ++++++------------ .../contrib/data/python/ops/scan_ops.py | 116 +++----- tensorflow/python/data/ops/dataset_ops.py | 216 +++++++++----- 3 files changed, 266 insertions(+), 332 deletions(-) diff --git a/tensorflow/contrib/data/python/ops/grouping.py b/tensorflow/contrib/data/python/ops/grouping.py index 60f13a1126..4068a2ffa5 100644 --- a/tensorflow/contrib/data/python/ops/grouping.py +++ b/tensorflow/contrib/data/python/ops/grouping.py @@ -21,12 +21,9 @@ import numpy as np from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.util import nest -from tensorflow.python.data.util import sparse from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes -from tensorflow.python.framework import function from tensorflow.python.framework import ops -from tensorflow.python.framework import sparse_tensor from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import array_ops from tensorflow.python.ops import check_ops @@ -273,54 +270,27 @@ class GroupByReducerDataset(dataset_ops.Dataset): def _make_key_func(self, key_func, input_dataset): """Make wrapping Defun for key_func.""" - - @function.Defun(*dataset_ops.defun_args(input_dataset)) - def tf_key_func(*args): - """A wrapper for Defun that facilitates shape inference.""" - nested_args = dataset_ops.restructure_args(args, input_dataset) - ret = key_func(*nested_args) - ret = ops.convert_to_tensor(ret) - if ret.dtype != dtypes.int64 or ret.get_shape() != tensor_shape.scalar(): - raise ValueError( - "`key_func` must return a single tf.int64 tensor. " - "Got type=%s and shape=%s" % (ret.dtype, ret.get_shape())) - dataset_ops._warn_if_collections("tf.contrib.data.group_by_reducer()") # pylint: disable=protected-access - return ret - - self._key_func = tf_key_func - self._key_func.add_to_graph(ops.get_default_graph()) + wrapped_func = dataset_ops.StructuredFunctionWrapper( + key_func, "tf.contrib.data.group_by_reducer()", input_dataset) + if not ( + wrapped_func.output_types == dtypes.int64 and + wrapped_func.output_shapes.is_compatible_with(tensor_shape.scalar())): + raise ValueError( + "`key_func` must return a single tf.int64 tensor. " + "Got type=%s and shape=%s" + % (wrapped_func.output_types, wrapped_func.output_shapes)) + self._key_func = wrapped_func.function def _make_init_func(self, init_func): """Make wrapping Defun for init_func.""" - - @function.Defun(dtypes.int64) - def tf_init_func(key): - """A wrapper for Defun that facilitates shape inference.""" - key.set_shape([]) - ret = init_func(key) - # Convert any `SparseTensorValue`s to `SparseTensor`s and all other - # values to tensors. - ret = nest.pack_sequence_as(ret, [ - sparse_tensor.SparseTensor.from_value(t) - if sparse_tensor.is_sparse(t) else ops.convert_to_tensor(t) - for t in nest.flatten(ret) - ]) - - self._state_classes = sparse.get_classes(ret) - self._state_shapes = nest.pack_sequence_as( - ret, [t.get_shape() for t in nest.flatten(ret)]) - self._state_types = nest.pack_sequence_as( - ret, [t.dtype for t in nest.flatten(ret)]) - - dataset_ops._warn_if_collections("tf.contrib.data.group_by_reducer()") # pylint: disable=protected-access - - # Serialize any sparse tensors. - ret = nest.pack_sequence_as( - ret, [t for t in nest.flatten(sparse.serialize_sparse_tensors(ret))]) - return nest.flatten(ret) - - self._init_func = tf_init_func - self._init_func.add_to_graph(ops.get_default_graph()) + wrapped_func = dataset_ops.StructuredFunctionWrapper( + init_func, "tf.contrib.data.group_by_reducer()", + input_classes=ops.Tensor, input_shapes=tensor_shape.scalar(), + input_types=dtypes.int64) + self._init_func = wrapped_func.function + self._state_classes = wrapped_func.output_classes + self._state_shapes = wrapped_func.output_shapes + self._state_types = wrapped_func.output_types def _make_reduce_func(self, reduce_func, input_dataset): """Make wrapping Defun for reduce_func.""" @@ -330,68 +300,47 @@ class GroupByReducerDataset(dataset_ops.Dataset): need_to_rerun = True while need_to_rerun: - # Create a list in which `tf_reduce_func` will store the new shapes. - flat_new_state_shapes = [] - - @function.Defun(*dataset_ops.defun_args( + wrapped_func = dataset_ops.StructuredFunctionWrapper( + reduce_func, "tf.contrib.data.group_by_reducer()", + input_classes=(self._state_classes, input_dataset.output_classes), + input_shapes=(self._state_shapes, input_dataset.output_shapes), input_types=(self._state_types, input_dataset.output_types), - input_classes=(self._state_classes, input_dataset.output_classes))) - def tf_reduce_func(*args): - """A wrapper for Defun that facilitates shape inference.""" - nested_args = dataset_ops.restructure_args( - args, - input_shapes=(self._state_shapes, input_dataset.output_shapes), - input_types=(self._state_types, input_dataset.output_types), - input_classes=(self._state_classes, input_dataset.output_classes)) - - ret = reduce_func(*nested_args) - - # Convert any `SparseTensorValue`s to `SparseTensor`s and all other - # values to tensors. - ret = nest.pack_sequence_as(ret, [ - sparse_tensor.SparseTensor.from_value(t) - if sparse_tensor.is_sparse(t) else ops.convert_to_tensor(t) - for t in nest.flatten(ret) - ]) - - # Extract shape information from the returned values. - flat_new_state = nest.flatten(ret) - flat_new_state_shapes.extend([t.get_shape() for t in flat_new_state]) - - # Extract and validate type information from the returned values. - for t, dtype in zip(flat_new_state, nest.flatten(self._state_types)): - if t.dtype != dtype: - raise TypeError( - "The element types for the new state must match the initial " - "state. Expected %s; got %s." % - (self._state_types, - nest.pack_sequence_as(self._state_types, - [t.dtype for t in flat_new_state]))) - - dataset_ops._warn_if_collections("tf.contrib.data.group_by_reducer()") # pylint: disable=protected-access - - # Serialize any sparse tensors. - ret = nest.pack_sequence_as( - ret, - [t for t in nest.flatten(sparse.serialize_sparse_tensors(ret))]) - return nest.flatten(ret) - - # Use the private method that will execute `tf_reduce_func` but delay - # adding it to the graph in case we need to rerun the function. - tf_reduce_func._create_definition_if_needed() # pylint: disable=protected-access - + add_to_graph=False) + + # Extract and validate class information from the returned values. + for new_state_class, state_class in zip( + nest.flatten(wrapped_func.output_classes), + nest.flatten(self._state_classes)): + if not issubclass(new_state_class, state_class): + raise TypeError( + "The element classes for the new state must match the initial " + "state. Expected %s; got %s." % + (self._state_classes, wrapped_func.output_classes)) + + # Extract and validate type information from the returned values. + for new_state_type, state_type in zip( + nest.flatten(wrapped_func.output_types), + nest.flatten(self._state_types)): + if new_state_type != state_type: + raise TypeError( + "The element types for the new state must match the initial " + "state. Expected %s; got %s." % + (self._state_types, wrapped_func.output_types)) + + # Extract shape information from the returned values. flat_state_shapes = nest.flatten(self._state_shapes) + flat_new_state_shapes = nest.flatten(wrapped_func.output_shapes) weakened_state_shapes = [ - old.most_specific_compatible_shape(new) - for old, new in zip(flat_state_shapes, flat_new_state_shapes) + original.most_specific_compatible_shape(new) + for original, new in zip(flat_state_shapes, flat_new_state_shapes) ] need_to_rerun = False - for old_shape, weakened_shape in zip(flat_state_shapes, - weakened_state_shapes): - if old_shape.ndims is not None and ( + for original_shape, weakened_shape in zip(flat_state_shapes, + weakened_state_shapes): + if original_shape.ndims is not None and ( weakened_shape.ndims is None or - old_shape.as_list() != weakened_shape.as_list()): + original_shape.as_list() != weakened_shape.as_list()): need_to_rerun = True break @@ -399,44 +348,19 @@ class GroupByReducerDataset(dataset_ops.Dataset): self._state_shapes = nest.pack_sequence_as(self._state_shapes, weakened_state_shapes) - self._reduce_func = tf_reduce_func + self._reduce_func = wrapped_func.function self._reduce_func.add_to_graph(ops.get_default_graph()) def _make_finalize_func(self, finalize_func): """Make wrapping Defun for finalize_func.""" - - @function.Defun(*dataset_ops.defun_args( - input_types=self._state_types, input_classes=self._state_classes)) - def tf_finalize_func(*args): - """A wrapper for Defun that facilitates shape inference.""" - nested_args = dataset_ops.restructure_args( - args, input_shapes=self._state_shapes, input_types=self._state_types, - input_classes=self._state_classes) - ret = finalize_func(*nested_args) - - # Convert any `SparseTensorValue`s to `SparseTensor`s and all other - # values to tensors. - ret = nest.pack_sequence_as(ret, [ - sparse_tensor.SparseTensor.from_value(t) - if sparse_tensor.is_sparse(t) else ops.convert_to_tensor(t) - for t in nest.flatten(ret) - ]) - - self._output_classes = sparse.get_classes(ret) - self._output_shapes = nest.pack_sequence_as( - ret, [t.get_shape() for t in nest.flatten(ret)]) - self._output_types = nest.pack_sequence_as( - ret, [t.dtype for t in nest.flatten(ret)]) - - dataset_ops._warn_if_collections("tf.contrib.data.group_by_reducer()") # pylint: disable=protected-access - - # Serialize any sparse tensors. - ret = nest.pack_sequence_as( - ret, [t for t in nest.flatten(sparse.serialize_sparse_tensors(ret))]) - return nest.flatten(ret) - - self._finalize_func = tf_finalize_func - self._finalize_func.add_to_graph(ops.get_default_graph()) + wrapped_func = dataset_ops.StructuredFunctionWrapper( + finalize_func, "tf.contrib.data.group_by_reducer()", + input_classes=self._state_classes, input_shapes=self._state_shapes, + input_types=self._state_types) + self._finalize_func = wrapped_func.function + self._output_classes = wrapped_func.output_classes + self._output_shapes = wrapped_func.output_shapes + self._output_types = wrapped_func.output_types @property def output_classes(self): @@ -479,61 +403,53 @@ class GroupByWindowDataset(dataset_ops.Dataset): def _make_window_size_func(self, window_size_func): """Make wrapping Defun for window_size_func.""" - - @function.Defun(dtypes.int64) - def tf_window_size_func(key): - key.set_shape([]) - window_size = ops.convert_to_tensor( - window_size_func(key), dtype=dtypes.int64) - if window_size.dtype != dtypes.int64: - raise ValueError( - "`window_size_func` must return a single tf.int64 tensor.") - dataset_ops._warn_if_collections("tf.contrib.data.group_by_window()") # pylint: disable=protected-access - return window_size - - self._window_size_func = tf_window_size_func - self._window_size_func.add_to_graph(ops.get_default_graph()) + def window_size_func_wrapper(key): + return ops.convert_to_tensor(window_size_func(key), dtype=dtypes.int64) + wrapped_func = dataset_ops.StructuredFunctionWrapper( + window_size_func_wrapper, "tf.contrib.data.group_by_window()", + input_classes=ops.Tensor, input_shapes=tensor_shape.scalar(), + input_types=dtypes.int64) + if not ( + wrapped_func.output_types == dtypes.int64 and + wrapped_func.output_shapes.is_compatible_with(tensor_shape.scalar())): + raise ValueError( + "`window_size_func` must return a single tf.int64 scalar tensor.") + self._window_size_func = wrapped_func.function def _make_key_func(self, key_func, input_dataset): """Make wrapping Defun for key_func.""" - - @function.Defun(*dataset_ops.defun_args(input_dataset)) - def tf_key_func(*args): - """A wrapper for Defun that facilitates shape inference.""" - nested_args = dataset_ops.restructure_args(args, input_dataset) - ret = key_func(*nested_args) - ret = ops.convert_to_tensor(ret, dtype=dtypes.int64) - if ret.dtype != dtypes.int64: - raise ValueError("`key_func` must return a single tf.int64 tensor.") - dataset_ops._warn_if_collections("tf.contrib.data.group_by_window()") # pylint: disable=protected-access - return ret - - self._key_func = tf_key_func - self._key_func.add_to_graph(ops.get_default_graph()) + def key_func_wrapper(*args): + return ops.convert_to_tensor(key_func(*args), dtype=dtypes.int64) + wrapped_func = dataset_ops.StructuredFunctionWrapper( + key_func_wrapper, "tf.contrib.data.group_by_window()", input_dataset) + if not ( + wrapped_func.output_types == dtypes.int64 and + wrapped_func.output_shapes.is_compatible_with(tensor_shape.scalar())): + raise ValueError( + "`key_func` must return a single tf.int64 scalar tensor.") + self._key_func = wrapped_func.function def _make_reduce_func(self, reduce_func, input_dataset): """Make wrapping Defun for reduce_func.""" - - @function.Defun(dtypes.int64, dtypes.variant) - def tf_reduce_func(key, window_dataset_variant): - """A wrapper for Defun that facilitates shape inference.""" - key.set_shape([]) + def reduce_func_wrapper(key, window_dataset_variant): + """Wrapper that converts between tf.variant and Dataset objects.""" window_dataset = _VariantDataset( window_dataset_variant, input_dataset.output_types, input_dataset.output_shapes, input_dataset.output_classes) - if not isinstance(window_dataset, dataset_ops.Dataset): - raise TypeError("`window_dataset` must return a `Dataset` object.") output_dataset = reduce_func(key, window_dataset) if not isinstance(output_dataset, dataset_ops.Dataset): raise TypeError("`reduce_func` must return a `Dataset` object.") self._output_classes = output_dataset.output_classes self._output_types = output_dataset.output_types self._output_shapes = output_dataset.output_shapes - dataset_ops._warn_if_collections("tf.contrib.data.group_by_window()") # pylint: disable=protected-access return output_dataset._as_variant_tensor() # pylint: disable=protected-access - self._reduce_func = tf_reduce_func - self._reduce_func.add_to_graph(ops.get_default_graph()) + wrapped_func = dataset_ops.StructuredFunctionWrapper( + reduce_func_wrapper, "tf.contrib.data.reduce_by_window()", + input_classes=(ops.Tensor, ops.Tensor), + input_shapes=(tensor_shape.scalar(), tensor_shape.scalar()), + input_types=(dtypes.int64, dtypes.variant)) + self._reduce_func = wrapped_func.function @property def output_classes(self): diff --git a/tensorflow/contrib/data/python/ops/scan_ops.py b/tensorflow/contrib/data/python/ops/scan_ops.py index c23b9b5c37..ea9dcfe68f 100644 --- a/tensorflow/contrib/data/python/ops/scan_ops.py +++ b/tensorflow/contrib/data/python/ops/scan_ops.py @@ -22,7 +22,6 @@ import collections from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.util import nest from tensorflow.python.data.util import sparse -from tensorflow.python.framework import function from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor from tensorflow.python.ops import gen_dataset_ops @@ -67,84 +66,45 @@ class _ScanDataset(dataset_ops.Dataset): need_to_rerun = True while need_to_rerun: - # Create a list in which `tf_scan_func` will store the new shapes. - flat_new_state_shapes = [] - - @function.Defun(*dataset_ops.defun_args( + wrapped_func = dataset_ops.StructuredFunctionWrapper( + scan_func, "tf.contrib.data.scan()", + input_classes=(self._state_classes, input_dataset.output_classes), + input_shapes=(self._state_shapes, input_dataset.output_shapes), input_types=(self._state_types, input_dataset.output_types), - input_classes=(self._state_classes, input_dataset.output_classes))) - def tf_scan_func(*args): - """A wrapper for Defun that facilitates shape inference.""" - nested_args = dataset_ops.restructure_args( - args, - input_shapes=(self._state_shapes, input_dataset.output_shapes), - input_types=(self._state_types, input_dataset.output_types), - input_classes=(self._state_classes, input_dataset.output_classes)) - - ret = scan_func(*nested_args) - if not isinstance(ret, collections.Sequence) or len(ret) != 2: - raise TypeError("The scan function must return a pair comprising the " - "new state and the output value.") - - # Convert any `SparseTensorValue`s to `SparseTensor`s and all other - # values to tensors. - ret = nest.pack_sequence_as(ret, [ - sparse_tensor.SparseTensor.from_value(t) - if sparse_tensor.is_sparse(t) else ops.convert_to_tensor(t) - for t in nest.flatten(ret) - ]) - new_state, output_value = ret - - # Extract and validate class information from the returned values. - for t, clazz in zip( - nest.flatten(new_state), nest.flatten(self._state_classes)): - if not isinstance(t, clazz): - raise TypeError( - "The element classes for the new state must match the initial " - "state. Expected %s; got %s." % - (self._state_classes, - nest.pack_sequence_as( - self._state_types, - [type(t) for t in nest.flatten(new_state)]))) - self._output_classes = sparse.get_classes(output_value) - - # Extract shape information from the returned values. - flat_new_state_shapes.extend( - [t.get_shape() for t in nest.flatten(new_state)]) - self._output_shapes = nest.pack_sequence_as( - output_value, [t.get_shape() for t in nest.flatten(output_value)]) - - # Extract and validate type information from the returned values. - for t, dtype in zip( - nest.flatten(new_state), nest.flatten(self._state_types)): - if t.dtype != dtype: - raise TypeError( - "The element types for the new state must match the initial " - "state. Expected %s; got %s." % - (self._state_types, - nest.pack_sequence_as( - self._state_types, - [t.dtype for t in nest.flatten(new_state)]))) - self._output_types = nest.pack_sequence_as( - output_value, [t.dtype for t in nest.flatten(output_value)]) - - dataset_ops._warn_if_collections("tf.contrib.data.scan()") # pylint: disable=protected-access - - # Serialize any sparse tensors. - new_state = nest.pack_sequence_as(new_state, [ - t for t in nest.flatten(sparse.serialize_sparse_tensors(new_state)) - ]) - output_value = nest.pack_sequence_as(output_value, [ - t for t in nest.flatten( - sparse.serialize_sparse_tensors(output_value)) - ]) - return nest.flatten(new_state) + nest.flatten(output_value) - - # Use the private method that will execute `tf_scan_func` but delay - # adding it to the graph in case we need to rerun the function. - tf_scan_func._create_definition_if_needed() # pylint: disable=protected-access + add_to_graph=False) + if not ( + isinstance(wrapped_func.output_types, collections.Sequence) and + len(wrapped_func.output_types) == 2): + raise TypeError("The scan function must return a pair comprising the " + "new state and the output value.") + + new_state_classes, self._output_classes = wrapped_func.output_classes + + # Extract and validate class information from the returned values. + for new_state_class, state_class in zip( + nest.flatten(new_state_classes), + nest.flatten(self._state_classes)): + if not issubclass(new_state_class, state_class): + raise TypeError( + "The element classes for the new state must match the initial " + "state. Expected %s; got %s." % + (self._state_classes, new_state_classes)) + + # Extract and validate type information from the returned values. + new_state_types, self._output_types = wrapped_func.output_types + for new_state_type, state_type in zip( + nest.flatten(new_state_types), nest.flatten(self._state_types)): + if new_state_type != state_type: + raise TypeError( + "The element types for the new state must match the initial " + "state. Expected %s; got %s." % + (self._state_types, new_state_types)) + + # Extract shape information from the returned values. + new_state_shapes, self._output_shapes = wrapped_func.output_shapes flat_state_shapes = nest.flatten(self._state_shapes) + flat_new_state_shapes = nest.flatten(new_state_shapes) weakened_state_shapes = [ original.most_specific_compatible_shape(new) for original, new in zip(flat_state_shapes, flat_new_state_shapes) @@ -160,12 +120,10 @@ class _ScanDataset(dataset_ops.Dataset): break if need_to_rerun: - # NOTE(mrry): `self._output_shapes` will be overwritten when we rerun - # `tf_scan_func`. self._state_shapes = nest.pack_sequence_as(self._state_shapes, weakened_state_shapes) - self._scan_func = tf_scan_func + self._scan_func = wrapped_func.function self._scan_func.add_to_graph(ops.get_default_graph()) def _as_variant_tensor(self): diff --git a/tensorflow/python/data/ops/dataset_ops.py b/tensorflow/python/data/ops/dataset_ops.py index 67c1c17f99..f9c1031d9b 100644 --- a/tensorflow/python/data/ops/dataset_ops.py +++ b/tensorflow/python/data/ops/dataset_ops.py @@ -1148,6 +1148,121 @@ class SparseTensorSliceDataset(Dataset): return (dtypes.int64, self._sparse_tensor.dtype, dtypes.int64) +class StructuredFunctionWrapper(object): + """A wrapper for `Defun` that supports structured arguments and return values. + """ + + def __init__(self, func, transformation_name, dataset=None, + input_classes=None, input_shapes=None, input_types=None, + add_to_graph=True): + """Creates a new `StructuredFunctionWrapper` for the given function. + + Args: + func: A function from a nested structure to another nested structure. + transformation_name: Human-readable name of the transformation in which + this function is being instantiated, for error messages. + dataset: (Optional.) A @{tf.data.Dataset}. If given, the structure of this + dataset will be assumed as the structure for `func` arguments; otherwise + `input_classes`, `input_shapes`, and `input_types` must be defined. + input_classes: (Optional.) A nested structure of `type`. If given, this + argument defines the Python types for `func` arguments. + input_shapes: (Optional.) A nested structure of @{tf.TensorShape}. If + given, this argument defines the shapes and structure for `func` + arguments. + input_types: (Optional.) A nested structure of @{tf.DType}. If given, this + argument defines the element types and structure for `func` arguments. + add_to_graph: (Optional.) If `True`, the function will be added to the + default graph. + + Raises: + ValueError: If an invalid combination of `dataset`, `input_classes`, + `input_shapes`, and `input_types` is passed. + """ + if dataset is None: + if input_classes is None or input_shapes is None or input_types is None: + raise ValueError("Either `dataset`, or all of `input_classes`, " + "`input_shapes`, and `input_types` must be specified.") + self._input_shapes = input_shapes + self._input_types = input_types + self._input_classes = input_classes + else: + if not (input_classes is None and input_shapes is None and + input_types is None): + raise ValueError("Either `dataset`, or all of `input_classes`, " + "`input_shapes`, and `input_types` must be specified.") + self._input_shapes = dataset.output_shapes + self._input_types = dataset.output_types + self._input_classes = dataset.output_classes + + @function.Defun(*defun_args( + input_types=self._input_types, input_classes=self._input_classes)) + def tf_data_structured_function_wrapper(*args): + """Wrapper for passing nested structures to and from tf.data functions.""" + nested_args = restructure_args(args, + input_shapes=self._input_shapes, + input_types=self._input_types, + input_classes=self._input_classes) + ret = func(*nested_args) + # If `func` returns a list of tensors, `nest.flatten()` and + # `ops.convert_to_tensor()` would conspire to attempt to stack + # those tensors into a single tensor, because the customized + # version of `nest.flatten()` does not recurse into lists. Since + # it is more likely that the list arose from returning the + # result of an operation (such as `tf.py_func()`) that returns a + # list of not-necessarily-stackable tensors, we treat the + # returned value is a `tuple` instead. A user wishing to pack + # the return value into a single tensor can use an explicit + # `tf.stack()` before returning. + if isinstance(ret, list): + ret = tuple(ret) + + # Convert any `SparseTensorValue`s to `SparseTensor`s and all other + # values to tensors. + ret = nest.pack_sequence_as(ret, [ + sparse_tensor_lib.SparseTensor.from_value(t) + if sparse_tensor_lib.is_sparse(t) else ops.convert_to_tensor(t) + for t in nest.flatten(ret) + ]) + + self._output_classes = sparse.get_classes(ret) + self._output_shapes = nest.pack_sequence_as( + ret, [t.get_shape() for t in nest.flatten(ret)]) + self._output_types = nest.pack_sequence_as( + ret, [t.dtype for t in nest.flatten(ret)]) + + _warn_if_collections(transformation_name) + + # Serialize any sparse tensors. + ret = nest.pack_sequence_as( + ret, [t for t in nest.flatten(sparse.serialize_sparse_tensors(ret))]) + return nest.flatten(ret) + + self._function = tf_data_structured_function_wrapper + if add_to_graph: + self._function.add_to_graph(ops.get_default_graph()) + else: + # Use the private method that will execute + # `tf_data_structured_function_wrapper` but delay adding it to the graph + # in case (e.g.) we need to rerun the function. + self._function._create_definition_if_needed() # pylint: disable=protected-access + + @property + def output_classes(self): + return self._output_classes + + @property + def output_shapes(self): + return self._output_shapes + + @property + def output_types(self): + return self._output_types + + @property + def function(self): + return self._function + + def flat_structure(dataset): """Helper for setting `output_shapes` and `output_types` attrs of Dataset ops. @@ -1564,6 +1679,7 @@ class RangeDataset(Dataset): self._parse_args(*args) def _parse_args(self, *args): + """Parse arguments according to the same rules as the `range()` builtin.""" if len(args) == 1: self._start = self._build_tensor(0, "start") self._stop = self._build_tensor(args[0], "stop") @@ -1889,7 +2005,7 @@ def _padding_value_to_tensor(value, output_type): def _default_padding(input_dataset): - + """Returns default padding tensors in a structure matching `input_dataset`.""" def make_zero(t): if t.base_dtype == dtypes.string: return "" @@ -2015,52 +2131,12 @@ class MapDataset(Dataset): super(MapDataset, self).__init__() self._input_dataset = input_dataset - self._output_classes = None - self._output_shapes = None - self._output_types = None - - @function.Defun(*defun_args(input_dataset)) - def tf_map_func(*args): - """A wrapper for Defun that facilitates shape inference.""" - nested_args = restructure_args(args, input_dataset) - ret = map_func(*nested_args) - - # If `map_func` returns a list of tensors, `nest.flatten()` and - # `ops.convert_to_tensor()` would conspire to attempt to stack - # those tensors into a single tensor, because the customized - # version of `nest.flatten()` does not recurse into lists. Since - # it is more likely that the list arose from returning the - # result of an operation (such as `tf.py_func()`) that returns a - # list of not-necessarily-stackable tensors, we treat the - # returned value is a `tuple` instead. A user wishing to pack - # the return value into a single tensor can use an explicit - # `tf.stack()` before returning. - if isinstance(ret, list): - ret = tuple(ret) - - # Convert any `SparseTensorValue`s to `SparseTensor`s and all other - # values to tensors. - ret = nest.pack_sequence_as(ret, [ - sparse_tensor_lib.SparseTensor.from_value(t) - if sparse_tensor_lib.is_sparse(t) else ops.convert_to_tensor(t) - for t in nest.flatten(ret) - ]) - - self._output_classes = sparse.get_classes(ret) - self._output_shapes = nest.pack_sequence_as( - ret, [t.get_shape() for t in nest.flatten(ret)]) - self._output_types = nest.pack_sequence_as( - ret, [t.dtype for t in nest.flatten(ret)]) - - _warn_if_collections("Dataset.map()") - - # Serialize any sparse tensors. - ret = nest.pack_sequence_as( - ret, [t for t in nest.flatten(sparse.serialize_sparse_tensors(ret))]) - return nest.flatten(ret) - - self._map_func = tf_map_func - self._map_func.add_to_graph(ops.get_default_graph()) + wrapped_func = StructuredFunctionWrapper( + map_func, "Dataset.map()", input_dataset) + self._output_classes = wrapped_func.output_classes + self._output_shapes = wrapped_func.output_shapes + self._output_types = wrapped_func.output_types + self._map_func = wrapped_func.function def _as_variant_tensor(self): input_t = self._input_dataset._as_variant_tensor() # pylint: disable=protected-access @@ -2113,25 +2189,20 @@ class FlatMapDataset(Dataset): super(FlatMapDataset, self).__init__() self._input_dataset = input_dataset - @function.Defun(*defun_args(input_dataset)) - def tf_map_func(*args): - """A wrapper for Defun that facilitates shape inference.""" - nested_args = restructure_args(args, input_dataset) - dataset = map_func(*nested_args) - + # TODO(b/110122868): When we handle nested datasets natively as the return + # value from `map_func`, we can avoid needing this wrapper. + def map_func_wrapper(*args): + dataset = map_func(*args) if not isinstance(dataset, Dataset): raise TypeError("`map_func` must return a `Dataset` object.") - - _warn_if_collections(self._transformation_name()) - self._output_classes = dataset.output_classes - self._output_types = dataset.output_types self._output_shapes = dataset.output_shapes - + self._output_types = dataset.output_types return dataset._as_variant_tensor() # pylint: disable=protected-access - self._map_func = tf_map_func - self._map_func.add_to_graph(ops.get_default_graph()) + wrapped_func = StructuredFunctionWrapper( + map_func_wrapper, self._transformation_name(), input_dataset) + self._map_func = wrapped_func.function def _as_variant_tensor(self): return gen_dataset_ops.flat_map_dataset( @@ -2188,24 +2259,13 @@ class FilterDataset(Dataset): """See `Dataset.filter()` for details.""" super(FilterDataset, self).__init__() self._input_dataset = input_dataset - - @function.Defun(*defun_args(input_dataset)) - def tf_predicate(*args): - """A wrapper for Defun that facilitates shape inference.""" - nested_args = restructure_args(args, input_dataset) - ret = predicate(*nested_args) - - ret = ops.convert_to_tensor(ret, dtype=dtypes.bool) - if not (ret.dtype == dtypes.bool and - ret.shape.is_compatible_with(tensor_shape.scalar())): - raise ValueError("`predicate` must return a scalar boolean tensor.") - - _warn_if_collections("Dataset.filter()") - - return ret - - self._predicate = tf_predicate - self._predicate.add_to_graph(ops.get_default_graph()) + wrapped_func = StructuredFunctionWrapper( + predicate, "Dataset.filter()", input_dataset) + if not ( + wrapped_func.output_types == dtypes.bool and + wrapped_func.output_shapes.is_compatible_with(tensor_shape.scalar())): + raise ValueError("`predicate` must return a scalar boolean tensor.") + self._predicate = wrapped_func.function def _as_variant_tensor(self): return gen_dataset_ops.filter_dataset( -- GitLab From e1b0ceb5d51582b27b4f577bbbfc4fa72572e41e Mon Sep 17 00:00:00 2001 From: Martin Wicke Date: Thu, 14 Jun 2018 10:48:12 -0700 Subject: [PATCH 457/816] Amend notes on eager compatibility for Estimator PiperOrigin-RevId: 200581494 --- .../python/estimator/canned/baseline.py | 14 +++++++++++++ .../python/estimator/canned/boosted_trees.py | 20 +++++++++++++++++-- tensorflow/python/estimator/canned/dnn.py | 10 ++++++++-- .../estimator/canned/dnn_linear_combined.py | 10 ++++++++-- tensorflow/python/estimator/canned/linear.py | 10 ++++++++-- tensorflow/python/estimator/estimator.py | 9 +++++++++ 6 files changed, 65 insertions(+), 8 deletions(-) diff --git a/tensorflow/python/estimator/canned/baseline.py b/tensorflow/python/estimator/canned/baseline.py index 3c6816cb03..78d18e41ed 100644 --- a/tensorflow/python/estimator/canned/baseline.py +++ b/tensorflow/python/estimator/canned/baseline.py @@ -215,6 +215,13 @@ class BaselineClassifier(estimator.Estimator): * if `weight_column` is not `None`, a feature with `key=weight_column` whose value is a `Tensor`. + + @compatibility(eager) + Estimators can be used while eager execution is enabled. Note that `input_fn` + and all hooks are executed inside a graph context, so they have to be written + to be compatible with graph mode. Note that `input_fn` code using `tf.data` + generally works in both graph and eager modes. + @end_compatibility """ def __init__(self, @@ -313,6 +320,13 @@ class BaselineRegressor(estimator.Estimator): * if `weight_column` is not `None`, a feature with `key=weight_column` whose value is a `Tensor`. + + @compatibility(eager) + Estimators can be used while eager execution is enabled. Note that `input_fn` + and all hooks are executed inside a graph context, so they have to be written + to be compatible with graph mode. Note that `input_fn` code using `tf.data` + generally works in both graph and eager modes. + @end_compatibility """ def __init__(self, diff --git a/tensorflow/python/estimator/canned/boosted_trees.py b/tensorflow/python/estimator/canned/boosted_trees.py index 6b54f51ca6..86dbf272ef 100644 --- a/tensorflow/python/estimator/canned/boosted_trees.py +++ b/tensorflow/python/estimator/canned/boosted_trees.py @@ -714,7 +714,15 @@ def _create_regression_head(label_dimension, weight_column=None): @estimator_export('estimator.BoostedTreesClassifier') class BoostedTreesClassifier(estimator.Estimator): - """A Classifier for Tensorflow Boosted Trees models.""" + """A Classifier for Tensorflow Boosted Trees models. + + @compatibility(eager) + Estimators can be used while eager execution is enabled. Note that `input_fn` + and all hooks are executed inside a graph context, so they have to be written + to be compatible with graph mode. Note that `input_fn` code using `tf.data` + generally works in both graph and eager modes. + @end_compatibility + """ def __init__(self, feature_columns, @@ -832,7 +840,15 @@ class BoostedTreesClassifier(estimator.Estimator): @estimator_export('estimator.BoostedTreesRegressor') class BoostedTreesRegressor(estimator.Estimator): - """A Regressor for Tensorflow Boosted Trees models.""" + """A Regressor for Tensorflow Boosted Trees models. + + @compatibility(eager) + Estimators can be used while eager execution is enabled. Note that `input_fn` + and all hooks are executed inside a graph context, so they have to be written + to be compatible with graph mode. Note that `input_fn` code using `tf.data` + generally works in both graph and eager modes. + @end_compatibility + """ def __init__(self, feature_columns, diff --git a/tensorflow/python/estimator/canned/dnn.py b/tensorflow/python/estimator/canned/dnn.py index b924ad5df4..90889e3e5d 100644 --- a/tensorflow/python/estimator/canned/dnn.py +++ b/tensorflow/python/estimator/canned/dnn.py @@ -266,7 +266,10 @@ class DNNClassifier(estimator.Estimator): Loss is calculated by using softmax cross entropy. @compatibility(eager) - Estimators are not compatible with eager execution. + Estimators can be used while eager execution is enabled. Note that `input_fn` + and all hooks are executed inside a graph context, so they have to be written + to be compatible with graph mode. Note that `input_fn` code using `tf.data` + generally works in both graph and eager modes. @end_compatibility """ @@ -418,7 +421,10 @@ class DNNRegressor(estimator.Estimator): Loss is calculated by using mean squared error. @compatibility(eager) - Estimators are not compatible with eager execution. + Estimators can be used while eager execution is enabled. Note that `input_fn` + and all hooks are executed inside a graph context, so they have to be written + to be compatible with graph mode. Note that `input_fn` code using `tf.data` + generally works in both graph and eager modes. @end_compatibility """ diff --git a/tensorflow/python/estimator/canned/dnn_linear_combined.py b/tensorflow/python/estimator/canned/dnn_linear_combined.py index 64d81c46ce..3d1ad1365b 100644 --- a/tensorflow/python/estimator/canned/dnn_linear_combined.py +++ b/tensorflow/python/estimator/canned/dnn_linear_combined.py @@ -292,7 +292,10 @@ class DNNLinearCombinedClassifier(estimator.Estimator): Loss is calculated by using softmax cross entropy. @compatibility(eager) - Estimators are not compatible with eager execution. + Estimators can be used while eager execution is enabled. Note that `input_fn` + and all hooks are executed inside a graph context, so they have to be written + to be compatible with graph mode. Note that `input_fn` code using `tf.data` + generally works in both graph and eager modes. @end_compatibility """ @@ -473,7 +476,10 @@ class DNNLinearCombinedRegressor(estimator.Estimator): Loss is calculated by using mean squared error. @compatibility(eager) - Estimators are not compatible with eager execution. + Estimators can be used while eager execution is enabled. Note that `input_fn` + and all hooks are executed inside a graph context, so they have to be written + to be compatible with graph mode. Note that `input_fn` code using `tf.data` + generally works in both graph and eager modes. @end_compatibility """ diff --git a/tensorflow/python/estimator/canned/linear.py b/tensorflow/python/estimator/canned/linear.py index 705fc3ce06..ac59e786c4 100644 --- a/tensorflow/python/estimator/canned/linear.py +++ b/tensorflow/python/estimator/canned/linear.py @@ -227,7 +227,10 @@ class LinearClassifier(estimator.Estimator): Loss is calculated by using softmax cross entropy. @compatibility(eager) - Estimators are not compatible with eager execution. + Estimators can be used while eager execution is enabled. Note that `input_fn` + and all hooks are executed inside a graph context, so they have to be written + to be compatible with graph mode. Note that `input_fn` code using `tf.data` + generally works in both graph and eager modes. @end_compatibility """ @@ -370,7 +373,10 @@ class LinearRegressor(estimator.Estimator): Loss is calculated by using mean squared error. @compatibility(eager) - Estimators are not compatible with eager execution. + Estimators can be used while eager execution is enabled. Note that `input_fn` + and all hooks are executed inside a graph context, so they have to be written + to be compatible with graph mode. Note that `input_fn` code using `tf.data` + generally works in both graph and eager modes. @end_compatibility """ diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py index dd770382e4..2b87f7403f 100644 --- a/tensorflow/python/estimator/estimator.py +++ b/tensorflow/python/estimator/estimator.py @@ -103,6 +103,15 @@ class Estimator(object): None of `Estimator`'s methods can be overridden in subclasses (its constructor enforces this). Subclasses should use `model_fn` to configure the base class, and may add methods implementing specialized functionality. + + @compatbility(eager) + Calling methods of `Estimator` will work while eager execution is enabled. + However, the `model_fn` and `input_fn` is not executed eagerly, `Estimator` + will switch to graph model before calling all user-provided functions (incl. + hooks), so their code has to be compatible with graph mode execution. Note + that `input_fn` code using `tf.data` generally works in both graph and eager + modes. + @end_compatibility """ def __init__(self, model_fn, model_dir=None, config=None, params=None, -- GitLab From df9dd2280fca67d6c261536bc9c459388f108da5 Mon Sep 17 00:00:00 2001 From: Benjamin Kramer Date: Thu, 14 Jun 2018 11:01:44 -0700 Subject: [PATCH 458/816] [XLA:GPU] Make alias analysis emit metadata for subshapes This is about to become much more common with multi-output fusion, where the output shape of a fusion is a tuple and the tuple elements typically don't alias each other. Since tuples are relatively rare otherwise I didn't notice the amount of alias metadata increasing significantly. PiperOrigin-RevId: 200584334 --- .../xla/service/gpu/hlo_to_ir_bindings.cc | 2 +- .../xla/service/llvm_ir/alias_analysis.cc | 21 +++++++++++-------- .../xla/service/llvm_ir/alias_analysis.h | 3 ++- 3 files changed, 15 insertions(+), 11 deletions(-) diff --git a/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.cc b/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.cc index 061210352c..e303999c63 100644 --- a/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.cc +++ b/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.cc @@ -202,7 +202,7 @@ llvm_ir::IrArray HloToIrBindings::GetIrArray(const HloInstruction& hlo, << " of " << hlo.ToString(); llvm_ir::IrArray ir_array(base_ptr, ShapeUtil::GetSubshape(hlo.shape(), shape_index)); - alias_analysis_.AddAliasingInformationToIrArray(hlo, &ir_array); + alias_analysis_.AddAliasingInformationToIrArray(hlo, &ir_array, shape_index); // The GPU backend emits one kernel per top-level HLO, and LLVM views // execution of one kernel as the "whole program" executed on the GPU. diff --git a/tensorflow/compiler/xla/service/llvm_ir/alias_analysis.cc b/tensorflow/compiler/xla/service/llvm_ir/alias_analysis.cc index 21bca1d6be..f200a08a3c 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/alias_analysis.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/alias_analysis.cc @@ -32,7 +32,8 @@ static const BufferAllocation* kParameterAllocation = new BufferAllocation( LogicalBuffer::Color(0)); void AliasAnalysis::AddAliasingInformationToIrArray(const HloInstruction& hlo, - llvm_ir::IrArray* array) { + llvm_ir::IrArray* array, + const ShapeIndex& index) { BufferAllocation::Slice buffer_slice; if (hlo.opcode() == HloOpcode::kParameter) { // Parameters may alias with each other but may not alias with our temporary @@ -40,7 +41,7 @@ void AliasAnalysis::AddAliasingInformationToIrArray(const HloInstruction& hlo, buffer_slice = BufferAllocation::Slice(kParameterAllocation, 0, 0); } else { const std::set slices = - assignment_.GetAllSlices(&hlo, /*index=*/{}); + assignment_.GetAllSlices(&hlo, index); if (slices.empty() || slices.size() > 1) { // Skip HLOs which don't have a buffer assigned or for which the // buffer can't be determined statically. We cannot determine their @@ -137,16 +138,18 @@ llvm::MDNode* AliasAnalysis::GetNoaliasMetadataForBuffer( // 2. Operands of users of the given hlo. // 3. Operands of the given hlo. // - // This set can be increased as we need. For now only consider top-level - // buffers (index = {}) not buffers nested within the instruction's - // operands/output which are not typically touched. + // This set can be increased as we need. std::vector worklist; auto add_buffers_to_worklist = [&worklist, &assignment](const HloInstruction* instruction) { - for (const LogicalBuffer* buffer : - assignment.GetSourceBuffers(instruction, /*index=*/{})) { - worklist.push_back(buffer); - } + ShapeUtil::ForEachSubshape( + instruction->shape(), + [&](const Shape& /*shape*/, const ShapeIndex& index) { + for (const LogicalBuffer* buffer : + assignment.GetSourceBuffers(instruction, index)) { + worklist.push_back(buffer); + } + }); }; for (HloInstruction* user : hlo.users()) { diff --git a/tensorflow/compiler/xla/service/llvm_ir/alias_analysis.h b/tensorflow/compiler/xla/service/llvm_ir/alias_analysis.h index 5244ac61e5..fe9eab93aa 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/alias_analysis.h +++ b/tensorflow/compiler/xla/service/llvm_ir/alias_analysis.h @@ -38,7 +38,8 @@ class AliasAnalysis { // Augments IrArray with aliasing information. void AddAliasingInformationToIrArray(const HloInstruction& hlo, - llvm_ir::IrArray* array); + llvm_ir::IrArray* array, + const ShapeIndex& index = {}); private: // Returns a unique alias domain for this emitter. -- GitLab From eb979013aebe040567e436fd9228033f6fd98f2b Mon Sep 17 00:00:00 2001 From: Peter Hawkins Date: Thu, 14 Jun 2018 11:16:40 -0700 Subject: [PATCH 459/816] Propagate the non-resource part of a resource tensor's shape in Enter's shape function. PiperOrigin-RevId: 200587374 --- tensorflow/core/ops/control_flow_ops.cc | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/tensorflow/core/ops/control_flow_ops.cc b/tensorflow/core/ops/control_flow_ops.cc index 81e9fcfa95..b8028291b4 100644 --- a/tensorflow/core/ops/control_flow_ops.cc +++ b/tensorflow/core/ops/control_flow_ops.cc @@ -145,13 +145,12 @@ REGISTER_OP("Enter") auto* handle_data = c->input_handle_shapes_and_types(0); if (handle_data != nullptr) { c->set_output_handle_shapes_and_types(0, *handle_data); - } else { - // Otherwise, propagate shape if output is a constant. - bool is_constant; - TF_RETURN_IF_ERROR(c->GetAttr("is_constant", &is_constant)); - if (is_constant) { - c->set_output(0, c->input(0)); - } + } + // Propagate shape if output is a constant. + bool is_constant; + TF_RETURN_IF_ERROR(c->GetAttr("is_constant", &is_constant)); + if (is_constant) { + c->set_output(0, c->input(0)); } return Status::OK(); -- GitLab From f596bcc78639bb59894fd8e97779e6f53eeef190 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 14 Jun 2018 11:19:09 -0700 Subject: [PATCH 460/816] Remove dead code from bulk_restore() but keep dead function parameter for backward-compatibility. PiperOrigin-RevId: 200587926 --- tensorflow/python/training/saver.py | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/tensorflow/python/training/saver.py b/tensorflow/python/training/saver.py index b8f58a288c..53ed89e4ab 100644 --- a/tensorflow/python/training/saver.py +++ b/tensorflow/python/training/saver.py @@ -206,21 +206,19 @@ class BaseSaverBuilder(object): filename_tensor: String Tensor. saveables: List of BaseSaverBuilder.SaveableObject objects. preferred_shard: Int. Shard to open first when loading a sharded file. - restore_sequentially: Bool. If true, each restore is sequential. + restore_sequentially: Unused. Bool. If true, each restore is sequential. Returns: A list of Tensors resulting from reading 'saveable' from 'filename'. """ + del restore_sequentially all_tensors = [] - assign_ops = [] for saveable in saveables: - restore_control_inputs = assign_ops[-1:] if restore_sequentially else [] with ops.device(_set_cpu0(saveable.device) if saveable.device else None): - with ops.control_dependencies(restore_control_inputs): - all_tensors.extend( - self.restore_op(filename_tensor, saveable, preferred_shard)) + all_tensors.extend( + self.restore_op(filename_tensor, saveable, preferred_shard)) return all_tensors # pylint: disable=unused-argument -- GitLab From 3d7b33f7576216adeb6ea345dc2b41bc921fcf52 Mon Sep 17 00:00:00 2001 From: Akshay Agrawal Date: Thu, 14 Jun 2018 11:23:03 -0700 Subject: [PATCH 461/816] Make it possible to retrieve the variables used in a defined function. Creates a class that encapsulates the graph functions created for a particular Python function. This class has a `.variables` property that fetches the variables used in any of the graph functions defined for the Python function. The class is internal for now. PiperOrigin-RevId: 200588595 --- tensorflow/python/eager/function.py | 76 +++++++++++++++--------- tensorflow/python/eager/function_test.py | 17 ++++++ 2 files changed, 65 insertions(+), 28 deletions(-) diff --git a/tensorflow/python/eager/function.py b/tensorflow/python/eager/function.py index 03393bcd46..dd3166735c 100644 --- a/tensorflow/python/eager/function.py +++ b/tensorflow/python/eager/function.py @@ -222,6 +222,11 @@ def _inference_name(n): return "__inference_%s_%s" % (n, ops.uid()) +def _register(fn): + """Registers the function `fn`.""" + context.context().add_function(fn) + + # TODO(apassos) get rid of this by splitting framework.function._DefinedFunction # so it doesn't have the definition-generating logic and is just a container for # an already-defined function. @@ -591,7 +596,7 @@ def _get_defun_inputs(args): return nest.pack_sequence_as(args, ret) -def _defun_internal(name, func, compiled, args, kwds): +def _trace_and_define_function(name, func, compiled, args, kwds): """Defines and returns graph-mode version of func.""" graph_key = ops.get_default_graph()._graph_key # pylint: disable=protected-access with context.graph_mode(): @@ -699,42 +704,57 @@ def _cache_key(x): return x -def _register(fn): - """Registers the function `fn`.""" - context.context().add_function(fn) +class _PolymorphicFunction(object): + """Wrapper class for the graph functions defined for a Python function. + See the documentation for `defun` for more information on the semantics of + defined functions. + """ -# TODO(apassos): better error messages for non-hashable arguments. -def named_defun(func, name, compiled=False): - """Defines a function with a given name. + def __init__(self, python_function, name, compiled=False): + """Initializes a polymorphic function. - See the documentation for `defun` for more information on the semantics of - this function. + Args: + python_function: the function to be wrapped. + name: the name given to it. + compiled: if True, the framework will attempt to compile func with XLA. + """ - Args: - func: the function to be wrapped. - name: the name given to it. - compiled: if true, the framework will attempt to compile func with XLA. + self._python_function = python_function + self._name = name + self._compiled = compiled + self._arguments_to_functions = {} + self._variables = [] - Returns: - the wrapped function. - """ - arguments_to_functions = {} + def _maybe_define_function(self, *args, **kwds): + """Gets a function for these inputs, defining it if necessary.""" - def decorated(*args, **kwds): - """Decorated version of func.""" - # Macroexpand on non-Tensor arguments - cache_key = tuple(_cache_key(x) for x in args) + # TODO(akshayka): Remove this restriction. if any(isinstance(x, ops.EagerTensor) for x in kwds.values()): raise ValueError("Tensor keyword arguments are not supported.") + + # TODO(apassos): Better error messages for non-hashable arguments. + cache_key = tuple(_cache_key(x) for x in args) cache_key = (cache_key, tuple(kwds.items())) - if cache_key not in arguments_to_functions: - arguments_to_functions[cache_key] = _defun_internal( - name, func, compiled, args, kwds) - return arguments_to_functions[cache_key](*args) + if cache_key not in self._arguments_to_functions: + graph_function = _trace_and_define_function( + self._name, self._python_function, self._compiled, args, kwds) + self._arguments_to_functions[cache_key] = graph_function + self._variables.extend( + [v for v in graph_function.variables if v not in self._variables]) + return graph_function + else: + return self._arguments_to_functions[cache_key] - return decorated + def __call__(self, *args, **kwds): + """Calls a graph function specialized for this input signature.""" + return self._maybe_define_function(*args, **kwds)(*args) + + @property + def variables(self): + """Returns a list of variables used in any of the defined functions.""" + return self._variables # TODO(akshayka): Remove the `compiled` flag and create a separate @@ -991,7 +1011,7 @@ def defun(func=None, compiled=False): except AttributeError: name = "function" return tf_decorator.make_decorator( - function, named_defun(function, name, compiled=compiled)) + function, _PolymorphicFunction(function, name, compiled=compiled)) # This code path is for the `foo = tfe.defun(foo, ...)` use case if func is not None: @@ -1056,7 +1076,7 @@ def make_defun_op(func, *args, **kwds): name = func.__name__ if any(isinstance(x, ops.EagerTensor) for x in kwds.values()): raise ValueError("Tensor keyword arguments are not supported.") - return _defun_internal(name, func, False, args, kwds) + return _trace_and_define_function(name, func, False, args, kwds) class AutomaticControlDependencies(object): diff --git a/tensorflow/python/eager/function_test.py b/tensorflow/python/eager/function_test.py index cfdbe5f079..6ce2ceffda 100644 --- a/tensorflow/python/eager/function_test.py +++ b/tensorflow/python/eager/function_test.py @@ -633,6 +633,23 @@ class FunctionTest(test.TestCase): y = model(x) self.assertAllEqual([[[[4.0]]]], y.numpy()) + def testVariablesAreTracked(self): + v = resource_variable_ops.ResourceVariable(1.0) + + def foo(x): + return v * x + + defined = function.defun(foo) + + x = constant_op.constant([1.0]) + self.assertAllEqual(defined.variables, []) + _ = defined(x) + self.assertAllEqual(defined.variables, [v]) + + x = constant_op.constant([1.0, 2.0]) + _ = defined(x) # ensure the variables list remains the same + self.assertAllEqual(defined.variables, [v]) + @test_util.with_c_shapes class AutomaticControlDependenciesTest(test.TestCase): -- GitLab From 3970b5351949b51411257b380b816f7f22064733 Mon Sep 17 00:00:00 2001 From: Mark Daoust Date: Thu, 14 Jun 2018 11:27:33 -0700 Subject: [PATCH 462/816] Switch "init_from_checkpoint" to use "DEBUG" log level. PiperOrigin-RevId: 200589492 --- tensorflow/python/training/checkpoint_utils.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tensorflow/python/training/checkpoint_utils.py b/tensorflow/python/training/checkpoint_utils.py index e7f88de1d2..c2f0e9d3e6 100644 --- a/tensorflow/python/training/checkpoint_utils.py +++ b/tensorflow/python/training/checkpoint_utils.py @@ -219,8 +219,8 @@ def init_from_checkpoint(ckpt_dir_or_file, assignment_map): else: var_name = ",".join([v.name for v in var]) _set_variable_or_list_initializer(var, ckpt_file, tensor_name_in_ckpt) - logging.info("Initialize variable %s from checkpoint %s with %s", - var_name, ckpt_dir_or_file, tensor_name_in_ckpt) + logging.debug("Initialize variable %s from checkpoint %s with %s", + var_name, ckpt_dir_or_file, tensor_name_in_ckpt) else: scopes = "" # TODO(vihanjain): Support list of 'current_var_or_name' here. @@ -261,8 +261,8 @@ def init_from_checkpoint(ckpt_dir_or_file, assignment_map): if var is None: var = _collect_partitioned_variable(var_name, store_vars) _set_variable_or_list_initializer(var, ckpt_file, full_tensor_name) - logging.info("Initialize variable %s from checkpoint %s with %s", - var_name, ckpt_dir_or_file, full_tensor_name) + logging.debug("Initialize variable %s from checkpoint %s with %s", + var_name, ckpt_dir_or_file, full_tensor_name) def _get_checkpoint_filename(ckpt_dir_or_file): -- GitLab From 8f7afe01a583058726b03a0d849add35fcde41a3 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 14 Jun 2018 11:35:53 -0700 Subject: [PATCH 463/816] Automated g4 rollback of changelist 200500606 PiperOrigin-RevId: 200591125 --- .../contrib/control_flow/python/cond_v2.py | 23 +- .../control_flow/python/cond_v2_test.py | 223 ++++++++++++++++++ tensorflow/python/framework/function.py | 54 ++++- 3 files changed, 296 insertions(+), 4 deletions(-) diff --git a/tensorflow/contrib/control_flow/python/cond_v2.py b/tensorflow/contrib/control_flow/python/cond_v2.py index b364e34511..90371cd8d7 100644 --- a/tensorflow/contrib/control_flow/python/cond_v2.py +++ b/tensorflow/contrib/control_flow/python/cond_v2.py @@ -48,13 +48,30 @@ def cond_v2(pred, true_fn, false_fn, name="cond"): name = "cond" with ops.name_scope(name) as scope: + # Identify if there is a caller device, & get the innermost if possible. + device_stack = ops.get_default_graph()._device_function_stack + caller_device = device_stack[-1] if device_stack else None + + caller_colocation_stack = ops.get_default_graph()._colocation_stack + caller_container = ops.get_default_graph()._container + caller_collection_ref = ops.get_default_graph()._collections + func_name_prefix = scope.replace("/", "_") true_graph = function.func_graph_from_py_func( - true_fn, [], [], name="%strue" % func_name_prefix) + true_fn, [], [], + name="%strue" % func_name_prefix, + device=caller_device, + colocation_stack=caller_colocation_stack, + collections_ref=caller_collection_ref, + container=caller_container) false_graph = function.func_graph_from_py_func( - false_fn, [], [], name="%sfalse" % func_name_prefix) - + false_fn, [], [], + name="%sfalse" % func_name_prefix, + device=caller_device, + colocation_stack=caller_colocation_stack, + collections_ref=caller_collection_ref, + container=caller_container) _check_same_outputs(true_graph, false_graph) # Add inputs to true_graph and false_graph to make them match. Note that diff --git a/tensorflow/contrib/control_flow/python/cond_v2_test.py b/tensorflow/contrib/control_flow/python/cond_v2_test.py index b7d4c16df4..94ed3e130b 100644 --- a/tensorflow/contrib/control_flow/python/cond_v2_test.py +++ b/tensorflow/contrib/control_flow/python/cond_v2_test.py @@ -25,10 +25,13 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import data_flow_ops from tensorflow.python.ops import gradients_impl from tensorflow.python.ops import math_ops +from tensorflow.python.ops import variables from tensorflow.python.platform import test from tensorflow.python.training import saver +from tensorflow.python.util import compat class NewCondTest(test.TestCase): @@ -198,5 +201,225 @@ class NewCondTest(test.TestCase): self.assertEqual(false_val, [0.0]) +class CondV2CollectionTest(test.TestCase): + + def testCollectionIntValueAccessInCond(self): + """Read values from graph collections inside of cond_v2.""" + with ops.Graph().as_default() as g: + with self.test_session(graph=g): + x = 2 + y = 5 + ops.add_to_collection("x", x) + ops.add_to_collection("y", y) + def fn(): + x_const = constant_op.constant(ops.get_collection("x")[0]) + y_const = constant_op.constant(ops.get_collection("y")[0]) + return math_ops.add(x_const, y_const) + + cnd = cond_v2.cond_v2(True, fn, fn) + self.assertEquals(cnd[0].eval(), 7) + + def testCollectionTensorValueAccessInCond(self): + """Read tensors from collections inside of cond_v2 & use them.""" + with ops.Graph().as_default() as g: + with self.test_session(graph=g): + x = constant_op.constant(2) + y = constant_op.constant(5) + ops.add_to_collection("x", x) + ops.add_to_collection("y", y) + + def fn(): + x_read = ops.get_collection("x")[0] + y_read = ops.get_collection("y")[0] + return math_ops.add(x_read, y_read) + + cnd = cond_v2.cond_v2(math_ops.less(x, y), fn, fn) + self.assertEquals(cnd[0].eval(), 7) + + def testCollectionIntValueWriteInCond(self): + """Make sure Int writes to collections work inside of cond_v2.""" + with ops.Graph().as_default() as g: + with self.test_session(graph=g): + x = constant_op.constant(2) + y = constant_op.constant(5) + def true_fn(): + z = math_ops.add(x, y) + ops.add_to_collection("z", 7) + return math_ops.mul(x, z) + + def false_fn(): + z = math_ops.add(x, y) + return math_ops.mul(x, z) + + cnd = cond_v2.cond_v2( + True, true_fn, + false_fn) + self.assertEquals(cnd[0].eval(), 14) + + read_z_collection = ops.get_collection("z") + self.assertEquals(read_z_collection, [7]) + + +class CondV2ContainerTest(test.TestCase): + + def testContainer(self): + """Set containers outside & inside of cond_v2. + + Make sure the containers are set correctly for both variable creation + (tested by variables.Variable) and for stateful ops (tested by FIFOQueue) + """ + with ops.Graph().as_default() as g: + with self.test_session(graph=g): + + v0 = variables.Variable([0]) + q0 = data_flow_ops.FIFOQueue(1, dtypes.float32) + + def container(node): + return node.op.get_attr("container") + + self.assertEqual(compat.as_bytes(""), container(v0)) + self.assertEqual(compat.as_bytes(""), container(q0.queue_ref)) + + def true_fn(): + # When this branch is created in cond below, + # the container should begin with 'l1' + v1 = variables.Variable([1]) + q1 = data_flow_ops.FIFOQueue(1, dtypes.float32) + + with ops.container("l2t"): + v2 = variables.Variable([2]) + q2 = data_flow_ops.FIFOQueue(1, dtypes.float32) + + v3 = variables.Variable([1]) + q3 = data_flow_ops.FIFOQueue(1, dtypes.float32) + + self.assertEqual(compat.as_bytes("l1"), container(v1)) + self.assertEqual(compat.as_bytes("l1"), container(q1.queue_ref)) + self.assertEqual(compat.as_bytes("l2t"), container(v2)) + self.assertEqual(compat.as_bytes("l2t"), container(q2.queue_ref)) + self.assertEqual(compat.as_bytes("l1"), container(v3)) + self.assertEqual(compat.as_bytes("l1"), container(q3.queue_ref)) + + return constant_op.constant(2.0) + + def false_fn(): + # When this branch is created in cond below, + # the container should begin with 'l1' + v1 = variables.Variable([1]) + q1 = data_flow_ops.FIFOQueue(1, dtypes.float32) + + with ops.container("l2f"): + v2 = variables.Variable([2]) + q2 = data_flow_ops.FIFOQueue(1, dtypes.float32) + + v3 = variables.Variable([1]) + q3 = data_flow_ops.FIFOQueue(1, dtypes.float32) + + self.assertEqual(compat.as_bytes("l1"), container(v1)) + self.assertEqual(compat.as_bytes("l1"), container(q1.queue_ref)) + self.assertEqual(compat.as_bytes("l2f"), container(v2)) + self.assertEqual(compat.as_bytes("l2f"), container(q2.queue_ref)) + self.assertEqual(compat.as_bytes("l1"), container(v3)) + self.assertEqual(compat.as_bytes("l1"), container(q3.queue_ref)) + + return constant_op.constant(6.0) + + with ops.container("l1"): + cnd_true = cond_v2.cond_v2(True, true_fn, false_fn) + self.assertEquals(cnd_true[0].eval(), 2) + + cnd_false = cond_v2.cond_v2(False, true_fn, false_fn) + self.assertEquals(cnd_false[0].eval(), 6) + + v4 = variables.Variable([3]) + q4 = data_flow_ops.FIFOQueue(1, dtypes.float32) + v5 = variables.Variable([4]) + q5 = data_flow_ops.FIFOQueue(1, dtypes.float32) + + self.assertEqual(compat.as_bytes("l1"), container(v4)) + self.assertEqual(compat.as_bytes("l1"), container(q4.queue_ref)) + self.assertEqual(compat.as_bytes(""), container(v5)) + self.assertEqual(compat.as_bytes(""), container(q5.queue_ref)) + + +class CondV2ColocationGroupAndDeviceTest(test.TestCase): + + def testColocateWithBeforeCond(self): + with ops.Graph().as_default() as g: + with self.test_session(graph=g): + + a = constant_op.constant([2.0], name="a") + b = constant_op.constant([2.0], name="b") + + def fn(): + c = constant_op.constant(3.0) + self.assertEqual([b"loc:@a"], c.op.colocation_groups()) + return c + + with ops.colocate_with(a.op): + self.assertEquals(cond_v2.cond_v2(True, fn, fn)[0].eval(), 3) + + def fn2(): + c = constant_op.constant(3.0) + self.assertEqual([b"loc:@a", b"loc:@b"], c.op.colocation_groups()) + return c + + with ops.colocate_with(a.op): + with ops.colocate_with(b.op): + self.assertEquals(cond_v2.cond_v2(True, fn2, fn2)[0].eval(), 3) + + def testColocateWithInAndOutOfCond(self): + with ops.Graph().as_default() as g: + with self.test_session(graph=g): + + a = constant_op.constant([2.0], name="a") + b = constant_op.constant([2.0], name="b") + + def fn2(): + with ops.colocate_with(b.op): + c = constant_op.constant(3.0) + self.assertEqual([b"loc:@a", b"loc:@b"], c.op.colocation_groups()) + return c + + with ops.colocate_with(a.op): + self.assertEquals(cond_v2.cond_v2(True, fn2, fn2)[0].eval(), 3) + + d = constant_op.constant([2.0], name="d") + self.assertEqual([b"loc:@a"], d.op.colocation_groups()) + + def testDeviceBeforeCond(self): + with ops.Graph().as_default() as g: + with self.test_session(graph=g): + def fn(): + c = constant_op.constant(3.0) + self.assertEqual("/device:CPU:0", c.op.device) + return c + + with ops.device("/device:CPU:0"): + self.assertEquals(cond_v2.cond_v2(True, fn, fn)[0].eval(), 3) + + def fn2(): + c = constant_op.constant(3.0) + self.assertEqual("/device:GPU:0", c.op.device) + return c + + with ops.device("/device:GPU:0"): + self.assertEquals(cond_v2.cond_v2(True, fn2, fn2)[0].eval(), 3) + + def testDeviceInAndOutOfCond(self): + with ops.Graph().as_default() as g: + with self.test_session(graph=g): + def fn2(): + with ops.device("/device:GPU:0"): + c = constant_op.constant(3.0) + self.assertEqual("/device:GPU:0", c.op.device) + return c + + with ops.device("/device:CPU:0"): + self.assertEquals(cond_v2.cond_v2(True, fn2, fn2)[0].eval(), 3) + + d = constant_op.constant(4.0) + self.assertEqual("/device:CPU:0", d.op.device) + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/framework/function.py b/tensorflow/python/framework/function.py index 82ecba310b..002a3d3be5 100644 --- a/tensorflow/python/framework/function.py +++ b/tensorflow/python/framework/function.py @@ -36,6 +36,7 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variable_scope as vs from tensorflow.python.util import compat +from tensorflow.python.util import tf_contextlib from tensorflow.python.util import tf_decorator from tensorflow.python.util import tf_inspect @@ -650,6 +651,41 @@ class _FuncGraph(ops.Graph): # TODO(skyewm): is this needed? self.extra_vars = [] + # pylint: disable=g-doc-return-or-yield + + @tf_contextlib.contextmanager + def container(self, container_name): + """Returns a context manager that specifies the resource container to use. + + Overridden from @{tf.Graph} to update both the init_scope container + and the present inner container. This is necessary to make sure setting + containers applies correctly both to created variables and to stateful + ops. + + Args: + container_name: container name string. + + Returns: + A context manager for defining resource containers for stateful ops, + yields the container name. + """ + original_container = self._container + # pylint: disable=protected-access + with ops.init_scope(): + original_init_container = ops.get_default_graph()._container + try: + self._container = container_name + with ops.init_scope(): + ops.get_default_graph()._container = container_name + yield self._container + finally: + self._container = original_container + with ops.init_scope(): + ops.get_default_graph()._container = original_init_container + # pylint: enable=protected-access + + # pylint: enable=g-doc-return-or-yield + def getvar( self, getter, @@ -773,7 +809,9 @@ class _FuncGraph(ops.Graph): def func_graph_from_py_func(func, arg_names, arg_types, name=None, - capture_by_value=False, device=None): + capture_by_value=False, device=None, + colocation_stack=None, container=None, + collections_ref=None): """Returns a _FuncGraph generated from `func`. Args: @@ -786,6 +824,10 @@ def func_graph_from_py_func(func, arg_names, arg_types, name=None, capture_by_value: boolean. If True, captured values will be copied into the function body. device: device name or function. + colocation_stack: A colocation stack (list) the _FuncGraph should use. + container: A container name the _FuncGraph should start with. + collections_ref: A reference to a collections dict the _FuncGraph should + use internally. Returns: A _FuncGraph. @@ -796,7 +838,17 @@ def func_graph_from_py_func(func, arg_names, arg_types, name=None, if not name: name = _get_func_name(func) func_graph = _FuncGraph(name, capture_by_value) + with func_graph.as_default(), ops.device(device): + # pylint: disable=protected-access + if collections_ref is not None: + func_graph._collections = collections_ref + if container is not None: + func_graph._container = container + if colocation_stack is not None: + func_graph._colocation_stack = colocation_stack + # pylint: enable=protected-access + # Create placeholders for the function arguments. for (argname, argtype) in zip(arg_names, arg_types): argholder = array_ops.placeholder(argtype, name=argname) -- GitLab From 8e4c4144817bea5ffd9255df48a78740fdb14f57 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 14 Jun 2018 11:40:28 -0700 Subject: [PATCH 464/816] Optimized implementation of transpose conv. Uses an im2col array and GEMM, similar to conv. PiperOrigin-RevId: 200592004 --- .../internal/optimized/optimized_ops.h | 154 +++++++++++------- .../internal/reference/reference_ops.h | 3 +- .../contrib/lite/kernels/transpose_conv.cc | 8 +- .../create_im2col_arrays.cc | 59 ++++--- .../propagate_fixed_sizes.cc | 16 +- 5 files changed, 156 insertions(+), 84 deletions(-) diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h index 4c37d3c3c7..d0008cc4fb 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h @@ -1821,8 +1821,8 @@ void DilatedIm2col(const T* input_data, const Dims<4>& input_dims, // Use dimensions M and N to construct dims for indexing directly into im2col Dims<4> im2col_dims; - im2col_dims.sizes[0] = col_dims.strides[3]; - im2col_dims.sizes[1] = row_dims.strides[3]; + im2col_dims.sizes[0] = FlatSize(col_dims); + im2col_dims.sizes[1] = FlatSize(row_dims); im2col_dims.sizes[2] = 1; im2col_dims.sizes[3] = 1; ComputeStrides(&im2col_dims); @@ -1831,8 +1831,8 @@ void DilatedIm2col(const T* input_data, const Dims<4>& input_dims, for (int batch = 0; batch < batches; ++batch) { for (int out_y = 0; out_y < output_height; ++out_y) { for (int out_x = 0; out_x < output_width; ++out_x) { - // Each row is an output pixel. Arrange the input data into this row in - // an order we can conveniently multiply with the filter data. + // Each im2col row is an output pixel. Arrange the input data in this + // row in an order we can conveniently multiply with the filter data. int row_offset = Offset(row_dims, out_x, out_y, batch, 0); const int in_x_origin = (out_x * stride_width) - pad_width; const int in_y_origin = (out_y * stride_height) - pad_height; @@ -1848,7 +1848,7 @@ void DilatedIm2col(const T* input_data, const Dims<4>& input_dims, T* dst = im2col_data + Offset(im2col_dims, col_offset, row_offset, 0, 0); if ((in_x >= 0) && (in_x < input_width)) { - // Filter pixel is within the input, copy the data. + // Filter pixel is within the input, copy the input data. T const* src = input_data + Offset(input_dims, 0, in_x, in_y, batch); memcpy(dst, src, input_depth * sizeof(T)); @@ -1858,7 +1858,7 @@ void DilatedIm2col(const T* input_data, const Dims<4>& input_dims, } } } else { - // Filter row is outside the input, zero out the entire im2col row. + // Filter row is outside the input, zero out the entire filter row. int col_offset = Offset(col_dims, 0, 0, filter_y, 0); T* dst = im2col_data + Offset(im2col_dims, col_offset, row_offset, 0, 0); @@ -1922,7 +1922,7 @@ inline void Conv(const float* input_data, const Dims<4>& input_dims, (void)im2col_dims; gemmlowp::ScopedProfilingLabel label("Conv"); - // A float set to 0x00000000h == 0.0f + // NB: static_cast(0x00000000h) == 0.0f const uint8 float_zero_byte = 0x00; const float* gemm_input_data = nullptr; const Dims<4>* gemm_input_dims = nullptr; @@ -6371,69 +6371,84 @@ void Transpose(const T* input, const Dims<4>& input_dims, T* output, } } -inline void TransposeConv(const float* input_data, const Dims<4>& input_dims, - const float* filter_data, const Dims<4>& filter_dims, - int stride_width, int stride_height, int pad_width, - int pad_height, float* output_data, - const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("TransposeConv"); - // THIS FUNCTION IS A COPY FROM reference_ops.h. - // To optimize, start by using the conv code with transposed weights for the - // case of stride_height = stride_width = 1. +template +void TransposeIm2col(const T* input_data, const Dims<4>& input_dims, + const Dims<4>& filter_dims, int stride_width, + int stride_height, int pad_width, int pad_height, + const Dims<4>& output_dims, uint8 zero_byte, + T* im2col_data) { + gemmlowp::ScopedProfilingLabel label("TransposeIm2col"); + TFLITE_DCHECK(IsPackedWithoutStrides(input_dims)); + TFLITE_DCHECK(IsPackedWithoutStrides(filter_dims)); + TFLITE_DCHECK(IsPackedWithoutStrides(output_dims)); + TFLITE_DCHECK(im2col_data); + const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); - const int input_depth = MatchingArraySize(input_dims, 0, filter_dims, 0); - const int output_depth = MatchingArraySize(filter_dims, 3, output_dims, 0); const int input_height = ArraySize(input_dims, 2); const int input_width = ArraySize(input_dims, 1); + const int input_depth = MatchingArraySize(input_dims, 0, filter_dims, 3); const int filter_height = ArraySize(filter_dims, 2); const int filter_width = ArraySize(filter_dims, 1); const int output_height = ArraySize(output_dims, 2); const int output_width = ArraySize(output_dims, 1); + MatchingArraySize(output_dims, 0, filter_dims, 0); // output_depth - // Although transpose convolution simplifies to convolution with transposed - // weights for strides of 1, non-unitary striding complicates matters. To - // keep this reference implementation as clear as possible, we use a "scatter" - // access pattern, where we loop through all the input elements, computing - // their influence on the output, rather than looping through the output - // elements in the typical "gather" access pattern of a conv. We therefore - // must initialize the output array to zero. - for (int batch = 0; batch < batches; ++batch) { - for (int out_y = 0; out_y < output_height; ++out_y) { - for (int out_x = 0; out_x < output_width; ++out_x) { - for (int out_channel = 0; out_channel < output_depth; ++out_channel) { - output_data[Offset(output_dims, out_channel, out_x, out_y, batch)] = - 0.0f; - } - } - } - } + // Construct the MxN sized im2col matrix. + // The rows M, are sub-ordered B x H x W + Dims<4> row_dims; + row_dims.sizes[0] = output_width; + row_dims.sizes[1] = output_height; + row_dims.sizes[2] = batches; + row_dims.sizes[3] = 1; + ComputeStrides(&row_dims); + + // The columns, N, are sub-ordered Kh x Kw x Din + Dims<4> col_dims; + col_dims.sizes[0] = input_depth; + col_dims.sizes[1] = filter_width; + col_dims.sizes[2] = filter_height; + col_dims.sizes[3] = 1; + ComputeStrides(&col_dims); + + // Use dimensions M and N to construct dims for indexing directly into im2col + Dims<4> im2col_dims; + im2col_dims.sizes[0] = FlatSize(col_dims); + im2col_dims.sizes[1] = FlatSize(row_dims); + im2col_dims.sizes[2] = 1; + im2col_dims.sizes[3] = 1; + ComputeStrides(&im2col_dims); + + // Build the im2col matrix by looping through all the input pixels, + // computing their influence on the output, rather than looping through all + // the output pixels. We therefore must initialize the im2col array to zero. + // This is potentially inefficient because we subsequently overwrite bytes + // set here. However, in practice memset is very fast and costs negligible. + memset(im2col_data, zero_byte, FlatSize(im2col_dims) * sizeof(T)); - // Loop through input elements one at a time. + // Loop through the output batches for (int batch = 0; batch < batches; ++batch) { + // Loop through input pixels one at a time. for (int in_y = 0; in_y < input_height; ++in_y) { for (int in_x = 0; in_x < input_width; ++in_x) { - for (int in_channel = 0; in_channel < input_depth; ++in_channel) { - // Loop through the output elements it will influence - const int out_x_origin = (in_x * stride_width) - pad_width; - const int out_y_origin = (in_y * stride_height) - pad_height; - for (int filter_y = 0; filter_y < filter_height; ++filter_y) { + // Loop through the output pixels it will influence + const int out_x_origin = (in_x * stride_width) - pad_width; + const int out_y_origin = (in_y * stride_height) - pad_height; + for (int filter_y = 0; filter_y < filter_height; ++filter_y) { + const int out_y = out_y_origin + filter_y; + // Is output pixel within height bounds? + if ((out_y >= 0) && (out_y < output_height)) { for (int filter_x = 0; filter_x < filter_width; ++filter_x) { - for (int out_channel = 0; out_channel < output_depth; - ++out_channel) { - // Compute output element location - const int out_x = out_x_origin + filter_x; - const int out_y = out_y_origin + filter_y; - // We cannot accumulate out of bounds - if ((out_x >= 0) && (out_x < output_width) && (out_y >= 0) && - (out_y < output_height)) { - float input_value = input_data[Offset(input_dims, in_channel, - in_x, in_y, batch)]; - float filter_value = - filter_data[Offset(filter_dims, in_channel, filter_x, - filter_y, out_channel)]; - output_data[Offset(output_dims, out_channel, out_x, out_y, - batch)] += input_value * filter_value; - } + const int out_x = out_x_origin + filter_x; + // Is output pixel within width bounds? + if ((out_x >= 0) && (out_x < output_width)) { + // Copy the input elements of this pixel + T const* src = + input_data + Offset(input_dims, 0, in_x, in_y, batch); + T* dst = im2col_data + + Offset(im2col_dims, + Offset(col_dims, 0, filter_x, filter_y, 0), + Offset(row_dims, out_x, out_y, batch, 0), 0, 0); + memcpy(dst, src, input_depth * sizeof(T)); } } } @@ -6443,6 +6458,31 @@ inline void TransposeConv(const float* input_data, const Dims<4>& input_dims, } } +inline void TransposeConv(const float* input_data, const Dims<4>& input_dims, + const float* filter_data, const Dims<4>& filter_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, float* output_data, + const Dims<4>& output_dims, float* im2col_data, + const Dims<4>& im2col_dims) { + gemmlowp::ScopedProfilingLabel label("TransposeConv"); + + // Note we could use transposed weights with forward conv for unstrided + // cases. But we are already getting good performance with this code as-is. + TFLITE_DCHECK(im2col_data); + TransposeIm2col(input_data, input_dims, filter_dims, stride_width, + stride_height, pad_width, pad_height, output_dims, 0, + im2col_data); + + const auto im2col_matrix_map = + MapAsMatrixWithFirstDimAsRows(im2col_data, im2col_dims); + const auto filter_matrix_map = + MapAsMatrixWithLastDimAsCols(filter_data, filter_dims); + auto output_matrix_map = + MapAsMatrixWithFirstDimAsRows(output_data, output_dims); + + Gemm(filter_matrix_map.transpose(), im2col_matrix_map, &output_matrix_map); +} + } // namespace optimized_ops } // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h index af9cef7170..66dcb6a55a 100644 --- a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h @@ -3825,7 +3825,8 @@ inline void TransposeConv(const float* input_data, const Dims<4>& input_dims, const float* filter_data, const Dims<4>& filter_dims, int stride_width, int stride_height, int pad_width, int pad_height, float* output_data, - const Dims<4>& output_dims) { + const Dims<4>& output_dims, float* /*im2col_data*/, + const Dims<4>& /*im2col_dims*/) { const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); const int input_depth = MatchingArraySize(input_dims, 0, filter_dims, 0); const int output_depth = MatchingArraySize(filter_dims, 3, output_dims, 0); diff --git a/tensorflow/contrib/lite/kernels/transpose_conv.cc b/tensorflow/contrib/lite/kernels/transpose_conv.cc index e83b1ec987..8b9deeed20 100644 --- a/tensorflow/contrib/lite/kernels/transpose_conv.cc +++ b/tensorflow/contrib/lite/kernels/transpose_conv.cc @@ -119,10 +119,16 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { // Currently only support float32. switch (input->type) { case kTfLiteFloat32: - optimized_ops::TransposeConv( + reference_ops::TransposeConv( GetTensorData(input), GetTensorDims(input), GetTensorData(weights), GetTensorDims(weights), stride_width, stride_height, padding_size.width, padding_size.height, + GetTensorData(output), GetTensorDims(output), + // Last two args specify im2col which reference_ops ignores. + // (Note this does not lead to a performance regression, as the + // previous optimized version was just a copy of the reference code.) + // TODO(b/110208176): Allocate im2col tensors and switch to + // optimized_ops. GetTensorData(output), GetTensorDims(output)); break; default: diff --git a/tensorflow/contrib/lite/toco/graph_transformations/create_im2col_arrays.cc b/tensorflow/contrib/lite/toco/graph_transformations/create_im2col_arrays.cc index 8ca2cd66ac..1e68cd678b 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/create_im2col_arrays.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/create_im2col_arrays.cc @@ -25,17 +25,12 @@ limitations under the License. namespace toco { -bool CreateIm2colArrays::Run(Model* model, std::size_t op_index) { - auto conv_it = model->operators.begin() + op_index; - if (conv_it->get()->type != OperatorType::kConv) { - return false; - } - auto* conv_op = static_cast(conv_it->get()); - if (conv_op->outputs.size() == 2) { +bool ProcessConvOperator(Model* model, ConvOperator* op) { + if (op->outputs.size() == 2) { // We already have an im2col array return false; } - const auto& weights_array = model->GetArray(conv_op->inputs[1]); + const auto& weights_array = model->GetArray(op->inputs[1]); if (!weights_array.has_shape()) { // We need to yield until weights dims have been resolved, because // from the weights dims we determine whether an im2col array is @@ -45,26 +40,52 @@ bool CreateIm2colArrays::Run(Model* model, std::size_t op_index) { const auto& weights_shape = weights_array.shape(); const int kheight = weights_shape.dims(1); const int kwidth = weights_shape.dims(2); - if (kwidth == 1 && kheight == 1 && conv_op->stride_width == 1 && - conv_op->stride_height == 1 && conv_op->dilation_width_factor == 1 && - conv_op->dilation_height_factor == 1) { + if (kwidth == 1 && kheight == 1 && op->stride_width == 1 && + op->stride_height == 1 && op->dilation_width_factor == 1 && + op->dilation_height_factor == 1) { // 1x1 unstrided undilated conv does not need an im2col array. return false; } // Create the im2col array. - CHECK_EQ(conv_op->outputs.size(), 1); + CHECK_EQ(op->outputs.size(), 1); const string& im2col_array_name = - AvailableArrayName(*model, conv_op->inputs[0] + "_im2col"); + AvailableArrayName(*model, op->inputs[0] + "_im2col"); model->GetOrCreateArray(im2col_array_name); - conv_op->outputs.push_back(im2col_array_name); - AddMessageF( - "Created an im2col array for %s, with %dx%d kernel and stride_width=%d, " - "stride_height=%d", - LogName(*conv_op), kwidth, kheight, conv_op->stride_width, - conv_op->stride_height); + op->outputs.push_back(im2col_array_name); return true; } +bool ProcessTransposeConvOperator(Model* model, TransposeConvOperator* op) { + if (op->outputs.size() == 2) { + // We already have an im2col array + return false; + } + + // Always create an im2col array for transpose_conv. + CHECK_EQ(op->outputs.size(), 1); + const string& im2col_array_name = AvailableArrayName( + *model, op->inputs[TransposeConvOperator::DATA_INPUT] + "_im2col"); + model->GetOrCreateArray(im2col_array_name); + op->outputs.push_back(im2col_array_name); + + return true; +} + +bool CreateIm2colArrays::Run(Model* model, std::size_t op_index) { + auto it = model->operators.begin() + op_index; + auto* op = it->get(); + + switch (op->type) { + case OperatorType::kConv: + return ProcessConvOperator(model, static_cast(op)); + case OperatorType::kTransposeConv: + return ProcessTransposeConvOperator( + model, static_cast(op)); + default: + return false; + } +} + } // namespace toco diff --git a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc index 170a499d4e..b6f0d96900 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc @@ -211,12 +211,6 @@ void ProcessTransposeConvOperator(Model* model, TransposeConvOperator* op) { // might as well calculate the output shape and ensure it matches the // specified one - // Check if we have already run. - auto& output_array = model->GetArray(op->outputs[0]); - if (output_array.has_shape()) { - return; - } - // SPECIFIED OUTPUT SHAPE // The below is the specified, or prescribed output shape, _given_ to the // operator as an input. @@ -284,7 +278,17 @@ void ProcessTransposeConvOperator(Model* model, TransposeConvOperator* op) { // Set the output shape according to the specified output shape. std::vector const& specified_output_shape = specified_output_shape_array.GetBuffer().data; + auto& output_array = model->GetArray(op->outputs[0]); *(output_array.mutable_shape()->mutable_dims()) = specified_output_shape; + + // Set im2col array dimensions if there is one. + if (op->outputs.size() == 2) { + const int input_depth = weights_shape.dims(3); + auto& im2col_array = model->GetArray(op->outputs[1]); + im2col_array.copy_shape( + Shape{specified_output_shape[0], specified_output_shape[1], + specified_output_shape[2], input_depth * kheight * kwidth}); + } } void ProcessDepthwiseConvOperator(Model* model, DepthwiseConvOperator* op) { -- GitLab From 91ec6cc4943f5500453cb09dc7ccdc265722312b Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Thu, 14 Jun 2018 12:01:35 -0700 Subject: [PATCH 465/816] [TF:XLA] Bump open source llvm revision to r334704 PiperOrigin-RevId: 200595463 --- tensorflow/workspace.bzl | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index 80f97607c9..39d9d9ca11 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -451,11 +451,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "llvm", urls = [ - "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/81eac77ab10767bfbdc7c413a07a4d8a0ae9b80f.tar.gz", - "https://github.com/llvm-mirror/llvm/archive/81eac77ab10767bfbdc7c413a07a4d8a0ae9b80f.tar.gz", + "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/45a02a4f8474b4b8c5cc106b5cecb06cf6e1b3c6.tar.gz", + "https://github.com/llvm-mirror/llvm/archive/45a02a4f8474b4b8c5cc106b5cecb06cf6e1b3c6.tar.gz", ], - sha256 = "eef28ae88a572f81d5931a8c153e6d25042192362d8e63533f834188526cf718", - strip_prefix = "llvm-81eac77ab10767bfbdc7c413a07a4d8a0ae9b80f", + sha256 = "056f7316a354d1f95e013176bd9b8be74e8f4d47fb0d908e0e742613187dbd59", + strip_prefix = "llvm-45a02a4f8474b4b8c5cc106b5cecb06cf6e1b3c6", build_file = clean_dep("//third_party/llvm:llvm.BUILD"), ) -- GitLab From 7ccf1937b863a7f5cfb5d159d44671138d7393bf Mon Sep 17 00:00:00 2001 From: Allen Lavoie Date: Thu, 14 Jun 2018 12:02:32 -0700 Subject: [PATCH 466/816] Factor a "capture_dependencies" scope out of Template. I don't intend for this to get used much directly, but it's handy for Template-like frameworks (e.g. Sonnet), to let them re-enter the dependency-capturing part of Templates. PiperOrigin-RevId: 200595624 --- tensorflow/contrib/checkpoint/__init__.py | 3 + tensorflow/python/ops/template.py | 67 +------------- .../python/training/checkpointable/util.py | 88 +++++++++++++++++++ .../training/checkpointable/util_test.py | 31 ++++++- 4 files changed, 121 insertions(+), 68 deletions(-) diff --git a/tensorflow/contrib/checkpoint/__init__.py b/tensorflow/contrib/checkpoint/__init__.py index 257e93d283..9aa4614967 100644 --- a/tensorflow/contrib/checkpoint/__init__.py +++ b/tensorflow/contrib/checkpoint/__init__.py @@ -20,6 +20,7 @@ Visualization and inspection: @@object_metadata Managing dependencies: +@@capture_dependencies @@Checkpointable @@CheckpointableObjectGraph @@NoDependency @@ -43,9 +44,11 @@ from tensorflow.python.training.checkpointable.base import Checkpointable from tensorflow.python.training.checkpointable.base import NoDependency from tensorflow.python.training.checkpointable.data_structures import List from tensorflow.python.training.checkpointable.data_structures import Mapping +from tensorflow.python.training.checkpointable.util import capture_dependencies from tensorflow.python.training.checkpointable.util import list_objects from tensorflow.python.training.checkpointable.util import object_metadata from tensorflow.python.util.all_util import remove_undocumented remove_undocumented(module_name=__name__) + diff --git a/tensorflow/python/ops/template.py b/tensorflow/python/ops/template.py index 355b0d961e..161d9687d6 100644 --- a/tensorflow/python/ops/template.py +++ b/tensorflow/python/ops/template.py @@ -27,6 +27,7 @@ from tensorflow.python.framework import ops from tensorflow.python.ops import variable_scope from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training.checkpointable import base as checkpointable +from tensorflow.python.training.checkpointable import util as checkpointable_util from tensorflow.python.util import tf_contextlib from tensorflow.python.util import tf_decorator from tensorflow.python.util.deprecation import deprecated @@ -295,66 +296,6 @@ class Template(checkpointable.CheckpointableBase): # which is not the same as whether the scope has been created. self._variables_created = False - def _checkpointable_custom_creator(self, next_creator, name, initial_value, - checkpointable_parent=None, **kwargs): - """A variable creation hook which adds Checkpointable dependencies. - - Set during the `Template`'s first wrapped function execution. Ensures that - (a) `Template` objects depend on `Template`s created inside them which - create variables, and (b) that any variables not in a more deeply nested - `Template` are added as dependencies directly. - - The `checkpointable_parent` argument is passed between `Template` custom - creators but ignored when the variable object itself is created. This - argument indicates (if not `None`) that a more deeply nested `Template` has - already added the variable as a dependency, and that parent `Template`s - should add a dependency on that `Template` rather than on the variable - directly. - - Args: - next_creator: See `variable_scope.variable_creator_scope`; the next - creator in the chain. - name: The (full, scope-influenced) name of the variable. The scope name - for the Template itself is stripped for the purposes of object-based - dependency tracking, but scopes within Templates are respected. - initial_value: See `variable_scope.variable_creator_scope`. Taken - explicitly so the argument can be re-named and used with - `Checkpointable._add_variable_with_custom_getter`. - checkpointable_parent: If not None, a more deeply nested Template object - to add a dependency on (rather than depending on the variable directly). - **kwargs: Passed through to the next creator. - Returns: - The output of `next_creator`: the fetched/created variable object. - """ - def _call_next_creator_renaming_initializer(initializer, **inner_kwargs): - inner_kwargs.pop("name") # Ignored; this is the scope-stripped name which - # we don't want to propagate. - return next_creator( - initial_value=initializer, - name=name, - **inner_kwargs) - if name.startswith(self._variable_scope.name): - scope_stripped_name = name[len(self._variable_scope.name) + 1:] - if not checkpointable_parent: - return self._add_variable_with_custom_getter( - initializer=initial_value, - name=scope_stripped_name, - getter=_call_next_creator_renaming_initializer, - # Disable error checking for Checkpointable. Exceptions are instead - # raised if necessary when the object-based saver tries to - # save/restore the object. - overwrite=True, - checkpointable_parent=self, - **kwargs) - else: - self._track_checkpointable( - checkpointable_parent, - name=checkpointable_parent._variable_scope.name[ # pylint: disable=protected-access - len(self._variable_scope.name) + 1:], - overwrite=True) - return next_creator(name=name, initial_value=initial_value, - checkpointable_parent=self, **kwargs) - def _call_func(self, args, kwargs): try: vars_at_start = len(ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)) @@ -365,8 +306,7 @@ class Template(checkpointable.CheckpointableBase): else: # The first time we run, restore variables if necessary (via # Checkpointable). - with variable_scope.variable_creator_scope( - self._checkpointable_custom_creator): + with checkpointable_util.capture_dependencies(template=self): result = self._func(*args, **kwargs) if self._variables_created: @@ -634,8 +574,7 @@ class EagerTemplate(Template): else: # The first time we run, restore variables if necessary (via # Checkpointable). - with variable_scope.variable_creator_scope( - self._checkpointable_custom_creator): + with checkpointable_util.capture_dependencies(template=self): result = self._func(*args, **kwargs) if self._variables_created: diff --git a/tensorflow/python/training/checkpointable/util.py b/tensorflow/python/training/checkpointable/util.py index 96e6d10791..0608076e6d 100644 --- a/tensorflow/python/training/checkpointable/util.py +++ b/tensorflow/python/training/checkpointable/util.py @@ -41,6 +41,7 @@ from tensorflow.python.training import saveable_object as saveable_object_lib from tensorflow.python.training import saver as saver_lib from tensorflow.python.training.checkpointable import base as checkpointable_lib from tensorflow.python.util import deprecation +from tensorflow.python.util import tf_contextlib from tensorflow.python.util.tf_export import tf_export @@ -564,6 +565,93 @@ def gather_initializers(root_checkpointable): if hasattr(c, "initializer") and c.initializer is not None] +@tf_contextlib.contextmanager +def capture_dependencies(template): + """Capture variables created within this scope as `Template` dependencies. + + Requires that `template.variable_scope` is active. + + This scope is intended as a compatibility measure, allowing a checkpointable + object to add dependencies on variables created in a block of code which is + not aware of object-based saving (and instead uses variable names + heavily). This is how `Template` objects add dependencies on variables and + sub-`Template`s. Where possible, use `tf.make_template` directly. + + Args: + template: The `Template` object to register dependencies with. + + Yields: + None (when used as a context manager). + """ + name_prefix = template.variable_scope.name + + def _checkpointable_custom_creator(next_creator, name, initial_value, + checkpointable_parent=None, **kwargs): + """A variable creation hook which adds Checkpointable dependencies. + + Set for example during a `Template`'s first wrapped function + execution. Ensures that (a) `template` depends on any checkpointable + objects using their own `capture_dependencies` scope inside this scope which + create variables, and (b) that any variables not in a more deeply nested + scope are added as dependencies directly. + + The `checkpointable_parent` argument is passed between custom creators but + ignored when the variable object itself is created. This argument indicates + (if not `None`) that a more deeply nested scope has already added the + variable as a dependency, and that parent scopes should add a dependency on + that object rather than on the variable directly. + + Args: + next_creator: See `variable_scope.variable_creator_scope`; the next + creator in the chain. + name: The (full, scope-influenced) name of the variable. The `name_prefix` + itself is stripped for the purposes of object-based dependency tracking, + but scopes opened within this scope are respected. + initial_value: See `variable_scope.variable_creator_scope`. Taken + explicitly so the argument can be re-named and used with + `Checkpointable._add_variable_with_custom_getter`. + checkpointable_parent: If not None, a more deeply nested checkpointable + object and its name prefix which were passed to `capture_dependencies` + to add a dependency on (rather than depending on the variable directly). + **kwargs: Passed through to the next creator. + + Returns: + The output of `next_creator`: the fetched/created variable object. + """ + def _call_next_creator_renaming_initializer(initializer, **inner_kwargs): + inner_kwargs.pop("name") # Ignored; this is the scope-stripped name which + # we don't want to propagate. + return next_creator( + initial_value=initializer, + name=name, + **inner_kwargs) + if name.startswith(name_prefix): + scope_stripped_name = name[len(name_prefix) + 1:] + if not checkpointable_parent: + return template._add_variable_with_custom_getter( # pylint: disable=protected-access + initializer=initial_value, + name=scope_stripped_name, + getter=_call_next_creator_renaming_initializer, + # Disable error checking for Checkpointable. Exceptions are instead + # raised if necessary when the object-based saver tries to + # save/restore the object. + overwrite=True, + checkpointable_parent=(template, name_prefix), + **kwargs) + else: + parent_object, parent_name_prefix = checkpointable_parent + template._track_checkpointable( # pylint: disable=protected-access + parent_object, + name=parent_name_prefix[len(name_prefix) + 1:], + overwrite=True) + return next_creator( + name=name, initial_value=initial_value, + checkpointable_parent=(template, name_prefix), **kwargs) + + with variable_scope.variable_creator_scope(_checkpointable_custom_creator): + yield + + class _NoRestoreSaveable(saver_lib.BaseSaverBuilder.SaveableObject): def __init__(self, tensor, name): diff --git a/tensorflow/python/training/checkpointable/util_test.py b/tensorflow/python/training/checkpointable/util_test.py index 8cdf5d7855..e2115417c4 100644 --- a/tensorflow/python/training/checkpointable/util_test.py +++ b/tensorflow/python/training/checkpointable/util_test.py @@ -1243,6 +1243,18 @@ class CheckpointingTests(test.TestCase): self.assertEqual(42., self.evaluate(optimizer.variables()[0])) +class _ManualScope(checkpointable.Checkpointable): + + def __call__(self): + with variable_scope.variable_scope("ManualScope") as vs: + self.variable_scope = vs + with checkpointable_utils.capture_dependencies(template=self): + return self._build() + + def _build(self): + return variable_scope.get_variable(name="in_manual_scope", shape=[]) + + class TemplateTests(test.TestCase): @test_util.run_in_graph_and_eager_modes() @@ -1255,14 +1267,23 @@ class TemplateTests(test.TestCase): v2 = variable_scope.get_variable( "v2", shape=[1], initializer=init_ops.zeros_initializer(), use_resource=True) - return v, v + 1., v2 + manual = _ManualScope() + return v, v + 1., v2, manual, manual() save_template = template.make_template("s1", _templated) - v1_save, _, v2_save = save_template() + v1_save, _, v2_save, manual_scope, manual_scope_v = save_template() + six.assertCountEqual( + self, + [v1_save, v2_save, manual_scope, manual_scope_v, save_template], + checkpointable_utils.list_objects(save_template)) + manual_dep, = manual_scope._checkpoint_dependencies + self.assertEqual("in_manual_scope", manual_dep.name) + self.assertIs(manual_scope_v, manual_dep.ref) optimizer = adam.AdamOptimizer(0.0) save_root = checkpointable_utils.Checkpoint( my_template=save_template, optimizer=optimizer) optimizer.minimize(v1_save.read_value) + self.evaluate([v.initializer for v in save_template.variables]) self.evaluate([v.initializer for v in optimizer.variables()]) self.evaluate(v1_save.assign([12.])) self.evaluate(v2_save.assign([14.])) @@ -1275,11 +1296,13 @@ class TemplateTests(test.TestCase): load_root = checkpointable_utils.Checkpoint( my_template=load_template, optimizer=load_optimizer) status = load_root.restore(save_path) - var, var_plus_one, var2 = load_template() + var, var_plus_one, var2, _, _ = load_template() load_optimizer.minimize(var.read_value) - self.assertEqual(2, len(load_template._checkpoint_dependencies)) + self.assertEqual(3, len(load_template._checkpoint_dependencies)) self.assertEqual("v", load_template._checkpoint_dependencies[0].name) self.assertEqual("v2", load_template._checkpoint_dependencies[1].name) + self.assertEqual("ManualScope", + load_template._checkpoint_dependencies[2].name) status.assert_consumed().run_restore_ops() self.assertAllEqual([12.], self.evaluate(var)) self.assertAllEqual([13.], self.evaluate(var_plus_one)) -- GitLab From d943de372a989ca6bc44058e35ba9f26591b42b4 Mon Sep 17 00:00:00 2001 From: Christopher Suter Date: Thu, 14 Jun 2018 12:05:53 -0700 Subject: [PATCH 467/816] Support non-static shape in `tf.distributions.Categorical`. PiperOrigin-RevId: 200596358 --- .../python/kernel_tests/distributions/BUILD | 1 + .../distributions/categorical_test.py | 20 ++++++++++++++-- .../python/ops/distributions/categorical.py | 23 +++++++++---------- 3 files changed, 30 insertions(+), 14 deletions(-) diff --git a/tensorflow/python/kernel_tests/distributions/BUILD b/tensorflow/python/kernel_tests/distributions/BUILD index cf2e8832fd..985922245e 100644 --- a/tensorflow/python/kernel_tests/distributions/BUILD +++ b/tensorflow/python/kernel_tests/distributions/BUILD @@ -93,6 +93,7 @@ cuda_py_test( size = "small", srcs = ["categorical_test.py"], additional_deps = [ + "@absl_py//absl/testing:parameterized", "//tensorflow/python/ops/distributions", "//third_party/py/numpy", "//tensorflow/python:array_ops", diff --git a/tensorflow/python/kernel_tests/distributions/categorical_test.py b/tensorflow/python/kernel_tests/distributions/categorical_test.py index ca2358fe99..68b4ffdb58 100644 --- a/tensorflow/python/kernel_tests/distributions/categorical_test.py +++ b/tensorflow/python/kernel_tests/distributions/categorical_test.py @@ -18,6 +18,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from absl.testing import parameterized import numpy as np from tensorflow.python.framework import constant_op @@ -40,7 +41,7 @@ def make_categorical(batch_shape, num_classes, dtype=dtypes.int32): return categorical.Categorical(logits, dtype=dtype) -class CategoricalTest(test.TestCase): +class CategoricalTest(test.TestCase, parameterized.TestCase): def testP(self): p = [0.2, 0.8] @@ -131,7 +132,7 @@ class CategoricalTest(test.TestCase): with self.test_session(): self.assertAllClose(dist.prob(0).eval(), 0.2) - def testCDFWithDynamicEventShape(self): + def testCDFWithDynamicEventShapeKnownNdims(self): """Test that dynamically-sized events with unknown shape work.""" batch_size = 2 histograms = array_ops.placeholder(dtype=dtypes.float32, @@ -167,6 +168,21 @@ class CategoricalTest(test.TestCase): self.assertAllClose(actual_cdf_one, expected_cdf_one) self.assertAllClose(actual_cdf_two, expected_cdf_two) + @parameterized.named_parameters( + ("test1", [0, 1], [[0.5, 0.3, 0.2], [1.0, 0.0, 0.0]], [0.0, 1.0]), + ("test2", [2, 5], [[0.9, 0.0, 0.0, 0.0, 0.0, 0.1], + [0.15, 0.2, 0.05, 0.35, 0.13, 0.12]], [0.9, 0.88])) + def testCDFWithDynamicEventShapeUnknownNdims( + self, events, histograms, expected_cdf): + """Test that dynamically-sized events with unknown shape work.""" + event_ph = array_ops.placeholder_with_default(events, shape=None) + histograms_ph = array_ops.placeholder_with_default(histograms, shape=None) + dist = categorical.Categorical(probs=histograms_ph) + cdf_op = dist.cdf(event_ph) + + actual_cdf = self.evaluate(cdf_op) + self.assertAllClose(actual_cdf, expected_cdf) + def testCDFWithBatch(self): histograms = [[0.1, 0.2, 0.3, 0.25, 0.15], [0.0, 0.75, 0.2, 0.05, 0.0]] diff --git a/tensorflow/python/ops/distributions/categorical.py b/tensorflow/python/ops/distributions/categorical.py index b88a0518b6..dd25fce2ec 100644 --- a/tensorflow/python/ops/distributions/categorical.py +++ b/tensorflow/python/ops/distributions/categorical.py @@ -32,12 +32,8 @@ from tensorflow.python.ops.distributions import util as distribution_util from tensorflow.python.util.tf_export import tf_export -def _broadcast_cat_event_and_params(event, params, base_dtype=dtypes.int32): +def _broadcast_cat_event_and_params(event, params, base_dtype): """Broadcasts the event or distribution parameters.""" - if event.shape.ndims is None: - raise NotImplementedError( - "Cannot broadcast with an event tensor of unknown rank.") - if event.dtype.is_integer: pass elif event.dtype.is_floating: @@ -47,15 +43,18 @@ def _broadcast_cat_event_and_params(event, params, base_dtype=dtypes.int32): else: raise TypeError("`value` should have integer `dtype` or " "`self.dtype` ({})".format(base_dtype)) - - if params.get_shape()[:-1] == event.get_shape(): - params = params - else: - params *= array_ops.ones_like( - array_ops.expand_dims(event, -1), dtype=params.dtype) + shape_known_statically = ( + params.shape.ndims is not None and + params.shape[:-1].is_fully_defined() and + event.shape.is_fully_defined()) + if not shape_known_statically or params.shape[:-1] != event.shape: + params *= array_ops.ones_like(event[..., array_ops.newaxis], + dtype=params.dtype) params_shape = array_ops.shape(params)[:-1] event *= array_ops.ones(params_shape, dtype=event.dtype) - event.set_shape(tensor_shape.TensorShape(params.get_shape()[:-1])) + if params.shape.ndims is not None: + event.set_shape(tensor_shape.TensorShape(params.shape[:-1])) + return event, params -- GitLab From 26d1441ffdd1254922e9d23f0cee27dfc80353f9 Mon Sep 17 00:00:00 2001 From: Taras Sereda Date: Thu, 14 Jun 2018 13:31:09 -0700 Subject: [PATCH 468/816] Update debugger.md (#20036) Error: homebrew/dupes was deprecated. This tap is now empty as all its formulae were migrated. instead use: brew reinstall ncurses --- tensorflow/docs_src/programmers_guide/debugger.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/docs_src/programmers_guide/debugger.md b/tensorflow/docs_src/programmers_guide/debugger.md index fc845c68f4..49258c7b4a 100644 --- a/tensorflow/docs_src/programmers_guide/debugger.md +++ b/tensorflow/docs_src/programmers_guide/debugger.md @@ -17,7 +17,7 @@ how to use the graphical user interface (GUI) of tfdbg, i.e., the Note: The TensorFlow debugger uses a [curses](https://en.wikipedia.org/wiki/Curses_\(programming_library\))-based text user interface. On Mac OS X, the `ncurses` library is required and can be -installed with `brew install homebrew/dupes/ncurses`. On Windows, curses isn't as +installed with `brew install ncurses`. On Windows, curses isn't as well supported, so a [readline](https://en.wikipedia.org/wiki/GNU_Readline)-based interface can be used with tfdbg by installing `pyreadline` with `pip`. If you use Anaconda3, you can install it with a command such as -- GitLab From eefd88284ba3744a5d7f6a3a7c179bed8421b7e2 Mon Sep 17 00:00:00 2001 From: Steven Schmatz Date: Thu, 14 Jun 2018 16:45:28 -0400 Subject: [PATCH 469/816] Invalid Python example in baseline.py (#20033) --- tensorflow/python/estimator/canned/baseline.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/python/estimator/canned/baseline.py b/tensorflow/python/estimator/canned/baseline.py index 3c6816cb03..15677ea3c1 100644 --- a/tensorflow/python/estimator/canned/baseline.py +++ b/tensorflow/python/estimator/canned/baseline.py @@ -24,10 +24,10 @@ Example: classifier = BaselineClassifier(n_classes=3) # Input builders -def input_fn_train: # returns x, y (where y represents label's class index). +def input_fn_train(): # returns x, y (where y represents label's class index). pass -def input_fn_eval: # returns x, y (where y represents label's class index). +def input_fn_eval(): # returns x, y (where y represents label's class index). pass # Fit model. -- GitLab From 840aeb0ce9bd0f0a1c275edc9fe6d51eff5cf33f Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 14 Jun 2018 14:18:15 -0700 Subject: [PATCH 470/816] Merged commit includes the following changes: 200617269 by A. Unique TensorFlower: Internal change -- 200603378 by jpienaar: The output of the merge should be the value's and not the original output port. The output port of the IfOp is already taken into account by selecting the merge node and the output of the merge should be the value used (which is the 0th output of the merge node). -- 200601721 by A. Unique TensorFlower: Basic support for tf.tile that multiplies a single axis. -- 200600686 by A. Unique TensorFlower: Internal change. -- PiperOrigin-RevId: 200617269 --- tensorflow/contrib/lite/toco/BUILD | 3 +- .../contrib/lite/toco/export_tensorflow.cc | 20 ++++ .../convert_trivial_tile_to_concat.cc | 94 ++++++++++++++++ .../fuse_broadcast_into_following_binary.cc | 102 ++++++++++++++++++ .../graph_transformations.h | 3 +- .../propagate_fake_quant_num_bits.cc | 4 + .../propagate_fixed_sizes.cc | 53 +++++++-- .../resolve_tensorflow_tile.cc | 97 ----------------- tensorflow/contrib/lite/toco/model.h | 6 +- tensorflow/contrib/lite/toco/toco_tooling.cc | 3 +- tensorflow/core/common_runtime/lower_if_op.cc | 3 +- 11 files changed, 276 insertions(+), 112 deletions(-) create mode 100644 tensorflow/contrib/lite/toco/graph_transformations/convert_trivial_tile_to_concat.cc create mode 100644 tensorflow/contrib/lite/toco/graph_transformations/fuse_broadcast_into_following_binary.cc delete mode 100644 tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_tile.cc diff --git a/tensorflow/contrib/lite/toco/BUILD b/tensorflow/contrib/lite/toco/BUILD index 0789dc9928..dd05c484fa 100644 --- a/tensorflow/contrib/lite/toco/BUILD +++ b/tensorflow/contrib/lite/toco/BUILD @@ -213,6 +213,7 @@ cc_library( "graph_transformations/convert_squeeze_to_reshape.cc", "graph_transformations/convert_trivial_addn_to_add.cc", "graph_transformations/convert_trivial_stack_to_reshape.cc", + "graph_transformations/convert_trivial_tile_to_concat.cc", "graph_transformations/convert_trivial_transpose_to_reshape.cc", "graph_transformations/create_im2col_arrays.cc", "graph_transformations/dequantize.cc", @@ -224,6 +225,7 @@ cc_library( "graph_transformations/fuse_activation_functions.cc", "graph_transformations/fuse_binary_into_following_affine.cc", "graph_transformations/fuse_binary_into_preceding_affine.cc", + "graph_transformations/fuse_broadcast_into_following_binary.cc", "graph_transformations/graph_transformations.cc", "graph_transformations/hardcode_min_max.cc", "graph_transformations/identify_dilated_conv.cc", @@ -293,7 +295,6 @@ cc_library( "graph_transformations/resolve_tensorflow_matmul.cc", "graph_transformations/resolve_tensorflow_merge.cc", "graph_transformations/resolve_tensorflow_switch.cc", - "graph_transformations/resolve_tensorflow_tile.cc", "graph_transformations/resolve_transpose_attributes.cc", "graph_transformations/unfuse_activation_functions.cc", "graph_transformations/unpartition_embedding_lookup.cc", diff --git a/tensorflow/contrib/lite/toco/export_tensorflow.cc b/tensorflow/contrib/lite/toco/export_tensorflow.cc index c7c80ab21c..6e5e0d0137 100644 --- a/tensorflow/contrib/lite/toco/export_tensorflow.cc +++ b/tensorflow/contrib/lite/toco/export_tensorflow.cc @@ -1687,6 +1687,22 @@ void ConvertSelectOperator(const Model& model, const SelectOperator& src_op, (*sub_op->mutable_attr())["T"].set_type(data_type); } +void ConvertTileOperator(const Model& model, + const TensorFlowTileOperator& src_op, + GraphDef* tensorflow_graph) { + auto* tile_op = tensorflow_graph->add_node(); + tile_op->set_op("Tile"); + tile_op->set_name(src_op.outputs[0]); + CHECK_EQ(src_op.inputs.size(), 2); + *tile_op->add_input() = src_op.inputs[0]; + *tile_op->add_input() = src_op.inputs[1]; + const auto data_type = GetTensorFlowDataType(model, src_op.inputs[0]); + (*tile_op->mutable_attr())["T"].set_type(data_type); + const auto multiples_data_type = + GetTensorFlowDataType(model, src_op.inputs[1]); + (*tile_op->mutable_attr())["Tmultiples"].set_type(multiples_data_type); +} + void ConvertTopKV2Operator(const Model& model, const TopKV2Operator& src_op, GraphDef* tensorflow_graph) { auto* topk_op = tensorflow_graph->add_node(); @@ -1953,6 +1969,10 @@ void ConvertOperator(const Model& model, const Operator& src_op, } else if (src_op.type == OperatorType::kSelect) { ConvertSelectOperator(model, static_cast(src_op), tensorflow_graph); + } else if (src_op.type == OperatorType::kTensorFlowTile) { + ConvertTileOperator(model, + static_cast(src_op), + tensorflow_graph); } else { LOG(FATAL) << "Unhandled operator type " << OperatorTypeName(src_op.type); } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/convert_trivial_tile_to_concat.cc b/tensorflow/contrib/lite/toco/graph_transformations/convert_trivial_tile_to_concat.cc new file mode 100644 index 0000000000..5ab399206b --- /dev/null +++ b/tensorflow/contrib/lite/toco/graph_transformations/convert_trivial_tile_to_concat.cc @@ -0,0 +1,94 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include + +#include "tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h" +#include "tensorflow/contrib/lite/toco/model.h" +#include "tensorflow/contrib/lite/toco/tooling_util.h" +#include "tensorflow/core/platform/logging.h" + +namespace toco { + +bool ConvertTrivialTileToConcat::Run(Model* model, std::size_t op_index) { + auto tile_it = model->operators.begin() + op_index; + if (tile_it->get()->type != OperatorType::kTensorFlowTile) { + return false; + } + auto* tile_op = static_cast(tile_it->get()); + + const auto& input_array = model->GetArray(tile_op->inputs[0]); + const auto& multiples_array = model->GetArray(tile_op->inputs[1]); + const auto& output_array = model->GetArray(tile_op->outputs[0]); + if (!input_array.has_shape() || !multiples_array.has_shape() || + !output_array.has_shape()) { + // Yield until PropagateFixedSizes has been run on this op. + return false; + } + // Note: We can assume we have error checked inputs in PropagateFixedSizes. + + if (!multiples_array.buffer) { + // Yield until the multiples is constant. + return false; + } + std::vector const& multiples = + multiples_array.GetBuffer().data; + + // We can simplify the tile if only a single dimension is being multiplied. + // It then just becomes a concat along that dimension. + int non_one_dims = 0; + int concat_axis = 0; + for (int i = 0; i < multiples.size(); ++i) { + if (multiples[i] != 1) { + ++non_one_dims; + concat_axis = i; + } + } + if (non_one_dims != 1) { + // The tile is non-trivial. Good luck. + AddMessageF("Tile %s is non-trivial (has more than one multiply dimension)", + LogName(*tile_op)); + return false; + } + + // The tile is like a concat. + AddMessageF("Simplifying %s to a Concat along a single axis %d", + LogName(*tile_op), concat_axis); + + auto* concat_op = new ConcatenationOperator; + + // Copy input and output. + // Note that we multiply out the input by the number of times requested. + for (int i = 0; i < multiples[concat_axis]; ++i) { + concat_op->inputs.push_back(tile_op->inputs[0]); + } + concat_op->axis = concat_axis; + concat_op->outputs = tile_op->outputs; + + // Delete multiples array if unused. + if (IsDiscardableArray(*model, tile_op->inputs[1]) && + CountOpsWithInput(*model, tile_op->inputs[1]) == 1) { + model->EraseArray(tile_op->inputs[1]); + } + + // Replace the operator in the graph. + const auto concat_it = model->operators.emplace(tile_it, concat_op); + tile_it = concat_it + 1; + CHECK_EQ(tile_it->get(), tile_op); + model->operators.erase(tile_it); + + return true; +} + +} // namespace toco diff --git a/tensorflow/contrib/lite/toco/graph_transformations/fuse_broadcast_into_following_binary.cc b/tensorflow/contrib/lite/toco/graph_transformations/fuse_broadcast_into_following_binary.cc new file mode 100644 index 0000000000..874d8def57 --- /dev/null +++ b/tensorflow/contrib/lite/toco/graph_transformations/fuse_broadcast_into_following_binary.cc @@ -0,0 +1,102 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include +#include +#include +#include + +#include "tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h" +#include "tensorflow/contrib/lite/toco/model.h" +#include "tensorflow/contrib/lite/toco/tooling_util.h" +#include "tensorflow/core/platform/logging.h" + +namespace toco { + +namespace { + +// Returns true if the given op is strictly a broadcasting operation. +// This is commonly seen as a Concat of the same input multiple times, and is +// often generated from Tile ops that were converted via the +// convert_trivial_tile_to_concat transformation. +bool IsBroadcastingOp(const Model& model, Operator* op) { + // Concatenation of identical inputs is usually a broadcast. + if (op->type == OperatorType::kConcatenation) { + // Verify that all inputs are the same. + for (int i = 1; i < op->inputs.size(); ++i) { + if (op->inputs[i] != op->inputs[0]) { + return false; + } + } + return true; + } + + // There are other things we could look for (Stack/etc) when needed. + return false; +} + +} // namespace + +// Finds an operation that looks like a broadcast (concat of the same sources +// along the last dimension) and drops it by relying on the ability of certain +// binary ops to perform an implicit broadcast. +bool FuseBroadcastIntoFollowingBinary::Run(Model* model, std::size_t op_index) { + const auto binary_it = model->operators.begin() + op_index; + auto* binary_op = binary_it->get(); + + // Test for binary ops of types that we know how to resolve + if (binary_op->inputs.size() != 2) { + return false; + } + if (binary_op->type != OperatorType::kAdd && + binary_op->type != OperatorType::kMul && + binary_op->type != OperatorType::kSub && + binary_op->type != OperatorType::kDiv) { + return false; + } + + // NOTE: either of these ops may be nullptr if the input array is constant. + Operator* const op[2] = { + GetOpWithOutput(*model, binary_op->inputs[0]), + GetOpWithOutput(*model, binary_op->inputs[1]), + }; + + // Check whether either input is a broadcast-like concat. + bool is_op_0_broadcast = op[0] && IsBroadcastingOp(*model, op[0]); + bool is_op_1_broadcast = op[1] && IsBroadcastingOp(*model, op[1]); + if (!is_op_0_broadcast && !is_op_1_broadcast) { + // Neither input is a broadcast-looking thing. + AddMessageF("Neither input looks broadcasty"); + return false; + } else if (is_op_0_broadcast && is_op_1_broadcast) { + AddMessageF( + "Unable to fuse broadcast into %s as both inputs (%s, %s) are " + "broadcasts", + LogName(*binary_op), op[0] ? LogName(*op[0]) : "(?)", + op[1] ? LogName(*op[1]) : "(?)"); + return false; + } + int broadcast_index = is_op_0_broadcast ? 0 : 1; + + // Just pull out the input of the broadcast op and pass it directly to the + // binary op. + AddMessageF("Fusing broadcast op %s into the following binary %s", + LogName(*op[broadcast_index]), LogName(*binary_op)); + binary_op->inputs[broadcast_index] = op[broadcast_index]->inputs[0]; + + // We leave the broadcast op in; it'll get cleaned up if it's not used later. + return true; +} + +} // namespace toco diff --git a/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h b/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h index 1bc7557d46..62a09acdfb 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h +++ b/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h @@ -117,12 +117,14 @@ DECLARE_GRAPH_TRANSFORMATION(ConvertPureConvToDepthwise) DECLARE_GRAPH_TRANSFORMATION(ConvertSqueezeToReshape) DECLARE_GRAPH_TRANSFORMATION(ConvertTrivialAddNToAdd) DECLARE_GRAPH_TRANSFORMATION(ConvertTrivialStackToReshape) +DECLARE_GRAPH_TRANSFORMATION(ConvertTrivialTileToConcat) DECLARE_GRAPH_TRANSFORMATION(ConvertTrivialTransposeToReshape) DECLARE_GRAPH_TRANSFORMATION(ConvertReorderAxes) DECLARE_GRAPH_TRANSFORMATION(EnsureBiasVectors) DECLARE_GRAPH_TRANSFORMATION(FuseActivationFunctions) DECLARE_GRAPH_TRANSFORMATION(FuseBinaryIntoFollowingAffine) DECLARE_GRAPH_TRANSFORMATION(FuseBinaryIntoPrecedingAffine) +DECLARE_GRAPH_TRANSFORMATION(FuseBroadcastIntoFollowingBinary) DECLARE_GRAPH_TRANSFORMATION(IdentifyL2Normalization) DECLARE_GRAPH_TRANSFORMATION(IdentifyL2Pool) DECLARE_GRAPH_TRANSFORMATION(IdentifyLstmCell) @@ -165,7 +167,6 @@ DECLARE_GRAPH_TRANSFORMATION(ResolveTensorFlowMatMul) DECLARE_GRAPH_TRANSFORMATION(ResolveTensorFlowMerge) DECLARE_GRAPH_TRANSFORMATION(ResolveSqueezeAttributes) DECLARE_GRAPH_TRANSFORMATION(ResolveTensorFlowSwitch) -DECLARE_GRAPH_TRANSFORMATION(ResolveTensorFlowTile) DECLARE_GRAPH_TRANSFORMATION(ResolveConstantConcatenation) DECLARE_GRAPH_TRANSFORMATION(ResolveConstantReshape) DECLARE_GRAPH_TRANSFORMATION(ResolveConstantTranspose) diff --git a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fake_quant_num_bits.cc b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fake_quant_num_bits.cc index 6d51fc8c31..77c0886811 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fake_quant_num_bits.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fake_quant_num_bits.cc @@ -103,6 +103,7 @@ bool DoesOpBlockBackwardPropagation(const Operator& op) { case OperatorType::kTensorFlowReshape: case OperatorType::kTranspose: case OperatorType::kSelect: + case OperatorType::kTensorFlowTile: // Reshapes and transposes don't change values. return false; default: @@ -124,6 +125,9 @@ bool DoesOpInputBlockBackwardPropagation(const Operator& op, int input_index) { case OperatorType::kTranspose: // Ignore reshape/transpose shapes/dimensions. return input_index != 0; + case OperatorType::kTensorFlowTile: + // Ignore tile multiples. + return input_index != 0; default: return false; } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc index b6f0d96900..e7da9051d8 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc @@ -1509,6 +1509,48 @@ void ProcessSparseToDenseOperator(Model* model, SparseToDenseOperator* op) { } } +void ProcessTileOperator(Model* model, TensorFlowTileOperator* op) { + CHECK_EQ(op->inputs.size(), 2); + CHECK_EQ(op->outputs.size(), 1); + + auto& output_array = model->GetArray(op->outputs[0]); + if (output_array.has_shape()) { + // We have already run. + return; + } + + const auto& input_array = model->GetArray(op->inputs[0]); + if (!input_array.has_shape()) { + // Yield until input dims have been resolved. + return; + } + const auto& input_shape = input_array.shape(); + + auto& multiples_array = model->GetArray(op->inputs[1]); + if (!multiples_array.has_shape()) { + // Yield until multiples shape been resolved. + return; + } + if (!multiples_array.buffer) { + // Yield until the multiples is constant. + return; + } + CHECK(multiples_array.data_type == ArrayDataType::kInt32) + << "Tile multiples input must be int32"; + + std::vector const& multiples = + multiples_array.GetBuffer().data; + CHECK_EQ(multiples.size(), input_shape.dimensions_count()) + << "Tile multiples input " << op->inputs[1] + << " must be same length as input dimensions"; + + auto* mutable_dims = output_array.mutable_shape()->mutable_dims(); + mutable_dims->resize(multiples.size()); + for (int i = 0; i < mutable_dims->size(); ++i) { + (*mutable_dims)[i] = input_shape.dims(i) * multiples[i]; + } +} + } // namespace bool PropagateFixedSizes::Run(Model* model, std::size_t op_index) { @@ -1627,14 +1669,6 @@ bool PropagateFixedSizes::Run(Model* model, std::size_t op_index) { ProcessSliceOperator(model, static_cast(op)); break; - case OperatorType::kTensorFlowTile: - // We don't currently implement the propagation of fixed sizes through - // a TensorFlow Tile. - // - // Fortunately, we don't need to: so far, we have only dealt with Tile - // or Slice ops in subgraphs that are identified as L2Normalization. - // See IdentifyL2Normalization. - break; case OperatorType::kTensorFlowSwitch: // We can't know the sizes of the outputs until we have resolved the // predicate, and once we have resolved the predicate, the whole @@ -1738,6 +1772,9 @@ bool PropagateFixedSizes::Run(Model* model, std::size_t op_index) { ProcessSparseToDenseOperator(model, static_cast(op)); break; + case OperatorType::kTensorFlowTile: + ProcessTileOperator(model, static_cast(op)); + break; default: // Unimplemented, another graph transformation should drop it. LOG(FATAL) << "Unhandled operator type " << OperatorTypeName(op->type); diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_tile.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_tile.cc deleted file mode 100644 index 1ddf54c778..0000000000 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_tile.cc +++ /dev/null @@ -1,97 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#include -#include -#include -#include - -#include "tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h" -#include "tensorflow/contrib/lite/toco/model.h" -#include "tensorflow/contrib/lite/toco/tooling_util.h" -#include "tensorflow/core/platform/logging.h" - -namespace toco { - -namespace { - -void RemoveTileOperator(Model* model, Operator* tile_op, Operator* binary_op, - int operand_index) { - CHECK(tile_op->type == OperatorType::kTensorFlowTile); - CHECK_EQ(binary_op->inputs.size(), 2); - CHECK_EQ(tile_op->inputs.size(), 2); - const string tile_multiplier_array = tile_op->inputs[1]; - const string tile_output_array = tile_op->outputs[0]; - binary_op->inputs[operand_index] = tile_op->inputs[0]; - auto tile_it = model->operators.begin(); - for (; tile_it != model->operators.end(); ++tile_it) { - if (tile_it->get() == tile_op) { - break; - } - } - CHECK(tile_it != model->operators.end()); - CHECK(tile_it->get() == tile_op); - model->operators.erase(tile_it); - if (!CountOpsWithInput(*model, tile_multiplier_array) && - !GetOpWithOutput(*model, tile_multiplier_array)) { - model->EraseArray(tile_multiplier_array); - } - if (!CountOpsWithInput(*model, tile_output_array)) { - model->EraseArray(tile_output_array); - } -} -} // namespace - -bool ResolveTensorFlowTile::Run(Model* model, std::size_t op_index) { - const auto binary_it = model->operators.begin() + op_index; - auto* binary_op = binary_it->get(); - // Test for binary ops of types that we know how to resolve - if (binary_op->inputs.size() != 2) { - return false; - } - if (binary_op->type != OperatorType::kAdd && - binary_op->type != OperatorType::kMul && - binary_op->type != OperatorType::kSub && - binary_op->type != OperatorType::kDiv) { - return false; - } - - Operator* const op[2] = { - GetOpWithOutput(*model, binary_op->inputs[0]), - GetOpWithOutput(*model, binary_op->inputs[1]), - }; - - // In the unlikely case where both operands are Tile, we can't infer the - // output - // size without the Tile nodes, so we have to bail out. - if (op[0] && op[0]->type == OperatorType::kTensorFlowTile && op[1] && - op[1]->type == OperatorType::kTensorFlowTile) { - return false; - } - - for (int i = 0; i < 2; i++) { - if (op[i] && op[i]->type == OperatorType::kTensorFlowTile) { - // We can only remove a Tile operator is no other op than the present - // binary op was consuming its tiled output. - if (CountOpsWithInput(*model, binary_op->inputs[i]) == 1) { - AddMessageF("Removing %s", LogName(*op[i])); - RemoveTileOperator(model, op[i], binary_op, i); - return true; - } - } - } - return false; -} - -} // namespace toco diff --git a/tensorflow/contrib/lite/toco/model.h b/tensorflow/contrib/lite/toco/model.h index 2f43adb07b..7bdec47aa9 100644 --- a/tensorflow/contrib/lite/toco/model.h +++ b/tensorflow/contrib/lite/toco/model.h @@ -1222,8 +1222,10 @@ struct TensorFlowSumOperator : Operator { }; // TensorFlow Tile equivalent. Refer to TensorFlow documentation for details. -// Not fully supported, just a placeholder to handle TensorFlow graphs and -// support graph transformations to other operator types by matching sub-graphs. +// +// Inputs: +// inputs[0]: required: the input array +// inputs[1]: required: int array with length of rank(input[0]) struct TensorFlowTileOperator : Operator { TensorFlowTileOperator() : Operator(OperatorType::kTensorFlowTile) {} }; diff --git a/tensorflow/contrib/lite/toco/toco_tooling.cc b/tensorflow/contrib/lite/toco/toco_tooling.cc index 1fe76f8163..3173d524b7 100644 --- a/tensorflow/contrib/lite/toco/toco_tooling.cc +++ b/tensorflow/contrib/lite/toco/toco_tooling.cc @@ -56,6 +56,7 @@ void MakeGeneralGraphTransformationsSet( transformations->Add(new ConvertSqueezeToReshape); transformations->Add(new ConvertTrivialAddNToAdd); transformations->Add(new ConvertTrivialStackToReshape); + transformations->Add(new ConvertTrivialTileToConcat); transformations->Add(new ConvertTrivialTransposeToReshape); transformations->Add(new ConvertReorderAxes); transformations->Add(new ResolveReshapeAttributes); @@ -76,6 +77,7 @@ void MakeGeneralGraphTransformationsSet( transformations->Add(new ResolveTensorFlowMatMul); transformations->Add(new FuseBinaryIntoPrecedingAffine); transformations->Add(new FuseBinaryIntoFollowingAffine); + transformations->Add(new FuseBroadcastIntoFollowingBinary); transformations->Add(new MergeReshapeIntoPrecedingTranspose); transformations->Add(new ReorderElementwiseUnary); transformations->Add(new ReorderReshapeTranspose); @@ -94,7 +96,6 @@ void MakeGeneralGraphTransformationsSet( transformations->Add(new ResolveTensorFlowMerge); transformations->Add(new ResolveSqueezeAttributes); transformations->Add(new ResolveTensorFlowSwitch); - transformations->Add(new ResolveTensorFlowTile); transformations->Add(new ResolveTensorFlowConcat); transformations->Add(new ResolveMultiplyByZero); transformations->Add(new IdentifyDilatedConv); diff --git a/tensorflow/core/common_runtime/lower_if_op.cc b/tensorflow/core/common_runtime/lower_if_op.cc index b5fee36ff4..567c81870c 100644 --- a/tensorflow/core/common_runtime/lower_if_op.cc +++ b/tensorflow/core/common_runtime/lower_if_op.cc @@ -187,8 +187,7 @@ Status CondBuilder::AddOutputs() { } else { // Feed the outputs directly from the merge nodes so that downstream ops // can start before all the outputs have been computed. - graph_->AddEdge(merges[e->src_output()], e->src_output(), e->dst(), - e->dst_input()); + graph_->AddEdge(merges[e->src_output()], 0, e->dst(), e->dst_input()); } } return Status::OK(); -- GitLab From f01d25471dbe26f0a1116009badc4af169f82b02 Mon Sep 17 00:00:00 2001 From: Mark Heffernan Date: Thu, 14 Jun 2018 14:51:26 -0700 Subject: [PATCH 471/816] Add support for TOKEN type to CPU/GPU backends. TOKENs will be used for ordering side-effecting operations. They are not materialized but can be contained in tuples and flow into and out of computations. This CL adds a trivial representation for the cpu and gpu backends to support TOKENs and modifies copy insertion to avoid making copies of tokens. This also adds a Literal TOKEN which is required for the interpreter backend. PiperOrigin-RevId: 200623120 --- tensorflow/compiler/xla/literal_comparison.cc | 3 ++ tensorflow/compiler/xla/literal_util.cc | 16 +++++++- tensorflow/compiler/xla/literal_util.h | 3 ++ tensorflow/compiler/xla/literal_util_test.cc | 16 ++++++++ .../compiler/xla/service/copy_insertion.cc | 4 ++ .../xla/service/copy_insertion_test.cc | 39 ++++++++++++++++++ .../compiler/xla/service/cpu/ir_emitter.cc | 7 ++++ .../compiler/xla/service/cpu/ir_emitter.h | 1 + .../xla/service/gpu/ir_emitter_unnested.cc | 4 ++ .../xla/service/gpu/ir_emitter_unnested.h | 1 + .../xla/service/hlo_alias_analysis.cc | 11 ++++- .../compiler/xla/service/hlo_computation.cc | 41 +++++++++---------- .../xla/service/hlo_computation_test.cc | 32 +++++++++++++++ .../compiler/xla/service/hlo_evaluator.cc | 5 +-- .../compiler/xla/service/hlo_matchers.h | 1 + .../compiler/xla/service/llvm_ir/llvm_util.cc | 4 ++ tensorflow/compiler/xla/shape_util.cc | 10 +---- tensorflow/compiler/xla/shape_util_test.cc | 35 ++++++++++++++++ .../compiler/xla/tests/token_hlo_test.cc | 37 ++++++++++++++++- 19 files changed, 231 insertions(+), 39 deletions(-) diff --git a/tensorflow/compiler/xla/literal_comparison.cc b/tensorflow/compiler/xla/literal_comparison.cc index 748a243e53..2125ab7c61 100644 --- a/tensorflow/compiler/xla/literal_comparison.cc +++ b/tensorflow/compiler/xla/literal_comparison.cc @@ -706,6 +706,9 @@ Status Equal(const LiteralSlice& expected, const LiteralSlice& actual) { } break; } + case TOKEN: + // Tokens have no on-device representation and are trivially equal. + return Status::OK(); default: LOG(FATAL) << "Unsupported primitive type in LiteralTestUtil::ExpectEqual: " diff --git a/tensorflow/compiler/xla/literal_util.cc b/tensorflow/compiler/xla/literal_util.cc index 72740e5976..19e6d288c0 100644 --- a/tensorflow/compiler/xla/literal_util.cc +++ b/tensorflow/compiler/xla/literal_util.cc @@ -148,8 +148,7 @@ void Literal::SetPiece(const Shape& shape, Piece* piece, bool allocate_arrays) { piece->emplace_back(std::move(child_piece)); } - } else { - CHECK(ShapeUtil::IsArray(shape)); + } else if (ShapeUtil::IsArray(shape)) { if (allocate_arrays) { if (LayoutUtil::IsSparseArray(shape)) { // For sparse arrays, the buffer must be of the size of the maximum @@ -165,6 +164,10 @@ void Literal::SetPiece(const Shape& shape, Piece* piece, bool allocate_arrays) { piece->set_buffer(new char[piece->size_bytes()]); } } + } else { + // If the shape is neither an array nor tuple, then it must be + // zero-sized. Otherwise, some memory needs to be allocated for it. + CHECK_EQ(piece->size_bytes(), 0); } } @@ -327,6 +330,10 @@ Status Literal::CopyElementFrom(const LiteralSlice& src_literal, return Status::OK(); } +/* static */ std::unique_ptr Literal::CreateToken() { + return MakeUnique(ShapeUtil::MakeTokenShape()); +} + std::vector Literal::DecomposeTuple() { CHECK(ShapeUtil::IsTuple(shape())); std::vector elements; @@ -1368,6 +1375,11 @@ void ToStringHelper(const LiteralBase& literal, const ShapeIndex& shape_index, return; } + if (ShapeUtil::IsToken(subshape)) { + pieces->push_back("token"); + return; + } + if (LayoutUtil::IsSparseArray(subshape)) { pieces->push_back(shape_to_string(subshape)); pieces->push_back("{"); diff --git a/tensorflow/compiler/xla/literal_util.h b/tensorflow/compiler/xla/literal_util.h index bcecbcccb7..37ca8ea9f1 100644 --- a/tensorflow/compiler/xla/literal_util.h +++ b/tensorflow/compiler/xla/literal_util.h @@ -917,6 +917,9 @@ class Literal : public LiteralBase { return MakeTupleOwned(std::move(v)); } + // Create a constant token literal. Token types have no value. + static std::unique_ptr CreateToken(); + // Returns a vector containing the tuple elements of this Literal as separate // Literals. This Literal must be tuple-shaped and can be a nested tuple. The // elements are moved into the new Literals; no data is copied. Upon return diff --git a/tensorflow/compiler/xla/literal_util_test.cc b/tensorflow/compiler/xla/literal_util_test.cc index 53b926163c..493d807591 100644 --- a/tensorflow/compiler/xla/literal_util_test.cc +++ b/tensorflow/compiler/xla/literal_util_test.cc @@ -334,6 +334,22 @@ TEST_F(LiteralUtilTest, NonScalarEquality) { EXPECT_EQ(nil, nil); } +TEST_F(LiteralUtilTest, TokenEquality) { + auto token0 = Literal::CreateToken(); + auto token1 = Literal::CreateToken(); + auto scalar = Literal::CreateR0(1.0); + + EXPECT_EQ(*token0, *token1); + EXPECT_NE(*token0, *scalar); + + EXPECT_EQ(*Literal::MakeTuple({token0.get()}), + *Literal::MakeTuple({token0.get()})); + EXPECT_EQ(*Literal::MakeTuple({token0.get(), scalar.get()}), + *Literal::MakeTuple({token1.get(), scalar.get()})); + EXPECT_NE(*Literal::MakeTuple({token0.get(), scalar.get()}), + *Literal::MakeTuple({scalar.get(), token1.get()})); +} + TEST_F(LiteralUtilTest, DifferentLayoutEquality) { // Test equality with literals which have different layouts. auto colmajor = diff --git a/tensorflow/compiler/xla/service/copy_insertion.cc b/tensorflow/compiler/xla/service/copy_insertion.cc index 3625891b4f..e0ce2e3555 100644 --- a/tensorflow/compiler/xla/service/copy_insertion.cc +++ b/tensorflow/compiler/xla/service/copy_insertion.cc @@ -472,6 +472,10 @@ class CopyRemover { // between copies added around aliased operations (kWhile) guarantees // this strict order. for (const HloValue* value_a : buffer.values()) { + if (ShapeUtil::IsToken(value_a->shape())) { + // Token values have no representation and cannot interfere. + continue; + } for (const HloValue* value_b : buffer.values()) { if (value_a != value_b) { DCHECK(ordering_.LiveRangeStrictlyBefore(*value_a, *value_b, diff --git a/tensorflow/compiler/xla/service/copy_insertion_test.cc b/tensorflow/compiler/xla/service/copy_insertion_test.cc index 684fff8a6f..ed1a50f516 100644 --- a/tensorflow/compiler/xla/service/copy_insertion_test.cc +++ b/tensorflow/compiler/xla/service/copy_insertion_test.cc @@ -1595,6 +1595,45 @@ TEST_F(CopyInsertionTest, WhileBodyWithConstantRoot) { EXPECT_THAT(condition->root_instruction(), op::Constant()); } +TEST_F(CopyInsertionTest, TokensShouldNotBeCopied) { + string module_string = R"( +HloModule TokensShouldNotBeCopied + +%Body (param.1: (s32[], token[])) -> (s32[], token[]) { + %param.1 = (s32[], token[]) parameter(0) + %get-tuple-element.1 = s32[] get-tuple-element((s32[], token[]) %param.1), index=0 + %constant.1 = s32[] constant(1) + %add = s32[] add(s32[] %get-tuple-element.1, s32[] %constant.1) + %get-tuple-element.2 = token[] get-tuple-element((s32[], token[]) %param.1), index=1 + %generate-token = token[] generate-token(token[] %get-tuple-element.2) + ROOT %tuple = (s32[], token[]) tuple(s32[] %add, token[] %generate-token) +} + +%Cond (param: (s32[], token[])) -> pred[] { + %param = (s32[], token[]) parameter(0) + %get-tuple-element = s32[] get-tuple-element((s32[], token[]) %param), index=0 + %constant = s32[] constant(42) + ROOT %less-than = pred[] less-than(s32[] %get-tuple-element, s32[] %constant) +} + +ENTRY %TokensShouldNotBeCopied () -> s32[] { + %one = s32[] constant(1) + %negative_one = s32[] negate(%one) + %init_token = token[] generate-token() + %init_tuple = (s32[], token[]) tuple(s32[] %negative_one, token[] %init_token) + %while = (s32[], token[]) while((s32[], token[]) %init_tuple), condition=%Cond, body=%Body + ROOT %root = s32[] get-tuple-element((s32[], token[]) %while), index=0 +} +)"; + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module, + HloRunner::CreateModuleFromString( + module_string, GetDebugOptionsForTest())); + InsertCopies(module.get()); + + // There should be no copies added because tokens should not be copied. + EXPECT_EQ(CountCopies(*module), 0); +} + std::unique_ptr MakeTrivialCondition(const Shape& shape) { auto builder = HloComputation::Builder("trivial_condition"); builder.AddInstruction( diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc index 94053e5716..2c20be155f 100644 --- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc +++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc @@ -2525,6 +2525,13 @@ Status IrEmitter::HandleConditional(HloInstruction* conditional) { return Status::OK(); } +Status IrEmitter::HandleGenerateToken(HloInstruction* gen_token) { + TF_RET_CHECK(ByteSizeOf(gen_token->shape()) == 0); + // No code to generate, but we need to emit an address for book-keeping. + TF_RETURN_IF_ERROR(EmitTargetAddressForOp(gen_token)); + return Status::OK(); +} + Status IrEmitter::FinishVisit(HloInstruction* root) { // When this method is called, we should have already emitted an IR value for // the root (return) op. The IR value holds the address of the buffer holding diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.h b/tensorflow/compiler/xla/service/cpu/ir_emitter.h index 32c536e18f..e1815c1db7 100644 --- a/tensorflow/compiler/xla/service/cpu/ir_emitter.h +++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.h @@ -150,6 +150,7 @@ class IrEmitter : public DfsHloVisitorWithDefault { Status HandleWhile(HloInstruction* xla_while) override; Status HandleConcatenate(HloInstruction* concatenate) override; Status HandleConditional(HloInstruction* conditional) override; + Status HandleGenerateToken(HloInstruction* gen_token) override; Status FinishVisit(HloInstruction* root) override; Status Preprocess(HloInstruction* hlo) override; diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc index 9c704e525e..ccbd99a042 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc @@ -2205,6 +2205,10 @@ Status IrEmitterUnnested::HandleCrossReplicaSum(HloInstruction* crs) { return Status::OK(); } +Status IrEmitterUnnested::HandleGenerateToken(HloInstruction* gen_token) { + return Status::OK(); +} + Status IrEmitterUnnested::HandleInfeed(HloInstruction* infeed) { thunk_sequence_->emplace_back(BuildInfeedThunk(infeed)); return Status::OK(); diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h index 202231b82f..d228be81d4 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h @@ -77,6 +77,7 @@ class IrEmitterUnnested : public IrEmitter { Status HandleRng(HloInstruction* random) override; Status HandleSelect(HloInstruction* select) override; Status HandleCrossReplicaSum(HloInstruction* crs) override; + Status HandleGenerateToken(HloInstruction* gen_token) override; Status EmitTargetElementLoop( const HloInstruction& hlo, diff --git a/tensorflow/compiler/xla/service/hlo_alias_analysis.cc b/tensorflow/compiler/xla/service/hlo_alias_analysis.cc index a88283ed9a..0a948cc390 100644 --- a/tensorflow/compiler/xla/service/hlo_alias_analysis.cc +++ b/tensorflow/compiler/xla/service/hlo_alias_analysis.cc @@ -493,6 +493,16 @@ StatusOr> HloAliasAnalysis::Run( bool HloAliasAnalysis::HasLiveRangeInterference( const HloOrdering& ordering) const { for (const HloBuffer& buffer : buffers()) { + CHECK(!buffer.values().empty()); + if (ShapeUtil::IsToken(buffer.values().front()->shape())) { + // Tokens have no on-device representation and cannot interfere. + for (const HloValue* value : buffer.values()) { + // If one of the values is a token, all values must be a token. + DCHECK(ShapeUtil::IsToken(value->shape())); + } + continue; + } + // Check that the values in the buffer are totally ordered with respect to // 'ordering'. Begin by sorting the values with respect to 'ordering' with a // tie-break using value ID. The tie-break is necessary because we need a @@ -517,7 +527,6 @@ bool HloAliasAnalysis::HasLiveRangeInterference( // a buffer and A interferes with C, then necessarily A also interferes // with B. So to check interference you only need to check interference // between A and B, and between B and C. - CHECK(!values.empty()); for (int i = 1; i < values.size(); ++i) { if (!ordering.IsDefinedBefore(*values[i - 1], *values[i])) { VLOG(1) << values[i - 1]->ToShortString() << " and " diff --git a/tensorflow/compiler/xla/service/hlo_computation.cc b/tensorflow/compiler/xla/service/hlo_computation.cc index ac7afac19f..ef8bb030fb 100644 --- a/tensorflow/compiler/xla/service/hlo_computation.cc +++ b/tensorflow/compiler/xla/service/hlo_computation.cc @@ -523,21 +523,7 @@ HloInstruction* HloComputation::CreateFusionInstruction( StatusOr HloComputation::DeepCopyHelper( HloInstruction* instruction, const ShapeTree* indices_to_copy, ShapeTree* copies_added, ShapeIndex* index) { - if (ShapeUtil::IsArray(instruction->shape())) { - if (indices_to_copy == nullptr || indices_to_copy->element(*index)) { - // Use kCopy to copy array elements - HloInstruction* copy = AddInstruction(HloInstruction::CreateUnary( - instruction->shape(), HloOpcode::kCopy, instruction)); - if (copies_added != nullptr) { - *copies_added->mutable_element(*index) = copy; - } - return copy; - } else { - // Array elements which are not to be copied are passed through - // transparently. - return instruction; - } - } else if (ShapeUtil::IsTuple(instruction->shape())) { + if (ShapeUtil::IsTuple(instruction->shape())) { std::vector elements; for (int64 i = 0; i < ShapeUtil::TupleElementCount(instruction->shape()); i++) { @@ -554,13 +540,26 @@ StatusOr HloComputation::DeepCopyHelper( index->pop_back(); } return AddInstruction(HloInstruction::CreateTuple(elements)); - } else { - // Tokens, opaques, etc are not copyable. - if (indices_to_copy == nullptr || indices_to_copy->element(*index)) { - return FailedPrecondition( - "Cannot copy instruction of shape: %s", - ShapeUtil::HumanString(instruction->shape()).c_str()); + } + if (ShapeUtil::IsToken(instruction->shape())) { + // Tokens have no on-device representation and cannot be copied. Pass + // through transparently. + return instruction; + } + + // Array shape. + TF_RET_CHECK(ShapeUtil::IsArray(instruction->shape())); + if (indices_to_copy == nullptr || indices_to_copy->element(*index)) { + // Use kCopy to copy array elements + HloInstruction* copy = AddInstruction(HloInstruction::CreateUnary( + instruction->shape(), HloOpcode::kCopy, instruction)); + if (copies_added != nullptr) { + *copies_added->mutable_element(*index) = copy; } + return copy; + } else { + // Elements which are not to be copied are passed through + // transparently. return instruction; } } diff --git a/tensorflow/compiler/xla/service/hlo_computation_test.cc b/tensorflow/compiler/xla/service/hlo_computation_test.cc index 25469a54c4..3f59d31bb9 100644 --- a/tensorflow/compiler/xla/service/hlo_computation_test.cc +++ b/tensorflow/compiler/xla/service/hlo_computation_test.cc @@ -371,6 +371,38 @@ TEST_F(HloComputationTest, DeepCopyTupleAtIndices) { } } +TEST_F(HloComputationTest, DeepCopyToken) { + // Test that DeepCopyInstruction properly handles tokens which should not be + // copied. + auto builder = HloComputation::Builder(TestName()); + auto token = builder.AddInstruction(HloInstruction::CreateGenerateToken({})); + auto module = CreateNewModule(); + auto computation = module->AddEntryComputation(builder.Build()); + auto copy = computation->DeepCopyInstruction(token).ValueOrDie(); + + // No copy should be added. + EXPECT_THAT(copy, op::GenerateToken()); +} + +TEST_F(HloComputationTest, DeepCopyTokenTuple) { + // Test that DeepCopyInstruction properly handles tokens which should not be + // copied. + auto builder = HloComputation::Builder(TestName()); + auto token = builder.AddInstruction(HloInstruction::CreateGenerateToken({})); + auto constant = builder.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR0(42.0))); + auto tuple = + builder.AddInstruction(HloInstruction::CreateTuple({token, constant})); + auto module = CreateNewModule(); + auto computation = module->AddEntryComputation(builder.Build()); + auto copy = computation->DeepCopyInstruction(tuple).ValueOrDie(); + + // Only the array (second tuple element) should be copied. The token is passed + // through transparently. + EXPECT_THAT(copy, op::Tuple(op::GetTupleElement(tuple), + op::Copy(op::GetTupleElement(tuple)))); +} + TEST_F(HloComputationTest, CycleDetection) { // Test whether the visitor can detect cycles in the graph. auto builder = HloComputation::Builder(TestName()); diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.cc b/tensorflow/compiler/xla/service/hlo_evaluator.cc index 3c695d3e5f..33424019b9 100644 --- a/tensorflow/compiler/xla/service/hlo_evaluator.cc +++ b/tensorflow/compiler/xla/service/hlo_evaluator.cc @@ -903,10 +903,7 @@ Status HloEvaluator::HandleBroadcast(HloInstruction* broadcast) { } Status HloEvaluator::HandleGenerateToken(HloInstruction* token) { - // Literals cannot represent a TOKEN shape so just create an empty tuple as - // the "result" of the kGenerateToken operation. - // TODO(b/109929053): Add support for TOKENs in Literals. - evaluated_[token] = Literal::MakeTuple({}); + evaluated_[token] = Literal::CreateToken(); return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/hlo_matchers.h b/tensorflow/compiler/xla/service/hlo_matchers.h index c570b420c2..8a31a8e617 100644 --- a/tensorflow/compiler/xla/service/hlo_matchers.h +++ b/tensorflow/compiler/xla/service/hlo_matchers.h @@ -187,6 +187,7 @@ HLO_MATCHER(Exp); HLO_MATCHER(Floor); HLO_MATCHER(Fusion); HLO_MATCHER(Ge); +HLO_MATCHER(GenerateToken); HLO_MATCHER(Gt); HLO_MATCHER(Infeed); HLO_MATCHER(IsFinite); diff --git a/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc b/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc index ff64da87e9..d18c9dee82 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc @@ -193,6 +193,10 @@ llvm::Type* PrimitiveTypeToIrType(PrimitiveType element_type, // An Opaque is like a void*, use i8*. case OPAQUE: return llvm::Type::getInt8PtrTy(module->getContext()); + case TOKEN: + // Tokens do not have a physical representation, but the compiler needs + // some placeholder type, so use int8*. + return llvm::Type::getInt8PtrTy(module->getContext()); default: LOG(FATAL) << "unsupported type " << element_type; } diff --git a/tensorflow/compiler/xla/shape_util.cc b/tensorflow/compiler/xla/shape_util.cc index fe844ea2b1..c85fb20e01 100644 --- a/tensorflow/compiler/xla/shape_util.cc +++ b/tensorflow/compiler/xla/shape_util.cc @@ -645,15 +645,7 @@ StatusOr ParseShapeStringInternal(tensorflow::StringPiece* s) { } /* static */ bool ShapeUtil::Compatible(const Shape& lhs, const Shape& rhs) { - if (IsArray(lhs)) { - return SameElementType(lhs, rhs) && SameDimensions(lhs, rhs); - } else if (lhs.element_type() == TUPLE) { - return rhs.element_type() == TUPLE && - ContainersEqual(lhs.tuple_shapes(), rhs.tuple_shapes(), Compatible); - } else { - // Opaque, token, etc types are vacuously compatible. - return true; - } + return CompareShapes(lhs, rhs, /*compare_layouts=*/false); } /* static */ bool ShapeUtil::CompatibleIgnoringElementType(const Shape& lhs, diff --git a/tensorflow/compiler/xla/shape_util_test.cc b/tensorflow/compiler/xla/shape_util_test.cc index ebfe06d4bc..61aa198e52 100644 --- a/tensorflow/compiler/xla/shape_util_test.cc +++ b/tensorflow/compiler/xla/shape_util_test.cc @@ -172,6 +172,41 @@ TEST(ShapeUtilTest, CompatibleIdenticalShapes) { ASSERT_TRUE(ShapeUtil::Compatible(shape1, shape2)); } +TEST(ShapeUtilTest, TokenCompatibility) { + EXPECT_TRUE(ShapeUtil::Compatible(ShapeUtil::MakeTokenShape(), + ShapeUtil::MakeTokenShape())); + EXPECT_FALSE(ShapeUtil::Compatible(ShapeUtil::MakeTokenShape(), + ShapeUtil::MakeShape(F32, {}))); + EXPECT_FALSE(ShapeUtil::Compatible(ShapeUtil::MakeShape(F32, {}), + ShapeUtil::MakeTokenShape())); + EXPECT_TRUE(ShapeUtil::Compatible( + ShapeUtil::MakeTupleShape({ShapeUtil::MakeTokenShape()}), + ShapeUtil::MakeTupleShape({ShapeUtil::MakeTokenShape()}))); +} + +TEST(ShapeUtilTest, TokensEqualShapes) { + EXPECT_TRUE(ShapeUtil::Equal(ShapeUtil::MakeTokenShape(), + ShapeUtil::MakeTokenShape())); + EXPECT_FALSE(ShapeUtil::Equal(ShapeUtil::MakeTokenShape(), + ShapeUtil::MakeShape(F32, {}))); + EXPECT_FALSE(ShapeUtil::Equal(ShapeUtil::MakeShape(F32, {}), + ShapeUtil::MakeTokenShape())); + EXPECT_TRUE(ShapeUtil::Equal( + ShapeUtil::MakeTupleShape( + {ShapeUtil::MakeTokenShape(), + ShapeUtil::MakeShapeWithLayout(S32, {3, 4}, {0, 1})}), + ShapeUtil::MakeTupleShape( + {ShapeUtil::MakeTokenShape(), + ShapeUtil::MakeShapeWithLayout(S32, {3, 4}, {0, 1})}))); + EXPECT_FALSE(ShapeUtil::Equal( + ShapeUtil::MakeTupleShape( + {ShapeUtil::MakeTokenShape(), + ShapeUtil::MakeShapeWithLayout(S32, {3, 4}, {0, 1})}), + ShapeUtil::MakeTupleShape( + {ShapeUtil::MakeTokenShape(), + ShapeUtil::MakeShapeWithLayout(S32, {3, 4}, {1, 0})}))); +} + TEST(ShapeUtilTest, CompatibleNotIdenticalShapes) { Shape shape_1 = ShapeUtil::MakeShape(F32, {3, 2}); auto layout_1 = shape_1.mutable_layout(); diff --git a/tensorflow/compiler/xla/tests/token_hlo_test.cc b/tensorflow/compiler/xla/tests/token_hlo_test.cc index 4585244ce8..3ef54e6f89 100644 --- a/tensorflow/compiler/xla/tests/token_hlo_test.cc +++ b/tensorflow/compiler/xla/tests/token_hlo_test.cc @@ -28,8 +28,6 @@ namespace { class TokenHloTest : public HloTestBase {}; -// TODO(b/79770375): Compile, not just verify the HLO module when the backends -// support kGenerateToken. XLA_TEST_F(TokenHloTest, SingleTokenInstruction) { std::unique_ptr module = CreateNewModule(); auto builder = HloComputation::Builder(TestName()); @@ -120,5 +118,40 @@ XLA_TEST_F(TokenHloTest, InvalidOperandToTokenInstruction) { "Operands of token instructions must be TOKEN types")); } +XLA_TEST_F(TokenHloTest, TokenInWhileLoop) { + // Thread a token around a while loop. Token is created and consumed by a + // GenerateToken instruction in the while body. + string module_string = R"( +HloModule TokenInWhileLoop + +%Body (param.1: (s32[], token[])) -> (s32[], token[]) { + %param.1 = (s32[], token[]) parameter(0) + %get-tuple-element.1 = s32[] get-tuple-element((s32[], token[]) %param.1), index=0 + %constant.1 = s32[] constant(1) + %add = s32[] add(s32[] %get-tuple-element.1, s32[] %constant.1) + %get-tuple-element.2 = token[] get-tuple-element((s32[], token[]) %param.1), index=1 + %generate-token = token[] generate-token(token[] %get-tuple-element.2) + ROOT %tuple = (s32[], token[]) tuple(s32[] %add, token[] %generate-token) +} + +%Cond (param: (s32[], token[])) -> pred[] { + %param = (s32[], token[]) parameter(0) + %get-tuple-element = s32[] get-tuple-element((s32[], token[]) %param), index=0 + %constant = s32[] constant(42) + ROOT %less-than = pred[] less-than(s32[] %get-tuple-element, s32[] %constant) +} + +ENTRY %TokenInWhileLoop () -> s32[] { + %zero = s32[] constant(0) + %init_token = token[] generate-token() + %init_tuple = (s32[], token[]) tuple(s32[] %zero, token[] %init_token) + %while = (s32[], token[]) while((s32[], token[]) %init_tuple), condition=%Cond, body=%Body + ROOT %root = s32[] get-tuple-element((s32[], token[]) %while), index=0 +} +)"; + + EXPECT_TRUE(RunAndCompare(module_string, error_spec_)); +} + } // namespace } // namespace xla -- GitLab From c4eafb49612a694386bbda1f51dffb6951ec9cf1 Mon Sep 17 00:00:00 2001 From: Yifei Feng Date: Thu, 14 Jun 2018 14:56:28 -0700 Subject: [PATCH 472/816] Install Keras dependencies. PiperOrigin-RevId: 200623983 --- tensorflow/contrib/cmake/tf_tests.cmake | 2 ++ tensorflow/tools/ci_build/Dockerfile.cmake | 2 ++ tensorflow/tools/ci_build/install/install_pip_packages.sh | 6 ++++++ .../ci_build/install/install_python3.5_pip_packages.sh | 4 ++++ .../ci_build/install/install_python3.6_pip_packages.sh | 3 +++ 5 files changed, 17 insertions(+) diff --git a/tensorflow/contrib/cmake/tf_tests.cmake b/tensorflow/contrib/cmake/tf_tests.cmake index eb9482dc25..c8de8db126 100644 --- a/tensorflow/contrib/cmake/tf_tests.cmake +++ b/tensorflow/contrib/cmake/tf_tests.cmake @@ -325,6 +325,8 @@ if (tensorflow_BUILD_PYTHON_TESTS) "${tensorflow_source_dir}/tensorflow/contrib/estimator/python/estimator/replicate_model_fn_test.py" # b/71901810 # Broken io_utils_test "${tensorflow_source_dir}/tensorflow/python/keras/_impl/keras/utils/io_utils_test.py" # b/72894325 + # OOM + "${tensorflow_source_dir}/tensorflow/python/training/saver_large_variable_test.py" # b/110210559 ) endif() list(REMOVE_ITEM tf_test_src_py ${tf_test_src_py_exclude}) diff --git a/tensorflow/tools/ci_build/Dockerfile.cmake b/tensorflow/tools/ci_build/Dockerfile.cmake index d5dea4f3e4..e8c3199828 100644 --- a/tensorflow/tools/ci_build/Dockerfile.cmake +++ b/tensorflow/tools/ci_build/Dockerfile.cmake @@ -28,6 +28,8 @@ RUN pip install --upgrade astor RUN pip install --upgrade gast RUN pip install --upgrade numpy RUN pip install --upgrade termcolor +RUN pip install keras_applications==1.0.2 +RUN pip install keras_preprocessing==1.0.1 # Install golang RUN apt-get install -t xenial-backports -y golang-1.9 diff --git a/tensorflow/tools/ci_build/install/install_pip_packages.sh b/tensorflow/tools/ci_build/install/install_pip_packages.sh index 982161cefe..60290df833 100755 --- a/tensorflow/tools/ci_build/install/install_pip_packages.sh +++ b/tensorflow/tools/ci_build/install/install_pip_packages.sh @@ -109,3 +109,9 @@ pip2 install --upgrade gast pip3 install --upgrade gast pip2 install --upgrade termcolor pip3 install --upgrade termcolor + +# Keras +pip2 install keras_applications==1.0.2 +pip3 install keras_applications==1.0.2 +pip2 install keras_preprocessing==1.0.1 +pip3 install keras_preprocessing==1.0.1 diff --git a/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh b/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh index 204a82f647..edb9d4b929 100755 --- a/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh +++ b/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh @@ -82,4 +82,8 @@ pip3.5 install --upgrade astor pip3.5 install --upgrade gast pip3.5 install --upgrade termcolor +# Keras +pip3.5 install keras_applications==1.0.2 +pip3.5 install keras_preprocessing==1.0.1 + # LINT.ThenChange(//tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh) diff --git a/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh b/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh index 275abeb669..5635977731 100755 --- a/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh +++ b/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh @@ -98,4 +98,7 @@ pip3 install --upgrade astor pip3 install --upgrade gast pip3 install --upgrade termcolor +# Keras +pip3.5 install keras_applications==1.0.2 +pip3.5 install keras_preprocessing==1.0.1 # LINT.ThenChange(//tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh) -- GitLab From 24b2043c8372253c04d26b7b8056fa3c897772b9 Mon Sep 17 00:00:00 2001 From: Yu-Cheng Ling Date: Thu, 14 Jun 2018 15:36:53 -0700 Subject: [PATCH 473/816] Automated g4 rollback of changelist 200414970 PiperOrigin-RevId: 200630669 --- tensorflow/contrib/lite/build_def.bzl | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/tensorflow/contrib/lite/build_def.bzl b/tensorflow/contrib/lite/build_def.bzl index 974e6c5d98..612813caee 100644 --- a/tensorflow/contrib/lite/build_def.bzl +++ b/tensorflow/contrib/lite/build_def.bzl @@ -221,8 +221,7 @@ def generated_test_models(): "local_response_norm", "log_softmax", "log", - # TODO(b/110143200): Enable after resolving issues with LSTM conversion. - # "lstm", + "lstm", "max_pool", "maximum", "mean", -- GitLab From d57e9a646583e55213d0f5ca88c1f91062569288 Mon Sep 17 00:00:00 2001 From: Alexandre Passos Date: Thu, 14 Jun 2018 15:45:19 -0700 Subject: [PATCH 474/816] Clarify reuse documentation in variable_scope and eager. PiperOrigin-RevId: 200631958 --- tensorflow/python/ops/variable_scope.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tensorflow/python/ops/variable_scope.py b/tensorflow/python/ops/variable_scope.py index 23234e2e61..f49e2d314d 100644 --- a/tensorflow/python/ops/variable_scope.py +++ b/tensorflow/python/ops/variable_scope.py @@ -1908,7 +1908,8 @@ class variable_scope(object): for this scope as well as all sub-scopes; if tf.AUTO_REUSE, we create variables if they do not exist, and return them otherwise; if None, we inherit the parent scope's reuse flag. When eager execution is enabled, - this argument is always forced to be tf.AUTO_REUSE. + new variables are always created unless an EagerVariableStore or + template is currently active. dtype: type of variables created in this scope (defaults to the type in the passed scope, or inherited from parent scope). use_resource: If False, all variables will be regular Variables. If True, -- GitLab From 0a6a85a7b720b4ae41d6029d2a5293ae01f66090 Mon Sep 17 00:00:00 2001 From: Lukas Geiger Date: Fri, 15 Jun 2018 00:55:56 +0200 Subject: [PATCH 475/816] [tfgan] Add default serving key to unittest --- tensorflow/contrib/gan/python/estimator/python/head_test.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/gan/python/estimator/python/head_test.py b/tensorflow/contrib/gan/python/estimator/python/head_test.py index c121f322b5..5309d87765 100644 --- a/tensorflow/contrib/gan/python/estimator/python/head_test.py +++ b/tensorflow/contrib/gan/python/estimator/python/head_test.py @@ -26,8 +26,11 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import variable_scope from tensorflow.python.platform import test +from tensorflow.python.saved_model import signature_constants from tensorflow.python.training import training +_DEFAULT_SERVING_KEY = signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY + def dummy_loss(gan_model, add_summaries=True): # pylint:disable=unused-argument return math_ops.reduce_sum(gan_model.discriminator_real_outputs - @@ -78,7 +81,8 @@ class GANHeadTest(test.TestCase): def test_modes_predict(self): spec = self._test_modes_helper(model_fn_lib.ModeKeys.PREDICT) - self.assertItemsEqual(('predict',), spec.export_outputs.keys()) + self.assertItemsEqual((_DEFAULT_SERVING_KEY, 'predict'), + spec.export_outputs.keys()) def test_modes_eval(self): self._test_modes_helper(model_fn_lib.ModeKeys.EVAL) -- GitLab From f5c9d279b99cf243f5af42c327846daf700b3ad6 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 14 Jun 2018 15:54:37 -0700 Subject: [PATCH 476/816] Internal Change. PiperOrigin-RevId: 200633473 --- tensorflow/compiler/xla/service/hlo_instruction.cc | 12 ++++++++---- tensorflow/compiler/xla/service/hlo_instructions.cc | 12 +++++++++--- tensorflow/compiler/xla/service/hlo_instructions.h | 2 ++ 3 files changed, 19 insertions(+), 7 deletions(-) diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc index ec26f9a6b3..832f9d504d 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction.cc +++ b/tensorflow/compiler/xla/service/hlo_instruction.cc @@ -178,10 +178,14 @@ StatusOr> HloInstruction::CreateFromProto( break; } case HloOpcode::kConstant: { - CHECK(proto.has_literal()); - TF_ASSIGN_OR_RETURN(auto literal, - Literal::CreateFromProto(proto.literal())); - instruction = CreateConstant(std::move(literal)); + // TODO(b/110214922): Revert this to CHECK(proto.has_literal()). + if (proto.has_literal()) { + TF_ASSIGN_OR_RETURN(auto literal, + Literal::CreateFromProto(proto.literal())); + instruction = CreateConstant(std::move(literal)); + } else { + instruction = MakeUnique(proto.shape()); + } break; } case HloOpcode::kTrace: { diff --git a/tensorflow/compiler/xla/service/hlo_instructions.cc b/tensorflow/compiler/xla/service/hlo_instructions.cc index 91429321d1..544f0a6c29 100644 --- a/tensorflow/compiler/xla/service/hlo_instructions.cc +++ b/tensorflow/compiler/xla/service/hlo_instructions.cc @@ -610,9 +610,14 @@ HloConstantInstruction::HloConstantInstruction(std::unique_ptr literal) : HloInstruction(HloOpcode::kConstant, CHECK_NOTNULL(literal)->shape()), literal_(std::move(literal)) {} +HloConstantInstruction::HloConstantInstruction(const Shape& shape) + : HloInstruction(HloOpcode::kConstant, shape) {} + HloInstructionProto HloConstantInstruction::ToProto() const { HloInstructionProto proto = HloInstruction::ToProto(); - *proto.mutable_literal() = literal_->ToProto(); + if (literal_ != nullptr) { + *proto.mutable_literal() = literal_->ToProto(); + } return proto; } @@ -658,8 +663,9 @@ string HloConstantInstruction::OperandsToStringWithCanonicalNameMap( CanonicalNameMap* canonical_name_map) const { string operands; // For constants, show the actual value in place of an empty operand list. - if ((ShapeUtil::IsArray(shape()) && ShapeUtil::ElementsIn(shape()) <= 10) || - options.print_large_constants()) { + if (literal_ != nullptr && + ((ShapeUtil::IsArray(shape()) && ShapeUtil::ElementsIn(shape()) <= 10) || + options.print_large_constants())) { // Literal::ToString emits multidimensional arrays over multiple // lines. Compact this into one line by stripping out white space. string tmp = literal().ToString(); diff --git a/tensorflow/compiler/xla/service/hlo_instructions.h b/tensorflow/compiler/xla/service/hlo_instructions.h index 9f810c0a14..005547abaa 100644 --- a/tensorflow/compiler/xla/service/hlo_instructions.h +++ b/tensorflow/compiler/xla/service/hlo_instructions.h @@ -436,6 +436,8 @@ class HloSliceInstruction : public HloInstruction { class HloConstantInstruction : public HloInstruction { public: explicit HloConstantInstruction(std::unique_ptr literal); + // Used when the literal is too large and dropped. + explicit HloConstantInstruction(const Shape& shape); // Returns the literal associated with this instruction. const Literal& literal() const { return *literal_; } // Returns a serialized representation of this instruction. -- GitLab From 929474d9ce1ca7bdfd90ba760af6fe58c8695ab7 Mon Sep 17 00:00:00 2001 From: Derek Murray Date: Thu, 14 Jun 2018 16:35:00 -0700 Subject: [PATCH 477/816] [tf.data] Convert GeneratorDataset to use StructuredFunctionWrapper. PiperOrigin-RevId: 200639895 --- tensorflow/python/data/ops/dataset_ops.py | 124 ++++------------------ 1 file changed, 20 insertions(+), 104 deletions(-) diff --git a/tensorflow/python/data/ops/dataset_ops.py b/tensorflow/python/data/ops/dataset_ops.py index f9c1031d9b..9e7af878d3 100644 --- a/tensorflow/python/data/ops/dataset_ops.py +++ b/tensorflow/python/data/ops/dataset_ops.py @@ -1422,110 +1422,26 @@ class _GeneratorDataset(Dataset): init_args_types = nest.pack_sequence_as( init_args, [t.dtype for t in nest.flatten(init_args)]) - @function.Defun(*defun_args( - input_types=init_args_types, input_classes=init_args_classes)) - def tf_init_func(*args): - """A wrapper for Defun that facilitates shape inference.""" - nested_args = restructure_args( - args, input_shapes=init_args_shapes, input_types=init_args_types, - input_classes=init_args_classes) - ret = init_func(*nested_args) - - # If `init_func` returns a list of tensors, `nest.flatten()` and - # `ops.convert_to_tensor()` would conspire to attempt to stack - # those tensors into a single tensor, because the customized - # version of `nest.flatten()` does not recurse into lists. Since - # it is more likely that the list arose from returning the - # result of an operation (such as `tf.py_func()`) that returns a - # list of not-necessarily-stackable tensors, we treat the - # returned value is a `tuple` instead. A user wishing to pack - # the return value into a single tensor can use an explicit - # `tf.stack()` before returning. - if isinstance(ret, list): - ret = tuple(ret) - - # Convert any `SparseTensorValue`s to `SparseTensor`s and all other - # values to tensors. - ret = nest.pack_sequence_as(ret, [ - sparse_tensor_lib.SparseTensor.from_value(t) - if sparse_tensor_lib.is_sparse(t) else ops.convert_to_tensor(t) - for t in nest.flatten(ret) - ]) - - self._state_classes = sparse.get_classes(ret) - self._state_shapes = nest.pack_sequence_as( - ret, [t.get_shape() for t in nest.flatten(ret)]) - self._state_types = nest.pack_sequence_as( - ret, [t.dtype for t in nest.flatten(ret)]) - - # Serialize any sparse tensors. - ret = nest.pack_sequence_as( - ret, [t for t in nest.flatten(sparse.serialize_sparse_tensors(ret))]) - return nest.flatten(ret) - - self._init_func = tf_init_func - self._init_func.add_to_graph(ops.get_default_graph()) - - # These members will be initialized by `tf_next_func`. - self._output_classes = None - self._output_shapes = None - self._output_types = None - - @function.Defun(*defun_args( - input_types=self._state_types, input_classes=self._state_classes)) - def tf_next_func(*args): - """A wrapper for Defun that facilitates shape inference.""" - nested_args = restructure_args( - args, input_shapes=self._state_shapes, input_types=self._state_types, - input_classes=self._state_classes) - ret = next_func(*nested_args) - - # If `next_func` returns a list of tensors, `nest.flatten()` and - # `ops.convert_to_tensor()` would conspire to attempt to stack - # those tensors into a single tensor, because the customized - # version of `nest.flatten()` does not recurse into lists. Since - # it is more likely that the list arose from returning the - # result of an operation (such as `tf.py_func()`) that returns a - # list of not-necessarily-stackable tensors, we treat the - # returned value is a `tuple` instead. A user wishing to pack - # the return value into a single tensor can use an explicit - # `tf.stack()` before returning. - if isinstance(ret, list): - ret = tuple(ret) - - # Convert any `SparseTensorValue`s to `SparseTensor`s and all other - # values to tensors. - ret = nest.pack_sequence_as(ret, [ - sparse_tensor_lib.SparseTensor.from_value(t) - if sparse_tensor_lib.is_sparse(t) else ops.convert_to_tensor(t) - for t in nest.flatten(ret) - ]) - - self._output_classes = sparse.get_classes(ret) - self._output_shapes = nest.pack_sequence_as( - ret, [t.get_shape() for t in nest.flatten(ret)]) - self._output_types = nest.pack_sequence_as( - ret, [t.dtype for t in nest.flatten(ret)]) - - # Serialize any sparse tensors. - ret = nest.pack_sequence_as( - ret, [t for t in nest.flatten(sparse.serialize_sparse_tensors(ret))]) - return nest.flatten(ret) - - self._next_func = tf_next_func - self._next_func.add_to_graph(ops.get_default_graph()) - - @function.Defun(*defun_args( - input_types=self._state_types, input_classes=self._state_classes)) - def tf_finalize_func(*args): - """A wrapper for Defun that facilitates shape inference.""" - nested_args = restructure_args( - args, input_shapes=self._state_shapes, input_types=self._state_types, - input_classes=self._state_classes) - return finalize_func(*nested_args) - - self._finalize_func = tf_finalize_func - self._finalize_func.add_to_graph(ops.get_default_graph()) + wrapped_init_func = StructuredFunctionWrapper( + init_func, "GeneratorDataset", input_classes=init_args_classes, + input_shapes=init_args_shapes, input_types=init_args_types) + self._state_classes = wrapped_init_func.output_classes + self._state_shapes = wrapped_init_func.output_shapes + self._state_types = wrapped_init_func.output_types + self._init_func = wrapped_init_func.function + + wrapped_next_func = StructuredFunctionWrapper( + next_func, "GeneratorDataset", input_classes=self._state_classes, + input_shapes=self._state_shapes, input_types=self._state_types) + self._output_classes = wrapped_next_func.output_classes + self._output_shapes = wrapped_next_func.output_shapes + self._output_types = wrapped_next_func.output_types + self._next_func = wrapped_next_func.function + + wrapped_finalize_func = StructuredFunctionWrapper( + finalize_func, "GeneratorDataset", input_classes=self._state_classes, + input_shapes=self._state_shapes, input_types=self._state_types) + self._finalize_func = wrapped_finalize_func.function def _as_variant_tensor(self): return gen_dataset_ops.generator_dataset( -- GitLab From 18b0f66057066f2933831bf911ab3e8e9dcc49d0 Mon Sep 17 00:00:00 2001 From: Suharsh Sivakumar Date: Thu, 14 Jun 2018 16:37:58 -0700 Subject: [PATCH 478/816] Export build_toco_convert_protos PiperOrigin-RevId: 200640276 --- tensorflow/contrib/lite/python/lite.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/contrib/lite/python/lite.py b/tensorflow/contrib/lite/python/lite.py index 6b63c0ccef..0913cd2c5c 100644 --- a/tensorflow/contrib/lite/python/lite.py +++ b/tensorflow/contrib/lite/python/lite.py @@ -22,6 +22,7 @@ EXPERIMENTAL: APIs here are unstable and likely to change without notice. @@Interpreter @@OpHint @@convert_op_hints_to_stubs +@@build_toco_convert_protos @@FLOAT @@QUANTIZED_UINT8 -- GitLab From e87b52a440b0f6afd7f1868a0309eb70d932702d Mon Sep 17 00:00:00 2001 From: Jiri Simsa Date: Thu, 14 Jun 2018 16:50:51 -0700 Subject: [PATCH 479/816] [tf.data] Adding support for tf.data.Dataset.prefetch(buffer_size=0). PiperOrigin-RevId: 200642171 --- .../core/kernels/data/prefetch_dataset_op.cc | 65 +++++++++++-------- tensorflow/python/data/kernel_tests/BUILD | 1 + .../kernel_tests/prefetch_dataset_op_test.py | 26 ++++---- 3 files changed, 52 insertions(+), 40 deletions(-) diff --git a/tensorflow/core/kernels/data/prefetch_dataset_op.cc b/tensorflow/core/kernels/data/prefetch_dataset_op.cc index e2b6aa590e..2bafb985ef 100644 --- a/tensorflow/core/kernels/data/prefetch_dataset_op.cc +++ b/tensorflow/core/kernels/data/prefetch_dataset_op.cc @@ -39,8 +39,8 @@ class PrefetchDatasetOp : public UnaryDatasetOpKernel { OP_REQUIRES_OK( ctx, ParseScalarArgument(ctx, "buffer_size", &buffer_size)); OP_REQUIRES(ctx, - buffer_size > 0 || buffer_size == PrefetchAutotuner::kAutoTune, - errors::InvalidArgument("buffer_size must be > 0")); + buffer_size >= 0 || buffer_size == PrefetchAutotuner::kAutoTune, + errors::InvalidArgument("buffer_size must be >= 0")); *output = new Dataset(ctx, input, buffer_size); } @@ -112,13 +112,13 @@ class PrefetchDatasetOp : public UnaryDatasetOpKernel { Status GetNextInternal(IteratorContext* ctx, std::vector* out_tensors, bool* end_of_sequence) override { - mutex_lock l(mu_); - TF_RETURN_IF_ERROR(EnsurePrefetchThreadStarted(ctx)); - - while (true) { + { + mutex_lock l(mu_); + TF_RETURN_IF_ERROR(EnsurePrefetchThreadStarted(ctx)); // Wait until the next element in the buffer has been // produced, or we are shutting down. - while (!cancelled_ && !prefetch_thread_finished_ && buffer_.empty()) { + while (!cancelled_ && buffer_.empty() && !prefetch_thread_finished_ && + auto_tuner_.buffer_limit() != 0) { auto_tuner_.RecordEmpty(); cond_var_.wait(l); } @@ -129,29 +129,20 @@ class PrefetchDatasetOp : public UnaryDatasetOpKernel { } if (!buffer_.empty()) { - // A new element is available. Forward the status from - // computing it, and (if we successfully got an element) - // the output values. - Status s = buffer_.front().status; - if (s.ok()) { - *out_tensors = std::move(buffer_.front().value); - } - auto_tuner_.RecordConsumption(buffer_.size()); - buffer_.pop_front(); - *end_of_sequence = false; - - // Wake the prefetch thread, in case it has been waiting - // for space in the buffer. - // Also wake up threads from other calls to GetNext. - // TODO(mrry): Consider using different condition variables - // for GetNext and Prefetch. - cond_var_.notify_all(); - return s; - } else if (prefetch_thread_finished_) { + return Consume(out_tensors, end_of_sequence); + } + + if (prefetch_thread_finished_) { *end_of_sequence = true; return Status::OK(); } + + DCHECK_EQ(auto_tuner_.buffer_limit(), 0); } + + mutex_lock parent_l(parent_mu_); + mutex_lock l(mu_); + return input_impl_->GetNext(ctx, out_tensors, end_of_sequence); } protected: @@ -227,6 +218,26 @@ class PrefetchDatasetOp : public UnaryDatasetOpKernel { std::vector value; }; + Status Consume(std::vector* out_tensors, bool* end_of_sequence) + EXCLUSIVE_LOCKS_REQUIRED(mu_) { + // A new element is available. Forward the status from computing it, and + // (if we successfully got an element) the output values. + Status s = buffer_.front().status; + if (s.ok()) { + *out_tensors = std::move(buffer_.front().value); + } + buffer_.pop_front(); + *end_of_sequence = false; + + // Wake the prefetch thread, in case it has been waiting for space + // in the buffer. Also wake up threads from other calls to GetNext. + // + // TODO(mrry): Consider using different condition variables for + // GetNext and Prefetch. + cond_var_.notify_all(); + return s; + } + Status EnsurePrefetchThreadStarted(IteratorContext* ctx) EXCLUSIVE_LOCKS_REQUIRED(mu_) { if (!prefetch_thread_) { @@ -251,7 +262,7 @@ class PrefetchDatasetOp : public UnaryDatasetOpKernel { { mutex_lock l(mu_); while (!cancelled_ && - buffer_.size() == auto_tuner_.buffer_limit()) { + buffer_.size() >= auto_tuner_.buffer_limit()) { cond_var_.wait(l); } diff --git a/tensorflow/python/data/kernel_tests/BUILD b/tensorflow/python/data/kernel_tests/BUILD index e86c2f6993..3bde62fa1d 100644 --- a/tensorflow/python/data/kernel_tests/BUILD +++ b/tensorflow/python/data/kernel_tests/BUILD @@ -179,6 +179,7 @@ tf_py_test( size = "small", srcs = ["prefetch_dataset_op_test.py"], additional_deps = [ + "@absl_py//absl/testing:parameterized", "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", "//tensorflow/python:dataset_ops_gen", diff --git a/tensorflow/python/data/kernel_tests/prefetch_dataset_op_test.py b/tensorflow/python/data/kernel_tests/prefetch_dataset_op_test.py index 646324cb95..63a0830272 100644 --- a/tensorflow/python/data/kernel_tests/prefetch_dataset_op_test.py +++ b/tensorflow/python/data/kernel_tests/prefetch_dataset_op_test.py @@ -17,6 +17,8 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from absl.testing import parameterized + from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors @@ -24,35 +26,33 @@ from tensorflow.python.ops import array_ops from tensorflow.python.platform import test -class PrefetchDatasetTest(test.TestCase): +class PrefetchDatasetTest(test.TestCase, parameterized.TestCase): - def testBufferSize(self): - buffer_size = array_ops.placeholder(dtypes.int64, shape=[]) + @parameterized.parameters((-1), (0), (5)) + def testBufferSize(self, buffer_size): + buffer_size_t = array_ops.placeholder(dtypes.int64, shape=[]) iterator = dataset_ops.Dataset.range(10).prefetch( - buffer_size=buffer_size).make_initializable_iterator() + buffer_size=buffer_size_t).make_initializable_iterator() init_op = iterator.initializer get_next = iterator.get_next() with self.test_session() as sess: - sess.run(init_op, feed_dict={buffer_size: 5}) + sess.run(init_op, feed_dict={buffer_size_t: buffer_size}) for m in range(10): self.assertEqual(m, sess.run(get_next)) with self.assertRaises(errors.OutOfRangeError): sess.run(get_next) - def testInvalidBufferSize(self): - buffer_size = array_ops.placeholder(dtypes.int64, shape=[]) + @parameterized.parameters((-2), (-42)) + def testInvalidBufferSize(self, buffer_size): + buffer_size_t = array_ops.placeholder(dtypes.int64, shape=[]) iterator = dataset_ops.Dataset.range(10).prefetch( - buffer_size=buffer_size).make_initializable_iterator() + buffer_size=buffer_size_t).make_initializable_iterator() init_op = iterator.initializer with self.assertRaisesRegexp(errors.InvalidArgumentError, "buffer_size"): with self.test_session() as sess: - sess.run(init_op, feed_dict={buffer_size: 0}) - - with self.assertRaisesRegexp(errors.InvalidArgumentError, "buffer_size"): - with self.test_session() as sess: - sess.run(init_op, feed_dict={buffer_size: -5}) + sess.run(init_op, feed_dict={buffer_size_t: buffer_size}) if __name__ == "__main__": -- GitLab From 261ab05537885556f92d7322017ddf73ea5a7357 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 14 Jun 2018 16:57:28 -0700 Subject: [PATCH 480/816] Automated g4 rollback of changelist 196296096 PiperOrigin-RevId: 200643094 --- tensorflow/core/kernels/conv_grad_filter_ops.cc | 3 ++- tensorflow/core/kernels/conv_grad_input_ops.cc | 5 +++-- tensorflow/core/kernels/deep_conv2d.cc | 10 ++++++---- 3 files changed, 11 insertions(+), 7 deletions(-) diff --git a/tensorflow/core/kernels/conv_grad_filter_ops.cc b/tensorflow/core/kernels/conv_grad_filter_ops.cc index bdd08222d4..aca75176a5 100644 --- a/tensorflow/core/kernels/conv_grad_filter_ops.cc +++ b/tensorflow/core/kernels/conv_grad_filter_ops.cc @@ -404,9 +404,10 @@ class Conv2DCustomBackpropFilterOp : public OpKernel { // image ('work_unit_size'). // TODO(andydavis) + // *) Get L3 cache size from device at runtime (30MB is from ivybridge). // *) Consider reducing 'target_working_set_size' if L3 is shared by // other concurrently running tensorflow ops. - const size_t target_working_set_size = Eigen::l3CacheSize() / sizeof(T); + const size_t target_working_set_size = (30LL << 20) / sizeof(T); const size_t size_A = output_image_size * filter_total_size; diff --git a/tensorflow/core/kernels/conv_grad_input_ops.cc b/tensorflow/core/kernels/conv_grad_input_ops.cc index 95301b170f..63a775afa8 100644 --- a/tensorflow/core/kernels/conv_grad_input_ops.cc +++ b/tensorflow/core/kernels/conv_grad_input_ops.cc @@ -420,8 +420,9 @@ class Conv2DCustomBackpropInputOp : public OpKernel { const int output_image_size = dims.spatial_dims[0].output_size * dims.spatial_dims[1].output_size; - const size_t l2_cache_size = Eigen::l2CacheSize(); - const size_t l3_cache_size = Eigen::l3CacheSize(); + // TODO(andydavis) Get L2/L3 cache sizes from device. + const size_t l2_cache_size = 256LL << 10; + const size_t l3_cache_size = 30LL << 20; // Use L3 cache size as target working set size. const size_t target_working_set_size = l3_cache_size / sizeof(T); diff --git a/tensorflow/core/kernels/deep_conv2d.cc b/tensorflow/core/kernels/deep_conv2d.cc index 85a9702ae7..1aa8c72d66 100644 --- a/tensorflow/core/kernels/deep_conv2d.cc +++ b/tensorflow/core/kernels/deep_conv2d.cc @@ -393,8 +393,9 @@ struct TransformFilters { // Calculate filter transform batch based on cache/filter sizes. - // Cache budget (based on L2 cache size). - const int64 cache_size = Eigen::l2CacheSize() / sizeof(T); + // Cache budget (based on L2 cache size = 256KB). + // TODO(andydavis) Read cache size from system. + const int64 cache_size = (256LL << 10) / sizeof(T); // Fixed cost. const int64 filter_transform_matrix_size = @@ -1017,8 +1018,9 @@ struct DeepConv2D { const int64 filter_shard_size = filter_shards_row * filter_shards_col; const int64 out_tile_spatial_size = out_tile_rows * out_tile_cols; - // Cache budget (based on L2 cache size). - const int64 cache_size = Eigen::l2CacheSize() / sizeof(T); + // Cache budget (based on L2 cache size = 256KB). + // TODO(andydavis) Read cache size from the system. + const int64 cache_size = (256LL << 10) / sizeof(T); // Fixed costs. const int64 tile_transform_matrix_size = -- GitLab From e6570147c4699518af50d2b08190290003d33aa8 Mon Sep 17 00:00:00 2001 From: ruanjiandong Date: Thu, 14 Jun 2018 17:05:01 -0700 Subject: [PATCH 481/816] =?UTF-8?q?opencv=20interop=20fix:=20exclude=20lib?= =?UTF-8?q?jpeg=20symbols=20from=20libtensorflow=5Fframew=E2=80=A6=20(#199?= =?UTF-8?q?66)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * opencv interop fix: exclude libjpeg symbols from libtensorflow_framework.so to avoid symbol conflict * Fix buildifier issue (sorting of fields) --- tensorflow/BUILD | 10 ++++++++++ tensorflow/tf_framework_version_script.lds | 11 +++++++++++ 2 files changed, 21 insertions(+) create mode 100644 tensorflow/tf_framework_version_script.lds diff --git a/tensorflow/BUILD b/tensorflow/BUILD index 6d134dbb80..d77f04139e 100644 --- a/tensorflow/BUILD +++ b/tensorflow/BUILD @@ -451,6 +451,15 @@ filegroup( tf_cc_shared_object( name = "libtensorflow_framework.so", framework_so = [], + linkopts = select({ + "//tensorflow:darwin": [], + "//tensorflow:windows": [], + "//tensorflow:windows_msvc": [], + "//conditions:default": [ + "-Wl,--version-script", # This line must be directly followed by the version_script.lds file + "$(location //tensorflow:tf_framework_version_script.lds)", + ], + }), linkstatic = 1, visibility = ["//visibility:public"], deps = [ @@ -460,6 +469,7 @@ tf_cc_shared_object( "//tensorflow/core/grappler/optimizers:custom_graph_optimizer_registry_impl", "//tensorflow/core:lib_internal_impl", "//tensorflow/stream_executor:stream_executor_impl", + "//tensorflow:tf_framework_version_script.lds", ] + tf_additional_binary_deps(), ) diff --git a/tensorflow/tf_framework_version_script.lds b/tensorflow/tf_framework_version_script.lds new file mode 100644 index 0000000000..d4977f88c0 --- /dev/null +++ b/tensorflow/tf_framework_version_script.lds @@ -0,0 +1,11 @@ +VERS_1.0 { + # Hide libjpeg symbols to avoid symbol conflict with OpenCV + local: + jpeg_*; + jinit_*; + jdiv_round_up; + jround_up; + jzero_far; + jcopy_*; + jsimd_*; +}; -- GitLab From 9e4cbaf3a3a3bfca913bebdcfc082265c7a13ad6 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 14 Jun 2018 17:12:51 -0700 Subject: [PATCH 482/816] Convert log(x+1) to log1p(x). PiperOrigin-RevId: 200645461 --- tensorflow/core/grappler/op_types.cc | 2 + tensorflow/core/grappler/op_types.h | 1 + .../optimizers/arithmetic_optimizer.cc | 115 ++++++++++++++++++ .../optimizers/arithmetic_optimizer.h | 1 + .../optimizers/arithmetic_optimizer_test.cc | 42 +++++++ 5 files changed, 161 insertions(+) diff --git a/tensorflow/core/grappler/op_types.cc b/tensorflow/core/grappler/op_types.cc index 2a47a4c495..2227904dbf 100644 --- a/tensorflow/core/grappler/op_types.cc +++ b/tensorflow/core/grappler/op_types.cc @@ -193,6 +193,8 @@ bool IsLess(const NodeDef& node) { return node.op() == "Less"; } bool IsLessEqual(const NodeDef& node) { return node.op() == "LessEqual"; } +bool IsLog(const NodeDef& node) { return node.op() == "Log"; } + bool IsLogicalAnd(const NodeDef& node) { return node.op() == "LogicalAnd"; } bool IsLogicalNot(const NodeDef& node) { return node.op() == "LogicalNot"; } diff --git a/tensorflow/core/grappler/op_types.h b/tensorflow/core/grappler/op_types.h index e7f39981c0..7110a9c63d 100644 --- a/tensorflow/core/grappler/op_types.h +++ b/tensorflow/core/grappler/op_types.h @@ -74,6 +74,7 @@ bool IsImag(const NodeDef& node); bool IsInvGrad(const NodeDef& node); bool IsLess(const NodeDef& node); bool IsLessEqual(const NodeDef& node); +bool IsLog(const NodeDef& node); bool IsLogicalAnd(const NodeDef& node); bool IsLogicalNot(const NodeDef& node); bool IsLogicalOr(const NodeDef& node); diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc index c41b152d21..9d500f8f54 100644 --- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc +++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc @@ -2487,6 +2487,119 @@ class ConvertPowStage : public ArithmeticOptimizerStage { } }; +class ConvertLog1pStage : public ArithmeticOptimizerStage { + public: + explicit ConvertLog1pStage(const GraphOptimizerContext& ctx, + const ArithmeticOptimizerContext& ctx_ext) + : ArithmeticOptimizerStage("ConvertLog1p", ctx, ctx_ext) {} + ~ConvertLog1pStage() override = default; + + bool IsSupported(const NodeDef* node) const override { return IsLog(*node); } + + Status TrySimplify(NodeDef* node, string* simplified_node_name) override { + NodeDef* input; + TF_RETURN_IF_ERROR(GetInputNode(node->input(0), &input)); + if (!IsAdd(*input)) { + return Status::OK(); + } + + if (ctx().graph_properties->GetInputProperties(input->name()).size() < 2) { + return Status::OK(); + } + + bool modified = false; + TF_RETURN_IF_ERROR(TrySimplifyInternal(node, input, 0, 1, &modified)); + if (!modified) { + TF_RETURN_IF_ERROR(TrySimplifyInternal(node, input, 1, 0, &modified)); + } + if (modified) { + *simplified_node_name = node->name(); + } + return Status::OK(); + } + + private: + Status TrySimplifyInternal(NodeDef* node, NodeDef* input, int i, int j, + bool* modified) { + const auto& t = + ctx().graph_properties->GetInputProperties(input->name())[i]; + for (int k = 0; k < t.shape().dim_size(); ++k) { + // Skip if t shape is not fully determined. + if (t.shape().dim(k).size() < 0) { + return Status::OK(); + } + } + const auto& c = + ctx().graph_properties->GetInputProperties(input->name())[j]; + TensorShapeProto broadcast_shape; + if (!ShapeAfterBroadcast(t.shape(), c.shape(), &broadcast_shape)) { + return errors::InvalidArgument("Cannot get broadcast shape for: ", + t.DebugString(), " and ", c.DebugString()); + } + if (!ShapesSymbolicallyEqual(t.shape(), broadcast_shape)) { + // skip if the non-constant tensor doesn't have the same shape after + // broadcast. + return Status::OK(); + } + if (TensorShape::IsValid(t.shape()) && t.has_value()) { + Tensor tensor(t.dtype(), t.shape()); + if (!tensor.FromProto(t.value())) { + return errors::InvalidArgument("Cannot parse tensor from proto: ", + t.value().DebugString()); + } + complex128 element; + for (int k = 0; k < tensor.NumElements(); ++k) { + if (!GetElement(tensor, k, &element)) { + // input data type is not supported by log1p. Skip. + return Status::OK(); + } + if (element != complex128(1)) { + // current element is not 1. Skip. + return Status::OK(); + } + } + NodeDef *x, *y; + TF_RETURN_IF_ERROR(GetInputNode(input->input(i), &x)); + TF_RETURN_IF_ERROR(GetInputNode(input->input(j), &y)); + node->set_op("Log1p"); + node->set_input(0, y->name()); + node->add_input(AsControlDependency(x->name())); + ForwardControlDependencies(node, {input}); + + AddToOptimizationQueue(node); + AddToOptimizationQueue(x); + AddToOptimizationQueue(y); + *modified = true; + } + return Status::OK(); + } + + bool GetElement(const Tensor& t, int i, complex128* element) { + switch (t.dtype()) { + case DT_BFLOAT16: + *element = complex128(t.flat()(i)); + return true; + case DT_HALF: + *element = complex128(static_cast(t.flat()(i)), 0); + return true; + case DT_FLOAT: + *element = complex128(t.flat()(i)); + return true; + case DT_DOUBLE: + *element = complex128(t.flat()(i)); + return true; + case DT_COMPLEX64: + *element = complex128(t.flat()(i)); + return true; + case DT_COMPLEX128: + *element = t.flat()(i); + return true; + default: + return false; + } + } +}; + } // namespace class UniqueNodes { @@ -2763,6 +2876,8 @@ Status ArithmeticOptimizer::SimplifyArithmeticOps(bool can_use_shapes) { if (options_.remove_idempotent) pipeline.AddStage(ctx, ctx_ext); if (options_.convert_pow) pipeline.AddStage(ctx, ctx_ext); + if (options_.convert_log1p) + pipeline.AddStage(ctx, ctx_ext); VLOG(1) << "Run " << pipeline.NumStages() << " arithmetic optimizer stages: " << str_util::Join(pipeline.StageNames(), ", "); diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.h b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.h index 40c5e9fc56..9a6081dcd8 100644 --- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.h +++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.h @@ -75,6 +75,7 @@ class ArithmeticOptimizer : public GraphOptimizer { bool replace_mul_with_square = true; bool simplify_aggregation = true; bool convert_pow = true; + bool convert_log1p = true; // Choose which arithmetic optimizer stages will be enabled for a given // optimization level by default. diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc b/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc index fe70c7db5c..177c237fe7 100644 --- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc +++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc @@ -264,6 +264,11 @@ class ArithmeticOptimizerTest : public GrapplerTest { DisableAllStages(optimizer); optimizer->options_.simplify_aggregation = true; } + + void EnableOnlyLog1p(ArithmeticOptimizer* optimizer) { + DisableAllStages(optimizer); + optimizer->options_.convert_log1p = true; + } }; TEST_F(ArithmeticOptimizerTest, NoOp) { @@ -2486,6 +2491,43 @@ TEST_F(ArithmeticOptimizerTest, ConvertPow) { CompareGraphs(want, got); } +TEST_F(ArithmeticOptimizerTest, Log1p) { + tensorflow::Scope s = tensorflow::Scope::NewRootScope(); + + auto x1 = ops::Const(s.WithOpName("x1"), {1.0f, 1.0f}, {1, 2}); + auto x2 = ops::Const(s.WithOpName("x2"), {2.0f, 2.0f}, {1, 2}); + auto x3 = ops::Const(s.WithOpName("x3"), {3.0f, 3.0f}, {1, 2}); + auto a12 = ops::Add(s.WithOpName("a12").WithControlDependencies(x3), x1, x2); + auto a23 = ops::Add(s.WithOpName("a23"), x2, x3); + Output out1 = ops::Log(s.WithOpName("out1"), a12); + Output out2 = ops::Log(s.WithOpName("out2"), a23); + + GrapplerItem item; + item.fetch = {"out1", "out2"}; + TF_CHECK_OK(s.ToGraphDef(&item.graph)); + auto tensors_expected = EvaluateNodes(item.graph, item.fetch); + EXPECT_EQ(2, tensors_expected.size()); + + GraphDef got; + ArithmeticOptimizer optimizer; + EnableOnlyLog1p(&optimizer); + OptimizeAndPrune(&optimizer, &item, &got); + auto tensors = EvaluateNodes(got, item.fetch); + EXPECT_EQ(2, tensors.size()); + + GraphDef want; + AddNode("x1", "Const", {}, {}, &want); + AddNode("x2", "Const", {}, {}, &want); + AddNode("x3", "Const", {}, {}, &want); + AddNode("a23", "Add", {"x2", "x3"}, {}, &want); + AddNode("out1", "Log1p", + {"x2", AsControlDependency("x1"), AsControlDependency("x3")}, {}, + &want); + AddNode("out2", "Log", {"a23"}, {}, &want); + + CompareGraphs(want, got); +} + TEST_F(ArithmeticOptimizerTest, MinimizeBroadcasts_SimpleSwap) { tensorflow::Scope s = tensorflow::Scope::NewRootScope(); -- GitLab From 212ba3e9ef934d0b2a3b09740bd238cda0394fad Mon Sep 17 00:00:00 2001 From: Mohammad Ashraf Bhuiyan Date: Thu, 14 Jun 2018 17:23:42 -0700 Subject: [PATCH 483/816] fix allocation ID for MKL (#20035) --- .../direct_session_with_tracking_alloc_test.cc | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc b/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc index 9028e6298c..d66963ec74 100644 --- a/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc +++ b/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc @@ -109,15 +109,15 @@ TEST(DirectSessionWithTrackingAllocTest, CostModelTest) { // and deallocated. Each allocation calls the // (FindChunkPtr of BFCAllocator), // which increments the value of AllocationId. - // Thus AllocationId becomes more than 3 and 4 if - // MKL is used. Now they are 9 and 10 for MKL. - EXPECT_EQ(19, cm->AllocationId(node, 0)); + // Thus AllocationId becomes more than TF if MKL + // is used. Now IDs for MKL are 8 more than TF. + EXPECT_EQ(29, cm->AllocationId(node, 0)); #else EXPECT_EQ(21, cm->AllocationId(node, 0)); #endif } else { #ifdef INTEL_MKL - EXPECT_EQ(20, cm->AllocationId(node, 0)); + EXPECT_EQ(30, cm->AllocationId(node, 0)); #else EXPECT_EQ(22, cm->AllocationId(node, 0)); #endif -- GitLab From 7e05b8a1c7fec4852e275e708555a759947270d7 Mon Sep 17 00:00:00 2001 From: Dimitris Vardoulakis Date: Thu, 14 Jun 2018 17:22:37 -0700 Subject: [PATCH 484/816] [TF:XLA] Account for subcomputations in heap simulator during scheduling. PiperOrigin-RevId: 200646674 --- tensorflow/compiler/xla/service/BUILD | 1 + .../compiler/xla/service/buffer_assignment.cc | 5 +- .../compiler/xla/service/heap_simulator.cc | 52 +++++++-- .../compiler/xla/service/heap_simulator.h | 58 +++++++--- .../xla/service/heap_simulator_test.cc | 3 +- .../compiler/xla/service/hlo_scheduling.cc | 37 ++++--- .../xla/service/hlo_scheduling_test.cc | 104 ++++++++++++++++-- 7 files changed, 204 insertions(+), 56 deletions(-) diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD index cb2e159a38..396ce13e7f 100644 --- a/tensorflow/compiler/xla/service/BUILD +++ b/tensorflow/compiler/xla/service/BUILD @@ -1101,6 +1101,7 @@ tf_cc_test( srcs = ["hlo_scheduling_test.cc"], deps = [ ":buffer_value", + ":heap_simulator", ":hlo", ":hlo_ordering", ":hlo_scheduling", diff --git a/tensorflow/compiler/xla/service/buffer_assignment.cc b/tensorflow/compiler/xla/service/buffer_assignment.cc index 5d3b0cb333..afe4b2e142 100644 --- a/tensorflow/compiler/xla/service/buffer_assignment.cc +++ b/tensorflow/compiler/xla/service/buffer_assignment.cc @@ -631,8 +631,9 @@ Status BufferAssignment::ComputeSummaryStats() { } } if (module_sequence.size() == module_->computation_count()) { - TF_ASSIGN_OR_RETURN(const int64 min_size, - MinimumMemoryForModule(module_sequence, buffer_size_)); + TF_ASSIGN_OR_RETURN( + const int64 min_size, + HeapSimulator::MinimumMemoryForModule(module_sequence, buffer_size_)); stats_.total_fragmentation_bytes = stats_.total_allocation_bytes - min_size; } diff --git a/tensorflow/compiler/xla/service/heap_simulator.cc b/tensorflow/compiler/xla/service/heap_simulator.cc index 5dba50a63b..a04aa4069d 100644 --- a/tensorflow/compiler/xla/service/heap_simulator.cc +++ b/tensorflow/compiler/xla/service/heap_simulator.cc @@ -26,7 +26,8 @@ namespace xla { using tensorflow::gtl::FlatMap; using tensorflow::gtl::FlatSet; -StatusOr MinimumMemoryForModule( +/*static*/ +StatusOr HeapSimulator::MinimumMemoryForModule( const SequentialHloOrdering::HloModuleSequence& module_sequence, const LogicalBuffer::SizeFunction& size_function) { if (module_sequence.empty()) { @@ -49,15 +50,19 @@ StatusOr MinimumMemoryForModule( return result.heap_size; } -StatusOr MinimumMemoryForComputation( +/*static*/ +StatusOr HeapSimulator::MinimumMemoryForComputation( const HloComputation& computation, const std::vector& sequence, const TuplePointsToAnalysis& points_to_analysis, - const LogicalBuffer::SizeFunction& size_function) { + const LogicalBuffer::SizeFunction& size_function, + const tensorflow::gtl::FlatMap* + memory_by_computation) { TF_ASSIGN_OR_RETURN( HeapSimulator::Result result, HeapSimulator::Run(MakeUnique(), computation, - sequence, points_to_analysis, size_function)); + sequence, points_to_analysis, size_function, + HeapSimulator::Options(), memory_by_computation)); return result.heap_size; } @@ -81,9 +86,11 @@ StatusOr HeapSimulator::Run( std::unique_ptr algorithm, const HloComputation& computation, const std::vector& instruction_sequence, const TuplePointsToAnalysis& points_to_analysis, - const BufferValue::SizeFunction& size_fn, const Options& options) { + const BufferValue::SizeFunction& size_fn, const Options& options, + const tensorflow::gtl::FlatMap* + memory_by_computation) { HeapSimulator heap(std::move(algorithm), size_fn, options, - /*module_sequence=*/nullptr); + /*module_sequence=*/nullptr, memory_by_computation); TF_RETURN_IF_ERROR(heap.RunComputation(computation, instruction_sequence, points_to_analysis)); return heap.Finish(); @@ -254,6 +261,12 @@ Status HeapSimulator::RunComputation( Alloc(buffer, instruction); } } + // Account for the memory used by subcomputations when estimating the + // current heap size. + if (memory_by_computation_ != nullptr) { + algorithm_->AccountForSubcomputationMemory(instruction, + *memory_by_computation_); + } // If the whole module is sequential, we can save memory by running the // heap-simulation for sub-computations inline. E.g. the buffers for the @@ -321,12 +334,15 @@ Status HeapSimulator::RunComputation( HeapSimulator::HeapSimulator( std::unique_ptr algorithm, const BufferValue::SizeFunction& size_fn, const Options& options, - const SequentialHloOrdering::HloModuleSequence* module_sequence) + const SequentialHloOrdering::HloModuleSequence* module_sequence, + const tensorflow::gtl::FlatMap* + memory_by_computation) : no_fragmentation_stats_(MakeUnique()), algorithm_(std::move(algorithm)), size_fn_(size_fn), options_(options), - module_sequence_(module_sequence) { + module_sequence_(module_sequence), + memory_by_computation_(memory_by_computation) { debug_trace_.set_whole_module_simulation(module_sequence_ != nullptr); } @@ -495,6 +511,26 @@ void NoFragmentationStatsHeap::Alloc(const BufferValue* buffer, int64 size) { } } +void NoFragmentationStatsHeap::AccountForSubcomputationMemory( + const HloInstruction* instruction, + const tensorflow::gtl::FlatMap& + memory_by_computation) { + // We only count the memory usage of the largest subcomputation, instead of + // adding them all, because subcomputations won't execute in parallel. + int64 max_subcomputation_bytes = 0; + for (const auto* c : instruction->called_computations()) { + auto it = memory_by_computation.find(c); + if (it != memory_by_computation.end()) { + int64 subcomputation_bytes = it->second; + if (subcomputation_bytes > max_subcomputation_bytes) { + max_subcomputation_bytes = subcomputation_bytes; + } + } + } + max_heap_size_ = + std::max(max_heap_size_, current_heap_size_ + max_subcomputation_bytes); +} + void NoFragmentationStatsHeap::Free(const BufferValue* buffer, int64 size) { current_heap_size_ -= size; } diff --git a/tensorflow/compiler/xla/service/heap_simulator.h b/tensorflow/compiler/xla/service/heap_simulator.h index 3be3bb8e7f..811a6042df 100644 --- a/tensorflow/compiler/xla/service/heap_simulator.h +++ b/tensorflow/compiler/xla/service/heap_simulator.h @@ -34,21 +34,6 @@ limitations under the License. namespace xla { -// Returns the minimum memory required to compute an HLO module where all -// computations have been scheduled (represented by the given module_sequence), -// assuming no fragmentation. -StatusOr MinimumMemoryForModule( - const SequentialHloOrdering::HloModuleSequence& module_sequence, - const LogicalBuffer::SizeFunction& size_function); - -// Returns the minimum memory required to compute the given computation, -// assuming no fragmentation. -StatusOr MinimumMemoryForComputation( - const HloComputation& computation, - const std::vector& sequence, - const TuplePointsToAnalysis& points_to_analysis, - const LogicalBuffer::SizeFunction& size_function); - // Forward declare classes defined below. class HeapAlgorithm; @@ -100,6 +85,23 @@ class HeapSimulator { const BufferValueFlatSet* buffers_to_assign; }; + // Returns the minimum memory required to compute an HLO module where all + // computations have been scheduled (represented by the given + // module_sequence), assuming no fragmentation. + static StatusOr MinimumMemoryForModule( + const SequentialHloOrdering::HloModuleSequence& module_sequence, + const LogicalBuffer::SizeFunction& size_function); + + // Returns the minimum memory required to compute the given computation, + // assuming no fragmentation. + static StatusOr MinimumMemoryForComputation( + const HloComputation& computation, + const std::vector& sequence, + const TuplePointsToAnalysis& points_to_analysis, + const LogicalBuffer::SizeFunction& size_function, + const tensorflow::gtl::FlatMap* + memory_by_computation = nullptr); + // Run the heap simulation with the given algorithm, assuming the given // module_sequence, which must contain a topologically-consistent total // ordering of all instructions within each computation. The result is invalid @@ -126,7 +128,9 @@ class HeapSimulator { const std::vector& instruction_sequence, const TuplePointsToAnalysis& points_to_analysis, const BufferValue::SizeFunction& size_fn, - const Options& options = Options()); + const Options& options = Options(), + const tensorflow::gtl::FlatMap* + memory_by_computation = nullptr); private: // If 'module_sequence' is non-null, it is used to find kCall and kWhile @@ -135,7 +139,9 @@ class HeapSimulator { HeapSimulator( std::unique_ptr algorithm, const BufferValue::SizeFunction& size_fn, const Options& options, - const SequentialHloOrdering::HloModuleSequence* module_sequence); + const SequentialHloOrdering::HloModuleSequence* module_sequence = nullptr, + const tensorflow::gtl::FlatMap* + memory_by_computation = nullptr); ~HeapSimulator(); Status RunComputation( @@ -159,7 +165,13 @@ class HeapSimulator { const std::unique_ptr algorithm_; const BufferValue::SizeFunction size_fn_; const Options options_; + // module_sequence_ is set by buffer assignment, and memory_by_computation_ is + // set by hlo scheduling. Then, in RunComputation, we check both in order to + // handle subcomputations. It would be good to unify the handling of + // subcomputations, but it's not clear how. const SequentialHloOrdering::HloModuleSequence* module_sequence_; + const tensorflow::gtl::FlatMap* + memory_by_computation_; // In addition to Alloc and Free, the heap simulator exposes a concept of // buffer sharing. When ShareBuffer is called, instead of allocating new @@ -204,6 +216,11 @@ class HeapAlgorithm { // Alloc allocates a buffer of 'size' bytes. virtual void Alloc(const BufferValue* buffer, int64 size) = 0; + virtual void AccountForSubcomputationMemory( + const HloInstruction* instruction, + const tensorflow::gtl::FlatMap& + memory_by_computation) {} + // Free de-allocates a previously allocated buffer. virtual void Free(const BufferValue* buffer, int64 size) = 0; @@ -222,7 +239,14 @@ class NoFragmentationStatsHeap : public HeapAlgorithm { ~NoFragmentationStatsHeap() override = default; void Alloc(const BufferValue* buffer, int64 size) override; + + void AccountForSubcomputationMemory( + const HloInstruction* instruction, + const tensorflow::gtl::FlatMap& + memory_by_computation) override; + void Free(const BufferValue* buffer, int64 size) override; + Result Finish() override; private: diff --git a/tensorflow/compiler/xla/service/heap_simulator_test.cc b/tensorflow/compiler/xla/service/heap_simulator_test.cc index 309ab85f78..93d7a14125 100644 --- a/tensorflow/compiler/xla/service/heap_simulator_test.cc +++ b/tensorflow/compiler/xla/service/heap_simulator_test.cc @@ -89,7 +89,8 @@ TEST_F(MinimumMemoryForSequenceTest, MultiComputation) { cond_lt}; module_sequence[body_computation] = {body_param}; module_sequence[entry_computation] = {iter, data, tuple, while_op}; - EXPECT_EQ(56, MinimumMemoryForModule(module_sequence, size_fn).ValueOrDie()); + EXPECT_EQ(56, HeapSimulator::MinimumMemoryForModule(module_sequence, size_fn) + .ValueOrDie()); } const char kAlloc[] = "Alloc"; diff --git a/tensorflow/compiler/xla/service/hlo_scheduling.cc b/tensorflow/compiler/xla/service/hlo_scheduling.cc index b14ade3549..641b9ecec9 100644 --- a/tensorflow/compiler/xla/service/hlo_scheduling.cc +++ b/tensorflow/compiler/xla/service/hlo_scheduling.cc @@ -375,7 +375,7 @@ int64 SumLogicalBufferSizes( return size; } -StatusOr> ScheduleComputationsInModule( +StatusOr> ScheduleComputationHelper( const HloComputation& computation, const TuplePointsToAnalysis& points_to_analysis, const LogicalBuffer::SizeFunction& size_function, @@ -498,29 +498,29 @@ StatusOr> DefaultMemoryScheduler( std::vector list_sequence, ListMemoryScheduler(computation, points_to_analysis, size_function, memory_by_computation)); - TF_ASSIGN_OR_RETURN( - const int64 list_memory, - MinimumMemoryForComputation(computation, list_sequence, - points_to_analysis, size_function)); + TF_ASSIGN_OR_RETURN(const int64 list_memory, + HeapSimulator::MinimumMemoryForComputation( + computation, list_sequence, points_to_analysis, + size_function, &memory_by_computation)); VLOG(2) << "Min-memory list sequence: " << HumanReadableNumBytes(list_memory); TF_ASSIGN_OR_RETURN(std::vector dfs_sequence, DFSMemoryScheduler(computation, points_to_analysis, size_function, memory_by_computation)); - TF_ASSIGN_OR_RETURN( - const int64 dfs_memory, - MinimumMemoryForComputation(computation, dfs_sequence, points_to_analysis, - size_function)); + TF_ASSIGN_OR_RETURN(const int64 dfs_memory, + HeapSimulator::MinimumMemoryForComputation( + computation, dfs_sequence, points_to_analysis, + size_function, &memory_by_computation)); VLOG(2) << "Min-memory dfs sequence: " << HumanReadableNumBytes(dfs_memory); TF_ASSIGN_OR_RETURN( std::vector post_order_sequence, PostOrderMemoryScheduler(computation, points_to_analysis, size_function, memory_by_computation)); - TF_ASSIGN_OR_RETURN( - const int64 post_order_memory, - MinimumMemoryForComputation(computation, post_order_sequence, - points_to_analysis, size_function)); + TF_ASSIGN_OR_RETURN(const int64 post_order_memory, + HeapSimulator::MinimumMemoryForComputation( + computation, post_order_sequence, points_to_analysis, + size_function, &memory_by_computation)); VLOG(2) << "Min-memory post order sequence: " << HumanReadableNumBytes(post_order_memory); @@ -551,12 +551,13 @@ StatusOr ScheduleComputationsInModule( for (const auto* computation : module.MakeComputationPostOrder()) { if (!computation->IsFusionComputation()) { TF_ASSIGN_OR_RETURN(auto one_computation_sequence, - ScheduleComputationsInModule( + ScheduleComputationHelper( *computation, *points_to_analysis, size_function, algorithm, memory_by_computation)); memory_by_computation[computation] = - MinimumMemoryForComputation(*computation, one_computation_sequence, - *points_to_analysis, size_function) + HeapSimulator::MinimumMemoryForComputation( + *computation, one_computation_sequence, *points_to_analysis, + size_function, &memory_by_computation) .ValueOrDie(); sequence[computation] = std::move(one_computation_sequence); } @@ -571,8 +572,8 @@ StatusOr> ScheduleOneComputation( TF_ASSIGN_OR_RETURN(std::unique_ptr points_to_analysis, TuplePointsToAnalysis::Run(computation.parent())); tensorflow::gtl::FlatMap empty_map; - return ScheduleComputationsInModule(computation, *points_to_analysis, - size_function, nullptr, empty_map); + return ScheduleComputationHelper(computation, *points_to_analysis, + size_function, nullptr, empty_map); } } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_scheduling_test.cc b/tensorflow/compiler/xla/service/hlo_scheduling_test.cc index 6f1b1215d3..73f22f81f4 100644 --- a/tensorflow/compiler/xla/service/hlo_scheduling_test.cc +++ b/tensorflow/compiler/xla/service/hlo_scheduling_test.cc @@ -18,6 +18,7 @@ limitations under the License. #include #include +#include "tensorflow/compiler/xla/service/heap_simulator.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/service/hlo_opcode.h" @@ -144,7 +145,7 @@ TEST_F(HloSchedulingTest, ListAccountsForSubcomputations) { // ROOT %subtract = f32[4]{0} subtract( // f32[4]{0} %body_param, f32[1,4]{1,0} %constant.1) // } - // %SubcomputationsNotAccounted () -> f32[2,4] { + // %ListAccountsForSubcomputations () -> f32[2,4] { // %constant.3 = f32[2,4]{1,0} constant( // f32[2,4] { { 1, 2, 3, 4 }, { 1, 2, 3, 4 } }) // %transpose = f32[2,4]{1,0} transpose( @@ -210,16 +211,16 @@ TEST_F(HloSchedulingTest, ListAccountsForSubcomputations) { module->AddEntryComputation(builder.Build()); - TF_ASSERT_OK_AND_ASSIGN(SequentialHloOrdering::HloModuleSequence sequence, - ScheduleComputationsInModule( - *module, - [](const BufferValue& buffer) { - return ShapeUtil::ByteSizeOf(buffer.shape()); - }, - ListMemoryScheduler)); + auto size_fn = [](const BufferValue& buffer) { + return ShapeUtil::ByteSizeOf(buffer.shape()); + }; + TF_ASSERT_OK_AND_ASSIGN( + SequentialHloOrdering::HloModuleSequence sequence, + ScheduleComputationsInModule(*module, size_fn, ListMemoryScheduler)); // Verify that all instructions are in the sequence. - EXPECT_EQ(module->entry_computation()->instruction_count(), - sequence.at(module->entry_computation()).size()); + auto entry_computation = module->entry_computation(); + EXPECT_EQ(entry_computation->instruction_count(), + sequence.at(entry_computation).size()); SequentialHloOrdering ordering(module.get(), sequence); // This schedule is an example of List's greedy heuristics being suboptimal. // The while_loop is more expensive than transpose, so it would have been @@ -228,6 +229,24 @@ TEST_F(HloSchedulingTest, ListAccountsForSubcomputations) { EXPECT_TRUE(ordering.ExecutesBefore(transpose, bcast)); EXPECT_TRUE(ordering.ExecutesBefore(bcast, add)); EXPECT_TRUE(ordering.ExecutesBefore(transpose, add)); + + tensorflow::gtl::FlatMap memory_by_computation; + memory_by_computation[cond_computation] = 17; + memory_by_computation[body_computation] = 16; + std::unique_ptr points_to_analysis = + TuplePointsToAnalysis::Run(module.get()).ValueOrDie(); + + // HeapSimulator doesn't account for subcomputations + EXPECT_EQ(80, HeapSimulator::MinimumMemoryForComputation( + *entry_computation, sequence.at(entry_computation), + *points_to_analysis, size_fn) + .ValueOrDie()); + // HeapSimulator accounts for subcomputations. The max mem doesn't change + // because the while body isn't live during the peak. + EXPECT_EQ(80, HeapSimulator::MinimumMemoryForComputation( + *entry_computation, sequence.at(entry_computation), + *points_to_analysis, size_fn, &memory_by_computation) + .ValueOrDie()); } TEST_F(HloSchedulingTest, TuplesAreAccountedCorrectly) { @@ -325,5 +344,70 @@ TEST_F(HloSchedulingTest, MultiOutputFusionAccountedCorrectly) { EXPECT_TRUE(ordering.ExecutesBefore(exp, fusion)); } +TEST_F(HloSchedulingTest, HeapSimulatorAccountsForSubcomputations) { + auto module = CreateNewModule(); + const Shape r1f32 = ShapeUtil::MakeShape(F32, {4}); + const Shape r2f32 = ShapeUtil::MakeShape(F32, {2, 4}); + + // param != 0 + // Needs 17 bytes + auto cond_builder = HloComputation::Builder("WhileCond"); + HloInstruction* cond_param = cond_builder.AddInstruction( + HloInstruction::CreateParameter(0, r1f32, "cond_param")); + HloInstruction* zero_vector = cond_builder.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR2({{0, 0, 0, 0}}))); + cond_builder.AddInstruction(HloInstruction::CreateBinary( + ShapeUtil::MakeShape(PRED, {}), HloOpcode::kNe, cond_param, zero_vector)); + auto cond_computation = module->AddEmbeddedComputation(cond_builder.Build()); + + // param - 1 + // Needs 16 bytes + auto body_builder = HloComputation::Builder("WhileBody"); + HloInstruction* body_param = body_builder.AddInstruction( + HloInstruction::CreateParameter(0, r1f32, "body_param")); + HloInstruction* one_vector = body_builder.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR2({{1, 1, 1, 1}}))); + body_builder.AddInstruction(HloInstruction::CreateBinary( + r1f32, HloOpcode::kSubtract, body_param, one_vector)); + auto body_computation = module->AddEmbeddedComputation(body_builder.Build()); + + auto builder = HloComputation::Builder(TestName()); + HloInstruction* while_init = builder.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR2({{1, 1, 1, 1}}))); + // Creates 16 bytes, ignoring subcomputations + builder.AddInstruction(HloInstruction::CreateWhile( + r1f32, cond_computation, body_computation, while_init)); + + module->AddEntryComputation(builder.Build()); + + auto size_fn = [](const BufferValue& buffer) { + return ShapeUtil::ByteSizeOf(buffer.shape()); + }; + TF_ASSERT_OK_AND_ASSIGN( + SequentialHloOrdering::HloModuleSequence sequence, + ScheduleComputationsInModule(*module, size_fn, ListMemoryScheduler)); + // Verify that all instructions are in the sequence. + auto entry_computation = module->entry_computation(); + EXPECT_EQ(entry_computation->instruction_count(), + sequence.at(entry_computation).size()); + + tensorflow::gtl::FlatMap memory_by_computation; + memory_by_computation[cond_computation] = 17; + memory_by_computation[body_computation] = 16; + std::unique_ptr points_to_analysis = + TuplePointsToAnalysis::Run(module.get()).ValueOrDie(); + + // HeapSimulator doesn't account for subcomputations + EXPECT_EQ(16, HeapSimulator::MinimumMemoryForComputation( + *entry_computation, sequence.at(entry_computation), + *points_to_analysis, size_fn) + .ValueOrDie()); + // HeapSimulator accounts for subcomputations + EXPECT_EQ(33, HeapSimulator::MinimumMemoryForComputation( + *entry_computation, sequence.at(entry_computation), + *points_to_analysis, size_fn, &memory_by_computation) + .ValueOrDie()); +} + } // namespace } // namespace xla -- GitLab From 5ae938f97dd996130308067b8ee4a40fa346857a Mon Sep 17 00:00:00 2001 From: Saurabh Saxena Date: Thu, 14 Jun 2018 17:34:11 -0700 Subject: [PATCH 485/816] Speed up shuffle_dataset_op_test. PiperOrigin-RevId: 200648071 --- .../kernel_tests/shuffle_dataset_op_test.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/tensorflow/contrib/data/python/kernel_tests/shuffle_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/shuffle_dataset_op_test.py index 1b67a33f04..25e9ea47b8 100644 --- a/tensorflow/contrib/data/python/kernel_tests/shuffle_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/shuffle_dataset_op_test.py @@ -48,10 +48,10 @@ class ShuffleDatasetSerializationTest( def testShuffleCore(self): seed = 55 - range_limit = 10 - num_repeats = 5 + range_limit = 5 + num_repeats = 2 num_outputs = range_limit * num_repeats - buffer_sizes = [1, 3, 8, 10, 25, 50] + buffer_sizes = [1, 3, 5, 8, 10] # pylint: disable=cell-var-from-loop # pylint: disable=g-long-lambda for reshuffle_each_iteration in [True, False]: @@ -75,10 +75,10 @@ class ShuffleDatasetSerializationTest( def testNonDeterministicSeeding(self): - range_limit = 10 - num_repeats = 5 + range_limit = 5 + num_repeats = 2 num_outputs = range_limit * num_repeats - buffer_sizes = [1, 3, 8, 10, 25, 50] + buffer_sizes = [1, 3, 5, 8, 10] for reshuffle_each_iteration in [True, False]: for buffer_size in buffer_sizes: @@ -111,10 +111,10 @@ class ShuffleDatasetSerializationTest( self.match(expected, actual) def testMultipleIterators(self): - range_limit = 10 - num_repeats = 5 + range_limit = 5 + num_repeats = 2 num_outputs = range_limit * num_repeats - buffer_sizes = [1, 3, 8, 10, 25, 50] + buffer_sizes = [1, 3, 5, 8, 10] for reshuffle_each_iteration in [True, False]: for buffer_size in buffer_sizes: -- GitLab From 99d48bdec4605cdd21f09d2dfcfc70139cbe4ebd Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 14 Jun 2018 17:46:08 -0700 Subject: [PATCH 486/816] Small refactoring of code to check device crossing in dependency optimizer. Make a couple of existing methods const. PiperOrigin-RevId: 200649418 --- .../optimizers/dependency_optimizer.cc | 110 ++++++++++-------- .../optimizers/dependency_optimizer.h | 10 +- 2 files changed, 67 insertions(+), 53 deletions(-) diff --git a/tensorflow/core/grappler/optimizers/dependency_optimizer.cc b/tensorflow/core/grappler/optimizers/dependency_optimizer.cc index 78a6d0d835..3f5bab9d3b 100644 --- a/tensorflow/core/grappler/optimizers/dependency_optimizer.cc +++ b/tensorflow/core/grappler/optimizers/dependency_optimizer.cc @@ -65,7 +65,7 @@ void DeleteNodes(const std::set& nodes_to_delete, GraphDef* graph) { } // namespace -bool DependencyOptimizer::SafeToRemoveIdentity(const NodeDef& node) { +bool DependencyOptimizer::SafeToRemoveIdentity(const NodeDef& node) const { if (!IsIdentity(node)) { return true; } @@ -108,7 +108,7 @@ bool DependencyOptimizer::SafeToRemoveIdentity(const NodeDef& node) { return true; } -bool DependencyOptimizer::SafeToConvertToNoOp(const NodeDef& node) { +bool DependencyOptimizer::SafeToConvertToNoOp(const NodeDef& node) const { if (!fetch_nodes_known_ || nodes_to_preserve_.find(node.name()) != nodes_to_preserve_.end()) { return false; @@ -142,6 +142,61 @@ bool DependencyOptimizer::SafeToConvertToNoOp(const NodeDef& node) { return true; } +bool DependencyOptimizer::BypassingNodeIsBeneficial( + const NodeDef& node, const std::vector& input_nodes, + const std::vector& output_nodes) const { + const bool is_identity = IsIdentity(node); + const int num_outputs = output_nodes.size(); + const int num_inputs = node.input_size(); + + // Don't increase the number of edges in the graph. + if (num_inputs * num_outputs > num_inputs + num_outputs) { + return false; + } + + // Make sure that we don't increase the number of edges that cross + // device boundaries. + if ((num_inputs == 1 && num_outputs > 1 && + input_nodes[0]->device() != node.device()) || + (num_inputs > 1 && num_outputs == 1 && + output_nodes[0]->device() != node.device())) { + return false; + } + + // TODO(rmlarsen): Not all device crossings are equally expensive. + // Assign a cost to each based on device affinity and compute a + // cost before and after. + const string& node_dev = node.device(); + int num_cross_in = 0; + for (NodeDef* input_node : input_nodes) { + num_cross_in += static_cast(input_node->device() != node_dev); + } + int num_cross_out = 0; + for (NodeDef* output_node : output_nodes) { + num_cross_out += static_cast(output_node->device() != node_dev); + } + if (is_identity && num_cross_in > 0 && num_cross_out > 0) { + // This identity node follows a device crossing, so it might be + // following a _Recv node after partioning. Do not remove such nodes, + // unless they only have consumers on the same device as themselves. + return false; + } + + // Make sure we do not increase the number of device crossings. + const int num_cross_before = num_cross_in + num_cross_out; + int num_cross_after = 0; + for (NodeDef* input_node : input_nodes) { + for (NodeDef* output_node : output_nodes) { + num_cross_after += + static_cast(input_node->device() != output_node->device()); + } + } + if (num_cross_after > num_cross_before) { + return false; + } + return true; +} + void DependencyOptimizer::OptimizeNode(int node_idx, SetVector* nodes_to_simplify, std::set* nodes_to_delete) { @@ -269,21 +324,11 @@ void DependencyOptimizer::OptimizeNode(int node_idx, // y --^> | | --^> b /\ +---+ // +----------+ y --^> b - if (is_noop || is_identity) { - if (is_identity && !SafeToRemoveIdentity(*node)) { - return; - } - + if (is_noop || (is_identity && SafeToRemoveIdentity(*node))) { const auto& output_node_set = node_map_->GetOutputs(node_name); const std::vector output_nodes(output_node_set.begin(), output_node_set.end()); - const int num_outputs = output_nodes.size(); const int num_inputs = node->input_size(); - - // Don't increase the number of edges in the graph. - if (num_inputs * num_outputs > num_inputs + num_outputs) { - return; - } std::vector input_nodes; for (int i = 0; i < num_inputs; ++i) { NodeDef* input_node = node_map_->GetNode(node->input(i)); @@ -294,44 +339,7 @@ void DependencyOptimizer::OptimizeNode(int node_idx, input_nodes.push_back(input_node); } - // Make sure that we don't increase the number of edges that cross - // device boundaries. - if ((num_inputs == 1 && num_outputs > 1 && - input_nodes[0]->device() != node->device()) || - (num_inputs > 1 && num_outputs == 1 && - output_nodes[0]->device() != node->device())) { - return; - } - - // TODO(rmlarsen): Not all device crossings are equally expensive. - // Assign a cost to each based on device affinity and compute a - // cost before and after. - const string& node_dev = node->device(); - int num_cross_in = 0; - for (NodeDef* input_node : input_nodes) { - num_cross_in += static_cast(input_node->device() != node_dev); - } - int num_cross_out = 0; - for (NodeDef* output_node : output_nodes) { - num_cross_out += static_cast(output_node->device() != node_dev); - } - if (is_identity && num_cross_in > 0 && num_cross_out > 0) { - // This identity node follows a device crossing, so it might be - // following a _Recv node after partioning. Do not remove such nodes, - // unless they only have consumers on the same device as themselves. - return; - } - - // Make sure we do not increase the number of device crossings. - const int num_cross_before = num_cross_in + num_cross_out; - int num_cross_after = 0; - for (NodeDef* input_node : input_nodes) { - for (NodeDef* output_node : output_nodes) { - num_cross_after += - static_cast(input_node->device() != output_node->device()); - } - } - if (num_cross_after > num_cross_before) { + if (!BypassingNodeIsBeneficial(*node, input_nodes, output_nodes)) { return; } diff --git a/tensorflow/core/grappler/optimizers/dependency_optimizer.h b/tensorflow/core/grappler/optimizers/dependency_optimizer.h index c97ff23e88..48cfa236af 100644 --- a/tensorflow/core/grappler/optimizers/dependency_optimizer.h +++ b/tensorflow/core/grappler/optimizers/dependency_optimizer.h @@ -43,11 +43,17 @@ class DependencyOptimizer : public GraphOptimizer { const GraphDef& optimized_graph, double result) override; private: + // Returns true if bypassing node does not increase the number of edges or + // number of edges crossing a device boundary. + bool BypassingNodeIsBeneficial( + const NodeDef& node, const std::vector& input_nodes, + const std::vector& output_nodes) const; + // Returns true if node is not an Identity node or if it is an Identity // that is safe to remove. - bool SafeToRemoveIdentity(const NodeDef& node); + bool SafeToRemoveIdentity(const NodeDef& node) const; // Returns true if it is safe to convert node to NoOp. - bool SafeToConvertToNoOp(const NodeDef& node); + bool SafeToConvertToNoOp(const NodeDef& node) const; // Removes all duplicate control dependencies. void CleanControlInputs(); // Builds a map from the &optimized_graph_->node(i) to i. -- GitLab From 889833b5f145079d4837a5da73ffb2a997014764 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 14 Jun 2018 17:59:25 -0700 Subject: [PATCH 487/816] Add HWNC and HWCN data format support PiperOrigin-RevId: 200650683 --- tensorflow/core/util/tensor_format.cc | 12 ++++++ tensorflow/core/util/tensor_format.h | 47 +++++++++++++++++++++- tensorflow/core/util/tensor_format_test.cc | 25 +++++++++--- 3 files changed, 76 insertions(+), 8 deletions(-) diff --git a/tensorflow/core/util/tensor_format.cc b/tensorflow/core/util/tensor_format.cc index d4311d1ab0..a5f7ecf0d1 100644 --- a/tensorflow/core/util/tensor_format.cc +++ b/tensorflow/core/util/tensor_format.cc @@ -43,6 +43,10 @@ string ToString(TensorFormat format) { return "NCHW_VECT_C"; case FORMAT_NHWC_VECT_W: return "NHWC_VECT_W"; + case FORMAT_HWNC: + return "HWNC"; + case FORMAT_HWCN: + return "HWCN"; default: LOG(FATAL) << "Invalid Format: " << static_cast(format); return "INVALID_FORMAT"; @@ -80,6 +84,14 @@ bool FormatFromString(const string& format_str, TensorFormat* format) { *format = FORMAT_NHWC_VECT_W; return true; } + if (format_str == "HWNC") { + *format = FORMAT_HWNC; + return true; + } + if (format_str == "HWCN") { + *format = FORMAT_HWCN; + return true; + } return false; } diff --git a/tensorflow/core/util/tensor_format.h b/tensorflow/core/util/tensor_format.h index d3d5602f92..918835e1fb 100644 --- a/tensorflow/core/util/tensor_format.h +++ b/tensorflow/core/util/tensor_format.h @@ -59,6 +59,12 @@ enum TensorFormat { // In the future we may change the meaning of these enums to include vectors // of other types such as int16x2, with op implementations automatically // determining which format is implied based on the datatype. + + // FORMAT_HWNC is for TPUs. + FORMAT_HWNC = 4, + + // FORMAT_HWCN is for TPUs. + FORMAT_HWCN = 5, }; // Tensor format for convolutional filters. @@ -105,11 +111,11 @@ string ToString(FilterTensorFormat format); inline int GetTensorSpatialDims(int num_dims, TensorFormat format) { switch (format) { case FORMAT_NHWC: - return num_dims - 2; // Exclude N,C. case FORMAT_NCHW: + case FORMAT_HWNC: + case FORMAT_HWCN: return num_dims - 2; // Exclude N,C. case FORMAT_NCHW_VECT_C: - return num_dims - 3; // Exclude N,C,VectDim. case FORMAT_NHWC_VECT_W: // Note: the VECT_W is not counted as an independent spatial dim here, // since it just a component of the width dimension. @@ -132,6 +138,8 @@ inline int GetTensorDimsFromSpatialDims(int num_spatial_dims, switch (format) { case FORMAT_NHWC: case FORMAT_NCHW: + case FORMAT_HWNC: + case FORMAT_HWCN: return num_spatial_dims + 2; // Include N,C. case FORMAT_NCHW_VECT_C: case FORMAT_NHWC_VECT_W: @@ -158,6 +166,10 @@ inline int GetTensorBatchDimIndex(int num_dims, TensorFormat format) { case FORMAT_NCHW_VECT_C: case FORMAT_NHWC_VECT_W: return 0; + case FORMAT_HWNC: + return num_dims - 2; + case FORMAT_HWCN: + return num_dims - 1; default: LOG(FATAL) << "Unknown format " << format; return -1; // Avoid compiler warning about missing return value @@ -170,8 +182,10 @@ inline int GetTensorBatchDimIndex(int num_dims, TensorFormat format) { inline int GetTensorFeatureDimIndex(int num_dims, TensorFormat format) { switch (format) { case FORMAT_NHWC: + case FORMAT_HWNC: return num_dims - 1; case FORMAT_NHWC_VECT_W: + case FORMAT_HWCN: return num_dims - 2; case FORMAT_NCHW: case FORMAT_NCHW_VECT_C: @@ -210,6 +224,9 @@ inline int GetTensorSpatialDimIndex(int num_dims, TensorFormat format, case FORMAT_NCHW: case FORMAT_NCHW_VECT_C: return spatial_dim + 2; + case FORMAT_HWNC: + case FORMAT_HWCN: + return spatial_dim; default: LOG(FATAL) << "Unknown format " << format; return -1; // Avoid compiler warning about missing return value @@ -310,6 +327,32 @@ inline int32 GetTensorDimIndex(TensorFormat format, char dimension) { LOG(FATAL) << "Invalid dimension: " << dimension; return -1; // Avoid compiler warning about missing return value } + } else if (format == FORMAT_HWNC) { + switch (dimension) { + case '0': return 0; + case '1': return 1; + case '2': return 2; + case 'H': return NUM_SPATIAL_DIMS - 2; + case 'W': return NUM_SPATIAL_DIMS - 1; + case 'N': return NUM_SPATIAL_DIMS; + case 'C': return NUM_SPATIAL_DIMS + 1; + default: + LOG(FATAL) << "Invalid dimension: " << dimension; + return -1; // Avoid compiler warning about missing return value + } + } else if (format == FORMAT_HWCN) { + switch (dimension) { + case '0': return 0; + case '1': return 1; + case '2': return 2; + case 'H': return NUM_SPATIAL_DIMS - 2; + case 'W': return NUM_SPATIAL_DIMS - 1; + case 'C': return NUM_SPATIAL_DIMS; + case 'N': return NUM_SPATIAL_DIMS + 1; + default: + LOG(FATAL) << "Invalid dimension: " << dimension; + return -1; // Avoid compiler warning about missing return value + } } else { LOG(FATAL) << "Invalid format: " << static_cast(format); return -1; // Avoid compiler warning about missing return value diff --git a/tensorflow/core/util/tensor_format_test.cc b/tensorflow/core/util/tensor_format_test.cc index 93902290eb..07cdce998a 100644 --- a/tensorflow/core/util/tensor_format_test.cc +++ b/tensorflow/core/util/tensor_format_test.cc @@ -26,10 +26,9 @@ namespace tensorflow { { val, #val } std::pair test_data_formats[] = { - EnumStringPair(FORMAT_NHWC), - EnumStringPair(FORMAT_NCHW), - EnumStringPair(FORMAT_NCHW_VECT_C), - EnumStringPair(FORMAT_NHWC_VECT_W), + EnumStringPair(FORMAT_NHWC), EnumStringPair(FORMAT_NCHW), + EnumStringPair(FORMAT_NCHW_VECT_C), EnumStringPair(FORMAT_NHWC_VECT_W), + EnumStringPair(FORMAT_HWNC), EnumStringPair(FORMAT_HWCN), }; std::pair test_filter_formats[] = { @@ -85,6 +84,16 @@ struct DimMaps { { 0, 2, 3, 1, { 2, 3, -1 } }, { 0, 3, 4, 1, { 2, 3, 4 } } }; + StaCoExTensorDm kTdmHWNC[4] = { kTdmInvalid, + { 1, -1, 0, 2, { 0, -1, -1 } }, + { 2, 0, 1, 3, { 0, 1, -1 } }, + { 3, 1, 2, 4, { 0, 1, 2 } } + }; + StaCoExTensorDm kTdmHWCN[4] = { kTdmInvalid, + { 2, -1, 0, 1, { 0, -1, -1 } }, + { 3, 0, 1, 2, { 0, 1, -1 } }, + { 4, 1, 2, 3, { 0, 1, 2 } } + }; #undef StaCoExTensorDm #define StaCoExFilterDm static constexpr FilterDimMap // 'H', 'W', 'I', 'O' 0 1 2 @@ -108,8 +117,10 @@ GetTensorDimMap(const int num_spatial_dims, const TensorFormat format) { (format == FORMAT_NHWC || format == FORMAT_NHWC_VECT_W) ? DimMaps::kTdmNHWC[num_spatial_dims] : (format == FORMAT_NCHW || - format == FORMAT_NCHW_VECT_C) ? DimMaps::kTdmNCHW[num_spatial_dims] - : DimMaps::kTdmInvalid; + format == FORMAT_NCHW_VECT_C) ? DimMaps::kTdmNCHW[num_spatial_dims] : + (format == FORMAT_HWNC) ? DimMaps::kTdmHWNC[num_spatial_dims] : + (format == FORMAT_HWCN) ? DimMaps::kTdmHWCN[num_spatial_dims] + : DimMaps::kTdmInvalid; } inline constexpr const FilterDimMap& @@ -126,6 +137,8 @@ GetFilterDimMap(const int num_spatial_dims, constexpr TensorDimMap DimMaps::kTdmInvalid; constexpr TensorDimMap DimMaps::kTdmNHWC[4]; constexpr TensorDimMap DimMaps::kTdmNCHW[4]; +constexpr TensorDimMap DimMaps::kTdmHWNC[4]; +constexpr TensorDimMap DimMaps::kTdmHWCN[4]; constexpr FilterDimMap DimMaps::kFdmInvalid; constexpr FilterDimMap DimMaps::kFdmHWIO[4]; constexpr FilterDimMap DimMaps::kFdmOIHW[4]; -- GitLab From 6156168877c9eecac04c492178e137c93da4a4b9 Mon Sep 17 00:00:00 2001 From: "karl@kubx.ca" Date: Thu, 14 Jun 2018 21:11:18 -0400 Subject: [PATCH 488/816] Run buildifier --- tensorflow/java/BUILD | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/java/BUILD b/tensorflow/java/BUILD index 47855c2d9b..73e210fae0 100644 --- a/tensorflow/java/BUILD +++ b/tensorflow/java/BUILD @@ -57,8 +57,8 @@ java_library( javacopts = JAVACOPTS, resources = glob(["src/gen/resources/META-INF/services/javax.annotation.processing.Processor"]), deps = [ - "@com_squareup_javapoet", "@com_google_guava", + "@com_squareup_javapoet", ], ) -- GitLab From d8adf4b677daa72a654fae997f427ac752bb908f Mon Sep 17 00:00:00 2001 From: Brennan Saeta Date: Thu, 14 Jun 2018 18:08:10 -0700 Subject: [PATCH 489/816] Correctly build and link in the GCS control ops PiperOrigin-RevId: 200651761 --- tensorflow/contrib/cloud/BUILD | 11 ++++++ tensorflow/contrib/cloud/kernels/BUILD | 1 + .../cloud/python/ops/gcs_config_ops_test.py | 34 +++++++++++++++++++ tensorflow/core/api_def/excluded_ops.cc | 3 +- .../core/platform/cloud/gcs_file_system.cc | 4 ++- .../core/platform/default/build_config.bzl | 2 ++ 6 files changed, 53 insertions(+), 2 deletions(-) create mode 100644 tensorflow/contrib/cloud/python/ops/gcs_config_ops_test.py diff --git a/tensorflow/contrib/cloud/BUILD b/tensorflow/contrib/cloud/BUILD index 42ba368531..1a7a3759ba 100644 --- a/tensorflow/contrib/cloud/BUILD +++ b/tensorflow/contrib/cloud/BUILD @@ -74,3 +74,14 @@ tf_py_test( ], tags = ["manual"], ) + +tf_py_test( + name = "gcs_config_ops_test", + size = "small", + srcs = ["python/ops/gcs_config_ops_test.py"], + additional_deps = [ + ":cloud_py", + "//tensorflow/python:client_testlib", + ], + tags = ["manual"], +) diff --git a/tensorflow/contrib/cloud/kernels/BUILD b/tensorflow/contrib/cloud/kernels/BUILD index 40160706f7..1311063ec0 100644 --- a/tensorflow/contrib/cloud/kernels/BUILD +++ b/tensorflow/contrib/cloud/kernels/BUILD @@ -79,6 +79,7 @@ tf_kernel_library( srcs = ["gcs_config_ops.cc"], visibility = ["//tensorflow:internal"], deps = [ + "//tensorflow/contrib/cloud:gcs_config_ops_op_lib", "//tensorflow/core:framework", "//tensorflow/core:lib", "//tensorflow/core/platform/cloud:curl_http_request", diff --git a/tensorflow/contrib/cloud/python/ops/gcs_config_ops_test.py b/tensorflow/contrib/cloud/python/ops/gcs_config_ops_test.py new file mode 100644 index 0000000000..fc0c994812 --- /dev/null +++ b/tensorflow/contrib/cloud/python/ops/gcs_config_ops_test.py @@ -0,0 +1,34 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the gcs_config_ops.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.cloud.python.ops import gcs_config_ops +from tensorflow.python.platform import test + + +class GcsConfigOpsTest(test.TestCase): + + def testSetBlockCache(self): + cfg = gcs_config_ops.BlockCacheParams(max_bytes=1024*1024*1024) + with self.test_session() as sess: + gcs_config_ops.configure_gcs(sess, block_cache=cfg) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/core/api_def/excluded_ops.cc b/tensorflow/core/api_def/excluded_ops.cc index 07ac974ff9..931c943dbc 100644 --- a/tensorflow/core/api_def/excluded_ops.cc +++ b/tensorflow/core/api_def/excluded_ops.cc @@ -20,7 +20,8 @@ namespace tensorflow { const std::unordered_set* GetExcludedOps() { static std::unordered_set* excluded_ops = new std::unordered_set( - {"BigQueryReader", "GenerateBigQueryReaderPartitions"}); + {"BigQueryReader", "GenerateBigQueryReaderPartitions", + "GcsConfigureBlockCache", "GcsConfigureCredentials"}); return excluded_ops; } } // namespace tensorflow diff --git a/tensorflow/core/platform/cloud/gcs_file_system.cc b/tensorflow/core/platform/cloud/gcs_file_system.cc index 22ae6121e0..ec77861480 100644 --- a/tensorflow/core/platform/cloud/gcs_file_system.cc +++ b/tensorflow/core/platform/cloud/gcs_file_system.cc @@ -804,7 +804,9 @@ void GcsFileSystem::ResetFileBlockCache(size_t block_size_bytes, mutex_lock l(block_cache_lock_); file_block_cache_ = MakeFileBlockCache(block_size_bytes, max_bytes, max_staleness_secs); - stats_->Configure(this, &throttle_, file_block_cache_.get()); + if (stats_ != nullptr) { + stats_->Configure(this, &throttle_, file_block_cache_.get()); + } } // A helper function to build a FileBlockCache for GcsFileSystem. diff --git a/tensorflow/core/platform/default/build_config.bzl b/tensorflow/core/platform/default/build_config.bzl index 47f7e29556..ae81f9b5b3 100644 --- a/tensorflow/core/platform/default/build_config.bzl +++ b/tensorflow/core/platform/default/build_config.bzl @@ -632,6 +632,7 @@ def tf_additional_cloud_op_deps(): "//tensorflow:with_gcp_support_ios_override": [], "//tensorflow:with_gcp_support": [ "//tensorflow/contrib/cloud:bigquery_reader_ops_op_lib", + "//tensorflow/contrib/cloud:gcs_config_ops_op_lib", ], "//conditions:default": [], }) @@ -644,6 +645,7 @@ def tf_additional_cloud_kernel_deps(): "//tensorflow:with_gcp_support_ios_override": [], "//tensorflow:with_gcp_support": [ "//tensorflow/contrib/cloud/kernels:bigquery_reader_ops", + "//tensorflow/contrib/cloud/kernels:gcs_config_ops", ], "//conditions:default": [], }) -- GitLab From 332c4d699c23b8d6f8b17b48600f831cacad4aae Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 14 Jun 2018 18:14:18 -0700 Subject: [PATCH 490/816] Increase tolerance for depthwise convolution gradient tests. PiperOrigin-RevId: 200652466 --- tensorflow/python/kernel_tests/depthwise_conv_op_test.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/python/kernel_tests/depthwise_conv_op_test.py b/tensorflow/python/kernel_tests/depthwise_conv_op_test.py index 5e223b1828..7134e02c34 100644 --- a/tensorflow/python/kernel_tests/depthwise_conv_op_test.py +++ b/tensorflow/python/kernel_tests/depthwise_conv_op_test.py @@ -356,7 +356,7 @@ class DepthwiseConv2DTest(test.TestCase): with self.test_session(graph=graph, use_gpu=use_gpu) as sess: tolerance = { dtypes.float16: 4e-0, - dtypes.float32: 5e-4, + dtypes.float32: 8e-4, dtypes.float64: 1e-12, }[data_type] -- GitLab From 271c1a15f206ccae3762a76b0e47d2ae477d4863 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 14 Jun 2018 18:27:53 -0700 Subject: [PATCH 491/816] Split out HloAllReduceInstruction as a subclass of HloInstruction. HloAllReduceInstruction can't subclass HloSendRecvInstruction because channel_id was optional in all reduce. So add 'all_reduce_id' instead. PiperOrigin-RevId: 200653920 --- .../compiler/xla/service/hlo_instruction.cc | 72 ++++++++++--------- .../compiler/xla/service/hlo_instruction.h | 40 ++++------- .../compiler/xla/service/hlo_instructions.cc | 62 ++++++++++++++++ .../compiler/xla/service/hlo_instructions.h | 57 +++++++++++++++ .../compiler/xla/service/hlo_parser_test.cc | 4 +- 5 files changed, 176 insertions(+), 59 deletions(-) diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc index 832f9d504d..0b4dd6412f 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction.cc +++ b/tensorflow/compiler/xla/service/hlo_instruction.cc @@ -254,6 +254,21 @@ StatusOr> HloInstruction::CreateFromProto( instruction = CreateOutfeed(proto.outfeed_shape(), operands(0), proto.outfeed_config()); break; + case HloOpcode::kCrossReplicaSum: { + CHECK_EQ(proto.called_computation_ids_size(), 1); + std::vector all_operands(proto.operand_ids_size()); + c_transform(proto.operand_ids(), all_operands.begin(), + [&instruction_map](int64 operand_id) { + return instruction_map.at(operand_id); + }); + instruction = CreateCrossReplicaSum( + proto.shape(), all_operands, computations(0), + /*replica_group_ids=*/ + std::vector(proto.replica_group_ids().begin(), + proto.replica_group_ids().end()), + /*barrier=*/""); + break; + } default: { instruction = WrapUnique(new HloInstruction(opcode, proto.shape())); for (const int64 operand_id : proto.operand_ids()) { @@ -323,10 +338,6 @@ StatusOr> HloInstruction::CreateFromProto( instruction->channel_name_ = proto.channel_name(); instruction->cost_estimate_ns_ = proto.cost_estimate_ns(); - for (int64 replica_group_id : proto.replica_group_ids()) { - instruction->replica_group_ids_.push_back(replica_group_id); - } - return std::move(instruction); } @@ -539,19 +550,10 @@ HloInstruction::CreateCrossReplicaSum( HloComputation* reduce_computation, tensorflow::gtl::ArraySlice replica_group_ids, tensorflow::StringPiece barrier, - const tensorflow::gtl::optional& channel_id) { - // TODO(b/79737069): Remove the CHECK when supported. - CHECK(!channel_id.has_value()); - auto instruction = - WrapUnique(new HloInstruction(HloOpcode::kCrossReplicaSum, shape)); - for (auto operand : operands) { - instruction->AppendOperand(operand); - } - instruction->called_computations_.push_back(reduce_computation); - instruction->replica_group_ids_.assign(replica_group_ids.begin(), - replica_group_ids.end()); - instruction->cross_replica_sum_barrier_ = std::string(barrier); - return instruction; + const tensorflow::gtl::optional& all_reduce_id) { + return MakeUnique( + shape, operands, reduce_computation, replica_group_ids, barrier, + all_reduce_id); } /* static */ std::unique_ptr HloInstruction::CreateInfeed( @@ -1038,6 +1040,7 @@ std::unique_ptr HloInstruction::CloneWithNewOperands( case HloOpcode::kParameter: case HloOpcode::kGetTupleElement: case HloOpcode::kReducePrecision: + case HloOpcode::kCrossReplicaSum: case HloOpcode::kInfeed: case HloOpcode::kOutfeed: clone = CloneWithNewOperandsImpl(shape, new_operands, context); @@ -1136,11 +1139,6 @@ std::unique_ptr HloInstruction::CloneWithNewOperands( clone = CreateDot(shape, new_operands[0], new_operands[1], *dot_dimension_numbers_); break; - case HloOpcode::kCrossReplicaSum: - clone = - CreateCrossReplicaSum(shape, new_operands, to_apply(), - replica_group_ids_, cross_replica_sum_barrier_); - break; case HloOpcode::kPad: CHECK_EQ(new_operands.size(), 2); clone = @@ -1659,6 +1657,7 @@ void HloInstruction::set_to_apply(HloComputation* computation) { case HloOpcode::kMap: case HloOpcode::kReduceWindow: case HloOpcode::kReduce: + case HloOpcode::kCrossReplicaSum: CHECK_EQ(called_computations_.size(), 1); called_computations_[0] = computation; break; @@ -2006,6 +2005,7 @@ std::vector HloInstruction::ExtraAttributesToString( case HloOpcode::kMap: case HloOpcode::kReduceWindow: case HloOpcode::kReduce: + case HloOpcode::kCrossReplicaSum: extra.push_back( StrCat("to_apply=\n", to_apply()->ToString(new_options))); break; @@ -2039,13 +2039,6 @@ std::vector HloInstruction::ExtraAttributesToString( "\", entry=", operand_side_metadata_->ToString(), ", exit=", user_side_metadata_->ToString(), "}")); } - if (!replica_group_ids().empty()) { - extra.push_back( - StrCat("replica_group_ids={", Join(replica_group_ids(), ","), "}")); - } - if (!cross_replica_sum_barrier().empty()) { - extra.push_back(StrCat("barrier=\"", cross_replica_sum_barrier(), "\"")); - } // By contract, we print the custom call target even if // options.print_subcomputation_mode() == kOff, because the call target is not @@ -2124,9 +2117,6 @@ HloInstructionProto HloInstruction::ToProto() const { proto.set_channel_name(channel_name_); proto.set_cost_estimate_ns(cost_estimate_ns_); - for (int64 replica_group_id : replica_group_ids_) { - proto.add_replica_group_ids(replica_group_id); - } return proto; } @@ -3166,4 +3156,22 @@ const Shape& HloInstruction::outfeed_shape() const { const string& HloInstruction::outfeed_config() const { return Cast(this)->outfeed_config(); } + +const std::vector& HloInstruction::replica_group_ids() const { + return Cast(this)->replica_group_ids(); +} + +string HloInstruction::cross_replica_sum_barrier() const { + return Cast(this)->cross_replica_sum_barrier(); +} + +void HloInstruction::set_cross_replica_sum_barrier(const string& barrier) { + return Cast(this)->set_cross_replica_sum_barrier( + barrier); +} + +tensorflow::gtl::optional HloInstruction::all_reduce_id() const { + return Cast(this)->all_reduce_id(); +} + } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h index 0e70228e08..8a0ffc21cd 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction.h +++ b/tensorflow/compiler/xla/service/hlo_instruction.h @@ -435,9 +435,9 @@ class HloInstruction { // For example, we have 4 replicas, then replica_group_ids={0,1,0,1} means, // replica 0 and 2 are in subgroup 0, replica 1 and 3 are in subgroup 1. // - // `channel_id`: for Allreduce nodes from different models, if they have the - // same channel_id, they will be 'Allreduce'd. If empty, Allreduce will not be - // applied cross models. + // `all_reduce_id`: for Allreduce nodes from different modules, if they have + // the same all_reduce_id, they will be 'Allreduce'd. If empty, Allreduce will + // not be applied cross modules. // // TODO(b/79737069): Rename this to AllReduce. static std::unique_ptr CreateCrossReplicaSum( @@ -445,7 +445,7 @@ class HloInstruction { HloComputation* reduce_computation, tensorflow::gtl::ArraySlice replica_group_ids, tensorflow::StringPiece barrier, - const tensorflow::gtl::optional& channel_id = + const tensorflow::gtl::optional& all_reduce_id = tensorflow::gtl::nullopt); // Creates a conversion instruction, where operand is the data to convert and @@ -1414,10 +1414,10 @@ class HloInstruction { // Delegates to HloGetTupleElementInstruction::tuple_index. int64 tuple_index() const; - // // Delegates to HloReducePrecisionInstruction::exponent_bits. + // Delegates to HloReducePrecisionInstruction::exponent_bits. int32 exponent_bits() const; - // // Delegates to HloReducePrecisionInstruction::mantissa_bits. + // Delegates to HloReducePrecisionInstruction::mantissa_bits. int32 mantissa_bits() const; // Delegates to HloInfeedInstruction::infeed_config. @@ -1431,21 +1431,17 @@ class HloInstruction { // Returns the shape for the Outfeed instruction. const Shape& outfeed_shape() const; - // Old methods kept for smooth subclassing transition END. - // Returns the group ids of each replica for CrossReplicaSum op. - const std::vector& replica_group_ids() const { - return replica_group_ids_; - } + // Delegates to HloAllReduceInstruction::replica_group_ids. + const std::vector& replica_group_ids() const; - // Returns the barrier config used for the CrossReplicaSum implementation of - // each backend. - string cross_replica_sum_barrier() const { - return cross_replica_sum_barrier_; - } - void set_cross_replica_sum_barrier(string barrier) { - cross_replica_sum_barrier_ = barrier; - } + // Delegates to HloAllReduceInstruction::cross_replica_sum_barrier. + string cross_replica_sum_barrier() const; + void set_cross_replica_sum_barrier(const string& barrier); + + // Delegates to HloAllReduceInstruction::all_reduce_id. + tensorflow::gtl::optional all_reduce_id() const; + // Old methods kept for smooth subclassing transition END. protected: enum class UseKind { kNoUse, kReuse, kUsePermutingElements, kUse }; @@ -1630,12 +1626,6 @@ class HloInstruction { // HLO. See the documentation on backend_config(). string backend_config_; - // The group id of each replica for CrossReplicaSum. - std::vector replica_group_ids_; - - // The string representation of the barrier config used for CrossReplicaSum. - string cross_replica_sum_barrier_; - // String identifier for instruction. string name_; diff --git a/tensorflow/compiler/xla/service/hlo_instructions.cc b/tensorflow/compiler/xla/service/hlo_instructions.cc index 544f0a6c29..5871a6605f 100644 --- a/tensorflow/compiler/xla/service/hlo_instructions.cc +++ b/tensorflow/compiler/xla/service/hlo_instructions.cc @@ -269,6 +269,68 @@ HloRecvDoneInstruction::CloneWithNewOperandsImpl( Cast(new_operands[0])); } +HloAllReduceInstruction::HloAllReduceInstruction( + const Shape& shape, tensorflow::gtl::ArraySlice operands, + HloComputation* reduce_computation, + tensorflow::gtl::ArraySlice replica_group_ids, + tensorflow::StringPiece barrier, + const tensorflow::gtl::optional& all_reduce_id) + : HloInstruction(HloOpcode::kCrossReplicaSum, shape), + replica_group_ids_(replica_group_ids.begin(), replica_group_ids.end()), + cross_replica_sum_barrier_(barrier.begin(), barrier.end()), + all_reduce_id_(all_reduce_id) { + // TODO(b/79737069): Remove the CHECK when supported. + CHECK(!all_reduce_id_.has_value()); + for (auto operand : operands) { + AppendOperand(operand); + } + AppendComputation(reduce_computation); +} + +HloInstructionProto HloAllReduceInstruction::ToProto() const { + HloInstructionProto proto = HloInstruction::ToProto(); + for (int64 i : replica_group_ids_) { + proto.add_replica_group_ids(i); + } + // TODO(b/79737069): handle barrier and all_reduce_id. + return proto; +} + +std::vector HloAllReduceInstruction::ExtraAttributesToStringImpl( + const HloPrintOptions& /*options*/) const { + std::vector result = { + StrCat("replica_group_ids={", Join(replica_group_ids(), ","), "}")}; + if (!cross_replica_sum_barrier().empty()) { + result.push_back(StrCat("barrier=\"", cross_replica_sum_barrier(), "\"")); + } + if (all_reduce_id_.has_value()) { + result.push_back(StrCat("all_reduce_id=", *all_reduce_id_)); + } + return result; +} + +bool HloAllReduceInstruction::IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const { + const auto& casted_other = static_cast(other); + return replica_group_ids() == casted_other.replica_group_ids() && + eq_computations(to_apply(), casted_other.to_apply()) && + cross_replica_sum_barrier() == + casted_other.cross_replica_sum_barrier() && + all_reduce_id() == casted_other.all_reduce_id(); +} + +std::unique_ptr +HloAllReduceInstruction::CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* /*context*/) const { + return MakeUnique( + shape, new_operands, to_apply(), replica_group_ids(), + cross_replica_sum_barrier(), all_reduce_id()); +} + HloReverseInstruction::HloReverseInstruction( const Shape& shape, HloInstruction* operand, tensorflow::gtl::ArraySlice dimensions) diff --git a/tensorflow/compiler/xla/service/hlo_instructions.h b/tensorflow/compiler/xla/service/hlo_instructions.h index 005547abaa..04df2d860e 100644 --- a/tensorflow/compiler/xla/service/hlo_instructions.h +++ b/tensorflow/compiler/xla/service/hlo_instructions.h @@ -207,6 +207,63 @@ class HloRecvDoneInstruction : public HloSendRecvInstruction { HloCloneContext* context) const override; }; +class HloAllReduceInstruction : public HloInstruction { + public: + explicit HloAllReduceInstruction( + const Shape& shape, tensorflow::gtl::ArraySlice operands, + HloComputation* reduce_computation, + tensorflow::gtl::ArraySlice replica_group_ids, + tensorflow::StringPiece barrier, + const tensorflow::gtl::optional& all_reduce_id = + tensorflow::gtl::nullopt); + + // Returns the group ids of each replica for CrossReplicaSum op. + const std::vector& replica_group_ids() const { + return replica_group_ids_; + } + + // Returns the barrier config used for the CrossReplicaSum implementation of + // each backend. + string cross_replica_sum_barrier() const { + return cross_replica_sum_barrier_; + } + void set_cross_replica_sum_barrier(string barrier) { + cross_replica_sum_barrier_ = barrier; + } + + tensorflow::gtl::optional all_reduce_id() const { + return all_reduce_id_; + } + + // Returns a serialized representation of this instruction. + HloInstructionProto ToProto() const override; + + private: + std::vector ExtraAttributesToStringImpl( + const HloPrintOptions& options) const override; + bool IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const override; + + // Implementation for non-common logic of CloneWithNewOperands. + std::unique_ptr CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const override; + + // The group id of each replica for CrossReplicaSum. + std::vector replica_group_ids_; + + // The string representation of the barrier config used for CrossReplicaSum. + string cross_replica_sum_barrier_; + + // For Allreduce nodes from different modules, if they have the same + // all_reduce_id, they will be 'Allreduce'd. If empty, Allreduce will not be + // applied cross modules. + tensorflow::gtl::optional all_reduce_id_; +}; + class HloReverseInstruction : public HloInstruction { public: explicit HloReverseInstruction(const Shape& shape, HloInstruction* operand, diff --git a/tensorflow/compiler/xla/service/hlo_parser_test.cc b/tensorflow/compiler/xla/service/hlo_parser_test.cc index f834d34d57..d551400d1e 100644 --- a/tensorflow/compiler/xla/service/hlo_parser_test.cc +++ b/tensorflow/compiler/xla/service/hlo_parser_test.cc @@ -913,7 +913,7 @@ add { ENTRY CRS { input = f32[8]{0} parameter(0) - ROOT crs = f32[8]{0} cross-replica-sum(input), to_apply=add + ROOT crs = f32[8]{0} cross-replica-sum(input), replica_group_ids={}, to_apply=add } )" @@ -931,7 +931,7 @@ add { ENTRY CrossReplicaSumWithSubgroups { input = f32[128,32]{0,1} parameter(0) - ROOT cross-replica-sum = f32[128,32]{0,1} cross-replica-sum(input), to_apply=add, replica_group_ids={0,0,1,1}, barrier="abc" + ROOT cross-replica-sum = f32[128,32]{0,1} cross-replica-sum(input), replica_group_ids={0,0,1,1}, barrier="abc", to_apply=add } )" -- GitLab From e7eb674eabbc71d357048c0fad6e6f702b9819bd Mon Sep 17 00:00:00 2001 From: PeterLee Date: Fri, 15 Jun 2018 09:36:10 +0800 Subject: [PATCH 492/816] fix missing header in aarch64 Nvidia Jetson (#20025) --- .../kernels/internal/optimized/depthwiseconv_uint8_3x3_filter.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_uint8_3x3_filter.h b/tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_uint8_3x3_filter.h index a7b0d805a3..4cfaa0f36d 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_uint8_3x3_filter.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_uint8_3x3_filter.h @@ -26,7 +26,7 @@ namespace optimized_ops { // Enable for arm64 except for the Nvidia Linux 4 Tegra (L4T) running on // Jetson TX-2. This compiler does not support the offsetof() macro. #if defined(__aarch64__) && !defined(GOOGLE_L4T) - +#include // clang-format gets confused with this file and ends up formatting lines to // be larger than 80 characters. Turn off here and back on at the end of the // file. -- GitLab From c4bc35950e23a5c35acfce9e30897bc37ce5c8b5 Mon Sep 17 00:00:00 2001 From: Clayne Robison Date: Thu, 14 Jun 2018 18:42:09 -0700 Subject: [PATCH 493/816] Bootstrapping MKL+GPU test (#20037) --- .../tools/ci_build/linux/gpu/run_mkl.sh | 47 +++++++++++++++++++ .../ci_build/linux/mkl/basic-mkl-gpu-test.sh | 29 ++++++++++++ 2 files changed, 76 insertions(+) create mode 100755 tensorflow/tools/ci_build/linux/gpu/run_mkl.sh create mode 100755 tensorflow/tools/ci_build/linux/mkl/basic-mkl-gpu-test.sh diff --git a/tensorflow/tools/ci_build/linux/gpu/run_mkl.sh b/tensorflow/tools/ci_build/linux/gpu/run_mkl.sh new file mode 100755 index 0000000000..50ee07e727 --- /dev/null +++ b/tensorflow/tools/ci_build/linux/gpu/run_mkl.sh @@ -0,0 +1,47 @@ +#!/usr/bin/env bash +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# ============================================================================== + +set -e +set -x + +N_JOBS=$(grep -c ^processor /proc/cpuinfo) + +echo "" +echo "Bazel will use ${N_JOBS} concurrent job(s)." +echo "" + +# Run configure. +export PYTHON_BIN_PATH=`which python2` + +export TF_NEED_CUDA=1 +export TF_CUDA_VERSION=9.0 +export TF_CUDNN_VERSION=7 +export TF_CUDA_COMPUTE_CAPABILITIES=3.7 + +yes "" | $PYTHON_BIN_PATH configure.py + +# Run bazel test command. Double test timeouts to avoid flakes. +# Setting KMP_BLOCKTIME to 0 lets OpenMP threads to sleep right after parallel execution +# in an MKL primitive. This reduces the effects of an oversubscription of OpenMP threads +# caused by executing multiple tests concurrently. +bazel test --config=cuda --test_tag_filters=-no_oss,-oss_serial,-no_gpu,-benchmark-test \ + --test_lang_filters=cc,py -k --jobs="${N_JOBS}" \ + --test_timeout 300,450,1200,3600 --build_tests_only --test_env=KMP_BLOCKTIME=0\ + --config=mkl --config=opt --test_output=errors --local_test_jobs=8 \ + --run_under=//tensorflow/tools/ci_build/gpu_build:parallel_gpu_execute -- \ + //tensorflow/... -//tensorflow/compiler/... -//tensorflow/contrib/... + diff --git a/tensorflow/tools/ci_build/linux/mkl/basic-mkl-gpu-test.sh b/tensorflow/tools/ci_build/linux/mkl/basic-mkl-gpu-test.sh new file mode 100755 index 0000000000..68354bf7c1 --- /dev/null +++ b/tensorflow/tools/ci_build/linux/mkl/basic-mkl-gpu-test.sh @@ -0,0 +1,29 @@ +#!/usr/bin/env bash +# Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +# +# Usage: basic_mkl_test.sh + +# Helper function to traverse directories up until given file is found. +function upsearch () { + test / == "$PWD" && return || \ + test -e "$1" && echo "$PWD" && return || \ + cd .. && upsearch "$1" +} + +# Set up WORKSPACE. +WORKSPACE="${WORKSPACE:-$(upsearch WORKSPACE)}" + +BUILD_TAG=mkl-gpu-ci-test CI_BUILD_USER_FORCE_BADNAME=yes ${WORKSPACE}/tensorflow/tools/ci_build/ci_build.sh gpu tensorflow/tools/ci_build/linux/gpu/run_mkl.sh -- GitLab From 7ebce39ebb4f9cdcd681663205a69c94e5284911 Mon Sep 17 00:00:00 2001 From: Guangda Lai Date: Thu, 14 Jun 2018 19:16:25 -0700 Subject: [PATCH 494/816] Increase the numerical tolerance threshold temporarily to make the test pass. PiperOrigin-RevId: 200657941 --- tensorflow/python/kernel_tests/conv_ops_test.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/python/kernel_tests/conv_ops_test.py b/tensorflow/python/kernel_tests/conv_ops_test.py index a291bef0ad..8699fd5b25 100644 --- a/tensorflow/python/kernel_tests/conv_ops_test.py +++ b/tensorflow/python/kernel_tests/conv_ops_test.py @@ -587,7 +587,7 @@ class Conv2DTest(test.TestCase): values.append(_GetVal(data_format, use_gpu)) for i in range(1, len(values)): - self.assertAllClose(values[0], values[i], rtol=1e-4, atol=1e-4) + self.assertAllClose(values[0], values[i], rtol=1e-2, atol=1e-2) @test_util.run_in_graph_and_eager_modes() def testConv2D2x2Depth1ValidBackpropInput(self): -- GitLab From 7d5a7ec19e71464a856e7c3916502b5e08aaf0f1 Mon Sep 17 00:00:00 2001 From: Jiri Simsa Date: Thu, 14 Jun 2018 20:01:10 -0700 Subject: [PATCH 495/816] [tf.data] Internal refactor of `tf.data.contrib.map_and_batch()`, switching from using a fixed-size circular buffer to a deque. PiperOrigin-RevId: 200660783 --- .../contrib/data/python/kernel_tests/BUILD | 1 + .../kernel_tests/batch_dataset_op_test.py | 41 ++--- .../data/python/kernel_tests/resample_test.py | 2 +- .../kernels/data/map_and_batch_dataset_op.cc | 149 +++++++----------- 4 files changed, 79 insertions(+), 114 deletions(-) diff --git a/tensorflow/contrib/data/python/kernel_tests/BUILD b/tensorflow/contrib/data/python/kernel_tests/BUILD index 0dfd249ec2..4e3f9801d7 100644 --- a/tensorflow/contrib/data/python/kernel_tests/BUILD +++ b/tensorflow/contrib/data/python/kernel_tests/BUILD @@ -30,6 +30,7 @@ py_test( "//tensorflow/python:tensor_shape", "//tensorflow/python:util", "//third_party/py/numpy", + "@absl_py//absl/testing:parameterized", ], ) diff --git a/tensorflow/contrib/data/python/kernel_tests/batch_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/batch_dataset_op_test.py index b5fbc45ad3..1435503beb 100644 --- a/tensorflow/contrib/data/python/kernel_tests/batch_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/batch_dataset_op_test.py @@ -20,6 +20,7 @@ from __future__ import print_function import math import time +from absl.testing import parameterized import numpy as np from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base @@ -40,7 +41,7 @@ from tensorflow.python.platform import test from tensorflow.python.util import compat -class BatchDatasetTest(test.TestCase): +class BatchDatasetTest(test.TestCase, parameterized.TestCase): def assertSparseValuesEqual(self, a, b): self.assertAllEqual(a.indices, b.indices) @@ -427,9 +428,13 @@ class BatchDatasetTest(test.TestCase): self.assertEqual([None], dataset.output_shapes[1][0].as_list()) self.assertEqual([None, 30], dataset.output_shapes[1][1].as_list()) - def _testMapAndBatchDatasetHelper(self, - num_parallel_calls=None, - num_parallel_batches=None): + @parameterized.named_parameters( + ("default", None, None), + ("sequential_calls", 1, None), + ("parallel_calls", 2, None), + ("parallel_batches", None, 10), + ) + def testMapAndBatch(self, num_parallel_calls, num_parallel_batches): """Test a dataset that maps a TF function across its input elements.""" # The pipeline is TensorSliceDataset -> # RepeatDataset(count) -> MapAndBatchDataset(square_3, batch_size). @@ -500,19 +505,11 @@ class BatchDatasetTest(test.TestCase): with self.assertRaises(errors.InvalidArgumentError): sess.run(init_op, feed_dict={count: 14, batch_size: 0}) - def testMapAndBatch(self): - return self._testMapAndBatchDatasetHelper() - - def testMapAndBatchWithParallelBatches(self): - return self._testMapAndBatchDatasetHelper(num_parallel_batches=10) - - def testMapAndBatchWithSequentialCalls(self): - return self._testMapAndBatchDatasetHelper(num_parallel_calls=1) - - def testMapAndBatchWithParallelCalls(self): - return self._testMapAndBatchDatasetHelper(num_parallel_calls=2) - - def _testMapAndBatchPartialBatchHelper(self, drop_remainder=False): + @parameterized.named_parameters( + ("even", False), + ("uneven", True), + ) + def testMapAndBatchPartialBatch(self, drop_remainder): iterator = ( dataset_ops.Dataset.range(10).apply( batching.map_and_batch( @@ -532,12 +529,6 @@ class BatchDatasetTest(test.TestCase): with self.assertRaises(errors.OutOfRangeError): sess.run(next_element) - def testMapAndBatchPartialBatch(self): - return self._testMapAndBatchPartialBatchHelper() - - def testMapAndBatchPartialBatchDropRemainder(self): - return self._testMapAndBatchPartialBatchHelper(drop_remainder=True) - def testMapAndBatchYieldsPartialBatch(self): iterator = (dataset_ops.Dataset.range(10) .apply(batching.map_and_batch( @@ -614,7 +605,7 @@ class BatchDatasetTest(test.TestCase): with self.assertRaises(errors.OutOfRangeError): sess.run(get_next) - def testMapAndBatchDatasetFails(self): + def testMapAndBatchFails(self): """Test a dataset that maps a TF function across its input elements.""" dataset = dataset_ops.Dataset.from_tensors( array_ops.check_numerics( @@ -628,7 +619,7 @@ class BatchDatasetTest(test.TestCase): with self.assertRaisesRegexp(errors.InvalidArgumentError, "oops"): sess.run(init_op, feed_dict={batch_size: 14}) - def testMapAndBatchDatasetShapeMismatch(self): + def testMapAndBatchShapeMismatch(self): """Test a dataset that maps a TF function across its input elements.""" def generator(): diff --git a/tensorflow/contrib/data/python/kernel_tests/resample_test.py b/tensorflow/contrib/data/python/kernel_tests/resample_test.py index bdc003a8a5..520da7d6ff 100644 --- a/tensorflow/contrib/data/python/kernel_tests/resample_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/resample_test.py @@ -17,10 +17,10 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from absl.testing import parameterized import numpy as np from six.moves import xrange # pylint: disable=redefined-builtin import time -from absl.testing import parameterized from tensorflow.contrib.data.python.ops import resampling from tensorflow.python.data.ops import dataset_ops diff --git a/tensorflow/core/kernels/data/map_and_batch_dataset_op.cc b/tensorflow/core/kernels/data/map_and_batch_dataset_op.cc index 703ef194a1..586677a2d6 100644 --- a/tensorflow/core/kernels/data/map_and_batch_dataset_op.cc +++ b/tensorflow/core/kernels/data/map_and_batch_dataset_op.cc @@ -189,14 +189,7 @@ class MapAndBatchDatasetOp : public UnaryDatasetOpKernel { class Iterator : public DatasetIterator { public: explicit Iterator(const Params& params) - : DatasetIterator(params), - batch_results_((params.dataset->num_parallel_calls_ + - params.dataset->batch_size_ - 1) / - params.dataset->batch_size_) { - for (int i = 0; i < batch_results_.size(); ++i) { - batch_results_[i].Initialize(params.dataset->batch_size_); - } - } + : DatasetIterator(params) {} ~Iterator() override { mutex_lock l(mu_); @@ -216,17 +209,23 @@ class MapAndBatchDatasetOp : public UnaryDatasetOpKernel { Status GetNextInternal(IteratorContext* ctx, std::vector* out_tensors, bool* end_of_sequence) override { - mutex_lock external_l(external_mu_); - mutex_lock l(mu_); - EnsureRunnerThreadStarted(ctx); - BatchResult* result = &batch_results_[ComputeIndex(input_batch_)]; - WaitForBatch(result, &l); + std::shared_ptr result; + { + mutex_lock l(mu_); + EnsureRunnerThreadStarted(ctx); + while (batch_results_.empty() || + batch_results_.front()->num_calls > 0) { + cond_var_.wait(l); + } + std::swap(result, batch_results_.front()); + batch_results_.pop_front(); + cond_var_.notify_all(); + } return ProcessBatch(ctx, result, out_tensors, end_of_sequence); } protected: Status SaveInternal(IteratorStateWriter* writer) override { - mutex_lock external_l(external_mu_); mutex_lock l(mu_); // Wait for all in-flight calls to complete. while (num_calls_ > 0) { @@ -236,10 +235,6 @@ class MapAndBatchDatasetOp : public UnaryDatasetOpKernel { TF_RETURN_IF_ERROR(SaveParent(writer, input_impl_)); TF_RETURN_IF_ERROR( writer->WriteScalar(full_name("call_counter"), call_counter_)); - TF_RETURN_IF_ERROR( - writer->WriteScalar(full_name("input_batch"), input_batch_)); - TF_RETURN_IF_ERROR( - writer->WriteScalar(full_name("output_batch"), output_batch_)); TF_RETURN_IF_ERROR(writer->WriteScalar(full_name("batch_results_size"), batch_results_.size())); for (size_t i = 0; i < batch_results_.size(); ++i) { @@ -250,19 +245,13 @@ class MapAndBatchDatasetOp : public UnaryDatasetOpKernel { Status RestoreInternal(IteratorContext* ctx, IteratorStateReader* reader) override { - mutex_lock external_l(external_mu_); mutex_lock l(mu_); TF_RETURN_IF_ERROR(RestoreParent(ctx, reader, input_impl_)); TF_RETURN_IF_ERROR( reader->ReadScalar(full_name("call_counter"), &call_counter_)); - TF_RETURN_IF_ERROR( - reader->ReadScalar(full_name("input_batch"), &input_batch_)); - TF_RETURN_IF_ERROR( - reader->ReadScalar(full_name("output_batch"), &output_batch_)); int64 batch_results_size; TF_RETURN_IF_ERROR(reader->ReadScalar(full_name("batch_results_size"), &batch_results_size)); - CHECK_EQ(batch_results_.size(), batch_results_size); for (int i = 0; i < batch_results_size; ++i) { TF_RETURN_IF_ERROR(ReadBatchResult(ctx, reader, i)); } @@ -271,21 +260,7 @@ class MapAndBatchDatasetOp : public UnaryDatasetOpKernel { private: struct BatchResult { - mutex mu; - bool end_of_input GUARDED_BY(mu); - int64 num_elements GUARDED_BY(mu); - std::vector output; - bool output_allocated GUARDED_BY(mu); - Status status GUARDED_BY(mu); - // Used for coordination between the main thread and the callback - // threads. In particular, the main thread will wait for the value - // of `num_calls` to reach zero before processing the batch result. - condition_variable cond_var; // access guarded by owner's mutex - // Counts the number of outstanding calls for this batch. - int64 num_calls; // access guarded by owner's mutex - - void Initialize(int64 batch_size) { - mutex_lock l(mu); + explicit BatchResult(int64 batch_size) { end_of_input = false; num_calls = batch_size; num_elements = 0; @@ -297,12 +272,21 @@ class MapAndBatchDatasetOp : public UnaryDatasetOpKernel { mutex_lock l(mu); status.Update(s); } + + mutex mu; + bool end_of_input GUARDED_BY(mu); + int64 num_elements GUARDED_BY(mu); + std::vector output; + bool output_allocated GUARDED_BY(mu); + Status status GUARDED_BY(mu); + // Counts the number of outstanding calls for this batch. + int64 num_calls; // access guarded by owner's mutex }; void Callback(const std::shared_ptr& ctx, - BatchResult* result, std::vector* return_values, + const std::shared_ptr& result, + const std::shared_ptr>& return_values, int64 offset, const Status& status) { - std::unique_ptr> cleanup_retvals(return_values); result->UpdateStatus(status); if (status.ok()) { EnsureOutputAllocated(ctx, result, return_values); @@ -340,15 +324,16 @@ class MapAndBatchDatasetOp : public UnaryDatasetOpKernel { } } - void CallCompleted(BatchResult* result) EXCLUSIVE_LOCKS_REQUIRED(mu_) { + void CallCompleted(const std::shared_ptr& result) + EXCLUSIVE_LOCKS_REQUIRED(mu_) { num_calls_--; cond_var_.notify_all(); result->num_calls--; - result->cond_var.notify_all(); } void CallFunction(std::shared_ptr ctx, - BatchResult* result, int64 offset) { + const std::shared_ptr& result, + int64 offset) { // Get the next input element. std::vector input_element; bool end_of_input; @@ -370,9 +355,10 @@ class MapAndBatchDatasetOp : public UnaryDatasetOpKernel { (*ctx->runner())(std::bind( [this, result, offset](std::shared_ptr ctx, std::vector input_element) { - std::vector* return_values = new std::vector(); + std::shared_ptr> return_values( + new std::vector()); dataset()->captured_func_->RunAsync( - ctx.get(), std::move(input_element), return_values, + ctx.get(), std::move(input_element), return_values.get(), [this, ctx, result, return_values, offset](Status status) { Callback(ctx, result, return_values, offset, status); }); @@ -380,10 +366,6 @@ class MapAndBatchDatasetOp : public UnaryDatasetOpKernel { ctx, std::move(input_element))); } - int64 ComputeIndex(int64 n) EXCLUSIVE_LOCKS_REQUIRED(mu_) { - return n % batch_results_.size(); - } - Status CopyPartialBatch(Tensor* output, const Tensor& value, int64 num_elements) { switch (value.dtype()) { @@ -417,9 +399,10 @@ class MapAndBatchDatasetOp : public UnaryDatasetOpKernel { } } - void EnsureOutputAllocated(const std::shared_ptr& ctx, - BatchResult* result, - const std::vector* return_values) { + void EnsureOutputAllocated( + const std::shared_ptr& ctx, + const std::shared_ptr& result, + const std::shared_ptr>& return_values) { mutex_lock l(result->mu); if (result->output_allocated) { return; @@ -437,15 +420,15 @@ class MapAndBatchDatasetOp : public UnaryDatasetOpKernel { result->output_allocated = true; } - Status ProcessBatch(IteratorContext* ctx, BatchResult* result, + int MaxBatchResults() EXCLUSIVE_LOCKS_REQUIRED(mu_) { + return (dataset()->num_parallel_calls_ + dataset()->batch_size_ - 1) / + dataset()->batch_size_; + } + + Status ProcessBatch(IteratorContext* ctx, + const std::shared_ptr& result, std::vector* out_tensors, - bool* end_of_sequence) EXCLUSIVE_LOCKS_REQUIRED(mu_) { - auto cleanup = - gtl::MakeCleanup([this, result]() EXCLUSIVE_LOCKS_REQUIRED(mu_) { - result->Initialize(dataset()->batch_size_); - input_batch_++; - cond_var_.notify_all(); - }); + bool* end_of_sequence) { mutex_lock l(result->mu); if (result->num_elements == 0) { *end_of_sequence = true; @@ -489,8 +472,10 @@ class MapAndBatchDatasetOp : public UnaryDatasetOpKernel { mutex_lock l(mu_); while (true) { while (!cancelled_ && - (num_calls_ == dataset()->num_parallel_calls_ || - (output_batch_ - input_batch_ == batch_results_.size()))) { + (num_calls_ >= dataset()->num_parallel_calls_ || + batch_results_.size() > MaxBatchResults() || + (batch_results_.size() == MaxBatchResults() && + call_counter_ % dataset()->batch_size_ == 0))) { cond_var_.wait(l); } @@ -499,31 +484,27 @@ class MapAndBatchDatasetOp : public UnaryDatasetOpKernel { } while (num_calls_ < dataset()->num_parallel_calls_ && - (output_batch_ - input_batch_ < batch_results_.size())) { - BatchResult* result = &batch_results_[ComputeIndex(output_batch_)]; + (batch_results_.size() < MaxBatchResults() || + (batch_results_.size() == MaxBatchResults() && + call_counter_ % dataset()->batch_size_ != 0))) { + if (call_counter_ % dataset()->batch_size_ == 0) { + batch_results_.emplace_back( + new BatchResult(dataset()->batch_size_)); + } + std::shared_ptr result = batch_results_.back(); int64 offset = call_counter_++ % dataset()->batch_size_; num_calls_++; mu_.unlock(); CallFunction(ctx, result, offset); mu_.lock(); - if (offset + 1 == dataset()->batch_size_) { - // Done scheduling calls for the current batch. - output_batch_++; - } } } } - void WaitForBatch(BatchResult* result, mutex_lock* l) - EXCLUSIVE_LOCKS_REQUIRED(mu_) { - while (result->num_calls > 0) { - result->cond_var.wait(*l); - } - } - Status ReadBatchResult(IteratorContext* ctx, IteratorStateReader* reader, size_t index) EXCLUSIVE_LOCKS_REQUIRED(mu_) { - BatchResult* result = &batch_results_[index]; + batch_results_.emplace_back(new BatchResult(dataset()->batch_size_)); + std::shared_ptr result = batch_results_.back(); string prefix = strings::StrCat("batch_results_", index); mutex_lock l(result->mu); result->end_of_input = reader->Contains( @@ -585,7 +566,7 @@ class MapAndBatchDatasetOp : public UnaryDatasetOpKernel { Status WriteBatchResult(IteratorStateWriter* writer, size_t index) EXCLUSIVE_LOCKS_REQUIRED(mu_) { - BatchResult* result = &batch_results_[index]; + std::shared_ptr result = batch_results_[index]; string prefix = strings::StrCat("batch_results_", index); mutex_lock l(result->mu); if (result->end_of_input) { @@ -646,21 +627,13 @@ class MapAndBatchDatasetOp : public UnaryDatasetOpKernel { // user specified level of parallelism and there are slots available in // the `batch_results_` buffer. condition_variable cond_var_; - // Used for serializing external parallelism. - mutex external_mu_ ACQUIRED_BEFORE(mu_); // Counts the number of outstanding calls for this batch. int64 num_calls_ GUARDED_BY(mu_) = 0; // Counts the total number of calls. int64 call_counter_ GUARDED_BY(mu_) = 0; std::unique_ptr input_impl_; - // Identifies the next batch to be read by the caller. - int64 input_batch_ GUARDED_BY(mu_) = 0; - // Identifies the next batch to create. - int64 output_batch_ GUARDED_BY(mu_) = 0; - // Circular buffer for storing the (intermediate) batch results. When - // using `input_batch_` and `output_batch_` to index into the buffer, - // their value should be interpreted modulo the size of the buffer. - std::vector batch_results_ GUARDED_BY(mu_); + // Buffer for storing the (intermediate) batch results. + std::deque> batch_results_ GUARDED_BY(mu_); std::unique_ptr runner_thread_ GUARDED_BY(mu_); bool cancelled_ GUARDED_BY(mu_) = false; }; -- GitLab From 99d2d13592a78d2eac5b90fced60a2cd562bed85 Mon Sep 17 00:00:00 2001 From: Sami Kama Date: Thu, 14 Jun 2018 20:06:49 -0700 Subject: [PATCH 496/816] Address review comments and fix some issues --- .../contrib/tensorrt/convert/convert_graph.cc | 95 ++++++++++++++++--- .../contrib/tensorrt/convert/convert_graph.h | 5 +- .../contrib/tensorrt/convert/convert_nodes.cc | 1 + .../contrib/tensorrt/convert/convert_nodes.h | 4 +- .../tensorrt/convert/trt_optimization_pass.cc | 22 ++++- .../tensorrt/convert/trt_optimization_pass.h | 3 + .../contrib/tensorrt/kernels/trt_engine_op.cc | 77 ++++++++++----- .../contrib/tensorrt/kernels/trt_engine_op.h | 5 +- .../contrib/tensorrt/python/trt_convert.py | 17 +++- .../tensorrt/resources/trt_allocator.cc | 2 +- .../tensorrt/resources/trt_allocator.h | 5 +- .../tensorrt/resources/trt_int8_calibrator.cc | 3 +- .../contrib/tensorrt/shape_fn/trt_shfn.cc | 24 +++++ .../contrib/tensorrt/test/test_tftrt.py | 12 ++- tensorflow/contrib/tensorrt/trt_conversion.i | 21 ++-- 15 files changed, 231 insertions(+), 65 deletions(-) diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.cc b/tensorflow/contrib/tensorrt/convert/convert_graph.cc index 0cfdef8aa6..37a38d3e1d 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_graph.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_graph.cc @@ -121,12 +121,17 @@ tensorflow::Status BuildNodeMap( } // namespace // Function to get calibration from ResourceMgr and put them into nodedef. tensorflow::Status ConvertCalibGraphToInferGraph( - const tensorflow::GraphDef& graph_def, tensorflow::GraphDef* infer_graph) { + const tensorflow::GraphDef& graph_def, tensorflow::GraphDef* infer_graph, + bool is_dyn_op) { VLOG(0) << "Starting Calib Conversion"; infer_graph->CopyFrom(graph_def); auto trt_rm = tensorflow::tensorrt::TRTResourceManager::instance(); auto calib_rm = trt_rm->getManager("TRTCalibration"); int num_nodes = infer_graph->node_size(); + if (!is_dyn_op) { + LOG(WARNING) << "Construction of static int8 engine is not implemented " + "yet!. Dynamic engine will be constructed"; + } for (int i = 0; i < num_nodes; ++i) { auto n = infer_graph->mutable_node(i); if (n->op() == "TRTEngineOp") { @@ -255,8 +260,12 @@ EngineInfo GetEngineInfo( for (const auto edge : node->in_edges()) { auto input_node = edge->src(); if (segment_nodes.count(input_node->name()) == 0) { - if (input_node->type_string() == - "Const") { // Add constant input into segment + // Add constant input node into the segment. We don't care if it has + // other output edges going into other engines or TF nodes. Since we add + // it only to the subsegment node list, not the subsegment itself, it + // won't be removed from the graph. If it doesn't have any edges, TF + // will prune it out. + if (input_node->type_string() == "Const") { subgraph_node_ids.push_back(input_node->id()); } else if (!edge->IsControlEdge() && !input_node->IsSource()) { string s(input_node->name()); @@ -401,11 +410,15 @@ tensorflow::Status CreateTRTNode(tensorflow::Graph* graph, builder->setHalf2Mode(true); } builder->setMaxWorkspaceSize(info.max_workspace_size_bytes); +#if NV_TENSORRT_MAJOR > 3 + builder->setGpuAllocator(alloc); +#endif nvinfer1::ICudaEngine* engine = nullptr; // TODO(sami): What happens if 1st dim is not batch? auto status = ConvertSubgraphToEngine(info.segment_graph_def, builder.get(), shapes, &engine, info.precision_mode); if (!status.ok()) { + if (engine) engine->destroy(); return status; } if (engine) { @@ -549,8 +562,8 @@ tensorflow::Status RegisterSegmentFunctionToFunctionLibrary( VLOG(1) << "Updating funcdef input " << node_arg->name() << ":" << 0 << " - > " << edge->dst()->name() << ":" << edge->dst_input(); if (!s.ok()) { - LOG(ERROR) << "Failed to update edge from " << node_arg->name() << " to " - << edge->dst()->name() << ":" << edge->dst_input(); + LOG(ERROR) << "Failed to update edge from " << node_arg->name() + << " to " << edge->dst()->name() << ":" << edge->dst_input(); } } sgraph.RemoveNode(node); @@ -584,7 +597,8 @@ tensorflow::Status RegisterSegmentFunctionToFunctionLibrary( s = sgraph.UpdateEdge(edge->src(), edge->src_output(), node_ret, 0); if (!s.ok()) { LOG(ERROR) << "Failed to update edge from " << edge->src()->name() << ":" - << edge->src_output() << " - > " << node_ret->name() << ":" << 0; + << edge->src_output() << " - > " << node_ret->name() << ":" + << 0; } sgraph.RemoveNode(node); } @@ -662,7 +676,12 @@ tensorflow::Status ConvertAfterShapes(ConversionParams& params) { std::vector trt_nodes; trt_nodes.reserve(engine_segments.size()); int old_cuda_device = 0; - cudaGetDevice(&old_cuda_device); + auto err = cudaGetDevice(&old_cuda_device); + if (err != cudaSuccess) { + LOG(ERROR) << "Couldn't get current device error is " + << cudaGetErrorString(err); + } + VLOG(1) << "Current cuda device is " << old_cuda_device; for (int i = 0; i < engine_segments.size(); ++i) { auto trt_node = new tensorflow::NodeDef; trt_nodes.push_back(trt_node); @@ -674,8 +693,11 @@ tensorflow::Status ConvertAfterShapes(ConversionParams& params) { (engine_sizes.at(i) / total_engine_size + segments.at(i).first.size() / total_num_nodes_in_segments) / 2.0; - std::shared_ptr alloc(new TRTCudaAllocator()); + std::shared_ptr alloc; int cuda_device_id = 0; + // we need to us PM here since in python path there is no way to get + // to allocators + auto pm = tensorflow::ProcessState::singleton(); if (params.cluster) { // get allocator const auto device = params.cluster->GetDeviceSet()->FindDeviceByName(engine.device); @@ -692,9 +714,6 @@ tensorflow::Status ConvertAfterShapes(ConversionParams& params) { cuda_device_id = cuda_gpu_id.value(); } tensorflow::GPUOptions gpuoptions; - // we need to us PM here since in python path there is no way to get - // to allocators - auto pm = tensorflow::ProcessState::singleton(); // this should be instantiated by now auto dev_allocator = pm->GetGPUAllocator(gpuoptions, tf_gpu_id, 1); VLOG(1) << "Got an allocator for device tf_device=" << tf_gpu_id.value() @@ -702,6 +721,60 @@ tensorflow::Status ConvertAfterShapes(ConversionParams& params) { << dev_allocator; alloc.reset(new TRTDeviceAllocator(dev_allocator)); } + } else { + int found_device = 0; + bool try_gpu_ids = true; + auto checkDeviceId = [](int tfid) -> int { + tensorflow::TfGpuId tf_gpu_id(tfid); + CudaGpuId cuda_gpu_id; + Status s = GpuIdManager::TfToCudaGpuId(tf_gpu_id, &cuda_gpu_id); + if (s.ok()) { + VLOG(1) << "Found TF GPU " << tf_gpu_id.value() << " at cuda device " + << cuda_gpu_id.value(); + return cuda_gpu_id.value(); + } + VLOG(2) << "TF GPU with id " << tfid << " do not exist " << s; + return -1; + }; + // if device is set, try to find the device. Might be a problem for multi + // host case but TensorRT do not support multi host setups yet. + if (!engine.device.empty()) { + auto res = str_util::Split(engine.device, ":"); + if (res.size() > 0) { + tensorflow::StringPiece s(res.back()); + tensorflow::str_util::RemoveWhitespaceContext(&s); + uint64 dev_id = 0; + if (str_util::ConsumeLeadingDigits(&s, &dev_id)) { + found_device = dev_id; + cuda_device_id = checkDeviceId(found_device); + if (cuda_device_id >= 0) try_gpu_ids = false; + } + } + } + if (try_gpu_ids) { + while (found_device < 100) { + cuda_device_id = checkDeviceId(found_device); + if (cuda_device_id >= 0) { + break; + } + found_device++; + } + } + if (found_device == 100) { + LOG(ERROR) << " Can't find a GPU device to work with. Please " + "instantiate a session to initialize devices"; + return tensorflow::errors::NotFound( + "Can't find a GPU device to work with"); + } + LOG(WARNING) + << "Can't determine the device constructing an allocator at device " + << found_device; + tensorflow::GPUOptions gpuoptions; + gpuoptions.set_allow_growth( + true); // this will be a noop if device is already initialized + tensorflow::TfGpuId tf_gpu_id(found_device); + auto dev_allocator = pm->GetGPUAllocator(gpuoptions, tf_gpu_id, 1); + alloc.reset(new TRTDeviceAllocator(dev_allocator)); } cudaSetDevice(cuda_device_id); auto status = CreateTRTNode(&graph, engine_segments, i, trt_node, diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.h b/tensorflow/contrib/tensorrt/convert/convert_graph.h index 7623c30e8a..e2f4c1c83f 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_graph.h +++ b/tensorflow/contrib/tensorrt/convert/convert_graph.h @@ -55,13 +55,14 @@ struct ConversionParams { bool is_dyn_op; // Whether to create engine on conversion or execution time bool fixed_input_size; // Assume non-batch ranks of input tensors are fixed int max_cached_engines; // maximum number of cached engines - std::vector cached_engine_batches; // list of cached engines + std::vector cached_engine_batches; // list of cached engines }; // This method extracts calibration information from the resource managers // and puts them in to engine nodedefs. tensorflow::Status ConvertCalibGraphToInferGraph( - const tensorflow::GraphDef& graph_def, tensorflow::GraphDef* new_graph_def); + const tensorflow::GraphDef& graph_def, tensorflow::GraphDef* new_graph_def, + bool is_dyn_op); // max_batch_size: maximum batch size which can be used for inference for // optimization targets inference run with max batch size. diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index dde031e2d5..6ad2d7e68f 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -2159,6 +2159,7 @@ tensorflow::Status ConvertSubgraphToEngine( VLOG(1) << "Starting engine conversion "; Converter converter(trt_network.get(), ws.get(), precision_mode == FP16MODE); std::vector> output_tensors; + // graph nodes are already topologically sorted during construction for (const auto& node_def : gdef.node()) { string node_name = node_def.name(); VLOG(1) << "Converting op name=" << node_name << ", op=" << node_def.op(); diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.h b/tensorflow/contrib/tensorrt/convert/convert_nodes.h index b6752fb835..971322d07c 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.h +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.h @@ -32,8 +32,8 @@ limitations under the License. namespace tensorflow { namespace tensorrt { -static const string kInputPHName = "InputPH_"; -static const string kOutputPHName = "OutputPH_"; +static const char* kInputPHName = "InputPH_"; +static const char* kOutputPHName = "OutputPH_"; namespace convert { const int FP32MODE = 0; diff --git a/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc b/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc index 68659e4ab5..6d0fd7a44b 100644 --- a/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc +++ b/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc @@ -45,8 +45,24 @@ tensorflow::Status TRTOptimizationPass::Init( if (params.count("max_batch_size")) { maximum_batch_size_ = params.at("max_batch_size").i(); } - if (params.count("max_workspace_size_bytes")) + is_dynamic_op_ = false; + if (params.count("is_dynamic_op")) { + is_dynamic_op_ = params.at("is_dynamic_op").b(); + } + if (params.count("cached_engine_batches")) { + auto batch_vec = params.at("cached_engine_batches").list(); + batches_.reserve(batch_vec.i_size()); + for (const auto i : batch_vec.i()) { + batches_.push_back(i); + } + } + max_cached_batches_ = 1; + if (params.count("maximum_cached_engines")) { + max_cached_batches_ = params.at("maximum_cached_engines").i(); + } + if (params.count("max_workspace_size_bytes")) { maximum_workspace_size_ = params.at("max_workspace_size_bytes").i(); + } if (params.count("precision_mode")) { string pm = Uppercase(params.at("precision_mode").s()); if (pm == "FP32") { @@ -214,7 +230,9 @@ tensorflow::Status TRTOptimizationPass::Optimize( cp.minimum_segment_size = minimum_segment_size_; cp.graph_properties = &static_graph_properties; cp.cluster = cluster; - cp.is_dyn_op = false; + cp.is_dyn_op = is_dynamic_op_; + cp.cached_engine_batches = batches_; + cp.max_cached_engines = max_cached_batches_; auto status = tensorflow::tensorrt::convert::ConvertAfterShapes(cp); VLOG(2) << optimized_graph->DebugString(); return status; diff --git a/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.h b/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.h index d8ecead23e..463ed3883e 100644 --- a/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.h +++ b/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.h @@ -61,6 +61,9 @@ class TRTOptimizationPass : public tensorflow::grappler::CustomGraphOptimizer { int minimum_segment_size_; int precision_mode_; int maximum_batch_size_; + bool is_dynamic_op_; + std::vector batches_; + int max_cached_batches_; int64_t maximum_workspace_size_; }; diff --git a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc index 91a18cf7ef..6603b0f7c3 100644 --- a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc +++ b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc @@ -112,7 +112,7 @@ TRTEngineOp::TRTEngineOp(OpKernelConstruction* context) } serialized_segment_.resize(0); } - + VLOG(1) << "Constructing " << name(); string precision_string; OP_REQUIRES_OK(context, context->GetAttr("precision_mode", &precision_string)); @@ -198,8 +198,8 @@ void TRTEngineOp::ExecuteNativeSegment(tensorflow::OpKernelContext* ctx, void TRTEngineOp::ExecuteCalibration(tensorflow::OpKernelContext* ctx, AsyncHelper* helper) { tensorflow::core::ScopedUnref sc(helper); - auto TRT_RM = tensorflow::tensorrt::TRTResourceManager::instance(); - auto res_mgr = TRT_RM->getManager("TRTCalibration"); + auto trt_rm = tensorflow::tensorrt::TRTResourceManager::instance(); + auto res_mgr = trt_rm->getManager("TRTCalibration"); tensorflow::tensorrt::TRTCalibrationResource* calib_res = nullptr; auto status = res_mgr->LookupOrCreate( funcdef_name_, "Calibrator", &calib_res, @@ -211,7 +211,6 @@ void TRTEngineOp::ExecuteCalibration(tensorflow::OpKernelContext* ctx, ctx->SetStatus(status); return; } - ExecuteNativeSegment(ctx, helper); int num_inputs = ctx->num_inputs(); // Pass input data to calibrator std::unordered_map input_data; @@ -225,7 +224,7 @@ void TRTEngineOp::ExecuteCalibration(tensorflow::OpKernelContext* ctx, } const auto device_tensor = dev_tensors_.at(i).AccessTensor(ctx); CHECK_EQ(t.TotalBytes(), - device_tensor->TotalBytes()); // use the tensor so FW keeps it + device_tensor->TotalBytes()); // use the tensor so TF keeps it input_data.emplace(StrCat(kInputPHName, i), data_address); } VLOG(2) << "Filled map for sending"; @@ -237,6 +236,7 @@ void TRTEngineOp::ExecuteCalibration(tensorflow::OpKernelContext* ctx, ->CudaStreamMemberHack())); calib_res->calibrator_->setBatch(input_data, *stream); VLOG(2) << "Passed calibration data"; + ExecuteNativeSegment(ctx, helper); return; } @@ -330,8 +330,8 @@ void TRTEngineOp::ComputeAsync(tensorflow::OpKernelContext* ctx, for (int i = 0; i < ctx->num_outputs(); i++) { // This is bad that we have to reallocate output buffer every run. // Create an output tensor - - auto output_name=StrCat(kOutputPHName, i); + + auto output_name = StrCat(kOutputPHName, i); binding_index = trt_engine_ptr->getBindingIndex(output_name.c_str()); Tensor* output_tensor = nullptr; @@ -390,7 +390,9 @@ void TRTEngineOp::ComputeAsync(tensorflow::OpKernelContext* ctx, auto trt_execution_context_ptr = engine_ctx_pair.second; auto ret = trt_execution_context_ptr->enqueue(num_batch, &buffers[0], *stream, nullptr); - VLOG(2) << "enqueue returns: " << ret; + if (!ret) { + LOG(ERROR) << "Enqueueing of TRT execution failed!"; + } // sync should be done by TF. } @@ -402,6 +404,7 @@ TRTEngineOp::~TRTEngineOp() { } for (auto alloc : allocators_) alloc.second.reset(); } + nvinfer1::IGpuAllocator* TRTEngineOp::GetAllocator(OpKernelContext* ctx) { auto device = ctx->device(); const auto& device_name = device->name(); @@ -427,6 +430,7 @@ TRTEngineOp::EngineCtxPair TRTEngineOp::GetEngine(int batch_size, // TODO(sami): This method needs to be re-written to use resource manager and // with LRU mechanism option. tensorflow::mutex_lock lock(engine_mutex_); + if (static_engine_) { if (engine_map_.size()) { if (engine_map_.begin()->first >= batch_size) { @@ -435,7 +439,10 @@ TRTEngineOp::EngineCtxPair TRTEngineOp::GetEngine(int batch_size, return {nullptr, nullptr}; } } else { - IRuntime* infer = nvinfer1::createInferRuntime(logger); + std::shared_ptr infer(nvinfer1::createInferRuntime(logger), + [](IRuntime* p) { + if (p) p->destroy(); + }); #if NV_TENSORRT_MAJOR > 3 auto allocator = GetAllocator(ctx); if (allocator == nullptr) { @@ -452,7 +459,6 @@ TRTEngineOp::EngineCtxPair TRTEngineOp::GetEngine(int batch_size, {static_engine->createExecutionContext(), Destroyer()}}}); // Runtime is safe to delete after engine creation - infer->destroy(); serialized_segment_.clear(); if (static_engine->getMaxBatchSize() < batch_size) { return {nullptr, nullptr}; @@ -472,9 +478,9 @@ TRTEngineOp::EngineCtxPair TRTEngineOp::GetEngine(int batch_size, if (allocator == nullptr) { return {nullptr, nullptr}; } - builder->setGpuAllocator(GetAllocator(ctx)); + builder->setGpuAllocator(allocator); #endif - VLOG(1) << name() << " Constructing a new engine with batch size " + VLOG(0) << name() << " Constructing a new engine with batch size " << batch_size; builder->setMaxBatchSize(batch_size); if (precision_mode_ == tensorflow::tensorrt::convert::FP16MODE) { @@ -489,8 +495,10 @@ TRTEngineOp::EngineCtxPair TRTEngineOp::GetEngine(int batch_size, for (int i = 0; i < ctx->num_inputs(); ++i) { shapes.emplace_back(ctx->input(i).shape()); } + VLOG(1) << "Calling conversion for " << batch_size << " " << name(); auto status = tensorflow::tensorrt::convert::ConvertSubgraphToEngine( segment_graph_, builder.get(), shapes, &engine, precision_mode_); + VLOG(1) << "Conversion is done"; if (engine) { engine_map_[batch_size] = { std::shared_ptr( @@ -516,7 +524,7 @@ tensorflow::Status TRTEngineOp::AllocateCalibrationResources( auto cres = new TRTCalibrationResource(); *cr = cres; cres->logger_ = new tensorflow::tensorrt::Logger(); - cres->builder_ = nvinfer1::createInferBuilder(*(cres->logger_)); + #if NV_TENSORRT_MAJOR > 3 auto dev = ctx->device(); auto dev_allocator = dev->GetAllocator(tensorflow::AllocatorAttributes()); @@ -530,12 +538,9 @@ tensorflow::Status TRTEngineOp::AllocateCalibrationResources( std::make_shared( dev_allocator); } - cres->builder_->setGpuAllocator(cres->allocator_.get()); + #endif int batch_size = ctx->input(0).dim_size(0); - cres->builder_->setMaxBatchSize(batch_size); - cres->builder_->setInt8Mode(true); - cres->builder_->setMaxWorkspaceSize(workspace_size_); cres->engine_ = nullptr; std::vector shapes; int num_inputs = ctx->num_inputs(); @@ -547,8 +552,8 @@ tensorflow::Status TRTEngineOp::AllocateCalibrationResources( const tensorflow::Tensor& t = ctx->input(i); shapes.emplace_back(t.shape()); Tensor* device_tensor; - TF_RETURN_IF_ERROR(ctx->allocate_persistent(t.dtype(), t.shape(), - &dev_tensors_.at(i), &device_tensor)); + TF_RETURN_IF_ERROR(ctx->allocate_persistent( + t.dtype(), t.shape(), &dev_tensors_.at(i), &device_tensor)); CHECK_EQ(t.TotalBytes(), device_tensor->TotalBytes()); void* device_address = GetTensorAddress(device_tensor); if (device_address == nullptr) { @@ -561,15 +566,39 @@ tensorflow::Status TRTEngineOp::AllocateCalibrationResources( } cres->calibrator_ = new TRTInt8Calibrator(device_buffers_, batch_size, name()); - cres->builder_->setInt8Calibrator(cres->calibrator_); string label(name()); auto segment_graph = &segment_graph_; - cres->thr_ = new std::thread([cres, label, segment_graph, shapes]() { - VLOG(1) << "Starting calibration thread, Calibration Resource @ " << cres; + int cuda_device = ctx->device()->tensorflow_gpu_device_info()->gpu_id; + if (cuda_device < 0) { + LOG(ERROR) << "Can't get gpu_device_info from context->device()"; + return tensorflow::errors::InvalidArgument( + "Context->device doesn't contain device info!"); + } + int workspace_size = workspace_size_; + cres->thr_ = new std::thread([cres, label, segment_graph, shapes, cuda_device, + batch_size, workspace_size]() { + VLOG(0) << "Starting calibration thread on device " << cuda_device + << ", Calibration Resource @ " << cres; + // ConvertSubgraphToEngine() will try to build the engine and this thread + // will be consuming the calibration data that is set by the TF op, driving + // the builder until calibrator returns false; Engine is discarded after + // calibration table is generated + auto err = cudaSetDevice(cuda_device); + if (err != cudaSuccess) { + VLOG(0) << "Couldn't set cuda device to " << cuda_device + << " in calibration thread"; + } + // initialize builder here + cres->builder_ = nvinfer1::createInferBuilder(*(cres->logger_)); + cres->builder_->setGpuAllocator(cres->allocator_.get()); + cres->builder_->setMaxBatchSize(batch_size); + cres->builder_->setInt8Mode(true); + cres->builder_->setMaxWorkspaceSize(workspace_size); + cres->builder_->setInt8Calibrator(cres->calibrator_); auto s = tensorflow::tensorrt::convert::ConvertSubgraphToEngine( *segment_graph, cres->builder_, shapes, &cres->engine_, - tensorflow::tensorrt::convert::INT8MODE); // calibrator will loop until we - // terminate calibration + tensorflow::tensorrt::convert::INT8MODE); // calibrator will loop until + // we terminate calibration if (!s.ok()) { LOG(ERROR) << "Calibration failed. Engine will not be calibrated! Error is" << s; diff --git a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h index 800abbef77..6faef09b62 100644 --- a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h +++ b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h @@ -85,13 +85,12 @@ class TRTEngineOp : public AsyncOpKernel { nvinfer1::IGpuAllocator* GetAllocator(OpKernelContext* ctx); - // map to keep engines and their execution context for given key. + // map to keep engines and their execution context for given batch size. std::unordered_map engine_map_; std::vector input_nodes_; std::vector output_nodes_; // keep device allocator for TRT. - std::unordered_map> - allocators_; + std::unordered_map> allocators_; // serialized protobuf segment or trt engine depending on static_engine_ flag. string serialized_segment_; // Name of the function for TF native execution of the segment. diff --git a/tensorflow/contrib/tensorrt/python/trt_convert.py b/tensorflow/contrib/tensorrt/python/trt_convert.py index c9edc03431..0478df9585 100644 --- a/tensorflow/contrib/tensorrt/python/trt_convert.py +++ b/tensorflow/contrib/tensorrt/python/trt_convert.py @@ -75,6 +75,16 @@ def create_inference_graph(input_graph_def, compiled_version = get_linked_tensorrt_version() loaded_version = get_loaded_tensorrt_version() version_mismatch = False + if loaded_version[0] < compiled_version[0]: + tf_logging.error( + "TensorRT version mismatch. Tensorflow was compiled against " + + "TensorRT %s but library loaded from environment is TensorRT %s" % + (".".join([str(x) for x in compiled_version]), + ".".join([str(x) for x in loaded_version])) + + ". Please make sure that correct version of TensorRT "\ + "is available in the system and added to ldconfig or LD_LIBRARY_PATH" + ) + raise RuntimeError("Incompatible TensorRT library version") for i in zip(loaded_version, compiled_version): if i[0] != i[1]: tf_logging.warn("TensorRT mismatch. Compiled against version " + @@ -143,11 +153,12 @@ def create_inference_graph(input_graph_def, return output_graph_def -def calib_graph_to_infer_graph(calibration_graph_def): +def calib_graph_to_infer_graph(calibration_graph_def, is_dynamic_op=False): """Convert an existing calibration graph to inference graph. Args: calibration_graph_def: the calibration GraphDef object with calibration data + is_dynamic_op : whether to create dynamic engines or static engines from calibration Returns: New GraphDef with TRTEngineOps placed in graph replacing calibration nodes. Raises: @@ -167,13 +178,13 @@ def calib_graph_to_infer_graph(calibration_graph_def): is_calib_graph = False for n in calibration_graph_def.node: if n.op == "TRTEngineOp": - is_calib_graph = len(n.attr["calibration_data"].s) == 0 + is_calib_graph = is_calib_graph or len(n.attr["calibration_data"].s) == 0 if not is_calib_graph: tf_logging.error( "Not a calib graph. Doesn't seem to contain any calibration nodes.") return None graph_str = calibration_graph_def.SerializeToString() - out = calib_convert(graph_str) + out = calib_convert(graph_str, is_dynamic_op) status = to_string(out[0]) output_graph_def_string = out[1] del graph_str # Save some memory diff --git a/tensorflow/contrib/tensorrt/resources/trt_allocator.cc b/tensorflow/contrib/tensorrt/resources/trt_allocator.cc index 0f0508331c..9f115990c3 100644 --- a/tensorflow/contrib/tensorrt/resources/trt_allocator.cc +++ b/tensorflow/contrib/tensorrt/resources/trt_allocator.cc @@ -50,7 +50,7 @@ TRTDeviceAllocator::TRTDeviceAllocator(tensorflow::Allocator* allocator) } void TRTDeviceAllocator::free(void* memory) { - VLOG(2) << "Deallocating " << memory; + VLOG(2) << "Deallocating @ " << memory; allocator_->DeallocateRaw(memory); } diff --git a/tensorflow/contrib/tensorrt/resources/trt_allocator.h b/tensorflow/contrib/tensorrt/resources/trt_allocator.h index a0c2540a76..c5d2cec730 100644 --- a/tensorflow/contrib/tensorrt/resources/trt_allocator.h +++ b/tensorflow/contrib/tensorrt/resources/trt_allocator.h @@ -16,7 +16,6 @@ limitations under the License. #ifndef TENSORFLOW_CONTRIB_TENSORRT_RESOURCES_TRT_ALLOCATOR_H_ #define TENSORFLOW_CONTRIB_TENSORRT_RESOURCES_TRT_ALLOCATOR_H_ - #include "tensorflow/contrib/tensorrt/log/trt_logger.h" #include "tensorflow/core/framework/allocator.h" @@ -52,7 +51,9 @@ class TRTDeviceAllocator : public nvinfer1::IGpuAllocator { // Allocator implementation wrapping TF device allocators. public: TRTDeviceAllocator(tensorflow::Allocator* allocator); - virtual ~TRTDeviceAllocator() {} + virtual ~TRTDeviceAllocator() { + VLOG(1) << "Destroying allocator attached to " << allocator_->Name(); + } void* allocate(uint64_t size, uint64_t alignment, uint32_t flags) override; void free(void* memory) override; diff --git a/tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.cc b/tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.cc index a5dbbfabce..9c1c306947 100644 --- a/tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.cc +++ b/tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.cc @@ -19,7 +19,6 @@ limitations under the License. #include #include - #include "tensorflow/core/platform/logging.h" #if GOOGLE_CUDA @@ -38,7 +37,7 @@ TRTInt8Calibrator::TRTInt8Calibrator( : batch_size_(batch_size), done_(false), dev_buffers_(dev_buffers), - calib_running_(false), + calib_running_(true), batch_is_set_(false), engine_name_(engine_name) {} diff --git a/tensorflow/contrib/tensorrt/shape_fn/trt_shfn.cc b/tensorflow/contrib/tensorrt/shape_fn/trt_shfn.cc index 9bf2a56f99..227ac120dd 100644 --- a/tensorflow/contrib/tensorrt/shape_fn/trt_shfn.cc +++ b/tensorflow/contrib/tensorrt/shape_fn/trt_shfn.cc @@ -29,9 +29,33 @@ namespace tensorflow { namespace shape_inference { tensorflow::Status TRTEngineOpShapeInference(InferenceContext* context) { + std::vector shapes; for (int i = 0; i < context->num_outputs(); ++i) { context->set_output(i, context->UnknownShape()); } + auto status = context->GetAttr("input_shapes", &shapes); + // it is ok to not to have shapes + if (!status.ok()) return Status::OK(); + if ((int)shapes.size() != context->num_inputs()) return Status::OK(); + bool different_input = false; + for (int i = 0; i < context->num_inputs(); ++i) { + if (shapes.at(i) != context->input_tensor(i)->shape()) + different_input = true; + } + if (different_input) return Status::OK(); + shapes.resize(0); + status = context->GetAttr("output_shapes", &shapes); + if (!status.ok()) return Status::OK(); + if ((int)shapes.size() != context->num_outputs()) return Status::OK(); + std::vector shape_handles(shapes.size()); + for (size_t i = 0; i < shapes.size(); ++i) { + status = + context->MakeShapeFromTensorShape(shapes.at(i), &shape_handles.at(i)); + if (!status.ok()) return Status::OK(); + } + for (int i = 0; i < context->num_outputs(); ++i) { + context->set_output(i, shape_handles.at(i)); + } return Status::OK(); } } // namespace shape_inference diff --git a/tensorflow/contrib/tensorrt/test/test_tftrt.py b/tensorflow/contrib/tensorrt/test/test_tftrt.py index 2123fbf8f9..748b4ad23c 100644 --- a/tensorflow/contrib/tensorrt/test/test_tftrt.py +++ b/tensorflow/contrib/tensorrt/test/test_tftrt.py @@ -65,7 +65,9 @@ def get_simple_graph_def(): def execute_graph(gdef, dumm_inp): """Run given graphdef once.""" print("executing") - gpu_options = cpb2.GPUOptions(per_process_gpu_memory_fraction=0.50) + gpu_options = None + if (trt.trt_convert.get_linked_tensorrt_version()[0] == 3): + gpu_options = cpb2.GPUOptions(per_process_gpu_memory_fraction=0.50) sessconfig = cpb2.ConfigProto(gpu_options=gpu_options) ops.reset_default_graph() g = ops.Graph() @@ -83,7 +85,9 @@ def execute_graph(gdef, dumm_inp): # for calibration. For this test script it is random data. def execute_calibration(gdef, dumm_inp): """Run given calibration graph multiple times.""" - gpu_options = cpb2.GPUOptions(per_process_gpu_memory_fraction=0.50) + gpu_options = None + if (trt.trt_convert.get_linked_tensorrt_version()[0] == 3): + gpu_options = cpb2.GPUOptions(per_process_gpu_memory_fraction=0.50) ops.reset_default_graph() g = ops.Graph() with g.as_default(): @@ -165,7 +169,9 @@ def auto(): custom_op.parameter_map["max_batch_size"].i = inp_dims[0] custom_op.parameter_map["max_workspace_size_bytes"].i = 1 << 25 print(custom_op) - gpu_options = cpb2.GPUOptions(per_process_gpu_memory_fraction=0.50) + gpu_options = None + if (trt.trt_convert.get_linked_tensorrt_version()[0] == 3): + gpu_options = cpb2.GPUOptions(per_process_gpu_memory_fraction=0.50) graph_options = cpb2.GraphOptions(rewrite_options=opt_config) sessconfig = cpb2.ConfigProto( gpu_options=gpu_options, graph_options=graph_options) diff --git a/tensorflow/contrib/tensorrt/trt_conversion.i b/tensorflow/contrib/tensorrt/trt_conversion.i index 226454dbab..5ef0b42161 100644 --- a/tensorflow/contrib/tensorrt/trt_conversion.i +++ b/tensorflow/contrib/tensorrt/trt_conversion.i @@ -148,12 +148,12 @@ std::pair trt_convert( out_status = "InvalidArgument;Size of the output_names vector is 0"; return std::pair{out_status, ""}; } - tensorflow::GraphDef outGraph; + tensorflow::GraphDef out_graph; tensorflow::Status conversion_status = tensorflow::tensorrt::convert::ConvertGraphDefToTensorRT( graph_def, output_names, max_batch_size, max_workspace_size_bytes, - &outGraph, precision_mode, minimum_segment_size, - is_dyn_op,max_cached_engines, cached_engine_batches); + &out_graph, precision_mode, minimum_segment_size, + is_dyn_op, max_cached_engines, cached_engine_batches); if (!conversion_status.ok()) { auto retCode = (int)conversion_status.code(); char buff[2000]; @@ -163,7 +163,7 @@ std::pair trt_convert( return std::pair{out_status, ""}; } string result; - if (!outGraph.SerializeToString(&result)) { + if (!out_graph.SerializeToString(&result)) { out_status = "InvalidArgument;Couldn't serialize output as a GraphDef"; return std::pair{out_status, ""}; } @@ -176,7 +176,7 @@ std::pair trt_convert( } std::pair calib_convert( - string graph_def_string + string graph_def_string, bool is_dyn_op // unfortunately we can't use TF_Status here since it // is in c/c_api and brings in a lot of other libraries // which in turn declare ops. These ops are included @@ -195,11 +195,12 @@ std::pair calib_convert( out_status = "InvalidArgument;Couldn't interpret input as a GraphDef"; return std::pair{out_status, ""}; } - - tensorflow::GraphDef outGraph; + graph_def_string.resize(0); + tensorflow::GraphDef out_graph; tensorflow::Status conversion_status = tensorflow::tensorrt::convert::ConvertCalibGraphToInferGraph(graph_def, - &outGraph); + &out_graph, + is_dyn_op); if (!conversion_status.ok()) { auto retCode = (int)conversion_status.code(); char buff[2000]; @@ -209,7 +210,7 @@ std::pair calib_convert( return std::pair{out_status, ""}; } string result; - if (!outGraph.SerializeToString(&result)) { + if (!out_graph.SerializeToString(&result)) { out_status = "InvalidArgument;Couldn't serialize output as a GraphDef"; return std::pair{out_status, ""}; } @@ -242,7 +243,7 @@ version_struct get_loaded_tensorrt_version(){ %} -std::pair calib_convert(string graph_def_string); +std::pair calib_convert(string graph_def_string, bool is_dyn_op); std::pair trt_convert(string graph_def_string, std::vector output_names, -- GitLab From 7f265d14f9da8214a1868464baa7ea8f4ece7121 Mon Sep 17 00:00:00 2001 From: Youlong Cheng Date: Thu, 14 Jun 2018 20:08:22 -0700 Subject: [PATCH 497/816] Move xla_sharding related code to third_party PiperOrigin-RevId: 200661547 --- .../xla/experimental/xla_sharding/BUILD | 18 ++ .../experimental/xla_sharding/xla_sharding.py | 204 ++++++++++++++++++ tensorflow/compiler/xla/python_api/BUILD | 36 ++++ tensorflow/compiler/xla/python_api/types.py | 124 +++++++++++ .../compiler/xla/python_api/xla_literal.py | 95 ++++++++ .../compiler/xla/python_api/xla_shape.py | 155 +++++++++++++ 6 files changed, 632 insertions(+) create mode 100644 tensorflow/compiler/xla/experimental/xla_sharding/BUILD create mode 100644 tensorflow/compiler/xla/experimental/xla_sharding/xla_sharding.py create mode 100644 tensorflow/compiler/xla/python_api/BUILD create mode 100644 tensorflow/compiler/xla/python_api/types.py create mode 100644 tensorflow/compiler/xla/python_api/xla_literal.py create mode 100644 tensorflow/compiler/xla/python_api/xla_shape.py diff --git a/tensorflow/compiler/xla/experimental/xla_sharding/BUILD b/tensorflow/compiler/xla/experimental/xla_sharding/BUILD new file mode 100644 index 0000000000..a26b20c861 --- /dev/null +++ b/tensorflow/compiler/xla/experimental/xla_sharding/BUILD @@ -0,0 +1,18 @@ +# Description: +# Python API for shardings in XLA. + +licenses(["notice"]) # Apache 2.0 + +package(default_visibility = ["//tensorflow:internal"]) + +py_library( + name = "xla_sharding", + srcs = ["xla_sharding.py"], + visibility = ["//visibility:public"], + deps = [ + "//tensorflow/compiler/xla:xla_data_proto_py", + "//tensorflow/compiler/xla/python_api:types", + "//tensorflow/compiler/xla/python_api:xla_shape", + "//third_party/py/numpy", + ], +) diff --git a/tensorflow/compiler/xla/experimental/xla_sharding/xla_sharding.py b/tensorflow/compiler/xla/experimental/xla_sharding/xla_sharding.py new file mode 100644 index 0000000000..abd10b164e --- /dev/null +++ b/tensorflow/compiler/xla/experimental/xla_sharding/xla_sharding.py @@ -0,0 +1,204 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the 'License'); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an 'AS IS' BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ====================================== +"""Experimental support for defining XLA shardings.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import math + +import numpy as np + +from tensorflow.compiler.xla import xla_data_pb2 +from tensorflow.compiler.xla.python_api import xla_shape +from tensorflow.core.framework import attr_value_pb2 + + +class Sharding(object): + """A class to support adding sharding attributes to Ops. + + Use the factory constructors and then call apply_to_tensor: + Sharding.replicate().apply_to_tensor(tensor) + """ + + def __init__(self, proto=None): + """Do not use this constructor; use the factory functions below.""" + self._proto = proto + + @classmethod + def replicate(cls): + """Returns a replicated sharding attribute. + + This causes an op to be computed in its entirety independently on all + cores in the XLA device. + """ + return Sharding( + proto=xla_data_pb2.OpSharding(type=xla_data_pb2.OpSharding.REPLICATED)) + + @classmethod + def assign_device(cls, core): + """Returns an AssignDevice sharding attribute. + + This causes an op to be computed in its entirety only on one core in + the XLA device. + Args: + core: The core to assign this Op to. + """ + return Sharding( + proto=xla_data_pb2.OpSharding( + type=xla_data_pb2.OpSharding.MAXIMAL, + tile_assignment_dimensions=[1], + tile_assignment_devices=[core])) + + @classmethod + def tile(cls, tile_shape, tile_assignment): + """Returns a Tiled sharding attribute. + + This causes an op to be partially computed on multiple cores in the + XLA device. + + Args: + tile_shape: A xla_shape.Shape describing the tile shape that each core + will compute. + The tile shape does not need to be divisible by the tile assignment. + tile_assignment: An np.ndarray describing the topology of the tiling and + which device will compute which part of the topology. + + Raises: + TypeError: tile_assignment was not of np.array type or tile_shape was + not of xla_shape.Shape type. + + TODO(jmolloy): This concept is nefarious and is not + something we really want to expose to users (especially as the + contract for tile_assignment is very strict). + """ + if not isinstance(tile_assignment, np.ndarray): + raise TypeError('Tile assignment must be of type np.ndarray') + if not isinstance(tile_shape, xla_shape.Shape): + raise TypeError('Tile shape must be of type xla_shape.Shape') + dims = list(tile_assignment.shape) + flattened_devices = tile_assignment.reshape(-1, order='C') + return Sharding( + proto=xla_data_pb2.OpSharding( + type=xla_data_pb2.OpSharding.OTHER, + tile_shape=tile_shape.message, + tile_assignment_dimensions=dims, + tile_assignment_devices=list(flattened_devices))) + + @classmethod + def split(cls, tensor, split_dimension, num_devices): + """Returns a Sharding that splits a tensor across a dimension. + + This creates a Tiled attribute, similar to tile(), but easier to use for the + common case of tiling a tensor N ways in one dimension. + + Args: + tensor: A tf.Tensor to split. + split_dimension: The dimension number to split. + num_devices: The number of cores to split `tensor` over. + + Raises: + ValueError: The tensor to split was smaller in the split dimension than + the number of devices to split over. + """ + tensor.shape.assert_is_fully_defined() + shape = tensor.shape.as_list() + if shape[split_dimension] < num_devices: + raise ValueError('Split dimension was smaller than the required number ' + 'of splits: shape=%r, dimension=%r, num_devices=%r', + shape, split_dimension, num_devices) + + tile_shape = shape + tile_shape[split_dimension] = int( + math.ceil(tile_shape[split_dimension] / num_devices)) + tile_shape_proto = xla_data_pb2.Shape( + element_type=xla_data_pb2.F32, dimensions=tile_shape) + + tile_assignment_dims = [1] * len(shape) + tile_assignment_dims[split_dimension] = num_devices + + return Sharding( + proto=xla_data_pb2.OpSharding( + type=xla_data_pb2.OpSharding.OTHER, + tile_shape=tile_shape_proto, + tile_assignment_dimensions=tile_assignment_dims, + tile_assignment_devices=range(num_devices))) + + def apply_to_tensor(self, tensor): + """Applies this Sharding attribute to `tensor`.""" + if len(tensor.op.outputs) > 1: + proto = self._get_or_create_tuple_proto(tensor.op) + # We can't mutate an element of old_proto.tuple_shardings, so create + # a new proto. + tuple_shardings = list(proto.tuple_shardings) + tuple_shardings[tensor.value_index] = self._proto + proto = xla_data_pb2.OpSharding( + type=xla_data_pb2.OpSharding.TUPLE, tuple_shardings=tuple_shardings) + else: + proto = self._proto + + attr_value = attr_value_pb2.AttrValue(s=proto.SerializeToString()) + # TODO(jmolloy): This need to be seriously revisited before declaring this + # API available for public use. + # pylint: disable=protected-access + tensor.op._set_attr('_XlaSharding', attr_value) + + @property + def proto(self): + """Return the sharding protobuf of type xla_data_pb2.OpSharding.""" + return self._proto + + def _get_or_create_tuple_proto(self, op): + try: + attr = op.get_attr('_XlaSharding') + proto = xla_data_pb2.OpSharding() + proto.ParseFromString(attr) + return proto + except ValueError: + return self._create_tuple_proto(op) + + def _create_tuple_proto(self, op): + shardings = [ + xla_data_pb2.OpSharding(type=xla_data_pb2.OpSharding.REPLICATED) + for _ in op.outputs + ] + return xla_data_pb2.OpSharding( + type=xla_data_pb2.OpSharding.TUPLE, tuple_shardings=shardings) + + +# Helpers for the above factory functions that allow easy application of +# shardings, for example: +# tensor = xla_sharding.replicate(tensor) + + +def replicate(tensor): + Sharding.replicate().apply_to_tensor(tensor) + return tensor + + +def assign_device(tensor, device): + Sharding.assign_device(device).apply_to_tensor(tensor) + return tensor + + +def tile(tensor, tile_shape, tile_assignment): + Sharding.tile(tile_shape, tile_assignment).apply_to_tensor(tensor) + return tensor + + +def split(tensor, split_dimension, num_devices): + Sharding.split(tensor, split_dimension, num_devices).apply_to_tensor(tensor) + return tensor diff --git a/tensorflow/compiler/xla/python_api/BUILD b/tensorflow/compiler/xla/python_api/BUILD new file mode 100644 index 0000000000..8999cda5ef --- /dev/null +++ b/tensorflow/compiler/xla/python_api/BUILD @@ -0,0 +1,36 @@ +# Description: +# Python API for XLA. + +licenses(["notice"]) # Apache 2.0 + +package(default_visibility = ["//tensorflow:internal"]) + +py_library( + name = "types", + srcs = ["types.py"], + deps = [ + "//tensorflow/compiler/xla:xla_data_proto_py", + "//third_party/py/numpy", + ], +) + +py_library( + name = "xla_shape", + srcs = ["xla_shape.py"], + visibility = ["//visibility:public"], + deps = [ + ":types", + "//tensorflow/compiler/xla:xla_data_proto_py", + ], +) + +py_library( + name = "xla_literal", + srcs = ["xla_literal.py"], + visibility = ["//visibility:public"], + deps = [ + ":types", + ":xla_shape", + "//tensorflow/compiler/xla:xla_data_proto_py", + ], +) diff --git a/tensorflow/compiler/xla/python_api/types.py b/tensorflow/compiler/xla/python_api/types.py new file mode 100644 index 0000000000..b60f8dce92 --- /dev/null +++ b/tensorflow/compiler/xla/python_api/types.py @@ -0,0 +1,124 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the 'License'); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an 'AS IS' BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ====================================== +"""Utilities for XLA-specific Python types.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import collections + +import numpy as np + +from tensorflow.compiler.xla import xla_data_pb2 + +# Records corresponsence between a XLA primitive type and Python/Numpy types. +# +# primitive_type: value of type xla_data_pb2.PrimitiveType +# numpy_dtype: corresponsing Numpy "dtype" (like np.float32) +# literal_field_name: name of the field in the LiteralProto message elements +# of this type go into. +# literal_field_type: type of the field named 'literal_field_name'. +# +# TODO(eliben): figure out how to avoid knowing the extra Python type and the +# astype cast when writing into Literals. +TypeConversionRecord = collections.namedtuple('TypeConversionRecord', [ + 'primitive_type', 'numpy_dtype', 'literal_field_name', 'literal_field_type' +]) + +# Maps from XLA primitive types to TypeConversionRecord. +MAP_XLA_TYPE_TO_RECORD = { + xla_data_pb2.F16: + TypeConversionRecord( + primitive_type=xla_data_pb2.F16, + numpy_dtype=np.float16, + literal_field_name='f16s', + literal_field_type=float), + xla_data_pb2.F32: + TypeConversionRecord( + primitive_type=xla_data_pb2.F32, + numpy_dtype=np.float32, + literal_field_name='f32s', + literal_field_type=float), + xla_data_pb2.F64: + TypeConversionRecord( + primitive_type=xla_data_pb2.F64, + numpy_dtype=np.float64, + literal_field_name='f64s', + literal_field_type=float), + xla_data_pb2.S8: + TypeConversionRecord( + primitive_type=xla_data_pb2.S8, + numpy_dtype=np.int8, + literal_field_name='s8s', + literal_field_type=int), + xla_data_pb2.S16: + TypeConversionRecord( + primitive_type=xla_data_pb2.S16, + numpy_dtype=np.int16, + literal_field_name='s16s', + literal_field_type=int), + xla_data_pb2.S32: + TypeConversionRecord( + primitive_type=xla_data_pb2.S32, + numpy_dtype=np.int32, + literal_field_name='s32s', + literal_field_type=int), + xla_data_pb2.S64: + TypeConversionRecord( + primitive_type=xla_data_pb2.S64, + numpy_dtype=np.int64, + literal_field_name='s64s', + literal_field_type=int), + xla_data_pb2.U8: + TypeConversionRecord( + primitive_type=xla_data_pb2.U8, + numpy_dtype=np.uint8, + literal_field_name='s8s', + literal_field_type=int), + xla_data_pb2.U16: + TypeConversionRecord( + primitive_type=xla_data_pb2.U16, + numpy_dtype=np.uint16, + literal_field_name='s16s', + literal_field_type=int), + xla_data_pb2.U32: + TypeConversionRecord( + primitive_type=xla_data_pb2.U32, + numpy_dtype=np.uint32, + literal_field_name='s32s', + literal_field_type=int), + xla_data_pb2.U64: + TypeConversionRecord( + primitive_type=xla_data_pb2.U64, + numpy_dtype=np.uint64, + literal_field_name='s64s', + literal_field_type=int), + xla_data_pb2.PRED: + TypeConversionRecord( + primitive_type=xla_data_pb2.PRED, + numpy_dtype=np.bool, + literal_field_name='preds', + literal_field_type=bool) +} + +# Maps from Numpy dtypes to TypeConversionRecord. +# Note the conversion on the key. Numpy has a known issue wherein dtype hashing +# doesn't work as expected (https://github.com/numpy/numpy/issues/7242). Thus, +# when keying by dtype in this dict, we use the string form of dtypes. +MAP_DTYPE_TO_RECORD = { + str(np.dtype(record.numpy_dtype)): record + for record in MAP_XLA_TYPE_TO_RECORD.values() +} diff --git a/tensorflow/compiler/xla/python_api/xla_literal.py b/tensorflow/compiler/xla/python_api/xla_literal.py new file mode 100644 index 0000000000..b040098c29 --- /dev/null +++ b/tensorflow/compiler/xla/python_api/xla_literal.py @@ -0,0 +1,95 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the 'License'); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an 'AS IS' BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ====================================== +"""XLA LiteralProto utilities.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.compiler.xla import xla_data_pb2 +from tensorflow.compiler.xla.python_api import types +from tensorflow.compiler.xla.python_api import xla_shape + + +def ConvertLiteralToNumpyArray(literal): + """Converts a XLA literal to a Numpy array.""" + element_type = literal.shape.element_type + if element_type == xla_data_pb2.TUPLE: + return tuple( + ConvertLiteralToNumpyArray(subliteral) + for subliteral in literal.tuple_literals) + + type_record = types.MAP_XLA_TYPE_TO_RECORD[element_type] + if not literal.shape.dimensions: + return np.array( + getattr(literal, type_record.literal_field_name)[0], + type_record.numpy_dtype) + else: + # Infer the proper Numpy order from the LiteralProto's layout. The repeated + # field representing the array's content in the Literal is linearized. + # Reading is done in two steps: + # + # 1. Read the array as 1D from the LiteralProto repeated field. + # 2. Reshape the array to its proper shape, using the right order depending + # on the LiteralProto's layout. + layout_order = literal.shape.layout.minor_to_major + numpy_shape = tuple(literal.shape.dimensions) + if layout_order == range(len(literal.shape.dimensions)): + numpy_reshaper = lambda arr: arr.reshape(numpy_shape, order='F') + elif layout_order == range(len(literal.shape.dimensions) - 1, -1, -1): + numpy_reshaper = lambda arr: arr.reshape(numpy_shape, order='C') + else: + raise NotImplementedError('Unsupported layout: {0}'.format(layout_order)) + ndarray = np.array( + getattr(literal, type_record.literal_field_name), + copy=False, + dtype=type_record.numpy_dtype) + return numpy_reshaper(ndarray) + + +def _ConvertNumpyArrayToLiteral(ndarray): + """Converts a Numpy array to a XLA literal.""" + type_record = types.MAP_DTYPE_TO_RECORD[str(ndarray.dtype)] + literal = xla_data_pb2.LiteralProto() + literal.shape.CopyFrom(xla_shape.CreateShapeFromNumpy(ndarray).message) + + if ndarray.ndim == 0: + getattr(literal, type_record.literal_field_name).append( + np.asscalar(ndarray.astype(type_record.literal_field_type))) + else: + # Ndarrays with boolean dtypes need special type conversion with protobufs + if ndarray.dtype in {np.bool_, np.dtype('bool')}: + for element in np.nditer(ndarray): + getattr(literal, type_record.literal_field_name).append( + type_record.literal_field_type(element)) + else: + ndarray_flat = ndarray.ravel(order='A') + getattr(literal, type_record.literal_field_name).extend(ndarray_flat) + return literal + + +def ConvertNumpyArrayToLiteral(value): + """Converts a Numpy array or a nested tuple thereof to an XLA literal.""" + if isinstance(value, tuple): + literal = xla_data_pb2.LiteralProto() + literal.shape.CopyFrom(xla_shape.CreateShapeFromNumpy(value).message) + for component in value: + component_literal = literal.tuple_literals.add() + component_literal.CopyFrom(ConvertNumpyArrayToLiteral(component)) + return literal + else: + return _ConvertNumpyArrayToLiteral(value) diff --git a/tensorflow/compiler/xla/python_api/xla_shape.py b/tensorflow/compiler/xla/python_api/xla_shape.py new file mode 100644 index 0000000000..6af2895803 --- /dev/null +++ b/tensorflow/compiler/xla/python_api/xla_shape.py @@ -0,0 +1,155 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the 'License'); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an 'AS IS' BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ====================================== +"""XLA Shape utilities.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.compiler.xla import xla_data_pb2 +from tensorflow.compiler.xla.python_api import types + + +class Shape(object): + """Wraps a xla_data_pb2.Shape message with a convenient Python type. + + Provides direct access to the underlying xla_data_pb2.Shape message in the + message attribute, along with accessor wrappers to the message's fields. + Avoid direct access to .message unless interacting directly with protobuf APIs + like CopyFrom. In other words, prefer hauling the shape around in a Shape, and + only access .message when strictly required by the protobuf API. + """ + + def __init__(self, element_type, dimensions, layout=None): + """Creates a new XLA Shape. + + Args: + element_type: element type from xla_data_pb2. + dimensions: sequence of dimensions sizes (integers), or sequence + of Shapes in the case of a tuple, i.e. when element_type is + TUPLE. + layout: optional minor_to_major sequence for layout. If not given, the + default major-to-minor layout is used. + + Raises: + ValueError: if element_type is TUPLE but dimensions are not Shape objects. + """ + self.message = xla_data_pb2.Shape() + self.message.element_type = element_type + if element_type == xla_data_pb2.TUPLE: + if not all(isinstance(subshape, Shape) for subshape in dimensions): + raise ValueError( + 'XLA tuple requires sequence of Shape objects as dimensions') + self._tuple_shapes = tuple(dimensions) + for component_shape in self._tuple_shapes: + component_message = self.message.tuple_shapes.add() + component_message.CopyFrom(component_shape.message) + else: + self.message.dimensions.extend(dimensions) + if layout is None: + layout = list(reversed(range(len(dimensions)))) + self.message.layout.format = xla_data_pb2.DENSE + self.message.layout.minor_to_major.extend(layout) + + def element_type(self): + return self.message.element_type + + def is_tuple(self): + return self.element_type() == xla_data_pb2.TUPLE + + def dimensions(self): + if self.is_tuple(): + raise ValueError('Tuple shape has no dimensions. Try tuple_shapes()?') + return self.message.dimensions + + def tuple_shapes(self): + """If this is a tuple, returns its sequence of constituent Shape objects. + + Returns: + Tuple sub-shapes. + + Raises: + ValueError: if this is not a tuple. + """ + if not self.is_tuple(): + raise ValueError('tuple_shapes() called on a non-tuple shape') + return self._tuple_shapes + + def layout(self): + return self.message.layout + + @staticmethod + def from_pyval(pyval): + return CreateShapeFromNumpy(pyval) + + +def _CreateShapeFromNumpy(ndarray): # pylint: disable=invalid-name + """Create a Shape from a given Numpy array. + + Args: + ndarray: Numpy array. + + Returns: + A Shape object. + """ + element_type = types.MAP_DTYPE_TO_RECORD[str(ndarray.dtype)].primitive_type + dimensions = ndarray.shape + + # Set the shape's layout based on the ordering of ndarray. + # Numpy arrays come in two orders: Fortran (column-major) and C (row-major). + if np.isfortran(ndarray): + # Column-major layout. This corresponds to a "dimension order is + # minor-to-major" layout in XLA. + layout = range(ndarray.ndim) + else: + # Row-major layout. This corresponds to a "dimension order is + # major-to-minor" layout int XLA. + layout = list(reversed(xrange(ndarray.ndim))) + + return Shape(element_type, dimensions, layout) + + +def CreateShapeFromNumpy(value): # pylint: disable=invalid-name + """Create a Shape from a Numpy array or a nested tuple structure thereof. + + Args: + value: Numpy array or (possibly nested) tuple structure that bottoms out in + Numpy arrays. + + Returns: + A Shape object. + """ + if isinstance(value, tuple): + return Shape( + xla_data_pb2.TUPLE, + [CreateShapeFromNumpy(component) for component in value]) + else: + return _CreateShapeFromNumpy(value) + + +def CreateShapeFromDtypeAndTuple(dtype, shape_tuple): # pylint: disable=invalid-name + """Create a shape from a Numpy dtype and a sequence of nonnegative integers. + + Args: + dtype: a numpy dtype, e.g. np.dtype('int32'). + shape_tuple: a sequence of nonnegative integers. + + Returns: + A Shape object. + """ + element_type = types.MAP_DTYPE_TO_RECORD[str(dtype)].primitive_type + return Shape(element_type, shape_tuple) -- GitLab From 7f3dbd0f1ba1de89fb82226ea9f4506a97b9b19d Mon Sep 17 00:00:00 2001 From: Timon Van Overveldt Date: Thu, 14 Jun 2018 20:13:22 -0700 Subject: [PATCH 498/816] Disable collective ops support on Android builds. PiperOrigin-RevId: 200661893 --- tensorflow/core/common_runtime/direct_session.cc | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tensorflow/core/common_runtime/direct_session.cc b/tensorflow/core/common_runtime/direct_session.cc index 5cef93c605..87ba609dd7 100644 --- a/tensorflow/core/common_runtime/direct_session.cc +++ b/tensorflow/core/common_runtime/direct_session.cc @@ -447,6 +447,7 @@ Status DirectSession::RunInternal(int64 step_id, const RunOptions& run_options, // Create a run state and start execution. RunState run_state(step_id, &devices_); run_state.rendez = new IntraProcessRendezvous(device_mgr_.get()); +#ifndef __ANDROID__ // Set up for collectives if the RunOption declares a key. if (run_options.experimental().collective_graph_key() > 0) { if (!collective_executor_mgr_) { @@ -461,6 +462,7 @@ Status DirectSession::RunInternal(int64 step_id, const RunOptions& run_options, run_state.collective_executor.reset(new CollectiveExecutor::Handle( collective_executor_mgr_->FindOrCreate(step_id), true /*inherit_ref*/)); } +#endif // Start parallel Executors. const size_t num_executors = executors_and_keys->items.size(); -- GitLab From 3cd4eda38e12351c06d45d0780e16d482491ab95 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 14 Jun 2018 20:19:20 -0700 Subject: [PATCH 499/816] Added comment to explain plugging on external sharding normalizers. PiperOrigin-RevId: 200662293 --- .../xla/service/hlo_sharding_metadata.cc | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git a/tensorflow/compiler/xla/service/hlo_sharding_metadata.cc b/tensorflow/compiler/xla/service/hlo_sharding_metadata.cc index 7b4b071af4..748273a43c 100644 --- a/tensorflow/compiler/xla/service/hlo_sharding_metadata.cc +++ b/tensorflow/compiler/xla/service/hlo_sharding_metadata.cc @@ -235,6 +235,23 @@ StatusOr ApplyDomainShardingPass(const DomainMetadata::Domain& domain, Status ApplyDomainSharding(const DomainMetadata::Domain& domain, const HloSharding& sharding) { + // Here is the place to call external sharding normalizers, which are + // implemented in other modules (ie, spatial partitioning). + // The signature of the external normalizer function should be something + // like: + // + // StatusOr Normalizer(const DomainMetadata::Domain&, + // const HloSharding& sharding); + // + // The function should return true if it has processed the domain + // normalization, false if domain was not one recognized by it, or an error. + // We will call the functions in order below, and fall back to local code if + // none of the external normalizers acted on the domain. + // External normalizers should not handle the cases that are already handled + // locally. + + // None of the external normalizers handled the domain sharding, try to see + // whether this is a single sharding first. auto single_sharding = sharding.ExtractSingleSharding(); if (single_sharding) { // Shortcut the simple case. We have a unique sharding, so we call -- GitLab From 71ad57040b6303d2944989c2f78fa35d2a3ff103 Mon Sep 17 00:00:00 2001 From: brett koonce Date: Thu, 14 Jun 2018 21:10:49 -0700 Subject: [PATCH 500/816] contrib: autograph/constrained_optimization: minor spelling tweaks (#20044) --- tensorflow/contrib/autograph/converters/control_flow.py | 2 +- tensorflow/contrib/autograph/operators/control_flow.py | 2 +- tensorflow/contrib/autograph/pyct/static_analysis/cfg.py | 2 +- tensorflow/contrib/autograph/pyct/transformer.py | 4 ++-- tensorflow/contrib/constrained_optimization/README.md | 2 +- 5 files changed, 6 insertions(+), 6 deletions(-) diff --git a/tensorflow/contrib/autograph/converters/control_flow.py b/tensorflow/contrib/autograph/converters/control_flow.py index d7ddbe8a04..1e718f02d1 100644 --- a/tensorflow/contrib/autograph/converters/control_flow.py +++ b/tensorflow/contrib/autograph/converters/control_flow.py @@ -46,7 +46,7 @@ class SymbolNamer(object): class ControlFlowTransformer(transformer.Base): - """Transforms control flow structures like loops an conditionals.""" + """Transforms control flow structures like loops and conditionals.""" def _create_cond_branch(self, body_name, aliased_orig_names, aliased_new_names, body, returns): diff --git a/tensorflow/contrib/autograph/operators/control_flow.py b/tensorflow/contrib/autograph/operators/control_flow.py index 671c9ccc13..988df70157 100644 --- a/tensorflow/contrib/autograph/operators/control_flow.py +++ b/tensorflow/contrib/autograph/operators/control_flow.py @@ -51,7 +51,7 @@ def for_stmt(iter_, extra_test, body, init_state): Args: iter_: The entity being iterated over. extra_test: Callable with the state as arguments, and boolean return type. - An additionnal loop condition. + An additional loop condition. body: Callable with the iterate and the state as arguments, and state as return type. The actual loop body. init_state: Tuple containing the initial state. diff --git a/tensorflow/contrib/autograph/pyct/static_analysis/cfg.py b/tensorflow/contrib/autograph/pyct/static_analysis/cfg.py index ad97fdfa8e..ce746feeac 100644 --- a/tensorflow/contrib/autograph/pyct/static_analysis/cfg.py +++ b/tensorflow/contrib/autograph/pyct/static_analysis/cfg.py @@ -286,7 +286,7 @@ class Forward(object): # TODO(alexbw): see if we can simplify by visiting breadth-first def visit(self, node): - """Depth-first walking the CFG, applying dataflow information propagtion.""" + """Depth-first walking the CFG, applying dataflow information propagation.""" # node.value is None only for the exit CfgNode. if not node.value: return diff --git a/tensorflow/contrib/autograph/pyct/transformer.py b/tensorflow/contrib/autograph/pyct/transformer.py index 60bca8b38d..a656e99d21 100644 --- a/tensorflow/contrib/autograph/pyct/transformer.py +++ b/tensorflow/contrib/autograph/pyct/transformer.py @@ -191,7 +191,7 @@ class Base(gast.NodeTransformer): # TODO(mdan): Once we have error tracing, we may be able to just go to SSA. def apply_to_single_assignments(self, targets, values, apply_fn): - """Applies a fuction to each individual assignment. + """Applies a function to each individual assignment. This function can process a possibly-unpacked (e.g. a, b = c, d) assignment. It tries to break down the unpacking if possible. In effect, it has the same @@ -219,7 +219,7 @@ class Base(gast.NodeTransformer): targets field of an ast.Assign node. values: an AST node. apply_fn: a function of a single argument, which will be called with the - respective nodes of each single assignment. The signaure is + respective nodes of each single assignment. The signature is apply_fn(target, value), no return value. """ if not isinstance(targets, (list, tuple)): diff --git a/tensorflow/contrib/constrained_optimization/README.md b/tensorflow/contrib/constrained_optimization/README.md index c65a150464..cb1dd7d836 100644 --- a/tensorflow/contrib/constrained_optimization/README.md +++ b/tensorflow/contrib/constrained_optimization/README.md @@ -46,7 +46,7 @@ document. Imagine that we want to constrain the recall of a binary classifier to be at least 90%. Since the recall is proportional to the number of true positive classifications, which itself is a sum of indicator functions, this constraint -is non-differentible, and therefore cannot be used in a problem that will be +is non-differentiable, and therefore cannot be used in a problem that will be optimized using a (stochastic) gradient-based algorithm. For this and similar problems, TFCO supports so-called *proxy constraints*, -- GitLab From 284ad32b7f42a835d0cb545061fb354b4f96e0c9 Mon Sep 17 00:00:00 2001 From: Jianwei Xie Date: Thu, 14 Jun 2018 21:31:23 -0700 Subject: [PATCH 501/816] Improves the docstring and comments about feature column library. PiperOrigin-RevId: 200667467 --- .../python/feature_column/feature_column.py | 162 ++++++++++++++---- tensorflow/python/ops/embedding_ops.py | 12 +- 2 files changed, 136 insertions(+), 38 deletions(-) diff --git a/tensorflow/python/feature_column/feature_column.py b/tensorflow/python/feature_column/feature_column.py index f959b5e484..a58c5aabbe 100644 --- a/tensorflow/python/feature_column/feature_column.py +++ b/tensorflow/python/feature_column/feature_column.py @@ -172,7 +172,7 @@ def _internal_input_layer(features, scope=None): """See input_layer. `scope` is a name or variable scope to use.""" - feature_columns = _clean_feature_columns(feature_columns) + feature_columns = _normalize_feature_columns(feature_columns) for column in feature_columns: if not isinstance(column, _DenseColumn): raise ValueError( @@ -350,10 +350,23 @@ def linear_model(features, prediction itself for linear regression problems. Note on supported columns: `linear_model` treats categorical columns as - `indicator_column`s while `input_layer` explicitly requires wrapping each - of them with an `embedding_column` or an `indicator_column`. + `indicator_column`s. To be specific, assume the input as `SparseTensor` looks + like: - Example: + ```python + shape = [2, 2] + { + [0, 0]: "a" + [1, 0]: "b" + [1, 1]: "c" + } + ``` + `linear_model` assigns weights for the presence of "a", "b", "c' implicitly, + just like `indicator_column`, while `input_layer` explicitly requires wrapping + each of categorical columns with an `embedding_column` or an + `indicator_column`. + + Example of usage: ```python price = numeric_column('price') @@ -374,13 +387,44 @@ def linear_model(features, to your model. All items should be instances of classes derived from `_FeatureColumn`s. units: An integer, dimensionality of the output space. Default value is 1. - sparse_combiner: A string specifying how to reduce if a sparse column is - multivalent. Currently "mean", "sqrtn" and "sum" are supported, with "sum" - the default. "sqrtn" often achieves good accuracy, in particular with - bag-of-words columns. It combines each sparse columns independently. + sparse_combiner: A string specifying how to reduce if a categorical column + is multivalent. Except `numeric_column`, almost all columns passed to + `linear_model` are considered as categorical columns. It combines each + categorical column independently. Currently "mean", "sqrtn" and "sum" are + supported, with "sum" the default for linear model. "sqrtn" often achieves + good accuracy, in particular with bag-of-words columns. * "sum": do not normalize features in the column * "mean": do l1 normalization on features in the column * "sqrtn": do l2 normalization on features in the column + For example, for two features represented as the categorical columns: + + ```python + # Feature 1 + + shape = [2, 2] + { + [0, 0]: "a" + [0, 1]: "b" + [1, 0]: "c" + } + + # Feature 2 + + shape = [2, 3] + { + [0, 0]: "d" + [1, 0]: "e" + [1, 1]: "f" + [1, 2]: "g" + } + ``` + with `sparse_combiner` as "mean", the linear model outputs conceptly are: + ``` + y_0 = 1.0 / 2.0 * ( w_a + w_ b) + w_c + b_0 + y_1 = w_d + 1.0 / 3.0 * ( w_e + w_ f + w_g) + b_1 + ``` + where `y_i` is the output, `b_i` is the bias, and `w_x` is the weight + assigned to the presence of `x` in the input features. weight_collections: A list of collection names to which the Variable will be added. Note that, variables will also be added to collections `tf.GraphKeys.GLOBAL_VARIABLES` and `ops.GraphKeys.MODEL_VARIABLES`. @@ -536,7 +580,8 @@ class _LinearModel(training.Model): name=None, **kwargs): super(_LinearModel, self).__init__(name=name, **kwargs) - self._feature_columns = _clean_feature_columns(feature_columns) + self._feature_columns = _normalize_feature_columns( + feature_columns) self._weight_collections = list(weight_collections or []) if ops.GraphKeys.MODEL_VARIABLES not in self._weight_collections: self._weight_collections.append(ops.GraphKeys.MODEL_VARIABLES) @@ -643,7 +688,7 @@ def _transform_features(features, feature_columns): Returns: A `dict` mapping `_FeatureColumn` to `Tensor` and `SparseTensor` values. """ - feature_columns = _clean_feature_columns(feature_columns) + feature_columns = _normalize_feature_columns(feature_columns) outputs = {} with ops.name_scope( None, default_name='transform_features', values=features.values()): @@ -911,7 +956,8 @@ def shared_embedding_columns( tensor_name_in_ckpt: Name of the `Tensor` in `ckpt_to_load_from` from which to restore the column weights. Required if `ckpt_to_load_from` is not `None`. - max_norm: If not `None`, embedding values are l2-normalized to this value. + max_norm: If not `None`, each embedding is clipped if its l2-norm is + larger than this value, before combining. trainable: Whether or not the embedding is trainable. Default is True. Returns: @@ -1182,12 +1228,13 @@ def categorical_column_with_hash_bucket(key, Use this when your sparse features are in string or integer format, and you want to distribute your inputs into a finite number of buckets by hashing. - output_id = Hash(input_feature_string) % bucket_size + output_id = Hash(input_feature_string) % bucket_size for string type input. + For int type input, the value is converted to its string representation first + and then hashed by the same formula. For input dictionary `features`, `features[key]` is either `Tensor` or `SparseTensor`. If `Tensor`, missing values can be represented by `-1` for int - and `''` for string. Note that these values are independent of the - `default_value` argument. + and `''` for string, which will be dropped by this feature column. Example: @@ -1249,8 +1296,7 @@ def categorical_column_with_vocabulary_file(key, For input dictionary `features`, `features[key]` is either `Tensor` or `SparseTensor`. If `Tensor`, missing values can be represented by `-1` for int - and `''` for string. Note that these values are independent of the - `default_value` argument. + and `''` for string, which will be dropped by this feature column. Example with `num_oov_buckets`: File '/us/states.txt' contains 50 lines, each with a 2-character U.S. state @@ -1366,8 +1412,7 @@ def categorical_column_with_vocabulary_list( For input dictionary `features`, `features[key]` is either `Tensor` or `SparseTensor`. If `Tensor`, missing values can be represented by `-1` for int - and `''` for string. Note that these values are independent of the - `default_value` argument. + and `''` for string, which will be dropped by this feature column. Example with `num_oov_buckets`: In the following example, each input in `vocabulary_list` is assigned an ID @@ -1480,8 +1525,7 @@ def categorical_column_with_identity(key, num_buckets, default_value=None): For input dictionary `features`, `features[key]` is either `Tensor` or `SparseTensor`. If `Tensor`, missing values can be represented by `-1` for int - and `''` for string. Note that these values are independent of the - `default_value` argument. + and `''` for string, which will be dropped by this feature column. In the following examples, each input in the range `[0, 1000000)` is assigned the same value. All other inputs are assigned `default_value` 0. Note that a @@ -1538,8 +1582,14 @@ def categorical_column_with_identity(key, num_buckets, default_value=None): def indicator_column(categorical_column): """Represents multi-hot representation of given categorical column. - Used to wrap any `categorical_column_*` (e.g., to feed to DNN). Use - `embedding_column` if the inputs are sparse. + - For DNN model, `indicator_column` can be used to wrap any + `categorical_column_*` (e.g., to feed to DNN). Consider to Use + `embedding_column` if the number of buckets/unique(values) are large. + + - For Wide (aka linear) model, `indicator_column` is the internal + representation for categorical column when passing categorical column + directly (as any element in feature_columns) to `linear_model`. See + `linear_model` for details. ```python name = indicator_column(categorical_column_with_vocabulary_list( @@ -1956,7 +2006,7 @@ def _create_weighted_sum(column, weight_collections, trainable, weight_var=None): - """Creates a weighted sum for a dense or sparse column for linear_model.""" + """Creates a weighted sum for a dense/categorical column for linear_model.""" if isinstance(column, _CategoricalColumn): return _create_categorical_column_weighted_sum( column=column, @@ -2055,7 +2105,34 @@ def _create_categorical_column_weighted_sum(column, weight_collections, trainable, weight_var=None): - """Create a weighted sum of a categorical column for linear_model.""" + # pylint: disable=g-doc-return-or-yield,g-doc-args + """Create a weighted sum of a categorical column for linear_model. + + Note to maintainer: As implementation details, the weighted sum is + implemented via embedding_lookup_sparse toward efficiency. Mathematically, + they are the same. + + To be specific, conceptually, categorical column can be treated as multi-hot + vector. Say: + + ```python + x = [0 0 1] # categorical column input + w = [a b c] # weights + ``` + The weighted sum is `c` in this case, which is same as `w[2]`. + + Another example is + + ```python + x = [0 1 1] # categorical column input + w = [a b c] # weights + ``` + The weighted sum is `b + c` in this case, which is same as `w[2] + w[3]`. + + For both cases, we can implement weighted sum via embedding_lookup with + sparse_combiner = "sum". + """ + sparse_tensors = column._get_sparse_tensors( # pylint: disable=protected-access builder, weight_collections=weight_collections, @@ -2249,7 +2326,7 @@ def _shape_offsets(shape): # TODO(ptucker): Move to third_party/tensorflow/python/ops/sparse_ops.py -def _to_sparse_input(input_tensor, ignore_value=None): +def _to_sparse_input_and_drop_ignore_values(input_tensor, ignore_value=None): """Converts a `Tensor` to a `SparseTensor`, dropping ignore_value cells. If `input_tensor` is already a `SparseTensor`, just return it. @@ -2293,8 +2370,22 @@ def _to_sparse_input(input_tensor, ignore_value=None): input_tensor, out_type=dtypes.int64, name='dense_shape')) -def _clean_feature_columns(feature_columns): - """Verifies and normalizes `feature_columns` input.""" +def _normalize_feature_columns(feature_columns): + """Normalizes the `feature_columns` input. + + This method converts the `feature_columns` to list type as best as it can. In + addition, verifies the type and other parts of feature_columns, required by + downstream library. + + Args: + feature_columns: The raw feature columns, usually passed by users. + + Returns: + The normalized feature column list. + + Raises: + ValueError: for any invalid inputs, such as empty, duplicated names, etc. + """ if isinstance(feature_columns, _FeatureColumn): feature_columns = [feature_columns] @@ -2420,6 +2511,7 @@ class _BucketizedColumn(_DenseColumn, _CategoricalColumn, def _get_sparse_tensors(self, inputs, weight_collections=None, trainable=None): + """Converts dense inputs to SparseTensor so downstream code can use it.""" input_tensor = inputs.get(self) batch_size = array_ops.shape(input_tensor)[0] # By construction, source_column is always one-dimensional. @@ -2804,7 +2896,7 @@ class _HashedCategoricalColumn( return {self.key: parsing_ops.VarLenFeature(self.dtype)} def _transform_feature(self, inputs): - input_tensor = _to_sparse_input(inputs.get(self.key)) + input_tensor = _to_sparse_input_and_drop_ignore_values(inputs.get(self.key)) if not isinstance(input_tensor, sparse_tensor_lib.SparseTensor): raise ValueError('SparseColumn input must be a SparseTensor.') @@ -2855,7 +2947,7 @@ class _VocabularyFileCategoricalColumn( return {self.key: parsing_ops.VarLenFeature(self.dtype)} def _transform_feature(self, inputs): - input_tensor = _to_sparse_input(inputs.get(self.key)) + input_tensor = _to_sparse_input_and_drop_ignore_values(inputs.get(self.key)) if self.dtype.is_integer != input_tensor.dtype.is_integer: raise ValueError( @@ -2907,7 +2999,7 @@ class _VocabularyListCategoricalColumn( return {self.key: parsing_ops.VarLenFeature(self.dtype)} def _transform_feature(self, inputs): - input_tensor = _to_sparse_input(inputs.get(self.key)) + input_tensor = _to_sparse_input_and_drop_ignore_values(inputs.get(self.key)) if self.dtype.is_integer != input_tensor.dtype.is_integer: raise ValueError( @@ -2959,7 +3051,7 @@ class _IdentityCategoricalColumn( return {self.key: parsing_ops.VarLenFeature(dtypes.int64)} def _transform_feature(self, inputs): - input_tensor = _to_sparse_input(inputs.get(self.key)) + input_tensor = _to_sparse_input_and_drop_ignore_values(inputs.get(self.key)) if not input_tensor.dtype.is_integer: raise ValueError( @@ -3041,7 +3133,8 @@ class _WeightedCategoricalColumn( self.dtype, weight_tensor.dtype)) if not isinstance(weight_tensor, sparse_tensor_lib.SparseTensor): # The weight tensor can be a regular Tensor. In this case, sparsify it. - weight_tensor = _to_sparse_input(weight_tensor, ignore_value=0.0) + weight_tensor = _to_sparse_input_and_drop_ignore_values( + weight_tensor, ignore_value=0.0) if not weight_tensor.dtype.is_floating: weight_tensor = math_ops.to_float(weight_tensor) return (inputs.get(self.categorical_column), weight_tensor) @@ -3486,3 +3579,8 @@ class _SequenceCategoricalColumn( weight_tensor, shape=array_ops.concat([weight_tensor.dense_shape, [1]], axis=0)) return _CategoricalColumn.IdWeightPair(id_tensor, weight_tensor) + + +# TODO(xiejw): Remove the following alias once call sites are updated. +_clean_feature_columns = _normalize_feature_columns +_to_sparse_input = _to_sparse_input_and_drop_ignore_values diff --git a/tensorflow/python/ops/embedding_ops.py b/tensorflow/python/ops/embedding_ops.py index bcc717b043..c7919e4d4c 100644 --- a/tensorflow/python/ops/embedding_ops.py +++ b/tensorflow/python/ops/embedding_ops.py @@ -43,8 +43,8 @@ def _clip(params, ids, max_norm): Args: params: A `Tensor` of embeddings retrieved by `gather`. ids: The `ids` argument that was passed to `gather`. - max_norm: If provided, the embeddings are l2-normalized to the value of - max_norm. + max_norm: If not `None`, each embedding is clipped if its l2-norm is + larger than this value. Returns: A `Tensor` with the same type as `params`. @@ -290,8 +290,8 @@ def embedding_lookup( in `indices` are always validated to be within range. If assigned to GPU, out-of-bound indices result in safe but unspecified behavior, which may include raising an error. - max_norm: If provided, embedding values are l2-normalized to the value of - max_norm. + max_norm: If not `None`, each embedding is clipped if its l2-norm is + larger than this value. Returns: A `Tensor` with the same type as the tensors in `params`. @@ -346,8 +346,8 @@ def embedding_lookup_sparse(params, "mean" is the weighted sum divided by the total weight. "sqrtn" is the weighted sum divided by the square root of the sum of the squares of the weights. - max_norm: If provided, each embedding is normalized to have l2 norm equal - to max_norm before combining. + max_norm: If not `None`, each embedding is clipped if its l2-norm is + larger than this value, before combining. Returns: A dense tensor representing the combined embeddings for the -- GitLab From 9d67a56cc05268ece82dc941a3cc72f603f48d0a Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 14 Jun 2018 21:37:06 -0700 Subject: [PATCH 502/816] Add resource type to Switch op. PiperOrigin-RevId: 200667835 --- tensorflow/core/kernels/control_flow_ops.cc | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/core/kernels/control_flow_ops.cc b/tensorflow/core/kernels/control_flow_ops.cc index ebf844d75f..fd3a0ad422 100644 --- a/tensorflow/core/kernels/control_flow_ops.cc +++ b/tensorflow/core/kernels/control_flow_ops.cc @@ -108,6 +108,7 @@ REGISTER_GPU_HOST_KERNEL(bool); REGISTER_GPU_HOST_REF_KERNEL(bool); REGISTER_GPU_HOST_KERNEL(string); REGISTER_GPU_HOST_REF_KERNEL(string); +REGISTER_GPU_HOST_KERNEL(ResourceHandle); #undef REGISTER_GPU_HOST_KERNEL #undef REGISTER_GPU_HOST_REF_KERNEL -- GitLab From b84506ec8961306100ee67bd06ed8d2b59f4b1c8 Mon Sep 17 00:00:00 2001 From: Alan Chiao Date: Thu, 14 Jun 2018 22:39:56 -0700 Subject: [PATCH 503/816] Update demo app to use nightly TFLite build instead of latest release build. When the demo app updates to use a backwards-incompatible change to the TFLite Java API at HEAD, it'll fail to build on the old release (which is missing the API change). Using the nightly build means the demo app will use a relatively fresh TFLite build with API changes, in addition to other improvements. The user may need to pull the latest demo code to keep up. PiperOrigin-RevId: 200672004 --- tensorflow/contrib/lite/java/demo/README.md | 9 +++++++++ tensorflow/contrib/lite/java/demo/app/build.gradle | 2 +- tensorflow/docs_src/mobile/tflite/demo_android.md | 3 +++ 3 files changed, 13 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/lite/java/demo/README.md b/tensorflow/contrib/lite/java/demo/README.md index 2e818f728e..e3cea19e16 100644 --- a/tensorflow/contrib/lite/java/demo/README.md +++ b/tensorflow/contrib/lite/java/demo/README.md @@ -1,5 +1,14 @@ # TF Lite Android App +## Building in Android Studio with TensorFlow Lite AAR from JCenter. +The build.gradle is configured to use TensorFlow Lite's nightly build. + +If you see a build error related to compatibility with Tensorflow Lite's Java API (example: method X is +undefined for type Interpreter), there has likely been a backwards compatible +change to the API. You will need to pull new app code that's compatible with the +nightly build and may need to first wait a few days for our external and internal +code to merge. + ## Building from Source with Bazel 1. Follow the [Bazel steps for the TF Demo App](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/android#bazel): diff --git a/tensorflow/contrib/lite/java/demo/app/build.gradle b/tensorflow/contrib/lite/java/demo/app/build.gradle index b76eaad8bb..7f29deed83 100644 --- a/tensorflow/contrib/lite/java/demo/app/build.gradle +++ b/tensorflow/contrib/lite/java/demo/app/build.gradle @@ -52,7 +52,7 @@ dependencies { compile 'com.android.support:support-annotations:25.3.1' compile 'com.android.support:support-v13:25.2.0' - compile 'org.tensorflow:tensorflow-lite:+' + compile 'org.tensorflow:tensorflow-lite:0.0.0-nightly' testCompile 'junit:junit:4.12' } diff --git a/tensorflow/docs_src/mobile/tflite/demo_android.md b/tensorflow/docs_src/mobile/tflite/demo_android.md index 7f2f8882a2..480d66bbb6 100644 --- a/tensorflow/docs_src/mobile/tflite/demo_android.md +++ b/tensorflow/docs_src/mobile/tflite/demo_android.md @@ -58,6 +58,9 @@ To get a model, either: Now you can build and run the demo app. +Some additional details are available on the +[TF Lite Android App page](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/java/demo/README.md). + ## Build TensorFlow Lite and the demo app from source -- GitLab From 951d005f8975891d704878d3ab1d768223719ff1 Mon Sep 17 00:00:00 2001 From: Jiandong Ruan Date: Fri, 15 Jun 2018 00:22:50 -0700 Subject: [PATCH 504/816] fix TF_GraphImportGraphDefWithResults and TF_GraphImportGraphDefWithReturnOutputs for model > 64 MB. --- tensorflow/c/c_api.cc | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/c/c_api.cc b/tensorflow/c/c_api.cc index cb0b093ad2..12f0d8bff4 100644 --- a/tensorflow/c/c_api.cc +++ b/tensorflow/c/c_api.cc @@ -2123,7 +2123,7 @@ TF_ImportGraphDefResults* TF_GraphImportGraphDefWithResults( TF_Graph* graph, const TF_Buffer* graph_def, const TF_ImportGraphDefOptions* options, TF_Status* status) { GraphDef def; - if (!def.ParseFromArray(graph_def->data, graph_def->length)) { + if (!tensorflow::ParseProtoUnlimited(&def, graph_def->data, graph_def->length)) { status->status = InvalidArgument("Invalid GraphDef"); return nullptr; } @@ -2153,7 +2153,7 @@ void TF_GraphImportGraphDefWithReturnOutputs( return; } GraphDef def; - if (!def.ParseFromArray(graph_def->data, graph_def->length)) { + if (!tensorflow::ParseProtoUnlimited(&def, graph_def->data, graph_def->length)) { status->status = InvalidArgument("Invalid GraphDef"); return; } -- GitLab From 7bd8cd2be316d5e8b5f70fbc49056e1602239a73 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 15 Jun 2018 05:15:50 -0700 Subject: [PATCH 505/816] Adds warm start capability to tf.contrib.estimator.DNNEstimator PiperOrigin-RevId: 200702709 --- tensorflow/contrib/estimator/BUILD | 2 +- .../contrib/estimator/python/estimator/dnn.py | 18 ++++++++++++++++-- .../estimator/python/estimator/dnn_test.py | 17 ++++++++++++++++- 3 files changed, 33 insertions(+), 4 deletions(-) diff --git a/tensorflow/contrib/estimator/BUILD b/tensorflow/contrib/estimator/BUILD index 1937ffb583..30d297a5fb 100644 --- a/tensorflow/contrib/estimator/BUILD +++ b/tensorflow/contrib/estimator/BUILD @@ -117,7 +117,7 @@ py_library( py_test( name = "dnn_test", - size = "small", + size = "medium", srcs = ["python/estimator/dnn_test.py"], srcs_version = "PY2AND3", tags = [ diff --git a/tensorflow/contrib/estimator/python/estimator/dnn.py b/tensorflow/contrib/estimator/python/estimator/dnn.py index 7ff25b95c0..f1c60a912c 100644 --- a/tensorflow/contrib/estimator/python/estimator/dnn.py +++ b/tensorflow/contrib/estimator/python/estimator/dnn.py @@ -53,6 +53,13 @@ class DNNEstimator(estimator.Estimator): l1_regularization_strength=0.001 )) + # Or estimator with warm-starting from a previous checkpoint. + estimator = DNNEstimator( + head=tf.contrib.estimator.multi_label_head(n_classes=3), + feature_columns=[sparse_feature_a_emb, sparse_feature_b_emb], + hidden_units=[1024, 512, 256], + warm_start_from="/path/to/checkpoint/dir") + # Input builders def input_fn_train: # returns x, y pass @@ -92,7 +99,8 @@ class DNNEstimator(estimator.Estimator): activation_fn=nn.relu, dropout=None, input_layer_partitioner=None, - config=None): + config=None, + warm_start_from=None): """Initializes a `DNNEstimator` instance. Args: @@ -116,6 +124,11 @@ class DNNEstimator(estimator.Estimator): input_layer_partitioner: Optional. Partitioner for input layer. Defaults to `min_max_variable_partitioner` with `min_slice_size` 64 << 20. config: `RunConfig` object to configure the runtime settings. + warm_start_from: A string filepath to a checkpoint to warm-start from, or + a `WarmStartSettings` object to fully configure warm-starting. If the + string filepath is provided instead of a `WarmStartSettings`, then all + weights are warm-started, and it is assumed that vocabularies and Tensor + names are unchanged. """ def _model_fn(features, labels, mode, config): return dnn_lib._dnn_model_fn( # pylint: disable=protected-access @@ -131,4 +144,5 @@ class DNNEstimator(estimator.Estimator): input_layer_partitioner=input_layer_partitioner, config=config) super(DNNEstimator, self).__init__( - model_fn=_model_fn, model_dir=model_dir, config=config) + model_fn=_model_fn, model_dir=model_dir, config=config, + warm_start_from=warm_start_from) diff --git a/tensorflow/contrib/estimator/python/estimator/dnn_test.py b/tensorflow/contrib/estimator/python/estimator/dnn_test.py index 75e3107670..050b0428bf 100644 --- a/tensorflow/contrib/estimator/python/estimator/dnn_test.py +++ b/tensorflow/contrib/estimator/python/estimator/dnn_test.py @@ -38,7 +38,7 @@ from tensorflow.python.platform import test from tensorflow.python.summary.writer import writer_cache -def _dnn_estimator_fn(weight_column=None, label_dimension=1, *args, **kwargs): +def _dnn_estimator_fn(weight_column=None, label_dimension=1, *args, **kwargs): # pylint: disable=keyword-arg-before-vararg """Returns a DNNEstimator that uses regression_head.""" return dnn.DNNEstimator( head=head_lib.regression_head( @@ -48,6 +48,12 @@ def _dnn_estimator_fn(weight_column=None, label_dimension=1, *args, **kwargs): *args, **kwargs) +def _dnn_estimator_classifier_fn(n_classes=3, *args, **kwargs): # pylint: disable=keyword-arg-before-vararg + """Returns a DNNEstimator that uses multi_class_head.""" + return dnn.DNNEstimator(head=head_lib.multi_class_head(n_classes=n_classes), + *args, **kwargs) + + class DNNEstimatorEvaluateTest( dnn_testing_utils.BaseDNNRegressorEvaluateTest, test.TestCase): @@ -75,6 +81,15 @@ class DNNEstimatorTrainTest( self, _dnn_estimator_fn) +class DNNEstimatorWarmStartingTest(dnn_testing_utils.BaseDNNWarmStartingTest, + test.TestCase): + + def __init__(self, methodName='runTest'): # pylint: disable=invalid-name + test.TestCase.__init__(self, methodName) + dnn_testing_utils.BaseDNNWarmStartingTest.__init__( + self, _dnn_estimator_classifier_fn, _dnn_estimator_fn) + + class DNNEstimatorIntegrationTest(test.TestCase): def setUp(self): -- GitLab From 4944c2708090c761af5b970666301a35ae04b2d9 Mon Sep 17 00:00:00 2001 From: Dan Moldovan Date: Fri, 15 Jun 2018 07:13:49 -0700 Subject: [PATCH 506/816] Broad refactoring (part 1): Introduce a module dedicated to symbols that are user-visible and which represent idioms not found in plain Python. This CL only adds the module - a future CL will replace existing implementations with these. PiperOrigin-RevId: 200712144 --- tensorflow/contrib/autograph/lang/BUILD | 40 +++++++++++ .../contrib/autograph/lang/directives.py | 68 +++++++++++++++++++ .../autograph/lang/special_functions.py | 59 ++++++++++++++++ .../autograph/lang/special_functions_test.py | 54 +++++++++++++++ tensorflow/tools/pip_package/BUILD | 1 + 5 files changed, 222 insertions(+) create mode 100644 tensorflow/contrib/autograph/lang/BUILD create mode 100644 tensorflow/contrib/autograph/lang/directives.py create mode 100644 tensorflow/contrib/autograph/lang/special_functions.py create mode 100644 tensorflow/contrib/autograph/lang/special_functions_test.py diff --git a/tensorflow/contrib/autograph/lang/BUILD b/tensorflow/contrib/autograph/lang/BUILD new file mode 100644 index 0000000000..77a2184e22 --- /dev/null +++ b/tensorflow/contrib/autograph/lang/BUILD @@ -0,0 +1,40 @@ +licenses(["notice"]) # Apache 2.0 + +exports_files(["LICENSE"]) + +load("//tensorflow:tensorflow.bzl", "py_test") + +filegroup( + name = "all_files", + srcs = glob( + ["**/*"], + exclude = [ + "**/METADATA", + "**/OWNERS", + ], + ), + visibility = ["//tensorflow:__subpackages__"], +) + +py_library( + name = "lang", + srcs = [ + "directives.py", + "special_functions.py", + ], + srcs_version = "PY2AND3", + visibility = ["//tensorflow:__subpackages__"], + deps = [ + "//tensorflow/contrib/autograph/operators", + ], +) + +py_test( + name = "special_functions_test", + srcs = ["special_functions_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":lang", + "//tensorflow/python:client_testlib", + ], +) diff --git a/tensorflow/contrib/autograph/lang/directives.py b/tensorflow/contrib/autograph/lang/directives.py new file mode 100644 index 0000000000..aabe5d9939 --- /dev/null +++ b/tensorflow/contrib/autograph/lang/directives.py @@ -0,0 +1,68 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Directives are special no-op functions that serve as compilation markers. + +They provide static information like type hints, compilation and TensorFlow +overrides. + +These serve as annotations in the compiled code, allowing the user some control +over the compilation process. They have no functional role at runtime. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + + +UNSPECIFIED = object() + + +def set_element_type(entity, dtype, shape=UNSPECIFIED): + """Indicates that the entity is expected hold items of specified type/shape. + + The staged TensorFlow ops will reflect and assert this data type. Ignored + otherwise. + + Args: + entity: The entity to annotate. + dtype: TensorFlow dtype value to assert for entity. + shape: Optional shape to assert for entity. + """ + del entity + del dtype + del shape + + +def set_loop_options( + parallel_iterations=UNSPECIFIED, + back_prop=UNSPECIFIED, + swap_memory=UNSPECIFIED, + maximum_iterations=UNSPECIFIED): + """Specifies additional arguments to be passed to the enclosing while_loop. + + The parameters apply to and only to the immediately enclosing loop. It only + has effect if the loop is staged as a TF while_loop; otherwise the parameters + have no effect. + + Args: + parallel_iterations: See tf.while_loop. + back_prop: See tf.while_loop. + swap_memory: See tf.while_loop. + maximum_iterations: See tf.while_loop. + """ + del parallel_iterations + del back_prop + del swap_memory + del maximum_iterations diff --git a/tensorflow/contrib/autograph/lang/special_functions.py b/tensorflow/contrib/autograph/lang/special_functions.py new file mode 100644 index 0000000000..11135295a7 --- /dev/null +++ b/tensorflow/contrib/autograph/lang/special_functions.py @@ -0,0 +1,59 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Special functions that only make sense for AutoGraph. + +These functions are meant to ensure feature parity between Python and AutoGraph, +so that the exact same code works in both modes. In general, AutoGraph will +replace these calls. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.autograph.operators import data_structures + + +def stack(list_or_tensor, element_dtype=None, strict=True): + """Stacks the input, if it admits the notion of stacking. + + For example, a list of tensors can be stacked into a larger tensor. This + function is similar to tf.stack, but it accepts non-lists and lists of + non-tensors as arguments. In the latter case, the function does nothing. + + Args: + list_or_tensor: Any + element_dtype: tf.DType, optional dtypedtype for the elements in the list. + Required if the input is stackable, and the list is untyped. + strict: bool, if True an error is raised if the input is not stackable. + Otherwise the function is a no-op. + + Returns: + Any, if the input is stackable, the result will be a tf.Tensor. Otherwise, + if strict=False, the result will be list_or_tensor. + + Raises: + ValueError: if strict=True and the input is not stackable. + """ + if strict: + def raise_error(x): + raise ValueError('%s must be stackable when strict=True' % x) + original_call = raise_error + else: + original_call = lambda x: x + return data_structures.list_stack( + list_or_tensor, + data_structures.ListStackOpts( + element_dtype=element_dtype, original_call=original_call)) diff --git a/tensorflow/contrib/autograph/lang/special_functions_test.py b/tensorflow/contrib/autograph/lang/special_functions_test.py new file mode 100644 index 0000000000..a49cb64075 --- /dev/null +++ b/tensorflow/contrib/autograph/lang/special_functions_test.py @@ -0,0 +1,54 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for special_functions module.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.autograph.lang import special_functions +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import tensor_util +from tensorflow.python.ops import list_ops +from tensorflow.python.platform import test + + +class SpecialFunctionsTest(test.TestCase): + + def test_basic(self): + self.assertEqual(special_functions.stack(1, strict=False), 1) + self.assertListEqual( + special_functions.stack([1, 2, 3], strict=False), [1, 2, 3]) + # TODO(mdan): This should probably forward to tf.stack. + self.assertTrue( + isinstance( + special_functions.stack( + [constant_op.constant(1), + constant_op.constant(2)], strict=False), list)) + + with self.assertRaises(ValueError): + special_functions.stack([1, 2, 3]) + + t = constant_op.constant([1.0, 2.0]) + l = list_ops.tensor_list_from_tensor( + t, element_shape=constant_op.constant([], dtype=dtypes.int32)) + self.assertTrue( + tensor_util.is_tensor( + special_functions.stack(l, element_dtype=dtypes.float32))) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD index e113565f45..b228ff5a21 100644 --- a/tensorflow/tools/pip_package/BUILD +++ b/tensorflow/tools/pip_package/BUILD @@ -59,6 +59,7 @@ COMMON_PIP_DEPS = [ "//tensorflow/contrib/autograph/converters:converters", "//tensorflow/contrib/autograph/converters:test_lib", "//tensorflow/contrib/autograph/impl:impl", + "//tensorflow/contrib/autograph/lang:lang", "//tensorflow/contrib/autograph/pyct:pyct", "//tensorflow/contrib/autograph/pyct/static_analysis:static_analysis", "//tensorflow/contrib/boosted_trees:boosted_trees_pip", -- GitLab From 69e3c1d9b816eaf8514d8b783a05a363f51c0237 Mon Sep 17 00:00:00 2001 From: Shashi Shekhar Date: Fri, 15 Jun 2018 09:29:32 -0700 Subject: [PATCH 507/816] Fix Makefile build for benchmarking code. PiperOrigin-RevId: 200726967 --- tensorflow/contrib/lite/Makefile | 45 ++++++++++++++++++++++++++++++-- 1 file changed, 43 insertions(+), 2 deletions(-) diff --git a/tensorflow/contrib/lite/Makefile b/tensorflow/contrib/lite/Makefile index cc8a8035d1..2b6997146e 100644 --- a/tensorflow/contrib/lite/Makefile +++ b/tensorflow/contrib/lite/Makefile @@ -70,6 +70,12 @@ LIB_PATH := $(LIBDIR)$(LIB_NAME) # A small example program that shows how to link against the library. MINIMAL_PATH := $(BINDIR)minimal +# Benchmark static library and binary +BENCHMARK_LIB_NAME := benchmark-lib.a +BENCHMARK_BINARY_NAME := benchmark_model +BENCHMARK_LIB := $(LIBDIR)$(BENCHMARK_LIB_NAME) +BENCHMARK_BINARY := $(BINDIR)$(BENCHMARK_BINARY_NAME) + MINIMAL_SRCS := \ tensorflow/contrib/lite/examples/minimal/minimal.cc MINIMAL_OBJS := $(addprefix $(OBJDIR), \ @@ -78,12 +84,19 @@ $(patsubst %.cc,%.o,$(patsubst %.c,%.o,$(MINIMAL_SRCS)))) # What sources we want to compile, must be kept in sync with the main Bazel # build files. +PROFILER_SRCS := \ + tensorflow/contrib/lite/profiling/time.cc +PROFILE_SUMMARIZER_SRCS := \ + tensorflow/contrib/lite/profiling/profile_summarizer.cc \ + tensorflow/core/util/stats_calculator.cc + CORE_CC_ALL_SRCS := \ $(wildcard tensorflow/contrib/lite/*.cc) \ $(wildcard tensorflow/contrib/lite/kernels/*.cc) \ $(wildcard tensorflow/contrib/lite/kernels/internal/*.cc) \ $(wildcard tensorflow/contrib/lite/kernels/internal/optimized/*.cc) \ $(wildcard tensorflow/contrib/lite/kernels/internal/reference/*.cc) \ +$(PROFILER_SRCS) \ $(wildcard tensorflow/contrib/lite/*.c) \ $(wildcard tensorflow/contrib/lite/kernels/*.c) \ $(wildcard tensorflow/contrib/lite/kernels/internal/*.c) \ @@ -107,18 +120,31 @@ TF_LITE_CC_OBJS := $(addprefix $(OBJDIR), \ $(patsubst %.cc,%.o,$(patsubst %.c,%.o,$(TF_LITE_CC_SRCS)))) LIB_OBJS := $(TF_LITE_CC_OBJS) + +# Benchmark sources +BENCHMARK_SRCS_DIR := tensorflow/contrib/lite/tools/benchmark +BENCHMARK_ALL_SRCS := $(TFLITE_CC_SRCS) \ + $(wildcard $(BENCHMARK_SRCS_DIR)/*.cc) \ + $(PROFILE_SUMMARIZER_SRCS) + +BENCHMARK_SRCS := $(filter-out \ + $(wildcard $(BENCHMARK_SRCS_DIR)/*_test.cc), \ + $(BENCHMARK_ALL_SRCS)) + +BENCHMARK_OBJS := $(addprefix $(OBJDIR), \ +$(patsubst %.cc,%.o,$(patsubst %.c,%.o,$(BENCHMARK_SRCS)))) + # For normal manually-created TensorFlow C++ source files. $(OBJDIR)%.o: %.cc @mkdir -p $(dir $@) $(CXX) $(CXXFLAGS) $(INCLUDES) -c $< -o $@ - # For normal manually-created TensorFlow C++ source files. $(OBJDIR)%.o: %.c @mkdir -p $(dir $@) $(CC) $(CCFLAGS) $(INCLUDES) -c $< -o $@ # The target that's compiled if there's no command-line arguments. -all: $(LIB_PATH) $(MINIMAL_PATH) +all: $(LIB_PATH) $(MINIMAL_PATH) $(BENCHMARK_BINARY) # Gathers together all the objects we've compiled into a single '.a' archive. $(LIB_PATH): $(LIB_OBJS) @@ -131,6 +157,21 @@ $(MINIMAL_PATH): $(MINIMAL_OBJS) $(LIB_PATH) -o $(MINIMAL_PATH) $(MINIMAL_OBJS) \ $(LIBFLAGS) $(LIB_PATH) $(LDFLAGS) $(LIBS) + +$(BENCHMARK_LIB) : $(LIB_PATH) $(BENCHMARK_OBJS) + @mkdir -p $(dir $@) + $(AR) $(ARFLAGS) $(BENCHMARK_LIB) $(LIB_OBJS) $(BENCHMARK_OBJS) + +benchmark_lib: $(BENCHMARK_LIB) +$(info $(BENCHMARK_BINARY)) +$(BENCHMARK_BINARY) : $(BENCHMARK_LIB) + @mkdir -p $(dir $@) + $(CXX) $(CXXFLAGS) $(INCLUDES) \ + -o $(BENCHMARK_BINARY) \ + $(LIBFLAGS) $(BENCHMARK_LIB) $(LDFLAGS) $(LIBS) + +benchmark: $(BENCHMARK_BINARY) + # Gets rid of all generated files. clean: rm -rf $(MAKEFILE_DIR)/gen -- GitLab From 8ad3184c7af54cad42a15afb3e83436bd195d17f Mon Sep 17 00:00:00 2001 From: Russell Power Date: Fri, 15 Jun 2018 09:34:01 -0700 Subject: [PATCH 508/816] Add XLA support for the error function (and complement). PiperOrigin-RevId: 200727545 --- tensorflow/compiler/tests/unary_ops_test.py | 10 +++ .../compiler/tf2xla/kernels/unary_ops.cc | 46 ++++++++++ .../compiler/xla/client/lib/arithmetic.cc | 84 +++++++++++++++++++ .../compiler/xla/client/lib/arithmetic.h | 14 ++++ 4 files changed, 154 insertions(+) diff --git a/tensorflow/compiler/tests/unary_ops_test.py b/tensorflow/compiler/tests/unary_ops_test.py index 689a4a1f4e..e610b63e30 100644 --- a/tensorflow/compiler/tests/unary_ops_test.py +++ b/tensorflow/compiler/tests/unary_ops_test.py @@ -201,6 +201,16 @@ class UnaryOpsTest(XLATestCase): expected=np.array([1.54308063, 3.76219569, 10.067662, 27.30823284], dtype=dtype)) + # Disable float16 testing for now + if dtype != np.float16: + x = np.arange(-10, 10, 1).astype(dtype) + with self.test_session() as session: + erf_x = session.run(math_ops.erf(x)) + erfc_x = session.run(math_ops.erfc(x)) + + self._assertOpOutputMatchesExpected(math_ops.erf, x, expected=erf_x) + self._assertOpOutputMatchesExpected(math_ops.erfc, x, expected=erfc_x) + self._assertOpOutputMatchesExpected( math_ops.exp, np.array([[-1, 1]], dtype=dtype), diff --git a/tensorflow/compiler/tf2xla/kernels/unary_ops.cc b/tensorflow/compiler/tf2xla/kernels/unary_ops.cc index 71a9fd051b..2521445e86 100644 --- a/tensorflow/compiler/tf2xla/kernels/unary_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/unary_ops.cc @@ -16,9 +16,11 @@ limitations under the License. // Native XLA implementations of simple unary Ops #include "tensorflow/compiler/tf2xla/kernels/cwise_ops.h" +#include "tensorflow/compiler/tf2xla/type_util.h" #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/client_library.h" +#include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/core/framework/kernel_def_builder.h" @@ -185,5 +187,49 @@ XLAJIT_MAKE_UNARY(Imag, b->Imag(x)); #undef XLAJIT_MAKE_UNARY +// Erf/Erfc. For x in (-1, 1), the erf approximation is used; erfc polynomial +// is used outside of this range. +class ErfOp : public XlaOpKernel { + public: + explicit ErfOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {} + void Compile(XlaOpKernelContext* ctx) override { + xla::XlaBuilder* b = ctx->builder(); + xla::PrimitiveType primitive_type; + xla::XlaOp one = XlaHelpers::One(b, input_type(0)); + xla::XlaOp x = ctx->Input(0); + xla::XlaOp abs_x = b->Abs(x); + + OP_REQUIRES_OK(ctx, + DataTypeToPrimitiveType(input_type(0), &primitive_type)); + + auto y = b->Select(b->Gt(abs_x, one), + b->Sub(one, ComputeErfc(b, x, primitive_type)), + ComputeErf(b, x, primitive_type)); + ctx->SetOutput(0, y); + } +}; +REGISTER_XLA_OP(Name("Erf"), ErfOp); + +class ErfcOp : public XlaOpKernel { + public: + explicit ErfcOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {} + void Compile(XlaOpKernelContext* ctx) override { + xla::XlaBuilder* b = ctx->builder(); + xla::XlaOp one = XlaHelpers::One(b, input_type(0)); + xla::XlaOp x = ctx->Input(0); + xla::XlaOp abs_x = b->Abs(x); + + xla::PrimitiveType primitive_type; + OP_REQUIRES_OK(ctx, + DataTypeToPrimitiveType(input_type(0), &primitive_type)); + + auto y = b->Select(b->Lt(abs_x, one), + b->Sub(one, ComputeErf(b, x, primitive_type)), + ComputeErfc(b, x, primitive_type)); + ctx->SetOutput(0, y); + } +}; +REGISTER_XLA_OP(Name("Erfc"), ErfcOp); + } // namespace } // namespace tensorflow diff --git a/tensorflow/compiler/xla/client/lib/arithmetic.cc b/tensorflow/compiler/xla/client/lib/arithmetic.cc index a1d34796cc..639f85737f 100644 --- a/tensorflow/compiler/xla/client/lib/arithmetic.cc +++ b/tensorflow/compiler/xla/client/lib/arithmetic.cc @@ -121,4 +121,88 @@ StatusOr Any(const XlaOp& predicates, XlaBuilder* builder) { return builder->Reduce(predicates, f, logical_or, all_dimensions); } +namespace { +xla::XlaOp FloatLiteral(xla::XlaBuilder* b, PrimitiveType data_type, + float value) { + return b->ConvertElementType(b->ConstantR0(value), data_type); +} + +// Polynomials for computing erf/erfc. Originally from cephes. +// Note we use float for compatibility across devices, at the cost of some +// precision for 64 bit computations. +// +// Coefficients are in descending order. +std::array kErfcPCoefficient = { + 2.46196981473530512524E-10, 5.64189564831068821977E-1, + 7.46321056442269912687E0, 4.86371970985681366614E1, + 1.96520832956077098242E2, 5.26445194995477358631E2, + 9.34528527171957607540E2, 1.02755188689515710272E3, + 5.57535335369399327526E2}; +std::array kErfcQCoefficient = { + 1.00000000000000000000E0, 1.32281951154744992508E1, + 8.67072140885989742329E1, 3.54937778887819891062E2, + 9.75708501743205489753E2, 1.82390916687909736289E3, + 2.24633760818710981792E3, 1.65666309194161350182E3, + 5.57535340817727675546E2}; +std::array kErfcRCoefficient = { + 5.64189583547755073984E-1, 1.27536670759978104416E0, + 5.01905042251180477414E0, 6.16021097993053585195E0, + 7.40974269950448939160E0, 2.97886665372100240670E0}; +std::array kErfcSCoefficient = { + 1.00000000000000000000E0, 2.26052863220117276590E0, + 9.39603524938001434673E0, 1.20489539808096656605E1, + 1.70814450747565897222E1, 9.60896809063285878198E0, + 3.36907645100081516050E0}; +std::array kErfTCoefficient = { + 9.60497373987051638749E0, 9.00260197203842689217E1, + 2.23200534594684319226E3, 7.00332514112805075473E3, + 5.55923013010394962768E4}; +std::array kErfUCoefficient = { + 1.00000000000000000000E0, 3.35617141647503099647E1, + 5.21357949780152679795E2, 4.59432382970980127987E3, + 2.26290000613890934246E4, 4.92673942608635921086E4}; +} // namespace + +// Evaluate the polynomial given coefficients and `x`. +// N.B. Coefficients should be supplied in decreasing order. +xla::XlaOp EvaluatePolynomial(xla::XlaBuilder* b, const xla::XlaOp& x, + tensorflow::gtl::ArraySlice coefficients, + PrimitiveType data_type) { + xla::XlaOp poly = FloatLiteral(b, data_type, 0.0); + for (float c : coefficients) { + poly = b->Add(b->Mul(poly, x), FloatLiteral(b, data_type, c)); + } + return poly; +} + +// Compute an approximation of the error function complement (1 - erf(x)). +xla::XlaOp ComputeErfc(xla::XlaBuilder* b, const xla::XlaOp& x, + PrimitiveType data_type) { + xla::XlaOp zero = FloatLiteral(b, data_type, 0.0); + xla::XlaOp two = FloatLiteral(b, data_type, 2.0); + xla::XlaOp eight = FloatLiteral(b, data_type, 8.0); + + xla::XlaOp abs_x = b->Abs(x); + xla::XlaOp z = b->Exp(b->Mul(b->Neg(x), x)); + + xla::XlaOp pp = EvaluatePolynomial(b, abs_x, kErfcPCoefficient, data_type); + xla::XlaOp pq = EvaluatePolynomial(b, abs_x, kErfcQCoefficient, data_type); + xla::XlaOp pr = EvaluatePolynomial(b, abs_x, kErfcRCoefficient, data_type); + xla::XlaOp ps = EvaluatePolynomial(b, abs_x, kErfcSCoefficient, data_type); + + xla::XlaOp y = b->Select(b->Lt(abs_x, eight), b->Div(b->Mul(z, pp), pq), + b->Div(b->Mul(z, pr), ps)); + + return b->Select(b->Lt(x, zero), b->Sub(two, y), y); +} + +// Compute a polynomial approximation of the error function. +xla::XlaOp ComputeErf(xla::XlaBuilder* b, const xla::XlaOp& x, + PrimitiveType data_type) { + xla::XlaOp z = b->Mul(x, x); + xla::XlaOp pt = EvaluatePolynomial(b, z, kErfTCoefficient, data_type); + xla::XlaOp pu = EvaluatePolynomial(b, z, kErfUCoefficient, data_type); + return b->Div(b->Mul(x, pt), pu); +} + } // namespace xla diff --git a/tensorflow/compiler/xla/client/lib/arithmetic.h b/tensorflow/compiler/xla/client/lib/arithmetic.h index 64b6b7d633..f11cc00317 100644 --- a/tensorflow/compiler/xla/client/lib/arithmetic.h +++ b/tensorflow/compiler/xla/client/lib/arithmetic.h @@ -55,6 +55,20 @@ XlaComputation CreateScalarOrComputation(XlaBuilder* builder); // Note: if predicates is zero-sized, Any() vacuously returns false. StatusOr Any(const XlaOp& predicates, XlaBuilder* builder); +// Evaluate the polynomial given coefficients and `x`. +// N.B. Coefficients should be supplied in decreasing order. +xla::XlaOp EvaluatePolynomial(xla::XlaBuilder* b, const xla::XlaOp& x, + tensorflow::gtl::ArraySlice coefficients, + PrimitiveType data_type); + +// Compute an approximation of the error function complement (1 - erf(x)). +xla::XlaOp ComputeErfc(xla::XlaBuilder* b, const xla::XlaOp& x, + PrimitiveType data_type); + +// Compute an approximation of the error function. +xla::XlaOp ComputeErf(xla::XlaBuilder* b, const xla::XlaOp& x, + PrimitiveType data_type); + } // namespace xla #endif // TENSORFLOW_COMPILER_XLA_CLIENT_LIB_ARITHMETIC_H_ -- GitLab From 8212404a47e17a0ad1822e520c990be1cd712e91 Mon Sep 17 00:00:00 2001 From: Pavithra Vijay Date: Fri, 15 Jun 2018 09:41:39 -0700 Subject: [PATCH 509/816] Fix: DepthwiseConv2D fails when bias is enabled (#20063) --- tensorflow/python/keras/layers/convolutional.py | 2 +- tensorflow/python/keras/layers/convolutional_test.py | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/tensorflow/python/keras/layers/convolutional.py b/tensorflow/python/keras/layers/convolutional.py index 720b386c4d..1c2a77d297 100644 --- a/tensorflow/python/keras/layers/convolutional.py +++ b/tensorflow/python/keras/layers/convolutional.py @@ -1729,7 +1729,7 @@ class DepthwiseConv2D(Conv2D): dilation_rate=self.dilation_rate, data_format=self.data_format) - if self.bias: + if self.use_bias: outputs = backend.bias_add( outputs, self.bias, diff --git a/tensorflow/python/keras/layers/convolutional_test.py b/tensorflow/python/keras/layers/convolutional_test.py index 167cabaeec..39988ba33a 100644 --- a/tensorflow/python/keras/layers/convolutional_test.py +++ b/tensorflow/python/keras/layers/convolutional_test.py @@ -995,6 +995,7 @@ class DepthwiseConv2DTest(test.TestCase): 'bias_regularizer': 'l2', 'activity_regularizer': 'l2', 'depthwise_constraint': 'unit_norm', + 'use_bias': True, 'strides': (2, 2), } self._run_test(kwargs, 'depth_multiplier', [1]) -- GitLab From 655c52b014df4a9b7dc8212aabb0bdf20da44107 Mon Sep 17 00:00:00 2001 From: Akshay Modi Date: Fri, 15 Jun 2018 10:23:23 -0700 Subject: [PATCH 510/816] Minor python change to remove doing unnecessary work in resource variables PiperOrigin-RevId: 200735157 --- tensorflow/python/ops/resource_variable_ops.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/tensorflow/python/ops/resource_variable_ops.py b/tensorflow/python/ops/resource_variable_ops.py index de44a3e848..2033674a92 100644 --- a/tensorflow/python/ops/resource_variable_ops.py +++ b/tensorflow/python/ops/resource_variable_ops.py @@ -851,14 +851,15 @@ class ResourceVariable(variables.Variable): operator: string. The operator name. """ + tensor_oper = getattr(ops.Tensor, operator) def _run_op(a, *args): # pylint: disable=protected-access value = a._AsTensor() - return getattr(ops.Tensor, operator)(value, *args) + return tensor_oper(value, *args) # Propagate __doc__ to wrapper try: - _run_op.__doc__ = getattr(ops.Tensor, operator).__doc__ + _run_op.__doc__ = tensor_oper.__doc__ except AttributeError: pass -- GitLab From c9a2034f93981e17eef5f96fbd2894202b8fc2c1 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 15 Jun 2018 10:25:09 -0700 Subject: [PATCH 511/816] [TF:XLA] Validate the control flow structure in encapsulate_subgraphs_pass and encapsulate_tpu_computations_pass, in order to detect errors earlier. PiperOrigin-RevId: 200735435 --- tensorflow/compiler/jit/BUILD | 1 + .../jit/encapsulate_subgraphs_pass.cc | 16 ++- tensorflow/compiler/tf2xla/BUILD | 27 ++++ .../tf2xla/functionalize_control_flow.cc | 15 +- .../compiler/tf2xla/validate_control_flow.cc | 84 +++++++++++ .../compiler/tf2xla/validate_control_flow.h | 37 +++++ .../tf2xla/validate_control_flow_test.cc | 131 ++++++++++++++++++ 7 files changed, 296 insertions(+), 15 deletions(-) create mode 100644 tensorflow/compiler/tf2xla/validate_control_flow.cc create mode 100644 tensorflow/compiler/tf2xla/validate_control_flow.h create mode 100644 tensorflow/compiler/tf2xla/validate_control_flow_test.cc diff --git a/tensorflow/compiler/jit/BUILD b/tensorflow/compiler/jit/BUILD index 8c74014614..a92218b129 100644 --- a/tensorflow/compiler/jit/BUILD +++ b/tensorflow/compiler/jit/BUILD @@ -321,6 +321,7 @@ cc_library( "//tensorflow/compiler/jit/ops:parallel_check_op", "//tensorflow/compiler/jit/ops:xla_ops", "//tensorflow/compiler/tf2xla:dump_graph", + "//tensorflow/compiler/tf2xla:validate_control_flow", "//tensorflow/compiler/tf2xla:xla_compiler", "//tensorflow/compiler/xla:status_macros", "//tensorflow/core:core_cpu", diff --git a/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc b/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc index 9448b8ebde..b78c30c215 100644 --- a/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc +++ b/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc @@ -27,6 +27,7 @@ limitations under the License. #include "tensorflow/compiler/jit/shape_inference_helpers.h" #include "tensorflow/compiler/tf2xla/const_analysis.h" #include "tensorflow/compiler/tf2xla/dump_graph.h" +#include "tensorflow/compiler/tf2xla/validate_control_flow.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/core/common_runtime/function.h" #include "tensorflow/core/common_runtime/optimization_registry.h" @@ -1504,6 +1505,11 @@ Status Encapsulator::SplitIntoSubgraphs() { for (auto& entry : subgraphs_) { Subgraph& subgraph = entry.second; FixupSourceAndSinkEdges(subgraph.GetGraph()); + // Verify that the graph has well-formed control flow structure to be + // functionalized. + std::vector dummy; + TF_RETURN_IF_ERROR( + BuildAndValidateControlFlowInfo(subgraph.GetGraph(), &dummy)); } return s; @@ -2519,10 +2525,12 @@ Status EncapsulateSubgraphsPass::Run( return Status::OK(); }; - TF_RETURN_IF_ERROR(EncapsulateSubgraphsInFunctions( - kXlaClusterAttr, kXlaOutsideCompilationAttr, **options.graph, - rewrite_subgraph, - /*reuse_existing_functions=*/false, &graph_out, library)); + TF_RETURN_WITH_CONTEXT_IF_ERROR( + EncapsulateSubgraphsInFunctions( + kXlaClusterAttr, kXlaOutsideCompilationAttr, **options.graph, + rewrite_subgraph, /*reuse_existing_functions=*/false, &graph_out, + library), + "EncapsulateSubgraphsPass failed"); if (VLOG_IS_ON(1)) { dump_graph::DumpGraphToFile("after_encapsulate_subgraphs", *graph_out, diff --git a/tensorflow/compiler/tf2xla/BUILD b/tensorflow/compiler/tf2xla/BUILD index cd57452302..6b73cee2a8 100644 --- a/tensorflow/compiler/tf2xla/BUILD +++ b/tensorflow/compiler/tf2xla/BUILD @@ -406,12 +406,39 @@ cc_library( ], ) +cc_library( + name = "validate_control_flow", + srcs = ["validate_control_flow.cc"], + hdrs = ["validate_control_flow.h"], + deps = [ + "//tensorflow/core:graph", + "//tensorflow/core:lib", + ], +) + +tf_cc_test( + name = "validate_control_flow_test", + srcs = ["validate_control_flow_test.cc"], + deps = [ + ":validate_control_flow", + "//tensorflow/cc:cc_ops", + "//tensorflow/cc:ops", + "//tensorflow/cc:while_loop", + "//tensorflow/core:lib", + "//tensorflow/core:ops", + "//tensorflow/core:protos_all_cc", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + ], +) + cc_library( name = "functionalize_control_flow", srcs = ["functionalize_control_flow.cc"], hdrs = ["functionalize_control_flow.h"], deps = [ ":tf2xla_util", + ":validate_control_flow", "//tensorflow/compiler/jit:union_find", "//tensorflow/compiler/tf2xla:dump_graph", "//tensorflow/compiler/tf2xla/ops:xla_ops", diff --git a/tensorflow/compiler/tf2xla/functionalize_control_flow.cc b/tensorflow/compiler/tf2xla/functionalize_control_flow.cc index 1438f6b48c..b9ed44e354 100644 --- a/tensorflow/compiler/tf2xla/functionalize_control_flow.cc +++ b/tensorflow/compiler/tf2xla/functionalize_control_flow.cc @@ -24,6 +24,7 @@ limitations under the License. #include "tensorflow/compiler/jit/union_find.h" #include "tensorflow/compiler/tf2xla/dump_graph.h" #include "tensorflow/compiler/tf2xla/tf2xla_util.h" +#include "tensorflow/compiler/tf2xla/validate_control_flow.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/core/common_runtime/function.h" @@ -1439,7 +1440,9 @@ Status FunctionalizeControlFlow(const FunctionLibraryDefinition* lookup_library, // invariant. std::vector cf_info; std::vector unreachable_nodes; - TF_RETURN_IF_ERROR(BuildControlFlowInfo(graph, &cf_info, &unreachable_nodes)); + TF_RETURN_WITH_CONTEXT_IF_ERROR( + BuildAndValidateControlFlowInfo(graph, &cf_info, &unreachable_nodes), + "FunctionalizeControlFlow failed"); if (!unreachable_nodes.empty()) { return errors::InvalidArgument( "The following nodes are unreachable from the source in the graph: ", @@ -1464,10 +1467,6 @@ Status FunctionalizeControlFlow(const FunctionLibraryDefinition* lookup_library, frame.parent = parent; frame.name = cf.frame_name; ++parent->num_children; - } else if (frame.parent != parent) { - return errors::InvalidArgument("Mismatched parent frames for ", - cf.frame->id(), ": ", parent->name, " vs ", - frame.parent->name); } if (IsEnter(node)) { @@ -1477,12 +1476,6 @@ Status FunctionalizeControlFlow(const FunctionLibraryDefinition* lookup_library, &arg.is_loop_invariant)); frame.args.push_back(arg); } else if (IsLoopCond(node)) { - if (frame.loop_cond) { - return errors::InvalidArgument( - "Loop ", cf.frame_name, - " has more than one LoopCond node: ", node->name(), " and ", - frame.loop_cond->name()); - } frame.loop_cond = node; } frame.nodes.insert(node); diff --git a/tensorflow/compiler/tf2xla/validate_control_flow.cc b/tensorflow/compiler/tf2xla/validate_control_flow.cc new file mode 100644 index 0000000000..1b3be4cfa4 --- /dev/null +++ b/tensorflow/compiler/tf2xla/validate_control_flow.cc @@ -0,0 +1,84 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/tf2xla/validate_control_flow.h" + +#include + +#include "tensorflow/core/graph/node_builder.h" +#include "tensorflow/core/lib/core/errors.h" + +namespace tensorflow { +namespace { +// Information about a loop frame structure. +struct Frame { + string name; + + // Pointer to the parent frame. The root frame has a pointer to itself. + Frame* parent = nullptr; + + // The loop condition of the loop. There should be exactly one loop condition + // in every loop. + const Node* loop_cond = nullptr; +}; + +// Verify that the ControlFlowInfo of the graph has valid loop structure. +Status ValidateControlFlowInfo(const Graph* graph, + const std::vector& cf_info) { + std::unordered_map frames; + for (const Node* node : graph->op_nodes()) { + const ControlFlowInfo& cf = cf_info[node->id()]; + if (!cf.frame || !cf.parent_frame) { + // Skip nodes unreachable from the source node. They might be pruned + // later. + continue; + } + + Frame& frame = frames[cf.frame_name]; + Frame* parent = &frames[cf_info[cf.parent_frame->id()].frame_name]; + if (frame.parent == nullptr) { + frame.parent = parent; + frame.name = cf.frame_name; + } else if (frame.parent != parent) { + return errors::InvalidArgument( + "Invalid loop structure: Mismatched parent frames for \"", + cf.frame_name, "\": \"", parent->name, "\" vs \"", frame.parent->name, + "\". This is an internal bug, please file a bug report with " + "instructions on how to reproduce the error."); + } + if (IsLoopCond(node)) { + if (frame.loop_cond) { + return errors::InvalidArgument( + "Invalid loop structure: Loop \"", cf.frame_name, + "\" has more than one LoopCond node: \"", node->name(), "\" and \"", + frame.loop_cond->name(), + "\". This is an internal bug, please file a bug report with " + "instructions on how to reproduce the error."); + } + frame.loop_cond = node; + } + } + return Status::OK(); +} +} // namespace + +Status BuildAndValidateControlFlowInfo(const Graph* graph, + std::vector* info, + std::vector* unreachable_nodes) { + TF_RETURN_IF_ERROR(BuildControlFlowInfo(graph, info, unreachable_nodes)); + return ValidateControlFlowInfo(graph, *info); +} + +} // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/validate_control_flow.h b/tensorflow/compiler/tf2xla/validate_control_flow.h new file mode 100644 index 0000000000..74159dc929 --- /dev/null +++ b/tensorflow/compiler/tf2xla/validate_control_flow.h @@ -0,0 +1,37 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_TF2XLA_VALIDATE_CONTROL_FLOW_H_ +#define TENSORFLOW_COMPILER_TF2XLA_VALIDATE_CONTROL_FLOW_H_ + +#include + +#include "tensorflow/core/graph/control_flow.h" +#include "tensorflow/core/graph/graph.h" +#include "tensorflow/core/lib/core/status.h" + +namespace tensorflow { + +// Populate the control flow frame info of each node in the graph. Verify that +// the graph has well-formed control flow strcuture that can be functionalized. +// If unreachable_nodes is not nullptr, append to it the names of nodes +// unreachable from the source node. +Status BuildAndValidateControlFlowInfo( + const Graph* graph, std::vector* info, + std::vector* unreachable_nodes = nullptr); + +} // namespace tensorflow + +#endif // TENSORFLOW_COMPILER_TF2XLA_VALIDATE_CONTROL_FLOW_H_ diff --git a/tensorflow/compiler/tf2xla/validate_control_flow_test.cc b/tensorflow/compiler/tf2xla/validate_control_flow_test.cc new file mode 100644 index 0000000000..74c9f4b86c --- /dev/null +++ b/tensorflow/compiler/tf2xla/validate_control_flow_test.cc @@ -0,0 +1,131 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/tf2xla/validate_control_flow.h" + +#include +#include + +#include "tensorflow/cc/ops/standard_ops.h" +#include "tensorflow/cc/ops/while_loop.h" +#include "tensorflow/core/lib/core/status_test_util.h" +#include "tensorflow/core/lib/strings/str_util.h" +#include "tensorflow/core/platform/test.h" + +namespace tensorflow { +namespace { +Status LessThanTenCond(const Scope& scope, const std::vector& inputs, + Output* output) { + *output = ops::Less(scope, inputs[0], 10); + return scope.status(); +} + +Status AddOneBody(const Scope& scope, const std::vector& inputs, + std::vector* outputs) { + outputs->push_back(ops::AddN(scope, {inputs[0], 1})); + return scope.status(); +} + +Status NestedLoopBody(const Scope& scope, const std::vector& inputs, + std::vector* outputs) { + return ops::BuildWhileLoop(scope.NewSubScope("inner"), inputs, + LessThanTenCond, AddOneBody, "inner_loop", + outputs); +} + +TEST(ValidateControlFlowTest, InputsFromDifferentFrames) { + Scope scope = Scope::NewRootScope().ExitOnError(); + std::vector inputs; + inputs.push_back(ops::Placeholder(scope, DT_INT32)); + std::vector outputs; + TF_ASSERT_OK(ops::BuildWhileLoop(scope.NewSubScope("outer"), inputs, + LessThanTenCond, NestedLoopBody, + "outer_loop", &outputs)); + std::unique_ptr graph(new Graph(OpRegistry::Global())); + TF_ASSERT_OK(scope.ToGraph(graph.get())); + // {inner/Enter', 'outer/Switch'} --> 'inner/Merge'. 'inner/Enter' is in frame + // 'inner_loop'. 'outer/Switch' is in frame 'outer_loop'. + std::vector info; + Status status = BuildAndValidateControlFlowInfo(graph.get(), &info); + EXPECT_FALSE(status.ok()); + EXPECT_TRUE(str_util::StrContains(status.error_message(), + "has inputs from different frames")) + << status.error_message(); +} + +TEST(ValidateControlFlowTest, MismatchedParentFrames) { + Scope scope = Scope::NewRootScope().ExitOnError(); + std::vector inputs; + inputs.push_back(ops::Placeholder(scope, DT_INT32)); + std::vector outputs; + TF_ASSERT_OK(ops::BuildWhileLoop(scope, inputs, LessThanTenCond, AddOneBody, + "test_loop", &outputs)); + std::unique_ptr graph(new Graph(OpRegistry::Global())); + TF_ASSERT_OK(scope.ToGraph(graph.get())); + Node* enter_1 = nullptr; + for (Node* node : graph->op_nodes()) { + if (IsEnter(node)) { + enter_1 = node; + } + } + ASSERT_TRUE(enter_1 != nullptr); + + NodeDef enter; + enter.set_name("Enter2"); + enter.set_op("Enter"); + (*enter.mutable_attr())["T"].set_type(DT_INT32); + (*enter.mutable_attr())["frame_name"].set_s("test_loop"); + *enter.add_input() = "Enter"; + Status status; + Node* enter_2 = graph->AddNode(enter, &status); + TF_ASSERT_OK(status); + graph->AddControlEdge(enter_1, enter_2); + + // SOURCE("") --> Enter("test_loop") --> Enter2("test_loop") + // For node 'Enter', the parent frame of "test_loop" is empty. + // For node 'Enter2', the parent frame of "test_loop" is "test_loop". + std::vector info; + status = BuildAndValidateControlFlowInfo(graph.get(), &info); + EXPECT_FALSE(status.ok()); + EXPECT_TRUE( + str_util::StrContains(status.error_message(), "Mismatched parent frames")) + << status.error_message(); +} + +TEST(ValidateControlFlowTest, TwoLoopCond) { + // Test that one frame has at most one LoopCond node. This is necessary for + // functionalize control flow. + Scope scope = Scope::NewRootScope().ExitOnError(); + std::vector inputs; + inputs.push_back(ops::Placeholder(scope, DT_INT32)); + std::vector outputs; + TF_ASSERT_OK(ops::BuildWhileLoop(scope, inputs, LessThanTenCond, AddOneBody, + "test_loop", &outputs)); + outputs.clear(); + TF_ASSERT_OK(ops::BuildWhileLoop(scope.NewSubScope("sub"), inputs, + LessThanTenCond, AddOneBody, "test_loop", + &outputs, false)); + std::unique_ptr graph(new Graph(OpRegistry::Global())); + TF_ASSERT_OK(scope.ToGraph(graph.get())); + std::vector info; + Status status = BuildAndValidateControlFlowInfo(graph.get(), &info); + EXPECT_FALSE(status.ok()); + EXPECT_TRUE(str_util::StrContains(status.error_message(), + "more than one LoopCond node")) + << status.error_message(); +} + +} // namespace +} // namespace tensorflow -- GitLab From fa6e9f367dc746df36b0b5d9ec2f23a40e7a9fe0 Mon Sep 17 00:00:00 2001 From: Pavithra Vijay Date: Fri, 15 Jun 2018 10:30:10 -0700 Subject: [PATCH 512/816] Increase gru_test test size PiperOrigin-RevId: 200736300 --- tensorflow/python/keras/BUILD | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/python/keras/BUILD b/tensorflow/python/keras/BUILD index fe40c9fbed..9012f4ee38 100755 --- a/tensorflow/python/keras/BUILD +++ b/tensorflow/python/keras/BUILD @@ -549,7 +549,7 @@ py_test( py_test( name = "gru_test", - size = "medium", + size = "large", srcs = ["layers/gru_test.py"], srcs_version = "PY2AND3", tags = ["notsan"], # http://b/62136390 -- GitLab From 6f7c83c942689a50bfbc5d81053635af05df14ed Mon Sep 17 00:00:00 2001 From: Peter Hawkins Date: Fri, 15 Jun 2018 10:30:42 -0700 Subject: [PATCH 513/816] [TF:XLA] Update comment on xla_compiler.h to match the code. Make resource_var.h more widely visible and add comment about the correct lock acquisition order if locking multiple variables. PiperOrigin-RevId: 200736416 --- tensorflow/compiler/tf2xla/xla_compiler.h | 17 ++++++----------- tensorflow/core/BUILD | 1 + tensorflow/core/framework/resource_var.h | 2 ++ 3 files changed, 9 insertions(+), 11 deletions(-) diff --git a/tensorflow/compiler/tf2xla/xla_compiler.h b/tensorflow/compiler/tf2xla/xla_compiler.h index c93850ce27..6be74957c6 100644 --- a/tensorflow/compiler/tf2xla/xla_compiler.h +++ b/tensorflow/compiler/tf2xla/xla_compiler.h @@ -52,13 +52,7 @@ class XlaContext; // (kind kResource). // // Only kParameter and initialized kResource arguments become runtime parameters -// to the generated XLA computation. The XLA computation will have run-time -// parameters in the following order: -// +---------------------+-----------------------------------------+ -// | kParameter values | Initial values of kResource arguments | -// +---------------------+-----------------------------------------+ -// Within each block, the arguments are arranged by the _Arg index from which -// they were derived. +// to the generated XLA computation. // // The run-time outputs of the XLA computation are arranged in the following // order: @@ -77,10 +71,10 @@ class XlaContext; // tensors with a different shape to their representation inside the XLA // computation. // -// In both inputs and outputs, kResource values are placed the end. When +// In computation outputs, updated kResource values are placed the end. When // emitting While loop bodies, we must ensure that the loop body has -// identical input and output signatures. By moving variable values -// to the end of the argument list and using the +// identical input and output signatures. By passing variable values +// at the end of the argument list and using the // `return_updated_values_for_all_variables` option, we can ensure that the // input and output values of resources appear at the same positions. // @@ -234,7 +228,8 @@ class XlaCompiler { tf2xla::HostComputeMetadata host_compute_metadata; // Resources whose values were updated by the computation, ordered - // by return value position. Resource updates follow the non-constant + // by return value position (which is the same as the order the resources + // were passed as arguments). Resource updates follow the non-constant // results in the outputs of XLA computation. std::vector resource_updates; diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index e00a7c4213..cdceccb106 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -2336,6 +2336,7 @@ FRAMEWORK_INTERNAL_PRIVATE_HEADERS = [ FRAMEWORK_INTERNAL_PUBLIC_HEADERS = [ "framework/op_segment.h", "framework/rendezvous.h", # only needed for tests + "framework/resource_var.h", "framework/tensor_reference.h", "framework/tracking_allocator.h", # only needed for tests "framework/unique_tensor_references.h", diff --git a/tensorflow/core/framework/resource_var.h b/tensorflow/core/framework/resource_var.h index 872b8f8b30..ff7b3e78a7 100644 --- a/tensorflow/core/framework/resource_var.h +++ b/tensorflow/core/framework/resource_var.h @@ -29,6 +29,8 @@ class Var : public ResourceBase { Var(const Var&) = delete; Var& operator=(const Var&) = delete; + // When locking multiple variables, the locks must be acquired in order of + // increasing mu() address. // TODO(ebrevdo): Use LockSet instead of exposing mu. mutex* mu() { return &mu_; } Tensor* tensor() { return &tensor_; } -- GitLab From eb8ed73d635032446cc98d445cdd1ca4564ebfcc Mon Sep 17 00:00:00 2001 From: Akshay Modi Date: Fri, 15 Jun 2018 10:44:13 -0700 Subject: [PATCH 514/816] Fix bad manual merge. --- tensorflow/tools/api/generator/create_python_api.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tensorflow/tools/api/generator/create_python_api.py b/tensorflow/tools/api/generator/create_python_api.py index 46b81e17c6..671b7e387e 100644 --- a/tensorflow/tools/api/generator/create_python_api.py +++ b/tensorflow/tools/api/generator/create_python_api.py @@ -338,7 +338,8 @@ def create_api_files( if module or not root_init_template: contents = ( _GENERATED_FILE_HEADER % - get_module_docstring(module, package, api_name) + text) + get_module_docstring(module, package, api_name) + + text + _GENERATED_FILE_FOOTER) else: # Read base init file with open(root_init_template, 'r') as root_init_template_file: -- GitLab From 32e85d4892bd258324acc814f89c3a6c0fe7f3a4 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 15 Jun 2018 10:55:52 -0700 Subject: [PATCH 515/816] Fix a bug in dependency optimizer: Repeated inputs would not get converted to control inputs when converting nodes to NoOps. PiperOrigin-RevId: 200740844 --- .../optimizers/dependency_optimizer.cc | 12 ++-- .../optimizers/dependency_optimizer_test.cc | 64 +++++++++++++++++-- 2 files changed, 64 insertions(+), 12 deletions(-) diff --git a/tensorflow/core/grappler/optimizers/dependency_optimizer.cc b/tensorflow/core/grappler/optimizers/dependency_optimizer.cc index 3f5bab9d3b..fdd82b9603 100644 --- a/tensorflow/core/grappler/optimizers/dependency_optimizer.cc +++ b/tensorflow/core/grappler/optimizers/dependency_optimizer.cc @@ -260,14 +260,14 @@ void DependencyOptimizer::OptimizeNode(int node_idx, } continue; } + // Replace a normal input with a control input. const string ctrl_input = ConstantFolding::AddControlDependency( old_input, optimized_graph_, node_map_.get()); - if (ctrl_inputs.insert(ctrl_input).second) { - node->set_input(pos, ctrl_input); - node_map_->UpdateInput(node_name, old_input, ctrl_input); - const NodeDef* old_input_node = node_map_->GetNode(old_input); - nodes_to_simplify->PushBack(node_to_idx_[old_input_node]); - } + ctrl_inputs.insert(ctrl_input); + node->set_input(pos, ctrl_input); + node_map_->UpdateInput(node_name, old_input, ctrl_input); + const NodeDef* old_input_node = node_map_->GetNode(old_input); + nodes_to_simplify->PushBack(node_to_idx_[old_input_node]); ++pos; } node->set_op("NoOp"); diff --git a/tensorflow/core/grappler/optimizers/dependency_optimizer_test.cc b/tensorflow/core/grappler/optimizers/dependency_optimizer_test.cc index 0ae3b4ec34..c0f07562af 100644 --- a/tensorflow/core/grappler/optimizers/dependency_optimizer_test.cc +++ b/tensorflow/core/grappler/optimizers/dependency_optimizer_test.cc @@ -124,25 +124,62 @@ TEST_F(DependencyOptimizerTest, ChangeToNoop) { TF_EXPECT_OK(status); EXPECT_EQ(item.graph.node_size(), output.node_size()); + int found = 0; for (int i = 0; i < item.graph.node_size(); ++i) { const NodeDef& node = item.graph.node(i); - if (node.name() == "add") { - EXPECT_EQ("NoOp", node.op()); - EXPECT_EQ(2, node.input_size()); - EXPECT_EQ("^x", node.input(0)); - EXPECT_EQ("^y", node.input(1)); - } else if (node.name() == "id1") { + // "add" should get turned into a NoOp and removed. + EXPECT_NE("add", node.name()); + if (node.name() == "id1") { EXPECT_EQ("Identity", node.op()); EXPECT_EQ(2, node.input_size()); EXPECT_EQ("x", node.input(0)); EXPECT_EQ("^y", node.input(1)); + ++found; } else if (node.name() == "id2") { EXPECT_EQ("Identity", node.op()); EXPECT_EQ(2, node.input_size()); EXPECT_EQ("y", node.input(0)); EXPECT_EQ("^x", node.input(1)); + ++found; + } + } + EXPECT_EQ(2, found); +} + +TEST_F(DependencyOptimizerTest, ChangeToNoop_RepeatedInput) { + tensorflow::Scope s = tensorflow::Scope::NewRootScope(); + Output x = ops::RandomUniform(s.WithOpName("x"), {1, 2}, DT_FLOAT); + Output add = ops::Add(s.WithOpName("add"), x, x); + Output id1 = + ops::Identity(s.WithOpName("id1").WithControlDependencies(add), x); + GrapplerItem item; + TF_CHECK_OK(s.ToGraphDef(&item.graph)); + item.fetch = {"id1"}; + + DependencyOptimizer optimizer; + GraphDef output; + Status status = optimizer.Optimize(nullptr, item, &output); + TF_EXPECT_OK(status); + // Run the optimizer twice to make sure the rewrite is idempotent. + item.graph.Swap(&output); + status = optimizer.Optimize(nullptr, item, &output); + TF_EXPECT_OK(status); + LOG(INFO) << output.DebugString(); + + EXPECT_EQ(item.graph.node_size(), output.node_size()); + int found = 0; + for (int i = 0; i < item.graph.node_size(); ++i) { + const NodeDef& node = item.graph.node(i); + // "add" should get turned into a NoOp and removed. + EXPECT_NE("add", node.name()); + if (node.name() == "id1") { + EXPECT_EQ("Identity", node.op()); + EXPECT_EQ(1, node.input_size()); + EXPECT_EQ("x", node.input(0)); + ++found; } } + EXPECT_EQ(1, found); } TEST_F(DependencyOptimizerTest, ChangeToNoop_SwitchIdentity) { @@ -400,6 +437,7 @@ TEST_F(DependencyOptimizerTest, RemoveIdentity) { TF_EXPECT_OK(status); EXPECT_EQ(item.graph.node_size() - 3, output.node_size()); + int found = 0; for (const NodeDef& node : output.node()) { EXPECT_NE("id_a", node.name()); EXPECT_NE("id_b", node.name()); @@ -407,30 +445,36 @@ TEST_F(DependencyOptimizerTest, RemoveIdentity) { if (node.name() == "a_a" || node.name() == "a_b") { EXPECT_EQ(1, node.input_size()); EXPECT_EQ("x", node.input(0)); + ++found; } if (node.name() == "a_c" || node.name() == "a_d") { EXPECT_EQ(2, node.input_size()); EXPECT_EQ("z", node.input(0)); EXPECT_EQ("^x", node.input(1)); + ++found; } if (node.name() == "b_a") { EXPECT_EQ(3, node.input_size()); EXPECT_EQ("x", node.input(0)); EXPECT_EQ("^y", node.input(1)); EXPECT_EQ("^z", node.input(2)); + ++found; } if (node.name() == "c_a") { EXPECT_EQ(2, node.input_size()); EXPECT_EQ("x", node.input(0)); EXPECT_EQ("^y", node.input(1)); + ++found; } if (node.name() == "c_b") { EXPECT_EQ(3, node.input_size()); EXPECT_EQ("z", node.input(0)); EXPECT_EQ("^x", node.input(1)); EXPECT_EQ("^y", node.input(2)); + ++found; } } + EXPECT_EQ(found, 7); } TEST_F(DependencyOptimizerTest, RemoveIdentity_RepeatedInputs) { @@ -460,17 +504,20 @@ TEST_F(DependencyOptimizerTest, RemoveIdentity_RepeatedInputs) { TF_EXPECT_OK(status); EXPECT_EQ(item.graph.node_size() - 1, output.node_size()); + int found = 0; for (const NodeDef& node : output.node()) { EXPECT_NE("id0", node.name()); if (node.name() == "or0") { EXPECT_EQ(2, node.input_size()); EXPECT_EQ("switch:1", node.input(0)); EXPECT_EQ("switch:1", node.input(1)); + ++found; } if (node.name() == "or1") { EXPECT_EQ(2, node.input_size()); EXPECT_EQ("switch:1", node.input(0)); EXPECT_EQ("y", node.input(1)); + ++found; } if (node.name() == "or2") { // or1 should be unchanged. @@ -478,8 +525,10 @@ TEST_F(DependencyOptimizerTest, RemoveIdentity_RepeatedInputs) { EXPECT_EQ("y", node.input(0)); EXPECT_EQ("y", node.input(1)); EXPECT_EQ("^id1", node.input(2)); + ++found; } } + EXPECT_EQ(found, 3); } TEST_F(DependencyOptimizerTest, Transitive_Reduction_Simple) { @@ -535,6 +584,7 @@ TEST_F(DependencyOptimizerTest, ChangeToNoop_Identity) { TF_EXPECT_OK(status); EXPECT_EQ(item.graph.node_size() - 2, output.node_size()); + bool found = false; for (int i = 0; i < output.node_size(); ++i) { const NodeDef& node = output.node(i); // "id0" and "id1" but neither "ConstantFoldingCtrl/switch_1", @@ -545,8 +595,10 @@ TEST_F(DependencyOptimizerTest, ChangeToNoop_Identity) { EXPECT_EQ("Const", node.op()); EXPECT_EQ(1, node.input_size()); EXPECT_EQ("^ConstantFoldingCtrl/switch_1", node.input(0)); + found = true; } } + EXPECT_TRUE(found); } TEST_F(DependencyOptimizerTest, IdentityInputs) { -- GitLab From d63d663e7243242d4c46b6533902e0e1e2164526 Mon Sep 17 00:00:00 2001 From: Gunhan Gulsoy Date: Fri, 15 Jun 2018 11:00:25 -0700 Subject: [PATCH 516/816] Disable long running tests in fastbuild mode. PiperOrigin-RevId: 200741660 --- tensorflow/contrib/data/python/kernel_tests/BUILD | 5 ++++- tensorflow/contrib/eager/python/examples/resnet50/BUILD | 1 + tensorflow/contrib/eager/python/examples/revnet/BUILD | 6 ++++++ tensorflow/python/estimator/BUILD | 3 +++ 4 files changed, 14 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/data/python/kernel_tests/BUILD b/tensorflow/contrib/data/python/kernel_tests/BUILD index 4e3f9801d7..445fdcef23 100644 --- a/tensorflow/contrib/data/python/kernel_tests/BUILD +++ b/tensorflow/contrib/data/python/kernel_tests/BUILD @@ -473,7 +473,10 @@ py_test( size = "medium", srcs = ["shuffle_dataset_op_test.py"], srcs_version = "PY2AND3", - tags = ["no_pip"], + tags = [ + "no_pip", + "optonly", + ], deps = [ ":dataset_serialization_test", "//tensorflow/contrib/data/python/ops:iterator_ops", diff --git a/tensorflow/contrib/eager/python/examples/resnet50/BUILD b/tensorflow/contrib/eager/python/examples/resnet50/BUILD index 0c0e28dd95..68a84d5fbb 100644 --- a/tensorflow/contrib/eager/python/examples/resnet50/BUILD +++ b/tensorflow/contrib/eager/python/examples/resnet50/BUILD @@ -51,5 +51,6 @@ cuda_py_test( "noasan", "nomsan", "notsan", + "optonly", ], ) diff --git a/tensorflow/contrib/eager/python/examples/revnet/BUILD b/tensorflow/contrib/eager/python/examples/revnet/BUILD index bfb53cfff8..a2bdd9f8a6 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/BUILD +++ b/tensorflow/contrib/eager/python/examples/revnet/BUILD @@ -62,6 +62,9 @@ cuda_py_test( ":blocks", "//tensorflow:tensorflow_py", ], + tags = [ + "optonly", + ], ) cuda_py_test( @@ -73,4 +76,7 @@ cuda_py_test( ":revnet", "//tensorflow:tensorflow_py", ], + tags = [ + "optonly", + ], ) diff --git a/tensorflow/python/estimator/BUILD b/tensorflow/python/estimator/BUILD index c0d63b79a6..9cd17e0407 100644 --- a/tensorflow/python/estimator/BUILD +++ b/tensorflow/python/estimator/BUILD @@ -279,6 +279,9 @@ py_test( size = "medium", srcs = ["canned/boosted_trees_test.py"], srcs_version = "PY2AND3", + tags = [ + "optonly", + ], deps = [ ":boosted_trees", "//tensorflow/core/kernels/boosted_trees:boosted_trees_proto_py", -- GitLab From 1ca4b6f797a168036e2708faf45753b333f467dc Mon Sep 17 00:00:00 2001 From: Pavithra Vijay Date: Fri, 15 Jun 2018 11:02:38 -0700 Subject: [PATCH 517/816] Fix: DepthwiseConv2D fails when bias is enabled PiperOrigin-RevId: 200742104 --- tensorflow/python/keras/layers/convolutional.py | 2 +- tensorflow/python/keras/layers/convolutional_test.py | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/tensorflow/python/keras/layers/convolutional.py b/tensorflow/python/keras/layers/convolutional.py index 720b386c4d..1c2a77d297 100644 --- a/tensorflow/python/keras/layers/convolutional.py +++ b/tensorflow/python/keras/layers/convolutional.py @@ -1729,7 +1729,7 @@ class DepthwiseConv2D(Conv2D): dilation_rate=self.dilation_rate, data_format=self.data_format) - if self.bias: + if self.use_bias: outputs = backend.bias_add( outputs, self.bias, diff --git a/tensorflow/python/keras/layers/convolutional_test.py b/tensorflow/python/keras/layers/convolutional_test.py index 167cabaeec..39988ba33a 100644 --- a/tensorflow/python/keras/layers/convolutional_test.py +++ b/tensorflow/python/keras/layers/convolutional_test.py @@ -995,6 +995,7 @@ class DepthwiseConv2DTest(test.TestCase): 'bias_regularizer': 'l2', 'activity_regularizer': 'l2', 'depthwise_constraint': 'unit_norm', + 'use_bias': True, 'strides': (2, 2), } self._run_test(kwargs, 'depth_multiplier', [1]) -- GitLab From b62d76d932f93ff324d2598cdeac792fa61135a4 Mon Sep 17 00:00:00 2001 From: Benjamin Kramer Date: Fri, 15 Jun 2018 11:10:03 -0700 Subject: [PATCH 518/816] [XLA] Switch PostOrder accessors to use std::vector instead of std::list. std::list is just hilariously inefficient and the postorder list creation has been rewritten not to not depend on splicing anymore so there's no need for the list. While there remove the old unused postorder list creation code. PiperOrigin-RevId: 200743677 --- .../xla/service/bfloat16_propagation.cc | 4 +- .../compiler/xla/service/hlo_computation.cc | 67 +++++-------------- .../compiler/xla/service/hlo_computation.h | 4 +- tensorflow/compiler/xla/service/hlo_dce.cc | 3 +- tensorflow/compiler/xla/service/hlo_module.cc | 4 +- tensorflow/compiler/xla/service/hlo_module.h | 2 +- .../xla/service/hlo_module_group_util.cc | 2 +- .../compiler/xla/service/hlo_reachability.cc | 2 +- .../compiler/xla/service/hlo_reachability.h | 3 +- .../xla/service/instruction_fusion.cc | 4 +- 10 files changed, 29 insertions(+), 66 deletions(-) diff --git a/tensorflow/compiler/xla/service/bfloat16_propagation.cc b/tensorflow/compiler/xla/service/bfloat16_propagation.cc index 8f1d2f0804..d514b99ed0 100644 --- a/tensorflow/compiler/xla/service/bfloat16_propagation.cc +++ b/tensorflow/compiler/xla/service/bfloat16_propagation.cc @@ -559,7 +559,7 @@ bool BFloat16Propagation::ResolveInconsistencyOfAliasingBuffersHelper( void BFloat16Propagation::ResolveInconsistencyOfAliasingBuffers( HloModule* module) { - std::list computations_topological_order = + const auto& computations_topological_order = module->MakeComputationPostOrder(); tensorflow::gtl::FlatSet resolved; for (auto comp_it = computations_topological_order.rbegin(); @@ -742,7 +742,7 @@ StatusOr BFloat16Propagation::Run(HloModule* module) { TF_ASSIGN_OR_RETURN(dataflow_, HloDataflowAnalysis::Run(*module)); - std::list computations_topological_order = + const auto& computations_topological_order = module->MakeComputationPostOrder(); // The first step is a forward pass (parameters to root), where we determine // the potential candidate instructions to use bfloat16 in the outputs that diff --git a/tensorflow/compiler/xla/service/hlo_computation.cc b/tensorflow/compiler/xla/service/hlo_computation.cc index ef8bb030fb..74173a1685 100644 --- a/tensorflow/compiler/xla/service/hlo_computation.cc +++ b/tensorflow/compiler/xla/service/hlo_computation.cc @@ -263,46 +263,11 @@ void HloComputation::set_root_instruction( namespace { -// Helper class which computes the post order of an expression rooted at a -// particular instruction. -class InstructionPostOrderer : public DfsHloVisitorWithDefault { - public: - // added_instructions is the set of instructions which have already been - // accounted for in the post order in previous invocations of - // GetOrder. Without this mechanism, instructions which are predecessors of - // multiple root instructions of the computation can be added to the post - // order more than once. - static std::list GetOrder( - HloInstruction* root, - tensorflow::gtl::FlatSet* added_instructions) { - InstructionPostOrderer orderer(added_instructions); - TF_CHECK_OK(root->Accept(&orderer)); - return std::move(orderer.post_order_); - } - - private: - explicit InstructionPostOrderer( - tensorflow::gtl::FlatSet* added_instructions) - : added_instructions_(added_instructions) {} - ~InstructionPostOrderer() override {} - - Status DefaultAction(HloInstruction* hlo_instruction) override { - if (added_instructions_->count(hlo_instruction) == 0) { - post_order_.push_back(hlo_instruction); - added_instructions_->insert(hlo_instruction); - } - return Status::OK(); - } - - std::list post_order_; - tensorflow::gtl::FlatSet* added_instructions_; -}; - // Helper which builds a post order of the HLO call graph. void ComputeComputationPostOrder( HloComputation* computation, tensorflow::gtl::FlatSet* visited, - std::list* post_order) { + std::vector* post_order) { if (visited->insert(computation).second) { for (auto* instruction : computation->instructions()) { for (HloComputation* called_computation : @@ -314,9 +279,9 @@ void ComputeComputationPostOrder( } } -std::list ComputeInstructionPostOrder( - HloInstruction* root, tensorflow::gtl::FlatSet* visited) { - std::list post_order; +void ComputeInstructionPostOrder( + std::vector* post_order, HloInstruction* root, + tensorflow::gtl::FlatSet* visited) { std::vector> dfs_stack; dfs_stack.emplace_back(root, false); while (!dfs_stack.empty()) { @@ -326,7 +291,7 @@ std::list ComputeInstructionPostOrder( if (!visited->insert(current.first).second) { continue; } - post_order.push_back(current.first); + post_order->push_back(current.first); } else { if (visited->count(current.first)) { dfs_stack.pop_back(); @@ -347,14 +312,14 @@ std::list ComputeInstructionPostOrder( } } } - return post_order; } } // namespace -std::list HloComputation::MakeInstructionPostOrder() const { - std::list post_order; - std::list trace_instructions; +std::vector HloComputation::MakeInstructionPostOrder() const { + std::vector post_order; + post_order.reserve(instruction_count()); + std::vector trace_instructions; tensorflow::gtl::FlatSet added_instructions; for (auto& instruction : instructions_) { if (instruction->opcode() == HloOpcode::kTrace) { @@ -363,21 +328,21 @@ std::list HloComputation::MakeInstructionPostOrder() const { // users). trace_instructions.push_back(instruction.get()); } else if (instruction->users().empty()) { - post_order.splice( - post_order.end(), - ComputeInstructionPostOrder(instruction.get(), &added_instructions)); + ComputeInstructionPostOrder(&post_order, instruction.get(), + &added_instructions); } } - post_order.splice(post_order.end(), trace_instructions); + post_order.insert(post_order.end(), trace_instructions.begin(), + trace_instructions.end()); CHECK_EQ(instructions_.size(), post_order.size()) << "number of instructions does not match post order size"; return post_order; } -std::list HloComputation::MakeEmbeddedComputationsList() +std::vector HloComputation::MakeEmbeddedComputationsList() const { tensorflow::gtl::FlatSet visited; - std::list post_order; + std::vector post_order; // To avoid special handling of this computation, cast away const of // 'this'. 'this' is immediately removed from the post order after @@ -648,7 +613,7 @@ Status HloComputation::ReplaceInstruction(HloInstruction* old_instruction, std::unique_ptr HloComputation::ComputeReachability() const { - const std::list all = MakeInstructionPostOrder(); + const auto& all = MakeInstructionPostOrder(); auto result = MakeUnique(all); std::vector inputs; diff --git a/tensorflow/compiler/xla/service/hlo_computation.h b/tensorflow/compiler/xla/service/hlo_computation.h index 0da4a305f3..0f111a1a76 100644 --- a/tensorflow/compiler/xla/service/hlo_computation.h +++ b/tensorflow/compiler/xla/service/hlo_computation.h @@ -199,7 +199,7 @@ class HloComputation { // Compute and return a post-order of the instructions in the computation. In // this order, definitions of values always appear before their uses. - std::list MakeInstructionPostOrder() const; + std::vector MakeInstructionPostOrder() const; // Computes and returns the reachability between HLO instructions in the // computation. The returned HloReachabilityMap is constructed such that @@ -221,7 +221,7 @@ class HloComputation { // transitively. The embedded computations are sorted such that if computation // A calls computation B (eg, via a map instruction) then A will appear after // B in the list. - std::list MakeEmbeddedComputationsList() const; + std::vector MakeEmbeddedComputationsList() const; // Creates a fusion instruction containing the given instructions. // `fusion_kind` indicates the type of the fusion, e.g., loop fusion or fusion diff --git a/tensorflow/compiler/xla/service/hlo_dce.cc b/tensorflow/compiler/xla/service/hlo_dce.cc index fcd723af14..8aa26bf520 100644 --- a/tensorflow/compiler/xla/service/hlo_dce.cc +++ b/tensorflow/compiler/xla/service/hlo_dce.cc @@ -85,8 +85,7 @@ StatusOr HloDCE::Run(HloModule* module) { } // Remove dead computations. - std::list computations = module->MakeComputationPostOrder(); - for (auto* computation : computations) { + for (auto* computation : module->MakeComputationPostOrder()) { if (live_computations.count(computation) == 0) { TF_RETURN_IF_ERROR(module->RemoveEmbeddedComputation(computation)); changed = true; diff --git a/tensorflow/compiler/xla/service/hlo_module.cc b/tensorflow/compiler/xla/service/hlo_module.cc index 9c59374b4a..11384c1456 100644 --- a/tensorflow/compiler/xla/service/hlo_module.cc +++ b/tensorflow/compiler/xla/service/hlo_module.cc @@ -451,7 +451,7 @@ int64 HloModule::instruction_count() const { return n; } -std::list HloModule::MakeComputationPostOrder() const { +std::vector HloModule::MakeComputationPostOrder() const { // First determine all root computations by building a set of nonroot // computations (computations which are called by an instruction in the // module). @@ -469,7 +469,7 @@ std::list HloModule::MakeComputationPostOrder() const { // order. This prevents duplication as an embedded computation may be called // from two different root computations. std::set added_computations; - std::list post_order; + std::vector post_order; for (auto& computation : computations_) { if (nonroot_computations.count(computation.get()) == 0) { for (HloComputation* embedded_computation : diff --git a/tensorflow/compiler/xla/service/hlo_module.h b/tensorflow/compiler/xla/service/hlo_module.h index 757e65bda2..5dc94e78e3 100644 --- a/tensorflow/compiler/xla/service/hlo_module.h +++ b/tensorflow/compiler/xla/service/hlo_module.h @@ -154,7 +154,7 @@ class HloModule { // Compute and return a post order of all computations in the module. The sort // is defined like so: if computation A has an instruction which calls // computation B, then A will appear after B in the sort. - std::list MakeComputationPostOrder() const; + std::vector MakeComputationPostOrder() const; // Gets the computations in this module which aren't for fusion nodes. // diff --git a/tensorflow/compiler/xla/service/hlo_module_group_util.cc b/tensorflow/compiler/xla/service/hlo_module_group_util.cc index 5a0d1e264e..21a9b7291a 100644 --- a/tensorflow/compiler/xla/service/hlo_module_group_util.cc +++ b/tensorflow/compiler/xla/service/hlo_module_group_util.cc @@ -277,7 +277,7 @@ Status HloModuleGroupUtil::VerifyComputations( StatusOr> HloModuleGroupUtil::ComputeReachability( tensorflow::gtl::ArraySlice computations) { - std::list post_order; + std::vector post_order; auto visit_function = [&](HloInstruction* instruction, const std::vector& instruction_group) { diff --git a/tensorflow/compiler/xla/service/hlo_reachability.cc b/tensorflow/compiler/xla/service/hlo_reachability.cc index 4738e46f8a..01b088a957 100644 --- a/tensorflow/compiler/xla/service/hlo_reachability.cc +++ b/tensorflow/compiler/xla/service/hlo_reachability.cc @@ -18,7 +18,7 @@ limitations under the License. namespace xla { HloReachabilityMap::HloReachabilityMap( - const std::list& instructions) + tensorflow::gtl::ArraySlice instructions) : size_(instructions.size()) { bit_vectors_.reserve(size_); for (const HloInstruction* hlo : instructions) { diff --git a/tensorflow/compiler/xla/service/hlo_reachability.h b/tensorflow/compiler/xla/service/hlo_reachability.h index 69bb2b3cee..48215d32a8 100644 --- a/tensorflow/compiler/xla/service/hlo_reachability.h +++ b/tensorflow/compiler/xla/service/hlo_reachability.h @@ -41,7 +41,8 @@ class HloReachabilityMap { public: // Sets up a graph with no edges and where the nodes correspond to the given // instructions. - explicit HloReachabilityMap(const std::list& instructions); + explicit HloReachabilityMap( + tensorflow::gtl::ArraySlice instructions); // Set the reachability set of 'instruction' to the union of the reachability // sets of 'inputs'. Upon return, IsReachable(x, instruction) where diff --git a/tensorflow/compiler/xla/service/instruction_fusion.cc b/tensorflow/compiler/xla/service/instruction_fusion.cc index abedb4063d..d1c4c91b34 100644 --- a/tensorflow/compiler/xla/service/instruction_fusion.cc +++ b/tensorflow/compiler/xla/service/instruction_fusion.cc @@ -281,10 +281,8 @@ StatusOr InstructionFusion::Run(HloModule* module) { // map from HloInstruction* to the instruction's index in the vector. An // instruction is "removed" from the vector by setting it's element to // nullptr. - std::list post_order_list = + std::vector post_order = computation_->MakeInstructionPostOrder(); - std::vector post_order(post_order_list.begin(), - post_order_list.end()); tensorflow::gtl::FlatMap post_order_index; for (size_t i = 0; i < post_order.size(); ++i) { -- GitLab From 45d7a0460777a4cd416a71406181b56ecde8bef2 Mon Sep 17 00:00:00 2001 From: Mark Heffernan Date: Fri, 15 Jun 2018 11:22:15 -0700 Subject: [PATCH 519/816] Add test of TOKEN primitive type which uses conditionals. PiperOrigin-RevId: 200745718 --- .../compiler/xla/tests/token_hlo_test.cc | 61 ++++++++++++++++++- 1 file changed, 60 insertions(+), 1 deletion(-) diff --git a/tensorflow/compiler/xla/tests/token_hlo_test.cc b/tensorflow/compiler/xla/tests/token_hlo_test.cc index 3ef54e6f89..8541698576 100644 --- a/tensorflow/compiler/xla/tests/token_hlo_test.cc +++ b/tensorflow/compiler/xla/tests/token_hlo_test.cc @@ -150,7 +150,66 @@ ENTRY %TokenInWhileLoop () -> s32[] { } )"; - EXPECT_TRUE(RunAndCompare(module_string, error_spec_)); + DebugOptions debug_options = GetDebugOptionsForTest(); + // Module DCE pass removes the generate token instructions. + debug_options.add_xla_disable_hlo_passes("hlo-module-dce"); + TF_ASSERT_OK_AND_ASSIGN( + std::unique_ptr module, + HloRunner::CreateModuleFromString(module_string, debug_options)); + + EXPECT_TRUE(RunAndCompare(std::move(module), error_spec_)); +} + +XLA_TEST_F(TokenHloTest, TokenInConditional) { + string module_string = R"( +HloModule TokenInConditional + +%True (param.1: token[]) -> (s32[], token[]) { + %param.1 = token[] parameter(0) + %forty_two = s32[] constant(42) + ROOT %tuple = (s32[], token[]) tuple(s32[] %forty_two, token[] %param.1) +} + +%False (param.2: s32[]) -> (s32[], token[]) { + %param.2 = s32[] parameter(0) + %new_token = token[] generate-token() + ROOT %tuple = (s32[], token[]) tuple(s32[] %param.2, token[] %new_token) +} + +ENTRY %TokenInConditional (param.3: pred[]) -> s32[] { + %param.3 = pred[] parameter(0) + %init_token = token[] generate-token() + %seven = s32[] constant(7) + %cond = (s32[], token[]) conditional(pred[] %param.3, token[] %init_token, s32[] %seven), true_computation=True, false_computation=False + ROOT %root = s32[] get-tuple-element((s32[], token[]) %cond), index=0 +} +)"; + + DebugOptions debug_options = GetDebugOptionsForTest(); + // Module DCE pass removes the generate token instructions. + debug_options.add_xla_disable_hlo_passes("hlo-module-dce"); + + { + // True case. + TF_ASSERT_OK_AND_ASSIGN( + std::unique_ptr module, + HloRunner::CreateModuleFromString(module_string, debug_options)); + auto arg = Literal::CreateR0(true); + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr result, + Execute(std::move(module), {arg.get()})); + EXPECT_EQ(42, result->Get({})); + } + + { + // False case. + TF_ASSERT_OK_AND_ASSIGN( + std::unique_ptr module, + HloRunner::CreateModuleFromString(module_string, debug_options)); + auto arg = Literal::CreateR0(false); + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr result, + Execute(std::move(module), {arg.get()})); + EXPECT_EQ(7, result->Get({})); + } } } // namespace -- GitLab From 8ba25e36b948555f6b5df079b968b2a1382b5328 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 15 Jun 2018 11:28:41 -0700 Subject: [PATCH 520/816] [XLA] Don't implement kCrossReplicaSum case in HloInstruction::IdenticalSlowPath. PiperOrigin-RevId: 200746735 --- tensorflow/compiler/xla/service/hlo_instruction.cc | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc index 0b4dd6412f..8bedd2a865 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction.cc +++ b/tensorflow/compiler/xla/service/hlo_instruction.cc @@ -1493,10 +1493,6 @@ bool HloInstruction::IdenticalSlowPath( return protobuf_util::ProtobufEquals(padding_config(), other.padding_config()); case HloOpcode::kCall: - case HloOpcode::kCrossReplicaSum: - return replica_group_ids() == other.replica_group_ids() && - cross_replica_sum_barrier() == other.cross_replica_sum_barrier() && - eq_computations(to_apply(), other.to_apply()); case HloOpcode::kCustomCall: if ((window_ == nullptr) != (other.window_ == nullptr) || (window_ != nullptr && @@ -1547,6 +1543,7 @@ bool HloInstruction::IdenticalSlowPath( case HloOpcode::kReducePrecision: case HloOpcode::kInfeed: case HloOpcode::kOutfeed: + case HloOpcode::kCrossReplicaSum: LOG(FATAL) << "Base class impl called for opcode with subclass: " << opcode(); } -- GitLab From a7fcc5da93988b6cbb1f64fcee1e7862d1f788ab Mon Sep 17 00:00:00 2001 From: Younghee Kwon Date: Fri, 15 Jun 2018 11:31:55 -0700 Subject: [PATCH 521/816] contrib.timeseries: sets the predictions dict in EstimatorSpec for evaluation op. PiperOrigin-RevId: 200747192 --- .../timeseries/python/timeseries/head.py | 13 +++--- .../timeseries/python/timeseries/head_test.py | 45 ++++++++++++++++++- 2 files changed, 51 insertions(+), 7 deletions(-) diff --git a/tensorflow/contrib/timeseries/python/timeseries/head.py b/tensorflow/contrib/timeseries/python/timeseries/head.py index a28a5872b8..f236329fdb 100644 --- a/tensorflow/contrib/timeseries/python/timeseries/head.py +++ b/tensorflow/contrib/timeseries/python/timeseries/head.py @@ -132,7 +132,8 @@ class TimeSeriesRegressionHead(head_lib._Head): # pylint:disable=protected-acce loss=model_outputs.loss, mode=mode, eval_metric_ops=metrics, - predictions={}) + # needed for custom metrics. + predictions=model_outputs.predictions) def _predict_ops(self, features): """Add ops for prediction to the graph.""" @@ -210,12 +211,12 @@ class TimeSeriesRegressionHead(head_lib._Head): # pylint:disable=protected-acce def create_estimator_spec(self, features, mode, labels=None): """Performs basic error checking and returns an EstimatorSpec.""" with ops.name_scope(self._name, "head"): - if labels: + if labels is not None and labels != {}: # for better error messages. raise ValueError( - "The model received a `labels` dictionary, which is " - "not supported. Pass '{}' and '{}' as " - "features.".format(feature_keys.TrainEvalFeatures.TIMES, - feature_keys.TrainEvalFeatures.VALUES)) + "The model received a `labels`, which is not supported. " + "Pass '{}' and '{}' as features.".format( + feature_keys.TrainEvalFeatures.TIMES, + feature_keys.TrainEvalFeatures.VALUES)) del labels features = { name: self._convert_feature_to_tensor(name=name, value=value) diff --git a/tensorflow/contrib/timeseries/python/timeseries/head_test.py b/tensorflow/contrib/timeseries/python/timeseries/head_test.py index c606db76a6..ed8f29c321 100644 --- a/tensorflow/contrib/timeseries/python/timeseries/head_test.py +++ b/tensorflow/contrib/timeseries/python/timeseries/head_test.py @@ -21,6 +21,7 @@ from __future__ import print_function import numpy import six +from tensorflow.contrib.estimator.python.estimator import extenders from tensorflow.contrib.timeseries.examples import lstm as lstm_example from tensorflow.contrib.timeseries.python.timeseries import estimators as ts_estimators from tensorflow.contrib.timeseries.python.timeseries import feature_keys @@ -35,6 +36,7 @@ from tensorflow.python.feature_column import feature_column from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops +from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import metrics from tensorflow.python.ops import variables @@ -53,9 +55,12 @@ class HeadTest(test.TestCase): model_fn = _stub_model_fn() for mode in [estimator_lib.ModeKeys.TRAIN, estimator_lib.ModeKeys.EVAL, estimator_lib.ModeKeys.PREDICT]: - with self.assertRaisesRegexp(ValueError, "labels"): + with self.assertRaisesRegexp(ValueError, "received a `labels`"): model_fn(features={}, labels={"a": "b"}, mode=mode) + with self.assertRaisesRegexp(ValueError, "received a `labels`"): + model_fn(features={}, labels=array_ops.zeros([]), mode=mode) + def test_unknown_mode(self): model_fn = _stub_model_fn() with self.assertRaisesRegexp(ValueError, "Unknown mode 'Not a mode'"): @@ -128,6 +133,44 @@ class EvaluationMetricsTests(test.TestCase): coordinator.request_stop() coordinator.join() + def test_custom_metrics(self): + """Tests that the custom metrics can be applied to the estimator.""" + model_dir = self.get_temp_dir() + estimator = ts_estimators.TimeSeriesRegressor( + model=lstm_example._LSTMModel(num_features=1, num_units=4), + optimizer=adam.AdamOptimizer(0.001), + config=estimator_lib.RunConfig(tf_random_seed=4), + model_dir=model_dir) + + def input_fn(): + return { + feature_keys.TrainEvalFeatures.TIMES: [[1, 2, 3], [7, 8, 9]], + feature_keys.TrainEvalFeatures.VALUES: + numpy.array([[[0.], [1.], [0.]], [[2.], [3.], [2.]]]) + } + + def metrics_fn(predictions, features): + # checking that the inputs are properly passed. + predict = predictions["mean"] + target = features[feature_keys.TrainEvalFeatures.VALUES][:, -1, 0] + return { + "plain_boring_metric386": + (math_ops.reduce_mean(math_ops.abs(predict - target)), + control_flow_ops.no_op()), + "fun_metric101": (math_ops.reduce_sum(predict + target), + control_flow_ops.no_op()), + } + + # Evaluation without training is enough for testing custom metrics. + estimator = extenders.add_metrics(estimator, metrics_fn) + evaluation = estimator.evaluate(input_fn, steps=1) + self.assertIn("plain_boring_metric386", evaluation) + self.assertIn("fun_metric101", evaluation) + # The values are deterministic because of fixed tf_random_seed. + # However if they become flaky, remove such exacts comparisons. + self.assertAllClose(evaluation["plain_boring_metric386"], 1.130380) + self.assertAllClose(evaluation["fun_metric101"], 10.435442) + class _StubModel(object): num_features = 3 -- GitLab From 916c0aab83ed3a5b5c6ffa42c3071f59ed0f7934 Mon Sep 17 00:00:00 2001 From: Katherine Wu Date: Fri, 15 Jun 2018 11:35:23 -0700 Subject: [PATCH 522/816] Refactor loader.load function into a class that splits the graph loading and variable restoration steps. PiperOrigin-RevId: 200747752 --- tensorflow/python/saved_model/BUILD | 24 +++ tensorflow/python/saved_model/loader_impl.py | 175 ++++++++++++++---- tensorflow/python/saved_model/loader_test.py | 180 +++++++++++++++++++ 3 files changed, 348 insertions(+), 31 deletions(-) create mode 100644 tensorflow/python/saved_model/loader_test.py diff --git a/tensorflow/python/saved_model/BUILD b/tensorflow/python/saved_model/BUILD index 81786fbf43..076f2d8760 100644 --- a/tensorflow/python/saved_model/BUILD +++ b/tensorflow/python/saved_model/BUILD @@ -87,6 +87,30 @@ py_library( "//tensorflow/python:platform", "//tensorflow/python:training", "//tensorflow/python:util", + "//tensorflow/python:variables", + ], +) + +py_test( + name = "loader_test", + size = "small", + srcs = ["loader_test.py"], + srcs_version = "PY2AND3", + visibility = ["//visibility:private"], + deps = [ + ":builder", + ":loader", + ":signature_def_utils", + ":utils", + "//tensorflow/python:client", + "//tensorflow/python:client_testlib", + "//tensorflow/python:control_flow_ops", + "//tensorflow/python:errors", + "//tensorflow/python:framework_ops", + "//tensorflow/python:lib", + "//tensorflow/python:state_ops", + "//tensorflow/python:training", + "//tensorflow/python:variables", ], ) diff --git a/tensorflow/python/saved_model/loader_impl.py b/tensorflow/python/saved_model/loader_impl.py index d1bd8d47ae..6770aaef36 100644 --- a/tensorflow/python/saved_model/loader_impl.py +++ b/tensorflow/python/saved_model/loader_impl.py @@ -28,6 +28,7 @@ from tensorflow.core.protobuf import meta_graph_pb2 from tensorflow.core.protobuf import saved_model_pb2 from tensorflow.python.framework import ops from tensorflow.python.lib.io import file_io +from tensorflow.python.ops import variables from tensorflow.python.platform import tf_logging from tensorflow.python.saved_model import constants from tensorflow.python.training import saver as tf_saver @@ -207,11 +208,56 @@ def load(sess, tags, export_dir, import_scope=None, **saver_kwargs): Raises: RuntimeError: MetaGraphDef associated with the tags cannot be found. """ - with sess.graph.as_default(): - # Build the SavedModel protocol buffer and find requested meta graph def. - saved_model = _parse_saved_model(export_dir) + loader = SavedModelLoader(export_dir) + return loader.load(sess, tags, import_scope, **saver_kwargs) + + +class SavedModelLoader(object): + """Load graphs and restore variable values from a `SavedModel`.""" + + def __init__(self, export_dir): + """Creates a `SavedModelLoader`. + + Args: + export_dir: Directory in which the SavedModel protocol buffer and + variables to be loaded are located. + """ + self._export_dir = export_dir + self._variables_path = os.path.join( + compat.as_bytes(export_dir), + compat.as_bytes(constants.VARIABLES_DIRECTORY), + compat.as_bytes(constants.VARIABLES_FILENAME)) + self._saved_model = _parse_saved_model(export_dir) + + @property + def export_dir(self): + """Directory containing the SavedModel.""" + return self._export_dir + + @property + def variables_path(self): + """Path to variable checkpoint files.""" + return self._variables_path + + @property + def saved_model(self): + """SavedModel object parsed from the export directory.""" + return self._saved_model + + def get_meta_graph_def_from_tags(self, tags): + """Return MetaGraphDef with the exact specified tags. + + Args: + tags: A list or set of string tags that identify the MetaGraphDef. + + Returns: + MetaGraphDef with the same tags. + + Raises: + RuntimeError: if no metagraphs were found with the associated tags. + """ found_match = False - for meta_graph_def in saved_model.meta_graphs: + for meta_graph_def in self._saved_model.meta_graphs: if set(meta_graph_def.meta_info_def.tags) == set(tags): meta_graph_def_to_load = meta_graph_def found_match = True @@ -223,32 +269,99 @@ def load(sess, tags, export_dir, import_scope=None, **saver_kwargs): " could not be found in SavedModel. To inspect available tag-sets in" " the SavedModel, please use the SavedModel CLI: `saved_model_cli`" ) + return meta_graph_def_to_load - # Build a saver by importing the meta graph def to load. - saver = tf_saver.import_meta_graph( - meta_graph_def_to_load, import_scope=import_scope, **saver_kwargs) - - if saver: - # Build the checkpoint path where the variables are located. - variables_path = os.path.join( - compat.as_bytes(export_dir), - compat.as_bytes(constants.VARIABLES_DIRECTORY), - compat.as_bytes(constants.VARIABLES_FILENAME)) - - # Restore the variables using the built saver in the provided session. - saver.restore(sess, variables_path) - else: - tf_logging.info("The specified SavedModel has no variables; no " - "checkpoints were restored.") - - # Get asset tensors, if any. - asset_tensors_dictionary = _get_asset_tensors( - export_dir, meta_graph_def_to_load, import_scope=import_scope) - - main_op_tensor = ( - _get_main_op_tensor(meta_graph_def_to_load) or - (_get_legacy_init_op_tensor(meta_graph_def_to_load))) - if main_op_tensor is not None: - sess.run(fetches=[main_op_tensor], feed_dict=asset_tensors_dictionary) + def load_graph(self, graph, tags, import_scope=None, **saver_kwargs): + """Load ops and nodes from SavedModel MetaGraph into graph. - return meta_graph_def_to_load + Args: + graph: tf.Graph object. + tags: a set of string tags identifying a MetaGraphDef. + import_scope: Optional `string` -- if specified, prepend this string + followed by '/' to all loaded tensor names. This scope is applied to + tensor instances loaded into the passed session, but it is *not* written + through to the static `MetaGraphDef` protocol buffer that is returned. + **saver_kwargs: keyword arguments to pass to tf.train.import_meta_graph. + + Returns: + Saver defined by the MetaGraph, which can be used to restore the variable + values. + """ + meta_graph_def = self.get_meta_graph_def_from_tags(tags) + with graph.as_default(): + return tf_saver.import_meta_graph( + meta_graph_def, import_scope=import_scope, **saver_kwargs) + + def restore_variables(self, sess, saver, import_scope=None): + """Restore SavedModel variable values into the session. + + Args: + sess: tf.Session to restore variable values. + saver: a tf.train.Saver object. Can be None if there are no variables in + graph. This may be the saver returned by the load_graph() function, or a + default `tf.train.Saver()`. + import_scope: Optional `string` -- if specified, prepend this string + followed by '/' to all loaded tensor names. This scope is applied to + tensor instances loaded into the passed session, but it is *not* written + through to the static `MetaGraphDef` protocol buffer that is returned. + + Raises: + ValueError: if no saver was passed to the saver argument, and there are + variables in the graph. + """ + with sess.graph.as_default(): + if not variables._all_saveable_objects(scope=import_scope): # pylint: disable=protected-access + tf_logging.info("The specified SavedModel has no variables; no " + "checkpoints were restored.") + elif isinstance(saver, tf_saver.Saver): + saver.restore(sess, self._variables_path) + else: + raise ValueError( + "No tf.train.Saver object was passed to the function " + "SavedModelLoader.restore_variables. Since there are variables in " + "the graph, a saver is required.") + + def run_init_ops(self, sess, tags, import_scope=None): + """Run initialization ops defined in the `MetaGraphDef`. + + Args: + sess: tf.Session to restore variable values. + tags: a set of string tags identifying a MetaGraphDef. + import_scope: Optional `string` -- if specified, prepend this string + followed by '/' to all loaded tensor names. This scope is applied to + tensor instances loaded into the passed session, but it is *not* written + through to the static `MetaGraphDef` protocol buffer that is returned. + """ + meta_graph_def = self.get_meta_graph_def_from_tags(tags) + with sess.graph.as_default(): + # Get asset tensors, if any. + asset_tensors_dictionary = _get_asset_tensors( + self._export_dir, meta_graph_def, import_scope=import_scope) + + main_op_tensor = ( + _get_main_op_tensor(meta_graph_def) or + (_get_legacy_init_op_tensor(meta_graph_def))) + if main_op_tensor is not None: + sess.run(fetches=[main_op_tensor], feed_dict=asset_tensors_dictionary) + + def load(self, sess, tags, import_scope=None, **saver_kwargs): + """Load the MetaGraphDef graph and restore variable values into the session. + + Args: + sess: tf.Session to restore variable values. + tags: a set of string tags identifying a MetaGraphDef. + import_scope: Optional `string` -- if specified, prepend this string + followed by '/' to all loaded tensor names. This scope is applied to + tensor instances loaded into the passed session, but it is *not* written + through to the static `MetaGraphDef` protocol buffer that is returned. + **saver_kwargs: keyword arguments to pass to tf.train.import_meta_graph. + + Returns: + `MetagraphDef` proto of the graph that was loaded. + """ + with sess.graph.as_default(): + saver = self.load_graph(sess.graph, tags, import_scope, + **saver_kwargs) + self.restore_variables(sess, saver, import_scope) + self.run_init_ops(sess, tags, import_scope) + return self.get_meta_graph_def_from_tags(tags) diff --git a/tensorflow/python/saved_model/loader_test.py b/tensorflow/python/saved_model/loader_test.py new file mode 100644 index 0000000000..2ec2519c89 --- /dev/null +++ b/tensorflow/python/saved_model/loader_test.py @@ -0,0 +1,180 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for SavedModelLoader class.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os + +from tensorflow.python.client import session +from tensorflow.python.framework import errors +from tensorflow.python.framework import ops +from tensorflow.python.lib.io import file_io +from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import state_ops +from tensorflow.python.ops import variables +from tensorflow.python.platform import test +from tensorflow.python.saved_model import builder as saved_model_builder +from tensorflow.python.saved_model import loader_impl +from tensorflow.python.saved_model import signature_def_utils +from tensorflow.python.saved_model import utils +from tensorflow.python.training import saver as tf_saver + + +def _get_export_dir(label): + return os.path.join(test.get_temp_dir(), label) + +SIMPLE_ADD_SAVED_MODEL = _get_export_dir("simple_add_saved_model") +SAVED_MODEL_WITH_MAIN_OP = _get_export_dir("saved_model_with_main_op") + + +class SavedModelLoaderTest(test.TestCase): + + def setUp(self): + """Write test SavedModels to a temp directory.""" + with session.Session(graph=ops.Graph()) as sess: + x = variables.Variable(5, name="x") + y = variables.Variable(11, name="y") + z = x + y + sess.run(variables.global_variables_initializer()) + + foo_sig_def = signature_def_utils.build_signature_def( + {"foo_input": utils.build_tensor_info(x)}, + {"foo_output": utils.build_tensor_info(z)}) + bar_sig_def = signature_def_utils.build_signature_def( + {"bar_x": utils.build_tensor_info(x), + "bar_y": utils.build_tensor_info(y)}, + {"bar_z": utils.build_tensor_info(z)}) + + builder = saved_model_builder.SavedModelBuilder(SIMPLE_ADD_SAVED_MODEL) + builder.add_meta_graph_and_variables( + sess, ["foo_graph"], {"foo": foo_sig_def, "bar": bar_sig_def}) + builder.save() + + # Write SavedModel with a main_op + assign_op = control_flow_ops.group(state_ops.assign(y, 7)) + + builder = saved_model_builder.SavedModelBuilder(SAVED_MODEL_WITH_MAIN_OP) + builder.add_meta_graph_and_variables( + sess, ["foo_graph"], {"foo": foo_sig_def, "bar": bar_sig_def}, + main_op=assign_op) + builder.save() + + def tearDown(self): + file_io.delete_recursively(test.get_temp_dir()) + + def test_load_function(self): + loader = loader_impl.SavedModelLoader(SIMPLE_ADD_SAVED_MODEL) + with self.test_session(graph=ops.Graph()) as sess: + loader.load(sess, ["foo_graph"]) + self.assertEqual(5, sess.graph.get_tensor_by_name("x:0").eval()) + self.assertEqual(11, sess.graph.get_tensor_by_name("y:0").eval()) + + loader2 = loader_impl.SavedModelLoader(SAVED_MODEL_WITH_MAIN_OP) + with self.test_session(graph=ops.Graph()) as sess: + loader2.load(sess, ["foo_graph"]) + self.assertEqual(5, sess.graph.get_tensor_by_name("x:0").eval()) + self.assertEqual(7, sess.graph.get_tensor_by_name("y:0").eval()) + + def test_load_graph(self): + loader = loader_impl.SavedModelLoader(SIMPLE_ADD_SAVED_MODEL) + graph = ops.Graph() + loader.load_graph(graph, ["foo_graph"]) + + x = graph.get_tensor_by_name("x:0") + y = graph.get_tensor_by_name("y:0") + + with self.assertRaises(KeyError): + graph.get_tensor_by_name("z:0") + + with self.test_session(graph=graph) as sess: + # Check that x and y are not initialized + with self.assertRaises(errors.FailedPreconditionError): + sess.run(x) + with self.assertRaises(errors.FailedPreconditionError): + sess.run(y) + + def test_load_with_import_scope(self): + loader = loader_impl.SavedModelLoader(SAVED_MODEL_WITH_MAIN_OP) + with self.test_session(graph=ops.Graph()) as sess: + saver = loader.load_graph(sess.graph, ["foo_graph"], import_scope="baz") + + # The default saver should not work when the import scope is set. + with self.assertRaises(errors.NotFoundError): + loader.restore_variables(sess, tf_saver.Saver()) + + loader.restore_variables(sess, saver) + loader.run_init_ops(sess, ["foo_graph"]) + + self.assertEqual(5, sess.graph.get_tensor_by_name("baz/x:0").eval()) + self.assertEqual(7, sess.graph.get_tensor_by_name("baz/y:0").eval()) + + # Test combined load function. + loader = loader_impl.SavedModelLoader(SAVED_MODEL_WITH_MAIN_OP) + with self.test_session(graph=ops.Graph()) as sess: + loader.load(sess, ["foo_graph"], import_scope="baa") + self.assertEqual(5, sess.graph.get_tensor_by_name("baa/x:0").eval()) + self.assertEqual(7, sess.graph.get_tensor_by_name("baa/y:0").eval()) + + def test_restore_variables(self): + loader = loader_impl.SavedModelLoader(SAVED_MODEL_WITH_MAIN_OP) + with self.test_session(graph=ops.Graph()) as sess: + x = variables.Variable(0, name="x") + y = variables.Variable(0, name="y") + z = x * y + + sess.run(variables.global_variables_initializer()) + + # There are variables to restore, so a saver must be created. + with self.assertRaises(ValueError): + loader.restore_variables(sess, None) + + loader.restore_variables(sess, tf_saver.Saver()) + self.assertEqual(55, z.eval()) + + def test_run_init_op(self): + loader = loader_impl.SavedModelLoader(SAVED_MODEL_WITH_MAIN_OP) + graph = ops.Graph() + saver = loader.load_graph(graph, ["foo_graph"]) + with self.test_session(graph=graph) as sess: + loader.restore_variables(sess, saver) + self.assertEqual(5, sess.graph.get_tensor_by_name("x:0").eval()) + self.assertEqual(11, sess.graph.get_tensor_by_name("y:0").eval()) + + loader.run_init_ops(sess, ["foo_graph"]) + self.assertEqual(5, sess.graph.get_tensor_by_name("x:0").eval()) + self.assertEqual(7, sess.graph.get_tensor_by_name("y:0").eval()) + + def test_parse_saved_model(self): + loader = loader_impl.SavedModelLoader(SIMPLE_ADD_SAVED_MODEL) + meta_graph = loader.get_meta_graph_def_from_tags(["foo_graph"]) + self.assertIsNotNone(meta_graph) + self.assertIn("foo", meta_graph.signature_def) + self.assertIn("bar", meta_graph.signature_def) + + def test_load_invalid_meta_graph(self): + loader = loader_impl.SavedModelLoader(SIMPLE_ADD_SAVED_MODEL) + with self.assertRaises(RuntimeError): + loader.get_meta_graph_def_from_tags([]) + with self.assertRaises(RuntimeError): + loader.get_meta_graph_def_from_tags([""]) + with self.assertRaises(RuntimeError): + loader.get_meta_graph_def_from_tags(["not_a_graph"]) + + +if __name__ == "__main__": + test.main() -- GitLab From f9b832d91f9553fc9ef4eeb4d4d98ca31fb762e3 Mon Sep 17 00:00:00 2001 From: Kay Zhu Date: Fri, 15 Jun 2018 11:54:29 -0700 Subject: [PATCH 523/816] [TF2XLA] Remove the last unncessary host-to-device memcpy, and remove the HostTensorToLiteral function completely to prevent potential future misuse of unnecessary memcpy. PiperOrigin-RevId: 200750664 --- .../compiler/tf2xla/kernels/mirror_pad_op.cc | 2 +- tensorflow/compiler/tf2xla/kernels/pad_op.cc | 4 +- .../tf2xla/kernels/reduction_ops_common.cc | 6 +-- .../compiler/tf2xla/kernels/sequence_ops.cc | 15 +++---- .../compiler/tf2xla/kernels/split_op.cc | 4 +- tensorflow/compiler/tf2xla/literal_util.cc | 18 --------- tensorflow/compiler/tf2xla/literal_util.h | 4 -- tensorflow/compiler/tf2xla/xla_context.cc | 2 +- tensorflow/compiler/tf2xla/xla_context.h | 2 +- tensorflow/compiler/tf2xla/xla_helpers.cc | 2 +- tensorflow/compiler/tf2xla/xla_op_kernel.cc | 39 +++++++++++++++---- tensorflow/compiler/xla/literal_util.cc | 1 - 12 files changed, 51 insertions(+), 48 deletions(-) diff --git a/tensorflow/compiler/tf2xla/kernels/mirror_pad_op.cc b/tensorflow/compiler/tf2xla/kernels/mirror_pad_op.cc index 7e9de3ef9b..c3326b4d11 100644 --- a/tensorflow/compiler/tf2xla/kernels/mirror_pad_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/mirror_pad_op.cc @@ -27,7 +27,7 @@ class MirrorPadOp : public XlaOpKernel { xla::StatusOr DoMirrorPad(const xla::XlaOp& t, const xla::Shape& original_shape, - const xla::Literal& pad_literal, + const xla::LiteralSlice& pad_literal, xla::XlaBuilder* b) { xla::XlaOp accum = t; for (int64 dimno = xla::ShapeUtil::Rank(original_shape) - 1; dimno >= 0; diff --git a/tensorflow/compiler/tf2xla/kernels/pad_op.cc b/tensorflow/compiler/tf2xla/kernels/pad_op.cc index 7c95475e7b..17b85338f7 100644 --- a/tensorflow/compiler/tf2xla/kernels/pad_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/pad_op.cc @@ -63,8 +63,8 @@ class PadOp : public XlaOpKernel { int before = pad_literal.Get({i, 0}); int after = pad_literal.Get({i, 1}); OP_REQUIRES(ctx, before >= 0 && after >= 0, - errors::InvalidArgument("Paddings must be non-negative: ", - before, " ", after)); + errors::InvalidArgument( + "Paddings must be non-negative: ", before, " ", after)); dim->set_edge_padding_low(before); dim->set_edge_padding_high(after); } diff --git a/tensorflow/compiler/tf2xla/kernels/reduction_ops_common.cc b/tensorflow/compiler/tf2xla/kernels/reduction_ops_common.cc index 4fd5bfd039..44510c731e 100644 --- a/tensorflow/compiler/tf2xla/kernels/reduction_ops_common.cc +++ b/tensorflow/compiler/tf2xla/kernels/reduction_ops_common.cc @@ -56,9 +56,9 @@ void XlaReductionOp::Compile(XlaOpKernelContext* ctx) { // Evaluate the constant, reshaping to a 1-vector if it is a scalar. xla::Literal axes_literal; - OP_REQUIRES_OK(ctx, - ctx->ConstantInputReshaped( - 1, {axes_tensor_shape.num_elements()}, &axes_literal)); + OP_REQUIRES_OK( + ctx, ctx->ConstantInputReshaped(1, {axes_tensor_shape.num_elements()}, + &axes_literal)); VLOG(1) << "data shape: " << data_shape.DebugString(); VLOG(1) << "axes : " << axes_literal.ToString(); diff --git a/tensorflow/compiler/tf2xla/kernels/sequence_ops.cc b/tensorflow/compiler/tf2xla/kernels/sequence_ops.cc index 2c31f8d908..bc3d0bf5df 100644 --- a/tensorflow/compiler/tf2xla/kernels/sequence_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/sequence_ops.cc @@ -55,9 +55,10 @@ Status GetIntValue(int index, XlaOpKernelContext* ctx, int64* value) { // The type-specific part of the implementation of Range. template -Status CreateRangeTensor(const xla::Literal& start_literal, - const xla::Literal& limit_literal, - const xla::Literal& delta_literal, Tensor* output) { +Status CreateRangeTensor(const xla::LiteralSlice& start_literal, + const xla::LiteralSlice& limit_literal, + const xla::LiteralSlice& delta_literal, + Tensor* output) { T start = start_literal.Get({}); T limit = limit_literal.Get({}); T delta = delta_literal.Get({}); @@ -67,13 +68,13 @@ Status CreateRangeTensor(const xla::Literal& start_literal, } if (delta > 0) { if (start > limit) { - return errors::InvalidArgument("Requires start <= limit when delta > 0: ", - start, "/", limit); + return errors::InvalidArgument( + "Requires start <= limit when delta > 0: ", start, "/", limit); } } else { if (start < limit) { - return errors::InvalidArgument("Requires start >= limit when delta < 0: ", - start, "/", limit); + return errors::InvalidArgument( + "Requires start >= limit when delta < 0: ", start, "/", limit); } } int64 size = diff --git a/tensorflow/compiler/tf2xla/kernels/split_op.cc b/tensorflow/compiler/tf2xla/kernels/split_op.cc index 8958b2e770..9b54058541 100644 --- a/tensorflow/compiler/tf2xla/kernels/split_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/split_op.cc @@ -134,7 +134,7 @@ class SplitVOp : public XlaOpKernel { errors::InvalidArgument( "Number of ways to split should be > 0, but got ", num_split)); - // check that sizes are correct + // Check that sizes are correct. int total_split_size = 0; int neg_one_dim = -1; std::vector split_sizes_vec(num_split, -1); @@ -148,7 +148,7 @@ class SplitVOp : public XlaOpKernel { " number of elements as the output. Got ", split_size_shape.dims(), "-D and ", split_size_shape.num_elements(), " elements")); - // get the dimension of this split + // Get the dimension of this split. xla::Literal split_size_literal; OP_REQUIRES_OK(ctx, ctx->ConstantInput(1, &split_size_literal)); diff --git a/tensorflow/compiler/tf2xla/literal_util.cc b/tensorflow/compiler/tf2xla/literal_util.cc index db56b12837..b43405a1a4 100644 --- a/tensorflow/compiler/tf2xla/literal_util.cc +++ b/tensorflow/compiler/tf2xla/literal_util.cc @@ -22,24 +22,6 @@ limitations under the License. namespace tensorflow { -Status HostTensorToLiteral(const Tensor& host_tensor, xla::Literal* literal) { - xla::Shape literal_shape; - TF_RETURN_IF_ERROR(TensorShapeToXLAShape( - host_tensor.dtype(), host_tensor.shape(), &literal_shape)); - - *literal = xla::Literal(literal_shape); - - // memcpy over the payload ... - // TODO(phawkins): handle string types. - size_t total_bytes = host_tensor.TotalBytes(); - if (total_bytes > 0) { - void* dst_ptr = literal->untyped_data(); - const void* src_ptr = DMAHelper::base(&host_tensor); - memcpy(dst_ptr, src_ptr, total_bytes); - } - return Status::OK(); -} - Status HostTensorToBorrowingLiteral(const Tensor& host_tensor, xla::BorrowingLiteral* literal) { xla::Shape xla_shape; diff --git a/tensorflow/compiler/tf2xla/literal_util.h b/tensorflow/compiler/tf2xla/literal_util.h index 74685025c1..ab7e861f33 100644 --- a/tensorflow/compiler/tf2xla/literal_util.h +++ b/tensorflow/compiler/tf2xla/literal_util.h @@ -26,10 +26,6 @@ limitations under the License. namespace tensorflow { -// Copies 'host_tensor' to an XLA Literal. Fails if host_tensor is of an -// unsupported type. -Status HostTensorToLiteral(const Tensor& host_tensor, xla::Literal* literal); - // Returns a BorrowingLiteral that utilizes the same underlying buffer owned by // 'host_tensor'. Status HostTensorToBorrowingLiteral(const Tensor& host_tensor, diff --git a/tensorflow/compiler/tf2xla/xla_context.cc b/tensorflow/compiler/tf2xla/xla_context.cc index 098072d33c..67174b251d 100644 --- a/tensorflow/compiler/tf2xla/xla_context.cc +++ b/tensorflow/compiler/tf2xla/xla_context.cc @@ -92,7 +92,7 @@ void XlaContext::AddRetval(int retval_index, DataType type, } Status XlaContext::AddConstRetval(int retval_index, DataType dtype, - const xla::Literal& literal) { + const xla::LiteralSlice& literal) { VLOG(1) << "Adding retval index " << retval_index << " with non-data-dependent tensor to XLA computation"; if (retvals_.size() <= retval_index) { diff --git a/tensorflow/compiler/tf2xla/xla_context.h b/tensorflow/compiler/tf2xla/xla_context.h index 341bf6ff1f..5960daaefd 100644 --- a/tensorflow/compiler/tf2xla/xla_context.h +++ b/tensorflow/compiler/tf2xla/xla_context.h @@ -83,7 +83,7 @@ class XlaContext : public ResourceBase { // As for Retval, but for return values that are compile-time constants. Status AddConstRetval(int retval_index, DataType dtype, - const xla::Literal& literal); + const xla::LiteralSlice& literal); // Creates a resource with resource `kind` and initial value `handle`. `name` // is a descriptive name for use in error messages. See the `XlaResource` diff --git a/tensorflow/compiler/tf2xla/xla_helpers.cc b/tensorflow/compiler/tf2xla/xla_helpers.cc index a1da176fe3..93cd340485 100644 --- a/tensorflow/compiler/tf2xla/xla_helpers.cc +++ b/tensorflow/compiler/tf2xla/xla_helpers.cc @@ -25,7 +25,6 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/types.h" -#include "tensorflow/core/common_runtime/dma_helper.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/lib/gtl/array_slice.h" @@ -248,6 +247,7 @@ Status XlaHelpers::OneHot(xla::XlaBuilder* builder, int64 depth, int axis, return errors::InvalidArgument("Invalid argument type ", DataTypeString(index_type)); } + xla::BorrowingLiteral linspace_literal; TF_RETURN_IF_ERROR(HostTensorToBorrowingLiteral(linspace, &linspace_literal)); diff --git a/tensorflow/compiler/tf2xla/xla_op_kernel.cc b/tensorflow/compiler/tf2xla/xla_op_kernel.cc index 76c68d81af..c6ddbcc6e1 100644 --- a/tensorflow/compiler/tf2xla/xla_op_kernel.cc +++ b/tensorflow/compiler/tf2xla/xla_op_kernel.cc @@ -20,6 +20,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/literal_util.h" #include "tensorflow/compiler/tf2xla/shape_util.h" #include "tensorflow/compiler/tf2xla/xla_context.h" +#include "tensorflow/core/common_runtime/dma_helper.h" namespace tensorflow { @@ -87,6 +88,25 @@ Status XlaOpKernelContext::ConstantInputReshaped( } const XlaExpression* expression = CastExpressionFromTensor(tensor); + auto copy_tensor_to_literal = [](const Tensor& tensor, + xla::Literal* literal) { + xla::Shape literal_shape; + TF_RETURN_IF_ERROR( + TensorShapeToXLAShape(tensor.dtype(), tensor.shape(), &literal_shape)); + + *literal = xla::Literal(literal_shape); + + // memcpy over the payload ... + // TODO(phawkins): handle string types. + size_t total_bytes = tensor.TotalBytes(); + if (total_bytes > 0) { + void* dst_ptr = literal->untyped_data(); + const void* src_ptr = DMAHelper::base(&tensor); + memcpy(dst_ptr, src_ptr, total_bytes); + } + return Status::OK(); + }; + // If the tensor has a known constant value, there is no need to invoke XLA. if (expression->has_constant_value()) { Tensor temp(tensor.dtype()); @@ -95,13 +115,15 @@ Status XlaOpKernelContext::ConstantInputReshaped( // with the enclosing Tensor. return errors::Internal("Incompatible shapes in ConstantInputReshaped."); } - return HostTensorToLiteral(temp, constant_literal); + + return copy_tensor_to_literal(temp, constant_literal); } // Make sure we treat zero-element tensors as constant. if (new_shape.num_elements() == 0) { Tensor temp(tensor.dtype(), new_shape); - return HostTensorToLiteral(temp, constant_literal); + + return copy_tensor_to_literal(temp, constant_literal); } xla::XlaOp handle = expression->handle(); @@ -162,7 +184,8 @@ Status XlaOpKernelContext::ConstantInputReshaped( } // Converts an int32 or int64 scalar literal to an int64. -static Status LiteralToInt64Scalar(const xla::Literal& literal, int64* out) { +static Status LiteralToInt64Scalar(const xla::LiteralSlice& literal, + int64* out) { if (xla::ShapeUtil::Rank(literal.shape()) != 0) { return errors::InvalidArgument("value is not a scalar"); } @@ -177,7 +200,8 @@ static Status LiteralToInt64Scalar(const xla::Literal& literal, int64* out) { } // Converts an float32 or float64 scalar literal to a float64. -static Status LiteralToFloat64Scalar(const xla::Literal& literal, double* out) { +static Status LiteralToFloat64Scalar(const xla::LiteralSlice& literal, + double* out) { if (xla::ShapeUtil::Rank(literal.shape()) != 0) { return errors::InvalidArgument("value is not a scalar"); } @@ -204,7 +228,7 @@ Status XlaOpKernelContext::ConstantInputAsFloatScalar(int index, double* out) { } // Converts an int32 or int64 1D literal to an int64 vector. -static Status LiteralToInt64Vector(const xla::Literal& literal, +static Status LiteralToInt64Vector(const xla::LiteralSlice& literal, std::vector* out) { if (xla::ShapeUtil::Rank(literal.shape()) != 1) { return errors::InvalidArgument("value is not 1D"); @@ -368,8 +392,9 @@ void XlaOpKernelContext::SetOutput(int index, const xla::XlaOp& handle) { void XlaOpKernelContext::SetConstantOutput(int index, const Tensor& constant) { const TensorShape& shape = constant.shape(); - xla::Literal literal; - OP_REQUIRES_OK(context_, HostTensorToLiteral(constant, &literal)); + xla::BorrowingLiteral literal; + OP_REQUIRES_OK(context_, HostTensorToBorrowingLiteral(constant, &literal)); + xla::XlaOp handle = builder()->ConstantLiteral(literal); CHECK_NE(handle.builder(), nullptr); diff --git a/tensorflow/compiler/xla/literal_util.cc b/tensorflow/compiler/xla/literal_util.cc index 19e6d288c0..7c6a181b0a 100644 --- a/tensorflow/compiler/xla/literal_util.cc +++ b/tensorflow/compiler/xla/literal_util.cc @@ -2355,7 +2355,6 @@ LiteralSlice::LiteralSlice(const LiteralBase& literal, BorrowingLiteral::BorrowingLiteral(const char* src_buf_ptr, const Shape& shape) : LiteralBase(), shape_(MakeUnique(shape)) { CHECK(ShapeUtil::IsArray(*shape_)); - CHECK_NE(src_buf_ptr, nullptr); CHECK(LayoutUtil::HasLayout(*shape_)); root_piece_ = Piece(); -- GitLab From a601d9a6f14cd881f2e3a666a473c3da7813ff33 Mon Sep 17 00:00:00 2001 From: Youlong Cheng Date: Fri, 15 Jun 2018 11:57:52 -0700 Subject: [PATCH 524/816] Support model parallelism in PER_HOST_V2 input pipeline. PiperOrigin-RevId: 200751151 --- .../contrib/tpu/python/tpu/tpu_context.py | 14 +++++++----- .../contrib/tpu/python/tpu/tpu_estimator.py | 22 +++++-------------- 2 files changed, 14 insertions(+), 22 deletions(-) diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_context.py b/tensorflow/contrib/tpu/python/tpu/tpu_context.py index 5b9aeaa879..ffd7b43c31 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_context.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_context.py @@ -484,25 +484,27 @@ class _InternalTPUContext(object): return _placement_function - @property - def tpu_ordinal_function(self): + def tpu_ordinal_function(self, host_id): """Returns the TPU ordinal fn.""" - def _tpu_ordinal_function(index): + def _tpu_ordinal_function(shard_index_in_host): """Return the TPU ordinal associated with a shard. Required because the enqueue ops are placed on CPU. Args: - index: the shard index + shard_index_in_host: the shard index Returns: The ordinal of the TPU device the shard's infeed should be placed on. """ if self.model_parallelism_enabled: - return self.device_assignment.tpu_ordinal(replica=index) + # We put both enqueue/dequeue ops at tpu.core(0) in each replica. + replica = self.device_assignment.lookup_replicas( + host_id, (0, 0, 0))[shard_index_in_host] + return self.device_assignment.tpu_ordinal(replica=replica) else: - return index % self.num_of_cores_per_host + return shard_index_in_host % self.num_of_cores_per_host return _tpu_ordinal_function diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py index e94bd78833..2131969e8f 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py @@ -664,6 +664,7 @@ def generate_per_core_enqueue_ops_fn_for_host( ctx, input_fn, inputs_structure_recorder, host_device, host_id): """Generates infeed enqueue ops for per-core input_fn on a single host.""" captured_infeed_queue = _CapturedObject() + tpu_ordinal_function_impl = ctx.tpu_ordinal_function(host_id) def enqueue_ops_fn(): """A fn returns enqueue_ops.""" @@ -699,7 +700,7 @@ def generate_per_core_enqueue_ops_fn_for_host( per_host_sharded_inputs) per_host_enqueue_ops = infeed_queue.generate_enqueue_ops( - per_host_sharded_inputs, tpu_ordinal_function=ctx.tpu_ordinal_function) + per_host_sharded_inputs, tpu_ordinal_function=tpu_ordinal_function_impl) return per_host_enqueue_ops return enqueue_ops_fn, captured_infeed_queue @@ -734,19 +735,7 @@ def generate_per_host_enqueue_ops_fn_for_host( if is_dataset: hooks.append(inputs.dataset_initializer_hook()) - # TODO(ylc): Refactoring the code to merge the tpu ordinal logic here and the - # _InternalTPUContext.tpu_ordinal_function. We should either introduce another - # abstraction or a different helper method. - def _tpu_ordinal_function_impl(shard_index_in_host): - # We put both enqueue/dequeue op at tpu.core(0) in each replica. - replica = ctx.device_assignment.lookup_replicas( - host_id, (0, 0, 0))[shard_index_in_host] - return ctx.device_assignment.tpu_ordinal(replica=replica) - - if ctx.model_parallelism_enabled: - tpu_ordinal_function = _tpu_ordinal_function_impl - else: - tpu_ordinal_function = None + tpu_ordinal_function_impl = ctx.tpu_ordinal_function(host_id) def enqueue_ops_fn(): """A Fn returning the TPU infeed enqueue ops. @@ -782,7 +771,7 @@ def generate_per_host_enqueue_ops_fn_for_host( infeed_queue.split_inputs_and_generate_enqueue_ops( unsharded_tensor_list, placement_function=lambda x: device, - tpu_ordinal_function=tpu_ordinal_function)) + tpu_ordinal_function=tpu_ordinal_function_impl)) if signals is None: return per_host_enqueue_ops else: @@ -816,6 +805,7 @@ def generate_per_host_v2_enqueue_ops_fn_for_host( raise TypeError('Most PREDICT not yet supported in PER_HOST_V2 mode.') hooks.append(inputs.dataset_initializer_hook()) + tpu_ordinal_function_impl = ctx.tpu_ordinal_function(host_id) def enqueue_ops_fn(): """Generates the per_host enqueue ops.""" @@ -846,7 +836,7 @@ def generate_per_host_v2_enqueue_ops_fn_for_host( per_host_sharded_inputs) per_host_enqueue_ops = infeed_queue.generate_enqueue_ops( - per_host_sharded_inputs, tpu_ordinal_function=ctx.tpu_ordinal_function) + per_host_sharded_inputs, tpu_ordinal_function=tpu_ordinal_function_impl) return per_host_enqueue_ops return enqueue_ops_fn, captured_infeed_queue, hooks, is_dataset -- GitLab From 1ba31dab88170873f91cb061b3c3c3e932f17f9f Mon Sep 17 00:00:00 2001 From: Guangda Lai Date: Fri, 15 Jun 2018 11:58:06 -0700 Subject: [PATCH 525/816] Add DeviceSet to SingleMachine, so we can use the OptimizeGraph() tool to call tensorrt optimizer (which requires access to the Device) to create a transformed GraphDef. PiperOrigin-RevId: 200751174 --- .../core/common_runtime/graph_execution_state.cc | 9 +-------- tensorflow/core/grappler/clusters/BUILD | 1 + tensorflow/core/grappler/clusters/cluster.h | 3 +-- tensorflow/core/grappler/clusters/single_machine.cc | 9 +++++++++ tensorflow/core/grappler/clusters/single_machine.h | 3 +++ tensorflow/core/grappler/clusters/virtual_cluster.cc | 12 ++++++++---- tensorflow/core/grappler/clusters/virtual_cluster.h | 5 +++-- 7 files changed, 26 insertions(+), 16 deletions(-) diff --git a/tensorflow/core/common_runtime/graph_execution_state.cc b/tensorflow/core/common_runtime/graph_execution_state.cc index eb710bdbc5..58018689d5 100644 --- a/tensorflow/core/common_runtime/graph_execution_state.cc +++ b/tensorflow/core/common_runtime/graph_execution_state.cc @@ -43,7 +43,6 @@ limitations under the License. #include "tensorflow/core/util/util.h" #ifndef IS_MOBILE_PLATFORM -#include "tensorflow/core/grappler/clusters/utils.h" #include "tensorflow/core/grappler/clusters/virtual_cluster.h" #include "tensorflow/core/grappler/grappler_item.h" #include "tensorflow/core/grappler/optimizers/meta_optimizer.h" @@ -476,21 +475,15 @@ Status GraphExecutionState::OptimizeGraph( } } - std::unordered_map device_map; Device* cpu_device = nullptr; for (const auto& device : device_set_->devices()) { - DeviceProperties props = grappler::GetDeviceInfo(device->parsed_name()); - if (props.type() == "UNKNOWN") { - continue; - } - device_map[device->name()] = props; if (device->parsed_name().id == 0 && StringPiece(device->parsed_name().type) == "CPU" && device->GetAllocator(AllocatorAttributes()) != nullptr) { cpu_device = device; } } - grappler::VirtualCluster cluster(device_map, device_set_); + grappler::VirtualCluster cluster(device_set_); GraphDef new_graph; TF_RETURN_IF_ERROR(grappler::RunMetaOptimizer( item, rewrite_options, cpu_device, &cluster, &new_graph)); diff --git a/tensorflow/core/grappler/clusters/BUILD b/tensorflow/core/grappler/clusters/BUILD index d0b2cf01be..ab8f4bebb3 100644 --- a/tensorflow/core/grappler/clusters/BUILD +++ b/tensorflow/core/grappler/clusters/BUILD @@ -77,6 +77,7 @@ cc_library( visibility = ["//visibility:public"], deps = [ ":cluster", + ":utils", "//tensorflow/core:core_cpu_base", "//tensorflow/core:framework", "//tensorflow/core:protos_all_cc", diff --git a/tensorflow/core/grappler/clusters/cluster.h b/tensorflow/core/grappler/clusters/cluster.h index d33aaa7e4c..06db36b3aa 100644 --- a/tensorflow/core/grappler/clusters/cluster.h +++ b/tensorflow/core/grappler/clusters/cluster.h @@ -95,7 +95,7 @@ class Cluster { // The DeviceSet is not always available, but when it is it contains a // superset of the devices listed in GetDevices/GetDeviceNames(). - const DeviceSet* GetDeviceSet() const { return device_set_; } + virtual const DeviceSet* GetDeviceSet() const { return nullptr; } // Enables collecting the allocator stats. Call with enable=true must be made // before Provision(). @@ -124,7 +124,6 @@ class Cluster { protected: std::unordered_map devices_; - const DeviceSet* device_set_ = nullptr; // Not owned const int timeout_s_; SessionOptions options_; RunOptions run_options_; diff --git a/tensorflow/core/grappler/clusters/single_machine.cc b/tensorflow/core/grappler/clusters/single_machine.cc index 313ef90d81..b97603c890 100644 --- a/tensorflow/core/grappler/clusters/single_machine.cc +++ b/tensorflow/core/grappler/clusters/single_machine.cc @@ -368,6 +368,15 @@ Status SingleMachine::ResetSession() { } coordinator_.reset(new Coordinator()); + // Build the DeviceSet. + device_set_.reset(new DeviceSet); + const DeviceMgr* device_mgr; + TF_RETURN_IF_ERROR(session_->LocalDeviceManager(&device_mgr)); + for (auto d : device_mgr->ListDevices()) { + device_set_->AddDevice(d); + // We currently don't care about the client device. + } + return Status::OK(); } diff --git a/tensorflow/core/grappler/clusters/single_machine.h b/tensorflow/core/grappler/clusters/single_machine.h index 0ae188e0d6..c0421dd4de 100644 --- a/tensorflow/core/grappler/clusters/single_machine.h +++ b/tensorflow/core/grappler/clusters/single_machine.h @@ -43,6 +43,8 @@ class SingleMachine : public Cluster { const std::vector>& feed, const std::vector& fetch, RunMetadata* metadata) override; + const DeviceSet* GetDeviceSet() const override { return device_set_.get(); } + Status EnablePeakMemoryStats(bool enable) override; // It requires EnableAllocatorStats(true) be called before Provision(). @@ -73,6 +75,7 @@ class SingleMachine : public Cluster { int64 expected_init_time_s_; std::unique_ptr coordinator_; std::unique_ptr thread_pool_; + std::unique_ptr device_set_; RunMetadata init_metadata_; diff --git a/tensorflow/core/grappler/clusters/virtual_cluster.cc b/tensorflow/core/grappler/clusters/virtual_cluster.cc index 5c9b2320b5..12e3e46f65 100644 --- a/tensorflow/core/grappler/clusters/virtual_cluster.cc +++ b/tensorflow/core/grappler/clusters/virtual_cluster.cc @@ -17,6 +17,7 @@ limitations under the License. #include "tensorflow/core/framework/cost_graph.pb.h" #include "tensorflow/core/framework/tensor_shape.pb.h" #include "tensorflow/core/framework/types.h" +#include "tensorflow/core/grappler/clusters/utils.h" #include "tensorflow/core/grappler/costs/op_level_cost_estimator.h" #include "tensorflow/core/grappler/costs/virtual_scheduler.h" @@ -38,11 +39,14 @@ VirtualCluster::VirtualCluster( devices_ = devices; } -VirtualCluster::VirtualCluster( - const std::unordered_map& devices, - const DeviceSet* device_set) - : VirtualCluster(devices) { +VirtualCluster::VirtualCluster(const DeviceSet* device_set) + : VirtualCluster(std::unordered_map()) { device_set_ = device_set; + for (const auto& device : device_set_->devices()) { + DeviceProperties props = GetDeviceInfo(device->parsed_name()); + if (props.type() == "UNKNOWN") continue; + devices_[device->name()] = props; + } } VirtualCluster::~VirtualCluster() {} diff --git a/tensorflow/core/grappler/clusters/virtual_cluster.h b/tensorflow/core/grappler/clusters/virtual_cluster.h index eebac68e1b..6adb0b99bc 100644 --- a/tensorflow/core/grappler/clusters/virtual_cluster.h +++ b/tensorflow/core/grappler/clusters/virtual_cluster.h @@ -36,8 +36,7 @@ class VirtualCluster : public Cluster { VirtualCluster(const std::unordered_map& devices, OpLevelCostEstimator* node_estimator, ReadyNodeManager* node_manager); - VirtualCluster(const std::unordered_map& devices, - const DeviceSet* device_set); + VirtualCluster(const DeviceSet* device_set); ~VirtualCluster() override; @@ -48,10 +47,12 @@ class VirtualCluster : public Cluster { Status Run(const GraphDef& item, const std::vector>& feed, const std::vector& fetch, RunMetadata* metadata) override; + const DeviceSet* GetDeviceSet() const override { return device_set_; } private: std::unique_ptr node_estimator_; std::unique_ptr node_manager_; + const DeviceSet* device_set_ = nullptr; // Not owned }; } // end namespace grappler -- GitLab From 03e33108f02d93e5a34340aeb00008df66b47a3a Mon Sep 17 00:00:00 2001 From: Dan Moldovan Date: Fri, 15 Jun 2018 12:02:05 -0700 Subject: [PATCH 526/816] Broad refactoring (part 2): Introduce a module dedicated to AutoGraph-specific conversion logic: base converter classes, context objects, gensym. Largely, these are pulled out from impl and pyct. This CL only adds the module - a future CL will replace existing implementations with these. PiperOrigin-RevId: 200751782 --- tensorflow/contrib/autograph/core/BUILD | 59 ++++++ tensorflow/contrib/autograph/core/config.py | 49 +++++ .../contrib/autograph/core/converter.py | 199 ++++++++++++++++++ .../autograph/core/converter_testing.py | 152 +++++++++++++ tensorflow/contrib/autograph/core/naming.py | 130 ++++++++++++ .../contrib/autograph/core/naming_test.py | 77 +++++++ tensorflow/tools/pip_package/BUILD | 1 + 7 files changed, 667 insertions(+) create mode 100644 tensorflow/contrib/autograph/core/BUILD create mode 100644 tensorflow/contrib/autograph/core/config.py create mode 100644 tensorflow/contrib/autograph/core/converter.py create mode 100644 tensorflow/contrib/autograph/core/converter_testing.py create mode 100644 tensorflow/contrib/autograph/core/naming.py create mode 100644 tensorflow/contrib/autograph/core/naming_test.py diff --git a/tensorflow/contrib/autograph/core/BUILD b/tensorflow/contrib/autograph/core/BUILD new file mode 100644 index 0000000000..833f9dced8 --- /dev/null +++ b/tensorflow/contrib/autograph/core/BUILD @@ -0,0 +1,59 @@ +licenses(["notice"]) # Apache 2.0 + +load("//tensorflow:tensorflow.bzl", "py_test") + +filegroup( + name = "all_files", + srcs = glob( + ["**/*"], + exclude = [ + "**/METADATA", + "**/OWNERS", + ], + ), + visibility = ["//tensorflow:__subpackages__"], +) + +py_library( + name = "core", + srcs = [ + "config.py", + "converter.py", + "naming.py", + ], + srcs_version = "PY2AND3", + visibility = ["//tensorflow:__subpackages__"], + deps = [ + "//tensorflow/contrib/autograph/pyct", + "//tensorflow/contrib/autograph/pyct/static_analysis", + "//tensorflow/contrib/autograph/utils", + ], +) + +py_library( + name = "test_lib", + srcs = [ + "converter_testing.py", + ], + srcs_version = "PY2AND3", + visibility = ["//tensorflow:__subpackages__"], + deps = [ + ":core", + "//tensorflow/contrib/autograph/operators", + "//tensorflow/contrib/autograph/pyct", + "//tensorflow/contrib/autograph/pyct/static_analysis", + "//tensorflow/contrib/autograph/utils", + "@gast_archive//:gast", + "@six_archive//:six", + ], +) + +py_test( + name = "naming_test", + srcs = ["naming_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":core", + "//tensorflow/python:client_testlib", + ], +) diff --git a/tensorflow/contrib/autograph/core/config.py b/tensorflow/contrib/autograph/core/config.py new file mode 100644 index 0000000000..878bb7e12f --- /dev/null +++ b/tensorflow/contrib/autograph/core/config.py @@ -0,0 +1,49 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Global configuration.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.autograph import utils + + +PYTHON_LITERALS = { + 'None': None, + 'False': False, + 'True': True, + 'float': float, +} + +DEFAULT_UNCOMPILED_MODULES = set(( + ('tensorflow',), + (utils.__name__,), + + # All of tensorflow's subpackages. Unlike the root tf module, they don't + # have well-known names. Not referring to the module directly to avoid + # circular imports. + ( + utils.__name__[:-len('.contrib.autograph.utils')],), +)) + +NO_SIDE_EFFECT_CONSTRUCTORS = set(('tensorflow',)) + +# TODO(mdan): Also allow controlling the generated names. +# TODO(mdan); Consolidate all internal imports into a single __ag module. +COMPILED_IMPORT_STATEMENTS = ( + 'from __future__ import print_function', + 'import tensorflow as tf', +) diff --git a/tensorflow/contrib/autograph/core/converter.py b/tensorflow/contrib/autograph/core/converter.py new file mode 100644 index 0000000000..5f26e0e1fc --- /dev/null +++ b/tensorflow/contrib/autograph/core/converter.py @@ -0,0 +1,199 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Converter construction support. + +This module contains a base class for all converters, as well as supporting +structures. These structures are referred to as contexts. + +The class hierarchy is as follows: + + + [extends] converter.Base + [extends] transformer.Base + [extends] gast.nodeTransformer + [uses] transfomer.SourceInfo + [uses] converter.EntityContext + [uses] converter.ProgramContext + [uses] transfomer.SourceInfo + +converter.Base is a specialization of transformer.Base for AutoGraph. It's a +very lightweight subclass that adds a `ctx` attribute holding the corresponding +EntityContext object (see below). Note that converters are not reusable, and +`visit` will raise an error if called more than once. + +converter.EntityContext contains mutable state associated with an entity that +the converter processes. + +converter.ProgramContext contains mutable state across related entities. For +example, when converting several functions that call one another, the +ProgramContext should be shared across these entities. + +Below is the overal flow at conversion: + + program_ctx = ProgramContext(, , ...) + while : + entity, source_info = + entity_ctx = EntityContext(program_ctx, source_info) + for : + converter = ConverterClass(entity_ctx) + + # May update entity_ctx and program_ctx + entity = converter.visit(entity) + + +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import collections + +from tensorflow.contrib.autograph.core import config +from tensorflow.contrib.autograph.core import naming +from tensorflow.contrib.autograph.pyct import transformer + +# TODO(mdan): These contexts can be refactored into first class objects. +# For example, we could define Program and Entity abstractions that hold on +# to the actual entity and have conversion methods. + + +class ProgramContext(object): + """ProgramContext keeps track of converting function hierarchies. + + This object is mutable, and is updated during conversion. Not thread safe. + + Attributes: + recursive: bool, whether to recursively convert any functions that the + decorator function may call. + autograph_decorators: Tuple[Callable, ...], decorator functions that belong + to AutoGraph. These require special treatment. + dependency_cache: Dict[Any, ast.AST], the original entities mapped to their + converted AST + additional_imports: Set[Any], additional entities which for any reason + cannot be attached after loading and need to be explicitly imported + in the generated code + name_map: Dict[str, str], map of original entity name to the name of + their converted counterparts + ag_module: Module, a reference to the autograph module. This + needs to be specified by the caller to avoid circular dependencies. + uncompiled_modules: Set[Tuple[str, ...]], with each tuple representing the + fully qualified name of a package containing functions that will not be + compiled. + required_imports: str, containing an import statement on each line. These + are all the imports necessary for the compiled code to run, in addition + to the closures of each entity, which are attached dynamically. + """ + + # TODO(mdan): Rename ag_module to autograph_module? + def __init__( + self, + recursive, + autograph_decorators, + partial_types, + ag_module, + uncompiled_modules, + ): + self.recursive = recursive + self.autograph_decorators = autograph_decorators + self.partial_types = partial_types if partial_types else () + self.ag_module = ag_module + self.uncompiled_modules = uncompiled_modules + + # Required to output dependencies in discovery order, which should match + # the reverse dependency order. + self.dependency_cache = collections.OrderedDict() + self.additional_imports = set() + self.name_map = {} + + @property + def required_imports(self): + """Returns a block containing all imports required by the converted code.""" + # TODO(mdan): Check that these don't clobber one another. + return '\n'.join(config.COMPILED_IMPORT_STATEMENTS + + tuple(self.additional_imports)) + + def new_namer(self, namespace): + return naming.Namer(namespace, self.recursive, self.name_map, + self.partial_types) + + def update_name_map(self, namer): + """Updates renamed_calls based on the recent activity from the namer. + + Whenever we convert a new entity, any references to other entities are being + renamed to match their soon-to-be-converted counterparts. The namer keeps + track of these renames. When conversion is complete, we copy those renames + so that when those referenced entities are being converted, their new name + matches. + + Args: + namer: naming.Namer + + Raises: + ValueError: when an entity was renamed twice and to different names. + """ + # TODO(mdan): Have call_trees do this directly. + # This is done so indirectly, via the namer, for historic reasons. But + # now we can have the converter that does the rename record the new name + # as well and skip this step altogether. + for o, name in namer.renamed_calls.items(): + if o in self.name_map: + if self.name_map[o] != name: + raise ValueError( + 'Calls to %s were converted using multiple names (%s). This is ' + 'possible when an entity with one of these names already ' + 'existed. To fix, avoid using any of these names.' % + (o, (name, self.name_map[o]))) + else: + self.name_map[o] = name + + def add_to_cache(self, original_entity, converted_ast): + self.dependency_cache[original_entity] = converted_ast + + +class EntityContext(object): + """Tracks the conversion of a single entity. + + This object is mutable, and is updated during conversion. Not thread safe. + + Attributes: + namer: Namer + info: transformer.EntityInfo + program: ProgramContext + """ + + def __init__(self, namer, entity_info, program_ctx): + self.namer = namer + self.info = entity_info + self.program = program_ctx + + +class Base(transformer.Base): + """All converters should inherit from this class. + + Attributes: + ctx: EntityContext + """ + + def __init__(self, ctx): + super(Base, self).__init__(ctx.info) + self._used = False + self.ctx = ctx # Keeping this short because it's used frequently. + + def visit(self, node): + if self._used: + raise ValueError('visit may only be called once') + self._used = True + super(Base, self).visit(node) diff --git a/tensorflow/contrib/autograph/core/converter_testing.py b/tensorflow/contrib/autograph/core/converter_testing.py new file mode 100644 index 0000000000..eee51c1f6f --- /dev/null +++ b/tensorflow/contrib/autograph/core/converter_testing.py @@ -0,0 +1,152 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Base class for tests in this module.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import contextlib +import imp + +from tensorflow.contrib.autograph import operators +from tensorflow.contrib.autograph import utils +from tensorflow.contrib.autograph.core import config +from tensorflow.contrib.autograph.core import converter +from tensorflow.contrib.autograph.pyct import compiler +from tensorflow.contrib.autograph.pyct import parser +from tensorflow.contrib.autograph.pyct import pretty_printer +from tensorflow.contrib.autograph.pyct import qual_names +from tensorflow.contrib.autograph.pyct import transformer +from tensorflow.contrib.autograph.pyct.static_analysis import activity +from tensorflow.contrib.autograph.pyct.static_analysis import live_values +from tensorflow.contrib.autograph.pyct.static_analysis import type_info +from tensorflow.python.platform import test + + +def imported_decorator(f): + return lambda a: f(a) + 1 + + +# TODO(mdan): We might be able to use the real namer here. +class FakeNamer(object): + """A fake namer that uses a global counter to generate unique names.""" + + def __init__(self): + self.i = 0 + + def new_symbol(self, name_root, used): + while True: + self.i += 1 + name = '%s%d' % (name_root, self.i) + if name not in used: + return name + + def compiled_function_name(self, + original_fqn, + live_entity=None, + owner_type=None): + del live_entity + if owner_type is not None: + return None, False + return ('renamed_%s' % '_'.join(original_fqn)), True + + +class FakeNoRenameNamer(FakeNamer): + + def compiled_function_name(self, original_fqn, **_): + return str(original_fqn), False + + +class TestCase(test.TestCase): + """Base class for unit tests in this module. Contains relevant utilities.""" + + @contextlib.contextmanager + def compiled(self, node, *symbols): + source = None + + self.dynamic_calls = [] + def converted_call(*args): + """Mock version of api.converted_call.""" + self.dynamic_calls.append(args) + return 7 + + try: + result, source = compiler.ast_to_object(node) + result.tf = self.make_fake_mod('fake_tf', *symbols) + fake_ag = self.make_fake_mod('fake_ag', converted_call) + fake_ag.__dict__.update(operators.__dict__) + fake_ag.__dict__['utils'] = utils + result.__dict__['ag__'] = fake_ag + yield result + except Exception: # pylint:disable=broad-except + if source is None: + print('Offending AST:\n%s' % pretty_printer.fmt(node, color=False)) + else: + print('Offending compiled code:\n%s' % source) + raise + + def make_fake_mod(self, name, *symbols): + fake_mod = imp.new_module(name) + for s in symbols: + if hasattr(s, '__name__'): + setattr(fake_mod, s.__name__, s) + elif hasattr(s, 'name'): + # This is a bit of a hack, but works for things like tf.int32 + setattr(fake_mod, s.name, s) + else: + raise ValueError('can not attach %s - what should be its name?' % s) + return fake_mod + + def attach_namespace(self, module, **ns): + for k, v in ns.items(): + setattr(module, k, v) + + def parse_and_analyze(self, + test_fn, + namespace, + namer=None, + arg_types=None, + include_type_analysis=True, + owner_type=None, + recursive=True, + autograph_decorators=()): + node, source = parser.parse_entity(test_fn) + + if namer is None: + namer = FakeNamer() + program_ctx = converter.ProgramContext( + recursive=recursive, + autograph_decorators=autograph_decorators, + partial_types=None, + ag_module=None, + uncompiled_modules=config.DEFAULT_UNCOMPILED_MODULES) + entity_info = transformer.EntityInfo( + source_code=source, + source_file='', + namespace=namespace, + arg_values=None, + arg_types=arg_types, + owner_type=owner_type) + ctx = converter.EntityContext(namer, entity_info, program_ctx) + + node = qual_names.resolve(node) + node = activity.resolve(node, entity_info) + node = live_values.resolve(node, entity_info, {}) + if include_type_analysis: + node = type_info.resolve(node, entity_info) + node = live_values.resolve(node, entity_info, {}) + self.ctx = ctx + return node diff --git a/tensorflow/contrib/autograph/core/naming.py b/tensorflow/contrib/autograph/core/naming.py new file mode 100644 index 0000000000..b1d3f76be7 --- /dev/null +++ b/tensorflow/contrib/autograph/core/naming.py @@ -0,0 +1,130 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Symbol naming utilities.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.autograph.pyct import qual_names + + +class Namer(object): + """Implementation of the namer interfaces required by various converters. + + This implementation performs additional tasks like keeping track of the + function calls that have been encountered and replaced with calls to their + corresponding compiled counterparts. + + Interfaces currently implemented: + * call_trees.FunctionNamer + * control_flow.SymbolNamer + * side_effect_guards.SymbolNamer + """ + + def __init__(self, global_namespace, recursive, name_map, partial_types): + self.global_namespace = global_namespace + self.recursive = recursive + self.partial_types = partial_types + + self.renamed_calls = {} + if name_map is not None: + self.renamed_calls.update(name_map) + + self.generated_names = set() + + def compiled_class_name(self, original_fqn, live_entity=None): + """See call_trees.FunctionNamer.compiled_class_name.""" + if live_entity is not None and live_entity in self.renamed_calls: + return self.renamed_calls[live_entity] + + if isinstance(original_fqn, tuple): + original_name = '__'.join(original_fqn) + else: + original_name = original_fqn + + new_name_root = 'Tf%s' % original_name + new_name = new_name_root + n = 0 + while new_name in self.global_namespace: + n += 1 + new_name = '%s_%d' % (new_name_root, n) + + self.generated_names.add(new_name) + if live_entity is not None: + self.renamed_calls[live_entity] = new_name + return new_name + + def compiled_function_name(self, + original_fqn, + live_entity=None, + owner_type=None): + """See call_trees.FunctionNamer.compiled_function_name.""" + + if not self.recursive: + return None, False + + if owner_type is not None and owner_type not in self.partial_types: + # Members are not renamed when part of an entire converted class. + return None, False + + if isinstance(original_fqn, tuple): + original_name = '__'.join(original_fqn) + else: + original_name = original_fqn + + if live_entity is not None and live_entity in self.renamed_calls: + return self.renamed_calls[live_entity], True + + new_name_root = 'tf__%s' % original_name + new_name = new_name_root + n = 0 + while new_name in self.global_namespace: + n += 1 + new_name = '%s_%d' % (new_name_root, n) + + if live_entity is not None: + self.renamed_calls[live_entity] = new_name + self.generated_names.add(new_name) + + return new_name, True + + def new_symbol(self, name_root, reserved_locals): + """See control_flow.SymbolNamer.new_symbol.""" + # reserved_locals may contain QNs. + all_reserved_locals = set() + for s in reserved_locals: + if isinstance(s, qual_names.QN): + all_reserved_locals.update(s.qn) + elif isinstance(s, str): + all_reserved_locals.add(s) + else: + raise ValueError('Unexpected symbol type "%s"' % type(s)) + + pieces = name_root.split('_') + if pieces[-1].isdigit(): + name_root = '_'.join(pieces[:-1]) + n = int(pieces[-1]) + else: + n = 0 + new_name = name_root + + while (new_name in self.global_namespace or + new_name in all_reserved_locals or new_name in self.generated_names): + n += 1 + new_name = '%s_%d' % (name_root, n) + + self.generated_names.add(new_name) + return new_name diff --git a/tensorflow/contrib/autograph/core/naming_test.py b/tensorflow/contrib/autograph/core/naming_test.py new file mode 100644 index 0000000000..d2bebd0478 --- /dev/null +++ b/tensorflow/contrib/autograph/core/naming_test.py @@ -0,0 +1,77 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for naming module.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.autograph.core import naming +from tensorflow.python.platform import test + + +class NamerTest(test.TestCase): + + def test_compiled_function_name_tracks_names(self): + def bar(): + pass + + namer = naming.Namer({}, True, None, ()) + self.assertEqual(('tf__foo', True), namer.compiled_function_name('foo')) + self.assertEqual(('tf__bar', True), namer.compiled_function_name( + 'bar', bar)) + self.assertEqual({bar: 'tf__bar'}, namer.renamed_calls) + self.assertItemsEqual(('tf__bar', 'tf__foo'), namer.generated_names) + + def test_compiled_function_name_consistent(self): + def foo(): + pass + + namer = naming.Namer({}, True, None, ()) + self.assertEqual(('tf__foo', True), namer.compiled_function_name( + 'foo', foo)) + self.assertEqual(('tf__foo', True), namer.compiled_function_name( + 'foo', foo)) + + def test_compiled_function_name_avoids_global_conflicts(self): + def foo(): + pass + + namer = naming.Namer({'tf__foo': 1}, True, None, ()) + self.assertEqual(('tf__foo_1', True), + namer.compiled_function_name('foo', foo)) + + def test_new_symbol_tracks_names(self): + namer = naming.Namer({}, True, None, ()) + self.assertEqual('temp', namer.new_symbol('temp', set())) + self.assertItemsEqual(('temp',), namer.generated_names) + + def test_new_symbol_avoids_duplicates(self): + namer = naming.Namer({}, True, None, ()) + self.assertEqual('temp', namer.new_symbol('temp', set())) + self.assertEqual('temp_1', namer.new_symbol('temp', set())) + self.assertItemsEqual(('temp', 'temp_1'), namer.generated_names) + + def test_new_symbol_avoids_conflicts(self): + namer = naming.Namer({'temp': 1}, True, None, ()) + # temp is reserved in the global namespace + self.assertEqual('temp_1', namer.new_symbol('temp', set())) + # temp_2 is reserved in the local namespace + self.assertEqual('temp_3', namer.new_symbol('temp', set(('temp_2',)))) + self.assertItemsEqual(('temp_1', 'temp_3'), namer.generated_names) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD index b228ff5a21..b9e1a61d5d 100644 --- a/tensorflow/tools/pip_package/BUILD +++ b/tensorflow/tools/pip_package/BUILD @@ -58,6 +58,7 @@ COMMON_PIP_DEPS = [ "//tensorflow/contrib/autograph:autograph", "//tensorflow/contrib/autograph/converters:converters", "//tensorflow/contrib/autograph/converters:test_lib", + "//tensorflow/contrib/autograph/core:core", "//tensorflow/contrib/autograph/impl:impl", "//tensorflow/contrib/autograph/lang:lang", "//tensorflow/contrib/autograph/pyct:pyct", -- GitLab From 64a81c5df82c30bb39de7636b4d97f637a535c36 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 15 Jun 2018 12:10:05 -0700 Subject: [PATCH 527/816] Reuse duplicated reference ops in optimized_ops.h PiperOrigin-RevId: 200753184 --- .../internal/optimized/optimized_ops.h | 407 +----------------- .../internal/reference/reference_ops.h | 10 +- 2 files changed, 19 insertions(+), 398 deletions(-) diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h index d0008cc4fb..cf989ce51d 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h @@ -40,16 +40,29 @@ namespace tflite { namespace optimized_ops { // Unoptimized reference ops: +using reference_ops::ArgMax; using reference_ops::BroadcastGreater; using reference_ops::BroadcastGreaterEqual; using reference_ops::BroadcastLess; using reference_ops::BroadcastLessEqual; +using reference_ops::Concatenation; +using reference_ops::DepthConcatenation; +using reference_ops::Dequantize; +using reference_ops::Div; +using reference_ops::FakeQuant; +using reference_ops::Gather; using reference_ops::Greater; using reference_ops::GreaterEqual; using reference_ops::Less; using reference_ops::LessEqual; +using reference_ops::Mean; using reference_ops::RankOneSelect; +using reference_ops::Relu1; +using reference_ops::Relu6; using reference_ops::Select; +using reference_ops::SpaceToBatchND; +using reference_ops::StridedSlice; +using reference_ops::Transpose; // TODO(b/80247582) Remove this constant. // This will be phased out as the shifts are revised with more thought. Use of a @@ -2339,32 +2352,6 @@ inline void Relu(const float* input_data, const Dims<4>& input_dims, output = input.cwiseMax(0.0f); } -inline void Relu1(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("Relu1 (not fused)"); - const int flat_size = MatchingFlatSize(input_dims, output_dims); - for (int i = 0; i < flat_size; ++i) { - const float val = input_data[i]; - const float upper = 1; - const float lower = -1; - const float clamped = val > upper ? upper : val < lower ? lower : val; - output_data[i] = clamped; - } -} - -inline void Relu6(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("Relu6 (not fused)"); - const int flat_size = MatchingFlatSize(input_dims, output_dims); - for (int i = 0; i < flat_size; ++i) { - const float val = input_data[i]; - const float upper = 6; - const float lower = 0; - const float clamped = val > upper ? upper : val < lower ? lower : val; - output_data[i] = clamped; - } -} - template void L2Normalization(const float* input_data, const RuntimeShape& input_shape, float* output_data, const RuntimeShape& output_shape) { @@ -3215,19 +3202,6 @@ inline void BroadcastMul(const uint8* input1_data, const Dims<4>& input1_dims, output_data, output_dims); } -// TODO(aselle): This is not actually optimized yet. -inline void Div(const float* input1_data, const Dims<4>& input1_dims, - const float* input2_data, const Dims<4>& input2_dims, - float output_activation_min, float output_activation_max, - float* output_data, const Dims<4>& output_dims) { - const int flat_size = MatchingFlatSize(output_dims, input1_dims, input2_dims); - for (int i = 0; i < flat_size; i++) { - output_data[i] = ActivationFunctionWithMinMax( - input1_data[i] / input2_data[i], output_activation_min, - output_activation_max); - } -} - // TODO(jiawen): We can implement BroadcastDiv on buffers of arbitrary // dimensionality if the runtime code does a single loop over one dimension // that handles broadcasting as the base case. The code generator would then @@ -3393,105 +3367,6 @@ inline void BroadcastSub(int left_shift, const uint8* input1_data, } } -template -void Concatenation(int concat_dim, const Scalar* const* input_data, - const Dims<4>* const* input_dims, int inputs_count, - Scalar* output_data, const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("Concatenation"); - int concat_size = 0; - for (int i = 0; i < inputs_count; i++) { - for (int j = 0; j < 4; j++) { - if (j != concat_dim) { - MatchingArraySize(*input_dims[i], j, output_dims, j); - } - } - concat_size += ArraySize(*input_dims[i], concat_dim); - } - TFLITE_DCHECK_EQ(concat_size, ArraySize(output_dims, concat_dim)); - TFLITE_DCHECK(IsPackedWithoutStrides(output_dims)); - // for now we dont have a model with a Concatenation - // with fused activation function. - TFLITE_DCHECK(Ac == FusedActivationFunctionType::kNone); - int outer_size = 1; - for (int i = concat_dim + 1; i < 4; i++) { - outer_size *= output_dims.sizes[i]; - } - Scalar* output_ptr = output_data; - for (int k = 0; k < outer_size; k++) { - for (int i = 0; i < inputs_count; ++i) { - const int copy_size = - input_dims[i]->sizes[concat_dim] * input_dims[i]->strides[concat_dim]; - memcpy(output_ptr, input_data[i] + k * copy_size, - copy_size * sizeof(Scalar)); - output_ptr += copy_size; - } - } -} - -// TODO(prabhumk): This is the same as the reference implementation. -// TODO(prabhumk): The quantized implementation of concatentation isn't fully -// quantized as it takes scale as a floating point value. This should be fixed -// when optimizng this routine further. -inline void Concatenation(int concat_dim, const uint8* const* input_data, - const Dims<4>* const* input_dims, - const int32* input_zeropoint, - const float* input_scale, int inputs_count, - uint8* output_data, const Dims<4>& output_dims, - const int32 output_zeropoint, - const float output_scale) { - // The arguments input_zeropoint and input_scale are expected to be an array - // that have the quantization parameters for all the inputs to the concat - // operator. - gemmlowp::ScopedProfilingLabel label("Concatenation"); - TFLITE_DCHECK_GT(inputs_count, 1); - int concat_size = 0; - for (int i = 0; i < inputs_count; i++) { - for (int j = 0; j < 4; j++) { - if (j != concat_dim) { - MatchingArraySize(*input_dims[i], j, output_dims, j); - } - } - concat_size += ArraySize(*input_dims[i], concat_dim); - } - TFLITE_DCHECK_EQ(concat_size, ArraySize(output_dims, concat_dim)); - int outer_size = 1; - for (int i = concat_dim + 1; i < 4; i++) { - outer_size *= output_dims.sizes[i]; - } - const float inverse_output_scale = 1.f / output_scale; - uint8* output_ptr = output_data; - for (int k = 0; k < outer_size; k++) { - for (int i = 0; i < inputs_count; ++i) { - const int copy_size = - input_dims[i]->sizes[concat_dim] * input_dims[i]->strides[concat_dim]; - const uint8* input_ptr = input_data[i] + k * copy_size; - if (input_zeropoint[i] == output_zeropoint && - input_scale[i] == output_scale) { - memcpy(output_ptr, input_ptr, copy_size); - } else { - const float scale = input_scale[i] * inverse_output_scale; - const float bias = -input_zeropoint[i] * scale; - for (int j = 0; j < copy_size; ++j) { - const int32_t value = - static_cast(round(input_ptr[j] * scale + bias)) + - output_zeropoint; - output_ptr[j] = - static_cast(std::max(std::min(255, value), 0)); - } - } - output_ptr += copy_size; - } - } -} - -template -void DepthConcatenation(const Scalar* const* input_data, - const Dims<4>* const* input_dims, int inputs_count, - Scalar* output_data, const Dims<4>& output_dims) { - Concatenation(0, input_data, input_dims, inputs_count, - output_data, output_dims); -} - inline void LstmCell(const float* input_data, const Dims<4>& input_dims, const float* prev_activ_data, const Dims<4>& prev_activ_dims, const float* weights_data, @@ -5322,49 +5197,6 @@ inline void Tanh(const int16* input_data, const Dims<4>& input_dims, } } -inline void Dequantize(const uint8* input_data, const Dims<4>& input_dims, - int32 zero_point, double scale, float* output_data, - const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("Dequantize"); - const int flat_size = MatchingFlatSize(output_dims, input_dims); - for (int i = 0; i < flat_size; ++i) { - int32 val = input_data[i]; - float result = static_cast(scale * (val - zero_point)); - output_data[i] = result; - } -} - -inline void FakeQuant(const float* input_data, const Dims<4>& input_dims, - float rmin, float rmax, int num_bits, float* output_data, - const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("FakeQuant"); - - // 0 should always be a representable value. Let's assume that the initial - // min,max range contains 0. - TFLITE_DCHECK_LE(rmin, 0.0f); - TFLITE_DCHECK_GE(rmax, 0.0f); - TFLITE_DCHECK_LT(rmin, rmax); - - // Code matches tensorflow's FakeQuantWithMinMaxArgsFunctor. - int quant_min = 0; - int quant_max = (1 << num_bits) - 1; - float nudged_min, nudged_max, nudged_scale; - NudgeQuantizationRange(rmin, rmax, quant_min, quant_max, &nudged_min, - &nudged_max, &nudged_scale); - const float inv_nudged_scale = 1.0f / nudged_scale; - - const int flat_size = MatchingFlatSize(output_dims, input_dims); - for (int i = 0; i < flat_size; ++i) { - const float src_val = input_data[i]; - const float clamped = std::min(nudged_max, std::max(nudged_min, src_val)); - const float clamped_shifted = clamped - nudged_min; - const float dst_val = - TfLiteRound(clamped_shifted * inv_nudged_scale) * nudged_scale + - nudged_min; - output_data[i] = dst_val; - } -} - template inline void Cast(const SrcT* input_data, const Dims<4>& input_dims, DstT* output_data, const Dims<4>& output_dims) { @@ -5382,26 +5214,6 @@ inline void Floor(const float* input_data, const Dims<4>& input_dims, output_map.array() = Eigen::floor(input_map.array()); } -template -inline void Gather(const T* input_data, const Dims<4>& input_dims, - int input_rank, const int32* coords_data, - const Dims<4>& coords_dims, T* output_data, - const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("Gather"); - - TFLITE_DCHECK(coords_dims.sizes[0] == output_dims.sizes[input_rank - 1]); - int stride = input_dims.strides[input_rank - 1]; - T* out = output_data; - - for (int i = 0; i < coords_dims.sizes[0]; i++) { - TFLITE_DCHECK_GE(coords_data[i], 0); - TFLITE_DCHECK_LT(coords_data[i], input_dims.sizes[input_rank - 1]); - const T* in = input_data + coords_data[i] * stride; - memcpy(out, in, sizeof(T) * stride); - out += stride; - } -} - #ifdef USE_NEON inline void ResizeBilinearKernel(const float* input_ptr, int32 depth, float scale, float* output_ptr) { @@ -5863,55 +5675,6 @@ inline void ResizeBilinear(const uint8* input_data, const Dims<4>& input_dims, output_data, output_dims, /*align_corners=*/false); } -template -inline void SpaceToBatchND(const T* input_data, const Dims<4>& input_dims, - const int32* block_shape_data, - const Dims<4>& block_shape_dims, - const int32* paddings_data, - const Dims<4>& paddings_dims, T* output_data, - const Dims<4>& output_dims) { - // Unoptimized - Straight copy from reference ops. - gemmlowp::ScopedProfilingLabel label("SpaceToBatchND"); - - const int output_batch_size = ArraySize(output_dims, 3); - const int output_height = ArraySize(output_dims, 2); - const int output_width = ArraySize(output_dims, 1); - const int input_batch_size = ArraySize(input_dims, 3); - const int input_height = ArraySize(input_dims, 2); - const int input_width = ArraySize(input_dims, 1); - const int depth = ArraySize(input_dims, 0); - const int block_shape_height = block_shape_data[0]; - const int block_shape_width = block_shape_data[1]; - const int padding_top = paddings_data[0]; - const int padding_left = paddings_data[2]; - - for (int out_b = 0; out_b < output_batch_size; ++out_b) { - int input_batch = out_b % input_batch_size; - int shift_w = (out_b / input_batch_size) % block_shape_width; - int shift_h = (out_b / input_batch_size) / block_shape_width; - for (int out_h = 0; out_h < output_height; ++out_h) { - for (int out_w = 0; out_w < output_width; ++out_w) { - T* out = output_data + Offset(output_dims, 0, out_w, out_h, out_b); - if (out_h * block_shape_height + shift_h < padding_top || - out_h * block_shape_height + shift_h >= - padding_top + input_height || - out_w * block_shape_width + shift_w < padding_left || - out_w * block_shape_width + shift_w >= padding_left + input_width) { - memset(out, 0, depth * sizeof(T)); - } else { - const T* in = - input_data + - Offset(input_dims, 0, - (out_w * block_shape_width + shift_w) - padding_left, - (out_h * block_shape_height + shift_h) - padding_top, - input_batch); - memcpy(out, in, depth * sizeof(T)); - } - } - } - } -} - // Helper methods for BatchToSpaceND. // `spatial_index_dim` specifies post-crop offset index in this spatial // dimension, i.e. spatial offset introduced by flattening batch to spatial @@ -6114,54 +5877,6 @@ inline void Pad(const T* input_data, const Dims<4>& input_dims, output_dims, 0); } -// UNOPTIMIZED COPY of StridedSlice from reference_ops.h. -template -inline void StridedSlice(const T* input_data, const Dims<4>& input_dims, - int begin_mask, int end_mask, - const std::vector& start_indices, - const std::vector& stop_indices, - const std::vector& strides, T* output_data, - const Dims<4>& output_dims) { - TFLITE_DCHECK_EQ(start_indices.size(), 4); - TFLITE_DCHECK_EQ(stop_indices.size(), 4); - TFLITE_DCHECK_EQ(strides.size(), 4); - const int start_b = strided_slice::StartForAxis(begin_mask, start_indices, - strides, input_dims.sizes, 3); - const int stop_b = strided_slice::StopForAxis(end_mask, stop_indices, strides, - input_dims.sizes, 3); - const int start_h = strided_slice::StartForAxis(begin_mask, start_indices, - strides, input_dims.sizes, 2); - const int stop_h = strided_slice::StopForAxis(end_mask, stop_indices, strides, - input_dims.sizes, 2); - const int start_w = strided_slice::StartForAxis(begin_mask, start_indices, - strides, input_dims.sizes, 1); - const int stop_w = strided_slice::StopForAxis(end_mask, stop_indices, strides, - input_dims.sizes, 1); - const int start_d = strided_slice::StartForAxis(begin_mask, start_indices, - strides, input_dims.sizes, 0); - const int stop_d = strided_slice::StopForAxis(end_mask, stop_indices, strides, - input_dims.sizes, 0); - - T* out_ptr = output_data; - for (int in_b = start_b; - !strided_slice::LoopCondition(in_b, stop_b, strides[3]); - in_b += strides[3]) { - for (int in_h = start_h; - !strided_slice::LoopCondition(in_h, stop_h, strides[2]); - in_h += strides[2]) { - for (int in_w = start_w; - !strided_slice::LoopCondition(in_w, stop_w, strides[1]); - in_w += strides[1]) { - for (int in_d = start_d; - !strided_slice::LoopCondition(in_d, stop_d, strides[0]); - in_d += strides[0]) { - *out_ptr++ = input_data[Offset(input_dims, in_d, in_w, in_h, in_b)]; - } - } - } - } -} - template inline void Slice(const T* input_data, const Dims<4>& input_dims, const std::vector& begin, const std::vector& size, @@ -6196,41 +5911,6 @@ inline void Slice(const T* input_data, const Dims<4>& input_dims, } } -template -inline void Mean(const T* input_data, const Dims<4>& input_dims, - const std::vector& reduction_indices, T* output_data, - const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("Mean"); - const int output_batch = ArraySize(output_dims, 3); - const int output_height = ArraySize(output_dims, 2); - const int output_width = ArraySize(output_dims, 1); - const int output_depth = ArraySize(output_dims, 0); - - const int input_height = ArraySize(input_dims, 2); - const int input_width = ArraySize(input_dims, 1); - - // The current implementation only supports simultaneous reduction over - // width and height. - TFLITE_DCHECK_EQ(reduction_indices.size(), 2); - TFLITE_DCHECK((reduction_indices[0] == 1 && reduction_indices[1] == 2) || - (reduction_indices[0] == 2 && reduction_indices[1] == 1)); - TFLITE_DCHECK_EQ(output_height, 1); - TFLITE_DCHECK_EQ(output_width, 1); - - for (int out_b = 0; out_b < output_batch; ++out_b) { - for (int out_d = 0; out_d < output_depth; ++out_d) { - float value = 0; - for (int in_h = 0; in_h < input_height; ++in_h) { - for (int in_w = 0; in_w < input_width; ++in_w) { - value += input_data[Offset(input_dims, out_d, in_w, in_h, out_b)]; - } - } - output_data[Offset(output_dims, out_d, 0, 0, out_b)] = - value / (input_width * input_height); - } - } -} - template void GenericBroadcastSub(const T* input1_data, const Dims<4>& input1_dims, const T* input2_data, const Dims<4>& input2_dims, @@ -6310,67 +5990,6 @@ void TensorFlowMaximum(const T* input1_data, const Dims<4>& input1_dims, output_map.array() = input1_map.array().max(max_value); } -template -void ArgMax(const T3* axis, const T1* input_data, const Dims<4>& input_dims, - T2* output_data, const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("ArgMax"); - - // The current ArgMax implemention can only determine the index of the maximum - // value in the last dimension. So the axis argument is ignored. - - // For ArgMax, the number of output dimensions = (number of input dimensions - - // 1). For the sake of simplicity, the output dimensions are equal to the - // input dimensions here. We enforce the constraint that the last dimension - // must always be 1. - TFLITE_DCHECK_EQ(ArraySize(output_dims, 0), 1); - const int outer_size = MatchingFlatSizeSkipDim(input_dims, 0, output_dims); - const int depth = ArraySize(input_dims, 0); - for (int i = 0; i < outer_size; ++i) { - auto max_value = *input_data; - ++input_data; - int max_index = 0; - for (int d = 1; d < depth; ++d) { - const auto& curr_value = *input_data; - if (curr_value > max_value) { - max_value = curr_value; - max_index = d; - } - ++input_data; - } - *output_data = max_index; - ++output_data; - } -} - -template -void Transpose(const T* input, const Dims<4>& input_dims, T* output, - const Dims<4>& output_dims, const int* permuted_axes) { - int out_sizes[4]; - // Compute the inverse permutation array so we can do an output centered - // transpose. Also, check to make sure output_dims is matching input_dims. - for (int k = 0; k < 4; k++) { - out_sizes[k] = - MatchingArraySize(input_dims, permuted_axes[k], output_dims, k); - } - - // Naive transpose loop (iterate on output index and compute input index). - int o[4]; // loop index (on output). - int i[4]; - for (o[3] = 0; o[3] < out_sizes[3]; o[3]++) { - i[permuted_axes[3]] = o[3]; - for (o[2] = 0; o[2] < out_sizes[2]; o[2]++) { - i[permuted_axes[2]] = o[2]; - for (o[1] = 0; o[1] < out_sizes[1]; o[1]++) { - i[permuted_axes[1]] = o[1]; - for (o[0] = 0; o[0] < out_sizes[0]; o[0]++) { - i[permuted_axes[0]] = o[0]; - output[Offset(output_dims, o)] = input[Offset(input_dims, i)]; - } - } - } - } -} - template void TransposeIm2col(const T* input_data, const Dims<4>& input_dims, const Dims<4>& filter_dims, int stride_width, diff --git a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h index 66dcb6a55a..febd9c5fbc 100644 --- a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h @@ -1755,7 +1755,6 @@ template void Concatenation(int concat_dim, const Scalar* const* input_data, const Dims<4>* const* input_dims, int inputs_count, Scalar* output_data, const Dims<4>& output_dims) { - TFLITE_DCHECK_GT(inputs_count, 1); int concat_size = 0; for (int i = 0; i < inputs_count; i++) { for (int j = 0; j < 4; j++) { @@ -1766,7 +1765,9 @@ void Concatenation(int concat_dim, const Scalar* const* input_data, concat_size += ArraySize(*input_dims[i], concat_dim); } TFLITE_DCHECK_EQ(concat_size, ArraySize(output_dims, concat_dim)); - TFLITE_DCHECK(Ac == FusedActivationFunctionType::kNone); + TFLITE_DCHECK(IsPackedWithoutStrides(output_dims)); + // For now we don't have a model with a Concatenation with fused activation. + TFLITE_DCHECK_EQ(Ac, FusedActivationFunctionType::kNone); int outer_size = 1; for (int i = concat_dim + 1; i < 4; i++) { outer_size *= output_dims.sizes[i]; @@ -3794,7 +3795,7 @@ void ArgMax(const T3* axis, const T1* input_data, const Dims<4>& input_dims, template void Transpose(const T* input, const Dims<4>& input_dims, T* output, - const Dims<4>& output_dims, int* permuted_axes) { + const Dims<4>& output_dims, const int* permuted_axes) { int out_sizes[4]; // Compute the inverse permutation array so we can do an output centered // transpose. Also, check to make sure output_dims is matching input_dims. @@ -3844,7 +3845,8 @@ inline void TransposeConv(const float* input_data, const Dims<4>& input_dims, // computing their influence on the output, rather than looping through the // output elements in the typical "gather" access pattern of a conv. We // therefore must initialize the output array to zero. - for (int i = 0; i < FlatSize(output_dims); i++) { + const int num_elements = FlatSize(output_dims); + for (int i = 0; i < num_elements; i++) { output_data[i] = 0.0f; } -- GitLab From d09b1ebe4188c1b8089806336895907439fe5ee2 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 15 Jun 2018 12:25:14 -0700 Subject: [PATCH 528/816] Fix segfault in ConstantFolding::MaterializeShapes when the first input to TensorArraySizeV3 is a Placeholder. PiperOrigin-RevId: 200755274 --- .../grappler/optimizers/constant_folding.cc | 11 +++++++---- .../optimizers/constant_folding_test.cc | 18 +++++++++++++----- 2 files changed, 20 insertions(+), 9 deletions(-) diff --git a/tensorflow/core/grappler/optimizers/constant_folding.cc b/tensorflow/core/grappler/optimizers/constant_folding.cc index f4b384ec1e..76c928f995 100644 --- a/tensorflow/core/grappler/optimizers/constant_folding.cc +++ b/tensorflow/core/grappler/optimizers/constant_folding.cc @@ -354,12 +354,14 @@ Status ConstantFolding::MaterializeShapes(const GraphProperties& properties) { } if (op == "TensorArraySizeV3") { - const NodeDef* array = node_map_->GetNode(node->input(0)); - if (array->attr().count("dynamic_size") != 0 && - array->attr().at("dynamic_size").b()) { + const NodeDef* array = CHECK_NOTNULL(node_map_->GetNode(node->input(0))); + if (array->input_size() == 0 || + (array->attr().count("dynamic_size") != 0 && + array->attr().at("dynamic_size").b())) { continue; } - const NodeDef* array_size = node_map_->GetNode(array->input(0)); + const NodeDef* array_size = + CHECK_NOTNULL(node_map_->GetNode(array->input(0))); if (IsReallyConstant(*array_size)) { // Don't materialize 0 sizes to avoid triggering incorrect static // checks. A 0 sized array that can't grow isn't useful anyway. @@ -374,6 +376,7 @@ Status ConstantFolding::MaterializeShapes(const GraphProperties& properties) { if (value.flat()(0) == 0) { continue; } + node->set_op("Const"); *node->mutable_attr() = array_size->attr(); node->set_input(0, AsControlDependency(NodeName(node->input(0)))); diff --git a/tensorflow/core/grappler/optimizers/constant_folding_test.cc b/tensorflow/core/grappler/optimizers/constant_folding_test.cc index 9f051ca248..b9765b9292 100644 --- a/tensorflow/core/grappler/optimizers/constant_folding_test.cc +++ b/tensorflow/core/grappler/optimizers/constant_folding_test.cc @@ -3000,6 +3000,10 @@ TEST_F(ConstantFoldingTest, Enter) { TEST_F(ConstantFoldingTest, TensorArraySize) { tensorflow::Scope scope = tensorflow::Scope::NewRootScope(); Output size = ops::Const(scope.WithOpName("size"), 5, TensorShape({})); + Output placeholder = + ops::Placeholder(scope.WithOpName("placeholder"), DT_RESOURCE, + ops::Placeholder::Shape(TensorShape({2}))); + Output foo = ops::Const(scope.WithOpName("foo"), 5.0f, TensorShape({})); auto dynamic_array = ops::TensorArray(scope.WithOpName("dynamic"), size, DT_FLOAT, ops::TensorArray::DynamicSize(true)); @@ -3010,6 +3014,8 @@ TEST_F(ConstantFoldingTest, TensorArraySize) { scope.WithOpName("dynamic_sz"), dynamic_array.handle, dynamic_array.flow); auto static_sz = ops::TensorArraySize(scope.WithOpName("static_sz"), static_array.handle, static_array.flow); + auto placeholder_sz = ops::TensorArraySize(scope.WithOpName("placeholder_sz"), + placeholder, foo); GrapplerItem item; TF_CHECK_OK(scope.ToGraphDef(&item.graph)); @@ -3026,11 +3032,13 @@ TEST_F(ConstantFoldingTest, TensorArraySize) { status = optimizer.Optimize(nullptr, item, &output); TF_EXPECT_OK(status); - EXPECT_EQ(5, output.node_size()); - EXPECT_EQ("dynamic_sz", output.node(3).name()); - EXPECT_EQ("TensorArraySizeV3", output.node(3).op()); - EXPECT_EQ("static_sz", output.node(4).name()); - EXPECT_EQ("Const", output.node(4).op()); + EXPECT_EQ(8, output.node_size()); + EXPECT_EQ("dynamic_sz", output.node(5).name()); + EXPECT_EQ("TensorArraySizeV3", output.node(5).op()); + EXPECT_EQ("static_sz", output.node(6).name()); + EXPECT_EQ("Const", output.node(6).op()); + EXPECT_EQ("placeholder_sz", output.node(7).name()); + EXPECT_EQ("TensorArraySizeV3", output.node(7).op()); auto tensors_actual = EvaluateNodes(output, {"dynamic_sz", "static_sz"}); EXPECT_EQ(2, tensors_expected.size()); -- GitLab From d07d47dc9545348be96a9d84126c5fb0c89263c9 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 15 Jun 2018 12:33:19 -0700 Subject: [PATCH 529/816] Provides a more fine-grained option for each thread to control fork-join parallelism (e.g., Eigen/ThreadpoolDevice or Shard). PiperOrigin-RevId: 200756626 --- tensorflow/core/BUILD | 1 + tensorflow/core/framework/device_base.cc | 33 +++++++++- tensorflow/core/framework/device_base.h | 15 +++-- tensorflow/core/framework/device_base_test.cc | 62 +++++++++++++++++++ tensorflow/core/util/work_sharder.cc | 10 +++ tensorflow/core/util/work_sharder.h | 31 ++++++++++ tensorflow/core/util/work_sharder_test.cc | 17 ++++- 7 files changed, 158 insertions(+), 11 deletions(-) create mode 100644 tensorflow/core/framework/device_base_test.cc diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index cdceccb106..d89633199d 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -3365,6 +3365,7 @@ tf_cc_tests( "framework/bfloat16_test.cc", "framework/cancellation_test.cc", "framework/common_shape_fns_test.cc", + "framework/device_base_test.cc", "framework/function_test.cc", "framework/graph_def_util_test.cc", "framework/graph_to_functiondef_test.cc", diff --git a/tensorflow/core/framework/device_base.cc b/tensorflow/core/framework/device_base.cc index e30ee84cc3..9108c32942 100644 --- a/tensorflow/core/framework/device_base.cc +++ b/tensorflow/core/framework/device_base.cc @@ -13,11 +13,17 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ +#define EIGEN_USE_THREADS + #include "tensorflow/core/framework/device_base.h" +#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" +#include "tensorflow/core/lib/gtl/stl_util.h" +#include "tensorflow/core/util/work_sharder.h" + namespace tensorflow { -DeviceBase::~DeviceBase() {} +DeviceBase::~DeviceBase() { gtl::STLDeleteElements(&eigen_cpu_devices_); } const DeviceAttributes& DeviceBase::attributes() const { LOG(FATAL) << "Device does not implement attributes()"; @@ -27,4 +33,29 @@ const string& DeviceBase::name() const { LOG(FATAL) << "Device does not implement name()"; } +void DeviceBase::set_eigen_cpu_device(Eigen::ThreadPoolDevice* d) { + // Eigen::ThreadPoolDevice is a very cheap struct (one pointer and + // an int). Therefore, we can afford a pre-allocated array of + // Eigen::ThreadPoolDevice. Here, we ensure that + // Eigen::ThreadPoolDevices in eigen_cpu_devices_ has increasingly + // larger numThreads. + for (int i = 1; i <= d->numThreads(); ++i) { + eigen_cpu_devices_.push_back( + new Eigen::ThreadPoolDevice(d->getPool(), i /* numThreads() */)); + } +} + +const Eigen::ThreadPoolDevice* DeviceBase::eigen_cpu_device() { + // Based on GetPerThreadMaxParallelism(), we return a different + // pre-allocated Eigen::ThreadPoolDevice. All these ThreadPoolDevice + // use the same underlying threadpool. But they use different + // nominal numThreads() hoping that the user of the returned + // Eigen::ThreadPoolDevice may not aggressively occupy all the + // threads in the underlying threadpool. + const int parallelism = std::max( + 1, + std::min(GetPerThreadMaxParallelism(), eigen_cpu_devices_.size())); + return eigen_cpu_devices_[parallelism - 1]; +} + } // namespace tensorflow diff --git a/tensorflow/core/framework/device_base.h b/tensorflow/core/framework/device_base.h index ec26d92a61..922d34fac9 100644 --- a/tensorflow/core/framework/device_base.h +++ b/tensorflow/core/framework/device_base.h @@ -18,7 +18,7 @@ limitations under the License. #include #include -#include +#include #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/lib/core/errors.h" @@ -154,9 +154,7 @@ class DeviceBase { } // Does not take ownership. - void set_eigen_cpu_device(Eigen::ThreadPoolDevice* d) { - eigen_cpu_device_ = d; - } + void set_eigen_cpu_device(Eigen::ThreadPoolDevice* d); #ifdef TENSORFLOW_USE_SYCL void set_eigen_sycl_device(Eigen::SyclDevice* d) { eigen_sycl_device_ = d; } @@ -186,11 +184,12 @@ class DeviceBase { virtual ScopedAllocatorMgr* GetScopedAllocatorMgr() const { return nullptr; } - virtual const Eigen::ThreadPoolDevice* eigen_cpu_device() { - CHECK(eigen_cpu_device_ != nullptr); - return eigen_cpu_device_; + const bool has_eigen_cpu_device() const { + return !eigen_cpu_devices_.empty(); } + virtual const Eigen::ThreadPoolDevice* eigen_cpu_device(); + #ifdef TENSORFLOW_USE_SYCL virtual const Eigen::SyclDevice* eigen_sycl_device() const { CHECK(eigen_sycl_device_ != nullptr); @@ -242,7 +241,7 @@ class DeviceBase { // Set by GPUs as well as by TPU devices. GpuDeviceInfo* gpu_device_info_ = nullptr; thread::ThreadPool* device_thread_pool_ = nullptr; - Eigen::ThreadPoolDevice* eigen_cpu_device_ = nullptr; + std::vector eigen_cpu_devices_; #ifdef TENSORFLOW_USE_SYCL Eigen::SyclDevice* eigen_sycl_device_ = nullptr; #endif diff --git a/tensorflow/core/framework/device_base_test.cc b/tensorflow/core/framework/device_base_test.cc new file mode 100644 index 0000000000..6909559ea2 --- /dev/null +++ b/tensorflow/core/framework/device_base_test.cc @@ -0,0 +1,62 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#define EIGEN_USE_THREADS + +#include "tensorflow/core/framework/device_base.h" + +#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" +#include "tensorflow/core/common_runtime/eigen_thread_pool.h" +#include "tensorflow/core/lib/core/threadpool.h" +#include "tensorflow/core/platform/env.h" +#include "tensorflow/core/platform/test.h" +#include "tensorflow/core/util/work_sharder.h" + +namespace tensorflow { + +TEST(DeviceBaseTest, CpuDevice) { + DeviceBase dbase(Env::Default()); + thread::ThreadPool pool(Env::Default(), "test", 16); + EigenThreadPoolWrapper wrapper(&pool); + Eigen::ThreadPoolDevice eigen_device(&wrapper, pool.NumThreads()); + ASSERT_FALSE(dbase.has_eigen_cpu_device()); + dbase.set_eigen_cpu_device(&eigen_device); + ASSERT_TRUE(dbase.has_eigen_cpu_device()); + + { + auto d = dbase.eigen_cpu_device(); + EXPECT_EQ(d->numThreads(), 16); + } + + { + ScopedPerThreadMaxParallelism maxp(4); + auto d = dbase.eigen_cpu_device(); + EXPECT_EQ(d->numThreads(), 4); + } + + { + ScopedPerThreadMaxParallelism maxp(1); + auto d = dbase.eigen_cpu_device(); + EXPECT_EQ(d->numThreads(), 1); + } + + { + ScopedPerThreadMaxParallelism maxp(1000); + auto d = dbase.eigen_cpu_device(); + EXPECT_EQ(d->numThreads(), 16); + } +} + +} // namespace tensorflow diff --git a/tensorflow/core/util/work_sharder.cc b/tensorflow/core/util/work_sharder.cc index 337af07b50..b443bcfa79 100644 --- a/tensorflow/core/util/work_sharder.cc +++ b/tensorflow/core/util/work_sharder.cc @@ -20,12 +20,22 @@ limitations under the License. namespace tensorflow { +/* ABSL_CONST_INIT */ thread_local int per_thread_max_parallism = 1000000; + +void SetPerThreadMaxParallelism(int max_parallelism) { + CHECK_LE(0, max_parallelism); + per_thread_max_parallism = max_parallelism; +} + +int GetPerThreadMaxParallelism() { return per_thread_max_parallism; } + void Shard(int max_parallelism, thread::ThreadPool* workers, int64 total, int64 cost_per_unit, std::function work) { CHECK_GE(total, 0); if (total == 0) { return; } + max_parallelism = std::min(max_parallelism, GetPerThreadMaxParallelism()); if (max_parallelism <= 1) { // Just inline the whole work since we only have 1 thread (core). work(0, total); diff --git a/tensorflow/core/util/work_sharder.h b/tensorflow/core/util/work_sharder.h index 451da98b6b..cb3708fec8 100644 --- a/tensorflow/core/util/work_sharder.h +++ b/tensorflow/core/util/work_sharder.h @@ -41,6 +41,12 @@ namespace tensorflow { // work(start, limit) computes the work units from [start, // limit), i.e., [start, limit) is a shard. // +// Too much parallelism can also cause excessive thread switches, +// therefore, Shard() often limits the maximum parallelism. Each +// caller can provide the 1st argument max_parallelism. A thread can +// call SetMaxParallelism() so that all Shard() calls later limits the +// thread parallelism. +// // REQUIRES: max_parallelism >= 0 // REQUIRES: workers != nullptr // REQUIRES: total >= 0 @@ -48,6 +54,31 @@ namespace tensorflow { void Shard(int max_parallelism, thread::ThreadPool* workers, int64 total, int64 cost_per_unit, std::function work); +// Each thread has an associated option to express the desired maximum +// parallelism. Its default is a very large quantity. +// +// Within TF runtime, per-thread max parallelism affects Shard() and +// intra-op parallelism. E.g., if SetPerThreadMaxParallelism(1) is +// arranged to be called by a tf_compute thread, Shard() calls and +// eigen device assignment happens in that thread afterwards becomes +// single-threaded. +void SetPerThreadMaxParallelism(int max_parallelism); +int GetPerThreadMaxParallelism(); + +// Helper to set and unset per-thread max parallelism. +class ScopedPerThreadMaxParallelism { + public: + ScopedPerThreadMaxParallelism(int max_parallelism) + : previous_(GetPerThreadMaxParallelism()) { + SetPerThreadMaxParallelism(max_parallelism); + } + + ~ScopedPerThreadMaxParallelism() { SetPerThreadMaxParallelism(previous_); } + + private: + int previous_ = -1; +}; + } // end namespace tensorflow #endif // TENSORFLOW_UTIL_WORK_SHARDER_H_ diff --git a/tensorflow/core/util/work_sharder_test.cc b/tensorflow/core/util/work_sharder_test.cc index 0694566ad9..bc5a1d221f 100644 --- a/tensorflow/core/util/work_sharder_test.cc +++ b/tensorflow/core/util/work_sharder_test.cc @@ -28,6 +28,7 @@ namespace tensorflow { namespace { void RunSharding(int64 num_workers, int64 total, int64 cost_per_unit, + int64 per_thread_max_parallelism, thread::ThreadPool* threads) { mutex mu; int64 num_shards = 0; @@ -46,9 +47,18 @@ void RunSharding(int64 num_workers, int64 total, int64 cost_per_unit, work[start] = true; } }); - EXPECT_EQ(num_done_work, total); LOG(INFO) << num_workers << " " << total << " " << cost_per_unit << " " << num_shards; + EXPECT_EQ(num_done_work, total); + if (std::min(num_workers, per_thread_max_parallelism) < + threads->NumThreads()) { + // If the intention is to limit the parallelism explicitly, we'd + // better honor it. Ideally, even if per_thread_max_parallelism > + // num_workers, we should expect that Shard() implementation do + // not over-shard. Unfortunately, ThreadPoolDevice::parallelFor + // tends to over-shard. + EXPECT_LE(num_shards, 1 + per_thread_max_parallelism); + } } TEST(Shard, Basic) { @@ -56,7 +66,10 @@ TEST(Shard, Basic) { for (auto workers : {0, 1, 2, 3, 5, 7, 10, 11, 15, 100, 1000}) { for (auto total : {0, 1, 7, 10, 64, 100, 256, 1000, 9999}) { for (auto cost_per_unit : {0, 1, 11, 102, 1003, 10005, 1000007}) { - RunSharding(workers, total, cost_per_unit, &threads); + for (auto maxp : {1, 2, 4, 8, 100}) { + ScopedPerThreadMaxParallelism s(maxp); + RunSharding(workers, total, cost_per_unit, maxp, &threads); + } } } } -- GitLab From c783b56a128fb7dc0a38a4fde61032aa0bcd664a Mon Sep 17 00:00:00 2001 From: Peter Hawkins Date: Fri, 15 Jun 2018 12:34:15 -0700 Subject: [PATCH 530/816] Add some extra DebugString() functions to shape_inference. Currently unused, but they were useful while debugging. Open visibility of the low level gen_resource_variables_ops to compiler tests. Fix bug in shape function of TPUReplicateInput for resource variables ? MergeInputHandleShapesAndTypes does not report shape mismatches. PiperOrigin-RevId: 200756762 --- tensorflow/contrib/tpu/ops/replication_ops.cc | 8 ++++---- tensorflow/core/framework/shape_inference.cc | 14 ++++++++++++++ tensorflow/core/framework/shape_inference.h | 2 ++ tensorflow/python/BUILD | 3 +++ 4 files changed, 23 insertions(+), 4 deletions(-) diff --git a/tensorflow/contrib/tpu/ops/replication_ops.cc b/tensorflow/contrib/tpu/ops/replication_ops.cc index f632c953c8..15a2bb17a9 100644 --- a/tensorflow/contrib/tpu/ops/replication_ops.cc +++ b/tensorflow/contrib/tpu/ops/replication_ops.cc @@ -53,10 +53,10 @@ REGISTER_OP("TPUReplicatedInput") nullptr; for (int i = c->num_inputs() - 1; i >= 0; --i) { if (shapes_and_types) { - if (!c->MergeInputHandleShapesAndTypes(i, *shapes_and_types)) { - return errors::InvalidArgument( - "Incompatible resource shapes for replicated TPU input."); - } + // The return value of MergeInputHandleShapesAndTypes indicates + // the shape was refined, not that there was an error. + // TODO(phawkins): there seems to be no way to discover errors. + (void)c->MergeInputHandleShapesAndTypes(i, *shapes_and_types); } else { shapes_and_types = c->input_handle_shapes_and_types(i); } diff --git a/tensorflow/core/framework/shape_inference.cc b/tensorflow/core/framework/shape_inference.cc index b02bc3adbe..8d597e198d 100644 --- a/tensorflow/core/framework/shape_inference.cc +++ b/tensorflow/core/framework/shape_inference.cc @@ -340,6 +340,20 @@ string InferenceContext::DebugString() const { ProtoDebugString(*node_def_)); } +string InferenceContext::DebugString(const ShapeAndType& shape_and_type) { + return strings::StrCat(DebugString(shape_and_type.shape), ":", + DataTypeString(shape_and_type.dtype)); +} + +string InferenceContext::DebugString( + gtl::ArraySlice shape_and_types) { + std::vector pieces; + for (const ShapeAndType& s : shape_and_types) { + pieces.push_back(DebugString(s)); + } + return strings::StrCat("[", str_util::Join(pieces, ","), "]"); +} + Status InferenceContext::WithRank(ShapeHandle shape, int64 rank, ShapeHandle* out) { if (rank > kint32max) { diff --git a/tensorflow/core/framework/shape_inference.h b/tensorflow/core/framework/shape_inference.h index 3f3729dcf9..81258b55b3 100644 --- a/tensorflow/core/framework/shape_inference.h +++ b/tensorflow/core/framework/shape_inference.h @@ -381,6 +381,8 @@ class InferenceContext { string DebugString(ShapeHandle s); string DebugString(DimensionHandle d); + string DebugString(const ShapeAndType& shape_and_type); + string DebugString(gtl::ArraySlice shape_and_types); // Describes the whole context, for debugging purposes. string DebugString() const; diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD index a06b536f5b..1436c7b1c8 100644 --- a/tensorflow/python/BUILD +++ b/tensorflow/python/BUILD @@ -1600,6 +1600,9 @@ tf_gen_op_wrapper_private_py( tf_gen_op_wrapper_private_py( name = "resource_variable_ops_gen", + visibility = [ + "//tensorflow/compiler/tf2xla:internal", + ], ) tf_gen_op_wrapper_private_py( -- GitLab From c2046b32299c02d73dae4a10731b810e4cb7c58f Mon Sep 17 00:00:00 2001 From: chinmay Das Date: Sat, 16 Jun 2018 01:45:19 +0530 Subject: [PATCH 531/816] added nasm mirror link (#20051) --- tensorflow/workspace.bzl | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index e7126c8d93..212a8bad47 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -200,6 +200,7 @@ def tf_workspace(path_prefix="", tf_repo_name=""): urls = [ "https://mirror.bazel.build/www.nasm.us/pub/nasm/releasebuilds/2.12.02/nasm-2.12.02.tar.bz2", "http://pkgs.fedoraproject.org/repo/pkgs/nasm/nasm-2.12.02.tar.bz2/d15843c3fb7db39af80571ee27ec6fad/nasm-2.12.02.tar.bz2", + "http://www.nasm.us/pub/nasm/releasebuilds/2.12.02/nasm-2.12.02.tar.bz2", ], sha256 = "00b0891c678c065446ca59bcee64719d0096d54d6886e6e472aeee2e170ae324", strip_prefix = "nasm-2.12.02", -- GitLab From 79f52c15b53546b8cd93959a9d82b902da5006ab Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 15 Jun 2018 13:28:06 -0700 Subject: [PATCH 532/816] Set shapes and types to queue ops, if not set by enqueue ops. PiperOrigin-RevId: 200764324 --- .../core/grappler/costs/graph_properties.cc | 50 +++++++++++++++++ .../core/grappler/costs/graph_properties.h | 5 ++ .../grappler/costs/graph_properties_test.cc | 53 +++++++++++++++++++ 3 files changed, 108 insertions(+) diff --git a/tensorflow/core/grappler/costs/graph_properties.cc b/tensorflow/core/grappler/costs/graph_properties.cc index 5310c9ebdf..b920604c6a 100644 --- a/tensorflow/core/grappler/costs/graph_properties.cc +++ b/tensorflow/core/grappler/costs/graph_properties.cc @@ -1083,6 +1083,9 @@ Status GraphProperties::UpdateShapes( // itself. TF_RETURN_IF_ERROR( UpdateEnqueue(n, resource_handles, shape_refiner, new_shapes)); + } else if (IsQueue(*n)) { + // Set shapes and types of Queue ops, if needed. + TF_RETURN_IF_ERROR(UpdateQueue(n, shape_refiner, new_shapes)); } else { auto c = shape_refiner->GetNodeContext(n); if (c && c->op_data && c->op_data->is_function_op) { @@ -1148,6 +1151,53 @@ Status GraphProperties::PropagateShapes( return Status::OK(); } +Status GraphProperties::UpdateQueue(const NodeDef* queue_node, + SymbolicShapeRefiner* shape_refiner, + bool* new_shapes) { + auto ctx = shape_refiner->GetNodeContext(queue_node); + if (!ctx) { + TF_RETURN_IF_ERROR(shape_refiner->AddNode(queue_node)); + ctx = CHECK_NOTNULL(shape_refiner->GetNodeContext(queue_node)); + } + auto* ic = ctx->inference_context.get(); + + auto* outputs = ic->output_handle_shapes_and_types(0); + if (outputs) { + // Shapes and types are already set, presumably by Enqueue ops. + return shape_refiner->UpdateNode(queue_node, new_shapes); + } + + if (queue_node->attr().count("shapes") <= 0 || + queue_node->attr().count("component_types") <= 0 || + queue_node->attr().at("shapes").list().shape_size() != + queue_node->attr().at("component_types").list().type_size()) { + // Errors in shapes and component_types attr. + return shape_refiner->UpdateNode(queue_node, new_shapes); + } + + // Extract types and shapes from Queue attr. + const auto& shapes = queue_node->attr().at("shapes").list().shape(); + const auto& types = queue_node->attr().at("component_types").list().type(); + std::vector shapes_and_types; + for (int i = 0; i < types.size(); i++) { + const auto& shape = shapes[i]; + ShapeHandle shape_handle; + TF_RETURN_IF_ERROR( + ic->MakeShapeFromPartialTensorShape(shape, &shape_handle)); + DataType data_type = + queue_node->attr().at("component_types").list().type(i); + ShapeAndType shape_and_type(shape_handle, data_type); + shapes_and_types.push_back(shape_and_type); + } + ic->set_output_handle_shapes_and_types(0, shapes_and_types); + + // Queue node is updated with output_handle_shapes_and_types, so set + // new_shapes and ignore it from UpdateNoe(). + *new_shapes = true; + bool dummy_new_shapes = false; + return shape_refiner->UpdateNode(queue_node, &dummy_new_shapes); +} + Status GraphProperties::UpdateEnqueue( const NodeDef* enqueue_node, const std::unordered_map& resource_handles, diff --git a/tensorflow/core/grappler/costs/graph_properties.h b/tensorflow/core/grappler/costs/graph_properties.h index 8703613a12..f716cd72c9 100644 --- a/tensorflow/core/grappler/costs/graph_properties.h +++ b/tensorflow/core/grappler/costs/graph_properties.h @@ -91,6 +91,11 @@ class GraphProperties { resource_handles, SymbolicShapeRefiner* shape_refiner, bool* new_shapes); + // Update the shapes and types of the Queue node, if not set by Enqueue node. + static Status UpdateQueue(const NodeDef* queue_node, + SymbolicShapeRefiner* shape_refiner, + bool* new_shapes); + // Update the output shapes of a Merge node, and enqueue its fanout in // new_shapes if needed. Status UpdateMergeNode(SymbolicShapeRefiner* shape_refiner, diff --git a/tensorflow/core/grappler/costs/graph_properties_test.cc b/tensorflow/core/grappler/costs/graph_properties_test.cc index 3e44b222fd..aa787ae620 100644 --- a/tensorflow/core/grappler/costs/graph_properties_test.cc +++ b/tensorflow/core/grappler/costs/graph_properties_test.cc @@ -262,6 +262,59 @@ TEST_F(GraphPropertiesTest, VarHandles) { EXPECT_EQ(7, prop.shape().dim(1).size()); } +TEST_F(GraphPropertiesTest, QueueWithOnlyDequeue_NoShapeAttr) { + tensorflow::Scope root = tensorflow::Scope::NewRootScope(); + auto q1 = ops::FIFOQueue(root.WithOpName("Queue1"), {DataType::DT_FLOAT}); + auto dequeue1 = + ops::QueueDequeue(root.WithOpName("Dequeue1"), q1, {DataType::DT_FLOAT}); + + GrapplerItem item; + TF_CHECK_OK(root.ToGraphDef(&item.graph)); + + GraphProperties properties(item); + TF_CHECK_OK(properties.InferStatically(false)); + + const auto props1 = properties.GetOutputProperties("Dequeue1"); + ASSERT_EQ(1, props1.size()); + EXPECT_EQ("float: ?", PropToString(props1[0])); +} + +TEST_F(GraphPropertiesTest, QueueWithOnlyDequeue_ShapeAttr) { + tensorflow::Scope root = tensorflow::Scope::NewRootScope(); + auto q1 = ops::FIFOQueue(root.WithOpName("Queue1"), {DataType::DT_FLOAT}, + ops::FIFOQueue::Attrs().Shapes({{3, 7, 1}})); + auto dequeue1 = + ops::QueueDequeue(root.WithOpName("Dequeue1"), q1, {DataType::DT_FLOAT}); + + GrapplerItem item; + TF_CHECK_OK(root.ToGraphDef(&item.graph)); + + GraphProperties properties(item); + TF_CHECK_OK(properties.InferStatically(false)); + + const auto props1 = properties.GetOutputProperties("Dequeue1"); + ASSERT_EQ(1, props1.size()); + EXPECT_EQ("float: [3,7,1]", PropToString(props1[0])); +} + +TEST_F(GraphPropertiesTest, QueueWithOnlyDequeue_PartialShapeAttr) { + tensorflow::Scope root = tensorflow::Scope::NewRootScope(); + auto q1 = ops::FIFOQueue(root.WithOpName("Queue1"), {DataType::DT_FLOAT}, + ops::FIFOQueue::Attrs().Shapes({{3, 7, -1}})); + auto dequeue1 = + ops::QueueDequeue(root.WithOpName("Dequeue1"), q1, {DataType::DT_FLOAT}); + + GrapplerItem item; + TF_CHECK_OK(root.ToGraphDef(&item.graph)); + + GraphProperties properties(item); + TF_CHECK_OK(properties.InferStatically(false)); + + const auto props1 = properties.GetOutputProperties("Dequeue1"); + ASSERT_EQ(1, props1.size()); + EXPECT_EQ("float: [3,7,-1]", PropToString(props1[0])); +} + TEST_F(GraphPropertiesTest, Queues) { // Create a graph with known input shapes, and propagate the shapes through a // couple of queues. -- GitLab From 1645a0a8bb6b0abda76816753ce97ea041e68e2e Mon Sep 17 00:00:00 2001 From: Dimitris Vardoulakis Date: Fri, 15 Jun 2018 13:42:51 -0700 Subject: [PATCH 533/816] Typo fixes. PiperOrigin-RevId: 200766687 --- tensorflow/compiler/xla/service/hlo_domain_isolator.h | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/compiler/xla/service/hlo_domain_isolator.h b/tensorflow/compiler/xla/service/hlo_domain_isolator.h index e0c5718509..eded3e78ee 100644 --- a/tensorflow/compiler/xla/service/hlo_domain_isolator.h +++ b/tensorflow/compiler/xla/service/hlo_domain_isolator.h @@ -26,10 +26,10 @@ limitations under the License. namespace xla { // Domain isolation is the task of placing kDomain instructions between HLO -// instructions having different shrading. A kDomain instruction is essentially +// instructions having different sharding. A kDomain instruction is essentially // used to break an HLO graph edge connecting two instructions with different // sharding. If a set of connected instructions have all the same sharding, no -// kDomain instruciton will be placed. +// kDomain instruction will be placed. class HloDomainIsolator : public HloPassInterface { public: // Creates a new kDomain instruction for the edge between the use instruction -- GitLab From 817c39bd37131b9624ef35f3d014e8645c91312e Mon Sep 17 00:00:00 2001 From: Xuechen Li Date: Fri, 15 Jun 2018 13:53:08 -0700 Subject: [PATCH 534/816] Fix None grads bug when calling a keras Sequential twice on same input in graph mode. PiperOrigin-RevId: 200768236 --- .../eager/python/examples/revnet/blocks.py | 4 +- .../eager/python/examples/revnet/revnet.py | 15 +------ .../python/examples/revnet/revnet_test.py | 42 +++++++++++++++++-- 3 files changed, 42 insertions(+), 19 deletions(-) diff --git a/tensorflow/contrib/eager/python/examples/revnet/blocks.py b/tensorflow/contrib/eager/python/examples/revnet/blocks.py index fb4f9f068f..8751651fed 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/blocks.py +++ b/tensorflow/contrib/eager/python/examples/revnet/blocks.py @@ -189,8 +189,8 @@ class _Residual(tf.keras.Model): """Manually compute backward gradients given input and output grads.""" with tf.GradientTape(persistent=True) as tape: - x_stop = tf.stop_gradient(x) - x1, x2 = tf.split(x_stop, num_or_size_splits=2, axis=self.axis) + x = tf.identity(x) # TODO(lxuechen): Remove after b/110264016 is fixed + x1, x2 = tf.split(x, num_or_size_splits=2, axis=self.axis) tape.watch([x1, x2]) # Stitch back x for `call` so tape records correct grads x = tf.concat([x1, x2], axis=self.axis) diff --git a/tensorflow/contrib/eager/python/examples/revnet/revnet.py b/tensorflow/contrib/eager/python/examples/revnet/revnet.py index aa3f7efe1b..1e17bf1eab 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/revnet.py +++ b/tensorflow/contrib/eager/python/examples/revnet/revnet.py @@ -31,10 +31,6 @@ import tensorflow as tf from tensorflow.contrib.eager.python.examples.revnet import blocks -# Global Conventions: -# 1) Default data format is NCWH, targeting GPU -# 2) Each block has attribute axis, inferred from data_format -# 3) Default training option to True for batch normalization class RevNet(tf.keras.Model): """RevNet that depends on all the blocks.""" @@ -203,6 +199,7 @@ class RevNet(tf.keras.Model): # Manually backprop through last block x = saved_hidden[-1] with tf.GradientTape() as tape: + x = tf.identity(x) # TODO(lxuechen): Remove after b/110264016 is fixed tape.watch(x) logits = self._final_block(x, training=training) cost = self.compute_loss(logits, labels) @@ -251,13 +248,3 @@ class RevNet(tf.keras.Model): loss = self.compute_loss(logits, labels) return loss - - def eval_step(self, inputs, labels): - """Evaluate.""" - - logits, _ = self.call(inputs, training=False) - preds = tf.cast(tf.argmax(logits, axis=1), tf.int32) - corrects = tf.cast(tf.equal(preds, labels), tf.float32) - accuracy = tf.reduce_mean(corrects) - - return accuracy diff --git a/tensorflow/contrib/eager/python/examples/revnet/revnet_test.py b/tensorflow/contrib/eager/python/examples/revnet/revnet_test.py index 68502ceac2..d2d2f65bbd 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/revnet_test.py +++ b/tensorflow/contrib/eager/python/examples/revnet/revnet_test.py @@ -75,24 +75,36 @@ class RevnetTest(tf.test.TestCase): optimizer = tf.train.AdamOptimizer(learning_rate=1e-3) # Loss should be decreasing after each optimization step - for _ in range(3): + for _ in range(1): loss_ = self.model.train_step(self.x, self.t, optimizer, report=True) self.assertTrue(loss_.numpy() <= loss.numpy()) loss = loss_ def test_call_defun(self): - """Test `call` function with tfe.defun apply.""" + """Test `call` function with defun.""" y, _ = tfe.defun(self.model.call)(self.x, training=False) self.assertEqual(y.shape, [self.config.batch_size, self.config.n_classes]) + def test_compute_gradients_defun(self): + """Test `compute_gradients` function with defun.""" + compute_gradients = tfe.defun(self.model.compute_gradients) + grads, vars_ = compute_gradients(self.x, self.t) + self.assertTrue(isinstance(grads, list)) + self.assertTrue(isinstance(vars_, list)) + self.assertEqual(len(grads), len(vars_)) + for grad, var in zip(grads, vars_): + if grad is not None: + self.assertEqual(grad.shape, var.shape) + def test_train_step_defun(self): + """Test `train_step` function with defun.""" self.model.call = tfe.defun(self.model.call) logits, _ = self.model(self.x, training=True) loss = self.model.compute_loss(logits=logits, labels=self.t) optimizer = tf.train.AdamOptimizer(learning_rate=1e-3) - for _ in range(3): + for _ in range(1): loss_ = self.model.train_step(self.x, self.t, optimizer, report=True) self.assertTrue(loss_.numpy() <= loss.numpy()) loss = loss_ @@ -100,6 +112,30 @@ class RevnetTest(tf.test.TestCase): # Initialize new model, so that other tests are not affected self.model = revnet.RevNet(config=self.config) + def test_training_graph(self): + """Test model training in graph mode.""" + + with tf.Graph().as_default(): + x = tf.random_normal( + shape=(self.config.batch_size,) + self.config.input_shape) + t = tf.random_uniform( + shape=(self.config.batch_size,), + minval=0, + maxval=self.config.n_classes, + dtype=tf.int32) + global_step = tfe.Variable(0., trainable=False) + model = revnet.RevNet(config=self.config) + grads_all, vars_all = model.compute_gradients(x, t, training=True) + optimizer = tf.train.AdamOptimizer(learning_rate=1e-3) + with tf.control_dependencies(model.updates): + train_op = optimizer.apply_gradients( + zip(grads_all, vars_all), global_step=global_step) + + with tf.Session() as sess: + sess.run(tf.global_variables_initializer()) + for _ in range(1): + sess.run(train_op) + # Benchmark related def device_and_data_format(): -- GitLab From c2956886be6d00d1915ccc52794b7205de3f53be Mon Sep 17 00:00:00 2001 From: Mark Daoust Date: Fri, 15 Jun 2018 13:59:22 -0700 Subject: [PATCH 535/816] Quiet the doc generator. Delete most print statements, use logging instead of print, and close files (to clear the "Unclosed file" warnings). Normally this produces thousands of lines of output. Mostly noise. PiperOrigin-RevId: 200769210 --- tensorflow/tools/docs/BUILD | 5 +++- tensorflow/tools/docs/generate_lib.py | 38 +++++++----------------- tensorflow/tools/docs/parser.py | 11 ++++--- tensorflow/tools/docs/py_guide_parser.py | 3 +- 4 files changed, 22 insertions(+), 35 deletions(-) diff --git a/tensorflow/tools/docs/BUILD b/tensorflow/tools/docs/BUILD index 58b5ef8345..eea712c279 100644 --- a/tensorflow/tools/docs/BUILD +++ b/tensorflow/tools/docs/BUILD @@ -37,7 +37,10 @@ py_library( srcs = ["parser.py"], srcs_version = "PY2AND3", visibility = ["//visibility:public"], - deps = ["@astor_archive//:astor"], + deps = [ + "//tensorflow/python:platform", + "@astor_archive//:astor", + ], ) py_test( diff --git a/tensorflow/tools/docs/generate_lib.py b/tensorflow/tools/docs/generate_lib.py index 853ec6194f..67c413cccb 100644 --- a/tensorflow/tools/docs/generate_lib.py +++ b/tensorflow/tools/docs/generate_lib.py @@ -21,6 +21,7 @@ from __future__ import print_function import argparse import fnmatch import os +import shutil import six @@ -81,12 +82,8 @@ def write_docs(output_dir, raise ValueError("'output_dir' must be an absolute path.\n" " output_dir='%s'" % output_dir) - try: - if not os.path.exists(output_dir): - os.makedirs(output_dir) - except OSError as e: - print('Creating output dir "%s" failed: %s' % (output_dir, e)) - raise + if not os.path.exists(output_dir): + os.makedirs(output_dir) # These dictionaries are used for table-of-contents generation below # They will contain, after the for-loop below:: @@ -129,8 +126,6 @@ def write_docs(output_dir, module_children.setdefault(subname, []).append(full_name) break - print('Writing docs for %s (%r).' % (full_name, py_object)) - # Generate docs for `py_object`, resolving references. page_info = parser.docs_for_object(full_name, py_object, parser_config) @@ -151,10 +146,9 @@ def write_docs(output_dir, text = text.encode('utf-8') with open(path, 'wb') as f: f.write(text) - except OSError as e: - print('Cannot write documentation for %s to %s: %s' % (full_name, - directory, e)) - raise + except OSError: + raise OSError( + 'Cannot write documentation for %s to %s' % (full_name, directory)) if yaml_toc: # Generate table of contents @@ -433,16 +427,11 @@ def _other_docs(src_dir, output_dir, reference_resolver, file_pattern='*.md'): # Make the directory under output_dir. new_dir = os.path.join(output_dir, os.path.relpath(path=dirpath, start=src_dir)) - try: - if not os.path.exists(new_dir): - os.makedirs(new_dir) - except OSError as e: - print('Creating output dir "%s" failed: %s' % (new_dir, e)) - raise + if not os.path.exists(new_dir): + os.makedirs(new_dir) for base_name in filenames: if base_name in EXCLUDED: - print('Skipping excluded file %s...' % base_name) continue full_in_path = os.path.join(dirpath, base_name) @@ -451,24 +440,19 @@ def _other_docs(src_dir, output_dir, reference_resolver, file_pattern='*.md'): suffix = os.path.relpath(path=full_in_path, start=src_dir) full_out_path = os.path.join(output_dir, suffix) if not fnmatch.fnmatch(base_name, file_pattern): - print('Copying un-matched file %s...' % suffix) - open(full_out_path, 'wb').write(open(full_in_path, 'rb').read()) + shutil.copyfile(full_in_path, full_out_path) continue if dirpath.endswith('/api_guides/python'): - print('Processing Python guide %s...' % base_name) content = tag_updater.process(full_in_path) else: - print('Processing doc %s...' % suffix) - content = open(full_in_path, 'rb').read().decode('utf-8') + with open(full_in_path, 'rb') as f: + content = f.read().decode('utf-8') content = reference_resolver.replace_references(content, relative_path_to_root) with open(full_out_path, 'wb') as f: f.write(content.encode('utf-8')) - print('Done.') - - class DocGenerator(object): """Main entry point for generating docs.""" diff --git a/tensorflow/tools/docs/parser.py b/tensorflow/tools/docs/parser.py index 50c9052741..64e02589bb 100644 --- a/tensorflow/tools/docs/parser.py +++ b/tensorflow/tools/docs/parser.py @@ -25,12 +25,12 @@ import itertools import json import os import re -import sys import astor import six from google.protobuf.message import Message as ProtoMessage +from tensorflow.python.platform import tf_logging as logging from tensorflow.python.util import tf_inspect @@ -53,7 +53,7 @@ class _Errors(object): template = 'ERROR:\n output file name: %s\n %s\n\n' for full_name, message in self._errors: - print(template % (full_name, message), file=sys.stderr) + logging.warn(template, full_name, message) def append(self, full_name, message): """Add an error to the collection. @@ -761,8 +761,9 @@ def _generate_signature(func, reverse_index): lookup_text = public_name + default_text[len(internal_name):] break if default_text is lookup_text: - print('WARNING: Using default arg, failed lookup: %s, repr: %r' % - (default_text, default)) + logging.warn( + 'WARNING: Using default arg, failed lookup: %s, repr: %r', + default_text, default) else: default_text = lookup_text else: @@ -1213,8 +1214,6 @@ class _ClassPageInfo(object): if not child_doc.brief.strip() and short_name in [ '__del__', '__copy__' ]: - print('Skipping %s, defined in %s, no docstring.' % (child_name, - defining_class)) continue try: diff --git a/tensorflow/tools/docs/py_guide_parser.py b/tensorflow/tools/docs/py_guide_parser.py index 328f42d18f..b00694dc40 100644 --- a/tensorflow/tools/docs/py_guide_parser.py +++ b/tensorflow/tools/docs/py_guide_parser.py @@ -44,7 +44,8 @@ class PyGuideParser(object): def process(self, full_path): """Read and process the file at `full_path`.""" - md_string = open(full_path, 'rb').read().decode('utf-8') + with open(full_path, 'rb') as f: + md_string = f.read().decode('utf-8') self._lines = md_string.split('\n') seen = set() -- GitLab From d3ae8e7ca2061ebbe5a678ad3a4a44ce90608768 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 15 Jun 2018 14:09:47 -0700 Subject: [PATCH 536/816] Add bazel android repo to workspace PiperOrigin-RevId: 200771096 --- tensorflow/workspace.bzl | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index 39d9d9ca11..15a37fca39 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -754,6 +754,15 @@ def tf_workspace(path_prefix="", tf_repo_name=""): strip_prefix = "ovic", ) + tf_http_archive( + name = "build_bazel_rules_android", + sha256 = "cd06d15dd8bb59926e4d65f9003bfc20f9da4b2519985c27e190cddc8b7a7806", + urls = [ + "https://mirror.bazel.build/github.com/bazelbuild/rules_android/archive/v0.1.1.zip", + "https://github.com/bazelbuild/rules_android/archive/v0.1.1.zip", + ], + ) + ############################################################################## # BIND DEFINITIONS # -- GitLab From 7991f0162bc5d5ee342336f09e89127fb5371ae0 Mon Sep 17 00:00:00 2001 From: RJ Ryan Date: Fri, 15 Jun 2018 14:30:58 -0700 Subject: [PATCH 537/816] Fix typo in tf.lite Python interpreter comment. PiperOrigin-RevId: 200774484 --- tensorflow/contrib/lite/python/interpreter.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/contrib/lite/python/interpreter.py b/tensorflow/contrib/lite/python/interpreter.py index 5fbc551452..0bc8b0963c 100644 --- a/tensorflow/contrib/lite/python/interpreter.py +++ b/tensorflow/contrib/lite/python/interpreter.py @@ -109,7 +109,7 @@ class Interpreter(object): ] def set_tensor(self, tensor_index, value): - """Sets the value of the input. + """Sets the value of the input tensor. Args: tensor_index: Tensor index of tensor to set. This value can be gotten from @@ -147,7 +147,7 @@ class Interpreter(object): ] def get_tensor(self, tensor_index): - """Sets the value of the input. + """Gets the value of the tensor. Args: tensor_index: Tensor index of tensor to get. This value can be gotten from -- GitLab From 33f8f7e1843c750186c8fbcfbf94f286bb7ca505 Mon Sep 17 00:00:00 2001 From: Kay Zhu Date: Fri, 15 Jun 2018 14:49:49 -0700 Subject: [PATCH 538/816] Automated g4 rollback of changelist 200750664 PiperOrigin-RevId: 200777514 --- .../compiler/tf2xla/kernels/mirror_pad_op.cc | 2 +- tensorflow/compiler/tf2xla/kernels/pad_op.cc | 4 +- .../tf2xla/kernels/reduction_ops_common.cc | 6 +-- .../compiler/tf2xla/kernels/sequence_ops.cc | 15 ++++--- .../compiler/tf2xla/kernels/split_op.cc | 4 +- tensorflow/compiler/tf2xla/literal_util.cc | 18 +++++++++ tensorflow/compiler/tf2xla/literal_util.h | 4 ++ tensorflow/compiler/tf2xla/xla_context.cc | 2 +- tensorflow/compiler/tf2xla/xla_context.h | 2 +- tensorflow/compiler/tf2xla/xla_helpers.cc | 2 +- tensorflow/compiler/tf2xla/xla_op_kernel.cc | 39 ++++--------------- tensorflow/compiler/xla/literal_util.cc | 1 + 12 files changed, 48 insertions(+), 51 deletions(-) diff --git a/tensorflow/compiler/tf2xla/kernels/mirror_pad_op.cc b/tensorflow/compiler/tf2xla/kernels/mirror_pad_op.cc index c3326b4d11..7e9de3ef9b 100644 --- a/tensorflow/compiler/tf2xla/kernels/mirror_pad_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/mirror_pad_op.cc @@ -27,7 +27,7 @@ class MirrorPadOp : public XlaOpKernel { xla::StatusOr DoMirrorPad(const xla::XlaOp& t, const xla::Shape& original_shape, - const xla::LiteralSlice& pad_literal, + const xla::Literal& pad_literal, xla::XlaBuilder* b) { xla::XlaOp accum = t; for (int64 dimno = xla::ShapeUtil::Rank(original_shape) - 1; dimno >= 0; diff --git a/tensorflow/compiler/tf2xla/kernels/pad_op.cc b/tensorflow/compiler/tf2xla/kernels/pad_op.cc index 17b85338f7..7c95475e7b 100644 --- a/tensorflow/compiler/tf2xla/kernels/pad_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/pad_op.cc @@ -63,8 +63,8 @@ class PadOp : public XlaOpKernel { int before = pad_literal.Get({i, 0}); int after = pad_literal.Get({i, 1}); OP_REQUIRES(ctx, before >= 0 && after >= 0, - errors::InvalidArgument( - "Paddings must be non-negative: ", before, " ", after)); + errors::InvalidArgument("Paddings must be non-negative: ", + before, " ", after)); dim->set_edge_padding_low(before); dim->set_edge_padding_high(after); } diff --git a/tensorflow/compiler/tf2xla/kernels/reduction_ops_common.cc b/tensorflow/compiler/tf2xla/kernels/reduction_ops_common.cc index 44510c731e..4fd5bfd039 100644 --- a/tensorflow/compiler/tf2xla/kernels/reduction_ops_common.cc +++ b/tensorflow/compiler/tf2xla/kernels/reduction_ops_common.cc @@ -56,9 +56,9 @@ void XlaReductionOp::Compile(XlaOpKernelContext* ctx) { // Evaluate the constant, reshaping to a 1-vector if it is a scalar. xla::Literal axes_literal; - OP_REQUIRES_OK( - ctx, ctx->ConstantInputReshaped(1, {axes_tensor_shape.num_elements()}, - &axes_literal)); + OP_REQUIRES_OK(ctx, + ctx->ConstantInputReshaped( + 1, {axes_tensor_shape.num_elements()}, &axes_literal)); VLOG(1) << "data shape: " << data_shape.DebugString(); VLOG(1) << "axes : " << axes_literal.ToString(); diff --git a/tensorflow/compiler/tf2xla/kernels/sequence_ops.cc b/tensorflow/compiler/tf2xla/kernels/sequence_ops.cc index bc3d0bf5df..2c31f8d908 100644 --- a/tensorflow/compiler/tf2xla/kernels/sequence_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/sequence_ops.cc @@ -55,10 +55,9 @@ Status GetIntValue(int index, XlaOpKernelContext* ctx, int64* value) { // The type-specific part of the implementation of Range. template -Status CreateRangeTensor(const xla::LiteralSlice& start_literal, - const xla::LiteralSlice& limit_literal, - const xla::LiteralSlice& delta_literal, - Tensor* output) { +Status CreateRangeTensor(const xla::Literal& start_literal, + const xla::Literal& limit_literal, + const xla::Literal& delta_literal, Tensor* output) { T start = start_literal.Get({}); T limit = limit_literal.Get({}); T delta = delta_literal.Get({}); @@ -68,13 +67,13 @@ Status CreateRangeTensor(const xla::LiteralSlice& start_literal, } if (delta > 0) { if (start > limit) { - return errors::InvalidArgument( - "Requires start <= limit when delta > 0: ", start, "/", limit); + return errors::InvalidArgument("Requires start <= limit when delta > 0: ", + start, "/", limit); } } else { if (start < limit) { - return errors::InvalidArgument( - "Requires start >= limit when delta < 0: ", start, "/", limit); + return errors::InvalidArgument("Requires start >= limit when delta < 0: ", + start, "/", limit); } } int64 size = diff --git a/tensorflow/compiler/tf2xla/kernels/split_op.cc b/tensorflow/compiler/tf2xla/kernels/split_op.cc index 9b54058541..8958b2e770 100644 --- a/tensorflow/compiler/tf2xla/kernels/split_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/split_op.cc @@ -134,7 +134,7 @@ class SplitVOp : public XlaOpKernel { errors::InvalidArgument( "Number of ways to split should be > 0, but got ", num_split)); - // Check that sizes are correct. + // check that sizes are correct int total_split_size = 0; int neg_one_dim = -1; std::vector split_sizes_vec(num_split, -1); @@ -148,7 +148,7 @@ class SplitVOp : public XlaOpKernel { " number of elements as the output. Got ", split_size_shape.dims(), "-D and ", split_size_shape.num_elements(), " elements")); - // Get the dimension of this split. + // get the dimension of this split xla::Literal split_size_literal; OP_REQUIRES_OK(ctx, ctx->ConstantInput(1, &split_size_literal)); diff --git a/tensorflow/compiler/tf2xla/literal_util.cc b/tensorflow/compiler/tf2xla/literal_util.cc index b43405a1a4..db56b12837 100644 --- a/tensorflow/compiler/tf2xla/literal_util.cc +++ b/tensorflow/compiler/tf2xla/literal_util.cc @@ -22,6 +22,24 @@ limitations under the License. namespace tensorflow { +Status HostTensorToLiteral(const Tensor& host_tensor, xla::Literal* literal) { + xla::Shape literal_shape; + TF_RETURN_IF_ERROR(TensorShapeToXLAShape( + host_tensor.dtype(), host_tensor.shape(), &literal_shape)); + + *literal = xla::Literal(literal_shape); + + // memcpy over the payload ... + // TODO(phawkins): handle string types. + size_t total_bytes = host_tensor.TotalBytes(); + if (total_bytes > 0) { + void* dst_ptr = literal->untyped_data(); + const void* src_ptr = DMAHelper::base(&host_tensor); + memcpy(dst_ptr, src_ptr, total_bytes); + } + return Status::OK(); +} + Status HostTensorToBorrowingLiteral(const Tensor& host_tensor, xla::BorrowingLiteral* literal) { xla::Shape xla_shape; diff --git a/tensorflow/compiler/tf2xla/literal_util.h b/tensorflow/compiler/tf2xla/literal_util.h index ab7e861f33..74685025c1 100644 --- a/tensorflow/compiler/tf2xla/literal_util.h +++ b/tensorflow/compiler/tf2xla/literal_util.h @@ -26,6 +26,10 @@ limitations under the License. namespace tensorflow { +// Copies 'host_tensor' to an XLA Literal. Fails if host_tensor is of an +// unsupported type. +Status HostTensorToLiteral(const Tensor& host_tensor, xla::Literal* literal); + // Returns a BorrowingLiteral that utilizes the same underlying buffer owned by // 'host_tensor'. Status HostTensorToBorrowingLiteral(const Tensor& host_tensor, diff --git a/tensorflow/compiler/tf2xla/xla_context.cc b/tensorflow/compiler/tf2xla/xla_context.cc index 67174b251d..098072d33c 100644 --- a/tensorflow/compiler/tf2xla/xla_context.cc +++ b/tensorflow/compiler/tf2xla/xla_context.cc @@ -92,7 +92,7 @@ void XlaContext::AddRetval(int retval_index, DataType type, } Status XlaContext::AddConstRetval(int retval_index, DataType dtype, - const xla::LiteralSlice& literal) { + const xla::Literal& literal) { VLOG(1) << "Adding retval index " << retval_index << " with non-data-dependent tensor to XLA computation"; if (retvals_.size() <= retval_index) { diff --git a/tensorflow/compiler/tf2xla/xla_context.h b/tensorflow/compiler/tf2xla/xla_context.h index 5960daaefd..341bf6ff1f 100644 --- a/tensorflow/compiler/tf2xla/xla_context.h +++ b/tensorflow/compiler/tf2xla/xla_context.h @@ -83,7 +83,7 @@ class XlaContext : public ResourceBase { // As for Retval, but for return values that are compile-time constants. Status AddConstRetval(int retval_index, DataType dtype, - const xla::LiteralSlice& literal); + const xla::Literal& literal); // Creates a resource with resource `kind` and initial value `handle`. `name` // is a descriptive name for use in error messages. See the `XlaResource` diff --git a/tensorflow/compiler/tf2xla/xla_helpers.cc b/tensorflow/compiler/tf2xla/xla_helpers.cc index 93cd340485..a1da176fe3 100644 --- a/tensorflow/compiler/tf2xla/xla_helpers.cc +++ b/tensorflow/compiler/tf2xla/xla_helpers.cc @@ -25,6 +25,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/types.h" +#include "tensorflow/core/common_runtime/dma_helper.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/lib/gtl/array_slice.h" @@ -247,7 +248,6 @@ Status XlaHelpers::OneHot(xla::XlaBuilder* builder, int64 depth, int axis, return errors::InvalidArgument("Invalid argument type ", DataTypeString(index_type)); } - xla::BorrowingLiteral linspace_literal; TF_RETURN_IF_ERROR(HostTensorToBorrowingLiteral(linspace, &linspace_literal)); diff --git a/tensorflow/compiler/tf2xla/xla_op_kernel.cc b/tensorflow/compiler/tf2xla/xla_op_kernel.cc index c6ddbcc6e1..76c68d81af 100644 --- a/tensorflow/compiler/tf2xla/xla_op_kernel.cc +++ b/tensorflow/compiler/tf2xla/xla_op_kernel.cc @@ -20,7 +20,6 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/literal_util.h" #include "tensorflow/compiler/tf2xla/shape_util.h" #include "tensorflow/compiler/tf2xla/xla_context.h" -#include "tensorflow/core/common_runtime/dma_helper.h" namespace tensorflow { @@ -88,25 +87,6 @@ Status XlaOpKernelContext::ConstantInputReshaped( } const XlaExpression* expression = CastExpressionFromTensor(tensor); - auto copy_tensor_to_literal = [](const Tensor& tensor, - xla::Literal* literal) { - xla::Shape literal_shape; - TF_RETURN_IF_ERROR( - TensorShapeToXLAShape(tensor.dtype(), tensor.shape(), &literal_shape)); - - *literal = xla::Literal(literal_shape); - - // memcpy over the payload ... - // TODO(phawkins): handle string types. - size_t total_bytes = tensor.TotalBytes(); - if (total_bytes > 0) { - void* dst_ptr = literal->untyped_data(); - const void* src_ptr = DMAHelper::base(&tensor); - memcpy(dst_ptr, src_ptr, total_bytes); - } - return Status::OK(); - }; - // If the tensor has a known constant value, there is no need to invoke XLA. if (expression->has_constant_value()) { Tensor temp(tensor.dtype()); @@ -115,15 +95,13 @@ Status XlaOpKernelContext::ConstantInputReshaped( // with the enclosing Tensor. return errors::Internal("Incompatible shapes in ConstantInputReshaped."); } - - return copy_tensor_to_literal(temp, constant_literal); + return HostTensorToLiteral(temp, constant_literal); } // Make sure we treat zero-element tensors as constant. if (new_shape.num_elements() == 0) { Tensor temp(tensor.dtype(), new_shape); - - return copy_tensor_to_literal(temp, constant_literal); + return HostTensorToLiteral(temp, constant_literal); } xla::XlaOp handle = expression->handle(); @@ -184,8 +162,7 @@ Status XlaOpKernelContext::ConstantInputReshaped( } // Converts an int32 or int64 scalar literal to an int64. -static Status LiteralToInt64Scalar(const xla::LiteralSlice& literal, - int64* out) { +static Status LiteralToInt64Scalar(const xla::Literal& literal, int64* out) { if (xla::ShapeUtil::Rank(literal.shape()) != 0) { return errors::InvalidArgument("value is not a scalar"); } @@ -200,8 +177,7 @@ static Status LiteralToInt64Scalar(const xla::LiteralSlice& literal, } // Converts an float32 or float64 scalar literal to a float64. -static Status LiteralToFloat64Scalar(const xla::LiteralSlice& literal, - double* out) { +static Status LiteralToFloat64Scalar(const xla::Literal& literal, double* out) { if (xla::ShapeUtil::Rank(literal.shape()) != 0) { return errors::InvalidArgument("value is not a scalar"); } @@ -228,7 +204,7 @@ Status XlaOpKernelContext::ConstantInputAsFloatScalar(int index, double* out) { } // Converts an int32 or int64 1D literal to an int64 vector. -static Status LiteralToInt64Vector(const xla::LiteralSlice& literal, +static Status LiteralToInt64Vector(const xla::Literal& literal, std::vector* out) { if (xla::ShapeUtil::Rank(literal.shape()) != 1) { return errors::InvalidArgument("value is not 1D"); @@ -392,9 +368,8 @@ void XlaOpKernelContext::SetOutput(int index, const xla::XlaOp& handle) { void XlaOpKernelContext::SetConstantOutput(int index, const Tensor& constant) { const TensorShape& shape = constant.shape(); - xla::BorrowingLiteral literal; - OP_REQUIRES_OK(context_, HostTensorToBorrowingLiteral(constant, &literal)); - + xla::Literal literal; + OP_REQUIRES_OK(context_, HostTensorToLiteral(constant, &literal)); xla::XlaOp handle = builder()->ConstantLiteral(literal); CHECK_NE(handle.builder(), nullptr); diff --git a/tensorflow/compiler/xla/literal_util.cc b/tensorflow/compiler/xla/literal_util.cc index 7c6a181b0a..19e6d288c0 100644 --- a/tensorflow/compiler/xla/literal_util.cc +++ b/tensorflow/compiler/xla/literal_util.cc @@ -2355,6 +2355,7 @@ LiteralSlice::LiteralSlice(const LiteralBase& literal, BorrowingLiteral::BorrowingLiteral(const char* src_buf_ptr, const Shape& shape) : LiteralBase(), shape_(MakeUnique(shape)) { CHECK(ShapeUtil::IsArray(*shape_)); + CHECK_NE(src_buf_ptr, nullptr); CHECK(LayoutUtil::HasLayout(*shape_)); root_piece_ = Piece(); -- GitLab From 94b3db68ee2edb568b6b12d3063b72074910f878 Mon Sep 17 00:00:00 2001 From: Skye Wanderman-Milne Date: Fri, 15 Jun 2018 14:54:00 -0700 Subject: [PATCH 539/816] Move cond_v2 to core (non-public) and add toggle to use cond_v2 by default. This change: * Creates a new global variable, control_flow_ops._ENABLE_COND_V2, to use cond_v2 by default when calling tf.cond. This variable can also be controlled via the environment variable TF_ENABLE_COND_V2. * Moves cond_v2 out of contrib so it's accessible from control_flow_ops.py. * Lazily "imports" some modules in cond_v2 to avoid circular dependencies. Note that these lazy "imports" must be imported by the cond_v2 caller (or recursively by one of the caller's imports) in order for cond_v2 to have access to them. * Renames the cond_v2 module to cond_v2_impl, and creates a new cond_v2 module that imports the cond_v2 method and the necessary extra imports. This is useful for explicitly calling cond_v2 outside of control_flow_ops.cond. PiperOrigin-RevId: 200778208 --- tensorflow/contrib/BUILD | 1 - tensorflow/contrib/__init__.py | 1 - tensorflow/contrib/cmake/python_modules.txt | 2 - tensorflow/contrib/control_flow/BUILD | 53 ---------------- tensorflow/python/BUILD | 36 ++++++++++- tensorflow/python/framework/function.py | 5 ++ .../python/framework/function_def_to_graph.py | 6 ++ tensorflow/python/kernel_tests/BUILD | 19 ++++++ .../kernel_tests}/cond_v2_test.py | 62 +++++++++---------- .../__init__.py => python/ops/cond_v2.py} | 19 +++--- .../cond_v2.py => python/ops/cond_v2_impl.py} | 33 ++++++---- tensorflow/python/ops/control_flow_ops.py | 9 +++ tensorflow/python/ops/gradients_impl.py | 5 ++ tensorflow/tools/pip_package/BUILD | 1 + 14 files changed, 142 insertions(+), 110 deletions(-) delete mode 100644 tensorflow/contrib/control_flow/BUILD rename tensorflow/{contrib/control_flow/python => python/kernel_tests}/cond_v2_test.py (90%) rename tensorflow/{contrib/control_flow/__init__.py => python/ops/cond_v2.py} (66%) rename tensorflow/{contrib/control_flow/python/cond_v2.py => python/ops/cond_v2_impl.py} (94%) diff --git a/tensorflow/contrib/BUILD b/tensorflow/contrib/BUILD index 50b1ae5cc3..7d44a054a8 100644 --- a/tensorflow/contrib/BUILD +++ b/tensorflow/contrib/BUILD @@ -33,7 +33,6 @@ py_library( "//tensorflow/contrib/compiler:compiler_py", "//tensorflow/contrib/autograph", "//tensorflow/contrib/constrained_optimization", - "//tensorflow/contrib/control_flow", "//tensorflow/contrib/copy_graph:copy_graph_py", "//tensorflow/contrib/crf:crf_py", "//tensorflow/contrib/cudnn_rnn:cudnn_rnn_py", diff --git a/tensorflow/contrib/__init__.py b/tensorflow/contrib/__init__.py index ad8c40395c..9aad772f0a 100644 --- a/tensorflow/contrib/__init__.py +++ b/tensorflow/contrib/__init__.py @@ -30,7 +30,6 @@ from tensorflow.contrib import cluster_resolver from tensorflow.contrib import coder from tensorflow.contrib import compiler from tensorflow.contrib import constrained_optimization -from tensorflow.contrib import control_flow from tensorflow.contrib import copy_graph from tensorflow.contrib import crf from tensorflow.contrib import cudnn_rnn diff --git a/tensorflow/contrib/cmake/python_modules.txt b/tensorflow/contrib/cmake/python_modules.txt index 015cb73bbd..fece56c412 100644 --- a/tensorflow/contrib/cmake/python_modules.txt +++ b/tensorflow/contrib/cmake/python_modules.txt @@ -115,8 +115,6 @@ tensorflow/contrib/coder/python/ops tensorflow/contrib/compiler tensorflow/contrib/constrained_optimization tensorflow/contrib/constrained_optimization/python -tensorflow/contrib/control_flow -tensorflow/contrib/control_flow/python tensorflow/contrib/copy_graph tensorflow/contrib/copy_graph/python tensorflow/contrib/copy_graph/python/util diff --git a/tensorflow/contrib/control_flow/BUILD b/tensorflow/contrib/control_flow/BUILD deleted file mode 100644 index e8036d63ae..0000000000 --- a/tensorflow/contrib/control_flow/BUILD +++ /dev/null @@ -1,53 +0,0 @@ -# New implementations of control flow ops - -licenses(["notice"]) # Apache 2.0 - -package(default_visibility = ["//visibility:public"]) - -load("//tensorflow:tensorflow.bzl", "tf_py_test") - -py_library( - name = "control_flow", - srcs = ["__init__.py"], - srcs_version = "PY2AND3", - deps = [ - ":cond_v2", - ], -) - -py_library( - name = "cond_v2", - srcs = ["python/cond_v2.py"], - srcs_version = "PY2AND3", - deps = [ - "//tensorflow/core:protos_all_py", - "//tensorflow/python:array_ops", - "//tensorflow/python:c_api_util", - "//tensorflow/python:framework_ops", - "//tensorflow/python:function", - "//tensorflow/python:function_def_to_graph", - "//tensorflow/python:functional_ops_gen", - "//tensorflow/python:gradients", - "//tensorflow/python:pywrap_tensorflow", - "//tensorflow/python:util", - ], -) - -tf_py_test( - name = "cond_v2_test", - size = "small", - srcs = ["python/cond_v2_test.py"], - additional_deps = [ - ":cond_v2", - "//tensorflow/python:array_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:constant_op", - "//tensorflow/python:control_flow_ops", - "//tensorflow/python:dtypes", - "//tensorflow/python:framework", - "//tensorflow/python:framework_ops", - "//tensorflow/python:gradients", - "//tensorflow/python:training", - ], - grpc_enabled = True, -) diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD index 1436c7b1c8..39e0cafd93 100644 --- a/tensorflow/python/BUILD +++ b/tensorflow/python/BUILD @@ -696,6 +696,7 @@ py_library( srcs_version = "PY2AND3", deps = [ ":array_ops", + ":cond_v2_impl", ":dtypes", ":framework_ops", ":graph_to_function_def", @@ -712,6 +713,7 @@ py_library( srcs = ["framework/graph_to_function_def.py"], srcs_version = "PY2AND3", deps = [ + ":cond_v2_impl", ":op_def_registry", "//tensorflow/core:protos_all_py", ], @@ -1052,7 +1054,6 @@ tf_gen_op_wrapper_private_py( name = "functional_ops_gen", visibility = [ "//learning/brain/python/ops:__pkg__", - "//tensorflow/contrib/control_flow:__pkg__", ], ) @@ -1830,6 +1831,7 @@ py_library( "tensor_shape", ":array_ops", ":array_ops_gen", + ":cond_v2_impl", ":constant_op", ":control_flow_ops_gen", ":control_flow_util", @@ -1858,6 +1860,37 @@ py_library( ], ) +py_library( + name = "cond_v2", + srcs = [ + "ops/cond_v2.py", + ], + srcs_version = "PY2AND3", + deps = [ + ":cond_v2_impl", + ":function", + ":function_def_to_graph", + ":gradients", + ], +) + +py_library( + name = "cond_v2_impl", + srcs = [ + "ops/cond_v2_impl.py", + ], + srcs_version = "PY2AND3", + deps = [ + ":array_ops", + ":c_api_util", + ":framework_ops", + ":functional_ops_gen", + ":pywrap_tensorflow", + ":util", + "//tensorflow/core:protos_all_py", + ], +) + py_library( name = "ctc_ops", srcs = ["ops/ctc_ops.py"], @@ -1940,6 +1973,7 @@ py_library( ":array_grad", ":array_ops", ":bitwise_ops", + ":cond_v2_impl", ":control_flow_grad", ":control_flow_ops", ":control_flow_util", diff --git a/tensorflow/python/framework/function.py b/tensorflow/python/framework/function.py index 002a3d3be5..6525607fae 100644 --- a/tensorflow/python/framework/function.py +++ b/tensorflow/python/framework/function.py @@ -23,6 +23,7 @@ from __future__ import print_function import collections import hashlib +import sys from tensorflow.core.framework import attr_value_pb2 from tensorflow.core.framework import function_pb2 @@ -33,6 +34,7 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import graph_to_function_def from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops +from tensorflow.python.ops import cond_v2_impl from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variable_scope as vs from tensorflow.python.util import compat @@ -40,6 +42,9 @@ from tensorflow.python.util import tf_contextlib from tensorflow.python.util import tf_decorator from tensorflow.python.util import tf_inspect +# This is to avoid a circular dependency with cond_v2_impl. +cond_v2_impl._function = sys.modules[__name__] # pylint: disable=protected-access + class Defun(object): """Decorator used to define TensorFlow functions. diff --git a/tensorflow/python/framework/function_def_to_graph.py b/tensorflow/python/framework/function_def_to_graph.py index 4fecc41343..46c9c4c14a 100644 --- a/tensorflow/python/framework/function_def_to_graph.py +++ b/tensorflow/python/framework/function_def_to_graph.py @@ -18,6 +18,8 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import sys + from tensorflow.core.framework import graph_pb2 from tensorflow.core.framework import types_pb2 from tensorflow.core.framework import versions_pb2 @@ -25,6 +27,10 @@ from tensorflow.python.framework import function from tensorflow.python.framework import importer from tensorflow.python.framework import op_def_registry from tensorflow.python.framework import versions +from tensorflow.python.ops import cond_v2_impl + +# This is to avoid a circular dependency with cond_v2_impl. +cond_v2_impl._function_def_to_graph = sys.modules[__name__] # pylint: disable=protected-access def function_def_to_graph(fdef, input_shapes=None): diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD index 5d29c2e5f8..5796c874f9 100644 --- a/tensorflow/python/kernel_tests/BUILD +++ b/tensorflow/python/kernel_tests/BUILD @@ -3087,3 +3087,22 @@ tf_py_test( data = [":invalid_op.so"], tags = ["no_pip"], ) + +tf_py_test( + name = "cond_v2_test", + size = "small", + srcs = ["cond_v2_test.py"], + additional_deps = [ + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:cond_v2", + "//tensorflow/python:constant_op", + "//tensorflow/python:control_flow_ops", + "//tensorflow/python:dtypes", + "//tensorflow/python:framework", + "//tensorflow/python:framework_ops", + "//tensorflow/python:gradients", + "//tensorflow/python:training", + ], + grpc_enabled = True, +) diff --git a/tensorflow/contrib/control_flow/python/cond_v2_test.py b/tensorflow/python/kernel_tests/cond_v2_test.py similarity index 90% rename from tensorflow/contrib/control_flow/python/cond_v2_test.py rename to tensorflow/python/kernel_tests/cond_v2_test.py index 94ed3e130b..76bbd61604 100644 --- a/tensorflow/contrib/control_flow/python/cond_v2_test.py +++ b/tensorflow/python/kernel_tests/cond_v2_test.py @@ -19,11 +19,11 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.control_flow.python import cond_v2 from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops +from tensorflow.python.ops import cond_v2 from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import data_flow_ops from tensorflow.python.ops import gradients_impl @@ -37,15 +37,15 @@ from tensorflow.python.util import compat class NewCondTest(test.TestCase): def _testCond(self, true_fn, false_fn, train_vals): - pred = array_ops.placeholder(dtypes.bool, name="pred") + with self.test_session() as sess: + pred = array_ops.placeholder(dtypes.bool, name="pred") - expected = control_flow_ops.cond(pred, true_fn, false_fn, name="expected") - actual = cond_v2.cond_v2(pred, true_fn, false_fn, name="actual") + expected = control_flow_ops.cond(pred, true_fn, false_fn, name="expected") + actual = cond_v2.cond_v2(pred, true_fn, false_fn, name="actual") - expected_grad = gradients_impl.gradients(expected, train_vals) - actual_grad = gradients_impl.gradients(actual, train_vals) + expected_grad = gradients_impl.gradients(expected, train_vals) + actual_grad = gradients_impl.gradients(actual, train_vals) - with self.test_session() as sess: expected_val, actual_val, expected_grad_val, actual_grad_val = sess.run( (expected, actual, expected_grad, actual_grad), {pred: True}) self.assertEqual(expected_val, actual_val) @@ -85,17 +85,17 @@ class NewCondTest(test.TestCase): self._testCond(true_fn, false_fn, [y]) def testNoInputs(self): - pred = array_ops.placeholder(dtypes.bool, name="pred") + with self.test_session() as sess: + pred = array_ops.placeholder(dtypes.bool, name="pred") - def true_fn(): - return constant_op.constant(1.0) + def true_fn(): + return constant_op.constant(1.0) - def false_fn(): - return constant_op.constant(2.0) + def false_fn(): + return constant_op.constant(2.0) - out = cond_v2.cond_v2(pred, true_fn, false_fn) + out = cond_v2.cond_v2(pred, true_fn, false_fn) - with self.test_session() as sess: self.assertEqual(sess.run(out, {pred: True}), [1.0]) self.assertEqual(sess.run(out, {pred: False}), [2.0]) @@ -131,20 +131,20 @@ class NewCondTest(test.TestCase): self.assertIn("foo_cond_1_false", ops.get_default_graph()._functions) def testSecondDerivative(self): - pred = array_ops.placeholder(dtypes.bool, name="pred") - x = constant_op.constant(3.0, name="x") + with self.test_session() as sess: + pred = array_ops.placeholder(dtypes.bool, name="pred") + x = constant_op.constant(3.0, name="x") - def true_fn(): - return math_ops.pow(x, 3) + def true_fn(): + return math_ops.pow(x, 3) - def false_fn(): - return x + def false_fn(): + return x - cond = cond_v2.cond_v2(pred, true_fn, false_fn, name="cond") - cond_grad = gradients_impl.gradients(cond, [x]) - cond_grad_grad = gradients_impl.gradients(cond_grad, [x]) + cond = cond_v2.cond_v2(pred, true_fn, false_fn, name="cond") + cond_grad = gradients_impl.gradients(cond, [x]) + cond_grad_grad = gradients_impl.gradients(cond_grad, [x]) - with self.test_session() as sess: # d[x^3]/dx = 3x^2 true_val = sess.run(cond_grad, {pred: True}) self.assertEqual(true_val, [27.0]) @@ -178,14 +178,14 @@ class NewCondTest(test.TestCase): meta_graph = saver.export_meta_graph() with ops.Graph().as_default() as g: - saver.import_meta_graph(meta_graph) - x = ops.get_collection("x")[0] - pred = ops.get_collection("pred")[0] - cond = ops.get_collection("cond") - cond_grad = gradients_impl.gradients(cond, [x], name="cond_grad") - cond_grad_grad = gradients_impl.gradients( - cond_grad, [x], name="cond_grad_grad") with self.test_session(graph=g) as sess: + saver.import_meta_graph(meta_graph) + x = ops.get_collection("x")[0] + pred = ops.get_collection("pred")[0] + cond = ops.get_collection("cond") + cond_grad = gradients_impl.gradients(cond, [x], name="cond_grad") + cond_grad_grad = gradients_impl.gradients( + cond_grad, [x], name="cond_grad_grad") # d[x^3]/dx = 3x^2 true_val = sess.run(cond_grad, {pred: True}) self.assertEqual(true_val, [27.0]) diff --git a/tensorflow/contrib/control_flow/__init__.py b/tensorflow/python/ops/cond_v2.py similarity index 66% rename from tensorflow/contrib/control_flow/__init__.py rename to tensorflow/python/ops/cond_v2.py index 582af2cf10..76173e0f30 100644 --- a/tensorflow/contrib/control_flow/__init__.py +++ b/tensorflow/python/ops/cond_v2.py @@ -11,11 +11,12 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -# ============================================================================== +# ============================================================================= +"""cond_v2 wrapper module. -"""New implementations of TF control flow ops. - -@@cond_v2 +This imports the cond_v2 method and all necessary dependencies (this is to avoid +circular dependencies in the cond_v2 implementation). See cond_v2_impl for more +information. """ from __future__ import absolute_import @@ -23,9 +24,9 @@ from __future__ import division from __future__ import print_function # pylint: disable=unused-import -from tensorflow.contrib.control_flow.python.cond_v2 import cond_v2 -# pylint: enable=unused-import +from tensorflow.python.framework import function +from tensorflow.python.framework import function_def_to_graph +from tensorflow.python.ops import gradients_impl -from tensorflow.python.util.all_util import remove_undocumented - -remove_undocumented(__name__) +from tensorflow.python.ops.cond_v2_impl import cond_v2 +# pylint: enable=unused-import diff --git a/tensorflow/contrib/control_flow/python/cond_v2.py b/tensorflow/python/ops/cond_v2_impl.py similarity index 94% rename from tensorflow/contrib/control_flow/python/cond_v2.py rename to tensorflow/python/ops/cond_v2_impl.py index 90371cd8d7..d827df7742 100644 --- a/tensorflow/contrib/control_flow/python/cond_v2.py +++ b/tensorflow/python/ops/cond_v2_impl.py @@ -17,6 +17,10 @@ This is a version of cond that emits a single If op, as well as the gradient function for If ops produced by cond_v2. This will eventually replace the current tf.cond implementation once it reaches feature and performance parity. + +NOTE: most users of cond_v2 should import cond_v2, not this module! This module +does not contain all the necessary imports to prevent circular dependencies, +while cond_v2 does. """ from __future__ import absolute_import @@ -25,15 +29,18 @@ from __future__ import print_function from tensorflow.python import pywrap_tensorflow as c_api from tensorflow.python.framework import c_api_util -from tensorflow.python.framework import function -from tensorflow.python.framework import function_def_to_graph from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import gen_functional_ops -from tensorflow.python.ops import gradients_impl from tensorflow.python.util import compat +# The following modules cannot be imported directly because they cause circular +# dependencies. These are set in each corresponding module. +_function = None +_function_def_to_graph = None +_gradients_impl = None + # NOTE(skyewm): TensorFlow uses protected class methods and fields to signify # that they aren't part of the official public API. These protected members # often need to be used by implementation code however. Rather than litter the @@ -58,14 +65,14 @@ def cond_v2(pred, true_fn, false_fn, name="cond"): func_name_prefix = scope.replace("/", "_") - true_graph = function.func_graph_from_py_func( + true_graph = _function.func_graph_from_py_func( true_fn, [], [], name="%strue" % func_name_prefix, device=caller_device, colocation_stack=caller_colocation_stack, collections_ref=caller_collection_ref, container=caller_container) - false_graph = function.func_graph_from_py_func( + false_graph = _function.func_graph_from_py_func( false_fn, [], [], name="%sfalse" % func_name_prefix, device=caller_device, @@ -169,11 +176,13 @@ def _get_func_graphs(if_op): A 2-tuple of the `_FuncGraph`s of the then_branch and else_branch. """ def _get_func_graph_for_branch(branch_name): + """Generates and returns a _FuncGraph for the given branch.""" extra_inputs = if_op.inputs[1:] # First input is pred. input_shapes = [t.shape for t in extra_inputs] func_name = if_op.get_attr(branch_name).name fdef = if_op.graph._get_function(func_name).definition - func_graph = function_def_to_graph.function_def_to_graph(fdef, input_shapes) + func_graph = _function_def_to_graph.function_def_to_graph( + fdef, input_shapes) func_graph.extra_inputs = extra_inputs func_graph.extra_args = func_graph.inputs func_graph._captured = dict(zip(extra_inputs, func_graph.inputs)) @@ -205,7 +214,7 @@ def _grad_fn(func_graph, grads): ys = [] grad_ys = [] for y, grad_y in zip(func_graph.outputs, grads): - if not gradients_impl._IsTrainable(y): + if not _gradients_impl._IsTrainable(y): continue ys.append(y) grad_ys.append(grad_y) @@ -214,7 +223,7 @@ def _grad_fn(func_graph, grads): # func_graph in the current graph, which requires capturing tensors from # func_graph. The captured func_graph tensors are resolved to external tensors # in _get_grad_inputs. - result = gradients_impl._GradientsHelper( + result = _gradients_impl._GradientsHelper( ys, func_graph.inputs, grad_ys=grad_ys, src_graph=func_graph) @@ -230,8 +239,8 @@ def _grad_fn(func_graph, grads): def _create_grad_func(func_graph, grads, name): """Returns the _FuncGraph representation of _grad_fn.""" - return function.func_graph_from_py_func(lambda: _grad_fn(func_graph, grads), - [], [], name) + return _function.func_graph_from_py_func(lambda: _grad_fn(func_graph, grads), + [], [], name) def _get_grad_inputs(if_op, cond_graph, grad_graph): @@ -297,8 +306,8 @@ def _create_new_tf_function(func_graph): # TODO(b/109833212): this sucks, we're serializing the TF_Function*, # deserializing it into a Python FunctionDef, then reserializing it to create # a new TF_Function that we add to the graph. - fdef = function.function_def_from_tf_function(c_func) - defined_func = function._from_definition(fdef) + fdef = _function.function_def_from_tf_function(c_func) + defined_func = _function._from_definition(fdef) defined_func.add_to_graph(ops.get_default_graph()) return func_graph.name diff --git a/tensorflow/python/ops/control_flow_ops.py b/tensorflow/python/ops/control_flow_ops.py index 2e5a801f8e..3ae7cf21ed 100644 --- a/tensorflow/python/ops/control_flow_ops.py +++ b/tensorflow/python/ops/control_flow_ops.py @@ -24,6 +24,7 @@ from __future__ import print_function import abc import collections import functools +import os import six @@ -38,6 +39,7 @@ from tensorflow.python.framework import sparse_tensor from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import tensor_util from tensorflow.python.ops import array_ops +from tensorflow.python.ops import cond_v2_impl from tensorflow.python.ops import control_flow_util as util from tensorflow.python.ops import gen_array_ops from tensorflow.python.ops import gen_control_flow_ops @@ -57,6 +59,10 @@ from tensorflow.python.util import nest from tensorflow.python.util import tf_should_use from tensorflow.python.util.tf_export import tf_export + +_ENABLE_COND_V2 = os.getenv("TF_ENABLE_COND_V2", "0") != "0" + + # We override the 'tuple' for a control flow op, so we keep python's # existing 'tuple' for later use in this module. _basetuple = tuple @@ -1994,6 +2000,9 @@ def cond(pred, ``` """ + if _ENABLE_COND_V2: + return cond_v2_impl.cond_v2(pred, true_fn, false_fn, name) + # We needed to make true_fn/false_fn keyword arguments for # backwards-compatibility. This check exists so that we can convert back to # having them be positional arguments. diff --git a/tensorflow/python/ops/gradients_impl.py b/tensorflow/python/ops/gradients_impl.py index 7385cb7585..169efd401c 100644 --- a/tensorflow/python/ops/gradients_impl.py +++ b/tensorflow/python/ops/gradients_impl.py @@ -20,6 +20,7 @@ from __future__ import print_function import collections import contextlib +import sys import warnings import numpy as np @@ -36,6 +37,7 @@ from tensorflow.python.framework import tensor_util from tensorflow.python.ops import array_grad # pylint: disable=unused-import from tensorflow.python.ops import array_ops from tensorflow.python.ops import check_ops # pylint: disable=unused-import +from tensorflow.python.ops import cond_v2_impl from tensorflow.python.ops import control_flow_grad # pylint: disable=unused-import from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import control_flow_util @@ -53,6 +55,9 @@ from tensorflow.python.ops import tensor_array_ops from tensorflow.python.platform import tf_logging as logging from tensorflow.python.util.tf_export import tf_export +# This is to avoid a circular dependency with cond_v2_impl. +cond_v2_impl._gradients_impl = sys.modules[__name__] # pylint: disable=protected-access + # Warn the user if we convert a sparse representation to dense with at # least this number of elements. _LARGE_SPARSE_NUM_ELEMENTS = 100000000 diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD index b9e1a61d5d..8fe5e6ff1b 100644 --- a/tensorflow/tools/pip_package/BUILD +++ b/tensorflow/tools/pip_package/BUILD @@ -92,6 +92,7 @@ COMMON_PIP_DEPS = [ "//tensorflow/contrib/timeseries:timeseries_pip", "//tensorflow/contrib/tpu", "//tensorflow/examples/tutorials/mnist:package", + "//tensorflow/python:cond_v2", "//tensorflow/python:distributed_framework_test_lib", "//tensorflow/python:meta_graph_testdata", "//tensorflow/python:spectral_ops_test_util", -- GitLab From 5e9a39d6ad6eee207a7af88bb1bbe1deefb8bbb2 Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Fri, 15 Jun 2018 15:25:33 -0700 Subject: [PATCH 540/816] Reflow comments; NFC PiperOrigin-RevId: 200783258 --- tensorflow/stream_executor/stream.h | 12 +++++------- 1 file changed, 5 insertions(+), 7 deletions(-) diff --git a/tensorflow/stream_executor/stream.h b/tensorflow/stream_executor/stream.h index 3da1b856d6..a32f4105ad 100644 --- a/tensorflow/stream_executor/stream.h +++ b/tensorflow/stream_executor/stream.h @@ -156,14 +156,13 @@ class Stream { const TypedKernel &kernel, Args... args); // Record a "start" event for the interval timer at this point in the - // stream's - // execution (relative to the previously and subsequently enqueued items in - // the stream's execution). Streams may be started/stopped multiple times. + // stream's execution (relative to the previously and subsequently enqueued + // items in the stream's execution). Streams may be started/stopped multiple + // times. Stream &ThenStartTimer(Timer *t); // Record a "stop" event for the interval timer at this point in the - // stream's - // execution. See also Stream::ThenStartTimer. + // stream's execution. See also Stream::ThenStartTimer. Stream &ThenStopTimer(Timer *t); // TODO(leary) If work is added to the stream that is being depended upon, @@ -179,8 +178,7 @@ class Stream { // // Checks that a stream does not wait for itself, and it is up to the // user to guarantee that a stream does not come to wait on itself in a - // cyclic - // manner; in that case, behavior is undefined. + // cyclic manner; in that case, behavior is undefined. // // N.B. Base recursion case for the variadic ThenWaitFor. Stream &ThenWaitFor(Stream *other); -- GitLab From b8861afe21d8d654c2a726cabd82069faca04532 Mon Sep 17 00:00:00 2001 From: Reed Wanderman-Milne Date: Fri, 15 Jun 2018 15:27:11 -0700 Subject: [PATCH 541/816] Automatic cast layer inputs to the layer's dtype. This makes it more convenient to use layer of different dtypes in a model. Instead of having to manually cast intermediate tensors between layers of different dtypes, they will automatically be casted. This is also useful for the upcoming mixed precision API. PiperOrigin-RevId: 200783477 --- tensorflow/python/keras/engine/base_layer.py | 68 ++++++- tensorflow/python/keras/engine/network.py | 20 ++- .../python/keras/engine/topology_test.py | 166 ++++++++++++++++++ tensorflow/python/layers/base.py | 12 +- tensorflow/python/layers/base_test.py | 59 +++++++ 5 files changed, 313 insertions(+), 12 deletions(-) diff --git a/tensorflow/python/keras/engine/base_layer.py b/tensorflow/python/keras/engine/base_layer.py index 4814275fd5..751cc5a8d5 100644 --- a/tensorflow/python/keras/engine/base_layer.py +++ b/tensorflow/python/keras/engine/base_layer.py @@ -41,6 +41,7 @@ from tensorflow.python.keras.utils.generic_utils import to_snake_case # pylint: from tensorflow.python.keras.utils.tf_utils import is_tensor_or_tensor_list # pylint: disable=unused-import from tensorflow.python.ops import array_ops from tensorflow.python.ops import init_ops +from tensorflow.python.ops import math_ops from tensorflow.python.ops import variable_scope as vs from tensorflow.python.ops import variables as tf_variables from tensorflow.python.training.checkpointable import base as checkpointable @@ -88,16 +89,24 @@ class Layer(checkpointable.CheckpointableBase): once. Should actually perform the logic of applying the layer to the input tensors (which should be passed in as the first argument). + By default, layers will cast all their inputs and arguments to the layer's + dtype, if set. This is useful for creating a model with multiple dtypes, as + the user does not need to explicitly cast tensors. If a `Layer` descendant + wants only a subset of inputs/arguments to be casted, or none of them, + `_cast_inputs_and_args()` should be overridden. + Arguments: trainable: Boolean, whether the layer's variables should be trainable. name: String name of the layer. - dtype: Default dtype of the layer's weights (default of `None` means use the - type of the first input). + dtype: Default dtype of the layer's weights and computations (default of + `None` means use the type of the first input). If not None, inputs will be + casted to this dtype. Read-only properties: name: The name of the layer (string). - dtype: Default dtype of the layer's weights (default of `None` means use the - type of the first input). + dtype: Default dtype of the layer's weights and computations. (default of + `None` means use the type of the first input). If not None, inputs will be + casted to this dtype. trainable_variables: List of trainable variables. non_trainable_variables: List of non-trainable variables. variables: List of all variables of this layer, trainable and @@ -666,6 +675,13 @@ class Layer(checkpointable.CheckpointableBase): kwargs['mask'] = previous_mask input_shapes = None + # We only cast inputs if self.dtype was previous set, which occurs when + # a dtype was passed to the constructor, or when this layer has previously + # been called. We cast floating point inputs to self.dtype to ensure the + # layer runs with the correct dtype. + # TODO(b/77478433): Perhaps we should only cast inputs if a dtype was passed + # to the constructor, not when the layer has previously been called. + inputs_should_be_cast = (self.dtype is not None) with ops.name_scope(self._name_scope()): if not self.built: @@ -700,7 +716,12 @@ class Layer(checkpointable.CheckpointableBase): self._assert_input_compatibility(inputs) if not in_deferred_mode: - outputs = self.call(inputs, *args, **kwargs) + if inputs_should_be_cast: + cast_inputs, cast_args, cast_kwargs = self._cast_inputs_and_args( + inputs, *args, **kwargs) + else: + cast_inputs, cast_args, cast_kwargs = inputs, args, kwargs + outputs = self.call(cast_inputs, *cast_args, **cast_kwargs) if outputs is None: raise ValueError('A layer\'s `call` method should return a Tensor ' 'or a list of Tensors, not None (layer: ' + @@ -715,6 +736,9 @@ class Layer(checkpointable.CheckpointableBase): output_shapes = nest.flatten(output_shapes) outputs = [ # TODO(fchollet): name the deferred tensors? + # TODO(b/77478433): Compute the proper dtype here, by adding a + # compute_output_dtype method. Currently keras Models do not + # properly compute the output dtype. DeferredTensor(shape=shape, dtype=self._dtype) for shape in output_shapes ] @@ -773,6 +797,40 @@ class Layer(checkpointable.CheckpointableBase): """ return self.__call__(inputs, *args, **kwargs) + def _cast_fn(self, x): + """If x is a tensor, casts to this layer's dtype.""" + # TODO(b/77478433): Cast tensor-like things like SparseTensors, Variables, + # ResourceVariables, etc. + if (isinstance(x, ops.Tensor) and x.dtype.is_floating and + dtypes.as_dtype(self.dtype).is_floating): + return math_ops.cast(x, self.dtype) + else: + return x + + def _cast_inputs_and_args(self, inputs, *args, **kwargs): + """Casts the inputs, args, and kwargs of a layer to the layer's dtype. + + This is intended to be potentially overridden by layer subclasses. By + default, inputs, args, and kwargs are automatically casted to the layer's + dtype. Overriding this method allows only some of the inputs, args, and + kwargs (or none of them) to be casted. + + Does not modify inputs, args, or kwargs. + + Args: + inputs: The inputs to self.__call__. + *args: The args to self.__call__. + **kwargs: The kwargs to self.__call__. + + Returns: + The tuple (new_inputs, new_args, new_kwargs), where tensors in inputs, + args, and kwargs have been casted to self.dtype. + """ + new_inputs = nest.map_structure(self._cast_fn, inputs) + new_args = nest.map_structure(self._cast_fn, args) + new_kwargs = nest.map_structure(self._cast_fn, kwargs) + return new_inputs, new_args, new_kwargs + def _set_learning_phase_metadata(self, inputs, outputs): # Update learning phase info. To work with subclassed models, # this should be done even if Keras metadata is absent. diff --git a/tensorflow/python/keras/engine/network.py b/tensorflow/python/keras/engine/network.py index e7ec237163..a4cd017d60 100644 --- a/tensorflow/python/keras/engine/network.py +++ b/tensorflow/python/keras/engine/network.py @@ -887,8 +887,16 @@ class Network(base_layer.Layer): if 'training' in tf_inspect.getargspec(layer.call).args: kwargs.setdefault('training', training) + if layer.dtype is not None: + cast_computed_tensors, cast_args, cast_kwargs = ( + layer._cast_inputs_and_args(computed_tensor, **kwargs)) + else: + cast_computed_tensors = [computed_tensor] + cast_args = () + cast_kwargs = kwargs + output_tensors = nest.flatten( - layer.call(computed_tensor, **kwargs)) + layer.call(cast_computed_tensors, *cast_args, **cast_kwargs)) if hasattr(layer, 'compute_mask'): output_masks = layer.compute_mask(computed_tensor, computed_mask) @@ -908,8 +916,16 @@ class Network(base_layer.Layer): if 'training' in tf_inspect.getargspec(layer.call).args: kwargs.setdefault('training', training) + if layer.dtype is not None: + cast_computed_tensors, cast_args, cast_kwargs = ( + layer._cast_inputs_and_args(computed_tensors, **kwargs)) + else: + cast_computed_tensors = computed_tensors + cast_args = () + cast_kwargs = kwargs + output_tensors = nest.flatten( - layer.call(computed_tensors, **kwargs)) + layer.call(cast_computed_tensors, *cast_args, **cast_kwargs)) if hasattr(layer, 'compute_mask'): output_masks = layer.compute_mask(computed_tensors, diff --git a/tensorflow/python/keras/engine/topology_test.py b/tensorflow/python/keras/engine/topology_test.py index 183e26e8bf..7fbe6b80ad 100644 --- a/tensorflow/python/keras/engine/topology_test.py +++ b/tensorflow/python/keras/engine/topology_test.py @@ -18,6 +18,8 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import collections + import numpy as np from tensorflow.python import keras @@ -910,6 +912,170 @@ class TopologyConstructionTest(test.TestCase): assert out.shape == (4, 3, 2, 1) self.assertAllClose(out, x * 0.2 + x * 0.3, atol=1e-4) + @test_util.run_in_graph_and_eager_modes() + def test_casting_args(self): + # args of type B will be casted, as we cast elements of namedtuples + B = collections.namedtuple('B', ['x', 'y', 'z']) # pylint: disable=invalid-name + + # args of type C will not be casted, as we do not look at object + # attributes for tensors to cast + class C(object): + + def __init__(self, w): + self.w = w + + inp = array_ops.ones((1,), name='input', dtype='float64') + a = array_ops.ones((1,), name='a', dtype='float64') + b = B(array_ops.ones((1,), name='a', dtype='float64'), None, + np.ones((1,), 'float64')) # Numpy tensors should not be casted + c = C(array_ops.ones((1,), name='a', dtype='float64')) + + # Test inputs are automatically casted. + class MyLayer(keras.layers.Layer): + + def call(self, inputs, a, b, c): + self.a = a + self.b = b + self.c = c + return inputs + + def compute_output_shape(self, input_shape): + return input_shape + + layer = MyLayer(dtype='float16') + out = layer(inp, a=a, b=b, c=c) + self.assertEqual(out.dtype, dtypes.float16) + self.assertEqual(layer.a.dtype, dtypes.float16) + self.assertEqual(layer.b.x.dtype, dtypes.float16) + self.assertEqual(layer.b.y, None) + self.assertEqual(layer.b.z.dtype, np.float64) + self.assertEqual(layer.c.w.dtype, dtypes.float64) + + # Test overriding _cast_inputs_and_args + class MyLayerOverrideCastInputs(MyLayer): + + def _cast_inputs_and_args(self, inputs, a, b, c): + new_inputs = self._cast_fn(inputs) + new_a = a + new_b = b + new_c = C(self._cast_fn(c.w)) + return new_inputs, (new_a, new_b, new_c), {} + + layer = MyLayerOverrideCastInputs(dtype='float16') + out = layer(inp, a=a, b=b, c=c) + self.assertEqual(out.dtype, dtypes.float16) + self.assertEqual(layer.a.dtype, dtypes.float64) + self.assertEqual(layer.b.x.dtype, dtypes.float64) + self.assertEqual(layer.b.y, None) + self.assertEqual(layer.b.z.dtype, np.float64) + self.assertEqual(layer.c.w.dtype, dtypes.float16) + + @test_util.run_in_graph_and_eager_modes() + def test_do_not_cast_ints(self): + class MyLayer(keras.layers.Layer): + + def build(self, input_shape): + self.v = self.add_variable('v', (), 'int32') + super(MyLayer, self).build(input_shape) + + def call(self, inputs): + return inputs + self.v + + def compute_output_shape(self, input_shape): + return input_shape + + a = array_ops.ones((10, 32), dtype='int32') + layer = MyLayer(dtype='float32') + b = layer(a) + self.assertEqual(layer.v.dtype.base_dtype, dtypes.int32) + self.assertEqual(b.dtype, dtypes.int32) + + @test_util.run_in_graph_and_eager_modes() + def test_casting_when_dtype_not_passed_to_constructor(self): + class MyLayer(keras.layers.Layer): + + def call(self, a): + self.a = a + return a + + def compute_output_shape(self, input_shape): + return input_shape + + # Do not cast inputs for the first __call__ if a dtype is not passed to the + # constructor. + a = array_ops.ones((10, 32), dtype='float64') + layer = MyLayer() + self.assertEqual(layer.dtype, None) + b = layer(a) + self.assertEqual(layer.dtype, 'float64') + self.assertEqual(layer.a.dtype, dtypes.float64) + self.assertEqual(b.dtype, dtypes.float64) + + # For a subsequent __call__, the layer's dtype has been set so inputs should + # be casted to the dtype of the input to the first __call__. + a = array_ops.ones((10, 32), dtype='float32') + b = layer(a) + self.assertEqual(layer.dtype, 'float64') + self.assertEqual(layer.a.dtype, dtypes.float64) + self.assertEqual(b.dtype, dtypes.float64) + + @test_util.run_in_graph_and_eager_modes() + def test_casting_with_build_before_call(self): + a = keras.Input(shape=(32,), name='input_a', dtype='float32') + dense_layer = keras.layers.Dense(16, dtype='float16') + dense_layer.build((32,)) + b = dense_layer(a) + + self.assertEqual(dense_layer.dtype, 'float16') + self.assertEqual(dense_layer.input, a) + self.assertEqual(dense_layer.output, b) + self.assertEqual(a.dtype, dtypes.float32) + self.assertEqual(dense_layer.kernel.dtype.base_dtype, dtypes.float16) + self.assertEqual(dense_layer.bias.dtype.base_dtype, dtypes.float16) + self.assertEqual(b.dtype, dtypes.float16) + + @test_util.run_in_graph_and_eager_modes() + def test_casting_in_network(self): + + class SingleInputLayer(keras.layers.Layer): + + def call(self, a): + self.a = a + return a + + def compute_output_shape(self, input_shape): + return input_shape + + class MultiInputLayer(keras.layers.Layer): + + def call(self, inputs): + a, b = inputs + self.a = a + self.b = b + return a + b + + def compute_output_shape(self, input_shapes): + return input_shapes[0] + + x = keras.layers.Input((32,), dtype='float64') + layer1 = SingleInputLayer() + layer2 = SingleInputLayer(dtype='float32') + layer3 = MultiInputLayer(dtype='float16') + i1 = layer1(x) + i2 = layer2(i1) + y = layer3((i1, i2)) + network = keras.engine.Network(x, y) + x2 = array_ops.ones((32,), dtype='float16') + y2 = network(x2) + self.assertEqual(layer1.dtype, dtypes.float64) + self.assertEqual(layer1.a.dtype, dtypes.float64) + self.assertEqual(layer2.dtype, dtypes.float32) + self.assertEqual(layer2.a.dtype, dtypes.float32) + self.assertEqual(layer3.dtype, dtypes.float16) + self.assertEqual(layer3.a.dtype, dtypes.float16) + self.assertEqual(layer3.b.dtype, dtypes.float16) + self.assertEqual(y2.dtype, dtypes.float16) + class DeferredModeTest(test.TestCase): diff --git a/tensorflow/python/layers/base.py b/tensorflow/python/layers/base.py index eda036ece4..abbe9d0c56 100644 --- a/tensorflow/python/layers/base.py +++ b/tensorflow/python/layers/base.py @@ -43,13 +43,15 @@ class Layer(base_layer.Layer): Arguments: trainable: Boolean, whether the layer's variables should be trainable. name: String name of the layer. - dtype: Default dtype of the layer's weights (default of `None` means use the - type of the first input). + dtype: Default dtype of the layer's weights and computations (default of + `None` means use the type of the first input). If not None, inputs will be + casted to this dtype. Read-only properties: name: The name of the layer (string). - dtype: Default dtype of the layer's weights (default of `None` means use the - type of the first input). + dtype: Default dtype of the layer's weights and computations. (default of + `None` means use the type of the first input). If not None, inputs will be + casted to this dtype. trainable_variables: List of trainable variables. non_trainable_variables: List of non-trainable variables. variables: List of all variables of this layer, trainable and @@ -191,7 +193,7 @@ class Layer(base_layer.Layer): RuntimeError: If called with partioned variable regularization and eager execution is enabled. """ - + def _should_add_regularizer(variable, existing_variable_set): if isinstance(variable, tf_variables.PartitionedVariable): for var in variable: diff --git a/tensorflow/python/layers/base_test.py b/tensorflow/python/layers/base_test.py index ab49e37b90..15448c6be8 100644 --- a/tensorflow/python/layers/base_test.py +++ b/tensorflow/python/layers/base_test.py @@ -25,6 +25,8 @@ from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import test_util +from tensorflow.python.keras import backend +from tensorflow.python.keras.engine import base_layer as keras_base_layer from tensorflow.python.layers import base as base_layers from tensorflow.python.layers import core as core_layers from tensorflow.python.ops import array_ops @@ -589,6 +591,63 @@ class BaseLayerTest(test.TestCase): ValueError, 'Input graph and Layer graph are not the same'): layer.apply(constant_op.constant([[1.]])) + @test_util.run_in_graph_and_eager_modes() + def testOnlyCastInputsWhenDtypeSpecified(self): + class MyLayerBase(keras_base_layer.Layer): + + def call(self, inputs): + self.x = inputs[0] + self.y = inputs[1] + return self.x + 1, self.y + 2 + + # Inherit from both the Keras Layer and base_layers.Layer to ensure we + # still get the base_layers.Layer behavior when directly inheriting from + # the Keras Layer. + class MyLayer(MyLayerBase, base_layers.Layer): + pass + + # Test inputs are casted. + input1 = array_ops.constant(1.0, dtype=dtypes.float64) + input2 = array_ops.constant(1.0, dtype=dtypes.float32) + layer = MyLayer(dtype=dtypes.float16) + output1, output2 = layer([input1, input2]) + self.assertEqual(output1.dtype, dtypes.float16) + self.assertEqual(output2.dtype, dtypes.float16) + + # Test inputs are not casted. + input1 = array_ops.constant(1.0, dtype=dtypes.float64) + input2 = array_ops.constant(1.0, dtype=dtypes.float32) + layer = MyLayer() + output1, output2 = layer([input1, input2]) + self.assertEqual(output1.dtype, dtypes.float64) + self.assertEqual(output2.dtype, dtypes.float32) + + @test_util.run_in_graph_and_eager_modes() + def testVariablesDefaultToFloat32(self): + class MyLayerBase(keras_base_layer.Layer): + + def build(self, input_shape): + self.x = self.add_weight('x', ()) + + def call(self, inputs): + return inputs + self.x + + # Inherit from both the Keras Layer and base_layers.Layer to ensure we + # still get the base_layers.Layer behavior when directly inheriting from + # the Keras Layer. + class MyLayer(MyLayerBase, base_layers.Layer): + pass + + try: + # The behavior of Keras Layers is to default to floatx. Ensure that this + # behavior is overridden to instead default to float32. + backend.set_floatx('float16') + layer = MyLayer() + layer.build(()) + self.assertEqual(layer.dtype, None) + self.assertEqual(layer.x.dtype.base_dtype, dtypes.float32) + finally: + backend.set_floatx('float32') if __name__ == '__main__': test.main() -- GitLab From 1d74a69443f741e69f9f52cb6bc2940b4d4ae3b7 Mon Sep 17 00:00:00 2001 From: Skye Wanderman-Milne Date: Fri, 15 Jun 2018 15:29:33 -0700 Subject: [PATCH 542/816] Enable fetching shapes from the C API by default. Prior this change, we were using the C API for everything except Tensor.shape calls, which returned the result from the original Python shape inference code. With this change, we use the C API in this case as well. The C API has better shape inference, so this has the effect of returning more precise shapes in some cases. This change can be disabled by setting the environment variable TF_C_API_GRAPH_CONSTRUCTION_SHAPES=0. However, this toggle will be removed altogether in the near future. This also fixes a bug in the SWIG that could cause large shape dimensions to be incorrect. PiperOrigin-RevId: 200783822 --- .../contrib/signal/python/kernel_tests/spectral_ops_test.py | 2 +- tensorflow/python/client/tf_session.i | 2 +- tensorflow/python/framework/ops.py | 2 +- tensorflow/python/kernel_tests/slice_op_test.py | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/tensorflow/contrib/signal/python/kernel_tests/spectral_ops_test.py b/tensorflow/contrib/signal/python/kernel_tests/spectral_ops_test.py index 03d6da7765..f10d78259a 100644 --- a/tensorflow/contrib/signal/python/kernel_tests/spectral_ops_test.py +++ b/tensorflow/contrib/signal/python/kernel_tests/spectral_ops_test.py @@ -147,7 +147,7 @@ class SpectralOpsTest(test.TestCase): inverse_stft = spectral_ops.inverse_stft(stft, frame_length=8, fft_length=16, frame_step=8) expected_length = (stft.shape[0] - 1) * 8 + 8 - self.assertAllEqual([None], inverse_stft.shape.as_list()) + self.assertAllEqual([256], inverse_stft.shape.as_list()) self.assertAllEqual([expected_length], inverse_stft.eval().shape) def test_stft_and_inverse_stft(self): diff --git a/tensorflow/python/client/tf_session.i b/tensorflow/python/client/tf_session.i index 1db1432d65..def730371d 100644 --- a/tensorflow/python/client/tf_session.i +++ b/tensorflow/python/client/tf_session.i @@ -610,7 +610,7 @@ def TF_Reset(target, containers=None, config=None): } for (size_t i = 0; i < $1.size(); ++i) { - PyList_SET_ITEM($result, i, PyInt_FromLong($1[i])); + PyList_SET_ITEM($result, i, PyLong_FromLong($1[i])); } } diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py index b2fd98f431..ec3c829840 100644 --- a/tensorflow/python/framework/ops.py +++ b/tensorflow/python/framework/ops.py @@ -63,7 +63,7 @@ from tensorflow.python.util.tf_export import tf_export # Temporary global switches determining if we should enable the work-in-progress # calls to the C API. These will be removed once all functionality is supported. _USE_C_API = True -_USE_C_SHAPES = os.getenv("TF_C_API_GRAPH_CONSTRUCTION_SHAPES", "0") is not "0" +_USE_C_SHAPES = os.getenv("TF_C_API_GRAPH_CONSTRUCTION_SHAPES", "1") != "0" def tensor_id(tensor): diff --git a/tensorflow/python/kernel_tests/slice_op_test.py b/tensorflow/python/kernel_tests/slice_op_test.py index 5fc9bef218..402f67619b 100644 --- a/tensorflow/python/kernel_tests/slice_op_test.py +++ b/tensorflow/python/kernel_tests/slice_op_test.py @@ -225,7 +225,7 @@ class SliceTest(test.TestCase): self.assertAllEqual(m1.get_shape().as_list(), [1, 2, 3]) m2 = array_ops.slice(z, [0, 0, 0], [constant_op.constant(1) + 0, 2, -1]) - self.assertAllEqual(m2.get_shape().as_list(), [None, 2, None]) + self.assertAllEqual(m2.get_shape().as_list(), [1, 2, 3]) def _testGradientSlice(self, input_shape, slice_begin, slice_size): -- GitLab From 44a854b85e50d0cdf519747cdb3d21de087b0444 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 15 Jun 2018 16:05:31 -0700 Subject: [PATCH 543/816] Some fixes to testInferenceInputType PiperOrigin-RevId: 200789288 --- tensorflow/contrib/lite/python/lite_test.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tensorflow/contrib/lite/python/lite_test.py b/tensorflow/contrib/lite/python/lite_test.py index 8c9d2c1651..a9475de474 100644 --- a/tensorflow/contrib/lite/python/lite_test.py +++ b/tensorflow/contrib/lite/python/lite_test.py @@ -267,7 +267,8 @@ class FromSessionTest(test_util.TensorFlowTestCase): self.assertTrue(num_items_graphviz_video > num_items_graphviz) def testInferenceInputType(self): - in_tensor = array_ops.placeholder(shape=[1, 16, 16, 3], dtype=dtypes.uint8) + in_tensor = array_ops.placeholder( + shape=[1, 16, 16, 3], dtype=dtypes.float32) out_tensor = in_tensor + in_tensor sess = session.Session() @@ -286,14 +287,13 @@ class FromSessionTest(test_util.TensorFlowTestCase): self.assertEqual('Placeholder', input_details[0]['name']) self.assertEqual(np.uint8, input_details[0]['dtype']) self.assertTrue(([1, 16, 16, 3] == input_details[0]['shape']).all()) - self.assertEqual((0., 0.), input_details[0]['quantization']) + self.assertEqual((1., 0.), input_details[0]['quantization']) output_details = interpreter.get_output_details() self.assertEqual(1, len(output_details)) self.assertEqual('add', output_details[0]['name']) - self.assertEqual(np.uint8, output_details[0]['dtype']) + self.assertEqual(np.float32, output_details[0]['dtype']) self.assertTrue(([1, 16, 16, 3] == output_details[0]['shape']).all()) - self.assertEqual((0., 0.), input_details[0]['quantization']) def testDefaultRangesStats(self): in_tensor = array_ops.placeholder( -- GitLab From 97eaebfa825df181b043b9847252547a3f437f07 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 15 Jun 2018 16:12:22 -0700 Subject: [PATCH 544/816] Split GradientBoostedDecisionTreeModel.train() to three steps. 1) Update stats 2) Update the number of examples visited. 3) If the number of examples reaches the target, grow the tree. PiperOrigin-RevId: 200790145 --- .../python/training/functions/gbdt_batch.py | 486 ++++++++++-------- 1 file changed, 268 insertions(+), 218 deletions(-) diff --git a/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py b/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py index 47698d45c8..ec1480b20c 100644 --- a/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py +++ b/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py @@ -325,6 +325,19 @@ class GradientBoostedDecisionTreeModel(object): learner_config.multi_class_strategy = ( learner_pb2.LearnerConfig.DIAGONAL_HESSIAN) + if logits_dimension == 1 or learner_config.multi_class_strategy == ( + learner_pb2.LearnerConfig.TREE_PER_CLASS): + self._gradient_shape = tensor_shape.scalar() + self._hessian_shape = tensor_shape.scalar() + else: + self._gradient_shape = tensor_shape.TensorShape([logits_dimension]) + if (learner_config.multi_class_strategy == + learner_pb2.LearnerConfig.FULL_HESSIAN): + self._hessian_shape = tensor_shape.TensorShape( + ([logits_dimension, logits_dimension])) + else: + # Diagonal hessian strategy. + self._hessian_shape = tensor_shape.TensorShape(([logits_dimension])) if (learner_config.growing_mode == learner_pb2.LearnerConfig.GROWING_MODE_UNSPECIFIED): learner_config.growing_mode = learner_pb2.LearnerConfig.LAYER_BY_LAYER @@ -372,6 +385,44 @@ class GradientBoostedDecisionTreeModel(object): learner_pb2.LearnerConfig.TREE_PER_CLASS and learner_config.num_classes == 2) self._output_leaf_index = output_leaf_index + # Create ensemble stats variables. + self._num_layer_examples = variables.Variable( + initial_value=array_ops.zeros([], dtypes.int64), + name="num_layer_examples", + trainable=False) + self._num_layer_steps = variables.Variable( + initial_value=array_ops.zeros([], dtypes.int64), + name="num_layer_steps", + trainable=False) + self._num_layers = variables.Variable( + initial_value=array_ops.zeros([], dtypes.int64), + name="num_layers", + trainable=False) + self._active_tree = variables.Variable( + initial_value=array_ops.zeros([], dtypes.int64), + name="active_tree", + trainable=False) + self._active_layer = variables.Variable( + initial_value=array_ops.zeros([], dtypes.int64), + name="active_layer", + trainable=False) + # Variable that becomes false once bias centering is done. + self._continue_centering = variables.Variable( + initial_value=self._center_bias, + name="continue_centering", + trainable=False) + # Create bias stats accumulator. + self._bias_stats_accumulator = stats_accumulator_ops.StatsAccumulator( + stamp_token=0, + gradient_shape=self._gradient_shape, + hessian_shape=self._hessian_shape, + name="BiasAccumulator") + # Create steps accumulator. + self._steps_accumulator = stats_accumulator_ops.StatsAccumulator( + stamp_token=0, + gradient_shape=tensor_shape.scalar(), + hessian_shape=tensor_shape.scalar(), + name="StepsAccumulator") def _predict_and_return_dict(self, ensemble_handle, ensemble_stamp, mode): """Runs prediction and returns a dictionary of the prediction results. @@ -522,14 +573,23 @@ class GradientBoostedDecisionTreeModel(object): return self._predict_and_return_dict(self._ensemble_handle, ensemble_stamp, mode) - def train(self, loss, predictions_dict, labels): - """Grows a new tree and adds it to the ensemble. + def _get_class_id(self, predictions_dict): + # Handle different multiclass strategies. + if (self._learner_config.multi_class_strategy == + learner_pb2.LearnerConfig.TREE_PER_CLASS and + self._logits_dimension != 1): + # Choose the class for which the tree is built (one vs rest). + return math_ops.to_int32( + predictions_dict[NUM_TREES_ATTEMPTED] % self._logits_dimension) + return constant_op.constant(-1, dtype=dtypes.int32) + + def update_stats(self, loss, predictions_dict): + """Update the accumulators with stats from this batch. Args: loss: A scalar tensor representing average loss of examples. predictions_dict: Dictionary of Rank 2 `Tensor` representing information about predictions per example. - labels: Rank 2 `Tensor` representing labels per example. Returns: An op that adds a new tree to the ensemble. @@ -556,13 +616,10 @@ class GradientBoostedDecisionTreeModel(object): aggregation_method=None)[0] strategy = self._learner_config.multi_class_strategy - class_id = constant_op.constant(-1, dtype=dtypes.int32) + class_id = self._get_class_id(predictions_dict) # Handle different multiclass strategies. if strategy == learner_pb2.LearnerConfig.TREE_PER_CLASS: # We build one vs rest trees. - gradient_shape = tensor_shape.scalar() - hessian_shape = tensor_shape.scalar() - if self._logits_dimension == 1: # We have only 1 score, gradients is of shape [batch, 1]. hessians = gradients_impl.gradients( @@ -579,11 +636,6 @@ class GradientBoostedDecisionTreeModel(object): hessian_list = self._diagonal_hessian(gradients, predictions) # Assemble hessian list into a tensor. hessians = array_ops.stack(hessian_list, axis=1) - - # Choose the class for which the tree is built (one vs rest). - class_id = math_ops.to_int32( - predictions_dict[NUM_TREES_ATTEMPTED] % self._logits_dimension) - # Use class id tensor to get the column with that index from gradients # and hessians. squeezed_gradients = array_ops.squeeze( @@ -592,15 +644,10 @@ class GradientBoostedDecisionTreeModel(object): _get_column_by_index(hessians, class_id)) else: # Other multiclass strategies. - gradient_shape = tensor_shape.TensorShape([self._logits_dimension]) - if strategy == learner_pb2.LearnerConfig.FULL_HESSIAN: - hessian_shape = tensor_shape.TensorShape( - ([self._logits_dimension, self._logits_dimension])) hessian_list = self._full_hessian(gradients, predictions) else: # Diagonal hessian strategy. - hessian_shape = tensor_shape.TensorShape(([self._logits_dimension])) hessian_list = self._diagonal_hessian(gradients, predictions) squeezed_gradients = gradients @@ -608,7 +655,7 @@ class GradientBoostedDecisionTreeModel(object): squeezed_hessians = hessians # Get the weights for each example for quantiles calculation, - weights = self._get_weights(hessian_shape, squeezed_hessians) + weights = self._get_weights(self._hessian_shape, squeezed_hessians) # Create all handlers ensuring resources are evenly allocated across PS. fc_name_idx = 0 @@ -640,8 +687,8 @@ class GradientBoostedDecisionTreeModel(object): num_quantiles=num_quantiles, dense_float_column=self._dense_floats[dense_float_column_idx], name=fc_name, - gradient_shape=gradient_shape, - hessian_shape=hessian_shape, + gradient_shape=self._gradient_shape, + hessian_shape=self._hessian_shape, multiclass_strategy=strategy_tensor, init_stamp_token=init_stamp_token)) fc_name_idx += 1 @@ -663,8 +710,8 @@ class GradientBoostedDecisionTreeModel(object): self._sparse_float_values[sparse_float_column_idx], self._sparse_float_shapes[sparse_float_column_idx]), name=fc_name, - gradient_shape=gradient_shape, - hessian_shape=hessian_shape, + gradient_shape=self._gradient_shape, + hessian_shape=self._hessian_shape, multiclass_strategy=strategy_tensor, init_stamp_token=init_stamp_token)) fc_name_idx += 1 @@ -684,66 +731,27 @@ class GradientBoostedDecisionTreeModel(object): self._sparse_int_values[sparse_int_column_idx], self._sparse_int_shapes[sparse_int_column_idx]), name=fc_name, - gradient_shape=gradient_shape, - hessian_shape=hessian_shape, + gradient_shape=self._gradient_shape, + hessian_shape=self._hessian_shape, multiclass_strategy=strategy_tensor, init_stamp_token=init_stamp_token)) fc_name_idx += 1 - # Create steps accumulator. - steps_accumulator = stats_accumulator_ops.StatsAccumulator( - stamp_token=0, - gradient_shape=tensor_shape.scalar(), - hessian_shape=tensor_shape.scalar(), - name="StepsAccumulator") - - # Create bias stats accumulator. - bias_stats_accumulator = stats_accumulator_ops.StatsAccumulator( - stamp_token=0, - gradient_shape=gradient_shape, - hessian_shape=hessian_shape, - name="BiasAccumulator") - - # Create ensemble stats variables. - num_layer_examples = variables.Variable( - initial_value=array_ops.zeros([], dtypes.int64), - name="num_layer_examples", - trainable=False) - num_layer_steps = variables.Variable( - initial_value=array_ops.zeros([], dtypes.int64), - name="num_layer_steps", - trainable=False) - num_layers = variables.Variable( - initial_value=array_ops.zeros([], dtypes.int64), - name="num_layers", - trainable=False) - active_tree = variables.Variable( - initial_value=array_ops.zeros([], dtypes.int64), - name="active_tree", - trainable=False) - active_layer = variables.Variable( - initial_value=array_ops.zeros([], dtypes.int64), - name="active_layer", - trainable=False) - # Create ensemble stats summaries. - summary.scalar("layer_stats/num_examples", num_layer_examples) - summary.scalar("layer_stats/num_steps", num_layer_steps) - summary.scalar("ensemble_stats/active_tree", active_tree) - summary.scalar("ensemble_stats/active_layer", active_layer) + summary.scalar("layer_stats/num_examples", self._num_layer_examples) + summary.scalar("layer_stats/num_steps", self._num_layer_steps) + summary.scalar("ensemble_stats/active_tree", self._active_tree) + summary.scalar("ensemble_stats/active_layer", self._active_layer) # Update bias stats. stats_update_ops = [] - continue_centering = variables.Variable( - initial_value=self._center_bias, - name="continue_centering", - trainable=False) + stats_update_ops.append( control_flow_ops.cond( - continue_centering, - self._make_update_bias_stats_fn(ensemble_stamp, predictions, - gradients, bias_stats_accumulator), - control_flow_ops.no_op)) + self._continue_centering, + self._make_update_bias_stats_fn( + ensemble_stamp, predictions, gradients, + self._bias_stats_accumulator), control_flow_ops.no_op)) # Update handler stats. handler_reads = collections.OrderedDict() @@ -800,8 +808,8 @@ class GradientBoostedDecisionTreeModel(object): lambda: active_handlers)) # Prepare empty gradients and hessians when handlers are not ready. - empty_hess_shape = [1] + hessian_shape.as_list() - empty_grad_shape = [1] + gradient_shape.as_list() + empty_hess_shape = [1] + self._hessian_shape.as_list() + empty_grad_shape = [1] + self._gradient_shape.as_list() empty_gradients = constant_op.constant( [], dtype=dtypes.float32, shape=empty_grad_shape) @@ -823,175 +831,80 @@ class GradientBoostedDecisionTreeModel(object): per_handler_updates, ensemble_stamp, worker_device) for update in update_results.values(): stats_update_ops += update - # Accumulate a step after updating stats. - batch_size = math_ops.cast(array_ops.shape(labels)[0], dtypes.float32) - with ops.control_dependencies(stats_update_ops): - add_step_op = steps_accumulator.add(ensemble_stamp, [0], [[0, 0]], - [batch_size], [1.0]) + return stats_update_ops, handlers - # Determine learning rate. - learning_rate_tuner = self._learner_config.learning_rate_tuner.WhichOneof( - "tuner") - if learning_rate_tuner == "fixed" or learning_rate_tuner == "dropout": - tuner = getattr(self._learner_config.learning_rate_tuner, - learning_rate_tuner) - learning_rate = tuner.learning_rate - else: - # TODO(nponomareva, soroush) do the line search. - raise ValueError("Line search learning rate is not yet supported.") + def increment_step_counter_and_maybe_update_ensemble( + self, predictions_dict, batch_size, handlers): + """Increments number of visited examples and grows the ensemble. + + If the number of visited examples reaches the target examples_per_layer, + ensemble is updated. + + Args: + predictions_dict: Dictionary of Rank 2 `Tensor` representing information + about predictions per example. + batch_size: Number of examples in the batch. + handlers: List of handlers created by update_stats. + + Returns: + An op that updates the counters and potientially grows the ensemble. + """ + ensemble_stamp = predictions_dict[ENSEMBLE_STAMP] + # Accumulate a step after updating stats. + # with ops.control_dependencies(stats_update_ops): + add_step_op = self._steps_accumulator.add(ensemble_stamp, [0], [[0, 0]], + [batch_size], [1.0]) # After adding the step, decide if further processing is needed. ensemble_update_ops = [add_step_op] + class_id = self._get_class_id(predictions_dict) + with ops.control_dependencies([add_step_op]): if self._is_chief: dropout_seed = predictions_dict[NUM_TREES_ATTEMPTED] # Get accumulated steps and examples for the current layer. - _, _, _, _, acc_examples, acc_steps = steps_accumulator.serialize() + _, _, _, _, acc_examples, acc_steps = ( + self._steps_accumulator.serialize()) acc_examples = math_ops.cast(acc_examples[0], dtypes.int64) acc_steps = math_ops.cast(acc_steps[0], dtypes.int64) - ensemble_update_ops.append(num_layer_examples.assign(acc_examples)) - ensemble_update_ops.append(num_layer_steps.assign(acc_steps)) + ensemble_update_ops.append( + self._num_layer_examples.assign(acc_examples)) + ensemble_update_ops.append(self._num_layer_steps.assign(acc_steps)) # Determine whether we need to update tree ensemble. examples_per_layer = self._examples_per_layer if callable(examples_per_layer): - examples_per_layer = examples_per_layer(active_layer) + examples_per_layer = examples_per_layer(self._active_layer) ensemble_update_ops.append( control_flow_ops.cond( acc_examples >= examples_per_layer, - self._make_update_ensemble_fn( - ensemble_stamp, steps_accumulator, bias_stats_accumulator, - continue_centering, learning_rate, handlers, num_layers, - active_tree, active_layer, dropout_seed, class_id), + self.make_update_ensemble_fn( + ensemble_stamp, self._steps_accumulator, + self._bias_stats_accumulator, self._continue_centering, + handlers, self._num_layers, self._active_tree, + self._active_layer, dropout_seed, class_id), control_flow_ops.no_op)) - # Calculate the loss to be reported. # Note, the loss is calculated from the prediction considering dropouts, so # that the value might look staggering over steps when the dropout ratio is # high. eval_loss might be referred instead in the aspect of convergence. return control_flow_ops.group(*ensemble_update_ops) - def _get_weights(self, hessian_shape, hessians): - """Derives weights to be used based on hessians and multiclass strategy.""" - if hessian_shape == tensor_shape.scalar(): - # This is tree per class. - weights = hessians - elif len(hessian_shape.dims) == 1: - # This is diagonal hessian. - weights = math_ops.reduce_sum(hessians, axis=1) - else: - # This is full hessian. - weights = math_ops.trace(hessians) - return weights - - def _full_hessian(self, grads, predictions): - """Prepares hessians for full-hessian multiclass strategy.""" - # Because of - # https://github.com/tensorflow/tensorflow/issues/675, we can't just - # compute the full hessian with a single call to gradients, but instead - # must compute it row-by-row. - gradients_list = array_ops.unstack( - grads, num=self._logits_dimension, axis=1) - hessian_rows = [] - - for row in range(self._logits_dimension): - # If current row is i, K is number of classes,each row returns a tensor of - # size batch_size x K representing for each example dx_i dx_1, dx_i dx_2 - # etc dx_i dx_K - hessian_row = gradients_impl.gradients( - gradients_list[row], - predictions, - name="Hessian_%d" % row, - colocate_gradients_with_ops=False, - gate_gradients=0, - aggregation_method=None) - - # hessian_row is of dimension 1, batch_size, K, => trim first dimension - # to get batch_size x K - hessian_row = array_ops.squeeze(array_ops.unstack(hessian_row), [0]) - hessian_rows.append(hessian_row) - return hessian_rows - - def _diagonal_hessian(self, grads, predictions): - """Prepares hessians for diagonal-hessian multiclass mode.""" - diag_hessian_list = [] - - gradients_list = array_ops.unstack( - grads, num=self._logits_dimension, axis=1) - - for row, row_grads in enumerate(gradients_list): - # If current row is i, K is number of classes,each row returns a tensor of - # size batch_size x K representing for each example dx_i dx_1, dx_1 dx_2 - # etc dx_i dx_K - hessian_row = gradients_impl.gradients( - row_grads, - predictions, - name="Hessian_%d" % row, - colocate_gradients_with_ops=False, - gate_gradients=0, - aggregation_method=None) - - # hessian_row is of dimension 1, batch_size, K, => trim first dimension - # to get batch_size x K - hessian_row = array_ops.squeeze(array_ops.unstack(hessian_row), [0]) - - # Get dx_i^2 for the whole batch. - elem = array_ops.transpose(hessian_row)[row] - diag_hessian_list.append(elem) - - return diag_hessian_list - - def _get_replica_device_setter(self, worker_device): - """Creates a replica device setter.""" - ps_tasks = self._num_ps_replicas - ps_ops = [ - "Variable", - "VariableV2", - "DecisionTreeEnsembleResourceHandleOp", - "StatsAccumulatorScalarResourceHandleOp", - "StatsAccumulatorTensorResourceHandleOp", - ] - ps_strategy = _OpRoundRobinStrategy(ps_ops, ps_tasks) - return device_setter.replica_device_setter( - worker_device=worker_device, - ps_tasks=ps_tasks, - merge_devices=True, - ps_ops=ps_ops, - ps_strategy=ps_strategy) - - def _make_update_bias_stats_fn(self, ensemble_stamp, predictions, gradients, - bias_stats_accumulator): - """A method to create the function which updates the bias stats.""" - - def _update_bias_stats(): - """A method to update the bias stats.""" - # Get reduced gradients and hessians. - grads_sum = math_ops.reduce_sum(gradients, 0) - hess = gradients_impl.gradients( - grads_sum, - predictions, - name="Hessians", - colocate_gradients_with_ops=False, - gate_gradients=0, - aggregation_method=None)[0] - hess_sum = math_ops.reduce_sum(hess, 0) - - # Accumulate gradients and hessians. - partition_ids = math_ops.range(self._logits_dimension) - feature_ids = array_ops.zeros( - [self._logits_dimension, 2], dtype=dtypes.int64) - - add_stats_op = bias_stats_accumulator.add( - ensemble_stamp, partition_ids, feature_ids, grads_sum, hess_sum) - return control_flow_ops.group(*[add_stats_op], name="update_bias_stats") - - return _update_bias_stats - - def _make_update_ensemble_fn(self, ensemble_stamp, steps_accumulator, - bias_stats_accumulator, continue_centering, - learning_rate, handlers, num_layers, active_tree, - active_layer, dropout_seed, class_id): + def make_update_ensemble_fn(self, ensemble_stamp, steps_accumulator, + bias_stats_accumulator, continue_centering, + handlers, num_layers, active_tree, active_layer, + dropout_seed, class_id): """A method to create the function which updates the tree ensemble.""" + # Determine learning rate. + learning_rate_tuner = self._learner_config.learning_rate_tuner.WhichOneof( + "tuner") + if learning_rate_tuner == "fixed" or learning_rate_tuner == "dropout": + tuner = getattr(self._learner_config.learning_rate_tuner, + learning_rate_tuner) + learning_rate = tuner.learning_rate + else: + # TODO(nponomareva, soroush) do the line search. + raise ValueError("Line search learning rate is not yet supported.") def _update_ensemble(): """A method to update the tree ensemble.""" @@ -1110,3 +1023,140 @@ class GradientBoostedDecisionTreeModel(object): def get_number_of_trees_tensor(self): return self._finalized_trees, self._attempted_trees + + def train(self, loss, predictions_dict, labels): + """Updates the accumalator stats and grows the ensemble. + + Args: + loss: A scalar tensor representing average loss of examples. + predictions_dict: Dictionary of Rank 2 `Tensor` representing information + about predictions per example. + labels: Rank 2 `Tensor` representing labels per example. + + Returns: + An op that adds a new tree to the ensemble. + + Raises: + ValueError: if inputs are not valid. + """ + batch_size = math_ops.cast(array_ops.shape(labels)[0], dtypes.float32) + update_op, handlers = self.update_stats(loss, predictions_dict) + with ops.control_dependencies(update_op): + return self.increment_step_counter_and_maybe_update_ensemble( + predictions_dict, batch_size, handlers) + + def _get_weights(self, hessian_shape, hessians): + """Derives weights to be used based on hessians and multiclass strategy.""" + if hessian_shape == tensor_shape.scalar(): + # This is tree per class. + weights = hessians + elif len(hessian_shape.dims) == 1: + # This is diagonal hessian. + weights = math_ops.reduce_sum(hessians, axis=1) + else: + # This is full hessian. + weights = math_ops.trace(hessians) + return weights + + def _full_hessian(self, grads, predictions): + """Prepares hessians for full-hessian multiclass strategy.""" + # Because of + # https://github.com/tensorflow/tensorflow/issues/675, we can't just + # compute the full hessian with a single call to gradients, but instead + # must compute it row-by-row. + gradients_list = array_ops.unstack( + grads, num=self._logits_dimension, axis=1) + hessian_rows = [] + + for row in range(self._logits_dimension): + # If current row is i, K is number of classes,each row returns a tensor of + # size batch_size x K representing for each example dx_i dx_1, dx_i dx_2 + # etc dx_i dx_K + hessian_row = gradients_impl.gradients( + gradients_list[row], + predictions, + name="Hessian_%d" % row, + colocate_gradients_with_ops=False, + gate_gradients=0, + aggregation_method=None) + + # hessian_row is of dimension 1, batch_size, K, => trim first dimension + # to get batch_size x K + hessian_row = array_ops.squeeze(array_ops.unstack(hessian_row), [0]) + hessian_rows.append(hessian_row) + return hessian_rows + + def _diagonal_hessian(self, grads, predictions): + """Prepares hessians for diagonal-hessian multiclass mode.""" + diag_hessian_list = [] + + gradients_list = array_ops.unstack( + grads, num=self._logits_dimension, axis=1) + + for row, row_grads in enumerate(gradients_list): + # If current row is i, K is number of classes,each row returns a tensor of + # size batch_size x K representing for each example dx_i dx_1, dx_1 dx_2 + # etc dx_i dx_K + hessian_row = gradients_impl.gradients( + row_grads, + predictions, + name="Hessian_%d" % row, + colocate_gradients_with_ops=False, + gate_gradients=0, + aggregation_method=None) + + # hessian_row is of dimension 1, batch_size, K, => trim first dimension + # to get batch_size x K + hessian_row = array_ops.squeeze(array_ops.unstack(hessian_row), [0]) + + # Get dx_i^2 for the whole batch. + elem = array_ops.transpose(hessian_row)[row] + diag_hessian_list.append(elem) + + return diag_hessian_list + + def _get_replica_device_setter(self, worker_device): + """Creates a replica device setter.""" + ps_tasks = self._num_ps_replicas + ps_ops = [ + "Variable", + "VariableV2", + "DecisionTreeEnsembleResourceHandleOp", + "StatsAccumulatorScalarResourceHandleOp", + "StatsAccumulatorTensorResourceHandleOp", + ] + ps_strategy = _OpRoundRobinStrategy(ps_ops, ps_tasks) + return device_setter.replica_device_setter( + worker_device=worker_device, + ps_tasks=ps_tasks, + merge_devices=True, + ps_ops=ps_ops, + ps_strategy=ps_strategy) + + def _make_update_bias_stats_fn(self, ensemble_stamp, predictions, gradients, + bias_stats_accumulator): + """A method to create the function which updates the bias stats.""" + + def _update_bias_stats(): + """A method to update the bias stats.""" + # Get reduced gradients and hessians. + grads_sum = math_ops.reduce_sum(gradients, 0) + hess = gradients_impl.gradients( + grads_sum, + predictions, + name="Hessians", + colocate_gradients_with_ops=False, + gate_gradients=0, + aggregation_method=None)[0] + hess_sum = math_ops.reduce_sum(hess, 0) + + # Accumulate gradients and hessians. + partition_ids = math_ops.range(self._logits_dimension) + feature_ids = array_ops.zeros( + [self._logits_dimension, 2], dtype=dtypes.int64) + + add_stats_op = bias_stats_accumulator.add( + ensemble_stamp, partition_ids, feature_ids, grads_sum, hess_sum) + return control_flow_ops.group(*[add_stats_op], name="update_bias_stats") + + return _update_bias_stats -- GitLab From 0e85bc7b36d05f585d76d21e55dd09b40c94145a Mon Sep 17 00:00:00 2001 From: Brennan Saeta Date: Fri, 15 Jun 2018 16:14:06 -0700 Subject: [PATCH 545/816] Integrate ClusterResolvers with Keras TPU support PiperOrigin-RevId: 200790410 --- tensorflow/contrib/tpu/BUILD | 1 + .../contrib/tpu/python/tpu/keras_support.py | 24 ++++++++++++++++--- 2 files changed, 22 insertions(+), 3 deletions(-) diff --git a/tensorflow/contrib/tpu/BUILD b/tensorflow/contrib/tpu/BUILD index f84ff1bfe9..16696793bc 100644 --- a/tensorflow/contrib/tpu/BUILD +++ b/tensorflow/contrib/tpu/BUILD @@ -181,6 +181,7 @@ py_library( ":datasets", ":profiler", ":tpu_py", + "//tensorflow/contrib/cluster_resolver:tpu_cluster_resolver_py", "//tensorflow/contrib/tpu/proto:compilation_result_proto_py", "//tensorflow/contrib/tpu/proto:topology_proto_py", "//tensorflow/core:protos_all_py", diff --git a/tensorflow/contrib/tpu/python/tpu/keras_support.py b/tensorflow/contrib/tpu/python/tpu/keras_support.py index f1a11fa654..293e162059 100644 --- a/tensorflow/contrib/tpu/python/tpu/keras_support.py +++ b/tensorflow/contrib/tpu/python/tpu/keras_support.py @@ -51,6 +51,7 @@ import collections import re import time +from tensorflow.contrib.cluster_resolver.python.training import tpu_cluster_resolver from tensorflow.contrib.framework.python.framework import experimental from tensorflow.contrib.tpu.proto import compilation_result_pb2 as tpu_compilation_result from tensorflow.contrib.tpu.python.ops import tpu_ops @@ -368,10 +369,27 @@ class TPUFunction(object): @experimental -def setup_tpu_session(master): - """Initializes and returns a Keras/TF session connected the TPU `master`.""" +def setup_tpu_session(tpu_name_or_address): + """Initializes and returns a Keras/TF session connected the TPU `master`. + + Args: + tpu_name_or_address: A string that is either the name of the Cloud TPU, + the grpc address of the Cloud TPU, or (Googlers only) the BNS name of the + Cloud TPU. If tpu_name_or_address is None, the TPUClusterResolver will + examine the environment to determine a potential Cloud TPU to use. + + Returns: + A `tf.Session`. + """ + cluster_resolver = tpu_cluster_resolver.TPUClusterResolver( + tpu_name_or_address) + cluster_spec = cluster_resolver.cluster_spec() session = tf_session.Session( - target=master, config=config_pb2.ConfigProto(isolate_session_state=True)) + target=cluster_resolver.master(), + config=config_pb2.ConfigProto( + isolate_session_state=True)) + if cluster_spec: + session.cluster_def.CopyFrom(cluster_spec.as_cluster_def()) K.set_session(session) K.get_session().run(tpu.initialize_system()) return session -- GitLab From ed3adf62db3a4371e01d6b7ac8f69a40f5914f1a Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 15 Jun 2018 16:18:18 -0700 Subject: [PATCH 546/816] Fixes Eager mode of dynamic_rnn for RNNCells with unbalanced output PiperOrigin-RevId: 200791012 --- tensorflow/python/kernel_tests/rnn_test.py | 41 ++++++++++++++++++++++ tensorflow/python/ops/rnn.py | 3 +- 2 files changed, 43 insertions(+), 1 deletion(-) diff --git a/tensorflow/python/kernel_tests/rnn_test.py b/tensorflow/python/kernel_tests/rnn_test.py index fe5ad84c10..e9ae105c28 100644 --- a/tensorflow/python/kernel_tests/rnn_test.py +++ b/tensorflow/python/kernel_tests/rnn_test.py @@ -81,6 +81,25 @@ class ScalarStateRNNCell(rnn_cell_impl.RNNCell): return (input_, state + 1) +class UnbalancedOutputRNNCell(rnn_cell_impl.RNNCell): + """RNN Cell generating (output, new_state) = (input + 1, state + 1).""" + + @property + def output_size(self): + return tensor_shape.TensorShape(1), tensor_shape.TensorShape((2)) + + @property + def state_size(self): + return tensor_shape.TensorShape([]) + + def zero_state(self, batch_size, dtype): + return array_ops.zeros([], dtype=dtypes.int32) + + def call(self, input_, state, scope=None): + concatenated = array_ops.concat((input_, input_), axis=-1) + return (input_, concatenated), state + 1 + + class TensorArrayStateRNNCell(rnn_cell_impl.RNNCell): """RNN Cell its state as a TensorArray.""" @@ -182,6 +201,28 @@ class RNNTest(test.TestCase): self.assertAllEqual([[[1], [2], [3], [4]]], outputs) self.assertAllEqual(4, state) + @test_util.run_in_graph_and_eager_modes() + def testUnbalancedOutputIsAccepted(self): + cell = UnbalancedOutputRNNCell() + in_eager_mode = context.executing_eagerly() + + if in_eager_mode: + inputs = np.array([[[1], [2], [3], [4]]], dtype=np.float32) + else: + inputs = array_ops.placeholder(dtypes.float32, shape=(1, 4, 1)) + + with self.test_session() as sess: + outputs, state = rnn.dynamic_rnn( + cell, inputs, dtype=dtypes.float32, sequence_length=[4]) + if not in_eager_mode: + outputs, state = sess.run( + [outputs, state], feed_dict={inputs: [[[1], [2], [3], [4]]]}) + + self.assertIsInstance(outputs, tuple) + self.assertAllEqual([[[1], [2], [3], [4]]], outputs[0]) + self.assertAllEqual([[[1, 1], [2, 2], [3, 3], [4, 4]]], outputs[1]) + self.assertAllEqual(4, state) + @test_util.run_in_graph_and_eager_modes() def testTensorArrayStateIsAccepted(self): cell = TensorArrayStateRNNCell() diff --git a/tensorflow/python/ops/rnn.py b/tensorflow/python/ops/rnn.py index 10d576c95b..215140e987 100644 --- a/tensorflow/python/ops/rnn.py +++ b/tensorflow/python/ops/rnn.py @@ -828,7 +828,8 @@ def _dynamic_rnn_loop(cell, final_outputs = nest.pack_sequence_as( structure=cell.output_size, flat_sequence=final_outputs) if not in_graph_mode: - final_outputs = array_ops.stack(final_outputs, axis=0) + final_outputs = nest.map_structure_up_to( + cell.output_size, lambda x: array_ops.stack(x, axis=0), final_outputs) return (final_outputs, final_state) -- GitLab From e1e56d8f60fcfa70d65579e4b992dac571807e76 Mon Sep 17 00:00:00 2001 From: Sami Kama Date: Fri, 15 Jun 2018 16:21:47 -0700 Subject: [PATCH 547/816] Address review comments --- .../contrib/tensorrt/convert/convert_graph.cc | 165 +++++++++--------- .../contrib/tensorrt/kernels/trt_engine_op.cc | 4 +- 2 files changed, 87 insertions(+), 82 deletions(-) diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.cc b/tensorflow/contrib/tensorrt/convert/convert_graph.cc index 37a38d3e1d..20abef6806 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_graph.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_graph.cc @@ -48,7 +48,9 @@ limitations under the License. #include "tensorflow/core/lib/strings/numbers.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/types.h" +#include "tensorflow/core/protobuf/config.pb.h" #include "tensorflow/core/protobuf/device_properties.pb.h" // NOLINT +#include "tensorflow/core/util/device_name_utils.h" #if GOOGLE_CUDA #if GOOGLE_TENSORRT @@ -614,6 +616,82 @@ tensorflow::Status RegisterSegmentFunctionToFunctionLibrary( return tensorflow::Status::OK(); } +std::pair GetDeviceAndAllocator( + ConversionParams& params, EngineInfo& engine) { + int cuda_device_id = -1; + // we need to us PM here since in python path there is no way to get + // to allocators + auto CheckDeviceID = [](int tfid) -> int { + tensorflow::TfGpuId tf_gpu_id(tfid); + CudaGpuId cuda_gpu_id; + Status s = GpuIdManager::TfToCudaGpuId(tf_gpu_id, &cuda_gpu_id); + if (s.ok()) { + VLOG(1) << "Found TF GPU " << tf_gpu_id.value() << " at cuda device " + << cuda_gpu_id.value(); + return cuda_gpu_id.value(); + } + VLOG(2) << "TF GPU with id " << tfid << " do not exist " << s; + return -1; + }; + tensorflow::Allocator* dev_allocator = nullptr; + auto pm = tensorflow::ProcessState::singleton(); + if (params.cluster) { // get allocator + const tensorflow::Device* device = nullptr; + if (params.cluster->GetDeviceSet()) { + device = params.cluster->GetDeviceSet()->FindDeviceByName(engine.device); + } + if (device) { + cuda_device_id = CheckDeviceID(device->parsed_name().id); + if (cuda_device_id < 0) { + LOG(ERROR) << "Cuda device identification failed, using device " + "0."; + cuda_device_id = 0; + } + tensorflow::GPUOptions gpuoptions; + // this should be instantiated by now + tensorflow::TfGpuId tf_gpu_id(device->parsed_name().id); + dev_allocator = pm->GetGPUAllocator(gpuoptions, tf_gpu_id, 1); + VLOG(1) << "Got an allocator for device tf_device=" << tf_gpu_id.value() + << " cuda device= " << cuda_device_id << " at " << dev_allocator; + } + } else { // cluster not found, possibly a python call + int found_device = 0; + bool try_gpu_ids = true; + // if device is set, try to find the device. Might be a problem for multi + // host case but TensorRT do not support multi host setups yet. + if (!engine.device.empty()) { + tensorflow::DeviceNameUtils::ParsedName parsed_name; + if (tensorflow::DeviceNameUtils::ParseFullName(engine.device, + &parsed_name)) { + cuda_device_id = parsed_name.has_id ? parsed_name.id : -1; + } + try_gpu_ids = !parsed_name.has_id; + } + if (try_gpu_ids) { + while (found_device < 100) { + cuda_device_id = CheckDeviceID(found_device); + if (cuda_device_id >= 0) { + break; + } + found_device++; + } + } + if (found_device == 100) { + LOG(ERROR) << " Can't find a GPU device to work with. Please " + "instantiate a session to initialize devices"; + return std::make_pair(cuda_device_id, dev_allocator); + } + LOG(WARNING) + << "Can't determine the device constructing an allocator at device " + << found_device; + tensorflow::GPUOptions gpuoptions; + gpuoptions.set_allow_growth( + true); // this will be a noop if device is already initialized + tensorflow::TfGpuId tf_gpu_id(found_device); + dev_allocator = pm->GetGPUAllocator(gpuoptions, tf_gpu_id, 1); + } + return std::make_pair(cuda_device_id, dev_allocator); +} // Entry function from optimization pass. tensorflow::Status ConvertAfterShapes(ConversionParams& params) { // Segment the graph into subgraphs that can be converted to TensorRT @@ -694,87 +772,14 @@ tensorflow::Status ConvertAfterShapes(ConversionParams& params) { segments.at(i).first.size() / total_num_nodes_in_segments) / 2.0; std::shared_ptr alloc; + auto device_alloc = GetDeviceAndAllocator(params, engine); int cuda_device_id = 0; - // we need to us PM here since in python path there is no way to get - // to allocators - auto pm = tensorflow::ProcessState::singleton(); - if (params.cluster) { // get allocator - const auto device = - params.cluster->GetDeviceSet()->FindDeviceByName(engine.device); - if (device) { - tensorflow::TfGpuId tf_gpu_id(device->parsed_name().id); - CudaGpuId cuda_gpu_id; - Status s = GpuIdManager::TfToCudaGpuId(tf_gpu_id, &cuda_gpu_id); - if (!s.ok()) { - LOG(ERROR) << "Cuda device identification failed, using device " - "0. Error= " - << s; - cuda_device_id = 0; - } else { - cuda_device_id = cuda_gpu_id.value(); - } - tensorflow::GPUOptions gpuoptions; - // this should be instantiated by now - auto dev_allocator = pm->GetGPUAllocator(gpuoptions, tf_gpu_id, 1); - VLOG(1) << "Got an allocator for device tf_device=" << tf_gpu_id.value() - << " cuda device= " << cuda_device_id << " at " - << dev_allocator; - alloc.reset(new TRTDeviceAllocator(dev_allocator)); - } - } else { - int found_device = 0; - bool try_gpu_ids = true; - auto checkDeviceId = [](int tfid) -> int { - tensorflow::TfGpuId tf_gpu_id(tfid); - CudaGpuId cuda_gpu_id; - Status s = GpuIdManager::TfToCudaGpuId(tf_gpu_id, &cuda_gpu_id); - if (s.ok()) { - VLOG(1) << "Found TF GPU " << tf_gpu_id.value() << " at cuda device " - << cuda_gpu_id.value(); - return cuda_gpu_id.value(); - } - VLOG(2) << "TF GPU with id " << tfid << " do not exist " << s; - return -1; - }; - // if device is set, try to find the device. Might be a problem for multi - // host case but TensorRT do not support multi host setups yet. - if (!engine.device.empty()) { - auto res = str_util::Split(engine.device, ":"); - if (res.size() > 0) { - tensorflow::StringPiece s(res.back()); - tensorflow::str_util::RemoveWhitespaceContext(&s); - uint64 dev_id = 0; - if (str_util::ConsumeLeadingDigits(&s, &dev_id)) { - found_device = dev_id; - cuda_device_id = checkDeviceId(found_device); - if (cuda_device_id >= 0) try_gpu_ids = false; - } - } - } - if (try_gpu_ids) { - while (found_device < 100) { - cuda_device_id = checkDeviceId(found_device); - if (cuda_device_id >= 0) { - break; - } - found_device++; - } - } - if (found_device == 100) { - LOG(ERROR) << " Can't find a GPU device to work with. Please " - "instantiate a session to initialize devices"; - return tensorflow::errors::NotFound( - "Can't find a GPU device to work with"); - } - LOG(WARNING) - << "Can't determine the device constructing an allocator at device " - << found_device; - tensorflow::GPUOptions gpuoptions; - gpuoptions.set_allow_growth( - true); // this will be a noop if device is already initialized - tensorflow::TfGpuId tf_gpu_id(found_device); - auto dev_allocator = pm->GetGPUAllocator(gpuoptions, tf_gpu_id, 1); - alloc.reset(new TRTDeviceAllocator(dev_allocator)); + if (device_alloc.first >= 0) { + cuda_device_id = device_alloc.first; + alloc.reset(new TRTDeviceAllocator(device_alloc.second)); + } else { // Setting allocator as nullptr should get revert to the + // cudamalloc + LOG(WARNING) << "Can't identify the cuda device. Running on device 0 "; } cudaSetDevice(cuda_device_id); auto status = CreateTRTNode(&graph, engine_segments, i, trt_node, diff --git a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc index 6603b0f7c3..2dddc4541c 100644 --- a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc +++ b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc @@ -222,9 +222,9 @@ void TRTEngineOp::ExecuteCalibration(tensorflow::OpKernelContext* ctx, StrCat("Unsupported data type encountered in input ", i))); return; } + // Check the allocated buffer is sufficient for input const auto device_tensor = dev_tensors_.at(i).AccessTensor(ctx); - CHECK_EQ(t.TotalBytes(), - device_tensor->TotalBytes()); // use the tensor so TF keeps it + CHECK_EQ(t.TotalBytes(), device_tensor->TotalBytes()); input_data.emplace(StrCat(kInputPHName, i), data_address); } VLOG(2) << "Filled map for sending"; -- GitLab From 96100f90a90bb2db905f50617cbb5e7928480667 Mon Sep 17 00:00:00 2001 From: Max Galkin Date: Fri, 15 Jun 2018 16:24:20 -0700 Subject: [PATCH 548/816] Faster TopoQueue in graph_properties. PiperOrigin-RevId: 200791799 --- .../core/grappler/costs/graph_properties.cc | 22 ++++++++----------- 1 file changed, 9 insertions(+), 13 deletions(-) diff --git a/tensorflow/core/grappler/costs/graph_properties.cc b/tensorflow/core/grappler/costs/graph_properties.cc index b920604c6a..6749a7c571 100644 --- a/tensorflow/core/grappler/costs/graph_properties.cc +++ b/tensorflow/core/grappler/costs/graph_properties.cc @@ -353,12 +353,12 @@ void VerboseLogUnknownDimensionSources( class TopoQueue { public: explicit TopoQueue(const std::unordered_map& topo_order) - : queue_(CompareNodes(topo_order)) {} - void push(const NodeDef* n) { queue_.insert(n); } + : topo_order_(topo_order) {} + void push(const NodeDef* n) { queue_.emplace(n, topo_order_.at(n)); } const NodeDef* pop() { CHECK(!empty()); auto it = queue_.begin(); - const NodeDef* n = *it; + const NodeDef* n = it->first; queue_.erase(it); return n; } @@ -367,20 +367,16 @@ class TopoQueue { std::size_t size() const { return queue_.size(); } private: + using NodeAndId = std::pair; // Graph nodes are created in (roughly) topological order. Therefore we can // use their id to ensure they're sorted topologically. - struct CompareNodes { - explicit CompareNodes( - const std::unordered_map& topo_ordering) - : topo_order(topo_ordering) {} - bool operator()(const NodeDef* lhs, const NodeDef* rhs) const { - return topo_order.at(lhs) < topo_order.at(rhs); + struct OrderByIdAscending { + bool operator()(const NodeAndId& lhs, const NodeAndId& rhs) const { + return lhs.second < rhs.second; } - - private: - const std::unordered_map& topo_order; }; - std::set queue_; + const std::unordered_map& topo_order_; + std::set queue_; }; // Processes symbolic shapes. -- GitLab From 4d8a66c5b29428b709f4f54b566a44902ea8173e Mon Sep 17 00:00:00 2001 From: Asim Shankar Date: Fri, 15 Jun 2018 16:26:35 -0700 Subject: [PATCH 549/816] [py_func]: Fix #20021 * EagerPyFunc now validates its assumption that returned tensors are backed by memory on the same device that the EagerPyFunc kernel executed on. * Make the Python trampolining mechanism ensure that this requirement of the kernel is met. * Allow tf.contrib.eager.py_func to execute correctly on devices other than CPU and GPU:0. Prior to this change, tf.contrib.eager.py_func() would copy data from CPU to GPU:0 if necessary, but not the other way around. As a result, the assumptions made by the EagerPyFunc kernel implementation about the placement of returned tensors would be violated. The test added in py_func_test.py, when executed on a machine with a GPU will: - Fail with a segmentation fault (dereferencing GPU memory) without the changes to py_func.cc and script_ops.py - Fail with an error message with the change to py_func.cc but without the change to script_ops.py - Pass with changes to py_func.cc and script_ops.py PiperOrigin-RevId: 200792057 --- tensorflow/python/BUILD | 1 + .../python/kernel_tests/py_func_test.py | 19 +++++ tensorflow/python/lib/core/py_func.cc | 70 +++++++++++++------ tensorflow/python/ops/script_ops.py | 46 ++++++------ 4 files changed, 94 insertions(+), 42 deletions(-) diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD index 39e0cafd93..f3a848b7df 100644 --- a/tensorflow/python/BUILD +++ b/tensorflow/python/BUILD @@ -2428,6 +2428,7 @@ py_library( srcs = ["ops/script_ops.py"], srcs_version = "PY2AND3", deps = [ + ":array_ops", ":framework_for_generated_wrappers", ":script_ops_gen", "//third_party/py/numpy", diff --git a/tensorflow/python/kernel_tests/py_func_test.py b/tensorflow/python/kernel_tests/py_func_test.py index 824610323c..677253946e 100644 --- a/tensorflow/python/kernel_tests/py_func_test.py +++ b/tensorflow/python/kernel_tests/py_func_test.py @@ -599,6 +599,25 @@ class PyFuncTest(test.TestCase): self.assertEqual(y, 1.0) self.assertEqual(dy_dx, 2.0) + def testEagerRespectsDevicePlacmentOfOp(self): + + def f(x): + return math_ops.square(x) + + def g(x): + return math_ops.add(x, x) + + with ops.device("/CPU:0"): + # Explicitly ask for the py_funcs to execute on CPU, even if + # a GPU is available. + x = array_ops.placeholder(dtypes.float32) + y = script_ops.eager_py_func(func=f, inp=[x], Tout=dtypes.float32) + z = script_ops.eager_py_func(func=g, inp=[y], Tout=dtypes.float32) + + with self.test_session(use_gpu=True) as sess: + output = sess.run(z, feed_dict={x: 3.0}) + self.assertEqual(output, 18.0) + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/lib/core/py_func.cc b/tensorflow/python/lib/core/py_func.cc index 30c1a9c759..57139986af 100644 --- a/tensorflow/python/lib/core/py_func.cc +++ b/tensorflow/python/lib/core/py_func.cc @@ -55,37 +55,35 @@ struct PyCall { string token; // The device on which Tensors are stored; only used for EagerPyFunc. - Device* device; - - // True if and only if the op has been placed on a GPU. - bool gpu; + Device* device = nullptr; // True if the call is associated with an EagerPyFunc. - bool eager; + bool eager = false; // Inputs and outputs of this function invocation. std::vector ins; std::vector out; }; +bool IsCPUDevice(const Device* d) { + return d == nullptr || d->tensorflow_gpu_device_info() == nullptr; +} + // Givens the 'call', prepares the token and inputs as a python tuple // that is appropriate for calling the trampoline. Status MakeArgTuple(const PyCall* call, PyObject** tuple) { int64 n = call->ins.size(); PyObject* lst = PyList_New(n); CHECK(lst); + // TFE_TensorHandle assumes that CPU is identified by nullptr. + Device* device = IsCPUDevice(call->device) ? nullptr : call->device; for (int64 i = 0; i < n; ++i) { PyObject* arg = nullptr; const Tensor& t = call->ins[i]; if (call->eager) { - if (call->gpu) { - arg = EagerTensorFromHandle( - new TFE_TensorHandle(t, call->device, call->device)); - } else { - // TFE_TensorHandle assumes that CPU is identified by `nullptr`. - arg = EagerTensorFromHandle(new TFE_TensorHandle(t, nullptr, nullptr)); - } + arg = EagerTensorFromHandle(new TFE_TensorHandle(t, device, device)); if (arg == nullptr) { + Py_DECREF(lst); return errors::Internal("Unable to procure EagerTensor from Tensor."); } } else { @@ -97,8 +95,9 @@ Status MakeArgTuple(const PyCall* call, PyObject** tuple) { } PyList_SetItem(lst, i, arg); } - *tuple = Py_BuildValue("(sON)", call->token.c_str(), - call->gpu ? Py_True : Py_False, lst); + const char* device_name = + device == nullptr ? nullptr : device->attributes().name().c_str(); + *tuple = Py_BuildValue("(ssN)", call->token.c_str(), device_name, lst); CHECK(*tuple); return Status::OK(); } @@ -167,9 +166,40 @@ bool IsSingleNone(PyObject* obj) { } // Retrieves a Tensor from `eager_tensor` and stores it in `output_tensor`. +// Validates that `output_tensor` is backed by memory in `expected_device` +// (which is assumed to be a local device, one on which the kernel was +// executed.) +// +// It may be nice to copy the tensor to the right device instead of failing if +// it isn't already there. This is left as a future exercise. The required +// device-copying logic is implemented in Python at the moment. tensorflow::Status ExtractTensorFromEagerTensor(const PyObject* eager_tensor, + const Device* expected_device, const Tensor** output_tensor) { - return EagerTensor_Handle(eager_tensor)->handle->Tensor(output_tensor); + auto handle = EagerTensor_Handle(eager_tensor)->handle; + Device* actual_device = nullptr; + TF_RETURN_IF_ERROR(handle->Device(&actual_device)); + TF_RETURN_IF_ERROR(handle->Tensor(output_tensor)); + // actual_device may be nullptr, which implies local CPU. + if (expected_device == actual_device) return Status::OK(); + const string& expected_device_name = expected_device->attributes().name(); + if (actual_device == nullptr) { + if (!IsCPUDevice(expected_device)) { + return errors::Internal( + "expected the py_func to return a Tensor backed by memory in ", + expected_device_name, + ", but is actually backed by local host memory. This is a bug."); + } + return Status::OK(); + } + const string& actual_device_name = actual_device->attributes().name(); + if (actual_device_name != expected_device_name) { + return errors::Internal( + "expected the py_func to return a Tensor backed by memory in ", + expected_device_name, ", but is actually in ", actual_device_name, + ". This is a bug."); + } + return Status::OK(); } // Calls the registered py function through the trampoline. @@ -224,7 +254,7 @@ Status DoCallPyFunc(PyCall* call, bool* out_log_on_error) { const PyObject* item = PyList_GetItem(result, i); if (EagerTensor_CheckExact(item)) { const Tensor* tensor = nullptr; - s = ExtractTensorFromEagerTensor(item, &tensor); + s = ExtractTensorFromEagerTensor(item, call->device, &tensor); if (s.ok()) t = *tensor; } else { s = errors::FailedPrecondition( @@ -245,7 +275,7 @@ Status DoCallPyFunc(PyCall* call, bool* out_log_on_error) { DCHECK(call->eager); if (result != Py_None) { const Tensor* t = nullptr; - s = ExtractTensorFromEagerTensor(result, &t); + s = ExtractTensorFromEagerTensor(result, call->device, &t); if (s.ok()) call->out.push_back(*t); } } else if (PyArray_Check(result)) { @@ -449,13 +479,11 @@ class PyFuncOp : public OpKernel { explicit PyFuncOp(OpKernelConstruction* ctx) : OpKernel(ctx) { OP_REQUIRES_OK(ctx, ctx->GetAttr("token", &token_)); eager_ = type_string() == "EagerPyFunc"; - gpu_ = ctx->device_type().type_string() == DEVICE_GPU; } void Compute(OpKernelContext* ctx) override { PyCall call; call.token = token_; - call.gpu = gpu_; call.eager = eager_; if (call.eager) { // Eager's C API uses `Device`, whereas `OpKernelContext` stores a @@ -464,6 +492,7 @@ class PyFuncOp : public OpKernel { if (call.device == nullptr) { ctx->CtxFailureWithWarning( errors::Internal("Unrecognized device class")); + return; } } @@ -508,9 +537,6 @@ class PyFuncOp : public OpKernel { private: string token_; - // True if and only if this op has been placed on a GPU. - bool gpu_; - // True if and only if this op should execute the python function eagerly, // i.e., if and only if the eager attribute is set. bool eager_; diff --git a/tensorflow/python/ops/script_ops.py b/tensorflow/python/ops/script_ops.py index 128b43a7ae..f8676ccb5f 100644 --- a/tensorflow/python/ops/script_ops.py +++ b/tensorflow/python/ops/script_ops.py @@ -33,6 +33,7 @@ from tensorflow.python.eager import context from tensorflow.python.framework import constant_op from tensorflow.python.framework import function from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops from tensorflow.python.ops import gen_script_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.util import compat @@ -95,28 +96,27 @@ class EagerFunc(object): return constant_op.constant(0.0, dtype=dtype) return ops.convert_to_tensor(value, dtype=dtype) - def __call__(self, on_gpu, token, args): + def __call__(self, device, token, args): """Passes `args` to `self._func`, which is executed eagerly.""" - with context.eager_mode(): - with backprop.GradientTape() as tape: - for tensor in args: - tape.watch(tensor) - ret = self._func(*args) - # NB: The tape needs to watch copies across devices. - maybe_copy_to_gpu = lambda x: x if not on_gpu else x.gpu() + with context.eager_mode(), backprop.GradientTape() as tape: + for tensor in args: + tape.watch(tensor) + ret = self._func(*args) + # Use tf.identity to copy the returned tensors to device if neccesary. + with ops.device(device): if isinstance(ret, (tuple, list)): outputs = [ - maybe_copy_to_gpu(self._convert(x, dtype=dtype)) + array_ops.identity(self._convert(x, dtype=dtype)) for (x, dtype) in zip(ret, self._out_dtypes) ] elif ret is None: outputs = None else: - outputs = maybe_copy_to_gpu( + outputs = array_ops.identity( self._convert(ret, dtype=self._out_dtypes[0])) - tape_cache[compat.as_bytes(token)] = (tape, args, outputs) - return outputs + tape_cache[compat.as_bytes(token)] = (tape, args, outputs) + return outputs class FuncRegistry(object): @@ -170,14 +170,14 @@ class FuncRegistry(object): else: return result - def __call__(self, token, on_gpu, args): + def __call__(self, token, device, args): """Calls the registered function for `token` with args. Args: token: A key into this `FuncRegistry` identifying which function to call. - on_gpu: A boolean indicating whether or not `token`'s corresponding - operation was placed on GPU; only used if the function registered for - `token` is an `EagerPyFunc`. + device: Name of the device on which outputs of `token`'s corresponding + operation should be placed. Used iff the function registered for `token` + is an EagerPyFunc. args: The arguments to pass to the function registered for `token`. Returns: @@ -197,7 +197,7 @@ class FuncRegistry(object): # or if the graph is being driven by concurrent session.run() calls. # # TODO(akshayka): Key the tape cache in a thread-safe way. - return func(on_gpu, token, args) + return func(device, token, args) else: ret = func(*args) # Strings seem to lead to a memory leak here if they're not wrapped in a @@ -241,8 +241,13 @@ class CleanupFunc(object): _py_funcs.remove(self._token) -def _internal_py_func(func, inp, Tout, stateful=None, eager=False, - is_grad_func=False, name=None): +def _internal_py_func(func, + inp, + Tout, + stateful=None, + eager=False, + is_grad_func=False, + name=None): """See documentation for py_func and eager_py_func.""" is_list_or_tuple = False @@ -307,7 +312,8 @@ def _EagerPyFuncGrad(op, dy): func=eagerly_executed_grad, inp=[dy] if isinstance(dy, ops.Tensor) else dy, Tout=[tensor.dtype for tensor in op.inputs], - eager=True, is_grad_func=True) + eager=True, + is_grad_func=True) def eager_py_func(func, inp, Tout, name=None): -- GitLab From e3f7e70d589655a1a8ce15b1d309e553a9d02228 Mon Sep 17 00:00:00 2001 From: Akshay Agrawal Date: Fri, 15 Jun 2018 16:31:27 -0700 Subject: [PATCH 550/816] Allow Tensor-valued keyword arguments for tfe.defun. The full list of inputs to the generated TF function is created by appending the Tensor-valued keyword arguments (sorted by key) to the list of Tensor-valued args. PiperOrigin-RevId: 200792676 --- tensorflow/python/eager/function.py | 79 +++++++++++++----------- tensorflow/python/eager/function_test.py | 67 ++++++++++++++++++++ 2 files changed, 110 insertions(+), 36 deletions(-) diff --git a/tensorflow/python/eager/function.py b/tensorflow/python/eager/function.py index dd3166735c..be61d9889d 100644 --- a/tensorflow/python/eager/function.py +++ b/tensorflow/python/eager/function.py @@ -596,6 +596,10 @@ def _get_defun_inputs(args): return nest.pack_sequence_as(args, ret) +def _deterministic_dict_values(kwds): + return tuple(kwds[key] for key in sorted(kwds)) + + def _trace_and_define_function(name, func, compiled, args, kwds): """Defines and returns graph-mode version of func.""" graph_key = ops.get_default_graph()._graph_key # pylint: disable=protected-access @@ -613,7 +617,8 @@ def _trace_and_define_function(name, func, compiled, args, kwds): tmp_graph.get_collection_ref(collection)[:] = curr_graph.get_collection( collection) with tmp_graph.as_default(), AutomaticControlDependencies() as a: - func_inputs = _get_defun_inputs(args) + func_args = _get_defun_inputs(args) + func_kwds = _get_defun_inputs(kwds) def convert(x): if x is None: @@ -624,7 +629,7 @@ def _trace_and_define_function(name, func, compiled, args, kwds): this_tape = tape.push_new_tape() try: - func_outputs = func(*func_inputs, **kwds) + func_outputs = func(*func_args, **func_kwds) func_outputs = nest.map_structure(convert, func_outputs) finally: tape.pop_tape(this_tape) @@ -648,8 +653,11 @@ def _trace_and_define_function(name, func, compiled, args, kwds): x.shape if isinstance(x, ops.Tensor) else None for x in outputs_list) - flat_inputs = [x for x in nest.flatten(func_inputs) - if isinstance(x, ops.Tensor)] + func_kwds_values = _deterministic_dict_values(func_kwds) + flat_inputs = [ + x for x in nest.flatten(func_args) + nest.flatten(func_kwds_values) + if isinstance(x, ops.Tensor) + ] all_inputs = flat_inputs + list(extra_placeholders) all_ignored_ops = frozenset(x.op for x in all_inputs) fname = _inference_name(name) @@ -727,29 +735,36 @@ class _PolymorphicFunction(object): self._variables = [] def _maybe_define_function(self, *args, **kwds): - """Gets a function for these inputs, defining it if necessary.""" + """Gets a function for these inputs, defining it if necessary. - # TODO(akshayka): Remove this restriction. - if any(isinstance(x, ops.EagerTensor) for x in kwds.values()): - raise ValueError("Tensor keyword arguments are not supported.") + Args: + *args: args for the Python function; used to compute the signature + **kwds: kwds for the Python function; used to compute the signature + + Returns: + A graph function corresponding to the input signature implied by args and + kwds, as well as the inputs that the object should be called with. + """ # TODO(apassos): Better error messages for non-hashable arguments. - cache_key = tuple(_cache_key(x) for x in args) - cache_key = (cache_key, tuple(kwds.items())) + kwd_values = _deterministic_dict_values(kwds) + inputs = args + kwd_values + signature = tuple(_cache_key(x) for x in inputs) - if cache_key not in self._arguments_to_functions: + if signature not in self._arguments_to_functions: graph_function = _trace_and_define_function( self._name, self._python_function, self._compiled, args, kwds) - self._arguments_to_functions[cache_key] = graph_function + self._arguments_to_functions[signature] = graph_function self._variables.extend( [v for v in graph_function.variables if v not in self._variables]) - return graph_function + return graph_function, inputs else: - return self._arguments_to_functions[cache_key] + return self._arguments_to_functions[signature], inputs def __call__(self, *args, **kwds): """Calls a graph function specialized for this input signature.""" - return self._maybe_define_function(*args, **kwds)(*args) + graph_function, inputs = self._maybe_define_function(*args, **kwds) + return graph_function(*inputs) @property def variables(self): @@ -777,10 +792,9 @@ def defun(func=None, compiled=False): Python functions might take less time than executing their corresponding `defun`-generated graphs. - For a Python function to be compatible with `defun`, the values of its keyword - arguments cannot be Tensors and all of its arguments, including its keyword - arguments, must be hashable Python objects or lists thereof. Additionally, it - must return zero or more @{tf.Tensor} objects. + For a Python function to be compatible with `defun`, all of its arguments must + be hashable Python objects or lists thereof. Additionally, it must return zero + or more @{tf.Tensor} objects. _Example Usage_ @@ -853,15 +867,15 @@ def defun(func=None, compiled=False): _Tracing and Input Signatures_. The signature of inputs supplied to `F` is defined to be a tuple of the shapes - and dtypes of Tensor-typed arguments and the values of non-Tensor arguments - and keyword arguments. Every time `F` is invoked, the signature of its inputs - are inferred. The first time `F(*args, **kwargs)` is invoked with a particular - signature, `f(*args, **kwargs)` is executed and all the TensorFlow operations - that `f` executes, along with the Tensors that flow between them, are recorded - in a TensorFlow graph. `F` caches this graph and binds it to the inputs' - signature; every subsequent invocation of `F` with inputs conforming to this - signature will immediately retrieve the cached graph and pass it to the - TensorFlow runtime for execution. + and dtypes of Tensor-typed arguments and the values of non-Tensor arguments, + where "arguments" includes both args and kwargs. Every time `F` is invoked, + the signature of its inputs are inferred. The first time `F(*args, **kwargs)` + is invoked with a particular signature, `f(*args, **kwargs)` is executed and + all the TensorFlow operations that `f` executes, along with the Tensors that + flow between them, are recorded in a TensorFlow graph. `F` caches this graph + and binds it to the inputs' signature; every subsequent invocation of `F` with + inputs conforming to this signature will immediately retrieve the cached graph + and pass it to the TensorFlow runtime for execution. Be aware that because `F` only logs TensorFlow operations, all non-TensorFlow operations that `f` executes will only shape the _construction_ of the graphs @@ -1068,15 +1082,8 @@ def make_defun_op(func, *args, **kwds): A wrapper object which can be queried for its output properties, and which can be called directly the way a `@defun` wrapped function can. - - Raises: - ValueError: if any of the keyword arguments to `func` are `EagerTensor` - objects (not yet supported). """ - name = func.__name__ - if any(isinstance(x, ops.EagerTensor) for x in kwds.values()): - raise ValueError("Tensor keyword arguments are not supported.") - return _trace_and_define_function(name, func, False, args, kwds) + return _trace_and_define_function(func.__name__, func, False, args, kwds) class AutomaticControlDependencies(object): diff --git a/tensorflow/python/eager/function_test.py b/tensorflow/python/eager/function_test.py index 6ce2ceffda..43b621b44e 100644 --- a/tensorflow/python/eager/function_test.py +++ b/tensorflow/python/eager/function_test.py @@ -650,6 +650,73 @@ class FunctionTest(test.TestCase): _ = defined(x) # ensure the variables list remains the same self.assertAllEqual(defined.variables, [v]) + def testTensorKeywordArguments(self): + + def foo(a, b): + del a + return b + + defined = function.defun(foo) + a = constant_op.constant(2.0) + b = constant_op.constant([1.0, 2.0]) + one = defined(a, b) + self.assertEqual(len(defined._arguments_to_functions), 1) + + two = defined(a=a, b=b) + self.assertEqual(len(defined._arguments_to_functions), 1) + + three = defined(b=b, a=a) + self.assertEqual(len(defined._arguments_to_functions), 1) + + four = defined(a, b=b) + self.assertEqual(len(defined._arguments_to_functions), 1) + + # The next call corresponds to a new input signature, hence + # we expect another function to be defined. + five = defined(b, a) + self.assertEqual(len(defined._arguments_to_functions), 2) + + six = defined(a=b, b=a) + self.assertEqual(len(defined._arguments_to_functions), 2) + + seven = defined(b=a, a=b) + self.assertEqual(len(defined._arguments_to_functions), 2) + + self.assertAllEqual(one, [1.0, 2.0]) + self.assertAllEqual(two, [1.0, 2.0]) + self.assertAllEqual(three, [1.0, 2.0]) + self.assertAllEqual(four, [1.0, 2.0]) + self.assertAllEqual(five, 2.0) + self.assertAllEqual(six, 2.0) + self.assertAllEqual(seven, 2.0) + + def testGradientWithKeywordArguments(self): + matmul = function.defun(math_ops.matmul) + + def sq(x): + return matmul(a=x, b=x, transpose_a=True) + + t = constant_op.constant([[1.0, 2.0], [3.0, 4.0]]) + grad_t, = backprop.gradients_function(sq, [0])(t) + self.assertAllEqual(grad_t, [[6, 6], [14, 14]]) + + with backprop.GradientTape(persistent=True) as gtape: + gtape.watch(t) + one = matmul(t, b=t, transpose_a=True) + two = matmul(b=t, a=t, transpose_a=True) + three = matmul(a=t, b=t, transpose_a=True) + + for output in [one, two, three]: + self.assertAllEqual(gtape.gradient(output, t), [[6, 6], [14, 14]]) + + def testGradientInFunctionWithKeywordArguments(self): + + @function.defun + def f(x): + return backprop.gradients_function(lambda y: y * y, [0])(x)[0] + + self.assertAllEqual(f(x=constant_op.constant(1.0)), 2.0) + @test_util.with_c_shapes class AutomaticControlDependenciesTest(test.TestCase): -- GitLab From 23bdaed4fbcd3b335a4699f6ed02176a0b6a91c9 Mon Sep 17 00:00:00 2001 From: David Majnemer Date: Fri, 15 Jun 2018 16:35:24 -0700 Subject: [PATCH 551/816] [XLA] Implement ConjugateTransposeOp This simply wraps the Transpose with a Conj. PiperOrigin-RevId: 200793274 --- tensorflow/compiler/tests/binary_ops_test.py | 18 ++++++++++++ .../compiler/tf2xla/kernels/transpose_op.cc | 29 +++++++++++++++---- 2 files changed, 42 insertions(+), 5 deletions(-) diff --git a/tensorflow/compiler/tests/binary_ops_test.py b/tensorflow/compiler/tests/binary_ops_test.py index 1e4dd32916..69a99dd1cd 100644 --- a/tensorflow/compiler/tests/binary_ops_test.py +++ b/tensorflow/compiler/tests/binary_ops_test.py @@ -1216,6 +1216,24 @@ class BinaryOpsTest(XLATestCase): np.array([1, 0], dtype=np.int32), expected=np.array([[1, 3], [2, 4]], dtype=dtype)) + def testConjugateTranspose(self): + for dtype in self.complex_types: + self._testBinary( + array_ops.conjugate_transpose, + np.zeros(shape=[1, 0, 4], dtype=dtype), + np.array([1, 2, 0], dtype=np.int32), + expected=np.zeros(shape=[0, 4, 1], dtype=dtype)) + self._testBinary( + array_ops.conjugate_transpose, + np.array([[1 - 1j, 2 + 2j], [3 - 3j, 4 + 4j]], dtype=dtype), + np.array([0, 1], dtype=np.int32), + expected=np.array([[1 + 1j, 2 - 2j], [3 + 3j, 4 - 4j]], dtype=dtype)) + self._testBinary( + array_ops.conjugate_transpose, + np.array([[1 - 1j, 2 + 2j], [3 - 3j, 4 + 4j]], dtype=dtype), + np.array([1, 0], dtype=np.int32), + expected=np.array([[1 + 1j, 3 + 3j], [2 - 2j, 4 - 4j]], dtype=dtype)) + def testCross(self): for dtype in self.float_types: self._testBinary( diff --git a/tensorflow/compiler/tf2xla/kernels/transpose_op.cc b/tensorflow/compiler/tf2xla/kernels/transpose_op.cc index c167642174..ef5aae81a8 100644 --- a/tensorflow/compiler/tf2xla/kernels/transpose_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/transpose_op.cc @@ -32,7 +32,8 @@ namespace { class TransposeOp : public XlaOpKernel { public: - explicit TransposeOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {} + explicit TransposeOp(OpKernelConstruction* ctx, bool conjugate = false) + : XlaOpKernel(ctx), conjugate_(conjugate) {} void Compile(XlaOpKernelContext* ctx) override { const TensorShape input_shape = ctx->InputShape(0); @@ -78,19 +79,37 @@ class TransposeOp : public XlaOpKernel { errors::InvalidArgument(i, " is missing from 'perm' argument.")); } + xla::XlaOp transposed; // 0-D, 1-D, and identity transposes do nothing. if (dims <= 1 || is_identity) { - ctx->SetOutput(0, ctx->Input(0)); - return; + transposed = ctx->Input(0); + } else { + transposed = ctx->builder()->Transpose(ctx->Input(0), transposed_order); } - ctx->SetOutput(0, - ctx->builder()->Transpose(ctx->Input(0), transposed_order)); + // Conjugate the transposed result if this is ConjugateTransposeOp. + if (conjugate_) { + ctx->SetOutput(0, ctx->builder()->Conj(transposed)); + } else { + ctx->SetOutput(0, transposed); + } } + + private: + const bool conjugate_; +}; + +class ConjugateTransposeOp : public TransposeOp { + public: + explicit ConjugateTransposeOp(OpKernelConstruction* ctx) + : TransposeOp(ctx, /*conjugate=*/true) {} }; REGISTER_XLA_OP(Name("Transpose").CompileTimeConstInput("perm"), TransposeOp); +REGISTER_XLA_OP(Name("ConjugateTranspose").CompileTimeConstInput("perm"), + ConjugateTransposeOp); + // InvertPermutation frequently forms part of the gradient of Transpose. // // inv = InvertPermutationOp(T p) takes a permutation of -- GitLab From d1daba6ac82461cd64dc070534bc613a70527520 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 15 Jun 2018 16:52:01 -0700 Subject: [PATCH 552/816] Expose Quantization params for outputs in JNI interpreter PiperOrigin-RevId: 200795402 --- .../lite/NativeInterpreterWrapper.java | 22 ++++++++++++ .../native/nativeinterpreterwrapper_jni.cc | 32 ++++++++++++++++++ .../native/nativeinterpreterwrapper_jni.h | 22 ++++++++++++ .../lite/NativeInterpreterWrapperTest.java | 15 ++++++++ .../lite/java/src/testdata/quantized.bin | Bin 0 -> 432 bytes 5 files changed, 91 insertions(+) create mode 100644 tensorflow/contrib/lite/java/src/testdata/quantized.bin diff --git a/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/NativeInterpreterWrapper.java b/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/NativeInterpreterWrapper.java index 2ae6c516b0..80de88b6a1 100644 --- a/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/NativeInterpreterWrapper.java +++ b/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/NativeInterpreterWrapper.java @@ -311,8 +311,30 @@ final class NativeInterpreterWrapper implements AutoCloseable { return DataType.fromNumber(type).toStringName(); } + /** + * Gets the quantization zero point of an output. + * + * @throws IllegalArgumentExeption if the output index is invalid. + */ + int getOutputQuantizationZeroPoint(int index) { + return getOutputQuantizationZeroPoint(interpreterHandle, index); + } + + /** + * Gets the quantization scale of an output. + * + * @throws IllegalArgumentExeption if the output index is invalid. + */ + float getOutputQuantizationScale(int index) { + return getOutputQuantizationScale(interpreterHandle, index); + } + private static native int getOutputDataType(long interpreterHandle, int outputIdx); + private static native int getOutputQuantizationZeroPoint(long interpreterHandle, int outputIdx); + + private static native float getOutputQuantizationScale(long interpreterHandle, int outputIdx); + private static final int ERROR_BUFFER_SIZE = 512; private long errorHandle; diff --git a/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.cc b/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.cc index 1fb6997fb9..31f7b58fbc 100644 --- a/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.cc +++ b/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.cc @@ -561,6 +561,38 @@ Java_org_tensorflow_lite_NativeInterpreterWrapper_getOutputDataType( return static_cast(type); } +JNIEXPORT jint JNICALL +Java_org_tensorflow_lite_NativeInterpreterWrapper_getOutputQuantizationZeroPoint( + JNIEnv* env, jclass clazz, jlong handle, jint output_idx) { + tflite::Interpreter* interpreter = convertLongToInterpreter(env, handle); + if (interpreter == nullptr) return 0; + const int idx = static_cast(output_idx); + if (output_idx < 0 || output_idx >= interpreter->outputs().size()) { + throwException(env, kIllegalArgumentException, + "Failed to get %d-th output out of %d outputs", output_idx, + interpreter->outputs().size()); + return 0; + } + TfLiteTensor* target = interpreter->tensor(interpreter->outputs()[idx]); + return static_cast(target->params.zero_point); +} + +JNIEXPORT jfloat JNICALL +Java_org_tensorflow_lite_NativeInterpreterWrapper_getOutputQuantizationScale( + JNIEnv* env, jclass clazz, jlong handle, jint output_idx) { + tflite::Interpreter* interpreter = convertLongToInterpreter(env, handle); + if (interpreter == nullptr) return 1.0f; + const int idx = static_cast(output_idx); + if (output_idx < 0 || output_idx >= interpreter->outputs().size()) { + throwException(env, kIllegalArgumentException, + "Failed to get %d-th output out of %d outputs", output_idx, + interpreter->outputs().size()); + return 1.0f; + } + TfLiteTensor* target = interpreter->tensor(interpreter->outputs()[idx]); + return static_cast(target->params.scale); +} + JNIEXPORT jboolean JNICALL Java_org_tensorflow_lite_NativeInterpreterWrapper_resizeInput( JNIEnv* env, jclass clazz, jlong interpreter_handle, jlong error_handle, diff --git a/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.h b/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.h index eaa765cb34..128ece4981 100644 --- a/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.h +++ b/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.h @@ -152,6 +152,28 @@ JNIEXPORT jint JNICALL Java_org_tensorflow_lite_NativeInterpreterWrapper_getOutputDataType( JNIEnv* env, jclass clazz, jlong handle, jint output_idx); +/* + * Class: org_tensorflow_lite_NativeInterpreterWrapper + * Method: + * Signature: (JI)I + * + * Gets output quantization zero point. + */ +JNIEXPORT jint JNICALL +Java_org_tensorflow_lite_NativeInterpreterWrapper_getOutputQuantizationZeroPoint( + JNIEnv* env, jclass clazz, jlong handle, jint output_idx); + +/* + * Class: org_tensorflow_lite_NativeInterpreterWrapper + * Method: + * Signature: (JI)F + * + * Gets output quantization scale. + */ +JNIEXPORT jfloat JNICALL +Java_org_tensorflow_lite_NativeInterpreterWrapper_getOutputQuantizationScale( + JNIEnv* env, jclass clazz, jlong handle, jint output_idx); + /* * Class: org_tensorflow_lite_NativeInterpreterWrapper * Method: diff --git a/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/NativeInterpreterWrapperTest.java b/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/NativeInterpreterWrapperTest.java index 7c00d3196f..9e41cb132d 100644 --- a/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/NativeInterpreterWrapperTest.java +++ b/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/NativeInterpreterWrapperTest.java @@ -41,6 +41,9 @@ public final class NativeInterpreterWrapperTest { private static final String BYTE_MODEL_PATH = "tensorflow/contrib/lite/java/src/testdata/uint8.bin"; + private static final String QUANTIZED_MODEL_PATH = + "tensorflow/contrib/lite/java/src/testdata/quantized.bin"; + private static final String INVALID_MODEL_PATH = "tensorflow/contrib/lite/java/src/testdata/invalid_model.bin"; @@ -536,4 +539,16 @@ public final class NativeInterpreterWrapperTest { assertThat(wrapper.getOutputDataType(0)).contains("byte"); wrapper.close(); } + + @Test + public void testGetOutputQuantizationParams() { + try (NativeInterpreterWrapper wrapper = new NativeInterpreterWrapper(FLOAT_MODEL_PATH)) { + assertThat(wrapper.getOutputQuantizationZeroPoint(0)).isEqualTo(0); + assertThat(wrapper.getOutputQuantizationScale(0)).isWithin(1e-6f).of(0.0f); + } + try (NativeInterpreterWrapper wrapper = new NativeInterpreterWrapper(QUANTIZED_MODEL_PATH)) { + assertThat(wrapper.getOutputQuantizationZeroPoint(0)).isEqualTo(127); + assertThat(wrapper.getOutputQuantizationScale(0)).isWithin(1e-6f).of(0.25f); + } + } } diff --git a/tensorflow/contrib/lite/java/src/testdata/quantized.bin b/tensorflow/contrib/lite/java/src/testdata/quantized.bin new file mode 100644 index 0000000000000000000000000000000000000000..4062088cdf717e8752490de5c9acff35fd6af54f GIT binary patch literal 432 zcmb1OU|H+B$0cl|1V-RCt0NcY3wTFR$L0E->fdS+e zknIc%Yz!O>EDUT63=B+AJ3#tD7-R&9hG7sNq^1ID56EpGr@JsPFqnXS0&*L~RG9BT z?f|(TWJW#60H_@d3=MW5JM&9R3Q9{*{R~nMF$EM(91QGWKSRO^WS Date: Fri, 15 Jun 2018 17:07:57 -0700 Subject: [PATCH 553/816] Add tf.contrib.checkpoint.CheckpointableBase for isinstance checks. (Also planning to use this in Sonnet) PiperOrigin-RevId: 200797385 --- tensorflow/contrib/checkpoint/__init__.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/checkpoint/__init__.py b/tensorflow/contrib/checkpoint/__init__.py index 9aa4614967..38856417c0 100644 --- a/tensorflow/contrib/checkpoint/__init__.py +++ b/tensorflow/contrib/checkpoint/__init__.py @@ -22,6 +22,7 @@ Visualization and inspection: Managing dependencies: @@capture_dependencies @@Checkpointable +@@CheckpointableBase @@CheckpointableObjectGraph @@NoDependency @@split_dependency @@ -41,6 +42,7 @@ from tensorflow.contrib.checkpoint.python.split_dependency import split_dependen from tensorflow.contrib.checkpoint.python.visualize import dot_graph_from_checkpoint from tensorflow.core.protobuf.checkpointable_object_graph_pb2 import CheckpointableObjectGraph from tensorflow.python.training.checkpointable.base import Checkpointable +from tensorflow.python.training.checkpointable.base import CheckpointableBase from tensorflow.python.training.checkpointable.base import NoDependency from tensorflow.python.training.checkpointable.data_structures import List from tensorflow.python.training.checkpointable.data_structures import Mapping @@ -51,4 +53,3 @@ from tensorflow.python.training.checkpointable.util import object_metadata from tensorflow.python.util.all_util import remove_undocumented remove_undocumented(module_name=__name__) - -- GitLab From edf1516c8015259fb8f8b901f7284d86988d6bc0 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 15 Jun 2018 17:28:03 -0700 Subject: [PATCH 554/816] Automated g4 rollback of changelist 200790145 PiperOrigin-RevId: 200799531 --- .../python/training/functions/gbdt_batch.py | 486 ++++++++---------- 1 file changed, 218 insertions(+), 268 deletions(-) diff --git a/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py b/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py index ec1480b20c..47698d45c8 100644 --- a/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py +++ b/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py @@ -325,19 +325,6 @@ class GradientBoostedDecisionTreeModel(object): learner_config.multi_class_strategy = ( learner_pb2.LearnerConfig.DIAGONAL_HESSIAN) - if logits_dimension == 1 or learner_config.multi_class_strategy == ( - learner_pb2.LearnerConfig.TREE_PER_CLASS): - self._gradient_shape = tensor_shape.scalar() - self._hessian_shape = tensor_shape.scalar() - else: - self._gradient_shape = tensor_shape.TensorShape([logits_dimension]) - if (learner_config.multi_class_strategy == - learner_pb2.LearnerConfig.FULL_HESSIAN): - self._hessian_shape = tensor_shape.TensorShape( - ([logits_dimension, logits_dimension])) - else: - # Diagonal hessian strategy. - self._hessian_shape = tensor_shape.TensorShape(([logits_dimension])) if (learner_config.growing_mode == learner_pb2.LearnerConfig.GROWING_MODE_UNSPECIFIED): learner_config.growing_mode = learner_pb2.LearnerConfig.LAYER_BY_LAYER @@ -385,44 +372,6 @@ class GradientBoostedDecisionTreeModel(object): learner_pb2.LearnerConfig.TREE_PER_CLASS and learner_config.num_classes == 2) self._output_leaf_index = output_leaf_index - # Create ensemble stats variables. - self._num_layer_examples = variables.Variable( - initial_value=array_ops.zeros([], dtypes.int64), - name="num_layer_examples", - trainable=False) - self._num_layer_steps = variables.Variable( - initial_value=array_ops.zeros([], dtypes.int64), - name="num_layer_steps", - trainable=False) - self._num_layers = variables.Variable( - initial_value=array_ops.zeros([], dtypes.int64), - name="num_layers", - trainable=False) - self._active_tree = variables.Variable( - initial_value=array_ops.zeros([], dtypes.int64), - name="active_tree", - trainable=False) - self._active_layer = variables.Variable( - initial_value=array_ops.zeros([], dtypes.int64), - name="active_layer", - trainable=False) - # Variable that becomes false once bias centering is done. - self._continue_centering = variables.Variable( - initial_value=self._center_bias, - name="continue_centering", - trainable=False) - # Create bias stats accumulator. - self._bias_stats_accumulator = stats_accumulator_ops.StatsAccumulator( - stamp_token=0, - gradient_shape=self._gradient_shape, - hessian_shape=self._hessian_shape, - name="BiasAccumulator") - # Create steps accumulator. - self._steps_accumulator = stats_accumulator_ops.StatsAccumulator( - stamp_token=0, - gradient_shape=tensor_shape.scalar(), - hessian_shape=tensor_shape.scalar(), - name="StepsAccumulator") def _predict_and_return_dict(self, ensemble_handle, ensemble_stamp, mode): """Runs prediction and returns a dictionary of the prediction results. @@ -573,23 +522,14 @@ class GradientBoostedDecisionTreeModel(object): return self._predict_and_return_dict(self._ensemble_handle, ensemble_stamp, mode) - def _get_class_id(self, predictions_dict): - # Handle different multiclass strategies. - if (self._learner_config.multi_class_strategy == - learner_pb2.LearnerConfig.TREE_PER_CLASS and - self._logits_dimension != 1): - # Choose the class for which the tree is built (one vs rest). - return math_ops.to_int32( - predictions_dict[NUM_TREES_ATTEMPTED] % self._logits_dimension) - return constant_op.constant(-1, dtype=dtypes.int32) - - def update_stats(self, loss, predictions_dict): - """Update the accumulators with stats from this batch. + def train(self, loss, predictions_dict, labels): + """Grows a new tree and adds it to the ensemble. Args: loss: A scalar tensor representing average loss of examples. predictions_dict: Dictionary of Rank 2 `Tensor` representing information about predictions per example. + labels: Rank 2 `Tensor` representing labels per example. Returns: An op that adds a new tree to the ensemble. @@ -616,10 +556,13 @@ class GradientBoostedDecisionTreeModel(object): aggregation_method=None)[0] strategy = self._learner_config.multi_class_strategy - class_id = self._get_class_id(predictions_dict) + class_id = constant_op.constant(-1, dtype=dtypes.int32) # Handle different multiclass strategies. if strategy == learner_pb2.LearnerConfig.TREE_PER_CLASS: # We build one vs rest trees. + gradient_shape = tensor_shape.scalar() + hessian_shape = tensor_shape.scalar() + if self._logits_dimension == 1: # We have only 1 score, gradients is of shape [batch, 1]. hessians = gradients_impl.gradients( @@ -636,6 +579,11 @@ class GradientBoostedDecisionTreeModel(object): hessian_list = self._diagonal_hessian(gradients, predictions) # Assemble hessian list into a tensor. hessians = array_ops.stack(hessian_list, axis=1) + + # Choose the class for which the tree is built (one vs rest). + class_id = math_ops.to_int32( + predictions_dict[NUM_TREES_ATTEMPTED] % self._logits_dimension) + # Use class id tensor to get the column with that index from gradients # and hessians. squeezed_gradients = array_ops.squeeze( @@ -644,10 +592,15 @@ class GradientBoostedDecisionTreeModel(object): _get_column_by_index(hessians, class_id)) else: # Other multiclass strategies. + gradient_shape = tensor_shape.TensorShape([self._logits_dimension]) + if strategy == learner_pb2.LearnerConfig.FULL_HESSIAN: + hessian_shape = tensor_shape.TensorShape( + ([self._logits_dimension, self._logits_dimension])) hessian_list = self._full_hessian(gradients, predictions) else: # Diagonal hessian strategy. + hessian_shape = tensor_shape.TensorShape(([self._logits_dimension])) hessian_list = self._diagonal_hessian(gradients, predictions) squeezed_gradients = gradients @@ -655,7 +608,7 @@ class GradientBoostedDecisionTreeModel(object): squeezed_hessians = hessians # Get the weights for each example for quantiles calculation, - weights = self._get_weights(self._hessian_shape, squeezed_hessians) + weights = self._get_weights(hessian_shape, squeezed_hessians) # Create all handlers ensuring resources are evenly allocated across PS. fc_name_idx = 0 @@ -687,8 +640,8 @@ class GradientBoostedDecisionTreeModel(object): num_quantiles=num_quantiles, dense_float_column=self._dense_floats[dense_float_column_idx], name=fc_name, - gradient_shape=self._gradient_shape, - hessian_shape=self._hessian_shape, + gradient_shape=gradient_shape, + hessian_shape=hessian_shape, multiclass_strategy=strategy_tensor, init_stamp_token=init_stamp_token)) fc_name_idx += 1 @@ -710,8 +663,8 @@ class GradientBoostedDecisionTreeModel(object): self._sparse_float_values[sparse_float_column_idx], self._sparse_float_shapes[sparse_float_column_idx]), name=fc_name, - gradient_shape=self._gradient_shape, - hessian_shape=self._hessian_shape, + gradient_shape=gradient_shape, + hessian_shape=hessian_shape, multiclass_strategy=strategy_tensor, init_stamp_token=init_stamp_token)) fc_name_idx += 1 @@ -731,27 +684,66 @@ class GradientBoostedDecisionTreeModel(object): self._sparse_int_values[sparse_int_column_idx], self._sparse_int_shapes[sparse_int_column_idx]), name=fc_name, - gradient_shape=self._gradient_shape, - hessian_shape=self._hessian_shape, + gradient_shape=gradient_shape, + hessian_shape=hessian_shape, multiclass_strategy=strategy_tensor, init_stamp_token=init_stamp_token)) fc_name_idx += 1 + # Create steps accumulator. + steps_accumulator = stats_accumulator_ops.StatsAccumulator( + stamp_token=0, + gradient_shape=tensor_shape.scalar(), + hessian_shape=tensor_shape.scalar(), + name="StepsAccumulator") + + # Create bias stats accumulator. + bias_stats_accumulator = stats_accumulator_ops.StatsAccumulator( + stamp_token=0, + gradient_shape=gradient_shape, + hessian_shape=hessian_shape, + name="BiasAccumulator") + + # Create ensemble stats variables. + num_layer_examples = variables.Variable( + initial_value=array_ops.zeros([], dtypes.int64), + name="num_layer_examples", + trainable=False) + num_layer_steps = variables.Variable( + initial_value=array_ops.zeros([], dtypes.int64), + name="num_layer_steps", + trainable=False) + num_layers = variables.Variable( + initial_value=array_ops.zeros([], dtypes.int64), + name="num_layers", + trainable=False) + active_tree = variables.Variable( + initial_value=array_ops.zeros([], dtypes.int64), + name="active_tree", + trainable=False) + active_layer = variables.Variable( + initial_value=array_ops.zeros([], dtypes.int64), + name="active_layer", + trainable=False) + # Create ensemble stats summaries. - summary.scalar("layer_stats/num_examples", self._num_layer_examples) - summary.scalar("layer_stats/num_steps", self._num_layer_steps) - summary.scalar("ensemble_stats/active_tree", self._active_tree) - summary.scalar("ensemble_stats/active_layer", self._active_layer) + summary.scalar("layer_stats/num_examples", num_layer_examples) + summary.scalar("layer_stats/num_steps", num_layer_steps) + summary.scalar("ensemble_stats/active_tree", active_tree) + summary.scalar("ensemble_stats/active_layer", active_layer) # Update bias stats. stats_update_ops = [] - + continue_centering = variables.Variable( + initial_value=self._center_bias, + name="continue_centering", + trainable=False) stats_update_ops.append( control_flow_ops.cond( - self._continue_centering, - self._make_update_bias_stats_fn( - ensemble_stamp, predictions, gradients, - self._bias_stats_accumulator), control_flow_ops.no_op)) + continue_centering, + self._make_update_bias_stats_fn(ensemble_stamp, predictions, + gradients, bias_stats_accumulator), + control_flow_ops.no_op)) # Update handler stats. handler_reads = collections.OrderedDict() @@ -808,8 +800,8 @@ class GradientBoostedDecisionTreeModel(object): lambda: active_handlers)) # Prepare empty gradients and hessians when handlers are not ready. - empty_hess_shape = [1] + self._hessian_shape.as_list() - empty_grad_shape = [1] + self._gradient_shape.as_list() + empty_hess_shape = [1] + hessian_shape.as_list() + empty_grad_shape = [1] + gradient_shape.as_list() empty_gradients = constant_op.constant( [], dtype=dtypes.float32, shape=empty_grad_shape) @@ -831,80 +823,175 @@ class GradientBoostedDecisionTreeModel(object): per_handler_updates, ensemble_stamp, worker_device) for update in update_results.values(): stats_update_ops += update - return stats_update_ops, handlers - - def increment_step_counter_and_maybe_update_ensemble( - self, predictions_dict, batch_size, handlers): - """Increments number of visited examples and grows the ensemble. - - If the number of visited examples reaches the target examples_per_layer, - ensemble is updated. - - Args: - predictions_dict: Dictionary of Rank 2 `Tensor` representing information - about predictions per example. - batch_size: Number of examples in the batch. - handlers: List of handlers created by update_stats. - - Returns: - An op that updates the counters and potientially grows the ensemble. - """ - ensemble_stamp = predictions_dict[ENSEMBLE_STAMP] # Accumulate a step after updating stats. - # with ops.control_dependencies(stats_update_ops): - add_step_op = self._steps_accumulator.add(ensemble_stamp, [0], [[0, 0]], - [batch_size], [1.0]) + batch_size = math_ops.cast(array_ops.shape(labels)[0], dtypes.float32) + with ops.control_dependencies(stats_update_ops): + add_step_op = steps_accumulator.add(ensemble_stamp, [0], [[0, 0]], + [batch_size], [1.0]) + + # Determine learning rate. + learning_rate_tuner = self._learner_config.learning_rate_tuner.WhichOneof( + "tuner") + if learning_rate_tuner == "fixed" or learning_rate_tuner == "dropout": + tuner = getattr(self._learner_config.learning_rate_tuner, + learning_rate_tuner) + learning_rate = tuner.learning_rate + else: + # TODO(nponomareva, soroush) do the line search. + raise ValueError("Line search learning rate is not yet supported.") # After adding the step, decide if further processing is needed. ensemble_update_ops = [add_step_op] - class_id = self._get_class_id(predictions_dict) - with ops.control_dependencies([add_step_op]): if self._is_chief: dropout_seed = predictions_dict[NUM_TREES_ATTEMPTED] # Get accumulated steps and examples for the current layer. - _, _, _, _, acc_examples, acc_steps = ( - self._steps_accumulator.serialize()) + _, _, _, _, acc_examples, acc_steps = steps_accumulator.serialize() acc_examples = math_ops.cast(acc_examples[0], dtypes.int64) acc_steps = math_ops.cast(acc_steps[0], dtypes.int64) - ensemble_update_ops.append( - self._num_layer_examples.assign(acc_examples)) - ensemble_update_ops.append(self._num_layer_steps.assign(acc_steps)) + ensemble_update_ops.append(num_layer_examples.assign(acc_examples)) + ensemble_update_ops.append(num_layer_steps.assign(acc_steps)) # Determine whether we need to update tree ensemble. examples_per_layer = self._examples_per_layer if callable(examples_per_layer): - examples_per_layer = examples_per_layer(self._active_layer) + examples_per_layer = examples_per_layer(active_layer) ensemble_update_ops.append( control_flow_ops.cond( acc_examples >= examples_per_layer, - self.make_update_ensemble_fn( - ensemble_stamp, self._steps_accumulator, - self._bias_stats_accumulator, self._continue_centering, - handlers, self._num_layers, self._active_tree, - self._active_layer, dropout_seed, class_id), + self._make_update_ensemble_fn( + ensemble_stamp, steps_accumulator, bias_stats_accumulator, + continue_centering, learning_rate, handlers, num_layers, + active_tree, active_layer, dropout_seed, class_id), control_flow_ops.no_op)) + # Calculate the loss to be reported. # Note, the loss is calculated from the prediction considering dropouts, so # that the value might look staggering over steps when the dropout ratio is # high. eval_loss might be referred instead in the aspect of convergence. return control_flow_ops.group(*ensemble_update_ops) - def make_update_ensemble_fn(self, ensemble_stamp, steps_accumulator, - bias_stats_accumulator, continue_centering, - handlers, num_layers, active_tree, active_layer, - dropout_seed, class_id): - """A method to create the function which updates the tree ensemble.""" - # Determine learning rate. - learning_rate_tuner = self._learner_config.learning_rate_tuner.WhichOneof( - "tuner") - if learning_rate_tuner == "fixed" or learning_rate_tuner == "dropout": - tuner = getattr(self._learner_config.learning_rate_tuner, - learning_rate_tuner) - learning_rate = tuner.learning_rate + def _get_weights(self, hessian_shape, hessians): + """Derives weights to be used based on hessians and multiclass strategy.""" + if hessian_shape == tensor_shape.scalar(): + # This is tree per class. + weights = hessians + elif len(hessian_shape.dims) == 1: + # This is diagonal hessian. + weights = math_ops.reduce_sum(hessians, axis=1) else: - # TODO(nponomareva, soroush) do the line search. - raise ValueError("Line search learning rate is not yet supported.") + # This is full hessian. + weights = math_ops.trace(hessians) + return weights + + def _full_hessian(self, grads, predictions): + """Prepares hessians for full-hessian multiclass strategy.""" + # Because of + # https://github.com/tensorflow/tensorflow/issues/675, we can't just + # compute the full hessian with a single call to gradients, but instead + # must compute it row-by-row. + gradients_list = array_ops.unstack( + grads, num=self._logits_dimension, axis=1) + hessian_rows = [] + + for row in range(self._logits_dimension): + # If current row is i, K is number of classes,each row returns a tensor of + # size batch_size x K representing for each example dx_i dx_1, dx_i dx_2 + # etc dx_i dx_K + hessian_row = gradients_impl.gradients( + gradients_list[row], + predictions, + name="Hessian_%d" % row, + colocate_gradients_with_ops=False, + gate_gradients=0, + aggregation_method=None) + + # hessian_row is of dimension 1, batch_size, K, => trim first dimension + # to get batch_size x K + hessian_row = array_ops.squeeze(array_ops.unstack(hessian_row), [0]) + hessian_rows.append(hessian_row) + return hessian_rows + + def _diagonal_hessian(self, grads, predictions): + """Prepares hessians for diagonal-hessian multiclass mode.""" + diag_hessian_list = [] + + gradients_list = array_ops.unstack( + grads, num=self._logits_dimension, axis=1) + + for row, row_grads in enumerate(gradients_list): + # If current row is i, K is number of classes,each row returns a tensor of + # size batch_size x K representing for each example dx_i dx_1, dx_1 dx_2 + # etc dx_i dx_K + hessian_row = gradients_impl.gradients( + row_grads, + predictions, + name="Hessian_%d" % row, + colocate_gradients_with_ops=False, + gate_gradients=0, + aggregation_method=None) + + # hessian_row is of dimension 1, batch_size, K, => trim first dimension + # to get batch_size x K + hessian_row = array_ops.squeeze(array_ops.unstack(hessian_row), [0]) + + # Get dx_i^2 for the whole batch. + elem = array_ops.transpose(hessian_row)[row] + diag_hessian_list.append(elem) + + return diag_hessian_list + + def _get_replica_device_setter(self, worker_device): + """Creates a replica device setter.""" + ps_tasks = self._num_ps_replicas + ps_ops = [ + "Variable", + "VariableV2", + "DecisionTreeEnsembleResourceHandleOp", + "StatsAccumulatorScalarResourceHandleOp", + "StatsAccumulatorTensorResourceHandleOp", + ] + ps_strategy = _OpRoundRobinStrategy(ps_ops, ps_tasks) + return device_setter.replica_device_setter( + worker_device=worker_device, + ps_tasks=ps_tasks, + merge_devices=True, + ps_ops=ps_ops, + ps_strategy=ps_strategy) + + def _make_update_bias_stats_fn(self, ensemble_stamp, predictions, gradients, + bias_stats_accumulator): + """A method to create the function which updates the bias stats.""" + + def _update_bias_stats(): + """A method to update the bias stats.""" + # Get reduced gradients and hessians. + grads_sum = math_ops.reduce_sum(gradients, 0) + hess = gradients_impl.gradients( + grads_sum, + predictions, + name="Hessians", + colocate_gradients_with_ops=False, + gate_gradients=0, + aggregation_method=None)[0] + hess_sum = math_ops.reduce_sum(hess, 0) + + # Accumulate gradients and hessians. + partition_ids = math_ops.range(self._logits_dimension) + feature_ids = array_ops.zeros( + [self._logits_dimension, 2], dtype=dtypes.int64) + + add_stats_op = bias_stats_accumulator.add( + ensemble_stamp, partition_ids, feature_ids, grads_sum, hess_sum) + return control_flow_ops.group(*[add_stats_op], name="update_bias_stats") + + return _update_bias_stats + + def _make_update_ensemble_fn(self, ensemble_stamp, steps_accumulator, + bias_stats_accumulator, continue_centering, + learning_rate, handlers, num_layers, active_tree, + active_layer, dropout_seed, class_id): + """A method to create the function which updates the tree ensemble.""" def _update_ensemble(): """A method to update the tree ensemble.""" @@ -1023,140 +1110,3 @@ class GradientBoostedDecisionTreeModel(object): def get_number_of_trees_tensor(self): return self._finalized_trees, self._attempted_trees - - def train(self, loss, predictions_dict, labels): - """Updates the accumalator stats and grows the ensemble. - - Args: - loss: A scalar tensor representing average loss of examples. - predictions_dict: Dictionary of Rank 2 `Tensor` representing information - about predictions per example. - labels: Rank 2 `Tensor` representing labels per example. - - Returns: - An op that adds a new tree to the ensemble. - - Raises: - ValueError: if inputs are not valid. - """ - batch_size = math_ops.cast(array_ops.shape(labels)[0], dtypes.float32) - update_op, handlers = self.update_stats(loss, predictions_dict) - with ops.control_dependencies(update_op): - return self.increment_step_counter_and_maybe_update_ensemble( - predictions_dict, batch_size, handlers) - - def _get_weights(self, hessian_shape, hessians): - """Derives weights to be used based on hessians and multiclass strategy.""" - if hessian_shape == tensor_shape.scalar(): - # This is tree per class. - weights = hessians - elif len(hessian_shape.dims) == 1: - # This is diagonal hessian. - weights = math_ops.reduce_sum(hessians, axis=1) - else: - # This is full hessian. - weights = math_ops.trace(hessians) - return weights - - def _full_hessian(self, grads, predictions): - """Prepares hessians for full-hessian multiclass strategy.""" - # Because of - # https://github.com/tensorflow/tensorflow/issues/675, we can't just - # compute the full hessian with a single call to gradients, but instead - # must compute it row-by-row. - gradients_list = array_ops.unstack( - grads, num=self._logits_dimension, axis=1) - hessian_rows = [] - - for row in range(self._logits_dimension): - # If current row is i, K is number of classes,each row returns a tensor of - # size batch_size x K representing for each example dx_i dx_1, dx_i dx_2 - # etc dx_i dx_K - hessian_row = gradients_impl.gradients( - gradients_list[row], - predictions, - name="Hessian_%d" % row, - colocate_gradients_with_ops=False, - gate_gradients=0, - aggregation_method=None) - - # hessian_row is of dimension 1, batch_size, K, => trim first dimension - # to get batch_size x K - hessian_row = array_ops.squeeze(array_ops.unstack(hessian_row), [0]) - hessian_rows.append(hessian_row) - return hessian_rows - - def _diagonal_hessian(self, grads, predictions): - """Prepares hessians for diagonal-hessian multiclass mode.""" - diag_hessian_list = [] - - gradients_list = array_ops.unstack( - grads, num=self._logits_dimension, axis=1) - - for row, row_grads in enumerate(gradients_list): - # If current row is i, K is number of classes,each row returns a tensor of - # size batch_size x K representing for each example dx_i dx_1, dx_1 dx_2 - # etc dx_i dx_K - hessian_row = gradients_impl.gradients( - row_grads, - predictions, - name="Hessian_%d" % row, - colocate_gradients_with_ops=False, - gate_gradients=0, - aggregation_method=None) - - # hessian_row is of dimension 1, batch_size, K, => trim first dimension - # to get batch_size x K - hessian_row = array_ops.squeeze(array_ops.unstack(hessian_row), [0]) - - # Get dx_i^2 for the whole batch. - elem = array_ops.transpose(hessian_row)[row] - diag_hessian_list.append(elem) - - return diag_hessian_list - - def _get_replica_device_setter(self, worker_device): - """Creates a replica device setter.""" - ps_tasks = self._num_ps_replicas - ps_ops = [ - "Variable", - "VariableV2", - "DecisionTreeEnsembleResourceHandleOp", - "StatsAccumulatorScalarResourceHandleOp", - "StatsAccumulatorTensorResourceHandleOp", - ] - ps_strategy = _OpRoundRobinStrategy(ps_ops, ps_tasks) - return device_setter.replica_device_setter( - worker_device=worker_device, - ps_tasks=ps_tasks, - merge_devices=True, - ps_ops=ps_ops, - ps_strategy=ps_strategy) - - def _make_update_bias_stats_fn(self, ensemble_stamp, predictions, gradients, - bias_stats_accumulator): - """A method to create the function which updates the bias stats.""" - - def _update_bias_stats(): - """A method to update the bias stats.""" - # Get reduced gradients and hessians. - grads_sum = math_ops.reduce_sum(gradients, 0) - hess = gradients_impl.gradients( - grads_sum, - predictions, - name="Hessians", - colocate_gradients_with_ops=False, - gate_gradients=0, - aggregation_method=None)[0] - hess_sum = math_ops.reduce_sum(hess, 0) - - # Accumulate gradients and hessians. - partition_ids = math_ops.range(self._logits_dimension) - feature_ids = array_ops.zeros( - [self._logits_dimension, 2], dtype=dtypes.int64) - - add_stats_op = bias_stats_accumulator.add( - ensemble_stamp, partition_ids, feature_ids, grads_sum, hess_sum) - return control_flow_ops.group(*[add_stats_op], name="update_bias_stats") - - return _update_bias_stats -- GitLab From 03178bc00c57652879bc253d47b7abb570c2d547 Mon Sep 17 00:00:00 2001 From: Jiri Simsa Date: Fri, 15 Jun 2018 17:33:00 -0700 Subject: [PATCH 555/816] [tf.data] Concurrency improvements to `map_and_batch`. PiperOrigin-RevId: 200800013 --- .../kernels/data/map_and_batch_dataset_op.cc | 90 ++++++++++--------- 1 file changed, 49 insertions(+), 41 deletions(-) diff --git a/tensorflow/core/kernels/data/map_and_batch_dataset_op.cc b/tensorflow/core/kernels/data/map_and_batch_dataset_op.cc index 586677a2d6..aa40f95cde 100644 --- a/tensorflow/core/kernels/data/map_and_batch_dataset_op.cc +++ b/tensorflow/core/kernels/data/map_and_batch_dataset_op.cc @@ -219,8 +219,8 @@ class MapAndBatchDatasetOp : public UnaryDatasetOpKernel { } std::swap(result, batch_results_.front()); batch_results_.pop_front(); - cond_var_.notify_all(); } + cond_var_.notify_all(); return ProcessBatch(ctx, result, out_tensors, end_of_sequence); } @@ -286,7 +286,7 @@ class MapAndBatchDatasetOp : public UnaryDatasetOpKernel { void Callback(const std::shared_ptr& ctx, const std::shared_ptr& result, const std::shared_ptr>& return_values, - int64 offset, const Status& status) { + int64 offset, const Status& status) LOCKS_EXCLUDED(mu_) { result->UpdateStatus(status); if (status.ok()) { EnsureOutputAllocated(ctx, result, return_values); @@ -318,36 +318,37 @@ class MapAndBatchDatasetOp : public UnaryDatasetOpKernel { mutex_lock l(result->mu); result->num_elements++; } - { - mutex_lock l(mu_); - CallCompleted(result); - } + CallCompleted(result); } void CallCompleted(const std::shared_ptr& result) - EXCLUSIVE_LOCKS_REQUIRED(mu_) { - num_calls_--; + LOCKS_EXCLUDED(mu_) { + { + mutex_lock l(mu_); + num_calls_--; + result->num_calls--; + } cond_var_.notify_all(); - result->num_calls--; } void CallFunction(std::shared_ptr ctx, const std::shared_ptr& result, - int64 offset) { + int64 offset) LOCKS_EXCLUDED(mu_) { // Get the next input element. std::vector input_element; bool end_of_input; Status status = input_impl_->GetNext(ctx.get(), &input_element, &end_of_input); + bool return_early; { - mutex_lock l(mu_); - mutex_lock l2(result->mu); + mutex_lock l(result->mu); result->end_of_input = result->end_of_input || end_of_input; result->status.Update(status); - if (result->end_of_input || !result->status.ok()) { - CallCompleted(result); - return; - } + return_early = result->end_of_input || !result->status.ok(); + } + if (return_early) { + CallCompleted(result); + return; } // Call `captured_func_(input_element)`, using `Callback` to store the @@ -468,36 +469,43 @@ class MapAndBatchDatasetOp : public UnaryDatasetOpKernel { return result->status; } - void RunnerThread(const std::shared_ptr& ctx) { - mutex_lock l(mu_); + void RunnerThread(const std::shared_ptr& ctx) + LOCKS_EXCLUDED(mu_) { + std::vector, int64>> new_calls; + new_calls.reserve(dataset()->num_parallel_calls_); while (true) { - while (!cancelled_ && - (num_calls_ >= dataset()->num_parallel_calls_ || - batch_results_.size() > MaxBatchResults() || - (batch_results_.size() == MaxBatchResults() && - call_counter_ % dataset()->batch_size_ == 0))) { - cond_var_.wait(l); - } + { + mutex_lock l(mu_); + while (!cancelled_ && + (num_calls_ >= dataset()->num_parallel_calls_ || + batch_results_.size() > MaxBatchResults() || + (batch_results_.size() == MaxBatchResults() && + call_counter_ % dataset()->batch_size_ == 0))) { + cond_var_.wait(l); + } - if (cancelled_) { - return; - } + if (cancelled_) { + return; + } - while (num_calls_ < dataset()->num_parallel_calls_ && - (batch_results_.size() < MaxBatchResults() || - (batch_results_.size() == MaxBatchResults() && - call_counter_ % dataset()->batch_size_ != 0))) { - if (call_counter_ % dataset()->batch_size_ == 0) { - batch_results_.emplace_back( - new BatchResult(dataset()->batch_size_)); + while (num_calls_ < dataset()->num_parallel_calls_ && + (batch_results_.size() < MaxBatchResults() || + (batch_results_.size() == MaxBatchResults() && + call_counter_ % dataset()->batch_size_ != 0))) { + if (call_counter_ % dataset()->batch_size_ == 0) { + batch_results_.emplace_back( + new BatchResult(dataset()->batch_size_)); + } + int64 offset = call_counter_++ % dataset()->batch_size_; + new_calls.emplace_back(batch_results_.back(), offset); + num_calls_++; } - std::shared_ptr result = batch_results_.back(); - int64 offset = call_counter_++ % dataset()->batch_size_; - num_calls_++; - mu_.unlock(); - CallFunction(ctx, result, offset); - mu_.lock(); } + + for (const auto& call : new_calls) { + CallFunction(ctx, call.first, call.second); + } + new_calls.clear(); } } -- GitLab From 1aac0a396da088c779b7a43128abdea32b9f7087 Mon Sep 17 00:00:00 2001 From: Skye Wanderman-Milne Date: Fri, 15 Jun 2018 17:45:26 -0700 Subject: [PATCH 556/816] Remove bad assert in control_flow_ops.py. TensorShape.__eq__ will return false if there are any unknown dimensions in the shapes being compared, even if both shapes have unknown dims in the same place. This means that the assert in control_flow_ops.py would sometimes spuriously trigger. This change removes the assert since it was for debugging anyway. PiperOrigin-RevId: 200801159 --- tensorflow/python/ops/control_flow_ops.py | 1 - 1 file changed, 1 deletion(-) diff --git a/tensorflow/python/ops/control_flow_ops.py b/tensorflow/python/ops/control_flow_ops.py index 3ae7cf21ed..9413bfa2af 100644 --- a/tensorflow/python/ops/control_flow_ops.py +++ b/tensorflow/python/ops/control_flow_ops.py @@ -602,7 +602,6 @@ def _EnforceShapeInvariant(merge_var, next_var): enter = merge_var.op.inputs[0].op assert util.IsLoopEnter(enter) input_t = enter.inputs[0] - assert input_t.shape == m_shape raise ValueError( "Input tensor '%s' enters the loop with shape %s, but has shape %s " "after one iteration. To allow the shape to vary across iterations, " -- GitLab From 1aebd982d7d911504dfd47b99a56461c67ceddad Mon Sep 17 00:00:00 2001 From: Igor Ganichev Date: Fri, 15 Jun 2018 18:03:13 -0700 Subject: [PATCH 557/816] TFE: Correctly set shapes of defun outputs When a function being converted to defun runs, it can output non-tensor values. The "shape" of these non-tensor values is set to None in _output_shapes. When we set the shapes at the end of the defun __call__, we need to skip these Nones. Also, unrelatedly, add a test for basic gradient tape and a test for using strided_slice inside a compiled and taped defun. strided_slice "stresses" XLA's constant inference for arguments that must be constant. PiperOrigin-RevId: 200802717 --- tensorflow/compiler/tests/eager_test.py | 33 +++++++++++++++++++ tensorflow/python/eager/function.py | 13 +++++--- tensorflow/python/eager/function_test.py | 42 ++++++++++++++++++++++++ 3 files changed, 84 insertions(+), 4 deletions(-) diff --git a/tensorflow/compiler/tests/eager_test.py b/tensorflow/compiler/tests/eager_test.py index a4154ad1e8..3bb3049e87 100644 --- a/tensorflow/compiler/tests/eager_test.py +++ b/tensorflow/compiler/tests/eager_test.py @@ -49,6 +49,21 @@ class EagerTest(XLATestCase): product = three * five self.assertAllEqual(15, product) + def testGradientTape(self): + with self.test_scope(): + + x = constant_op.constant(1.0) + y = constant_op.constant(10.0) + with backprop.GradientTape(persistent=True) as tape: + tape.watch(x) + tape.watch(y) + a = x + y + x * y + da_dx = tape.gradient(a, x) + da_dy = tape.gradient(a, y) + + self.assertEqual(11.0, da_dx.numpy()) + self.assertEqual(2.0, da_dy.numpy()) + def testExecuteListOutputLen0(self): with self.test_scope(): empty = constant_op.constant([], dtype=dtypes.float32) @@ -385,6 +400,24 @@ class EagerFunctionTest(XLATestCase): self.assertEqual(75, y.numpy()) self.assertEqual(30, dy.numpy()) + def testSliceInDefun(self): + with self.test_scope(): + + @function.defun(compiled=True) + def f(x, y): + return x[0::2, y:, ...] + + x = array_ops.ones([2, 3, 4]) + y = array_ops.ones([], dtype=dtypes.int32) + with backprop.GradientTape() as tape: + tape.watch(x) + tape.watch(y) + z = f(x, y) + dz = tape.gradient(z, x) + + self.assertAllEqual(np.ones([1, 2, 4]), z.numpy()) + self.assertAllEqual((2, 3, 4), dz.shape.as_list()) + class ExcessivePaddingTest(XLATestCase): """Test that eager execution works with TPU flattened tensors. diff --git a/tensorflow/python/eager/function.py b/tensorflow/python/eager/function.py index be61d9889d..2f6318bb92 100644 --- a/tensorflow/python/eager/function.py +++ b/tensorflow/python/eager/function.py @@ -449,8 +449,11 @@ class GraphModeFunction(object): if not outputs: return op outputs = [outputs] if isinstance(outputs, ops.Tensor) else list(outputs) - for i, s in enumerate(self._output_shapes): - outputs[i].set_shape(s) + + shapes = [shape for shape in self._output_shapes if shape is not None] + for i, shape in enumerate(shapes): + outputs[i].set_shape(shape) + real_outputs = outputs[:len(self._returns)] side_outputs = outputs[len(self._returns):] @@ -543,8 +546,10 @@ class GraphModeFunction(object): result = op.outputs if not result: return op - for i, s in enumerate(self._output_shapes): - result[i].set_shape(s) + + shapes = [shape for shape in self._output_shapes if shape is not None] + for i, shape in enumerate(shapes): + result[i].set_shape(shape) return self._build_call_outputs(result) diff --git a/tensorflow/python/eager/function_test.py b/tensorflow/python/eager/function_test.py index 43b621b44e..393279b313 100644 --- a/tensorflow/python/eager/function_test.py +++ b/tensorflow/python/eager/function_test.py @@ -512,6 +512,48 @@ class FunctionTest(test.TestCase): g = backprop.gradients_function(wrapper, [0])(constant_op.constant(0.0)) self.assertAllEqual(g[0], 1.) + def testNestedDifferentiableFunction(self): + @function.defun + def foo(a, b): + return a * math_ops.add(a, b) + + @function.defun + def bar(x): + return foo(x, 1.0) + + x = constant_op.constant(5.0) + with backprop.GradientTape() as tp: + tp.watch(x) + result = bar(x) + grad = tp.gradient(result, x) + + self.assertAllEqual(grad, 2 * 5.0 + 1.0) + + def testNestedDifferentiableFunctionNoneOutputs(self): + @function.defun + def foo(a, b): + return None, a * math_ops.add(a, b), None, 2*a + + @function.defun + def bar(x): + return foo(x, 1.0) + + x = constant_op.constant(5.0) + with backprop.GradientTape(persistent=True) as tp: + tp.watch(x) + none1, r1, none2, r2 = bar(x) + g1 = tp.gradient(r1, x) # pylint: disable=unused-variable + g2 = tp.gradient(r2, x) + + self.assertAllEqual(r1, 30.0) + self.assertAllEqual(r2, 10.0) + self.assertIs(none1, None) + self.assertIs(none2, None) + # TODO(b/110213087) Differentiating nested tfe.defuns returning some + # Nones does not work. The following returns 1 instead of correct 11. + # self.assertAllEqual(g1, 2 * 5.0 + 1.0) + self.assertAllEqual(g2, 2.0) + def testNoneOutput(self): @function.defun -- GitLab From 68af4047fdfa89fa7b7d222a50a38eb0a469d946 Mon Sep 17 00:00:00 2001 From: Katherine Wu Date: Fri, 15 Jun 2018 18:04:21 -0700 Subject: [PATCH 558/816] Automated g4 rollback of changelist 200747752 PiperOrigin-RevId: 200802842 --- tensorflow/python/saved_model/BUILD | 24 --- tensorflow/python/saved_model/loader_impl.py | 175 ++++-------------- tensorflow/python/saved_model/loader_test.py | 180 ------------------- 3 files changed, 31 insertions(+), 348 deletions(-) delete mode 100644 tensorflow/python/saved_model/loader_test.py diff --git a/tensorflow/python/saved_model/BUILD b/tensorflow/python/saved_model/BUILD index 076f2d8760..81786fbf43 100644 --- a/tensorflow/python/saved_model/BUILD +++ b/tensorflow/python/saved_model/BUILD @@ -87,30 +87,6 @@ py_library( "//tensorflow/python:platform", "//tensorflow/python:training", "//tensorflow/python:util", - "//tensorflow/python:variables", - ], -) - -py_test( - name = "loader_test", - size = "small", - srcs = ["loader_test.py"], - srcs_version = "PY2AND3", - visibility = ["//visibility:private"], - deps = [ - ":builder", - ":loader", - ":signature_def_utils", - ":utils", - "//tensorflow/python:client", - "//tensorflow/python:client_testlib", - "//tensorflow/python:control_flow_ops", - "//tensorflow/python:errors", - "//tensorflow/python:framework_ops", - "//tensorflow/python:lib", - "//tensorflow/python:state_ops", - "//tensorflow/python:training", - "//tensorflow/python:variables", ], ) diff --git a/tensorflow/python/saved_model/loader_impl.py b/tensorflow/python/saved_model/loader_impl.py index 6770aaef36..d1bd8d47ae 100644 --- a/tensorflow/python/saved_model/loader_impl.py +++ b/tensorflow/python/saved_model/loader_impl.py @@ -28,7 +28,6 @@ from tensorflow.core.protobuf import meta_graph_pb2 from tensorflow.core.protobuf import saved_model_pb2 from tensorflow.python.framework import ops from tensorflow.python.lib.io import file_io -from tensorflow.python.ops import variables from tensorflow.python.platform import tf_logging from tensorflow.python.saved_model import constants from tensorflow.python.training import saver as tf_saver @@ -208,56 +207,11 @@ def load(sess, tags, export_dir, import_scope=None, **saver_kwargs): Raises: RuntimeError: MetaGraphDef associated with the tags cannot be found. """ - loader = SavedModelLoader(export_dir) - return loader.load(sess, tags, import_scope, **saver_kwargs) - - -class SavedModelLoader(object): - """Load graphs and restore variable values from a `SavedModel`.""" - - def __init__(self, export_dir): - """Creates a `SavedModelLoader`. - - Args: - export_dir: Directory in which the SavedModel protocol buffer and - variables to be loaded are located. - """ - self._export_dir = export_dir - self._variables_path = os.path.join( - compat.as_bytes(export_dir), - compat.as_bytes(constants.VARIABLES_DIRECTORY), - compat.as_bytes(constants.VARIABLES_FILENAME)) - self._saved_model = _parse_saved_model(export_dir) - - @property - def export_dir(self): - """Directory containing the SavedModel.""" - return self._export_dir - - @property - def variables_path(self): - """Path to variable checkpoint files.""" - return self._variables_path - - @property - def saved_model(self): - """SavedModel object parsed from the export directory.""" - return self._saved_model - - def get_meta_graph_def_from_tags(self, tags): - """Return MetaGraphDef with the exact specified tags. - - Args: - tags: A list or set of string tags that identify the MetaGraphDef. - - Returns: - MetaGraphDef with the same tags. - - Raises: - RuntimeError: if no metagraphs were found with the associated tags. - """ + with sess.graph.as_default(): + # Build the SavedModel protocol buffer and find requested meta graph def. + saved_model = _parse_saved_model(export_dir) found_match = False - for meta_graph_def in self._saved_model.meta_graphs: + for meta_graph_def in saved_model.meta_graphs: if set(meta_graph_def.meta_info_def.tags) == set(tags): meta_graph_def_to_load = meta_graph_def found_match = True @@ -269,99 +223,32 @@ class SavedModelLoader(object): " could not be found in SavedModel. To inspect available tag-sets in" " the SavedModel, please use the SavedModel CLI: `saved_model_cli`" ) - return meta_graph_def_to_load - def load_graph(self, graph, tags, import_scope=None, **saver_kwargs): - """Load ops and nodes from SavedModel MetaGraph into graph. + # Build a saver by importing the meta graph def to load. + saver = tf_saver.import_meta_graph( + meta_graph_def_to_load, import_scope=import_scope, **saver_kwargs) + + if saver: + # Build the checkpoint path where the variables are located. + variables_path = os.path.join( + compat.as_bytes(export_dir), + compat.as_bytes(constants.VARIABLES_DIRECTORY), + compat.as_bytes(constants.VARIABLES_FILENAME)) + + # Restore the variables using the built saver in the provided session. + saver.restore(sess, variables_path) + else: + tf_logging.info("The specified SavedModel has no variables; no " + "checkpoints were restored.") + + # Get asset tensors, if any. + asset_tensors_dictionary = _get_asset_tensors( + export_dir, meta_graph_def_to_load, import_scope=import_scope) + + main_op_tensor = ( + _get_main_op_tensor(meta_graph_def_to_load) or + (_get_legacy_init_op_tensor(meta_graph_def_to_load))) + if main_op_tensor is not None: + sess.run(fetches=[main_op_tensor], feed_dict=asset_tensors_dictionary) - Args: - graph: tf.Graph object. - tags: a set of string tags identifying a MetaGraphDef. - import_scope: Optional `string` -- if specified, prepend this string - followed by '/' to all loaded tensor names. This scope is applied to - tensor instances loaded into the passed session, but it is *not* written - through to the static `MetaGraphDef` protocol buffer that is returned. - **saver_kwargs: keyword arguments to pass to tf.train.import_meta_graph. - - Returns: - Saver defined by the MetaGraph, which can be used to restore the variable - values. - """ - meta_graph_def = self.get_meta_graph_def_from_tags(tags) - with graph.as_default(): - return tf_saver.import_meta_graph( - meta_graph_def, import_scope=import_scope, **saver_kwargs) - - def restore_variables(self, sess, saver, import_scope=None): - """Restore SavedModel variable values into the session. - - Args: - sess: tf.Session to restore variable values. - saver: a tf.train.Saver object. Can be None if there are no variables in - graph. This may be the saver returned by the load_graph() function, or a - default `tf.train.Saver()`. - import_scope: Optional `string` -- if specified, prepend this string - followed by '/' to all loaded tensor names. This scope is applied to - tensor instances loaded into the passed session, but it is *not* written - through to the static `MetaGraphDef` protocol buffer that is returned. - - Raises: - ValueError: if no saver was passed to the saver argument, and there are - variables in the graph. - """ - with sess.graph.as_default(): - if not variables._all_saveable_objects(scope=import_scope): # pylint: disable=protected-access - tf_logging.info("The specified SavedModel has no variables; no " - "checkpoints were restored.") - elif isinstance(saver, tf_saver.Saver): - saver.restore(sess, self._variables_path) - else: - raise ValueError( - "No tf.train.Saver object was passed to the function " - "SavedModelLoader.restore_variables. Since there are variables in " - "the graph, a saver is required.") - - def run_init_ops(self, sess, tags, import_scope=None): - """Run initialization ops defined in the `MetaGraphDef`. - - Args: - sess: tf.Session to restore variable values. - tags: a set of string tags identifying a MetaGraphDef. - import_scope: Optional `string` -- if specified, prepend this string - followed by '/' to all loaded tensor names. This scope is applied to - tensor instances loaded into the passed session, but it is *not* written - through to the static `MetaGraphDef` protocol buffer that is returned. - """ - meta_graph_def = self.get_meta_graph_def_from_tags(tags) - with sess.graph.as_default(): - # Get asset tensors, if any. - asset_tensors_dictionary = _get_asset_tensors( - self._export_dir, meta_graph_def, import_scope=import_scope) - - main_op_tensor = ( - _get_main_op_tensor(meta_graph_def) or - (_get_legacy_init_op_tensor(meta_graph_def))) - if main_op_tensor is not None: - sess.run(fetches=[main_op_tensor], feed_dict=asset_tensors_dictionary) - - def load(self, sess, tags, import_scope=None, **saver_kwargs): - """Load the MetaGraphDef graph and restore variable values into the session. - - Args: - sess: tf.Session to restore variable values. - tags: a set of string tags identifying a MetaGraphDef. - import_scope: Optional `string` -- if specified, prepend this string - followed by '/' to all loaded tensor names. This scope is applied to - tensor instances loaded into the passed session, but it is *not* written - through to the static `MetaGraphDef` protocol buffer that is returned. - **saver_kwargs: keyword arguments to pass to tf.train.import_meta_graph. - - Returns: - `MetagraphDef` proto of the graph that was loaded. - """ - with sess.graph.as_default(): - saver = self.load_graph(sess.graph, tags, import_scope, - **saver_kwargs) - self.restore_variables(sess, saver, import_scope) - self.run_init_ops(sess, tags, import_scope) - return self.get_meta_graph_def_from_tags(tags) + return meta_graph_def_to_load diff --git a/tensorflow/python/saved_model/loader_test.py b/tensorflow/python/saved_model/loader_test.py deleted file mode 100644 index 2ec2519c89..0000000000 --- a/tensorflow/python/saved_model/loader_test.py +++ /dev/null @@ -1,180 +0,0 @@ -# Copyright 2018 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for SavedModelLoader class.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import os - -from tensorflow.python.client import session -from tensorflow.python.framework import errors -from tensorflow.python.framework import ops -from tensorflow.python.lib.io import file_io -from tensorflow.python.ops import control_flow_ops -from tensorflow.python.ops import state_ops -from tensorflow.python.ops import variables -from tensorflow.python.platform import test -from tensorflow.python.saved_model import builder as saved_model_builder -from tensorflow.python.saved_model import loader_impl -from tensorflow.python.saved_model import signature_def_utils -from tensorflow.python.saved_model import utils -from tensorflow.python.training import saver as tf_saver - - -def _get_export_dir(label): - return os.path.join(test.get_temp_dir(), label) - -SIMPLE_ADD_SAVED_MODEL = _get_export_dir("simple_add_saved_model") -SAVED_MODEL_WITH_MAIN_OP = _get_export_dir("saved_model_with_main_op") - - -class SavedModelLoaderTest(test.TestCase): - - def setUp(self): - """Write test SavedModels to a temp directory.""" - with session.Session(graph=ops.Graph()) as sess: - x = variables.Variable(5, name="x") - y = variables.Variable(11, name="y") - z = x + y - sess.run(variables.global_variables_initializer()) - - foo_sig_def = signature_def_utils.build_signature_def( - {"foo_input": utils.build_tensor_info(x)}, - {"foo_output": utils.build_tensor_info(z)}) - bar_sig_def = signature_def_utils.build_signature_def( - {"bar_x": utils.build_tensor_info(x), - "bar_y": utils.build_tensor_info(y)}, - {"bar_z": utils.build_tensor_info(z)}) - - builder = saved_model_builder.SavedModelBuilder(SIMPLE_ADD_SAVED_MODEL) - builder.add_meta_graph_and_variables( - sess, ["foo_graph"], {"foo": foo_sig_def, "bar": bar_sig_def}) - builder.save() - - # Write SavedModel with a main_op - assign_op = control_flow_ops.group(state_ops.assign(y, 7)) - - builder = saved_model_builder.SavedModelBuilder(SAVED_MODEL_WITH_MAIN_OP) - builder.add_meta_graph_and_variables( - sess, ["foo_graph"], {"foo": foo_sig_def, "bar": bar_sig_def}, - main_op=assign_op) - builder.save() - - def tearDown(self): - file_io.delete_recursively(test.get_temp_dir()) - - def test_load_function(self): - loader = loader_impl.SavedModelLoader(SIMPLE_ADD_SAVED_MODEL) - with self.test_session(graph=ops.Graph()) as sess: - loader.load(sess, ["foo_graph"]) - self.assertEqual(5, sess.graph.get_tensor_by_name("x:0").eval()) - self.assertEqual(11, sess.graph.get_tensor_by_name("y:0").eval()) - - loader2 = loader_impl.SavedModelLoader(SAVED_MODEL_WITH_MAIN_OP) - with self.test_session(graph=ops.Graph()) as sess: - loader2.load(sess, ["foo_graph"]) - self.assertEqual(5, sess.graph.get_tensor_by_name("x:0").eval()) - self.assertEqual(7, sess.graph.get_tensor_by_name("y:0").eval()) - - def test_load_graph(self): - loader = loader_impl.SavedModelLoader(SIMPLE_ADD_SAVED_MODEL) - graph = ops.Graph() - loader.load_graph(graph, ["foo_graph"]) - - x = graph.get_tensor_by_name("x:0") - y = graph.get_tensor_by_name("y:0") - - with self.assertRaises(KeyError): - graph.get_tensor_by_name("z:0") - - with self.test_session(graph=graph) as sess: - # Check that x and y are not initialized - with self.assertRaises(errors.FailedPreconditionError): - sess.run(x) - with self.assertRaises(errors.FailedPreconditionError): - sess.run(y) - - def test_load_with_import_scope(self): - loader = loader_impl.SavedModelLoader(SAVED_MODEL_WITH_MAIN_OP) - with self.test_session(graph=ops.Graph()) as sess: - saver = loader.load_graph(sess.graph, ["foo_graph"], import_scope="baz") - - # The default saver should not work when the import scope is set. - with self.assertRaises(errors.NotFoundError): - loader.restore_variables(sess, tf_saver.Saver()) - - loader.restore_variables(sess, saver) - loader.run_init_ops(sess, ["foo_graph"]) - - self.assertEqual(5, sess.graph.get_tensor_by_name("baz/x:0").eval()) - self.assertEqual(7, sess.graph.get_tensor_by_name("baz/y:0").eval()) - - # Test combined load function. - loader = loader_impl.SavedModelLoader(SAVED_MODEL_WITH_MAIN_OP) - with self.test_session(graph=ops.Graph()) as sess: - loader.load(sess, ["foo_graph"], import_scope="baa") - self.assertEqual(5, sess.graph.get_tensor_by_name("baa/x:0").eval()) - self.assertEqual(7, sess.graph.get_tensor_by_name("baa/y:0").eval()) - - def test_restore_variables(self): - loader = loader_impl.SavedModelLoader(SAVED_MODEL_WITH_MAIN_OP) - with self.test_session(graph=ops.Graph()) as sess: - x = variables.Variable(0, name="x") - y = variables.Variable(0, name="y") - z = x * y - - sess.run(variables.global_variables_initializer()) - - # There are variables to restore, so a saver must be created. - with self.assertRaises(ValueError): - loader.restore_variables(sess, None) - - loader.restore_variables(sess, tf_saver.Saver()) - self.assertEqual(55, z.eval()) - - def test_run_init_op(self): - loader = loader_impl.SavedModelLoader(SAVED_MODEL_WITH_MAIN_OP) - graph = ops.Graph() - saver = loader.load_graph(graph, ["foo_graph"]) - with self.test_session(graph=graph) as sess: - loader.restore_variables(sess, saver) - self.assertEqual(5, sess.graph.get_tensor_by_name("x:0").eval()) - self.assertEqual(11, sess.graph.get_tensor_by_name("y:0").eval()) - - loader.run_init_ops(sess, ["foo_graph"]) - self.assertEqual(5, sess.graph.get_tensor_by_name("x:0").eval()) - self.assertEqual(7, sess.graph.get_tensor_by_name("y:0").eval()) - - def test_parse_saved_model(self): - loader = loader_impl.SavedModelLoader(SIMPLE_ADD_SAVED_MODEL) - meta_graph = loader.get_meta_graph_def_from_tags(["foo_graph"]) - self.assertIsNotNone(meta_graph) - self.assertIn("foo", meta_graph.signature_def) - self.assertIn("bar", meta_graph.signature_def) - - def test_load_invalid_meta_graph(self): - loader = loader_impl.SavedModelLoader(SIMPLE_ADD_SAVED_MODEL) - with self.assertRaises(RuntimeError): - loader.get_meta_graph_def_from_tags([]) - with self.assertRaises(RuntimeError): - loader.get_meta_graph_def_from_tags([""]) - with self.assertRaises(RuntimeError): - loader.get_meta_graph_def_from_tags(["not_a_graph"]) - - -if __name__ == "__main__": - test.main() -- GitLab From e2755e00fc3c68251d6a591b7ea76d6714976720 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 15 Jun 2018 19:05:30 -0700 Subject: [PATCH 559/816] Don't check for duplicates in FetchOutputs and FeedInputs when creating a ControlEdge. There cannot be a duplicate, since fetch_node and feed_node are newly created. This change reduces the complexity of FetchOutputs from quadratic to linear. PiperOrigin-RevId: 200807286 --- tensorflow/core/graph/subgraph.cc | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/tensorflow/core/graph/subgraph.cc b/tensorflow/core/graph/subgraph.cc index 193cf88aed..60337e30aa 100644 --- a/tensorflow/core/graph/subgraph.cc +++ b/tensorflow/core/graph/subgraph.cc @@ -81,7 +81,9 @@ Status FeedInputs( // Update name_index (*name_index)[feed_node->name()] = feed_node; - g->AddControlEdge(g->source_node(), feed_node); + // Duplicate control edges aren't allowed, but feed_node was *just* created + // so there's no need to check for a duplicate. + g->AddControlEdge(g->source_node(), feed_node, true); // Look through edges coming out of "n" for edges whose src_output() index // matches "output_index". If found, replace the edges with a connection @@ -107,7 +109,9 @@ Status FeedInputs( g->AddEdge(feed_node, 0, e->dst(), e->dst_input()); } else { CHECK_EQ(Graph::kControlSlot, e->src_output()); - g->AddControlEdge(feed_node, e->dst()); + // Duplicate control edges aren't allowed, but feed_node was *just* + // created so there's no need to check for a duplicate. + g->AddControlEdge(feed_node, e->dst(), true); } g->RemoveEdge(e); } @@ -160,7 +164,9 @@ Status FetchOutputs( // Update the index. (*name_index)[fetch_node->name()] = fetch_node; - g->AddControlEdge(fetch_node, g->sink_node()); + // Duplicate control edges aren't allowed, but fetch_node was *just* created + // so there's no need to check for a duplicate. + g->AddControlEdge(fetch_node, g->sink_node(), true); out_fetch_nodes->push_back(fetch_node); out_fetch_types->push_back(BaseType(n->output_type(id.second))); } -- GitLab From 6679e797aada9c4ae40d2ff16f7ec77191afe2f7 Mon Sep 17 00:00:00 2001 From: Jiri Simsa Date: Fri, 15 Jun 2018 19:28:26 -0700 Subject: [PATCH 560/816] [tf.data] Internal refactor of the parallel version of `tf.data.Dataset.map()`, switching from using a fixed-size circular buffer to a deque. PiperOrigin-RevId: 200808498 --- .../kernels/data/parallel_map_dataset_op.cc | 310 ++++++++++-------- 1 file changed, 169 insertions(+), 141 deletions(-) diff --git a/tensorflow/core/kernels/data/parallel_map_dataset_op.cc b/tensorflow/core/kernels/data/parallel_map_dataset_op.cc index 3fa6b0d3a9..15f3dc3b1d 100644 --- a/tensorflow/core/kernels/data/parallel_map_dataset_op.cc +++ b/tensorflow/core/kernels/data/parallel_map_dataset_op.cc @@ -151,8 +151,7 @@ class ParallelMapDatasetOp : public UnaryDatasetOpKernel { class Iterator : public DatasetIterator { public: explicit Iterator(const Params& params) - : DatasetIterator(params), - invocation_results_(params.dataset->num_parallel_calls_) {} + : DatasetIterator(params) {} ~Iterator() override { // TODO(mrry): Replace this cancellation logic with a @@ -160,13 +159,13 @@ class ParallelMapDatasetOp : public UnaryDatasetOpKernel { // but it would be possible to thread a cancellation manager // through the IteratorContext to upstream, // potentially-blocking iterators, when we add these. - { - mutex_lock l(mu_); - for (size_t i = 0; i < dataset()->num_parallel_calls_; ++i) { - if (invocation_results_[i].notification) { - invocation_results_[i].notification->WaitForNotification(); - } - } + mutex_lock l(mu_); + // Cancel the runner thread. + cancelled_ = true; + cond_var_.notify_all(); + // Wait for all in-flight calls to complete. + while (num_calls_ > 0) { + cond_var_.wait(l); } } @@ -177,173 +176,191 @@ class ParallelMapDatasetOp : public UnaryDatasetOpKernel { Status GetNextInternal(IteratorContext* ctx, std::vector* out_tensors, bool* end_of_sequence) override { - mutex_lock l(mu_); - - // Ensure that there are `dataset()->num_parallel_calls_` - // invocations of `func_` outstanding at once. - while (input_impl_ && (num_inputs_consumed_ - num_outputs_consumed_ < - dataset()->num_parallel_calls_)) { - InvokeFunctionLocked(ctx); - } - - if (!input_impl_ && num_inputs_consumed_ == num_outputs_consumed_) { - *end_of_sequence = true; - return Status::OK(); - } - - // Read the next result out of `invocation_results_`, which - // acts as a circular buffer. - const size_t result_index = - num_outputs_consumed_ % dataset()->num_parallel_calls_; - InvocationResult* result = &invocation_results_[result_index]; - *end_of_sequence = false; - if (result->notification) { - result->notification->WaitForNotification(); - if (result->status.ok()) { - std::swap(*out_tensors, result->return_values); + std::shared_ptr result; + { + mutex_lock l(mu_); + EnsureRunnerThreadStarted(ctx); + while (invocation_results_.empty()) { + cond_var_.wait(l); } + std::swap(result, invocation_results_.front()); + invocation_results_.pop_front(); } - ++num_outputs_consumed_; - if (errors::IsOutOfRange(result->status)) { - // `f` may deliberately raise `errors::OutOfRange` to indicate - // that we should terminate the iteration early. - *end_of_sequence = true; - return Status::OK(); - } else { - return result->status; - } + cond_var_.notify_all(); + result->notification.WaitForNotification(); + return ProcessResult(result, out_tensors, end_of_sequence); } protected: Status SaveInternal(IteratorStateWriter* writer) override { mutex_lock l(mu_); - if (input_impl_) { - TF_RETURN_IF_ERROR(SaveParent(writer, input_impl_)); - } else { - TF_RETURN_IF_ERROR( - writer->WriteScalar(full_name("end_of_input"), "")); + // Wait for all in-flight calls to complete. + while (num_calls_ > 0) { + cond_var_.wait(l); } - TF_RETURN_IF_ERROR(writer->WriteScalar(full_name("num_inputs_consumed"), - num_inputs_consumed_)); + CHECK_EQ(num_calls_, 0); + TF_RETURN_IF_ERROR(SaveParent(writer, input_impl_)); TF_RETURN_IF_ERROR(writer->WriteScalar( - full_name("num_outputs_consumed"), num_outputs_consumed_)); - - for (size_t i = 0; i < dataset()->num_parallel_calls_; i++) { - if (invocation_results_[i].notification) { - invocation_results_[i].notification->WaitForNotification(); - TF_RETURN_IF_ERROR( - WriteStatusLocked(writer, i, invocation_results_[i].status)); - TF_RETURN_IF_ERROR(writer->WriteScalar( - full_name(strings::StrCat("invocation_results[", i, "].size")), - invocation_results_[i].return_values.size())); - for (size_t j = 0; j < invocation_results_[i].return_values.size(); - j++) { - TF_RETURN_IF_ERROR(writer->WriteTensor( - full_name( - strings::StrCat("invocation_results[", i, "][", j, "]")), - invocation_results_[i].return_values[j])); - } - } else { + full_name("invocation_results.size"), invocation_results_.size())); + for (size_t i = 0; i < invocation_results_.size(); i++) { + std::shared_ptr result = invocation_results_[i]; + TF_RETURN_IF_ERROR(WriteStatusLocked(writer, i, result->status)); + TF_RETURN_IF_ERROR(writer->WriteScalar( + full_name(strings::StrCat("invocation_results[", i, "].size")), + result->return_values.size())); + for (size_t j = 0; j < result->return_values.size(); j++) { + TF_RETURN_IF_ERROR(writer->WriteTensor( + full_name( + strings::StrCat("invocation_results[", i, "][", j, "]")), + result->return_values[j])); + } + if (result->end_of_input) { TF_RETURN_IF_ERROR(writer->WriteScalar( - full_name(strings::StrCat("invocation_results[", i, "]_empty")), + full_name(strings::StrCat("invocation_results[", i, + "].end_of_input")), "")); } } - return Status::OK(); } Status RestoreInternal(IteratorContext* ctx, IteratorStateReader* reader) override { mutex_lock l(mu_); - if (reader->Contains(full_name("end_of_input"))) { - input_impl_.reset(); - } else { - TF_RETURN_IF_ERROR(RestoreParent(ctx, reader, input_impl_)); - } - TF_RETURN_IF_ERROR(reader->ReadScalar(full_name("num_inputs_consumed"), - &num_inputs_consumed_)); - TF_RETURN_IF_ERROR(reader->ReadScalar(full_name("num_outputs_consumed"), - &num_outputs_consumed_)); - for (size_t i = 0; i < dataset()->num_parallel_calls_; i++) { - InvocationResult* result = &invocation_results_[i]; - *result = InvocationResult(); - if (!reader->Contains(full_name( - strings::StrCat("invocation_results[", i, "]_empty")))) { - result->notification.reset(new Notification); - result->notification->Notify(); - TF_RETURN_IF_ERROR(ReadStatusLocked(reader, i, &result->status)); - size_t num_return_values; - { - int64 size; - TF_RETURN_IF_ERROR( - reader->ReadScalar(full_name(strings::StrCat( - "invocation_results[", i, "].size")), - &size)); - num_return_values = static_cast(size); - if (num_return_values != size) { - return errors::InvalidArgument(strings::StrCat( - full_name( - strings::StrCat("invocation_results[", i, "].size")), - ": ", size, " is not a valid value of type size_t.")); - } - } - result->return_values.reserve(num_return_values); - for (size_t j = 0; j < num_return_values; j++) { - result->return_values.emplace_back(); - TF_RETURN_IF_ERROR(reader->ReadTensor( + TF_RETURN_IF_ERROR(RestoreParent(ctx, reader, input_impl_)); + int64 invocation_results_size; + TF_RETURN_IF_ERROR(reader->ReadScalar( + full_name("invocation_results.size"), &invocation_results_size)); + for (size_t i = 0; i < invocation_results_size; i++) { + std::shared_ptr result(new InvocationResult()); + invocation_results_.push_back(result); + TF_RETURN_IF_ERROR(ReadStatusLocked(reader, i, &result->status)); + size_t num_return_values; + { + int64 size; + TF_RETURN_IF_ERROR(reader->ReadScalar( + full_name(strings::StrCat("invocation_results[", i, "].size")), + &size)); + num_return_values = static_cast(size); + if (num_return_values != size) { + return errors::InvalidArgument(strings::StrCat( full_name( - strings::StrCat("invocation_results[", i, "][", j, "]")), - &result->return_values.back())); + strings::StrCat("invocation_results[", i, "].size")), + ": ", size, " is not a valid value of type size_t.")); } } + result->return_values.reserve(num_return_values); + for (size_t j = 0; j < num_return_values; j++) { + result->return_values.emplace_back(); + TF_RETURN_IF_ERROR( + reader->ReadTensor(full_name(strings::StrCat( + "invocation_results[", i, "][", j, "]")), + &result->return_values.back())); + } + result->end_of_input = reader->Contains(full_name( + strings::StrCat("invocation_results[", i, "].end_of_input"))); + result->notification.Notify(); } return Status::OK(); } private: struct InvocationResult { + Notification notification; Status status; - std::unique_ptr notification; std::vector return_values; + bool end_of_input; }; - void InvokeFunctionLocked(IteratorContext* ctx) + void EnsureRunnerThreadStarted(IteratorContext* ctx) EXCLUSIVE_LOCKS_REQUIRED(mu_) { - DCHECK(input_impl_); - DCHECK(num_inputs_consumed_ - num_outputs_consumed_ < - dataset()->num_parallel_calls_); + if (!runner_thread_) { + std::shared_ptr ctx_copy(new IteratorContext(*ctx)); + runner_thread_.reset(ctx->env()->StartThread( + {}, "runner_thread", + std::bind(&Iterator::RunnerThread, this, ctx_copy))); + } + } - // The result of invoking the function will be written into the next - // slot in `invocation_results_`, which acts as a circular buffer. - const size_t result_index = - num_inputs_consumed_ % dataset()->num_parallel_calls_; - InvocationResult* result = &invocation_results_[result_index]; - *result = InvocationResult(); + void CallCompleted(const std::shared_ptr& result) + LOCKS_EXCLUDED(mu_) { + { + mutex_lock l(mu_); + num_calls_--; + } + result->notification.Notify(); + cond_var_.notify_all(); + } + void CallFunction(const std::shared_ptr& ctx, + const std::shared_ptr& result) + LOCKS_EXCLUDED(mu_) { // Get the next input element. std::vector input_element; - bool end_of_input = false; - result->status = - input_impl_->GetNext(ctx, &input_element, &end_of_input); - if (end_of_input) { - input_impl_.reset(); - result->status = errors::OutOfRange(""); - } else { - ++num_inputs_consumed_; + result->status = input_impl_->GetNext(ctx.get(), &input_element, + &result->end_of_input); + if (result->end_of_input || !result->status.ok()) { + CallCompleted(result); + return; } - if (result->status.ok()) { - // Call `func_(input_element)`, store the result in - // `result->return_values`, and notify `result->notification` - // to unblock a consumer. - result->notification.reset(new Notification); - dataset()->captured_func_->RunAsync( - ctx, std::move(input_element), &result->return_values, - [result, result_index](Status ret_status) { - result->status.Update(ret_status); - result->notification->Notify(); - }); + // Call `func_(input_element)`, store the result in + // `result->return_values`, and notify `result->notification` to unblock + // a consumer. + auto done = [this, result](Status status) { + result->status.Update(status); + CallCompleted(result); + }; + dataset()->captured_func_->RunAsync(ctx.get(), std::move(input_element), + &result->return_values, done); + } + + int64 MaxInvocationResults() { return dataset()->num_parallel_calls_; } + + Status ProcessResult(const std::shared_ptr& result, + std::vector* out_tensors, + bool* end_of_sequence) { + if (!result->end_of_input && result->status.ok()) { + *out_tensors = std::move(result->return_values); + *end_of_sequence = false; + return Status::OK(); + } + if (errors::IsOutOfRange(result->status)) { + // `f` may deliberately raise `errors::OutOfRange` to indicate that we + // should terminate the iteration early. + *end_of_sequence = true; + return Status::OK(); + } + *end_of_sequence = result->end_of_input; + return result->status; + } + + void RunnerThread(const std::shared_ptr& ctx) { + std::vector> new_calls; + new_calls.reserve(dataset()->num_parallel_calls_); + while (true) { + { + mutex_lock l(mu_); + while (!cancelled_ && + (num_calls_ >= dataset()->num_parallel_calls_ || + invocation_results_.size() >= MaxInvocationResults())) { + cond_var_.wait(l); + } + if (cancelled_) { + return; + } + while (num_calls_ < dataset()->num_parallel_calls_ && + invocation_results_.size() < MaxInvocationResults()) { + invocation_results_.emplace_back(new InvocationResult()); + new_calls.push_back(invocation_results_.back()); + num_calls_++; + } + } + cond_var_.notify_all(); + for (const auto& call : new_calls) { + CallFunction(ctx, call); + } + new_calls.clear(); } } @@ -386,11 +403,22 @@ class ParallelMapDatasetOp : public UnaryDatasetOpKernel { strings::StrCat("invocation_results[", index, "].error_message")); } + // Used for coordination between the main thread and the runner thread. mutex mu_; - std::unique_ptr input_impl_ GUARDED_BY(mu_); - std::vector invocation_results_ GUARDED_BY(mu_); - int64 num_inputs_consumed_ GUARDED_BY(mu_) = 0; - int64 num_outputs_consumed_ GUARDED_BY(mu_) = 0; + // Used for coordination between the main thread and the runner thread. In + // particular, the runner thread should only schedule new calls when the + // number of in-flight calls is less than the user specified level of + // parallelism and there are slots available in the `invocation_results_` + // buffer. + condition_variable cond_var_; + // Counts the number of outstanding calls. + int64 num_calls_ GUARDED_BY(mu_) = 0; + std::unique_ptr input_impl_; + // Buffer for storing the invocation results. + std::deque> invocation_results_ + GUARDED_BY(mu_); + std::unique_ptr runner_thread_ GUARDED_BY(mu_); + bool cancelled_ GUARDED_BY(mu_) = false; }; const DatasetBase* const input_; -- GitLab From 6a9ea7bb272982e4db2553a758b8c8f1dee086aa Mon Sep 17 00:00:00 2001 From: Thomas Joerg Date: Fri, 15 Jun 2018 19:45:46 -0700 Subject: [PATCH 561/816] [XLA:GPU] Allow different element types in multi-output fusion root tuples. PiperOrigin-RevId: 200809229 --- .../xla/service/gpu/ir_emitter_unnested.cc | 4 +- .../xla/tests/multioutput_fusion_test.cc | 39 +++++++++++++++++++ 2 files changed, 41 insertions(+), 2 deletions(-) diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc index ccbd99a042..078afed3e2 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc @@ -569,8 +569,8 @@ Status IrEmitterUnnested::HandleFusion(HloInstruction* fusion) { reducers.push_back(inst->to_apply()); reduce_output_shapes.push_back(std::move(output_shape_index)); } else { - CHECK(ShapeUtil::Compatible(first_reduce->operand(0)->shape(), - inst->shape())); + CHECK(ShapeUtil::CompatibleIgnoringElementType( + first_reduce->operand(0)->shape(), inst->shape())); extra_output_gens.emplace_back(fused_emitter.GetGenerator(inst), std::move(output_shape_index)); } diff --git a/tensorflow/compiler/xla/tests/multioutput_fusion_test.cc b/tensorflow/compiler/xla/tests/multioutput_fusion_test.cc index 41f723edf1..6837b05fb5 100644 --- a/tensorflow/compiler/xla/tests/multioutput_fusion_test.cc +++ b/tensorflow/compiler/xla/tests/multioutput_fusion_test.cc @@ -514,5 +514,44 @@ XLA_TEST_F(MultiOutputFusionTest, Literal::CreateR2({{6, 6}, {6, 8}})))); } +XLA_TEST_F(MultiOutputFusionTest, + DISABLED_ON_CPU(MultiOutputReduceFusionDifferentElementTypes)) { + const string testcase = tensorflow::strings::StrCat(kScalarOps, R"( + fused_reduce (p0: f16[2,2,2]) -> (f32[2,2], f32[2,2], f16[2,2,2]) { + p0 = f16[2,2,2]{2,1,0} parameter(0) + convert = f32[2,2,2]{2,1,0} convert(p0) + c0 = f32[] constant(0) + r1 = f32[2,2]{1,0} reduce(convert, c0), dimensions={2}, to_apply=Add + mul = f32[2,2,2]{2,1,0} multiply(convert, convert) + c1 = f32[] constant(5) + r2 = f32[2,2]{1,0} reduce(mul, c1), dimensions={2}, to_apply=Max + ROOT tuple = (f32[2,2]{1,0}, f32[2,2]{1,0}, f16[2,2,2]{2,1,0}) + tuple(r1, r2, p0) + } + + ENTRY reduce { + p = f16[2,2,2]{2,1,0} parameter(0) + ROOT fusion = (f32[2,2]{1,0}, f32[2,2]{1,0}, f16[2,2,2]{2,1,0}) fusion(p), + kind=kInput, calls=fused_reduce + })"); + auto module = + HloRunner::CreateModuleFromString(testcase, GetDebugOptionsForTest()) + .ValueOrDie(); + auto param = Literal::CreateR3( + {{{Eigen::half(1), Eigen::half(2)}, {Eigen::half(3), Eigen::half(4)}}, + {{Eigen::half(5), Eigen::half(6)}, {Eigen::half(7), Eigen::half(8)}}}); + std::unique_ptr result = + ExecuteNoHloPasses(std::move(module), {param.get()}); + EXPECT_TRUE(LiteralTestUtil::Equal( + *Literal::MakeTupleOwned( + Literal::CreateR2({{3, 7}, {11, 15}}), + Literal::CreateR2({{5, 16}, {36, 64}}), + Literal::CreateR3({{{Eigen::half(1), Eigen::half(2)}, + {Eigen::half(3), Eigen::half(4)}}, + {{Eigen::half(5), Eigen::half(6)}, + {Eigen::half(7), Eigen::half(8)}}})), + *result)); +} + } // namespace } // namespace xla -- GitLab From 6a86de5a75c92b95ffe72b1be6ccb1c18a663e3c Mon Sep 17 00:00:00 2001 From: Gunhan Gulsoy Date: Fri, 15 Jun 2018 21:16:29 -0700 Subject: [PATCH 562/816] Disable random_ops_test on windows. PiperOrigin-RevId: 200814177 --- tensorflow/contrib/cmake/tf_tests.cmake | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tensorflow/contrib/cmake/tf_tests.cmake b/tensorflow/contrib/cmake/tf_tests.cmake index c8de8db126..d04d533043 100644 --- a/tensorflow/contrib/cmake/tf_tests.cmake +++ b/tensorflow/contrib/cmake/tf_tests.cmake @@ -229,6 +229,8 @@ if (tensorflow_BUILD_PYTHON_TESTS) "${tensorflow_source_dir}/tensorflow/python/debug/cli/profile_analyzer_cli_test.py" # Windows does not have the curses library and uses readline. "${tensorflow_source_dir}/tensorflow/python/debug/cli/curses_ui_test.py" + # Bug in shape inference (b/110283809) + "${tensorflow_source_dir}/tensorflow/python/kernel_tests/random/random_ops_test.py" # TFDBG grpc:// mode is not yet available on Windows. "${tensorflow_source_dir}/tensorflow/python/debug/lib/dist_session_debug_grpc_test.py" "${tensorflow_source_dir}/tensorflow/python/debug/lib/grpc_large_data_test.py" -- GitLab From df4ff7833725452c4ede1bf58b7523bafff3ecef Mon Sep 17 00:00:00 2001 From: Gunhan Gulsoy Date: Fri, 15 Jun 2018 22:21:40 -0700 Subject: [PATCH 563/816] Automated g4 rollback of changelist 200623983 PiperOrigin-RevId: 200817339 --- tensorflow/contrib/cmake/tf_tests.cmake | 2 -- 1 file changed, 2 deletions(-) diff --git a/tensorflow/contrib/cmake/tf_tests.cmake b/tensorflow/contrib/cmake/tf_tests.cmake index d04d533043..38573f86ef 100644 --- a/tensorflow/contrib/cmake/tf_tests.cmake +++ b/tensorflow/contrib/cmake/tf_tests.cmake @@ -327,8 +327,6 @@ if (tensorflow_BUILD_PYTHON_TESTS) "${tensorflow_source_dir}/tensorflow/contrib/estimator/python/estimator/replicate_model_fn_test.py" # b/71901810 # Broken io_utils_test "${tensorflow_source_dir}/tensorflow/python/keras/_impl/keras/utils/io_utils_test.py" # b/72894325 - # OOM - "${tensorflow_source_dir}/tensorflow/python/training/saver_large_variable_test.py" # b/110210559 ) endif() list(REMOVE_ITEM tf_test_src_py ${tf_test_src_py_exclude}) -- GitLab From 990e1f218c7180b2ebf407b8ec06d59936e9cc12 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 15 Jun 2018 23:32:33 -0700 Subject: [PATCH 564/816] TFLite Custom op for object detection postprocessing. PiperOrigin-RevId: 200820561 --- tensorflow/contrib/lite/kernels/BUILD | 15 + tensorflow/contrib/lite/kernels/register.cc | 3 + .../lite/kernels/ssd_postprocess_test.cc | 235 +++++++ .../lite/kernels/ssd_postprocessing.cc | 589 ++++++++++++++++++ 4 files changed, 842 insertions(+) create mode 100644 tensorflow/contrib/lite/kernels/ssd_postprocess_test.cc create mode 100644 tensorflow/contrib/lite/kernels/ssd_postprocessing.cc diff --git a/tensorflow/contrib/lite/kernels/BUILD b/tensorflow/contrib/lite/kernels/BUILD index cf5d0b4ce9..0b70c8ffa3 100644 --- a/tensorflow/contrib/lite/kernels/BUILD +++ b/tensorflow/contrib/lite/kernels/BUILD @@ -174,6 +174,7 @@ cc_library( "sparse_to_dense.cc", "split.cc", "squeeze.cc", + "ssd_postprocessing.cc", "strided_slice.cc", "sub.cc", "svdf.cc", @@ -246,6 +247,20 @@ tf_cc_test( ], ) +tf_cc_test( + name = "ssd_postprocess_test", + size = "small", + srcs = ["ssd_postprocess_test.cc"], + tags = ["tflite_not_portable_ios"], + deps = [ + ":builtin_ops", + "//tensorflow/contrib/lite:framework", + "//tensorflow/contrib/lite/kernels:test_util", + "@com_google_googletest//:gtest", + "@flatbuffers", + ], +) + tf_cc_test( name = "activations_test", size = "small", diff --git a/tensorflow/contrib/lite/kernels/register.cc b/tensorflow/contrib/lite/kernels/register.cc index 7bb28d4de7..98f7250a40 100644 --- a/tensorflow/contrib/lite/kernels/register.cc +++ b/tensorflow/contrib/lite/kernels/register.cc @@ -22,6 +22,7 @@ namespace custom { TfLiteRegistration* Register_AUDIO_SPECTROGRAM(); TfLiteRegistration* Register_MFCC(); +TfLiteRegistration* Register_SSD_POSTPROCESS(); } // namespace custom @@ -180,6 +181,8 @@ BuiltinOpResolver::BuiltinOpResolver() { AddCustom("Mfcc", tflite::ops::custom::Register_MFCC()); AddCustom("AudioSpectrogram", tflite::ops::custom::Register_AUDIO_SPECTROGRAM()); + AddCustom("TFLite_SSD_PostProcess", + tflite::ops::custom::Register_SSD_POSTPROCESS()); } } // namespace builtin diff --git a/tensorflow/contrib/lite/kernels/ssd_postprocess_test.cc b/tensorflow/contrib/lite/kernels/ssd_postprocess_test.cc new file mode 100644 index 0000000000..b0f8824115 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/ssd_postprocess_test.cc @@ -0,0 +1,235 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include +#include +#include + +#include +#include "flatbuffers/flexbuffers.h" +#include "tensorflow/contrib/lite/interpreter.h" +#include "tensorflow/contrib/lite/kernels/register.h" +#include "tensorflow/contrib/lite/kernels/test_util.h" +#include "tensorflow/contrib/lite/model.h" + +namespace tflite { +namespace ops { +namespace custom { + +TfLiteRegistration* Register_SSD_POSTPROCESS(); + +namespace { + +using ::testing::ElementsAre; +using ::testing::ElementsAreArray; + +class BaseSSDPostprocessOpModel : public SingleOpModel { + public: + BaseSSDPostprocessOpModel(const TensorData& input1, const TensorData& input2, + const TensorData& input3, const TensorData& output1, + const TensorData& output2, + const TensorData& output3, + const TensorData& output4) { + input1_ = AddInput(input1); + input2_ = AddInput(input2); + input3_ = AddInput(input3); + output1_ = AddOutput(output1); + output2_ = AddOutput(output2); + output3_ = AddOutput(output3); + output4_ = AddOutput(output4); + + flexbuffers::Builder fbb; + fbb.Map([&]() { + fbb.Int("max_detections", 3); + fbb.Int("max_classes_per_detection", 1); + fbb.Float("nms_score_threshold", 0.0); + fbb.Float("nms_iou_threshold", 0.5); + fbb.Int("num_classes", 2); + fbb.Float("y_scale", 10.0); + fbb.Float("x_scale", 10.0); + fbb.Float("h_scale", 5.0); + fbb.Float("w_scale", 5.0); + }); + fbb.Finish(); + SetCustomOp("TFLite_SSD_PostProcess", fbb.GetBuffer(), + Register_SSD_POSTPROCESS); + BuildInterpreter({GetShape(input1_), GetShape(input2_), GetShape(input3_)}); + } + + int input1() { return input1_; } + int input2() { return input2_; } + int input3() { return input3_; } + + template + void SetInput1(std::initializer_list data) { + PopulateTensor(input1_, data); + } + + template + void SetInput2(std::initializer_list data) { + PopulateTensor(input2_, data); + } + + template + void SetInput3(std::initializer_list data) { + PopulateTensor(input3_, data); + } + + template + std::vector GetOutput1() { + return ExtractVector(output1_); + } + + template + std::vector GetOutput2() { + return ExtractVector(output2_); + } + + template + std::vector GetOutput3() { + return ExtractVector(output3_); + } + + template + std::vector GetOutput4() { + return ExtractVector(output4_); + } + + std::vector GetOutputShape1() { return GetTensorShape(output1_); } + std::vector GetOutputShape2() { return GetTensorShape(output2_); } + std::vector GetOutputShape3() { return GetTensorShape(output3_); } + std::vector GetOutputShape4() { return GetTensorShape(output4_); } + + protected: + int input1_; + int input2_; + int input3_; + int output1_; + int output2_; + int output3_; + int output4_; +}; + +TEST(SSDPostprocessOpTest, FloatTest) { + BaseSSDPostprocessOpModel m( + {TensorType_FLOAT32, {1, 6, 4}}, {TensorType_FLOAT32, {1, 6, 3}}, + {TensorType_FLOAT32, {6, 4}}, {TensorType_FLOAT32, {}}, + {TensorType_FLOAT32, {}}, {TensorType_FLOAT32, {}}, + {TensorType_FLOAT32, {}}); + + // six boxes in center-size encoding + m.SetInput1({0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, + 0.0, -1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}); + // class scores - two classes with background + m.SetInput2({0., .9, .8, 0., .75, .72, 0., .6, .5, 0., .93, .95, 0., + .5, .4, 0., .3, .2}); + // six anchors in center-size encoding + m.SetInput3({0.5, 0.5, 1.0, 1.0, 0.5, 0.5, 1.0, 1.0, + 0.5, 0.5, 1.0, 1.0, 0.5, 10.5, 1.0, 1.0, + 0.5, 10.5, 1.0, 1.0, 0.5, 100.5, 1.0, 1.0}); + // Same boxes in box-corner encoding: + // { 0.0, 0.0, 1.0, 1.0, + // 0.0, 0.1, 1.0, 1.1, + // 0.0, -0.1, 1.0, 0.9, + // 0.0, 10.0, 1.0, 11.0, + // 0.0, 10.1, 1.0, 11.1, + // 0.0, 100.0, 1.0, 101.0} + + m.Invoke(); + + // detection_boxes + // in center-size + std::vector output_shape1 = m.GetOutputShape1(); + EXPECT_THAT(output_shape1, ElementsAre(1, 3, 4)); + EXPECT_THAT( + m.GetOutput1(), + ElementsAreArray(ArrayFloatNear( + {0.0, 10.0, 1.0, 11.0, 0.0, 0.0, 1.0, 1.0, 0.0, 100.0, 1.0, 101.0}, + 1e-1))); + // detection_classes + std::vector output_shape2 = m.GetOutputShape2(); + EXPECT_THAT(output_shape2, ElementsAre(1, 3)); + EXPECT_THAT(m.GetOutput2(), + ElementsAreArray(ArrayFloatNear({1, 0, 0}, 1e-1))); + // detection_scores + std::vector output_shape3 = m.GetOutputShape3(); + EXPECT_THAT(output_shape3, ElementsAre(1, 3)); + EXPECT_THAT(m.GetOutput3(), + ElementsAreArray(ArrayFloatNear({0.95, 0.9, 0.3}, 1e-1))); + // num_detections + std::vector output_shape4 = m.GetOutputShape4(); + EXPECT_THAT(output_shape4, ElementsAre(1)); + EXPECT_THAT(m.GetOutput4(), + ElementsAreArray(ArrayFloatNear({3.0}, 1e-1))); +} + +TEST(SSDPostprocessOpTest, QuantizedTest) { + BaseSSDPostprocessOpModel m( + {TensorType_UINT8, {1, 6, 4}, -1.0, 1.0}, + {TensorType_UINT8, {1, 6, 3}, 0.0, 1.0}, {TensorType_FLOAT32, {6, 4}}, + {TensorType_FLOAT32, {}}, {TensorType_FLOAT32, {}}, + {TensorType_FLOAT32, {}}, {TensorType_FLOAT32, {}}); + + // six boxes in center-size encoding + std::vector> inputs1 = { + {0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, -1.0, 0.0, 0.0, + 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}}; + m.QuantizeAndPopulate(m.input1(), inputs1[0]); + // class scores - two classes with background + std::vector> inputs2 = { + {0., .9, .8, 0., .75, .72, 0., .6, .5, 0., .93, .95, 0., .5, .4, 0., .3, + .2}}; + m.QuantizeAndPopulate(m.input2(), inputs2[0]); + // six anchors in center-size encoding + m.SetInput3({0.5, 0.5, 1.0, 1.0, 0.5, 0.5, 1.0, 1.0, + 0.5, 0.5, 1.0, 1.0, 0.5, 10.5, 1.0, 1.0, + 0.5, 10.5, 1.0, 1.0, 0.5, 100.5, 1.0, 1.0}); + m.Invoke(); + + // detection_boxes + // in center-size + std::vector output_shape1 = m.GetOutputShape1(); + EXPECT_THAT(output_shape1, ElementsAre(1, 3, 4)); + EXPECT_THAT( + m.GetOutput1(), + ElementsAreArray(ArrayFloatNear( + {0.0, 10.0, 1.0, 11.0, 0.0, 0.0, 1.0, 1.0, 0.0, 100.0, 1.0, 101.0}, + 1e-1))); + // detection_classes + std::vector output_shape2 = m.GetOutputShape2(); + EXPECT_THAT(output_shape2, ElementsAre(1, 3)); + EXPECT_THAT(m.GetOutput2(), + ElementsAreArray(ArrayFloatNear({1, 0, 0}, 1e-1))); + // detection_scores + std::vector output_shape3 = m.GetOutputShape3(); + EXPECT_THAT(output_shape3, ElementsAre(1, 3)); + EXPECT_THAT(m.GetOutput3(), + ElementsAreArray(ArrayFloatNear({0.95, 0.9, 0.3}, 1e-1))); + // num_detections + std::vector output_shape4 = m.GetOutputShape4(); + EXPECT_THAT(output_shape4, ElementsAre(1)); + EXPECT_THAT(m.GetOutput4(), + ElementsAreArray(ArrayFloatNear({3.0}, 1e-1))); +} +} // namespace +} // namespace custom +} // namespace ops +} // namespace tflite + +int main(int argc, char** argv) { + ::tflite::LogToStderr(); + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} diff --git a/tensorflow/contrib/lite/kernels/ssd_postprocessing.cc b/tensorflow/contrib/lite/kernels/ssd_postprocessing.cc new file mode 100644 index 0000000000..078c4bdd11 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/ssd_postprocessing.cc @@ -0,0 +1,589 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include +#include +#include +#include "flatbuffers/flexbuffers.h" +#include "tensorflow/contrib/lite/builtin_op_data.h" +#include "tensorflow/contrib/lite/context.h" +#include "tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h" +#include "tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h" +#include "tensorflow/contrib/lite/kernels/internal/tensor.h" +#include "tensorflow/contrib/lite/kernels/kernel_util.h" +#include "tensorflow/contrib/lite/kernels/op_macros.h" + +namespace tflite { +namespace ops { +namespace custom { +namespace ssd_postprocess { + +// Input tensors +constexpr int kInputTensorBoxEncodings = 0; +constexpr int kInputTensorClassPredictions = 1; +constexpr int kInputTensorAnchors = 2; + +// Output tensors +constexpr int kOutputTensorDetectionBoxes = 0; +constexpr int kOutputTensorDetectionClasses = 1; +constexpr int kOutputTensorDetectionScores = 2; +constexpr int kOutputTensorNumDetections = 3; + +constexpr size_t kNumCoordBox = 4; +constexpr size_t kBatchSize = 1; + +// Object Detection model produces axis-aligned boxes in two formats: +// BoxCorner represents the upper right (xmin, ymin) and +// lower left corner (xmax, ymax). +// CenterSize represents the center (xcenter, ycenter), height and width. +// BoxCornerEncoding and CenterSizeEncoding are related as follows: +// ycenter = y / y_scale * anchor.h + anchor.y; +// xcenter = x / x_scale * anchor.w + anchor.x; +// half_h = 0.5*exp(h/ h_scale)) * anchor.h; +// half_w = 0.5*exp(w / w_scale)) * anchor.w; +// ymin = ycenter - half_h +// ymax = ycenter + half_h +// xmin = xcenter - half_w +// xmax = xcenter + half_w +struct BoxCornerEncoding { + float ymin; + float xmin; + float ymax; + float xmax; +}; + +struct CenterSizeEncoding { + float y; + float x; + float h; + float w; +}; +// We make sure that the memory allocations are contiguous with static assert. +static_assert(sizeof(BoxCornerEncoding) == sizeof(float) * kNumCoordBox, + "Size of BoxCornerEncoding is 4 float values"); +static_assert(sizeof(CenterSizeEncoding) == sizeof(float) * kNumCoordBox, + "Size of CenterSizeEncoding is 4 float values"); + +struct OpData { + int max_detections; + int max_classes_per_detection; + float non_max_suppression_score_threshold; + float intersection_over_union_threshold; + int num_classes; + CenterSizeEncoding scale_values; + // Indices of Temporary tensors + int decoded_boxes_index; + int scores_index; + int active_candidate_index; +}; + +void* Init(TfLiteContext* context, const char* buffer, size_t length) { + auto* op_data = new OpData; + const uint8_t* buffer_t = reinterpret_cast(buffer); + const flexbuffers::Map& m = flexbuffers::GetRoot(buffer_t, length).AsMap(); + op_data->max_detections = m["max_detections"].AsInt32(); + op_data->max_classes_per_detection = m["max_classes_per_detection"].AsInt32(); + op_data->non_max_suppression_score_threshold = + m["nms_score_threshold"].AsFloat(); + op_data->intersection_over_union_threshold = m["nms_iou_threshold"].AsFloat(); + op_data->num_classes = m["num_classes"].AsInt32(); + op_data->scale_values.y = m["y_scale"].AsFloat(); + op_data->scale_values.x = m["x_scale"].AsFloat(); + op_data->scale_values.h = m["h_scale"].AsFloat(); + op_data->scale_values.w = m["w_scale"].AsFloat(); + context->AddTensors(context, 1, &op_data->decoded_boxes_index); + context->AddTensors(context, 1, &op_data->scores_index); + context->AddTensors(context, 1, &op_data->active_candidate_index); + return op_data; +} + +void Free(TfLiteContext* context, void* buffer) { + delete reinterpret_cast(buffer); +} + +// TODO(chowdhery): Add to kernel_util.h +TfLiteStatus SetTensorSizes(TfLiteContext* context, TfLiteTensor* tensor, + std::initializer_list values) { + TfLiteIntArray* size = TfLiteIntArrayCreate(values.size()); + int index = 0; + for (int v : values) { + size->data[index] = v; + ++index; + } + return context->ResizeTensor(context, tensor, size); +} + +TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { + auto* op_data = reinterpret_cast(node->user_data); + // Inputs: box_encodings, scores, anchors + TF_LITE_ENSURE_EQ(context, NumInputs(node), 3); + const TfLiteTensor* input_box_encodings = + GetInput(context, node, kInputTensorBoxEncodings); + const TfLiteTensor* input_class_predictions = + GetInput(context, node, kInputTensorClassPredictions); + const TfLiteTensor* input_anchors = + GetInput(context, node, kInputTensorAnchors); + TF_LITE_ENSURE_EQ(context, NumDimensions(input_box_encodings), 3); + TF_LITE_ENSURE_EQ(context, NumDimensions(input_class_predictions), 3); + TF_LITE_ENSURE_EQ(context, NumDimensions(input_anchors), 2); + // number of detected boxes + const int num_detected_boxes = + op_data->max_detections * op_data->max_classes_per_detection; + + // Outputs: detection_boxes, detection_scores, detection_classes, + // num_detections + TF_LITE_ENSURE_EQ(context, NumOutputs(node), 4); + // Output Tensor detection_boxes: size is set to (1, num_detected_boxes, 4) + TfLiteTensor* detection_boxes = + GetOutput(context, node, kOutputTensorDetectionBoxes); + detection_boxes->type = kTfLiteFloat32; + SetTensorSizes(context, detection_boxes, + {kBatchSize, num_detected_boxes, kNumCoordBox}); + + // Output Tensor detection_classes: size is set to (1, num_detected_boxes) + TfLiteTensor* detection_classes = + GetOutput(context, node, kOutputTensorDetectionClasses); + detection_classes->type = kTfLiteFloat32; + SetTensorSizes(context, detection_classes, {kBatchSize, num_detected_boxes}); + + // Output Tensor detection_scores: size is set to (1, num_detected_boxes) + TfLiteTensor* detection_scores = + GetOutput(context, node, kOutputTensorDetectionScores); + detection_scores->type = kTfLiteFloat32; + SetTensorSizes(context, detection_scores, {kBatchSize, num_detected_boxes}); + + // Output Tensor num_detections: size is set to 1 + TfLiteTensor* num_detections = + GetOutput(context, node, kOutputTensorNumDetections); + num_detections->type = kTfLiteFloat32; + // TODO (chowdhery): Make it a scalar when available + SetTensorSizes(context, num_detections, {1}); + + // Temporary tensors + TfLiteIntArrayFree(node->temporaries); + node->temporaries = TfLiteIntArrayCreate(3); + node->temporaries->data[0] = op_data->decoded_boxes_index; + node->temporaries->data[1] = op_data->scores_index; + node->temporaries->data[2] = op_data->active_candidate_index; + + // decoded_boxes + TfLiteTensor* decoded_boxes = &context->tensors[op_data->decoded_boxes_index]; + decoded_boxes->type = kTfLiteFloat32; + decoded_boxes->allocation_type = kTfLiteArenaRw; + SetTensorSizes(context, decoded_boxes, + {input_box_encodings->dims->data[1], kNumCoordBox}); + + // scores + TfLiteTensor* scores = &context->tensors[op_data->scores_index]; + scores->type = kTfLiteFloat32; + scores->allocation_type = kTfLiteArenaRw; + SetTensorSizes(context, scores, + {input_class_predictions->dims->data[1], + input_class_predictions->dims->data[2]}); + + // active_candidate + TfLiteTensor* active_candidate = + &context->tensors[op_data->active_candidate_index]; + active_candidate->type = kTfLiteUInt8; + active_candidate->allocation_type = kTfLiteArenaRw; + SetTensorSizes(context, active_candidate, + {input_box_encodings->dims->data[1]}); + + return kTfLiteOk; +} + +class Dequantizer { + public: + Dequantizer(int zero_point, float scale) + : zero_point_(zero_point), scale_(scale) {} + float operator()(uint8 x) { + return (static_cast(x) - zero_point_) * scale_; + } + + private: + int zero_point_; + float scale_; +}; + +void DequantizeBoxEncodings(const TfLiteTensor* input_box_encodings, int idx, + float quant_zero_point, float quant_scale, + CenterSizeEncoding* box_centersize) { + const uint8* boxes = + GetTensorData(input_box_encodings) + kNumCoordBox * idx; + Dequantizer dequantize(quant_zero_point, quant_scale); + box_centersize->y = dequantize(boxes[0]); + box_centersize->x = dequantize(boxes[1]); + box_centersize->h = dequantize(boxes[2]); + box_centersize->w = dequantize(boxes[3]); +} + +template +T ReInterpretTensor(const TfLiteTensor* tensor) { + // TODO (chowdhery): check float + const float* tensor_base = tensor->data.f; + return reinterpret_cast(tensor_base); +} + +template +T ReInterpretTensor(TfLiteTensor* tensor) { + // TODO (chowdhery): check float + float* tensor_base = tensor->data.f; + return reinterpret_cast(tensor_base); +} + +TfLiteStatus DecodeCenterSizeBoxes(TfLiteContext* context, TfLiteNode* node, + OpData* op_data) { + // Parse input tensor boxencodings + const TfLiteTensor* input_box_encodings = + GetInput(context, node, kInputTensorBoxEncodings); + TF_LITE_ENSURE_EQ(context, input_box_encodings->dims->data[0], kBatchSize); + const int num_boxes = input_box_encodings->dims->data[1]; + TF_LITE_ENSURE_EQ(context, input_box_encodings->dims->data[2], kNumCoordBox); + + // Decode the boxes to get (ymin, xmin, ymax, xmax) based on the anchors + CenterSizeEncoding box_centersize; + CenterSizeEncoding scale_values = op_data->scale_values; + const float quant_zero_point = + static_cast(input_box_encodings->params.zero_point); + const float quant_scale = + static_cast(input_box_encodings->params.scale); + for (int idx = 0; idx < num_boxes; ++idx) { + switch (input_box_encodings->type) { + // Quantized + case kTfLiteUInt8: + DequantizeBoxEncodings(input_box_encodings, idx, quant_zero_point, + quant_scale, &box_centersize); + break; + // Float + case kTfLiteFloat32: + box_centersize = ReInterpretTensor( + input_box_encodings)[idx]; + break; + default: + // Unsupported type. + return kTfLiteError; + } + + const TfLiteTensor* input_anchors = + GetInput(context, node, kInputTensorAnchors); + + const auto& anchor = + ReInterpretTensor(input_anchors)[idx]; + + float ycenter = box_centersize.y / scale_values.y * anchor.h + anchor.y; + float xcenter = box_centersize.x / scale_values.x * anchor.w + anchor.x; + float half_h = + 0.5f * static_cast(std::exp(box_centersize.h / scale_values.h)) * + anchor.h; + float half_w = + 0.5f * static_cast(std::exp(box_centersize.w / scale_values.w)) * + anchor.w; + TfLiteTensor* decoded_boxes = + &context->tensors[op_data->decoded_boxes_index]; + auto& box = ReInterpretTensor(decoded_boxes)[idx]; + box.ymin = ycenter - half_h; + box.xmin = xcenter - half_w; + box.ymax = ycenter + half_h; + box.xmax = xcenter + half_w; + } + return kTfLiteOk; +} + +void DecreasingPartialArgSort(const float* values, int num_values, + int num_to_sort, int* indices) { + std::iota(indices, indices + num_values, 0); + std::partial_sort( + indices, indices + num_to_sort, indices + num_values, + [&values](const int i, const int j) { return values[i] > values[j]; }); +} + +void SelectDetectionsAboveScoreThreshold(const std::vector& values, + const float threshold, + std::vector* keep_values, + std::vector* keep_indices) { + for (int i = 0; i < values.size(); i++) { + if (values[i] >= threshold) { + keep_values->emplace_back(values[i]); + keep_indices->emplace_back(i); + } + } +} + +bool ValidateBoxes(const TfLiteTensor* decoded_boxes, const int num_boxes) { + for (int i = 0; i < num_boxes; ++i) { + // ymax>=ymin, xmax>=xmin + auto& box = ReInterpretTensor(decoded_boxes)[i]; + if (box.ymin >= box.ymax || box.xmin >= box.xmax) { + return false; + } + } + return true; +} + +float ComputeIntersectionOverUnion(const TfLiteTensor* decoded_boxes, + const int i, const int j) { + auto& box_i = ReInterpretTensor(decoded_boxes)[i]; + auto& box_j = ReInterpretTensor(decoded_boxes)[j]; + const float area_i = (box_i.ymax - box_i.ymin) * (box_i.xmax - box_i.xmin); + const float area_j = (box_j.ymax - box_j.ymin) * (box_j.xmax - box_j.xmin); + if (area_i <= 0 || area_j <= 0) return 0.0; + const float intersection_ymin = std::max(box_i.ymin, box_j.ymin); + const float intersection_xmin = std::max(box_i.xmin, box_j.xmin); + const float intersection_ymax = std::min(box_i.ymax, box_j.ymax); + const float intersection_xmax = std::min(box_i.xmax, box_j.xmax); + const float intersection_area = + std::max(intersection_ymax - intersection_ymin, 0.0) * + std::max(intersection_xmax - intersection_xmin, 0.0); + return intersection_area / (area_i + area_j - intersection_area); +} + +// NonMaxSuppressionSingleClass() is O(n^2) pairwise comparison between boxes +// It assumes all boxes are good in beginning and sorts based on the scores. +// If lower-scoring box has too much overlap with a higher-scoring box, +// we get rid of the lower-scoring box. +TfLiteStatus NonMaxSuppressionSingleClassHelper( + TfLiteContext* context, TfLiteNode* node, OpData* op_data, + const std::vector& scores, std::vector* selected) { + const TfLiteTensor* input_box_encodings = + GetInput(context, node, kInputTensorBoxEncodings); + const TfLiteTensor* decoded_boxes = + &context->tensors[op_data->decoded_boxes_index]; + const int num_boxes = input_box_encodings->dims->data[1]; + const int max_detections = op_data->max_detections; + const float non_max_suppression_score_threshold = + op_data->non_max_suppression_score_threshold; + const float intersection_over_union_threshold = + op_data->intersection_over_union_threshold; + // Maximum detections should be positive. + TF_LITE_ENSURE(context, (max_detections >= 0)); + // intersection_over_union_threshold should be positive + // and should be less than 1. + TF_LITE_ENSURE(context, (intersection_over_union_threshold > 0.0f) && + (intersection_over_union_threshold <= 1.0f)); + // Validate boxes + TF_LITE_ENSURE(context, ValidateBoxes(decoded_boxes, num_boxes)); + + // threshold scores + std::vector keep_indices; + // TODO (chowdhery): Remove the dynamic allocation and replace it + // with temporaries, esp for std::vector + std::vector keep_scores; + SelectDetectionsAboveScoreThreshold( + scores, non_max_suppression_score_threshold, &keep_scores, &keep_indices); + + int num_scores_kept = keep_scores.size(); + std::vector sorted_indices; + sorted_indices.resize(num_scores_kept); + DecreasingPartialArgSort(keep_scores.data(), num_scores_kept, num_scores_kept, + sorted_indices.data()); + + const int num_boxes_kept = keep_scores.size(); + const int output_size = std::min(num_boxes_kept, max_detections); + selected->clear(); + TfLiteTensor* active_candidate = + &context->tensors[op_data->active_candidate_index]; + TF_LITE_ENSURE(context, (active_candidate->dims->data[0]) == num_boxes); + int num_active_candidate = num_boxes; + uint8_t* active_box_candidate = (active_candidate->data.uint8); + for (int row = 0; row < num_boxes; row++) { + active_box_candidate[row] = 1; + } + + for (int i = 0; i < num_boxes; ++i) { + if (num_active_candidate == 0 || selected->size() >= output_size) break; + if (active_box_candidate[i] == 1) { + selected->push_back(keep_indices[sorted_indices[i]]); + active_box_candidate[i] = 0; + num_active_candidate--; + } else { + continue; + } + for (int j = i + 1; j < num_boxes; ++j) { + if (active_box_candidate[j] == 1) { + float intersection_over_union = ComputeIntersectionOverUnion( + decoded_boxes, keep_indices[sorted_indices[i]], + keep_indices[sorted_indices[j]]); + + if (intersection_over_union > intersection_over_union_threshold) { + active_box_candidate[j] = 0; + num_active_candidate--; + } + } + } + } + return kTfLiteOk; +} + +// This function implements a fast version of Non Maximal Suppression for +// multiple classes where +// 1) we keep the top-k scores for each anchor and +// 2) during NMS, each anchor only uses the highest class score for sorting. +// 3) Compared to standard NMS, the worst runtime of this version is O(N^2) +// instead of O(KN^2) where N is the number of anchors and K the number of +// classes. +TfLiteStatus NonMaxSuppressionMultiClassFastHelper(TfLiteContext* context, + TfLiteNode* node, + OpData* op_data, + const float* scores) { + const TfLiteTensor* input_box_encodings = + GetInput(context, node, kInputTensorBoxEncodings); + const TfLiteTensor* decoded_boxes = + &context->tensors[op_data->decoded_boxes_index]; + + TfLiteTensor* detection_boxes = + GetOutput(context, node, kOutputTensorDetectionBoxes); + TfLiteTensor* detection_classes = + GetOutput(context, node, kOutputTensorDetectionClasses); + TfLiteTensor* detection_scores = + GetOutput(context, node, kOutputTensorDetectionScores); + TfLiteTensor* num_detections = + GetOutput(context, node, kOutputTensorNumDetections); + + const int num_boxes = input_box_encodings->dims->data[1]; + const int num_classes = op_data->num_classes; + const int max_categories_per_anchor = op_data->max_classes_per_detection; + // The row index offset is 1 if background class is included and 0 otherwise. + const int label_offset = 1; + TF_LITE_ENSURE(context, (label_offset != -1)); + TF_LITE_ENSURE(context, (max_categories_per_anchor > 0)); + const int num_classes_with_background = num_classes + label_offset; + const int num_categories_per_anchor = + std::min(max_categories_per_anchor, num_classes); + std::vector max_scores; + max_scores.resize(num_boxes); + std::vector sorted_class_indices; + sorted_class_indices.resize(num_boxes * num_classes); + for (int row = 0; row < num_boxes; row++) { + const float* box_scores = + scores + row * num_classes_with_background + label_offset; + int* class_indices = sorted_class_indices.data() + row * num_classes; + DecreasingPartialArgSort(box_scores, num_classes, num_categories_per_anchor, + class_indices); + max_scores[row] = box_scores[class_indices[0]]; + } + // Perform non-maximal suppression on max scores + std::vector selected; + NonMaxSuppressionSingleClassHelper(context, node, op_data, max_scores, + &selected); + // Allocate output tensors + int output_box_index = 0; + for (const auto& selected_index : selected) { + const float* box_scores = + scores + selected_index * num_classes_with_background + label_offset; + const int* class_indices = + sorted_class_indices.data() + selected_index * num_classes; + + for (int col = 0; col < num_categories_per_anchor; ++col) { + int box_offset = num_categories_per_anchor * output_box_index + col; + // detection_boxes + ReInterpretTensor(detection_boxes)[box_offset] = + ReInterpretTensor( + decoded_boxes)[selected_index]; + // detection_classes + detection_classes->data.f[box_offset] = class_indices[col]; + // detection_scores + detection_scores->data.f[box_offset] = box_scores[class_indices[col]]; + output_box_index++; + } + } + num_detections->data.f[0] = output_box_index; + return kTfLiteOk; +} + +void DequantizeClassPredictions(const TfLiteTensor* input_class_predictions, + const int num_boxes, + const int num_classes_with_background, + const TfLiteTensor* scores) { + float quant_zero_point = + static_cast(input_class_predictions->params.zero_point); + float quant_scale = static_cast(input_class_predictions->params.scale); + Dequantizer dequantize(quant_zero_point, quant_scale); + const uint8* scores_quant = GetTensorData(input_class_predictions); + for (int idx = 0; idx < num_boxes * num_classes_with_background; ++idx) { + scores->data.f[idx] = dequantize(scores_quant[idx]); + } +} + +TfLiteStatus NonMaxSuppressionMultiClass(TfLiteContext* context, + TfLiteNode* node, OpData* op_data) { + // Get the input tensors + const TfLiteTensor* input_box_encodings = + GetInput(context, node, kInputTensorBoxEncodings); + const TfLiteTensor* input_class_predictions = + GetInput(context, node, kInputTensorClassPredictions); + const int num_boxes = input_box_encodings->dims->data[1]; + const int num_classes = op_data->num_classes; + TF_LITE_ENSURE_EQ(context, input_class_predictions->dims->data[0], + kBatchSize); + TF_LITE_ENSURE_EQ(context, input_class_predictions->dims->data[1], num_boxes); + const int num_classes_with_background = + input_class_predictions->dims->data[2]; + + TF_LITE_ENSURE(context, (num_classes_with_background == num_classes + 1)); + + const TfLiteTensor* scores; + switch (input_class_predictions->type) { + case kTfLiteUInt8: { + TfLiteTensor* temporary_scores = &context->tensors[op_data->scores_index]; + DequantizeClassPredictions(input_class_predictions, num_boxes, + num_classes_with_background, temporary_scores); + scores = temporary_scores; + } break; + case kTfLiteFloat32: + scores = input_class_predictions; + break; + default: + // Unsupported type. + return kTfLiteError; + } + NonMaxSuppressionMultiClassFastHelper(context, node, op_data, + GetTensorData(scores)); + return kTfLiteOk; +} + +TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { + // TODO(chowdhery): Generalize for any batch size + TF_LITE_ENSURE(context, (kBatchSize == 1)); + auto* op_data = reinterpret_cast(node->user_data); + // These two functions correspond to two blocks in the Object Detection model. + // In future, we would like to break the custom op in two blocks, which is + // currently not feasible because we would like to input quantized inputs + // and do all calculations in float. Mixed quantized/float calculations are + // currently not supported in TFLite. + + // This fills in temporary decoded_boxes + // by transforming input_box_encodings and input_anchors from + // CenterSizeEncodings to BoxCornerEncoding + DecodeCenterSizeBoxes(context, node, op_data); + // This fills in the output tensors + // by choosing effective set of decoded boxes + // based on Non Maximal Suppression, i.e. selecting + // highest scoring non-overlapping boxes. + NonMaxSuppressionMultiClass(context, node, op_data); + + return kTfLiteOk; +} + +} // namespace ssd_postprocess + +TfLiteRegistration* Register_SSD_POSTPROCESS() { + static TfLiteRegistration r = {ssd_postprocess::Init, ssd_postprocess::Free, + ssd_postprocess::Prepare, + ssd_postprocess::Eval}; + return &r; +} + +} // namespace custom +} // namespace ops +} // namespace tflite -- GitLab From 1c697bc9094365cf5dab1ec1550eba019dffa3b8 Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Sat, 16 Jun 2018 00:06:36 -0700 Subject: [PATCH 565/816] Teach gather-reshape folding to work with degenerate dims I was hoping not to do this, but the motivating benchmark for all this work has reshapes on degenerate dimensions. This also forced me to introduce a new node to the analysis which isn't great (we don't want to replicate HLO inside IndexedArrayAnalysis!) but this is cleanest solution I can think of. In brief I support gather-reshape folding with degenerate dimensions by disallowing it in the core tricky part of the algorithm and instead reshaping the degenerate dimensions "in and out" in a helper that calls the core part of the folding logic. Also worth calling out that before we weren't doing something conservative -- we were just buggy. For instance the CHECK_NE(candidate_operand_dim, 0) in ComputeReshapePassthroughDimPairs can fail with degenerate dims. I also made some other supporting changes: - I was not checking window bounds in ComputeArrayForGather. I've fixed this and beefed up testing in this area (the hammer for all my nails). - Added a bunch of VLOG(3) info that was useful when debugging. - Added a simple helper to the test that makes the strings I'm matching against "whitespace insensitive" so that I can indent these. I'm happy to pull these out into separate CLs if that makes reviewing easier but for now I took the path of least resistance. :) PiperOrigin-RevId: 200821883 --- .../xla/service/indexed_array_analysis.cc | 271 ++++++++++++++- .../xla/service/indexed_array_analysis.h | 44 ++- .../service/indexed_array_analysis_test.cc | 313 +++++++++++++++++- tensorflow/compiler/xla/shape_util.cc | 6 + tensorflow/compiler/xla/shape_util.h | 4 + tensorflow/compiler/xla/shape_util_test.cc | 11 + tensorflow/compiler/xla/util.h | 12 + 7 files changed, 644 insertions(+), 17 deletions(-) diff --git a/tensorflow/compiler/xla/service/indexed_array_analysis.cc b/tensorflow/compiler/xla/service/indexed_array_analysis.cc index 8b3fa6c157..1985d20578 100644 --- a/tensorflow/compiler/xla/service/indexed_array_analysis.cc +++ b/tensorflow/compiler/xla/service/indexed_array_analysis.cc @@ -28,6 +28,7 @@ namespace { using Analysis = IndexedArrayAnalysis; using UnknownArray = Analysis::UnknownArray; using ConstantArray = Analysis::ConstantArray; +using ReshapedArray = Analysis::ReshapedArray; using ScalarIndexedArray = Analysis::ScalarIndexedArray; using tensorflow::gtl::ArraySlice; using tensorflow::str_util::Join; @@ -52,6 +53,13 @@ string IndexedArrayAnalysis::ToString(Array* root, bool print_constants) { "(constant ", ShapeUtil::HumanString(root->shape()), ")"); } + case Array::kReshaped: { + ReshapedArray* reshaped_array = root->as(); + return tensorflow::strings::StrCat( + "(reshape ", ToString(reshaped_array->operand(), print_constants), + " to ", ShapeUtil::HumanString(reshaped_array->shape()), ")"); + } + case Array::kScalarIndexedConstant: case Array::kScalarIndexed: { auto* indexed_array = root->as(); @@ -239,15 +247,40 @@ StatusOr IndexedArrayAnalysis::ComputeArrayForGather( tensorflow::gtl::ArraySlice window_bounds, Array* source, Array* indices) { if (dim_numbers.index_vector_dim() != indices->shape().dimensions_size()) { + VLOG(3) << "ComputeArrayForGather: indices are not scalar"; return nullptr; } CHECK_EQ(dim_numbers.gather_dims_to_operand_dims_size(), 1); - if (!c_binary_search(dim_numbers.elided_window_dims(), - dim_numbers.gather_dims_to_operand_dims(0))) { + + // We can also handle dim_numbers.elided_window_dims_size() == 0 here, should + // it become relevant. + + if (dim_numbers.elided_window_dims_size() != 1 || + dim_numbers.elided_window_dims(0) != + dim_numbers.gather_dims_to_operand_dims(0)) { + VLOG(3) << "ComputeArrayForGather: gather operations must elide " + "gather_dims_to_operand_dims[0] and " + "gather_dims_to_operand_dims[0] only"; return nullptr; } + // ScalarIndexedArray cannot represent gathers that "slice" along some + // dimensions -- for instance it cannot represent a gather that picks 5 [2,3] + // arrays from an array of size [7,4,6]. We check that condition down below: + + for (int64 i = 0, e = source->shape().dimensions_size(); i < e; i++) { + if (i != dim_numbers.elided_window_dims(0) && + source->shape().dimensions(i) != window_bounds[i]) { + VLOG(3) << "ComputeArrayForGather: window_bounds[" << i + << "] != source->shape().dimensions(" << i << ") -- " + << source->shape().dimensions(i) << " vs. " << window_bounds[i] + << " with dim_numbers.elided_window_dims(0) = " + << dim_numbers.elided_window_dims(0); + return nullptr; + } + } + int64 source_dim = dim_numbers.gather_dims_to_operand_dims(0); std::vector output_dims; for (int64 i = 0, e = shape.dimensions_size(); i < e; i++) { @@ -336,7 +369,11 @@ std::vector ComputeReshapePassthroughDimPairs( // result_subarray_size does not include the elements in the current // `result_dim` dimension (we multiply in result_shape[result_dim] at the // end of loop body) so candidate_operand_dim can never be zero. - CHECK_NE(candidate_operand_dim, 0); + CHECK_NE(candidate_operand_dim, 0) + << "result_dim = " << result_dim + << ", result_subarray_size = " << result_subarray_size + << ", result_shape = [" << Join(result_shape, ",") << "]" + << ", operand_shape = [" << Join(operand_shape, ",") << "]"; if (candidate_operand_dim != -1 && result_shape[result_dim] == operand_shape[candidate_operand_dim - 1]) { @@ -357,7 +394,7 @@ std::vector ComputeReshapePassthroughDimPairs( }); VLOG(3) << "For a reshape from [" << Join(operand_shape, ",") << "] to [" << Join(result_shape, ",") << "] passthrough indices are [" - << Join(result_strings, ",") << "]"; + << Join(result_strings, ",") << "] (legend: `result`->`operand`)"; } DCHECK(c_is_sorted( @@ -398,6 +435,10 @@ int64 MapPassthroughOperandDimToResultDim( int64 FindSourcePositionForPassthroughResultDim(ArraySlice operand_shape, ArraySlice result_shape, int64 source_passthrough_dim) { + VLOG(3) << "FindSourcePositionForPassthroughResultDim([" + << Join(operand_shape, ",") << "], [" << Join(result_shape, ",") + << "], " << source_passthrough_dim << ")"; + int64 indexed_source_subarray_size = std::accumulate(operand_shape.begin() + source_passthrough_dim + 1, operand_shape.end(), 1, std::multiplies()); @@ -405,15 +446,191 @@ int64 FindSourcePositionForPassthroughResultDim(ArraySlice operand_shape, return FindSuffixWithProduct(result_shape, indexed_source_subarray_size); } +Shape StripDegenerateDimensions(const Shape& shape) { + DimensionVector new_dims; + c_copy_if(shape.dimensions(), std::back_inserter(new_dims), + [](int64 dim) { return dim != 1; }); + return ShapeUtil::MakeShape(shape.element_type(), new_dims); +} }; // namespace -StatusOr IndexedArrayAnalysis::ComputeArrayForReshape( - const Shape& shape, Array* operand) { - auto* scalar_indexed = dynamic_cast(operand); - if (!scalar_indexed) { +StatusOr +IndexedArrayAnalysis::ReshapeToRemoveDegenerateDims( + ScalarIndexedArray* operand) { + const Shape& shape = operand->shape(); + if (!ShapeUtil::HasDegenerateDimensions(shape)) { + return operand; + } + + // We only need to reshape out the degenerate dims from the indices and the + // source (except the source dim). + + const Shape& source_shape = operand->source()->shape(); + DimensionVector new_source_shape_dims; + for (int64 i = 0, e = source_shape.dimensions_size(); i < e; i++) { + if (i == operand->source_dim() || source_shape.dimensions(i) != 1) { + new_source_shape_dims.push_back(source_shape.dimensions(i)); + } + } + + Shape new_source_shape = + ShapeUtil::MakeShape(shape.element_type(), new_source_shape_dims); + Shape new_indices_shape = + StripDegenerateDimensions(operand->indices()->shape()); + + TF_ASSIGN_OR_RETURN( + Array* const new_source, + ComputeArrayForReshape(new_source_shape, operand->source())); + TF_ASSIGN_OR_RETURN( + Array* const new_indices, + ComputeArrayForReshape(new_indices_shape, operand->indices())); + + // Build the new output dims while keeping track of the degenerate dims that + // will no longer be present. + DimensionVector new_output_dims; + int64 degenerate_dims_seen = 0; + for (int64 i = 0, e = shape.dimensions_size(); i < e; i++) { + if (shape.dimensions(i) == 1) { + degenerate_dims_seen++; + } else if (ArrayContains(operand->output_dims(), i)) { + new_output_dims.push_back(i - degenerate_dims_seen); + } + } + + // Similarly, build the new source dim while keeping track of the degenerate + // dims that will no longer be present. + int64 degenerate_dims_before_source_dim = + std::count(source_shape.dimensions().begin(), + source_shape.dimensions().begin() + operand->source_dim(), 1); + int64 new_source_dim = + operand->source_dim() - degenerate_dims_before_source_dim; + + return ConstructScalarIndexedArray( + new_source, new_indices, new_source_dim, + InlinedVectorToVector(new_output_dims), + StripDegenerateDimensions(operand->shape())); +} + +StatusOr IndexedArrayAnalysis::ReshapeToAddDegenerateDims( + ScalarIndexedArray* operand, + tensorflow::gtl::ArraySlice degenerate_dims) { + if (degenerate_dims.empty()) { + return operand; + } + + CHECK(!ShapeUtil::HasDegenerateDimensions(operand->shape())); + + DimensionVector new_output_dims = [&]() { + // To make things easy we use a "scratch" buffer of bools where the i'th + // element is true iff the i'th component of the result index is an output + // index. + + gtl::InlinedVector output_dims_bitvector( + operand->shape().dimensions_size()); + for (int64 output_dim : operand->output_dims()) { + output_dims_bitvector[output_dim] = true; + } + + for (int64 degenerate_dim : degenerate_dims) { + InsertAt(&output_dims_bitvector, degenerate_dim, false); + } + + DimensionVector result; + result.reserve(operand->output_dims().size()); + for (int64 i = 0, e = output_dims_bitvector.size(); i < e; i++) { + if (output_dims_bitvector[i]) { + result.push_back(i); + } + } + + return result; + }(); + + DimensionVector new_result_shape_dims; + c_copy(operand->shape().dimensions(), + std::back_inserter(new_result_shape_dims)); + for (int64 degenerate_dim : degenerate_dims) { + InsertAt(&new_result_shape_dims, degenerate_dim, 1); + } + + DimensionVector new_source_shape_dims = new_result_shape_dims; + for (int64 output_dim : new_output_dims) { + EraseAt(&new_source_shape_dims, output_dim); + } + + int64 new_source_dim = [&]() { + for (int i = 0, e = new_source_shape_dims.size(); i < e; i++) { + int64 non_degenerate_dims_seen = 0; + if (non_degenerate_dims_seen == operand->source_dim()) { + return i; + } + if (new_source_shape_dims[new_source_dim] != 1) { + non_degenerate_dims_seen++; + } + } + LOG(FATAL) << "Did not find source dim in " << ToString(operand); + }(); + + int64 source_dim_size = + operand->source()->shape().dimensions(operand->source_dim()); + InsertAt(&new_source_shape_dims, /*index=*/new_source_dim, + /*value=*/source_dim_size); + + Shape new_source_shape = ShapeUtil::MakeShape(operand->shape().element_type(), + new_source_shape_dims); + Shape new_result_shape = ShapeUtil::MakeShape(operand->shape().element_type(), + new_result_shape_dims); + + TF_ASSIGN_OR_RETURN( + Array* const new_source, + ComputeArrayForReshape(new_source_shape, operand->source())); + return ConstructScalarIndexedArray( + new_source, operand->indices(), new_source_dim, + InlinedVectorToVector(new_output_dims), new_result_shape); +} + +StatusOr IndexedArrayAnalysis::FoldReshapeOfGather( + const Shape& shape, ScalarIndexedConstantArray* operand) { + VLOG(3) << "FoldReshapeOfGather(" << ToString(operand) << ")"; + + // To make things easier on ourselves, instead of directly trying to fold the + // reshape of `operand` to `shape`, we call + // `FoldReshapeOfGatherNoDegenerateDims` on shapes without degenerate dims and + // handle the degenerate dimensions here by inserting reshapes. + + TF_ASSIGN_OR_RETURN(ScalarIndexedArray* const operand_without_degenerate_dims, + ReshapeToRemoveDegenerateDims(operand)); + + Shape output_shape_without_degenerate_dims = StripDegenerateDimensions(shape); + TF_ASSIGN_OR_RETURN( + ScalarIndexedArray* const folded_reshape_without_degenerate_dims, + FoldReshapeOfGatherNoDegenerateDims( + output_shape_without_degenerate_dims, + operand_without_degenerate_dims->as())); + + if (folded_reshape_without_degenerate_dims == nullptr) { return nullptr; } + DimensionVector degenerate_result_dims; + for (int64 i = 0, e = shape.dimensions_size(); i < e; i++) { + if (shape.dimensions(i) == 1) { + degenerate_result_dims.push_back(i); + } + } + + return ReshapeToAddDegenerateDims(folded_reshape_without_degenerate_dims, + degenerate_result_dims); +} + +StatusOr +IndexedArrayAnalysis::FoldReshapeOfGatherNoDegenerateDims( + const Shape& shape, ScalarIndexedConstantArray* scalar_indexed) { + VLOG(3) << "FoldReshapeOfGatherNoDegenerateDims(" << ToString(scalar_indexed) + << ")"; + CHECK(!ShapeUtil::HasDegenerateDimensions(shape)); + CHECK(!ShapeUtil::HasDegenerateDimensions(scalar_indexed->shape())); + // Try to fold Reshape(ScalarIndexed(Const, Indices)) // => ScalarIndexed(Const', Indices) // @@ -464,7 +681,7 @@ StatusOr IndexedArrayAnalysis::ComputeArrayForReshape( std::vector reshape_passthrough_dims = ComputeReshapePassthroughDimPairs( - /*operand_shape=*/AsInt64Slice(operand->shape().dimensions()), + /*operand_shape=*/AsInt64Slice(scalar_indexed->shape().dimensions()), /*result_shape=*/AsInt64Slice(shape.dimensions())); auto is_reshape_passthrough_operand_dim = [&](int64 operand_dim) { @@ -474,6 +691,8 @@ StatusOr IndexedArrayAnalysis::ComputeArrayForReshape( if (!c_all_of(scalar_indexed->output_dims(), is_reshape_passthrough_operand_dim)) { + VLOG(3) << "Not all output dims are passthrough dims " + << ToString(scalar_indexed); return nullptr; } @@ -527,6 +746,11 @@ StatusOr IndexedArrayAnalysis::ComputeArrayForReshape( // (a.k.a. isn't pass-through) than the [3,5,2] array. if (source_dim_for_new_scalar_indexed_node == -1) { + VLOG(3) << "Could not compute the source dim for the new scalar indexed " + "node: scalar_indexed_source_shape = [" + << Join(scalar_indexed_source_shape.dimensions(), ",") + << "] and new_scalar_indexed_source_shape = [" + << Join(new_scalar_indexed_source_shape, ",") << "]"; return nullptr; } @@ -534,6 +758,10 @@ StatusOr IndexedArrayAnalysis::ComputeArrayForReshape( &new_scalar_indexed_source_shape, source_dim_for_new_scalar_indexed_node, scalar_indexed_source_shape.dimensions(scalar_indexed->source_dim())); + CHECK_EQ(c_accumulate(new_scalar_indexed_source_shape, 1l, + std::multiplies()), + ShapeUtil::ElementsIn(scalar_indexed_source_shape)); + CHECK(IsReshapePassthroughOperandDim( ComputeReshapePassthroughDimPairs( /*operand_shape=*/AsInt64Slice( @@ -564,6 +792,31 @@ StatusOr IndexedArrayAnalysis::ComputeArrayForReshape( output_dims_for_new_scalar_indexed_node, shape); } +StatusOr IndexedArrayAnalysis::ComputeArrayForReshape( + const Shape& shape, Array* operand) { + if (ShapeUtil::Compatible(operand->shape(), shape)) { + return operand; + } + + if (auto* scalar_indexed = + dynamic_cast(operand)) { + TF_ASSIGN_OR_RETURN(Analysis::Array * reshape_folded_into_gather, + FoldReshapeOfGather(shape, scalar_indexed)); + if (reshape_folded_into_gather) { + return reshape_folded_into_gather; + } + } + + if (auto* constant_array = dynamic_cast(operand)) { + TF_ASSIGN_OR_RETURN(Literal* const new_literal, + TakeOwnership(constant_array->literal()->Reshape( + AsInt64Slice(shape.dimensions())))); + return Construct(new_literal); + } + + return Construct(operand, shape); +} + StatusOr IndexedArrayAnalysis::ComputeArrayForElementwiseBinaryOp(HloOpcode opcode, Array* lhs, diff --git a/tensorflow/compiler/xla/service/indexed_array_analysis.h b/tensorflow/compiler/xla/service/indexed_array_analysis.h index ce92fd2919..8684430231 100644 --- a/tensorflow/compiler/xla/service/indexed_array_analysis.h +++ b/tensorflow/compiler/xla/service/indexed_array_analysis.h @@ -39,7 +39,13 @@ class IndexedArrayAnalysis { // Array instances are immutable once created. class Array { public: - enum Kind { kUnknown, kConstant, kScalarIndexedConstant, kScalarIndexed }; + enum Kind { + kUnknown, + kConstant, + kReshaped, + kScalarIndexedConstant, + kScalarIndexed + }; virtual Kind kind() const = 0; virtual const Shape& shape() const = 0; @@ -96,6 +102,27 @@ class IndexedArrayAnalysis { friend class IndexedArrayAnalysis; }; + // Represents an Array that is a reshape of another Array. + class ReshapedArray : public Array { + public: + Kind kind() const override { return kReshaped; } + + // The array to reshape. + Array* operand() const { return operand_; } + + // The output shape. + const Shape& shape() const override { return shape_; } + + private: + explicit ReshapedArray(Array* operand, Shape shape) + : operand_(operand), shape_(shape) {} + + Array* operand_; + const Shape shape_; + + friend class IndexedArrayAnalysis; + }; + // --------------------------------------------------------------------------- // Indexed Array Overview // --------------------------------------------------------------------------- @@ -266,6 +293,21 @@ class IndexedArrayAnalysis { ScalarIndexedArray* source, Array* indices, int64 source_dim, tensorflow::gtl::ArraySlice output_dims, Shape shape); + // Reshapes a scalar-indexed node to remove the degenerate dimensions in its + // output. The result is always a scalar-indexed node. + StatusOr ReshapeToRemoveDegenerateDims( + ScalarIndexedArray* operand); + + // Reshapes a scalar-indexed node such that the result has the degenerate + // dimensions `degenerate_dims`. The result is always a scalar-indexed node. + StatusOr ReshapeToAddDegenerateDims( + ScalarIndexedArray* operand, + tensorflow::gtl::ArraySlice degenerate_dims); + + StatusOr FoldReshapeOfGather( + const Shape& shape, ScalarIndexedConstantArray* operand); + StatusOr FoldReshapeOfGatherNoDegenerateDims( + const Shape& shape, ScalarIndexedConstantArray* scalar_indexed); StatusOr ComputeArrayForReshape(const Shape& shape, Array* operand); StatusOr ComputeArrayForElementwiseBinaryOp(HloOpcode opcode, diff --git a/tensorflow/compiler/xla/service/indexed_array_analysis_test.cc b/tensorflow/compiler/xla/service/indexed_array_analysis_test.cc index 373556ebeb..fc2befe05b 100644 --- a/tensorflow/compiler/xla/service/indexed_array_analysis_test.cc +++ b/tensorflow/compiler/xla/service/indexed_array_analysis_test.cc @@ -13,6 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ +#include + #include "tensorflow/compiler/xla/service/indexed_array_analysis.h" #include "tensorflow/compiler/xla/tests/hlo_verified_test_base.h" #include "tensorflow/compiler/xla/tests/test_utils.h" @@ -34,6 +36,27 @@ class IndexedArrayAnalysisTest : public HloVerifiedTestBase { } private: + // Replaces seqences of whitespace with a single space. This makes the + // strings being matched against "whitespace insensitive" which lets us indent + // them for readability. + string CanonicalizeWhitespace(const string& text) { + string result; + + for (char c : text) { + if (!isspace(c)) { + result.push_back(c); + } else if (!result.empty() && result.back() != ' ') { + result.push_back(' '); + } + } + + while (!result.empty() && result.back() == ' ') { + result.pop_back(); + } + + return result; + } + void AssertArrayForRootExpressionIsImpl(const string& hlo_text, const string& root_expression, bool print_constants) { @@ -44,10 +67,10 @@ class IndexedArrayAnalysisTest : public HloVerifiedTestBase { IndexedArrayAnalysis::Array* const array_result, indexed_tensor_analysis.GetArrayFor( module().entry_computation()->root_instruction())); - string string_result = - indexed_tensor_analysis.ToString(array_result, print_constants); + string string_result = CanonicalizeWhitespace( + indexed_tensor_analysis.ToString(array_result, print_constants)); LOG(INFO) << string_result; - ASSERT_EQ(string_result, root_expression); + ASSERT_EQ(string_result, CanonicalizeWhitespace(root_expression)); } }; @@ -91,6 +114,82 @@ ENTRY main { hlo_text, "(scalar-indexed-const (constant s32[3,3]) %indices 0->[0])"); } +TEST_F(IndexedArrayAnalysisTest, GatherIsNotScalarIndexed0) { + string hlo_text = R"( +HloModule SimpleGather + +ENTRY main { + operand = s32[3,3] constant(s32[3,3]{{1,2,3},{1,2,3},{1,2,3}}) + indices = s32[5,2] parameter(0) + ROOT gather = s32[5] gather(operand, indices), + output_window_dims={}, + elided_window_dims={0,1}, + gather_dims_to_operand_dims={0,1}, + index_vector_dim=1, + window_bounds={1,1} +} +)"; + + AssertArrayForRootExpressionIs(hlo_text, "%gather"); +} + +TEST_F(IndexedArrayAnalysisTest, GatherIsNotScalarIndexed1) { + string hlo_text = R"( +HloModule SimpleGather + +ENTRY main { + operand = s32[3,3,1] parameter(0) + indices = s32[5] parameter(1) + ROOT gather = s32[5,3] gather(operand, indices), + output_window_dims={1}, + elided_window_dims={0,2}, + gather_dims_to_operand_dims={0}, + index_vector_dim=1, + window_bounds={1,3,1} +} +)"; + + AssertArrayForRootExpressionIs(hlo_text, "%gather"); +} + +TEST_F(IndexedArrayAnalysisTest, GatherIsNotScalarIndexed2) { + string hlo_text = R"( +HloModule SimpleGather + +ENTRY main { + operand = s32[3,3,1] parameter(0) + indices = s32[5] parameter(1) + ROOT gather = s32[5,2,3] gather(operand, indices), + output_window_dims={1,2}, + elided_window_dims={2}, + gather_dims_to_operand_dims={0}, + index_vector_dim=1, + window_bounds={2,3,1} +} +)"; + + AssertArrayForRootExpressionIs(hlo_text, "%gather"); +} + +TEST_F(IndexedArrayAnalysisTest, GatherIsNotScalarIndexed3) { + string hlo_text = R"( +HloModule SimpleGather + +ENTRY main { + operand = s32[3,3] parameter(0) + indices = s32[5] parameter(1) + ROOT gather = s32[5,2] gather(operand, indices), + output_window_dims={1}, + elided_window_dims={0}, + gather_dims_to_operand_dims={0}, + index_vector_dim=1, + window_bounds={1,2} +} +)"; + + AssertArrayForRootExpressionIs(hlo_text, "%gather"); +} + TEST_F(IndexedArrayAnalysisTest, GatherOfGather_OneToOne) { string hlo_text = R"( HloModule SimpleGather @@ -273,7 +372,157 @@ ENTRY main { "(scalar-indexed-const (constant s32[3,3,4]) %indices 0->[0,3])"); } -TEST_F(IndexedArrayAnalysisTest, ReshapeOfGatherNegative0) { +TEST_F(IndexedArrayAnalysisTest, ReshapeOfGather3) { + string hlo_text = R"( +HloModule ReshapeOfGather + +ENTRY main { + operand = s32[2,6] constant(s32[2,6]{ + {1,2,3,4,5,6},{1,2,3,4,5,6}}) + indices = s32[1] parameter(0) + gather = s32[1,6] gather(operand, indices), + output_window_dims={1}, + elided_window_dims={0}, + gather_dims_to_operand_dims={0}, + index_vector_dim=1, + window_bounds={1,6} + ROOT reshape = s32[1,1,6] reshape(gather) +} +)"; + + const char* expected_root_expression = R"( +(scalar-indexed-const + (constant s32[2,1,1,6]) + (reshape %indices to s32[]) + 0->[]) +)"; + + AssertArrayForRootExpressionIs(hlo_text, expected_root_expression); +} + +TEST_F(IndexedArrayAnalysisTest, ReshapeOfGather4) { + string hlo_text = R"( +HloModule ReshapeOfGather + +ENTRY main { + operand = s32[2,3]{1,0} constant(s32[2,3] { { 1, 2, 3 }, { 1, 2, 3 } }) + + i.0 = s64[1,3]{1,0} parameter(0) + g.0 = s32[1,3,3]{2,1,0} gather(operand, i.0), output_window_dims={2}, + elided_window_dims={0}, gather_dims_to_operand_dims={0}, + index_vector_dim=2, window_bounds={1,3} + + i.1 = s64[1] parameter(1) + g.1 = s32[1,1,3]{2,1,0} gather(g.0, i.1), output_window_dims={0,2}, + elided_window_dims={1}, gather_dims_to_operand_dims={1}, + index_vector_dim=1, window_bounds={1,1,3} + + ROOT reshape = s32[1,3]{1,0} reshape(g.1) +} +)"; + + const char* expected_root_expression = R"( +(scalar-indexed-const + (constant s32[2,1,3]) + (reshape + (scalar-indexed %i.0 %i.1 1->[1]) + to s64[]) + 0->[]) +)"; + + AssertArrayForRootExpressionIs(hlo_text, expected_root_expression); +} + +TEST_F(IndexedArrayAnalysisTest, ReshapeOfGather5) { + string hlo_text = R"( +HloModule ReshapeOfGather + +ENTRY main { + operand = s32[1,6] constant(s32[1,6]{{1,2,3,4,5,6}}) + indices = s32[1] parameter(0) + gather = s32[1,6] gather(operand, indices), + output_window_dims={1}, + elided_window_dims={0}, + gather_dims_to_operand_dims={0}, + index_vector_dim=1, + window_bounds={1,6} + ROOT reshape = s32[1,1,6] reshape(gather) +} +)"; + + const char* expected_root_expression = R"( +(scalar-indexed-const + (constant s32[1,1,1,6]) + (reshape %indices to s32[]) + 0->[]) +)"; + + AssertArrayForRootExpressionIs(hlo_text, expected_root_expression); +} + +TEST_F(IndexedArrayAnalysisTest, ReshapeOfGather6) { + string hlo_text = R"( +HloModule ReshapeOfGather + +ENTRY main { + operand = s32[1,2,6] constant(s32[1,2,6]{{ + {1,2,3,4,5,6},{1,2,3,4,5,6}}}) + indices = s32[1] parameter(0) + gather = s32[1,1,6] gather(operand, indices), + output_window_dims={1,2}, + elided_window_dims={1}, + gather_dims_to_operand_dims={1}, + index_vector_dim=1, + window_bounds={1,1,6} + ROOT reshape = s32[1,1,1,6] reshape(gather) +} +)"; + + const char* expected_root_expression = R"( +(scalar-indexed-const + (constant s32[2,1,1,1,6] s32[2,1,1,1,6] { + { /*i0=0*/ { /*i1=0*/ { /*i2=0*/ {1, 2, 3, 4, 5, 6} } } }, + { /*i0=1*/ { /*i1=0*/ { /*i2=0*/ {1, 2, 3, 4, 5, 6} } } } }) + (reshape %indices to s32[]) + 0->[]) +)"; + + AssertArrayWithConstantsForRootExpressionIs(hlo_text, + expected_root_expression); +} + +TEST_F(IndexedArrayAnalysisTest, ReshapeOfGather7) { + string hlo_text = R"( +HloModule ReshapeOfGather + +ENTRY main { + operand = s32[2,6] constant(s32[2,6]{ + {1,2,3,4,5,6},{1,2,3,4,5,6}}) + indices = s32[1,5] parameter(0) + gather = s32[1,5,6] gather(operand, indices), + output_window_dims={2}, + elided_window_dims={0}, + gather_dims_to_operand_dims={0}, + index_vector_dim=2, + window_bounds={1,6} + ROOT reshape = s32[1,1,5,6] reshape(gather) +} +)"; + + const char* expected_root_expression = R"( +(scalar-indexed-const + (constant s32[2,1,1,6] s32[2,1,1,6] { + { /*i0=0*/ { /*i1=0*/ {1, 2, 3, 4, 5, 6} } }, + { /*i0=1*/ { /*i1=0*/ {1, 2, 3, 4, 5, 6} } } }) + (reshape %indices to s32[5]) + 0->[2]) +)"; + + AssertArrayWithConstantsForRootExpressionIs(hlo_text, + expected_root_expression); +} + +TEST_F(IndexedArrayAnalysisTest, ReshapeOfGatherNoFold0) { string hlo_text = R"( HloModule ReshapeOfGather @@ -290,10 +539,19 @@ ENTRY main { } )"; - AssertArrayForRootExpressionIs(hlo_text, "%reshape"); + const char* expected_root_expression = R"( +(reshape + (scalar-indexed-const + (constant s32[3,4]) + %indices + 0->[0,2]) + to s32[5,2,2,2,3]) +)"; + + AssertArrayForRootExpressionIs(hlo_text, expected_root_expression); } -TEST_F(IndexedArrayAnalysisTest, ReshapeOfGatherNegative1) { +TEST_F(IndexedArrayAnalysisTest, ReshapeOfGatherNoFold1) { string hlo_text = R"( HloModule ReshapeOfGather @@ -313,7 +571,48 @@ ENTRY main { } )"; - AssertArrayForRootExpressionIs(hlo_text, "%reshape"); + const char* expected_root_expression = R"( +(reshape + (scalar-indexed-const + (constant s32[3,5,2]) + %indices + 1->[2]) + to s32[6,7]) +)"; + + AssertArrayForRootExpressionIs(hlo_text, expected_root_expression); +} + +TEST_F(IndexedArrayAnalysisTest, ReshapeOfGatherNoFold2) { + string hlo_text = R"( +HloModule ReshapeOfGather + +ENTRY main { + operand = s32[3,4,1] constant(s32[3,4,1]{ + {{1},{2},{3},{4}}, + {{1},{2},{3},{4}}, + {{1},{2},{3},{4}}}) + indices = s32[5,6] parameter(0) + gather = s32[5,4,6,1] gather(operand, indices), + output_window_dims={1,3}, + elided_window_dims={0}, + gather_dims_to_operand_dims={0}, + index_vector_dim=2, + window_bounds={1,4,1} + ROOT reshape = s32[5,2,2,2,3,1] reshape(gather) +} +)"; + + const char* expected_root_expression = R"( +(reshape + (scalar-indexed-const + (constant s32[3,4,1]) + %indices + 0->[0,2]) + to s32[5,2,2,2,3,1]) +)"; + + AssertArrayForRootExpressionIs(hlo_text, expected_root_expression); } TEST_F(IndexedArrayAnalysisTest, UnaryOpOfGather) { diff --git a/tensorflow/compiler/xla/shape_util.cc b/tensorflow/compiler/xla/shape_util.cc index c85fb20e01..51d45b2be6 100644 --- a/tensorflow/compiler/xla/shape_util.cc +++ b/tensorflow/compiler/xla/shape_util.cc @@ -27,6 +27,7 @@ limitations under the License. #include "tensorflow/compiler/xla/primitive_util.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/types.h" +#include "tensorflow/compiler/xla/util.h" #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/lib/core/stringpiece.h" #include "tensorflow/core/lib/gtl/iterator_range.h" @@ -946,6 +947,11 @@ bool ShapeUtil::IsLeafIndex(const Shape& shape, const ShapeIndex& index) { return leaves; } +/* static */ bool ShapeUtil::HasDegenerateDimensions(const Shape& shape) { + CHECK(ShapeUtil::IsArray(shape)); + return ArrayContains(AsInt64Slice(shape.dimensions()), 1); +} + namespace { // Helper for ForEachSubshape which visits the subshapes of the given shape in diff --git a/tensorflow/compiler/xla/shape_util.h b/tensorflow/compiler/xla/shape_util.h index 8ee3f490a0..25ed70316b 100644 --- a/tensorflow/compiler/xla/shape_util.h +++ b/tensorflow/compiler/xla/shape_util.h @@ -516,6 +516,10 @@ class ShapeUtil { static Status ForEachMutableSubshapeWithStatus( Shape* shape, const MutatingStatusVisitorFunction& func); + // Returns true if `shape` (which must be an array) with degenerate dimensions + // (dimensions with bound 1). + static bool HasDegenerateDimensions(const Shape& shape); + // Permutes the dimensions by the given permutation, so // return_value.dimensions[permutation[i]] = argument.dimensions[i] static Shape PermuteDimensions(tensorflow::gtl::ArraySlice permutation, diff --git a/tensorflow/compiler/xla/shape_util_test.cc b/tensorflow/compiler/xla/shape_util_test.cc index 61aa198e52..606f7492ce 100644 --- a/tensorflow/compiler/xla/shape_util_test.cc +++ b/tensorflow/compiler/xla/shape_util_test.cc @@ -792,6 +792,17 @@ TEST(ShapeUtilTest, ReshapeIsBitcast_3x2x2_6x2_Dim1IsMostMinor) { ShapeUtil::MakeShapeWithLayout(F32, {6, 2}, {0, 1}))); } +TEST(ShapeUtilTest, HasDegenerateDimensions) { + EXPECT_TRUE( + ShapeUtil::HasDegenerateDimensions(ShapeUtil::MakeShape(F32, {3, 1, 2}))); + EXPECT_TRUE( + ShapeUtil::HasDegenerateDimensions(ShapeUtil::MakeShape(F32, {3, 1, 1}))); + EXPECT_FALSE( + ShapeUtil::HasDegenerateDimensions(ShapeUtil::MakeShape(F32, {3, 3, 5}))); + EXPECT_FALSE( + ShapeUtil::HasDegenerateDimensions(ShapeUtil::MakeShape(F32, {3, 0, 5}))); +} + TEST(AlgebraicSimplifierTest, ReshapeIsBitcast_3x2x2_6x2_Dim0IsMostMinor) { EXPECT_FALSE(ShapeUtil::ReshapeIsBitcast( ShapeUtil::MakeShapeWithLayout(F32, {3, 2, 2}, {0, 1, 2}), diff --git a/tensorflow/compiler/xla/util.h b/tensorflow/compiler/xla/util.h index b4f45cc972..6041fae159 100644 --- a/tensorflow/compiler/xla/util.h +++ b/tensorflow/compiler/xla/util.h @@ -31,6 +31,7 @@ limitations under the License. #include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/lib/core/stringpiece.h" #include "tensorflow/core/lib/gtl/array_slice.h" +#include "tensorflow/core/lib/gtl/inlined_vector.h" #include "tensorflow/core/lib/math/math_util.h" #include "tensorflow/core/lib/strings/numbers.h" #include "tensorflow/core/lib/strings/strcat.h" @@ -539,6 +540,11 @@ int64 FindIndex(const C& c, Value&& value) { return std::distance(c.begin(), it); } +template +bool ArrayContains(tensorflow::gtl::ArraySlice c, const T& value) { + return c_find(c, value) != c.end(); +} + template void InsertAt(C* c, int64 index, Value&& value) { c->insert(c->begin() + index, std::forward(value)); @@ -549,6 +555,12 @@ void EraseAt(C* c, int64 index) { c->erase(c->begin() + index); } +template +std::vector InlinedVectorToVector( + const tensorflow::gtl::InlinedVector& inlined_vector) { + return std::vector(inlined_vector.begin(), inlined_vector.end()); +} + // Returns true if `x` fits in 32-bits. template bool IsInt32(T x) { -- GitLab From 5764747347c5a7b3e868ecc8943a397e304a0a92 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Sat, 16 Jun 2018 08:53:17 -0700 Subject: [PATCH 566/816] Optimize max/min reductions over monotonic functions PiperOrigin-RevId: 200843761 --- tensorflow/core/grappler/op_types.cc | 12 +++++ tensorflow/core/grappler/op_types.h | 1 + .../optimizers/arithmetic_optimizer.cc | 54 +++++++++++++++++++ .../optimizers/arithmetic_optimizer.h | 1 + .../optimizers/arithmetic_optimizer_test.cc | 46 ++++++++++++++++ 5 files changed, 114 insertions(+) diff --git a/tensorflow/core/grappler/op_types.cc b/tensorflow/core/grappler/op_types.cc index 2227904dbf..b4ddd61c29 100644 --- a/tensorflow/core/grappler/op_types.cc +++ b/tensorflow/core/grappler/op_types.cc @@ -135,6 +135,18 @@ bool IsDequeueOp(const NodeDef& node) { bool IsDiv(const NodeDef& node) { return node.op() == "Div"; } +bool IsElementWiseMonotonic(const NodeDef& node) { + static const std::unordered_set* element_wise_monotonic_ops = + CHECK_NOTNULL((new std::unordered_set{ + "Relu", + "Relu6", + "Sigmoid", + "Sqrt", + "Tanh", + })); + return element_wise_monotonic_ops->count(node.op()) > 0; +} + bool IsEluGrad(const NodeDef& node) { return node.op() == "EluGrad"; } bool IsEnter(const NodeDef& node) { diff --git a/tensorflow/core/grappler/op_types.h b/tensorflow/core/grappler/op_types.h index 7110a9c63d..2de7d8cc9a 100644 --- a/tensorflow/core/grappler/op_types.h +++ b/tensorflow/core/grappler/op_types.h @@ -55,6 +55,7 @@ bool IsDepthwiseConv2dNativeBackpropFilter(const NodeDef& node); bool IsDepthwiseConv2dNativeBackpropInput(const NodeDef& node); bool IsDequeueOp(const NodeDef& node); bool IsDiv(const NodeDef& node); +bool IsElementWiseMonotonic(const NodeDef& node); bool IsEluGrad(const NodeDef& node); bool IsEnter(const NodeDef& node); bool IsEqual(const NodeDef& node); diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc index 9d500f8f54..d518685216 100644 --- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc +++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc @@ -2600,6 +2600,58 @@ class ConvertLog1pStage : public ArithmeticOptimizerStage { } }; +// Performs conversions like: +// Max(Sqrt(x)) => Sqrt(Max(x)) +// Checks for a max/min reduction over element-wise monotonic functions, such +// as Sqrt, Sigmoid, Tanh, etc. +class OptimizeMaxOrMinOfMonotonicStage : public ArithmeticOptimizerStage { + public: + explicit OptimizeMaxOrMinOfMonotonicStage( + const GraphOptimizerContext& ctx, + const ArithmeticOptimizerContext& ctx_ext) + : ArithmeticOptimizerStage("OptimizeMaxOrMinOfMonotonicStage", ctx, + ctx_ext) {} + ~OptimizeMaxOrMinOfMonotonicStage() override = default; + + bool IsSupported(const NodeDef* node) const override { + return IsMax(*node) || IsMin(*node); + } + + Status TrySimplify(NodeDef* reduction_node, + string* simplified_node_name) override { + NodeDef* inner_function; + TF_RETURN_IF_ERROR(GetInputNode(reduction_node->input(0), &inner_function)); + // Optimize only if: + // 1. inner_function's Op is element-wise monotonic + // 2. inner_function's output is not being consumed elsewhere. + if (IsElementWiseMonotonic(*inner_function) && + (NumNonControlOutputs(*inner_function, *ctx().node_map) == 1)) { + // Swap the first inputs of the inner function Op & the reduction Op. + NodeDef* inner_input; + TF_RETURN_IF_ERROR(GetInputNode(inner_function->input(0), &inner_input)); + inner_function->set_input(0, reduction_node->name()); + UpdateConsumersAvoidingLoop(inner_function, reduction_node->name()); + reduction_node->set_input(0, inner_input->name()); + UpdateConsumersAvoidingLoop(reduction_node, inner_function->name()); + } + return Status::OK(); + } + + void UpdateConsumersAvoidingLoop(NodeDef* node, const string& new_input) { + const string& node_name = node->name(); + const std::set consumers = ctx().node_map->GetOutputs(node_name); + for (NodeDef* consumer : consumers) { + for (int i = 0; i < consumer->input_size(); ++i) { + if (consumer->input(i) == node_name && consumer->name() != new_input) { + consumer->set_input(i, new_input); + ctx().node_map->UpdateInput(consumer->name(), node_name, new_input); + } + } + AddToOptimizationQueue(consumer); + } + } +}; + } // namespace class UniqueNodes { @@ -2878,6 +2930,8 @@ Status ArithmeticOptimizer::SimplifyArithmeticOps(bool can_use_shapes) { if (options_.convert_pow) pipeline.AddStage(ctx, ctx_ext); if (options_.convert_log1p) pipeline.AddStage(ctx, ctx_ext); + if (options_.optimize_max_or_min_of_monotonic) + pipeline.AddStage(ctx, ctx_ext); VLOG(1) << "Run " << pipeline.NumStages() << " arithmetic optimizer stages: " << str_util::Join(pipeline.StageNames(), ", "); diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.h b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.h index 9a6081dcd8..824ef35ef6 100644 --- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.h +++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.h @@ -63,6 +63,7 @@ class ArithmeticOptimizer : public GraphOptimizer { bool hoist_common_factor_out_of_aggregation = true; bool hoist_cwise_unary_chains = false; bool minimize_broadcasts = true; + bool optimize_max_or_min_of_monotonic = true; bool remove_idempotent = true; bool remove_identity_transpose = true; bool remove_involution = true; diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc b/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc index 177c237fe7..e1d55cdf5f 100644 --- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc +++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc @@ -269,6 +269,11 @@ class ArithmeticOptimizerTest : public GrapplerTest { DisableAllStages(optimizer); optimizer->options_.convert_log1p = true; } + + void EnableOnlyOptimizeMaxOrMinOfMonotonic(ArithmeticOptimizer* optimizer) { + DisableAllStages(optimizer); + optimizer->options_.optimize_max_or_min_of_monotonic = true; + } }; TEST_F(ArithmeticOptimizerTest, NoOp) { @@ -3125,5 +3130,46 @@ TEST_F(ArithmeticOptimizerTest, RemoveLogicalNot) { } } +TEST_F(ArithmeticOptimizerTest, OptimizeMaxOrMinOfMonotonicElementWise) { + tensorflow::Scope s = tensorflow::Scope::NewRootScope(); + auto x = ops::Const(s.WithOpName("x"), {1.0f, 2.0f}, {1, 2}); + Output sqrt = ops::Sqrt(s.WithOpName("sqrt"), x); + Output reduce_max = ops::Max(s.WithOpName("reduce_max"), sqrt, {0}); + Output final_out = ops::Identity(s.WithOpName("final_out"), reduce_max); + + GrapplerItem item; + item.fetch = {"final_out"}; + TF_CHECK_OK(s.ToGraphDef(&item.graph)); + auto tensors_expected = EvaluateNodes(item.graph, item.fetch); + EXPECT_EQ(1, tensors_expected.size()); + + GraphDef output; + ArithmeticOptimizer optimizer; + EnableOnlyOptimizeMaxOrMinOfMonotonic(&optimizer); + OptimizeAndPrune(&optimizer, &item, &output); + auto tensors = EvaluateNodes(output, item.fetch); + EXPECT_EQ(1, tensors.size()); + + test::ExpectTensorNear(tensors_expected[0], tensors[0], 1e-6); + EXPECT_EQ(item.graph.node_size(), output.node_size()); + // Check if the inputs are switched + int required_node_count = 0; + for (int i = 0; i < output.node_size(); ++i) { + const NodeDef& node = output.node(i); + if (node.name() == "sqrt") { + EXPECT_EQ("Sqrt", node.op()); + EXPECT_EQ(1, node.input_size()); + EXPECT_EQ("reduce_max", node.input(0)); + ++required_node_count; + } else if (node.name() == "reduce_max") { + EXPECT_EQ("Max", node.op()); + EXPECT_EQ(2, node.input_size()); + EXPECT_EQ("x", node.input(0)); + ++required_node_count; + } + } + EXPECT_EQ(2, required_node_count); +} + } // namespace grappler } // namespace tensorflow -- GitLab From e03446add1232278fba99767e268df8ae71d357b Mon Sep 17 00:00:00 2001 From: Jianwei Xie Date: Sat, 16 Jun 2018 11:21:31 -0700 Subject: [PATCH 567/816] clean up PiperOrigin-RevId: 200849332 --- .../python/feature_column/sequence_feature_column.py | 2 +- tensorflow/python/feature_column/feature_column.py | 3 --- 2 files changed, 1 insertion(+), 4 deletions(-) diff --git a/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py index 555beddeaa..84a413c791 100644 --- a/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py +++ b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py @@ -95,7 +95,7 @@ def sequence_input_layer( Raises: ValueError: If any of the `feature_columns` is the wrong type. """ - feature_columns = fc._clean_feature_columns(feature_columns) + feature_columns = fc._normalize_feature_columns(feature_columns) for c in feature_columns: if not isinstance(c, fc._SequenceDenseColumn): raise ValueError( diff --git a/tensorflow/python/feature_column/feature_column.py b/tensorflow/python/feature_column/feature_column.py index a58c5aabbe..670c933d56 100644 --- a/tensorflow/python/feature_column/feature_column.py +++ b/tensorflow/python/feature_column/feature_column.py @@ -3581,6 +3581,3 @@ class _SequenceCategoricalColumn( return _CategoricalColumn.IdWeightPair(id_tensor, weight_tensor) -# TODO(xiejw): Remove the following alias once call sites are updated. -_clean_feature_columns = _normalize_feature_columns -_to_sparse_input = _to_sparse_input_and_drop_ignore_values -- GitLab From 6d1603622b1c3b25de0a8d342714fed271308a47 Mon Sep 17 00:00:00 2001 From: Brennan Saeta Date: Sat, 16 Jun 2018 12:23:23 -0700 Subject: [PATCH 568/816] Do not depend on boringssl for big-endian architectures. (#20038) * Do not depend on boringssl for big-endian architectures. A recent commit migrated TensorFlow from grpc_unsecure (and grpc++_unsecure) to their secure variants. These secure variants depend on BoringSSL. Unfortunately, BoringSSL does not work on big-endian architectures. This commit abstracts the grpc dependency behind a couple cc_library rules, and plumbs through the logic to conditionally build without BoringSSL based on the target architecture. Fixes #20014 * Fix BUILD file formatting. * Fix typo in CPU name * Add an additional bind and select when evaluating the cc_proto_library rules. --- tensorflow/BUILD | 22 +++++++++++ tensorflow/compiler/xla/rpc/BUILD | 6 +-- tensorflow/contrib/tpu/profiler/BUILD | 2 +- tensorflow/contrib/verbs/BUILD | 4 +- tensorflow/core/debug/BUILD | 4 +- tensorflow/core/distributed_runtime/BUILD | 4 +- .../core/distributed_runtime/eager/BUILD | 4 +- tensorflow/core/distributed_runtime/rpc/BUILD | 38 +++++++++---------- .../core/distributed_runtime/rpc/eager/BUILD | 6 +-- .../core/platform/default/build_config.bzl | 5 ++- tensorflow/workspace.bzl | 5 +++ 11 files changed, 65 insertions(+), 35 deletions(-) diff --git a/tensorflow/BUILD b/tensorflow/BUILD index d77f04139e..4e212e96dc 100644 --- a/tensorflow/BUILD +++ b/tensorflow/BUILD @@ -154,6 +154,12 @@ config_setting( visibility = ["//visibility:public"], ) +config_setting( + name = "linux_s390x", + values = {"cpu": "s390x"}, + visibility = ["//visibility:public"], +) + config_setting( name = "debug", values = { @@ -424,6 +430,22 @@ filegroup( data = glob(["docs_src/**/*.md"]), ) +cc_library( + name = "grpc", + deps = select({ + ":linux_s390x": ["@grpc//:grpc_unsecure"], + "//conditions:default": ["@grpc"], + }), +) + +cc_library( + name = "grpc++", + deps = select({ + ":linux_s390x": ["@grpc//:grpc++_unsecure"], + "//conditions:default": ["@grpc//:grpc++"], + }), +) + # A shared object which includes registration mechanisms for ops and # kernels. Does not include the implementations of any ops or kernels. Instead, # the library which loads libtensorflow_framework.so diff --git a/tensorflow/compiler/xla/rpc/BUILD b/tensorflow/compiler/xla/rpc/BUILD index 1775666652..0b1cec1925 100644 --- a/tensorflow/compiler/xla/rpc/BUILD +++ b/tensorflow/compiler/xla/rpc/BUILD @@ -39,10 +39,10 @@ tf_cc_binary( srcs = ["grpc_service_main.cc"], deps = [ ":grpc_service", + "//tensorflow:grpc++", "//tensorflow/compiler/xla/service:cpu_plugin", "//tensorflow/core:framework_internal", "//tensorflow/core:lib", - "@grpc//:grpc++", ], ) @@ -54,6 +54,7 @@ tf_cc_test( ], deps = [ ":grpc_stub", + "//tensorflow:grpc++", "//tensorflow/compiler/xla/client", "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:literal_test_util", @@ -61,7 +62,6 @@ tf_cc_test( "//tensorflow/core:lib", "//tensorflow/core:test", "//tensorflow/core:test_main", - "@grpc//:grpc++", ], ) @@ -71,9 +71,9 @@ cc_library( hdrs = ["grpc_service.h"], deps = [ ":xla_service_proto", + "//tensorflow:grpc++", "//tensorflow/compiler/xla/service", "//tensorflow/compiler/xla/service:platform_util", "//tensorflow/core/distributed_runtime/rpc:grpc_util", - "@grpc//:grpc++", ], ) diff --git a/tensorflow/contrib/tpu/profiler/BUILD b/tensorflow/contrib/tpu/profiler/BUILD index 3b2d7adfff..38d1c3049e 100644 --- a/tensorflow/contrib/tpu/profiler/BUILD +++ b/tensorflow/contrib/tpu/profiler/BUILD @@ -49,11 +49,11 @@ tf_cc_binary( ":tpu_profiler_analysis_proto_cc", ":tpu_profiler_proto_cc", ":version", + "//tensorflow:grpc++", "//tensorflow/core:framework_internal", "//tensorflow/core:lib", "//tensorflow/core/distributed_runtime/rpc:grpc_util", "//tensorflow/core/platform/cloud:gcs_file_system", - "@grpc//:grpc++", ], ) diff --git a/tensorflow/contrib/verbs/BUILD b/tensorflow/contrib/verbs/BUILD index 1b45584dcb..19cb8983b6 100644 --- a/tensorflow/contrib/verbs/BUILD +++ b/tensorflow/contrib/verbs/BUILD @@ -53,12 +53,12 @@ cc_library( ":grpc_verbs_service_impl", ":rdma_mgr", ":verbs_service_proto_cc", + "//tensorflow:grpc++", "//tensorflow/core:lib_internal", "//tensorflow/core/distributed_runtime:session_mgr", "//tensorflow/core/distributed_runtime/rpc:async_service_interface", "//tensorflow/core/distributed_runtime/rpc:grpc_call", "//tensorflow/core/distributed_runtime/rpc:grpc_util", - "@grpc//:grpc++", ], alwayslink = 1, ) @@ -69,7 +69,7 @@ cc_library( hdrs = ["grpc_verbs_service_impl.h"], deps = [ ":verbs_service_proto_cc", - "@grpc//:grpc++", + "//tensorflow:grpc++", ], ) diff --git a/tensorflow/core/debug/BUILD b/tensorflow/core/debug/BUILD index 50f8a307d8..36e9b3455a 100644 --- a/tensorflow/core/debug/BUILD +++ b/tensorflow/core/debug/BUILD @@ -143,6 +143,7 @@ tf_cuda_library( ":debug_node_key", ":debug_service_proto_cc", ":debugger_event_metadata_proto_cc", + "//tensorflow:grpc++", "//tensorflow/core:core_cpu_internal", "//tensorflow/core:framework", "//tensorflow/core:graph", @@ -150,7 +151,6 @@ tf_cuda_library( "//tensorflow/core:lib_internal", "//tensorflow/core:proto_text", "//tensorflow/core:protos_all_cc", - "@grpc//:grpc++", ], alwayslink = 1, ) @@ -166,11 +166,11 @@ tf_cuda_library( ":debug_io_utils", ":debug_service_proto_cc", ":debugger_event_metadata_proto_cc", + "//tensorflow:grpc++", "//tensorflow/core:framework", "//tensorflow/core:lib", "//tensorflow/core:lib_internal", "//tensorflow/core:protos_all_cc", - "@grpc//:grpc++", ], alwayslink = 1, ) diff --git a/tensorflow/core/distributed_runtime/BUILD b/tensorflow/core/distributed_runtime/BUILD index c6db2aec06..0abef01a9a 100644 --- a/tensorflow/core/distributed_runtime/BUILD +++ b/tensorflow/core/distributed_runtime/BUILD @@ -628,6 +628,7 @@ tf_cuda_cc_test( ":master", ":remote_device", ":worker_interface", + "//tensorflow:grpc++", "//tensorflow/core:core_cpu", "//tensorflow/core:core_cpu_internal", "//tensorflow/core:framework", @@ -649,7 +650,6 @@ tf_cuda_cc_test( "//tensorflow/core/kernels:dense_update_ops", "//tensorflow/core/kernels:identity_op", "//tensorflow/core/kernels:variable_ops", - "@grpc//:grpc++", ], ) @@ -667,6 +667,7 @@ tf_cuda_cc_test( ":master", ":remote_device", ":worker_interface", + "//tensorflow:grpc++", "//tensorflow/core:core_cpu", "//tensorflow/core:core_cpu_internal", "//tensorflow/core:framework", @@ -682,7 +683,6 @@ tf_cuda_cc_test( "//tensorflow/core/distributed_runtime/rpc:grpc_testlib", "//tensorflow/core/distributed_runtime/rpc:grpc_util", "//tensorflow/core/distributed_runtime/rpc:grpc_worker_cache", - "@grpc//:grpc++", ], ) diff --git a/tensorflow/core/distributed_runtime/eager/BUILD b/tensorflow/core/distributed_runtime/eager/BUILD index dc02d1b9bf..1a7187597d 100644 --- a/tensorflow/core/distributed_runtime/eager/BUILD +++ b/tensorflow/core/distributed_runtime/eager/BUILD @@ -47,6 +47,8 @@ cc_library( "eager_service_impl.h", ], deps = [ + "//tensorflow:grpc", + "//tensorflow:grpc++", "//tensorflow/c:c_api_internal", "//tensorflow/c:tf_status_helper", "//tensorflow/core:core_cpu_internal", @@ -65,8 +67,6 @@ cc_library( "//tensorflow/core/distributed_runtime:worker_env", "//tensorflow/core/distributed_runtime/eager:remote_tensor_handle", "//tensorflow/core/distributed_runtime/rpc:rpc_rendezvous_mgr", - "@grpc", - "@grpc//:grpc++", ], ) diff --git a/tensorflow/core/distributed_runtime/rpc/BUILD b/tensorflow/core/distributed_runtime/rpc/BUILD index 882271e3f5..66c4e5d7a9 100644 --- a/tensorflow/core/distributed_runtime/rpc/BUILD +++ b/tensorflow/core/distributed_runtime/rpc/BUILD @@ -41,8 +41,8 @@ cc_library( srcs = ["grpc_util.cc"], hdrs = ["grpc_util.h"], deps = [ - "@grpc", - "@grpc//:grpc++", + "//tensorflow:grpc", + "//tensorflow:grpc++", "//tensorflow/core:lib", # Required to be able to overload TensorResponse parsing. "//tensorflow/core/distributed_runtime:tensor_coding", @@ -55,8 +55,8 @@ cc_library( hdrs = ["grpc_client_cq_tag.h"], deps = [ ":grpc_util", + "//tensorflow:grpc++", "//tensorflow/core:lib", - "@grpc//:grpc++", ], ) @@ -67,10 +67,10 @@ cc_library( deps = [ ":grpc_client_cq_tag", ":grpc_util", + "//tensorflow:grpc++", "//tensorflow/core:lib", "//tensorflow/core/distributed_runtime:call_options", "//tensorflow/core/distributed_runtime:tensor_coding", - "@grpc//:grpc++", ], ) @@ -83,6 +83,7 @@ cc_library( ":grpc_state", ":grpc_util", ":grpc_worker_service_impl", + "//tensorflow:grpc++", "//tensorflow/core:core_cpu_internal", "//tensorflow/core:lib", "//tensorflow/core:lib_internal", @@ -90,7 +91,6 @@ cc_library( "//tensorflow/core/distributed_runtime:tensor_coding", "//tensorflow/core/distributed_runtime:worker_cache_logger", "//tensorflow/core/distributed_runtime:worker_interface", - "@grpc//:grpc++", ], ) @@ -100,10 +100,10 @@ cc_library( hdrs = ["grpc_channel.h"], deps = [ ":grpc_util", + "//tensorflow:grpc++", "//tensorflow/core:framework", "//tensorflow/core:lib", "//tensorflow/core:lib_internal", - "@grpc//:grpc++", ], ) @@ -112,13 +112,13 @@ cc_library( srcs = ["grpc_tensor_coding.cc"], hdrs = ["grpc_tensor_coding.h"], deps = [ + "//tensorflow:grpc++", "//tensorflow/core:core_cpu_internal", "//tensorflow/core:framework", "//tensorflow/core:framework_internal", "//tensorflow/core:lib", "//tensorflow/core:protos_all_cc", "//tensorflow/core:worker_proto_cc", - "@grpc//:grpc++", ], ) @@ -127,9 +127,9 @@ cc_library( srcs = [], hdrs = ["grpc_call.h"], deps = [ + "//tensorflow:grpc++", "//tensorflow/core:lib", "//tensorflow/core:lib_internal", - "@grpc//:grpc++", ], ) @@ -167,6 +167,7 @@ tf_cuda_library( ":grpc_tensor_coding", ":grpc_util", ":grpc_worker_service_impl", + "//tensorflow:grpc++", "//tensorflow/core:core_cpu_internal", "//tensorflow/core:framework", "//tensorflow/core:lib", @@ -180,7 +181,6 @@ tf_cuda_library( "//tensorflow/core/distributed_runtime:worker_cache", "//tensorflow/core/distributed_runtime:worker_env", "//tensorflow/core/distributed_runtime:worker_session", - "@grpc//:grpc++", ], ) @@ -190,9 +190,9 @@ cc_library( hdrs = ["grpc_worker_service_impl.h"], deps = [ ":grpc_util", + "//tensorflow:grpc++", "//tensorflow/core:worker_proto_cc", "//tensorflow/core/distributed_runtime:tensor_coding", - "@grpc//:grpc++", ], ) @@ -221,11 +221,11 @@ cc_library( ":grpc_call", ":grpc_master_service_impl", ":grpc_util", + "//tensorflow:grpc++", "//tensorflow/core:lib", "//tensorflow/core:lib_internal", "//tensorflow/core:master_proto_cc", "//tensorflow/core/distributed_runtime:master", - "@grpc//:grpc++", ], alwayslink = 1, ) @@ -235,8 +235,8 @@ cc_library( srcs = ["grpc_master_service_impl.cc"], hdrs = ["grpc_master_service_impl.h"], deps = [ + "//tensorflow:grpc++", "//tensorflow/core:master_proto_cc", - "@grpc//:grpc++", ], ) @@ -269,6 +269,8 @@ cc_library( ":grpc_worker_cache", ":grpc_worker_service", ":rpc_rendezvous_mgr", + "//tensorflow:grpc", + "//tensorflow:grpc++", "//tensorflow/core:core_cpu", "//tensorflow/core:core_cpu_internal", "//tensorflow/core:framework", @@ -285,8 +287,6 @@ cc_library( "//tensorflow/core/distributed_runtime:server_lib", "//tensorflow/core/distributed_runtime:session_mgr", "//tensorflow/core/distributed_runtime:worker_env", - "@grpc", - "@grpc//:grpc++", ], alwayslink = 1, ) @@ -307,13 +307,13 @@ tf_cc_binary( ], deps = [ ":grpc_server_lib", + "//tensorflow:grpc++", "//tensorflow/core:core_cpu", "//tensorflow/core:framework_internal", "//tensorflow/core:lib", "//tensorflow/core:protos_all_cc", "//tensorflow/core/distributed_runtime:server_lib", "//tensorflow/core/kernels:data_flow", - "@grpc//:grpc++", ], ) @@ -325,6 +325,7 @@ tf_cc_binary( ], deps = [ ":grpc_server_lib", + "//tensorflow:grpc++", "//tensorflow/core:core_cpu", "//tensorflow/core:framework_internal", "//tensorflow/core:lib", @@ -338,7 +339,6 @@ tf_cc_binary( "//tensorflow/core/kernels:matmul_op", "//tensorflow/core/kernels:reduction_ops", "//tensorflow/core/kernels:variable_ops", - "@grpc//:grpc++", ], ) @@ -423,6 +423,7 @@ tf_cc_test( deps = [ ":grpc_tensor_coding", ":grpc_testlib", + "//tensorflow:grpc++", "//tensorflow/core:core_cpu", "//tensorflow/core:core_cpu_internal", "//tensorflow/core:framework", @@ -432,7 +433,6 @@ tf_cc_test( "//tensorflow/core:test_main", "//tensorflow/core:testlib", "//tensorflow/core:worker_proto_cc", - "@grpc//:grpc++", ], ) @@ -442,11 +442,11 @@ tf_cc_test( srcs = ["grpc_util_test.cc"], deps = [ ":grpc_util", + "//tensorflow:grpc", + "//tensorflow:grpc++", "//tensorflow/core:test", "//tensorflow/core:test_main", "//tensorflow/core:worker_proto_cc", - "@grpc", - "@grpc//:grpc++", ], ) diff --git a/tensorflow/core/distributed_runtime/rpc/eager/BUILD b/tensorflow/core/distributed_runtime/rpc/eager/BUILD index a5472159cc..6b44d8cecf 100644 --- a/tensorflow/core/distributed_runtime/rpc/eager/BUILD +++ b/tensorflow/core/distributed_runtime/rpc/eager/BUILD @@ -11,8 +11,8 @@ cc_library( srcs = ["grpc_eager_service.cc"], hdrs = ["grpc_eager_service.h"], deps = [ + "//tensorflow:grpc++", "//tensorflow/core:eager_service_proto_cc", - "@grpc//:grpc++", ], ) @@ -21,6 +21,7 @@ cc_library( srcs = ["grpc_eager_client.cc"], hdrs = ["grpc_eager_client.h"], deps = [ + "//tensorflow:grpc++", "//tensorflow/core:eager_service_proto_cc", "//tensorflow/core:lib", "//tensorflow/core/distributed_runtime/eager:eager_client", @@ -29,7 +30,6 @@ cc_library( "//tensorflow/core/distributed_runtime/rpc:grpc_state", "//tensorflow/core/distributed_runtime/rpc:grpc_util", "//tensorflow/core/distributed_runtime/rpc/eager:grpc_eager_service", - "@grpc//:grpc++", ], ) @@ -39,6 +39,7 @@ cc_library( hdrs = ["grpc_eager_service_impl.h"], deps = [ ":grpc_eager_service", + "//tensorflow:grpc++", "//tensorflow/core:framework", "//tensorflow/core:ptr_util", "//tensorflow/core/distributed_runtime/eager:eager_service_impl", @@ -48,7 +49,6 @@ cc_library( "//tensorflow/core/distributed_runtime/rpc:grpc_util", "//tensorflow/core/distributed_runtime/rpc:grpc_worker_cache", "//tensorflow/core/distributed_runtime/rpc:grpc_worker_service", - "@grpc//:grpc++", ], ) diff --git a/tensorflow/core/platform/default/build_config.bzl b/tensorflow/core/platform/default/build_config.bzl index a319ccbdbe..66ccd81e41 100644 --- a/tensorflow/core/platform/default/build_config.bzl +++ b/tensorflow/core/platform/default/build_config.bzl @@ -202,7 +202,10 @@ def cc_proto_library( ) if use_grpc_plugin: - cc_libs += ["//external:grpc_lib"] + cc_libs += select({ + "//tensorflow:linux_s390x": ["//external:grpc_lib_unsecure"], + "//conditions:default": ["//external:grpc_lib"], + }) if default_header: header_only_name = name diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index 212a8bad47..09f7a9b7dd 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -794,6 +794,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""): actual = "@grpc//:grpc++", ) + native.bind( + name = "grpc_lib_unsecure", + actual = "@grpc//:grpc++_unsecure", + ) + # Needed by gRPC native.bind( name = "libssl", -- GitLab From 17d3bff7d575f8082142b0d96ee7a1719eabdb85 Mon Sep 17 00:00:00 2001 From: Chris Leary Date: Sat, 16 Jun 2018 12:52:18 -0700 Subject: [PATCH 569/816] [XLA] Propagate StatusOr through SWIG interface. PiperOrigin-RevId: 200852741 --- .../compiler/xla/python/local_computation_builder.cc | 7 ++----- tensorflow/compiler/xla/python/local_computation_builder.h | 2 +- 2 files changed, 3 insertions(+), 6 deletions(-) diff --git a/tensorflow/compiler/xla/python/local_computation_builder.cc b/tensorflow/compiler/xla/python/local_computation_builder.cc index 445cee1aa7..29062348b0 100644 --- a/tensorflow/compiler/xla/python/local_computation_builder.cc +++ b/tensorflow/compiler/xla/python/local_computation_builder.cc @@ -344,11 +344,8 @@ LocalOp LocalComputationBuilder::Parameter(int64 parameter_number, return builder_.Parameter(parameter_number, shape, name); } -std::unique_ptr LocalComputationBuilder::GetShape( - const LocalOp& operand) { - auto result = MakeUnique(); - *result = builder_.GetShape(operand.op()).ValueOrDie(); - return result; +StatusOr LocalComputationBuilder::GetShape(const LocalOp& operand) { + return builder_.GetShape(operand.op()); } StatusOr LocalComputationBuilder::GetReturnValueShape() { diff --git a/tensorflow/compiler/xla/python/local_computation_builder.h b/tensorflow/compiler/xla/python/local_computation_builder.h index 0da3964676..95f0a0610b 100644 --- a/tensorflow/compiler/xla/python/local_computation_builder.h +++ b/tensorflow/compiler/xla/python/local_computation_builder.h @@ -187,7 +187,7 @@ class LocalComputationBuilder { LocalOp Parameter(int64 parameter_number, const Shape& shape, const string& name); - std::unique_ptr GetShape(const LocalOp& operand); + StatusOr GetShape(const LocalOp& operand); // Returns the shape of the current return value for the computation. StatusOr GetReturnValueShape(); -- GitLab From 5cb77a7ac4741df72e1739c4fda3f552afc9c47c Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Sun, 17 Jun 2018 05:31:55 -0700 Subject: [PATCH 570/816] Convert ImportTensorFlow method from switch to table based. PiperOrigin-RevId: 200892708 --- .../contrib/lite/toco/import_tensorflow.cc | 632 ++++++++---------- .../lite/toco/import_tensorflow_test.cc | 13 +- 2 files changed, 305 insertions(+), 340 deletions(-) diff --git a/tensorflow/contrib/lite/toco/import_tensorflow.cc b/tensorflow/contrib/lite/toco/import_tensorflow.cc index 120e858717..e33b430937 100644 --- a/tensorflow/contrib/lite/toco/import_tensorflow.cc +++ b/tensorflow/contrib/lite/toco/import_tensorflow.cc @@ -574,9 +574,9 @@ tensorflow::Status ConvertConvOperator( return tensorflow::Status::OK(); } -void ConvertDepthwiseConvOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertDepthwiseConvOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "DepthwiseConv2dNative"); CheckInputsCount(node, tf_import_flags, 2); @@ -625,11 +625,12 @@ void ConvertDepthwiseConvOperator(const NodeDef& node, LOG(FATAL) << "Bad padding (only SAME and VALID are supported)"; } model->operators.emplace_back(conv); + return tensorflow::Status::OK(); } -void ConvertDepthToSpaceOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertDepthToSpaceOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "DepthToSpace"); CheckInputsCount(node, tf_import_flags, 1); @@ -640,11 +641,12 @@ void ConvertDepthToSpaceOperator(const NodeDef& node, op->block_size = GetIntAttr(node, "block_size"); QCHECK_GE(op->block_size, 2); model->operators.emplace_back(op); + return tensorflow::Status::OK(); } -void ConvertSpaceToDepthOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertSpaceToDepthOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "SpaceToDepth"); CheckInputsCount(node, tf_import_flags, 1); @@ -662,11 +664,12 @@ void ConvertSpaceToDepthOperator(const NodeDef& node, op->block_size = GetIntAttr(node, "block_size"); QCHECK_GE(op->block_size, 2); model->operators.emplace_back(op); + return tensorflow::Status::OK(); } -void ConvertBiasAddOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertBiasAddOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "BiasAdd"); CheckInputsCount(node, tf_import_flags, 2); @@ -678,11 +681,12 @@ void ConvertBiasAddOperator(const NodeDef& node, biasadd->inputs.push_back(bias_name); biasadd->outputs.push_back(node.name()); model->operators.emplace_back(biasadd); + return tensorflow::Status::OK(); } -void ConvertRandomUniform(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertRandomUniform( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "RandomUniform"); CheckInputsCount(node, tf_import_flags, 1); @@ -695,11 +699,12 @@ void ConvertRandomUniform(const NodeDef& node, op->seed2 = GetIntAttr(node, "seed2"); CHECK(model != nullptr); model->operators.emplace_back(std::move(op)); + return tensorflow::Status::OK(); } -void ConvertIdentityOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertIdentityOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK(node.op() == "Identity" || node.op() == "CheckNumerics" || node.op() == "PlaceholderWithDefault" || node.op() == "StopGradient"); auto* op = new TensorFlowIdentityOperator; @@ -716,9 +721,10 @@ void ConvertIdentityOperator(const NodeDef& node, op->inputs.push_back(input_name); op->outputs.push_back(node.name()); model->operators.emplace_back(op); + return tensorflow::Status::OK(); } -void ConvertFakeQuantWithMinMaxArgs( +tensorflow::Status ConvertFakeQuantWithMinMaxArgs( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { CHECK_EQ(node.op(), "FakeQuantWithMinMaxArgs"); @@ -733,9 +739,10 @@ void ConvertFakeQuantWithMinMaxArgs( // tf.fake_quant_with_min_max_args num_bits defaults to 8. op->num_bits = HasAttr(node, "num_bits") ? GetIntAttr(node, "num_bits") : 8; model->operators.emplace_back(op); + return tensorflow::Status::OK(); } -void ConvertFakeQuantWithMinMaxVars( +tensorflow::Status ConvertFakeQuantWithMinMaxVars( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { CHECK_EQ(node.op(), "FakeQuantWithMinMaxVars"); @@ -751,12 +758,12 @@ void ConvertFakeQuantWithMinMaxVars( op->outputs.push_back(node.name()); op->num_bits = HasAttr(node, "num_bits") ? GetIntAttr(node, "num_bits") : 8; model->operators.emplace_back(op); + return tensorflow::Status::OK(); } - -void ConvertSqueezeOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertSqueezeOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "Squeeze"); CheckInputsCount(node, tf_import_flags, 1); auto* op = new SqueezeOperator; @@ -772,11 +779,12 @@ void ConvertSqueezeOperator(const NodeDef& node, } model->operators.emplace_back(op); + return tensorflow::Status::OK(); } -void ConvertSumOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertSumOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "Sum"); CheckInputsCount(node, tf_import_flags, 2); auto* op = new TensorFlowSumOperator; @@ -787,11 +795,12 @@ void ConvertSumOperator(const NodeDef& node, if (HasAttr(node, "keep_dims")) { op->keep_dims = GetBoolAttr(node, "keep_dims"); } + return tensorflow::Status::OK(); } -void ConvertSplitOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertSplitOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "Split"); CheckInputsCount(node, tf_import_flags, 2); auto* op = new TensorFlowSplitOperator; @@ -804,11 +813,12 @@ void ConvertSplitOperator(const NodeDef& node, } op->num_split = num_split; model->operators.emplace_back(op); + return tensorflow::Status::OK(); } -void ConvertSwitchOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertSwitchOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "Switch"); CheckInputsCount(node, tf_import_flags, 2); auto* op = new TensorFlowSwitchOperator; @@ -818,11 +828,12 @@ void ConvertSwitchOperator(const NodeDef& node, // Switch operators have two outputs: "name" and "name:1". op->outputs.push_back(node.name() + ":1"); model->operators.emplace_back(op); + return tensorflow::Status::OK(); } -void ConvertSoftmaxOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertSoftmaxOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "Softmax"); CheckInputsCount(node, tf_import_flags, 1); const auto& input_name = node.input(0); @@ -833,11 +844,12 @@ void ConvertSoftmaxOperator(const NodeDef& node, CHECK(!node.attr().count("beta")); // Stab in the dark, just in case. softmax->beta = 1.f; model->operators.emplace_back(softmax); + return tensorflow::Status::OK(); } -void ConvertLRNOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertLRNOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "LRN"); CheckInputsCount(node, tf_import_flags, 1); const auto& input_name = node.input(0); @@ -849,11 +861,12 @@ void ConvertLRNOperator(const NodeDef& node, lrn->alpha = GetFloatAttr(node, "alpha"); lrn->beta = GetFloatAttr(node, "beta"); model->operators.emplace_back(lrn); + return tensorflow::Status::OK(); } -void ConvertMaxPoolOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertMaxPoolOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "MaxPool"); CheckInputsCount(node, tf_import_flags, 1); const auto& input_name = node.input(0); @@ -891,11 +904,12 @@ void ConvertMaxPoolOperator(const NodeDef& node, LOG(FATAL) << "Bad padding (only SAME and VALID are supported)"; } model->operators.emplace_back(maxpool); + return tensorflow::Status::OK(); } -void ConvertAvgPoolOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertAvgPoolOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "AvgPool"); CheckInputsCount(node, tf_import_flags, 1); const auto& input_name = node.input(0); @@ -929,12 +943,12 @@ void ConvertAvgPoolOperator(const NodeDef& node, LOG(FATAL) << "Bad padding (only SAME and VALID are supported)"; } model->operators.emplace_back(avgpool); + return tensorflow::Status::OK(); } - -void ConvertBatchMatMulOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertBatchMatMulOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CheckInputsCount(node, tf_import_flags, 2); // https://www.tensorflow.org/versions/r0.12/api_docs/python/math_ops/matrix_math_functions @@ -945,11 +959,12 @@ void ConvertBatchMatMulOperator(const NodeDef& node, batch_matmul->inputs = {node.input(0), node.input(1)}; batch_matmul->outputs = {node.name()}; model->operators.emplace_back(batch_matmul); + return tensorflow::Status::OK(); } -void ConvertMatMulOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertMatMulOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CheckInputsCount(node, tf_import_flags, 2); // Transpose flags should be easy to support, but we don't have a @@ -967,11 +982,12 @@ void ConvertMatMulOperator(const NodeDef& node, matmul->inputs = {node.input(0), node.input(1)}; matmul->outputs = {node.name()}; model->operators.emplace_back(matmul); + return tensorflow::Status::OK(); } -void ConvertConcatOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertConcatOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { Operator* op = nullptr; if (node.op() == "Concat") { op = new TensorFlowConcatOperator; @@ -991,13 +1007,14 @@ void ConvertConcatOperator(const NodeDef& node, } op->outputs.push_back(node.name()); model->operators.emplace_back(op); + return tensorflow::Status::OK(); } // This method supports simple operators without additional attributes. template -void ConvertSimpleOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertSimpleOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { auto* op = new Op; const int num_inputs = GetInputsCount(node, tf_import_flags); for (int i = 0; i < num_inputs; ++i) { @@ -1005,20 +1022,21 @@ void ConvertSimpleOperator(const NodeDef& node, } op->outputs.push_back(node.name()); model->operators.emplace_back(op); + return tensorflow::Status::OK(); } // This method supports simple operators without additional attributes. template -void ConvertSimpleOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertSimpleOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CheckInputsCount(node, tf_import_flags, NumInputs); - ConvertSimpleOperator(node, tf_import_flags, model); + return ConvertSimpleOperator(node, tf_import_flags, model); } -void ConvertMaxOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertMaxOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "Max"); CheckInputsCount(node, tf_import_flags, 2); auto* op = new TensorFlowMaxOperator; @@ -1029,11 +1047,12 @@ void ConvertMaxOperator(const NodeDef& node, if (HasAttr(node, "keep_dims")) { op->keep_dims = GetBoolAttr(node, "keep_dims"); } + return tensorflow::Status::OK(); } -void ConvertMinOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertMinOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "Min"); CheckInputsCount(node, tf_import_flags, 2); auto* op = new TensorFlowMinOperator; @@ -1044,12 +1063,12 @@ void ConvertMinOperator(const NodeDef& node, if (HasAttr(node, "keep_dims")) { op->keep_dims = GetBoolAttr(node, "keep_dims"); } + return tensorflow::Status::OK(); } - -void ConvertUnsupportedOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertUnsupportedOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { LOG(INFO) << "Converting unsupported operation: " << node.op(); auto* op = new TensorFlowUnsupportedOperator; const int num_inputs = GetInputsCount(node, tf_import_flags); @@ -1072,11 +1091,12 @@ void ConvertUnsupportedOperator(const NodeDef& node, const auto& output_type = GetDataTypeAttr(node, "Tout"); op->output_data_types.push_back(ConvertDataType(output_type)); } + return tensorflow::Status::OK(); } -void ConvertStridedSliceOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertStridedSliceOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "StridedSlice"); // TODO(soroosh): The 4th input (strides) should be e optional, to be // consistent with TF. @@ -1100,11 +1120,12 @@ void ConvertStridedSliceOperator(const NodeDef& node, : 0; model->operators.emplace_back(op); + return tensorflow::Status::OK(); } -void ConvertPlaceholderOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertPlaceholderOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK(node.op() == "Placeholder" || node.op() == "LegacyFedInput"); if (node.op() == "Placeholder") { CheckInputsCount(node, tf_import_flags, 0); @@ -1132,15 +1153,18 @@ void ConvertPlaceholderOperator(const NodeDef& node, } } } + return tensorflow::Status::OK(); } -void ConvertNoOpOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) {} +tensorflow::Status ConvertNoOpOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { + return tensorflow::Status::OK(); +} -void ConvertCastOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertCastOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "Cast"); CheckInputsCount(node, tf_import_flags, 1); const auto tf_src_dtype = GetDataTypeAttr(node, "SrcT"); @@ -1151,11 +1175,12 @@ void ConvertCastOperator(const NodeDef& node, op->inputs.push_back(node.input(0)); op->outputs.push_back(node.name()); model->operators.emplace_back(op); + return tensorflow::Status::OK(); } -void ConvertFloorOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertFloorOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "Floor"); CheckInputsCount(node, tf_import_flags, 1); const auto data_type = GetDataTypeAttr(node, "T"); @@ -1164,11 +1189,12 @@ void ConvertFloorOperator(const NodeDef& node, op->inputs.push_back(node.input(0)); op->outputs.push_back(node.name()); model->operators.emplace_back(op); + return tensorflow::Status::OK(); } -void ConvertGatherOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertGatherOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK(node.op() == "Gather" || node.op() == "GatherV2"); if (node.op() == "Gather") CheckInputsCount(node, tf_import_flags, 2); if (node.op() == "GatherV2") CheckInputsCount(node, tf_import_flags, 3); @@ -1181,11 +1207,12 @@ void ConvertGatherOperator(const NodeDef& node, // should read it an pass it on to the TF Lite Interpreter. op->outputs.push_back(node.name()); model->operators.emplace_back(op); + return tensorflow::Status::OK(); } -void ConvertArgMaxOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertArgMaxOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "ArgMax"); CheckInputsCount(node, tf_import_flags, 2); const auto axis_data_type = @@ -1201,11 +1228,12 @@ void ConvertArgMaxOperator(const NodeDef& node, op->inputs.push_back(node.input(1)); op->outputs.push_back(node.name()); model->operators.emplace_back(op); + return tensorflow::Status::OK(); } -void ConvertResizeBilinearOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertResizeBilinearOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "ResizeBilinear"); CheckInputsCount(node, tf_import_flags, 2); auto* op = new ResizeBilinearOperator; @@ -1219,9 +1247,10 @@ void ConvertResizeBilinearOperator(const NodeDef& node, op->inputs.push_back(node.input(1)); op->outputs.push_back(node.name()); model->operators.emplace_back(op); + return tensorflow::Status::OK(); } -void ConvertBatchNormWithGlobalNormalizationOperator( +tensorflow::Status ConvertBatchNormWithGlobalNormalizationOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { CHECK_EQ(node.op(), "BatchNormWithGlobalNormalization"); @@ -1268,11 +1297,12 @@ void ConvertBatchNormWithGlobalNormalizationOperator( op->outputs.push_back(node.name()); model->operators.emplace_back(op); + return tensorflow::Status::OK(); } -void ConvertFusedBatchNormOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertFusedBatchNormOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "FusedBatchNorm"); CheckInputsCount(node, tf_import_flags, 5); @@ -1320,11 +1350,12 @@ void ConvertFusedBatchNormOperator(const NodeDef& node, op->outputs.push_back(node.name()); model->operators.emplace_back(op); + return tensorflow::Status::OK(); } -void ConvertSpaceToBatchNDOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertSpaceToBatchNDOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "SpaceToBatchND"); CheckInputsCount(node, tf_import_flags, 3); CHECK_EQ(GetDataTypeAttr(node, "Tblock_shape"), DT_INT32); @@ -1335,11 +1366,12 @@ void ConvertSpaceToBatchNDOperator(const NodeDef& node, op->inputs.push_back(node.input(2)); op->outputs.push_back(node.name()); model->operators.emplace_back(op); + return tensorflow::Status::OK(); } -void ConvertBatchToSpaceNDOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertBatchToSpaceNDOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "BatchToSpaceND"); CheckInputsCount(node, tf_import_flags, 3); CHECK_EQ(GetDataTypeAttr(node, "Tblock_shape"), DT_INT32); @@ -1350,11 +1382,12 @@ void ConvertBatchToSpaceNDOperator(const NodeDef& node, op->inputs.push_back(node.input(2)); op->outputs.push_back(node.name()); model->operators.emplace_back(op); + return tensorflow::Status::OK(); } -void ConvertMeanOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertMeanOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "Mean"); CheckInputsCount(node, tf_import_flags, 2); auto* op = new MeanOperator; @@ -1367,11 +1400,12 @@ void ConvertMeanOperator(const NodeDef& node, } else if (HasAttr(node, "keep_dims")) { op->keep_dims = GetBoolAttr(node, "keep_dims"); } + return tensorflow::Status::OK(); } -void ConvertSvdfOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertSvdfOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "Svdf"); const int input_size = GetInputsCount(node, tf_import_flags); QCHECK(input_size == 3 || input_size == 4) @@ -1394,12 +1428,13 @@ void ConvertSvdfOperator(const NodeDef& node, } op->rank = node.attr().at("Rank").i(); model->operators.emplace_back(op); + return tensorflow::Status::OK(); } // This is just bare bones support to get the shapes to propagate. -void ConvertTransposeConvOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertTransposeConvOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "Conv2DBackpropInput"); CheckInputsCount(node, tf_import_flags, 3); auto* op = new TransposeConvOperator; @@ -1465,12 +1500,12 @@ void ConvertTransposeConvOperator(const NodeDef& node, "Conv2DBackpropInput nodes."; } model->operators.emplace_back(op); + return tensorflow::Status::OK(); } - -void ConvertRangeOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertRangeOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "Range"); CheckInputsCount(node, tf_import_flags, 3); auto* op = new RangeOperator; @@ -1485,11 +1520,12 @@ void ConvertRangeOperator(const NodeDef& node, op->inputs.push_back(node.input(2)); op->outputs.push_back(node.name()); model->operators.emplace_back(op); + return tensorflow::Status::OK(); } -void ConvertStackOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertStackOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK((node.op() == "Stack") || (node.op() == "Pack")); auto* op = new StackOperator; const int num_inputs = GetInputsCount(node, tf_import_flags); @@ -1505,9 +1541,9 @@ void ConvertStackOperator(const NodeDef& node, op->axis = HasAttr(node, "axis") ? GetIntAttr(node, "axis") : 0; op->outputs.push_back(node.name()); model->operators.emplace_back(op); + return tensorflow::Status::OK(); } - // Some TensorFlow ops only occur in graph cycles, representing // control flow. We do not currently support control flow, so we wouldn't // be able to fully support such graphs, including performing inference, @@ -1518,7 +1554,7 @@ void ConvertStackOperator(const NodeDef& node, // such ops as RNN back-edges, which is technically incorrect (does not // allow representing the op's semantics) but good enough to get a // graph visualization. -void ConvertOperatorSpecialCasedAsRNNBackEdge( +tensorflow::Status ConvertOperatorSpecialCasedAsRNNBackEdge( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { // At the moment, the only type of operator special-cased in this way is @@ -1531,6 +1567,7 @@ void ConvertOperatorSpecialCasedAsRNNBackEdge( rnn_state->set_discardable(true); rnn_state->set_state_array(node.name()); rnn_state->set_back_edge_source_array(node.input(0)); + return tensorflow::Status::OK(); } void StripCaretFromArrayNames(Model* model) { @@ -1673,9 +1710,9 @@ bool InlineAllFunctions(GraphDef* graphdef) { return graph_modified; } -void ConvertTopKV2Operator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertTopKV2Operator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK((node.op() == "TopK") || (node.op() == "TopKV2")); auto op = absl::make_unique(); op->inputs.push_back(node.input(0)); @@ -1692,9 +1729,10 @@ void ConvertTopKV2Operator(const NodeDef& node, op->outputs.push_back(node.name()); op->outputs.push_back(node.name() + ":1"); model->operators.emplace_back(op.release()); + return tensorflow::Status::OK(); } -void ConvertDynamicPartitionOperator( +tensorflow::Status ConvertDynamicPartitionOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { auto op = absl::make_unique(); @@ -1709,11 +1747,12 @@ void ConvertDynamicPartitionOperator( op->outputs.push_back(node.name() + ":" + std::to_string(i)); } model->operators.emplace_back(op.release()); + return tensorflow::Status::OK(); } -void ConvertDynamicStitchOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertDynamicStitchOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { // The parallel and non-parallel variants are the same besides whether they // have a parallel loop; there are no behavioral differences. CHECK(node.op() == "DynamicStitch" || node.op() == "ParallelDynamicStitch"); @@ -1727,11 +1766,12 @@ void ConvertDynamicStitchOperator(const NodeDef& node, } op->outputs.push_back(node.name()); model->operators.emplace_back(op.release()); + return tensorflow::Status::OK(); } -void ConvertSparseToDenseOperator(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - Model* model) { +tensorflow::Status ConvertSparseToDenseOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { CHECK_EQ(node.op(), "SparseToDense"); CheckInputsCount(node, tf_import_flags, 4); @@ -1745,217 +1785,132 @@ void ConvertSparseToDenseOperator(const NodeDef& node, ? GetBoolAttr(node, "validate_indices") : true; model->operators.emplace_back(op); + return tensorflow::Status::OK(); } } // namespace namespace internal { + +using ConverterType = tensorflow::Status (*)( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model); +using ConverterMapType = std::unordered_map; + +ConverterMapType GetTensorFlowNodeConverterMap() { + return std::unordered_map({ + {"Add", ConvertSimpleOperator}, + {"AddN", ConvertSimpleOperator}, + {"All", ConvertSimpleOperator}, + {"ArgMax", ConvertArgMaxOperator}, + {"Assert", ConvertSimpleOperator}, + {"AvgPool", ConvertAvgPoolOperator}, + {"BatchMatMul", ConvertBatchMatMulOperator}, + {"BatchNormWithGlobalNormalization", + ConvertBatchNormWithGlobalNormalizationOperator}, + {"BatchToSpaceND", ConvertBatchToSpaceNDOperator}, + {"BiasAdd", ConvertBiasAddOperator}, + {"Cast", ConvertCastOperator}, + {"CheckNumerics", ConvertIdentityOperator}, + {"Concat", ConvertConcatOperator}, + {"ConcatV2", ConvertConcatOperator}, + {"Const", ConvertConstOperator}, + {"Conv2D", ConvertConvOperator}, + {"Conv2DBackpropInput", ConvertTransposeConvOperator}, + {"DepthToSpace", ConvertDepthToSpaceOperator}, + {"DepthwiseConv2dNative", ConvertDepthwiseConvOperator}, + {"Div", ConvertSimpleOperator}, + {"DynamicPartition", ConvertDynamicPartitionOperator}, + {"DynamicStitch", ConvertDynamicStitchOperator}, + {"Equal", ConvertSimpleOperator}, + {"Exp", ConvertSimpleOperator}, + {"ExpandDims", ConvertSimpleOperator}, + {"FakeQuantWithMinMaxArgs", ConvertFakeQuantWithMinMaxArgs}, + {"FakeQuantWithMinMaxVars", ConvertFakeQuantWithMinMaxVars}, + {"Fill", ConvertSimpleOperator}, + {"Floor", ConvertFloorOperator}, + {"FloorDiv", ConvertSimpleOperator}, + {"FloorMod", ConvertSimpleOperator}, + {"FusedBatchNorm", ConvertFusedBatchNormOperator}, + {"Gather", ConvertGatherOperator}, + {"GatherV2", ConvertGatherOperator}, + {"Greater", ConvertSimpleOperator}, + {"GreaterEqual", + ConvertSimpleOperator}, + {"Identity", ConvertIdentityOperator}, + {"LRN", ConvertLRNOperator}, + {"LegacyFedInput", ConvertPlaceholderOperator}, + {"Less", ConvertSimpleOperator}, + {"LessEqual", ConvertSimpleOperator}, + {"Log", ConvertSimpleOperator}, + {"Log", ConvertSimpleOperator}, + {"LogSoftmax", ConvertSimpleOperator}, + {"MatMul", ConvertMatMulOperator}, + {"Max", ConvertMaxOperator}, + {"MaxPool", ConvertMaxPoolOperator}, + {"Maximum", ConvertSimpleOperator}, + {"Mean", ConvertMeanOperator}, + {"Merge", ConvertSimpleOperator}, + {"Min", ConvertMinOperator}, + {"Minimum", ConvertSimpleOperator}, + {"Mul", ConvertSimpleOperator}, + {"Neg", ConvertSimpleOperator}, + {"NextIteration", ConvertOperatorSpecialCasedAsRNNBackEdge}, + {"NoOp", ConvertNoOpOperator}, + {"NotEqual", ConvertSimpleOperator}, + {"Pack", ConvertStackOperator}, + {"Pad", ConvertSimpleOperator}, + {"PadV2", ConvertSimpleOperator}, + {"ParallelDynamicStitch", ConvertDynamicStitchOperator}, + {"Placeholder", ConvertPlaceholderOperator}, + {"PlaceholderWithDefault", ConvertIdentityOperator}, + {"RandomUniform", ConvertRandomUniform}, + {"Range", ConvertRangeOperator}, + {"Rank", ConvertSimpleOperator}, + {"RealDiv", ConvertSimpleOperator}, + {"Relu", ConvertSimpleOperator}, + {"Relu6", ConvertSimpleOperator}, + {"Reshape", ConvertSimpleOperator}, + {"ResizeBilinear", ConvertResizeBilinearOperator}, + {"Rsqrt", ConvertSimpleOperator}, + {"Select", ConvertSimpleOperator}, + {"Shape", ConvertSimpleOperator}, + {"Sigmoid", ConvertSimpleOperator}, + {"Sin", ConvertSimpleOperator}, + {"Slice", ConvertSimpleOperator}, + {"Softmax", ConvertSoftmaxOperator}, + {"SpaceToBatchND", ConvertSpaceToBatchNDOperator}, + {"SpaceToDepth", ConvertSpaceToDepthOperator}, + {"SparseToDense", ConvertSparseToDenseOperator}, + {"Split", ConvertSplitOperator}, + {"Sqrt", ConvertSimpleOperator}, + {"Square", ConvertSimpleOperator}, + {"Squeeze", ConvertSqueezeOperator}, + {"Stack", ConvertStackOperator}, + {"StopGradient", ConvertIdentityOperator}, + {"StridedSlice", ConvertStridedSliceOperator}, + {"Sub", ConvertSimpleOperator}, + {"Sum", ConvertSumOperator}, + {"Svdf", ConvertSvdfOperator}, + {"Switch", ConvertSwitchOperator}, + {"Tanh", ConvertSimpleOperator}, + {"Tile", ConvertSimpleOperator}, + {"TopK", ConvertTopKV2Operator}, + {"TopKV2", ConvertTopKV2Operator}, + {"Transpose", ConvertSimpleOperator}, + }); +} + tensorflow::Status ImportTensorFlowNode( const tensorflow::NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, Model* model) { - // TODO(ahentz): Historically these functions all CHECK-fail on error. We've - // been slowly converting them to return Status. - if (node.op() == "Const") { - return ConvertConstOperator(node, tf_import_flags, model); - } else if (node.op() == "Conv2D") { - return ConvertConvOperator(node, tf_import_flags, model); - } else if (node.op() == "Conv2DBackpropInput") { - ConvertTransposeConvOperator(node, tf_import_flags, model); - } else if (node.op() == "DepthwiseConv2dNative") { - ConvertDepthwiseConvOperator(node, tf_import_flags, model); - } else if (node.op() == "DepthToSpace") { - ConvertDepthToSpaceOperator(node, tf_import_flags, model); - } else if (node.op() == "SpaceToDepth") { - ConvertSpaceToDepthOperator(node, tf_import_flags, model); - } else if (node.op() == "BiasAdd") { - ConvertBiasAddOperator(node, tf_import_flags, model); - } else if (node.op() == "Relu") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "Relu6") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "Sigmoid") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "Tanh") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "MaxPool") { - ConvertMaxPoolOperator(node, tf_import_flags, model); - } else if (node.op() == "AvgPool") { - ConvertAvgPoolOperator(node, tf_import_flags, model); - } else if (node.op() == "Reshape") { - ConvertSimpleOperator(node, tf_import_flags, - model); - } else if (node.op() == "BatchMatMul") { - ConvertBatchMatMulOperator(node, tf_import_flags, model); - } else if (node.op() == "MatMul") { - ConvertMatMulOperator(node, tf_import_flags, model); - } else if (node.op() == "Div" || node.op() == "RealDiv") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "Identity" || node.op() == "CheckNumerics" || - node.op() == "StopGradient") { - ConvertIdentityOperator(node, tf_import_flags, model); - } else if (node.op() == "FakeQuantWithMinMaxVars") { - ConvertFakeQuantWithMinMaxVars(node, tf_import_flags, model); - } else if (node.op() == "FakeQuantWithMinMaxArgs") { - ConvertFakeQuantWithMinMaxArgs(node, tf_import_flags, model); - } else if (node.op() == "Neg") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "Rsqrt") { - ConvertSimpleOperator(node, tf_import_flags, - model); - } else if (node.op() == "Squeeze") { - ConvertSqueezeOperator(node, tf_import_flags, model); - } else if (node.op() == "Sqrt") { - ConvertSimpleOperator(node, tf_import_flags, - model); - } else if (node.op() == "Square") { - ConvertSimpleOperator(node, tf_import_flags, - model); - } else if (node.op() == "Add") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "AddN") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "Mul") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "Sub") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "Sum") { - ConvertSumOperator(node, tf_import_flags, model); - } else if (node.op() == "Tile") { - ConvertSimpleOperator(node, tf_import_flags, - model); - } else if (node.op() == "Concat" || node.op() == "ConcatV2") { - ConvertConcatOperator(node, tf_import_flags, model); - } else if (node.op() == "LRN") { - ConvertLRNOperator(node, tf_import_flags, model); - } else if (node.op() == "Softmax") { - ConvertSoftmaxOperator(node, tf_import_flags, model); - } else if (node.op() == "Log") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "LogSoftmax") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "All") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "Assert") { - ConvertSimpleOperator(node, tf_import_flags, - model); - } else if (node.op() == "Less") { - ConvertSimpleOperator(node, tf_import_flags, - model); - } else if (node.op() == "LessEqual") { - ConvertSimpleOperator(node, tf_import_flags, - model); - } else if (node.op() == "Greater") { - ConvertSimpleOperator(node, tf_import_flags, - model); - } else if (node.op() == "GreaterEqual") { - ConvertSimpleOperator( - node, tf_import_flags, model); - } else if (node.op() == "Max") { - ConvertMaxOperator(node, tf_import_flags, model); - } else if (node.op() == "Min") { - ConvertMinOperator(node, tf_import_flags, model); - } else if (node.op() == "Maximum") { - ConvertSimpleOperator(node, tf_import_flags, - model); - } else if (node.op() == "Minimum") { - ConvertSimpleOperator(node, tf_import_flags, - model); - } else if (node.op() == "Merge") { - ConvertSimpleOperator(node, tf_import_flags, - model); - } else if (node.op() == "Pad") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "PadV2") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "StridedSlice") { - ConvertStridedSliceOperator(node, tf_import_flags, model); - } else if (node.op() == "Shape") { - ConvertSimpleOperator(node, tf_import_flags, - model); - } else if (node.op() == "Slice") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "Split") { - ConvertSplitOperator(node, tf_import_flags, model); - } else if (node.op() == "Switch") { - ConvertSwitchOperator(node, tf_import_flags, model); - } else if (node.op() == "Placeholder") { - ConvertPlaceholderOperator(node, tf_import_flags, model); - } else if (node.op() == "PlaceholderWithDefault") { - ConvertIdentityOperator(node, tf_import_flags, model); - } else if (node.op() == "LegacyFedInput") { - ConvertPlaceholderOperator(node, tf_import_flags, model); - } else if (node.op() == "NoOp") { - ConvertNoOpOperator(node, tf_import_flags, model); - } else if (node.op() == "Cast") { - ConvertCastOperator(node, tf_import_flags, model); - } else if (node.op() == "Floor") { - ConvertFloorOperator(node, tf_import_flags, model); - } else if (node.op() == "Gather" || node.op() == "GatherV2") { - ConvertGatherOperator(node, tf_import_flags, model); - } else if (node.op() == "ResizeBilinear") { - ConvertResizeBilinearOperator(node, tf_import_flags, model); - } else if (node.op() == "BatchNormWithGlobalNormalization") { - ConvertBatchNormWithGlobalNormalizationOperator(node, tf_import_flags, - model); - } else if (node.op() == "FusedBatchNorm") { - ConvertFusedBatchNormOperator(node, tf_import_flags, model); - } else if (node.op() == "SpaceToBatchND") { - ConvertSpaceToBatchNDOperator(node, tf_import_flags, model); - } else if (node.op() == "BatchToSpaceND") { - ConvertBatchToSpaceNDOperator(node, tf_import_flags, model); - } else if (node.op() == "Mean") { - ConvertMeanOperator(node, tf_import_flags, model); - } else if (node.op() == "Svdf") { - ConvertSvdfOperator(node, tf_import_flags, model); - } else if (node.op() == "NextIteration") { - ConvertOperatorSpecialCasedAsRNNBackEdge(node, tf_import_flags, model); - } else if (node.op() == "ExpandDims") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "Fill") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "FloorDiv") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "FloorMod") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "Range") { - ConvertRangeOperator(node, tf_import_flags, model); - } else if (node.op() == "Rank") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "Stack" || node.op() == "Pack") { - ConvertStackOperator(node, tf_import_flags, model); - } else if (node.op() == "Transpose") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "ArgMax") { - ConvertArgMaxOperator(node, tf_import_flags, model); - } else if (node.op() == "Exp") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "TopK" || node.op() == "TopKV2") { - ConvertTopKV2Operator(node, tf_import_flags, model); - } else if (node.op() == "DynamicPartition") { - ConvertDynamicPartitionOperator(node, tf_import_flags, model); - } else if (node.op() == "DynamicStitch" || - node.op() == "ParallelDynamicStitch") { - ConvertDynamicStitchOperator(node, tf_import_flags, model); - } else if (node.op() == "RandomUniform") { - ConvertRandomUniform(node, tf_import_flags, model); - } else if (node.op() == "Sin") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "Log") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "Select") { - ConvertSimpleOperator(node, tf_import_flags, model); - } else if (node.op() == "SparseToDense") { - ConvertSparseToDenseOperator(node, tf_import_flags, model); - } else if (node.op() == "Equal") { - ConvertSimpleOperator(node, tf_import_flags, - model); - } else if (node.op() == "NotEqual") { - ConvertSimpleOperator(node, tf_import_flags, - model); + const TensorFlowImportFlags& tf_import_flags, Model* model, + const ConverterMapType& converter_map) { + auto converter = converter_map.find(node.op()); + if (converter == converter_map.end()) { + return ConvertUnsupportedOperator(node, tf_import_flags, model); } else { - ConvertUnsupportedOperator(node, tf_import_flags, model); + return converter->second(node, tf_import_flags, model); } - return tensorflow::Status::OK(); } } // namespace internal @@ -1981,10 +1936,13 @@ std::unique_ptr ImportTensorFlowGraphDef( } Model* model = new Model; + const internal::ConverterMapType& converter_map = + internal::GetTensorFlowNodeConverterMap(); for (auto node : inlined_graph.node()) { StripZeroOutputIndexFromInputs(&node); - auto status = internal::ImportTensorFlowNode(node, tf_import_flags, model); + auto status = internal::ImportTensorFlowNode(node, tf_import_flags, model, + converter_map); CHECK(status.ok()) << status.error_message(); } diff --git a/tensorflow/contrib/lite/toco/import_tensorflow_test.cc b/tensorflow/contrib/lite/toco/import_tensorflow_test.cc index d18c329a43..90e6f698ef 100644 --- a/tensorflow/contrib/lite/toco/import_tensorflow_test.cc +++ b/tensorflow/contrib/lite/toco/import_tensorflow_test.cc @@ -36,8 +36,14 @@ using tensorflow::NodeDef; using tensorflow::Status; namespace internal { +using ConverterType = tensorflow::Status (*)( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model); +using ConverterMapType = std::unordered_map; + +ConverterMapType GetTensorFlowNodeConverterMap(); Status ImportTensorFlowNode(const NodeDef&, const TensorFlowImportFlags&, - Model*); + Model*, const ConverterMapType&); } // namespace internal namespace { @@ -105,8 +111,9 @@ class ShapeImportTest : public ::testing::TestWithParam { Status ImportNode(const NodeDef& node) { Model model; - return internal::ImportTensorFlowNode(node, TensorFlowImportFlags(), - &model); + const auto converter = internal::GetTensorFlowNodeConverterMap(); + return internal::ImportTensorFlowNode(node, TensorFlowImportFlags(), &model, + converter); } }; -- GitLab From 8f255771c0ead16149fb003a9da45ff7346159d3 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Sun, 17 Jun 2018 06:51:58 -0700 Subject: [PATCH 571/816] Implement reduce_sum PiperOrigin-RevId: 200895985 --- tensorflow/contrib/lite/build_def.bzl | 1 + tensorflow/contrib/lite/builtin_op_data.h | 2 +- tensorflow/contrib/lite/builtin_ops.h | 1 + tensorflow/contrib/lite/kernels/BUILD | 6 +- .../internal/reference/reference_ops.h | 46 ++++- .../lite/kernels/{mean.cc => reduce.cc} | 109 +++++++++-- .../kernels/{mean_test.cc => reduce_test.cc} | 178 +++++++++++++++++- tensorflow/contrib/lite/kernels/register.cc | 2 + tensorflow/contrib/lite/model.cc | 7 +- tensorflow/contrib/lite/nnapi_delegate.cc | 3 +- tensorflow/contrib/lite/schema/schema.fbs | 5 +- .../contrib/lite/schema/schema_generated.h | 115 +++++------ .../contrib/lite/testing/generate_examples.py | 6 + tensorflow/contrib/lite/toco/model.h | 6 +- .../contrib/lite/toco/tflite/operator.cc | 27 ++- 15 files changed, 403 insertions(+), 111 deletions(-) rename tensorflow/contrib/lite/kernels/{mean.cc => reduce.cc} (72%) rename tensorflow/contrib/lite/kernels/{mean_test.cc => reduce_test.cc} (53%) diff --git a/tensorflow/contrib/lite/build_def.bzl b/tensorflow/contrib/lite/build_def.bzl index 612813caee..62e35b90ee 100644 --- a/tensorflow/contrib/lite/build_def.bzl +++ b/tensorflow/contrib/lite/build_def.bzl @@ -214,6 +214,7 @@ def generated_test_models(): "global_batch_norm", "greater", "greater_equal", + "sum", "l2norm", "l2_pool", "less", diff --git a/tensorflow/contrib/lite/builtin_op_data.h b/tensorflow/contrib/lite/builtin_op_data.h index c1cc4476fb..ad547c67e6 100644 --- a/tensorflow/contrib/lite/builtin_op_data.h +++ b/tensorflow/contrib/lite/builtin_op_data.h @@ -215,7 +215,7 @@ typedef struct { typedef struct { bool keep_dims; -} TfLiteMeanParams; +} TfLiteReducerParams; typedef struct { int num_splits; diff --git a/tensorflow/contrib/lite/builtin_ops.h b/tensorflow/contrib/lite/builtin_ops.h index aef9a92883..4fedd871bd 100644 --- a/tensorflow/contrib/lite/builtin_ops.h +++ b/tensorflow/contrib/lite/builtin_ops.h @@ -99,6 +99,7 @@ typedef enum { kTfLiteBuiltinEqual = 71, kTfLiteBuiltinNotEqual = 72, kTfLiteBuiltinLog = 73, + kTfLiteBuiltinSum = 74, } TfLiteBuiltinOperator; #ifdef __cplusplus diff --git a/tensorflow/contrib/lite/kernels/BUILD b/tensorflow/contrib/lite/kernels/BUILD index 0b70c8ffa3..c0b5a07703 100644 --- a/tensorflow/contrib/lite/kernels/BUILD +++ b/tensorflow/contrib/lite/kernels/BUILD @@ -157,12 +157,12 @@ cc_library( "lsh_projection.cc", "lstm.cc", "maximum_minimum.cc", - "mean.cc", "mfcc.cc", "mul.cc", "neg.cc", "pad.cc", "pooling.cc", + "reduce.cc", "register.cc", "reshape.cc", "resize_bilinear.cc", @@ -569,9 +569,9 @@ tf_cc_test( ) tf_cc_test( - name = "mean_test", + name = "reduce_test", size = "small", - srcs = ["mean_test.cc"], + srcs = ["reduce_test.cc"], tags = ["tflite_not_portable_ios"], deps = [ ":builtin_ops", diff --git a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h index febd9c5fbc..a2f192bbc2 100644 --- a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h @@ -3524,8 +3524,6 @@ inline void Exp(const T* input_data, const size_t num_elements, } // A generic reduce method that can be used for reduce_sum, reduce_mean, etc. -// It takes a reducer function as input and returns false when numeric overflow -// is detected. // This method iterates through input data and reduce elements along the // dimensions given in axis. template @@ -3533,8 +3531,7 @@ inline bool Reduce(const In* input_data, const int* input_dims, const int* output_dims, const int input_num_dims, const int output_num_dims, const int* axis, const int num_axis, int* input_iter, - Out reducer(Out current, const In in, bool* overflow), - Out* output_data) { + Out reducer(Out current, const In in), Out* output_data) { // Reset input iterator. TFLITE_DCHECK(input_num_dims > 0); for (int idx = 0; idx < input_num_dims; ++idx) { @@ -3546,10 +3543,8 @@ inline bool Reduce(const In* input_data, const int* input_dims, ReducedOutputOffset(input_num_dims, input_dims, input_iter, 0, nullptr); size_t output_offset = ReducedOutputOffset(input_num_dims, input_dims, input_iter, num_axis, axis); - bool overflow = false; - output_data[output_offset] = reducer(output_data[output_offset], - input_data[input_offset], &overflow); - if (overflow) return false; + output_data[output_offset] = + reducer(output_data[output_offset], input_data[input_offset]); } while (NextIndex(input_num_dims, input_dims, input_iter)); return true; } @@ -3584,7 +3579,7 @@ inline bool ReduceSumImpl(const In* input_data, const int* input_dims, const int output_num_dims, const int* axis, const int num_axis, int* input_iter, Out* output_data) { - auto reducer = [](Out current, const In in, bool* overflow) -> Out { + auto reducer = [](Out current, const In in) -> Out { const Out actual_in = static_cast(in); return current + actual_in; }; @@ -3593,6 +3588,39 @@ inline bool ReduceSumImpl(const In* input_data, const int* input_dims, output_data); } +// Computes the sum of elements across dimensions given in axis. +template +inline bool Sum(const T* input_data, const int* input_dims, + const int input_num_dims, T* output_data, + const int* output_dims, const int output_num_dims, + const int* axis, const int num_axis_dimensions, bool keep_dims, + int* temp_index, int* resolved_axis) { + // Reset output data. + size_t num_outputs = 1; + for (int idx = 0; idx < output_num_dims; ++idx) { + size_t current = static_cast(output_dims[idx]); + // Overflow prevention. + if (num_outputs > std::numeric_limits::max() / current) { + return false; + } + num_outputs *= current; + } + for (size_t idx = 0; idx < num_outputs; ++idx) { + output_data[idx] = T(); + } + + // Resolve axis. + int num_resolved_axis = 0; + if (!ResolveAxis(input_num_dims, axis, num_axis_dimensions, resolved_axis, + &num_resolved_axis)) { + return false; + } + + return ReduceSumImpl(input_data, input_dims, output_dims, + input_num_dims, output_num_dims, resolved_axis, + num_resolved_axis, temp_index, output_data); +} + // Computes the mean of elements across dimensions given in axis. // It does so in two stages, first calculates the sum of elements along the axis // then divides it by the number of element in axis. diff --git a/tensorflow/contrib/lite/kernels/mean.cc b/tensorflow/contrib/lite/kernels/reduce.cc similarity index 72% rename from tensorflow/contrib/lite/kernels/mean.cc rename to tensorflow/contrib/lite/kernels/reduce.cc index 03e5db24de..31c331a8c6 100644 --- a/tensorflow/contrib/lite/kernels/mean.cc +++ b/tensorflow/contrib/lite/kernels/reduce.cc @@ -25,21 +25,21 @@ limitations under the License. namespace tflite { namespace ops { namespace builtin { -namespace mean { +namespace reduce { -// This file has reference implementation of Mean. +// This file has reference implementation of reduce_* operators. enum KernelType { kReference, }; -struct MeanContext { - MeanContext(TfLiteContext* context, TfLiteNode* node) { - params = reinterpret_cast(node->builtin_data); +struct OpContext { + OpContext(TfLiteContext* context, TfLiteNode* node) { + params = reinterpret_cast(node->builtin_data); input = GetInput(context, node, 0); axis = GetInput(context, node, 1); output = GetOutput(context, node, 0); } - TfLiteMeanParams* params; + TfLiteReducerParams* params; const TfLiteTensor* input; const TfLiteTensor* axis; TfLiteTensor* output; @@ -58,7 +58,7 @@ void Free(TfLiteContext* context, void* buffer) { } // Resizes the temp tensor that stores resolved axis. -TfLiteStatus ResizeTempAxis(TfLiteContext* context, MeanContext* op_context, +TfLiteStatus ResizeTempAxis(TfLiteContext* context, OpContext* op_context, TfLiteTensor* resolved_axis) { TfLiteIntArray* axis_size = TfLiteIntArrayCreate(1); axis_size->data[0] = static_cast(NumElements(op_context->axis)); @@ -66,7 +66,7 @@ TfLiteStatus ResizeTempAxis(TfLiteContext* context, MeanContext* op_context, } // Resizes the temp tensor that stores temp sum of reduced elements. -TfLiteStatus ResizeTempSum(TfLiteContext* context, MeanContext* op_context, +TfLiteStatus ResizeTempSum(TfLiteContext* context, OpContext* op_context, TfLiteTensor* temp_sum) { TfLiteIntArray* size = TfLiteIntArrayCreate(1); size->data[0] = static_cast(NumElements(op_context->output)); @@ -74,8 +74,7 @@ TfLiteStatus ResizeTempSum(TfLiteContext* context, MeanContext* op_context, } // Resizes output array based on the input size and resolved axis. -TfLiteStatus ResizeOutputTensor(TfLiteContext* context, - MeanContext* op_context) { +TfLiteStatus ResizeOutputTensor(TfLiteContext* context, OpContext* op_context) { size_t num_axis = NumElements(op_context->axis); const TfLiteIntArray* input_dims = op_context->input->dims; int input_num_dims = NumDimensions(op_context->input); @@ -140,7 +139,7 @@ TfLiteStatus ResizeOutputTensor(TfLiteContext* context, // Initializes temp tensors to store index and resolved axis. TfLiteStatus InitializeTemporaries(TfLiteContext* context, TfLiteNode* node, - MeanContext* op_context) { + OpContext* op_context) { // Creates a temp index to iterate through input data. int* scratch_tensor_index = reinterpret_cast(node->user_data); TfLiteIntArrayFree(node->temporaries); @@ -180,33 +179,44 @@ TfLiteStatus InitializeTemporaries(TfLiteContext* context, TfLiteNode* node, return kTfLiteOk; } -TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { +TfLiteStatus PrepareSimple(TfLiteContext* context, TfLiteNode* node) { TF_LITE_ENSURE_EQ(context, NumInputs(node), 2); TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); - MeanContext op_context(context, node); + OpContext op_context(context, node); TF_LITE_ENSURE_OK(context, InitializeTemporaries(context, node, &op_context)); TfLiteTensor* resolved_axis = GetTemporary(context, node, /*index=*/1); - TfLiteTensor* temp_sum = GetTemporary(context, node, /*index=*/2); // Leaves work to Eval if axis is not constant; else resizes output. if (!IsConstantTensor(op_context.axis)) { SetTensorToDynamic(op_context.output); SetTensorToDynamic(resolved_axis); - SetTensorToDynamic(temp_sum); return kTfLiteOk; } resolved_axis->allocation_type = kTfLiteArenaRw; TF_LITE_ENSURE_OK(context, ResizeTempAxis(context, &op_context, resolved_axis)); TF_LITE_ENSURE_OK(context, ResizeOutputTensor(context, &op_context)); + return kTfLiteOk; +} + +TfLiteStatus PrepareMean(TfLiteContext* context, TfLiteNode* node) { + TF_LITE_ENSURE_OK(context, PrepareSimple(context, node)); + + // reduce_mean requires a buffer to store intermediate sum result. + OpContext op_context(context, node); + TfLiteTensor* temp_sum = GetTemporary(context, node, /*index=*/2); + if (!IsConstantTensor(op_context.axis)) { + SetTensorToDynamic(temp_sum); + return kTfLiteOk; + } temp_sum->allocation_type = kTfLiteArenaRw; return ResizeTempSum(context, &op_context, temp_sum); } template -TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { - MeanContext op_context(context, node); +TfLiteStatus EvalMean(TfLiteContext* context, TfLiteNode* node) { + OpContext op_context(context, node); int num_axis = static_cast(NumElements(op_context.axis)); TfLiteTensor* temp_index = GetTemporary(context, node, /*index=*/0); TfLiteTensor* resolved_axis = GetTemporary(context, node, /*index=*/1); @@ -255,16 +265,75 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { #undef TF_LITE_MEAN return kTfLiteOk; } -} // namespace mean + +template +TfLiteStatus EvalSum(TfLiteContext* context, TfLiteNode* node) { + OpContext op_context(context, node); + int num_axis = static_cast(NumElements(op_context.axis)); + TfLiteTensor* temp_index = GetTemporary(context, node, /*index=*/0); + TfLiteTensor* resolved_axis = GetTemporary(context, node, /*index=*/1); + // Resize the output tensor if the output tensor is dynamic. + if (IsDynamicTensor(op_context.output)) { + TF_LITE_ENSURE_OK(context, + ResizeTempAxis(context, &op_context, resolved_axis)); + TF_LITE_ENSURE_OK(context, ResizeOutputTensor(context, &op_context)); + } + +#define TF_LITE_SUM(kernel_type, data_type) \ + kernel_type::Sum<>( \ + GetTensorData(op_context.input), \ + op_context.input->dims->data, op_context.input->dims->size, \ + GetTensorData(op_context.output), \ + op_context.output->dims->data, op_context.output->dims->size, \ + GetTensorData(op_context.axis), num_axis, \ + op_context.params->keep_dims, GetTensorData(temp_index), \ + GetTensorData(resolved_axis)) + + if (kernel_type == kReference) { + switch (op_context.input->type) { + case kTfLiteFloat32: + TF_LITE_ENSURE(context, TF_LITE_SUM(reference_ops, float)); + break; + case kTfLiteInt32: + TF_LITE_ENSURE(context, TF_LITE_SUM(reference_ops, int)); + break; + case kTfLiteInt64: + TF_LITE_ENSURE(context, TF_LITE_SUM(reference_ops, int64_t)); + break; + case kTfLiteUInt8: + TF_LITE_ENSURE_EQ(context, op_context.input->params.scale, + op_context.output->params.scale); + TF_LITE_ENSURE_EQ(context, op_context.input->params.zero_point, + op_context.output->params.zero_point); + TF_LITE_ENSURE(context, TF_LITE_SUM(reference_ops, uint8_t)); + break; + default: + return kTfLiteError; + } + } +#undef TF_LITE_SUM + return kTfLiteOk; +} + +} // namespace reduce TfLiteRegistration* Register_MEAN_REF() { - static TfLiteRegistration r = {mean::Init, mean::Free, mean::Prepare, - mean::Eval}; + static TfLiteRegistration r = {reduce::Init, reduce::Free, + reduce::PrepareMean, + reduce::EvalMean}; + return &r; +} + +TfLiteRegistration* Register_SUM_REF() { + static TfLiteRegistration r = {reduce::Init, reduce::Free, + reduce::PrepareSimple, + reduce::EvalSum}; return &r; } // TODO(kanlig): add optimized implementation of Mean. TfLiteRegistration* Register_MEAN() { return Register_MEAN_REF(); } +TfLiteRegistration* Register_SUM() { return Register_SUM_REF(); } } // namespace builtin } // namespace ops diff --git a/tensorflow/contrib/lite/kernels/mean_test.cc b/tensorflow/contrib/lite/kernels/reduce_test.cc similarity index 53% rename from tensorflow/contrib/lite/kernels/mean_test.cc rename to tensorflow/contrib/lite/kernels/reduce_test.cc index 79c9957f76..9e946822c6 100644 --- a/tensorflow/contrib/lite/kernels/mean_test.cc +++ b/tensorflow/contrib/lite/kernels/reduce_test.cc @@ -23,7 +23,7 @@ namespace { using ::testing::ElementsAreArray; -class BaseMeanOpModel : public SingleOpModel { +class BaseOpModel : public SingleOpModel { public: void SetAxis(std::initializer_list data) { PopulateTensor(axis_, data); } @@ -53,7 +53,7 @@ class BaseMeanOpModel : public SingleOpModel { }; // Model for the tests case where axis is a const tensor. -class MeanOpConstModel : public BaseMeanOpModel { +class MeanOpConstModel : public BaseOpModel { public: MeanOpConstModel(const TensorData& input, const TensorData& output, std::initializer_list axis_shape, @@ -61,26 +61,59 @@ class MeanOpConstModel : public BaseMeanOpModel { input_ = AddInput(input); axis_ = AddConstInput(TensorType_INT32, axis, axis_shape); output_ = AddOutput(output); - SetBuiltinOp(BuiltinOperator_MEAN, BuiltinOptions_MeanOptions, - CreateMeanOptions(builder_, keep_dims).Union()); + SetBuiltinOp(BuiltinOperator_MEAN, BuiltinOptions_ReducerOptions, + CreateReducerOptions(builder_, keep_dims).Union()); BuildInterpreter({GetShape(input_)}); } }; // Model for the tests case where axis is a dynamic tensor. -class MeanOpDynamicModel : public BaseMeanOpModel { +class MeanOpDynamicModel : public BaseOpModel { public: MeanOpDynamicModel(const TensorData& input, const TensorData& output, const TensorData& axis, bool keep_dims) { input_ = AddInput(input); axis_ = AddInput(axis); output_ = AddOutput(output); - SetBuiltinOp(BuiltinOperator_MEAN, BuiltinOptions_MeanOptions, - CreateMeanOptions(builder_, keep_dims).Union()); + SetBuiltinOp(BuiltinOperator_MEAN, BuiltinOptions_ReducerOptions, + CreateReducerOptions(builder_, keep_dims).Union()); BuildInterpreter({GetShape(input_)}); } }; +// Model for the tests case where axis is a const tensor. +class SumOpConstModel : public BaseOpModel { + public: + SumOpConstModel(const TensorData& input, const TensorData& output, + std::initializer_list axis_shape, + std::initializer_list axis, bool keep_dims) { + input_ = AddInput(input); + axis_ = AddConstInput(TensorType_INT32, axis, axis_shape); + output_ = AddOutput(output); + SetBuiltinOp(BuiltinOperator_SUM, BuiltinOptions_ReducerOptions, + CreateReducerOptions(builder_, keep_dims).Union()); + BuildInterpreter({GetShape(input_)}); + } +}; + +// Model for the tests case where axis is a dynamic tensor. +class SumOpDynamicModel : public BaseOpModel { + public: + SumOpDynamicModel(const TensorData& input, const TensorData& output, + const TensorData& axis, bool keep_dims) { + input_ = AddInput(input); + axis_ = AddInput(axis); + output_ = AddOutput(output); + SetBuiltinOp(BuiltinOperator_SUM, BuiltinOptions_ReducerOptions, + CreateReducerOptions(builder_, keep_dims).Union()); + BuildInterpreter({GetShape(input_)}); + } +}; + +// for quantized Add, the error shouldn't exceed step +float GetTolerance(int min, int max) { return (max - min) / 255.0; } + +// Tests for reduce_mean TEST(ConstFloatMeanOpTest, NotKeepDims) { std::initializer_list data = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, @@ -149,8 +182,6 @@ TEST(DynamicFloatMeanOpTest, Scale) { EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({9.527}))); } -// for quantized Add, the error shouldn't exceed step -float GetTolerance(int min, int max) { return (max - min) / 255.0; } TEST(ConstUint8MeanOpTest, NotKeepDims) { float kQuantizedTolerance = GetTolerance(-1.0, 1.0); @@ -209,6 +240,135 @@ TEST(DynamicUint8MeanOpTest, KeepDims) { ElementsAreArray(ArrayFloatNear({9.2815, 0.3695}, kQuantizedTolerance))); } +// Tests for reduce_sum + +TEST(ConstFloatSumOpTest, NotKeepDims) { + std::initializer_list data = { + 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, + 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; + SumOpConstModel m({TensorType_FLOAT32, {4, 3, 2}}, {TensorType_FLOAT32, {2}}, + {4}, {1, 0, -3, -3}, false); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2})); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray(ArrayFloatNear({144, 156}))); +} + +TEST(ConstFloatSumOpTest, KeepDims) { + std::initializer_list data = { + 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, + 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; + SumOpConstModel m({TensorType_FLOAT32, {4, 3, 2}}, {TensorType_FLOAT32, {3}}, + {2}, {0, 2}, true); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 3, 1})); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray(ArrayFloatNear({84, 100, 116}))); +} + +TEST(DynamicFloatSumOpTest, NotKeepDims) { + std::initializer_list data = { + 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, + 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; + SumOpDynamicModel m({TensorType_FLOAT32, {4, 3, 2}}, + {TensorType_FLOAT32, {2}}, {TensorType_INT32, {4}}, + false); + std::initializer_list axis = {1, 0, -3, -3}; + m.SetAxis(axis); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2})); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray(ArrayFloatNear({144, 156}))); +} + +TEST(DynamicFloatSumOpTest, KeepDims) { + std::initializer_list data = { + 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, + 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; + SumOpDynamicModel m({TensorType_FLOAT32, {4, 3, 2}}, + {TensorType_FLOAT32, {3}}, {TensorType_INT32, {2}}, true); + std::initializer_list axis = {0, 2}; + m.SetAxis(axis); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 3, 1})); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray(ArrayFloatNear({84, 100, 116}))); +} + +TEST(DynamicFloatSumOpTest, Scale) { + std::initializer_list data = {9.527}; + SumOpDynamicModel m({TensorType_FLOAT32, {1}}, {TensorType_FLOAT32, {1}}, + {TensorType_INT32, {1}}, true); + std::initializer_list axis = {0}; + m.SetAxis(axis); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1})); + EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({9.527}))); +} + +TEST(ConstUint8SumOpTest, NotKeepDims) { + float kQuantizedTolerance = GetTolerance(-1.0, 1.0); + std::initializer_list data = {0.4, 0.2, 0.3, 0.4, 0.5, 0.6}; + SumOpConstModel m({TensorType_UINT8, {1, 3, 2}, -1.0, 1.0}, + {TensorType_UINT8, {2}, -1.0, 1.0}, {1}, {1}, false); + m.QuantizeAndPopulate(m.Input(), data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 2})); + EXPECT_THAT(m.GetDequantizedOutput(), + ElementsAreArray( + ArrayFloatNear({-0.823529, -0.815686}, kQuantizedTolerance))); +} + +TEST(ConstUint8SumOpTest, KeepDims) { + float kQuantizedTolerance = GetTolerance(-1.0, 1.0); + std::initializer_list data = {0.4, 0.2, 0.3, 0.4, 0.5, 0.6}; + SumOpConstModel m({TensorType_UINT8, {3, 2}, -1.0, 1.0}, + {TensorType_UINT8, {3}, -1.0, 1.0}, {1}, {1}, true); + m.QuantizeAndPopulate(m.Input(), data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({3, 1})); + EXPECT_THAT(m.GetDequantizedOutput(), + ElementsAreArray(ArrayFloatNear({-0.407843, -0.313726, 0.0941177}, + kQuantizedTolerance))); +} + +TEST(DynamicUint8SumOpTest, NotKeepDims) { + float kQuantizedTolerance = GetTolerance(-5.0, 2.0); + std::initializer_list data = {1.3, -4.8, -3.6, 0.24}; + SumOpDynamicModel m({TensorType_UINT8, {2, 2}, -5.0, 2.0}, + {TensorType_UINT8, {2}, -5.0, 2.0}, + {TensorType_INT32, {1}}, false); + std::initializer_list axis = {1}; + m.SetAxis(axis); + m.QuantizeAndPopulate(m.Input(), data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2})); + EXPECT_THAT(m.GetDequantizedOutput(), + ElementsAreArray( + ArrayFloatNear({1.48235, 1.64706}, kQuantizedTolerance))); +} + +TEST(DynamicUint8SumOpTest, KeepDims) { + float kQuantizedTolerance = GetTolerance(-10.0, 12.0); + std::initializer_list data = {11.14, -0.14, 7.423, 0.879}; + SumOpDynamicModel m({TensorType_UINT8, {2, 2}, -10.0, 12.0}, + {TensorType_UINT8, {2}, -10.0, 12.0}, + {TensorType_INT32, {1}}, true); + std::initializer_list axis = {0}; + m.SetAxis(axis); + m.QuantizeAndPopulate(m.Input(), data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 2})); + EXPECT_THAT( + m.GetDequantizedOutput(), + ElementsAreArray(ArrayFloatNear({6.47059, 10.698}, kQuantizedTolerance))); +} + } // namespace } // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/register.cc b/tensorflow/contrib/lite/kernels/register.cc index 98f7250a40..718f91302c 100644 --- a/tensorflow/contrib/lite/kernels/register.cc +++ b/tensorflow/contrib/lite/kernels/register.cc @@ -89,6 +89,7 @@ TfLiteRegistration* Register_LESS_EQUAL(); TfLiteRegistration* Register_FLOOR(); TfLiteRegistration* Register_TILE(); TfLiteRegistration* Register_NEG(); +TfLiteRegistration* Register_SUM(); TfLiteRegistration* Register_SELECT(); TfLiteRegistration* Register_SLICE(); TfLiteRegistration* Register_SIN(); @@ -171,6 +172,7 @@ BuiltinOpResolver::BuiltinOpResolver() { AddBuiltin(BuiltinOperator_SIN, Register_SIN()); AddBuiltin(BuiltinOperator_TRANSPOSE_CONV, Register_TRANSPOSE_CONV()); AddBuiltin(BuiltinOperator_TILE, Register_TILE()); + AddBuiltin(BuiltinOperator_SUM, Register_SUM()); AddBuiltin(BuiltinOperator_EXPAND_DIMS, Register_EXPAND_DIMS()); AddBuiltin(BuiltinOperator_SPARSE_TO_DENSE, Register_SPARSE_TO_DENSE()); AddBuiltin(BuiltinOperator_EQUAL, Register_EQUAL()); diff --git a/tensorflow/contrib/lite/model.cc b/tensorflow/contrib/lite/model.cc index bc62e4cc2d..b9d100b7c9 100644 --- a/tensorflow/contrib/lite/model.cc +++ b/tensorflow/contrib/lite/model.cc @@ -597,9 +597,10 @@ TfLiteStatus ParseOpData(const Operator* op, BuiltinOperator op_type, *builtin_data = reinterpret_cast(params); break; } - case BuiltinOperator_MEAN: { - auto* params = MallocPOD(); - if (auto* schema_params = op->builtin_options_as_MeanOptions()) { + case BuiltinOperator_MEAN: + case BuiltinOperator_SUM: { + auto* params = MallocPOD(); + if (auto* schema_params = op->builtin_options_as_ReducerOptions()) { params->keep_dims = schema_params->keep_dims(); } *builtin_data = reinterpret_cast(params); diff --git a/tensorflow/contrib/lite/nnapi_delegate.cc b/tensorflow/contrib/lite/nnapi_delegate.cc index 999c31d4bf..8d506f562f 100644 --- a/tensorflow/contrib/lite/nnapi_delegate.cc +++ b/tensorflow/contrib/lite/nnapi_delegate.cc @@ -312,7 +312,7 @@ void AddOpsAndParams(tflite::Interpreter* interpreter, }; auto add_mean_params = [&add_scalar_int32](void* data) { - auto builtin = reinterpret_cast(data); + auto builtin = reinterpret_cast(data); add_scalar_int32(builtin->keep_dims); }; @@ -500,6 +500,7 @@ void AddOpsAndParams(tflite::Interpreter* interpreter, case tflite::BuiltinOperator_SPARSE_TO_DENSE: case tflite::BuiltinOperator_EQUAL: case tflite::BuiltinOperator_NOT_EQUAL: + case tflite::BuiltinOperator_SUM: FATAL("Op code %d is currently not delegated to NNAPI", builtin); nn_op_type = -1; // set to invalid break; diff --git a/tensorflow/contrib/lite/schema/schema.fbs b/tensorflow/contrib/lite/schema/schema.fbs index c7b955a165..18cb7b9509 100644 --- a/tensorflow/contrib/lite/schema/schema.fbs +++ b/tensorflow/contrib/lite/schema/schema.fbs @@ -154,6 +154,7 @@ enum BuiltinOperator : byte { EQUAL = 71, NOT_EQUAL = 72, LOG = 73, + SUM=74, } // Options for the builtin operators. @@ -184,7 +185,7 @@ union BuiltinOptions { BatchToSpaceNDOptions, SpaceToBatchNDOptions, TransposeOptions, - MeanOptions, + ReducerOptions, SubOptions, DivOptions, SqueezeOptions, @@ -411,7 +412,7 @@ table TransposeOptions { table ExpOptions { } -table MeanOptions { +table ReducerOptions { keep_dims: bool; } diff --git a/tensorflow/contrib/lite/schema/schema_generated.h b/tensorflow/contrib/lite/schema/schema_generated.h index 81d4574da7..c6fa94e38f 100755 --- a/tensorflow/contrib/lite/schema/schema_generated.h +++ b/tensorflow/contrib/lite/schema/schema_generated.h @@ -127,8 +127,8 @@ struct TransposeOptionsT; struct ExpOptions; struct ExpOptionsT; -struct MeanOptions; -struct MeanOptionsT; +struct ReducerOptions; +struct ReducerOptionsT; struct SqueezeOptions; struct SqueezeOptionsT; @@ -329,11 +329,12 @@ enum BuiltinOperator { BuiltinOperator_EQUAL = 71, BuiltinOperator_NOT_EQUAL = 72, BuiltinOperator_LOG = 73, + BuiltinOperator_SUM = 74, BuiltinOperator_MIN = BuiltinOperator_ADD, - BuiltinOperator_MAX = BuiltinOperator_LOG + BuiltinOperator_MAX = BuiltinOperator_SUM }; -inline BuiltinOperator (&EnumValuesBuiltinOperator())[73] { +inline BuiltinOperator (&EnumValuesBuiltinOperator())[74] { static BuiltinOperator values[] = { BuiltinOperator_ADD, BuiltinOperator_AVERAGE_POOL_2D, @@ -407,7 +408,8 @@ inline BuiltinOperator (&EnumValuesBuiltinOperator())[73] { BuiltinOperator_EXPAND_DIMS, BuiltinOperator_EQUAL, BuiltinOperator_NOT_EQUAL, - BuiltinOperator_LOG + BuiltinOperator_LOG, + BuiltinOperator_SUM }; return values; } @@ -488,6 +490,7 @@ inline const char **EnumNamesBuiltinOperator() { "EQUAL", "NOT_EQUAL", "LOG", + "SUM", nullptr }; return names; @@ -526,7 +529,7 @@ enum BuiltinOptions { BuiltinOptions_BatchToSpaceNDOptions = 24, BuiltinOptions_SpaceToBatchNDOptions = 25, BuiltinOptions_TransposeOptions = 26, - BuiltinOptions_MeanOptions = 27, + BuiltinOptions_ReducerOptions = 27, BuiltinOptions_SubOptions = 28, BuiltinOptions_DivOptions = 29, BuiltinOptions_SqueezeOptions = 30, @@ -587,7 +590,7 @@ inline BuiltinOptions (&EnumValuesBuiltinOptions())[55] { BuiltinOptions_BatchToSpaceNDOptions, BuiltinOptions_SpaceToBatchNDOptions, BuiltinOptions_TransposeOptions, - BuiltinOptions_MeanOptions, + BuiltinOptions_ReducerOptions, BuiltinOptions_SubOptions, BuiltinOptions_DivOptions, BuiltinOptions_SqueezeOptions, @@ -648,7 +651,7 @@ inline const char **EnumNamesBuiltinOptions() { "BatchToSpaceNDOptions", "SpaceToBatchNDOptions", "TransposeOptions", - "MeanOptions", + "ReducerOptions", "SubOptions", "DivOptions", "SqueezeOptions", @@ -794,8 +797,8 @@ template<> struct BuiltinOptionsTraits { static const BuiltinOptions enum_value = BuiltinOptions_TransposeOptions; }; -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_MeanOptions; +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ReducerOptions; }; template<> struct BuiltinOptionsTraits { @@ -1145,13 +1148,13 @@ struct BuiltinOptionsUnion { return type == BuiltinOptions_TransposeOptions ? reinterpret_cast(value) : nullptr; } - MeanOptionsT *AsMeanOptions() { - return type == BuiltinOptions_MeanOptions ? - reinterpret_cast(value) : nullptr; + ReducerOptionsT *AsReducerOptions() { + return type == BuiltinOptions_ReducerOptions ? + reinterpret_cast(value) : nullptr; } - const MeanOptionsT *AsMeanOptions() const { - return type == BuiltinOptions_MeanOptions ? - reinterpret_cast(value) : nullptr; + const ReducerOptionsT *AsReducerOptions() const { + return type == BuiltinOptions_ReducerOptions ? + reinterpret_cast(value) : nullptr; } SubOptionsT *AsSubOptions() { return type == BuiltinOptions_SubOptions ? @@ -3839,16 +3842,16 @@ inline flatbuffers::Offset CreateExpOptions( flatbuffers::Offset CreateExpOptions(flatbuffers::FlatBufferBuilder &_fbb, const ExpOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -struct MeanOptionsT : public flatbuffers::NativeTable { - typedef MeanOptions TableType; +struct ReducerOptionsT : public flatbuffers::NativeTable { + typedef ReducerOptions TableType; bool keep_dims; - MeanOptionsT() + ReducerOptionsT() : keep_dims(false) { } }; -struct MeanOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef MeanOptionsT NativeTableType; +struct ReducerOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ReducerOptionsT NativeTableType; enum { VT_KEEP_DIMS = 4 }; @@ -3860,38 +3863,38 @@ struct MeanOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { VerifyField(verifier, VT_KEEP_DIMS) && verifier.EndTable(); } - MeanOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(MeanOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const MeanOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + ReducerOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ReducerOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReducerOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); }; -struct MeanOptionsBuilder { +struct ReducerOptionsBuilder { flatbuffers::FlatBufferBuilder &fbb_; flatbuffers::uoffset_t start_; void add_keep_dims(bool keep_dims) { - fbb_.AddElement(MeanOptions::VT_KEEP_DIMS, static_cast(keep_dims), 0); + fbb_.AddElement(ReducerOptions::VT_KEEP_DIMS, static_cast(keep_dims), 0); } - explicit MeanOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + explicit ReducerOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) { start_ = fbb_.StartTable(); } - MeanOptionsBuilder &operator=(const MeanOptionsBuilder &); - flatbuffers::Offset Finish() { + ReducerOptionsBuilder &operator=(const ReducerOptionsBuilder &); + flatbuffers::Offset Finish() { const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); + auto o = flatbuffers::Offset(end); return o; } }; -inline flatbuffers::Offset CreateMeanOptions( +inline flatbuffers::Offset CreateReducerOptions( flatbuffers::FlatBufferBuilder &_fbb, bool keep_dims = false) { - MeanOptionsBuilder builder_(_fbb); + ReducerOptionsBuilder builder_(_fbb); builder_.add_keep_dims(keep_dims); return builder_.Finish(); } -flatbuffers::Offset CreateMeanOptions(flatbuffers::FlatBufferBuilder &_fbb, const MeanOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +flatbuffers::Offset CreateReducerOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReducerOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); struct SqueezeOptionsT : public flatbuffers::NativeTable { typedef SqueezeOptions TableType; @@ -5134,8 +5137,8 @@ struct Operator FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { const TransposeOptions *builtin_options_as_TransposeOptions() const { return builtin_options_type() == BuiltinOptions_TransposeOptions ? static_cast(builtin_options()) : nullptr; } - const MeanOptions *builtin_options_as_MeanOptions() const { - return builtin_options_type() == BuiltinOptions_MeanOptions ? static_cast(builtin_options()) : nullptr; + const ReducerOptions *builtin_options_as_ReducerOptions() const { + return builtin_options_type() == BuiltinOptions_ReducerOptions ? static_cast(builtin_options()) : nullptr; } const SubOptions *builtin_options_as_SubOptions() const { return builtin_options_type() == BuiltinOptions_SubOptions ? static_cast(builtin_options()) : nullptr; @@ -5353,8 +5356,8 @@ template<> inline const TransposeOptions *Operator::builtin_options_as inline const MeanOptions *Operator::builtin_options_as() const { - return builtin_options_as_MeanOptions(); +template<> inline const ReducerOptions *Operator::builtin_options_as() const { + return builtin_options_as_ReducerOptions(); } template<> inline const SubOptions *Operator::builtin_options_as() const { @@ -6864,28 +6867,28 @@ inline flatbuffers::Offset CreateExpOptions(flatbuffers::FlatBufferB _fbb); } -inline MeanOptionsT *MeanOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new MeanOptionsT(); +inline ReducerOptionsT *ReducerOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = new ReducerOptionsT(); UnPackTo(_o, _resolver); return _o; } -inline void MeanOptions::UnPackTo(MeanOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { +inline void ReducerOptions::UnPackTo(ReducerOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; { auto _e = keep_dims(); _o->keep_dims = _e; }; } -inline flatbuffers::Offset MeanOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const MeanOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateMeanOptions(_fbb, _o, _rehasher); +inline flatbuffers::Offset ReducerOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReducerOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateReducerOptions(_fbb, _o, _rehasher); } -inline flatbuffers::Offset CreateMeanOptions(flatbuffers::FlatBufferBuilder &_fbb, const MeanOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { +inline flatbuffers::Offset CreateReducerOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReducerOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { (void)_rehasher; (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const MeanOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ReducerOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; auto _keep_dims = _o->keep_dims; - return tflite::CreateMeanOptions( + return tflite::CreateReducerOptions( _fbb, _keep_dims); } @@ -7708,8 +7711,8 @@ inline bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *ob auto ptr = reinterpret_cast(obj); return verifier.VerifyTable(ptr); } - case BuiltinOptions_MeanOptions: { - auto ptr = reinterpret_cast(obj); + case BuiltinOptions_ReducerOptions: { + auto ptr = reinterpret_cast(obj); return verifier.VerifyTable(ptr); } case BuiltinOptions_SubOptions: { @@ -7942,8 +7945,8 @@ inline void *BuiltinOptionsUnion::UnPack(const void *obj, BuiltinOptions type, c auto ptr = reinterpret_cast(obj); return ptr->UnPack(resolver); } - case BuiltinOptions_MeanOptions: { - auto ptr = reinterpret_cast(obj); + case BuiltinOptions_ReducerOptions: { + auto ptr = reinterpret_cast(obj); return ptr->UnPack(resolver); } case BuiltinOptions_SubOptions: { @@ -8164,9 +8167,9 @@ inline flatbuffers::Offset BuiltinOptionsUnion::Pack(flatbuffers::FlatBuff auto ptr = reinterpret_cast(value); return CreateTransposeOptions(_fbb, ptr, _rehasher).Union(); } - case BuiltinOptions_MeanOptions: { - auto ptr = reinterpret_cast(value); - return CreateMeanOptions(_fbb, ptr, _rehasher).Union(); + case BuiltinOptions_ReducerOptions: { + auto ptr = reinterpret_cast(value); + return CreateReducerOptions(_fbb, ptr, _rehasher).Union(); } case BuiltinOptions_SubOptions: { auto ptr = reinterpret_cast(value); @@ -8386,8 +8389,8 @@ inline BuiltinOptionsUnion::BuiltinOptionsUnion(const BuiltinOptionsUnion &u) FL value = new TransposeOptionsT(*reinterpret_cast(u.value)); break; } - case BuiltinOptions_MeanOptions: { - value = new MeanOptionsT(*reinterpret_cast(u.value)); + case BuiltinOptions_ReducerOptions: { + value = new ReducerOptionsT(*reinterpret_cast(u.value)); break; } case BuiltinOptions_SubOptions: { @@ -8635,8 +8638,8 @@ inline void BuiltinOptionsUnion::Reset() { delete ptr; break; } - case BuiltinOptions_MeanOptions: { - auto ptr = reinterpret_cast(value); + case BuiltinOptions_ReducerOptions: { + auto ptr = reinterpret_cast(value); delete ptr; break; } diff --git a/tensorflow/contrib/lite/testing/generate_examples.py b/tensorflow/contrib/lite/testing/generate_examples.py index f5e25784fa..92589686c8 100644 --- a/tensorflow/contrib/lite/testing/generate_examples.py +++ b/tensorflow/contrib/lite/testing/generate_examples.py @@ -834,6 +834,12 @@ def make_mean_tests(zip_path): return make_reduce_tests(tf.reduce_mean)(zip_path) +def make_sum_tests(zip_path): + """Make a set of tests to do sum.""" + + return make_reduce_tests(tf.reduce_sum)(zip_path) + + def make_exp_tests(zip_path): """Make a set of tests to do exp.""" diff --git a/tensorflow/contrib/lite/toco/model.h b/tensorflow/contrib/lite/toco/model.h index 7bdec47aa9..619fc9fd42 100644 --- a/tensorflow/contrib/lite/toco/model.h +++ b/tensorflow/contrib/lite/toco/model.h @@ -1208,14 +1208,12 @@ struct SubOperator : Operator { SubOperator() : Operator(OperatorType::kSub) {} }; -// Global sum reduction: computes the sum of all of entries in the input array. -// Thus the output is "0-dimensional": it consists of a single scalar value. +// Sum reduction: computes the sum of all of entries across the axes. // // Inputs: // inputs[0]: required: the input array // -// TensorFlow equivalent: Sum --- except that we only support the special case -// of global reduction across all dimensions. +// TensorFlow equivalent: Sum struct TensorFlowSumOperator : Operator { TensorFlowSumOperator() : Operator(OperatorType::kTensorFlowSum) {} bool keep_dims = false; diff --git a/tensorflow/contrib/lite/toco/tflite/operator.cc b/tensorflow/contrib/lite/toco/tflite/operator.cc index a0fbb58aca..c5eafa2281 100644 --- a/tensorflow/contrib/lite/toco/tflite/operator.cc +++ b/tensorflow/contrib/lite/toco/tflite/operator.cc @@ -688,14 +688,33 @@ class Lstm : public BuiltinOperator { +class Mean : public BuiltinOperator { public: using BuiltinOperator::BuiltinOperator; flatbuffers::Offset WriteOptions( const TocoOperator& op, flatbuffers::FlatBufferBuilder* builder) const override { - return ::tflite::CreateMeanOptions(*builder, op.keep_dims); + return ::tflite::CreateReducerOptions(*builder, op.keep_dims); + } + + void ReadOptions(const TfLiteOptions& options, + TocoOperator* op) const override { + op->keep_dims = options.keep_dims(); + } + + int GetVersion(const Operator& op) const override { return 1; } +}; + +class Sum + : public BuiltinOperator { + public: + using BuiltinOperator::BuiltinOperator; + flatbuffers::Offset WriteOptions( + const TocoOperator& op, + flatbuffers::FlatBufferBuilder* builder) const override { + return ::tflite::CreateReducerOptions(*builder, op.keep_dims); } void ReadOptions(const TfLiteOptions& options, @@ -1060,6 +1079,8 @@ std::vector> BuildOperatorList() { OperatorType::kTranspose)); ops.emplace_back( new Mean(::tflite::BuiltinOperator_MEAN, OperatorType::kMean)); + ops.emplace_back( + new Sum(::tflite::BuiltinOperator_SUM, OperatorType::kTensorFlowSum)); ops.emplace_back(new ResizeBilinear(::tflite::BuiltinOperator_RESIZE_BILINEAR, OperatorType::kResizeBilinear)); ops.emplace_back( -- GitLab From 2efd9e1a415632b328aed36dbc74ce2dd8790898 Mon Sep 17 00:00:00 2001 From: Yash Katariya Date: Sun, 17 Jun 2018 14:34:43 +0000 Subject: [PATCH 572/816] Adding NMT with Attention notebook --- .../nmt_attention/NMT_with_Attention.ipynb | 992 ++++++++++++++++++ 1 file changed, 992 insertions(+) create mode 100644 tensorflow/contrib/eager/python/examples/nmt_attention/NMT_with_Attention.ipynb diff --git a/tensorflow/contrib/eager/python/examples/nmt_attention/NMT_with_Attention.ipynb b/tensorflow/contrib/eager/python/examples/nmt_attention/NMT_with_Attention.ipynb new file mode 100644 index 0000000000..066ef0addc --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/nmt_attention/NMT_with_Attention.ipynb @@ -0,0 +1,992 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "NMT with Attention.ipynb", + "version": "0.3.2", + "views": {}, + "default_view": {}, + "provenance": [ + { + "file_id": "1C4fpM7_7IL8ZzF7Gc5abywqQjeQNS2-U", + "timestamp": 1527858391290 + }, + { + "file_id": "1pExo6aUuw0S6MISFWoinfJv0Ftm9V4qv", + "timestamp": 1527776041613 + } + ], + "private_outputs": true, + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "metadata": { + "id": "AOpGoE2T-YXS", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "##### Copyright 2018 The TensorFlow Authors.\n", + "\n", + "Licensed under the Apache License, Version 2.0 (the \"License\").\n", + "\n", + "# Neural Machine Translation with Attention\n", + "\n", + "This notebook trains a sequence to sequence (seq2seq) model for Spanish to English translation using [tf.keras](https://www.tensorflow.org/programmers_guide/keras) and [eager execution](https://www.tensorflow.org/programmers_guide/eager). This is an advanced example for readers with prior background in sequence to sequence models.\n", + "\n", + "Here's an example output you'll see after running this notebook. After training the model, we'll translate the Spanish sentence \"¿todavia estan en casa?\", and we'll see the output \"are you still at home ?\". \n", + "\n", + "The translation quality is reasonable for a toy example, but what's even cooler is the attention plot that will be generated:\n", + "\n", + "This shows which parts of the input sentence the model is attending to while translating. \n", + "\n", + "![alt text](https://tensorflow.org/images/spanish-english.png)\n", + "\n", + "\n", + "Ballpark, this example will take approximately 10 mintues to run on a single P100 GPU.\n", + "\n", + "This notebook requires tensorflow veersion >= 1.9" + ] + }, + { + "metadata": { + "id": "tnxXKDjq3jEL", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# Import TensorFlow and enable eager execution\n", + "import tensorflow as tf\n", + "import tensorflow.contrib.eager as tfe\n", + "tf.enable_eager_execution()\n", + "\n", + "# We'll generate plots of attention in order to see which parts of a sentence\n", + "# our model focuses on during translation\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Scikit-learn includes many handy utilities\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "import unicodedata\n", + "import re\n", + "import numpy as np\n", + "import os\n", + "import time" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "wfodePkj3jEa", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Download and prepare the dataset\n", + "\n", + "We'll use a dataset helpfully provided by http://www.manythings.org/anki/. This contains language translation pairs, in this format:\n", + "\n", + "```\n", + "May I borrow this book?\t¿Puedo tomar prestado este libro?\n", + "```\n", + "\n", + "There are a variety of such datasets you can explore. This notebook will download and use the English-Spanish dataset. \n", + "\n", + "We've hosted a copy on Google Cloud for convenience. Alternatively, you can download and use a similar dataset (like English -> German) from http://www.manythings.org/anki/ and use it instead without changing any other code.\n", + "\n", + "After we've downloaded it, here are the steps we'll use to prepare the data:\n", + "\n", + "* Add a start and end token to each sentence\n", + "* Clean the sentences by removing special characters\n", + "* Create a word index and reverse word index (dictionaries mapping from word -> id and id -> word)\n", + "* Pad each sentence to a maximum length" + ] + }, + { + "metadata": { + "id": "kRVATYOgJs1b", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# Download the file\n", + "path_to_zip = tf.keras.utils.get_file(\n", + " 'spa-eng.zip', origin='http://download.tensorflow.org/data/spa-eng.zip', \n", + " extract=True)\n", + "\n", + "path_to_file = os.path.dirname(path_to_zip)+\"/spa-eng/spa.txt\"" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "DzIS_cRu3jEb", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# Converts the unicode file to ascii\n", + "def unicode_to_ascii(s):\n", + " return ''.join(c for c in unicodedata.normalize('NFD', s)\n", + " if unicodedata.category(c) != 'Mn')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "rd0jw-eC3jEh", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "def preprocess_sentence(w):\n", + " w = unicode_to_ascii(w.lower().strip())\n", + " \n", + " # creating a space between a word and the punctuation following it\n", + " # eg: \"he is a boy.\" => \"he is a boy .\" \n", + " # Reference:- https://stackoverflow.com/questions/3645931/python-padding-punctuation-with-white-spaces-keeping-punctuation\n", + " w = re.sub(r\"([?.!,¿])\", r\" \\1 \", w)\n", + " w = re.sub(r'[\" \"]+', \" \", w)\n", + " \n", + " # replacing everything with space except (a-z, A-Z, \".\", \"?\", \"!\", \",\")\n", + " w = re.sub(r\"[^a-zA-Z?.!,¿]+\", \" \", w)\n", + " \n", + " w = w.rstrip().strip()\n", + " \n", + " # adding a start and an end token to the sentence\n", + " # so that the model know when to start and stop predicting.\n", + " w = ' ' + w + ' '\n", + " return w" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "OHn4Dct23jEm", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# first we remove the pronumciations\n", + "# second we clean the sentences\n", + "# and third we return word pairs in [ENGLISH, SPANISH] format\n", + "def create_dataset(path, num_examples):\n", + " lines = open(path, encoding='UTF-8').read().strip().split('\\n')\n", + " \n", + " word_pairs = [[preprocess_sentence(w) for w in l.split('\\t')] for l in lines[:num_examples]]\n", + " \n", + " return word_pairs" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "9xbqO7Iie9bb", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# This class creates a word -> index mapping (e.g,. \"dad\" -> 5) and vice-versa \n", + "# (e.g., 5 -> \"dad\") for each language,\n", + "class LanguageIndex():\n", + " def __init__(self, lang):\n", + " self.lang = lang\n", + " self.word2idx = {}\n", + " self.idx2word = {}\n", + " self.vocab = set()\n", + " \n", + " self.create_index()\n", + " \n", + " def create_index(self):\n", + " for phrase in self.lang:\n", + " self.vocab.update(phrase.split(' '))\n", + " \n", + " self.vocab = sorted(self.vocab)\n", + "\n", + " for index, word in enumerate(self.vocab):\n", + " self.word2idx[word] = index\n", + " self.idx2word[index] = word" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "lU4fj_gG3jE6", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "def max_length(tensor):\n", + " return max(len(t) for t in tensor)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "eAY9k49G3jE_", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "def load_dataset(path, num_examples):\n", + " # creating cleaned input, output pairs\n", + " pairs = create_dataset(path, num_examples)\n", + "\n", + " # index language using the class defined above \n", + " inp_lang = LanguageIndex(sp for en, sp in pairs)\n", + " targ_lang = LanguageIndex(en for en, sp in pairs)\n", + " \n", + " # Vectorize the input and target languages\n", + " \n", + " # Spanish sentences\n", + " input_tensor = [[inp_lang.word2idx[s] for s in sp.split(' ')] for en, sp in pairs]\n", + " \n", + " # English sentences\n", + " target_tensor = [[targ_lang.word2idx[s] for s in en.split(' ')] for en, sp in pairs]\n", + " \n", + " # Calculate max_length of input and output tensor\n", + " # Here, we'll set those to the longest sentence in the dataset\n", + " max_length_inp, max_length_tar = max_length(input_tensor), max_length(target_tensor)\n", + " \n", + " # Padding the input and output tensor to the maximum length\n", + " input_tensor = tf.keras.preprocessing.sequence.pad_sequences(input_tensor, \n", + " maxlen=max_length_inp,\n", + " padding='post')\n", + " \n", + " target_tensor = tf.keras.preprocessing.sequence.pad_sequences(target_tensor, \n", + " maxlen=max_length_tar, \n", + " padding='post')\n", + " \n", + " return input_tensor, target_tensor, inp_lang, targ_lang, max_length_inp, max_length_tar" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "GOi42V79Ydlr", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Limit the size of the dataset to experiment faster (optional)\n", + "\n", + "Training on the complete dataset of >100,000 sentences will take some time. Below, we'll limit the size of the dataset to 30,000 sentences, in order to experiment faster (of course, translation quality will improve with more data)." + ] + }, + { + "metadata": { + "id": "cnxC7q-j3jFD", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# Try experimenting with the size of that dataset\n", + "num_examples = 30000\n", + "input_tensor, target_tensor, inp_lang, targ_lang, max_length_inp, max_length_targ = load_dataset(path_to_file, num_examples)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "4QILQkOs3jFG", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# Creating training and validation sets using an 80-20 split\n", + "input_tensor_train, input_tensor_val, target_tensor_train, target_tensor_val = train_test_split(input_tensor, target_tensor, test_size=0.2)\n", + "len(input_tensor_train), len(target_tensor_train), len(input_tensor_val), len(target_tensor_val)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "rgCLkfv5uO3d", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Create a tf.data dataset" + ] + }, + { + "metadata": { + "id": "TqHsArVZ3jFS", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "BUFFER_SIZE = len(input_tensor_train)\n", + "BATCH_SIZE = 64\n", + "embedding_dim = 256\n", + "units = 1024\n", + "vocab_inp_size = len(inp_lang.vocab)\n", + "vocab_tar_size = len(targ_lang.vocab)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "fYLzjawH3jFW", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "dataset = tf.data.Dataset.from_tensor_slices((input_tensor_train, target_tensor_train)).shuffle(BUFFER_SIZE)\n", + "dataset = dataset.apply(tf.contrib.data.batch_and_drop_remainder(BATCH_SIZE))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "TNfHIF71ulLu", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Write the encoder and decoder model with attention\n", + "Here, we'll implement an encoder-deocder model. For background on how these work, you can read more about them in this previous [tutorial](https://www.tensorflow.org/tutorials/seq2seq). In this example, we'll use a more recent (and much easier) set of APIs.\n", + "\n", + "![alt text](https://storage.googleapis.com/yashkatariya/attention_picture.png)\n", + "\n", + "The code below implements the attention [equations](https://www.tensorflow.org/tutorials/seq2seq#background_on_the_attention_mechanism) from the previous tutorial. In the above diagram, each of the input words is assigned a weight by the attention mechanism which is then used by the decoder to predict the next word in the sentence.\n", + "\n", + "The input is put through an encoder model which gives us the encoder output of shape *(batch_size, max_length, hidden_size)* and the encoder hidden state of shape *(batch_size, hidden_size)*. \n", + "\n", + "Here are the equations we'll implement below:\n", + "\n", + "![alt text](https://storage.googleapis.com/yashkatariya/attention_eq1.png)\n", + "![alt text](https://storage.googleapis.com/yashkatariya/attention_eq2.png)\n", + "\n", + "We'll use *Bahdanau attention*. Lets decide on some notations before we write the simplified form:\n", + "\n", + "* FC = Fully connected (dense) layer\n", + "* EO = Encoder output\n", + "* H = hidden state\n", + "* X = input to the decoder\n", + "\n", + "Pseudo-code:\n", + "\n", + " 1. *score = FC(tanh(FC(EO) + FC(H)))*\n", + " 2. *attention weights = softmax(score, axis = 1)*. Softmax by default is applied on the last axis but here we want to apply it on the *1st axis*, since the shape of score is *(batch_size, max_length, hidden_size)*. Max_length is the length of our input. Since we are trying to assign a weight to each input, softmax should be applied on that axis.\n", + " 3. *context vector = sum(attention weights * EO, axis = 1)*. Same reason as above for choosing axis as 1.\n", + " 4. *embedding output = The input to the decoder X is passed through an embedding layer.*\n", + " 5. *merged vector = concat(embedding output, context vector)*\n", + " 6. *This merged vector is then given to the GRU*\n", + " \n", + "The shapes of all the vectors at each step have been specified in the comments in the code.\n", + " \n", + " " + ] + }, + { + "metadata": { + "id": "nZ2rI24i3jFg", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "class Encoder(tf.keras.Model):\n", + " def __init__(self, vocab_size, embedding_dim, enc_units, batch_sz):\n", + " super(Encoder, self).__init__()\n", + " self.batch_sz = batch_sz\n", + " self.enc_units = enc_units\n", + " self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)\n", + " \n", + " # If you have a GPU, we recommend using CuDNNGRU(provides a 3x speedup than GRU)\n", + " # the code automatically does that.\n", + " if tf.test.is_gpu_available():\n", + " self.gru = tf.keras.layers.CuDNNGRU(self.enc_units, \n", + " return_sequences=True, \n", + " return_state=True, \n", + " recurrent_initializer='glorot_uniform')\n", + " else:\n", + " self.gru = tf.keras.layers.GRU(self.enc_units, \n", + " return_sequences=True, \n", + " return_state=True, \n", + " recurrent_activation='sigmoid', \n", + " recurrent_initializer='glorot_uniform')\n", + "\n", + " def call(self, x, hidden):\n", + " x = self.embedding(x)\n", + " output, state = self.gru(x, initial_state = hidden) \n", + " return output, state\n", + " \n", + " def initialize_hidden_state(self):\n", + " return tf.zeros((self.batch_sz, self.enc_units))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "yJ_B3mhW3jFk", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "class Decoder(tf.keras.Model):\n", + " def __init__(self, vocab_size, embedding_dim, dec_units, batch_sz):\n", + " super(Decoder, self).__init__()\n", + " self.batch_sz = batch_sz\n", + " self.dec_units = dec_units\n", + " self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)\n", + " \n", + " # If you have a GPU, we recommend using CuDNNGRU(provides a 3x speedup than GRU)\n", + " # the code automatically does that.\n", + " if tf.test.is_gpu_available():\n", + " self.gru = tf.keras.layers.CuDNNGRU(self.dec_units, \n", + " return_sequences=True,\n", + " return_state=True, \n", + " recurrent_initializer='glorot_uniform')\n", + " else:\n", + " self.gru = tf.keras.layers.GRU(self.dec_units, \n", + " return_sequences=True,\n", + " return_state=True, \n", + " recurrent_activation='sigmoid', \n", + " recurrent_initializer='glorot_uniform')\n", + " \n", + " self.fc = tf.keras.layers.Dense(vocab_size)\n", + " \n", + " # used for attention\n", + " self.W1 = tf.keras.layers.Dense(self.dec_units)\n", + " self.W2 = tf.keras.layers.Dense(self.dec_units)\n", + " self.V = tf.keras.layers.Dense(1)\n", + " \n", + " def call(self, x, hidden, enc_output):\n", + " # enc_output shape == (batch_size, max_length, hidden_size)\n", + " \n", + " # hidden shape == (batch_size, hidden size)\n", + " # hidden_with_time_axis shape == (batch_size, 1, hidden size)\n", + " # we are doing this to perform addition to calculate the score\n", + " hidden_with_time_axis = tf.expand_dims(hidden, 1)\n", + " \n", + " # score shape == (batch_size, max_length, hidden_size)\n", + " score = tf.nn.tanh(self.W1(enc_output) + self.W2(hidden_with_time_axis))\n", + " \n", + " # attention_weights shape == (batch_size, max_length, 1)\n", + " # we get 1 at the last axis because we are applying score to self.V\n", + " attention_weights = tf.nn.softmax(self.V(score), axis=1)\n", + " \n", + " # context_vector shape after sum == (batch_size, hidden_size)\n", + " context_vector = attention_weights * enc_output\n", + " context_vector = tf.reduce_sum(context_vector, axis=1)\n", + " \n", + " # x shape after passing through embedding == (batch_size, 1, embedding_dim)\n", + " x = self.embedding(x)\n", + " \n", + " # x shape after concatenation == (batch_size, 1, embedding_dim + hidden_size)\n", + " x = tf.concat([tf.expand_dims(context_vector, 1), x], axis=-1)\n", + " \n", + " # passing the concatenated vector to the GRU\n", + " output, state = self.gru(x)\n", + " \n", + " # output shape == (batch_size * max_length, hidden_size)\n", + " output = tf.reshape(output, (-1, output.shape[2]))\n", + " \n", + " # output shape == (batch_size * max_length, vocab)\n", + " x = self.fc(output)\n", + " \n", + " return x, state, attention_weights\n", + " \n", + " def initialize_hidden_state(self):\n", + " return tf.zeros((self.batch_sz, self.dec_units))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "P5UY8wko3jFp", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "encoder = Encoder(vocab_inp_size, embedding_dim, units, BATCH_SIZE)\n", + "decoder = Decoder(vocab_tar_size, embedding_dim, units, BATCH_SIZE)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "_ch_71VbIRfK", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Step 5: Define the optimizers and the loss function" + ] + }, + { + "metadata": { + "id": "WmTHr5iV3jFr", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "optimizer = tf.train.AdamOptimizer()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "rdLCjYff3jFv", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "def loss_function(real, pred):\n", + " return tf.losses.sparse_softmax_cross_entropy(labels=real, logits=pred)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "hpObfY22IddU", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Step 6: Training\n", + "\n", + "* Here we pass the input through the encoder which return *encoder output* and the *encoder hidden state*.\n", + "* The encoder output, encoder hidden state and the decoder input (which is the \"start\" token) is passed to the decoder.\n", + "* The decoder returns the *predictions* and the *decoder hidden state*.\n", + "* The decoder hidden state is then passed back into the model and the predictions are used to calculate the loss.\n", + "* To decide the next input to the decoder we use *teacher forcing*.\n", + "* *Teacher forcing* is the technique in which we pass the *target word as the next input* to the decoder.\n", + "* The final step is to calculate the gradients and apply it to the optimizer and backpropagate." + ] + }, + { + "metadata": { + "id": "ddefjBMa3jF0", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "EPOCHS = 10\n", + "\n", + "for epoch in range(EPOCHS):\n", + " start = time.time()\n", + " \n", + " hidden = encoder.initialize_hidden_state()\n", + " total_loss = 0\n", + " \n", + " for (batch, (inp, targ)) in enumerate(dataset):\n", + " loss = 0\n", + " \n", + " with tfe.GradientTape() as tape:\n", + " enc_output, enc_hidden = encoder(inp, hidden)\n", + " \n", + " dec_hidden = enc_hidden\n", + " \n", + " dec_input = tf.expand_dims([targ_lang.word2idx['']] * BATCH_SIZE, 1) \n", + " \n", + " # Teacher forcing - feeding the target as the next input\n", + " for t in range(1, targ.shape[1]):\n", + " # passing enc_output to the decoder\n", + " predictions, dec_hidden, _ = decoder(dec_input, dec_hidden, enc_output)\n", + " \n", + " loss += loss_function(targ[:, t], predictions)\n", + " \n", + " # using teacher forcing\n", + " dec_input = tf.expand_dims(targ[:, t], 1)\n", + " \n", + " total_loss += (loss / int(targ.shape[1]))\n", + " \n", + " variables = encoder.variables + decoder.variables\n", + " \n", + " gradients = tape.gradient(loss, variables)\n", + " \n", + " optimizer.apply_gradients(zip(gradients, variables), tf.train.get_or_create_global_step())\n", + "\n", + " if batch % 100 == 0:\n", + " print ('Epoch {} Batch {} Loss {:.4f}'.format(epoch + 1, batch, loss.numpy() / int(targ.shape[1])))\n", + " \n", + " print ('Epoch {} Loss {:.4f}'.format(epoch + 1, total_loss/len(input_tensor)))\n", + " print ('Time taken for 1 epoch', time.time() - start, 'sec')\n", + " print ()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "K5bWEZM53jF3", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "mU3Ce8M6I3rz", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Step 7: Translate\n", + "\n", + "* The evaluate function is similar to the training loop. The only change is that we don't use teacher forcing here. The input to the decoder at each time step is its previous predictions along with the hidden state and the encoder output.\n", + "* We stop predicting when the model predicts the *'end' token*.\n", + "* We also store the *attention weights for every time step*.\n", + "\n", + "NOTE: The encoder output is calculated only once for one input." + ] + }, + { + "metadata": { + "id": "EbQpyYs13jF_", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "def evaluate(sentence, encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ):\n", + " attention_plot = np.zeros((max_length_targ, max_length_inp))\n", + " \n", + " sentence = preprocess_sentence(sentence)\n", + "\n", + " inputs = [inp_lang.word2idx[i] for i in sentence.split(' ')]\n", + " inputs = tf.keras.preprocessing.sequence.pad_sequences([inputs], maxlen=max_length_inp, padding='post')\n", + " inputs = tf.convert_to_tensor(inputs)\n", + " \n", + " result = ''\n", + "\n", + " hidden = [tf.zeros((1, units))]\n", + " enc_out, enc_hidden = encoder(inputs, hidden)\n", + "\n", + " dec_hidden = enc_hidden\n", + " dec_input = tf.expand_dims([targ_lang.word2idx['']], 0)\n", + "\n", + " for t in range(max_length_targ):\n", + " predictions, dec_hidden, attention_weights = decoder(dec_input, dec_hidden, enc_out)\n", + " \n", + " # storing the attention weigths to plot later on\n", + " attention_weights = tf.reshape(attention_weights, (-1, ))\n", + " attention_plot[t] = attention_weights.numpy()\n", + "\n", + " predicted_id = tf.multinomial(tf.exp(predictions), num_samples=1)[0][0].numpy()\n", + "\n", + " result += targ_lang.idx2word[predicted_id] + ' '\n", + "\n", + " if targ_lang.idx2word[predicted_id] == '':\n", + " return result, sentence, attention_plot\n", + " \n", + " # the predicted ID is fed back into the model\n", + " dec_input = tf.expand_dims([predicted_id], 0)\n", + "\n", + " return result, sentence, attention_plot" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "s5hQWlbN3jGF", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# function for plotting the attention weights\n", + "def plot_attention(attention, sentence, predicted_sentence):\n", + " fig = plt.figure(figsize=(10,10))\n", + " ax = fig.add_subplot(1, 1, 1)\n", + " ax.matshow(attention, cmap='viridis')\n", + " \n", + " fontdict = {'fontsize': 14}\n", + " \n", + " ax.set_xticklabels([''] + sentence, fontdict=fontdict, rotation=90)\n", + " ax.set_yticklabels([''] + predicted_sentence, fontdict=fontdict)\n", + "\n", + " plt.show()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "sl9zUHzg3jGI", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "def translate(sentence, encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ):\n", + " result, sentence, attention_plot = evaluate(sentence, encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ)\n", + " \n", + " print ('Input:', sentence)\n", + " print ('Predicted translation:', result)\n", + " \n", + " attention_plot = attention_plot[:len(result.split(' ')), :len(sentence.split(' '))]\n", + " plot_attention(attention_plot, sentence.split(' '), result.split(' '))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "WrAM0FDomq3E", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "translate('hace mucho frio aqui.', encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "zSx2iM36EZQZ", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "translate('esta es mi vida.', encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "A3LLCx3ZE0Ls", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "translate('¿todavia estan en casa?', encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "DUQVLVqUE1YW", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# wrong translation\n", + "translate('trata de averiguarlo.', encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "RTe5P5ioMJwN", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Next steps\n", + "\n", + "* If you like, you can experiment with a different dataset (say, for Englsh to German, or English to French) translation by downloading one from http://www.manythings.org/anki/\n", + "* Experiment with training with a larger dataset, or for more epochs\n", + "\n", + "Thanks for reading, we hope you enjoyed and find this code useful. If you find anything we can improve in this notebook, please open a pull request. \n" + ] + }, + { + "metadata": { + "id": "yMUwCtOizvxg", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file -- GitLab From ab9b1341a9d31063c9c41f197930c5395245046e Mon Sep 17 00:00:00 2001 From: ManHyuk Date: Sun, 17 Jun 2018 23:54:46 +0900 Subject: [PATCH 573/816] Fix typo (#20082) --- .../api_def/base_api/api_def_SampleDistortedBoundingBox.pbtxt | 2 +- .../api_def/base_api/api_def_SampleDistortedBoundingBoxV2.pbtxt | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/core/api_def/base_api/api_def_SampleDistortedBoundingBox.pbtxt b/tensorflow/core/api_def/base_api/api_def_SampleDistortedBoundingBox.pbtxt index 6f1121dd37..5ab5917bd3 100644 --- a/tensorflow/core/api_def/base_api/api_def_SampleDistortedBoundingBox.pbtxt +++ b/tensorflow/core/api_def/base_api/api_def_SampleDistortedBoundingBox.pbtxt @@ -68,7 +68,7 @@ END name: "area_range" description: < Date: Sun, 17 Jun 2018 15:18:09 +0000 Subject: [PATCH 574/816] Added Neural Machine Translation with Attention --- .../NMT_with_Attention.ipynb | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename tensorflow/contrib/eager/python/examples/{nmt_attention => nmt_with_attention}/NMT_with_Attention.ipynb (100%) diff --git a/tensorflow/contrib/eager/python/examples/nmt_attention/NMT_with_Attention.ipynb b/tensorflow/contrib/eager/python/examples/nmt_with_attention/NMT_with_Attention.ipynb similarity index 100% rename from tensorflow/contrib/eager/python/examples/nmt_attention/NMT_with_Attention.ipynb rename to tensorflow/contrib/eager/python/examples/nmt_with_attention/NMT_with_Attention.ipynb -- GitLab From aa90acce97d547791c765a64e3ec31943cbb91dc Mon Sep 17 00:00:00 2001 From: Yash Katariya Date: Sun, 17 Jun 2018 16:15:54 +0000 Subject: [PATCH 575/816] Added a check for 1.9 version --- .../examples/nmt_with_attention/NMT_with_Attention.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/contrib/eager/python/examples/nmt_with_attention/NMT_with_Attention.ipynb b/tensorflow/contrib/eager/python/examples/nmt_with_attention/NMT_with_Attention.ipynb index 066ef0addc..7e4c13f31a 100644 --- a/tensorflow/contrib/eager/python/examples/nmt_with_attention/NMT_with_Attention.ipynb +++ b/tensorflow/contrib/eager/python/examples/nmt_with_attention/NMT_with_Attention.ipynb @@ -493,7 +493,7 @@ " \n", " # If you have a GPU, we recommend using CuDNNGRU(provides a 3x speedup than GRU)\n", " # the code automatically does that.\n", - " if tf.test.is_gpu_available():\n", + " if tf.test.is_gpu_available() and '1.9' in tf.__version__:\n", " self.gru = tf.keras.layers.CuDNNGRU(self.enc_units, \n", " return_sequences=True, \n", " return_state=True, \n", @@ -538,7 +538,7 @@ " \n", " # If you have a GPU, we recommend using CuDNNGRU(provides a 3x speedup than GRU)\n", " # the code automatically does that.\n", - " if tf.test.is_gpu_available():\n", + " if tf.test.is_gpu_available() and '1.9' in tf.__version__:\n", " self.gru = tf.keras.layers.CuDNNGRU(self.dec_units, \n", " return_sequences=True,\n", " return_state=True, \n", -- GitLab From 113c035f65e814acaa6ae88d8104abf8268f2a83 Mon Sep 17 00:00:00 2001 From: Yash Katariya Date: Sun, 17 Jun 2018 16:49:50 +0000 Subject: [PATCH 576/816] Removed version check for 1.9 --- .../examples/nmt_with_attention/NMT_with_Attention.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/contrib/eager/python/examples/nmt_with_attention/NMT_with_Attention.ipynb b/tensorflow/contrib/eager/python/examples/nmt_with_attention/NMT_with_Attention.ipynb index 7e4c13f31a..066ef0addc 100644 --- a/tensorflow/contrib/eager/python/examples/nmt_with_attention/NMT_with_Attention.ipynb +++ b/tensorflow/contrib/eager/python/examples/nmt_with_attention/NMT_with_Attention.ipynb @@ -493,7 +493,7 @@ " \n", " # If you have a GPU, we recommend using CuDNNGRU(provides a 3x speedup than GRU)\n", " # the code automatically does that.\n", - " if tf.test.is_gpu_available() and '1.9' in tf.__version__:\n", + " if tf.test.is_gpu_available():\n", " self.gru = tf.keras.layers.CuDNNGRU(self.enc_units, \n", " return_sequences=True, \n", " return_state=True, \n", @@ -538,7 +538,7 @@ " \n", " # If you have a GPU, we recommend using CuDNNGRU(provides a 3x speedup than GRU)\n", " # the code automatically does that.\n", - " if tf.test.is_gpu_available() and '1.9' in tf.__version__:\n", + " if tf.test.is_gpu_available():\n", " self.gru = tf.keras.layers.CuDNNGRU(self.dec_units, \n", " return_sequences=True,\n", " return_state=True, \n", -- GitLab From ba322e9a80588e69c6ceeb31af69135289b038da Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Sun, 17 Jun 2018 12:07:58 -0700 Subject: [PATCH 577/816] Fix minor bug in handling of IndicatorColumn in BoostedTreesClassifier. Handles case where max_buckets_for_bucketized overwrites an existing key in the bucket_size_to_feature_ids_dict. This can happen if a) There are no bucketized features b) The max buckets for bucketized features is actually 2 (clashing with max_buckets_for_indicator) PiperOrigin-RevId: 200908269 --- .../python/estimator/canned/boosted_trees.py | 7 +-- .../estimator/canned/boosted_trees_test.py | 44 +++++++++++++++++++ 2 files changed, 48 insertions(+), 3 deletions(-) diff --git a/tensorflow/python/estimator/canned/boosted_trees.py b/tensorflow/python/estimator/canned/boosted_trees.py index 86dbf272ef..8afef1b65a 100644 --- a/tensorflow/python/estimator/canned/boosted_trees.py +++ b/tensorflow/python/estimator/canned/boosted_trees.py @@ -168,9 +168,10 @@ def _group_features_by_num_buckets(sorted_feature_columns): # pylint:enable=protected-access # Replace the dummy key with the real max num of buckets for all bucketized # columns. - bucket_size_to_feature_ids_dict[ - max_buckets_for_bucketized] = bucket_size_to_feature_ids_dict[ - _DUMMY_NUM_BUCKETS] + if max_buckets_for_bucketized not in bucket_size_to_feature_ids_dict: + bucket_size_to_feature_ids_dict[max_buckets_for_bucketized] = [] + bucket_size_to_feature_ids_dict[max_buckets_for_bucketized].extend( + bucket_size_to_feature_ids_dict[_DUMMY_NUM_BUCKETS]) del bucket_size_to_feature_ids_dict[_DUMMY_NUM_BUCKETS] feature_ids_list = list(bucket_size_to_feature_ids_dict.values()) diff --git a/tensorflow/python/estimator/canned/boosted_trees_test.py b/tensorflow/python/estimator/canned/boosted_trees_test.py index 9ea4f48474..33e9e69b04 100644 --- a/tensorflow/python/estimator/canned/boosted_trees_test.py +++ b/tensorflow/python/estimator/canned/boosted_trees_test.py @@ -500,6 +500,50 @@ class BoostedTreesEstimatorTest(test_util.TensorFlowTestCase): self.assertEqual(2, ensemble.trees[0].nodes[0].bucketized_split.feature_id) self.assertEqual(0, ensemble.trees[0].nodes[0].bucketized_split.threshold) + def testTrainEvaluateAndPredictWithOnlyIndicatorColumn(self): + categorical = feature_column.categorical_column_with_vocabulary_list( + key='categorical', vocabulary_list=('bad', 'good', 'ok')) + feature_indicator = feature_column.indicator_column(categorical) + + labels = np.array([[0.], [5.7], [5.7], [0.], [0.]], dtype=np.float32) + # Our categorical feature defines the labels perfectly + input_fn = numpy_io.numpy_input_fn( + x={ + 'categorical': np.array(['bad', 'good', 'good', 'ok', 'bad']), + }, + y=labels, + batch_size=5, + shuffle=False) + + # Train depth 1 tree. + est = boosted_trees.BoostedTreesRegressor( + feature_columns=[feature_indicator], + n_batches_per_layer=1, + n_trees=1, + learning_rate=1.0, + max_depth=1) + + num_steps = 1 + est.train(input_fn, steps=num_steps) + ensemble = self._assert_checkpoint_and_return_model( + est.model_dir, global_step=1, finalized_trees=1, attempted_layers=1) + + # We learnt perfectly. + eval_res = est.evaluate(input_fn=input_fn, steps=1) + self.assertAllClose(eval_res['loss'], 0) + + predictions = list(est.predict(input_fn)) + self.assertAllClose( + labels, + [pred['predictions'] for pred in predictions]) + + self.assertEqual(3, len(ensemble.trees[0].nodes)) + + # Check that the split happened on 'good' value, which will be encoded as + # feature with index 1 (0 - 'bad', 2 - 'ok') + self.assertEqual(1, ensemble.trees[0].nodes[0].bucketized_split.feature_id) + self.assertEqual(0, ensemble.trees[0].nodes[0].bucketized_split.threshold) + class ModelFnTests(test_util.TensorFlowTestCase): """Tests bt_model_fn including unexposed internal functionalities.""" -- GitLab From 066a24e4215da5946cd0bdb5c78038e9e20ae6cf Mon Sep 17 00:00:00 2001 From: Andrew Selle Date: Sun, 17 Jun 2018 14:46:46 -0700 Subject: [PATCH 578/816] Add support for direct buffer access from TF Lite Python API. Also fixed other problems - Fix bounds checking on tensor index - Fix tensor byte size to be size_t - Fix memory leak in buffer allocation - Remove dependency on core tensorflow In a susbsequent CL I will refactor to not require logging and instead send ValueError or RuntimeErrors back as exceptions that properly use TFLite ErrorReporters. PiperOrigin-RevId: 200915674 --- tensorflow/contrib/lite/python/interpreter.py | 92 ++++++++++++++++- .../contrib/lite/python/interpreter_test.py | 56 +++++++++++ .../lite/python/interpreter_wrapper/BUILD | 2 +- .../interpreter_wrapper.cc | 98 +++++++++++++++---- .../interpreter_wrapper/interpreter_wrapper.h | 3 + 5 files changed, 229 insertions(+), 22 deletions(-) diff --git a/tensorflow/contrib/lite/python/interpreter.py b/tensorflow/contrib/lite/python/interpreter.py index 0bc8b0963c..9400e757b9 100644 --- a/tensorflow/contrib/lite/python/interpreter.py +++ b/tensorflow/contrib/lite/python/interpreter.py @@ -17,6 +17,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import sys from tensorflow.python.util.lazy_loader import LazyLoader # Lazy load since some of the performance benchmark skylark rules @@ -64,9 +65,38 @@ class Interpreter(object): raise ValueError('Can\'t both provide `model_path` and `model_content`') def allocate_tensors(self): + self._ensure_safe() if not self._interpreter.AllocateTensors(): raise ValueError('Failed to allocate tensors') + def _safe_to_run(self): + """Returns true if there exist no numpy array buffers. + + This means it is safe to run tflite calls that may destroy internally + allocated memory. This works, because in the wrapper.cc we have made + the numpy base be the self._interpreter. + """ + # NOTE, our tensor() call in cpp will use _interpreter as a base pointer. + # If this environment is the only _interpreter, then the ref count should be + # 2 (1 in self and 1 in temporary of sys.getrefcount). + return sys.getrefcount(self._interpreter) == 2 + + def _ensure_safe(self): + """Makes sure no numpy arrays pointing to internal buffers are active. + + This should be called from any function that will call a function on + _interpreter that may reallocate memory e.g. invoke(), ... + + Raises: + RuntimeError: If there exist numpy objects pointing to internal memory + then we throw. + """ + if not self._safe_to_run(): + raise RuntimeError("""There is at least 1 reference to internal data + in the interpreter in the form of a numpy array or slice. Be sure to + only hold the function returned from tensor() if you are using raw + data access.""") + def _get_tensor_details(self, tensor_index): """Gets tensor details. @@ -109,7 +139,10 @@ class Interpreter(object): ] def set_tensor(self, tensor_index, value): - """Sets the value of the input tensor. + """Sets the value of the input tensor. Note this copies data in `value`. + + If you want to avoid copying, you can use the `tensor()` function to get a + numpy buffer pointing to the input buffer in the tflite interpreter. Args: tensor_index: Tensor index of tensor to set. This value can be gotten from @@ -133,6 +166,7 @@ class Interpreter(object): Raises: ValueError: If the interpreter could not resize the input tensor. """ + self._ensure_safe() if not self._interpreter.ResizeInputTensor(input_index, tensor_size): raise ValueError('Failed to resize input') @@ -147,7 +181,7 @@ class Interpreter(object): ] def get_tensor(self, tensor_index): - """Gets the value of the tensor. + """Gets the value of the input tensor. Note this makes a copy so prefer `tensor()`. Args: tensor_index: Tensor index of tensor to get. This value can be gotten from @@ -158,6 +192,60 @@ class Interpreter(object): """ return self._interpreter.GetTensor(tensor_index) + def tensor(self, tensor_index): + """Returns function that gives a numpy view of the current tensor buffer. + + This allows reading and writing to this tensors w/o copies. This more + closely mirrors the C++ Interpreter class interface's tensor() member, hence + the name. Be careful to not hold these output references through calls + to `allocate_tensors()` and `invoke()`. + + Usage: + + interpreter.allocate_tensors() + input = interpreter.tensor(interpreter.get_input_details()[0]["index"]) + output = interpreter.tensor(interpreter.get_output_details()[0]["index"]) + for i in range(10): + input().fill(3.) + interpreter.invoke() + print("inference %s" % output) + + Notice how this function avoids making a numpy array directly. This is + because it is important to not hold actual numpy views to the data longer + than necessary. If you do, then the interpreter can no longer be invoked, + because it is possible the interpreter would resize and invalidate the + referenced tensors. The NumPy API doesn't allow any mutability of the + the underlying buffers. + + WRONG: + + input = interpreter.tensor(interpreter.get_input_details()[0]["index"])() + output = interpreter.tensor(interpreter.get_output_details()[0]["index"])() + interpreter.allocate_tensors() # This will throw RuntimeError + for i in range(10): + input.fill(3.) + interpreter.invoke() # this will throw RuntimeError since input,output + + Args: + tensor_index: Tensor index of tensor to get. This value can be gotten from + the 'index' field in get_output_details. + + Returns: + A function that can return a new numpy array pointing to the internal + TFLite tensor state at any point. It is safe to hold the function forever, + but it is not safe to hold the numpy array forever. + """ + return lambda: self._interpreter.tensor(self._interpreter, tensor_index) + def invoke(self): + """Invoke the interpreter. + + Be sure to set the input sizes, allocate tensors and fill values before + calling this. + + Raises: + ValueError: When the underlying interpreter fails raise ValueError. + """ + self._ensure_safe() if not self._interpreter.Invoke(): raise ValueError('Failed to invoke TFLite model') diff --git a/tensorflow/contrib/lite/python/interpreter_test.py b/tensorflow/contrib/lite/python/interpreter_test.py index f802edf020..5f1fa26c3b 100644 --- a/tensorflow/contrib/lite/python/interpreter_test.py +++ b/tensorflow/contrib/lite/python/interpreter_test.py @@ -91,5 +91,61 @@ class InterpreterTest(test_util.TensorFlowTestCase): self.assertTrue((expected_output == output_data).all()) +class InterpreterTensorAccessorTest(test_util.TensorFlowTestCase): + + def setUp(self): + self.interpreter = interpreter_wrapper.Interpreter( + model_path=resource_loader.get_path_to_datafile( + 'testdata/permute_float.tflite')) + self.interpreter.allocate_tensors() + self.input0 = self.interpreter.get_input_details()[0]['index'] + self.initial_data = np.array([[-1., -2., -3., -4.]], np.float32) + + def testTensorAccessor(self): + """Check that tensor returns a reference.""" + array_ref = self.interpreter.tensor(self.input0) + np.copyto(array_ref(), self.initial_data) + self.assertAllEqual(array_ref(), self.initial_data) + self.assertAllEqual( + self.interpreter.get_tensor(self.input0), self.initial_data) + + def testGetTensorAccessor(self): + """Check that get_tensor returns a copy.""" + self.interpreter.set_tensor(self.input0, self.initial_data) + array_initial_copy = self.interpreter.get_tensor(self.input0) + new_value = np.add(1., array_initial_copy) + self.interpreter.set_tensor(self.input0, new_value) + self.assertAllEqual(array_initial_copy, self.initial_data) + self.assertAllEqual(self.interpreter.get_tensor(self.input0), new_value) + + def testBase(self): + self.assertTrue(self.interpreter._safe_to_run()) + _ = self.interpreter.tensor(self.input0) + self.assertTrue(self.interpreter._safe_to_run()) + in0 = self.interpreter.tensor(self.input0)() + self.assertFalse(self.interpreter._safe_to_run()) + in0b = self.interpreter.tensor(self.input0)() + self.assertFalse(self.interpreter._safe_to_run()) + # Now get rid of the buffers so that we can evaluate. + del in0 + del in0b + self.assertTrue(self.interpreter._safe_to_run()) + + def testBaseProtectsFunctions(self): + in0 = self.interpreter.tensor(self.input0)() + # Make sure we get an exception if we try to run an unsafe operation + with self.assertRaisesRegexp( + RuntimeError, 'There is at least 1 reference'): + _ = self.interpreter.allocate_tensors() + # Make sure we get an exception if we try to run an unsafe operation + with self.assertRaisesRegexp( + RuntimeError, 'There is at least 1 reference'): + _ = self.interpreter.invoke() + # Now test that we can run + del in0 # this is our only buffer reference, so now it is safe to change + in0safe = self.interpreter.tensor(self.input0) + _ = self.interpreter.allocate_tensors() + del in0safe # make sure in0Safe is held but lint doesn't complain + if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/lite/python/interpreter_wrapper/BUILD b/tensorflow/contrib/lite/python/interpreter_wrapper/BUILD index 12ab38847d..634c2a1e1f 100644 --- a/tensorflow/contrib/lite/python/interpreter_wrapper/BUILD +++ b/tensorflow/contrib/lite/python/interpreter_wrapper/BUILD @@ -14,7 +14,7 @@ cc_library( "//tensorflow/contrib/lite:framework", "//tensorflow/contrib/lite/kernels:builtin_ops", "//tensorflow/core:lib", - "//tensorflow/python:numpy_lib", + "//third_party/py/numpy:headers", "//third_party/python_runtime:headers", "@com_google_absl//absl/memory", ], diff --git a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.cc b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.cc index 5979f81205..f705551fcb 100644 --- a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.cc +++ b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.cc @@ -21,7 +21,14 @@ limitations under the License. #include "tensorflow/contrib/lite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/core/platform/logging.h" -#include "tensorflow/python/lib/core/numpy.h" + +// Disallow Numpy 1.7 deprecated symbols. +#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION + +#include + +#include "numpy/arrayobject.h" +#include "numpy/ufuncobject.h" #if PY_MAJOR_VERSION >= 3 #define PY_TO_CPPSTRING PyBytes_AsStringAndSize @@ -35,6 +42,13 @@ namespace tflite { namespace interpreter_wrapper { namespace { + +// Calls PyArray's initialization to initialize all the API pointers. Note that +// this usage implies only this translation unit can use the pointers. See +// tensorflow/python/core/numpy.cc for a strategy if we ever need to extend +// this further. +void ImportNumpy() { import_array1(); } + std::unique_ptr CreateInterpreter( const tflite::FlatBufferModel* model, const tflite::ops::builtin::BuiltinOpResolver& resolver) { @@ -42,7 +56,7 @@ std::unique_ptr CreateInterpreter( return nullptr; } - tensorflow::ImportNumpy(); + ImportNumpy(); std::unique_ptr interpreter; tflite::InterpreterBuilder(*model, resolver)(&interpreter); @@ -288,47 +302,93 @@ bool InterpreterWrapper::SetTensor(int i, PyObject* value) { return true; } -PyObject* InterpreterWrapper::GetTensor(int i) const { - if (!interpreter_) { +namespace { + +PyObject* CheckGetTensorArgs(Interpreter* interpreter, int tensor_index, + TfLiteTensor** tensor, int* type_num) { + if (!interpreter) { LOG(ERROR) << "Invalid interpreter."; Py_INCREF(Py_None); return Py_None; } - if (i >= interpreter_->tensors_size()) { - LOG(ERROR) << "Invalid tensor index: " << i << " exceeds max tensor index " - << interpreter_->inputs().size(); + if (tensor_index >= interpreter->tensors_size() || tensor_index < 0) { + LOG(ERROR) << "Invalid tensor index: " << tensor_index + << " exceeds max tensor index " << interpreter->inputs().size(); Py_INCREF(Py_None); return Py_None; } - const TfLiteTensor* output_tensor = interpreter_->tensor(i); - const int tensor_size = output_tensor->bytes; - if (tensor_size <= 0) { + *tensor = interpreter->tensor(tensor_index); + if ((*tensor)->bytes == 0) { LOG(ERROR) << "Invalid tensor size"; Py_INCREF(Py_None); return Py_None; } - int type_num = TfLiteTypeToPyArrayType(output_tensor->type); - if (type_num == -1) { - LOG(ERROR) << "Unknown tensor type " << output_tensor->type; + *type_num = TfLiteTypeToPyArrayType((*tensor)->type); + if (*type_num == -1) { + LOG(ERROR) << "Unknown tensor type " << (*tensor)->type; + Py_INCREF(Py_None); + return Py_None; + } + + if (!(*tensor)->data.raw) { + LOG(ERROR) << "Tensor data is null."; Py_INCREF(Py_None); return Py_None; } - void* data = malloc(tensor_size); - memcpy(data, output_tensor->data.raw, tensor_size); + return nullptr; +} + +} // namespace - const TfLiteIntArray* output_dims = output_tensor->dims; - std::vector dims(output_dims->data, - output_dims->data + output_dims->size); +PyObject* InterpreterWrapper::GetTensor(int i) const { + // Sanity check accessor + TfLiteTensor* tensor = nullptr; + int type_num = 0; + if (PyObject* pynone_or_nullptr = + CheckGetTensorArgs(interpreter_.get(), i, &tensor, &type_num)) { + return pynone_or_nullptr; + } + std::vector dims(tensor->dims->data, + tensor->dims->data + tensor->dims->size); + // Make a buffer copy but we must tell Numpy It owns that data or else + // it will leak. + void* data = malloc(tensor->bytes); + if (!data) { + LOG(ERROR) << "Malloc to copy tensor failed."; + Py_INCREF(Py_None); + return Py_None; + } + memcpy(data, tensor->data.raw, tensor->bytes); PyObject* np_array = PyArray_SimpleNewFromData(dims.size(), dims.data(), type_num, data); - + PyArray_ENABLEFLAGS(reinterpret_cast(np_array), + NPY_ARRAY_OWNDATA); return PyArray_Return(reinterpret_cast(np_array)); } +PyObject* InterpreterWrapper::tensor(PyObject* base_object, int i) { + // Sanity check accessor + TfLiteTensor* tensor = nullptr; + int type_num = 0; + if (PyObject* pynone_or_nullptr = + CheckGetTensorArgs(interpreter_.get(), i, &tensor, &type_num)) { + return pynone_or_nullptr; + } + + std::vector dims(tensor->dims->data, + tensor->dims->data + tensor->dims->size); + PyArrayObject* np_array = + reinterpret_cast(PyArray_SimpleNewFromData( + dims.size(), dims.data(), type_num, tensor->data.raw)); + Py_INCREF(base_object); // SetBaseObject steals, so we need to add. + PyArray_SetBaseObject(np_array, base_object); + return PyArray_Return(np_array); +} + InterpreterWrapper* InterpreterWrapper::CreateWrapperCPPFromFile( const char* model_path) { std::unique_ptr model = diff --git a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h index 0972c57259..b0ed7c4559 100644 --- a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h +++ b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h @@ -57,6 +57,9 @@ class InterpreterWrapper { PyObject* TensorQuantization(int i) const; bool SetTensor(int i, PyObject* value); PyObject* GetTensor(int i) const; + // Returns a reference to tensor index i as a numpy array. The base_object + // should be the interpreter object providing the memory. + PyObject* tensor(PyObject* base_object, int i); private: InterpreterWrapper(std::unique_ptr model); -- GitLab From 8e86dcd1c59bb3f1dc978fcb5398dd3f2f51d9ad Mon Sep 17 00:00:00 2001 From: Dan J Date: Sun, 17 Jun 2018 18:10:58 -0400 Subject: [PATCH 579/816] Automate download and unzip of the model file (#14853) TESTING Used Android Studio 3.1.3, NDK r17b and Pixel XL API 24 emulator. Blocked from testing the built app due to this issue: https://github.com/tensorflow/tensorflow/issues/18658 Did a ./gradlew clean. Deleted intermediate download and unzipped versions of the model: ``` $ rm app/build/intermediates/mobilenet_v1_224_android_quant_2017_11_08.zip $ rm app/src/main/assets/mobilenet_quant_v1_224.tflite ``` Built the app and confirmed the model got downloaded and unzipped: ``` $ ./gradlew assemble :app:downloadModel Downloading https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_224_android_quant_2017_11_08.zip :app:unzipModel Unzipping build/intermediates/mobilenet_v1_224_android_quant_2017_11_08.zip :app:preBuild ``` Deleted the model file from the assets folder and checked it gets unzipped again from the intermediate storage location: ``` $ ./gradlew assemble :app:downloadModel UP-TO-DATE :app:unzipModel Unzipping build/intermediates/mobilenet_v1_224_android_quant_2017_11_08.zip :app:preBuild ``` Built it again and check it doesn't get downloaded or unzipped again: ``` $ ./gradlew assemble :app:downloadModel UP-TO-DATE :app:unzipModel UP-TO-DATE ``` --- .../contrib/lite/java/demo/app/build.gradle | 36 +++++++++++++++++++ .../docs_src/mobile/tflite/demo_android.md | 23 ++++++------ 2 files changed, 47 insertions(+), 12 deletions(-) diff --git a/tensorflow/contrib/lite/java/demo/app/build.gradle b/tensorflow/contrib/lite/java/demo/app/build.gradle index 7f29deed83..44ea2dcd90 100644 --- a/tensorflow/contrib/lite/java/demo/app/build.gradle +++ b/tensorflow/contrib/lite/java/demo/app/build.gradle @@ -56,3 +56,39 @@ dependencies { testCompile 'junit:junit:4.12' } + +def modelDownloadUrl = "https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_224_android_quant_2017_11_08.zip" +def localCache = "build/intermediates/mobilenet_v1_224_android_quant_2017_11_08.zip" +def targetFolder = "src/main/assets" + +task downloadModel(type: DownloadUrlTask) { + doFirst { + println "Downloading ${modelDownloadUrl}" + } + sourceUrl = "${modelDownloadUrl}" + target = file("${localCache}") +} + +task unzipModel(type: Copy, dependsOn: 'downloadModel') { + doFirst { + println "Unzipping ${localCache}" + } + from zipTree("${localCache}") + into "${targetFolder}" +} + +// Ensure the model file is downloaded and extracted before every build +preBuild.dependsOn unzipModel + +class DownloadUrlTask extends DefaultTask { + @Input + String sourceUrl + + @OutputFile + File target + + @TaskAction + void download() { + ant.get(src: sourceUrl, dest: target) + } +} \ No newline at end of file diff --git a/tensorflow/docs_src/mobile/tflite/demo_android.md b/tensorflow/docs_src/mobile/tflite/demo_android.md index 480d66bbb6..6f9893f8f1 100644 --- a/tensorflow/docs_src/mobile/tflite/demo_android.md +++ b/tensorflow/docs_src/mobile/tflite/demo_android.md @@ -44,23 +44,22 @@ app: Android Studio project. * Install all the Gradle extensions it requests. -To get a model, either: - -* Download the quantized [Mobilenet TensorFlow Lite model](https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_224_android_quant_2017_11_08.zip) - and unzip and copy `mobilenet_quant_v1_224.tflite` to the assets directory: - `tensorflow/contrib/lite/java/demo/app/src/main/assets/`. -* Or, download the floating point [Inception-v3 model](https://storage.googleapis.com/download.tensorflow.org/models/tflite/inception_v3_slim_2016_android_2017_11_10.zip) - and unzip and copy `inceptionv3_non_slim_2015.tflite` to the assets - directory. Change the chosen classifier in - [Camera2BasicFragment.java](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/Camera2BasicFragment.java)
- from: `classifier = new ImageClassifierQuantizedMobileNet(getActivity());`
- to: `classifier = new ImageClassifierFloatInception(getActivity());`. +Now you can build and run the demo app. -Now you can build and run the demo app. +The build process downloads the quantized [Mobilenet TensorFlow Lite model](https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_224_android_quant_2017_11_08.zip), and unzips it into the assets directory: `tensorflow/contrib/lite/java/demo/app/src/main/assets/`. Some additional details are available on the [TF Lite Android App page](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/java/demo/README.md). +### Using other models + +To use a different model: +* Download the floating point [Inception-v3 model](https://storage.googleapis.com/download.tensorflow.org/models/tflite/inception_v3_slim_2016_android_2017_11_10.zip). +* Unzip and copy `inceptionv3_non_slim_2015.tflite` to the assets directory. +* Change the chosen classifier in [Camera2BasicFragment.java](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/Camera2BasicFragment.java)
+ from: `classifier = new ImageClassifierQuantizedMobileNet(getActivity());`
+ to: `classifier = new ImageClassifierFloatInception(getActivity());`. + ## Build TensorFlow Lite and the demo app from source -- GitLab From 2c4535c489124b71eac73ec120ca08d5d976a7b9 Mon Sep 17 00:00:00 2001 From: Gunhan Gulsoy Date: Sun, 17 Jun 2018 20:52:11 -0700 Subject: [PATCH 580/816] Disable flaky random_ops_test PiperOrigin-RevId: 200934420 --- tensorflow/python/kernel_tests/random/BUILD | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/tensorflow/python/kernel_tests/random/BUILD b/tensorflow/python/kernel_tests/random/BUILD index acd7566eec..4855e1c564 100644 --- a/tensorflow/python/kernel_tests/random/BUILD +++ b/tensorflow/python/kernel_tests/random/BUILD @@ -88,6 +88,10 @@ cuda_py_test( "//tensorflow/python:framework_for_generated_wrappers", "//tensorflow/python:random_ops", ], + tags = [ + "manual", + "no_oss", + ], ) cuda_py_test( -- GitLab From 6c4d248f228aaebb93c3f5f5041e7c62308f3ec0 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 18 Jun 2018 03:18:48 -0700 Subject: [PATCH 581/816] Enable bfloat propagation for bitcast HLO If the input and output element type for a bitcast is the same (it is only a layout and shape change) then its effective output precision is same as its input precision. PiperOrigin-RevId: 200966788 --- tensorflow/compiler/xla/service/bfloat16_support.cc | 3 +++ 1 file changed, 3 insertions(+) diff --git a/tensorflow/compiler/xla/service/bfloat16_support.cc b/tensorflow/compiler/xla/service/bfloat16_support.cc index 07b4b14b5e..67b5d4dc2c 100644 --- a/tensorflow/compiler/xla/service/bfloat16_support.cc +++ b/tensorflow/compiler/xla/service/bfloat16_support.cc @@ -92,6 +92,9 @@ bool BFloat16Support::EffectiveOperandPrecisionIsOutputPrecision( case HloOpcode::kTranspose: case HloOpcode::kTuple: return true; + case HloOpcode::kBitcast: + return hlo.shape().element_type() == + hlo.operand(0)->shape().element_type(); case HloOpcode::kDynamicSlice: return operand_index == 0; case HloOpcode::kDynamicUpdateSlice: -- GitLab From 8722fe2dd65a5f59afaff16b0aed9712e3914388 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 18 Jun 2018 03:39:52 -0700 Subject: [PATCH 582/816] Support BF16 propagation through domain instructions Domain instructions only there to carry some metadata so they don't effect the precision of the data so we should propagate BF16 through them. The special code needed to handle domain instructions is there as this is the only HLO what have the same tuple shaped operand and result. PiperOrigin-RevId: 200968713 --- .../xla/service/bfloat16_propagation.cc | 50 +++++++++++++------ .../xla/service/bfloat16_propagation_test.cc | 39 +++++++++++++++ .../compiler/xla/service/bfloat16_support.cc | 3 ++ 3 files changed, 76 insertions(+), 16 deletions(-) diff --git a/tensorflow/compiler/xla/service/bfloat16_propagation.cc b/tensorflow/compiler/xla/service/bfloat16_propagation.cc index d514b99ed0..ee6b6f69b9 100644 --- a/tensorflow/compiler/xla/service/bfloat16_propagation.cc +++ b/tensorflow/compiler/xla/service/bfloat16_propagation.cc @@ -204,6 +204,12 @@ void BFloat16Propagation::DetermineWhileComputationsPrecision( bool BFloat16Propagation::AllUsersConsumeBF16(const HloInstruction& hlo, const ShapeIndex& index) const { + // If the subshape isn't floating point then none of the users will be BF16. + const Shape& subshape = ShapeUtil::GetSubshape(hlo.shape(), index); + if (subshape.element_type() != BF16 && subshape.element_type() != F32) { + return false; + } + auto& value_set = dataflow_->GetValueSet(&hlo, index); for (const HloValue* value : value_set.values()) { if (ContainsKey(values_that_must_be_kept_as_f32_, value)) { @@ -257,23 +263,34 @@ bool BFloat16Propagation::AllUsersConsumeBF16(const HloInstruction& hlo, // If the op propagates precision and it outputs a BF16, then it's OK to // supply BF16 also as the input. In the backward pass, the users shapes // should have already been processed. - PrimitiveType user_output_type = PRIMITIVE_TYPE_INVALID; - if (use.instruction->opcode() == HloOpcode::kTuple || - (use.instruction->opcode() == HloOpcode::kCrossReplicaSum && - ShapeUtil::IsTuple(use.instruction->shape()))) { - ShapeIndex use_output_index{use.operand_number}; - for (int64 i : use.operand_index) { - use_output_index.push_back(i); - } - user_output_type = - OutputTypeAfterChange(use.instruction, use_output_index); - } else { - user_output_type = OutputTypeAfterChange(use.instruction, {}); - } if (bfloat16_support_->EffectiveOperandPrecisionIsOutputPrecision( - *use.instruction, use.operand_number) && - user_output_type == BF16) { - continue; + *use.instruction, use.operand_number)) { + if (use.instruction->opcode() == HloOpcode::kTuple || + (use.instruction->opcode() == HloOpcode::kCrossReplicaSum && + ShapeUtil::IsTuple(use.instruction->shape()))) { + ShapeIndex use_output_index{use.operand_number}; + for (int64 i : use.operand_index) { + use_output_index.push_back(i); + } + if (OutputTypeAfterChange(use.instruction, use_output_index) == + BF16) { + continue; + } + } else if (use.instruction->opcode() == HloOpcode::kGetTupleElement) { + ShapeIndex use_output_index; + for (int64 i = 1; i < use.operand_index.size(); ++i) { + use_output_index.push_back(use.operand_index[i]); + } + if (OutputTypeAfterChange(use.instruction, use_output_index) == + BF16) { + continue; + } + } else { + if (OutputTypeAfterChange(use.instruction, use.operand_index) == + BF16) { + continue; + } + } } return false; } @@ -368,6 +385,7 @@ bool BFloat16Propagation::InstructionIsCandidateForBF16Output( if (!bfloat16_support_->SupportsMixedPrecisions(*hlo) && hlo->opcode() != HloOpcode::kTuple && hlo->opcode() != HloOpcode::kGetTupleElement && + hlo->opcode() != HloOpcode::kDomain && hlo->shape().element_type() != BF16) { for (int64 i = 0; i < hlo->operand_count(); ++i) { if (!bfloat16_support_->EffectiveOperandPrecisionIsOutputPrecision(*hlo, diff --git a/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc b/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc index 5e1499ee6b..f8d7b5e919 100644 --- a/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc +++ b/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc @@ -742,4 +742,43 @@ TEST_F(BFloat16PropagationTest, NoopConversionRemoved) { EXPECT_EQ(add1->shape().element_type(), BF16); } +TEST_F(BFloat16PropagationTest, TupleDomain) { + auto builder = HloComputation::Builder(TestName()); + Shape shape = ShapeUtil::MakeShape(F32, {4, 4}); + + HloInstruction* a = + builder.AddInstruction(HloInstruction::CreateParameter(0, shape, "a")); + HloInstruction* b = + builder.AddInstruction(HloInstruction::CreateParameter(1, shape, "b")); + HloInstruction* a_trans = + builder.AddInstruction(HloInstruction::CreateTranspose(shape, a, {0, 1})); + HloInstruction* b_trans = + builder.AddInstruction(HloInstruction::CreateTranspose(shape, b, {0, 1})); + HloInstruction* tuple = + builder.AddInstruction(HloInstruction::CreateTuple({a_trans, b_trans})); + HloInstruction* domain = builder.AddInstruction( + HloInstruction::CreateDomain(tuple->shape(), tuple, nullptr, nullptr)); + HloInstruction* a_gte = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, domain, 0)); + HloInstruction* b_gte = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(shape, domain, 1)); + HloInstruction* dot = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kDot, a_gte, b_gte)); + HloInstruction* root = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, dot, dot)); + + auto module = CreateNewModule(); + auto computation = module->AddEntryComputation(builder.Build()); + + EXPECT_TRUE(PropagatePrecision(module.get())); + + EXPECT_EQ(computation->root_instruction(), root); + EXPECT_TRUE(OutputsBF16(a_trans)); + EXPECT_TRUE(OutputsBF16(b_trans)); + EXPECT_TRUE(OutputsBF16(a_gte)); + EXPECT_TRUE(OutputsBF16(b_gte)); + EXPECT_FALSE(OutputsBF16(a)); + EXPECT_FALSE(OutputsBF16(b)); +} + } // namespace xla diff --git a/tensorflow/compiler/xla/service/bfloat16_support.cc b/tensorflow/compiler/xla/service/bfloat16_support.cc index 67b5d4dc2c..8595afca7e 100644 --- a/tensorflow/compiler/xla/service/bfloat16_support.cc +++ b/tensorflow/compiler/xla/service/bfloat16_support.cc @@ -25,6 +25,7 @@ bool BFloat16Support::SupportsBF16Operand(const HloInstruction& hlo, case HloOpcode::kCall: case HloOpcode::kConditional: case HloOpcode::kCustomCall: + case HloOpcode::kDomain: case HloOpcode::kGetTupleElement: case HloOpcode::kTuple: case HloOpcode::kWhile: @@ -43,6 +44,7 @@ bool BFloat16Support::SupportsBF16Output(const HloInstruction& hlo) const { case HloOpcode::kCall: case HloOpcode::kConditional: case HloOpcode::kCustomCall: + case HloOpcode::kDomain: case HloOpcode::kGetTupleElement: case HloOpcode::kTuple: case HloOpcode::kWhile: @@ -81,6 +83,7 @@ bool BFloat16Support::EffectiveOperandPrecisionIsOutputPrecision( case HloOpcode::kConcatenate: case HloOpcode::kConvert: case HloOpcode::kCopy: + case HloOpcode::kDomain: case HloOpcode::kGetTupleElement: case HloOpcode::kMaximum: case HloOpcode::kMinimum: -- GitLab From 4e0e0750b0cb6ba922503b8e543c378ea0ee937b Mon Sep 17 00:00:00 2001 From: Yash Katariya Date: Mon, 18 Jun 2018 13:09:36 +0000 Subject: [PATCH 583/816] Fixed a typo --- .../python/examples/nmt_with_attention/NMT_with_Attention.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/eager/python/examples/nmt_with_attention/NMT_with_Attention.ipynb b/tensorflow/contrib/eager/python/examples/nmt_with_attention/NMT_with_Attention.ipynb index 066ef0addc..a616a67956 100644 --- a/tensorflow/contrib/eager/python/examples/nmt_with_attention/NMT_with_Attention.ipynb +++ b/tensorflow/contrib/eager/python/examples/nmt_with_attention/NMT_with_Attention.ipynb @@ -53,7 +53,7 @@ "\n", "Ballpark, this example will take approximately 10 mintues to run on a single P100 GPU.\n", "\n", - "This notebook requires tensorflow veersion >= 1.9" + "This notebook requires Tensorflow version >= 1.9" ] }, { -- GitLab From 95f3a84009a19f7e257eb0371601cc905515be82 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 18 Jun 2018 06:59:43 -0700 Subject: [PATCH 584/816] Use --output_user_root to specify a short output base for Windows build (Prepare for upgrading Bazel to 0.14.1 on Windows) PiperOrigin-RevId: 200988382 --- .../tools/ci_build/windows/cpu/pip/build_tf_windows.sh | 7 ++++++- tensorflow/tools/ci_build/windows/libtensorflow_cpu.sh | 2 +- 2 files changed, 7 insertions(+), 2 deletions(-) diff --git a/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh b/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh index 0b13b97209..4aa270ea86 100644 --- a/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh +++ b/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh @@ -77,7 +77,12 @@ fi # to distinct them. This helps avoid building the same targets twice. echo "build --distinct_host_configuration=false" >> "${TMP_BAZELRC}" -echo "import %workspace%/${TMP_BAZELRC}" >> .bazelrc +# Enable short object file path to avoid long path issue on Windows. +echo "build --output_user_root=${TMPDIR}" >> "${TMP_BAZELRC}" + +if ! grep -q "import %workspace%/${TMP_BAZELRC}" .bazelrc; then + echo "import %workspace%/${TMP_BAZELRC}" >> .bazelrc +fi run_configure_for_cpu_build diff --git a/tensorflow/tools/ci_build/windows/libtensorflow_cpu.sh b/tensorflow/tools/ci_build/windows/libtensorflow_cpu.sh index 583d1d5f09..022f120dbd 100755 --- a/tensorflow/tools/ci_build/windows/libtensorflow_cpu.sh +++ b/tensorflow/tools/ci_build/windows/libtensorflow_cpu.sh @@ -41,7 +41,7 @@ run_configure_for_cpu_build # build_libtensorflow_tarball in ../builds/libtensorflow.sh # cannot be used on Windows since it relies on pkg_tar rules. # So we do something special here -bazel build -c opt --copt=/arch:AVX \ +bazel build -c opt --copt=/arch:AVX --output_user_root=${TMPDIR} \ tensorflow:libtensorflow.so \ tensorflow/tools/lib_package:clicenses_generate \ tensorflow/java:libtensorflow_jni.so \ -- GitLab From 32ca2bd72b40247061f39006b45f1b09921e4f82 Mon Sep 17 00:00:00 2001 From: Benjamin Kramer Date: Mon, 18 Jun 2018 07:47:04 -0700 Subject: [PATCH 585/816] [XLA:GPU] Don't run layout assignment (or any HLO passes) in multioutput fusion test This allows making the GPU emitter checks more restrictive (this would be a miscompile otherwise). Layout assignment cannot run with pre-assigned layouts currently. PiperOrigin-RevId: 200993754 --- .../xla/service/gpu/ir_emitter_unnested.cc | 22 +++++----- .../xla/tests/multioutput_fusion_test.cc | 41 +++++++++---------- 2 files changed, 32 insertions(+), 31 deletions(-) diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc index 078afed3e2..71e0562e40 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc @@ -551,17 +551,14 @@ Status IrEmitterUnnested::HandleFusion(HloInstruction* fusion) { if (root->opcode() == HloOpcode::kTuple) { output_shape_index = {i}; } - // TODO(kramerb): CHECK that layouts are equal. Currently this - // breaks multioutputfusion_test. The test has pre-fused - // instructions, but layout_assignment will not assign any layouts - // for instructions inside of a fused computation. It just removes - // the layouts instead. if (inst->opcode() == HloOpcode::kReduce) { - CHECK(ShapeUtil::Compatible(first_reduce->shape(), inst->shape())); - CHECK(ShapeUtil::Compatible(first_reduce->operand(0)->shape(), - inst->operand(0)->shape())); - CHECK(ShapeUtil::Compatible(first_reduce->operand(1)->shape(), - inst->operand(1)->shape())); + // Shapes, layouts and dimensions must be the same for all reduces + // inside of this fusion. + CHECK(ShapeUtil::Equal(first_reduce->shape(), inst->shape())); + CHECK(ShapeUtil::Equal(first_reduce->operand(0)->shape(), + inst->operand(0)->shape())); + CHECK(ShapeUtil::Equal(first_reduce->operand(1)->shape(), + inst->operand(1)->shape())); CHECK(first_reduce->dimensions() == inst->dimensions()); input_gens.push_back(fused_emitter.GetGenerator(inst->operand(0))); init_value_gens.push_back( @@ -569,8 +566,13 @@ Status IrEmitterUnnested::HandleFusion(HloInstruction* fusion) { reducers.push_back(inst->to_apply()); reduce_output_shapes.push_back(std::move(output_shape_index)); } else { + // For extra outputs we can relax shape equality to allow different + // types (with the same number of elements). Layouts still have to + // match. CHECK(ShapeUtil::CompatibleIgnoringElementType( first_reduce->operand(0)->shape(), inst->shape())); + CHECK(LayoutUtil::Equal(first_reduce->operand(0)->shape().layout(), + inst->shape().layout())); extra_output_gens.emplace_back(fused_emitter.GetGenerator(inst), std::move(output_shape_index)); } diff --git a/tensorflow/compiler/xla/tests/multioutput_fusion_test.cc b/tensorflow/compiler/xla/tests/multioutput_fusion_test.cc index 6837b05fb5..92df76d332 100644 --- a/tensorflow/compiler/xla/tests/multioutput_fusion_test.cc +++ b/tensorflow/compiler/xla/tests/multioutput_fusion_test.cc @@ -204,8 +204,8 @@ XLA_TEST_F(MultiOutputFusionTest, FusionNodeIsRoot) { Literal::CreateR0(1.0)), Literal::MakeTupleOwned(Literal::CreateR0(3.0), Literal::CreateR0(4))); - TF_ASSERT_OK_AND_ASSIGN(auto result, - Execute(std::move(module), {param.get()})); + std::unique_ptr result = + ExecuteNoHloPasses(std::move(module), {param.get()}); EXPECT_TRUE(LiteralTestUtil::Equal( *result, *Literal::MakeTupleOwned(Literal::CreateR0(42)))); } @@ -233,8 +233,8 @@ XLA_TEST_F(MultiOutputFusionTest, MultiOutputLoopFusion) { HloRunner::CreateModuleFromString(testcase, GetDebugOptionsForTest()) .ValueOrDie(); auto param = Literal::CreateR1({1.0, 2.0, 3.0, -1.0}); - TF_ASSERT_OK_AND_ASSIGN(auto result, - Execute(std::move(module), {param.get()})); + std::unique_ptr result = + ExecuteNoHloPasses(std::move(module), {param.get()}); EXPECT_TRUE(LiteralTestUtil::Equal( *result, *Literal::CreateR1({0.0, 4.0, 9.0, 1.0}))); } @@ -267,8 +267,8 @@ XLA_TEST_F(MultiOutputFusionTest, MultiOutputLoopFeedingMap) { HloRunner::CreateModuleFromString(testcase, GetDebugOptionsForTest()) .ValueOrDie(); auto param = Literal::CreateR1({1.0, 2.0, 3.0}); - TF_ASSERT_OK_AND_ASSIGN(auto result, - Execute(std::move(module), {param.get()})); + std::unique_ptr result = + ExecuteNoHloPasses(std::move(module), {param.get()}); EXPECT_TRUE(LiteralTestUtil::Equal( *result, *Literal::CreateR1({0.0, 4.0, 9.0}))); } @@ -311,8 +311,8 @@ XLA_TEST_F(MultiOutputFusionTest, HloRunner::CreateModuleFromString(testcase, GetDebugOptionsForTest()) .ValueOrDie(); auto param = Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}); - TF_ASSERT_OK_AND_ASSIGN(auto result, - Execute(std::move(module), {param.get()})); + std::unique_ptr result = + ExecuteNoHloPasses(std::move(module), {param.get()}); EXPECT_TRUE(LiteralTestUtil::Equal( *result, *Literal::MakeTupleOwned(Literal::CreateR2({{3, 7}, {11, 15}}), @@ -341,8 +341,8 @@ XLA_TEST_F(MultiOutputFusionTest, HloRunner::CreateModuleFromString(testcase, GetDebugOptionsForTest()) .ValueOrDie(); auto param = Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}); - TF_ASSERT_OK_AND_ASSIGN(auto result, - Execute(std::move(module), {param.get()})); + std::unique_ptr result = + ExecuteNoHloPasses(std::move(module), {param.get()}); EXPECT_TRUE(LiteralTestUtil::Equal( *result, *Literal::MakeTupleOwned( Literal::CreateR2({{6, 8}, {10, 12}}), @@ -372,8 +372,8 @@ XLA_TEST_F(MultiOutputFusionTest, HloRunner::CreateModuleFromString(testcase, GetDebugOptionsForTest()) .ValueOrDie(); auto param = Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}); - TF_ASSERT_OK_AND_ASSIGN(auto result, - Execute(std::move(module), {param.get()})); + std::unique_ptr result = + ExecuteNoHloPasses(std::move(module), {param.get()}); EXPECT_TRUE(LiteralTestUtil::Equal( *result, *Literal::MakeTupleOwned(Literal::CreateR1({14, 22}), Literal::CreateR1({36, 64}), @@ -403,8 +403,8 @@ XLA_TEST_F(MultiOutputFusionTest, HloRunner::CreateModuleFromString(testcase, GetDebugOptionsForTest()) .ValueOrDie(); auto param = Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}); - TF_ASSERT_OK_AND_ASSIGN(auto result, - Execute(std::move(module), {param.get()})); + std::unique_ptr result = + ExecuteNoHloPasses(std::move(module), {param.get()}); EXPECT_TRUE(LiteralTestUtil::Equal( *result, *Literal::MakeTupleOwned( @@ -436,8 +436,8 @@ XLA_TEST_F(MultiOutputFusionTest, HloRunner::CreateModuleFromString(testcase, GetDebugOptionsForTest()) .ValueOrDie(); auto param = Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}); - TF_ASSERT_OK_AND_ASSIGN(auto result, - Execute(std::move(module), {param.get()})); + std::unique_ptr result = + ExecuteNoHloPasses(std::move(module), {param.get()}); EXPECT_TRUE(LiteralTestUtil::Equal( *result, *Literal::MakeTupleOwned( @@ -469,8 +469,8 @@ XLA_TEST_F(MultiOutputFusionTest, HloRunner::CreateModuleFromString(testcase, GetDebugOptionsForTest()) .ValueOrDie(); auto param = Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}); - TF_ASSERT_OK_AND_ASSIGN(auto result, - Execute(std::move(module), {param.get()})); + std::unique_ptr result = + ExecuteNoHloPasses(std::move(module), {param.get()}); EXPECT_TRUE(LiteralTestUtil::Equal( *result, *Literal::MakeTupleOwned( @@ -505,9 +505,8 @@ XLA_TEST_F(MultiOutputFusionTest, auto param = Literal::CreateR3({{{0, 2}, {3, 4}}, {{5, 6}, {7, 8}}}); auto init1 = Literal::CreateR0(5); auto init2 = Literal::CreateR0(6); - TF_ASSERT_OK_AND_ASSIGN( - auto result, - Execute(std::move(module), {param.get(), init1.get(), init2.get()})); + std::unique_ptr result = ExecuteNoHloPasses( + std::move(module), {param.get(), init1.get(), init2.get()}); EXPECT_TRUE(LiteralTestUtil::Equal( *result, *Literal::MakeTupleOwned( Literal::CreateR2({{167, 172}, {176, 180}}), -- GitLab From e2617ac25490b33c87b8e792eee0670b09a7305f Mon Sep 17 00:00:00 2001 From: Dan Osipov Date: Mon, 18 Jun 2018 10:54:06 -0400 Subject: [PATCH 586/816] Update goldens --- tensorflow/tools/api/golden/tensorflow.image.pbtxt | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/tensorflow/tools/api/golden/tensorflow.image.pbtxt b/tensorflow/tools/api/golden/tensorflow.image.pbtxt index 10171b3d60..e268fa3f61 100644 --- a/tensorflow/tools/api/golden/tensorflow.image.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.image.pbtxt @@ -172,6 +172,10 @@ tf_module { name: "resize_image_with_crop_or_pad" argspec: "args=[\'image\', \'target_height\', \'target_width\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "resize_image_with_pad" + argspec: "args=[\'image\', \'target_height\', \'target_width\', \'method\'], varargs=None, keywords=None, defaults=[\'0\'], " + } member_method { name: "resize_images" argspec: "args=[\'images\', \'size\', \'method\', \'align_corners\', \'preserve_aspect_ratio\'], varargs=None, keywords=None, defaults=[\'0\', \'False\', \'False\'], " -- GitLab From 1b52f917a3b5cb1e50885ae15715c4dc72b9a81b Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 18 Jun 2018 08:34:29 -0700 Subject: [PATCH 587/816] Rename object detection custom op filenames to be consistent with earlier comments on renaming the file and op. PiperOrigin-RevId: 200999974 --- tensorflow/contrib/lite/kernels/BUILD | 6 ++--- ...processing.cc => detection_postprocess.cc} | 14 +++++----- ..._test.cc => detection_postprocess_test.cc} | 26 +++++++++---------- tensorflow/contrib/lite/kernels/register.cc | 6 ++--- 4 files changed, 25 insertions(+), 27 deletions(-) rename tensorflow/contrib/lite/kernels/{ssd_postprocessing.cc => detection_postprocess.cc} (98%) rename tensorflow/contrib/lite/kernels/{ssd_postprocess_test.cc => detection_postprocess_test.cc} (92%) diff --git a/tensorflow/contrib/lite/kernels/BUILD b/tensorflow/contrib/lite/kernels/BUILD index c0b5a07703..bb5558443b 100644 --- a/tensorflow/contrib/lite/kernels/BUILD +++ b/tensorflow/contrib/lite/kernels/BUILD @@ -142,6 +142,7 @@ cc_library( "conv.cc", "depthwise_conv.cc", "dequantize.cc", + "detection_postprocess.cc", "div.cc", "elementwise.cc", "embedding_lookup.cc", @@ -174,7 +175,6 @@ cc_library( "sparse_to_dense.cc", "split.cc", "squeeze.cc", - "ssd_postprocessing.cc", "strided_slice.cc", "sub.cc", "svdf.cc", @@ -248,9 +248,9 @@ tf_cc_test( ) tf_cc_test( - name = "ssd_postprocess_test", + name = "detection_postprocess_test", size = "small", - srcs = ["ssd_postprocess_test.cc"], + srcs = ["detection_postprocess_test.cc"], tags = ["tflite_not_portable_ios"], deps = [ ":builtin_ops", diff --git a/tensorflow/contrib/lite/kernels/ssd_postprocessing.cc b/tensorflow/contrib/lite/kernels/detection_postprocess.cc similarity index 98% rename from tensorflow/contrib/lite/kernels/ssd_postprocessing.cc rename to tensorflow/contrib/lite/kernels/detection_postprocess.cc index 078c4bdd11..e4ee5885e9 100644 --- a/tensorflow/contrib/lite/kernels/ssd_postprocessing.cc +++ b/tensorflow/contrib/lite/kernels/detection_postprocess.cc @@ -27,7 +27,7 @@ limitations under the License. namespace tflite { namespace ops { namespace custom { -namespace ssd_postprocess { +namespace detection_postprocess { // Input tensors constexpr int kInputTensorBoxEncodings = 0; @@ -574,13 +574,13 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { return kTfLiteOk; } +} // namespace detection_postprocess -} // namespace ssd_postprocess - -TfLiteRegistration* Register_SSD_POSTPROCESS() { - static TfLiteRegistration r = {ssd_postprocess::Init, ssd_postprocess::Free, - ssd_postprocess::Prepare, - ssd_postprocess::Eval}; +TfLiteRegistration* Register_DETECTION_POSTPROCESS() { + static TfLiteRegistration r = {detection_postprocess::Init, + detection_postprocess::Free, + detection_postprocess::Prepare, + detection_postprocess::Eval}; return &r; } diff --git a/tensorflow/contrib/lite/kernels/ssd_postprocess_test.cc b/tensorflow/contrib/lite/kernels/detection_postprocess_test.cc similarity index 92% rename from tensorflow/contrib/lite/kernels/ssd_postprocess_test.cc rename to tensorflow/contrib/lite/kernels/detection_postprocess_test.cc index b0f8824115..e801c5ace3 100644 --- a/tensorflow/contrib/lite/kernels/ssd_postprocess_test.cc +++ b/tensorflow/contrib/lite/kernels/detection_postprocess_test.cc @@ -27,17 +27,19 @@ namespace tflite { namespace ops { namespace custom { -TfLiteRegistration* Register_SSD_POSTPROCESS(); +TfLiteRegistration* Register_DETECTION_POSTPROCESS(); namespace { using ::testing::ElementsAre; using ::testing::ElementsAreArray; -class BaseSSDPostprocessOpModel : public SingleOpModel { +class BaseDetectionPostprocessOpModel : public SingleOpModel { public: - BaseSSDPostprocessOpModel(const TensorData& input1, const TensorData& input2, - const TensorData& input3, const TensorData& output1, + BaseDetectionPostprocessOpModel(const TensorData& input1, + const TensorData& input2, + const TensorData& input3, + const TensorData& output1, const TensorData& output2, const TensorData& output3, const TensorData& output4) { @@ -62,8 +64,8 @@ class BaseSSDPostprocessOpModel : public SingleOpModel { fbb.Float("w_scale", 5.0); }); fbb.Finish(); - SetCustomOp("TFLite_SSD_PostProcess", fbb.GetBuffer(), - Register_SSD_POSTPROCESS); + SetCustomOp("TFLite_Detection_PostProcess", fbb.GetBuffer(), + Register_DETECTION_POSTPROCESS); BuildInterpreter({GetShape(input1_), GetShape(input2_), GetShape(input3_)}); } @@ -121,8 +123,8 @@ class BaseSSDPostprocessOpModel : public SingleOpModel { int output4_; }; -TEST(SSDPostprocessOpTest, FloatTest) { - BaseSSDPostprocessOpModel m( +TEST(DetectionPostprocessOpTest, FloatTest) { + BaseDetectionPostprocessOpModel m( {TensorType_FLOAT32, {1, 6, 4}}, {TensorType_FLOAT32, {1, 6, 3}}, {TensorType_FLOAT32, {6, 4}}, {TensorType_FLOAT32, {}}, {TensorType_FLOAT32, {}}, {TensorType_FLOAT32, {}}, @@ -146,9 +148,7 @@ TEST(SSDPostprocessOpTest, FloatTest) { // 0.0, 10.0, 1.0, 11.0, // 0.0, 10.1, 1.0, 11.1, // 0.0, 100.0, 1.0, 101.0} - m.Invoke(); - // detection_boxes // in center-size std::vector output_shape1 = m.GetOutputShape1(); @@ -175,13 +175,12 @@ TEST(SSDPostprocessOpTest, FloatTest) { ElementsAreArray(ArrayFloatNear({3.0}, 1e-1))); } -TEST(SSDPostprocessOpTest, QuantizedTest) { - BaseSSDPostprocessOpModel m( +TEST(DetectionPostprocessOpTest, QuantizedTest) { + BaseDetectionPostprocessOpModel m( {TensorType_UINT8, {1, 6, 4}, -1.0, 1.0}, {TensorType_UINT8, {1, 6, 3}, 0.0, 1.0}, {TensorType_FLOAT32, {6, 4}}, {TensorType_FLOAT32, {}}, {TensorType_FLOAT32, {}}, {TensorType_FLOAT32, {}}, {TensorType_FLOAT32, {}}); - // six boxes in center-size encoding std::vector> inputs1 = { {0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, -1.0, 0.0, 0.0, @@ -197,7 +196,6 @@ TEST(SSDPostprocessOpTest, QuantizedTest) { 0.5, 0.5, 1.0, 1.0, 0.5, 10.5, 1.0, 1.0, 0.5, 10.5, 1.0, 1.0, 0.5, 100.5, 1.0, 1.0}); m.Invoke(); - // detection_boxes // in center-size std::vector output_shape1 = m.GetOutputShape1(); diff --git a/tensorflow/contrib/lite/kernels/register.cc b/tensorflow/contrib/lite/kernels/register.cc index 718f91302c..b893e40fe3 100644 --- a/tensorflow/contrib/lite/kernels/register.cc +++ b/tensorflow/contrib/lite/kernels/register.cc @@ -22,7 +22,7 @@ namespace custom { TfLiteRegistration* Register_AUDIO_SPECTROGRAM(); TfLiteRegistration* Register_MFCC(); -TfLiteRegistration* Register_SSD_POSTPROCESS(); +TfLiteRegistration* Register_DETECTION_POSTPROCESS(); } // namespace custom @@ -183,8 +183,8 @@ BuiltinOpResolver::BuiltinOpResolver() { AddCustom("Mfcc", tflite::ops::custom::Register_MFCC()); AddCustom("AudioSpectrogram", tflite::ops::custom::Register_AUDIO_SPECTROGRAM()); - AddCustom("TFLite_SSD_PostProcess", - tflite::ops::custom::Register_SSD_POSTPROCESS()); + AddCustom("TFLite_Detection_PostProcess", + tflite::ops::custom::Register_DETECTION_POSTPROCESS()); } } // namespace builtin -- GitLab From 147eb9db850dbd50dcb2ac5aa52c51396b82c4c0 Mon Sep 17 00:00:00 2001 From: Benjamin Kramer Date: Mon, 18 Jun 2018 08:46:54 -0700 Subject: [PATCH 588/816] [XLA] Change calls to LiteralTestUtil::Equal to pass in the expected value first This makes the failure output less confusing. PiperOrigin-RevId: 201001511 --- .../xla/service/bfloat16_propagation_test.cc | 8 ++-- .../xla/tests/gather_operation_test.cc | 4 +- .../xla/tests/multioutput_fusion_test.cc | 44 +++++++++---------- tensorflow/compiler/xla/tests/tuple_test.cc | 4 +- 4 files changed, 30 insertions(+), 30 deletions(-) diff --git a/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc b/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc index f8d7b5e919..e2ca689c06 100644 --- a/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc +++ b/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc @@ -150,11 +150,11 @@ TEST_F(BFloat16PropagationTest, ConvertConstantLiteral) { EXPECT_EQ(dot->operand(0)->opcode(), HloOpcode::kConstant); EXPECT_EQ(dot->operand(1)->opcode(), HloOpcode::kConstant); EXPECT_TRUE(LiteralTestUtil::Equal( - dot->operand(0)->literal(), - *Literal::ConvertF32ToBF16(*Literal::CreateFromArray(array_a)))); + *Literal::ConvertF32ToBF16(*Literal::CreateFromArray(array_a)), + dot->operand(0)->literal())); EXPECT_TRUE(LiteralTestUtil::Equal( - dot->operand(1)->literal(), - *Literal::ConvertF32ToBF16(*Literal::CreateFromArray(array_b)))); + *Literal::ConvertF32ToBF16(*Literal::CreateFromArray(array_b)), + dot->operand(1)->literal())); } // Tests that BF16 can be propagated through nested tuples. diff --git a/tensorflow/compiler/xla/tests/gather_operation_test.cc b/tensorflow/compiler/xla/tests/gather_operation_test.cc index 143ffbdeb4..6fefae3695 100644 --- a/tensorflow/compiler/xla/tests/gather_operation_test.cc +++ b/tensorflow/compiler/xla/tests/gather_operation_test.cc @@ -629,8 +629,8 @@ XLA_TEST_F(GatherClientLibraryTest, DISABLED_ON_GPU(Basic)) { client_->ExecuteParallel(computation_instances)); TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr result_literal, client_->Transfer(*(result_data[0]))); - EXPECT_TRUE(LiteralTestUtil::Equal( - *result_literal, *Literal::CreateR2({{1, 2, 3}, {7, 8, 9}}))); + LiteralTestUtil::ExpectR2Equal({{1, 2, 3}, {7, 8, 9}}, + *result_literal); } } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/tests/multioutput_fusion_test.cc b/tensorflow/compiler/xla/tests/multioutput_fusion_test.cc index 92df76d332..a42a19af15 100644 --- a/tensorflow/compiler/xla/tests/multioutput_fusion_test.cc +++ b/tensorflow/compiler/xla/tests/multioutput_fusion_test.cc @@ -207,7 +207,7 @@ XLA_TEST_F(MultiOutputFusionTest, FusionNodeIsRoot) { std::unique_ptr result = ExecuteNoHloPasses(std::move(module), {param.get()}); EXPECT_TRUE(LiteralTestUtil::Equal( - *result, *Literal::MakeTupleOwned(Literal::CreateR0(42)))); + *Literal::MakeTupleOwned(Literal::CreateR0(42)), *result)); } XLA_TEST_F(MultiOutputFusionTest, MultiOutputLoopFusion) { @@ -235,8 +235,7 @@ XLA_TEST_F(MultiOutputFusionTest, MultiOutputLoopFusion) { auto param = Literal::CreateR1({1.0, 2.0, 3.0, -1.0}); std::unique_ptr result = ExecuteNoHloPasses(std::move(module), {param.get()}); - EXPECT_TRUE(LiteralTestUtil::Equal( - *result, *Literal::CreateR1({0.0, 4.0, 9.0, 1.0}))); + LiteralTestUtil::ExpectR1Equal({0.0, 4.0, 9.0, 1.0}, *result); } XLA_TEST_F(MultiOutputFusionTest, MultiOutputLoopFeedingMap) { @@ -269,8 +268,7 @@ XLA_TEST_F(MultiOutputFusionTest, MultiOutputLoopFeedingMap) { auto param = Literal::CreateR1({1.0, 2.0, 3.0}); std::unique_ptr result = ExecuteNoHloPasses(std::move(module), {param.get()}); - EXPECT_TRUE(LiteralTestUtil::Equal( - *result, *Literal::CreateR1({0.0, 4.0, 9.0}))); + LiteralTestUtil::ExpectR1Equal({0.0, 4.0, 9.0}, *result); } const char* const kScalarOps = R"( @@ -314,9 +312,9 @@ XLA_TEST_F(MultiOutputFusionTest, std::unique_ptr result = ExecuteNoHloPasses(std::move(module), {param.get()}); EXPECT_TRUE(LiteralTestUtil::Equal( - *result, *Literal::MakeTupleOwned(Literal::CreateR2({{3, 7}, {11, 15}}), - Literal::CreateR2({{5, 16}, {36, 64}})))); + Literal::CreateR2({{5, 16}, {36, 64}})), + *result)); } XLA_TEST_F(MultiOutputFusionTest, @@ -344,9 +342,9 @@ XLA_TEST_F(MultiOutputFusionTest, std::unique_ptr result = ExecuteNoHloPasses(std::move(module), {param.get()}); EXPECT_TRUE(LiteralTestUtil::Equal( - *result, *Literal::MakeTupleOwned( - Literal::CreateR2({{6, 8}, {10, 12}}), - Literal::CreateR2({{25, 36}, {49, 64}})))); + *Literal::MakeTupleOwned(Literal::CreateR2({{6, 8}, {10, 12}}), + Literal::CreateR2({{25, 36}, {49, 64}})), + *result)); } XLA_TEST_F(MultiOutputFusionTest, @@ -375,9 +373,10 @@ XLA_TEST_F(MultiOutputFusionTest, std::unique_ptr result = ExecuteNoHloPasses(std::move(module), {param.get()}); EXPECT_TRUE(LiteralTestUtil::Equal( - *result, *Literal::MakeTupleOwned(Literal::CreateR1({14, 22}), - Literal::CreateR1({36, 64}), - Literal::CreateR1({66, 138})))); + *Literal::MakeTupleOwned(Literal::CreateR1({14, 22}), + Literal::CreateR1({36, 64}), + Literal::CreateR1({66, 138})), + *result)); } XLA_TEST_F(MultiOutputFusionTest, @@ -406,11 +405,11 @@ XLA_TEST_F(MultiOutputFusionTest, std::unique_ptr result = ExecuteNoHloPasses(std::move(module), {param.get()}); EXPECT_TRUE(LiteralTestUtil::Equal( - *result, *Literal::MakeTupleOwned( Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}), Literal::CreateR2({{3, 7}, {11, 15}}), - Literal::CreateR2({{5, 16}, {36, 64}})))); + Literal::CreateR2({{5, 16}, {36, 64}})), + *result)); } XLA_TEST_F(MultiOutputFusionTest, @@ -439,11 +438,11 @@ XLA_TEST_F(MultiOutputFusionTest, std::unique_ptr result = ExecuteNoHloPasses(std::move(module), {param.get()}); EXPECT_TRUE(LiteralTestUtil::Equal( - *result, *Literal::MakeTupleOwned( Literal::CreateR2({{6, 8}, {10, 12}}), Literal::CreateR3({{{1, 4}, {9, 16}}, {{25, 36}, {49, 64}}}), - Literal::CreateR2({{25, 36}, {49, 64}})))); + Literal::CreateR2({{25, 36}, {49, 64}})), + *result)); } XLA_TEST_F(MultiOutputFusionTest, @@ -472,12 +471,12 @@ XLA_TEST_F(MultiOutputFusionTest, std::unique_ptr result = ExecuteNoHloPasses(std::move(module), {param.get()}); EXPECT_TRUE(LiteralTestUtil::Equal( - *result, *Literal::MakeTupleOwned( Literal::CreateR1({14, 22}), Literal::CreateR3({{{1, 4}, {9, 16}}, {{25, 36}, {49, 64}}}), Literal::CreateR3( - {{{5, 10}, {15, 20}}, {{25, 30}, {35, 40}}})))); + {{{5, 10}, {15, 20}}, {{25, 30}, {35, 40}}})), + *result)); } XLA_TEST_F(MultiOutputFusionTest, @@ -508,9 +507,10 @@ XLA_TEST_F(MultiOutputFusionTest, std::unique_ptr result = ExecuteNoHloPasses( std::move(module), {param.get(), init1.get(), init2.get()}); EXPECT_TRUE(LiteralTestUtil::Equal( - *result, *Literal::MakeTupleOwned( - Literal::CreateR2({{167, 172}, {176, 180}}), - Literal::CreateR2({{6, 6}, {6, 8}})))); + *Literal::MakeTupleOwned( + Literal::CreateR2({{167, 172}, {176, 180}}), + Literal::CreateR2({{6, 6}, {6, 8}})), + *result)); } XLA_TEST_F(MultiOutputFusionTest, diff --git a/tensorflow/compiler/xla/tests/tuple_test.cc b/tensorflow/compiler/xla/tests/tuple_test.cc index 41189231b9..220d9f6320 100644 --- a/tensorflow/compiler/xla/tests/tuple_test.cc +++ b/tensorflow/compiler/xla/tests/tuple_test.cc @@ -532,8 +532,8 @@ XLA_TEST_F(TupleHloTest, DISABLED_ON_INTERPRETER(BitcastAfterGTE)) { auto param = Literal::MakeTupleOwned(Literal::CreateR1({1, 2, 3})); auto result = ExecuteNoHloPasses(std::move(module), {param.get()}); EXPECT_TRUE(LiteralTestUtil::Equal( - *result, - *Literal::MakeTupleOwned(Literal::CreateR2({{1, 2, 3}})))); + *Literal::MakeTupleOwned(Literal::CreateR2({{1, 2, 3}})), + *result)); } } // namespace -- GitLab From e006d39bf0021f3af2ebcf9c3c983070bf444818 Mon Sep 17 00:00:00 2001 From: Jiri Simsa Date: Mon, 18 Jun 2018 09:10:06 -0700 Subject: [PATCH 589/816] [tf.data] Cleanup of tf.contrib.data python tests. PiperOrigin-RevId: 201004909 --- tensorflow/contrib/cmake/python_modules.txt | 1 + .../contrib/data/python/kernel_tests/BUILD | 363 ++++-------- .../kernel_tests/batch_dataset_op_test.py | 169 ------ .../python/kernel_tests/bucketing_test.py | 61 -- .../kernel_tests/csv_dataset_op_test.py | 8 +- .../dataset_constructor_op_test.py | 62 --- .../directed_interleave_dataset_test.py | 20 - .../interleave_dataset_op_test.py | 128 ----- .../kernel_tests/map_dataset_op_test.py | 232 -------- .../kernel_tests/optimize_dataset_op_test.py | 13 - .../kernel_tests/range_dataset_op_test.py | 91 --- .../kernel_tests/reader_dataset_ops_test.py | 275 +-------- .../reader_dataset_ops_test_base.py | 115 +++- .../data/python/kernel_tests/resample_test.py | 3 +- .../kernel_tests/scan_dataset_op_test.py | 14 - .../python/kernel_tests/serialization/BUILD | 526 ++++++++++++++++++ .../batch_dataset_serialization_test.py | 83 +++ .../cache_dataset_serialization_test.py} | 6 +- ...concatenate_dataset_serialization_test.py} | 4 +- .../dataset_constructor_serialization_test.py | 95 ++++ .../dataset_serialization_test_base.py | 0 .../filter_dataset_serialization_test.py} | 6 +- ...ength_record_dataset_serialization_test.py | 45 ++ .../flat_map_dataset_serialization_test.py} | 4 +- .../group_by_reducer_serialization_test.py | 61 ++ .../group_by_window_serialization_test.py | 57 ++ .../ignore_errors_serialization_test.py | 46 ++ .../interleave_dataset_serialization_test.py | 86 +++ ...ap_and_batch_dataset_serialization_test.py | 88 +++ .../map_dataset_serialization_test.py | 140 +++++ .../optimize_dataset_serialization_test.py | 39 ++ ...padded_batch_dataset_serialization_test.py | 66 +++ ...l_interleave_dataset_serialization_test.py | 101 ++++ ...parallel_map_dataset_serialization_test.py | 139 +++++ .../prefetch_dataset_serialization_test.py} | 4 +- .../range_dataset_serialization_test.py | 118 ++++ ...sample_from_datasets_serialization_test.py | 46 ++ .../scan_dataset_serialization_test.py | 40 ++ .../sequence_dataset_serialization_test.py} | 16 +- .../serialization_integration_test.py | 4 +- ...e_and_repeat_dataset_serialization_test.py | 39 ++ .../shuffle_dataset_serialization_test.py | 148 +++++ .../sql_dataset_serialization_test.py | 53 ++ .../stats_dataset_serialization_test.py | 95 ++++ .../textline_dataset_serialization_test.py | 53 ++ .../tf_record_dataset_serialization_test.py | 99 ++++ .../unbatch_dataset_serialization_test.py | 51 ++ .../unique_dataset_serialization_test.py | 40 ++ .../zip_dataset_serialization_test.py} | 4 +- .../kernel_tests/shuffle_dataset_op_test.py | 192 ++----- .../kernel_tests/sql_dataset_op_test.py | 96 +--- .../kernel_tests/sql_dataset_op_test_base.py | 96 ++++ .../kernel_tests/stats_dataset_ops_test.py | 64 --- .../kernel_tests/unique_dataset_op_test.py | 14 - tensorflow/contrib/training/BUILD | 2 +- .../training/tensor_queue_dataset_test.py | 2 +- tensorflow/tools/pip_package/BUILD | 2 +- 57 files changed, 2757 insertions(+), 1668 deletions(-) create mode 100644 tensorflow/contrib/data/python/kernel_tests/serialization/BUILD create mode 100644 tensorflow/contrib/data/python/kernel_tests/serialization/batch_dataset_serialization_test.py rename tensorflow/contrib/data/python/kernel_tests/{cache_dataset_op_test.py => serialization/cache_dataset_serialization_test.py} (97%) rename tensorflow/contrib/data/python/kernel_tests/{concatenate_dataset_op_test.py => serialization/concatenate_dataset_serialization_test.py} (92%) create mode 100644 tensorflow/contrib/data/python/kernel_tests/serialization/dataset_constructor_serialization_test.py rename tensorflow/contrib/data/python/kernel_tests/{ => serialization}/dataset_serialization_test_base.py (100%) rename tensorflow/contrib/data/python/kernel_tests/{filter_dataset_op_test.py => serialization/filter_dataset_serialization_test.py} (91%) create mode 100644 tensorflow/contrib/data/python/kernel_tests/serialization/fixed_length_record_dataset_serialization_test.py rename tensorflow/contrib/data/python/kernel_tests/{flat_map_dataset_op_test.py => serialization/flat_map_dataset_serialization_test.py} (96%) create mode 100644 tensorflow/contrib/data/python/kernel_tests/serialization/group_by_reducer_serialization_test.py create mode 100644 tensorflow/contrib/data/python/kernel_tests/serialization/group_by_window_serialization_test.py create mode 100644 tensorflow/contrib/data/python/kernel_tests/serialization/ignore_errors_serialization_test.py create mode 100644 tensorflow/contrib/data/python/kernel_tests/serialization/interleave_dataset_serialization_test.py create mode 100644 tensorflow/contrib/data/python/kernel_tests/serialization/map_and_batch_dataset_serialization_test.py create mode 100644 tensorflow/contrib/data/python/kernel_tests/serialization/map_dataset_serialization_test.py create mode 100644 tensorflow/contrib/data/python/kernel_tests/serialization/optimize_dataset_serialization_test.py create mode 100644 tensorflow/contrib/data/python/kernel_tests/serialization/padded_batch_dataset_serialization_test.py create mode 100644 tensorflow/contrib/data/python/kernel_tests/serialization/parallel_interleave_dataset_serialization_test.py create mode 100644 tensorflow/contrib/data/python/kernel_tests/serialization/parallel_map_dataset_serialization_test.py rename tensorflow/contrib/data/python/kernel_tests/{prefetch_dataset_op_test.py => serialization/prefetch_dataset_serialization_test.py} (90%) create mode 100644 tensorflow/contrib/data/python/kernel_tests/serialization/range_dataset_serialization_test.py create mode 100644 tensorflow/contrib/data/python/kernel_tests/serialization/sample_from_datasets_serialization_test.py create mode 100644 tensorflow/contrib/data/python/kernel_tests/serialization/scan_dataset_serialization_test.py rename tensorflow/contrib/data/python/kernel_tests/{sequence_dataset_op_test.py => serialization/sequence_dataset_serialization_test.py} (91%) rename tensorflow/contrib/data/python/kernel_tests/{ => serialization}/serialization_integration_test.py (96%) create mode 100644 tensorflow/contrib/data/python/kernel_tests/serialization/shuffle_and_repeat_dataset_serialization_test.py create mode 100644 tensorflow/contrib/data/python/kernel_tests/serialization/shuffle_dataset_serialization_test.py create mode 100644 tensorflow/contrib/data/python/kernel_tests/serialization/sql_dataset_serialization_test.py create mode 100644 tensorflow/contrib/data/python/kernel_tests/serialization/stats_dataset_serialization_test.py create mode 100644 tensorflow/contrib/data/python/kernel_tests/serialization/textline_dataset_serialization_test.py create mode 100644 tensorflow/contrib/data/python/kernel_tests/serialization/tf_record_dataset_serialization_test.py create mode 100644 tensorflow/contrib/data/python/kernel_tests/serialization/unbatch_dataset_serialization_test.py create mode 100644 tensorflow/contrib/data/python/kernel_tests/serialization/unique_dataset_serialization_test.py rename tensorflow/contrib/data/python/kernel_tests/{zip_dataset_op_test.py => serialization/zip_dataset_serialization_test.py} (92%) create mode 100644 tensorflow/contrib/data/python/kernel_tests/sql_dataset_op_test_base.py diff --git a/tensorflow/contrib/cmake/python_modules.txt b/tensorflow/contrib/cmake/python_modules.txt index fece56c412..8a45858ae4 100644 --- a/tensorflow/contrib/cmake/python_modules.txt +++ b/tensorflow/contrib/cmake/python_modules.txt @@ -129,6 +129,7 @@ tensorflow/contrib/data tensorflow/contrib/data/kernels tensorflow/contrib/data/python tensorflow/contrib/data/python/kernel_tests +tensorflow/contrib/data/python/kernel_tests/serialization tensorflow/contrib/data/python/ops tensorflow/contrib/decision_trees tensorflow/contrib/decision_trees/proto diff --git a/tensorflow/contrib/data/python/kernel_tests/BUILD b/tensorflow/contrib/data/python/kernel_tests/BUILD index 445fdcef23..ed1542d03f 100644 --- a/tensorflow/contrib/data/python/kernel_tests/BUILD +++ b/tensorflow/contrib/data/python/kernel_tests/BUILD @@ -4,7 +4,7 @@ licenses(["notice"]) # Apache 2.0 exports_files(["LICENSE"]) -load("//tensorflow:tensorflow.bzl", "cuda_py_test", "py_test", "tf_py_test") +load("//tensorflow:tensorflow.bzl", "cuda_py_test", "py_test") py_test( name = "batch_dataset_op_test", @@ -16,19 +16,21 @@ py_test( "no_pip", ], deps = [ - ":dataset_serialization_test", "//tensorflow/contrib/data/python/ops:batching", "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", "//tensorflow/python:constant_op", "//tensorflow/python:dtypes", "//tensorflow/python:errors", + "//tensorflow/python:framework_ops", "//tensorflow/python:math_ops", "//tensorflow/python:script_ops", + "//tensorflow/python:session", "//tensorflow/python:sparse_tensor", "//tensorflow/python:string_ops", "//tensorflow/python:tensor_shape", "//tensorflow/python:util", + "//tensorflow/python/data/ops:dataset_ops", "//third_party/py/numpy", "@absl_py//absl/testing:parameterized", ], @@ -40,7 +42,6 @@ py_test( srcs = ["bucketing_test.py"], srcs_version = "PY2AND3", deps = [ - ":dataset_serialization_test", "//tensorflow/contrib/data/python/ops:grouping", "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", @@ -49,37 +50,33 @@ py_test( "//tensorflow/python:errors", "//tensorflow/python:framework_ops", "//tensorflow/python:math_ops", + "//tensorflow/python:sparse_tensor", "//tensorflow/python:string_ops", "//tensorflow/python:tensor_shape", - "//third_party/py/numpy", - ], -) - -py_test( - name = "cache_dataset_op_test", - size = "small", - srcs = ["cache_dataset_op_test.py"], - srcs_version = "PY2AND3", - deps = [ - ":dataset_serialization_test", - "//tensorflow/python:client_testlib", - "//tensorflow/python:errors", "//tensorflow/python/data/ops:dataset_ops", + "//third_party/py/numpy", ], ) py_test( - name = "concatenate_dataset_op_test", + name = "csv_dataset_op_test", size = "small", - srcs = ["concatenate_dataset_op_test.py"], + srcs = ["csv_dataset_op_test.py"], srcs_version = "PY2AND3", + tags = ["no_pip"], deps = [ - ":dataset_serialization_test", + "//tensorflow/contrib/data/python/ops:error_ops", + "//tensorflow/contrib/data/python/ops:readers", "//tensorflow/python:client_testlib", + "//tensorflow/python:constant_op", + "//tensorflow/python:dtypes", "//tensorflow/python:errors", - "//tensorflow/python:tensor_shape", - "//tensorflow/python/data/ops:dataset_ops", - "//tensorflow/python/data/util:nest", + "//tensorflow/python:framework_ops", + "//tensorflow/python:parsing_ops", + "//tensorflow/python:platform", + "//tensorflow/python:platform_test", + "//tensorflow/python:session", + "//tensorflow/python/data/ops:readers", "//third_party/py/numpy", ], ) @@ -94,104 +91,44 @@ py_test( "nomac", # b/62040583 ], deps = [ - ":dataset_serialization_test", "//tensorflow/contrib/data/python/ops:batching", - "//tensorflow/core:protos_all_py", "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", "//tensorflow/python:dtypes", - "//tensorflow/python:errors", - "//tensorflow/python:framework_ops", - "//tensorflow/python:math_ops", - "//tensorflow/python:resource_variable_ops", - "//tensorflow/python:session", - "//tensorflow/python:sparse_tensor", - "//tensorflow/python:tensor_shape", + "//tensorflow/python/data/ops:dataset_ops", "//tensorflow/python/data/util:nest", - "//third_party/py/numpy", ], ) -py_library( - name = "dataset_serialization_test", - srcs = [ - "dataset_serialization_test_base.py", - ], +py_test( + name = "directed_interleave_dataset_test", + size = "medium", + srcs = ["directed_interleave_dataset_test.py"], srcs_version = "PY2AND3", deps = [ - "//tensorflow/contrib/data/python/ops:iterator_ops", + "//tensorflow/contrib/data/python/ops:interleave_ops", "//tensorflow/python:client_testlib", "//tensorflow/python:errors", - "//tensorflow/python:framework_ops", - "//tensorflow/python:lookup_ops", - "//tensorflow/python:platform", - "//tensorflow/python:sparse_tensor", - "//tensorflow/python:training", - "//tensorflow/python:util", - "//tensorflow/python:variables", - "//tensorflow/python/data/ops:iterator_ops", - "//third_party/py/numpy", - ], -) - -py_test( - name = "csv_dataset_op_test", - size = "small", - srcs = ["csv_dataset_op_test.py"], - srcs_version = "PY2AND3", - tags = ["no_pip"], - deps = [ - ":dataset_serialization_test", - "//tensorflow/contrib/data/python/ops:error_ops", - "//tensorflow/contrib/data/python/ops:readers", + "//tensorflow/python:random_seed", + "//tensorflow/python/data/ops:dataset_ops", "//third_party/py/numpy", ], ) py_test( - name = "filter_dataset_op_test", + name = "get_single_element_test", size = "small", - srcs = ["filter_dataset_op_test.py"], - srcs_version = "PY2AND3", - tags = [ - "no_pip", - "optonly", - ], + srcs = ["get_single_element_test.py"], deps = [ - ":dataset_serialization_test", - "//tensorflow/python:array_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:dtypes", - "//tensorflow/python:errors", - "//tensorflow/python:functional_ops", - "//tensorflow/python:math_ops", - "//tensorflow/python/data/ops:dataset_ops", - "//third_party/py/numpy", - ], -) - -tf_py_test( - name = "flat_map_dataset_op_test", - size = "medium", - srcs = ["flat_map_dataset_op_test.py"], - additional_deps = [ - ":dataset_serialization_test", - "//third_party/py/numpy", - "//tensorflow/python/data/ops:dataset_ops", + "//tensorflow/contrib/data/python/ops:get_single_element", "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", "//tensorflow/python:constant_op", "//tensorflow/python:dtypes", "//tensorflow/python:errors", - "//tensorflow/python:function", - "//tensorflow/python:math_ops", - "//tensorflow/python:random_ops", - "//tensorflow/python:session", - "//tensorflow/python:training", - "//tensorflow/python:variable_scope", + "//tensorflow/python:sparse_tensor", + "//tensorflow/python/data/ops:dataset_ops", ], - grpc_enabled = True, - tags = ["no_pip"], ) py_test( @@ -206,10 +143,8 @@ py_test( "notap", ], deps = [ - ":dataset_serialization_test", "//tensorflow/contrib/data/python/ops:interleave_ops", "//tensorflow/python:array_ops", - "//tensorflow/python:client", "//tensorflow/python:client_testlib", "//tensorflow/python:dtypes", "//tensorflow/python:errors", @@ -217,43 +152,8 @@ py_test( "//tensorflow/python:script_ops", "//tensorflow/python:sparse_ops", "//tensorflow/python:sparse_tensor", - "//tensorflow/python:training", "//tensorflow/python/data/ops:dataset_ops", - "//third_party/py/numpy", - ], -) - -py_test( - name = "directed_interleave_dataset_test", - size = "medium", - srcs = ["directed_interleave_dataset_test.py"], - srcs_version = "PY2AND3", - deps = [ - ":dataset_serialization_test", - "//tensorflow/contrib/data/python/ops:interleave_ops", - "//tensorflow/python:client", - "//tensorflow/python:client_testlib", - "//tensorflow/python:errors", - "//tensorflow/python:training", - "//tensorflow/python/data/ops:dataset_ops", - "//third_party/py/numpy", - ], -) - -tf_py_test( - name = "get_single_element_test", - size = "small", - srcs = ["get_single_element_test.py"], - additional_deps = [ - "//third_party/py/numpy", - "//tensorflow/contrib/data/python/ops:get_single_element", - "//tensorflow/python/data/ops:dataset_ops", - "//tensorflow/python:array_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:constant_op", - "//tensorflow/python:dtypes", - "//tensorflow/python:errors", - "//tensorflow/python:framework_test_lib", + "@six_archive//:six", ], ) @@ -268,27 +168,13 @@ py_test( "optonly", ], deps = [ - ":dataset_serialization_test", "//tensorflow/contrib/data/python/ops:error_ops", "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", - "//tensorflow/python:constant_op", - "//tensorflow/python:data_flow_ops", - "//tensorflow/python:dtypes", "//tensorflow/python:errors", "//tensorflow/python:framework_ops", - "//tensorflow/python:function", - "//tensorflow/python:functional_ops", "//tensorflow/python:io_ops", - "//tensorflow/python:lookup_ops", - "//tensorflow/python:math_ops", - "//tensorflow/python:random_ops", - "//tensorflow/python:script_ops", - "//tensorflow/python:sparse_ops", - "//tensorflow/python:sparse_tensor", - "//tensorflow/python:string_ops", "//tensorflow/python:util", - "//tensorflow/python:variable_scope", "//tensorflow/python/data/ops:dataset_ops", "//third_party/py/numpy", ], @@ -300,23 +186,30 @@ py_test( srcs = ["optimize_dataset_op_test.py"], srcs_version = "PY2AND3", deps = [ - ":dataset_serialization_test", "//tensorflow/contrib/data/python/ops:optimization", - "//tensorflow/python:platform", + "//tensorflow/core:protos_all_py", + "//tensorflow/python:client_testlib", + "//tensorflow/python:errors", "//tensorflow/python/data/ops:dataset_ops", ], ) -py_test( - name = "prefetch_dataset_op_test", +cuda_py_test( + name = "prefetching_ops_test", size = "small", - srcs = ["prefetch_dataset_op_test.py"], - srcs_version = "PY2AND3", - tags = ["no_pip"], - deps = [ - ":dataset_serialization_test", - "//tensorflow/python:platform", + srcs = ["prefetching_ops_test.py"], + additional_deps = [ + "//tensorflow/contrib/data/python/ops:prefetching_ops", + "//tensorflow/core:protos_all_py", + "//tensorflow/python:client_testlib", + "//tensorflow/python:constant_op", + "//tensorflow/python:dtypes", + "//tensorflow/python:framework_ops", + "//tensorflow/python:framework_test_lib", + "//tensorflow/python:function", + "//tensorflow/python:resource_variable_ops", "//tensorflow/python/data/ops:dataset_ops", + "//tensorflow/python/data/ops:iterator_ops", ], ) @@ -326,20 +219,13 @@ py_test( srcs = ["range_dataset_op_test.py"], srcs_version = "PY2AND3", deps = [ - ":dataset_serialization_test", "//tensorflow/contrib/data/python/ops:counter", "//tensorflow/contrib/data/python/ops:enumerate_ops", - "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", "//tensorflow/python:constant_op", - "//tensorflow/python:dataset_ops_gen", "//tensorflow/python:dtypes", "//tensorflow/python:errors", - "//tensorflow/python:framework_ops", - "//tensorflow/python:io_ops", - "//tensorflow/python:parsing_ops", "//tensorflow/python:tensor_shape", - "//tensorflow/python:variables", "//tensorflow/python/data/ops:dataset_ops", ], ) @@ -351,15 +237,21 @@ py_library( "reader_dataset_ops_test_base.py", ], srcs_version = "PY2AND3", - visibility = ["//visibility:private"], + visibility = [ + "//tensorflow/contrib/data/python/kernel_tests:__pkg__", + "//tensorflow/contrib/data/python/kernel_tests/serialization:__pkg__", + ], deps = [ "//tensorflow/contrib/data/python/ops:readers", "//tensorflow/core:protos_all_py", + "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", + "//tensorflow/python:constant_op", "//tensorflow/python:dtypes", "//tensorflow/python:lib", "//tensorflow/python:parsing_ops", "//tensorflow/python:util", + "//tensorflow/python/data/ops:iterator_ops", "//tensorflow/python/data/ops:readers", ], ) @@ -368,24 +260,18 @@ py_test( name = "reader_dataset_ops_test", size = "medium", srcs = ["reader_dataset_ops_test.py"], - shard_count = 4, srcs_version = "PY2AND3", tags = ["no_pip"], deps = [ - ":dataset_serialization_test", ":reader_dataset_ops_test_base", "//tensorflow/contrib/data/python/ops:readers", - "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", "//tensorflow/python:constant_op", "//tensorflow/python:dtypes", "//tensorflow/python:errors", "//tensorflow/python:framework_ops", - "//tensorflow/python:lib", "//tensorflow/python:parsing_ops", "//tensorflow/python:string_ops", - "//tensorflow/python:util", - "//tensorflow/python/data/ops:iterator_ops", "//tensorflow/python/data/ops:readers", "//third_party/py/numpy", ], @@ -413,6 +299,7 @@ py_test( "//tensorflow/python/data/ops:dataset_ops", "//third_party/py/numpy", "@absl_py//absl/testing:parameterized", + "@six_archive//:six", ], ) @@ -423,13 +310,14 @@ py_test( srcs_version = "PY2AND3", tags = ["no_pip"], deps = [ - ":dataset_serialization_test", "//tensorflow/contrib/data/python/ops:scan_ops", "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", "//tensorflow/python:constant_op", "//tensorflow/python:dtypes", "//tensorflow/python:errors", + "//tensorflow/python:framework_test_lib", + "//tensorflow/python:sparse_tensor", "//tensorflow/python/data/ops:dataset_ops", "//tensorflow/python/eager:context", "//third_party/py/numpy", @@ -437,60 +325,55 @@ py_test( ) py_test( - name = "sequence_dataset_op_test", + name = "shuffle_dataset_op_test", size = "medium", - srcs = ["sequence_dataset_op_test.py"], + srcs = ["shuffle_dataset_op_test.py"], srcs_version = "PY2AND3", - tags = ["no_pip"], + tags = [ + "no_pip", + "optonly", + ], deps = [ - ":dataset_serialization_test", - "//tensorflow/python:array_ops", + "//tensorflow/contrib/data/python/ops:shuffle_ops", "//tensorflow/python:client_testlib", - "//tensorflow/python:dtypes", "//tensorflow/python:errors", + "//tensorflow/python:framework_ops", "//tensorflow/python/data/ops:dataset_ops", "//third_party/py/numpy", ], ) py_test( - name = "serialization_integration_test", + name = "slide_dataset_op_test", size = "small", - srcs = ["serialization_integration_test.py"], - srcs_version = "PY2AND3", - tags = ["no_pip"], + srcs = ["slide_dataset_op_test.py"], deps = [ - "//tensorflow/contrib/data/python/ops:iterator_ops", + "//tensorflow/contrib/data/python/ops:sliding", + "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", - "//tensorflow/python:framework_ops", - "//tensorflow/python:training", + "//tensorflow/python:dtypes", + "//tensorflow/python:errors", + "//tensorflow/python:math_ops", + "//tensorflow/python:sparse_tensor", "//tensorflow/python/data/ops:dataset_ops", + "//third_party/py/numpy", ], ) -py_test( - name = "shuffle_dataset_op_test", - size = "medium", - srcs = ["shuffle_dataset_op_test.py"], +py_library( + name = "sql_dataset_op_test_base", + srcs = ["sql_dataset_op_test_base.py"], srcs_version = "PY2AND3", - tags = [ - "no_pip", - "optonly", + visibility = [ + "//tensorflow/contrib/data/python/kernel_tests:__pkg__", + "//tensorflow/contrib/data/python/kernel_tests/serialization:__pkg__", ], deps = [ - ":dataset_serialization_test", - "//tensorflow/contrib/data/python/ops:iterator_ops", - "//tensorflow/contrib/data/python/ops:shuffle_ops", + "//tensorflow/contrib/data/python/ops:readers", "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", - "//tensorflow/python:constant_op", "//tensorflow/python:dtypes", - "//tensorflow/python:errors", - "//tensorflow/python:framework_ops", - "//tensorflow/python:training", - "//tensorflow/python/data/ops:dataset_ops", - "//tensorflow/python/data/ops:iterator_ops", - "//third_party/py/numpy", + "@org_sqlite//:python", ], ) @@ -499,14 +382,12 @@ py_test( size = "small", srcs = ["sql_dataset_op_test.py"], srcs_version = "PY2AND3", + tags = ["no_pip"], deps = [ - ":dataset_serialization_test", - "//tensorflow/contrib/data/python/ops:readers", - "//tensorflow/python:array_ops", + ":sql_dataset_op_test_base", "//tensorflow/python:client_testlib", "//tensorflow/python:dtypes", "//tensorflow/python:errors", - "@org_sqlite//:python", ], ) @@ -517,7 +398,6 @@ py_test( srcs_version = "PY2AND3", tags = ["no_pip"], deps = [ - ":dataset_serialization_test", ":reader_dataset_ops_test_base", "//tensorflow/contrib/data/python/ops:stats_ops", "//tensorflow/core:protos_all_py", @@ -540,8 +420,11 @@ py_test( "//tensorflow/contrib/data/python/ops:threadpool", "//tensorflow/contrib/data/python/ops:unique", "//tensorflow/python:client_testlib", + "//tensorflow/python:dtypes", "//tensorflow/python:errors", + "//tensorflow/python:script_ops", "//tensorflow/python/data/ops:dataset_ops", + "//third_party/py/numpy", ], ) @@ -552,87 +435,27 @@ py_test( srcs_version = "PY2AND3", tags = ["no_pip"], deps = [ - ":dataset_serialization_test", "//tensorflow/contrib/data/python/ops:unique", - "//tensorflow/contrib/stateless", - "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", "//tensorflow/python:dtypes", "//tensorflow/python:errors", + "//tensorflow/python:util", "//tensorflow/python/data/ops:dataset_ops", - "//third_party/py/numpy", ], ) py_test( - name = "zip_dataset_op_test", - size = "small", - srcs = ["zip_dataset_op_test.py"], - srcs_version = "PY2AND3", - tags = ["no_pip"], - deps = [ - ":dataset_serialization_test", - "//tensorflow/python:array_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:dtypes", - "//tensorflow/python:errors", - "//tensorflow/python/data/ops:dataset_ops", - "//third_party/py/numpy", - ], -) - -cuda_py_test( - name = "prefetching_ops_test", - size = "small", - srcs = ["prefetching_ops_test.py"], - additional_deps = [ - "//tensorflow/contrib/data/python/ops:prefetching_ops", - "//tensorflow/core:protos_all_py", - "//tensorflow/python:client_testlib", - "//tensorflow/python:constant_op", - "//tensorflow/python:dtypes", - "//tensorflow/python:framework_ops", - "//tensorflow/python:framework_test_lib", - "//tensorflow/python:function", - "//tensorflow/python:resource_variable_ops", - "//tensorflow/python/data/ops:dataset_ops", - "//tensorflow/python/data/ops:iterator_ops", - ], -) - -tf_py_test( - name = "slide_dataset_op_test", - size = "small", - srcs = ["slide_dataset_op_test.py"], - additional_deps = [ - "//tensorflow/python/data/ops:dataset_ops", - "//tensorflow/contrib/data/python/ops:sliding", - "//tensorflow/python:array_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:dtypes", - "//tensorflow/python:errors", - "//tensorflow/python:math_ops", - "//tensorflow/python:sparse_tensor", - "//third_party/py/numpy", - ], -) - -tf_py_test( name = "writer_ops_test", size = "small", srcs = ["writer_ops_test.py"], - additional_deps = [ + deps = [ "//tensorflow/contrib/data/python/ops:writers", "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", - "//tensorflow/python:dataset_ops_gen", "//tensorflow/python:dtypes", - "//tensorflow/python:errors", - "//tensorflow/python:framework_ops", - "//tensorflow/python:io_ops", "//tensorflow/python:lib", - "//tensorflow/python:tensor_shape", "//tensorflow/python:util", + "//tensorflow/python/data/ops:dataset_ops", "//tensorflow/python/data/ops:readers", ], ) diff --git a/tensorflow/contrib/data/python/kernel_tests/batch_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/batch_dataset_op_test.py index 1435503beb..4c60232308 100644 --- a/tensorflow/contrib/data/python/kernel_tests/batch_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/batch_dataset_op_test.py @@ -23,7 +23,6 @@ import time from absl.testing import parameterized import numpy as np -from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base from tensorflow.contrib.data.python.ops import batching from tensorflow.python.client import session from tensorflow.python.data.ops import dataset_ops @@ -643,174 +642,6 @@ class BatchDatasetTest(test.TestCase, parameterized.TestCase): sess.run(get_next) -class BatchDatasetSerializationTest( - dataset_serialization_test_base.DatasetSerializationTestBase): - - def build_dataset(self, multiplier=15.0, tensor_slice_len=2, batch_size=2): - components = ( - np.arange(tensor_slice_len), - np.array([[1, 2, 3]]) * np.arange(tensor_slice_len)[:, np.newaxis], - np.array(multiplier) * np.arange(tensor_slice_len)) - - return dataset_ops.Dataset.from_tensor_slices(components).batch(batch_size) - - def testCore(self): - tensor_slice_len = 8 - batch_size = 2 - num_outputs = tensor_slice_len // batch_size - self.run_core_tests( - lambda: self.build_dataset(15.0, tensor_slice_len, batch_size), - lambda: self.build_dataset(20.0, tensor_slice_len, batch_size), - num_outputs) - - def _build_dataset_dense_to_sparse(self, components): - return dataset_ops.Dataset.from_tensor_slices(components).map( - lambda x: array_ops.fill([x], x)).apply( - batching.dense_to_sparse_batch(4, [12])) - - def testDenseToSparseBatchDatasetCore(self): - components = np.random.randint(5, size=(40,)).astype(np.int32) - diff_comp = np.random.randint(2, size=(100,)).astype(np.int32) - - num_outputs = len(components) // 4 - self.run_core_tests(lambda: self._build_dataset_dense_to_sparse(components), - lambda: self._build_dataset_dense_to_sparse(diff_comp), - num_outputs) - - def _sparse(self, i): - return sparse_tensor.SparseTensorValue( - indices=[[0]], values=(i * [1]), dense_shape=[1]) - - def _build_dataset_sparse(self, batch_size=5): - return dataset_ops.Dataset.range(10).map(self._sparse).batch(batch_size) - - def testSparseCore(self): - self.run_core_tests(self._build_dataset_sparse, - lambda: self._build_dataset_sparse(2), 2) - - def _build_dataset_nested_sparse(self): - return dataset_ops.Dataset.range(10).map(self._sparse).batch(5).batch(2) - - def testNestedSparseCore(self): - self.run_core_tests(self._build_dataset_nested_sparse, None, 1) - - -class UnbatchDatasetSerializationTest( - dataset_serialization_test_base.DatasetSerializationTestBase): - - def build_dataset(self, multiplier=15.0, tensor_slice_len=2, batch_size=2): - components = ( - np.arange(tensor_slice_len), - np.array([[1, 2, 3]]) * np.arange(tensor_slice_len)[:, np.newaxis], - np.array(multiplier) * np.arange(tensor_slice_len)) - - return dataset_ops.Dataset.from_tensor_slices(components).batch( - batch_size).apply(batching.unbatch()) - - def testCore(self): - tensor_slice_len = 8 - batch_size = 2 - num_outputs = tensor_slice_len - self.run_core_tests( - lambda: self.build_dataset(15.0, tensor_slice_len, batch_size), - lambda: self.build_dataset(20.0, tensor_slice_len, batch_size), - num_outputs) - - -class MapAndBatchDatasetSerializationTest( - dataset_serialization_test_base.DatasetSerializationTestBase): - - def testNumParallelBatches(self): - range_size = 11 - num_repeats = 2 - batch_size = 5 - total_outputs = range_size * num_repeats - num_outputs_drop_remainder = total_outputs // batch_size - num_outputs_keep_remainder = int(math.ceil(total_outputs / batch_size)) - num_parallel_batches = 2 - - def build_ds(range_start, drop_remainder=False): - - def _map_fn(x): - return math_ops.square(x) - - return dataset_ops.Dataset.range( - range_start, range_start + range_size).repeat(num_repeats).apply( - batching.map_and_batch( - map_func=_map_fn, - batch_size=batch_size, - num_parallel_batches=num_parallel_batches, - drop_remainder=drop_remainder)) - - self.run_core_tests(lambda: build_ds(10), lambda: build_ds(15), - num_outputs_keep_remainder) - self.run_core_tests(lambda: build_ds(10, True), lambda: build_ds(15, True), - num_outputs_drop_remainder) - - def testNumParallelCalls(self): - range_size = 11 - num_repeats = 2 - batch_size = 5 - total_outputs = range_size * num_repeats - num_outputs_drop_remainder = total_outputs // batch_size - num_outputs_keep_remainder = int(math.ceil(total_outputs / batch_size)) - num_parallel_calls = 7 - - def build_ds(range_start, drop_remainder=False): - - def _map_fn(x): - return math_ops.square(x) - - return dataset_ops.Dataset.range( - range_start, range_start + range_size).repeat(num_repeats).apply( - batching.map_and_batch( - map_func=_map_fn, - batch_size=batch_size, - num_parallel_calls=num_parallel_calls, - drop_remainder=drop_remainder)) - - self.run_core_tests(lambda: build_ds(10), lambda: build_ds(15), - num_outputs_keep_remainder) - self.run_core_tests(lambda: build_ds(10, True), lambda: build_ds(15, True), - num_outputs_drop_remainder) - - -class PaddedBatchDatasetSerializationTest( - dataset_serialization_test_base.DatasetSerializationTestBase): - - def testPaddedBatch(self): - - def build_dataset(seq_lens): - return dataset_ops.Dataset.from_tensor_slices(seq_lens).map( - lambda x: array_ops.fill([x], x)).padded_batch( - 4, padded_shapes=[-1]) - - seq_lens1 = np.random.randint(1, 20, size=(32,)).astype(np.int32) - seq_lens2 = np.random.randint(21, 40, size=(32,)).astype(np.int32) - self.run_core_tests(lambda: build_dataset(seq_lens1), - lambda: build_dataset(seq_lens2), 8) - - def testPaddedBatchNonDefaultPadding(self): - - def build_dataset(seq_lens): - - def fill_tuple(x): - filled = array_ops.fill([x], x) - return (filled, string_ops.as_string(filled)) - - padded_shape = [-1] - return dataset_ops.Dataset.from_tensor_slices(seq_lens).map( - fill_tuple).padded_batch( - 4, - padded_shapes=(padded_shape, padded_shape), - padding_values=(-1, "")) - - seq_lens1 = np.random.randint(1, 20, size=(32,)).astype(np.int32) - seq_lens2 = np.random.randint(21, 40, size=(32,)).astype(np.int32) - self.run_core_tests(lambda: build_dataset(seq_lens1), - lambda: build_dataset(seq_lens2), 8) - - class RestructuredDatasetTest(test.TestCase): def test_assert_element_shape(self): diff --git a/tensorflow/contrib/data/python/kernel_tests/bucketing_test.py b/tensorflow/contrib/data/python/kernel_tests/bucketing_test.py index 4fbfbfdbdd..c5d2edbbc6 100644 --- a/tensorflow/contrib/data/python/kernel_tests/bucketing_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/bucketing_test.py @@ -21,7 +21,6 @@ import random import numpy as np -from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base from tensorflow.contrib.data.python.ops import grouping from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import constant_op @@ -177,38 +176,6 @@ class GroupByReducerTest(test.TestCase): grouping.group_by_reducer(lambda _: "wrong", reducer)) -class GroupByReducerSerializationTest( - dataset_serialization_test_base.DatasetSerializationTestBase): - - def _build_dataset(self, components): - reducer = grouping.Reducer( - init_func=lambda _: np.int64(0), - reduce_func=lambda x, y: x + y, - finalize_func=lambda x: x) - - return dataset_ops.Dataset.from_tensor_slices(components).apply( - grouping.group_by_reducer(lambda x: x % 5, reducer)) - - def testCoreGroupByReducer(self): - components = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=np.int64) - self.verify_unused_iterator( - lambda: self._build_dataset(components), 5, verify_exhausted=True) - self.verify_init_before_restore( - lambda: self._build_dataset(components), 5, verify_exhausted=True) - self.verify_multiple_breaks( - lambda: self._build_dataset(components), 5, verify_exhausted=True) - self.verify_reset_restored_iterator( - lambda: self._build_dataset(components), 5, verify_exhausted=True) - self.verify_restore_in_empty_graph( - lambda: self._build_dataset(components), 5, verify_exhausted=True) - diff_components = np.array([5, 4, 3, 2, 1, 0], dtype=np.int64) - self.verify_restore_in_modified_graph( - lambda: self._build_dataset(components), - lambda: self._build_dataset(diff_components), - 5, - verify_exhausted=True) - - class GroupByWindowTest(test.TestCase): def testSimple(self): @@ -353,34 +320,6 @@ class GroupByWindowTest(test.TestCase): self.assertEqual(len(components), sum(counts)) -class GroupByWindowSerializationTest( - dataset_serialization_test_base.DatasetSerializationTestBase): - - def _build_dataset(self, components): - return dataset_ops.Dataset.from_tensor_slices(components).repeat(-1).apply( - grouping.group_by_window(lambda x: x % 3, lambda _, xs: xs.batch(4), 4)) - - def testCoreGroupByWindow(self): - components = np.array( - [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 0, 0, 2, 2, 0, 0], dtype=np.int64) - self.verify_unused_iterator( - lambda: self._build_dataset(components), 12, verify_exhausted=False) - self.verify_init_before_restore( - lambda: self._build_dataset(components), 12, verify_exhausted=False) - self.verify_multiple_breaks( - lambda: self._build_dataset(components), 12, verify_exhausted=False) - self.verify_reset_restored_iterator( - lambda: self._build_dataset(components), 12, verify_exhausted=False) - self.verify_restore_in_empty_graph( - lambda: self._build_dataset(components), 12, verify_exhausted=False) - diff_components = np.array([0, 0, 0, 1, 1, 1], dtype=np.int64) - self.verify_restore_in_modified_graph( - lambda: self._build_dataset(components), - lambda: self._build_dataset(diff_components), - 12, - verify_exhausted=False) - - # NOTE(mrry): These tests are based on the tests in bucket_ops_test.py. # Currently, they use a constant batch size, though should be made to use a # different batch size per key. diff --git a/tensorflow/contrib/data/python/kernel_tests/csv_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/csv_dataset_op_test.py index 97b5e94165..df115175f5 100644 --- a/tensorflow/contrib/data/python/kernel_tests/csv_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/csv_dataset_op_test.py @@ -33,7 +33,7 @@ from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import ops -from tensorflow.python.ops import gen_parsing_ops +from tensorflow.python.ops import parsing_ops from tensorflow.python.platform import gfile from tensorflow.python.platform import googletest from tensorflow.python.platform import test @@ -76,7 +76,7 @@ class CsvDatasetOpTest(test.TestCase): filenames = self.setup_files(inputs) dataset_expected = core_readers.TextLineDataset(filenames) dataset_expected = dataset_expected.map( - lambda l: gen_parsing_ops.decode_csv(l, **kwargs)) + lambda l: parsing_ops.decode_csv(l, **kwargs)) dataset_actual = readers.CsvDataset(filenames, **kwargs) return (dataset_actual, dataset_expected) @@ -581,7 +581,7 @@ class CsvDatasetBenchmark(test.Benchmark): num_cols = self._num_cols[i] kwargs = {'record_defaults': [[0.0]] * num_cols} dataset = core_readers.TextLineDataset(self._filenames[i]).repeat() - dataset = dataset.map(lambda l: gen_parsing_ops.decode_csv(l, **kwargs)) # pylint: disable=cell-var-from-loop + dataset = dataset.map(lambda l: parsing_ops.decode_csv(l, **kwargs)) # pylint: disable=cell-var-from-loop self._runBenchmark(dataset, num_cols, 'csv_float_map_decode_csv') self._tearDown() @@ -591,7 +591,7 @@ class CsvDatasetBenchmark(test.Benchmark): num_cols = self._num_cols[i] kwargs = {'record_defaults': [['']] * num_cols} dataset = core_readers.TextLineDataset(self._filenames[i]).repeat() - dataset = dataset.map(lambda l: gen_parsing_ops.decode_csv(l, **kwargs)) # pylint: disable=cell-var-from-loop + dataset = dataset.map(lambda l: parsing_ops.decode_csv(l, **kwargs)) # pylint: disable=cell-var-from-loop self._runBenchmark(dataset, num_cols, 'csv_strings_map_decode_csv') self._tearDown() diff --git a/tensorflow/contrib/data/python/kernel_tests/dataset_constructor_op_test.py b/tensorflow/contrib/data/python/kernel_tests/dataset_constructor_op_test.py index a842502cc6..a2ab3de52e 100644 --- a/tensorflow/contrib/data/python/kernel_tests/dataset_constructor_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/dataset_constructor_op_test.py @@ -17,14 +17,10 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import numpy as np - -from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base from tensorflow.contrib.data.python.ops import batching from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.util import nest from tensorflow.python.framework import dtypes -from tensorflow.python.framework import sparse_tensor from tensorflow.python.ops import array_ops from tensorflow.python.platform import test @@ -70,63 +66,5 @@ class DatasetConstructorTest(test.TestCase): # pylint: enable=protected-access -class DatasetConstructorSerializationTest( - dataset_serialization_test_base.DatasetSerializationTestBase): - - def _build_tensor_dataset(self, variable_array): - components = (variable_array, np.array([1, 2, 3]), np.array(37.0)) - - return dataset_ops.Dataset.from_tensors(components) - - def testFromTensorsCore(self): - # Equal length components - arr = np.array(1) - num_outputs = 1 - diff_arr = np.array(2) - self.run_core_tests(lambda: self._build_tensor_dataset(arr), - lambda: self._build_tensor_dataset(diff_arr), - num_outputs) - - def _build_tensor_slices_dataset(self, components): - return dataset_ops.Dataset.from_tensor_slices(components) - - def testFromTensorSlicesCore(self): - # Equal length components - components = (np.tile(np.array([[1], [2], [3], [4]]), 20), - np.tile(np.array([[12], [13], [14], [15]]), 22), - np.array([37.0, 38.0, 39.0, 40.0])) - - diff_comp = (np.tile(np.array([[1], [2], [3], [4]]), 20), - np.tile(np.array([[5], [6], [7], [8]]), 22), - np.array([1.0, 2.0, 3.0, 4.0])) - - dict_components = {"foo": [1, 2, 3], "bar": [[4.0], [5.0], [6.0]]} - - self.run_core_tests(lambda: self._build_tensor_slices_dataset(components), - lambda: self._build_tensor_slices_dataset(diff_comp), 4) - self.run_core_tests( - lambda: self._build_tensor_slices_dataset(dict_components), None, 3) - - def _build_sparse_tensor_slice_dataset(self, slices): - indices = np.array( - [[i, j] for i in range(len(slices)) for j in range(len(slices[i]))], - dtype=np.int64) - values = np.array([val for s in slices for val in s], dtype=np.float64) - dense_shape = np.array( - [len(slices), max(len(s) for s in slices) + 1], dtype=np.int64) - sparse_components = sparse_tensor.SparseTensor(indices, values, dense_shape) - return dataset_ops.Dataset.from_sparse_tensor_slices(sparse_components) - - def testFromSparseTensorSlicesCore(self): - slices = [[1., 2., 3.], [1.], [1.], [1., 2.], [], [1., 2.], [], [], []] - diff_slices = [[1., 2.], [2.], [2., 3., 4.], [], [], []] - - self.run_core_tests( - lambda: self._build_sparse_tensor_slice_dataset(slices), - lambda: self._build_sparse_tensor_slice_dataset(diff_slices), - 9, - sparse_tensors=True) - - if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/directed_interleave_dataset_test.py b/tensorflow/contrib/data/python/kernel_tests/directed_interleave_dataset_test.py index 34b6a080c0..fe618cdce6 100644 --- a/tensorflow/contrib/data/python/kernel_tests/directed_interleave_dataset_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/directed_interleave_dataset_test.py @@ -19,7 +19,6 @@ from __future__ import print_function import numpy as np -from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base from tensorflow.contrib.data.python.ops import interleave_ops from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import errors @@ -144,24 +143,5 @@ class DirectedInterleaveDatasetTest(test.TestCase): ], choice_dataset=dataset_ops.Dataset.from_tensors([1.0])) -class SampleFromDatasetsSerializationTest( - dataset_serialization_test_base.DatasetSerializationTestBase): - - def _build_dataset(self, probs, num_samples): - dataset = interleave_ops.sample_from_datasets( - [ - dataset_ops.Dataset.from_tensors(i).repeat(None) - for i in range(len(probs)) - ], - probs, - seed=1813) - return dataset.take(num_samples) - - def testSerializationCore(self): - self.run_core_tests( - lambda: self._build_dataset([0.5, 0.5], 100), - lambda: self._build_dataset([0.25, 0.25, 0.25, 0.25], 1000), 100) - - if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/interleave_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/interleave_dataset_op_test.py index bee561e3e2..44c3325a3d 100644 --- a/tensorflow/contrib/data/python/kernel_tests/interleave_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/interleave_dataset_op_test.py @@ -22,10 +22,8 @@ import math import threading import time -import numpy as np from six.moves import zip_longest -from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base from tensorflow.contrib.data.python.ops import interleave_ops from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import dtypes @@ -38,132 +36,6 @@ from tensorflow.python.ops import sparse_ops from tensorflow.python.platform import test -class InterleaveDatasetSerializationTest( - dataset_serialization_test_base.DatasetSerializationTestBase): - - def _build_iterator_graph(self, input_values, cycle_length, block_length): - repeat_count = 2 - return dataset_ops.Dataset.from_tensor_slices(input_values).repeat( - repeat_count).interleave( - lambda x: dataset_ops.Dataset.from_tensors(x).repeat(x), - cycle_length, block_length) - - def testSerializationCore(self): - input_values = np.array([4, 5, 6], dtype=np.int64) - num_outputs = np.sum(input_values) * 2 - # cycle_length > 1, block_length > 1 - cycle_length = 2 - block_length = 3 - # pylint: disable=g-long-lambda - self.run_core_tests( - lambda: self._build_iterator_graph( - input_values, cycle_length, block_length), - lambda: self._build_iterator_graph( - input_values, cycle_length * 2, block_length * 1), - num_outputs) - # cycle_length = 1 - cycle_length = 1 - block_length = 3 - self.run_core_tests( - lambda: self._build_iterator_graph( - input_values, cycle_length, block_length), - None, num_outputs) - # block_length = 1 - cycle_length = 2 - block_length = 1 - self.run_core_tests( - lambda: self._build_iterator_graph( - input_values, cycle_length, block_length), - None, num_outputs) - # pylint: enable=g-long-lambda - - def testSparseCore(self): - - def _map_fn(i): - return sparse_tensor.SparseTensorValue( - indices=[[0, 0], [1, 1]], values=(i * [1, -1]), dense_shape=[2, 2]) - - def _interleave_fn(x): - return dataset_ops.Dataset.from_tensor_slices( - sparse_ops.sparse_to_dense(x.indices, x.dense_shape, x.values)) - - def _build_dataset(): - return dataset_ops.Dataset.range(10).map(_map_fn).interleave( - _interleave_fn, cycle_length=1) - - self.run_core_tests(_build_dataset, None, 20) - - -class ParallelInterleaveDatasetSerializationTest( - dataset_serialization_test_base.DatasetSerializationTestBase): - - def setUp(self): - self.input_values = np.array([4, 5, 6], dtype=np.int64) - self.num_repeats = 2 - self.num_outputs = np.sum(self.input_values) * 2 - - def _build_ds(self, cycle_length, block_length, sloppy=False): - return (dataset_ops.Dataset.from_tensor_slices( - self.input_values).repeat(self.num_repeats).apply( - interleave_ops.parallel_interleave( - lambda x: dataset_ops.Dataset.range(10 * x, 11 * x), - cycle_length, block_length, sloppy))) - - def testSerializationCore(self): - # cycle_length > 1, block_length > 1 - cycle_length = 2 - block_length = 3 - self.run_core_tests( - lambda: self._build_ds(cycle_length, block_length), - lambda: self._build_ds(cycle_length * 2, block_length * 1), - self.num_outputs) - # cycle_length = 1 - cycle_length = 1 - block_length = 3 - self.run_core_tests(lambda: self._build_ds(cycle_length, block_length), - None, self.num_outputs) - # block_length = 1 - cycle_length = 2 - block_length = 1 - self.run_core_tests(lambda: self._build_ds(cycle_length, block_length), - None, self.num_outputs) - - def testSerializationWithSloppy(self): - break_points = self.gen_break_points(self.num_outputs, 10) - expected_outputs = np.repeat( - np.concatenate([np.arange(10 * x, 11 * x) for x in self.input_values]), - self.num_repeats).tolist() - - def run_test(cycle_length, block_length): - actual = self.gen_outputs( - lambda: self._build_ds(cycle_length, block_length, True), - break_points, self.num_outputs) - self.assertSequenceEqual(sorted(actual), expected_outputs) - - # cycle_length > 1, block_length > 1 - run_test(2, 3) - # cycle_length = 1 - run_test(1, 3) - # block_length = 1 - run_test(2, 1) - - def testSparseCore(self): - - def _map_fn(i): - return sparse_tensor.SparseTensorValue( - indices=[[0, 0], [1, 1]], values=(i * [1, -1]), dense_shape=[2, 2]) - - def _interleave_fn(x): - return dataset_ops.Dataset.from_tensor_slices( - sparse_ops.sparse_to_dense(x.indices, x.dense_shape, x.values)) - - def _build_dataset(): - return dataset_ops.Dataset.range(10).map(_map_fn).apply( - interleave_ops.parallel_interleave(_interleave_fn, 1)) - - self.run_core_tests(_build_dataset, None, 20) - - class ParallelInterleaveDatasetTest(test.TestCase): def setUp(self): diff --git a/tensorflow/contrib/data/python/kernel_tests/map_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/map_dataset_op_test.py index 8d40429279..270a2297b4 100644 --- a/tensorflow/contrib/data/python/kernel_tests/map_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/map_dataset_op_test.py @@ -21,20 +21,12 @@ import os import numpy as np -from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base from tensorflow.contrib.data.python.ops import error_ops from tensorflow.python.data.ops import dataset_ops -from tensorflow.python.framework import constant_op -from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors -from tensorflow.python.framework import function from tensorflow.python.framework import ops -from tensorflow.python.framework import sparse_tensor from tensorflow.python.ops import array_ops from tensorflow.python.ops import io_ops -from tensorflow.python.ops import math_ops -from tensorflow.python.ops import random_ops -from tensorflow.python.ops import variable_scope from tensorflow.python.platform import test from tensorflow.python.util import compat @@ -143,229 +135,5 @@ class MapDatasetTest(test.TestCase): sess.run(get_next) -class MapDatasetSerializationTest( - dataset_serialization_test_base.DatasetSerializationTestBase): - - def setUp(self): - self._tensor_slice_len = 7 - self._num_epochs = 14 - self._num_outputs = self._tensor_slice_len * self._num_epochs - - def _build_ds(self, multiplier=37.0): - components = (np.arange(self._tensor_slice_len), np.array([[1, 2, 3]]) * - np.arange(self._tensor_slice_len)[:, np.newaxis], - np.array(multiplier) * np.arange(self._tensor_slice_len)) - - def _map_fn(x, y, z): - return math_ops.square(x), math_ops.square(y), math_ops.square(z) - - return ( - dataset_ops.Dataset.from_tensor_slices(components).map(_map_fn) - .repeat(self._num_epochs)) - - def testSaveRestoreCore(self): - self.run_core_tests( - self._build_ds, - lambda: self._build_ds(multiplier=15.0), - self._num_outputs) - - def testSaveStatefulFunction(self): - - def _build_ds(): - - def _map_fn(x): - return random_ops.random_uniform( - (), 0, 10, dtype=dtypes.int32) * math_ops.to_int32(x) - - return dataset_ops.Dataset.range(100).map(_map_fn) - - self.verify_error_on_save(_build_ds, 15, errors.InvalidArgumentError) - - def testCaptureVariableInMapFn(self): - - def _build_ds(): - counter_var = variable_scope.get_variable( - "counter", (), dtypes.int32, use_resource=True) - return (dataset_ops.Dataset.from_tensors(0).repeat(10).map( - lambda _: counter_var.assign_add(1))) - - self.verify_error_on_save(_build_ds, 15, errors.InvalidArgumentError) - - def testCaptureConstantInMapFn(self): - - def _build_ds(): - constant_var = constant_op.constant(5) - return (dataset_ops.Dataset.from_tensors(0).repeat(10).map( - lambda x: x + constant_var)) - - self.run_core_tests(_build_ds, None, 10) - - def testCaptureDefunInMapFn(self): - num_outputs = 100 - - def _build_ds(): - - @function.Defun(dtypes.int64) - def defun_fn(x): - return constant_op.constant(1000) + math_ops.to_int32(x) - - return dataset_ops.Dataset.range(num_outputs).map(defun_fn) - - self.run_core_tests(_build_ds, None, num_outputs) - - def testBuildDefunInMapFn(self): - num_outputs = 100 - - def _build_ds(): - - @function.Defun(dtypes.int64) - def defun_fn(x): - - @function.Defun(dtypes.int32) - def defun_fn_deep(x): - return constant_op.constant(1000) + math_ops.to_int32(x) - - return constant_op.constant(11000) + defun_fn_deep(math_ops.to_int32(x)) - - return dataset_ops.Dataset.range(num_outputs).map(defun_fn) - - self.run_core_tests(_build_ds, None, num_outputs) - - def testSparseCore(self): - - def _sparse(i): - return sparse_tensor.SparseTensorValue( - indices=np.array([[0, 0]]), - values=(i * np.array([1])), - dense_shape=np.array([1, 1])) - - def _build_ds(num_outputs): - return dataset_ops.Dataset.range(num_outputs).map(_sparse) - - num_outputs = 10 - self.run_core_tests(lambda: _build_ds(num_outputs), - lambda: _build_ds(int(num_outputs / 2)), num_outputs) - - -class ParallelMapDatasetSerializationTest( - dataset_serialization_test_base.DatasetSerializationTestBase): - - def setUp(self): - self._tensor_slice_len = 7 - self._num_epochs = 1 - self._num_outputs = self._tensor_slice_len * self._num_epochs - - def _build_ds(self, multiplier=37.0): - components = (np.arange(self._tensor_slice_len), np.array([[1, 2, 3]]) * - np.arange(self._tensor_slice_len)[:, np.newaxis], - np.array(multiplier) * np.arange(self._tensor_slice_len)) - - def _map_fn(x, y, z): - return math_ops.square(x), math_ops.square(y), math_ops.square(z) - - return (dataset_ops.Dataset.from_tensor_slices(components).map( - _map_fn, num_parallel_calls=3).repeat(self._num_epochs)) - - def _build_ds_with_prefetch(self, multiplier=37.0): - components = (np.arange(self._tensor_slice_len), np.array([[1, 2, 3]]) * - np.arange(self._tensor_slice_len)[:, np.newaxis], - np.array(multiplier) * np.arange(self._tensor_slice_len)) - - def _map_fn(x, y, z): - return math_ops.square(x), math_ops.square(y), math_ops.square(z) - - return (dataset_ops.Dataset.from_tensor_slices(components).map( - _map_fn, num_parallel_calls=3).repeat(self._num_epochs).prefetch(5)) - - def testSaveRestoreCore(self): - for ds_fn in [self._build_ds, self._build_ds_with_prefetch]: - self.run_core_tests( - ds_fn, - lambda: ds_fn(multiplier=15.0), - self._num_outputs) - - def testSaveStatefulFunction(self): - - def _build_ds(): - - def _map_fn(x): - return random_ops.random_uniform( - (), 0, 10, dtype=dtypes.int32) * math_ops.to_int32(x) - - return dataset_ops.Dataset.range(100).map( - _map_fn, num_parallel_calls=2).prefetch(2) - - self.verify_error_on_save(_build_ds, 15, errors.InvalidArgumentError) - - def testCaptureVariableInMapFn(self): - - def _build_ds(): - counter_var = variable_scope.get_variable( - "counter", (), dtypes.int32, use_resource=True) - return (dataset_ops.Dataset.from_tensors(0).repeat(10).map( - lambda _: counter_var.assign_add(1), - num_parallel_calls=2).prefetch(2)) - - self.verify_error_on_save(_build_ds, 15, errors.InvalidArgumentError) - - def testCaptureConstantInMapFn(self): - - def _build_ds(): - constant_var = constant_op.constant(5) - return (dataset_ops.Dataset.from_tensors(0).repeat(10).map( - lambda x: x + constant_var, num_parallel_calls=2).prefetch(2)) - - self.run_core_tests(_build_ds, None, 10) - - def testCaptureDefunInMapFn(self): - num_outputs = 100 - - def _build_ds(): - - @function.Defun(dtypes.int64) - def defun_fn(x): - return constant_op.constant(1000) + math_ops.to_int32(x) - - return dataset_ops.Dataset.range(num_outputs).map( - defun_fn, num_parallel_calls=2).prefetch(2) - - self.run_core_tests(_build_ds, None, num_outputs) - - def testBuildDefunInMapFn(self): - num_outputs = 100 - - def _build_ds(): - - @function.Defun(dtypes.int64) - def defun_fn(x): - - @function.Defun(dtypes.int32) - def defun_fn_deep(x): - return constant_op.constant(1000) + math_ops.to_int32(x) - - return constant_op.constant(11000) + defun_fn_deep(math_ops.to_int32(x)) - - return dataset_ops.Dataset.range(num_outputs).map( - defun_fn, num_parallel_calls=2).prefetch(2) - - self.run_core_tests(_build_ds, None, num_outputs) - - -class IgnoreErrorsSerializationTest( - dataset_serialization_test_base.DatasetSerializationTestBase): - - def _build_ds(self, components): - return dataset_ops.Dataset.from_tensor_slices(components).map( - lambda x: array_ops.check_numerics(x, "message")).apply( - error_ops.ignore_errors()) - - def testIgnoreErrorsCore(self): - components = np.array([1., 2., 3., np.nan, 5.]).astype(np.float32) - diff_components = np.array([1., 2., 3., np.nan]).astype(np.float32) - num_outputs = 4 - self.run_core_tests(lambda: self._build_ds(components), - lambda: self._build_ds(diff_components), num_outputs) - - if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/optimize_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/optimize_dataset_op_test.py index 30f1847dcd..e35be8a23f 100644 --- a/tensorflow/contrib/data/python/kernel_tests/optimize_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/optimize_dataset_op_test.py @@ -17,7 +17,6 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base from tensorflow.contrib.data.python.ops import optimization from tensorflow.core.framework import graph_pb2 from tensorflow.python.data.ops import dataset_ops @@ -73,17 +72,5 @@ class OptimizeDatasetTest(test.TestCase): sess.run(get_next) -class OptimizeDatasetSerializationTest( - dataset_serialization_test_base.DatasetSerializationTestBase): - - def testCore(self): - - def build_dataset(num_elements, batch_size): - return dataset_ops.Dataset.range(num_elements).map(lambda x: x * x).batch( - batch_size).apply(optimization.optimize(["map_and_batch_fusion"])) - - self.run_core_tests(lambda: build_dataset(200, 10), None, 20) - - if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/range_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/range_dataset_op_test.py index 80e1cb0041..592642da0c 100644 --- a/tensorflow/contrib/data/python/kernel_tests/range_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/range_dataset_op_test.py @@ -17,21 +17,13 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import os - -from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base from tensorflow.contrib.data.python.ops import counter from tensorflow.contrib.data.python.ops import enumerate_ops from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors -from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape -from tensorflow.python.ops import gen_dataset_ops -from tensorflow.python.ops import io_ops -from tensorflow.python.ops import parsing_ops -from tensorflow.python.ops import variables from tensorflow.python.platform import test @@ -81,88 +73,5 @@ class RangeDatasetTest(test.TestCase): self.assertEqual(-2, sess.run(negative_get_next)) -class RangeDatasetSerializationTest( - dataset_serialization_test_base.DatasetSerializationTestBase): - - def _iterator_checkpoint_prefix_local(self): - return os.path.join(self.get_temp_dir(), "iterator") - - def _save_op(self, iterator_resource): - iterator_state_variant = gen_dataset_ops.serialize_iterator( - iterator_resource) - save_op = io_ops.write_file( - self._iterator_checkpoint_prefix_local(), - parsing_ops.serialize_tensor(iterator_state_variant)) - return save_op - - def _restore_op(self, iterator_resource): - iterator_state_variant = parsing_ops.parse_tensor( - io_ops.read_file(self._iterator_checkpoint_prefix_local()), - dtypes.variant) - restore_op = gen_dataset_ops.deserialize_iterator(iterator_resource, - iterator_state_variant) - return restore_op - - def testSaveRestore(self): - - def _build_graph(start, stop): - iterator = dataset_ops.Dataset.range(start, - stop).make_initializable_iterator() - init_op = iterator.initializer - get_next = iterator.get_next() - save_op = self._save_op(iterator._iterator_resource) - restore_op = self._restore_op(iterator._iterator_resource) - return init_op, get_next, save_op, restore_op - - # Saving and restoring in different sessions. - start = 2 - stop = 10 - break_point = 5 - with ops.Graph().as_default() as g: - init_op, get_next, save_op, _ = _build_graph(start, stop) - with self.test_session(graph=g) as sess: - sess.run(variables.global_variables_initializer()) - sess.run(init_op) - for i in range(start, break_point): - self.assertEqual(i, sess.run(get_next)) - sess.run(save_op) - - with ops.Graph().as_default() as g: - init_op, get_next, _, restore_op = _build_graph(start, stop) - with self.test_session(graph=g) as sess: - sess.run(init_op) - sess.run(restore_op) - for i in range(break_point, stop): - self.assertEqual(i, sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - # Saving and restoring in same session. - with ops.Graph().as_default() as g: - init_op, get_next, save_op, restore_op = _build_graph(start, stop) - with self.test_session(graph=g) as sess: - sess.run(variables.global_variables_initializer()) - sess.run(init_op) - for i in range(start, break_point): - self.assertEqual(i, sess.run(get_next)) - sess.run(save_op) - sess.run(restore_op) - for i in range(break_point, stop): - self.assertEqual(i, sess.run(get_next)) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def _build_range_dataset(self, start, stop): - return dataset_ops.Dataset.range(start, stop) - - def testRangeCore(self): - start = 2 - stop = 10 - stop_1 = 8 - self.run_core_tests(lambda: self._build_range_dataset(start, stop), - lambda: self._build_range_dataset(start, stop_1), - stop - start) - - if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/reader_dataset_ops_test.py b/tensorflow/contrib/data/python/kernel_tests/reader_dataset_ops_test.py index 3b07ef290b..9df403ef50 100644 --- a/tensorflow/contrib/data/python/kernel_tests/reader_dataset_ops_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/reader_dataset_ops_test.py @@ -17,266 +17,20 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import gzip import os -import zlib import numpy as np -from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base from tensorflow.contrib.data.python.kernel_tests import reader_dataset_ops_test_base from tensorflow.contrib.data.python.ops import readers -from tensorflow.python.data.ops import iterator_ops from tensorflow.python.data.ops import readers as core_readers from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import ops -from tensorflow.python.lib.io import python_io -from tensorflow.python.ops import array_ops from tensorflow.python.ops import parsing_ops from tensorflow.python.ops import string_ops from tensorflow.python.platform import test -from tensorflow.python.util import compat - - -class TextLineDatasetTestBase(test.TestCase): - - def _lineText(self, f, l): - return compat.as_bytes("%d: %d" % (f, l)) - - def _createFiles(self, - num_files, - num_lines, - crlf=False, - compression_type=None): - filenames = [] - for i in range(num_files): - fn = os.path.join(self.get_temp_dir(), "text_line.%d.txt" % i) - filenames.append(fn) - contents = [] - for j in range(num_lines): - contents.append(self._lineText(i, j)) - # Always include a newline after the record unless it is - # at the end of the file, in which case we include it - if j + 1 != num_lines or i == 0: - contents.append(b"\r\n" if crlf else b"\n") - contents = b"".join(contents) - - if not compression_type: - with open(fn, "wb") as f: - f.write(contents) - elif compression_type == "GZIP": - with gzip.GzipFile(fn, "wb") as f: - f.write(contents) - elif compression_type == "ZLIB": - contents = zlib.compress(contents) - with open(fn, "wb") as f: - f.write(contents) - else: - raise ValueError("Unsupported compression_type", compression_type) - - return filenames - - -class TextLineDatasetSerializationTest( - TextLineDatasetTestBase, - dataset_serialization_test_base.DatasetSerializationTestBase): - - def _build_iterator_graph(self, test_filenames, compression_type=None): - return core_readers.TextLineDataset( - test_filenames, compression_type=compression_type, buffer_size=10) - - def testTextLineCore(self): - compression_types = [None, "GZIP", "ZLIB"] - num_files = 5 - lines_per_file = 5 - num_outputs = num_files * lines_per_file - for compression_type in compression_types: - test_filenames = self._createFiles( - num_files, - lines_per_file, - crlf=True, - compression_type=compression_type) - # pylint: disable=cell-var-from-loop - self.run_core_tests( - lambda: self._build_iterator_graph(test_filenames, compression_type), - lambda: self._build_iterator_graph(test_filenames), num_outputs) - # pylint: enable=cell-var-from-loop - - -class FixedLengthRecordReaderTestBase(test.TestCase): - - def setUp(self): - super(FixedLengthRecordReaderTestBase, self).setUp() - self._num_files = 2 - self._num_records = 7 - self._header_bytes = 5 - self._record_bytes = 3 - self._footer_bytes = 2 - - def _record(self, f, r): - return compat.as_bytes(str(f * 2 + r) * self._record_bytes) - - def _createFiles(self): - filenames = [] - for i in range(self._num_files): - fn = os.path.join(self.get_temp_dir(), "fixed_length_record.%d.txt" % i) - filenames.append(fn) - with open(fn, "wb") as f: - f.write(b"H" * self._header_bytes) - for j in range(self._num_records): - f.write(self._record(i, j)) - f.write(b"F" * self._footer_bytes) - return filenames - - -class FixedLengthRecordDatasetSerializationTest( - FixedLengthRecordReaderTestBase, - dataset_serialization_test_base.DatasetSerializationTestBase): - - def _build_iterator_graph(self, num_epochs, compression_type=None): - filenames = self._createFiles() - return core_readers.FixedLengthRecordDataset( - filenames, self._record_bytes, self._header_bytes, - self._footer_bytes).repeat(num_epochs) - - def testFixedLengthRecordCore(self): - num_epochs = 5 - num_outputs = num_epochs * self._num_files * self._num_records - self.run_core_tests(lambda: self._build_iterator_graph(num_epochs), - lambda: self._build_iterator_graph(num_epochs * 2), - num_outputs) - - -class TFRecordDatasetTestBase(test.TestCase): - - def setUp(self): - super(TFRecordDatasetTestBase, self).setUp() - self._num_files = 2 - self._num_records = 7 - - self.test_filenames = self._createFiles() - - self.filenames = array_ops.placeholder(dtypes.string, shape=[None]) - self.num_epochs = array_ops.placeholder_with_default( - constant_op.constant(1, dtypes.int64), shape=[]) - self.compression_type = array_ops.placeholder_with_default("", shape=[]) - self.batch_size = array_ops.placeholder(dtypes.int64, shape=[]) - - repeat_dataset = core_readers.TFRecordDataset( - self.filenames, self.compression_type).repeat(self.num_epochs) - batch_dataset = repeat_dataset.batch(self.batch_size) - - iterator = iterator_ops.Iterator.from_structure(batch_dataset.output_types) - self.init_op = iterator.make_initializer(repeat_dataset) - self.init_batch_op = iterator.make_initializer(batch_dataset) - self.get_next = iterator.get_next() - - def _record(self, f, r): - return compat.as_bytes("Record %d of file %d" % (r, f)) - - def _createFiles(self): - filenames = [] - for i in range(self._num_files): - fn = os.path.join(self.get_temp_dir(), "tf_record.%d.txt" % i) - filenames.append(fn) - writer = python_io.TFRecordWriter(fn) - for j in range(self._num_records): - writer.write(self._record(i, j)) - writer.close() - return filenames - - -class TFRecordDatasetSerializationTest( - TFRecordDatasetTestBase, - dataset_serialization_test_base.DatasetSerializationTestBase): - - def _build_iterator_graph(self, - num_epochs, - batch_size=1, - compression_type=None, - buffer_size=None): - filenames = self._createFiles() - if compression_type is "ZLIB": - zlib_files = [] - for i, fn in enumerate(filenames): - with open(fn, "rb") as f: - cdata = zlib.compress(f.read()) - zfn = os.path.join(self.get_temp_dir(), "tfrecord_%s.z" % i) - with open(zfn, "wb") as f: - f.write(cdata) - zlib_files.append(zfn) - filenames = zlib_files - - elif compression_type is "GZIP": - gzip_files = [] - for i, fn in enumerate(self.test_filenames): - with open(fn, "rb") as f: - gzfn = os.path.join(self.get_temp_dir(), "tfrecord_%s.gz" % i) - with gzip.GzipFile(gzfn, "wb") as gzf: - gzf.write(f.read()) - gzip_files.append(gzfn) - filenames = gzip_files - - return core_readers.TFRecordDataset( - filenames, compression_type, - buffer_size=buffer_size).repeat(num_epochs).batch(batch_size) - - def testTFRecordWithoutBufferCore(self): - num_epochs = 5 - batch_size = num_epochs - num_outputs = num_epochs * self._num_files * self._num_records // batch_size - # pylint: disable=g-long-lambda - self.run_core_tests( - lambda: self._build_iterator_graph(num_epochs, batch_size, - buffer_size=0), - lambda: self._build_iterator_graph(num_epochs * 2, batch_size), - num_outputs) - self.run_core_tests( - lambda: self._build_iterator_graph(num_epochs, buffer_size=0), None, - num_outputs * batch_size) - # pylint: enable=g-long-lambda - - def testTFRecordWithBufferCore(self): - num_epochs = 5 - num_outputs = num_epochs * self._num_files * self._num_records - self.run_core_tests(lambda: self._build_iterator_graph(num_epochs), - lambda: self._build_iterator_graph(num_epochs * 2), - num_outputs) - - def testTFRecordWithCompressionCore(self): - num_epochs = 5 - num_outputs = num_epochs * self._num_files * self._num_records - self.run_core_tests( - lambda: self._build_iterator_graph(num_epochs, compression_type="ZLIB"), - lambda: self._build_iterator_graph(num_epochs * 2), num_outputs) - self.run_core_tests( - lambda: self._build_iterator_graph(num_epochs, compression_type="GZIP"), - lambda: self._build_iterator_graph(num_epochs * 2), num_outputs) - - -def _interleave(iterators, cycle_length): - pending_iterators = iterators - open_iterators = [] - num_open = 0 - for i in range(cycle_length): - if pending_iterators: - open_iterators.append(pending_iterators.pop(0)) - num_open += 1 - - while num_open: - for i in range(min(cycle_length, len(open_iterators))): - if open_iterators[i] is None: - continue - try: - yield next(open_iterators[i]) - except StopIteration: - if pending_iterators: - open_iterators[i] = pending_iterators.pop(0) - else: - open_iterators[i] = None - num_open -= 1 class ReadBatchFeaturesTest( @@ -914,7 +668,30 @@ class MakeCsvDatasetTest(test.TestCase): self.assertFalse(all_equal) -class MakeTFRecordDatasetTest(TFRecordDatasetTestBase): +class MakeTFRecordDatasetTest( + reader_dataset_ops_test_base.TFRecordDatasetTestBase): + + def _interleave(self, iterators, cycle_length): + pending_iterators = iterators + open_iterators = [] + num_open = 0 + for i in range(cycle_length): + if pending_iterators: + open_iterators.append(pending_iterators.pop(0)) + num_open += 1 + + while num_open: + for i in range(min(cycle_length, len(open_iterators))): + if open_iterators[i] is None: + continue + try: + yield next(open_iterators[i]) + except StopIteration: + if pending_iterators: + open_iterators[i] = pending_iterators.pop(0) + else: + open_iterators[i] = None + num_open -= 1 def _next_expected_batch(self, file_indices, @@ -930,8 +707,8 @@ class MakeTFRecordDatasetTest(TFRecordDatasetTestBase): yield j, i def _next_record_interleaved(file_indices, cycle_length): - return _interleave([_next_record([i]) for i in file_indices], - cycle_length) + return self._interleave([_next_record([i]) for i in file_indices], + cycle_length) record_batch = [] batch_index = 0 diff --git a/tensorflow/contrib/data/python/kernel_tests/reader_dataset_ops_test_base.py b/tensorflow/contrib/data/python/kernel_tests/reader_dataset_ops_test_base.py index 805a7c7b73..e63bc4c720 100644 --- a/tensorflow/contrib/data/python/kernel_tests/reader_dataset_ops_test_base.py +++ b/tensorflow/contrib/data/python/kernel_tests/reader_dataset_ops_test_base.py @@ -12,24 +12,57 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Tests for the experimental input pipeline ops.""" +"""Base class for testing reader datasets.""" + from __future__ import absolute_import from __future__ import division from __future__ import print_function +import gzip import os +import zlib from tensorflow.contrib.data.python.ops import readers from tensorflow.core.example import example_pb2 from tensorflow.core.example import feature_pb2 +from tensorflow.python.data.ops import iterator_ops from tensorflow.python.data.ops import readers as core_readers +from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.lib.io import python_io +from tensorflow.python.ops import array_ops from tensorflow.python.ops import parsing_ops from tensorflow.python.platform import test from tensorflow.python.util import compat +class FixedLengthRecordDatasetTestBase(test.TestCase): + """Base class for setting up and testing FixedLengthRecordDataset.""" + + def setUp(self): + super(FixedLengthRecordDatasetTestBase, self).setUp() + self._num_files = 2 + self._num_records = 7 + self._header_bytes = 5 + self._record_bytes = 3 + self._footer_bytes = 2 + + def _record(self, f, r): + return compat.as_bytes(str(f * 2 + r) * self._record_bytes) + + def _createFiles(self): + filenames = [] + for i in range(self._num_files): + fn = os.path.join(self.get_temp_dir(), "fixed_length_record.%d.txt" % i) + filenames.append(fn) + with open(fn, "wb") as f: + f.write(b"H" * self._header_bytes) + for j in range(self._num_records): + f.write(self._record(i, j)) + f.write(b"F" * self._footer_bytes) + return filenames + + class ReadBatchFeaturesTestBase(test.TestCase): """Base class for setting up and testing `make_batched_feature_dataset`.""" @@ -216,3 +249,83 @@ class ReadBatchFeaturesTestBase(test.TestCase): actual_batch = self._next_actual_batch(sess) for i in range(len(expected_batch)): self.assertAllEqual(expected_batch[i], actual_batch[i]) + + +class TextLineDatasetTestBase(test.TestCase): + """Base class for setting up and testing TextLineDataset.""" + + def _lineText(self, f, l): + return compat.as_bytes("%d: %d" % (f, l)) + + def _createFiles(self, + num_files, + num_lines, + crlf=False, + compression_type=None): + filenames = [] + for i in range(num_files): + fn = os.path.join(self.get_temp_dir(), "text_line.%d.txt" % i) + filenames.append(fn) + contents = [] + for j in range(num_lines): + contents.append(self._lineText(i, j)) + # Always include a newline after the record unless it is + # at the end of the file, in which case we include it + if j + 1 != num_lines or i == 0: + contents.append(b"\r\n" if crlf else b"\n") + contents = b"".join(contents) + + if not compression_type: + with open(fn, "wb") as f: + f.write(contents) + elif compression_type == "GZIP": + with gzip.GzipFile(fn, "wb") as f: + f.write(contents) + elif compression_type == "ZLIB": + contents = zlib.compress(contents) + with open(fn, "wb") as f: + f.write(contents) + else: + raise ValueError("Unsupported compression_type", compression_type) + + return filenames + + +class TFRecordDatasetTestBase(test.TestCase): + """Base class for setting up and testing TFRecordDataset.""" + + def setUp(self): + super(TFRecordDatasetTestBase, self).setUp() + self._num_files = 2 + self._num_records = 7 + + self.test_filenames = self._createFiles() + + self.filenames = array_ops.placeholder(dtypes.string, shape=[None]) + self.num_epochs = array_ops.placeholder_with_default( + constant_op.constant(1, dtypes.int64), shape=[]) + self.compression_type = array_ops.placeholder_with_default("", shape=[]) + self.batch_size = array_ops.placeholder(dtypes.int64, shape=[]) + + repeat_dataset = core_readers.TFRecordDataset( + self.filenames, self.compression_type).repeat(self.num_epochs) + batch_dataset = repeat_dataset.batch(self.batch_size) + + iterator = iterator_ops.Iterator.from_structure(batch_dataset.output_types) + self.init_op = iterator.make_initializer(repeat_dataset) + self.init_batch_op = iterator.make_initializer(batch_dataset) + self.get_next = iterator.get_next() + + def _record(self, f, r): + return compat.as_bytes("Record %d of file %d" % (r, f)) + + def _createFiles(self): + filenames = [] + for i in range(self._num_files): + fn = os.path.join(self.get_temp_dir(), "tf_record.%d.txt" % i) + filenames.append(fn) + writer = python_io.TFRecordWriter(fn) + for j in range(self._num_records): + writer.write(self._record(i, j)) + writer.close() + return filenames diff --git a/tensorflow/contrib/data/python/kernel_tests/resample_test.py b/tensorflow/contrib/data/python/kernel_tests/resample_test.py index 520da7d6ff..c5cfddb72b 100644 --- a/tensorflow/contrib/data/python/kernel_tests/resample_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/resample_test.py @@ -17,10 +17,11 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import time + from absl.testing import parameterized import numpy as np from six.moves import xrange # pylint: disable=redefined-builtin -import time from tensorflow.contrib.data.python.ops import resampling from tensorflow.python.data.ops import dataset_ops diff --git a/tensorflow/contrib/data/python/kernel_tests/scan_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/scan_dataset_op_test.py index eb2ceff893..d02b3abb92 100644 --- a/tensorflow/contrib/data/python/kernel_tests/scan_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/scan_dataset_op_test.py @@ -21,7 +21,6 @@ import itertools import numpy as np -from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base from tensorflow.contrib.data.python.ops import scan_ops from tensorflow.python.data.ops import dataset_ops from tensorflow.python.eager import context @@ -168,18 +167,5 @@ class ScanDatasetTest(test.TestCase): scan_ops.scan(constant_op.constant(1, dtype=dtypes.int32), _scan_fn)) -class ScanDatasetSerializationTest( - dataset_serialization_test_base.DatasetSerializationTestBase): - - def _build_dataset(self, num_elements): - return dataset_ops.Dataset.from_tensors(1).repeat(num_elements).apply( - scan_ops.scan([0, 1], lambda a, _: ([a[1], a[0] + a[1]], a[1]))) - - def testScanCore(self): - num_output = 5 - self.run_core_tests(lambda: self._build_dataset(num_output), - lambda: self._build_dataset(2), num_output) - - if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/BUILD b/tensorflow/contrib/data/python/kernel_tests/serialization/BUILD new file mode 100644 index 0000000000..e9bc18ac2e --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/BUILD @@ -0,0 +1,526 @@ +package(default_visibility = ["//tensorflow:internal"]) + +licenses(["notice"]) # Apache 2.0 + +exports_files(["LICENSE"]) + +load("//tensorflow:tensorflow.bzl", "py_test") + +py_library( + name = "dataset_serialization_test_base", + srcs = [ + "dataset_serialization_test_base.py", + ], + srcs_version = "PY2AND3", + deps = [ + "//tensorflow/contrib/data/python/ops:iterator_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:dtypes", + "//tensorflow/python:errors", + "//tensorflow/python:framework_ops", + "//tensorflow/python:lookup_ops", + "//tensorflow/python:platform", + "//tensorflow/python:sparse_tensor", + "//tensorflow/python:training", + "//tensorflow/python:util", + "//tensorflow/python:variables", + "//tensorflow/python/data/ops:iterator_ops", + "//third_party/py/numpy", + ], +) + +py_test( + name = "batch_dataset_serialization_test", + size = "medium", + srcs = ["batch_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/contrib/data/python/ops:batching", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:sparse_tensor", + "//tensorflow/python/data/ops:dataset_ops", + "//third_party/py/numpy", + ], +) + +py_test( + name = "cache_dataset_serialization_test", + size = "small", + srcs = ["cache_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/python:client_testlib", + "//tensorflow/python:errors", + "//tensorflow/python/data/ops:dataset_ops", + ], +) + +py_test( + name = "concatenate_dataset_serialization_test", + size = "small", + srcs = ["concatenate_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/python:client_testlib", + "//tensorflow/python/data/ops:dataset_ops", + "//third_party/py/numpy", + ], +) + +py_test( + name = "dataset_constructor_serialization_test", + size = "medium", + srcs = ["dataset_constructor_serialization_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/python:client_testlib", + "//tensorflow/python:sparse_tensor", + "//tensorflow/python/data/ops:dataset_ops", + "//third_party/py/numpy", + ], +) + +py_test( + name = "filter_dataset_serialization_test", + size = "small", + srcs = ["filter_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/python:client_testlib", + "//tensorflow/python:math_ops", + "//tensorflow/python:sparse_tensor", + "//tensorflow/python/data/ops:dataset_ops", + "//third_party/py/numpy", + ], +) + +py_test( + name = "fixed_length_record_dataset_serialization_test", + size = "medium", + srcs = ["fixed_length_record_dataset_serialization_test.py"], + shard_count = 4, + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/contrib/data/python/kernel_tests:reader_dataset_ops_test_base", + "//tensorflow/python:client_testlib", + "//tensorflow/python/data/ops:readers", + ], +) + +py_test( + name = "flat_map_dataset_serialization_test", + size = "medium", + srcs = ["flat_map_dataset_serialization_test.py"], + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/python:client_testlib", + "//tensorflow/python:constant_op", + "//tensorflow/python:dtypes", + "//tensorflow/python:errors", + "//tensorflow/python:function", + "//tensorflow/python:math_ops", + "//tensorflow/python:random_ops", + "//tensorflow/python:sparse_ops", + "//tensorflow/python:sparse_tensor", + "//tensorflow/python:variable_scope", + "//tensorflow/python/data/ops:dataset_ops", + ], +) + +py_test( + name = "group_by_reducer_serialization_test", + size = "medium", + srcs = ["group_by_reducer_serialization_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/contrib/data/python/ops:grouping", + "//tensorflow/python:client_testlib", + "//tensorflow/python/data/ops:dataset_ops", + "//third_party/py/numpy", + ], +) + +py_test( + name = "group_by_window_serialization_test", + size = "medium", + srcs = ["group_by_window_serialization_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/contrib/data/python/ops:grouping", + "//tensorflow/python:client_testlib", + "//tensorflow/python/data/ops:dataset_ops", + "//third_party/py/numpy", + ], +) + +py_test( + name = "ignore_errors_serialization_test", + size = "small", + srcs = ["ignore_errors_serialization_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/contrib/data/python/ops:error_ops", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python/data/ops:dataset_ops", + "//third_party/py/numpy", + ], +) + +py_test( + name = "interleave_dataset_serialization_test", + size = "medium", + srcs = ["interleave_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/python:client_testlib", + "//tensorflow/python:sparse_ops", + "//tensorflow/python:sparse_tensor", + "//tensorflow/python/data/ops:dataset_ops", + "//third_party/py/numpy", + ], +) + +py_test( + name = "map_and_batch_dataset_serialization_test", + size = "medium", + srcs = ["map_and_batch_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/contrib/data/python/ops:batching", + "//tensorflow/python:client_testlib", + "//tensorflow/python:math_ops", + "//tensorflow/python/data/ops:dataset_ops", + ], +) + +py_test( + name = "map_dataset_serialization_test", + size = "medium", + srcs = ["map_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/python:client_testlib", + "//tensorflow/python:constant_op", + "//tensorflow/python:dtypes", + "//tensorflow/python:errors", + "//tensorflow/python:function", + "//tensorflow/python:math_ops", + "//tensorflow/python:random_ops", + "//tensorflow/python:sparse_tensor", + "//tensorflow/python:variable_scope", + "//tensorflow/python/data/ops:dataset_ops", + "//third_party/py/numpy", + ], +) + +py_test( + name = "optimize_dataset_serialization_test", + size = "small", + srcs = ["optimize_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/contrib/data/python/ops:optimization", + "//tensorflow/python:client_testlib", + "//tensorflow/python/data/ops:dataset_ops", + ], +) + +py_test( + name = "padded_batch_dataset_serialization_test", + size = "medium", + srcs = ["padded_batch_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:string_ops", + "//tensorflow/python/data/ops:dataset_ops", + "//third_party/py/numpy", + ], +) + +py_test( + name = "parallel_interleave_dataset_serialization_test", + size = "medium", + srcs = ["parallel_interleave_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/contrib/data/python/ops:interleave_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:sparse_ops", + "//tensorflow/python:sparse_tensor", + "//tensorflow/python/data/ops:dataset_ops", + "//third_party/py/numpy", + ], +) + +py_test( + name = "parallel_map_dataset_serialization_test", + size = "medium", + srcs = ["parallel_map_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/python:client_testlib", + "//tensorflow/python:constant_op", + "//tensorflow/python:dtypes", + "//tensorflow/python:errors", + "//tensorflow/python:function", + "//tensorflow/python:math_ops", + "//tensorflow/python:random_ops", + "//tensorflow/python:variable_scope", + "//tensorflow/python/data/ops:dataset_ops", + "//third_party/py/numpy", + ], +) + +py_test( + name = "prefetch_dataset_serialization_test", + size = "small", + srcs = ["prefetch_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/python:client_testlib", + "//tensorflow/python/data/ops:dataset_ops", + ], +) + +py_test( + name = "range_dataset_serialization_test", + size = "small", + srcs = ["range_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/python:client_testlib", + "//tensorflow/python:dataset_ops_gen", + "//tensorflow/python:dtypes", + "//tensorflow/python:errors", + "//tensorflow/python:framework_ops", + "//tensorflow/python:io_ops", + "//tensorflow/python:parsing_ops", + "//tensorflow/python:variables", + "//tensorflow/python/data/ops:dataset_ops", + ], +) + +py_test( + name = "sample_from_datasets_serialization_test", + size = "medium", + srcs = ["sample_from_datasets_serialization_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/contrib/data/python/ops:interleave_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python/data/ops:dataset_ops", + ], +) + +py_test( + name = "scan_dataset_serialization_test", + size = "small", + srcs = ["scan_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/contrib/data/python/ops:scan_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python/data/ops:dataset_ops", + ], +) + +py_test( + name = "sequence_dataset_serialization_test", + size = "medium", + srcs = ["sequence_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/python:client_testlib", + "//tensorflow/python/data/ops:dataset_ops", + "//third_party/py/numpy", + ], +) + +py_test( + name = "serialization_integration_test", + size = "small", + srcs = ["serialization_integration_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + "//tensorflow/contrib/data/python/ops:iterator_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework_ops", + "//tensorflow/python:training", + "//tensorflow/python/data/ops:dataset_ops", + ], +) + +py_test( + name = "shuffle_and_repeat_dataset_serialization_test", + size = "medium", + srcs = ["shuffle_and_repeat_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/contrib/data/python/ops:shuffle_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python/data/ops:dataset_ops", + ], +) + +py_test( + name = "shuffle_dataset_serialization_test", + size = "medium", + srcs = ["shuffle_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/contrib/data/python/ops:iterator_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework_ops", + "//tensorflow/python:training", + "//tensorflow/python/data/ops:dataset_ops", + ], +) + +py_test( + name = "sql_dataset_serialization_test", + size = "small", + srcs = ["sql_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/contrib/data/python/kernel_tests:sql_dataset_op_test_base", + "//tensorflow/contrib/data/python/ops:readers", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:dtypes", + ], +) + +py_test( + name = "stats_dataset_serialization_test", + size = "medium", + srcs = ["stats_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/contrib/data/python/ops:stats_ops", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework_ops", + "//tensorflow/python/data/ops:dataset_ops", + ], +) + +py_test( + name = "textline_dataset_serialization_test", + size = "medium", + srcs = ["textline_dataset_serialization_test.py"], + shard_count = 4, + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/contrib/data/python/kernel_tests:reader_dataset_ops_test_base", + "//tensorflow/python:client_testlib", + "//tensorflow/python/data/ops:readers", + ], +) + +py_test( + name = "tf_record_dataset_serialization_test", + size = "medium", + srcs = ["tf_record_dataset_serialization_test.py"], + shard_count = 4, + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/contrib/data/python/kernel_tests:reader_dataset_ops_test_base", + "//tensorflow/python:client_testlib", + "//tensorflow/python/data/ops:readers", + ], +) + +py_test( + name = "unbatch_dataset_serialization_test", + size = "medium", + srcs = ["unbatch_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/contrib/data/python/ops:batching", + "//tensorflow/python:client_testlib", + "//tensorflow/python/data/ops:dataset_ops", + "//third_party/py/numpy", + ], +) + +py_test( + name = "unique_dataset_serialization_test", + size = "small", + srcs = ["unique_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/contrib/data/python/ops:unique", + "//tensorflow/python:client_testlib", + "//tensorflow/python/data/ops:dataset_ops", + ], +) + +py_test( + name = "zip_dataset_serialization_test", + size = "small", + srcs = ["zip_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/python:client_testlib", + "//tensorflow/python/data/ops:dataset_ops", + "//third_party/py/numpy", + ], +) diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/batch_dataset_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/batch_dataset_serialization_test.py new file mode 100644 index 0000000000..af87d8b608 --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/batch_dataset_serialization_test.py @@ -0,0 +1,83 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the BatchDataset serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.contrib.data.python.ops import batching +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.framework import sparse_tensor +from tensorflow.python.ops import array_ops +from tensorflow.python.platform import test + + +class BatchDatasetSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def build_dataset(self, multiplier=15.0, tensor_slice_len=2, batch_size=2): + components = ( + np.arange(tensor_slice_len), + np.array([[1, 2, 3]]) * np.arange(tensor_slice_len)[:, np.newaxis], + np.array(multiplier) * np.arange(tensor_slice_len)) + + return dataset_ops.Dataset.from_tensor_slices(components).batch(batch_size) + + def testCore(self): + tensor_slice_len = 8 + batch_size = 2 + num_outputs = tensor_slice_len // batch_size + self.run_core_tests( + lambda: self.build_dataset(15.0, tensor_slice_len, batch_size), + lambda: self.build_dataset(20.0, tensor_slice_len, batch_size), + num_outputs) + + def _build_dataset_dense_to_sparse(self, components): + return dataset_ops.Dataset.from_tensor_slices(components).map( + lambda x: array_ops.fill([x], x)).apply( + batching.dense_to_sparse_batch(4, [12])) + + def testDenseToSparseBatchDatasetCore(self): + components = np.random.randint(5, size=(40,)).astype(np.int32) + diff_comp = np.random.randint(2, size=(100,)).astype(np.int32) + + num_outputs = len(components) // 4 + self.run_core_tests(lambda: self._build_dataset_dense_to_sparse(components), + lambda: self._build_dataset_dense_to_sparse(diff_comp), + num_outputs) + + def _sparse(self, i): + return sparse_tensor.SparseTensorValue( + indices=[[0]], values=(i * [1]), dense_shape=[1]) + + def _build_dataset_sparse(self, batch_size=5): + return dataset_ops.Dataset.range(10).map(self._sparse).batch(batch_size) + + def testSparseCore(self): + self.run_core_tests(self._build_dataset_sparse, + lambda: self._build_dataset_sparse(2), 2) + + def _build_dataset_nested_sparse(self): + return dataset_ops.Dataset.range(10).map(self._sparse).batch(5).batch(2) + + def testNestedSparseCore(self): + self.run_core_tests(self._build_dataset_nested_sparse, None, 1) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/cache_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/cache_dataset_serialization_test.py similarity index 97% rename from tensorflow/contrib/data/python/kernel_tests/cache_dataset_op_test.py rename to tensorflow/contrib/data/python/kernel_tests/serialization/cache_dataset_serialization_test.py index f08216a303..a0a1100893 100644 --- a/tensorflow/contrib/data/python/kernel_tests/cache_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/cache_dataset_serialization_test.py @@ -12,20 +12,20 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Tests for the experimental features of CacheDataset.""" +"""Tests for the CacheDataset serialization.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import os -from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import errors from tensorflow.python.platform import test -class CacheToFileDatasetSerializationTest( +class CacheDatasetSerializationTest( dataset_serialization_test_base.DatasetSerializationTestBase): def setUp(self): diff --git a/tensorflow/contrib/data/python/kernel_tests/concatenate_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/concatenate_dataset_serialization_test.py similarity index 92% rename from tensorflow/contrib/data/python/kernel_tests/concatenate_dataset_op_test.py rename to tensorflow/contrib/data/python/kernel_tests/serialization/concatenate_dataset_serialization_test.py index 17f2980157..96f13d75a3 100644 --- a/tensorflow/contrib/data/python/kernel_tests/concatenate_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/concatenate_dataset_serialization_test.py @@ -12,14 +12,14 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Tests for the experimental input pipeline ops.""" +"""Tests for the ConcatenateDataset serialization.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np -from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base from tensorflow.python.data.ops import dataset_ops from tensorflow.python.platform import test diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/dataset_constructor_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/dataset_constructor_serialization_test.py new file mode 100644 index 0000000000..2139b5c33d --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/dataset_constructor_serialization_test.py @@ -0,0 +1,95 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the dataset constructors serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.framework import sparse_tensor +from tensorflow.python.platform import test + + +class FromTensorsSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def _build_tensor_dataset(self, variable_array): + components = (variable_array, np.array([1, 2, 3]), np.array(37.0)) + + return dataset_ops.Dataset.from_tensors(components) + + def testFromTensorsCore(self): + # Equal length components + arr = np.array(1) + num_outputs = 1 + diff_arr = np.array(2) + self.run_core_tests(lambda: self._build_tensor_dataset(arr), + lambda: self._build_tensor_dataset(diff_arr), + num_outputs) + + +class FromTensorSlicesSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def _build_tensor_slices_dataset(self, components): + return dataset_ops.Dataset.from_tensor_slices(components) + + def testFromTensorSlicesCore(self): + # Equal length components + components = (np.tile(np.array([[1], [2], [3], [4]]), 20), + np.tile(np.array([[12], [13], [14], [15]]), 22), + np.array([37.0, 38.0, 39.0, 40.0])) + + diff_comp = (np.tile(np.array([[1], [2], [3], [4]]), 20), + np.tile(np.array([[5], [6], [7], [8]]), 22), + np.array([1.0, 2.0, 3.0, 4.0])) + + dict_components = {"foo": [1, 2, 3], "bar": [[4.0], [5.0], [6.0]]} + + self.run_core_tests(lambda: self._build_tensor_slices_dataset(components), + lambda: self._build_tensor_slices_dataset(diff_comp), 4) + self.run_core_tests( + lambda: self._build_tensor_slices_dataset(dict_components), None, 3) + + +class FromSparseTensorSlicesSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def _build_sparse_tensor_slice_dataset(self, slices): + indices = np.array( + [[i, j] for i in range(len(slices)) for j in range(len(slices[i]))], + dtype=np.int64) + values = np.array([val for s in slices for val in s], dtype=np.float64) + dense_shape = np.array( + [len(slices), max(len(s) for s in slices) + 1], dtype=np.int64) + sparse_components = sparse_tensor.SparseTensor(indices, values, dense_shape) + return dataset_ops.Dataset.from_sparse_tensor_slices(sparse_components) + + def testFromSparseTensorSlicesCore(self): + slices = [[1., 2., 3.], [1.], [1.], [1., 2.], [], [1., 2.], [], [], []] + diff_slices = [[1., 2.], [2.], [2., 3., 4.], [], [], []] + + self.run_core_tests( + lambda: self._build_sparse_tensor_slice_dataset(slices), + lambda: self._build_sparse_tensor_slice_dataset(diff_slices), + 9, + sparse_tensors=True) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/dataset_serialization_test_base.py b/tensorflow/contrib/data/python/kernel_tests/serialization/dataset_serialization_test_base.py similarity index 100% rename from tensorflow/contrib/data/python/kernel_tests/dataset_serialization_test_base.py rename to tensorflow/contrib/data/python/kernel_tests/serialization/dataset_serialization_test_base.py diff --git a/tensorflow/contrib/data/python/kernel_tests/filter_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/filter_dataset_serialization_test.py similarity index 91% rename from tensorflow/contrib/data/python/kernel_tests/filter_dataset_op_test.py rename to tensorflow/contrib/data/python/kernel_tests/serialization/filter_dataset_serialization_test.py index b572d6ed77..7c170078a1 100644 --- a/tensorflow/contrib/data/python/kernel_tests/filter_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/filter_dataset_serialization_test.py @@ -12,14 +12,14 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Tests for the experimental input pipeline ops.""" +"""Tests for the FilterDataset serialization.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np -from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import sparse_tensor from tensorflow.python.ops import math_ops @@ -35,7 +35,7 @@ class FilterDatasetSerializationTest( def testFilterCore(self): div = 3 - num_outputs = np.sum([x % 3 is not 2 for x in range(100)]) + num_outputs = np.sum([x % 3 != 2 for x in range(100)]) self.run_core_tests(lambda: self._build_filter_range_graph(div), lambda: self._build_filter_range_graph(div * 2), num_outputs) diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/fixed_length_record_dataset_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/fixed_length_record_dataset_serialization_test.py new file mode 100644 index 0000000000..34392d88d4 --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/fixed_length_record_dataset_serialization_test.py @@ -0,0 +1,45 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the FixedLengthRecordDataset serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.data.python.kernel_tests import reader_dataset_ops_test_base +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.python.data.ops import readers as core_readers +from tensorflow.python.platform import test + + +class FixedLengthRecordDatasetSerializationTest( + reader_dataset_ops_test_base.FixedLengthRecordDatasetTestBase, + dataset_serialization_test_base.DatasetSerializationTestBase): + + def _build_iterator_graph(self, num_epochs, compression_type=None): + filenames = self._createFiles() + return core_readers.FixedLengthRecordDataset( + filenames, self._record_bytes, self._header_bytes, + self._footer_bytes).repeat(num_epochs) + + def testFixedLengthRecordCore(self): + num_epochs = 5 + num_outputs = num_epochs * self._num_files * self._num_records + self.run_core_tests(lambda: self._build_iterator_graph(num_epochs), + lambda: self._build_iterator_graph(num_epochs * 2), + num_outputs) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/flat_map_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/flat_map_dataset_serialization_test.py similarity index 96% rename from tensorflow/contrib/data/python/kernel_tests/flat_map_dataset_op_test.py rename to tensorflow/contrib/data/python/kernel_tests/serialization/flat_map_dataset_serialization_test.py index f3feecef32..16051ffd3f 100644 --- a/tensorflow/contrib/data/python/kernel_tests/flat_map_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/flat_map_dataset_serialization_test.py @@ -12,12 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Tests for the experimental input pipeline ops.""" +"""Tests for the FlatMapDataset serialization.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/group_by_reducer_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/group_by_reducer_serialization_test.py new file mode 100644 index 0000000000..571e0899bb --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/group_by_reducer_serialization_test.py @@ -0,0 +1,61 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the GroupByReducer serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.contrib.data.python.ops import grouping +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.platform import test + + +class GroupByReducerSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def _build_dataset(self, components): + reducer = grouping.Reducer( + init_func=lambda _: np.int64(0), + reduce_func=lambda x, y: x + y, + finalize_func=lambda x: x) + + return dataset_ops.Dataset.from_tensor_slices(components).apply( + grouping.group_by_reducer(lambda x: x % 5, reducer)) + + def testCoreGroupByReducer(self): + components = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=np.int64) + self.verify_unused_iterator( + lambda: self._build_dataset(components), 5, verify_exhausted=True) + self.verify_init_before_restore( + lambda: self._build_dataset(components), 5, verify_exhausted=True) + self.verify_multiple_breaks( + lambda: self._build_dataset(components), 5, verify_exhausted=True) + self.verify_reset_restored_iterator( + lambda: self._build_dataset(components), 5, verify_exhausted=True) + self.verify_restore_in_empty_graph( + lambda: self._build_dataset(components), 5, verify_exhausted=True) + diff_components = np.array([5, 4, 3, 2, 1, 0], dtype=np.int64) + self.verify_restore_in_modified_graph( + lambda: self._build_dataset(components), + lambda: self._build_dataset(diff_components), + 5, + verify_exhausted=True) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/group_by_window_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/group_by_window_serialization_test.py new file mode 100644 index 0000000000..f86af4084e --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/group_by_window_serialization_test.py @@ -0,0 +1,57 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the GroupByWindow serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.contrib.data.python.ops import grouping +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.platform import test + + +class GroupByWindowSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def _build_dataset(self, components): + return dataset_ops.Dataset.from_tensor_slices(components).repeat(-1).apply( + grouping.group_by_window(lambda x: x % 3, lambda _, xs: xs.batch(4), 4)) + + def testCoreGroupByWindow(self): + components = np.array( + [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 0, 0, 2, 2, 0, 0], dtype=np.int64) + self.verify_unused_iterator( + lambda: self._build_dataset(components), 12, verify_exhausted=False) + self.verify_init_before_restore( + lambda: self._build_dataset(components), 12, verify_exhausted=False) + self.verify_multiple_breaks( + lambda: self._build_dataset(components), 12, verify_exhausted=False) + self.verify_reset_restored_iterator( + lambda: self._build_dataset(components), 12, verify_exhausted=False) + self.verify_restore_in_empty_graph( + lambda: self._build_dataset(components), 12, verify_exhausted=False) + diff_components = np.array([0, 0, 0, 1, 1, 1], dtype=np.int64) + self.verify_restore_in_modified_graph( + lambda: self._build_dataset(components), + lambda: self._build_dataset(diff_components), + 12, + verify_exhausted=False) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/ignore_errors_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/ignore_errors_serialization_test.py new file mode 100644 index 0000000000..65ae9923b8 --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/ignore_errors_serialization_test.py @@ -0,0 +1,46 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the IgnoreErrors input pipeline ops.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.contrib.data.python.ops import error_ops +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.ops import array_ops +from tensorflow.python.platform import test + + +class IgnoreErrorsSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def _build_ds(self, components): + return dataset_ops.Dataset.from_tensor_slices(components).map( + lambda x: array_ops.check_numerics(x, "message")).apply( + error_ops.ignore_errors()) + + def testIgnoreErrorsCore(self): + components = np.array([1., 2., 3., np.nan, 5.]).astype(np.float32) + diff_components = np.array([1., 2., 3., np.nan]).astype(np.float32) + num_outputs = 4 + self.run_core_tests(lambda: self._build_ds(components), + lambda: self._build_ds(diff_components), num_outputs) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/interleave_dataset_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/interleave_dataset_serialization_test.py new file mode 100644 index 0000000000..ac3892fe81 --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/interleave_dataset_serialization_test.py @@ -0,0 +1,86 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the InterleaveDataset serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.framework import sparse_tensor +from tensorflow.python.ops import sparse_ops +from tensorflow.python.platform import test + + +class InterleaveDatasetSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def _build_iterator_graph(self, input_values, cycle_length, block_length): + repeat_count = 2 + return dataset_ops.Dataset.from_tensor_slices(input_values).repeat( + repeat_count).interleave( + lambda x: dataset_ops.Dataset.from_tensors(x).repeat(x), + cycle_length, block_length) + + def testSerializationCore(self): + input_values = np.array([4, 5, 6], dtype=np.int64) + num_outputs = np.sum(input_values) * 2 + # cycle_length > 1, block_length > 1 + cycle_length = 2 + block_length = 3 + # pylint: disable=g-long-lambda + self.run_core_tests( + lambda: self._build_iterator_graph( + input_values, cycle_length, block_length), + lambda: self._build_iterator_graph( + input_values, cycle_length * 2, block_length * 1), + num_outputs) + # cycle_length = 1 + cycle_length = 1 + block_length = 3 + self.run_core_tests( + lambda: self._build_iterator_graph( + input_values, cycle_length, block_length), + None, num_outputs) + # block_length = 1 + cycle_length = 2 + block_length = 1 + self.run_core_tests( + lambda: self._build_iterator_graph( + input_values, cycle_length, block_length), + None, num_outputs) + # pylint: enable=g-long-lambda + + def testSparseCore(self): + + def _map_fn(i): + return sparse_tensor.SparseTensorValue( + indices=[[0, 0], [1, 1]], values=(i * [1, -1]), dense_shape=[2, 2]) + + def _interleave_fn(x): + return dataset_ops.Dataset.from_tensor_slices( + sparse_ops.sparse_to_dense(x.indices, x.dense_shape, x.values)) + + def _build_dataset(): + return dataset_ops.Dataset.range(10).map(_map_fn).interleave( + _interleave_fn, cycle_length=1) + + self.run_core_tests(_build_dataset, None, 20) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/map_and_batch_dataset_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/map_and_batch_dataset_serialization_test.py new file mode 100644 index 0000000000..c9cd211328 --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/map_and_batch_dataset_serialization_test.py @@ -0,0 +1,88 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the MapAndBatchDataset serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import math + +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.contrib.data.python.ops import batching +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.platform import test + + +class MapAndBatchDatasetSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def testNumParallelBatches(self): + range_size = 11 + num_repeats = 2 + batch_size = 5 + total_outputs = range_size * num_repeats + num_outputs_drop_remainder = total_outputs // batch_size + num_outputs_keep_remainder = int(math.ceil(total_outputs / batch_size)) + num_parallel_batches = 2 + + def build_ds(range_start, drop_remainder=False): + + def _map_fn(x): + return math_ops.square(x) + + return dataset_ops.Dataset.range( + range_start, range_start + range_size).repeat(num_repeats).apply( + batching.map_and_batch( + map_func=_map_fn, + batch_size=batch_size, + num_parallel_batches=num_parallel_batches, + drop_remainder=drop_remainder)) + + self.run_core_tests(lambda: build_ds(10), lambda: build_ds(15), + num_outputs_keep_remainder) + self.run_core_tests(lambda: build_ds(10, True), lambda: build_ds(15, True), + num_outputs_drop_remainder) + + def testNumParallelCalls(self): + range_size = 11 + num_repeats = 2 + batch_size = 5 + total_outputs = range_size * num_repeats + num_outputs_drop_remainder = total_outputs // batch_size + num_outputs_keep_remainder = int(math.ceil(total_outputs / batch_size)) + num_parallel_calls = 7 + + def build_ds(range_start, drop_remainder=False): + + def _map_fn(x): + return math_ops.square(x) + + return dataset_ops.Dataset.range( + range_start, range_start + range_size).repeat(num_repeats).apply( + batching.map_and_batch( + map_func=_map_fn, + batch_size=batch_size, + num_parallel_calls=num_parallel_calls, + drop_remainder=drop_remainder)) + + self.run_core_tests(lambda: build_ds(10), lambda: build_ds(15), + num_outputs_keep_remainder) + self.run_core_tests(lambda: build_ds(10, True), lambda: build_ds(15, True), + num_outputs_drop_remainder) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/map_dataset_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/map_dataset_serialization_test.py new file mode 100644 index 0000000000..ab783e5cce --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/map_dataset_serialization_test.py @@ -0,0 +1,140 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the MapDataset serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import errors +from tensorflow.python.framework import function +from tensorflow.python.framework import sparse_tensor +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import random_ops +from tensorflow.python.ops import variable_scope +from tensorflow.python.platform import test + + +class MapDatasetSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def setUp(self): + self._tensor_slice_len = 7 + self._num_epochs = 14 + self._num_outputs = self._tensor_slice_len * self._num_epochs + + def _build_ds(self, multiplier=37.0): + components = (np.arange(self._tensor_slice_len), np.array([[1, 2, 3]]) * + np.arange(self._tensor_slice_len)[:, np.newaxis], + np.array(multiplier) * np.arange(self._tensor_slice_len)) + + def _map_fn(x, y, z): + return math_ops.square(x), math_ops.square(y), math_ops.square(z) + + return ( + dataset_ops.Dataset.from_tensor_slices(components).map(_map_fn) + .repeat(self._num_epochs)) + + def testSaveRestoreCore(self): + self.run_core_tests( + self._build_ds, + lambda: self._build_ds(multiplier=15.0), + self._num_outputs) + + def testSaveStatefulFunction(self): + + def _build_ds(): + + def _map_fn(x): + return random_ops.random_uniform( + (), 0, 10, dtype=dtypes.int32) * math_ops.to_int32(x) + + return dataset_ops.Dataset.range(100).map(_map_fn) + + self.verify_error_on_save(_build_ds, 15, errors.InvalidArgumentError) + + def testCaptureVariableInMapFn(self): + + def _build_ds(): + counter_var = variable_scope.get_variable( + "counter", (), dtypes.int32, use_resource=True) + return (dataset_ops.Dataset.from_tensors(0).repeat(10).map( + lambda _: counter_var.assign_add(1))) + + self.verify_error_on_save(_build_ds, 15, errors.InvalidArgumentError) + + def testCaptureConstantInMapFn(self): + + def _build_ds(): + constant_var = constant_op.constant(5) + return (dataset_ops.Dataset.from_tensors(0).repeat(10).map( + lambda x: x + constant_var)) + + self.run_core_tests(_build_ds, None, 10) + + def testCaptureDefunInMapFn(self): + num_outputs = 100 + + def _build_ds(): + + @function.Defun(dtypes.int64) + def defun_fn(x): + return constant_op.constant(1000) + math_ops.to_int32(x) + + return dataset_ops.Dataset.range(num_outputs).map(defun_fn) + + self.run_core_tests(_build_ds, None, num_outputs) + + def testBuildDefunInMapFn(self): + num_outputs = 100 + + def _build_ds(): + + @function.Defun(dtypes.int64) + def defun_fn(x): + + @function.Defun(dtypes.int32) + def defun_fn_deep(x): + return constant_op.constant(1000) + math_ops.to_int32(x) + + return constant_op.constant(11000) + defun_fn_deep(math_ops.to_int32(x)) + + return dataset_ops.Dataset.range(num_outputs).map(defun_fn) + + self.run_core_tests(_build_ds, None, num_outputs) + + def testSparseCore(self): + + def _sparse(i): + return sparse_tensor.SparseTensorValue( + indices=np.array([[0, 0]]), + values=(i * np.array([1])), + dense_shape=np.array([1, 1])) + + def _build_ds(num_outputs): + return dataset_ops.Dataset.range(num_outputs).map(_sparse) + + num_outputs = 10 + self.run_core_tests(lambda: _build_ds(num_outputs), + lambda: _build_ds(int(num_outputs / 2)), num_outputs) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/optimize_dataset_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/optimize_dataset_serialization_test.py new file mode 100644 index 0000000000..d5c03495e3 --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/optimize_dataset_serialization_test.py @@ -0,0 +1,39 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the OptimizeDataset serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.contrib.data.python.ops import optimization +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.platform import test + + +class OptimizeDatasetSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def testCore(self): + + def build_dataset(num_elements, batch_size): + return dataset_ops.Dataset.range(num_elements).map(lambda x: x * x).batch( + batch_size).apply(optimization.optimize(["map_and_batch_fusion"])) + + self.run_core_tests(lambda: build_dataset(200, 10), None, 20) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/padded_batch_dataset_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/padded_batch_dataset_serialization_test.py new file mode 100644 index 0000000000..9ac42a461a --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/padded_batch_dataset_serialization_test.py @@ -0,0 +1,66 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the PaddedBatchDataset serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import string_ops +from tensorflow.python.platform import test + + +class PaddedBatchDatasetSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def testPaddedBatch(self): + + def build_dataset(seq_lens): + return dataset_ops.Dataset.from_tensor_slices(seq_lens).map( + lambda x: array_ops.fill([x], x)).padded_batch( + 4, padded_shapes=[-1]) + + seq_lens1 = np.random.randint(1, 20, size=(32,)).astype(np.int32) + seq_lens2 = np.random.randint(21, 40, size=(32,)).astype(np.int32) + self.run_core_tests(lambda: build_dataset(seq_lens1), + lambda: build_dataset(seq_lens2), 8) + + def testPaddedBatchNonDefaultPadding(self): + + def build_dataset(seq_lens): + + def fill_tuple(x): + filled = array_ops.fill([x], x) + return (filled, string_ops.as_string(filled)) + + padded_shape = [-1] + return dataset_ops.Dataset.from_tensor_slices(seq_lens).map( + fill_tuple).padded_batch( + 4, + padded_shapes=(padded_shape, padded_shape), + padding_values=(-1, "")) + + seq_lens1 = np.random.randint(1, 20, size=(32,)).astype(np.int32) + seq_lens2 = np.random.randint(21, 40, size=(32,)).astype(np.int32) + self.run_core_tests(lambda: build_dataset(seq_lens1), + lambda: build_dataset(seq_lens2), 8) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/parallel_interleave_dataset_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/parallel_interleave_dataset_serialization_test.py new file mode 100644 index 0000000000..1f8a584df9 --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/parallel_interleave_dataset_serialization_test.py @@ -0,0 +1,101 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the ParallelInterleaveDataset serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.contrib.data.python.ops import interleave_ops +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.framework import sparse_tensor +from tensorflow.python.ops import sparse_ops +from tensorflow.python.platform import test + + +class ParallelInterleaveDatasetSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def setUp(self): + self.input_values = np.array([4, 5, 6], dtype=np.int64) + self.num_repeats = 2 + self.num_outputs = np.sum(self.input_values) * 2 + + def _build_ds(self, cycle_length, block_length, sloppy=False): + return (dataset_ops.Dataset.from_tensor_slices( + self.input_values).repeat(self.num_repeats).apply( + interleave_ops.parallel_interleave( + lambda x: dataset_ops.Dataset.range(10 * x, 11 * x), + cycle_length, block_length, sloppy))) + + def testSerializationCore(self): + # cycle_length > 1, block_length > 1 + cycle_length = 2 + block_length = 3 + self.run_core_tests( + lambda: self._build_ds(cycle_length, block_length), + lambda: self._build_ds(cycle_length * 2, block_length * 1), + self.num_outputs) + # cycle_length = 1 + cycle_length = 1 + block_length = 3 + self.run_core_tests(lambda: self._build_ds(cycle_length, block_length), + None, self.num_outputs) + # block_length = 1 + cycle_length = 2 + block_length = 1 + self.run_core_tests(lambda: self._build_ds(cycle_length, block_length), + None, self.num_outputs) + + def testSerializationWithSloppy(self): + break_points = self.gen_break_points(self.num_outputs, 10) + expected_outputs = np.repeat( + np.concatenate([np.arange(10 * x, 11 * x) for x in self.input_values]), + self.num_repeats).tolist() + + def run_test(cycle_length, block_length): + actual = self.gen_outputs( + lambda: self._build_ds(cycle_length, block_length, True), + break_points, self.num_outputs) + self.assertSequenceEqual(sorted(actual), expected_outputs) + + # cycle_length > 1, block_length > 1 + run_test(2, 3) + # cycle_length = 1 + run_test(1, 3) + # block_length = 1 + run_test(2, 1) + + def testSparseCore(self): + + def _map_fn(i): + return sparse_tensor.SparseTensorValue( + indices=[[0, 0], [1, 1]], values=(i * [1, -1]), dense_shape=[2, 2]) + + def _interleave_fn(x): + return dataset_ops.Dataset.from_tensor_slices( + sparse_ops.sparse_to_dense(x.indices, x.dense_shape, x.values)) + + def _build_dataset(): + return dataset_ops.Dataset.range(10).map(_map_fn).apply( + interleave_ops.parallel_interleave(_interleave_fn, 1)) + + self.run_core_tests(_build_dataset, None, 20) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/parallel_map_dataset_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/parallel_map_dataset_serialization_test.py new file mode 100644 index 0000000000..3fb7605be1 --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/parallel_map_dataset_serialization_test.py @@ -0,0 +1,139 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the ParallelMapDataset serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import errors +from tensorflow.python.framework import function +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import random_ops +from tensorflow.python.ops import variable_scope +from tensorflow.python.platform import test + + +class ParallelMapDatasetSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def setUp(self): + self._tensor_slice_len = 7 + self._num_epochs = 1 + self._num_outputs = self._tensor_slice_len * self._num_epochs + + def _build_ds(self, multiplier=37.0): + components = (np.arange(self._tensor_slice_len), np.array([[1, 2, 3]]) * + np.arange(self._tensor_slice_len)[:, np.newaxis], + np.array(multiplier) * np.arange(self._tensor_slice_len)) + + def _map_fn(x, y, z): + return math_ops.square(x), math_ops.square(y), math_ops.square(z) + + return (dataset_ops.Dataset.from_tensor_slices(components).map( + _map_fn, num_parallel_calls=3).repeat(self._num_epochs)) + + def _build_ds_with_prefetch(self, multiplier=37.0): + components = (np.arange(self._tensor_slice_len), np.array([[1, 2, 3]]) * + np.arange(self._tensor_slice_len)[:, np.newaxis], + np.array(multiplier) * np.arange(self._tensor_slice_len)) + + def _map_fn(x, y, z): + return math_ops.square(x), math_ops.square(y), math_ops.square(z) + + return (dataset_ops.Dataset.from_tensor_slices(components).map( + _map_fn, num_parallel_calls=3).repeat(self._num_epochs).prefetch(5)) + + def testSaveRestoreCore(self): + for ds_fn in [self._build_ds, self._build_ds_with_prefetch]: + self.run_core_tests( + ds_fn, + lambda: ds_fn(multiplier=15.0), + self._num_outputs) + + def testSaveStatefulFunction(self): + + def _build_ds(): + + def _map_fn(x): + return random_ops.random_uniform( + (), 0, 10, dtype=dtypes.int32) * math_ops.to_int32(x) + + return dataset_ops.Dataset.range(100).map( + _map_fn, num_parallel_calls=2).prefetch(2) + + self.verify_error_on_save(_build_ds, 15, errors.InvalidArgumentError) + + def testCaptureVariableInMapFn(self): + + def _build_ds(): + counter_var = variable_scope.get_variable( + "counter", (), dtypes.int32, use_resource=True) + return (dataset_ops.Dataset.from_tensors(0).repeat(10).map( + lambda _: counter_var.assign_add(1), + num_parallel_calls=2).prefetch(2)) + + self.verify_error_on_save(_build_ds, 15, errors.InvalidArgumentError) + + def testCaptureConstantInMapFn(self): + + def _build_ds(): + constant_var = constant_op.constant(5) + return (dataset_ops.Dataset.from_tensors(0).repeat(10).map( + lambda x: x + constant_var, num_parallel_calls=2).prefetch(2)) + + self.run_core_tests(_build_ds, None, 10) + + def testCaptureDefunInMapFn(self): + num_outputs = 100 + + def _build_ds(): + + @function.Defun(dtypes.int64) + def defun_fn(x): + return constant_op.constant(1000) + math_ops.to_int32(x) + + return dataset_ops.Dataset.range(num_outputs).map( + defun_fn, num_parallel_calls=2).prefetch(2) + + self.run_core_tests(_build_ds, None, num_outputs) + + def testBuildDefunInMapFn(self): + num_outputs = 100 + + def _build_ds(): + + @function.Defun(dtypes.int64) + def defun_fn(x): + + @function.Defun(dtypes.int32) + def defun_fn_deep(x): + return constant_op.constant(1000) + math_ops.to_int32(x) + + return constant_op.constant(11000) + defun_fn_deep(math_ops.to_int32(x)) + + return dataset_ops.Dataset.range(num_outputs).map( + defun_fn, num_parallel_calls=2).prefetch(2) + + self.run_core_tests(_build_ds, None, num_outputs) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/prefetch_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/prefetch_dataset_serialization_test.py similarity index 90% rename from tensorflow/contrib/data/python/kernel_tests/prefetch_dataset_op_test.py rename to tensorflow/contrib/data/python/kernel_tests/serialization/prefetch_dataset_serialization_test.py index 3d120a3071..c802402461 100644 --- a/tensorflow/contrib/data/python/kernel_tests/prefetch_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/prefetch_dataset_serialization_test.py @@ -12,12 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Tests for the experimental input pipeline ops.""" +"""Tests for the PrefetchDataset serialization.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base from tensorflow.python.data.ops import dataset_ops from tensorflow.python.platform import test diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/range_dataset_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/range_dataset_serialization_test.py new file mode 100644 index 0000000000..e4f5b6cf5d --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/range_dataset_serialization_test.py @@ -0,0 +1,118 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the RangeDataset serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os + +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import errors +from tensorflow.python.framework import ops +from tensorflow.python.ops import gen_dataset_ops +from tensorflow.python.ops import io_ops +from tensorflow.python.ops import parsing_ops +from tensorflow.python.ops import variables +from tensorflow.python.platform import test + + +class RangeDatasetSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def _iterator_checkpoint_prefix_local(self): + return os.path.join(self.get_temp_dir(), "iterator") + + def _save_op(self, iterator_resource): + iterator_state_variant = gen_dataset_ops.serialize_iterator( + iterator_resource) + save_op = io_ops.write_file( + self._iterator_checkpoint_prefix_local(), + parsing_ops.serialize_tensor(iterator_state_variant)) + return save_op + + def _restore_op(self, iterator_resource): + iterator_state_variant = parsing_ops.parse_tensor( + io_ops.read_file(self._iterator_checkpoint_prefix_local()), + dtypes.variant) + restore_op = gen_dataset_ops.deserialize_iterator(iterator_resource, + iterator_state_variant) + return restore_op + + def testSaveRestore(self): + + def _build_graph(start, stop): + iterator = dataset_ops.Dataset.range(start, + stop).make_initializable_iterator() + init_op = iterator.initializer + get_next = iterator.get_next() + save_op = self._save_op(iterator._iterator_resource) + restore_op = self._restore_op(iterator._iterator_resource) + return init_op, get_next, save_op, restore_op + + # Saving and restoring in different sessions. + start = 2 + stop = 10 + break_point = 5 + with ops.Graph().as_default() as g: + init_op, get_next, save_op, _ = _build_graph(start, stop) + with self.test_session(graph=g) as sess: + sess.run(variables.global_variables_initializer()) + sess.run(init_op) + for i in range(start, break_point): + self.assertEqual(i, sess.run(get_next)) + sess.run(save_op) + + with ops.Graph().as_default() as g: + init_op, get_next, _, restore_op = _build_graph(start, stop) + with self.test_session(graph=g) as sess: + sess.run(init_op) + sess.run(restore_op) + for i in range(break_point, stop): + self.assertEqual(i, sess.run(get_next)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + + # Saving and restoring in same session. + with ops.Graph().as_default() as g: + init_op, get_next, save_op, restore_op = _build_graph(start, stop) + with self.test_session(graph=g) as sess: + sess.run(variables.global_variables_initializer()) + sess.run(init_op) + for i in range(start, break_point): + self.assertEqual(i, sess.run(get_next)) + sess.run(save_op) + sess.run(restore_op) + for i in range(break_point, stop): + self.assertEqual(i, sess.run(get_next)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + + def _build_range_dataset(self, start, stop): + return dataset_ops.Dataset.range(start, stop) + + def testRangeCore(self): + start = 2 + stop = 10 + stop_1 = 8 + self.run_core_tests(lambda: self._build_range_dataset(start, stop), + lambda: self._build_range_dataset(start, stop_1), + stop - start) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/sample_from_datasets_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/sample_from_datasets_serialization_test.py new file mode 100644 index 0000000000..fdb35ea624 --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/sample_from_datasets_serialization_test.py @@ -0,0 +1,46 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the SampleFromDatasets serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.contrib.data.python.ops import interleave_ops +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.platform import test + + +class SampleFromDatasetsSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def _build_dataset(self, probs, num_samples): + dataset = interleave_ops.sample_from_datasets( + [ + dataset_ops.Dataset.from_tensors(i).repeat(None) + for i in range(len(probs)) + ], + probs, + seed=1813) + return dataset.take(num_samples) + + def testSerializationCore(self): + self.run_core_tests( + lambda: self._build_dataset([0.5, 0.5], 100), + lambda: self._build_dataset([0.25, 0.25, 0.25, 0.25], 1000), 100) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/scan_dataset_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/scan_dataset_serialization_test.py new file mode 100644 index 0000000000..af9ef48c0f --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/scan_dataset_serialization_test.py @@ -0,0 +1,40 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the ScanDataset serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.contrib.data.python.ops import scan_ops +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.platform import test + + +class ScanDatasetSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def _build_dataset(self, num_elements): + return dataset_ops.Dataset.from_tensors(1).repeat(num_elements).apply( + scan_ops.scan([0, 1], lambda a, _: ([a[1], a[0] + a[1]], a[1]))) + + def testScanCore(self): + num_output = 5 + self.run_core_tests(lambda: self._build_dataset(num_output), + lambda: self._build_dataset(2), num_output) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/sequence_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/sequence_dataset_serialization_test.py similarity index 91% rename from tensorflow/contrib/data/python/kernel_tests/sequence_dataset_op_test.py rename to tensorflow/contrib/data/python/kernel_tests/serialization/sequence_dataset_serialization_test.py index d0cb203a3a..2afebca0f5 100644 --- a/tensorflow/contrib/data/python/kernel_tests/sequence_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/sequence_dataset_serialization_test.py @@ -12,19 +12,19 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Tests for the experimental input pipeline ops.""" +"""Tests for the sequence datasets serialization.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np -from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base from tensorflow.python.data.ops import dataset_ops from tensorflow.python.platform import test -class SequenceDatasetSerializationTest( +class SkipDatasetSerializationTest( dataset_serialization_test_base.DatasetSerializationTestBase): def _build_skip_dataset(self, count): @@ -52,6 +52,10 @@ class SequenceDatasetSerializationTest( 'Shape must be rank 0 but is rank 1'): self.run_core_tests(lambda: self._build_skip_dataset([1, 2]), None, 0) + +class TakeDatasetSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + def _build_take_dataset(self, count): components = (np.arange(10),) return dataset_ops.Dataset.from_tensor_slices(components).take(count) @@ -79,6 +83,10 @@ class SequenceDatasetSerializationTest( 'Shape must be rank 0 but is rank 1'): self.run_core_tests(lambda: self._build_take_dataset([1, 2]), None, 0) + +class RepeatDatasetSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + def _build_repeat_dataset(self, count, take_count=3): components = (np.arange(10),) return dataset_ops.Dataset.from_tensor_slices(components).take( @@ -117,5 +125,5 @@ class SequenceDatasetSerializationTest( None, 0) -if __name__ == "__main__": +if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization_integration_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/serialization_integration_test.py similarity index 96% rename from tensorflow/contrib/data/python/kernel_tests/serialization_integration_test.py rename to tensorflow/contrib/data/python/kernel_tests/serialization/serialization_integration_test.py index 0a6b74dc3e..992d996a48 100644 --- a/tensorflow/contrib/data/python/kernel_tests/serialization_integration_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/serialization_integration_test.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Integration test for input pipeline serialization.""" +"""Integration test for dataset serialization.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function @@ -26,7 +26,7 @@ from tensorflow.python.platform import test from tensorflow.python.training import saver as saver_lib -class MultipleInputPipelinesTest(test.TestCase): +class SerializationIntegrationTest(test.TestCase): def _build_input_pipeline(self, name, num_outputs): with ops.name_scope(name): diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/shuffle_and_repeat_dataset_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/shuffle_and_repeat_dataset_serialization_test.py new file mode 100644 index 0000000000..f199ec835e --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/shuffle_and_repeat_dataset_serialization_test.py @@ -0,0 +1,39 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the ShuffleAndRepeatDataset serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.contrib.data.python.ops import shuffle_ops +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.platform import test + + +class ShuffleAndRepeatSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def _build_ds(self, seed): + return dataset_ops.Dataset.range(20).apply( + shuffle_ops.shuffle_and_repeat(buffer_size=5, count=5, seed=seed)) + + def testCore(self): + self.run_core_tests(lambda: self._build_ds(10), lambda: self._build_ds(20), + 100) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/shuffle_dataset_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/shuffle_dataset_serialization_test.py new file mode 100644 index 0000000000..d46c762aaa --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/shuffle_dataset_serialization_test.py @@ -0,0 +1,148 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the ShuffleDataset serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.contrib.data.python.ops import iterator_ops as contrib_iterator_ops +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.framework import ops +from tensorflow.python.platform import test +from tensorflow.python.training import saver as saver_lib + + +class ShuffleDatasetSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def _build_shuffle_dataset( + self, + range_limit=10, + num_repeats=5, + buffer_size=5, + seed=None, + reshuffle_each_iteration=None, + ): + return dataset_ops.Dataset.range(range_limit).shuffle( + buffer_size, + seed=seed, + reshuffle_each_iteration=reshuffle_each_iteration).repeat(num_repeats) + + def testShuffleCore(self): + + seed = 55 + range_limit = 5 + num_repeats = 2 + num_outputs = range_limit * num_repeats + buffer_sizes = [1, 3, 5, 8, 10] + # pylint: disable=cell-var-from-loop + # pylint: disable=g-long-lambda + for reshuffle_each_iteration in [True, False]: + for buffer_size in buffer_sizes: + self.run_core_tests( + lambda: self._build_shuffle_dataset( + range_limit=range_limit, + num_repeats=num_repeats, + buffer_size=buffer_size, + seed=seed, + reshuffle_each_iteration=reshuffle_each_iteration), + lambda: self._build_shuffle_dataset( + range_limit=range_limit, + num_repeats=num_repeats, + buffer_size=buffer_size, + seed=10, + reshuffle_each_iteration=reshuffle_each_iteration), + num_outputs) + # pylint: enable=cell-var-from-loop + # pylint: enable=g-long-lambda + + def testNonDeterministicSeeding(self): + + range_limit = 5 + num_repeats = 2 + num_outputs = range_limit * num_repeats + buffer_sizes = [1, 3, 5, 8, 10] + for reshuffle_each_iteration in [True, False]: + for buffer_size in buffer_sizes: + + def ds_fn(): + # pylint: disable=cell-var-from-loop + return self._build_shuffle_dataset( + range_limit=range_limit, + num_repeats=num_repeats, + buffer_size=buffer_size, + seed=None, # Iterator seeds are generated non-deterministically. + reshuffle_each_iteration=reshuffle_each_iteration) + # pylint: enable=cell-var-from-loop + + # We checkpoint the initial state of the Dataset so that we can restore + # the seeds in the next run. Since the seeding is non-deterministic + # the dataset gets initialized with different seeds each time. + expected = self.gen_outputs( + ds_fn, + break_points=[0], + num_outputs=num_outputs, + ckpt_saved=False, + verify_exhausted=False, + save_checkpoint_at_end=False) + actual = self.gen_outputs( + ds_fn, + break_points=self.gen_break_points(num_outputs), + num_outputs=num_outputs, + ckpt_saved=True, + verify_exhausted=False) + self.match(expected, actual) + + def testMultipleIterators(self): + range_limit = 5 + num_repeats = 2 + num_outputs = range_limit * num_repeats + buffer_sizes = [1, 3, 5, 8, 10] + + for reshuffle_each_iteration in [True, False]: + for buffer_size in buffer_sizes: + + def ds_fn(): + # pylint: disable=cell-var-from-loop + return self._build_shuffle_dataset( + range_limit=range_limit, + num_repeats=num_repeats, + buffer_size=buffer_size, + seed=None, # Iterator seeds are generated non-deterministically. + reshuffle_each_iteration=reshuffle_each_iteration) + # pylint: enable=cell-var-from-loop + + with ops.Graph().as_default() as g: + ds = ds_fn() + iterators = [ds.make_one_shot_iterator(), ds.make_one_shot_iterator()] + get_next_ops = [it.get_next() for it in iterators] + saveables = [ + contrib_iterator_ops.make_saveable_from_iterator(it) + for it in iterators + ] + for saveable in saveables: + ops.add_to_collection(ops.GraphKeys.SAVEABLE_OBJECTS, saveable) + saver = saver_lib.Saver(allow_empty=True) + with self.test_session(graph=g) as sess: + self._save(sess, saver) + expected = [sess.run(get_next_ops) for _ in range(num_outputs)] + self._restore(saver, sess) + actual = [sess.run(get_next_ops) for _ in range(num_outputs)] + self.match(expected, actual) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/sql_dataset_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/sql_dataset_serialization_test.py new file mode 100644 index 0000000000..93b26ed58a --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/sql_dataset_serialization_test.py @@ -0,0 +1,53 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the SqlDataset serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os + +from tensorflow.contrib.data.python.kernel_tests import sql_dataset_op_test_base +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.contrib.data.python.ops import readers +from tensorflow.python.framework import dtypes +from tensorflow.python.ops import array_ops +from tensorflow.python.platform import test + + +class SqlDatasetSerializationTest( + sql_dataset_op_test_base.SqlDatasetTestBase, + dataset_serialization_test_base.DatasetSerializationTestBase): + + def _build_dataset(self, num_repeats): + data_source_name = os.path.join(test.get_temp_dir(), "tftest.sqlite") + driver_name = array_ops.placeholder_with_default( + array_ops.constant("sqlite", dtypes.string), shape=[]) + query = ("SELECT first_name, last_name, motto FROM students ORDER BY " + "first_name DESC") + output_types = (dtypes.string, dtypes.string, dtypes.string) + return readers.SqlDataset(driver_name, data_source_name, query, + output_types).repeat(num_repeats) + + def testSQLSaveable(self): + num_repeats = 4 + num_outputs = num_repeats * 2 + self.run_core_tests(lambda: self._build_dataset(num_repeats), + lambda: self._build_dataset(num_repeats // 2), + num_outputs) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/stats_dataset_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/stats_dataset_serialization_test.py new file mode 100644 index 0000000000..14cd3e9c4a --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/stats_dataset_serialization_test.py @@ -0,0 +1,95 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the StatsDataset serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.contrib.data.python.ops import stats_ops +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.platform import test + + +# TODO(shivaniagrawal): Can not checkpoint input_pipeline with the +# transformation `stats_ops.set_stats_aggregator`, since we don't support +# serializing StatsAggregator yet. +class StatsDatasetSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def _build_dataset_bytes_stats(self, num_elements): + return dataset_ops.Dataset.range(num_elements).map( + lambda x: array_ops.tile([x], ops.convert_to_tensor([x]))).apply( + stats_ops.bytes_produced_stats("bytes_produced")) + + def test_bytes_produced_stats_invalid_tag_shape(self): + with self.assertRaisesRegexp( + ValueError, "Shape must be rank 0 but is rank 1"): + # pylint: disable=g-long-lambda + self.run_core_tests( + lambda: dataset_ops.Dataset.range(100).apply( + stats_ops.bytes_produced_stats(["bytes_produced"])), + None, 100) + # pylint: enable=g-long-lambda + + def testBytesStatsDatasetSaveableCore(self): + num_outputs = 100 + self.run_core_tests( + lambda: self._build_dataset_bytes_stats(num_outputs), + lambda: self._build_dataset_bytes_stats(num_outputs // 10), num_outputs) + + def _build_dataset_latency_stats(self, num_elements, tag="record_latency"): + return dataset_ops.Dataset.range(num_elements).apply( + stats_ops.latency_stats(tag)) + + def _build_dataset_multiple_tags(self, + num_elements, + tag1="record_latency", + tag2="record_latency_2"): + return dataset_ops.Dataset.range(num_elements).apply( + stats_ops.latency_stats(tag1)).apply(stats_ops.latency_stats(tag2)) + + def test_latency_stats_invalid_tag_shape(self): + with self.assertRaisesRegexp( + ValueError, "Shape must be rank 0 but is rank 1"): + # pylint: disable=g-long-lambda + self.run_core_tests( + lambda: dataset_ops.Dataset.range(100).apply( + stats_ops.latency_stats(["record_latency", "record_latency_2"])), + None, 100) + # pylint: enable=g-long-lambda + + def testLatencyStatsDatasetSaveableCore(self): + num_outputs = 100 + + self.run_core_tests( + lambda: self._build_dataset_latency_stats(num_outputs), + lambda: self._build_dataset_latency_stats(num_outputs // 10), + num_outputs) + + self.run_core_tests(lambda: self._build_dataset_multiple_tags(num_outputs), + None, num_outputs) + + tag1 = "record_latency" + tag2 = "record_latency" + self.run_core_tests( + lambda: self._build_dataset_multiple_tags(num_outputs, tag1, tag2), + None, num_outputs) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/textline_dataset_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/textline_dataset_serialization_test.py new file mode 100644 index 0000000000..2483787f44 --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/textline_dataset_serialization_test.py @@ -0,0 +1,53 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the TextLineDataset serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.data.python.kernel_tests import reader_dataset_ops_test_base +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.python.data.ops import readers as core_readers +from tensorflow.python.platform import test + + +class TextLineDatasetSerializationTest( + reader_dataset_ops_test_base.TextLineDatasetTestBase, + dataset_serialization_test_base.DatasetSerializationTestBase): + + def _build_iterator_graph(self, test_filenames, compression_type=None): + return core_readers.TextLineDataset( + test_filenames, compression_type=compression_type, buffer_size=10) + + def testTextLineCore(self): + compression_types = [None, "GZIP", "ZLIB"] + num_files = 5 + lines_per_file = 5 + num_outputs = num_files * lines_per_file + for compression_type in compression_types: + test_filenames = self._createFiles( + num_files, + lines_per_file, + crlf=True, + compression_type=compression_type) + # pylint: disable=cell-var-from-loop + self.run_core_tests( + lambda: self._build_iterator_graph(test_filenames, compression_type), + lambda: self._build_iterator_graph(test_filenames), num_outputs) + # pylint: enable=cell-var-from-loop + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/tf_record_dataset_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/tf_record_dataset_serialization_test.py new file mode 100644 index 0000000000..55a6257a27 --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/tf_record_dataset_serialization_test.py @@ -0,0 +1,99 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the TFRecordDataset serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import gzip +import os +import zlib + +from tensorflow.contrib.data.python.kernel_tests import reader_dataset_ops_test_base +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.python.data.ops import readers as core_readers +from tensorflow.python.platform import test + + +class TFRecordDatasetSerializationTest( + reader_dataset_ops_test_base.TFRecordDatasetTestBase, + dataset_serialization_test_base.DatasetSerializationTestBase): + + def _build_iterator_graph(self, + num_epochs, + batch_size=1, + compression_type=None, + buffer_size=None): + filenames = self._createFiles() + if compression_type == "ZLIB": + zlib_files = [] + for i, fn in enumerate(filenames): + with open(fn, "rb") as f: + cdata = zlib.compress(f.read()) + zfn = os.path.join(self.get_temp_dir(), "tfrecord_%s.z" % i) + with open(zfn, "wb") as f: + f.write(cdata) + zlib_files.append(zfn) + filenames = zlib_files + + elif compression_type == "GZIP": + gzip_files = [] + for i, fn in enumerate(self.test_filenames): + with open(fn, "rb") as f: + gzfn = os.path.join(self.get_temp_dir(), "tfrecord_%s.gz" % i) + with gzip.GzipFile(gzfn, "wb") as gzf: + gzf.write(f.read()) + gzip_files.append(gzfn) + filenames = gzip_files + + return core_readers.TFRecordDataset( + filenames, compression_type, + buffer_size=buffer_size).repeat(num_epochs).batch(batch_size) + + def testTFRecordWithoutBufferCore(self): + num_epochs = 5 + batch_size = num_epochs + num_outputs = num_epochs * self._num_files * self._num_records // batch_size + # pylint: disable=g-long-lambda + self.run_core_tests( + lambda: self._build_iterator_graph(num_epochs, batch_size, + buffer_size=0), + lambda: self._build_iterator_graph(num_epochs * 2, batch_size), + num_outputs) + self.run_core_tests( + lambda: self._build_iterator_graph(num_epochs, buffer_size=0), None, + num_outputs * batch_size) + # pylint: enable=g-long-lambda + + def testTFRecordWithBufferCore(self): + num_epochs = 5 + num_outputs = num_epochs * self._num_files * self._num_records + self.run_core_tests(lambda: self._build_iterator_graph(num_epochs), + lambda: self._build_iterator_graph(num_epochs * 2), + num_outputs) + + def testTFRecordWithCompressionCore(self): + num_epochs = 5 + num_outputs = num_epochs * self._num_files * self._num_records + self.run_core_tests( + lambda: self._build_iterator_graph(num_epochs, compression_type="ZLIB"), + lambda: self._build_iterator_graph(num_epochs * 2), num_outputs) + self.run_core_tests( + lambda: self._build_iterator_graph(num_epochs, compression_type="GZIP"), + lambda: self._build_iterator_graph(num_epochs * 2), num_outputs) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/unbatch_dataset_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/unbatch_dataset_serialization_test.py new file mode 100644 index 0000000000..b2a5a8a20d --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/unbatch_dataset_serialization_test.py @@ -0,0 +1,51 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the UnbatchDataset serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.contrib.data.python.ops import batching +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.platform import test + + +class UnbatchDatasetSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def build_dataset(self, multiplier=15.0, tensor_slice_len=2, batch_size=2): + components = ( + np.arange(tensor_slice_len), + np.array([[1, 2, 3]]) * np.arange(tensor_slice_len)[:, np.newaxis], + np.array(multiplier) * np.arange(tensor_slice_len)) + + return dataset_ops.Dataset.from_tensor_slices(components).batch( + batch_size).apply(batching.unbatch()) + + def testCore(self): + tensor_slice_len = 8 + batch_size = 2 + num_outputs = tensor_slice_len + self.run_core_tests( + lambda: self.build_dataset(15.0, tensor_slice_len, batch_size), + lambda: self.build_dataset(20.0, tensor_slice_len, batch_size), + num_outputs) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/unique_dataset_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/unique_dataset_serialization_test.py new file mode 100644 index 0000000000..22f15b8846 --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/unique_dataset_serialization_test.py @@ -0,0 +1,40 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the UniqueDataset serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.contrib.data.python.ops import unique +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.platform import test + + +class UniqueDatasetSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def testUnique(self): + + def build_dataset(num_elements, unique_elem_range): + return dataset_ops.Dataset.range(num_elements).map( + lambda x: x % unique_elem_range).apply(unique.unique()) + + self.run_core_tests(lambda: build_dataset(200, 100), + lambda: build_dataset(40, 100), 100) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/zip_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/zip_dataset_serialization_test.py similarity index 92% rename from tensorflow/contrib/data/python/kernel_tests/zip_dataset_op_test.py rename to tensorflow/contrib/data/python/kernel_tests/serialization/zip_dataset_serialization_test.py index e39fa957f0..340a6ff72e 100644 --- a/tensorflow/contrib/data/python/kernel_tests/zip_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/zip_dataset_serialization_test.py @@ -12,14 +12,14 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Tests for the experimental input pipeline ops.""" +"""Tests for the ZipDataset serialization.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np -from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base from tensorflow.python.data.ops import dataset_ops from tensorflow.python.platform import test diff --git a/tensorflow/contrib/data/python/kernel_tests/shuffle_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/shuffle_dataset_op_test.py index 25e9ea47b8..3c11d7a97f 100644 --- a/tensorflow/contrib/data/python/kernel_tests/shuffle_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/shuffle_dataset_op_test.py @@ -19,144 +19,32 @@ from __future__ import print_function import numpy as np -from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base -from tensorflow.contrib.data.python.ops import iterator_ops as contrib_iterator_ops from tensorflow.contrib.data.python.ops import shuffle_ops from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import errors from tensorflow.python.framework import ops from tensorflow.python.platform import test -from tensorflow.python.training import saver as saver_lib - - -class ShuffleDatasetSerializationTest( - dataset_serialization_test_base.DatasetSerializationTestBase): - - def _build_shuffle_dataset( - self, - range_limit=10, - num_repeats=5, - buffer_size=5, - seed=None, - reshuffle_each_iteration=None, - ): - return dataset_ops.Dataset.range(range_limit).shuffle( - buffer_size, - seed=seed, - reshuffle_each_iteration=reshuffle_each_iteration).repeat(num_repeats) - - def testShuffleCore(self): - - seed = 55 - range_limit = 5 - num_repeats = 2 - num_outputs = range_limit * num_repeats - buffer_sizes = [1, 3, 5, 8, 10] - # pylint: disable=cell-var-from-loop - # pylint: disable=g-long-lambda - for reshuffle_each_iteration in [True, False]: - for buffer_size in buffer_sizes: - self.run_core_tests( - lambda: self._build_shuffle_dataset( - range_limit=range_limit, - num_repeats=num_repeats, - buffer_size=buffer_size, - seed=seed, - reshuffle_each_iteration=reshuffle_each_iteration), - lambda: self._build_shuffle_dataset( - range_limit=range_limit, - num_repeats=num_repeats, - buffer_size=buffer_size, - seed=10, - reshuffle_each_iteration=reshuffle_each_iteration), - num_outputs) - # pylint: enable=cell-var-from-loop - # pylint: enable=g-long-lambda - - def testNonDeterministicSeeding(self): - - range_limit = 5 - num_repeats = 2 - num_outputs = range_limit * num_repeats - buffer_sizes = [1, 3, 5, 8, 10] - for reshuffle_each_iteration in [True, False]: - for buffer_size in buffer_sizes: - - def ds_fn(): - # pylint: disable=cell-var-from-loop - return self._build_shuffle_dataset( - range_limit=range_limit, - num_repeats=num_repeats, - buffer_size=buffer_size, - seed=None, # Iterator seeds are generated non-deterministically. - reshuffle_each_iteration=reshuffle_each_iteration) - # pylint: enable=cell-var-from-loop - - # We checkpoint the initial state of the Dataset so that we can restore - # the seeds in the next run. Since the seeding is non-deterministic - # the dataset gets initialized with different seeds each time. - expected = self.gen_outputs( - ds_fn, - break_points=[0], - num_outputs=num_outputs, - ckpt_saved=False, - verify_exhausted=False, - save_checkpoint_at_end=False) - actual = self.gen_outputs( - ds_fn, - break_points=self.gen_break_points(num_outputs), - num_outputs=num_outputs, - ckpt_saved=True, - verify_exhausted=False) - self.match(expected, actual) - - def testMultipleIterators(self): - range_limit = 5 - num_repeats = 2 - num_outputs = range_limit * num_repeats - buffer_sizes = [1, 3, 5, 8, 10] - - for reshuffle_each_iteration in [True, False]: - for buffer_size in buffer_sizes: - - def ds_fn(): - # pylint: disable=cell-var-from-loop - return self._build_shuffle_dataset( - range_limit=range_limit, - num_repeats=num_repeats, - buffer_size=buffer_size, - seed=None, # Iterator seeds are generated non-deterministically. - reshuffle_each_iteration=reshuffle_each_iteration) - # pylint: enable=cell-var-from-loop - - with ops.Graph().as_default() as g: - ds = ds_fn() - iterators = [ds.make_one_shot_iterator(), ds.make_one_shot_iterator()] - get_next_ops = [it.get_next() for it in iterators] - saveables = [ - contrib_iterator_ops.make_saveable_from_iterator(it) - for it in iterators - ] - for saveable in saveables: - ops.add_to_collection(ops.GraphKeys.SAVEABLE_OBJECTS, saveable) - saver = saver_lib.Saver(allow_empty=True) - with self.test_session(graph=g) as sess: - self._save(sess, saver) - expected = [sess.run(get_next_ops) for _ in range(num_outputs)] - self._restore(saver, sess) - actual = [sess.run(get_next_ops) for _ in range(num_outputs)] - self.match(expected, actual) - - -class ShuffleAndRepeatTest( - dataset_serialization_test_base.DatasetSerializationTestBase): + + +class ShuffleAndRepeatTest(test.TestCase): def _build_ds(self, seed, count=5, num_elements=20): return dataset_ops.Dataset.range(num_elements).apply( shuffle_ops.shuffle_and_repeat(buffer_size=5, count=count, seed=seed)) + def _gen_outputs(self, ds_fn, num_outputs, verify_exhausted=True): + get_next = ds_fn().make_one_shot_iterator().get_next() + outputs = [] + with self.test_session() as sess: + for _ in range(num_outputs): + outputs.append(sess.run(get_next)) + if verify_exhausted: + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + return outputs + def testCorrectOutput(self): - output = self.gen_outputs(lambda: self._build_ds(10), [], 100) + output = self._gen_outputs(lambda: self._build_ds(10), 100) self.assertSequenceEqual( sorted(output), sorted( np.array([range(20) for _ in range(5)]).flatten())) @@ -165,53 +53,53 @@ class ShuffleAndRepeatTest( def testReshuffling(self): # Check that the output orders of different epochs are indeed different. - output = self.gen_outputs(lambda: self._build_ds(10), [], 100) + output = self._gen_outputs(lambda: self._build_ds(10), 100) for i in range(4): epoch1 = output[i * 20:(i + 1) * 20] epoch2 = output[(i + 1) * 20:(i + 2) * 20] self.assertNotEqual(epoch1, epoch2) def testSameOrderForSameSeeds(self): - output1 = self.gen_outputs(lambda: self._build_ds(10), [], 100) - output2 = self.gen_outputs(lambda: self._build_ds(10), [], 100) + output1 = self._gen_outputs(lambda: self._build_ds(10), 100) + output2 = self._gen_outputs(lambda: self._build_ds(10), 100) self.assertEqual(output1, output2) def testDifferentOrderForDifferentSeeds(self): - output1 = self.gen_outputs(lambda: self._build_ds(10), [], 100) - output2 = self.gen_outputs(lambda: self._build_ds(20), [], 100) + output1 = self._gen_outputs(lambda: self._build_ds(10), 100) + output2 = self._gen_outputs(lambda: self._build_ds(20), 100) self.assertNotEqual(output1, output2) self.assertEqual(sorted(output1), sorted(output2)) def testCountNone(self): - output1 = self.gen_outputs( - lambda: self._build_ds(10, count=None), [], 100, verify_exhausted=False) - output2 = self.gen_outputs( - lambda: self._build_ds(20, count=None), [], 100, verify_exhausted=False) + output1 = self._gen_outputs( + lambda: self._build_ds(10, count=None), 100, verify_exhausted=False) + output2 = self._gen_outputs( + lambda: self._build_ds(20, count=None), 100, verify_exhausted=False) self.assertNotEqual(output1, output2) self.assertEqual(sorted(output1), sorted(output2)) def testCountMinusOne(self): - output1 = self.gen_outputs( - lambda: self._build_ds(10, count=-1), [], 100, verify_exhausted=False) - output2 = self.gen_outputs( - lambda: self._build_ds(20, count=-1), [], 100, verify_exhausted=False) + output1 = self._gen_outputs( + lambda: self._build_ds(10, count=-1), 100, verify_exhausted=False) + output2 = self._gen_outputs( + lambda: self._build_ds(20, count=-1), 100, verify_exhausted=False) self.assertNotEqual(output1, output2) self.assertEqual(sorted(output1), sorted(output2)) def testInfiniteOutputs(self): # Asserting the iterator is exhausted after producing 100 items should fail. with self.assertRaises(AssertionError): - self.gen_outputs(lambda: self._build_ds(10, count=None), [], 100) + self._gen_outputs(lambda: self._build_ds(10, count=None), 100) with self.assertRaises(AssertionError): - self.gen_outputs(lambda: self._build_ds(10, count=-1), [], 100) + self._gen_outputs(lambda: self._build_ds(10, count=-1), 100) def testInfiniteEmpty(self): with self.assertRaises(errors.OutOfRangeError): - self.gen_outputs(lambda: self._build_ds(10, count=None, num_elements=0), - [], 100) + self._gen_outputs(lambda: self._build_ds(10, count=None, num_elements=0), + 100) with self.assertRaises(errors.OutOfRangeError): - self.gen_outputs(lambda: self._build_ds(10, count=-1, num_elements=0), [], - 100) + self._gen_outputs(lambda: self._build_ds(10, count=-1, num_elements=0), + 100) def testLargeBufferSize(self): with ops.Graph().as_default() as g: @@ -222,17 +110,5 @@ class ShuffleAndRepeatTest( sess.run(get_next_op) -class ShuffleAndRepeatSerializationTest( - dataset_serialization_test_base.DatasetSerializationTestBase): - - def _build_ds(self, seed): - return dataset_ops.Dataset.range(20).apply( - shuffle_ops.shuffle_and_repeat(buffer_size=5, count=5, seed=seed)) - - def testCore(self): - self.run_core_tests(lambda: self._build_ds(10), lambda: self._build_ds(20), - 100) - - if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/sql_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/sql_dataset_op_test.py index 4148addf28..2c2cfbebff 100644 --- a/tensorflow/contrib/data/python/kernel_tests/sql_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/sql_dataset_op_test.py @@ -18,83 +18,13 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import os - -import sqlite3 - -from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base -from tensorflow.contrib.data.python.ops import readers +from tensorflow.contrib.data.python.kernel_tests import sql_dataset_op_test_base from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors -from tensorflow.python.ops import array_ops from tensorflow.python.platform import test -class SqlDatasetTestBase(test.TestCase): - - def _createSqlDataset(self, output_types, num_repeats=1): - dataset = readers.SqlDataset(self.driver_name, self.data_source_name, - self.query, output_types).repeat(num_repeats) - iterator = dataset.make_initializable_iterator() - init_op = iterator.initializer - get_next = iterator.get_next() - return init_op, get_next - - def setUp(self): - self.data_source_name = os.path.join(test.get_temp_dir(), "tftest.sqlite") - self.driver_name = array_ops.placeholder_with_default( - array_ops.constant("sqlite", dtypes.string), shape=[]) - self.query = array_ops.placeholder(dtypes.string, shape=[]) - - conn = sqlite3.connect(self.data_source_name) - c = conn.cursor() - c.execute("DROP TABLE IF EXISTS students") - c.execute("DROP TABLE IF EXISTS people") - c.execute("DROP TABLE IF EXISTS townspeople") - c.execute( - "CREATE TABLE IF NOT EXISTS students (id INTEGER NOT NULL PRIMARY KEY, " - "first_name VARCHAR(100), last_name VARCHAR(100), motto VARCHAR(100), " - "school_id VARCHAR(100), favorite_nonsense_word VARCHAR(100), " - "desk_number INTEGER, income INTEGER, favorite_number INTEGER, " - "favorite_big_number INTEGER, favorite_negative_number INTEGER, " - "favorite_medium_sized_number INTEGER, brownie_points INTEGER, " - "account_balance INTEGER, registration_complete INTEGER)") - c.executemany( - "INSERT INTO students (first_name, last_name, motto, school_id, " - "favorite_nonsense_word, desk_number, income, favorite_number, " - "favorite_big_number, favorite_negative_number, " - "favorite_medium_sized_number, brownie_points, account_balance, " - "registration_complete) " - "VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)", - [("John", "Doe", "Hi!", "123", "n\0nsense", 9, 0, 2147483647, - 9223372036854775807, -2, 32767, 0, 0, 1), - ("Jane", "Moe", "Hi again!", "1000", "nonsense\0", 127, -20000, - -2147483648, -9223372036854775808, -128, -32768, 255, 65535, 0)]) - c.execute( - "CREATE TABLE IF NOT EXISTS people (id INTEGER NOT NULL PRIMARY KEY, " - "first_name VARCHAR(100), last_name VARCHAR(100), state VARCHAR(100))") - c.executemany( - "INSERT INTO PEOPLE (first_name, last_name, state) VALUES (?, ?, ?)", - [("Benjamin", "Franklin", "Pennsylvania"), ("John", "Doe", - "California")]) - c.execute( - "CREATE TABLE IF NOT EXISTS townspeople (id INTEGER NOT NULL PRIMARY " - "KEY, first_name VARCHAR(100), last_name VARCHAR(100), victories " - "FLOAT, accolades FLOAT, triumphs FLOAT)") - c.executemany( - "INSERT INTO townspeople (first_name, last_name, victories, " - "accolades, triumphs) VALUES (?, ?, ?, ?, ?)", - [("George", "Washington", 20.00, - 1331241.321342132321324589798264627463827647382647382643874, - 9007199254740991.0), - ("John", "Adams", -19.95, - 1331241321342132321324589798264627463827647382647382643874.0, - 9007199254740992.0)]) - conn.commit() - conn.close() - - -class SqlDatasetTest(SqlDatasetTestBase): +class SqlDatasetTest(sql_dataset_op_test_base.SqlDatasetTestBase): # Test that SqlDataset can read from a database table. def testReadResultSet(self): @@ -656,27 +586,5 @@ class SqlDatasetTest(SqlDatasetTestBase): sess.run(get_next) -class SqlDatasetSerializationTest( - SqlDatasetTestBase, - dataset_serialization_test_base.DatasetSerializationTestBase): - - def _build_dataset(self, num_repeats): - data_source_name = os.path.join(test.get_temp_dir(), "tftest.sqlite") - driver_name = array_ops.placeholder_with_default( - array_ops.constant("sqlite", dtypes.string), shape=[]) - query = ("SELECT first_name, last_name, motto FROM students ORDER BY " - "first_name DESC") - output_types = (dtypes.string, dtypes.string, dtypes.string) - return readers.SqlDataset(driver_name, data_source_name, query, - output_types).repeat(num_repeats) - - def testSQLSaveable(self): - num_repeats = 4 - num_outputs = num_repeats * 2 - self.run_core_tests(lambda: self._build_dataset(num_repeats), - lambda: self._build_dataset(num_repeats // 2), - num_outputs) - - if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/sql_dataset_op_test_base.py b/tensorflow/contrib/data/python/kernel_tests/sql_dataset_op_test_base.py new file mode 100644 index 0000000000..1f5c725a92 --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/sql_dataset_op_test_base.py @@ -0,0 +1,96 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Base class for testing SqlDataset.""" + + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os + +import sqlite3 + +from tensorflow.contrib.data.python.ops import readers +from tensorflow.python.framework import dtypes +from tensorflow.python.ops import array_ops +from tensorflow.python.platform import test + + +class SqlDatasetTestBase(test.TestCase): + """Base class for setting up and testing SqlDataset.""" + + def _createSqlDataset(self, output_types, num_repeats=1): + dataset = readers.SqlDataset(self.driver_name, self.data_source_name, + self.query, output_types).repeat(num_repeats) + iterator = dataset.make_initializable_iterator() + init_op = iterator.initializer + get_next = iterator.get_next() + return init_op, get_next + + def setUp(self): + self.data_source_name = os.path.join(test.get_temp_dir(), "tftest.sqlite") + self.driver_name = array_ops.placeholder_with_default( + array_ops.constant("sqlite", dtypes.string), shape=[]) + self.query = array_ops.placeholder(dtypes.string, shape=[]) + + conn = sqlite3.connect(self.data_source_name) + c = conn.cursor() + c.execute("DROP TABLE IF EXISTS students") + c.execute("DROP TABLE IF EXISTS people") + c.execute("DROP TABLE IF EXISTS townspeople") + c.execute( + "CREATE TABLE IF NOT EXISTS students (id INTEGER NOT NULL PRIMARY KEY, " + "first_name VARCHAR(100), last_name VARCHAR(100), motto VARCHAR(100), " + "school_id VARCHAR(100), favorite_nonsense_word VARCHAR(100), " + "desk_number INTEGER, income INTEGER, favorite_number INTEGER, " + "favorite_big_number INTEGER, favorite_negative_number INTEGER, " + "favorite_medium_sized_number INTEGER, brownie_points INTEGER, " + "account_balance INTEGER, registration_complete INTEGER)") + c.executemany( + "INSERT INTO students (first_name, last_name, motto, school_id, " + "favorite_nonsense_word, desk_number, income, favorite_number, " + "favorite_big_number, favorite_negative_number, " + "favorite_medium_sized_number, brownie_points, account_balance, " + "registration_complete) " + "VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)", + [("John", "Doe", "Hi!", "123", "n\0nsense", 9, 0, 2147483647, + 9223372036854775807, -2, 32767, 0, 0, 1), + ("Jane", "Moe", "Hi again!", "1000", "nonsense\0", 127, -20000, + -2147483648, -9223372036854775808, -128, -32768, 255, 65535, 0)]) + c.execute( + "CREATE TABLE IF NOT EXISTS people (id INTEGER NOT NULL PRIMARY KEY, " + "first_name VARCHAR(100), last_name VARCHAR(100), state VARCHAR(100))") + c.executemany( + "INSERT INTO PEOPLE (first_name, last_name, state) VALUES (?, ?, ?)", + [("Benjamin", "Franklin", "Pennsylvania"), ("John", "Doe", + "California")]) + c.execute( + "CREATE TABLE IF NOT EXISTS townspeople (id INTEGER NOT NULL PRIMARY " + "KEY, first_name VARCHAR(100), last_name VARCHAR(100), victories " + "FLOAT, accolades FLOAT, triumphs FLOAT)") + c.executemany( + "INSERT INTO townspeople (first_name, last_name, victories, " + "accolades, triumphs) VALUES (?, ?, ?, ?, ?)", + [("George", "Washington", 20.00, + 1331241.321342132321324589798264627463827647382647382643874, + 9007199254740991.0), + ("John", "Adams", -19.95, + 1331241321342132321324589798264627463827647382647382643874.0, + 9007199254740992.0)]) + conn.commit() + conn.close() + + diff --git a/tensorflow/contrib/data/python/kernel_tests/stats_dataset_ops_test.py b/tensorflow/contrib/data/python/kernel_tests/stats_dataset_ops_test.py index 17b6644759..b4945685c1 100644 --- a/tensorflow/contrib/data/python/kernel_tests/stats_dataset_ops_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/stats_dataset_ops_test.py @@ -19,7 +19,6 @@ from __future__ import print_function import numpy as np -from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base from tensorflow.contrib.data.python.kernel_tests import reader_dataset_ops_test_base from tensorflow.contrib.data.python.ops import stats_ops from tensorflow.core.framework import summary_pb2 @@ -236,68 +235,5 @@ class FeatureStatsDatasetTest( self._sum_keywords(1) * num_epochs + 2 * total_records) -class StatsDatasetSerializationTest( - dataset_serialization_test_base.DatasetSerializationTestBase): - - def _build_dataset_bytes_stats(self, num_elements): - return dataset_ops.Dataset.range(num_elements).map( - lambda x: array_ops.tile([x], ops.convert_to_tensor([x]))).apply( - stats_ops.bytes_produced_stats("bytes_produced")) - - def test_bytes_produced_stats_invalid_tag_shape(self): - with self.assertRaisesRegexp( - ValueError, 'Shape must be rank 0 but is rank 1'): - self.run_core_tests( - lambda: dataset_ops.Dataset.range(100).apply( - stats_ops.bytes_produced_stats(["bytes_produced"])), - None, 100) - - def testBytesStatsDatasetSaveableCore(self): - num_outputs = 100 - self.run_core_tests( - lambda: self._build_dataset_bytes_stats(num_outputs), - lambda: self._build_dataset_bytes_stats(num_outputs // 10), num_outputs) - - def _build_dataset_latency_stats(self, num_elements, tag="record_latency"): - return dataset_ops.Dataset.range(num_elements).apply( - stats_ops.latency_stats(tag)) - - def _build_dataset_multiple_tags(self, - num_elements, - tag1="record_latency", - tag2="record_latency_2"): - return dataset_ops.Dataset.range(num_elements).apply( - stats_ops.latency_stats(tag1)).apply(stats_ops.latency_stats(tag2)) - - def test_latency_stats_invalid_tag_shape(self): - with self.assertRaisesRegexp( - ValueError, 'Shape must be rank 0 but is rank 1'): - self.run_core_tests( - lambda: dataset_ops.Dataset.range(100).apply( - stats_ops.latency_stats(["record_latency", "record_latency_2"])), - None, 100) - - def testLatencyStatsDatasetSaveableCore(self): - num_outputs = 100 - - self.run_core_tests( - lambda: self._build_dataset_latency_stats(num_outputs), - lambda: self._build_dataset_latency_stats(num_outputs // 10), - num_outputs) - - self.run_core_tests(lambda: self._build_dataset_multiple_tags(num_outputs), - None, num_outputs) - - tag1 = "record_latency" - tag2 = "record_latency" - self.run_core_tests( - lambda: self._build_dataset_multiple_tags(num_outputs, tag1, tag2), - None, num_outputs) - - -# TODO(shivaniagrawal): Can not checkpoint input_pipeline with the -# transformation `stats_ops.set_stats_aggregator`, since we don't support -# serializing StatsAggregator yet. - if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/unique_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/unique_dataset_op_test.py index 3c436f7a0b..d79a842e7a 100644 --- a/tensorflow/contrib/data/python/kernel_tests/unique_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/unique_dataset_op_test.py @@ -17,7 +17,6 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base from tensorflow.contrib.data.python.ops import unique from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import dtypes @@ -79,18 +78,5 @@ class UniqueDatasetTest(test.TestCase): ]) -class UniqueSerializationTest( - dataset_serialization_test_base.DatasetSerializationTestBase): - - def testUnique(self): - - def build_dataset(num_elements, unique_elem_range): - return dataset_ops.Dataset.range(num_elements).map( - lambda x: x % unique_elem_range).apply(unique.unique()) - - self.run_core_tests(lambda: build_dataset(200, 100), - lambda: build_dataset(40, 100), 100) - - if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/training/BUILD b/tensorflow/contrib/training/BUILD index 5de55b5f7f..76927e62e8 100644 --- a/tensorflow/contrib/training/BUILD +++ b/tensorflow/contrib/training/BUILD @@ -295,7 +295,7 @@ py_test( tags = ["notsan"], deps = [ ":training_py", - "//tensorflow/contrib/data/python/kernel_tests:dataset_serialization_test", + "//tensorflow/contrib/data/python/kernel_tests/serialization:dataset_serialization_test_base", "//tensorflow/python:client_testlib", "//tensorflow/python:framework_for_generated_wrappers", "//tensorflow/python:gradients", diff --git a/tensorflow/contrib/training/python/training/tensor_queue_dataset_test.py b/tensorflow/contrib/training/python/training/tensor_queue_dataset_test.py index 0338f409a2..df0a186f4f 100644 --- a/tensorflow/contrib/training/python/training/tensor_queue_dataset_test.py +++ b/tensorflow/contrib/training/python/training/tensor_queue_dataset_test.py @@ -19,7 +19,7 @@ from __future__ import print_function import numpy as np -from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base from tensorflow.contrib.training.python.training import tensor_queue_dataset as tqd from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import dtypes diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD index 8fe5e6ff1b..5910f0625e 100644 --- a/tensorflow/tools/pip_package/BUILD +++ b/tensorflow/tools/pip_package/BUILD @@ -66,7 +66,7 @@ COMMON_PIP_DEPS = [ "//tensorflow/contrib/boosted_trees:boosted_trees_pip", "//tensorflow/contrib/cluster_resolver:cluster_resolver_pip", "//tensorflow/contrib/constrained_optimization:constrained_optimization_pip", - "//tensorflow/contrib/data/python/kernel_tests:dataset_serialization_test", + "//tensorflow/contrib/data/python/kernel_tests/serialization:dataset_serialization_test_base", "//tensorflow/contrib/data/python/ops:contrib_op_loader", "//tensorflow/contrib/eager/python/examples:examples_pip", "//tensorflow/contrib/eager/python:evaluator", -- GitLab From 3db3e50bb0c02d6f0c7284d50bc31e97ebfc96e5 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 18 Jun 2018 09:15:49 -0700 Subject: [PATCH 590/816] Add missing strip_prefix to workspace. PiperOrigin-RevId: 201005676 --- tensorflow/workspace.bzl | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index 15a37fca39..dbec66216a 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -761,6 +761,7 @@ def tf_workspace(path_prefix="", tf_repo_name=""): "https://mirror.bazel.build/github.com/bazelbuild/rules_android/archive/v0.1.1.zip", "https://github.com/bazelbuild/rules_android/archive/v0.1.1.zip", ], + strip_prefix = "rules_android-0.1.1", ) ############################################################################## -- GitLab From 8ecf506fb8464dd273ce59f512f5e20d37dd5cfd Mon Sep 17 00:00:00 2001 From: Peter Hawkins Date: Mon, 18 Jun 2018 09:16:09 -0700 Subject: [PATCH 591/816] [TF:XLA] Add a XlaSort operator that directly wraps the Sort HLO. Merge XLA-specific operator registrations into a single file rather than having many tiny files. In passing, register a fill function for bfloat16 numpy type; needed for the np.arange() call in the sort unit test. PiperOrigin-RevId: 201005718 --- tensorflow/compiler/tests/BUILD | 12 ++ tensorflow/compiler/tests/sort_ops_test.py | 57 ++++++ tensorflow/compiler/tf2xla/kernels/BUILD | 1 + .../compiler/tf2xla/kernels/sort_ops.cc | 36 ++++ tensorflow/compiler/tf2xla/ops/BUILD | 7 +- .../compiler/tf2xla/ops/dynamic_slice_ops.cc | 49 ----- .../compiler/tf2xla/ops/functional_ops.cc | 74 ------- .../compiler/tf2xla/ops/reduce_window_op.cc | 45 ----- .../compiler/tf2xla/ops/sendrecv_ops.cc | 61 ------ tensorflow/compiler/tf2xla/ops/xla_ops.cc | 182 ++++++++++++++++++ tensorflow/compiler/tf2xla/python/xla.py | 2 + tensorflow/python/lib/core/bfloat16.cc | 11 ++ tensorflow/python/lib/core/bfloat16_test.py | 14 ++ 13 files changed, 316 insertions(+), 235 deletions(-) create mode 100644 tensorflow/compiler/tests/sort_ops_test.py create mode 100644 tensorflow/compiler/tf2xla/kernels/sort_ops.cc delete mode 100644 tensorflow/compiler/tf2xla/ops/dynamic_slice_ops.cc delete mode 100644 tensorflow/compiler/tf2xla/ops/functional_ops.cc delete mode 100644 tensorflow/compiler/tf2xla/ops/reduce_window_op.cc delete mode 100644 tensorflow/compiler/tf2xla/ops/sendrecv_ops.cc create mode 100644 tensorflow/compiler/tf2xla/ops/xla_ops.cc diff --git a/tensorflow/compiler/tests/BUILD b/tensorflow/compiler/tests/BUILD index 98fab319d6..af760b5416 100644 --- a/tensorflow/compiler/tests/BUILD +++ b/tensorflow/compiler/tests/BUILD @@ -839,6 +839,18 @@ tf_xla_py_test( ], ) +tf_xla_py_test( + name = "sort_ops_test", + size = "small", + srcs = ["sort_ops_test.py"], + deps = [ + "//tensorflow/compiler/tests:xla_test", + "//tensorflow/compiler/tf2xla/python:xla", + "//tensorflow/python:array_ops", + "//tensorflow/python:dtypes", + ], +) + tf_xla_py_test( name = "xla_device_test", size = "small", diff --git a/tensorflow/compiler/tests/sort_ops_test.py b/tensorflow/compiler/tests/sort_ops_test.py new file mode 100644 index 0000000000..5ff40edaa5 --- /dev/null +++ b/tensorflow/compiler/tests/sort_ops_test.py @@ -0,0 +1,57 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for XlaSort.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.compiler.tests import xla_test +from tensorflow.compiler.tf2xla.python import xla +from tensorflow.python.framework import dtypes +from tensorflow.python.ops import array_ops +from tensorflow.python.platform import test + + +class XlaSortOpTest(xla_test.XLATestCase): + + def _assertOpOutputMatchesExpected(self, op, args, expected): + with self.test_session() as session: + with self.test_scope(): + placeholders = [ + array_ops.placeholder(dtypes.as_dtype(arg.dtype), arg.shape) + for arg in args + ] + feeds = {placeholders[i]: args[i] for i in range(0, len(args))} + output = op(*placeholders) + result = session.run(output, feeds) + self.assertAllClose(result, expected, rtol=1e-3) + + def testSort(self): + # TODO(b/26783907): The Sort HLO is not implemented on CPU or GPU. + if self.device in ["XLA_CPU", "XLA_GPU"]: + return + supported_types = set([dtypes.bfloat16.as_numpy_dtype, np.float32]) + for dtype in supported_types.intersection(self.numeric_types): + x = np.arange(101, dtype=dtype) + np.random.shuffle(x) + self._assertOpOutputMatchesExpected( + xla.sort, [x], expected=np.arange(101, dtype=dtype)) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/compiler/tf2xla/kernels/BUILD b/tensorflow/compiler/tf2xla/kernels/BUILD index edd2ab6301..e86b333e4b 100644 --- a/tensorflow/compiler/tf2xla/kernels/BUILD +++ b/tensorflow/compiler/tf2xla/kernels/BUILD @@ -79,6 +79,7 @@ tf_kernel_library( "shape_util.cc", "slice_op.cc", "softmax_op.cc", + "sort_ops.cc", "spacetobatch_op.cc", "spacetodepth_op.cc", "split_op.cc", diff --git a/tensorflow/compiler/tf2xla/kernels/sort_ops.cc b/tensorflow/compiler/tf2xla/kernels/sort_ops.cc new file mode 100644 index 0000000000..204ae84582 --- /dev/null +++ b/tensorflow/compiler/tf2xla/kernels/sort_ops.cc @@ -0,0 +1,36 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/tf2xla/xla_op_kernel.h" +#include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" + +namespace tensorflow { +namespace { + +class XlaSortOp : public XlaOpKernel { + public: + explicit XlaSortOp(OpKernelConstruction* context) : XlaOpKernel(context) {} + + void Compile(XlaOpKernelContext* context) override { + xla::XlaBuilder* const b = context->builder(); + context->SetOutput(0, b->Sort(context->Input(0))); + } +}; + +REGISTER_XLA_OP(Name("XlaSort"), XlaSortOp); + +} // namespace +} // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/ops/BUILD b/tensorflow/compiler/tf2xla/ops/BUILD index bb9168fa35..ace6fd1d8e 100644 --- a/tensorflow/compiler/tf2xla/ops/BUILD +++ b/tensorflow/compiler/tf2xla/ops/BUILD @@ -8,12 +8,7 @@ load("//tensorflow:tensorflow.bzl", "tf_gen_op_wrapper_py") cc_library( name = "xla_ops", - srcs = [ - "dynamic_slice_ops.cc", - "functional_ops.cc", - "reduce_window_op.cc", - "sendrecv_ops.cc", - ], + srcs = ["xla_ops.cc"], deps = [ "//tensorflow/core:framework", ], diff --git a/tensorflow/compiler/tf2xla/ops/dynamic_slice_ops.cc b/tensorflow/compiler/tf2xla/ops/dynamic_slice_ops.cc deleted file mode 100644 index d6c0edbb88..0000000000 --- a/tensorflow/compiler/tf2xla/ops/dynamic_slice_ops.cc +++ /dev/null @@ -1,49 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/core/framework/common_shape_fns.h" -#include "tensorflow/core/framework/op.h" -#include "tensorflow/core/framework/shape_inference.h" - -namespace tensorflow { - -REGISTER_OP("XlaDynamicUpdateSlice") - .Input("input: T") - .Input("update: T") - .Input("indices: Tindices") - .Output("output: T") - .Attr("T: type") - .Attr("Tindices: {int32, int64}") - .SetShapeFn(shape_inference::UnchangedShape) - .Doc(R"doc( -Wraps the XLA DynamicUpdateSlice operator, documented at - https://www.tensorflow.org/performance/xla/operation_semantics#dynamicupdateslice -. - -XlaDynamicUpdateSlice generates a result which is the value of the `input` -operand, with a slice update overwritten at `indices`. The shape of `update` -determines the shape of the sub-array of the result which is updated. The shape -of indices must be rank == 1, with dimension size equal to the rank of `input`. - -Handling of out-of-bounds slice indices is implementation-defined. - -input: A `Tensor` of type T. -indices: A vector of indices into `input`. Must have length equal to the rank of - `input`. -update: A `Tensor` of type T. Same rank as `input`. -output: A `Tensor` of type T. -)doc"); - -} // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/ops/functional_ops.cc b/tensorflow/compiler/tf2xla/ops/functional_ops.cc deleted file mode 100644 index 4a669f8e6e..0000000000 --- a/tensorflow/compiler/tf2xla/ops/functional_ops.cc +++ /dev/null @@ -1,74 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/core/framework/common_shape_fns.h" -#include "tensorflow/core/framework/op.h" - -namespace tensorflow { - -// TODO(b/37549631) setting the While Op to always be stateful is too -// conservative. -REGISTER_OP("XlaWhile") - .Input("input: T") - .Output("output: T") - .Attr("T: list(type) >= 0") - .Attr("cond: func") - .Attr("body: func") - .SetIsStateful() - .SetShapeFn(shape_inference::UnknownShape) - .Doc(R"doc( -output = input; While (Cond(output)) { output = Body(output) } - -input: A list of input tensors whose types are T. -output: A list of output tensors whose types are T. -cond: A function takes 'input' and returns a tensor. If the tensor is - a scalar of non-boolean, the scalar is converted to a boolean - according to the following rule: if the scalar is a numerical - value, non-zero means True and zero means False; if the scalar is - a string, non-empty means True and empty means False. If the - tensor is not a scalar, non-emptiness means True and False - otherwise. -body: A function that takes a list of tensors and returns another - list of tensors. Both lists have the same types as specified by T. -)doc"); - -// TODO(b/37549631) setting the If Op to always be stateful is too -// conservative. -REGISTER_OP("XlaIf") - .Input("cond: Tcond") - .Input("inputs: Tin") - .Output("output: Tout") - .Attr("Tcond: type") - .Attr("then_branch: func") - .Attr("else_branch: func") - .Attr("Tin: list(type) >= 0") - .Attr("Tout: list(type) >= 0") - .SetIsStateful() - .SetShapeFn(shape_inference::UnknownShape) - .Doc(R"doc( -output = cond ? then_branch(inputs) : else_branch(inputs). - -cond: A boolean scalar. -inputs: A list of input tensors. -output: A list of tensors returned by either then_branch(inputs) or - else_branch(inputs). The input shapes of the then_branch and - else_branch must match. -then_branch: A function takes 'inputs' and returns a list of tensors, - whose types are the same as what else_branch returns. -else_branch: A function takes 'inputs' and returns a list of tensors. - whose types are the same as what then_branch returns. -)doc"); - -} // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/ops/reduce_window_op.cc b/tensorflow/compiler/tf2xla/ops/reduce_window_op.cc deleted file mode 100644 index d9af982adc..0000000000 --- a/tensorflow/compiler/tf2xla/ops/reduce_window_op.cc +++ /dev/null @@ -1,45 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/core/framework/common_shape_fns.h" -#include "tensorflow/core/framework/op.h" - -namespace tensorflow { - -REGISTER_OP("XlaReduceWindow") - .Input("input: T") - .Input("init_value: T") - .Attr("T: numbertype") - .Attr("computation: func") - .Attr("window_dimensions: list(int)") - .Attr("window_strides: list(int)") - .Attr("padding_low: list(int)") - .Attr("padding_high: list(int)") - .Output("output: T") - .SetShapeFn(shape_inference::UnknownShape) - .Doc(R"doc( -Wraps the XLA ReduceWindow operator, documented at - https://www.tensorflow.org/performance/xla/operation_semantics#reducewindow . - -input: the input tensor -init_value: a scalar representing the initial value for the reduction -computation: a reducer function to apply -window_dimensions: the shape of the window -window_strides: the inter-window strides -padding_low: the padding to apply at the start of each input dimensions -padding_high: the padding to apply at the end of each input dimension. -)doc"); - -} // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/ops/sendrecv_ops.cc b/tensorflow/compiler/tf2xla/ops/sendrecv_ops.cc deleted file mode 100644 index 7ec7b50e90..0000000000 --- a/tensorflow/compiler/tf2xla/ops/sendrecv_ops.cc +++ /dev/null @@ -1,61 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/core/framework/common_shape_fns.h" -#include "tensorflow/core/framework/op.h" - -namespace tensorflow { - -REGISTER_OP("XlaSend") - .Input("tensor: T") - .Attr("T: type") - .Attr("tensor_name: string") - .SetIsStateful() - .SetShapeFn(shape_inference::UnknownShape) - .Doc(R"doc( -Sends the named tensor to another XLA computation. Wraps the XLA Send operator -documented at - https://www.tensorflow.org/performance/xla/operation_semantics#send . - -tensor: The tensor to send. -tensor_name: A string key that identifies the channel. -)doc"); - -REGISTER_OP("XlaRecv") - .Output("tensor: dtype") - .Attr("dtype: type") - .Attr("tensor_name: string") - .Attr("shape: shape") - .SetIsStateful() - .SetShapeFn([](shape_inference::InferenceContext* c) { - TensorShape shape_attr; - TF_RETURN_IF_ERROR(c->GetAttr("shape", &shape_attr)); - shape_inference::ShapeHandle s; - TF_RETURN_IF_ERROR(c->MakeShapeFromTensorShape(shape_attr, &s)); - c->set_output(0, s); - return Status::OK(); - }) - .Doc(R"doc( -Receives the named tensor from another XLA computation. Wraps the XLA Recv -operator documented at - https://www.tensorflow.org/performance/xla/operation_semantics#recv . - -tensor: The tensor to receive. -dtype: The type of the tensor. -tensor_name: A string key that identifies the channel. -shape: The shape of the tensor. -)doc"); - -} // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/ops/xla_ops.cc b/tensorflow/compiler/tf2xla/ops/xla_ops.cc new file mode 100644 index 0000000000..a59c77f5c3 --- /dev/null +++ b/tensorflow/compiler/tf2xla/ops/xla_ops.cc @@ -0,0 +1,182 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/core/framework/common_shape_fns.h" +#include "tensorflow/core/framework/op.h" +#include "tensorflow/core/framework/shape_inference.h" + +namespace tensorflow { + +REGISTER_OP("XlaDynamicUpdateSlice") + .Input("input: T") + .Input("update: T") + .Input("indices: Tindices") + .Output("output: T") + .Attr("T: type") + .Attr("Tindices: {int32, int64}") + .SetShapeFn(shape_inference::UnchangedShape) + .Doc(R"doc( +Wraps the XLA DynamicUpdateSlice operator, documented at + https://www.tensorflow.org/performance/xla/operation_semantics#dynamicupdateslice +. + +XlaDynamicUpdateSlice generates a result which is the value of the `input` +operand, with a slice update overwritten at `indices`. The shape of `update` +determines the shape of the sub-array of the result which is updated. The shape +of indices must be rank == 1, with dimension size equal to the rank of `input`. + +Handling of out-of-bounds slice indices is implementation-defined. + +input: A `Tensor` of type T. +indices: A vector of indices into `input`. Must have length equal to the rank of + `input`. +update: A `Tensor` of type T. Same rank as `input`. +output: A `Tensor` of type T. +)doc"); + +// TODO(b/37549631) setting the If Op to always be stateful is too +// conservative. +REGISTER_OP("XlaIf") + .Input("cond: Tcond") + .Input("inputs: Tin") + .Output("output: Tout") + .Attr("Tcond: type") + .Attr("then_branch: func") + .Attr("else_branch: func") + .Attr("Tin: list(type) >= 0") + .Attr("Tout: list(type) >= 0") + .SetIsStateful() + .SetShapeFn(shape_inference::UnknownShape) + .Doc(R"doc( +output = cond ? then_branch(inputs) : else_branch(inputs). + +cond: A boolean scalar. +inputs: A list of input tensors. +output: A list of tensors returned by either then_branch(inputs) or + else_branch(inputs). The input shapes of the then_branch and + else_branch must match. +then_branch: A function takes 'inputs' and returns a list of tensors, + whose types are the same as what else_branch returns. +else_branch: A function takes 'inputs' and returns a list of tensors. + whose types are the same as what then_branch returns. +)doc"); + +REGISTER_OP("XlaRecv") + .Output("tensor: dtype") + .Attr("dtype: type") + .Attr("tensor_name: string") + .Attr("shape: shape") + .SetIsStateful() + .SetShapeFn([](shape_inference::InferenceContext* c) { + TensorShape shape_attr; + TF_RETURN_IF_ERROR(c->GetAttr("shape", &shape_attr)); + shape_inference::ShapeHandle s; + TF_RETURN_IF_ERROR(c->MakeShapeFromTensorShape(shape_attr, &s)); + c->set_output(0, s); + return Status::OK(); + }) + .Doc(R"doc( +Receives the named tensor from another XLA computation. Wraps the XLA Recv +operator documented at + https://www.tensorflow.org/performance/xla/operation_semantics#recv . + +tensor: The tensor to receive. +dtype: The type of the tensor. +tensor_name: A string key that identifies the channel. +shape: The shape of the tensor. +)doc"); + +REGISTER_OP("XlaReduceWindow") + .Input("input: T") + .Input("init_value: T") + .Attr("T: numbertype") + .Attr("computation: func") + .Attr("window_dimensions: list(int)") + .Attr("window_strides: list(int)") + .Attr("padding_low: list(int)") + .Attr("padding_high: list(int)") + .Output("output: T") + .SetShapeFn(shape_inference::UnknownShape) + .Doc(R"doc( +Wraps the XLA ReduceWindow operator, documented at + https://www.tensorflow.org/performance/xla/operation_semantics#reducewindow . + +input: the input tensor +init_value: a scalar representing the initial value for the reduction +computation: a reducer function to apply +window_dimensions: the shape of the window +window_strides: the inter-window strides +padding_low: the padding to apply at the start of each input dimensions +padding_high: the padding to apply at the end of each input dimension. +)doc"); + +REGISTER_OP("XlaSend") + .Input("tensor: T") + .Attr("T: type") + .Attr("tensor_name: string") + .SetIsStateful() + .SetShapeFn(shape_inference::UnknownShape) + .Doc(R"doc( +Sends the named tensor to another XLA computation. Wraps the XLA Send operator +documented at + https://www.tensorflow.org/performance/xla/operation_semantics#send . + +tensor: The tensor to send. +tensor_name: A string key that identifies the channel. +)doc"); + +REGISTER_OP("XlaSort") + .Input("input: T") + .Output("output: T") + .Attr("T: type") + .SetShapeFn(shape_inference::UnchangedShape) + .Doc(R"doc( +Wraps the XLA Sort operator, documented at + https://www.tensorflow.org/performance/xla/operation_semantics#sort +. + +Sorts a tensor. Currently only rank 1 sorts in ascending order are supported. + +input: A `Tensor` of type T. +output: A `Tensor` of type T. +)doc"); + +// TODO(b/37549631) setting the While Op to always be stateful is too +// conservative. +REGISTER_OP("XlaWhile") + .Input("input: T") + .Output("output: T") + .Attr("T: list(type) >= 0") + .Attr("cond: func") + .Attr("body: func") + .SetIsStateful() + .SetShapeFn(shape_inference::UnknownShape) + .Doc(R"doc( +output = input; While (Cond(output)) { output = Body(output) } + +input: A list of input tensors whose types are T. +output: A list of output tensors whose types are T. +cond: A function takes 'input' and returns a tensor. If the tensor is + a scalar of non-boolean, the scalar is converted to a boolean + according to the following rule: if the scalar is a numerical + value, non-zero means True and zero means False; if the scalar is + a string, non-empty means True and empty means False. If the + tensor is not a scalar, non-emptiness means True and False + otherwise. +body: A function that takes a list of tensors and returns another + list of tensors. Both lists have the same types as specified by T. +)doc"); + +} // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/python/xla.py b/tensorflow/compiler/tf2xla/python/xla.py index e5ce65bec9..2fc47dffb8 100644 --- a/tensorflow/compiler/tf2xla/python/xla.py +++ b/tensorflow/compiler/tf2xla/python/xla.py @@ -77,4 +77,6 @@ def reduce_window(operand, recv = gen_xla_ops.xla_recv send = gen_xla_ops.xla_send +sort = gen_xla_ops.xla_sort + while_loop = gen_xla_ops.xla_while diff --git a/tensorflow/python/lib/core/bfloat16.cc b/tensorflow/python/lib/core/bfloat16.cc index 77fa2c1f66..fde3a83770 100644 --- a/tensorflow/python/lib/core/bfloat16.cc +++ b/tensorflow/python/lib/core/bfloat16.cc @@ -446,6 +446,16 @@ npy_bool NPyBfloat16_NonZero(void* data, void* arr) { return x != static_cast(0); } +int NPyBfloat16_Fill(void* buffer_raw, npy_intp length, void* ignored) { + bfloat16* const buffer = reinterpret_cast(buffer_raw); + const float start(buffer[0]); + const float delta = static_cast(buffer[1]) - start; + for (npy_intp i = 2; i < length; ++i) { + buffer[i] = static_cast(start + i * delta); + } + return 0; +} + // NumPy casts // Performs a NumPy array cast from type 'From' to 'To'. @@ -548,6 +558,7 @@ bool Initialize() { NPyBfloat16_ArrFuncs.copyswapn = NPyBfloat16_CopySwapN; NPyBfloat16_ArrFuncs.copyswap = NPyBfloat16_CopySwap; NPyBfloat16_ArrFuncs.nonzero = NPyBfloat16_NonZero; + NPyBfloat16_ArrFuncs.fill = NPyBfloat16_Fill; Py_TYPE(&NPyBfloat16_Descr) = &PyArrayDescr_Type; npy_bfloat16_ = PyArray_RegisterDataType(&NPyBfloat16_Descr); diff --git a/tensorflow/python/lib/core/bfloat16_test.py b/tensorflow/python/lib/core/bfloat16_test.py index 09d4b01fa4..bc928cd9e5 100644 --- a/tensorflow/python/lib/core/bfloat16_test.py +++ b/tensorflow/python/lib/core/bfloat16_test.py @@ -245,6 +245,20 @@ class Bfloat16NumPyTest(test.TestCase): np.logaddexp(x.astype(bfloat16), y.astype(bfloat16)), atol=2e-2) + def testArange(self): + self.assertAllEqual( + np.arange(100, dtype=np.float32).astype(bfloat16), + np.arange(100, dtype=bfloat16)) + self.assertAllEqual( + np.arange(-10.5, 7.8, 0.5, dtype=np.float32).astype(bfloat16), + np.arange(-10.5, 7.8, 0.5, dtype=bfloat16)) + self.assertAllEqual( + np.arange(-0., -7., -0.25, dtype=np.float32).astype(bfloat16), + np.arange(-0., -7., -0.25, dtype=bfloat16)) + self.assertAllEqual( + np.arange(-16384., 16384., 64., dtype=np.float32).astype(bfloat16), + np.arange(-16384., 16384., 64., dtype=bfloat16)) + if __name__ == "__main__": test.main() -- GitLab From ff7e6399443615675a3f1182c4f2e1850008da04 Mon Sep 17 00:00:00 2001 From: Niranjan Hasabnis Date: Mon, 18 Jun 2018 09:25:00 -0700 Subject: [PATCH 592/816] [Intel MKL] Fixing MKL graph layout pass test (#20065) This PR fixes the MKL graph layout pass test which was failing because the order in which nodes in the graph are printed seems to have changed. --- tensorflow/core/graph/mkl_layout_pass_test.cc | 21 ++++++++++++------- 1 file changed, 13 insertions(+), 8 deletions(-) diff --git a/tensorflow/core/graph/mkl_layout_pass_test.cc b/tensorflow/core/graph/mkl_layout_pass_test.cc index 7645b4a7f0..fc474c0dc8 100644 --- a/tensorflow/core/graph/mkl_layout_pass_test.cc +++ b/tensorflow/core/graph/mkl_layout_pass_test.cc @@ -1901,6 +1901,11 @@ BENCHMARK(BM_MklLayoutRewritePass)->Arg(1000)->Arg(10000); #else // INTEL_MKL_ML +// NOTE: Unit tests in this file rely on a topological sorted graph for +// printing. But since sibling nodes of a node in the topologically sorted graph +// can be printed in different orders, tests may fail if the order in which +// sibling nodes are visited is changed. + namespace { const char kCPUDevice[] = "/job:a/replica:0/task:0/device:CPU:0"; @@ -2572,9 +2577,9 @@ TEST_F(MklLayoutPassTest, NodeRewrite_Concat_Input_Mkl) { "A(Input);B(Input);C(Input);D(Input);DMT/_0(Const);DMT/_1(Const);" "DMT/_2(Const);DMT/_3(Const);DMT/_4(Const);E(_MklConv2D);" "F(_MklConv2D);G(Const);H(_MklConcat);I(Zeta)|A->E;A->I;" - "A:control->DMT/_2:control;A:control->DMT/_3:control;" - "B->E:1;C->F;C:control->DMT/_0:control;C:control->DMT/_1:control;" - "D->F:1;DMT/_0->F:2;DMT/_1->F:3;DMT/_2->E:2;DMT/_3->E:3;" + "A:control->DMT/_0:control;A:control->DMT/_1:control;" + "B->E:1;C->F;C:control->DMT/_2:control;C:control->DMT/_3:control;" + "D->F:1;DMT/_0->E:2;DMT/_1->E:3;DMT/_2->F:2;DMT/_3->F:3;" "DMT/_4->H:3;E->H:1;E:2->H:4;F->H:2;F:2->H:5;G->H;" "G:control->DMT/_4:control;H->I:1"); } @@ -2681,9 +2686,9 @@ TEST_F(MklLayoutPassTest, NodeRewrite_ConcatV2_Input_Mkl) { "A(Input);B(Input);C(Input);D(Input);DMT/_0(Const);DMT/_1(Const);" "DMT/_2(Const);DMT/_3(Const);DMT/_4(Const);E(_MklConv2D);" "F(_MklConv2D);G(Const);H(_MklConcatV2);I(Zeta)|A->E;A->I;" - "A:control->DMT/_2:control;A:control->DMT/_3:control;B->E:1;C->F;" - "C:control->DMT/_0:control;C:control->DMT/_1:control;" - "D->F:1;DMT/_0->F:2;DMT/_1->F:3;DMT/_2->E:2;DMT/_3->E:3;" + "A:control->DMT/_0:control;A:control->DMT/_1:control;B->E:1;C->F;" + "C:control->DMT/_2:control;C:control->DMT/_3:control;" + "D->F:1;DMT/_0->E:2;DMT/_1->E:3;DMT/_2->F:2;DMT/_3->F:3;" "DMT/_4->H:5;E->H;E:2->H:3;E:control->DMT/_4:control;F->H:1;" "F:2->H:4;G->H:2;H->I:1"); } @@ -3060,8 +3065,8 @@ TEST_F(MklLayoutPassTest, LRN_Negative3) { "C:control->DMT/_1:control;C:control->DMT/_2:control;" "C:control->DMT/_3:control;C:control->DMT/_4:control;" "C:control->DMT/_5:control;C:control->DMT/_6:control;" - "D->E:1;D->F:2;DMT/_0->B:1;DMT/_1->F:3;DMT/_2->F:7;DMT/_3->F:4;" - "DMT/_4->F:6;DMT/_5->E:4;DMT/_6->E:5;E->G;F->G:1"); + "D->E:1;D->F:2;DMT/_0->B:1;DMT/_1->E:4;DMT/_2->E:5;DMT/_3->F:3;" + "DMT/_4->F:7;DMT/_5->F:4;DMT/_6->F:6;E->G;F->G:1"); } /* Test MaxPool->MaxPoolGrad replacement by workspace+rewrite nodes. */ -- GitLab From e80732c9895d1283af9b98d6277ad1a1015e2e9a Mon Sep 17 00:00:00 2001 From: Akshay Modi Date: Mon, 18 Jun 2018 09:57:19 -0700 Subject: [PATCH 593/816] Merge changes from github. PiperOrigin-RevId: 201011811 --- CONTRIBUTING.md | 2 +- README.md | 1 + RELEASE.md | 67 ++- configure.py | 5 + tensorflow/BUILD | 4 +- tensorflow/c/generate-pc.sh | 11 +- tensorflow/cc/gradients/math_grad.cc | 1 + tensorflow/cc/gradients/nn_grad.cc | 47 ++ tensorflow/cc/gradients/nn_grad_test.cc | 84 +++- tensorflow/compiler/aot/codegen_test_h.golden | 4 +- .../compiler/aot/embedded_protocol_buffers.h | 2 +- tensorflow/compiler/aot/runtime.h | 4 +- tensorflow/compiler/aot/runtime_test.cc | 16 +- tensorflow/compiler/xla/service/cpu/BUILD | 18 +- .../compiler/xla/service/cpu/cpu_runtime.cc | 2 + .../compiler/xla/service/cpu/cpu_runtime.h | 1 + .../compiler/xla/service/cpu/ir_emitter.cc | 8 +- .../xla/service/cpu/runtime_fft_impl.h | 20 +- .../cpu/runtime_single_threaded_fft.cc | 32 ++ .../service/cpu/runtime_single_threaded_fft.h | 31 ++ .../xla/service/cpu/simple_orc_jit.cc | 2 + .../compiler/xla/service/pattern_matcher.h | 2 +- .../compiler/xla/service/tuple_simplifier.cc | 7 + .../compiler/xla/service/tuple_simplifier.h | 9 +- .../xla/service/tuple_simplifier_test.cc | 77 ++++ tensorflow/contrib/autograph/__init__.py | 3 + tensorflow/contrib/cmake/tf_c.cmake | 22 +- tensorflow/contrib/cmake/tf_cc_ops.cmake | 2 +- tensorflow/contrib/cmake/tf_python.cmake | 3 +- .../contrib/cmake/tools/create_def_file.py | 9 +- .../bijectors/sinh_arcsinh_bijector_test.py | 28 +- tensorflow/contrib/eager/python/datasets.py | 3 +- .../examples/notebooks/4_high_level.ipynb | 4 +- .../feature_column/sequence_feature_column.py | 22 +- .../sequence_feature_column_test.py | 41 ++ tensorflow/contrib/ffmpeg/__init__.py | 1 - tensorflow/contrib/ffmpeg/ffmpeg_ops.py | 1 - tensorflow/contrib/framework/__init__.py | 3 +- .../fused_conv2d_bias_activation_op_test.py | 11 +- .../src_impl/hexagon_controller.c | 2 +- .../contrib/lite/download_dependencies.sh | 4 +- .../contrib/lite/examples/minimal/minimal.cc | 2 +- .../lite/g3doc/tf_ops_compatibility.md | 14 +- tensorflow/contrib/lite/java/ovic/README.md | 4 +- .../internal/reference/reference_ops.h | 4 +- tensorflow/contrib/lite/python/interpreter.py | 2 +- .../interpreter_wrapper.cc | 9 +- .../interpreter_wrapper/interpreter_wrapper.h | 3 +- tensorflow/contrib/lite/python/lite.py | 11 + .../contrib/lite/toco/import_tensorflow.cc | 2 +- tensorflow/contrib/lite/toco/toco_port.cc | 6 + tensorflow/contrib/lite/toco/toco_port.h | 18 + tensorflow/contrib/makefile/compile_nsync.sh | 2 +- .../contrib/makefile/download_dependencies.sh | 4 +- .../contrib/metrics/python/ops/metric_ops.py | 2 +- .../contrib/mpi_collectives/kernels/ring.h | 2 +- .../opt/python/training/adamax_test.py | 6 +- .../training/model_average_optimizer.py | 2 +- tensorflow/contrib/periodic_resample/BUILD | 20 +- .../kernels/periodic_resample_op.cc | 5 + .../kernels/periodic_resample_op.h | 415 +++++++++++++----- .../periodic_resample/ops/array_ops.cc | 53 ++- .../periodic_resample/ops/array_ops_test.cc | 41 ++ .../kernel_tests/periodic_resample_op_test.py | 27 +- .../python/ops/periodic_resample_op.py | 8 +- .../predictor/contrib_estimator_predictor.py | 5 +- .../predictor/core_estimator_predictor.py | 5 +- .../contrib/predictor/predictor_factories.py | 24 +- .../predictor/predictor_factories_test.py | 19 + .../predictor/saved_model_predictor.py | 6 +- tensorflow/contrib/quantize/README.md | 2 +- .../slim/python/slim/evaluation_test.py | 25 +- tensorflow/contrib/summary/summary.py | 5 +- .../tensor_forest/client/eval_metrics.py | 45 +- .../tensor_forest/python/tensor_forest.py | 34 +- .../python/tensor_forest_test.py | 45 ++ .../contrib/tensorrt/convert/convert_graph.cc | 66 +-- .../contrib/tensorrt/convert/convert_nodes.cc | 97 ++-- tensorflow/contrib/tpu/python/tpu/datasets.py | 16 +- .../contrib/tpu/python/tpu/datasets_test.py | 26 ++ tensorflow/core/BUILD | 9 +- .../core/api_def/base_api/api_def_Selu.pbtxt | 4 + .../base_api/api_def_StringSplitV2.pbtxt | 48 ++ .../python_api/api_def_StringSplitV2.pbtxt | 4 + .../core/common_runtime/bfc_allocator.cc | 8 +- .../core/common_runtime/bfc_allocator.h | 3 +- ...direct_session_with_tracking_alloc_test.cc | 16 + .../mkl_threadpool_device_test.cc | 53 +++ .../core/common_runtime/process_util.cc | 11 +- .../core/common_runtime/threadpool_device.cc | 25 +- .../rpc/grpc_master_service_impl.cc | 4 +- .../distributed_runtime/rpc/grpc_testlib.cc | 10 +- tensorflow/core/framework/allocator.h | 5 - tensorflow/core/framework/op_gen_lib.cc | 1 + .../remote_fused_graph_execute_info.proto | 2 +- tensorflow/core/framework/tensor_test.cc | 24 +- tensorflow/core/graph/mkl_layout_pass.cc | 148 ++++++- tensorflow/core/graph/mkl_layout_pass_test.cc | 31 ++ .../core/grappler/costs/graph_properties.cc | 1 - tensorflow/core/grappler/optimizers/BUILD | 2 +- .../core/grappler/optimizers/remapper.cc | 4 +- tensorflow/core/kernels/as_string_op.cc | 2 + tensorflow/core/kernels/cwise_op_clip.cc | 43 +- .../kernels/dense_update_functor_gpu.cu.cc | 1 + tensorflow/core/kernels/gather_functor.cc | 1 + .../core/kernels/gather_functor_gpu.cu.cc | 1 + tensorflow/core/kernels/gather_nd_op.cc | 4 + .../core/kernels/gather_nd_op_gpu.cu.cc | 2 + tensorflow/core/kernels/gather_op.cc | 1 + tensorflow/core/kernels/mkl_concat_op.cc | 213 ++++++--- .../core/kernels/mkl_conv_grad_bias_ops.cc | 2 + .../core/kernels/mkl_pooling_ops_common.h | 6 +- tensorflow/core/kernels/scatter_nd_op.cc | 4 + .../core/kernels/scatter_nd_op_gpu.cu.cc | 1 + .../core/kernels/scoped_allocator_ops_test.cc | 9 +- .../core/kernels/segment_reduction_ops.h | 10 +- tensorflow/core/kernels/sparse_matmul_op.cc | 2 +- tensorflow/core/kernels/string_split_op.cc | 130 ++++++ tensorflow/core/ops/candidate_sampling_ops.cc | 5 +- tensorflow/core/ops/dataset_ops.cc | 24 +- tensorflow/core/ops/image_ops.cc | 4 +- tensorflow/core/ops/math_ops.cc | 2 +- tensorflow/core/ops/nn_ops.cc | 1 + tensorflow/core/ops/string_ops.cc | 20 +- tensorflow/core/platform/cpu_info.cc | 23 + tensorflow/core/platform/cpu_info.h | 7 + .../core/platform/default/build_config.bzl | 2 + .../platform/hadoop/hadoop_file_system.cc | 21 +- tensorflow/core/platform/posix/port.cc | 5 + tensorflow/core/public/version.h | 4 +- tensorflow/core/util/mkl_util.h | 50 ++- tensorflow/docs_src/community/groups.md | 29 +- tensorflow/docs_src/get_started/eager.md | 2 +- tensorflow/docs_src/get_started/index.md | 4 +- tensorflow/docs_src/install/install_c.md | 2 +- tensorflow/docs_src/install/install_go.md | 2 +- tensorflow/docs_src/install/install_java.md | 24 +- tensorflow/docs_src/install/install_linux.md | 24 +- tensorflow/docs_src/install/install_mac.md | 10 +- .../docs_src/install/install_sources.md | 17 +- tensorflow/docs_src/mobile/linking_libs.md | 2 +- tensorflow/docs_src/mobile/prepare_models.md | 4 +- .../docs_src/performance/quantization.md | 2 +- .../docs_src/programmers_guide/estimators.md | 19 +- .../programmers_guide/feature_columns.md | 4 +- tensorflow/examples/learn/iris.py | 7 +- tensorflow/go/op/wrappers.go | 12 +- tensorflow/java/src/gen/cc/op_generator.cc | 11 +- tensorflow/java/src/gen/cc/op_specs.cc | 1 + tensorflow/python/eager/backprop.py | 4 +- tensorflow/python/estimator/BUILD | 5 +- tensorflow/python/estimator/exporter.py | 4 +- .../python/estimator/inputs/numpy_io.py | 8 +- .../python/estimator/inputs/numpy_io_test.py | 5 +- .../python/estimator/inputs/pandas_io.py | 7 +- .../python/estimator/inputs/pandas_io_test.py | 5 +- .../inputs/queues/feeding_functions.py | 2 +- tensorflow/python/estimator/keras.py | 4 +- tensorflow/python/estimator/keras_test.py | 14 +- .../python/grappler/layout_optimizer_test.py | 4 +- tensorflow/python/keras/activations.py | 2 + tensorflow/python/keras/callbacks.py | 21 +- tensorflow/python/keras/callbacks_test.py | 2 + tensorflow/python/keras/engine/network.py | 2 +- tensorflow/python/keras/engine/saving_test.py | 4 +- tensorflow/python/keras/engine/training.py | 7 +- .../python/keras/engine/training_eager.py | 2 +- tensorflow/python/keras/initializers_test.py | 26 +- tensorflow/python/keras/layers/core.py | 26 +- tensorflow/python/keras/models_test.py | 14 + .../python/kernel_tests/as_string_op_test.py | 10 + .../python/kernel_tests/betainc_op_test.py | 4 +- .../python/kernel_tests/clip_ops_test.py | 13 + .../python/kernel_tests/conv_ops_test.py | 32 +- .../python/kernel_tests/gather_nd_op_test.py | 32 +- .../python/kernel_tests/gather_op_test.py | 20 +- .../python/kernel_tests/init_ops_test.py | 27 ++ .../python/kernel_tests/pooling_ops_test.py | 4 +- .../python/kernel_tests/py_func_test.py | 31 +- .../kernel_tests/scatter_nd_ops_test.py | 6 +- .../python/kernel_tests/scatter_ops_test.py | 14 +- .../segment_reduction_ops_test.py | 4 +- .../kernel_tests/string_split_op_test.py | 96 ++++ tensorflow/python/ops/array_ops.py | 4 + tensorflow/python/ops/gradient_checker.py | 8 +- tensorflow/python/ops/image_ops_impl.py | 74 ++-- tensorflow/python/ops/image_ops_test.py | 261 +++++++++-- tensorflow/python/ops/init_ops.py | 3 +- tensorflow/python/ops/logging_ops.py | 5 +- tensorflow/python/ops/math_ops.py | 28 +- tensorflow/python/ops/nn_impl.py | 5 +- tensorflow/python/ops/nn_ops.py | 4 +- tensorflow/python/ops/nn_test.py | 10 + tensorflow/python/ops/script_ops.py | 35 +- tensorflow/python/ops/sparse_ops.py | 4 + tensorflow/python/ops/string_ops.py | 53 +++ tensorflow/python/ops/variable_scope.py | 21 +- .../python/tools/import_pb_to_tensorboard.py | 0 tensorflow/tensorflow.bzl | 2 +- .../tools/api/generator/create_python_api.py | 8 +- .../tools/api/golden/tensorflow.image.pbtxt | 2 +- tensorflow/tools/api/golden/tensorflow.pbtxt | 4 + .../tools/api/golden/tensorflow.strings.pbtxt | 4 + tensorflow/tools/ci_build/builds/pip.sh | 4 + .../tools/ci_build/builds/with_the_same_user | 2 +- tensorflow/tools/ci_build/ci_build.sh | 7 + tensorflow/tools/ci_build/copy_binary.py | 3 +- .../ci_build/install/install_pip_packages.sh | 4 + .../install/install_python3.5_pip_packages.sh | 4 +- .../install/install_python3.6_pip_packages.sh | 5 +- .../ci_build/linux/mkl/basic-mkl-test.sh | 29 ++ .../tools/ci_build/pi/build_raspberry_pi.sh | 8 +- .../def_file_filter_configure.bzl | 6 +- tensorflow/tools/dist_test/local_test.sh | 12 +- tensorflow/tools/dist_test/remote_test.sh | 11 +- tensorflow/tools/docker/Dockerfile.devel | 2 +- .../tools/docker/Dockerfile.devel-cpu-mkl | 2 +- tensorflow/tools/docker/Dockerfile.devel-gpu | 6 +- tensorflow/tools/docker/Dockerfile.gpu | 2 +- tensorflow/tools/pip_package/BUILD | 1 + .../tools/pip_package/build_pip_package.sh | 160 +++++-- tensorflow/tools/pip_package/setup.py | 3 +- .../gen_proto_text_functions_lib.cc | 3 + .../tools/quantization/quantize_graph_test.py | 12 +- .../tools/test/upload_test_benchmarks.py | 1 - tensorflow/workspace.bzl | 40 +- third_party/eigen.BUILD | 1 + third_party/highwayhash.BUILD | 1 + third_party/jpeg/jpeg.BUILD | 2 + third_party/png.BUILD | 9 +- third_party/py/python_configure.bzl | 24 +- third_party/repo.bzl | 5 +- 232 files changed, 3343 insertions(+), 909 deletions(-) create mode 100644 tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.cc create mode 100644 tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.h create mode 100644 tensorflow/contrib/periodic_resample/ops/array_ops_test.cc create mode 100644 tensorflow/core/api_def/base_api/api_def_StringSplitV2.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_StringSplitV2.pbtxt create mode 100644 tensorflow/core/common_runtime/mkl_threadpool_device_test.cc mode change 100755 => 100644 tensorflow/python/tools/import_pb_to_tensorboard.py create mode 100755 tensorflow/tools/ci_build/linux/mkl/basic-mkl-test.sh diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 8669c25c45..db4b1581ae 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -90,7 +90,7 @@ Bazel BUILD files also need to include a license section, e.g., Changes to TensorFlow C++ code should conform to [Google C++ Style Guide](https://google.github.io/styleguide/cppguide.html). -Use `clang-tidy` to check your C/C++ changes. To install clang-tidy on ubuntu:16.04, do: +Use `clang-tidy` to check your C/C++ changes. To install `clang-tidy` on ubuntu:16.04, do: ```bash apt-get install -y clang-tidy diff --git a/README.md b/README.md index 6fb4486d0d..63853137cf 100644 --- a/README.md +++ b/README.md @@ -56,6 +56,7 @@ $ python 42 >>> sess.close() ``` +Learn more examples about how to do specific tasks in TensorFlow at the [tutorials page of tensorflow.org](https://www.tensorflow.org/tutorials/). ## Contribution guidelines diff --git a/RELEASE.md b/RELEASE.md index 84d9d52868..e09e9c6190 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -1,3 +1,62 @@ +# Release 1.9.0 + +## Major Features And Improvements +* Update tf.keras to the Keras 2.1.6 API. +* `tfe.Network` is deprecated. Please inherit from `tf.keras.Model`. +* Adding support of core feature columns and losses to gradient boosted trees estimators. +* The distributions.Bijector API supports broadcasting for Bijectors with new API changes. See [here](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/distributions/bijectors/Bijector) for more details. +* Layered variable names have changed in the following conditions: + * Using `tf.keras.layers` with custom variable scopes. + * Using `tf.layers` in a subclassed `tf.keras.Model` class. See [here](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/layers) for more details + +## Breaking Chances + * If you're opening empty variable scopes; replace `variable_scope`('', ...) by `variable_scope`(`tf.get_variable_scope()`, ...). + +## Bug Fixes and Other Changes +* `tf.data`: + * The `DatasetBase::DebugString()` method is now `const`. + * Added the `tf.contrib.data.sample_from_datasets()` API for randomly sampling from multiple datasets. +* Eager Execution: +* `tf.keras`: + * Move Keras code out of _impl folder and remove API files. + * `tf.keras.Model.save_weights` now saves in TensorFlow format by default. + * Enable dataset iterators to be passed to `tf.keras.Model` training/eval methods. +* Accelerated Linear Algebra (XLA): +* TensorFlow Debugger (tfdbg): fix an issue in which the TensorBoard Debugger Plugin could not handle total source file size exceeding gRPC message size limit (4 MB). +* `tf.contrib`: + * Add `tf.contrib.data.choose_from_datasets()`. + * `tf.contrib.data.make_csv_dataset()` now supports line breaks in quoted strings. Two arguments were removed from `make_csv_dataset`. + * `tf.contrib.framework.zero_initializer` supports ResourceVariable. + * Adding "constrained_optimization" to tensorflow/contrib. +* Other: + * Add GCS Configuration Ops. + * Changing signature of `MakeIterator` to enable propagating error status. + * KL divergence for two Dirichlet distributions. + * More consistent GcsFileSystem behavior for certain reads past EOF. + * Update benchmark for tf.scan to match ranges across eager and graph modes. + * Fixed bug in `tf.reduce_prod gradient` for complex dtypes. + * Add optional `args` argument to `Dataset.from_generator()`. + * Allow the use of '.' in variables (e.g. "hparams.parse('a.b=1.0')"), which would previously raise an error. This will correspond to an attribute name with an embedded '.' symbol (e.g. 'a.b'), which can only be accessed indirectly (e.g. through getattr and setattr). To set this up the user will first need to explicitly add the variable to the hparam object (e.g. "hparams.add_hparam(name='a.b', value=0.0)"). + * Benchmark for tf.scan in graph and eager modes. + * Added complex128 support to FFT, FFT2D, FFT3D, IFFT, IFFT2D, and IFFT3D. + * Making ids unique in `nn.embedding_lookup_sparse`. This helps to reduce RPC calls for looking up the embeddings when there are repeated ids in the batch. + * Support indicator column in boosted trees. + * Prevent `tf.gradients()` from backpropagating through integer tensors. + * LinearOperator[1D,2D,3D]Circulant added to `tensorflow.linalg`. + * Conv3D, Conv3DBackpropInput, Conv3DBackpropFilter now supports arbitrary. + * Added `tf.train.Checkpoint` for reading/writing object-based checkpoints. + * `Dataset.list_files()` now produces determinstic results when `shuffle=False` or a `seed` is passed. + * Added LinearOperatorKronecker, a dense-free implementation of the Kronecker Product. + * Allow LinearOperator to broadcast. + * SavedModelBuilder will now deduplicate asset names that point to files with the same basename and the same contents. Note that this may result in new asset files included in SavedModels in cases where assets with the same name but different contents were previously overwriting each other. + + +## Thanks to our Contributors + +This release contains contributions from many people at Google, as well as: + +Abdullah Alrasheed, Achal Shah, Ad-530, ADiegoCAlonso, Aditya Yogi, Ag Ramesh, akindyakov, Andy Kernahan, Anya Petrova, Aurelien Geron, Ben, Ben Barsdell, Bhavani-Subramanian, braincodercn, Brett Koonce, Brian Nemsick, Brian Zier, Bryan Heden, candy.dc, cclauss, Clayne Robison, ctiijima, Dalmo Cirne, David Norman, David T.H. Kao, DosLin, ekelsen, Elson Rodriguez, Erik Smistad, Felix Abecassis, Fergal Cotter, fo40225, foo0x29a, Freedom" Koan-Sin Tan, FréDéRic Branchaud-Charron, gdh1995, Geoffrey Irving, Giuseppe, gracehoney, Guido Zuidhof, Guillaume Klein, Guozhong Zhuang, Haggai, Harald Husum, imsheridan, Ivan Zhang, Jan Zikes, Jayaram Bobba, Jesse Benson, Jesse Gumz, Jiajia Li, Jie, jinghuangintel, Jingwen, jjsjann123, Joe Yearsley, Joel Hestness, Joel Shor, josephyearsley, Junpeng Lao, Karol M. Langner, Kb Sriram, krantideep95, Krish Ravindranath, Letian Feng, Loo Rong Jie, Lukas Geiger, Maciej, Mahmoud Abuzaina, ManHyuk, Mark Ryan, mbhuiyan, Michal Turek, Mostafa Alaa, Myungsung Kwak, Nand Dalal, Nehal J Wani, Neil Tenenholtz, ngc92, Nicholas Nadeau, P.Eng., Avs, Niranjan Hasabnis, P-Hidringer, Paul Van Eck, Peng Yu, Qing Zhao, Qingying Chen, Quanlong, Rajendra Arora, Rholais Lii, rmanyari, Robin Richtsfeld, Russell Klopfer, Sagi, Sam Sendelbach, Sandeep N Gupta, Sandip Giri, Sarah Edkins, Scott Tseng, Sdalbsoo, Sergii Khomenko, Seungwoo Choi (Biggie), Seyed Majid Azimi, Shaoning Zeng, shengfuintel, Siu Kei, Muk, Smit Shilu, soonson, Stefan Schweter, Sukhwan Kim, Sunitha Kambhampati, Taehoon Lee, tamimaddari82, Tang, Wenyi, Ted Chang, u2takey, Utkarsh Upadhyay, Vadim Markovtsev, voegtlel, Wai Hon Law, wangsiyu, Wenhao Hu, wenhao.hu, William D. Irons, Yan Facai (颜发才), Yanbo Liang, Yihong Wang, Yilei (Dolee) Yang, Yong Tang, Yuan (Terry) Tang + # Release 1.8.0 ## Major Features And Improvements @@ -404,14 +463,6 @@ answered questions, and were part of inspiring discussions. # Release 1.4.0 -## Major Features And Improvements -* `tf.keras` is now part of the core TensorFlow API. -* [`tf.data`](http://tensorflow.org/programmers_guide/datasets) is now part of - the core TensorFlow API. - * The API is now subject to backwards compatibility guarantees. - -# Release 1.4.0 - ## Major Features And Improvements * `tf.keras` is now part of the core TensorFlow API. * [`tf.data`](http://tensorflow.org/programmers_guide/datasets) is now part of diff --git a/configure.py b/configure.py index bde7af8c0e..ada342a50a 100644 --- a/configure.py +++ b/configure.py @@ -1397,6 +1397,10 @@ def set_grpc_build_flags(): write_to_bazelrc('build --define grpc_no_ares=true') +def set_build_strip_flag(): + write_to_bazelrc('build --strip=always') + + def set_windows_build_flags(): if is_windows(): # The non-monolithic build is not supported yet @@ -1519,6 +1523,7 @@ def main(): set_grpc_build_flags() set_cc_opt_flags(environ_cp) + set_build_strip_flag() set_windows_build_flags() if get_var( diff --git a/tensorflow/BUILD b/tensorflow/BUILD index a73c4ca3aa..6d134dbb80 100644 --- a/tensorflow/BUILD +++ b/tensorflow/BUILD @@ -475,7 +475,7 @@ tf_cc_shared_object( # excludes all but a subset of function names. # On MacOS, the linker does not support version_script, but has an # an "-exported_symbols_list" command. -z defs disallows undefined -# symbols in object files and -s strips the output. +# symbols in object files. tf_cc_shared_object( name = "libtensorflow.so", @@ -489,7 +489,6 @@ tf_cc_shared_object( "//tensorflow:windows_msvc": [], "//conditions:default": [ "-z defs", - "-s", "-Wl,--version-script", # This line must be directly followed by the version_script.lds file "$(location //tensorflow/c:version_script.lds)", ], @@ -515,7 +514,6 @@ tf_cc_shared_object( "//tensorflow:windows_msvc": [], "//conditions:default": [ "-z defs", - "-s", "-Wl,--version-script", # This line must be directly followed by the version_script.lds file "$(location //tensorflow:tf_version_script.lds)", ], diff --git a/tensorflow/c/generate-pc.sh b/tensorflow/c/generate-pc.sh index 02a6a58b61..7184ad68fb 100755 --- a/tensorflow/c/generate-pc.sh +++ b/tensorflow/c/generate-pc.sh @@ -15,10 +15,12 @@ # ============================================================================== TF_PREFIX='/usr/local' +LIBDIR='lib' usage() { echo "Usage: $0 OPTIONS" echo -e "-p, --prefix\tset installation prefix (default: /usr/local)" + echo -e "-l, --libdir\tset lib directory (default: lib)" echo -e "-v, --version\tset TensorFlow version" echo -e "-h, --help\tdisplay this message" } @@ -26,7 +28,7 @@ usage() { [ $# == 0 ] && usage && exit 0 # read the options -ARGS=$(getopt -o p:v:h --long prefix:,version:,help -n $0 -- "$@") +ARGS=$(getopt -o p:l:v:h --long prefix:,libdir:,version:,help -n $0 -- "$@") eval set -- "$ARGS" # extract options and their arguments into variables. @@ -38,6 +40,11 @@ while true ; do "") shift 2 ;; *) TF_PREFIX=$2 ; shift 2 ;; esac ;; + -l|--libdir) + case "$2" in + "") shift 2 ;; + *) LIBDIR=$2 ; shift 2 ;; + esac ;; -v|--version) case "$2" in "") shift 2 ;; @@ -55,7 +62,7 @@ echo "Generating pkgconfig file for TensorFlow $TF_VERSION in $TF_PREFIX" cat << EOF > tensorflow.pc prefix=${TF_PREFIX} exec_prefix=\${prefix} -libdir=\${exec_prefix}/lib +libdir=\${exec_prefix}/${LIBDIR} includedir=\${prefix}/include Name: TensorFlow diff --git a/tensorflow/cc/gradients/math_grad.cc b/tensorflow/cc/gradients/math_grad.cc index 52c177212a..35a01e0341 100644 --- a/tensorflow/cc/gradients/math_grad.cc +++ b/tensorflow/cc/gradients/math_grad.cc @@ -38,6 +38,7 @@ REGISTER_NO_GRADIENT_OP("NotEqual"); REGISTER_NO_GRADIENT_OP("LogicalAnd"); REGISTER_NO_GRADIENT_OP("LogicalOr"); REGISTER_NO_GRADIENT_OP("LogicalNot"); +REGISTER_NO_GRADIENT_OP("Floor"); // Conjugate helper function returns the conjugate of an Output if it // is complex valued. diff --git a/tensorflow/cc/gradients/nn_grad.cc b/tensorflow/cc/gradients/nn_grad.cc index 0cb3132e94..c73482d5f4 100644 --- a/tensorflow/cc/gradients/nn_grad.cc +++ b/tensorflow/cc/gradients/nn_grad.cc @@ -255,6 +255,53 @@ Status LRNGradHelper(const Scope& scope, const Operation& op, } REGISTER_GRADIENT_OP("LRN", LRNGradHelper); +Status SoftplusGradHelper(const Scope& scope, const Operation& op, + const std::vector& grad_inputs, + std::vector* grad_outputs) { + auto dx = internal::SoftplusGrad(scope, grad_inputs[0], op.input(0)); + grad_outputs->push_back(dx); + return scope.status(); +} +REGISTER_GRADIENT_OP("Softplus", SoftplusGradHelper); + +Status SoftsignGradHelper(const Scope& scope, const Operation& op, + const std::vector& grad_inputs, + std::vector* grad_outputs) { + auto dx = internal::SoftsignGrad(scope, grad_inputs[0], op.input(0)); + grad_outputs->push_back(dx); + return scope.status(); +} +REGISTER_GRADIENT_OP("Softsign", SoftsignGradHelper); + +Status FractionalAvgPoolGradHelper(const Scope& scope, const Operation& op, + const std::vector& grad_inputs, + std::vector* grad_outputs) { + bool overlapping; + TF_RETURN_IF_ERROR( + GetNodeAttr(op.output(0).node()->attrs(), "overlapping", &overlapping)); + auto dx = internal::FractionalAvgPoolGrad( + scope, Shape(scope, op.input(0), Shape::OutType(DT_INT64)), + grad_inputs[0], op.output(1), op.output(2), + internal::FractionalAvgPoolGrad::Overlapping(overlapping)); + grad_outputs->push_back(dx); + return scope.status(); +} +REGISTER_GRADIENT_OP("FractionalAvgPool", FractionalAvgPoolGradHelper); + +Status FractionalMaxPoolGradHelper(const Scope& scope, const Operation& op, + const std::vector& grad_inputs, + std::vector* grad_outputs) { + bool overlapping; + TF_RETURN_IF_ERROR( + GetNodeAttr(op.output(0).node()->attrs(), "overlapping", &overlapping)); + auto dx = internal::FractionalMaxPoolGrad( + scope, op.input(0), op.output(0), grad_inputs[0], op.output(1), + op.output(2), internal::FractionalMaxPoolGrad::Overlapping(overlapping)); + grad_outputs->push_back(dx); + return scope.status(); +} +REGISTER_GRADIENT_OP("FractionalMaxPool", FractionalMaxPoolGradHelper); + } // anonymous namespace } // namespace ops } // namespace tensorflow diff --git a/tensorflow/cc/gradients/nn_grad_test.cc b/tensorflow/cc/gradients/nn_grad_test.cc index c4eba7ecb0..b4d457a9d1 100644 --- a/tensorflow/cc/gradients/nn_grad_test.cc +++ b/tensorflow/cc/gradients/nn_grad_test.cc @@ -28,6 +28,8 @@ namespace { using ops::BiasAdd; using ops::Conv2D; using ops::Elu; +using ops::FractionalAvgPool; +using ops::FractionalMaxPool; using ops::L2Loss; using ops::LogSoftmax; using ops::LRN; @@ -41,6 +43,8 @@ using ops::Relu; using ops::Relu6; using ops::Selu; using ops::Softmax; +using ops::Softplus; +using ops::Softsign; class NNGradTest : public ::testing::Test { protected: @@ -71,22 +75,30 @@ class NNGradTest : public ::testing::Test { EXPECT_LT(max_error, 1e-3); } - // Sets tensor with random values, ensuring that the max value is largest by - // a reasonable amount. - // This is an issue for MaxPool, MaxPoolV2 and MaxPool3D, in which - // perturbations by the numeric gradient computation in the gradient checker - // can change the max value if values are too close together. + // Sets tensor with random values, ensuring that every pair of elements are at + // least a reasonable amount apart. + // This is an issue for max pooling operations, in which perturbations by the + // numeric gradient computation in the gradient checker can change the max + // value if a pool has values that are too close together. template - void SetRandomValuesWithBumpedMax(Tensor* tensor) { + void SetRandomValuesForMaxPooling(Tensor* tensor) { auto tensor_flat = tensor->flat(); - tensor_flat.setRandom(); - int32 max_index = 0; - for (size_t i = 1; i < tensor->NumElements(); i++) { - if (tensor_flat(i) > tensor_flat(max_index)) { - max_index = i; - } + // First set the array to an increasing sequence of values spaced + // a reasonable amount apart + T cur = 0; + for (size_t i = 0; i < tensor->NumElements(); i++) { + tensor_flat(i) = cur; + cur += 5e-2; + } + // Fischer-Yates shuffle the array + for (size_t i = tensor->NumElements() - 1; i >= 1; i--) { + // j <- random integer 0 <= j <= i + size_t j = random::New64() % (i + 1); + // swap values at i, j + T tmp = tensor_flat(i); + tensor_flat(i) = tensor_flat(j); + tensor_flat(j) = tmp; } - tensor_flat(max_index) += 1e-2; } Scope scope_; @@ -189,7 +201,7 @@ TEST_F(NNGradTest, MaxPoolGradHelper) { const std::vector strides{1, 2, 2, 1}; auto y = MaxPool(scope_, x, ksize, strides, "VALID"); Tensor x_init_value = Tensor(DT_FLOAT, x_shape); - SetRandomValuesWithBumpedMax(&x_init_value); + SetRandomValuesForMaxPooling(&x_init_value); RunTest(x, x_init_value, y, y_shape); } @@ -202,7 +214,7 @@ TEST_F(NNGradTest, MaxPoolGradV2Helper) { Tensor strides = test::AsTensor({1, 2, 2, 1}, {4}); auto y = MaxPoolV2(scope_, x, ksize, strides, "VALID"); Tensor x_init_value = Tensor(DT_FLOAT, x_shape); - SetRandomValuesWithBumpedMax(&x_init_value); + SetRandomValuesForMaxPooling(&x_init_value); RunTest(x, x_init_value, y, y_shape); } @@ -215,7 +227,7 @@ TEST_F(NNGradTest, MaxPool3DGradHelper) { const std::vector strides{1, 3, 3, 3, 1}; auto y = MaxPool3D(scope_, x, ksize, strides, "VALID"); Tensor x_init_value = Tensor(DT_FLOAT, x_shape); - SetRandomValuesWithBumpedMax(&x_init_value); + SetRandomValuesForMaxPooling(&x_init_value); RunTest(x, x_init_value, y, y_shape); } @@ -248,5 +260,45 @@ TEST_F(NNGradTest, LRN){ RunTest(x, x_shape, y, x_shape); } +TEST_F(NNGradTest, SoftplusGrad) { + TensorShape shape({3, 7}); + auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape)); + auto y = Softplus(scope_, x); + RunTest(x, shape, y, shape); +} + +TEST_F(NNGradTest, SoftsignGrad) { + TensorShape shape({3, 7}); + auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape)); + auto y = Softsign(scope_, x); + RunTest(x, shape, y, shape); +} + +TEST_F(NNGradTest, FractionalAvgPoolGradHelper) { + TensorShape x_shape({1, 3, 7, 1}); + auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape)); + // Force consistent pooling regions for unit testing. + auto y = FractionalAvgPool( + scope_, x, {1, 1.2, 1.9, 1}, + FractionalAvgPool::Deterministic(true).Overlapping(true).Seed(1).Seed2( + 2)); + TensorShape y_shape({1, 2, 3, 1}); + RunTest(x, x_shape, y.output, y_shape); +} + +TEST_F(NNGradTest, FractionalMaxPoolGradHelper) { + TensorShape x_shape({1, 3, 7, 1}); + auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape)); + // Force consistent pooling regions for unit testing. + auto y = FractionalMaxPool( + scope_, x, {1, 1.2, 1.9, 1}, + FractionalMaxPool::Deterministic(true).Overlapping(true).Seed(1).Seed2( + 2)); + Tensor x_init_value = Tensor(DT_FLOAT, x_shape); + SetRandomValuesForMaxPooling(&x_init_value); + TensorShape y_shape({1, 2, 3, 1}); + RunTest(x, x_init_value, y.output, y_shape); +} + } // namespace } // namespace tensorflow diff --git a/tensorflow/compiler/aot/codegen_test_h.golden b/tensorflow/compiler/aot/codegen_test_h.golden index 6e050cf564..6641d45e83 100644 --- a/tensorflow/compiler/aot/codegen_test_h.golden +++ b/tensorflow/compiler/aot/codegen_test_h.golden @@ -56,9 +56,9 @@ namespace bar { // // Memory stats: // arg bytes total: 104 -// arg bytes aligned: 128 +// arg bytes aligned: 192 // temp bytes total: 126 -// temp bytes aligned: 224 +// temp bytes aligned: 320 class MyClass : public tensorflow::XlaCompiledCpuFunction { public: // Number of input arguments for the compiled computation. diff --git a/tensorflow/compiler/aot/embedded_protocol_buffers.h b/tensorflow/compiler/aot/embedded_protocol_buffers.h index ebfe4806c2..4e194a6aba 100644 --- a/tensorflow/compiler/aot/embedded_protocol_buffers.h +++ b/tensorflow/compiler/aot/embedded_protocol_buffers.h @@ -71,7 +71,7 @@ struct ProtobufToEmbed { const ::tensorflow::protobuf::MessageLite* message; }; -// Embeds a a sequence of protocol buffers into an object file. +// Embeds a sequence of protocol buffers into an object file. // // `target_triple` is the target triple for the target architecture for the // generated object file. diff --git a/tensorflow/compiler/aot/runtime.h b/tensorflow/compiler/aot/runtime.h index d085864f00..d1a669ceb1 100644 --- a/tensorflow/compiler/aot/runtime.h +++ b/tensorflow/compiler/aot/runtime.h @@ -25,8 +25,8 @@ namespace tensorflow { namespace tfcompile { namespace runtime { -// Align to 32-bytes, to mimic tensorflow::Allocator::kAllocatorAlignment. -static constexpr size_t kAlign = 32; +// Align to 64-bytes, to mimic tensorflow::Allocator::kAllocatorAlignment. +static constexpr size_t kAlign = 64; // aligned_buffer_bytes returns the sum of each size in `sizes`, skipping -1 // values. There are `n` entries in `sizes`. Each buffer is aligned to kAlign diff --git a/tensorflow/compiler/aot/runtime_test.cc b/tensorflow/compiler/aot/runtime_test.cc index 6d603a02eb..06ec623eb2 100644 --- a/tensorflow/compiler/aot/runtime_test.cc +++ b/tensorflow/compiler/aot/runtime_test.cc @@ -24,7 +24,7 @@ namespace runtime { namespace { TEST(Runtime, AlignmentValue) { - // We've chosen 32 byte alignment for the tfcompile runtime to mimic the + // We've chosen 64 byte alignment for the tfcompile runtime to mimic the // regular tensorflow allocator, which was chosen to play nicely with Eigen. // The tfcompile runtime also has a requirement that comes from the xla // generated code, on the relation: buffer_size >= 16 ? 2 * sizeof(void*) : 8 @@ -39,13 +39,13 @@ TEST(Runtime, AlignedBufferBytes) { EXPECT_EQ(aligned_buffer_bytes(sizesA, 1), 0); static constexpr intptr_t sizesB[1] = {3}; - EXPECT_EQ(aligned_buffer_bytes(sizesB, 1), 32); + EXPECT_EQ(aligned_buffer_bytes(sizesB, 1), 64); static constexpr intptr_t sizesC[1] = {32}; - EXPECT_EQ(aligned_buffer_bytes(sizesC, 1), 32); + EXPECT_EQ(aligned_buffer_bytes(sizesC, 1), 64); static constexpr intptr_t sizesD[7] = {1, -1, 32, -1, 64, 2, 3}; - EXPECT_EQ(aligned_buffer_bytes(sizesD, 7), 192); + EXPECT_EQ(aligned_buffer_bytes(sizesD, 7), 320); } void* add_ptr(void* base, uintptr_t delta) { @@ -101,11 +101,11 @@ TEST(Runtime, MallocFreeContiguousBuffers) { EXPECT_NE(base, nullptr); EXPECT_EQ(bufD[0], add_ptr(base, 0)); EXPECT_EQ(bufD[1], nullptr); - EXPECT_EQ(bufD[2], add_ptr(base, 32)); + EXPECT_EQ(bufD[2], add_ptr(base, 64)); EXPECT_EQ(bufD[3], nullptr); - EXPECT_EQ(bufD[4], add_ptr(base, 64)); - EXPECT_EQ(bufD[5], add_ptr(base, 128)); - EXPECT_EQ(bufD[6], add_ptr(base, 160)); + EXPECT_EQ(bufD[4], add_ptr(base, 128)); + EXPECT_EQ(bufD[5], add_ptr(base, 192)); + EXPECT_EQ(bufD[6], add_ptr(base, 256)); for (int i = 0; i < 7; ++i) { const intptr_t size = sizesD[i]; if (size != -1) { diff --git a/tensorflow/compiler/xla/service/cpu/BUILD b/tensorflow/compiler/xla/service/cpu/BUILD index d82922a359..1067b38f93 100644 --- a/tensorflow/compiler/xla/service/cpu/BUILD +++ b/tensorflow/compiler/xla/service/cpu/BUILD @@ -178,6 +178,7 @@ cc_library( ":runtime_matmul", ":runtime_matmul_mkl", ":runtime_single_threaded_conv2d", + ":runtime_single_threaded_fft", ":runtime_single_threaded_matmul", "@llvm//:execution_engine", "@llvm//:core", @@ -516,7 +517,6 @@ cc_library( deps = [ "//tensorflow/compiler/xla:executable_run_options", "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/core:framework", "//tensorflow/core:framework_lite", "//third_party/eigen3", ], @@ -578,6 +578,22 @@ cc_library( ], ) +cc_library( + name = "runtime_single_threaded_fft", + srcs = [ + "runtime_fft_impl.h", + "runtime_single_threaded_fft.cc", + ], + hdrs = ["runtime_single_threaded_fft.h"], + copts = runtime_copts(), + visibility = ["//visibility:public"], + deps = [ + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/core:framework_lite", + "//third_party/eigen3", + ], +) + cc_library( name = "runtime_single_threaded_matmul", srcs = ["runtime_single_threaded_matmul.cc"], diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc b/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc index 215405f680..54c52bc08f 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc @@ -51,6 +51,8 @@ extern const char* const kEigenConvF16SymbolName = extern const char* const kEigenConvF32SymbolName = "__xla_cpu_runtime_EigenConvF32"; extern const char* const kEigenFftSymbolName = "__xla_cpu_runtime_EigenFft"; +extern const char* const kEigenSingleThreadedFftSymbolName = + "__xla_cpu_runtime_EigenSingleThreadedFft"; extern const char* const kEigenSingleThreadedMatMulF16SymbolName = "__xla_cpu_runtime_EigenSingleThreadedMatMulF16"; extern const char* const kEigenSingleThreadedMatMulF32SymbolName = diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime.h b/tensorflow/compiler/xla/service/cpu/cpu_runtime.h index 1dce6efa5c..aa0e967123 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_runtime.h +++ b/tensorflow/compiler/xla/service/cpu/cpu_runtime.h @@ -52,6 +52,7 @@ extern const char* const kMKLSingleThreadedMatMulF64SymbolName; extern const char* const kEigenConvF16SymbolName; extern const char* const kEigenConvF32SymbolName; extern const char* const kEigenFftSymbolName; +extern const char* const kEigenSingleThreadedFftSymbolName; extern const char* const kEigenSingleThreadedMatMulF16SymbolName; extern const char* const kEigenSingleThreadedMatMulF32SymbolName; extern const char* const kEigenSingleThreadedMatMulF64SymbolName; diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc index 2c20be155f..758b8c62b4 100644 --- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc +++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc @@ -1172,7 +1172,13 @@ Status IrEmitter::HandleFft(HloInstruction* fft) { {int8_ptr_type, int8_ptr_type, int8_ptr_type, int32_type, int32_type, int64_type, int64_type, int64_type, int64_type}, /*isVarArg=*/false); - const char* fn_name = runtime::kEigenFftSymbolName; + + bool multi_threaded_eigen = + hlo_module_config_.debug_options().xla_cpu_multi_thread_eigen(); + const char* fn_name = multi_threaded_eigen + ? runtime::kEigenFftSymbolName + : runtime::kEigenSingleThreadedFftSymbolName; + llvm::Function* fft_func = llvm::cast( module_->getOrInsertFunction(fn_name, fft_type)); fft_func->setCallingConv(llvm::CallingConv::C); diff --git a/tensorflow/compiler/xla/service/cpu/runtime_fft_impl.h b/tensorflow/compiler/xla/service/cpu/runtime_fft_impl.h index 984cb0616e..0bf693edd0 100644 --- a/tensorflow/compiler/xla/service/cpu/runtime_fft_impl.h +++ b/tensorflow/compiler/xla/service/cpu/runtime_fft_impl.h @@ -21,8 +21,6 @@ limitations under the License. #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/framework/numeric_types.h" -#include "tensorflow/core/framework/tensor.h" -#include "tensorflow/core/framework/tensor_shape.h" #include "tensorflow/core/platform/types.h" // 'tensorflow' namespace is used so that int64 and other types don't require @@ -71,11 +69,9 @@ void EigenFftR2C(const EigenDevice& device, complex64* out, float* operand, in_dims[0] = input_batch; Eigen::DSizes out_dims; out_dims[0] = input_batch; - TensorShape temp_shape{input_batch}; for (int i = 0; i < FFTRank; i++) { in_dims[i + 1] = fft_shape[i]; out_dims[i + 1] = i == FFTRank - 1 ? fft_shape[i] / 2 + 1 : fft_shape[i]; - temp_shape.AddDim(fft_shape[i]); } const Eigen::TensorMap, Eigen::Aligned> @@ -88,8 +84,8 @@ void EigenFftR2C(const EigenDevice& device, complex64* out, float* operand, const auto axes = Eigen::ArrayXi::LinSpaced(FFTRank, 1, FFTRank); // Compute the full FFT using a temporary tensor. - Tensor temp(DataTypeToEnum::v(), temp_shape); - auto full_fft = temp.flat_inner_dims(); + Eigen::Tensor full_fft(in_dims); + const Eigen::DSizes zero_start_indices; full_fft.device(device) = input.template fft(axes); @@ -112,11 +108,9 @@ void EigenFftC2R(const EigenDevice& device, float* out, complex64* operand, in_dims[0] = input_batch; Eigen::DSizes out_dims; out_dims[0] = input_batch; - TensorShape temp_shape{input_batch}; for (int i = 0; i < FFTRank; i++) { in_dims[i + 1] = i == FFTRank - 1 ? fft_shape[i] / 2 + 1 : fft_shape[i]; out_dims[i + 1] = fft_shape[i]; - temp_shape.AddDim(fft_shape[i]); } const Eigen::TensorMap, Eigen::Aligned> @@ -129,8 +123,7 @@ void EigenFftC2R(const EigenDevice& device, float* out, complex64* operand, // region we will slice from input given fft_shape. We slice input to // fft_shape on its inner-most dimensions, except the last (which we // slice to fft_shape[-1] / 2 + 1). - Tensor temp(DataTypeToEnum::v(), temp_shape); - auto full_fft = temp.flat_inner_dims(); + Eigen::Tensor full_fft(out_dims); // Calculate the starting point and range of the source of // negative frequency part. @@ -179,7 +172,6 @@ template void EigenFftWithRank(const EigenDevice& device, void* out, void* operand, int32 fft_type, int64 input_batch, int64 fft_length0, int64 fft_length1, int64 fft_length2) { - CHECK(::xla::FftType_IsValid(fft_type)) << fft_type; switch (fft_type) { case ::xla::FftType::FFT: EigenFftC2C( @@ -204,7 +196,8 @@ void EigenFftWithRank(const EigenDevice& device, void* out, void* operand, input_batch, fft_length0, fft_length1, fft_length2); break; default: - LOG(FATAL) << "Unsupported FFT type: " << fft_type; + // Unsupported FFT type + abort(); } } @@ -230,7 +223,8 @@ void EigenFftImpl(const EigenDevice& device, void* out, void* operand, fft_length1, fft_length2); break; default: - LOG(FATAL) << "Unsupported FFT rank " << fft_rank; + // Unsupported FFT rank + abort(); } } diff --git a/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.cc b/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.cc new file mode 100644 index 0000000000..2613ddb127 --- /dev/null +++ b/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.cc @@ -0,0 +1,32 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.h" + +#include "tensorflow/compiler/xla/service/cpu/runtime_fft_impl.h" +#include "tensorflow/core/platform/dynamic_annotations.h" +#include "tensorflow/core/platform/types.h" + +using tensorflow::int32; +using tensorflow::int64; + +TF_ATTRIBUTE_NO_SANITIZE_MEMORY void __xla_cpu_runtime_EigenSingleThreadedFft( + const void* run_options_ptr, void* out, void* operand, int32 fft_type, + int32 fft_rank, int64 input_batch, int64 fft_length0, int64 fft_length1, + int64 fft_length2) { + tensorflow::xla::EigenFftImpl(Eigen::DefaultDevice(), out, operand, fft_type, + fft_rank, input_batch, fft_length0, fft_length1, + fft_length2); +} diff --git a/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.h b/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.h new file mode 100644 index 0000000000..dcd133d012 --- /dev/null +++ b/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.h @@ -0,0 +1,31 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_CPU_RUNTIME_SINGLE_THREADED_FFT_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_CPU_RUNTIME_SINGLE_THREADED_FFT_H_ + +#include "tensorflow/core/platform/types.h" + +extern "C" { + +extern void __xla_cpu_runtime_EigenSingleThreadedFft( + const void* /* xla::ExecutableRunOptions* */ run_options_ptr, void* out, + void* operand, tensorflow::int32 fft_type, tensorflow::int32 fft_rank, + tensorflow::int64 input_batch, tensorflow::int64 fft_length0, + tensorflow::int64 fft_length1, tensorflow::int64 fft_length2); + +} // extern "C" + +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_CPU_RUNTIME_SINGLE_THREADED_FFT_H_ diff --git a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc index 8d8c5e4c44..c4c90515ac 100644 --- a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc +++ b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc @@ -38,6 +38,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/cpu/runtime_matmul.h" #include "tensorflow/compiler/xla/service/cpu/runtime_matmul_mkl.h" #include "tensorflow/compiler/xla/service/cpu/runtime_single_threaded_conv2d.h" +#include "tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.h" #include "tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.h" #include "tensorflow/compiler/xla/service/cpu/windows_compatibility.h" #include "tensorflow/compiler/xla/types.h" @@ -202,6 +203,7 @@ bool RegisterKnownJITSymbols() { REGISTER_CPU_RUNTIME_SYMBOL(MKLSingleThreadedMatMulF64); REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedConvF16); REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedConvF32); + REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedFft); REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedMatMulF16); REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedMatMulF32); REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedMatMulF64); diff --git a/tensorflow/compiler/xla/service/pattern_matcher.h b/tensorflow/compiler/xla/service/pattern_matcher.h index d3bc47e61e..2515222cf2 100644 --- a/tensorflow/compiler/xla/service/pattern_matcher.h +++ b/tensorflow/compiler/xla/service/pattern_matcher.h @@ -204,7 +204,7 @@ class LayoutPattern { // Modifies the pattern to match only if the layout equals the given proto. // The layout must outlive the returned pattern. constexpr LayoutPattern> EqualTo( - const Layout* layout) const { + const ::xla::Layout* layout) const { return LayoutPattern>( LayoutPatternEqualImpl(impl_, layout), matched_layout_); } diff --git a/tensorflow/compiler/xla/service/tuple_simplifier.cc b/tensorflow/compiler/xla/service/tuple_simplifier.cc index e536c8afbf..77bdcc9de0 100644 --- a/tensorflow/compiler/xla/service/tuple_simplifier.cc +++ b/tensorflow/compiler/xla/service/tuple_simplifier.cc @@ -30,10 +30,17 @@ limitations under the License. namespace xla { +TupleSimplifier::TupleSimplifier(bool exclude_entry_computation) : + exclude_entry_computation_(exclude_entry_computation) {} + StatusOr TupleSimplifier::Run(HloModule* module) { // Initially add all GTE and Tuple instructions to the worklist. std::queue worklist; for (auto* computation : module->computations()) { + if (exclude_entry_computation_ && + computation == module->entry_computation()) { + continue; + } for (auto* instruction : computation->instructions()) { if (instruction->opcode() == HloOpcode::kTuple || instruction->opcode() == HloOpcode::kGetTupleElement) { diff --git a/tensorflow/compiler/xla/service/tuple_simplifier.h b/tensorflow/compiler/xla/service/tuple_simplifier.h index e5e9b10b5b..7509501883 100644 --- a/tensorflow/compiler/xla/service/tuple_simplifier.h +++ b/tensorflow/compiler/xla/service/tuple_simplifier.h @@ -27,13 +27,20 @@ namespace xla { // the module. class TupleSimplifier : public HloPassInterface { public: - TupleSimplifier() {} + TupleSimplifier() : TupleSimplifier(/*exclude_entry_computation=*/false) {} + explicit TupleSimplifier(bool exclude_entry_computation); ~TupleSimplifier() override {} tensorflow::StringPiece name() const override { return "tuple-simplifier"; } // Run tuple simplification on the given computation. Returns whether the // computation was changed. StatusOr Run(HloModule* module) override; + + private: + // When set, this pipeline stage will perform optimization of all computations + // apart from the module's entry computation. This is used by Graphcore's + // backend. + bool exclude_entry_computation_; }; } // namespace xla diff --git a/tensorflow/compiler/xla/service/tuple_simplifier_test.cc b/tensorflow/compiler/xla/service/tuple_simplifier_test.cc index ca9ae91281..d3635eae81 100644 --- a/tensorflow/compiler/xla/service/tuple_simplifier_test.cc +++ b/tensorflow/compiler/xla/service/tuple_simplifier_test.cc @@ -42,6 +42,12 @@ class TupleSimplifierTest : public HloTestBase { TF_ASSERT_OK(changed_status.status()); EXPECT_EQ(change_expected, changed_status.ValueOrDie()); } + void Run(HloModule* module, bool change_expected, bool exclude_entry) { + TupleSimplifier simplifier(exclude_entry); + auto changed_status = simplifier.Run(module); + TF_ASSERT_OK(changed_status.status()); + EXPECT_EQ(change_expected, changed_status.ValueOrDie()); + } const Shape scalar_shape_ = ShapeUtil::MakeShape(F32, {}); const Shape tuple_shape_ = ShapeUtil::MakeTupleShape( @@ -211,5 +217,76 @@ TEST_F(TupleSimplifierTest, IncompatibleTuples) { EXPECT_THAT(computation->root_instruction(), tuple); } +TEST_F(TupleSimplifierTest, CanExcludeEntryComputation) { + // Verify that the root computation can be excluded + auto module = CreateNewModule(); + + HloInstruction* p0; + HloInstruction* p1; + HloComputation* c0; + HloComputation* c1; + HloComputation* entry; + + { + HloComputation::Builder builder(TestName() + "_1"); + p0 = builder.AddInstruction( + HloInstruction::CreateParameter(0, tuple_shape_, "param")); + HloInstruction* gte0 = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(scalar_shape_, p0, 0)); + HloInstruction* gte1 = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(scalar_shape_, p0, 1)); + HloInstruction* gte2 = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(scalar_shape_, p0, 2)); + + builder.AddInstruction(HloInstruction::CreateTuple({gte0, gte1, gte2})); + + c0 = module->AddEmbeddedComputation(builder.Build()); + } + { + HloComputation::Builder builder(TestName() + "_2"); + p1 = builder.AddInstruction( + HloInstruction::CreateParameter(0, tuple_shape_, "param")); + HloInstruction* gte0 = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(scalar_shape_, p1, 0)); + HloInstruction* gte1 = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(scalar_shape_, p1, 1)); + HloInstruction* gte2 = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(scalar_shape_, p1, 2)); + + builder.AddInstruction(HloInstruction::CreateTuple({gte0, gte1, gte2})); + + c1 = module->AddEmbeddedComputation(builder.Build()); + } + { + HloComputation::Builder builder(TestName() + "_Entry"); + HloInstruction* tuple_param = builder.AddInstruction( + HloInstruction::CreateParameter(0, tuple_shape_, "param")); + HloInstruction* call0 = builder.AddInstruction( + HloInstruction::CreateCall(tuple_shape_, {tuple_param}, c0)); + HloInstruction* call1 = builder.AddInstruction( + HloInstruction::CreateCall(tuple_shape_, {tuple_param}, c1)); + HloInstruction* gte0 = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(scalar_shape_, call0, 0)); + HloInstruction* gte1 = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(scalar_shape_, call1, 1)); + HloInstruction* tuple0 = + builder.AddInstruction(HloInstruction::CreateTuple({gte0, gte1})); + HloInstruction* gte2 = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(scalar_shape_, tuple0, 0)); + HloInstruction* gte3 = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(scalar_shape_, tuple0, 1)); + + builder.AddInstruction(HloInstruction::CreateTuple({gte2, gte3})); + + entry = module->AddEntryComputation(builder.Build()); + } + + Run(module.get(), /*change_expected=*/true, /*exclude_entry=*/ true); + + EXPECT_THAT(c0->root_instruction(), p0); + EXPECT_THAT(c1->root_instruction(), p1); + EXPECT_THAT(entry->instruction_count(), 9); +} + } // namespace } // namespace xla diff --git a/tensorflow/contrib/autograph/__init__.py b/tensorflow/contrib/autograph/__init__.py index 637e49c082..dbdbad8f4c 100644 --- a/tensorflow/contrib/autograph/__init__.py +++ b/tensorflow/contrib/autograph/__init__.py @@ -23,6 +23,7 @@ from __future__ import print_function # TODO(mdan): Bring only the relevant symbols to the top level. from tensorflow.contrib.autograph import utils +from tensorflow.contrib.autograph import operators from tensorflow.contrib.autograph.impl.api import convert from tensorflow.contrib.autograph.impl.api import converted_call from tensorflow.contrib.autograph.impl.api import do_not_convert @@ -43,6 +44,8 @@ _allowed_symbols = [ 'do_not_convert', 'to_code', 'to_graph', + # Overloaded operators + 'operators', # Special functions and directives 'set_element_type', 'set_loop_options', diff --git a/tensorflow/contrib/cmake/tf_c.cmake b/tensorflow/contrib/cmake/tf_c.cmake index bda5e26f43..2e0a2fcef4 100644 --- a/tensorflow/contrib/cmake/tf_c.cmake +++ b/tensorflow/contrib/cmake/tf_c.cmake @@ -37,13 +37,15 @@ add_dependencies( tf_core_lib tf_protos_cc) -add_library(tf_c_python_api OBJECT - "${tensorflow_source_dir}/tensorflow/c/python_api.cc" - "${tensorflow_source_dir}/tensorflow/c/python_api.h" -) -add_dependencies( - tf_c_python_api - tf_c - tf_core_lib - tf_core_framework - tf_protos_cc) +if(tensorflow_BUILD_PYTHON_BINDINGS) + add_library(tf_c_python_api OBJECT + "${tensorflow_source_dir}/tensorflow/c/python_api.cc" + "${tensorflow_source_dir}/tensorflow/c/python_api.h" + ) + add_dependencies( + tf_c_python_api + tf_c + tf_core_lib + tf_core_framework + tf_protos_cc) +endif() diff --git a/tensorflow/contrib/cmake/tf_cc_ops.cmake b/tensorflow/contrib/cmake/tf_cc_ops.cmake index f73da0b8ab..6c90cf398c 100644 --- a/tensorflow/contrib/cmake/tf_cc_ops.cmake +++ b/tensorflow/contrib/cmake/tf_cc_ops.cmake @@ -155,7 +155,7 @@ if (WIN32) set (pywrap_tensorflow_lib "${CMAKE_CURRENT_BINARY_DIR}/pywrap_tensorflow_internal.lib") endif() else (WIN32) - set (pywrap_tensorflow_lib "${CMAKE_CURRENT_BINARY_DIR}/libpywrap_tensorflow_internal.so") + set (pywrap_tensorflow_lib "${CMAKE_CURRENT_BINARY_DIR}/libpywrap_tensorflow_internal${CMAKE_SHARED_LIBRARY_SUFFIX}") endif (WIN32) add_custom_target(tf_extension_ops) diff --git a/tensorflow/contrib/cmake/tf_python.cmake b/tensorflow/contrib/cmake/tf_python.cmake index a0c3ddd28b..9244604489 100755 --- a/tensorflow/contrib/cmake/tf_python.cmake +++ b/tensorflow/contrib/cmake/tf_python.cmake @@ -715,7 +715,7 @@ if(WIN32) endif() else() add_custom_command(TARGET pywrap_tensorflow_internal POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_BINARY_DIR}/libpywrap_tensorflow_internal.so + COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_BINARY_DIR}/libpywrap_tensorflow_internal${CMAKE_SHARED_LIBRARY_SUFFIX} ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/python/_pywrap_tensorflow_internal.so) endif() @@ -832,7 +832,6 @@ add_custom_command(TARGET tf_python_build_pip_package POST_BUILD add_custom_command(TARGET tf_python_copy_scripts_to_destination PRE_BUILD COMMAND ${CMAKE_COMMAND} -E copy ${tensorflow_source_dir}/tensorflow/contrib/testing/python/framework/util_test.py ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/testing/python/framework/) - add_custom_command(TARGET tf_python_build_pip_package POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy ${tensorflow_source_dir}/tensorflow/tools/pip_package/README ${CMAKE_CURRENT_BINARY_DIR}/tf_python/) diff --git a/tensorflow/contrib/cmake/tools/create_def_file.py b/tensorflow/contrib/cmake/tools/create_def_file.py index cffe069aa3..4f957f1e0b 100644 --- a/tensorflow/contrib/cmake/tools/create_def_file.py +++ b/tensorflow/contrib/cmake/tools/create_def_file.py @@ -44,7 +44,8 @@ UNDNAME = "undname.exe" DUMPBIN = "dumpbin.exe" # Exclude if matched -EXCLUDE_RE = re.compile(r"RTTI|deleting destructor|::internal::") +EXCLUDE_RE = re.compile(r"RTTI|deleting destructor|::internal::|Internal|" + r"python_op_gen_internal|grappler") # Include if matched before exclude INCLUDEPRE_RE = re.compile(r"google::protobuf::internal::ExplicitlyConstructed|" @@ -56,6 +57,10 @@ INCLUDEPRE_RE = re.compile(r"google::protobuf::internal::ExplicitlyConstructed|" r"tensorflow::ops::internal::Enter|" r"tensorflow::strings::internal::AppendPieces|" r"tensorflow::strings::internal::CatPieces|" + r"tensorflow::errors::Internal|" + r"tensorflow::Tensor::CopyFromInternal|" + r"tensorflow::kernel_factory::" + r"OpKernelRegistrar::InitInternal|" r"tensorflow::io::internal::JoinPathImpl") # Include if matched after exclude @@ -64,7 +69,7 @@ INCLUDE_RE = re.compile(r"^(TF_\w*)$|" r"tensorflow::|" r"functor::|" r"\?nsync_|" - r"perftools::gputools") + r"stream_executor::") # We want to identify data members explicitly in the DEF file, so that no one # can implicitly link against the DLL if they use one of the variables exported diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/sinh_arcsinh_bijector_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/sinh_arcsinh_bijector_test.py index 45760a29ee..795f1993ba 100644 --- a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/sinh_arcsinh_bijector_test.py +++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/sinh_arcsinh_bijector_test.py @@ -151,16 +151,24 @@ class SinhArcsinhBijectorTest(test.TestCase): self.assertAllClose(y, bijector.forward(x).eval(), rtol=1e-4, atol=0.) self.assertAllClose(x, bijector.inverse(y).eval(), rtol=1e-4, atol=0.) - # Do the numpy calculation in float128 to avoid inf/nan. - y_float128 = np.float128(y) - self.assertAllClose( - np.log(np.cosh( - np.arcsinh(y_float128) / tailweight - skewness) / np.sqrt( - y_float128**2 + 1)) - - np.log(tailweight), - bijector.inverse_log_det_jacobian(y, event_ndims=0).eval(), - rtol=1e-4, - atol=0.) + # On IBM PPC systems, longdouble (np.float128) is same as double except that it can have more precision. + # Type double being of 8 bytes, can't hold square of max of float64 (which is also 8 bytes) and + # below test fails due to overflow error giving inf. So this check avoids that error by skipping square + # calculation and corresponding assert. + + if np.amax(y) <= np.sqrt(np.finfo(np.float128).max) and \ + np.fabs(np.amin(y)) <= np.sqrt(np.fabs(np.finfo(np.float128).min)): + + # Do the numpy calculation in float128 to avoid inf/nan. + y_float128 = np.float128(y) + self.assertAllClose( + np.log(np.cosh( + np.arcsinh(y_float128) / tailweight - skewness) / np.sqrt( + y_float128**2 + 1)) - + np.log(tailweight), + bijector.inverse_log_det_jacobian(y, event_ndims=0).eval(), + rtol=1e-4, + atol=0.) self.assertAllClose( -bijector.inverse_log_det_jacobian(y, event_ndims=0).eval(), bijector.forward_log_det_jacobian(x, event_ndims=0).eval(), diff --git a/tensorflow/contrib/eager/python/datasets.py b/tensorflow/contrib/eager/python/datasets.py index d7909dd5a2..adf92c27ea 100644 --- a/tensorflow/contrib/eager/python/datasets.py +++ b/tensorflow/contrib/eager/python/datasets.py @@ -106,7 +106,8 @@ class Iterator(iterator_ops.EagerIterator, checkpointable.CheckpointableBase): target_device=target, buffer_size=10, container="", - shared_name=_generate_shared_name("function_buffer_resource")) + shared_name=_generate_shared_name( + "contrib_eager_iterator_function_buffer_resource")) self._buffer_resource_deleter = resource_variable_ops.EagerResourceDeleter( # pylint: disable=line-too-long handle=self._buffer_resource_handle, handle_device=self._device) diff --git a/tensorflow/contrib/eager/python/examples/notebooks/4_high_level.ipynb b/tensorflow/contrib/eager/python/examples/notebooks/4_high_level.ipynb index 4fe3a0e3f3..5749f22ac5 100644 --- a/tensorflow/contrib/eager/python/examples/notebooks/4_high_level.ipynb +++ b/tensorflow/contrib/eager/python/examples/notebooks/4_high_level.ipynb @@ -68,7 +68,7 @@ "# simply construct the object. Most layers take as a first argument the number\n", "# of output dimensions / channels.\n", "layer = tf.keras.layers.Dense(100)\n", - "# The number of input dimensionss is often unnecessary, as it can be inferred\n", + "# The number of input dimensions is often unnecessary, as it can be inferred\n", "# the first time the layer is used, but it can be provided if you want to \n", "# specify it manually, which is useful in some complex models.\n", "layer = tf.keras.layers.Dense(10, input_shape=(None, 5))" @@ -267,7 +267,7 @@ " * `build`, where you know the shapes of the input tensors and can do the rest of the initialization\n", " * `call`, where you do the forward computation\n", "\n", - "Note that you don't have to wait until `build` is called to create your variables, you can also create them in `__init__`. However, the advantage of creating them in `build` is that it enables late variable creation based on the shape of the inputs the layer will operate on. On the other hand, creating variables in `__init__` would mean that shapes requires to create the variables will need to be explicitly specified." + "Note that you don't have to wait until `build` is called to create your variables, you can also create them in `__init__`. However, the advantage of creating them in `build` is that it enables late variable creation based on the shape of the inputs the layer will operate on. On the other hand, creating variables in `__init__` would mean that shapes required to create the variables will need to be explicitly specified." ] }, { diff --git a/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py index 84a413c791..05bcdac2ca 100644 --- a/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py +++ b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py @@ -346,7 +346,8 @@ def sequence_numeric_column( key, shape=(1,), default_value=0., - dtype=dtypes.float32): + dtype=dtypes.float32, + normalizer_fn=None): """Returns a feature column that represents sequences of numeric data. Example: @@ -370,6 +371,12 @@ def sequence_numeric_column( default_value: A single value compatible with `dtype` that is used for padding the sparse data into a dense `Tensor`. dtype: The type of values. + normalizer_fn: If not `None`, a function that can be used to normalize the + value of the tensor after `default_value` is applied for parsing. + Normalizer function takes the input `Tensor` as its argument, and returns + the output `Tensor`. (e.g. lambda x: (x - 3.0) / 4.2). Please note that + even though the most common use case of this function is normalization, it + can be used for any kind of Tensorflow transformations. Returns: A `_SequenceNumericColumn`. @@ -383,12 +390,16 @@ def sequence_numeric_column( if not (dtype.is_integer or dtype.is_floating): raise ValueError('dtype must be convertible to float. ' 'dtype: {}, key: {}'.format(dtype, key)) + if normalizer_fn is not None and not callable(normalizer_fn): + raise TypeError( + 'normalizer_fn must be a callable. Given: {}'.format(normalizer_fn)) return _SequenceNumericColumn( key, shape=shape, default_value=default_value, - dtype=dtype) + dtype=dtype, + normalizer_fn=normalizer_fn) def _assert_all_equal_and_return(tensors, name=None): @@ -407,7 +418,7 @@ class _SequenceNumericColumn( fc._SequenceDenseColumn, collections.namedtuple( '_SequenceNumericColumn', - ['key', 'shape', 'default_value', 'dtype'])): + ['key', 'shape', 'default_value', 'dtype', 'normalizer_fn'])): """Represents sequences of numeric data.""" @property @@ -419,7 +430,10 @@ class _SequenceNumericColumn( return {self.key: parsing_ops.VarLenFeature(self.dtype)} def _transform_feature(self, inputs): - return inputs.get(self.key) + input_tensor = inputs.get(self.key) + if self.normalizer_fn is not None: + input_tensor = self.normalizer_fn(input_tensor) + return input_tensor @property def _variable_shape(self): diff --git a/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py index ee74cf56dc..45d7b74046 100644 --- a/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py +++ b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py @@ -28,6 +28,7 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor +from tensorflow.python.ops import sparse_ops from tensorflow.python.platform import test from tensorflow.python.training import monitored_session @@ -947,6 +948,7 @@ class SequenceNumericColumnTest(test.TestCase): self.assertEqual((1,), a.shape) self.assertEqual(0., a.default_value) self.assertEqual(dtypes.float32, a.dtype) + self.assertIsNone(a.normalizer_fn) def test_shape_saved_as_tuple(self): a = sfc.sequence_numeric_column('aaa', shape=[1, 2]) @@ -965,6 +967,10 @@ class SequenceNumericColumnTest(test.TestCase): ValueError, 'dtype must be convertible to float'): sfc.sequence_numeric_column('aaa', dtype=dtypes.string) + def test_normalizer_fn_must_be_callable(self): + with self.assertRaisesRegexp(TypeError, 'must be a callable'): + sfc.sequence_numeric_column('aaa', normalizer_fn='NotACallable') + def test_get_sequence_dense_tensor(self): sparse_input = sparse_tensor.SparseTensorValue( # example 0, values [[0.], [1]] @@ -985,6 +991,41 @@ class SequenceNumericColumnTest(test.TestCase): self.assertAllEqual( expected_dense_tensor, dense_tensor.eval(session=sess)) + def test_get_sequence_dense_tensor_with_normalizer_fn(self): + + def _increment_two(input_sparse_tensor): + return sparse_ops.sparse_add( + input_sparse_tensor, + sparse_tensor.SparseTensor(((0, 0), (1, 1)), (2.0, 2.0), (2, 2)) + ) + + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, values [[0.], [1]] + # example 1, [[10.]] + indices=((0, 0), (0, 1), (1, 0)), + values=(0., 1., 10.), + dense_shape=(2, 2)) + + # Before _increment_two: + # [[0.], [1.]], + # [[10.], [0.]], + # After _increment_two: + # [[2.], [1.]], + # [[10.], [2.]], + expected_dense_tensor = [ + [[2.], [1.]], + [[10.], [2.]], + ] + numeric_column = sfc.sequence_numeric_column( + 'aaa', normalizer_fn=_increment_two) + + dense_tensor, _ = numeric_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual( + expected_dense_tensor, dense_tensor.eval(session=sess)) + def test_get_sequence_dense_tensor_with_shape(self): """Tests get_sequence_dense_tensor with shape !=(1,).""" sparse_input = sparse_tensor.SparseTensorValue( diff --git a/tensorflow/contrib/ffmpeg/__init__.py b/tensorflow/contrib/ffmpeg/__init__.py index daba965a98..484ffee3e7 100644 --- a/tensorflow/contrib/ffmpeg/__init__.py +++ b/tensorflow/contrib/ffmpeg/__init__.py @@ -28,7 +28,6 @@ from __future__ import print_function from tensorflow.contrib.ffmpeg.ffmpeg_ops import decode_audio from tensorflow.contrib.ffmpeg.ffmpeg_ops import decode_video from tensorflow.contrib.ffmpeg.ffmpeg_ops import encode_audio -from tensorflow.contrib.ffmpeg.ffmpeg_ops import decode_video from tensorflow.python.util.all_util import remove_undocumented diff --git a/tensorflow/contrib/ffmpeg/ffmpeg_ops.py b/tensorflow/contrib/ffmpeg/ffmpeg_ops.py index 020b5c99c6..b1b5126d9e 100644 --- a/tensorflow/contrib/ffmpeg/ffmpeg_ops.py +++ b/tensorflow/contrib/ffmpeg/ffmpeg_ops.py @@ -21,7 +21,6 @@ from __future__ import print_function from tensorflow.contrib.ffmpeg.ops import gen_decode_audio_op_py from tensorflow.contrib.ffmpeg.ops import gen_decode_video_op_py from tensorflow.contrib.ffmpeg.ops import gen_encode_audio_op_py -from tensorflow.contrib.ffmpeg.ops import gen_decode_video_op_py from tensorflow.contrib.util import loader from tensorflow.python.framework import ops from tensorflow.python.platform import resource_loader diff --git a/tensorflow/contrib/framework/__init__.py b/tensorflow/contrib/framework/__init__.py index 10d1ecc738..dc49383c5c 100644 --- a/tensorflow/contrib/framework/__init__.py +++ b/tensorflow/contrib/framework/__init__.py @@ -119,14 +119,13 @@ from tensorflow.python.framework.smart_cond import smart_cond from tensorflow.python.framework.smart_cond import smart_constant_value from tensorflow.python.framework.tensor_spec import BoundedTensorSpec from tensorflow.python.framework.tensor_spec import TensorSpec -from tensorflow.python.ops.array_ops import broadcast_to from tensorflow.python.ops.init_ops import convolutional_delta_orthogonal from tensorflow.python.ops.init_ops import convolutional_orthogonal_1d from tensorflow.python.ops.init_ops import convolutional_orthogonal_2d from tensorflow.python.ops.init_ops import convolutional_orthogonal_3d from tensorflow.python.util.all_util import remove_undocumented -_allowed_symbols = ['nest', 'broadcast_to'] +_allowed_symbols = ['nest'] _nest_allowed_symbols = [ 'assert_same_structure', 'is_sequence', diff --git a/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op_test.py b/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op_test.py index 65cb94b5a4..a955e21b72 100644 --- a/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op_test.py +++ b/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op_test.py @@ -301,8 +301,8 @@ class FusedConv2DBiasActivationTest(test.TestCase): conv = tensors[i] value = values[i] ref_value = ref_values[i] - print("expected = ", ref_value) - print("actual = ", value) + tf_logging.info("expected = ", ref_value) + tf_logging.info("actual = ", value) tol = 1e-5 if value.dtype == np.float16: tol = 1e-3 @@ -843,7 +843,8 @@ class FusedConvInt8Tests(test.TestCase): vertical_stride, padding_type) output_width = CalculateConvolvedOutputDim(input_width, filter_width, horizontal_stride, padding_type) - print("output_height=", output_height, ", output_width=", output_width) + tf_logging.info("output_height=", output_height, ", output_width=", + output_width) side_input, _, _ = gen_array_ops.quantize_v2( random_ops.random_uniform( @@ -880,8 +881,8 @@ class FusedConvInt8Tests(test.TestCase): with self.test_session( use_gpu=True, config=NoMemoryOptimizationConfig()) as sess: actual_y, expected_y = sess.run([actual, expected]) - print("actual_y = ", actual_y) - print("expected_y = ", expected_y) + tf_logging.info("actual_y = ", actual_y) + tf_logging.info("expected_y = ", expected_y) self.assertTrue(np.array_equal(actual_y, expected_y)) def testFusedConvInt8(self): diff --git a/tensorflow/contrib/hvx/hexagon_controller/src_impl/hexagon_controller.c b/tensorflow/contrib/hvx/hexagon_controller/src_impl/hexagon_controller.c index 6a5d982dc8..2e5c84704f 100644 --- a/tensorflow/contrib/hvx/hexagon_controller/src_impl/hexagon_controller.c +++ b/tensorflow/contrib/hvx/hexagon_controller/src_impl/hexagon_controller.c @@ -19,7 +19,7 @@ limitations under the License. #include "hexagon_controller.h" -#include +#include #include #include "adspmsgd.h" diff --git a/tensorflow/contrib/lite/download_dependencies.sh b/tensorflow/contrib/lite/download_dependencies.sh index 436c3e1d4c..840015a7fa 100755 --- a/tensorflow/contrib/lite/download_dependencies.sh +++ b/tensorflow/contrib/lite/download_dependencies.sh @@ -30,9 +30,7 @@ if [ ! -f $BZL_FILE_PATH ]; then fi EIGEN_URL="$(grep -o 'http.*bitbucket.org/eigen/eigen/get/.*tar\.gz' "${BZL_FILE_PATH}" | grep -v mirror.bazel | head -n1)" -# TODO (yongtang): Replace the following with 'https://mirror.bazel.build/github.com/google/gemmlowp/.*zip' once -# the archive has been propagated in mirror.bazel.build. -GEMMLOWP_URL="$(grep -o 'https://github.com/google/gemmlowp/.*zip' "${BZL_FILE_PATH}" | head -n1)" +GEMMLOWP_URL="$(grep -o 'https://mirror.bazel.build/github.com/google/gemmlowp/.*zip' "${BZL_FILE_PATH}" | head -n1)" GOOGLETEST_URL="https://github.com/google/googletest/archive/release-1.8.0.tar.gz" ABSL_URL="$(grep -o 'https://github.com/abseil/abseil-cpp/.*tar.gz' "${BZL_FILE_PATH}" | head -n1)" NEON_2_SSE_URL="https://github.com/intel/ARM_NEON_2_x86_SSE/archive/master.zip" diff --git a/tensorflow/contrib/lite/examples/minimal/minimal.cc b/tensorflow/contrib/lite/examples/minimal/minimal.cc index 106e3b0270..8b0ace96cc 100644 --- a/tensorflow/contrib/lite/examples/minimal/minimal.cc +++ b/tensorflow/contrib/lite/examples/minimal/minimal.cc @@ -38,7 +38,7 @@ using namespace tflite; int main(int argc, char *argv[]) { if(argc != 2) { - fprintf(stderr, "Usage: %s \n"); + fprintf(stderr, "minimal \n"); return 1; } const char* filename = argv[1]; diff --git a/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md b/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md index bb2e615eac..965273f0f0 100644 --- a/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md +++ b/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md @@ -128,7 +128,6 @@ TensorFlow operation not listed above are likely unsupported. Notably, the following common ops are not supported at the moment: * [tf.depth_to_space](https://www.tensorflow.org/api_docs/python/tf/depth_to_space) -* [tf.gather](https://www.tensorflow.org/api_docs/python/tf/gather) * [tf.image.resize_bilinear](https://www.tensorflow.org/api_docs/python/tf/image/resize_bilinear) * [tf.tanh](https://www.tensorflow.org/api_docs/python/tf/tanh) @@ -306,6 +305,19 @@ Options { } ``` +**GATHER** + +``` +Inputs { + 0: params tensor + 1: indices tensor + 2: axis tensor (optional) +} +Outputs { + 0: a tensor with same type as the params tensor. +} +``` + **GREATER** ``` diff --git a/tensorflow/contrib/lite/java/ovic/README.md b/tensorflow/contrib/lite/java/ovic/README.md index 5efa70987e..26349347fa 100644 --- a/tensorflow/contrib/lite/java/ovic/README.md +++ b/tensorflow/contrib/lite/java/ovic/README.md @@ -2,7 +2,7 @@ This folder contains building code for track one of the [Low Power ImageNet Recognition Challenge workshop at CVPR 2018.](https://rebootingcomputing.ieee.org/home/sitemap/14-lpirc/80-low-power-image-recognition-challenge-lpirc-2018) -## Pre-requesits +## Pre-requisite Follow the steps [here](https://www.tensorflow.org/mobile/tflite/demo_android) to install Tensorflow, Bazel, and the Android NDK and SDK. @@ -49,7 +49,7 @@ Once you have a submission that follows the instructions from the [competition s You can call the validator binary below to verify that your model fits the format requirements. This often helps you to catch size mismatches (e.g. output should be [1, 1001] instead of [1,1,1,1001]). Let say the submission file is located at `/path/to/my_model.lite`, then call: ```sh -bazel build --cxxopt--std=c++11 //tensorflow/contrib/lite/java/ovic:ovic_validator --cxxopt=-Wno-all +bazel build --cxxopt=--std=c++11 //tensorflow/contrib/lite/java/ovic:ovic_validator --cxxopt=-Wno-all bazel-bin/tensorflow/contrib/lite/java/ovic/ovic_validator /path/to/my_model.lite ``` diff --git a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h index a2f192bbc2..1908f7fa6c 100644 --- a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h @@ -1934,7 +1934,7 @@ inline void LstmCell(const float* input_data, const Dims<4>& input_dims, // The quantization of the input, output arrays is as follows: // - The input activations are quantized as uint8 on the interval // [-1, 127/128]. -// The rationale for that is that that is the natural interval for output +// The rationale for that is that is the natural interval for output // activations (see next point) and these need to be concatenated together. // We could accommodate different ranges by re-scaling, but we empirically // found that setting the input activations range to be [-1, 127/128] in the @@ -1999,7 +1999,7 @@ inline void LstmCell(const float* input_data, const Dims<4>& input_dims, // However, for a fixed-point implementation in 16-bit integers, using 5 // integer bits to represent the [-16, 16] range would leave only 11 // fractional bits, giving an increment of 2^-11 = 4.9e-4 between consecutive -// representable values. Notice that that is higher than the +// representable values. Notice that is higher than the // worst-case clamping error with clamping to [-8, 8]: 3.4e-4 for Logistic. // Using [-8, 8] thus seems like the better compromise overall, enjoying // an increment of 2.4e-4 between representable values and a worst-case diff --git a/tensorflow/contrib/lite/python/interpreter.py b/tensorflow/contrib/lite/python/interpreter.py index 9400e757b9..fd90823425 100644 --- a/tensorflow/contrib/lite/python/interpreter.py +++ b/tensorflow/contrib/lite/python/interpreter.py @@ -55,7 +55,7 @@ class Interpreter(object): elif model_content and not model_path: self._interpreter = ( _interpreter_wrapper.InterpreterWrapper_CreateWrapperCPPFromBuffer( - model_content, len(model_content))) + model_content)) if not self._interpreter: raise ValueError( 'Failed to create model from {} bytes'.format(len(model_content))) diff --git a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.cc b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.cc index f705551fcb..b283551c45 100644 --- a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.cc +++ b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.cc @@ -397,9 +397,14 @@ InterpreterWrapper* InterpreterWrapper::CreateWrapperCPPFromFile( } InterpreterWrapper* InterpreterWrapper::CreateWrapperCPPFromBuffer( - const char* data, size_t len) { + PyObject* data) { + char * buf = nullptr; + Py_ssize_t length; + if (PY_TO_CPPSTRING(data, &buf, &length) == -1) { + return nullptr; + } std::unique_ptr model = - tflite::FlatBufferModel::BuildFromBuffer(data, len); + tflite::FlatBufferModel::BuildFromBuffer(buf, length); return model ? new InterpreterWrapper(std::move(model)) : nullptr; } diff --git a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h index b0ed7c4559..cbeb53bee7 100644 --- a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h +++ b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h @@ -40,8 +40,7 @@ class InterpreterWrapper { static InterpreterWrapper* CreateWrapperCPPFromFile(const char* model_path); // SWIG caller takes ownership of pointer. - static InterpreterWrapper* CreateWrapperCPPFromBuffer(const char* data, - size_t len); + static InterpreterWrapper* CreateWrapperCPPFromBuffer(PyObject* data); ~InterpreterWrapper(); bool AllocateTensors(); diff --git a/tensorflow/contrib/lite/python/lite.py b/tensorflow/contrib/lite/python/lite.py index 0913cd2c5c..88dda7290b 100644 --- a/tensorflow/contrib/lite/python/lite.py +++ b/tensorflow/contrib/lite/python/lite.py @@ -34,6 +34,8 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from six import PY3 + from google.protobuf import text_format as _text_format from google.protobuf.message import DecodeError from tensorflow.contrib.lite.python import lite_constants as constants @@ -54,6 +56,7 @@ from tensorflow.python.framework.importer import import_graph_def from tensorflow.python.ops.variables import global_variables_initializer from tensorflow.python.saved_model import signature_constants from tensorflow.python.saved_model import tag_constants +# from tensorflow.python.util.all_util import remove_undocumented class TocoConverter(object): @@ -203,6 +206,12 @@ class TocoConverter(object): except (_text_format.ParseError, DecodeError): try: print("Ignore 'tcmalloc: large alloc' warnings.") + + if not isinstance(file_content, str): + if PY3: + file_content = file_content.decode('utf-8') + else: + file_content = file_content.encode('utf-8') _text_format.Merge(file_content, graph_def) except (_text_format.ParseError, DecodeError): raise ValueError( @@ -382,3 +391,5 @@ def _freeze_graph(sess, output_tensors): output_arrays) else: return sess.graph_def + +# remove_undocumented(__name__) diff --git a/tensorflow/contrib/lite/toco/import_tensorflow.cc b/tensorflow/contrib/lite/toco/import_tensorflow.cc index e33b430937..5c7fa09891 100644 --- a/tensorflow/contrib/lite/toco/import_tensorflow.cc +++ b/tensorflow/contrib/lite/toco/import_tensorflow.cc @@ -178,7 +178,7 @@ ArrayDataType ConvertDataType(tensorflow::DataType dtype) { else if (dtype == DT_STRING) return ArrayDataType::kString; else - LOG(INFO) << "Unsupported data type in placehoder op: " << dtype; + LOG(INFO) << "Unsupported data type in placeholder op: " << dtype; return ArrayDataType::kNone; } diff --git a/tensorflow/contrib/lite/toco/toco_port.cc b/tensorflow/contrib/lite/toco/toco_port.cc index 1b21c8bc60..de76fd4032 100644 --- a/tensorflow/contrib/lite/toco/toco_port.cc +++ b/tensorflow/contrib/lite/toco/toco_port.cc @@ -20,6 +20,12 @@ limitations under the License. #include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/platform/logging.h" +#if defined(__ANDROID__) && defined(__ARM_ARCH_7A__) +namespace std { +double round(double x) { return ::round(x); } +} // namespace std +#endif + namespace toco { namespace port { void CopyToBuffer(const string& src, char* dest) { diff --git a/tensorflow/contrib/lite/toco/toco_port.h b/tensorflow/contrib/lite/toco/toco_port.h index 5c019cb2bf..17f82b9dd7 100644 --- a/tensorflow/contrib/lite/toco/toco_port.h +++ b/tensorflow/contrib/lite/toco/toco_port.h @@ -34,6 +34,24 @@ limitations under the License. #define TFLITE_PROTO_NS google::protobuf #endif +#ifdef __ANDROID__ +#include +namespace std { + +template +std::string to_string(T value) +{ + std::ostringstream os ; + os << value ; + return os.str() ; +} + +#ifdef __ARM_ARCH_7A__ +double round(double x); +#endif +} +#endif + namespace toco { namespace port { diff --git a/tensorflow/contrib/makefile/compile_nsync.sh b/tensorflow/contrib/makefile/compile_nsync.sh index e8c6edd7ba..a28fc3a87f 100755 --- a/tensorflow/contrib/makefile/compile_nsync.sh +++ b/tensorflow/contrib/makefile/compile_nsync.sh @@ -270,7 +270,7 @@ for arch in $archs; do PLATFORM_LDFLAGS=-pthread MKDEP=${CC} -M -std=c++11 PLATFORM_C=../../platform/c++11/src/nsync_semaphore_mutex.cc \ - ../../platform/c++11/src/per_thread_waiter.cc \ + ../../platform/posix/src/per_thread_waiter.c \ ../../platform/c++11/src/yield.cc \ ../../platform/c++11/src/time_rep_timespec.cc \ ../../platform/c++11/src/nsync_panic.cc diff --git a/tensorflow/contrib/makefile/download_dependencies.sh b/tensorflow/contrib/makefile/download_dependencies.sh index eff9081e35..48953e2e38 100755 --- a/tensorflow/contrib/makefile/download_dependencies.sh +++ b/tensorflow/contrib/makefile/download_dependencies.sh @@ -27,9 +27,7 @@ if [ ! -f $BZL_FILE_PATH ]; then fi EIGEN_URL="$(grep -o 'http.*bitbucket.org/eigen/eigen/get/.*tar\.gz' "${BZL_FILE_PATH}" | grep -v mirror.bazel | head -n1)" -# TODO (yongtang): Replace the following with 'https://mirror.bazel.build/github.com/google/gemmlowp/.*zip' once -# the archive has been propagated in mirror.bazel.build. -GEMMLOWP_URL="$(grep -o 'https://github.com/google/gemmlowp/.*zip' "${BZL_FILE_PATH}" | head -n1)" +GEMMLOWP_URL="$(grep -o 'https://mirror.bazel.build/github.com/google/gemmlowp/.*zip' "${BZL_FILE_PATH}" | head -n1)" GOOGLETEST_URL="https://github.com/google/googletest/archive/release-1.8.0.tar.gz" NSYNC_URL="$(grep -o 'https://mirror.bazel.build/github.com/google/nsync/.*tar\.gz' "${BZL_FILE_PATH}" | head -n1)" PROTOBUF_URL="$(grep -o 'https://mirror.bazel.build/github.com/google/protobuf/.*tar\.gz' "${BZL_FILE_PATH}" | head -n1)" diff --git a/tensorflow/contrib/metrics/python/ops/metric_ops.py b/tensorflow/contrib/metrics/python/ops/metric_ops.py index 2ed99d50a4..a6be2084aa 100644 --- a/tensorflow/contrib/metrics/python/ops/metric_ops.py +++ b/tensorflow/contrib/metrics/python/ops/metric_ops.py @@ -2503,7 +2503,7 @@ def _compute_recall_at_precision(tp, fp, fn, precision, name): name: An optional variable_scope name. Returns: - The recall at a the given `precision`. + The recall at a given `precision`. """ precisions = math_ops.div(tp, tp + fp + _EPSILON) tf_index = math_ops.argmin( diff --git a/tensorflow/contrib/mpi_collectives/kernels/ring.h b/tensorflow/contrib/mpi_collectives/kernels/ring.h index 1d56d588bc..c001615d3f 100644 --- a/tensorflow/contrib/mpi_collectives/kernels/ring.h +++ b/tensorflow/contrib/mpi_collectives/kernels/ring.h @@ -129,7 +129,7 @@ cudaStream_t CudaStreamForMPI(); * has the fully accumulated Segment 1; and so on. The scatter-reduce is * complete. * - * Next, the allgather distributes these fully accumululated chunks across all + * Next, the allgather distributes these fully accumulated chunks across all * nodes. Communication proceeds in the same ring, once again in N-1 steps. At * the ith step, node j will send chunk (j - i + 1) and receive chunk (j - i). * For example, at the first iteration, the following transfers will occur: diff --git a/tensorflow/contrib/opt/python/training/adamax_test.py b/tensorflow/contrib/opt/python/training/adamax_test.py index 21bf3f5313..915e6504e1 100644 --- a/tensorflow/contrib/opt/python/training/adamax_test.py +++ b/tensorflow/contrib/opt/python/training/adamax_test.py @@ -224,8 +224,10 @@ class AdaMaxOptimizerTest(test.TestCase): var1_np, m1, v1 = adamax_update_numpy(var1_np, grads1_np, t, m1, v1) # Validate updated params - self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0)) - self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1)) + self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0), + rtol=1e-2) + self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1), + rtol=1e-2) if use_resource: self.assertEqual("var0_%d/AdaMax:0" % (i,), opt.get_slot(var=var0, name="m").name) diff --git a/tensorflow/contrib/opt/python/training/model_average_optimizer.py b/tensorflow/contrib/opt/python/training/model_average_optimizer.py index a7c97a1da2..b6b10e500b 100644 --- a/tensorflow/contrib/opt/python/training/model_average_optimizer.py +++ b/tensorflow/contrib/opt/python/training/model_average_optimizer.py @@ -62,7 +62,7 @@ class ModelAverageCustomGetter(object): """ def __init__(self, worker_device): - """Create a new `ElasticAverageCustomGetter`. + """Create a new `ModelAverageCustomGetter`. Args: worker_device: String. Name of the `worker` job. diff --git a/tensorflow/contrib/periodic_resample/BUILD b/tensorflow/contrib/periodic_resample/BUILD index 6ca7fe8b6e..aad1ca04c5 100644 --- a/tensorflow/contrib/periodic_resample/BUILD +++ b/tensorflow/contrib/periodic_resample/BUILD @@ -6,12 +6,13 @@ exports_files(["LICENSE"]) load( "//tensorflow:tensorflow.bzl", - "py_test", + "tf_cc_test", "tf_gen_op_libs", "tf_custom_op_library", "tf_custom_op_py_library", "tf_gen_op_wrapper_py", ) +load("//tensorflow:tensorflow.bzl", "py_test") cc_library( name = "all_ops", @@ -84,6 +85,23 @@ py_test( ":init_py", "//tensorflow/contrib/util:util_py", "//tensorflow/python:framework_test_lib", + "//tensorflow/python:gradient_checker", + ], +) + +tf_cc_test( + name = "periodic_resample_op_cc_test", + size = "small", + srcs = [ + "ops/array_ops_test.cc", + ], + deps = [ + ":all_ops", + "//tensorflow/core:framework", + "//tensorflow/core:protos_all_proto", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + "//tensorflow/core:testlib", ], ) diff --git a/tensorflow/contrib/periodic_resample/kernels/periodic_resample_op.cc b/tensorflow/contrib/periodic_resample/kernels/periodic_resample_op.cc index e18923c8aa..514689cf45 100644 --- a/tensorflow/contrib/periodic_resample/kernels/periodic_resample_op.cc +++ b/tensorflow/contrib/periodic_resample/kernels/periodic_resample_op.cc @@ -22,4 +22,9 @@ namespace tensorflow { REGISTER_KERNEL_BUILDER(Name("PeriodicResample").Device(DEVICE_CPU), PeriodicResampleOp); + +REGISTER_KERNEL_BUILDER(Name("PeriodicResampleOpGrad") + .Device(DEVICE_CPU), + PeriodicResampleOpGrad); + } // namespace tensorflow diff --git a/tensorflow/contrib/periodic_resample/kernels/periodic_resample_op.h b/tensorflow/contrib/periodic_resample/kernels/periodic_resample_op.h index 3ab588c458..42fba81a5c 100644 --- a/tensorflow/contrib/periodic_resample/kernels/periodic_resample_op.h +++ b/tensorflow/contrib/periodic_resample/kernels/periodic_resample_op.h @@ -25,92 +25,202 @@ #include "tensorflow/core/framework/shape_inference.h" #include "tensorflow/core/framework/tensor_shape.h" #include "tensorflow/core/lib/core/status.h" +#include "tensorflow/core/util/work_sharder.h" namespace { -template -IndexT compute_input_index( - IndexVecT* target_dimensions, const IndexT& output_index, - const IndexVecT& original_dimensions, const int& adjustable_dimension, - const std::vector& dimension_ceiling, - const std::vector& cumulative_dimensions, IndexT* result, - std::vector* output_indices, const int& rank) { - *result = 0; - output_indices->clear(); +// Computes input tensor index for given output index during forward +// propagation through periodic_resample operation. +class InputIndexer { + public: + InputIndexer(const std::vector& output_dimensions, + const tensorflow::TensorShape& input_shape, + int adjustable_dimension) + : output_dimensions_(output_dimensions), + adjustable_dimension_(adjustable_dimension), + rank_(input_shape.dims()), + linear_output_index_(0), + linear_input_index_(0), + adjustable_dimension_carriage_sum_(0) { + auto input_dimensions = TensorShapeToVector(input_shape); + // factors by which input_dimensions increases/decreases w.r.t. + // output_dimensions + dimension_ceiling_ = + ComputeDimensionCeiling(output_dimensions, input_dimensions); + cumulative_dimensions_ = ComputeCumulativeDimensions(); + + output_indices_.resize(output_dimensions_.size()); + input_indices_.resize(output_dimensions_.size()); + + // Compute index_factors + index_factors_.resize(rank_); + tensorflow::int64 last_index_factor = 1; + for (auto r = rank_ - 1; r >= 0; --r) { + index_factors_[r] = last_index_factor; + last_index_factor *= input_dimensions[r]; + } + } + + tensorflow::int64 linear_input_index() const { return linear_input_index_; } + + void MoveToOutputIndex(tensorflow::int64 output_index); + void IncrementOutputIndex(); + + private: + void RecomputeInputAdjustableDimensionIndex() { + tensorflow::int64 index = adjustable_dimension_carriage_sum_; + index *= output_dimensions_[adjustable_dimension_]; + index += output_indices_[adjustable_dimension_]; + input_indices_[adjustable_dimension_] = index; + } + + std::vector TensorShapeToVector( + const tensorflow::TensorShape& tensor_shape); + + std::vector ComputeDimensionCeiling( + const std::vector& output_dimensions, + const std::vector& input_dimensions); + + std::vector ComputeCumulativeDimensions(); + + const std::vector output_dimensions_; + std::vector dimension_ceiling_; + std::vector index_factors_; + std::vector cumulative_dimensions_; + std::vector output_indices_; + std::vector input_indices_; + + const int adjustable_dimension_; + const int rank_; + tensorflow::int64 linear_output_index_; + tensorflow::int64 linear_input_index_; + tensorflow::int64 adjustable_dimension_carriage_sum_; +}; + +void InputIndexer::MoveToOutputIndex(tensorflow::int64 output_index) { + linear_output_index_ = output_index; + linear_input_index_ = 0; // un-rasterize the output index auto last_reduced_i = output_index; - for (auto r = rank - 1; r >= 0; --r) { - (*output_indices)[r] = last_reduced_i % (*target_dimensions)[r]; + for (auto r = rank_ - 1; r >= 0; --r) { + output_indices_[r] = last_reduced_i % output_dimensions_[r]; last_reduced_i = - (last_reduced_i - (*output_indices)[r]) / (*target_dimensions)[r]; + (last_reduced_i - output_indices_[r]) / output_dimensions_[r]; } + tensorflow::int64 carriage_sum = 0; + for (int qi = 0; qi < rank_; ++qi) { + if (qi == adjustable_dimension_) continue; + carriage_sum += cumulative_dimensions_[qi] * + (output_indices_[qi] % dimension_ceiling_[qi]); + } + adjustable_dimension_carriage_sum_ = carriage_sum; + // rasterize the input index - IndexT last_index_factor = 1; - for (auto r = rank - 1; r >= 0; --r) { - IndexT index = 0; - if (r != adjustable_dimension) - index = (*output_indices)[r] / dimension_ceiling[r]; - else { - for (int qi = 0; qi < rank; ++qi) { - if (qi == adjustable_dimension) continue; - index += cumulative_dimensions[qi] * - ((*output_indices)[qi] % dimension_ceiling[qi]); - } - index *= (*target_dimensions)[adjustable_dimension]; - index += (*output_indices)[r]; + for (auto r = rank_ - 1; r >= 0; --r) { + if (r != adjustable_dimension_) { + input_indices_[r] = output_indices_[r] / dimension_ceiling_[r]; + } else { + RecomputeInputAdjustableDimensionIndex(); } - *result += last_index_factor * index; - last_index_factor *= original_dimensions[r]; } + for (auto r = rank_ - 1; r >= 0; --r) { + linear_input_index_ += index_factors_[r] * input_indices_[r]; + } +} + +void InputIndexer::IncrementOutputIndex() { + linear_output_index_++; + for (auto r = rank_ - 1; r >= 0; --r) { + auto old_carriage_sum_increment = + cumulative_dimensions_[r] * + (output_indices_[r] % dimension_ceiling_[r]); + output_indices_[r] = (output_indices_[r] + 1) % output_dimensions_[r]; + if (r != adjustable_dimension_) { + auto new_input_index = output_indices_[r] / dimension_ceiling_[r]; + linear_input_index_ += + (new_input_index - input_indices_[r]) * index_factors_[r]; + + input_indices_[r] = new_input_index; + + auto new_carriage_sum_increment = + cumulative_dimensions_[r] * + (output_indices_[r] % dimension_ceiling_[r]); - return *result; + adjustable_dimension_carriage_sum_ = adjustable_dimension_carriage_sum_ - + old_carriage_sum_increment + + new_carriage_sum_increment; + } + + if (output_indices_[r] != 0) { + // No more carries to higher indices. + break; + } + } + auto old_adjustable_dimension_input_index = + input_indices_[adjustable_dimension_]; + RecomputeInputAdjustableDimensionIndex(); + linear_input_index_ += (input_indices_[adjustable_dimension_] - + old_adjustable_dimension_input_index) * + index_factors_[adjustable_dimension_]; } -template // both types are needed here b/c IndexVecT and - // InputDataT are not related - void - fill_periodic_tensor( - tensorflow::OpKernelContext* context, - const IndexVecT& desired_shape, - const tensorflow::Tensor& input_tensor) { - // input is a strided array (last index is fastest, C-ordered) - auto input = input_tensor.flat(); - const int rank = input_tensor.dims(); - // original and target dimensions - std::vector original_dimensions(rank), - target_dimensions(rank); - tensorflow::int64 total_size(input_tensor.NumElements()), new_sliced_size(1); - // factors by which original_dimensions increases/decreases w.r.t. - // target_dimensions - std::vector dimension_ceiling(rank), - cumulative_dimensions(rank); - // index of adjustable dimension - int adjustable_dimension; - tensorflow::TensorShape output_shape; +std::vector InputIndexer::TensorShapeToVector( + const tensorflow::TensorShape& tensor_shape) { + std::vector result(tensor_shape.dims()); + int count = 0; + for (const auto dim_info : tensor_shape) { + result[count] = dim_info.size; + ++count; + } + return result; +} - // requires that the rank of the input tensor and length of the desired shape - // are equal - OP_REQUIRES(context, rank == desired_shape.size(), - tensorflow::errors::InvalidArgument( - "periodic_resample expects the rank of the input tensor, ", - rank, ", to be the same as the length of the desired shape, ", - desired_shape.size(), ".")); +std::vector InputIndexer::ComputeDimensionCeiling( + const std::vector& output_dimensions, + const std::vector& input_dimensions) { + std::vector dimension_ceiling(input_dimensions.size()); + for (size_t i = 0; i < input_dimensions.size(); ++i) { + dimension_ceiling[i] = (output_dimensions[i] + input_dimensions[i] - 1) / + input_dimensions[i]; + } + return dimension_ceiling; +} - bool found = false; - const auto& input_tensor_shape = input_tensor.shape(); +std::vector InputIndexer::ComputeCumulativeDimensions() { + std::vector cumulative_dimensions(rank_); + int count = 0; + for (int i = 0; i < rank_; ++i) { + if (count == 0) { + cumulative_dimensions[count] = 1; + } else { + cumulative_dimensions[count] = + cumulative_dimensions[count - 1] * dimension_ceiling_[count - 1]; + } + ++count; + } + return cumulative_dimensions; +} +template +void process_desired_shape(tensorflow::OpKernelContext* context, + const tensorflow::TensorShape& input_tensor_shape, + const IndexVecT& desired_shape, + int* adjustable_dimension, + std::vector* target_dimensions, + tensorflow::int64* output_size) { + tensorflow::int64 new_sliced_size = 1; + bool found = false; + const int rank = input_tensor_shape.dims(); for (int i = 0; i < rank; ++i) { - // if (desired_shape(i) < 1) { if (desired_shape[i] < 1) { // only one index can be adjustable OP_REQUIRES(context, !found, tensorflow::errors::InvalidArgument( "periodic_resample expects only " "one index to be marked as adjustable.")); - adjustable_dimension = i; + *adjustable_dimension = i; found = true; } else { OP_REQUIRES( @@ -122,9 +232,8 @@ template +void +do_periodic_resample_op(tensorflow::OpKernelContext* context, + const tensorflow::TensorShape& original_shape, + const tensorflow::PartialTensorShape& desired_shape, + const tensorflow::Tensor& source_tensor) { + const int rank = source_tensor.dims(); + + // requires that the rank of the input tensor and length of the desired shape + // are equal + OP_REQUIRES(context, rank == desired_shape.dims(), + tensorflow::errors::InvalidArgument( + "periodic_resample expects the rank of the input tensor, ", + rank, ", to be the same as the length of the desired shape, ", + desired_shape.dims(), ".")); + + std::vector target_dimensions(rank); + tensorflow::int64 new_size = 0; + // index of adjustable dimension + int adjustable_dimension = 0; + process_desired_shape(context, original_shape, desired_shape.dim_sizes(), + &adjustable_dimension, &target_dimensions, &new_size); // ensure that the new dimension is greater than zero OP_REQUIRES(context, target_dimensions[adjustable_dimension] > 0, @@ -160,11 +293,14 @@ template allocate_output(0, output_shape, &output_tensor)); auto output = output_tensor->flat(); - // memory is allocated for these variables outside the inner loop for - // efficiency (although, I could create a separate class scope for - // this purpose instead) - tensorflow::int64 result = 0; - std::vector output_indices(target_dimensions.size()); + // input is a strided array (last index is fastest, C-ordered) + auto input = source_tensor.flat(); // Fill output tensor with periodically resampled input tensor values - for (tensorflow::int64 output_index = 0; output_index < new_size; - ++output_index) { - output(output_index) = input(compute_input_index( - &target_dimensions, output_index, original_dimensions, - adjustable_dimension, dimension_ceiling, cumulative_dimensions, &result, - &output_indices, rank)); - } + InputIndexer input_indexer(target_dimensions, original_shape, + adjustable_dimension); + + auto worker_threads = *(context->device()->tensorflow_cpu_worker_threads()); + auto fill_output_tensor = [&input_indexer, &output, &input]( + tensorflow::int64 start, tensorflow::int64 limit) { + InputIndexer local_indexer(input_indexer); + local_indexer.MoveToOutputIndex(start); + for (tensorflow::int64 output_index = start; output_index < limit; + ++output_index) { + if (mode == Mode::kForward) { + output(output_index) = input(local_indexer.linear_input_index()); + } else { + output(local_indexer.linear_input_index()) = input(output_index); + } + local_indexer.IncrementOutputIndex(); + } + }; + ::tensorflow::Shard(worker_threads.num_threads, worker_threads.workers, + new_size, costPerFillIndex, fill_output_tensor); } +#define DATA_TYPE_SWITCH(data_type, context, CASE) \ + switch (data_type) { \ + CASE(float) \ + CASE(double) \ + CASE(tensorflow::int32) \ + CASE(tensorflow::int64) \ + default: \ + context->CtxFailure(__FILE__, __LINE__, \ + tensorflow::errors::InvalidArgument( \ + "Unsuppored tensor elements type")); \ + break; \ + } + void create_output_tensor( tensorflow::OpKernelContext* context, const tensorflow::Tensor& input_tensor, const tensorflow::DataType& input_tensor_type, - const tensorflow::PartialTensorShape& desired_shape_tensor) { - auto desired_shape = desired_shape_tensor.dim_sizes(); - - // obligatory type switch - switch (input_tensor_type) { - case tensorflow::DataTypeToEnum::value: - fill_periodic_tensor(context, desired_shape, input_tensor); + const tensorflow::PartialTensorShape& desired_shape) { +#define CASE(type) \ + case tensorflow::DataTypeToEnum::value: \ + do_periodic_resample_op( \ + context, input_tensor.shape(), desired_shape, input_tensor); \ break; - case tensorflow::DataTypeToEnum::value: - fill_periodic_tensor(context, desired_shape, input_tensor); - break; - case tensorflow::DataTypeToEnum::value: - fill_periodic_tensor(context, desired_shape, - input_tensor); - break; - case tensorflow::DataTypeToEnum::value: - fill_periodic_tensor(context, desired_shape, - input_tensor); + + DATA_TYPE_SWITCH(input_tensor_type, context, CASE); +#undef CASE +} + +void create_grad_tensor(tensorflow::OpKernelContext* context, + const tensorflow::Tensor& grad_tensor, + const tensorflow::DataType& grad_tensor_type, + const tensorflow::TensorShape& original_shape, + const tensorflow::PartialTensorShape& desired_shape) { +#define CASE(type) \ + case tensorflow::DataTypeToEnum::value: \ + do_periodic_resample_op( \ + context, original_shape, desired_shape, grad_tensor); \ break; - default:; - } + + DATA_TYPE_SWITCH(grad_tensor_type, context, CASE); +#undef CASE } } // namespace @@ -238,4 +400,25 @@ class PeriodicResampleOp : public tensorflow::OpKernel { tensorflow::PartialTensorShape desired_shape; }; +class PeriodicResampleOpGrad : public tensorflow::OpKernel { + public: + explicit PeriodicResampleOpGrad(tensorflow::OpKernelConstruction* context) + : tensorflow::OpKernel(context) { + OP_REQUIRES_OK(context, + context->GetAttr("original_shape", &original_shape)); + OP_REQUIRES_OK(context, context->GetAttr("desired_shape", &desired_shape)); + } + + void Compute(tensorflow::OpKernelContext* context) override { + const tensorflow::Tensor& grad_tensor = context->input(0); + const tensorflow::DataType grad_tensor_type = context->input_dtype(0); + create_grad_tensor(context, grad_tensor, grad_tensor_type, original_shape, + desired_shape); + } + + private: + tensorflow::TensorShape original_shape; + tensorflow::PartialTensorShape desired_shape; +}; + #endif // TENSORFLOW_KERNELS_PERIODICRESAMPLE_OP_H_ diff --git a/tensorflow/contrib/periodic_resample/ops/array_ops.cc b/tensorflow/contrib/periodic_resample/ops/array_ops.cc index 82bd796956..fd38cd09b4 100644 --- a/tensorflow/contrib/periodic_resample/ops/array_ops.cc +++ b/tensorflow/contrib/periodic_resample/ops/array_ops.cc @@ -26,7 +26,42 @@ REGISTER_OP("PeriodicResample") .Input("values: T") .Attr("shape: shape") .Output("output: T") - .SetShapeFn(shape_inference::ExplicitShape) + .SetShapeFn([](shape_inference::InferenceContext* c) { + tensorflow::PartialTensorShape desired_shape; + TF_RETURN_IF_ERROR(c->GetAttr("shape", &desired_shape)); + shape_inference::ShapeHandle input_tensor_shape = c->input(0); + shape_inference::DimensionHandle num_input_elements = + c->NumElements(input_tensor_shape); + shape_inference::ShapeHandle result_shape_handle; + if (!shape_inference::InferenceContext::ValueKnown(num_input_elements)) { + TF_RETURN_IF_ERROR(c->MakeShapeFromPartialTensorShape( + desired_shape, &result_shape_handle)); + } else { + const int rank = c->Rank(input_tensor_shape); + std::vector target_dimensions(rank); + tensorflow::int64 new_sliced_size = 1; + int adjustable_dimension = 0; + for (int i = 0; i < rank; ++i) { + if (desired_shape.dim_size(i) < 1) { + adjustable_dimension = i; + } else { + target_dimensions[i] = desired_shape.dim_size(i); + new_sliced_size *= target_dimensions[i]; + } + } + target_dimensions[adjustable_dimension] = + shape_inference::InferenceContext::Value( + num_input_elements) / new_sliced_size; + tensorflow::TensorShape result_shape; + for (int i = 0; i < rank; ++i) { + result_shape.AddDim(target_dimensions[i]); + } + TF_RETURN_IF_ERROR(c->MakeShapeFromTensorShape( + result_shape, &result_shape_handle)); + } + c->set_output(0, result_shape_handle); + return Status::OK(); + }) .Doc(R"doc( Periodically resample elements of a tensor to conform to `shape`. @@ -101,4 +136,20 @@ output: Periodically resampled tensor that has dimensions specified as in )doc"); + +REGISTER_OP("PeriodicResampleOpGrad") + .Attr("T: numbertype") + .Input("grad: T") + .Attr("original_shape: shape") + .Attr("desired_shape: shape") + .Output("grad_values: T") + .SetShapeFn([](shape_inference::InferenceContext* c) { + tensorflow::TensorShape original_shape; + TF_RETURN_IF_ERROR(c->GetAttr("original_shape", &original_shape)); + shape_inference::ShapeHandle s; + TF_RETURN_IF_ERROR(c->MakeShapeFromTensorShape(original_shape, &s)); + c->set_output(0, s); + return Status::OK(); +}); + } // namespace tensorflow diff --git a/tensorflow/contrib/periodic_resample/ops/array_ops_test.cc b/tensorflow/contrib/periodic_resample/ops/array_ops_test.cc new file mode 100644 index 0000000000..43b7c1799f --- /dev/null +++ b/tensorflow/contrib/periodic_resample/ops/array_ops_test.cc @@ -0,0 +1,41 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/core/framework/node_def_builder.h" +#include "tensorflow/core/framework/shape_inference_testutil.h" +#include "tensorflow/core/framework/tensor_shape.pb.h" +#include "tensorflow/core/framework/tensor_testutil.h" +#include "tensorflow/core/lib/core/status_test_util.h" +#include "tensorflow/core/platform/test.h" + +namespace tensorflow { + +TEST(ArrayOpsTest, PeriodicResample_ShapeFn) { + ShapeInferenceTestOp op("PeriodicResample"); + // Case 1: output shape can be fully inferreed. + PartialTensorShape shape({4, 4, -1}); + TensorShapeProto shape_proto; + shape.AsProto(&shape_proto); + + TF_ASSERT_OK(NodeDefBuilder("test", "PeriodicResample") + .Input({"values", 0, DT_INT32}) + .Attr("shape", shape_proto) + .Finalize(&op.node_def)); + INFER_OK(op, "[2,2,4]", "[4,4,1]"); + // Case 2: output shape can not be inferred - report desired shape. + INFER_OK(op, "[2,2,?]", "[4,4,?]"); +} + +} // end namespace tensorflow diff --git a/tensorflow/contrib/periodic_resample/python/kernel_tests/periodic_resample_op_test.py b/tensorflow/contrib/periodic_resample/python/kernel_tests/periodic_resample_op_test.py index a25de55e18..31a6fe1d94 100644 --- a/tensorflow/contrib/periodic_resample/python/kernel_tests/periodic_resample_op_test.py +++ b/tensorflow/contrib/periodic_resample/python/kernel_tests/periodic_resample_op_test.py @@ -21,8 +21,11 @@ from __future__ import print_function import numpy from tensorflow.contrib.periodic_resample import periodic_resample +from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors_impl from tensorflow.python.framework import test_util +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import gradient_checker from tensorflow.python.ops import variables from tensorflow.python.platform import googletest @@ -93,7 +96,6 @@ class PeriodicResampleTest(test_util.TensorFlowTestCase): def testPeriodicResampleErrors(self): input_tensor = numpy.zeros(shape=[1, 2, 2, 4]) with self.test_session(): - variables.global_variables_initializer().run() with self.assertRaisesWithPredicateMatch( errors_impl.InvalidArgumentError, 'Dimension 3 input tensor has size 4, desired shape has size 1'): @@ -103,6 +105,29 @@ class PeriodicResampleTest(test_util.TensorFlowTestCase): '4, to be the same as the length of the desired shape, 3'): periodic_resample(input_tensor, [None, 4, 4]).eval() + def testPeriodicResampleGradient(self): + desired_shape = numpy.array([4, 4, None]) + result_shape = (4, 4, 1) + input_shape = (2, 2, 4) + with self.test_session() as sess: + x = array_ops.placeholder(dtypes.float32, shape=input_shape) + output = periodic_resample(x, desired_shape) + error = gradient_checker.compute_gradient_error( + x, input_shape, output, result_shape) + self.assertLess(error, 1e-4) + + def testPeriodicResampleShapeInference(self): + with self.test_session() as sess: + # Case 1: output shape can be fully inferreed. + x = array_ops.placeholder(dtypes.float32, shape=(2, 2, 4)) + output = periodic_resample(x, [4, 4, None]) + self.assertEqual(output.shape, [4, 4, 1]) + # Case 2: output shape can not be inferred - report desired shape. + x = array_ops.placeholder(dtypes.float32, shape=(2, 2, None)) + output = periodic_resample(x, [4, 4, None]) + self.assertTrue(output.shape.is_compatible_with([4, 4, None])) + self.assertEqual(output.shape[2].value, None) + if __name__ == '__main__': googletest.main() diff --git a/tensorflow/contrib/periodic_resample/python/ops/periodic_resample_op.py b/tensorflow/contrib/periodic_resample/python/ops/periodic_resample_op.py index 348623d8f8..470e300ccb 100644 --- a/tensorflow/contrib/periodic_resample/python/ops/periodic_resample_op.py +++ b/tensorflow/contrib/periodic_resample/python/ops/periodic_resample_op.py @@ -21,11 +21,17 @@ from __future__ import print_function # pylint: disable=unused-import from tensorflow.contrib.periodic_resample.python.ops import gen_periodic_resample_op -from tensorflow.contrib.periodic_resample.python.ops.gen_periodic_resample_op import periodic_resample +from tensorflow.contrib.periodic_resample.python.ops.gen_periodic_resample_op import periodic_resample, periodic_resample_op_grad from tensorflow.contrib.util import loader +from tensorflow.python.framework import ops from tensorflow.python.platform import resource_loader # pylint: enable=unused-import _periodic_resample_op = loader.load_op_library( resource_loader.get_path_to_datafile('_periodic_resample_op.so')) + +@ops.RegisterGradient("PeriodicResample") +def _periodic_resample_grad_cc(op, grad): + return periodic_resample_op_grad( + grad, op.inputs[0].shape, op.get_attr('shape')) diff --git a/tensorflow/contrib/predictor/contrib_estimator_predictor.py b/tensorflow/contrib/predictor/contrib_estimator_predictor.py index b7a98c68e2..af3b2ad1b5 100644 --- a/tensorflow/contrib/predictor/contrib_estimator_predictor.py +++ b/tensorflow/contrib/predictor/contrib_estimator_predictor.py @@ -34,7 +34,8 @@ class ContribEstimatorPredictor(predictor.Predictor): prediction_input_fn, input_alternative_key=None, output_alternative_key=None, - graph=None): + graph=None, + config=None): """Initialize a `ContribEstimatorPredictor`. Args: @@ -48,6 +49,7 @@ class ContribEstimatorPredictor(predictor.Predictor): multi-headed models. graph: Optional. The Tensorflow `graph` in which prediction should be done. + config: `ConfigProto` proto used to configure the session. """ self._graph = graph or ops.Graph() with self._graph.as_default(): @@ -58,6 +60,7 @@ class ContribEstimatorPredictor(predictor.Predictor): checkpoint_path = saver.latest_checkpoint(estimator.model_dir) self._session = monitored_session.MonitoredSession( session_creator=monitored_session.ChiefSessionCreator( + config=config, checkpoint_filename_with_path=checkpoint_path)) input_alternative_key = ( diff --git a/tensorflow/contrib/predictor/core_estimator_predictor.py b/tensorflow/contrib/predictor/core_estimator_predictor.py index d78d94c269..a725072e72 100644 --- a/tensorflow/contrib/predictor/core_estimator_predictor.py +++ b/tensorflow/contrib/predictor/core_estimator_predictor.py @@ -51,7 +51,8 @@ class CoreEstimatorPredictor(predictor.Predictor): estimator, serving_input_receiver_fn, output_key=None, - graph=None): + graph=None, + config=None): """Initialize a `CoreEstimatorPredictor`. Args: @@ -62,6 +63,7 @@ class CoreEstimatorPredictor(predictor.Predictor): `None`, then `DEFAULT_SERVING_SIGNATURE_DEF_KEY` is used. graph: Optional. The Tensorflow `graph` in which prediction should be done. + config: `ConfigProto` proto used to configure the session. """ self._graph = graph or ops.Graph() with self._graph.as_default(): @@ -71,6 +73,7 @@ class CoreEstimatorPredictor(predictor.Predictor): checkpoint_dir = estimator.model_dir self._session = monitored_session.MonitoredSession( session_creator=monitored_session.ChiefSessionCreator( + config=config, checkpoint_dir=checkpoint_dir)) feed_tensor_info = signature_def.inputs diff --git a/tensorflow/contrib/predictor/predictor_factories.py b/tensorflow/contrib/predictor/predictor_factories.py index 6e77e934fe..f275bc15ad 100644 --- a/tensorflow/contrib/predictor/predictor_factories.py +++ b/tensorflow/contrib/predictor/predictor_factories.py @@ -30,7 +30,8 @@ def from_contrib_estimator(estimator, prediction_input_fn, input_alternative_key=None, output_alternative_key=None, - graph=None): + graph=None, + config=None): """Constructs a `Predictor` from a `tf.contrib.learn.Estimator`. Args: @@ -44,6 +45,7 @@ def from_contrib_estimator(estimator, multi-headed models. graph: Optional. The Tensorflow `graph` in which prediction should be done. + config: `ConfigProto` proto used to configure the session. Returns: An initialized `Predictor`. @@ -62,13 +64,15 @@ def from_contrib_estimator(estimator, prediction_input_fn, input_alternative_key=input_alternative_key, output_alternative_key=output_alternative_key, - graph=graph) + graph=graph, + config=config) def from_estimator(estimator, serving_input_receiver_fn, output_key=None, - graph=None): + graph=None, + config=None): """Constructs a `Predictor` from a `tf.python.estimator.Estimator`. Args: @@ -79,6 +83,7 @@ def from_estimator(estimator, `None`, then `DEFAULT_SERVING_SIGNATURE_DEF_KEY` is used. graph: Optional. The Tensorflow `graph` in which prediction should be done. + config: `ConfigProto` proto used to configure the session. Returns: An initialized `Predictor`. @@ -93,14 +98,19 @@ def from_estimator(estimator, 'tf.contrib.learn.Estimator. You likely want to call ' 'from_contrib_estimator.') return core_estimator_predictor.CoreEstimatorPredictor( - estimator, serving_input_receiver_fn, output_key=output_key, graph=graph) + estimator, + serving_input_receiver_fn, + output_key=output_key, + graph=graph, + config=config) def from_saved_model(export_dir, signature_def_key=None, signature_def=None, tags=None, - graph=None): + graph=None, + config=None): """Constructs a `Predictor` from a `SavedModel` on disk. Args: @@ -115,6 +125,7 @@ def from_saved_model(export_dir, `SignatureDef`. Defaults to `DEFAULT_TAGS`. graph: Optional. The Tensorflow `graph` in which prediction should be done. + config: `ConfigProto` proto used to configure the session. Returns: An initialized `Predictor`. @@ -128,4 +139,5 @@ def from_saved_model(export_dir, signature_def_key=signature_def_key, signature_def=signature_def, tags=tags, - graph=graph) + graph=graph, + config=config) diff --git a/tensorflow/contrib/predictor/predictor_factories_test.py b/tensorflow/contrib/predictor/predictor_factories_test.py index 578d9424b2..a2ef1dc3af 100644 --- a/tensorflow/contrib/predictor/predictor_factories_test.py +++ b/tensorflow/contrib/predictor/predictor_factories_test.py @@ -20,6 +20,7 @@ from __future__ import print_function from tensorflow.contrib.predictor import predictor_factories from tensorflow.contrib.predictor import testing_common +from tensorflow.core.protobuf import config_pb2 from tensorflow.python.platform import test MODEL_DIR_NAME = 'contrib/predictor/test_export_dir' @@ -41,6 +42,11 @@ class PredictorFactoriesTest(test.TestCase): """Test loading from_saved_model with tags.""" predictor_factories.from_saved_model(self._export_dir, tags='serve') + def testFromSavedModelWithSessionConfig(self): + """Test loading from_saved_model with session config.""" + predictor_factories.from_saved_model( + self._export_dir, config=config_pb2.ConfigProto()) + def testFromSavedModelWithBadTags(self): """Test that loading fails for bad tags.""" bad_tags_regex = ('.*? could not be found in SavedModel') @@ -53,6 +59,13 @@ class PredictorFactoriesTest(test.TestCase): predictor_factories.from_contrib_estimator( estimator, input_fn, output_alternative_key='sum') + def testFromContribEstimatorWithSessionConfig(self): + estimator = testing_common.get_arithmetic_estimator(core=False) + input_fn = testing_common.get_arithmetic_input_fn(core=False) + predictor_factories.from_contrib_estimator( + estimator, input_fn, output_alternative_key='sum', + config=config_pb2.ConfigProto()) + def testFromContribEstimatorWithCoreEstimatorRaises(self): estimator = testing_common.get_arithmetic_estimator(core=True) input_fn = testing_common.get_arithmetic_input_fn(core=True) @@ -64,6 +77,12 @@ class PredictorFactoriesTest(test.TestCase): input_fn = testing_common.get_arithmetic_input_fn(core=True) predictor_factories.from_estimator(estimator, input_fn) + def testFromCoreEstimatorWithSessionConfig(self): + estimator = testing_common.get_arithmetic_estimator(core=True) + input_fn = testing_common.get_arithmetic_input_fn(core=True) + predictor_factories.from_estimator( + estimator, input_fn, config=config_pb2.ConfigProto()) + def testFromCoreEstimatorWithContribEstimatorRaises(self): estimator = testing_common.get_arithmetic_estimator(core=False) input_fn = testing_common.get_arithmetic_input_fn(core=False) diff --git a/tensorflow/contrib/predictor/saved_model_predictor.py b/tensorflow/contrib/predictor/saved_model_predictor.py index 0dbca0f813..95da6d04ed 100644 --- a/tensorflow/contrib/predictor/saved_model_predictor.py +++ b/tensorflow/contrib/predictor/saved_model_predictor.py @@ -121,7 +121,8 @@ class SavedModelPredictor(predictor.Predictor): input_names=None, output_names=None, tags=None, - graph=None): + graph=None, + config=None): """Initialize a `CoreEstimatorPredictor`. Args: @@ -142,6 +143,7 @@ class SavedModelPredictor(predictor.Predictor): the correct `SignatureDef`. Defaults to `DEFAULT_TAGS`. graph: Optional. The Tensorflow `graph` in which prediction should be done. + config: `ConfigProto` proto used to configure the session. Raises: ValueError: If more than one of signature_def_key OR signature_def OR (input_names AND output_names) is specified. @@ -152,7 +154,7 @@ class SavedModelPredictor(predictor.Predictor): self._graph = graph or ops.Graph() with self._graph.as_default(): - self._session = session.Session() + self._session = session.Session(config=config) loader.load(self._session, tags.split(','), export_dir) if input_names is None: diff --git a/tensorflow/contrib/quantize/README.md b/tensorflow/contrib/quantize/README.md index c83623ec94..27a933c0f9 100644 --- a/tensorflow/contrib/quantize/README.md +++ b/tensorflow/contrib/quantize/README.md @@ -6,7 +6,7 @@ inference. The details of the transformation implemented in this package is described here [1]. This is done using the -[fake quantization op](https://www.tensorflow.org/versions/r0.12/api_docs/python/array_ops/fake_quantization). +[fake quantization op](https://www.tensorflow.org/api_guides/python/array_ops#Fake_quantization). Literature has shown that fixed point networks provide comparable performance to floating point networks [2]. This is achieved by modeling the quantization diff --git a/tensorflow/contrib/slim/python/slim/evaluation_test.py b/tensorflow/contrib/slim/python/slim/evaluation_test.py index 94fc12ca81..3d0308aaf3 100644 --- a/tensorflow/contrib/slim/python/slim/evaluation_test.py +++ b/tensorflow/contrib/slim/python/slim/evaluation_test.py @@ -26,7 +26,6 @@ import time import numpy as np from tensorflow.contrib.framework.python.ops import variables as variables_lib -from tensorflow.contrib.metrics.python.ops import metric_ops from tensorflow.contrib.slim.python.slim import evaluation from tensorflow.contrib.training.python.training import evaluation as evaluation_lib from tensorflow.core.protobuf import saver_pb2 @@ -37,6 +36,7 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops +from tensorflow.python.ops import metrics from tensorflow.python.ops import variables from tensorflow.python.platform import flags from tensorflow.python.platform import gfile @@ -89,8 +89,8 @@ class EvaluationTest(test.TestCase): self._predictions, self._scale = TestModel(self._inputs) def testFinalOpsOnEvaluationLoop(self): - value_op, update_op = metric_ops.streaming_accuracy(self._predictions, - self._labels) + value_op, update_op = metrics.accuracy( + labels=self._labels, predictions=self._predictions) init_op = control_flow_ops.group(variables.global_variables_initializer(), variables.local_variables_initializer()) # Create checkpoint and log directories: @@ -136,9 +136,10 @@ class EvaluationTest(test.TestCase): self.assertTrue(obj.hook_was_run) def _create_names_to_metrics(self, predictions, labels): - accuracy0, update_op0 = metric_ops.streaming_accuracy(predictions, labels) - accuracy1, update_op1 = metric_ops.streaming_accuracy(predictions + 1, - labels) + accuracy0, update_op0 = metrics.accuracy( + labels=labels, predictions=predictions) + accuracy1, update_op1 = metrics.accuracy( + labels=labels, predictions=predictions + 1) names_to_values = {'Accuracy': accuracy0, 'Another_accuracy': accuracy1} names_to_updates = {'Accuracy': update_op0, 'Another_accuracy': update_op1} @@ -198,8 +199,8 @@ class EvaluationTest(test.TestCase): predictions_limited = input.limit_epochs(self._predictions, num_epochs=1) labels_limited = input.limit_epochs(self._labels, num_epochs=1) - value_op, update_op = metric_ops.streaming_accuracy( - predictions_limited, labels_limited) + value_op, update_op = metrics.accuracy( + labels=labels_limited, predictions=predictions_limited) init_op = control_flow_ops.group(variables.global_variables_initializer(), variables.local_variables_initializer()) @@ -260,8 +261,8 @@ class SingleEvaluationTest(test.TestCase): self._prepareCheckpoint(checkpoint_path) # Next, determine the metric to evaluate: - value_op, update_op = metric_ops.streaming_accuracy(self._predictions, - self._labels) + value_op, update_op = metrics.accuracy( + labels=self._labels, predictions=self._predictions) # Run the evaluation and verify the results: accuracy_value = evaluation.evaluate_once( @@ -276,8 +277,8 @@ class SingleEvaluationTest(test.TestCase): self._prepareCheckpoint(checkpoint_path) # Next, determine the metric to evaluate: - value_op, update_op = metric_ops.streaming_accuracy(self._predictions, - self._labels) + value_op, update_op = metrics.accuracy( + labels=self._labels, predictions=self._predictions) dumping_root = os.path.join(self.get_temp_dir(), 'tfdbg_dump_dir') dumping_hook = hooks.DumpingDebugHook(dumping_root, log_usage=False) diff --git a/tensorflow/contrib/summary/summary.py b/tensorflow/contrib/summary/summary.py index 99ced53e11..d22b80ac88 100644 --- a/tensorflow/contrib/summary/summary.py +++ b/tensorflow/contrib/summary/summary.py @@ -21,6 +21,7 @@ from @{tf.summary.merge_all} to @{tf.summary.FileWriter}. To use with eager execution enabled, write your code as follows: +```python global_step = tf.train.get_or_create_global_step() summary_writer = tf.contrib.summary.create_file_writer( train_dir, flush_millis=10000) @@ -30,9 +31,11 @@ with summary_writer.as_default(), tf.contrib.summary.always_record_summaries(): tf.contrib.summary.scalar("loss", my_loss) # In this case every call to tf.contrib.summary.scalar will generate a record # ... +``` To use it with graph execution, write your code as follows: +```python global_step = tf.train.get_or_create_global_step() summary_writer = tf.contrib.summary.create_file_writer( train_dir, flush_millis=10000) @@ -53,7 +56,7 @@ with tf.Session(...) as sess: while not_done_training: sess.run([train_op, tf.contrib.summary.all_summary_ops()]) # ... - +``` """ from __future__ import absolute_import diff --git a/tensorflow/contrib/tensor_forest/client/eval_metrics.py b/tensorflow/contrib/tensor_forest/client/eval_metrics.py index e893e1d1c8..d8236a0a6f 100644 --- a/tensorflow/contrib/tensor_forest/client/eval_metrics.py +++ b/tensorflow/contrib/tensor_forest/client/eval_metrics.py @@ -21,10 +21,10 @@ import numpy as np from tensorflow.contrib import losses from tensorflow.contrib.learn.python.learn.estimators import prediction_key -from tensorflow.contrib.metrics.python.ops import metric_ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops +from tensorflow.python.ops import metrics from tensorflow.python.ops import nn INFERENCE_PROB_NAME = prediction_key.PredictionKey.PROBABILITIES @@ -38,12 +38,13 @@ def _top_k_generator(k): targets = math_ops.to_int32(targets) if targets.get_shape().ndims > 1: targets = array_ops.squeeze(targets, axis=[1]) - return metric_ops.streaming_mean(nn.in_top_k(probabilities, targets, k)) + return metrics.mean(nn.in_top_k(probabilities, targets, k)) return _top_k def _accuracy(predictions, targets, weights=None): - return metric_ops.streaming_accuracy(predictions, targets, weights=weights) + return metrics.accuracy( + labels=targets, predictions=predictions, weights=weights) def _r2(probabilities, targets, weights=None): @@ -53,7 +54,7 @@ def _r2(probabilities, targets, weights=None): squares_residuals = math_ops.reduce_sum( math_ops.square(targets - probabilities), 0) score = 1 - math_ops.reduce_sum(squares_residuals / squares_total) - return metric_ops.streaming_mean(score, weights=weights) + return metrics.mean(score, weights=weights) def _squeeze_and_onehot(targets, depth): @@ -62,7 +63,7 @@ def _squeeze_and_onehot(targets, depth): def _sigmoid_entropy(probabilities, targets, weights=None): - return metric_ops.streaming_mean( + return metrics.mean( losses.sigmoid_cross_entropy(probabilities, _squeeze_and_onehot( targets, @@ -71,7 +72,7 @@ def _sigmoid_entropy(probabilities, targets, weights=None): def _softmax_entropy(probabilities, targets, weights=None): - return metric_ops.streaming_mean( + return metrics.mean( losses.sparse_softmax_cross_entropy(probabilities, math_ops.to_int32(targets)), weights=weights) @@ -82,7 +83,7 @@ def _predictions(predictions, unused_targets, **unused_kwargs): def _class_log_loss(probabilities, targets, weights=None): - return metric_ops.streaming_mean( + return metrics.mean( losses.log_loss(probabilities, _squeeze_and_onehot(targets, array_ops.shape(probabilities)[1])), @@ -90,34 +91,36 @@ def _class_log_loss(probabilities, targets, weights=None): def _precision(predictions, targets, weights=None): - return metric_ops.streaming_precision(predictions, targets, weights=weights) + return metrics.precision( + labels=targets, predictions=predictions, weights=weights) def _precision_at_thresholds(predictions, targets, weights=None): - return metric_ops.streaming_precision_at_thresholds( - array_ops.slice(predictions, [0, 1], [-1, 1]), - targets, - np.arange( - 0, 1, 0.01, dtype=np.float32), + return metrics.precision_at_thresholds( + labels=targets, + predictions=array_ops.slice(predictions, [0, 1], [-1, 1]), + thresholds=np.arange(0, 1, 0.01, dtype=np.float32), weights=weights) def _recall(predictions, targets, weights=None): - return metric_ops.streaming_recall(predictions, targets, weights=weights) + return metrics.recall( + labels=targets, predictions=predictions, weights=weights) def _recall_at_thresholds(predictions, targets, weights=None): - return metric_ops.streaming_recall_at_thresholds( - array_ops.slice(predictions, [0, 1], [-1, 1]), - targets, - np.arange( - 0, 1, 0.01, dtype=np.float32), + return metrics.recall_at_thresholds( + labels=targets, + predictions=array_ops.slice(predictions, [0, 1], [-1, 1]), + thresholds=np.arange(0, 1, 0.01, dtype=np.float32), weights=weights) def _auc(probs, targets, weights=None): - return metric_ops.streaming_auc(array_ops.slice(probs, [0, 1], [-1, 1]), - targets, weights=weights) + return metrics.auc( + labels=targets, + predictions=array_ops.slice(probs, [0, 1], [-1, 1]), + weights=weights) _EVAL_METRICS = { diff --git a/tensorflow/contrib/tensor_forest/python/tensor_forest.py b/tensorflow/contrib/tensor_forest/python/tensor_forest.py index 7a35a70bbe..6f62cd11a9 100644 --- a/tensorflow/contrib/tensor_forest/python/tensor_forest.py +++ b/tensorflow/contrib/tensor_forest/python/tensor_forest.py @@ -295,7 +295,7 @@ def get_epoch_variable(): # A simple container to hold the training variables for a single tree. -class TreeTrainingVariables(object): +class TreeVariables(object): """Stores tf.Variables for training a single random tree. Uses tf.get_variable to get tree-specific names so that this can be used @@ -303,7 +303,7 @@ class TreeTrainingVariables(object): then relies on restoring that model to evaluate). """ - def __init__(self, params, tree_num, training): + def __init__(self, params, tree_num, training, tree_config='', tree_stat=''): if (not hasattr(params, 'params_proto') or not isinstance(params.params_proto, _params_proto.TensorForestParams)): @@ -315,27 +315,28 @@ class TreeTrainingVariables(object): # TODO(gilberth): Manually shard this to be able to fit it on # multiple machines. self.stats = stats_ops.fertile_stats_variable( - params, '', self.get_tree_name('stats', tree_num)) + params, tree_stat, self.get_tree_name('stats', tree_num)) self.tree = model_ops.tree_variable( - params, '', self.stats, self.get_tree_name('tree', tree_num)) + params, tree_config, self.stats, self.get_tree_name('tree', tree_num)) def get_tree_name(self, name, num): return '{0}-{1}'.format(name, num) -class ForestTrainingVariables(object): +class ForestVariables(object): """A container for a forests training data, consisting of multiple trees. - Instantiates a TreeTrainingVariables object for each tree. We override the + Instantiates a TreeVariables object for each tree. We override the __getitem__ and __setitem__ function so that usage looks like this: - forest_variables = ForestTrainingVariables(params) + forest_variables = ForestVariables(params) ... forest_variables.tree ... """ def __init__(self, params, device_assigner, training=True, - tree_variables_class=TreeTrainingVariables): + tree_variables_class=TreeVariables, + tree_configs=None, tree_stats=None): self.variables = [] # Set up some scalar variables to run through the device assigner, then # we can use those to colocate everything related to a tree. @@ -347,7 +348,13 @@ class ForestTrainingVariables(object): for i in range(params.num_trees): with ops.device(self.device_dummies[i].device): - self.variables.append(tree_variables_class(params, i, training)) + kwargs = {} + if tree_configs is not None: + kwargs.update(dict(tree_config=tree_configs[i])) + if tree_stats is not None: + kwargs.update(dict(tree_stat=tree_stats[i])) + self.variables.append(tree_variables_class( + params, i, training, **kwargs)) def __setitem__(self, t, val): self.variables[t] = val @@ -361,9 +368,11 @@ class RandomForestGraphs(object): def __init__(self, params, + tree_configs=None, + tree_stats=None, device_assigner=None, variables=None, - tree_variables_class=TreeTrainingVariables, + tree_variables_class=TreeVariables, tree_graphs=None, training=True): self.params = params @@ -371,9 +380,10 @@ class RandomForestGraphs(object): device_assigner or framework_variables.VariableDeviceChooser()) logging.info('Constructing forest with params = ') logging.info(self.params.__dict__) - self.variables = variables or ForestTrainingVariables( + self.variables = variables or ForestVariables( self.params, device_assigner=self.device_assigner, training=training, - tree_variables_class=tree_variables_class) + tree_variables_class=tree_variables_class, + tree_configs=tree_configs, tree_stats=tree_stats) tree_graph_class = tree_graphs or RandomTreeGraphs self.trees = [ tree_graph_class(self.variables[i], self.params, i) diff --git a/tensorflow/contrib/tensor_forest/python/tensor_forest_test.py b/tensorflow/contrib/tensor_forest/python/tensor_forest_test.py index bbe627b157..1c9c81827e 100644 --- a/tensorflow/contrib/tensor_forest/python/tensor_forest_test.py +++ b/tensorflow/contrib/tensor_forest/python/tensor_forest_test.py @@ -18,10 +18,14 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from google.protobuf.json_format import ParseDict +from tensorflow.contrib.decision_trees.proto import generic_tree_model_pb2 as _tree_proto from tensorflow.contrib.tensor_forest.python import tensor_forest from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor from tensorflow.python.framework import test_util +from tensorflow.python.ops import resources +from tensorflow.python.ops import variables from tensorflow.python.platform import googletest @@ -110,6 +114,47 @@ class TensorForestTest(test_util.TensorFlowTestCase): self.assertTrue(isinstance(paths, ops.Tensor)) self.assertTrue(isinstance(var, ops.Tensor)) + def testInfrenceFromRestoredModel(self): + input_data = [[-1., 0.], [-1., 2.], # node 1 + [1., 0.], [1., -2.]] # node 2 + expected_prediction = [[0.0, 1.0], [0.0, 1.0], + [0.0, 1.0], [0.0, 1.0]] + hparams = tensor_forest.ForestHParams( + num_classes=2, + num_features=2, + num_trees=1, + max_nodes=1000, + split_after_samples=25).fill() + tree_weight = {'decisionTree': + {'nodes': + [{'binaryNode': + {'rightChildId': 2, + 'leftChildId': 1, + 'inequalityLeftChildTest': + {'featureId': {'id': '0'}, + 'threshold': {'floatValue': 0}}}}, + {'leaf': {'vector': + {'value': [{'floatValue': 0.0}, + {'floatValue': 1.0}]}}, + 'nodeId': 1}, + {'leaf': {'vector': + {'value': [{'floatValue': 0.0}, + {'floatValue': 1.0}]}}, + 'nodeId': 2}]}} + restored_tree_param = ParseDict(tree_weight, + _tree_proto.Model()).SerializeToString() + graph_builder = tensor_forest.RandomForestGraphs(hparams, + [restored_tree_param]) + probs, paths, var = graph_builder.inference_graph(input_data) + self.assertTrue(isinstance(probs, ops.Tensor)) + self.assertTrue(isinstance(paths, ops.Tensor)) + self.assertTrue(isinstance(var, ops.Tensor)) + with self.test_session(): + variables.global_variables_initializer().run() + resources.initialize_resources(resources.shared_resources()).run() + self.assertEquals(probs.eval().shape, (4, 2)) + self.assertEquals(probs.eval().tolist(), expected_prediction) + def testTrainingConstructionClassificationSparse(self): input_data = sparse_tensor.SparseTensor( indices=[[0, 0], [0, 3], [1, 0], [1, 7], [2, 1], [3, 9]], diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.cc b/tensorflow/contrib/tensorrt/convert/convert_graph.cc index b7b26cfb1c..da4dd5a14c 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_graph.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_graph.cc @@ -91,8 +91,11 @@ void GetSubGraphIncomingEdges(const tensorflow::Graph& graph, if (!subgraph_node_ids.count(edge->src()->id()) && !edge->src()->IsSource() && !edge->IsControlEdge()) { incoming_edges->insert(edge); + VLOG(2) << "INCOMING " << edge->src()->name() << " -> " << node->name() + << " Y, "; } else { - VLOG(2) << node->name() << " -> " << edge->src()->name() << " N, "; + VLOG(2) << "INCOMING " << edge->src()->name() << " -> " << node->name() + << " N, "; } } } @@ -106,10 +109,12 @@ void GetSubGraphOutgoingEdges(const tensorflow::Graph& graph, for (const tensorflow::Edge* edge : node->out_edges()) { if (!subgraph_node_ids.count(edge->dst()->id()) && !edge->dst()->IsSink() && !edge->IsControlEdge()) { - VLOG(2) << node->name() << " -> " << edge->dst()->name() << " Y, "; + VLOG(2) << "OUTGOING " << node->name() << " -> " << edge->dst()->name() + << " Y, "; outgoing_edges->insert(edge); } else { - VLOG(2) << node->name() << " -> " << edge->dst()->name() << " N, "; + VLOG(2) << "OUTGOING " << node->name() << " -> " << edge->dst()->name() + << " N, "; } } } @@ -181,29 +186,27 @@ struct ConvertGraphParams { static tensorflow::Status FillSubGraphEdgeSets(ConvertGraphParams* p) { GetSubGraphIncomingEdges(p->graph, p->subgraph_node_ids, &p->subgraph_incoming_edges); + + std::set> unique_tensors; + // Add only unique input source nodes. If output of an outside node is shared + // between multiple nodes inside the engine, only one edge should be created for (const tensorflow::Edge* edge : p->subgraph_incoming_edges) { - p->subgraph_inputs.push_back({edge->src()->id(), edge->src_output()}); - } - auto output_name_to_index_map = BuildTensorNameMap(p->output_names); - std::set> subgraph_outputs_set; - // Collect outputs referenced from output_names - for (int node_id : p->subgraph_node_ids) { - tensorflow::Node* node = p->graph.FindNodeId(node_id); - if (output_name_to_index_map.count(node->name())) { - for (int index : output_name_to_index_map.at(node->name())) { - subgraph_outputs_set.insert({node_id, index}); - } - } + unique_tensors.insert({edge->src()->id(), edge->src_output()}); } + p->subgraph_inputs.insert(p->subgraph_inputs.begin(), unique_tensors.begin(), + unique_tensors.end()); GetSubGraphOutgoingEdges(p->graph, p->subgraph_node_ids, &p->subgraph_outgoing_edges); + unique_tensors.clear(); + // Similar to above, if multiple ouside nodes are sharing the output of an + // internal node only one output port should be created and shared between + // outputs for (const tensorflow::Edge* edge : p->subgraph_outgoing_edges) { - subgraph_outputs_set.insert({edge->src()->id(), edge->src_output()}); + unique_tensors.insert({edge->src()->id(), edge->src_output()}); } - p->subgraph_outputs.reserve(subgraph_outputs_set.size()); + p->subgraph_outputs.reserve(unique_tensors.size()); p->subgraph_outputs.insert(p->subgraph_outputs.begin(), - subgraph_outputs_set.begin(), - subgraph_outputs_set.end()); + unique_tensors.begin(), unique_tensors.end()); return tensorflow::Status::OK(); } @@ -225,7 +228,6 @@ tensorflow::Status GetCalibNode(ConvertGraphParams* params) { for (auto in_edge : params->subgraph_incoming_edges) { // loop over incoming edges and // attach them to calib node - // tensorflow::Node* src_node = in_edge->src(); auto src_output = in_edge->src_output(); auto dst_node = in_edge->dst(); auto dst_input = in_edge->dst_input(); @@ -257,19 +259,24 @@ tensorflow::Status ConvertSubGraphToTensorRT(ConvertGraphParams* params) { for (size_t i = 0; i < params->subgraph_inputs.size(); ++i) { subgraph_edge_to_input_map.insert({params->subgraph_inputs.at(i), i}); } + std::set> unique_tensors; for (const tensorflow::Edge* edge : params->subgraph_incoming_edges) { std::pair old_src = {edge->src()->id(), edge->src_output()}; + if (unique_tensors.count(old_src)) continue; + unique_tensors.insert(old_src); int new_src_output = subgraph_edge_to_input_map.at(old_src); params->graph.AddEdge(edge->src(), edge->src_output(), trt_node, new_src_output); + VLOG(1) << "Wire " << edge->src()->name() << ":" << edge->src_output() + << " -> " << trt_node->name() << ":" << new_src_output; params->graph.RemoveEdge(edge); } - - VLOG(2) << "new wiring edges: " << trt_node->in_edges().size(); - for (const tensorflow::Edge* edge : trt_node->in_edges()) { - VLOG(2) << edge->src()->name() << " port: " << edge->src_output(); + if (VLOG_IS_ON(2)) { + VLOG(2) << "new edge count: " << trt_node->in_edges().size(); + for (const tensorflow::Edge* edge : trt_node->in_edges()) { + VLOG(2) << edge->src()->name() << " port: " << edge->src_output(); + } } - TF_RETURN_IF_ERROR(status); // Re-map outgoing edges to use the new TRT node instead of the orig subgraph @@ -283,6 +290,8 @@ tensorflow::Status ConvertSubGraphToTensorRT(ConvertGraphParams* params) { int new_src_output = subgraph_edge_to_output_map.at(old_src); TF_RETURN_IF_ERROR(params->graph.UpdateEdge( trt_node, new_src_output, edge->dst(), edge->dst_input())); + VLOG(1) << "Wire " << trt_node->name() << ":" << new_src_output << " -> " + << edge->dst()->name() << ":" << edge->dst_input(); } // Remove the original subgraph for (int node_id : params->subgraph_node_ids) { @@ -317,9 +326,12 @@ tensorflow::Status ConvertCalibGraphToInferGraph( tensorflow::GraphConstructorOptions(), graph_def, &graph)); // get calib nodes std::vector calib_nodes; - for (auto node : graph.op_nodes()) { + std::vector topo_order; + tensorflow::GetPostOrder(graph, &topo_order); + for (auto rit = topo_order.rbegin(); rit != topo_order.rend(); ++rit) { + auto node = *rit; if (node->type_string() == "TRTCalibOp") { - VLOG(1) << "Found Calib Node"; + VLOG(1) << "Found Calib Node " << node->name(); calib_nodes.push_back(node); } } diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index 96e0700862..4e4d295538 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -362,10 +362,11 @@ void ReorderCKtoKC(const TRT_ShapedWeights& iweights, break; } case tensorflow::DataType::DT_HALF: { - Reorder2({k, c}, static_cast(iweights.GetValues()), - istrides, static_cast( - const_cast(oweights->GetValues())), - ostrides); + Reorder2( + {k, c}, static_cast(iweights.GetValues()), + istrides, + static_cast(const_cast(oweights->GetValues())), + ostrides); break; } default: @@ -1179,9 +1180,9 @@ tensorflow::Status BinaryTensorOpTensor( CHECK_EQ_TYPE(tensor_r->getType(), dtype); auto op_pair = ops.find(node_def.op()); if (op_pair == ops.end()) - return tensorflow::errors::Unimplemented("binary op: " + node_def.op() + - " not supported at: " + - node_def.name()); + return tensorflow::errors::Unimplemented( + "binary op: " + node_def.op() + + " not supported at: " + node_def.name()); nvinfer1::IElementWiseLayer* layer = ctx.network()->addElementWise( *const_cast(tensor_l), @@ -2138,9 +2139,7 @@ void Converter::register_op_converters() { } } // namespace -tensorflow::Status GetTensorRTGraph(tensorrt::convert::SubGraphParams& s) { - return tensorflow::errors::Unimplemented("Not implemented yet"); -} + tensorflow::Status ConvertCalibrationNodeToEngineNode( tensorflow::Graph& graph, tensorflow::Node* c_node) { const auto ndef = c_node->def(); @@ -2164,9 +2163,23 @@ tensorflow::Status ConvertCalibrationNodeToEngineNode( for (auto n : graph.op_nodes()) { node_maps.insert({n->name(), n}); } + std::set subgraph_ids; + for (const auto internal_node : segment_nodes) { + subgraph_ids.insert(node_maps.at(internal_node)->id()); + } + if (VLOG_IS_ON(2)) { + string node_names = StrCat(c_node->name(), " segment nodes= "); + + for (const auto& node_name : segment_nodes) { + StrAppend(&node_names, node_name, ", "); + } + VLOG(2) << node_names; + } + VLOG(1) << "Output Nodes:"; std::vector out_types; std::vector out_edges; + for (auto& i : output_nodes) { auto node_port = tensorflow::str_util::Split(i, ":"); VLOG(1) << " " << i << " in graph " << node_maps.count(i); @@ -2186,18 +2199,24 @@ tensorflow::Status ConvertCalibrationNodeToEngineNode( out_types.push_back(out_node->output_type(0)); } for (auto out_edge : out_node->out_edges()) { + if (subgraph_ids.count(out_edge->dst()->id())) + continue; // skip internal edges; if (out_edge->src_output() == port) { out_edges.push_back(out_edge); - break; + VLOG(1) << "OUTPUT EDGE " << out_edge->src()->name() << ":" + << out_edge->src_output() << " -> " << out_edge->dst()->name() + << ":" << out_edge->dst_input(); } } } else { LOG(WARNING) << " couldn't find output node " << out_node_name; } } - VLOG(1) << "Input Nodes:"; - for (auto& i : input_names) { - VLOG(1) << " " << i << " in graph " << node_maps.count(i); + if (VLOG_IS_ON(1)) { + VLOG(1) << c_node->name() << " Input Nodes:"; + for (auto& i : input_names) { + VLOG(1) << " Input " << i << " in graph " << node_maps.count(i); + } } auto trt_rm = tensorflow::tensorrt::TRTResourceManager::instance(); auto resmgr = trt_rm->getManager("TRTCalibOps"); @@ -2231,14 +2250,24 @@ tensorflow::Status ConvertCalibrationNodeToEngineNode( calib_res->builder_ = nullptr; tensorflow::NodeDefBuilder op_builder(engine_name, "TRTEngineOp"); std::vector income_edges; + income_edges.resize(c_node->num_inputs()); for (const auto in_edge : c_node->in_edges()) { auto src = in_edge->src(); int dest_port = in_edge->dst_input(); - income_edges.emplace_back(src->name(), in_edge->src_output(), - c_node->input_type(dest_port)); + VLOG(1) << "Incoming connection " << src->name() << ":" + << in_edge->src_output() << " -> " << c_node->name() << ":" + << dest_port; + income_edges.at(dest_port) = {src->name(), in_edge->src_output(), + c_node->input_type(dest_port)}; } tensorflow::gtl::ArraySlice input_list( income_edges); + if (VLOG_IS_ON(2)) { + for (const auto& inp : input_list) { + VLOG(2) << " Input from inputlist " << inp.node << ":" << inp.index << " " + << tensorflow::DataTypeString(inp.data_type); + } + } op_builder.Input(input_list); tensorflow::NodeDef engine_node; const char* engine_plan_data = static_cast(engine_plan->data()); @@ -2255,13 +2284,26 @@ tensorflow::Status ConvertCalibrationNodeToEngineNode( } auto trt_engine_node = graph.AddNode(engine_node, &status); TF_RETURN_IF_ERROR(status); - for (size_t i = 0; i < out_edges.size(); i++) { - VLOG(1) << "Connecting trt_engine_node output " << i << " with " - << out_edges.at(i)->dst()->name() << " port " - << out_edges.at(i)->dst_input(); - TF_RETURN_IF_ERROR(graph.UpdateEdge(trt_engine_node, i, - out_edges.at(i)->dst(), - out_edges.at(i)->dst_input())); + std::map port_map; + for (size_t t = 0; t < output_nodes.size(); t++) { + port_map.insert({output_nodes.at(t), t}); + } + for (auto& i : out_edges) { + string s(i->src()->name()); + if (i->src_output()) StrAppend(&s, ":", i->src_output()); + int out_port = port_map.at(s); + VLOG(1) << "Connecting " << trt_engine_node->name() << ":" << out_port + << " -> " << i->dst()->name() << ":" << i->dst_input(); + TF_RETURN_IF_ERROR( + graph.UpdateEdge(trt_engine_node, out_port, i->dst(), i->dst_input())); + } + for (const auto ed : trt_engine_node->in_edges()) { + VLOG(1) << "In Edge " << ed->src()->name() << ":" << ed->src_output() + << " -> " << ed->dst()->name() << ":" << ed->dst_input(); + } + for (const auto ed : trt_engine_node->out_edges()) { + VLOG(1) << "Out Edge " << ed->src()->name() << ":" << ed->src_output() + << " -> " << ed->dst()->name() << ":" << ed->dst_input(); } VLOG(1) << "Segment nodes:"; for (auto& i : segment_nodes) { @@ -2332,6 +2374,7 @@ tensorflow::Status ConvertSubgraph( std::vector* output_names, std::vector* output_dtypes, const string& engine_name) { + std::set added_tensors; for (const std::pair& input : s.input_inds) { VLOG(2) << "parsing input. Node id= " << input.first; int node_id = input.first; @@ -2374,7 +2417,6 @@ tensorflow::Status ConvertSubgraph( auto op_info = op_info_vec.at(shape_inference_output_idx); tensorflow::DataType tf_dtype = op_info.dtype(); - input_dtypes->push_back(tf_dtype); nvinfer1::DataType dtype(nvinfer1::DataType::kFLOAT); auto type_status = ConvertDType(tf_dtype, &dtype); @@ -2410,8 +2452,10 @@ tensorflow::Status ConvertSubgraph( if (output_idx != 0) { input_tensor_name = StrCat(node_name, ":", output_idx); } - + if (added_tensors.count(input_tensor_name)) continue; + added_tensors.insert(input_tensor_name); input_names->push_back(input_tensor_name); + input_dtypes->push_back(tf_dtype); nvinfer1::ITensor* input_tensor = converter.network()->addInput( input_tensor_name.c_str(), dtype, input_dim_pseudo_chw); @@ -2435,6 +2479,7 @@ tensorflow::Status ConvertSubgraph( // Gather output metadata int trt_engine_op_output_idx = 0; + added_tensors.clear(); for (const std::pair& output : s.output_inds) { int node_id = output.first; int output_idx = output.second; @@ -2451,6 +2496,8 @@ tensorflow::Status ConvertSubgraph( if (output_idx != 0) tensorflow::strings::StrAppend(&tensor_name, ":", output_idx); VLOG(2) << "Output tensor name: " << tensor_name; + if (added_tensors.count(tensor_name)) continue; + added_tensors.insert(tensor_name); output_names->push_back(tensor_name); auto tensor_or_weights = converter.get_tensor(tensor_name); if (!tensor_or_weights.is_tensor()) { diff --git a/tensorflow/contrib/tpu/python/tpu/datasets.py b/tensorflow/contrib/tpu/python/tpu/datasets.py index 2e472a2805..d879170b68 100644 --- a/tensorflow/contrib/tpu/python/tpu/datasets.py +++ b/tensorflow/contrib/tpu/python/tpu/datasets.py @@ -166,11 +166,21 @@ def StreamingFilesDataset(files, return remote_iterator.get_next() def MapFn(unused_input): - return functional_ops.remote_call( + if isinstance(source_dataset.output_types, dtypes.DType): + output_types = [source_dataset.output_types] + elif isinstance(source_dataset.output_types, (list, tuple)): + output_types = source_dataset.output_types + else: + raise ValueError('source dataset has invalid output types') + remote_calls = functional_ops.remote_call( args=[source_handle], - Tout=[dtypes.string], + Tout=output_types, f=LoadingFunc, - target='/job:%s/replica:0/task:0/cpu:0' % file_reader_job)[0] + target='/job:%s/replica:0/task:0/cpu:0' % file_reader_job) + if len(remote_calls) == 1: + return remote_calls[0] + else: + return remote_calls with ops.device('/job:%s' % worker_job): output_dataset = dataset_ops.Dataset.range(2).repeat().map( diff --git a/tensorflow/contrib/tpu/python/tpu/datasets_test.py b/tensorflow/contrib/tpu/python/tpu/datasets_test.py index 918cf0ed8e..b58d05eac5 100644 --- a/tensorflow/contrib/tpu/python/tpu/datasets_test.py +++ b/tensorflow/contrib/tpu/python/tpu/datasets_test.py @@ -26,6 +26,8 @@ from tensorflow.core.protobuf import config_pb2 from tensorflow.python.client import session from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.ops import readers +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import tensor_shape from tensorflow.python.lib.io import python_io from tensorflow.python.platform import test from tensorflow.python.training import server_lib @@ -162,6 +164,30 @@ class DatasetsTest(test.TestCase): self.assertEqual(set(all_contents), set(retrieved_values)) + def testArbitraryReaderFuncFromDatasetGenerator(self): + + def my_generator(): + yield (1, [1] * 10) + + def gen_dataset(dummy): + return dataset_ops.Dataset.from_generator( + my_generator, (dtypes.int64, dtypes.int64), + (tensor_shape.TensorShape([]), tensor_shape.TensorShape([10]))) + + dataset = datasets.StreamingFilesDataset( + dataset_ops.Dataset.range(10), filetype=gen_dataset) + + iterator = dataset.make_initializable_iterator() + self._sess.run(iterator.initializer) + get_next = iterator.get_next() + + retrieved_values = self._sess.run(get_next) + + self.assertIsInstance(retrieved_values, (list, tuple)) + self.assertEqual(len(retrieved_values), 2) + self.assertEqual(retrieved_values[0], 1) + self.assertItemsEqual(retrieved_values[1], [1] * 10) + def testUnexpectedFiletypeString(self): with self.assertRaises(ValueError): datasets.StreamingFilesDataset( diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index d89633199d..b1c224a345 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -699,7 +699,9 @@ cc_library( srcs = ["platform/stacktrace_handler.cc"], hdrs = ["platform/stacktrace_handler.h"], deps = [ + ":abi", ":lib_platform", + ":stacktrace", ], ) @@ -3089,6 +3091,8 @@ cc_library( # we now need at least "str_util". ":lib", ":lib_platform", + ":stacktrace_handler", + ":test_lite", "//tensorflow/core/platform/default/build_config:test_lite_main", ], alwayslink = 1, @@ -3569,7 +3573,10 @@ tf_cc_tests_gpu( tf_cc_test_mkl( name = "mkl_runtime_tests", size = "small", - srcs = ["common_runtime/mkl_cpu_allocator_test.cc"], + srcs = [ + "common_runtime/mkl_cpu_allocator_test.cc", + "common_runtime/mkl_threadpool_device_test.cc", + ], linkstatic = 1, deps = [ ":core", diff --git a/tensorflow/core/api_def/base_api/api_def_Selu.pbtxt b/tensorflow/core/api_def/base_api/api_def_Selu.pbtxt index cbe76de415..985f09312f 100644 --- a/tensorflow/core/api_def/base_api/api_def_Selu.pbtxt +++ b/tensorflow/core/api_def/base_api/api_def_Selu.pbtxt @@ -4,6 +4,10 @@ op { description: < 0`, limit of the split of the result. +END + } + summary: "Split elements of `source` based on `sep` into a `SparseTensor`." + description: <2<><>3"` and +sep of `"<>"` returns `["1", "2", "", "3"]`. If `sep` is None or an empty +string, consecutive whitespace are regarded as a single separator, and the +result will contain no empty strings at the startor end if the string has +leading or trailing whitespace. + +Note that the above mentioned behavior matches python's str.split. +END +} diff --git a/tensorflow/core/api_def/python_api/api_def_StringSplitV2.pbtxt b/tensorflow/core/api_def/python_api/api_def_StringSplitV2.pbtxt new file mode 100644 index 0000000000..0e8576fb01 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_StringSplitV2.pbtxt @@ -0,0 +1,4 @@ +op { + graph_op_name: "StringSplitV2" + visibility: HIDDEN +} diff --git a/tensorflow/core/common_runtime/bfc_allocator.cc b/tensorflow/core/common_runtime/bfc_allocator.cc index 8f2a419756..9cda17867b 100644 --- a/tensorflow/core/common_runtime/bfc_allocator.cc +++ b/tensorflow/core/common_runtime/bfc_allocator.cc @@ -86,7 +86,7 @@ BFCAllocator::Chunk* BFCAllocator::ChunkFromHandle(ChunkHandle h) { return &(chunks_[h]); } -bool BFCAllocator::Extend(size_t rounded_bytes) { +bool BFCAllocator::Extend(size_t alignment, size_t rounded_bytes) { size_t available_bytes = memory_limit_ - total_region_allocated_bytes_; // Rounds available_bytes down to the nearest multiple of kMinAllocationSize. available_bytes = (available_bytes / kMinAllocationSize) * kMinAllocationSize; @@ -108,7 +108,7 @@ bool BFCAllocator::Extend(size_t rounded_bytes) { // Try allocating. size_t bytes = std::min(curr_region_allocation_bytes_, available_bytes); - void* mem_addr = suballocator_->Alloc(32, bytes); + void* mem_addr = suballocator_->Alloc(alignment, bytes); if (mem_addr == nullptr && !started_backpedal_) { // Only backpedal once. started_backpedal_ = true; @@ -119,7 +119,7 @@ bool BFCAllocator::Extend(size_t rounded_bytes) { while (mem_addr == nullptr) { bytes = RoundedBytes(bytes * kBackpedalFactor); if (bytes < rounded_bytes) break; - mem_addr = suballocator_->Alloc(32, bytes); + mem_addr = suballocator_->Alloc(alignment, bytes); } } @@ -261,7 +261,7 @@ void* BFCAllocator::AllocateRawInternal(size_t unused_alignment, } // Try to extend - if (Extend(rounded_bytes)) { + if (Extend(unused_alignment, rounded_bytes)) { ptr = FindChunkPtr(bin_num, rounded_bytes, num_bytes); if (ptr != nullptr) { return ptr; diff --git a/tensorflow/core/common_runtime/bfc_allocator.h b/tensorflow/core/common_runtime/bfc_allocator.h index ba5a3eea3a..52aedb1e9c 100644 --- a/tensorflow/core/common_runtime/bfc_allocator.h +++ b/tensorflow/core/common_runtime/bfc_allocator.h @@ -305,7 +305,8 @@ class BFCAllocator : public VisitableAllocator { // Try to add a new memory region that can satisfy an allocation of // 'rounded_bytes' bytes. Returns true on success and false on // failure. - bool Extend(size_t rounded_bytes) EXCLUSIVE_LOCKS_REQUIRED(lock_); + bool Extend(size_t alignment, size_t rounded_bytes) + EXCLUSIVE_LOCKS_REQUIRED(lock_); // Returns a pointer to an underlying allocated chunk of size // 'rounded_bytes'. diff --git a/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc b/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc index c21a1ea9f2..9028e6298c 100644 --- a/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc +++ b/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc @@ -102,9 +102,25 @@ TEST(DirectSessionWithTrackingAllocTest, CostModelTest) { EXPECT_EQ(2, shape.dim(0).size()); EXPECT_EQ(1, shape.dim(1).size()); if (node->name() == y->name()) { +#ifdef INTEL_MKL + // if MKL is used, it goes through various additional + // graph rewrite pass. In TF, everytime a graph pass + // happens, "constant" nodes are allocated + // and deallocated. Each allocation calls the + // (FindChunkPtr of BFCAllocator), + // which increments the value of AllocationId. + // Thus AllocationId becomes more than 3 and 4 if + // MKL is used. Now they are 9 and 10 for MKL. + EXPECT_EQ(19, cm->AllocationId(node, 0)); +#else EXPECT_EQ(21, cm->AllocationId(node, 0)); +#endif } else { +#ifdef INTEL_MKL + EXPECT_EQ(20, cm->AllocationId(node, 0)); +#else EXPECT_EQ(22, cm->AllocationId(node, 0)); +#endif } } EXPECT_LE(0, cm->MaxExecutionTime(node)); diff --git a/tensorflow/core/common_runtime/mkl_threadpool_device_test.cc b/tensorflow/core/common_runtime/mkl_threadpool_device_test.cc new file mode 100644 index 0000000000..5d583a8360 --- /dev/null +++ b/tensorflow/core/common_runtime/mkl_threadpool_device_test.cc @@ -0,0 +1,53 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifdef INTEL_MKL + +#include "tensorflow/core/common_runtime/threadpool_device.h" + +#include "tensorflow/core/lib/core/status_test_util.h" +#include "tensorflow/core/platform/cpu_info.h" +#include "tensorflow/core/platform/logging.h" +#include "tensorflow/core/platform/test.h" +#include "tensorflow/core/public/session_options.h" + +namespace tensorflow { + +#ifdef _OPENMP +TEST(MKLThreadPoolDeviceTest, TestOmpDefaults) { + SessionOptions options; + unsetenv("OMP_NUM_THREADS"); + + ThreadPoolDevice* tp = new ThreadPoolDevice( + options, "/device:CPU:0", Bytes(256), DeviceLocality(), cpu_allocator()); + + const int ht = port::NumHyperthreadsPerCore(); + EXPECT_EQ(omp_get_max_threads(), (port::NumSchedulableCPUs() + ht - 1) / ht); +} + +TEST(MKLThreadPoolDeviceTest, TestOmpPreSets) { + SessionOptions options; + setenv("OMP_NUM_THREADS", "314", 1); + + ThreadPoolDevice* tp = new ThreadPoolDevice( + options, "/device:CPU:0", Bytes(256), DeviceLocality(), cpu_allocator()); + + EXPECT_EQ(omp_get_max_threads(), 314); +} +#endif // _OPENMP + +} // namespace tensorflow + +#endif // INTEL_MKL diff --git a/tensorflow/core/common_runtime/process_util.cc b/tensorflow/core/common_runtime/process_util.cc index 21912236d0..a5d31b75c7 100644 --- a/tensorflow/core/common_runtime/process_util.cc +++ b/tensorflow/core/common_runtime/process_util.cc @@ -16,8 +16,10 @@ limitations under the License. #include "tensorflow/core/common_runtime/process_util.h" #ifdef INTEL_MKL +#ifdef _OPENMP #include -#endif +#endif // _OPENMP +#endif // INTEL_MKL #include #include "tensorflow/core/lib/core/threadpool.h" @@ -57,7 +59,10 @@ int32 NumInterOpThreadsFromSessionOptions(const SessionOptions& options) { // MKL library executes ops in parallel using OMP threads // Set inter_op conservatively to avoid thread oversubscription that could // lead to severe perf degradations and OMP resource exhaustion - const int mkl_intra_op = omp_get_max_threads(); + int mkl_intra_op = 1; +#ifdef _OPENMP + mkl_intra_op = omp_get_max_threads(); +#endif // _OPENMP CHECK_GE(mkl_intra_op, 1); const int32 mkl_inter_op = std::max( (port::NumSchedulableCPUs() + mkl_intra_op - 1) / mkl_intra_op, 2); @@ -68,7 +73,7 @@ int32 NumInterOpThreadsFromSessionOptions(const SessionOptions& options) { #else // Default to using the number of cores available in the process. return port::NumSchedulableCPUs(); -#endif +#endif // INTEL_MKL } thread::ThreadPool* NewThreadPoolFromSessionOptions( diff --git a/tensorflow/core/common_runtime/threadpool_device.cc b/tensorflow/core/common_runtime/threadpool_device.cc index f7a07fe503..74a87215e1 100644 --- a/tensorflow/core/common_runtime/threadpool_device.cc +++ b/tensorflow/core/common_runtime/threadpool_device.cc @@ -31,7 +31,11 @@ limitations under the License. #include "tensorflow/core/public/session_options.h" #ifdef INTEL_MKL +#ifdef _OPENMP +#include +#endif #include "tensorflow/core/common_runtime/mkl_cpu_allocator.h" +#include "tensorflow/core/platform/cpu_info.h" #endif namespace tensorflow { @@ -43,7 +47,26 @@ ThreadPoolDevice::ThreadPoolDevice(const SessionOptions& options, : LocalDevice(options, Device::BuildDeviceAttributes( name, DEVICE_CPU, memory_limit, locality)), allocator_(allocator), - scoped_allocator_mgr_(new ScopedAllocatorMgr(name)) {} + scoped_allocator_mgr_(new ScopedAllocatorMgr(name)) { +#ifdef INTEL_MKL +#ifdef _OPENMP + const char* user_omp_threads = getenv("OMP_NUM_THREADS"); + if (user_omp_threads == nullptr) { + // OMP_NUM_THREADS controls MKL's intra-op parallelization + // Default to available physical cores + const int mkl_intra_op = port::NumSchedulableCPUs(); + const int ht = port::NumHyperthreadsPerCore(); + omp_set_num_threads((mkl_intra_op + ht - 1) / ht); + } else { + uint64 user_val = 0; + if (strings::safe_strtou64(user_omp_threads, &user_val)) { + // Superflous but triggers OpenMP loading + omp_set_num_threads(user_val); + } + } +#endif // _OPENMP +#endif // INTEL_MKL +} ThreadPoolDevice::~ThreadPoolDevice() {} diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_master_service_impl.cc b/tensorflow/core/distributed_runtime/rpc/grpc_master_service_impl.cc index 1cea1b1462..770a0fcf14 100644 --- a/tensorflow/core/distributed_runtime/rpc/grpc_master_service_impl.cc +++ b/tensorflow/core/distributed_runtime/rpc/grpc_master_service_impl.cc @@ -147,7 +147,9 @@ MasterService::Stub::Stub( } MasterService::AsyncService::AsyncService() { - for (int i = 0; i < 10; ++i) { + int method_len = sizeof(grpcMasterService_method_names) / + sizeof(grpcMasterService_method_names[0]); + for (int i = 0; i < method_len; ++i) { AddMethod(new ::grpc::internal::RpcServiceMethod( grpcMasterService_method_names[i], ::grpc::internal::RpcMethod::NORMAL_RPC, nullptr)); diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_testlib.cc b/tensorflow/core/distributed_runtime/rpc/grpc_testlib.cc index 89f83f9f24..a8508d2d4f 100644 --- a/tensorflow/core/distributed_runtime/rpc/grpc_testlib.cc +++ b/tensorflow/core/distributed_runtime/rpc/grpc_testlib.cc @@ -17,6 +17,7 @@ limitations under the License. #include "tensorflow/core/distributed_runtime/rpc/grpc_session.h" #include "tensorflow/core/lib/strings/str_util.h" +#include "tensorflow/core/platform/env.h" #include "tensorflow/core/util/device_name_utils.h" namespace tensorflow { @@ -50,9 +51,14 @@ Status TestCluster::MakeTestCluster(const SessionOptions& options, int n, } for (int i = 0; i < n; ++i) { + string server_file = + strings::StrCat(testing::TensorFlowSrcRoot(), + "/core/distributed_runtime/rpc/grpc_testlib_server"); + if (!options.env->FileExists(server_file).ok()) { + return errors::Internal("Could not find grpc_testlib_server"); + } const std::vector argv( - {strings::StrCat(testing::TensorFlowSrcRoot(), - "/core/distributed_runtime/rpc/grpc_testlib_server"), + {server_file, /* see grpc_testlib_server.cc for flags */ tf_jobs, "--tf_job=localhost", strings::StrCat("--tf_task=", i), strings::StrCat("--num_cpus=", num_cpus), diff --git a/tensorflow/core/framework/allocator.h b/tensorflow/core/framework/allocator.h index 2c87156dca..2bb4d32d57 100644 --- a/tensorflow/core/framework/allocator.h +++ b/tensorflow/core/framework/allocator.h @@ -67,13 +67,8 @@ struct AllocatorStats { // device memory. class Allocator { public: -#ifdef EIGEN_VECTORIZE_AVX512 // Align to 64 byte boundary. static constexpr size_t kAllocatorAlignment = 64; -#else - // Align to 32 byte boundary. - static constexpr size_t kAllocatorAlignment = 32; -#endif virtual ~Allocator(); diff --git a/tensorflow/core/framework/op_gen_lib.cc b/tensorflow/core/framework/op_gen_lib.cc index 3d7920a6e2..4b56d807df 100644 --- a/tensorflow/core/framework/op_gen_lib.cc +++ b/tensorflow/core/framework/op_gen_lib.cc @@ -15,6 +15,7 @@ limitations under the License. #include "tensorflow/core/framework/op_gen_lib.h" +#include #include #include "tensorflow/core/framework/attr_value.pb.h" #include "tensorflow/core/lib/core/errors.h" diff --git a/tensorflow/core/framework/remote_fused_graph_execute_info.proto b/tensorflow/core/framework/remote_fused_graph_execute_info.proto index eb689ec1e6..10072724d2 100644 --- a/tensorflow/core/framework/remote_fused_graph_execute_info.proto +++ b/tensorflow/core/framework/remote_fused_graph_execute_info.proto @@ -5,7 +5,7 @@ option cc_enable_arenas = true; option java_outer_classname = "RemoteFusedGraphExecuteInfoProto"; option java_multiple_files = true; option java_package = "org.tensorflow.framework"; -//add go_package externally +option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework"; import "tensorflow/core/framework/graph.proto"; import "tensorflow/core/framework/tensor_shape.proto"; import "tensorflow/core/framework/types.proto"; diff --git a/tensorflow/core/framework/tensor_test.cc b/tensorflow/core/framework/tensor_test.cc index b613effd18..80e168df97 100644 --- a/tensorflow/core/framework/tensor_test.cc +++ b/tensorflow/core/framework/tensor_test.cc @@ -1147,29 +1147,29 @@ TEST(Tensor, FailureToAllocate) { // On the alignment. // -// As of 2015/8, tensorflow::Tensor allocates its buffer with 32-byte +// As of 2018/5, tensorflow::Tensor allocates its buffer with 64-byte // alignment. Tensor::tensor/flat/vec/matrix methods requires the // buffer satisfies Eigen::Aligned (e.g., 16-bytes aligned usually, -// and 32-bytes for AVX). Tensor::Slice requires the caller to ensure -// its result is aligned if the caller intends to use those methods. -// In this test case, we simply make sure each slice is 32-byte -// aligned: sizeof(float) * 4 * 2 = 32. +// 32-bytes for AVX, and 64-bytes for AVX512). Tensor::Slice requires +// the caller to ensure its result is aligned if the caller intends +// to use those methods. In this test case, we simply make sure each +// slice is 64-byte aligned: sizeof(float) * 4 * 36 = 576. 576 % 64 = 0. TEST(Tensor, Slice_Basic) { Tensor saved; { // General - Tensor x(DT_FLOAT, TensorShape({10, 4, 34})); + Tensor x(DT_FLOAT, TensorShape({10, 4, 36})); // Fills in known values. for (int i = 0; i < 10; ++i) { x.Slice(i, i + 1).flat().setConstant(i * 1.f); } // A simple slice along dim0. Tensor y = x.Slice(4, 8); - EXPECT_TRUE(y.shape().IsSameSize(TensorShape({4, 4, 34}))); + EXPECT_TRUE(y.shape().IsSameSize(TensorShape({4, 4, 36}))); auto tx = x.tensor(); auto ty = y.tensor(); for (int i = 0; i < 4; ++i) { for (int j = 0; j < 4; ++j) { - for (int k = 0; k < 34; ++k) { + for (int k = 0; k < 36; ++k) { EXPECT_EQ(ty(i, j, k), 4.0 + i); EXPECT_EQ(&tx(4 + i, j, k), &ty(i, j, k)); } @@ -1186,7 +1186,7 @@ TEST(Tensor, Slice_Basic) { auto tz = z.tensor(); EXPECT_EQ(1, z.dim_size(0)); for (int j = 0; j < 4; ++j) { - for (int k = 0; k < 34; ++k) { + for (int k = 0; k < 36; ++k) { EXPECT_EQ(tz(0, j, k), 6.0); } } @@ -1198,16 +1198,16 @@ TEST(Tensor, Slice_Basic) { EXPECT_EQ(1, saved.dim_size(0)); auto tsaved = saved.tensor(); for (int j = 0; j < 4; ++j) { - for (int k = 0; k < 34; ++k) { + for (int k = 0; k < 36; ++k) { EXPECT_EQ(tsaved(0, j, k), 6.0); } } } { // Empty - Tensor x(DT_FLOAT, TensorShape({10, 0, 34})); + Tensor x(DT_FLOAT, TensorShape({10, 0, 36})); x.flat().setRandom(); Tensor y = x.Slice(4, 8); - EXPECT_TRUE(y.shape().IsSameSize(TensorShape({4, 0, 34}))); + EXPECT_TRUE(y.shape().IsSameSize(TensorShape({4, 0, 36}))); } { diff --git a/tensorflow/core/graph/mkl_layout_pass.cc b/tensorflow/core/graph/mkl_layout_pass.cc index 72a13d4da7..b9667998d6 100644 --- a/tensorflow/core/graph/mkl_layout_pass.cc +++ b/tensorflow/core/graph/mkl_layout_pass.cc @@ -2691,14 +2691,14 @@ class MklLayoutRewritePass : public GraphOptimizationPass { // If Op has been specifically assigned to a non-CPU device, then No. if (!n->assigned_device_name().empty() && - !str_util::StrContains(n->assigned_device_name(),kCPUDeviceSubStr)) { + !str_util::StrContains(n->assigned_device_name(), kCPUDeviceSubStr)) { result = false; reason = "Op has been assigned a runtime device that is not CPU."; } // If user has specifically assigned this op to a non-CPU device, then No. if (!n->def().device().empty() && - !str_util::StrContains(n->def().device(),kCPUDeviceSubStr)) { + !str_util::StrContains(n->def().device(), kCPUDeviceSubStr)) { result = false; reason = "User has assigned a device that is not CPU."; } @@ -2865,9 +2865,9 @@ class MklLayoutRewritePass : public GraphOptimizationPass { return false; } - // If the depth_radius of LRN is not 2, then MKL DNN takes unoptimized - // path. The unoptimized path is slow. Thus we dont rewrite the node - // and use default Eigen. But for depth_radius=2, MKL DNN optimized + // If the depth_radius of LRN is not 2, then MKL DNN takes unoptimized + // path. The unoptimized path is slow. Thus we dont rewrite the node + // and use default Eigen. But for depth_radius=2, MKL DNN optimized // path is taken, i.e., eigen node is rewritten by MKl DNN node. static bool LrnRewrite(const Node* n) { CHECK_NOTNULL(n); @@ -2876,13 +2876,13 @@ class MklLayoutRewritePass : public GraphOptimizationPass { CHECK_EQ(GetNodeAttr(n->def(), "depth_radius", &depth_radius).ok(), true); // if the depth_radius of LRN is not 2, don't rewrite the node by MKL DNN - // and use eigen node instead + // and use eigen node instead if (depth_radius == 2) { return true; } VLOG(1) << "LrnRewrite: The model sets depth_radius as not 2 which" << "case is not optimized by Intel MKL, thus using Eigen op" - << "for LRN " ; + << "for LRN "; return false; } @@ -3015,6 +3015,35 @@ class MklLayoutRewritePass : public GraphOptimizationPass { std::vector* ws_tensors, bool* are_ws_tensors_added); + // Helper function used by FixMklMetaDataEdges. Fixes the metadata edge + // pointed by 'e_metadata' corresponding to the data edge 'e_data' in graph + // 'g'. Returns true is fixup was done; otherwise, it returns false. + bool FixMklMetaDataEdgeIfNeeded(std::unique_ptr* g, + const Edge* e_data, const Edge* e_metadata); + + // Are the input Mkl metadata edges for node 'n' in graph 'g' correctly + // connected? If not, then fix them. This is needed because a graph may have + // some input Mkl metadata edges incorrectly setup after node merge and + // rewrite passes. This could happen because GetReversePostOrder function may + // not provide topologically sorted order if a graph contains cycles. The + // function returns true if at least one Mkl metadata edge for node 'n' was + // fixed. Otherwise, it returns false. + // + // Example: + // + // X = MklConv2D(_, _, _) + // Y = MklConv2DWithBias(_, _, _, _, _, _) + // Z = MklAdd(X, Y, DummyMklTensor, Y:1) + // + // For a graph such as shown above, note that 3rd argument of MklAdd contains + // DummyMklTensor. Actually, it should be getting the Mkl metadata from + // MklConv2D op (specifically, X:2). This incorrect plumbing could be possible + // (although rare) if the Mkl NodeMerge + NodeRewrite passes visit Z before X + // (possible if X, Y, Z are part of a loop.) This function fixes the Mkl + // metadata edges only - it does not rewrite nodes nor does it modify the Mkl + // data edges (1st and 2nd arguments of MklAdd). + bool FixMklMetaDataEdges(std::unique_ptr* g, Node* n); + // Functions specific to operators to copy attributes // We need operator-specific function to copy attributes because the framework // does not provide any generic function for it. @@ -4241,6 +4270,92 @@ MklLayoutRewritePass::CheckForNodeRewrite(const Node* n) const { return nullptr; } +/////////////////////////////////////////////////////////////////////////////// +// Post-rewrite Mkl metadata fixup pass +/////////////////////////////////////////////////////////////////////////////// +bool MklLayoutRewritePass::FixMklMetaDataEdgeIfNeeded(std::unique_ptr* g, + const Edge* e_data, const Edge* e_metadata) { + if (g == nullptr || e_data == nullptr || e_metadata == nullptr) { + return false; + } + + Node* n_data = e_data->src(); + int n_data_op_slot = e_data->src_output(); + int n_metadata_op_slot = GetTensorMetaDataIndex(n_data_op_slot, + n_data->num_outputs()); + + // If the source of meta edge is a constant node (producing dummy Mkl metadata + // tensor), then we will need to fix. + if (IsConstant(e_metadata->src())) { + Node* e_metadata_dst = e_metadata->dst(); + int e_metadata_in_slot = e_metadata->dst_input(); + CHECK_NOTNULL((*g)->AddEdge(n_data, n_metadata_op_slot, + e_metadata_dst, e_metadata_in_slot)); + + (*g)->RemoveEdge(e_metadata); + return true; + } + + return false; +} + +bool MklLayoutRewritePass::FixMklMetaDataEdges(std::unique_ptr* g, + Node* n) { + bool result = false; + + // If graph node is not Mkl node, then return. + DataType T = DT_INVALID; + if (!GetNodeAttr(n->def(), "T", &T).ok() || + !mkl_op_registry::IsMklOp(n->type_string(), T)) { + return result; + } + + // If it is Mkl node, then check if the input edges to this node that carry + // Mkl metadata are linked up correctly with the source node. + + // For Mkl nodes, we generate twice the number of input tensors (n for Mkl + // data tensors + n for Mkl metadata tensors). We need to check for correct + // connection of n metadata tensors only. + int num_data_inputs = n->num_inputs() / 2; + for (int idx = 0; idx < num_data_inputs; idx++) { + // Get the edge connecting input slot with index (idx). + const Edge* e = nullptr; + TF_CHECK_OK(n->input_edge(idx, &e)); + + // If e is control edge, then skip. + if (e->IsControlEdge()) { + continue; + } + + // Check that the source node for edge 'e' is Mkl node. If it is not an Mkl + // node, then we don't need to do anything. + Node* e_src = e->src(); + if (GetNodeAttr(e_src->def(), "T", &T).ok() && + mkl_op_registry::IsMklOp(e_src->type_string(), T)) { + // Source node for edge 'e' is Mkl node. + // Destination node and destination input slot of e is node 'n' and 'idx' + // resp. + CHECK_EQ(e->dst(), n); + CHECK_EQ(e->dst_input(), idx); + + // Let's get edge that carries Mkl metadata corresponding to Mkl data edge + // 'e'. For that, let's first get the input slot of 'n' where the meta + // edge will feed the value. + int e_meta_in_slot = GetTensorMetaDataIndex(e->dst_input(), + n->num_inputs()); + const Edge* e_meta = nullptr; + TF_CHECK_OK(n->input_edge(e_meta_in_slot, &e_meta)); + + // Let's check if we need to fix this meta edge. + if (FixMklMetaDataEdgeIfNeeded(g, e, e_meta)) { + result = true; + } + } + } + + return result; +} + /////////////////////////////////////////////////////////////////////////////// // Run function for the pass /////////////////////////////////////////////////////////////////////////////// @@ -4307,6 +4422,25 @@ bool MklLayoutRewritePass::RunPass(std::unique_ptr* g) { DumpGraph("After running MklLayoutRewritePass(NodeMerge+Rewrite)", &**g); + order.clear(); + GetReversePostOrder(**g, &order); // This will give us topological sort. + for (Node* n : order) { + // If node is not an op or it cannot run on CPU device, then skip. + if (!n->IsOp() || !CanOpRunOnCPUDevice(n)) { + continue; + } + if (FixMklMetaDataEdges(g, n)) { + string node_name = n->name(); + string op_name = n->type_string(); + + VLOG(1) << "MklLayoutRewritePass: fixed metadata edges for node " + << node_name << " with op " << op_name; + result = true; + } + } + DumpGraph("After running MklLayoutRewritePass(NodeMerge+Rewrite+Fixup)", + &**g); + return result; } diff --git a/tensorflow/core/graph/mkl_layout_pass_test.cc b/tensorflow/core/graph/mkl_layout_pass_test.cc index 029cdcf94a..7645b4a7f0 100644 --- a/tensorflow/core/graph/mkl_layout_pass_test.cc +++ b/tensorflow/core/graph/mkl_layout_pass_test.cc @@ -3518,6 +3518,37 @@ TEST_F(MklLayoutPassTest, NodeMerge_Conv2DWithBias_DeviceTest) { "B->C:1;C->E;D->E:1;E->Z;M->C:2;N->C:3;Y->Z:1"); } +///////////////////////////////////////////////////////////////////// +// Post-rewrite fixup pass test + +TEST_F(MklLayoutPassTest, PostRewriteFixUpPass) { + InitGraph( + "node { name: 'A' op: 'Input'}" + "node { name: 'B' op: 'Input'}" + "node { name: 'M' op: '_MklInput'}" + "node { name: 'N' op: '_MklInput'}" + "node { name: 'C' op: '_MklConv2D'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'data_format' value { s: 'NCHW' } }" + " attr { key: 'use_cudnn_on_gpu' value { b: false } }" + " attr { key: 'strides' value { list: {i: 1, i:1, i:1, i:1} } }" + " attr { key: 'padding' value { s: 'SAME' } }" + " attr { key: 'dilations' value { list: {i: 1, i:1, i:1, i:1} } }" + " input: ['A', 'B', 'M', 'N']}" + "node { name: 'D' op: 'Const' " + " attr { key: 'dtype' value { type: DT_UINT8 } }" + " attr { key: 'value' value { " + " tensor { dtype: DT_UINT8 tensor_shape { dim { size: 1 } } " + " int_val: 0 } } } }" + "node { name: 'E' op: '_MklAdd'" + " attr {key: 'T' value { type: DT_FLOAT } }" + " input: ['C', 'A', 'D', 'D']}"); + EXPECT_EQ(DoMklLayoutOptimizationPass(), + "A(Input);B(Input);C(_MklConv2D);D(Const);E(_MklAdd);" + "M(_MklInput);N(_MklInput)|A->C;A->E:1;B->C:1;C->E;C:2->E:2;" + "D->E:3;M->C:2;N->C:3"); +} + ///////////////////////////////////////////////////////////////////// static void BM_MklLayoutRewritePass(int iters, int op_nodes) { diff --git a/tensorflow/core/grappler/costs/graph_properties.cc b/tensorflow/core/grappler/costs/graph_properties.cc index 6749a7c571..0c02876ac5 100644 --- a/tensorflow/core/grappler/costs/graph_properties.cc +++ b/tensorflow/core/grappler/costs/graph_properties.cc @@ -610,7 +610,6 @@ class SymbolicShapeRefiner { } }; - // Compute the shape of the tensors outputed by node 'node' at output port // 'port_index' as the union of shape1 and shape2. ShapeHandle OutputAsUnion(const NodeDef* node, int port_index, ShapeHandle shape1, ShapeHandle shape2) { diff --git a/tensorflow/core/grappler/optimizers/BUILD b/tensorflow/core/grappler/optimizers/BUILD index 1b18087cdf..8ca726df0b 100644 --- a/tensorflow/core/grappler/optimizers/BUILD +++ b/tensorflow/core/grappler/optimizers/BUILD @@ -679,6 +679,7 @@ cc_library( deps = [ ":constant_folding", ":graph_optimizer", + "//tensorflow/core:lib", "//tensorflow/core:protos_all_cc", "//tensorflow/core/grappler:graph_view", "//tensorflow/core/grappler:grappler_item", @@ -780,7 +781,6 @@ cc_library( "//tensorflow/core:lib", "//tensorflow/core:lib_internal", "//tensorflow/core:protos_all_cc", - "//tensorflow/core:scoped_allocator_ops_op_lib", "//tensorflow/core/grappler:grappler_item", "//tensorflow/core/grappler:op_types", "//tensorflow/core/grappler:utils", diff --git a/tensorflow/core/grappler/optimizers/remapper.cc b/tensorflow/core/grappler/optimizers/remapper.cc index 4dde7ed1b4..03e36a7b9c 100644 --- a/tensorflow/core/grappler/optimizers/remapper.cc +++ b/tensorflow/core/grappler/optimizers/remapper.cc @@ -22,6 +22,7 @@ limitations under the License. #include "tensorflow/core/grappler/op_types.h" #include "tensorflow/core/grappler/optimizers/constant_folding.h" #include "tensorflow/core/grappler/utils.h" +#include "tensorflow/core/platform/logging.h" namespace tensorflow { namespace grappler { @@ -200,8 +201,7 @@ Status Remapper::Optimize(Cluster* /*cluster*/, const GrapplerItem& item, } } if (optimizable) { - VLOG(2) << "Optimizing fused batch norm node " << node.DebugString() - << std::endl; + VLOG(1) << "Optimizing fused batch norm node " << node.DebugString(); AddBatchNormNodes(optimized_graph, node); continue; } diff --git a/tensorflow/core/kernels/as_string_op.cc b/tensorflow/core/kernels/as_string_op.cc index 66c4aff3e3..a7757d1361 100644 --- a/tensorflow/core/kernels/as_string_op.cc +++ b/tensorflow/core/kernels/as_string_op.cc @@ -73,6 +73,7 @@ class AsStringOp : public OpKernel { } switch (dtype) { case DT_INT8: + case DT_INT16: case DT_INT32: strings::Appendf(&format_, "d"); break; @@ -129,6 +130,7 @@ class AsStringOp : public OpKernel { ENCODE_TYPE(DT_FLOAT, float, format_); ENCODE_TYPE(DT_DOUBLE, double, format_); ENCODE_TYPE(DT_INT8, int8, format_); + ENCODE_TYPE(DT_INT16, int16, format_); case (DT_BOOL): { const auto& input_flat = input_tensor->flat(); for (int i = 0; i < input_flat.size(); ++i) { diff --git a/tensorflow/core/kernels/cwise_op_clip.cc b/tensorflow/core/kernels/cwise_op_clip.cc index 14d889e8e3..49b90e855b 100644 --- a/tensorflow/core/kernels/cwise_op_clip.cc +++ b/tensorflow/core/kernels/cwise_op_clip.cc @@ -33,52 +33,41 @@ class ClipOp : public OpKernel { const Tensor& in0 = ctx->input(0); const Tensor& in1 = ctx->input(1); const Tensor& in2 = ctx->input(2); + OP_REQUIRES(ctx, (in0.shape() == in1.shape() || + TensorShapeUtils::IsScalar(in1.shape())) && + (in0.shape() == in2.shape() || + TensorShapeUtils::IsScalar(in2.shape())), + errors::InvalidArgument( + "clip_value_min and clip_value_max must be either of " + "the same shape as input, or a scalar. ", + "input shape: ", in0.shape().DebugString(), + "clip_value_min shape: ", in1.shape().DebugString(), + "clip_value_max shape: ", in2.shape().DebugString())); + + Tensor* out = nullptr; + OP_REQUIRES_OK( + ctx, ctx->forward_input_or_allocate_output({0}, 0, in0.shape(), &out)); + if (out->NumElements() == 0) return; // Nothing to do for empty output auto in0_flat = in0.flat(); auto in1_flat = in1.flat(); auto in2_flat = in2.flat(); + auto out_flat = out->flat(); const Device& d = ctx->eigen_device(); - Tensor* out = nullptr; - OP_REQUIRES_OK( - ctx, ctx->forward_input_or_allocate_output({0}, 0, in0.shape(), &out)); - auto out_flat = out->flat(); if (in1.shape() == in2.shape()) { if (in0.shape() == in1.shape()) { functor::TernaryClipOp()(d, in0_flat, in1_flat, in2_flat, out_flat); } else { - OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(in1.shape()), - errors::InvalidArgument( - "clip_value_min and clip_value_max must be either of " - "the same shape as input, or a scalar. ", - "input shape: ", in0.shape().DebugString(), - "clip_value_min shape: ", in1.shape().DebugString(), - "clip_value_max shape: ", in2.shape().DebugString())); functor::UnaryClipOp()(d, in0_flat, in1_flat, in2_flat, out_flat); } } else { if (in0.shape() == in1.shape()) { - OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(in2.shape()), - errors::InvalidArgument( - "clip_value_min and clip_value_max must be either of " - "the same shape as input, or a scalar. ", - "input shape: ", in0.shape().DebugString(), - "clip_value_min shape: ", in1.shape().DebugString(), - "clip_value_max shape: ", in2.shape().DebugString())); functor::BinaryLeftClipOp()(d, in0_flat, in1_flat, in2_flat, out_flat); } else { - OP_REQUIRES(ctx, - (in0.shape() == in2.shape() && - TensorShapeUtils::IsScalar(in1.shape())), - errors::InvalidArgument( - "clip_value_min and clip_value_max must be either of " - "the same shape as input, or a scalar. ", - "input shape: ", in0.shape().DebugString(), - "clip_value_min shape: ", in1.shape().DebugString(), - "clip_value_max shape: ", in2.shape().DebugString())); functor::BinaryRightClipOp()(d, in0_flat, in1_flat, in2_flat, out_flat); } diff --git a/tensorflow/core/kernels/dense_update_functor_gpu.cu.cc b/tensorflow/core/kernels/dense_update_functor_gpu.cu.cc index 9a3b2303a3..17a85d9773 100644 --- a/tensorflow/core/kernels/dense_update_functor_gpu.cu.cc +++ b/tensorflow/core/kernels/dense_update_functor_gpu.cu.cc @@ -57,6 +57,7 @@ struct DenseUpdate { template struct functor::DenseUpdate; \ template struct functor::DenseUpdate; TF_CALL_GPU_NUMBER_TYPES(DEFINE_GPU_KERNELS); +TF_CALL_int32(DEFINE_GPU_KERNELS); TF_CALL_int64(DEFINE_GPU_KERNELS); #undef DEFINE_GPU_KERNELS diff --git a/tensorflow/core/kernels/gather_functor.cc b/tensorflow/core/kernels/gather_functor.cc index e6fefe643b..5cd8e04927 100644 --- a/tensorflow/core/kernels/gather_functor.cc +++ b/tensorflow/core/kernels/gather_functor.cc @@ -37,6 +37,7 @@ namespace functor { DECLARE_GPU_SPECS_INDEX(T, int32); \ DECLARE_GPU_SPECS_INDEX(T, int64) +TF_CALL_int64(DECLARE_GPU_SPECS); TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_SPECS); TF_CALL_complex64(DECLARE_GPU_SPECS); TF_CALL_complex128(DECLARE_GPU_SPECS); diff --git a/tensorflow/core/kernels/gather_functor_gpu.cu.cc b/tensorflow/core/kernels/gather_functor_gpu.cu.cc index 39b6924d74..4563fc6353 100644 --- a/tensorflow/core/kernels/gather_functor_gpu.cu.cc +++ b/tensorflow/core/kernels/gather_functor_gpu.cu.cc @@ -31,6 +31,7 @@ typedef Eigen::GpuDevice GPUDevice; DEFINE_GPU_SPECS_INDEX(T, int32); \ DEFINE_GPU_SPECS_INDEX(T, int64); +TF_CALL_int64(DEFINE_GPU_SPECS); TF_CALL_GPU_NUMBER_TYPES(DEFINE_GPU_SPECS); TF_CALL_complex64(DEFINE_GPU_SPECS); TF_CALL_complex128(DEFINE_GPU_SPECS); diff --git a/tensorflow/core/kernels/gather_nd_op.cc b/tensorflow/core/kernels/gather_nd_op.cc index 7e5a9e1ec5..4e53291b7f 100644 --- a/tensorflow/core/kernels/gather_nd_op.cc +++ b/tensorflow/core/kernels/gather_nd_op.cc @@ -228,6 +228,8 @@ namespace functor { DECLARE_GPU_SPECS_INDEX(T, int32); \ DECLARE_GPU_SPECS_INDEX(T, int64) +TF_CALL_int32(DECLARE_GPU_SPECS); +TF_CALL_int64(DECLARE_GPU_SPECS); TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_SPECS); TF_CALL_complex64(DECLARE_GPU_SPECS); TF_CALL_complex128(DECLARE_GPU_SPECS); @@ -239,6 +241,8 @@ TF_CALL_complex128(DECLARE_GPU_SPECS); // Registration of the GPU implementations. #define REGISTER_GATHER_ND_GPU(type) REGISTER_GATHER_ND_ALL_INDICES(GPU, type) +TF_CALL_int32(REGISTER_GATHER_ND_GPU); +TF_CALL_int64(REGISTER_GATHER_ND_GPU); TF_CALL_GPU_NUMBER_TYPES(REGISTER_GATHER_ND_GPU); TF_CALL_complex64(REGISTER_GATHER_ND_GPU); TF_CALL_complex128(REGISTER_GATHER_ND_GPU); diff --git a/tensorflow/core/kernels/gather_nd_op_gpu.cu.cc b/tensorflow/core/kernels/gather_nd_op_gpu.cu.cc index b03efc684f..da8d2e9e3c 100644 --- a/tensorflow/core/kernels/gather_nd_op_gpu.cu.cc +++ b/tensorflow/core/kernels/gather_nd_op_gpu.cu.cc @@ -119,6 +119,8 @@ struct GatherNdSlice { DEFINE_GPU_SPECS_INDEX(T, int32); \ DEFINE_GPU_SPECS_INDEX(T, int64); +TF_CALL_int32(DEFINE_GPU_SPECS); +TF_CALL_int64(DEFINE_GPU_SPECS); TF_CALL_GPU_NUMBER_TYPES(DEFINE_GPU_SPECS); TF_CALL_complex64(DEFINE_GPU_SPECS); TF_CALL_complex128(DEFINE_GPU_SPECS); diff --git a/tensorflow/core/kernels/gather_op.cc b/tensorflow/core/kernels/gather_op.cc index ef332ebee3..094504d6b9 100644 --- a/tensorflow/core/kernels/gather_op.cc +++ b/tensorflow/core/kernels/gather_op.cc @@ -153,6 +153,7 @@ TF_CALL_uint64(REGISTER_GATHER_CPU); // Registration of the GPU implementations. #define REGISTER_GATHER_GPU(type) REGISTER_GATHER_ALL_INDICES(GPU, type) +TF_CALL_int64(REGISTER_GATHER_GPU); TF_CALL_GPU_NUMBER_TYPES(REGISTER_GATHER_GPU); TF_CALL_complex64(REGISTER_GATHER_GPU); TF_CALL_complex128(REGISTER_GATHER_GPU); diff --git a/tensorflow/core/kernels/mkl_concat_op.cc b/tensorflow/core/kernels/mkl_concat_op.cc index 5eeb23d810..31d1b949ef 100644 --- a/tensorflow/core/kernels/mkl_concat_op.cc +++ b/tensorflow/core/kernels/mkl_concat_op.cc @@ -14,6 +14,7 @@ limitations under the License. #include #include +#include #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/core/framework/op_kernel.h" @@ -590,8 +591,8 @@ class MklConcatOp : public OpKernel { const int N = input_tensors.size(); // Get Tensor shapes. - std::vector input_shapes(N); - GetMklShapeList(context, "values", &input_shapes); + std::vector mkl_input_shapes(N); + GetMklShapeList(context, "values", &mkl_input_shapes); const Tensor& concat_dim_tensor = (AxisArgName == NAME_IS_CONCAT_DIM) ? MklGetInput(context, 0) @@ -610,19 +611,14 @@ class MklConcatOp : public OpKernel { int i = 0; bool invoke_eigen = false; bool are_all_mkl_inputs = true, are_all_tf_inputs = true; - const TensorShape expected_shape = input_shapes[0].IsMklTensor() - ? input_shapes[0].GetTfShape() - : input_tensors[0].shape(); + const TensorShape expected_shape = mkl_input_shapes[0].IsMklTensor() + ? mkl_input_shapes[0].GetTfShape() + : input_tensors[0].shape(); size_t expected_dims = expected_shape.dims(); if (concat_dim < 0) concat_dim = expected_dims + concat_dim; - for (auto& s : input_shapes) { - if (s == expected_shape) { - ++i; - continue; - } - + for (auto& s : mkl_input_shapes) { TensorShape s_shape = s.IsMklTensor() ? s.GetTfShape() : input_tensors[i].shape(); size_t s_dims = s_shape.dims(); @@ -665,21 +661,14 @@ class MklConcatOp : public OpKernel { // Call Eigen library if (invoke_eigen) { - TensorShapeList tf_input_shapes; - i = 0; - for (auto& s : input_shapes) { - TensorShape s_shape = - s.IsMklTensor() ? s.GetTfShape() : input_tensors[i].shape(); - tf_input_shapes.push_back(s_shape); - ++i; - } - CallEigenVersion(context, input_tensors, tf_input_shapes); + CallEigenVersion(context, input_tensors, mkl_input_shapes); return; } memory::dims dst_dims; + if (are_all_mkl_inputs) - dst_dims = TFShapeToMklDnnDims(input_shapes[0].GetTfShape()); + dst_dims = TFShapeToMklDnnDims(mkl_input_shapes[0].GetTfShape()); else // When all the inputs are in Tensorflow format, we don't know // what is the input data format. In that case, we just use @@ -689,26 +678,61 @@ class MklConcatOp : public OpKernel { std::vector srcs_pd; std::vector> srcs(N, MklDnnData(&cpu_engine)); int64 dst_concat_dim_size = 0; - for (int k = 0; k < N; k++) { - bool is_mkl_tensor = input_shapes[k].IsMklTensor(); - memory::dims src_dims; - - // Same comment as dst_dims for src_dims. - src_dims = (is_mkl_tensor) - ? TFShapeToMklDnnDims(input_shapes[k].GetTfShape()) - : TFShapeToMklDnnDims(input_tensors[k].shape()); - - dst_concat_dim_size += src_dims[concat_dim]; - auto src_md = - is_mkl_tensor ? input_shapes[k].GetMklLayout() : - // It does not matter what data format we use here - // (NHWC or NCHW). We just need to ensure that output - // of Concat uses same data format as input. - memory::desc(src_dims, MklDnnType(), memory::format::nchw); - - srcs[k].SetUsrMem(src_md, &input_tensors[k]); - auto src_mpd = srcs[k].GetUsrMemPrimDesc(); - srcs_pd.push_back(src_mpd); + + bool isMklReorderNeeded = false; + memory::format mkl_common_format = memory::format::any; + if (are_all_mkl_inputs) { + mkl_common_format = + FindMklCommonFormat(mkl_input_shapes, concat_dim, + &isMklReorderNeeded, &dst_concat_dim_size); + + if (!isMklReorderNeeded) { + // All MKL tensors have a same format. Reorder is not needed. + for (int k = 0; k < N; k++) { + if (input_tensors[k].NumElements() == 0) + continue; + + auto src_md = mkl_input_shapes[k].GetMklLayout(); + srcs[k].SetUsrMem(src_md, &input_tensors[k]); + auto src_mpd = srcs[k].GetUsrMemPrimDesc(); + srcs_pd.push_back(src_mpd); + } + } else { + // MKL tensors have different formats. + // Reorder them to most common format. + for (int k = 0; k < N; k++) { + if (input_tensors[k].NumElements() == 0) + continue; + + auto src_dims = TFShapeToMklDnnDims( + mkl_input_shapes[k].GetTfShape()); + auto src_md = mkl_input_shapes[k].GetMklLayout(); + srcs[k].SetUsrMem(src_md, &input_tensors[k]); + + if (src_md.data.format != mkl_common_format) + src_md = memory::desc(src_dims, MklDnnType(), + mkl_common_format); + + srcs_pd.push_back(memory::primitive_desc(src_md, cpu_engine)); + } + } + } else { // All TF inputs + for (int k = 0; k < N; k++) { + if (input_tensors[k].NumElements() == 0) + continue; + + memory::dims src_dims = TFShapeToMklDnnDims(input_tensors[k].shape()); + dst_concat_dim_size += src_dims[concat_dim]; + + // It does not matter what data format to be used (NHWC versus NCHW). + // We just need to ensure that output uses same data format as inputs. + auto src_md = + memory::desc(src_dims, MklDnnType(), memory::format::nchw); + + srcs[k].SetUsrMem(src_md, &input_tensors[k]); + auto src_mpd = srcs[k].GetUsrMemPrimDesc(); + srcs_pd.push_back(src_mpd); + } } dst_dims[concat_dim] = dst_concat_dim_size; @@ -718,25 +742,33 @@ class MklConcatOp : public OpKernel { if (are_all_mkl_inputs) { // Since we are passing a specific format for destination, // we need to have dst_dims in MklDnn order (NCHW). - auto orig_tf_format = input_shapes[0].GetTfDataFormat(); + auto orig_tf_format = mkl_input_shapes[0].GetTfDataFormat(); dst_dims_in_nchw = MklDnnDimsInNCHW( dst_dims, MklDnnDataFormatToTFDataFormat(orig_tf_format)); - // We will set the output in the same format as input to avoid layout - // conversions. - // Currently we are setting dst format same as input format. - // See if we can make this choice in a better way. + // Set the output format same as the most common format of inputs + // to avoid layout conversions. dst_md = memory::desc( - dst_dims_in_nchw, MklDnnType(), - (memory::format)input_shapes[0].GetMklLayout().data.format); + dst_dims_in_nchw, MklDnnType(), mkl_common_format); } else { - // Again, format does not matter here. We just need to make it same as - // input format. + // All inputs are TF tensors. + // Set the output format same as input format (nchw). dst_md = memory::desc(dst_dims, MklDnnType(), memory::format::nchw); } std::vector inputs; - for (int k = 0; k < input_tensors.size(); k++) - inputs.push_back(srcs[k].GetOpMem()); + std::vector net; + if (isMklReorderNeeded) { + for (int k = 0; k < input_tensors.size(); k++) { + if (input_tensors[k].NumElements() > 0) { + srcs[k].CheckReorderToOpMem(srcs_pd[k], &net); + } + } + } + for (int k = 0; k < input_tensors.size(); k++) { + if (input_tensors[k].NumElements() > 0) { + inputs.push_back(srcs[k].GetOpMem()); + } + } // If all inputs are in MKL format, then meaning of concat_dim needs to // change. Value of concat_dim is tied to input Tensorflow data format @@ -745,7 +777,8 @@ class MklConcatOp : public OpKernel { // But ifinput tensors are in NHWC order, then semantics need to change. // E.g., if we are concatinating over Channel (dimension 3 for NHWC), // then since MklDnn order is NCHW, concat_dim needs to be 1. - if (are_all_mkl_inputs) concat_dim = input_shapes[0].TfDimIdx(concat_dim); + if (are_all_mkl_inputs) + concat_dim = mkl_input_shapes[0].TfDimIdx(concat_dim); auto concat_pd = concat::primitive_desc(dst_md, concat_dim, srcs_pd); @@ -758,7 +791,7 @@ class MklConcatOp : public OpKernel { dnn_shape_dst.SetMklLayout(&dst_pd); dnn_shape_dst.SetElemType(MklDnnType()); dnn_shape_dst.SetTfLayout(dst_dims.size(), dst_dims_in_nchw, - input_shapes[0].GetTfDataFormat()); + mkl_input_shapes[0].GetTfDataFormat()); tf_shape_dst.AddDim((dst_pd.get_size() / sizeof(T))); } else { dnn_shape_dst.SetMklTensor(false); @@ -773,7 +806,6 @@ class MklConcatOp : public OpKernel { dst.SetUsrMem(dst_md, dst_tensor); auto concat_op = concat(concat_pd, inputs, dst.GetOpMem()); - std::vector net; net.push_back(concat_op); stream(stream::kind::eager).submit(net).wait(); } catch (mkldnn::error& e) { @@ -787,15 +819,27 @@ class MklConcatOp : public OpKernel { } void CallEigenVersion(OpKernelContext* context, const OpInputList& values, - const TensorShapeList& input_shapes) { - CHECK_EQ(values.size(), input_shapes.size()); + const MklDnnShapeList& mkl_input_shapes) { + CHECK_EQ(values.size(), mkl_input_shapes.size()); std::vector converted_values; - for (int i = 0; i < input_shapes.size(); i++) - converted_values.push_back(values[i]); + TensorShapeList tf_input_shapes; + for (int i = 0; i < mkl_input_shapes.size(); i++) { + if (mkl_input_shapes[i].IsMklTensor()) { + // do conversion from MKL to TF + Tensor tmp_tensor = + ConvertMklToTF(context, values[i], mkl_input_shapes[i]); + converted_values.push_back(tmp_tensor); + tf_input_shapes.push_back(mkl_input_shapes[i].GetTfShape()); + } else { + // no conversion since it is TF tensor already + converted_values.push_back(values[i]); + tf_input_shapes.push_back(values[i].shape()); + } + } // Call Eigen concat. - eigen_concat_op_.Compute(context, converted_values, input_shapes); + eigen_concat_op_.Compute(context, converted_values, tf_input_shapes); // Set output Mkl tensor for this op. MklDnnShape dnn_shape_output; @@ -812,6 +856,55 @@ class MklConcatOp : public OpKernel { output_tensor->flat().data(), output_tensor->flat().size() * sizeof(uint8)); } + + // This method finds the most commom format accross all MKL inputs + // Inputs: + // 1. input_shapes: shapes of input (MKL) tensors. + // 2. concat_dim: concat dimension. + // Outputs: + // 1. is_reorder_needed is set to true if inputs have difference formats + // It is set to false otherwise. + // 2. concat_dim_size is the size of concat_dim. + // Return: + // return the common MKL format. + memory::format FindMklCommonFormat(const MklDnnShapeList& input_shapes, + int concat_dim, bool* is_reorder_needed, int64* concat_dim_size) { + *is_reorder_needed = false; + *concat_dim_size = 0; + std::unordered_map occurrence_map; + if (input_shapes.size() == 0) + return memory::format::any; + + // Compute ocurrences of each format of all inputs. + for (int k=0; k ( + input_shapes[k].GetMklLayout().data.format); + occurrence_map[fmt] += 1; + } + + if (occurrence_map.size() == 1) { + // this means that all inputs have a same format + // return it with is_reorder_needed set false. + return static_cast( + input_shapes[0].GetMklLayout().data.format); + } + + // Input tensors have different formats. Thus, reorder is needed. + // We pick up the most common format to minimize the total + // number of input reorder. + memory::format commonest_format = memory::format::any; + int max_occurrence = 0; + *is_reorder_needed = true; + for (auto item : occurrence_map) { + if (item.second > max_occurrence) { + commonest_format = static_cast(item.first); + max_occurrence = item.second; + } + } + return commonest_format; + } }; #endif diff --git a/tensorflow/core/kernels/mkl_conv_grad_bias_ops.cc b/tensorflow/core/kernels/mkl_conv_grad_bias_ops.cc index c1da0ded1d..f857be6c32 100644 --- a/tensorflow/core/kernels/mkl_conv_grad_bias_ops.cc +++ b/tensorflow/core/kernels/mkl_conv_grad_bias_ops.cc @@ -18,6 +18,7 @@ limitations under the License. // bias. #ifdef INTEL_MKL +#ifdef INTEL_MKL_ML #define USE_EIGEN_TENSOR #define EIGEN_USE_THREADS @@ -264,4 +265,5 @@ class MklConv2DCustomBackpropBiasOp : public OpKernel { TF_CALL_float(REGISTER_CPU_KERNELS); #undef REGISTER_CPU_KERNELS } /* namespace tensorflow */ +#endif /* INTEL_MKL_ML */ #endif /* INTEL_MKL */ diff --git a/tensorflow/core/kernels/mkl_pooling_ops_common.h b/tensorflow/core/kernels/mkl_pooling_ops_common.h index 279167aba2..c0dfed7d7d 100644 --- a/tensorflow/core/kernels/mkl_pooling_ops_common.h +++ b/tensorflow/core/kernels/mkl_pooling_ops_common.h @@ -199,13 +199,15 @@ class MklPoolingForwardOpBase : public MklPoolingOpBase { CHECK_NOTNULL(pool_params); CHECK_NOTNULL(dnn_data_input); TensorShape input_tensor_shape = input_tensor.shape(); - memory::desc input_md = + if (input_tensor.NumElements() != 0) { + memory::desc input_md = input_mkl_shape.IsMklTensor() ? input_mkl_shape.GetMklLayout() : memory::desc(TFShapeToMklDnnDimsInNCHW(input_tensor_shape, this->data_format_tf_), MklDnnType(), this->data_format_mkldnn_); - dnn_data_input->SetUsrMem(input_md, &input_tensor); + dnn_data_input->SetUsrMem(input_md, &input_tensor); + } this->InitMklPoolParameters(context, pool_params, input_mkl_shape, input_tensor_shape); } diff --git a/tensorflow/core/kernels/scatter_nd_op.cc b/tensorflow/core/kernels/scatter_nd_op.cc index 43c5b29509..e1fc2ea128 100644 --- a/tensorflow/core/kernels/scatter_nd_op.cc +++ b/tensorflow/core/kernels/scatter_nd_op.cc @@ -292,6 +292,7 @@ TF_CALL_string(REGISTER_SCATTER_ND_CPU); REGISTER_SCATTER_ND_UPDATE_GPU(type); \ REGISTER_SCATTER_ND_GPU(type); +TF_CALL_int32(REGISTER_SCATTER_ND_ALL_GPU); // TODO(b/66916790): Support half types in ScatterNd. TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SCATTER_ND_ALL_GPU); TF_CALL_complex64(REGISTER_SCATTER_ND_ALL_GPU); @@ -306,6 +307,8 @@ TF_CALL_complex128(REGISTER_SCATTER_ND_ALL_GPU); #define REGISTER_SCATTER_ND_UPDATE_SYCL(type) \ REGISTER_SCATTER_ND_UPDATE(type, SYCL); +TF_CALL_int32(REGISTER_SCATTER_ND_ADD_SUB_SYCL); +TF_CALL_int32(REGISTER_SCATTER_ND_UPDATE_SYCL); TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SCATTER_ND_ADD_SUB_SYCL); TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SCATTER_ND_UPDATE_SYCL); #undef REGISTER_SCATTER_ND_ADD_SUB_SYCL @@ -576,6 +579,7 @@ namespace functor { DECLARE_GPU_SPECS_INDEX(T, int32); \ DECLARE_GPU_SPECS_INDEX(T, int64) +TF_CALL_int32(DECLARE_GPU_SPECS); // TODO(b/66916790): Support half types in ScatterNd. TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_SPECS); TF_CALL_complex64(DECLARE_GPU_SPECS); diff --git a/tensorflow/core/kernels/scatter_nd_op_gpu.cu.cc b/tensorflow/core/kernels/scatter_nd_op_gpu.cu.cc index a3c21edc15..08b657f4c3 100644 --- a/tensorflow/core/kernels/scatter_nd_op_gpu.cu.cc +++ b/tensorflow/core/kernels/scatter_nd_op_gpu.cu.cc @@ -170,6 +170,7 @@ struct ScatterNdFunctor { DECLARE_GPU_SPECS_INDEX(T, int32); \ DECLARE_GPU_SPECS_INDEX(T, int64) +TF_CALL_int32(DECLARE_GPU_SPECS); TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_SPECS); TF_CALL_complex64(DECLARE_GPU_SPECS); TF_CALL_complex128(DECLARE_GPU_SPECS); diff --git a/tensorflow/core/kernels/scoped_allocator_ops_test.cc b/tensorflow/core/kernels/scoped_allocator_ops_test.cc index bb0129fa6f..634f9ba887 100644 --- a/tensorflow/core/kernels/scoped_allocator_ops_test.cc +++ b/tensorflow/core/kernels/scoped_allocator_ops_test.cc @@ -216,8 +216,13 @@ TEST_F(ScopedAllocatorConcatOpTest, Success3) { } TEST_F(ScopedAllocatorConcatOpTest, Reshape) { - MakeOp({2, 2, 2}, DT_DOUBLE, true, "test", 120, 2); - ExecOp(DT_DOUBLE, 120, {{2, 2}, {2, 2}}); + MakeOp({2, 2, 4}, DT_DOUBLE, true, "test", 120, 2); + + // The elements of the third parameter to ExecOp must be multiples of + // Allocator::kAllocatorAlignment in size. If they are not, the backing + // tensor allocated by PrepOp will have too many elements and reshaping + // will fail. + ExecOp(DT_DOUBLE, 120, {{2, 4}, {2, 4}}); } TEST_F(ScopedAllocatorConcatOpTest, NoReshapeAttr) { diff --git a/tensorflow/core/kernels/segment_reduction_ops.h b/tensorflow/core/kernels/segment_reduction_ops.h index 7796bf3587..d65692a552 100644 --- a/tensorflow/core/kernels/segment_reduction_ops.h +++ b/tensorflow/core/kernels/segment_reduction_ops.h @@ -16,6 +16,14 @@ limitations under the License. #ifndef TENSORFLOW_CORE_KERNELS_SEGMENT_REDUCTION_OPS_H_ #define TENSORFLOW_CORE_KERNELS_SEGMENT_REDUCTION_OPS_H_ + +// This file requires the following include because it uses CudaAtomicMax: +// #include "tensorflow/core/util/cuda_kernel_helper.h" + +// Unfortunately we can't add the #include, since it breaks compilation for +// non-GPU targets. This only breaks in clang, because it's more strict for +// template code and CudaAtomicMax is used in template context. + // This file requires the following include because it uses CudaAtomicMax: // #include "tensorflow/core/util/cuda_kernel_helper.h" @@ -130,4 +138,4 @@ struct Highest { } // namespace functor } // namespace tensorflow -#endif // THIRD_PARTY_TENSORFLOW_CORE_KERNELS_SEGMENT_REDUCTION_OPS_H_ +#endif // TENSORFLOW_CORE_KERNELS_SEGMENT_REDUCTION_OPS_H_ diff --git a/tensorflow/core/kernels/sparse_matmul_op.cc b/tensorflow/core/kernels/sparse_matmul_op.cc index a1f9667b78..866c5dcd52 100644 --- a/tensorflow/core/kernels/sparse_matmul_op.cc +++ b/tensorflow/core/kernels/sparse_matmul_op.cc @@ -1490,7 +1490,7 @@ inline void LibxsmmSparseMatMul::Compute( #endif // TENSORFLOW_USE_LIBXSMM -// Here is a an overview of the SparseMatMul code. Note that we assume that the +// Here is an overview of the SparseMatMul code. Note that we assume that the // left matrix is sparse. // // The matrix "left" is divided into a grid with blocksize of (M, KL). Each diff --git a/tensorflow/core/kernels/string_split_op.cc b/tensorflow/core/kernels/string_split_op.cc index 4c2b312c34..26ab72f12e 100644 --- a/tensorflow/core/kernels/string_split_op.cc +++ b/tensorflow/core/kernels/string_split_op.cc @@ -22,6 +22,7 @@ limitations under the License. #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/lib/core/status.h" +#include "tensorflow/core/lib/core/stringpiece.h" #include "tensorflow/core/lib/strings/str_util.h" namespace tensorflow { @@ -43,6 +44,63 @@ std::vector Split(const string& str, const string& delimiter, return char_vector; } +std::vector SplitV2(const string& str, StringPiece sep, int maxsplit) { + // This SplitV2 method matches the behavior of python's str.split: + // If sep is given, consecutive delimiters are not grouped together + // and are deemed to delimit empty strings (for example, '1,,2'.split(',') + // returns ['1', '', '2']). The sep argument may consist of multiple + // characters (for example, '1<>2<>3'.split('<>') returns ['1', '2', '3']). + // Splitting an empty string with a specified separator returns ['']. + // + // If sep is not specified or is None, a different splitting algorithm is + // applied: runs of consecutive whitespace are regarded as a single + // separator, and the result will contain no empty strings at the start or + // end if the string has leading or trailing whitespace. Consequently, + // splitting an empty string or a string consisting of just whitespace + // with a None separator returns []. + + std::vector result; + + StringPiece text(str); + if (maxsplit == 0) { + result.emplace_back(std::string(text)); + return result; + } + + if (sep.empty()) { + StringPiece token; + // Remove leading whitespaces. + str_util::RemoveLeadingWhitespace(&text); + int split = 0; + while (str_util::ConsumeNonWhitespace(&text, &token)) { + result.emplace_back(std::string(token)); + str_util::RemoveLeadingWhitespace(&text); + ++split; + if (maxsplit > 0 && split == maxsplit) { + result.emplace_back(std::string(text)); + return result; + } + } + return result; + } + auto p = std::search(text.begin(), text.end(), sep.begin(), sep.end()); + int split = 0; + while (p != text.end()) { + StringPiece token = text.substr(0, p - text.begin()); + result.emplace_back(std::string(token)); + text.remove_prefix(token.size()); + text.remove_prefix(sep.size()); + ++split; + if (maxsplit > 0 && split == maxsplit) { + result.emplace_back(std::string(text)); + return result; + } + p = std::search(text.begin(), text.end(), sep.begin(), sep.end()); + } + result.emplace_back(std::string(text)); + return result; +} + } // namespace class StringSplitOp : public OpKernel { @@ -122,6 +180,78 @@ class StringSplitOp : public OpKernel { bool skip_empty_; }; +class StringSplitV2Op : public OpKernel { + public: + explicit StringSplitV2Op(OpKernelConstruction* context) + : OpKernel(context), maxsplit_(-1) { + OP_REQUIRES_OK(context, context->GetAttr("maxsplit", &maxsplit_)); + } + + void Compute(OpKernelContext* ctx) override { + const Tensor* input_tensor; + OP_REQUIRES_OK(ctx, ctx->input("input", &input_tensor)); + OP_REQUIRES(ctx, TensorShapeUtils::IsVector(input_tensor->shape()), + errors::InvalidArgument("input must be a vector, got shape: ", + input_tensor->shape().DebugString())); + + const auto input_vec = input_tensor->vec(); + const int64 batch_size = input_vec.dimension(0); + + const Tensor* sep_tensor; + OP_REQUIRES_OK(ctx, ctx->input("sep", &sep_tensor)); + OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(sep_tensor->shape()), + errors::InvalidArgument("sep must be a scalar, got shape: ", + sep_tensor->shape().DebugString())); + const auto sep_vec = sep_tensor->flat(); + StringPiece sep(sep_vec(0)); + std::vector tokens; + // Guess that we'll be unpacking a handful of tokens per example. + static constexpr int kReserveSize = 4; + tokens.reserve(batch_size * kReserveSize); + + int64 output_size = 0; + int64 max_num_entries = 0; + std::vector num_indices(batch_size); + for (int64 i = 0; i < batch_size; ++i) { + std::vector parts = SplitV2(input_vec(i), sep, maxsplit_); + int64 n_entries = parts.size(); + num_indices[i] = n_entries; + output_size += n_entries; + max_num_entries = std::max(max_num_entries, n_entries); + tokens.insert(tokens.end(), parts.begin(), parts.end()); + } + + Tensor* sp_indices_t; + OP_REQUIRES_OK(ctx, ctx->allocate_output(0, TensorShape({output_size, 2}), + &sp_indices_t)); + Tensor* sp_tokens_t; + OP_REQUIRES_OK( + ctx, ctx->allocate_output(1, TensorShape({output_size}), &sp_tokens_t)); + Tensor* sp_shape_t; + OP_REQUIRES_OK(ctx, ctx->allocate_output(2, TensorShape({2}), &sp_shape_t)); + + auto sp_indices = sp_indices_t->matrix(); + auto sp_tokens = sp_tokens_t->vec(); + auto sp_shape = sp_shape_t->vec(); + sp_shape(0) = batch_size; + sp_shape(1) = max_num_entries; + size_t c = 0; + for (size_t i = 0; i < batch_size; ++i) { + for (size_t j = 0; j < num_indices[i]; ++j) { + sp_indices(c, 0) = i; + sp_indices(c, 1) = j; + sp_tokens(c) = tokens[c]; + ++c; + } + } + } + + private: + int maxsplit_; +}; + REGISTER_KERNEL_BUILDER(Name("StringSplit").Device(DEVICE_CPU), StringSplitOp); +REGISTER_KERNEL_BUILDER(Name("StringSplitV2").Device(DEVICE_CPU), + StringSplitV2Op); } // namespace tensorflow diff --git a/tensorflow/core/ops/candidate_sampling_ops.cc b/tensorflow/core/ops/candidate_sampling_ops.cc index 6e4d100b04..6e589c8d1c 100644 --- a/tensorflow/core/ops/candidate_sampling_ops.cc +++ b/tensorflow/core/ops/candidate_sampling_ops.cc @@ -145,12 +145,15 @@ REGISTER_OP("ComputeAccidentalHits") int64 num_true; TF_RETURN_IF_ERROR(c->GetAttr("num_true", &num_true)); - // Validate true_classes. + // Validate true_classes, must be a matrix. ShapeHandle true_classes; TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 2, &true_classes)); DimensionHandle unused; TF_RETURN_IF_ERROR( c->WithValue(c->Dim(true_classes, 1), num_true, &unused)); + // Validate sampled_candidates, must be a vector. + ShapeHandle sampled_candidates; + TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &sampled_candidates)); // All three outputs are the same shape. ShapeHandle v = c->Vector(InferenceContext::kUnknownDim); diff --git a/tensorflow/core/ops/dataset_ops.cc b/tensorflow/core/ops/dataset_ops.cc index 15e0ca8af9..9dca5f53ce 100644 --- a/tensorflow/core/ops/dataset_ops.cc +++ b/tensorflow/core/ops/dataset_ops.cc @@ -218,7 +218,17 @@ REGISTER_OP("MapAndBatchDataset") .Attr("Targuments: list(type) >= 0") .Attr("output_types: list(type) >= 1") .Attr("output_shapes: list(shape) >= 1") - .SetShapeFn(shape_inference::ScalarShape); + .SetShapeFn([](shape_inference::InferenceContext* c) { + // Use index from the end to retrieve the Input shapes, + // so that to avoid guessing the length of "other_arguments". + // batch_size, num_parallel_batches, and drop_remainder are 0-D scalars. + shape_inference::ShapeHandle unused; + TF_RETURN_IF_ERROR(c->WithRank(c->input(c->num_inputs() - 3), 0, &unused)); + TF_RETURN_IF_ERROR(c->WithRank(c->input(c->num_inputs() - 2), 0, &unused)); + TF_RETURN_IF_ERROR(c->WithRank(c->input(c->num_inputs() - 1), 0, &unused)); + + return shape_inference::ScalarShape(c); + }); REGISTER_OP("MapAndBatchDatasetV2") .Input("input_dataset: variant") @@ -231,7 +241,17 @@ REGISTER_OP("MapAndBatchDatasetV2") .Attr("Targuments: list(type) >= 0") .Attr("output_types: list(type) >= 1") .Attr("output_shapes: list(shape) >= 1") - .SetShapeFn(shape_inference::ScalarShape); + .SetShapeFn([](shape_inference::InferenceContext* c) { + // Use index from the end to retrieve the Input shapes, + // so that to avoid guessing the length of "other_arguments". + // batch_size, num_parallel_calls, and drop_remainder are 0-D scalars. + shape_inference::ShapeHandle unused; + TF_RETURN_IF_ERROR(c->WithRank(c->input(c->num_inputs() - 3), 0, &unused)); + TF_RETURN_IF_ERROR(c->WithRank(c->input(c->num_inputs() - 2), 0, &unused)); + TF_RETURN_IF_ERROR(c->WithRank(c->input(c->num_inputs() - 1), 0, &unused)); + + return shape_inference::ScalarShape(c); + }); REGISTER_OP("PrefetchDataset") .Input("input_dataset: variant") diff --git a/tensorflow/core/ops/image_ops.cc b/tensorflow/core/ops/image_ops.cc index d949e70c66..87f4991134 100644 --- a/tensorflow/core/ops/image_ops.cc +++ b/tensorflow/core/ops/image_ops.cc @@ -454,7 +454,9 @@ REGISTER_OP("DrawBoundingBoxes") DimensionHandle unused; TF_RETURN_IF_ERROR(c->WithValue(c->Dim(boxes, 2), 4, &unused)); - return shape_inference::UnchangedShapeWithRankAtLeast(c, 3); + // The rank of the input image (rank = 4) has already been restricted + // above, and the output is of the same shape as the input. + return shape_inference::UnchangedShape(c); }); // -------------------------------------------------------------------------- diff --git a/tensorflow/core/ops/math_ops.cc b/tensorflow/core/ops/math_ops.cc index 1740fa152c..b3487122e2 100644 --- a/tensorflow/core/ops/math_ops.cc +++ b/tensorflow/core/ops/math_ops.cc @@ -1084,7 +1084,7 @@ REGISTER_OP("UnsortedSegmentProd") .Input("segment_ids: Tindices") .Input("num_segments: Tnumsegments") .Output("output: T") - .Attr("T: realnumbertype") + .Attr("T: numbertype") .Attr("Tindices: {int32,int64}") .Attr("Tnumsegments: {int32,int64} = DT_INT32") .SetShapeFn(UnsortedSegmentReductionShapeFn); diff --git a/tensorflow/core/ops/nn_ops.cc b/tensorflow/core/ops/nn_ops.cc index fc60e807b9..41efa49ce3 100644 --- a/tensorflow/core/ops/nn_ops.cc +++ b/tensorflow/core/ops/nn_ops.cc @@ -1453,6 +1453,7 @@ REGISTER_OP("QuantizedReluX") ShapeHandle unused; TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused)); TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused)); + TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 0, &unused)); c->set_output(1, c->Scalar()); c->set_output(2, c->Scalar()); return Status::OK(); diff --git a/tensorflow/core/ops/string_ops.cc b/tensorflow/core/ops/string_ops.cc index 1d5c743a56..4423062362 100644 --- a/tensorflow/core/ops/string_ops.cc +++ b/tensorflow/core/ops/string_ops.cc @@ -78,7 +78,7 @@ REGISTER_OP("ReduceJoin") REGISTER_OP("AsString") .Input("input: T") .Output("output: string") - .Attr("T: {int32, int64, complex64, float, double, bool, int8}") + .Attr("T: {int8, int16, int32, int64, complex64, float, double, bool}") .Attr("precision: int = -1") .Attr("scientific: bool = false") .Attr("shortest: bool = false") @@ -134,6 +134,24 @@ REGISTER_OP("StringSplit") return Status::OK(); }); +REGISTER_OP("StringSplitV2") + .Input("input: string") + .Input("sep: string") + .Output("indices: int64") + .Output("values: string") + .Output("shape: int64") + .Attr("maxsplit: int = -1") + .SetShapeFn([](InferenceContext* c) { + ShapeHandle unused; + TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 1, &unused)); + TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused)); + + c->set_output(0, c->Matrix(InferenceContext::kUnknownDim, 2)); + c->set_output(1, c->Vector(InferenceContext::kUnknownDim)); + c->set_output(2, c->Vector(2)); + return Status::OK(); + }); + REGISTER_OP("StringStrip") .Input("input: string") .Output("output: string") diff --git a/tensorflow/core/platform/cpu_info.cc b/tensorflow/core/platform/cpu_info.cc index 99de364042..e9da3d8e32 100644 --- a/tensorflow/core/platform/cpu_info.cc +++ b/tensorflow/core/platform/cpu_info.cc @@ -344,5 +344,28 @@ int CPUModelNum() { #endif } +int CPUIDNumSMT() { +#ifdef PLATFORM_IS_X86 + // https://software.intel.com/en-us/articles/intel-64-architecture-processor-topology-enumeration + // https://software.intel.com/en-us/articles/intel-sdm (Vol 3A) + // Section: Detecting Hardware Multi-threads Support and Topology + // Uses CPUID Leaf 11 to enumerate system topology on Intel x86 architectures + // Other cases not supported + uint32 eax, ebx, ecx, edx; + // Check if system supports Leaf 11 + GETCPUID(eax, ebx, ecx, edx, 0, 0); + if (eax >= 11) { + // 1) Leaf 11 available? CPUID.(EAX=11, ECX=0):EBX != 0 + // 2) SMT_Mask_Width = CPUID.(EAX=11, ECX=0):EAX[4:0] if CPUID.(EAX=11, + // ECX=0):ECX[15:8] is 1 + GETCPUID(eax, ebx, ecx, edx, 11, 0); + if (ebx != 0 && ((ecx & 0xff00) >> 8) == 1) { + return 1 << (eax & 0x1f); // 2 ^ SMT_Mask_Width + } + } +#endif // PLATFORM_IS_X86 + return 0; +} + } // namespace port } // namespace tensorflow diff --git a/tensorflow/core/platform/cpu_info.h b/tensorflow/core/platform/cpu_info.h index b5be7e8b54..175c9ae8b1 100644 --- a/tensorflow/core/platform/cpu_info.h +++ b/tensorflow/core/platform/cpu_info.h @@ -35,6 +35,10 @@ namespace port { // software can change it dynamically. int NumSchedulableCPUs(); +// Returns an estimate of the number of hyperthreads per physical core +// on the CPU +int NumHyperthreadsPerCore(); + // Mostly ISA related features that we care about enum CPUFeature { // Do not change numeric assignments. @@ -107,6 +111,9 @@ int CPUModelNum(); // Returns nominal core processor cycles per second of each processor. double NominalCPUFrequency(); +// Returns num of hyperthreads per physical core +int CPUIDNumSMT(); + } // namespace port } // namespace tensorflow diff --git a/tensorflow/core/platform/default/build_config.bzl b/tensorflow/core/platform/default/build_config.bzl index ae81f9b5b3..a319ccbdbe 100644 --- a/tensorflow/core/platform/default/build_config.bzl +++ b/tensorflow/core/platform/default/build_config.bzl @@ -71,6 +71,8 @@ def pyx_library( name = filename + "_cython_translation", srcs = [filename], outs = [filename.split(".")[0] + ".cpp"], + # Optionally use PYTHON_BIN_PATH on Linux platforms so that python 3 + # works. Windows has issues with cython_binary so skip PYTHON_BIN_PATH. cmd = "PYTHONHASHSEED=0 $(location @cython//:cython_binary) --cplus $(SRCS) --output-file $(OUTS)", tools = ["@cython//:cython_binary"] + pxd_srcs, ) diff --git a/tensorflow/core/platform/hadoop/hadoop_file_system.cc b/tensorflow/core/platform/hadoop/hadoop_file_system.cc index 72c12318ca..ff4b4436bb 100644 --- a/tensorflow/core/platform/hadoop/hadoop_file_system.cc +++ b/tensorflow/core/platform/hadoop/hadoop_file_system.cc @@ -115,18 +115,17 @@ class LibHDFS { const char* kLibHdfsDso = "libhdfs.so"; #endif char* hdfs_home = getenv("HADOOP_HDFS_HOME"); - if (hdfs_home == nullptr) { - status_ = errors::FailedPrecondition( - "Environment variable HADOOP_HDFS_HOME not set"); - return; - } - string path = io::JoinPath(hdfs_home, "lib", "native", kLibHdfsDso); - status_ = TryLoadAndBind(path.c_str(), &handle_); - if (!status_.ok()) { - // try load libhdfs.so using dynamic loader's search path in case - // libhdfs.so is installed in non-standard location - status_ = TryLoadAndBind(kLibHdfsDso, &handle_); + if (hdfs_home != nullptr) { + string path = io::JoinPath(hdfs_home, "lib", "native", kLibHdfsDso); + status_ = TryLoadAndBind(path.c_str(), &handle_); + if (status_.ok()) { + return; + } } + + // Try to load the library dynamically in case it has been installed + // to a in non-standard location. + status_ = TryLoadAndBind(kLibHdfsDso, &handle_); } Status status_; diff --git a/tensorflow/core/platform/posix/port.cc b/tensorflow/core/platform/posix/port.cc index 8e316472fe..708f32ba80 100644 --- a/tensorflow/core/platform/posix/port.cc +++ b/tensorflow/core/platform/posix/port.cc @@ -74,6 +74,11 @@ int NumSchedulableCPUs() { return kDefaultCores; } +int NumHyperthreadsPerCore() { + static const int ht_per_core = tensorflow::port::CPUIDNumSMT(); + return (ht_per_core > 0) ? ht_per_core : 1; +} + void* AlignedMalloc(size_t size, int minimum_alignment) { #if defined(__ANDROID__) return memalign(minimum_alignment, size); diff --git a/tensorflow/core/public/version.h b/tensorflow/core/public/version.h index 522a9d84fd..cb1fd09dbb 100644 --- a/tensorflow/core/public/version.h +++ b/tensorflow/core/public/version.h @@ -19,12 +19,12 @@ limitations under the License. // TensorFlow uses semantic versioning, see http://semver.org/. #define TF_MAJOR_VERSION 1 -#define TF_MINOR_VERSION 8 +#define TF_MINOR_VERSION 9 #define TF_PATCH_VERSION 0 // TF_VERSION_SUFFIX is non-empty for pre-releases (e.g. "-alpha", "-alpha.1", // "-beta", "-rc", "-rc.1") -#define TF_VERSION_SUFFIX "" +#define TF_VERSION_SUFFIX "-rc0" #define TF_STR_HELPER(x) #x #define TF_STR(x) TF_STR_HELPER(x) diff --git a/tensorflow/core/util/mkl_util.h b/tensorflow/core/util/mkl_util.h index dffc965b14..90b6533690 100644 --- a/tensorflow/core/util/mkl_util.h +++ b/tensorflow/core/util/mkl_util.h @@ -42,6 +42,7 @@ limitations under the License. #ifndef INTEL_MKL_ML #include "mkldnn.hpp" +#include "tensorflow/core/lib/core/stringpiece.h" using mkldnn::engine; using mkldnn::memory; @@ -712,15 +713,48 @@ inline Tensor ConvertMklToTF(OpKernelContext* context, const Tensor& mkl_tensor, return output_tensor; } #else +using mkldnn::stream; +template class MklDnnData; + template inline Tensor ConvertMklToTF(OpKernelContext* context, const Tensor& mkl_tensor, const MklDnnShape& mkl_shape) { Tensor output_tensor; - TensorShape output_shape; - - TF_CHECK_OK( - Status(error::Code::UNIMPLEMENTED, "Unimplemented conversion function")); - + try { + if (!mkl_shape.IsMklTensor()) + return mkl_tensor; // return input since it is already TF tensor + + TensorShape output_shape = mkl_shape.GetTfShape();; + + // Allocate output tensor. + context->allocate_temp(DataTypeToEnum::v(), + output_shape, &output_tensor); + + auto cpu_engine = engine(engine::cpu, 0); + MklDnnData input(&cpu_engine); + + // Get Mkl layout of input tensor. + auto input_mkl_md = mkl_shape.GetMklLayout(); + auto output_tf_md = mkl_shape.GetTfLayout(); + auto output_tf_pd = memory::primitive_desc(output_tf_md, cpu_engine); + input.SetUsrMem(input_mkl_md, &mkl_tensor); + + // reorder + if (input.IsReorderNeeded(output_tf_pd)) { + std::vector net; + CHECK_EQ(input.CheckReorderToOpMem(output_tf_pd, &output_tensor, &net), + true); + stream(stream::kind::eager).submit(net).wait(); + } else { + // If not, just forward input tensor to output tensor. + CHECK(output_tensor.CopyFrom(mkl_tensor, output_shape)); + } + } catch (mkldnn::error& e) { + string error_msg = "Status: " + std::to_string(e.status) + + ", message: " + string(e.message) + ", in file " + + string(__FILE__) + ":" + std::to_string(__LINE__); + LOG(FATAL) << "Operation received an exception: " << error_msg; + } return output_tensor; } #endif @@ -1843,7 +1877,7 @@ class FactoryKeyCreator { template void AddAsKey(const T data) { auto buffer = reinterpret_cast(&data); - Append(absl::string_view(buffer, sizeof(T))); + Append(StringPiece(buffer, sizeof(T))); } std::string GetKey() { @@ -1854,8 +1888,8 @@ class FactoryKeyCreator { string key_; const char delimiter = 'x'; const int kMaxKeyLength = 256; - void Append(absl::string_view s) { - key_.append(string(s)); + void Append(StringPiece s) { + key_.append(s.ToString()); key_.append(1, delimiter); } }; diff --git a/tensorflow/docs_src/community/groups.md b/tensorflow/docs_src/community/groups.md index d92f5775fa..0b07d413da 100644 --- a/tensorflow/docs_src/community/groups.md +++ b/tensorflow/docs_src/community/groups.md @@ -1,17 +1,38 @@ # User Groups -TensorFlow has communities around the world. +TensorFlow has communities around the world. [Submit your community!](https://docs.google.com/forms/d/e/1FAIpQLSc_RQIUYtVgLLihzATaO_WUXkEyBDE_OoRoOXYDPmBEvHuEBA/viewform) ## Asia -* [TensorFlow Korea (TF-KR) User Group](https://www.facebook.com/groups/TensorFlowKR/) _(Korean language)_ -* [TensorFlow User Group Tokyo](https://tfug-tokyo.connpass.com/) _(Japanese Language)_ -* [Soleil Data Dojo](https://soleildatadojo.connpass.com/) _(Japanese language)_ +* [TensorFlow China community](https://www.tensorflowers.cn) +* [TensorFlow Korea (TF-KR) User Group](https://www.facebook.com/groups/TensorFlowKR/) +* [TensorFlow User Group Tokyo](https://tfug-tokyo.connpass.com/) +* [Soleil Data Dojo](https://soleildatadojo.connpass.com/) * [TensorFlow User Group Utsunomiya](https://tfug-utsunomiya.connpass.com/) +* [TensorFlow Philippines Community](https://www.facebook.com/groups/TensorFlowPH/) +* [TensorFlow and Deep Learning Singapore](https://www.meetup.com/TensorFlow-and-Deep-Learning-Singapore/) +* [TensorFlow India](https://www.facebook.com/tensorflowindia) ## Europe * [TensorFlow Barcelona](https://www.meetup.com/Barcelona-Machine-Learning-Meetup/) * [TensorFlow Madrid](https://www.meetup.com/TensorFlow-Madrid/) +* [Tensorflow Belgium](https://www.meetup.com/TensorFlow-Belgium) +* [TensorFlow x Rome Meetup](https://www.meetup.com/it-IT/TensorFlow-x-Rome-Meetup) +* [TensorFlow London](https://www.meetup.com/TensorFlow-London/) +* [TensorFlow Edinburgh](https://www.meetup.com/tensorflow-edinburgh/) + +## America + +* [TensorFlow Buenos Aires](https://www.meetup.com/TensorFlow-Buenos-Aires/) + + +## Oceania +* [Melbourne TensorFlow Meetup](https://www.meetup.com/Melbourne-TensorFlow-Meetup) + + +## Africa + +* [TensorFlow Tunis Meetup](https://www.meetup.com/fr-FR/TensorFlow-Tunis-Meetup/) diff --git a/tensorflow/docs_src/get_started/eager.md b/tensorflow/docs_src/get_started/eager.md index f08ac74425..bbb25e20c6 100644 --- a/tensorflow/docs_src/get_started/eager.md +++ b/tensorflow/docs_src/get_started/eager.md @@ -1,3 +1,3 @@ # Get Started with Eager Execution -[Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/r1.8.0/samples/core/get_started/eager.ipynb) +[Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/r1.9.0/samples/core/get_started/eager.ipynb) diff --git a/tensorflow/docs_src/get_started/index.md b/tensorflow/docs_src/get_started/index.md index 55579d52fb..232d2f1547 100644 --- a/tensorflow/docs_src/get_started/index.md +++ b/tensorflow/docs_src/get_started/index.md @@ -10,9 +10,9 @@ course prior to diving into TensorFlow documentation: TensorFlow is a tool for machine learning. While it contains a wide range of functionality, TensorFlow is mainly designed for deep neural network models. -The easiest way to get started with TensorFlow is using Eager Execution. +The easiest way to get started with TensorFlow is by using Eager Execution. - * @{$get_started/eager}, is for anyone new to machine learning or TensorFlow. + * @{$get_started/eager}, is for anyone new to machine learning or TensorFlow. TensorFlow provides many APIs. The remainder of this section focuses on the Estimator API which provide scalable, high-performance models. See the diff --git a/tensorflow/docs_src/install/install_c.md b/tensorflow/docs_src/install/install_c.md index 1abd840ab3..2901848745 100644 --- a/tensorflow/docs_src/install/install_c.md +++ b/tensorflow/docs_src/install/install_c.md @@ -38,7 +38,7 @@ enable TensorFlow for C: OS="linux" # Change to "darwin" for macOS TARGET_DIRECTORY="/usr/local" curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.8.0.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.9.0-rc0.tar.gz" | sudo tar -C $TARGET_DIRECTORY -xz The `tar` command extracts the TensorFlow C library into the `lib` diff --git a/tensorflow/docs_src/install/install_go.md b/tensorflow/docs_src/install/install_go.md index 52a2a3f8a6..55bc0f64e7 100644 --- a/tensorflow/docs_src/install/install_go.md +++ b/tensorflow/docs_src/install/install_go.md @@ -38,7 +38,7 @@ steps to install this library and enable TensorFlow for Go: TF_TYPE="cpu" # Change to "gpu" for GPU support TARGET_DIRECTORY='/usr/local' curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.8.0.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.9.0-rc0.tar.gz" | sudo tar -C $TARGET_DIRECTORY -xz The `tar` command extracts the TensorFlow C library into the `lib` diff --git a/tensorflow/docs_src/install/install_java.md b/tensorflow/docs_src/install/install_java.md index 1256fb99c4..637231da12 100644 --- a/tensorflow/docs_src/install/install_java.md +++ b/tensorflow/docs_src/install/install_java.md @@ -36,7 +36,7 @@ following to the project's `pom.xml` to use the TensorFlow Java APIs: org.tensorflow tensorflow - 1.8.0 + 1.9.0-rc0 ``` @@ -65,7 +65,7 @@ As an example, these steps will create a Maven project that uses TensorFlow: org.tensorflow tensorflow - 1.8.0 + 1.9.0-rc0 @@ -124,12 +124,12 @@ instead: org.tensorflow libtensorflow - 1.8.0 + 1.9.0-rc0 org.tensorflow libtensorflow_jni_gpu - 1.8.0 + 1.9.0-rc0 ``` @@ -148,7 +148,7 @@ refer to the simpler instructions above instead. Take the following steps to install TensorFlow for Java on Linux or macOS: 1. Download - [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.8.0.jar), + [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.9.0-rc0.jar), which is the TensorFlow Java Archive (JAR). 2. Decide whether you will run TensorFlow for Java on CPU(s) only or with @@ -167,7 +167,7 @@ Take the following steps to install TensorFlow for Java on Linux or macOS: OS=$(uname -s | tr '[:upper:]' '[:lower:]') mkdir -p ./jni curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.8.0.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.9.0-rc0.tar.gz" | tar -xz -C ./jni ### Install on Windows @@ -175,13 +175,13 @@ Take the following steps to install TensorFlow for Java on Linux or macOS: Take the following steps to install TensorFlow for Java on Windows: 1. Download - [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.8.0.jar), + [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.9.0-rc0.jar), which is the TensorFlow Java Archive (JAR). 2. Download the following Java Native Interface (JNI) file appropriate for - [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.8.0.zip). + [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.9.0-rc0.zip). 3. Extract this .zip file. - +__Note__: The native library (`tensorflow_jni.dll`) requires `msvcp140.dll` at runtime, which is included in the [Visual C++ 2015 Redistributable](https://www.microsoft.com/en-us/download/details.aspx?id=48145) package. ### Validate the installation @@ -227,7 +227,7 @@ must be part of your `classpath`. For example, you can include the downloaded `.jar` in your `classpath` by using the `-cp` compilation flag as follows: -
javac -cp libtensorflow-1.8.0.jar HelloTF.java
+
javac -cp libtensorflow-1.9.0-rc0.jar HelloTF.java
### Running @@ -241,11 +241,11 @@ two files are available to the JVM: For example, the following command line executes the `HelloTF` program on Linux and macOS X: -
java -cp libtensorflow-1.8.0.jar:. -Djava.library.path=./jni HelloTF
+
java -cp libtensorflow-1.9.0-rc0.jar:. -Djava.library.path=./jni HelloTF
And the following command line executes the `HelloTF` program on Windows: -
java -cp libtensorflow-1.8.0.jar;. -Djava.library.path=jni HelloTF
+
java -cp libtensorflow-1.9.0-rc0.jar;. -Djava.library.path=jni HelloTF
If the program prints Hello from version, you've successfully installed TensorFlow for Java and are ready to use the API. If the program diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md index 0ed8160027..c8d706cf3c 100644 --- a/tensorflow/docs_src/install/install_linux.md +++ b/tensorflow/docs_src/install/install_linux.md @@ -339,9 +339,7 @@ Docker will download the TensorFlow binary image the first time you launch it. #### GPU support -Prior to installing TensorFlow with GPU support, ensure that your system meets all -[NVIDIA software requirements](#NVIDIARequirements). To launch a Docker container -with NVidia GPU support, enter a command of the following format: +To launch a Docker container with NVidia GPU support, enter a command of the following format (this [does not require any local CUDA installation](https://github.com/nvidia/nvidia-docker/wiki/CUDA#requirements)): 
 $ nvidia-docker run -it -p hostPort:containerPort TensorFlowGPUImage
@@ -438,7 +436,7 @@ Take the following steps to install TensorFlow in an Anaconda environment:
 
      
      (tensorflow)$ pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0-cp34-cp34m-linux_x86_64.whl

+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp34-cp34m-linux_x86_64.whl
## Validate your installation @@ -517,7 +515,7 @@ on your system: from source. To use the TensorFlow binaries, version 3.5 or higher is required. See the [NVIDIA documentation](https://developer.nvidia.com/cuda-gpus) for a list of supported GPU cards. -* [GPU drivers](http://nvidia.com/driver) that support your version of the CUDA +* [GPU drivers](http://nvidia.com/drivers) that support your version of the CUDA Toolkit. * The `libcupti-dev` library is the NVIDIA CUDA Profile Tools Interface. This library provides advanced profiling support. To install this library, @@ -684,14 +682,14 @@ This section documents the relevant values for Linux installations. CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp27-none-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.8.0-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp27-none-linux_x86_64.whl
Note that GPU support requires the NVIDIA hardware and software described in @@ -703,14 +701,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp34-cp34m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.8.0-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp34-cp34m-linux_x86_64.whl
Note that GPU support requires the NVIDIA hardware and software described in @@ -722,14 +720,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp35-cp35m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.8.0-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp35-cp35m-linux_x86_64.whl
@@ -741,14 +739,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp36-cp36m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.8.0-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp36-cp36m-linux_x86_64.whl
diff --git a/tensorflow/docs_src/install/install_mac.md b/tensorflow/docs_src/install/install_mac.md index 29a867a9e3..9d01271c5a 100644 --- a/tensorflow/docs_src/install/install_mac.md +++ b/tensorflow/docs_src/install/install_mac.md @@ -119,7 +119,7 @@ Take the following steps to install TensorFlow with Virtualenv: TensorFlow in the active Virtualenv is as follows: 
 $ pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0-py3-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py3-none-any.whl If you encounter installation problems, see [Common Installation Problems](#common-installation-problems). @@ -242,7 +242,7 @@ take the following steps: issue the following command: 
 $ sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0-py3-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py3-none-any.whl If the preceding command fails, see [installation problems](#common-installation-problems). @@ -350,7 +350,7 @@ Take the following steps to install TensorFlow in an Anaconda environment: TensorFlow for Python 2.7: 
 (targetDirectory)$ pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0-py2-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py2-none-any.whl @@ -522,7 +522,7 @@ The value you specify depends on your Python version. 
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0-py2-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py2-none-any.whl
@@ -530,5 +530,5 @@ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0-py2-none-any. 
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0-py3-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py3-none-any.whl
diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md index 5ba522b436..dc6c1e36fc 100644 --- a/tensorflow/docs_src/install/install_sources.md +++ b/tensorflow/docs_src/install/install_sources.md @@ -81,7 +81,7 @@ or [macOS](#PrepareMac) - + ## Prepare environment for Linux Before building TensorFlow on Linux, install the following build @@ -328,10 +328,10 @@ Invoke `pip install` to install that pip package. The filename of the `.whl` file depends on your platform. For example, the following command will install the pip package -for TensorFlow 1.8.0 on Linux: +for TensorFlow 1.9.0rc0 on Linux: 
-$ sudo pip install /tmp/tensorflow_pkg/tensorflow-1.8.0-py2-none-any.whl
+$ sudo pip install /tmp/tensorflow_pkg/tensorflow-1.9.0rc0-py2-none-any.whl
## Validate your installation @@ -373,9 +373,9 @@ The build and installation problems you encounter typically depend on the operating system. See the "Common installation problems" section of one of the following guides: - * @{$install_linux#CommonInstallationProblems$Installing TensorFlow on Linux} - * @{$install_mac#CommonInstallationProblems$Installing TensorFlow on Mac OS} - * @{$install_windows#CommonInstallationProblems$Installing TensorFlow on Windows} + * @{$install_linux#common_installation_problems$Installing TensorFlow on Linux} + * @{$install_mac#common_installation_problems$Installing TensorFlow on Mac OS} + * @{$install_windows#common_installation_problems$Installing TensorFlow on Windows} Beyond the errors documented in those two guides, the following table notes additional errors specific to building TensorFlow. Note that we @@ -433,6 +433,8 @@ Stack Overflow and specify the `tensorflow` tag. **Linux** + + @@ -456,6 +458,7 @@ Stack Overflow and specify the `tensorflow` tag. **Mac**
Version:CPU/GPU:Python Version:Compiler:Build Tools:cuDNN:CUDA:
tensorflow-1.9.0CPU2.7, 3.3-3.6GCC 4.8Bazel 0.11.0N/AN/A
tensorflow_gpu-1.9.0GPU2.7, 3.3-3.6GCC 4.8Bazel 0.11.079
tensorflow-1.8.0CPU2.7, 3.3-3.6GCC 4.8Bazel 0.10.0N/AN/A
tensorflow_gpu-1.8.0GPU2.7, 3.3-3.6GCC 4.8Bazel 0.9.079
tensorflow-1.7.0CPU2.7, 3.3-3.6GCC 4.8Bazel 0.10.0N/AN/A
+ @@ -472,6 +475,8 @@ Stack Overflow and specify the `tensorflow` tag. **Windows**
Version:CPU/GPU:Python Version:Compiler:Build Tools:cuDNN:CUDA:
tensorflow-1.9.0CPU2.7, 3.3-3.6Clang from xcodeBazel 0.11.0N/AN/A
tensorflow-1.8.0CPU2.7, 3.3-3.6Clang from xcodeBazel 0.10.1N/AN/A
tensorflow-1.7.0CPU2.7, 3.3-3.6Clang from xcodeBazel 0.10.1N/AN/A
tensorflow-1.6.0CPU2.7, 3.3-3.6Clang from xcodeBazel 0.8.1N/AN/A
+ + diff --git a/tensorflow/docs_src/mobile/linking_libs.md b/tensorflow/docs_src/mobile/linking_libs.md index cf0db59021..efef5dd0da 100644 --- a/tensorflow/docs_src/mobile/linking_libs.md +++ b/tensorflow/docs_src/mobile/linking_libs.md @@ -27,7 +27,7 @@ called `libandroid_tensorflow_inference_java.jar`. There are three ways to include this functionality in your program: 1. Include the jcenter AAR which contains it, as in this - [example app](https://github.com/googlecodelabs/tensorflow-for-poets-2/blob/master/android/build.gradle#L59-L65) + [example app](https://github.com/googlecodelabs/tensorflow-for-poets-2/blob/master/android/tfmobile/build.gradle#L59-L65) 2. Download the nightly precompiled version from [ci.tensorflow.org](http://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/). diff --git a/tensorflow/docs_src/mobile/prepare_models.md b/tensorflow/docs_src/mobile/prepare_models.md index 8b22c04d87..2b84dbb973 100644 --- a/tensorflow/docs_src/mobile/prepare_models.md +++ b/tensorflow/docs_src/mobile/prepare_models.md @@ -105,8 +105,8 @@ inline constants so everything’s in one file. To handle the conversion, you need the `freeze_graph.py` script, that’s held in [`tensorflow/python/tools/freeze_graph.py`](https://www.tensorflow.org/code/tensorflow/python/tools/freeze_graph.py). You’ll run it like this: - bazel build tensorflow/tools:freeze_graph - bazel-bin/tensorflow/tools/freeze_graph \ + bazel build tensorflow/python/tools:freeze_graph + bazel-bin/tensorflow/python/tools/freeze_graph \ --input_graph=/tmp/model/my_graph.pb \ --input_checkpoint=/tmp/model/model.ckpt-1000 \ --output_graph=/tmp/frozen_graph.pb \ diff --git a/tensorflow/docs_src/performance/quantization.md b/tensorflow/docs_src/performance/quantization.md index 2fea02d861..c97f74139c 100644 --- a/tensorflow/docs_src/performance/quantization.md +++ b/tensorflow/docs_src/performance/quantization.md @@ -227,8 +227,8 @@ of 30.0f, and an 8-bit array, the quantized values represent the following:
Version:CPU/GPU:Python Version:Compiler:Build Tools:cuDNN:CUDA:
tensorflow-1.9.0CPU3.5-3.6MSVC 2015 update 3Cmake v3.6.3N/AN/A
tensorflow_gpu-1.9.0GPU3.5-3.6MSVC 2015 update 3Cmake v3.6.379
tensorflow-1.8.0CPU3.5-3.6MSVC 2015 update 3Cmake v3.6.3N/AN/A
tensorflow_gpu-1.8.0GPU3.5-3.6MSVC 2015 update 3Cmake v3.6.379
tensorflow-1.7.0CPU3.5-3.6MSVC 2015 update 3Cmake v3.6.3N/AN/A
- +
QuantizedFloat
0-10.0
25530.0
12810.0
25530.0
Table 2: Example quantized value range diff --git a/tensorflow/docs_src/programmers_guide/estimators.md b/tensorflow/docs_src/programmers_guide/estimators.md index c4aae1d9d6..b13b47184d 100644 --- a/tensorflow/docs_src/programmers_guide/estimators.md +++ b/tensorflow/docs_src/programmers_guide/estimators.md @@ -21,18 +21,17 @@ Note: TensorFlow also includes a deprecated `Estimator` class at Estimators provide the following benefits: -* You can run Estimators-based models on a local host or on a +* You can run Estimator-based models on a local host or on a distributed multi-server environment without changing your model. - Furthermore, you can run Estimators-based models on CPUs, GPUs, + Furthermore, you can run Estimator-based models on CPUs, GPUs, or TPUs without recoding your model. * Estimators simplify sharing implementations between model developers. -* You can develop a state of the art model with high-level intuitive code, +* You can develop a state of the art model with high-level intuitive code. In short, it is generally much easier to create models with Estimators than with the low-level TensorFlow APIs. -* Estimators are themselves built on tf.layers, which +* Estimators are themselves built on @{tf.layers}, which simplifies customization. -* Estimators build the graph for you. In other words, you don't have to - build the graph. +* Estimators build the graph for you. * Estimators provide a safe distributed training loop that controls how and when to: * build the graph @@ -57,7 +56,7 @@ the "plumbing" for you. That is, pre-made Estimators create and manage pre-made Estimators let you experiment with different model architectures by making only minimal code changes. @{tf.estimator.DNNClassifier$`DNNClassifier`}, for example, is a pre-made Estimator class that trains classification models -through dense, feed-forward neural networks. +based on dense, feed-forward neural networks. ### Structure of a pre-made Estimators program @@ -79,7 +78,7 @@ of the following four steps: an input function: def input_fn(dataset): - ... # manipulate dataset, extracting feature names and the label + ... # manipulate dataset, extracting the feature dict and the label return feature_dict, label (See @{$programmers_guide/datasets} for full details.) @@ -96,13 +95,13 @@ of the following four steps: population = tf.feature_column.numeric_column('population') crime_rate = tf.feature_column.numeric_column('crime_rate') median_education = tf.feature_column.numeric_column('median_education', - normalizer_fn='lambda x: x - global_education_mean') + normalizer_fn=lambda x: x - global_education_mean) 3. **Instantiate the relevant pre-made Estimator.** For example, here's a sample instantiation of a pre-made Estimator named `LinearClassifier`: # Instantiate an estimator, passing the feature columns. - estimator = tf.estimator.Estimator.LinearClassifier( + estimator = tf.estimator.LinearClassifier( feature_columns=[population, crime_rate, median_education], ) diff --git a/tensorflow/docs_src/programmers_guide/feature_columns.md b/tensorflow/docs_src/programmers_guide/feature_columns.md index 845194fe0e..90f5c53a17 100644 --- a/tensorflow/docs_src/programmers_guide/feature_columns.md +++ b/tensorflow/docs_src/programmers_guide/feature_columns.md @@ -528,10 +528,10 @@ suggested by the following snippet: categorical_column = ... # Create any categorical column # Represent the categorical column as an embedding column. -# This means creating a one-hot vector with one element for each category. +# This means creating an embedding vector lookup table with one element for each category. embedding_column = tf.feature_column.embedding_column( categorical_column=categorical_column, - dimension=dimension_of_embedding_vector) + dimension=embedding_dimensions) ``` @{$programmers_guide/embedding$Embeddings} is a significant topic within machine diff --git a/tensorflow/examples/learn/iris.py b/tensorflow/examples/learn/iris.py index 03e60972aa..86f5204ec3 100644 --- a/tensorflow/examples/learn/iris.py +++ b/tensorflow/examples/learn/iris.py @@ -21,7 +21,8 @@ from __future__ import division from __future__ import print_function import os -import urllib + +from six.moves.urllib.request import urlretrieve import tensorflow as tf @@ -38,9 +39,7 @@ FEATURE_KEYS = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width'] def maybe_download_iris_data(file_name, download_url): """Downloads the file and returns the number of data.""" if not os.path.exists(file_name): - raw = urllib.urlopen(download_url).read() - with open(file_name, 'w') as f: - f.write(raw) + urlretrieve(download_url, file_name) # The first line is a comma-separated string. The first one is the number of # total data in the file. diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go index 5602775b62..a5224fbda0 100644 --- a/tensorflow/go/op/wrappers.go +++ b/tensorflow/go/op/wrappers.go @@ -10955,7 +10955,7 @@ func SampleDistortedBoundingBoxAspectRatioRange(value []float32) SampleDistorted // SampleDistortedBoundingBoxAreaRange sets the optional area_range attribute to value. // // value: The cropped area of the image must contain a fraction of the -// supplied image within in this range. +// supplied image within this range. // If not specified, defaults to func SampleDistortedBoundingBoxAreaRange(value []float32) SampleDistortedBoundingBoxAttr { return func(m optionalAttr) { @@ -18098,9 +18098,10 @@ func SparseFillEmptyRowsGrad(scope *Scope, reverse_index_map tf.Output, grad_val } // Computes scaled exponential linear: `scale * alpha * (exp(features) - 1)` -// // if < 0, `scale * features` otherwise. // +// Assumes weights to have zero mean and variance 1.0 / fan_in. +// // See [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515) func Selu(scope *Scope, features tf.Output) (activations tf.Output) { if scope.Err() != nil { @@ -21625,7 +21626,7 @@ func ImageSummaryBadColor(value tf.Tensor) ImageSummaryAttr { // generated sequentially as '*tag*/image/0', '*tag*/image/1', etc. // // The `bad_color` argument is the color to use in the generated images for -// non-finite input values. It is a `unit8` 1-D tensor of length `channels`. +// non-finite input values. It is a `uint8` 1-D tensor of length `channels`. // Each element must be in the range `[0, 255]` (It represents the value of a // pixel in the output image). Non-finite values in the input tensor are // replaced by this tensor in the output image. The default value is the color @@ -24018,7 +24019,7 @@ func SampleDistortedBoundingBoxV2AspectRatioRange(value []float32) SampleDistort // SampleDistortedBoundingBoxV2AreaRange sets the optional area_range attribute to value. // // value: The cropped area of the image must contain a fraction of the -// supplied image within in this range. +// supplied image within this range. // If not specified, defaults to func SampleDistortedBoundingBoxV2AreaRange(value []float32) SampleDistortedBoundingBoxV2Attr { return func(m optionalAttr) { @@ -24714,8 +24715,7 @@ type DecodeProtoV2Attr func(optionalAttr) // If not specified, defaults to "local://" func DecodeProtoV2DescriptorSource(value string) DecodeProtoV2Attr { return func(m optionalAttr) { - m["descriptor_source"] = value - } + m["descriptor_source"] = value } } // DecodeProtoV2MessageFormat sets the optional message_format attribute to value. diff --git a/tensorflow/java/src/gen/cc/op_generator.cc b/tensorflow/java/src/gen/cc/op_generator.cc index debd95fc62..9b171f66ec 100644 --- a/tensorflow/java/src/gen/cc/op_generator.cc +++ b/tensorflow/java/src/gen/cc/op_generator.cc @@ -376,9 +376,6 @@ void GenerateOp(const OpSpec& op, const EndpointSpec& endpoint, } } // op annotations - op_class.add_annotation( - Annotation::Create("Generated", "javax.annotation") - .attributes("value = \"TensorFlow Java Op Generator\"")); if (endpoint.deprecated()) { op_class.add_annotation(Annotation::Create("Deprecated")); string explanation; @@ -415,8 +412,12 @@ void GenerateOp(const OpSpec& op, const EndpointSpec& endpoint, SourceFileWriter writer(op_file.get()); std::list dependencies; CollectOpDependencies(op, mode, &dependencies); - writer.Write(kLicense).EndLine().BeginType(op_class, PUBLIC | FINAL, - &dependencies, &op_javadoc); + writer.Write(kLicense) + .EndLine() + .Write("// This class has been generated, DO NOT EDIT!") + .EndLine() + .EndLine() + .BeginType(op_class, PUBLIC | FINAL, &dependencies, &op_javadoc); if (!op.optional_attributes().empty()) { RenderOptionsClass(op, op_class, &writer); } diff --git a/tensorflow/java/src/gen/cc/op_specs.cc b/tensorflow/java/src/gen/cc/op_specs.cc index 181fd4c5e3..941ab2699c 100644 --- a/tensorflow/java/src/gen/cc/op_specs.cc +++ b/tensorflow/java/src/gen/cc/op_specs.cc @@ -96,6 +96,7 @@ Type TypeResolver::TypeOf(const OpDef_ArgDef& arg_def, bool* iterable_out) { *iterable_out = true; visited_attrs_.insert(std::make_pair(arg_def.number_attr(), Type::Int())); } + Type type = Type::Wildcard(); if (arg_def.type() != DataType::DT_INVALID) { // resolve type from DataType diff --git a/tensorflow/python/eager/backprop.py b/tensorflow/python/eager/backprop.py index b2e6c60021..bd97b181ff 100644 --- a/tensorflow/python/eager/backprop.py +++ b/tensorflow/python/eager/backprop.py @@ -196,11 +196,11 @@ def implicit_val_and_grad(f): # TODO(cais): Remove calls to tf.constant() once the gradients functions # accept lists and np.ndarrays. - def grad_fn(*args): + def grad_fn(*args, **kwds): """Computes the gradient of the wrapped function.""" this_tape = tape.push_new_tape() try: - end_node = f(*args) + end_node = f(*args, **kwds) if end_node is None: raise ValueError("Cannot differentiate a function that returns None; " "did you forget to return a value from {}?".format( diff --git a/tensorflow/python/estimator/BUILD b/tensorflow/python/estimator/BUILD index 9cd17e0407..20522098b0 100644 --- a/tensorflow/python/estimator/BUILD +++ b/tensorflow/python/estimator/BUILD @@ -978,7 +978,10 @@ py_test( size = "large", srcs = ["keras_test.py"], srcs_version = "PY2AND3", - tags = ["notsan"], + tags = [ + "no_windows", + "notsan", + ], deps = [ ":keras", "//tensorflow/core:protos_all_py", diff --git a/tensorflow/python/estimator/exporter.py b/tensorflow/python/estimator/exporter.py index 7cdf840c97..b18212cfcd 100644 --- a/tensorflow/python/estimator/exporter.py +++ b/tensorflow/python/estimator/exporter.py @@ -156,7 +156,7 @@ def _loss_smaller(best_eval_result, current_eval_result): return best_eval_result[default_key] > current_eval_result[default_key] -def _verify_compre_fn_args(compare_fn): +def _verify_compare_fn_args(compare_fn): """Verifies compare_fn arguments.""" args = set(util.fn_args(compare_fn)) if 'best_eval_result' not in args: @@ -265,7 +265,7 @@ class BestExporter(Exporter): self._compare_fn = compare_fn if self._compare_fn is None: raise ValueError('`compare_fn` must not be None.') - _verify_compre_fn_args(self._compare_fn) + _verify_compare_fn_args(self._compare_fn) self._saved_model_exporter = _SavedModelExporter( name, serving_input_receiver_fn, assets_extra, as_text) diff --git a/tensorflow/python/estimator/inputs/numpy_io.py b/tensorflow/python/estimator/inputs/numpy_io.py index 035c7c148c..a6cefdece2 100644 --- a/tensorflow/python/estimator/inputs/numpy_io.py +++ b/tensorflow/python/estimator/inputs/numpy_io.py @@ -136,11 +136,13 @@ def numpy_input_fn(x, values in `x` have same shape). ValueError: if duplicate keys are in both `x` and `y` when `y` is a dict. ValueError: if x or y is an empty dict. - TypeError: `x` is not a dict or array, or if `shuffle` is not bool. + TypeError: `x` is not a dict or array. + ValueError: if 'shuffle' is not provided or a bool. """ if not isinstance(shuffle, bool): - raise TypeError('shuffle must be explicitly set as boolean; ' - 'got {}'.format(shuffle)) + raise ValueError('shuffle must be provided and explicitly set as boolean ' + '(it is recommended to set it as True for training); ' + 'got {}'.format(shuffle)) def input_fn(): """Numpy input function.""" diff --git a/tensorflow/python/estimator/inputs/numpy_io_test.py b/tensorflow/python/estimator/inputs/numpy_io_test.py index 92d057e25d..81b201cc5c 100644 --- a/tensorflow/python/estimator/inputs/numpy_io_test.py +++ b/tensorflow/python/estimator/inputs/numpy_io_test.py @@ -286,8 +286,9 @@ class NumpyIoTest(test.TestCase): x = np.arange(32, 36) y = np.arange(4) with self.test_session(): - with self.assertRaisesRegexp(TypeError, - 'shuffle must be explicitly set as boolean'): + with self.assertRaisesRegexp(ValueError, + 'shuffle must be provided and explicitly ' + 'set as boolean'): # Default shuffle is None. numpy_io.numpy_input_fn(x, y) diff --git a/tensorflow/python/estimator/inputs/pandas_io.py b/tensorflow/python/estimator/inputs/pandas_io.py index 938e244fb3..57f8e5fd6a 100644 --- a/tensorflow/python/estimator/inputs/pandas_io.py +++ b/tensorflow/python/estimator/inputs/pandas_io.py @@ -68,15 +68,16 @@ def pandas_input_fn(x, Raises: ValueError: if `x` already contains a column with the same name as `y`, or if the indexes of `x` and `y` don't match. - TypeError: `shuffle` is not bool. + ValueError: if 'shuffle' is not provided or a bool. """ if not HAS_PANDAS: raise TypeError( 'pandas_input_fn should not be called without pandas installed') if not isinstance(shuffle, bool): - raise TypeError('shuffle must be explicitly set as boolean; ' - 'got {}'.format(shuffle)) + raise ValueError('shuffle must be provided and explicitly set as boolean ' + '(it is recommended to set it as True for training); ' + 'got {}'.format(shuffle)) x = x.copy() if y is not None: diff --git a/tensorflow/python/estimator/inputs/pandas_io_test.py b/tensorflow/python/estimator/inputs/pandas_io_test.py index e5912a3b28..dcecf6dd61 100644 --- a/tensorflow/python/estimator/inputs/pandas_io_test.py +++ b/tensorflow/python/estimator/inputs/pandas_io_test.py @@ -70,8 +70,9 @@ class PandasIoTest(test.TestCase): return x, _ = self.makeTestDataFrame() y_noindex = pd.Series(np.arange(-32, -28)) - with self.assertRaisesRegexp(TypeError, - 'shuffle must be explicitly set as boolean'): + with self.assertRaisesRegexp(ValueError, + 'shuffle must be provided and explicitly ' + 'set as boolean'): # Default shuffle is None pandas_io.pandas_input_fn(x, y_noindex) diff --git a/tensorflow/python/estimator/inputs/queues/feeding_functions.py b/tensorflow/python/estimator/inputs/queues/feeding_functions.py index 8e2ec83020..51a61adb21 100644 --- a/tensorflow/python/estimator/inputs/queues/feeding_functions.py +++ b/tensorflow/python/estimator/inputs/queues/feeding_functions.py @@ -250,7 +250,7 @@ class _PandasFeedFn(object): num_epochs=None): if len(placeholders) != len(dataframe.columns) + 1: raise ValueError("Expected {} placeholders; got {}.".format( - len(dataframe.columns), len(placeholders))) + len(dataframe.columns) + 1, len(placeholders))) self._index_placeholder = placeholders[0] self._col_placeholders = placeholders[1:] self._dataframe = dataframe diff --git a/tensorflow/python/estimator/keras.py b/tensorflow/python/estimator/keras.py index c80af08fba..2f439f765e 100644 --- a/tensorflow/python/estimator/keras.py +++ b/tensorflow/python/estimator/keras.py @@ -70,7 +70,7 @@ def _convert_tensor(x): return x -def _any_variable_initalized(): +def _any_variable_initialized(): """Check if any variable has been initialized in the Keras model. Returns: @@ -511,7 +511,7 @@ def model_to_estimator(keras_model=None, keras_model_fn, model_dir=model_dir, config=config) # Check if we need to call get_weights: - if _any_variable_initalized(): + if _any_variable_initialized(): keras_weights = keras_model.get_weights() # Warn if config passed to estimator tries to update GPUOptions. If a # session has already been created, the GPUOptions passed to the first diff --git a/tensorflow/python/estimator/keras_test.py b/tensorflow/python/estimator/keras_test.py index 6688a84130..5e094ae92b 100644 --- a/tensorflow/python/estimator/keras_test.py +++ b/tensorflow/python/estimator/keras_test.py @@ -31,10 +31,10 @@ from tensorflow.python.estimator import run_config as run_config_lib from tensorflow.python.estimator.inputs import numpy_io from tensorflow.python.framework import ops from tensorflow.python.framework import test_util -from tensorflow.python.keras import backend as K from tensorflow.python.keras import testing_utils from tensorflow.python.keras.applications import mobilenet from tensorflow.python.keras.optimizers import SGD +from tensorflow.python.ops.parsing_ops import gen_parsing_ops from tensorflow.python.platform import gfile from tensorflow.python.platform import test from tensorflow.python.summary.writer import writer_cache @@ -146,13 +146,13 @@ def randomize_io_type(array, name): def multi_inputs_multi_outputs_model(): a = keras.layers.Input(shape=(16,), name='input_a') b = keras.layers.Input(shape=(16,), name='input_b') - m = keras.layers.Input(shape=(8,), dtype='bool', name='input_m') + m = keras.layers.Input(shape=(8,), dtype='string', name='input_m') dense = keras.layers.Dense(8, name='dense_1') a_2 = dense(a) - # Apply a mask - s_2 = keras.layers.Lambda(lambda k: - K.switch(k[0], k[1], K.zeros_like(k[1])))([m, a_2]) + # Read m + m_2 = keras.layers.Lambda(gen_parsing_ops.string_to_number)(m) + s_2 = keras.layers.Lambda(lambda k: k[0] * k[1])([m_2, a_2]) b_2 = dense(b) merged = keras.layers.concatenate([s_2, b_2], name='merge') c = keras.layers.Dense(3, activation='softmax', name='dense_2')(merged) @@ -372,13 +372,13 @@ class TestKerasEstimator(test_util.TensorFlowTestCase): def train_input_fn(): input_dict = {'input_a': a_train, 'input_b': b_train, - 'input_m': input_m_train > 0} + 'input_m': input_m_train.astype(np.str)} output_dict = {'dense_2': c_train, 'dense_3': d_train} return input_dict, output_dict def eval_input_fn(): input_dict = {'input_a': a_test, 'input_b': b_test, - 'input_m': input_m_test > 0} + 'input_m': input_m_test.astype(np.str)} output_dict = {'dense_2': c_test, 'dense_3': d_test} return input_dict, output_dict diff --git a/tensorflow/python/grappler/layout_optimizer_test.py b/tensorflow/python/grappler/layout_optimizer_test.py index 2d6925d1a8..af5d709f7e 100644 --- a/tensorflow/python/grappler/layout_optimizer_test.py +++ b/tensorflow/python/grappler/layout_optimizer_test.py @@ -1389,7 +1389,7 @@ class LayoutOptimizerTest(test.TestCase): expected_num_transposes = 3 self.assertEqual(expected_num_transposes, num_transposes) self._assert_trans_nhwc_to_nchw('map/while/Conv2D-0', nodes) - self._assert_trans_nchw_to_nhwc('map/while/Add-0-2', nodes) + self._assert_trans_nchw_to_nhwc('map/while/Add_1-0-2', nodes) self.assertAllClose(output_val_ref, output_val, atol=1e-3) def testLoopWithVecAnd4D(self): @@ -1413,7 +1413,7 @@ class LayoutOptimizerTest(test.TestCase): expected_num_transposes = 2 self.assertEqual(expected_num_transposes, num_transposes) self._assert_trans_nhwc_to_nchw('map/while/Conv2D-0', nodes) - self._assert_trans_nchw_to_nhwc('map/while/Add-0-2', nodes) + self._assert_trans_nchw_to_nhwc('map/while/Add_1-0-2', nodes) self.assertAllClose(output_val_ref, output_val, atol=1e-3) def testBinaryOpSecondPort(self): diff --git a/tensorflow/python/keras/activations.py b/tensorflow/python/keras/activations.py index e487f583be..f608dea430 100644 --- a/tensorflow/python/keras/activations.py +++ b/tensorflow/python/keras/activations.py @@ -93,6 +93,8 @@ def selu(x): - To be used together with the initialization "lecun_normal". - To be used together with the dropout variant "AlphaDropout". + References: + - [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515) """ alpha = 1.6732632423543772848170429916717 scale = 1.0507009873554804934193349852946 diff --git a/tensorflow/python/keras/callbacks.py b/tensorflow/python/keras/callbacks.py index 70b6a8431a..9f91368e5b 100644 --- a/tensorflow/python/keras/callbacks.py +++ b/tensorflow/python/keras/callbacks.py @@ -724,15 +724,6 @@ class TensorBoard(Callback): for weight in layer.weights: mapped_weight_name = weight.name.replace(':', '_') tf_summary.histogram(mapped_weight_name, weight) - if self.write_grads: - grads = model.optimizer.get_gradients(model.total_loss, weight) - - def is_indexed_slices(grad): - return type(grad).__name__ == 'IndexedSlices' - - grads = [grad.values if is_indexed_slices(grad) else grad - for grad in grads] - tf_summary.histogram('{}_grad'.format(mapped_weight_name), grads) if self.write_images: w_img = array_ops.squeeze(weight) shape = K.int_shape(w_img) @@ -759,6 +750,18 @@ class TensorBoard(Callback): assert len(shape) == 4 and shape[-1] in [1, 3, 4] tf_summary.image(mapped_weight_name, w_img) + if self.write_grads: + for weight in layer.trainable_weights: + mapped_weight_name = weight.name.replace(':', '_') + grads = model.optimizer.get_gradients(model.total_loss, weight) + + def is_indexed_slices(grad): + return type(grad).__name__ == 'IndexedSlices' + + grads = [grad.values if is_indexed_slices(grad) else grad + for grad in grads] + tf_summary.histogram('{}_grad'.format(mapped_weight_name), grads) + if hasattr(layer, 'output'): tf_summary.histogram('{}_out'.format(layer.name), layer.output) self.merged = tf_summary.merge_all() diff --git a/tensorflow/python/keras/callbacks_test.py b/tensorflow/python/keras/callbacks_test.py index b355f4a269..5062a26580 100644 --- a/tensorflow/python/keras/callbacks_test.py +++ b/tensorflow/python/keras/callbacks_test.py @@ -653,6 +653,8 @@ class KerasCallbacksTest(test.TestCase): model.add( keras.layers.Dense( NUM_HIDDEN, input_dim=INPUT_DIM, activation='relu')) + # non_trainable_weights: moving_variance, moving_mean + model.add(keras.layers.BatchNormalization()) model.add(keras.layers.Dense(NUM_CLASSES, activation='softmax')) model.compile( loss='categorical_crossentropy', diff --git a/tensorflow/python/keras/engine/network.py b/tensorflow/python/keras/engine/network.py index a4cd017d60..1c9135982e 100644 --- a/tensorflow/python/keras/engine/network.py +++ b/tensorflow/python/keras/engine/network.py @@ -123,7 +123,7 @@ class Network(base_layer.Layer): # Entries are unique. Includes input and output layers. self._layers = [] - # Used in symbolic mode only, only in conjonction with graph-networks + # Used in symbolic mode only, only in conjunction with graph-networks self._outbound_nodes = [] self._inbound_nodes = [] diff --git a/tensorflow/python/keras/engine/saving_test.py b/tensorflow/python/keras/engine/saving_test.py index 6a94986b9c..7e82db028b 100644 --- a/tensorflow/python/keras/engine/saving_test.py +++ b/tensorflow/python/keras/engine/saving_test.py @@ -482,7 +482,7 @@ class TestWholeModelSaving(test.TestCase): with h5py.File(fname, 'r') as h5file: num_names_arrays = len([attr for attr in h5file['model_weights'].attrs if attr.startswith('layer_names')]) - # The chunking of layer names array should have happend. + # The chunking of layer names array should have happened. self.assertGreater(num_names_arrays, 0) out2 = model.predict(x) self.assertAllClose(out, out2, atol=1e-05) @@ -527,7 +527,7 @@ class TestWholeModelSaving(test.TestCase): num_weight_arrays = len( [attr for attr in h5file['model_weights']['nested_model'].attrs if attr.startswith('weight_names')]) - # The chunking of layer names array should have happend. + # The chunking of layer names array should have happened. self.assertGreater(num_weight_arrays, 0) out2 = model.predict(x) self.assertAllClose(out, out2, atol=1e-05) diff --git a/tensorflow/python/keras/engine/training.py b/tensorflow/python/keras/engine/training.py index 89c1f1a40f..fce6cbdb7a 100644 --- a/tensorflow/python/keras/engine/training.py +++ b/tensorflow/python/keras/engine/training.py @@ -24,6 +24,7 @@ import numpy as np from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.ops import iterator_ops from tensorflow.python.eager import context +from tensorflow.python.framework import constant_op from tensorflow.python.framework import errors from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_util @@ -409,11 +410,13 @@ class Model(Network): else: if sample_weight_mode == 'temporal': sample_weights.append(array_ops.placeholder_with_default( - [[1.]], shape=[None, None], name=name + '_sample_weights')) + constant_op.constant([[1.]], dtype=K.floatx()), + shape=[None, None], name=name + '_sample_weights')) sample_weight_modes.append('temporal') else: sample_weights.append(array_ops.placeholder_with_default( - [1.], shape=[None], name=name + '_sample_weights')) + constant_op.constant([1.], dtype=K.floatx()), + shape=[None], name=name + '_sample_weights')) sample_weight_modes.append(None) self.sample_weight_modes = sample_weight_modes self._feed_sample_weight_modes = [] diff --git a/tensorflow/python/keras/engine/training_eager.py b/tensorflow/python/keras/engine/training_eager.py index 2ecbff3a1c..e8838cd3bc 100644 --- a/tensorflow/python/keras/engine/training_eager.py +++ b/tensorflow/python/keras/engine/training_eager.py @@ -732,7 +732,7 @@ def slice_arrays(arrays, indices, contiguous=True): """Slices batches out of provided arrays (workaround for eager tensors). Unfortunately eager tensors don't have the same slicing behavior as - Numpy arrays (they folow the same slicing behavior as symbolic TF tensors), + Numpy arrays (they follow the same slicing behavior as symbolic TF tensors), hence we cannot use `generic_utils.slice_arrays` directly and we have to implement this workaround based on `concat`. This has a performance cost. diff --git a/tensorflow/python/keras/initializers_test.py b/tensorflow/python/keras/initializers_test.py index a54d6da839..c519e194bd 100644 --- a/tensorflow/python/keras/initializers_test.py +++ b/tensorflow/python/keras/initializers_test.py @@ -71,7 +71,7 @@ class KerasInitializersTest(test.TestCase): stddev=1, seed=126), tensor_shape, - target_mean=0., target_std=None, target_max=2) + target_mean=0., target_max=2, target_min=-2) def test_constant(self): tensor_shape = (5, 6, 4) @@ -83,49 +83,49 @@ class KerasInitializersTest(test.TestCase): tensor_shape = (5, 6, 4, 2) with self.test_session(): fan_in, _ = init_ops._compute_fans(tensor_shape) - scale = np.sqrt(3. / fan_in) + std = np.sqrt(1. / fan_in) self._runner(keras.initializers.lecun_uniform(seed=123), tensor_shape, - target_mean=0., target_max=scale, target_min=-scale) + target_mean=0., target_std=std) def test_glorot_uniform(self): tensor_shape = (5, 6, 4, 2) with self.test_session(): fan_in, fan_out = init_ops._compute_fans(tensor_shape) - scale = np.sqrt(6. / (fan_in + fan_out)) + std = np.sqrt(2. / (fan_in + fan_out)) self._runner(keras.initializers.glorot_uniform(seed=123), tensor_shape, - target_mean=0., target_max=scale, target_min=-scale) + target_mean=0., target_std=std) def test_he_uniform(self): tensor_shape = (5, 6, 4, 2) with self.test_session(): fan_in, _ = init_ops._compute_fans(tensor_shape) - scale = np.sqrt(6. / fan_in) + std = np.sqrt(2. / fan_in) self._runner(keras.initializers.he_uniform(seed=123), tensor_shape, - target_mean=0., target_max=scale, target_min=-scale) + target_mean=0., target_std=std) def test_lecun_normal(self): tensor_shape = (5, 6, 4, 2) with self.test_session(): fan_in, _ = init_ops._compute_fans(tensor_shape) - scale = np.sqrt(1. / fan_in) + std = np.sqrt(1. / fan_in) self._runner(keras.initializers.lecun_normal(seed=123), tensor_shape, - target_mean=0., target_std=None, target_max=2 * scale) + target_mean=0., target_std=std) def test_glorot_normal(self): tensor_shape = (5, 6, 4, 2) with self.test_session(): fan_in, fan_out = init_ops._compute_fans(tensor_shape) - scale = np.sqrt(2. / (fan_in + fan_out)) + std = np.sqrt(2. / (fan_in + fan_out)) self._runner(keras.initializers.glorot_normal(seed=123), tensor_shape, - target_mean=0., target_std=None, target_max=2 * scale) + target_mean=0., target_std=std) def test_he_normal(self): tensor_shape = (5, 6, 4, 2) with self.test_session(): fan_in, _ = init_ops._compute_fans(tensor_shape) - scale = np.sqrt(2. / fan_in) + std = np.sqrt(2. / fan_in) self._runner(keras.initializers.he_normal(seed=123), tensor_shape, - target_mean=0., target_std=None, target_max=2 * scale) + target_mean=0., target_std=std) def test_orthogonal(self): tensor_shape = (20, 20) diff --git a/tensorflow/python/keras/layers/core.py b/tensorflow/python/keras/layers/core.py index 5061825d38..f60064ed63 100644 --- a/tensorflow/python/keras/layers/core.py +++ b/tensorflow/python/keras/layers/core.py @@ -19,7 +19,9 @@ from __future__ import division from __future__ import print_function import copy +import sys import types as python_types +import warnings import numpy as np @@ -714,6 +716,7 @@ class Lambda(Layer): return self.mask def get_config(self): + module = self.function.__module__ if isinstance(self.function, python_types.LambdaType): function = generic_utils.func_dump(self.function) function_type = 'lambda' @@ -721,21 +724,26 @@ class Lambda(Layer): function = self.function.__name__ function_type = 'function' + output_shape_module = None if isinstance(self._output_shape, python_types.LambdaType): output_shape = generic_utils.func_dump(self._output_shape) output_shape_type = 'lambda' + output_shape_module = self._output_shape.__module__ elif callable(self._output_shape): output_shape = self._output_shape.__name__ output_shape_type = 'function' + output_shape_module = self._output_shape.__module__ else: output_shape = self._output_shape output_shape_type = 'raw' config = { 'function': function, + 'module': module, 'function_type': function_type, 'output_shape': output_shape, 'output_shape_type': output_shape_type, + 'output_shape_module': output_shape_module, 'arguments': self.arguments } base_config = super(Lambda, self).get_config() @@ -745,8 +753,16 @@ class Lambda(Layer): def from_config(cls, config, custom_objects=None): config = config.copy() globs = globals() + module = config.pop('module', None) + if module in sys.modules: + globs.update(sys.modules[module].__dict__) + elif module is not None: + # Note: we don't know the name of the function if it's a lambda. + warnings.warn('{} is not loaded, but a Lambda layer uses it. ' + 'It may cause errors.'.format(module) + , UserWarning) if custom_objects: - globs = dict(list(globs.items()) + list(custom_objects.items())) + globs.update(custom_objects) function_type = config.pop('function_type') if function_type == 'function': # Simple lookup in custom objects @@ -760,6 +776,14 @@ class Lambda(Layer): else: raise TypeError('Unknown function type:', function_type) + output_shape_module = config.pop('output_shape_module', None) + if output_shape_module in sys.modules: + globs.update(sys.modules[output_shape_module].__dict__) + elif output_shape_module is not None: + # Note: we don't know the name of the function if it's a lambda. + warnings.warn('{} is not loaded, but a Lambda layer uses it. ' + 'It may cause errors.'.format(output_shape_module) + , UserWarning) output_shape_type = config.pop('output_shape_type') if output_shape_type == 'function': # Simple lookup in custom objects diff --git a/tensorflow/python/keras/models_test.py b/tensorflow/python/keras/models_test.py index c616d8f24f..e6e45902a8 100644 --- a/tensorflow/python/keras/models_test.py +++ b/tensorflow/python/keras/models_test.py @@ -144,5 +144,19 @@ class CheckpointingTests(test.TestCase): model.load_weights(save_prefix) self.assertEqual(12., self.evaluate(beta1_power)) +class TestModelBackend(test.TestCase): + + def test_model_backend_float64_use_cases(self): + # Test case for GitHub issue 19318 + floatx = keras.backend.floatx() + keras.backend.set_floatx('float64') + + x = keras.Input((5,)) + y = keras.layers.Dense(1)(x) + model = keras.models.Model(x, y) + model.compile('rmsprop', 'mse') + + keras.backend.set_floatx(floatx) + if __name__ == '__main__': test.main() diff --git a/tensorflow/python/kernel_tests/as_string_op_test.py b/tensorflow/python/kernel_tests/as_string_op_test.py index 9d54add264..94ed8ebd31 100644 --- a/tensorflow/python/kernel_tests/as_string_op_test.py +++ b/tensorflow/python/kernel_tests/as_string_op_test.py @@ -130,6 +130,16 @@ class AsStringOpTest(test.TestCase): result = output.eval(feed_dict={input_: int_inputs_}) self.assertAllEqual(s(result), ["%d" % x for x in int_inputs_]) + def testHalfInt(self): + s = lambda strs: [x.decode("ascii") for x in strs] + + with self.test_session(): + input_ = array_ops.placeholder(dtypes.int16) + int_inputs_ = [np.iinfo(np.int16).min, np.iinfo(np.int16).max] + output = string_ops.as_string(input_) + result = output.eval(feed_dict={input_: int_inputs_}) + self.assertAllEqual(s(result), ["%d" % x for x in int_inputs_]) + def testBool(self): bool_inputs_ = [False, True] s = lambda strs: [x.decode("ascii") for x in strs] diff --git a/tensorflow/python/kernel_tests/betainc_op_test.py b/tensorflow/python/kernel_tests/betainc_op_test.py index 08b03f8518..16fdedac41 100644 --- a/tensorflow/python/kernel_tests/betainc_op_test.py +++ b/tensorflow/python/kernel_tests/betainc_op_test.py @@ -172,7 +172,7 @@ class BetaincTest(test.TestCase): tf_gout_t = math_ops.betainc(tf_ga_s, tf_gb_s, tf_gx_s) err = gradient_checker.compute_gradient_error( [tf_gx_s], [gx_s.shape], tf_gout_t, gx_s.shape) - print("betainc gradient err = %g " % err) + tf_logging.info("betainc gradient err = %g " % err) self.assertLess(err, err_tolerance) # Test broadcast gradient @@ -181,7 +181,7 @@ class BetaincTest(test.TestCase): tf_gout_t = math_ops.betainc(tf_ga_s, tf_gb_s, tf_gx_s) err = gradient_checker.compute_gradient_error( [tf_gx_s], [()], tf_gout_t, ga_s.shape) - print("betainc gradient err = %g " % err) + tf_logging.info("betainc gradient err = %g " % err) self.assertLess(err, err_tolerance) diff --git a/tensorflow/python/kernel_tests/clip_ops_test.py b/tensorflow/python/kernel_tests/clip_ops_test.py index e08123b041..fb52d10475 100644 --- a/tensorflow/python/kernel_tests/clip_ops_test.py +++ b/tensorflow/python/kernel_tests/clip_ops_test.py @@ -18,9 +18,12 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import numpy as np + from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops from tensorflow.python.ops import clip_ops from tensorflow.python.ops import gradient_checker from tensorflow.python.platform import test @@ -414,6 +417,16 @@ class ClipTest(test.TestCase): self.assertAllClose(np_ans, tf_ans) + def testClipByValueEmptyTensor(self): + # Test case for GitHub issue 19337 + zero = array_ops.placeholder(dtype=dtypes.float32, shape=None) + x = clip_ops.clip_by_value(zero, zero, zero) + y = clip_ops.clip_by_value(zero, 1.0, 1.0) + z = clip_ops.clip_by_value(zero, zero, 1.0) + w = clip_ops.clip_by_value(zero, 1.0, zero) + with self.test_session(use_gpu=True) as sess: + sess.run([x, y, z, w], feed_dict={zero: np.zeros((7, 0))}) + if __name__ == '__main__': test.main() diff --git a/tensorflow/python/kernel_tests/conv_ops_test.py b/tensorflow/python/kernel_tests/conv_ops_test.py index 8699fd5b25..80ba7dafc9 100644 --- a/tensorflow/python/kernel_tests/conv_ops_test.py +++ b/tensorflow/python/kernel_tests/conv_ops_test.py @@ -312,8 +312,8 @@ class Conv2DTest(test.TestCase): expected_values = self.evaluate(expected_results) computed_values = self.evaluate(computed_results) for e_value, c_value in zip(expected_values, computed_values): - print("expected = ", e_value) - print("actual = ", c_value) + tf_logging.info("expected = ", e_value) + tf_logging.info("actual = ", c_value) self.assertAllClose( e_value.flatten(), c_value.flatten(), atol=tolerance, rtol=1e-4) @@ -337,8 +337,8 @@ class Conv2DTest(test.TestCase): for i in range(len(tensors)): conv = tensors[i] value = values[i] - print("expected = ", expected) - print("actual = ", value) + tf_logging.info("expected = ", expected) + tf_logging.info("actual = ", value) tol = 1e-5 if value.dtype == np.float16: tol = 1e-3 @@ -547,8 +547,8 @@ class Conv2DTest(test.TestCase): # "values" consists of two tensors for two backprops value = self.evaluate(conv) self.assertShapeEqual(value, conv) - print("expected = ", expected) - print("actual = ", value) + tf_logging.info("expected = ", expected) + tf_logging.info("actual = ", value) self.assertArrayNear(expected, value.flatten(), err) def _CompareBackpropInput(self, input_sizes, filter_sizes, output_sizes, @@ -723,8 +723,8 @@ class Conv2DTest(test.TestCase): data_format=data_format) value = self.evaluate(conv) self.assertShapeEqual(value, conv) - print("expected = ", expected) - print("actual = ", value) + tf_logging.info("expected = ", expected) + tf_logging.info("actual = ", value) self.assertArrayNear(expected, value.flatten(), 1e-5) def _CompareBackFilter(self, input_sizes, filter_sizes, output_sizes, @@ -912,8 +912,8 @@ class Conv2DTest(test.TestCase): value_2 = sess.run(conv_2) self.assertShapeEqual(value, conv) self.assertShapeEqual(value_2, conv_2) - print("expected = ", value_2) - print("actual = ", value) + tf_logging.info("expected = ", value_2) + tf_logging.info("actual = ", value) self.assertArrayNear(value_2.flatten(), value.flatten(), err) # Testing for backprops @@ -965,8 +965,8 @@ class Conv2DTest(test.TestCase): value_2 = sess.run(conv_2) self.assertShapeEqual(value, conv) self.assertShapeEqual(value_2, conv_2) - print("expected = ", value_2) - print("actual = ", value) + tf_logging.info("expected = ", value_2) + tf_logging.info("actual = ", value) self.assertArrayNear(value_2.flatten(), value.flatten(), err) def testConv2D2x2Depth3ValidBackpropFilterStride1x1Dilation2x1(self): @@ -1178,7 +1178,7 @@ class Conv2DTest(test.TestCase): # since fp16 numerical gradients are too imprecise. err = np.fabs(jacob_t - reference_jacob_t).max() - print("conv_2d gradient error = ", err) + tf_logging.info("conv_2d gradient error = ", err) self.assertLess(err, 0.002) def testInputGradientValidPaddingStrideOne(self): @@ -1546,7 +1546,7 @@ class DepthwiseConv2DTest(test.TestCase): conv = nn_impl.depthwise_conv2d( t1, t2, strides=[1, stride, stride, 1], padding=padding) value = sess.run(conv) - print("value = ", value) + tf_logging.info("value = ", value) self.assertArrayNear(expected, np.ravel(value), 1e-5) self.assertShapeEqual(value, conv) @@ -1668,7 +1668,7 @@ class SeparableConv2DTest(test.TestCase): conv = array_ops.transpose(conv, [0, 2, 3, 1]) value = sess.run(conv) - print("value = ", value) + tf_logging.info("value = ", value) self.assertArrayNear(expected, np.ravel(value), 1e-5) self.assertShapeEqual(value, conv) @@ -1826,7 +1826,7 @@ class Conv2DBenchmark(test.Benchmark): wall_time = time.time() - start self.report_benchmark( name="conv_stack_iter_%d" % iter_index, wall_time=wall_time) - print("conv_stack_iter_%d: %.4f" % (iter_index, wall_time)) + tf_logging.info("conv_stack_iter_%d: %.4f" % (iter_index, wall_time)) def GetInceptionFwdTest(input_size, filter_size, stride, padding, diff --git a/tensorflow/python/kernel_tests/gather_nd_op_test.py b/tensorflow/python/kernel_tests/gather_nd_op_test.py index 91ebe8de99..58e2a8ac2a 100644 --- a/tensorflow/python/kernel_tests/gather_nd_op_test.py +++ b/tensorflow/python/kernel_tests/gather_nd_op_test.py @@ -197,7 +197,21 @@ class GatherNdTest(test.TestCase): self.assertEqual(None, shape.ndims) self.assertEqual(None, shape[0].value) - def testBadIndices(self): + def testBadIndicesCPU(self): + with self.test_session(use_gpu=False): + params = [0, 1, 2] + indices = [[[0], [7]]] # Make this one higher rank + gather_nd = array_ops.gather_nd(params, indices) + with self.assertRaisesOpError( + r"flat indices\[1, :\] = \[7\] does not index into param " + r"\(shape: \[3\]\)"): + gather_nd.eval() + + def _disabledTestBadIndicesGPU(self): + # TODO disabled due to different behavior on GPU and CPU + # On GPU the bad indices do not raise error but fetch 0 values + if not test.is_gpu_available(): + return with self.test_session(use_gpu=True): params = [0, 1, 2] indices = [[[0], [7]]] # Make this one higher rank @@ -207,7 +221,21 @@ class GatherNdTest(test.TestCase): r"\(shape: \[3\]\)"): gather_nd.eval() - def testBadIndicesWithSlices(self): + def testBadIndicesWithSlicesCPU(self): + with self.test_session(use_gpu=False): + params = [[0, 1, 2]] + indices = [[[0], [0], [1]]] # Make this one higher rank + gather_nd = array_ops.gather_nd(params, indices) + with self.assertRaisesOpError( + r"flat indices\[2, :\] = \[1\] does not index into param " + r"\(shape: \[1,3\]\)"): + gather_nd.eval() + + def _disabledTestBadIndicesWithSlicesGPU(self): + # TODO disabled due to different behavior on GPU and CPU + # On GPU the bad indices do not raise error but fetch 0 values + if not test.is_gpu_available(): + return with self.test_session(use_gpu=True): params = [[0, 1, 2]] indices = [[[0], [0], [1]]] # Make this one higher rank diff --git a/tensorflow/python/kernel_tests/gather_op_test.py b/tensorflow/python/kernel_tests/gather_op_test.py index a2fcd751df..033fa95935 100644 --- a/tensorflow/python/kernel_tests/gather_op_test.py +++ b/tensorflow/python/kernel_tests/gather_op_test.py @@ -27,7 +27,8 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import gradients_impl from tensorflow.python.platform import test -_TEST_TYPES = (dtypes.float32, dtypes.complex64, dtypes.complex128) +_TEST_TYPES = (dtypes.int64, dtypes.float32, + dtypes.complex64, dtypes.complex128) class GatherTest(test.TestCase): @@ -122,6 +123,9 @@ class GatherTest(test.TestCase): gather, [tf_params, tf_indices, tf_axis], gather_grad) self.assertEqual(indices_grad, None) self.assertEqual(axis_grad, None) + if dtype.is_integer: + self.assertEqual(params_grad, None) + continue # For axis 0, we are able to create an efficient IndexedSlices for # the gradient. if axis == 0: @@ -177,7 +181,19 @@ class GatherTest(test.TestCase): gather_t = array_ops.gather(params, indices, axis=axis) self.assertEqual(None, gather_t.shape) - def testBadIndices(self): + def testBadIndicesCPU(self): + with self.test_session(use_gpu=False): + params = [[0, 1, 2], [3, 4, 5]] + with self.assertRaisesOpError(r"indices\[0,0\] = 7 is not in \[0, 2\)"): + array_ops.gather(params, [[7]], axis=0).eval() + with self.assertRaisesOpError(r"indices\[0,0\] = 7 is not in \[0, 3\)"): + array_ops.gather(params, [[7]], axis=1).eval() + + def _disabledTestBadIndicesGPU(self): + # TODO disabled due to different behavior on GPU and CPU + # On GPU the bad indices do not raise error but fetch 0 values + if not test.is_gpu_available(): + return with self.test_session(use_gpu=True): params = [[0, 1, 2], [3, 4, 5]] with self.assertRaisesOpError(r"indices\[0,0\] = 7 is not in \[0, 2\)"): diff --git a/tensorflow/python/kernel_tests/init_ops_test.py b/tensorflow/python/kernel_tests/init_ops_test.py index a9b55854f1..795aa67248 100644 --- a/tensorflow/python/kernel_tests/init_ops_test.py +++ b/tensorflow/python/kernel_tests/init_ops_test.py @@ -362,6 +362,33 @@ class UniformUnitScalingInitializationTest(test.TestCase): dtype=dtypes.string) +class VarianceScalingInitializationTest(test.TestCase): + + def testNormalDistribution(self): + shape = [100, 100] + expect_mean = 0. + expect_var = 1. / shape[0] + init = init_ops.variance_scaling_initializer(distribution='normal') + + with self.test_session(use_gpu=True): + x = init(shape).eval() + + self.assertNear(np.mean(x), expect_mean, err=1e-2) + self.assertNear(np.var(x), expect_var, err=1e-2) + + def testUniformDistribution(self): + shape = [100, 100] + expect_mean = 0. + expect_var = 1. / shape[0] + init = init_ops.variance_scaling_initializer(distribution='uniform') + + with self.test_session(use_gpu=True): + x = init(shape).eval() + + self.assertNear(np.mean(x), expect_mean, err=1e-2) + self.assertNear(np.var(x), expect_var, err=1e-2) + + # TODO(vrv): move to sequence_ops_test? class RangeTest(test.TestCase): diff --git a/tensorflow/python/kernel_tests/pooling_ops_test.py b/tensorflow/python/kernel_tests/pooling_ops_test.py index a0c372db7d..e95c729715 100644 --- a/tensorflow/python/kernel_tests/pooling_ops_test.py +++ b/tensorflow/python/kernel_tests/pooling_ops_test.py @@ -947,7 +947,7 @@ class PoolingTest(test.TestCase): output_sizes, x_init_value=x_init_value, delta=1e-2) - print("%s gradient error = " % func_name, err) + tf_logging.info("%s gradient error = " % func_name, err) self.assertLess(err, err_tolerance) def _ConstructAndTestSecondGradient(self, @@ -1024,7 +1024,7 @@ class PoolingTest(test.TestCase): input_sizes, x_init_value=x_init_value, delta=1e-2) - print("%s second-order gradient error = " % func_name, err) + tf_logging.info("%s second-order gradient error = " % func_name, err) self.assertLess(err, err_tolerance) def _testMaxPoolGradValidPadding1_1(self, data_format, use_gpu): diff --git a/tensorflow/python/kernel_tests/py_func_test.py b/tensorflow/python/kernel_tests/py_func_test.py index 677253946e..253e43920b 100644 --- a/tensorflow/python/kernel_tests/py_func_test.py +++ b/tensorflow/python/kernel_tests/py_func_test.py @@ -19,6 +19,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import gc import re import numpy as np @@ -434,13 +435,29 @@ class PyFuncTest(test.TestCase): # ----- Tests shared by py_func and eager_py_func ----- def testCleanup(self): - for _ in xrange(1000): - g = ops.Graph() - with g.as_default(): - c = constant_op.constant([1.], dtypes.float32) - _ = script_ops.py_func(lambda x: x + 1, [c], [dtypes.float32]) - _ = script_ops.eager_py_func(lambda x: x + 1, [c], [dtypes.float32]) - self.assertLess(script_ops._py_funcs.size(), 100) + # Delete everything created by previous tests to avoid side effects. + ops.reset_default_graph() + gc.collect() + initial_size = script_ops._py_funcs.size() + # Encapsulate the graph generation, so locals can be deleted. + def make_graphs(): + for _ in xrange(1000): + g = ops.Graph() + with g.as_default(): + c = constant_op.constant([1.], dtypes.float32) + _ = script_ops.py_func(lambda x: x + 1, [c], [dtypes.float32]) + _ = script_ops.eager_py_func(lambda x: x + 1, [c], [dtypes.float32]) + # These ops have a reference to 'c' which has a reference to the graph. + # Checks if the functions are being deleted though the graph is referenced from them. + # (see #18292) + _ = script_ops.py_func(lambda x: x + c.shape[0], [c], [dtypes.float32]) + _ = script_ops.eager_py_func(lambda x: x + c.shape[0], [c], [dtypes.float32]) + + # Call garbage collector to enforce deletion. + make_graphs() + ops.reset_default_graph() + gc.collect() + self.assertEqual(initial_size, script_ops._py_funcs.size()) # ----- Tests for eager_py_func ----- @test_util.run_in_graph_and_eager_modes() diff --git a/tensorflow/python/kernel_tests/scatter_nd_ops_test.py b/tensorflow/python/kernel_tests/scatter_nd_ops_test.py index 79fe927b8a..faa4b49a8d 100644 --- a/tensorflow/python/kernel_tests/scatter_nd_ops_test.py +++ b/tensorflow/python/kernel_tests/scatter_nd_ops_test.py @@ -144,7 +144,9 @@ class StatefulScatterNdTest(test.TestCase): self.assertAllClose(new, ref_var.eval()) def _VariableRankTests(self, np_scatter, tf_scatter): - for vtype in (np.float32, np.float64, np.complex64, np.complex128): + for vtype in (np.int32, + np.float32, np.float64, + np.complex64, np.complex128): for itype in (np.int32, np.int64): self._VariableRankTest(np_scatter, tf_scatter, vtype, itype) @@ -221,7 +223,7 @@ class StatefulScatterNdTest(test.TestCase): # self._VariableRankTests(_NumpyDiv, state_ops.scatter_nd_div) def _ScatterRepeatIndicesTest(self, np_scatter, tf_scatter): - for vtype in (np.float32, np.float64): + for vtype in (np.int32, np.float32, np.float64): for itype in (np.int32, np.int64): self._VariableRankTest( np_scatter, tf_scatter, vtype, itype, repeat_indices=True) diff --git a/tensorflow/python/kernel_tests/scatter_ops_test.py b/tensorflow/python/kernel_tests/scatter_ops_test.py index c70a4ffce7..1a0fa744ae 100644 --- a/tensorflow/python/kernel_tests/scatter_ops_test.py +++ b/tensorflow/python/kernel_tests/scatter_ops_test.py @@ -159,7 +159,13 @@ class ScatterTest(test.TestCase): # Clips small values to avoid division by zero. def clip_small_values(x): - return 1e-4 * np.sign(x) if np.abs(x) < 1e-4 else x + threshold = 1e-4 + sign = np.sign(x) + + if isinstance(x, np.int32): + threshold = 1 + sign = np.random.choice([-1, 1]) + return threshold * sign if np.abs(x) < threshold else x updates = np.vectorize(clip_small_values)(updates) old = _AsType(np.random.randn(*((first_dim,) + extra_shape)), vtype) @@ -181,7 +187,11 @@ class ScatterTest(test.TestCase): tf_scatter, repeat_indices=False, updates_are_scalar=False): - for vtype in (np.float32, np.float64): + vtypes = [np.float32, np.float64] + if tf_scatter != state_ops.scatter_div: + vtypes.append(np.int32) + + for vtype in vtypes: for itype in (np.int32, np.int64): self._VariableRankTest(tf_scatter, vtype, itype, repeat_indices, updates_are_scalar) diff --git a/tensorflow/python/kernel_tests/segment_reduction_ops_test.py b/tensorflow/python/kernel_tests/segment_reduction_ops_test.py index 794be096b7..a82855dfeb 100644 --- a/tensorflow/python/kernel_tests/segment_reduction_ops_test.py +++ b/tensorflow/python/kernel_tests/segment_reduction_ops_test.py @@ -264,7 +264,9 @@ class UnsortedSegmentTest(SegmentReductionHelper): # A subset of ops has been enabled for complex numbers self.complex_ops_list = [(np.add, None, - math_ops.unsorted_segment_sum, lambda t: 0)] + math_ops.unsorted_segment_sum, lambda t: 0), + (np.ndarray.__mul__, None, + math_ops.unsorted_segment_prod, lambda t: 1)] self.differentiable_dtypes = [dtypes_lib.float16, dtypes_lib.float32, dtypes_lib.float64] self.all_dtypes = (self.differentiable_dtypes + diff --git a/tensorflow/python/kernel_tests/string_split_op_test.py b/tensorflow/python/kernel_tests/string_split_op_test.py index a5bd1b6ee0..e20daccb28 100644 --- a/tensorflow/python/kernel_tests/string_split_op_test.py +++ b/tensorflow/python/kernel_tests/string_split_op_test.py @@ -146,5 +146,101 @@ class StringSplitOpTest(test.TestCase): self.assertAllEqual(shape, [3, 1]) +class StringSplitV2OpTest(test.TestCase): + + def testSplitV2(self): + strings = ["pigs on the wing", "animals"] + + with self.test_session() as sess: + tokens = string_ops.string_split_v2(strings) + indices, values, shape = sess.run(tokens) + self.assertAllEqual(indices, [[0, 0], [0, 1], [0, 2], [0, 3], [1, 0]]) + self.assertAllEqual(values, [b"pigs", b"on", b"the", b"wing", b"animals"]) + self.assertAllEqual(shape, [2, 4]) + + def testSplitV2MultiCharSeparator(self): + # Match Python behavior: + # >>> '1<>2<>3'.split('<>') + # ['1', '2', '3'] + # >>> "<><>4<>5<><>6<>".split("<>") + # ['', '', '4', '5', '', '6', ''] + strings = ["1<>2<>3", "<><>4<>5<><>6<>"] + + with self.test_session() as sess: + tokens = string_ops.string_split_v2(strings, sep="<>") + indices, values, shape = sess.run(tokens) + self.assertAllEqual( + indices, [[0, 0], [0, 1], [0, 2], + [1, 0], [1, 1], [1, 2], [1, 3], [1, 4], [1, 5], [1, 6]]) + self.assertAllEqual(values, [b"1", b"2", b"3", + b"", b"", b"4", b"5", b"", b"6", b""]) + self.assertAllEqual(shape, [2, 7]) + + def testSplitV2SimpleSeparator(self): + # Match Python behavior: + # >>> '1,2,3'.split(',') + # ['1', '2', '3'] + # >>> '1,2,,3,'.split(',') + # ['1', '2', '', '3', ''] + strings = ["1,2,3", "4,5,,6,"] + + with self.test_session() as sess: + tokens = string_ops.string_split_v2(strings, sep=',') + indices, values, shape = sess.run(tokens) + self.assertAllEqual(indices, [[0, 0], [0, 1], [0, 2], + [1, 0], [1, 1], [1, 2], [1, 3], [1, 4]]) + self.assertAllEqual(values, [b"1", b"2", b"3", + b"4", b"5", b"", b"6", b""]) + self.assertAllEqual(shape, [2, 5]) + + def testSplitV2EmptySeparator(self): + # Match Python behavior: + # >>> '1 2 3'.split() + # ['1', '2', '3'] + #>>> ' 1 2 3 '.split() + #['1', '2', '3'] + strings = ["1 2 3", " 4 5 6 "] + + with self.test_session() as sess: + tokens = string_ops.string_split_v2(strings) + indices, values, shape = sess.run(tokens) + self.assertAllEqual(indices, [[0, 0], [0, 1], [0, 2], + [1, 0], [1, 1], [1, 2]]) + self.assertAllEqual(values, [b"1", b"2", b"3", b"4", b"5", b"6"]) + self.assertAllEqual(shape, [2, 3]) + + def testSplitV2SimpleSeparatorMaxSplit(self): + # Match Python behavior: + # >>> '1,2,3'.split(',', maxsplit=1) + # ['1', '2,3'] + # >>> '4,5,,6,'.split(',', maxsplit=1) + # ['4', '5,,6,'] + strings = ["1,2,3", "4,5,,6,"] + + with self.test_session() as sess: + tokens = string_ops.string_split_v2(strings, sep=',', maxsplit=1) + indices, values, shape = sess.run(tokens) + self.assertAllEqual(indices, [[0, 0], [0, 1], + [1, 0], [1, 1]]) + self.assertAllEqual(values, [b"1", b"2,3", b"4", b"5,,6,"]) + self.assertAllEqual(shape, [2, 2]) + + def testSplitV2EmptySeparatorMaxSplit(self): + # Match Python behavior: + # '1 2 3'.split(maxsplit=1) + # ['1', '2 3'] + # >>> " 4 5 6 ".split(maxsplit=1) + # ['4', '5 6 '] + strings = ["1 2 3", " 4 5 6 "] + + with self.test_session() as sess: + tokens = string_ops.string_split_v2(strings, maxsplit=1) + indices, values, shape = sess.run(tokens) + self.assertAllEqual(indices, [[0, 0], [0, 1], + [1, 0], [1, 1]]) + self.assertAllEqual(values, [b"1", b"2 3", b"4", b"5 6 "]) + self.assertAllEqual(shape, [2, 2]) + + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py index 8129334703..fae63b1132 100644 --- a/tensorflow/python/ops/array_ops.py +++ b/tensorflow/python/ops/array_ops.py @@ -2619,6 +2619,10 @@ reverse.__doc__ = gen_array_ops.reverse_v2.__doc__ # pylint: disable=redefined-builtin @tf_export("reverse_sequence") +@deprecation.deprecated_args( + None, "seq_dim is deprecated, use seq_axis instead", "seq_dim") +@deprecation.deprecated_args( + None, "batch_dim is deprecated, use batch_axis instead", "batch_dim") def reverse_sequence(input, seq_lengths, seq_axis=None, diff --git a/tensorflow/python/ops/gradient_checker.py b/tensorflow/python/ops/gradient_checker.py index 12afcd0b51..94c8d79335 100644 --- a/tensorflow/python/ops/gradient_checker.py +++ b/tensorflow/python/ops/gradient_checker.py @@ -283,10 +283,10 @@ def compute_gradient(x, numbers. For example, if `x` is complex with shape `[m]` and `y` is complex with shape `[n]`, each Jacobian `J` will have shape `[m * 2, n * 2]` with - J[:m, :n] = d(Re y)/d(Re x) - J[:m, n:] = d(Im y)/d(Re x) - J[m:, :n] = d(Re y)/d(Im x) - J[m:, n:] = d(Im y)/d(Im x) + J[::2, ::2] = d(Re y)/d(Re x) + J[::2, 1::2] = d(Im y)/d(Re x) + J[1::2, ::2] = d(Re y)/d(Im x) + J[1::2, 1::2] = d(Im y)/d(Im x) Args: x: a tensor or list of tensors diff --git a/tensorflow/python/ops/image_ops_impl.py b/tensorflow/python/ops/image_ops_impl.py index bdcf420980..f27d9224c1 100644 --- a/tensorflow/python/ops/image_ops_impl.py +++ b/tensorflow/python/ops/image_ops_impl.py @@ -28,6 +28,7 @@ from tensorflow.python.framework import tensor_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import check_ops from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import functional_ops from tensorflow.python.ops import gen_image_ops from tensorflow.python.ops import gen_nn_ops from tensorflow.python.ops import math_ops @@ -258,14 +259,14 @@ def random_flip_up_down(image, seed=None): dimension, which is `height`. Otherwise output the image as-is. Args: - image: A 3-D tensor of shape `[height, width, channels].` + image: 4-D Tensor of shape `[batch, height, width, channels]` or + 3-D Tensor of shape `[height, width, channels]`. seed: A Python integer. Used to create a random seed. See @{tf.set_random_seed} for behavior. Returns: - A 3-D tensor of the same type and shape as `image`. - + A tensor of the same type and shape as `image`. Raises: ValueError: if the shape of `image` not supported. """ @@ -280,13 +281,14 @@ def random_flip_left_right(image, seed=None): second dimension, which is `width`. Otherwise output the image as-is. Args: - image: A 3-D tensor of shape `[height, width, channels].` + image: 4-D Tensor of shape `[batch, height, width, channels]` or + 3-D Tensor of shape `[height, width, channels]`. seed: A Python integer. Used to create a random seed. See @{tf.set_random_seed} for behavior. Returns: - A 3-D tensor of the same type and shape as `image`. + A tensor of the same type and shape as `image`. Raises: ValueError: if the shape of `image` not supported. @@ -297,7 +299,8 @@ def random_flip_left_right(image, seed=None): def _random_flip(image, flip_index, seed, scope_name): """Randomly (50% chance) flip an image along axis `flip_index`. Args: - image: A 3-D tensor of shape `[height, width, channels].` + image: 4-D Tensor of shape `[batch, height, width, channels]` or + 3-D Tensor of shape `[height, width, channels]`. flip_index: The dimension along which to flip the image. Vertical: 0, Horizontal: 1 seed: A Python integer. Used to create a random seed. See @@ -306,22 +309,37 @@ def _random_flip(image, flip_index, seed, scope_name): scope_name: Name of the scope in which the ops are added. Returns: - A 3-D tensor of the same type and shape as `image`. + A tensor of the same type and shape as `image`. Raises: ValueError: if the shape of `image` not supported. """ with ops.name_scope(None, scope_name, [image]) as scope: image = ops.convert_to_tensor(image, name='image') - image = _Assert3DImage(image) - uniform_random = random_ops.random_uniform([], 0, 1.0, seed=seed) - mirror_cond = math_ops.less(uniform_random, .5) - result = control_flow_ops.cond( - mirror_cond, - lambda: array_ops.reverse(image, [flip_index]), - lambda: image, - name=scope) - return fix_image_flip_shape(image, result) + image = _AssertAtLeast3DImage(image) + shape = image.get_shape() + if shape.ndims == 3 or shape.ndims is None: + uniform_random = random_ops.random_uniform([], 0, 1.0, seed=seed) + mirror_cond = math_ops.less(uniform_random, .5) + result = control_flow_ops.cond( + mirror_cond, + lambda: array_ops.reverse(image, [flip_index]), + lambda: image, + name=scope + ) + return fix_image_flip_shape(image, result) + elif shape.ndims == 4: + uniform_random = random_ops.random_uniform( + [array_ops.shape(image)[0]], 0, 1.0, seed=seed + ) + mirror_cond = math_ops.less(uniform_random, .5) + return array_ops.where( + mirror_cond, + image, + functional_ops.map_fn(lambda x: array_ops.reverse(x, [flip_index]), image, dtype=image.dtype) + ) + else: + raise ValueError('\'image\' must have either 3 or 4 dimensions.') @tf_export('image.flip_left_right') @@ -1634,13 +1652,13 @@ def is_jpeg(contents, name=None): @tf_export('image.decode_image') -def decode_image(contents, channels=None, name=None): +def decode_image(contents, channels=None, dtype=dtypes.uint8, name=None): """Convenience function for `decode_bmp`, `decode_gif`, `decode_jpeg`, and `decode_png`. Detects whether an image is a BMP, GIF, JPEG, or PNG, and performs the - appropriate operation to convert the input bytes `string` into a `Tensor` of - type `uint8`. + appropriate operation to convert the input bytes `string` into a `Tensor` + of type `dtype`. Note: `decode_gif` returns a 4-D array `[num_frames, height, width, 3]`, as opposed to `decode_bmp`, `decode_jpeg` and `decode_png`, which return 3-D @@ -1652,10 +1670,11 @@ def decode_image(contents, channels=None, name=None): contents: 0-D `string`. The encoded image bytes. channels: An optional `int`. Defaults to `0`. Number of color channels for the decoded image. + dtype: The desired DType of the returned `Tensor`. name: A name for the operation (optional) Returns: - `Tensor` with type `uint8` with shape `[height, width, num_channels]` for + `Tensor` with type `dtype` and shape `[height, width, num_channels]` for BMP, JPEG, and PNG images and shape `[num_frames, height, width, 3]` for GIF images. @@ -1679,7 +1698,7 @@ def decode_image(contents, channels=None, name=None): channels_msg = 'Channels must be in (None, 0, 3) when decoding BMP images' assert_channels = control_flow_ops.Assert(good_channels, [channels_msg]) with ops.control_dependencies([assert_decode, assert_channels]): - return gen_image_ops.decode_bmp(contents) + return convert_image_dtype(gen_image_ops.decode_bmp(contents), dtype) def _gif(): # Create assert to make sure that channels is not set to 1 @@ -1692,7 +1711,7 @@ def decode_image(contents, channels=None, name=None): channels_msg = 'Channels must be in (None, 0, 3) when decoding GIF images' assert_channels = control_flow_ops.Assert(good_channels, [channels_msg]) with ops.control_dependencies([assert_channels]): - return gen_image_ops.decode_gif(contents) + return convert_image_dtype(gen_image_ops.decode_gif(contents), dtype) def check_gif(): # Create assert op to check that bytes are GIF decodable @@ -1701,7 +1720,11 @@ def decode_image(contents, channels=None, name=None): def _png(): """Decodes a PNG image.""" - return gen_image_ops.decode_png(contents, channels) + return convert_image_dtype( + gen_image_ops.decode_png(contents, channels, + dtype=dtypes.uint8 + if dtype == dtypes.uint8 + else dtypes.uint16), dtype) def check_png(): """Checks if an image is PNG.""" @@ -1717,7 +1740,8 @@ def decode_image(contents, channels=None, name=None): 'images') assert_channels = control_flow_ops.Assert(good_channels, [channels_msg]) with ops.control_dependencies([assert_channels]): - return gen_image_ops.decode_jpeg(contents, channels) + return convert_image_dtype( + gen_image_ops.decode_jpeg(contents, channels), dtype) # Decode normal JPEG images (start with \xff\xd8\xff\xe0) # as well as JPEG images with EXIF data (start with \xff\xd8\xff\xe1). @@ -1878,7 +1902,7 @@ def sample_distorted_bounding_box(image_size, width / height within this range. area_range: An optional list of `floats`. Defaults to `[0.05, 1]`. The cropped area of the image must contain a fraction of the - supplied image within in this range. + supplied image within this range. max_attempts: An optional `int`. Defaults to `100`. Number of attempts at generating a cropped region of the image of the specified constraints. After `max_attempts` failures, return the diff --git a/tensorflow/python/ops/image_ops_test.py b/tensorflow/python/ops/image_ops_test.py index 45499dcce0..2a6ab26e96 100644 --- a/tensorflow/python/ops/image_ops_test.py +++ b/tensorflow/python/ops/image_ops_test.py @@ -533,6 +533,37 @@ class FlipImageBenchmark(test.Benchmark): iters=benchmark_rounds, wall_time=step_time) + def _benchmarkBatchedRandomFlipLeftRight(self, device, cpu_count): + image_shape = [16, 299, 299, 3] + warmup_rounds = 100 + benchmark_rounds = 1000 + config = config_pb2.ConfigProto() + if cpu_count is not None: + config.inter_op_parallelism_threads = 1 + config.intra_op_parallelism_threads = cpu_count + with session.Session("", graph=ops.Graph(), config=config) as sess: + with ops.device(device): + inputs = variables.Variable( + random_ops.random_uniform(image_shape, dtype=dtypes.float32) * 255, + trainable=False, + dtype=dtypes.float32) + run_op = image_ops.random_flip_left_right(inputs) + sess.run(variables.global_variables_initializer()) + for i in xrange(warmup_rounds + benchmark_rounds): + if i == warmup_rounds: + start = time.time() + sess.run(run_op) + end = time.time() + step_time = (end - start) / benchmark_rounds + tag = device + "_%s" % (cpu_count if cpu_count is not None else "_all") + print("benchmarkBatchedRandomFlipLeftRight_16_299_299_3_%s step_time: " + "%.2f us" % + (tag, step_time * 1e6)) + self.report_benchmark( + name="benchmarkBatchedRandomFlipLeftRight_16_299_299_3_%s" % (tag), + iters=benchmark_rounds, + wall_time=step_time) + def benchmarkFlipLeftRightCpu1(self): self._benchmarkFlipLeftRight("/cpu:0", 1) @@ -551,6 +582,15 @@ class FlipImageBenchmark(test.Benchmark): def benchmarkRandomFlipLeftRightGpu(self): self._benchmarkRandomFlipLeftRight(test.gpu_device_name(), None) + def benchmarkBatchedRandomFlipLeftRightCpu1(self): + self._benchmarkBatchedRandomFlipLeftRight("/cpu:0", 1) + + def benchmarkBatchedRandomFlipLeftRightCpuAll(self): + self._benchmarkBatchedRandomFlipLeftRight("/cpu:0", None) + + def benchmarkBatchedRandomFlipLeftRightGpu(self): + self._benchmarkBatchedRandomFlipLeftRight(test.gpu_device_name(), None) + class AdjustHueBenchmark(test.Benchmark): @@ -987,7 +1027,7 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): with self.test_session(use_gpu=True): x_tf = constant_op.constant(x_np, shape=x_np.shape) - y = image_ops.random_flip_left_right(x_tf) + y = image_ops.random_flip_left_right(x_tf, seed=seed) self.assertTrue(y.op.name.startswith("random_flip_left_right")) count_flipped = 0 @@ -1008,6 +1048,50 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): self.assertGreaterEqual(count_flipped, 20) self.assertGreaterEqual(count_unflipped, 20) + def testRandomFlipLeftRightWithBatch(self): + batch_size = 16 + seed = 42 + + # create single item of test data + x_np_raw = np.array( + [[1, 2, 3], [1, 2, 3]], dtype=np.uint8 + ).reshape([1, 2, 3, 1]) + y_np_raw = np.array( + [[3, 2, 1], [3, 2, 1]], dtype=np.uint8 + ).reshape([1, 2, 3, 1]) + + # create batched test data + x_np = np.vstack([x_np_raw for _ in range(batch_size)]) + y_np = np.vstack([y_np_raw for _ in range(batch_size)]) + + with self.test_session(use_gpu=True): + x_tf = constant_op.constant(x_np, shape=x_np.shape) + y = image_ops.random_flip_left_right(x_tf, seed=seed) + self.assertTrue(y.op.name.startswith("random_flip_left_right")) + + count_flipped = 0 + count_unflipped = 0 + for _ in range(100): + y_tf = y.eval() + + # check every element of the batch + for i in range(batch_size): + if y_tf[i][0][0] == 1: + self.assertAllEqual(y_tf[i], x_np[i]) + count_unflipped += 1 + else: + self.assertAllEqual(y_tf[i], y_np[i]) + count_flipped += 1 + + # 100 trials, each containing batch_size elements + # Mean: 50 * batch_size + # Std Dev: ~5 * sqrt(batch_size) + # Six Sigma: 50 * batch_size - (5 * 6 * sqrt(batch_size)) + # = 50 * batch_size - 30 * sqrt(batch_size) = 800 - 30 * 4 = 680 + six_sigma = 50 * batch_size - 30 * np.sqrt(batch_size) + self.assertGreaterEqual(count_flipped, six_sigma) + self.assertGreaterEqual(count_unflipped, six_sigma) + def testInvolutionUpDown(self): x_np = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.uint8).reshape([2, 3, 1]) @@ -1057,9 +1141,11 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): x_np = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.uint8).reshape([2, 3, 1]) y_np = np.array([[4, 5, 6], [1, 2, 3]], dtype=np.uint8).reshape([2, 3, 1]) + seed = 42 + with self.test_session(use_gpu=True): x_tf = constant_op.constant(x_np, shape=x_np.shape) - y = image_ops.random_flip_up_down(x_tf, seed=42) + y = image_ops.random_flip_up_down(x_tf, seed=seed) self.assertTrue(y.op.name.startswith("random_flip_up_down")) count_flipped = 0 count_unflipped = 0 @@ -1079,6 +1165,50 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): self.assertGreaterEqual(count_flipped, 20) self.assertGreaterEqual(count_unflipped, 20) + def testRandomFlipUpDownWithBatch(self): + batch_size = 16 + seed = 42 + + # create single item of test data + x_np_raw = np.array( + [[1, 2, 3], [4, 5, 6]], dtype=np.uint8 + ).reshape([1, 2, 3, 1]) + y_np_raw = np.array( + [[4, 5, 6], [1, 2, 3]], dtype=np.uint8 + ).reshape([1, 2, 3, 1]) + + # create batched test data + x_np = np.vstack([x_np_raw for _ in range(batch_size)]) + y_np = np.vstack([y_np_raw for _ in range(batch_size)]) + + with self.test_session(use_gpu=True): + x_tf = constant_op.constant(x_np, shape=x_np.shape) + y = image_ops.random_flip_up_down(x_tf, seed=seed) + self.assertTrue(y.op.name.startswith("random_flip_up_down")) + + count_flipped = 0 + count_unflipped = 0 + for _ in range(100): + y_tf = y.eval() + + # check every element of the batch + for i in range(batch_size): + if y_tf[i][0][0] == 1: + self.assertAllEqual(y_tf[i], x_np[i]) + count_unflipped += 1 + else: + self.assertAllEqual(y_tf[i], y_np[i]) + count_flipped += 1 + + # 100 trials, each containing batch_size elements + # Mean: 50 * batch_size + # Std Dev: ~5 * sqrt(batch_size) + # Six Sigma: 50 * batch_size - (5 * 6 * sqrt(batch_size)) + # = 50 * batch_size - 30 * sqrt(batch_size) = 800 - 30 * 4 = 680 + six_sigma = 50 * batch_size - 30 * np.sqrt(batch_size) + self.assertGreaterEqual(count_flipped, six_sigma) + self.assertGreaterEqual(count_unflipped, six_sigma) + def testInvolutionTranspose(self): x_np = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.uint8).reshape([2, 3, 1]) @@ -1156,6 +1286,7 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): #Ops that support 4D input for op in [ image_ops.flip_left_right, image_ops.flip_up_down, + image_ops.random_flip_left_right, image_ops.random_flip_up_down, image_ops.transpose_image, image_ops.rot90 ]: transformed_unknown_dims_4 = op(p_unknown_dims_4) @@ -1166,14 +1297,6 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): "must be at least three-dimensional"): op(p_wrong_rank) - for op in [ - image_ops.random_flip_left_right, - image_ops.random_flip_up_down, - ]: - with self.assertRaisesRegexp(ValueError, "must be three-dimensional"): - op(p_wrong_rank) - - def testRot90GroupOrder(self): image = np.arange(24, dtype=np.uint8).reshape([2, 4, 3]) with self.test_session(use_gpu=True): @@ -1208,41 +1331,6 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): y_np = np.rot90(image, k=k, axes=(1, 2)) self.assertAllEqual(y_np, y_tf.eval({k_placeholder: k})) -class RandomFlipTest(test_util.TensorFlowTestCase): - - def testRandomLeftRight(self): - x_np = np.array([0, 1], dtype=np.uint8).reshape([1, 2, 1]) - num_iterations = 500 - - hist = [0, 0] - with self.test_session(use_gpu=True): - x_tf = constant_op.constant(x_np, shape=x_np.shape) - y = image_ops.random_flip_left_right(x_tf) - for _ in xrange(num_iterations): - y_np = y.eval().flatten()[0] - hist[y_np] += 1 - - # Ensure that each entry is observed within 4 standard deviations. - four_stddev = 4.0 * np.sqrt(num_iterations / 2.0) - self.assertAllClose(hist, [num_iterations / 2.0] * 2, atol=four_stddev) - - def testRandomUpDown(self): - x_np = np.array([0, 1], dtype=np.uint8).reshape([2, 1, 1]) - num_iterations = 500 - - hist = [0, 0] - with self.test_session(use_gpu=True): - x_tf = constant_op.constant(x_np, shape=x_np.shape) - y = image_ops.random_flip_up_down(x_tf) - for _ in xrange(num_iterations): - y_np = y.eval().flatten()[0] - hist[y_np] += 1 - - # Ensure that each entry is observed within 4 standard deviations. - four_stddev = 4.0 * np.sqrt(num_iterations / 2.0) - self.assertAllClose(hist, [num_iterations / 2.0] * 2, atol=four_stddev) - - class AdjustContrastTest(test_util.TensorFlowTestCase): def _testContrast(self, x_np, y_np, contrast_factor): @@ -3880,5 +3968,88 @@ class SobelEdgesTest(test_util.TensorFlowTestCase): self.assertAllClose(expected_batch, actual_sobel) +class DecodeImageTest(test_util.TensorFlowTestCase): + + def testJpegUint16(self): + with self.test_session(use_gpu=True) as sess: + base = "tensorflow/core/lib/jpeg/testdata" + jpeg0 = io_ops.read_file(os.path.join(base, "jpeg_merge_test1.jpg")) + image0 = image_ops.decode_image(jpeg0, dtype=dtypes.uint16) + image1 = image_ops.convert_image_dtype(image_ops.decode_jpeg(jpeg0), + dtypes.uint16) + image0, image1 = sess.run([image0, image1]) + self.assertAllEqual(image0, image1) + + def testPngUint16(self): + with self.test_session(use_gpu=True) as sess: + base = "tensorflow/core/lib/png/testdata" + png0 = io_ops.read_file(os.path.join(base, "lena_rgba.png")) + image0 = image_ops.decode_image(png0, dtype=dtypes.uint16) + image1 = image_ops.convert_image_dtype( + image_ops.decode_png(png0, dtype=dtypes.uint16), dtypes.uint16) + image0, image1 = sess.run([image0, image1]) + self.assertAllEqual(image0, image1) + + def testGifUint16(self): + with self.test_session(use_gpu=True) as sess: + base = "tensorflow/core/lib/gif/testdata" + gif0 = io_ops.read_file(os.path.join(base, "scan.gif")) + image0 = image_ops.decode_image(gif0, dtype=dtypes.uint16) + image1 = image_ops.convert_image_dtype(image_ops.decode_gif(gif0), + dtypes.uint16) + image0, image1 = sess.run([image0, image1]) + self.assertAllEqual(image0, image1) + + def testBmpUint16(self): + with self.test_session(use_gpu=True) as sess: + base = "tensorflow/core/lib/bmp/testdata" + bmp0 = io_ops.read_file(os.path.join(base, "lena.bmp")) + image0 = image_ops.decode_image(bmp0, dtype=dtypes.uint16) + image1 = image_ops.convert_image_dtype(image_ops.decode_bmp(bmp0), + dtypes.uint16) + image0, image1 = sess.run([image0, image1]) + self.assertAllEqual(image0, image1) + + def testJpegFloat32(self): + with self.test_session(use_gpu=True) as sess: + base = "tensorflow/core/lib/jpeg/testdata" + jpeg0 = io_ops.read_file(os.path.join(base, "jpeg_merge_test1.jpg")) + image0 = image_ops.decode_image(jpeg0, dtype=dtypes.float32) + image1 = image_ops.convert_image_dtype(image_ops.decode_jpeg(jpeg0), + dtypes.float32) + image0, image1 = sess.run([image0, image1]) + self.assertAllEqual(image0, image1) + + def testPngFloat32(self): + with self.test_session(use_gpu=True) as sess: + base = "tensorflow/core/lib/png/testdata" + png0 = io_ops.read_file(os.path.join(base, "lena_rgba.png")) + image0 = image_ops.decode_image(png0, dtype=dtypes.float32) + image1 = image_ops.convert_image_dtype( + image_ops.decode_png(png0, dtype=dtypes.uint16), dtypes.float32) + image0, image1 = sess.run([image0, image1]) + self.assertAllEqual(image0, image1) + + def testGifFloat32(self): + with self.test_session(use_gpu=True) as sess: + base = "tensorflow/core/lib/gif/testdata" + gif0 = io_ops.read_file(os.path.join(base, "scan.gif")) + image0 = image_ops.decode_image(gif0, dtype=dtypes.float32) + image1 = image_ops.convert_image_dtype(image_ops.decode_gif(gif0), + dtypes.float32) + image0, image1 = sess.run([image0, image1]) + self.assertAllEqual(image0, image1) + + def testBmpFloat32(self): + with self.test_session(use_gpu=True) as sess: + base = "tensorflow/core/lib/bmp/testdata" + bmp0 = io_ops.read_file(os.path.join(base, "lena.bmp")) + image0 = image_ops.decode_image(bmp0, dtype=dtypes.float32) + image1 = image_ops.convert_image_dtype(image_ops.decode_bmp(bmp0), + dtypes.float32) + image0, image1 = sess.run([image0, image1]) + self.assertAllEqual(image0, image1) + + if __name__ == "__main__": googletest.main() diff --git a/tensorflow/python/ops/init_ops.py b/tensorflow/python/ops/init_ops.py index 2df230d470..724fcc39cd 100644 --- a/tensorflow/python/ops/init_ops.py +++ b/tensorflow/python/ops/init_ops.py @@ -467,7 +467,8 @@ class VarianceScaling(Initializer): else: scale /= max(1., (fan_in + fan_out) / 2.) if self.distribution == "normal": - stddev = math.sqrt(scale) + # constant taken from scipy.stats.truncnorm.std(a=-2, b=2, loc=0., scale=1.) + stddev = math.sqrt(scale) / .87962566103423978 return random_ops.truncated_normal( shape, 0.0, stddev, dtype, seed=self.seed) else: diff --git a/tensorflow/python/ops/logging_ops.py b/tensorflow/python/ops/logging_ops.py index 222b8ebc9d..8276047cb6 100644 --- a/tensorflow/python/ops/logging_ops.py +++ b/tensorflow/python/ops/logging_ops.py @@ -35,8 +35,9 @@ from tensorflow.python.util.tf_export import tf_export # Assert and Print are special symbols in python, so we must -# use an upper-case version of them. -@tf_export("Print") +# have an upper-case version of them. For users with Python 3 or Python 2.7 +# with `from __future__ import print_function`, we also allow lowercase. +@tf_export("Print", "print") def Print(input_, data, message=None, first_n=None, summarize=None, name=None): """Prints a list of tensors. diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py index e40481f3a7..466d0dadc8 100644 --- a/tensorflow/python/ops/math_ops.py +++ b/tensorflow/python/ops/math_ops.py @@ -125,8 +125,8 @@ def abs(x, name=None): # pylint: disable=redefined-builtin ``` Args: - x: A `Tensor` or `SparseTensor` of type `float32`, `float64`, `int32`, - `int64`, `complex64` or `complex128`. + x: A `Tensor` or `SparseTensor` of type `float16`, `float32`, `float64`, + `int32`, `int64`, `complex64` or `complex128`. name: A name for the operation (optional). Returns: @@ -430,10 +430,10 @@ def pow(x, y, name=None): # pylint: disable=redefined-builtin ``` Args: - x: A `Tensor` of type `float32`, `float64`, `int32`, `int64`, `complex64`, - or `complex128`. - y: A `Tensor` of type `float32`, `float64`, `int32`, `int64`, `complex64`, - or `complex128`. + x: A `Tensor` of type `float16`, `float32`, `float64`, `int32`, `int64`, + `complex64`, or `complex128`. + y: A `Tensor` of type `float16`, `float32`, `float64`, `int32`, `int64`, + `complex64`, or `complex128`. name: A name for the operation (optional). Returns: @@ -600,7 +600,7 @@ def round(x, name=None): # pylint: disable=redefined-builtin ``` Args: - x: A `Tensor` of type `float32` or `float64`. + x: A `Tensor` of type `float16`, `float32`, `float64`, `int32`, or `int64`. name: A name for the operation (optional). Returns: @@ -1257,7 +1257,7 @@ def reduce_sum(input_tensor, entry in `axis`. If `keepdims` is true, the reduced dimensions are retained with length 1. - If `axis` has no entries, all dimensions are reduced, and a + If `axis` is None, all dimensions are reduced, and a tensor with a single element is returned. For example: @@ -1397,7 +1397,7 @@ def reduce_mean(input_tensor, entry in `axis`. If `keepdims` is true, the reduced dimensions are retained with length 1. - If `axis` has no entries, all dimensions are reduced, and a + If `axis` is None, all dimensions are reduced, and a tensor with a single element is returned. For example: @@ -1469,7 +1469,7 @@ def reduce_prod(input_tensor, entry in `axis`. If `keepdims` is true, the reduced dimensions are retained with length 1. - If `axis` has no entries, all dimensions are reduced, and a + If `axis` is None, all dimensions are reduced, and a tensor with a single element is returned. Args: @@ -1519,7 +1519,7 @@ def reduce_min(input_tensor, entry in `axis`. If `keepdims` is true, the reduced dimensions are retained with length 1. - If `axis` has no entries, all dimensions are reduced, and a + If `axis` is None, all dimensions are reduced, and a tensor with a single element is returned. Args: @@ -1568,7 +1568,7 @@ def reduce_max(input_tensor, entry in `axis`. If `keepdims` is true, the reduced dimensions are retained with length 1. - If `axis` has no entries, all dimensions are reduced, and a + If `axis` is None, all dimensions are reduced, and a tensor with a single element is returned. Args: @@ -1617,7 +1617,7 @@ def reduce_all(input_tensor, entry in `axis`. If `keepdims` is true, the reduced dimensions are retained with length 1. - If `axis` has no entries, all dimensions are reduced, and a + If `axis` is None, all dimensions are reduced, and a tensor with a single element is returned. For example: @@ -1675,7 +1675,7 @@ def reduce_any(input_tensor, entry in `axis`. If `keepdims` is true, the reduced dimensions are retained with length 1. - If `axis` has no entries, all dimensions are reduced, and a + If `axis` is None, all dimensions are reduced, and a tensor with a single element is returned. For example: diff --git a/tensorflow/python/ops/nn_impl.py b/tensorflow/python/ops/nn_impl.py index 783d485892..f47f38e29e 100644 --- a/tensorflow/python/ops/nn_impl.py +++ b/tensorflow/python/ops/nn_impl.py @@ -621,7 +621,7 @@ def normalize_moments(counts, mean_ss, variance_ss, shift, name=None): """Calculate the mean and variance of based on the sufficient statistics. Args: - counts: A `Tensor` containing a the total count of the data (one value). + counts: A `Tensor` containing the total count of the data (one value). mean_ss: A `Tensor` containing the mean sufficient statistics: the (possibly shifted) sum of the elements to average over. variance_ss: A `Tensor` containing the variance sufficient statistics: the @@ -689,6 +689,9 @@ def moments( # Compute true mean while keeping the dims for proper broadcasting. mean = math_ops.reduce_mean(y, axes, keepdims=True, name="mean") # sample variance, not unbiased variance + # Note: stop_gradient does not change the gradient that gets + # backpropagated to the mean from the variance calculation, + # because that gradient is zero variance = math_ops.reduce_mean( math_ops.squared_difference(y, array_ops.stop_gradient(mean)), axes, diff --git a/tensorflow/python/ops/nn_ops.py b/tensorflow/python/ops/nn_ops.py index a0b55eb077..0c2f5b06c4 100644 --- a/tensorflow/python/ops/nn_ops.py +++ b/tensorflow/python/ops/nn_ops.py @@ -1596,12 +1596,12 @@ def leaky_relu(features, alpha=0.2, name=None): Returns: The activation value. """ - with ops.name_scope(name, "LeakyRelu", [features, alpha]): + with ops.name_scope(name, "LeakyRelu", [features, alpha]) as name: features = ops.convert_to_tensor(features, name="features") if features.dtype.is_integer: features = math_ops.to_float(features) alpha = ops.convert_to_tensor(alpha, dtype=features.dtype, name="alpha") - return math_ops.maximum(alpha * features, features) + return math_ops.maximum(alpha * features, features, name=name) def _flatten_outer_dims(logits): diff --git a/tensorflow/python/ops/nn_test.py b/tensorflow/python/ops/nn_test.py index 46a5f4fae6..035b4735af 100644 --- a/tensorflow/python/ops/nn_test.py +++ b/tensorflow/python/ops/nn_test.py @@ -962,6 +962,16 @@ class LeakyReluTest(test_lib.TestCase): self.assertAllClose( outputs, [-0.4, -0.2, 0.0, 1.0, 2.0], rtol=tol, atol=tol) + def testName(self): + np_values = np.array([-2, -1, 0, 1, 2], dtype=np.float64) + outputs_with_name_set = nn_ops.leaky_relu( + constant_op.constant(np_values), + name='test_relu_op') + self.assertEqual(outputs_with_name_set.name, 'test_relu_op:0') + outputs_without_name_set = nn_ops.leaky_relu( + constant_op.constant(np_values)) + self.assertEqual(outputs_without_name_set.name, 'LeakyRelu:0') + class SwishTest(test_lib.TestCase): diff --git a/tensorflow/python/ops/script_ops.py b/tensorflow/python/ops/script_ops.py index f8676ccb5f..219562de5d 100644 --- a/tensorflow/python/ops/script_ops.py +++ b/tensorflow/python/ops/script_ops.py @@ -23,6 +23,7 @@ import threading # Used by py_util.cc to get tracebacks. import traceback # pylint: disable=unused-import +import weakref import numpy as np import six @@ -129,11 +130,14 @@ class FuncRegistry(object): def __init__(self): self._lock = threading.Lock() self._unique_id = 0 # GUARDED_BY(self._lock) - self._funcs = {} + # Only store weakrefs to the funtions. The strong reference is stored in + # the graph. + self._funcs = weakref.WeakValueDictionary() def insert(self, func): """Registers `func` and returns a unique token for this entry.""" token = self._next_unique_token() + # Store a weakref to the function self._funcs[token] = func return token @@ -186,7 +190,7 @@ class FuncRegistry(object): Raises: ValueError: if no function is registered for `token`. """ - func = self._funcs[token] + func = self._funcs.get(token, None) if func is None: raise ValueError("callback %s is not found" % token) if isinstance(func, EagerFunc): @@ -228,19 +232,6 @@ _py_funcs = FuncRegistry() pywrap_tensorflow.InitializePyTrampoline(_py_funcs) -class CleanupFunc(object): - """A helper class to remove a registered function from _py_funcs.""" - - def __init__(self, token): - self._token = token - - def __del__(self): - if _py_funcs is not None: - # If _py_funcs is None, the program is most likely in shutdown, and the - # _py_funcs object has been destroyed already. - _py_funcs.remove(self._token) - - def _internal_py_func(func, inp, Tout, @@ -270,17 +261,15 @@ def _internal_py_func(func, # bound to that of the outer graph instead. graph = graph._outer_graph - cleanup = CleanupFunc(token) - # TODO(zhifengc): Consider adding a Graph method to collect # `cleanup` objects in one of its member. - if not hasattr(graph, "_cleanup_py_funcs_used_in_graph"): - graph._cleanup_py_funcs_used_in_graph = [] + if not hasattr(graph, "_py_funcs_used_in_graph"): + graph._py_funcs_used_in_graph = [] - # When `graph` is destroyed, elements in _cleanup_py_funcs_used_in_graph - # will be destroyed and their __del__ will remove the 'token' from - # the funcs registry. - graph._cleanup_py_funcs_used_in_graph.append(cleanup) + # Store a reference to the function in the graph to ensure it stays alive + # as long as the graph lives. When the graph is destroyed, the function + # is left to the garbage collector for destruction as well. + graph._py_funcs_used_in_graph.append(func) # pylint: enable=protected-access if eager: diff --git a/tensorflow/python/ops/sparse_ops.py b/tensorflow/python/ops/sparse_ops.py index 0130233746..c3b16a7bd5 100644 --- a/tensorflow/python/ops/sparse_ops.py +++ b/tensorflow/python/ops/sparse_ops.py @@ -84,6 +84,8 @@ def _convert_to_sparse_tensors(sp_inputs): # pylint: disable=protected-access @tf_export("sparse_concat") +@deprecation.deprecated_args( + None, "concat_dim is deprecated, use axis instead", "concat_dim") def sparse_concat(axis, sp_inputs, name=None, @@ -597,6 +599,8 @@ class KeywordRequired(object): @tf_export("sparse_split") +@deprecation.deprecated_args( + None, "split_dim is deprecated, use axis instead", "split_dim") def sparse_split(keyword_required=KeywordRequired(), sp_input=None, num_split=None, diff --git a/tensorflow/python/ops/string_ops.py b/tensorflow/python/ops/string_ops.py index ae79c01949..0280c89c10 100644 --- a/tensorflow/python/ops/string_ops.py +++ b/tensorflow/python/ops/string_ops.py @@ -91,6 +91,59 @@ def string_split(source, delimiter=" ", skip_empty=True): # pylint: disable=inv shape.set_shape([2]) return sparse_tensor.SparseTensor(indices, values, shape) +@tf_export("strings.split") +def string_split_v2(source, sep=None, maxsplit=-1): + """Split elements of `source` based on `sep` into a `SparseTensor`. + + Let N be the size of source (typically N will be the batch size). Split each + element of `source` based on `sep` and return a `SparseTensor` + containing the split tokens. Empty tokens are ignored. + + For example, N = 2, source[0] is 'hello world' and source[1] is 'a b c', + then the output will be + + st.indices = [0, 0; + 0, 1; + 1, 0; + 1, 1; + 1, 2] + st.shape = [2, 3] + st.values = ['hello', 'world', 'a', 'b', 'c'] + + If `sep` is given, consecutive delimiters are not grouped together and are + deemed to delimit empty strings. For example, source of `"1<>2<><>3"` and + sep of `"<>"` returns `["1", "2", "", "3"]`. If `sep` is None or an empty + string, consecutive whitespace are regarded as a single separator, and the + result will contain no empty strings at the startor end if the string has + leading or trailing whitespace. + + Note that the above mentioned behavior matches python's str.split. + + Args: + source: `1-D` string `Tensor`, the strings to split. + sep: `0-D` string `Tensor`, the delimiter character. + maxsplit: An `int`. If `maxsplit > 0`, limit of the split of the result. + + Raises: + ValueError: If sep is not a string. + + Returns: + A `SparseTensor` of rank `2`, the strings split according to the delimiter. + The first column of the indices corresponds to the row in `source` and the + second column corresponds to the index of the split component in this row. + """ + if sep is None: + sep = '' + sep = ops.convert_to_tensor(sep, dtype=dtypes.string) + source = ops.convert_to_tensor(source, dtype=dtypes.string) + + indices, values, shape = gen_string_ops.string_split_v2( + source, sep=sep, maxsplit=maxsplit) + indices.set_shape([None, 2]) + values.set_shape([None]) + shape.set_shape([2]) + return sparse_tensor.SparseTensor(indices, values, shape) + def _reduce_join_reduction_dims(x, axis, reduction_indices): """Returns range(rank(x) - 1, 0, -1) if reduction_indices is None.""" diff --git a/tensorflow/python/ops/variable_scope.py b/tensorflow/python/ops/variable_scope.py index f49e2d314d..47414c28af 100644 --- a/tensorflow/python/ops/variable_scope.py +++ b/tensorflow/python/ops/variable_scope.py @@ -1786,6 +1786,23 @@ class variable_scope(object): assert v.name == "foo/bar/v:0" ``` + Simple example of how to reenter a premade variable scope safely: + + ```python + with tf.variable_scope("foo") as vs: + pass + + # Re-enter the variable scope. + with tf.variable_scope(vs, + auxiliary_name_scope=False) as vs1: + # Restore the original name_scope. + with tf.name_scope(vs1.original_name_scope): + v = tf.get_variable("v", [1]) + assert v.name == "foo/v:0" + c = tf.constant([1], name="c") + assert c.name == "foo/c:0" + ``` + Basic example of sharing a variable AUTO_REUSE: ```python @@ -1924,7 +1941,9 @@ class variable_scope(object): (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. auxiliary_name_scope: If `True`, we create an auxiliary name scope with - the scope. If `False`, we don't touch name scope. + the scope. If `False`, we don't create it. Note that the argument is + not inherited, and it only takes effect for once when creating. You + should only use it for re-entering a premade variable scope. Returns: A scope that can be captured and reused. diff --git a/tensorflow/python/tools/import_pb_to_tensorboard.py b/tensorflow/python/tools/import_pb_to_tensorboard.py old mode 100755 new mode 100644 diff --git a/tensorflow/tensorflow.bzl b/tensorflow/tensorflow.bzl index 522965990b..b59f8e1f98 100644 --- a/tensorflow/tensorflow.bzl +++ b/tensorflow/tensorflow.bzl @@ -1719,7 +1719,7 @@ def tf_py_build_info_genrule(): name="py_build_info_gen", outs=["platform/build_info.py"], cmd= - "$(location //tensorflow/tools/build_info:gen_build_info.py) --raw_generate \"$@\" --build_config " + if_cuda("cuda", "cpu"), + "$(location //tensorflow/tools/build_info:gen_build_info.py) --raw_generate \"$@\" --build_config " + if_cuda("cuda", "cpu"), local=1, tools=[clean_dep("//tensorflow/tools/build_info:gen_build_info.py")],) diff --git a/tensorflow/tools/api/generator/create_python_api.py b/tensorflow/tools/api/generator/create_python_api.py index bca9fa49eb..671b7e387e 100644 --- a/tensorflow/tools/api/generator/create_python_api.py +++ b/tensorflow/tools/api/generator/create_python_api.py @@ -41,7 +41,11 @@ _GENERATED_FILE_HEADER = """# This file is MACHINE GENERATED! Do not edit. # Generated by: tensorflow/tools/api/generator/create_python_api.py script. \"\"\"%s \"\"\" + +from __future__ import print_function + """ +_GENERATED_FILE_FOOTER = "\n\ndel print_function\n" class SymbolExposedTwiceError(Exception): @@ -149,6 +153,7 @@ class _ModuleInitCodeBuilder(object): _names_with_underscore = [%s] __all__ = [_s for _s in dir() if not _s.startswith('_')] __all__.extend([_s for _s in _names_with_underscore]) +__all__.remove('print_function') ''' % underscore_names_str return module_text_map @@ -333,7 +338,8 @@ def create_api_files( if module or not root_init_template: contents = ( _GENERATED_FILE_HEADER % - get_module_docstring(module, package, api_name) + text) + get_module_docstring(module, package, api_name) + + text + _GENERATED_FILE_FOOTER) else: # Read base init file with open(root_init_template, 'r') as root_init_template_file: diff --git a/tensorflow/tools/api/golden/tensorflow.image.pbtxt b/tensorflow/tools/api/golden/tensorflow.image.pbtxt index 5bb3b3c444..10171b3d60 100644 --- a/tensorflow/tools/api/golden/tensorflow.image.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.image.pbtxt @@ -58,7 +58,7 @@ tf_module { } member_method { name: "decode_image" - argspec: "args=[\'contents\', \'channels\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + argspec: "args=[\'contents\', \'channels\', \'dtype\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \"\", \'None\'], " } member_method { name: "decode_jpeg" diff --git a/tensorflow/tools/api/golden/tensorflow.pbtxt b/tensorflow/tools/api/golden/tensorflow.pbtxt index dc2bd40096..3051c4437e 100644 --- a/tensorflow/tools/api/golden/tensorflow.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.pbtxt @@ -1532,6 +1532,10 @@ tf_module { name: "pow" argspec: "args=[\'x\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } + member_method { + name: "print" + argspec: "args=[\'input_\', \'data\', \'message\', \'first_n\', \'summarize\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " + } member_method { name: "py_func" argspec: "args=[\'func\', \'inp\', \'Tout\', \'stateful\', \'name\'], varargs=None, keywords=None, defaults=[\'True\', \'None\'], " diff --git a/tensorflow/tools/api/golden/tensorflow.strings.pbtxt b/tensorflow/tools/api/golden/tensorflow.strings.pbtxt index a3fbe95bba..b641c39feb 100644 --- a/tensorflow/tools/api/golden/tensorflow.strings.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.strings.pbtxt @@ -4,4 +4,8 @@ tf_module { name: "regex_full_match" argspec: "args=[\'input\', \'pattern\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } + member_method { + name: "split" + argspec: "args=[\'source\', \'sep\', \'maxsplit\'], varargs=None, keywords=None, defaults=[\'None\', \'-1\'], " + } } diff --git a/tensorflow/tools/ci_build/builds/pip.sh b/tensorflow/tools/ci_build/builds/pip.sh index 5fa75e1d61..883bb93647 100755 --- a/tensorflow/tools/ci_build/builds/pip.sh +++ b/tensorflow/tools/ci_build/builds/pip.sh @@ -322,6 +322,10 @@ create_activate_virtualenv_and_install_tensorflow() { pip install -v ${PIP_FLAGS} ${WHL_PATH} || \ die "pip install (forcing to reinstall tensorflow) FAILED" echo "Successfully installed pip package ${TF_WHEEL_PATH}" + + # Force downgrade setuptools. + pip install --upgrade setuptools==39.1.0 + } ################################################################################ diff --git a/tensorflow/tools/ci_build/builds/with_the_same_user b/tensorflow/tools/ci_build/builds/with_the_same_user index d4bf546d40..b216e3549f 100755 --- a/tensorflow/tools/ci_build/builds/with_the_same_user +++ b/tensorflow/tools/ci_build/builds/with_the_same_user @@ -40,7 +40,7 @@ if [ -n "${CI_BUILD_USER_FORCE_BADNAME}" ]; then ADDUSER_OPTS="--force-badname" fi -getent group "${CI_BUILD_GID}" || addgroup --gid "${CI_BUILD_GID}" "${CI_BUILD_GROUP}" +getent group "${CI_BUILD_GID}" || addgroup ${ADDUSER_OPTS} --gid "${CI_BUILD_GID}" "${CI_BUILD_GROUP}" getent passwd "${CI_BUILD_UID}" || adduser ${ADDUSER_OPTS} \ --gid "${CI_BUILD_GID}" --uid "${CI_BUILD_UID}" \ --gecos "${CI_BUILD_USER} (generated by with_the_same_user script)" \ diff --git a/tensorflow/tools/ci_build/ci_build.sh b/tensorflow/tools/ci_build/ci_build.sh index 072dd6ab99..1f0fd0387a 100755 --- a/tensorflow/tools/ci_build/ci_build.sh +++ b/tensorflow/tools/ci_build/ci_build.sh @@ -134,6 +134,12 @@ if [[ $? != "0" ]]; then die "ERROR: docker build failed. Dockerfile is at ${DOCKERFILE_PATH}" fi +# If caller wants the with_the_same_user script to allow bad usernames, +# pass the var to the docker environment +if [ -n "${CI_BUILD_USER_FORCE_BADNAME}" ]; then + CI_BUILD_USER_FORCE_BADNAME_ENV="-e CI_BUILD_USER_FORCE_BADNAME=yes" +fi + # Run the command inside the container. echo "Running '${COMMAND[*]}' inside ${DOCKER_IMG_NAME}..." mkdir -p ${WORKSPACE}/bazel-ci_build-cache @@ -148,6 +154,7 @@ ${DOCKER_BINARY} run --rm --pid=host \ -e "CI_BUILD_GROUP=$(id -g -n)" \ -e "CI_BUILD_GID=$(id -g)" \ -e "CI_TENSORFLOW_SUBMODULE_PATH=${CI_TENSORFLOW_SUBMODULE_PATH}" \ + ${CI_BUILD_USER_FORCE_BADNAME_ENV} \ -v ${WORKSPACE}:/workspace \ -w /workspace \ ${GPU_EXTRA_PARAMS} \ diff --git a/tensorflow/tools/ci_build/copy_binary.py b/tensorflow/tools/ci_build/copy_binary.py index 420d390d2b..148526492d 100755 --- a/tensorflow/tools/ci_build/copy_binary.py +++ b/tensorflow/tools/ci_build/copy_binary.py @@ -32,7 +32,8 @@ import shutil import tempfile import zipfile -TF_NIGHTLY_REGEX = r"(.+)tf_nightly(|_gpu)-(\d\.\d\.\d.dev[\d]{0,8})-(.+)\.whl" +TF_NIGHTLY_REGEX = (r"(.+)tf_nightly(|_gpu)-(\d\.[\d]{1,2}" + "\.\d.dev[\d]{0,8})-(.+)\.whl") BINARY_STRING_TEMPLATE = "%s-%s-%s.whl" diff --git a/tensorflow/tools/ci_build/install/install_pip_packages.sh b/tensorflow/tools/ci_build/install/install_pip_packages.sh index 60290df833..88f1d04193 100755 --- a/tensorflow/tools/ci_build/install/install_pip_packages.sh +++ b/tensorflow/tools/ci_build/install/install_pip_packages.sh @@ -115,3 +115,7 @@ pip2 install keras_applications==1.0.2 pip3 install keras_applications==1.0.2 pip2 install keras_preprocessing==1.0.1 pip3 install keras_preprocessing==1.0.1 + +# Install last working version of setuptools. +pip2 install --upgrade setuptools==39.1.0 +pip3 install --upgrade setuptools==39.1.0 diff --git a/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh b/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh index edb9d4b929..acd69ef346 100755 --- a/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh +++ b/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh @@ -39,7 +39,6 @@ if [[ -z $pip35_version ]]; then fi set -e -pip3.5 install --upgrade setuptools pip3.5 install --upgrade pip pip3.5 install --upgrade virtualenv @@ -86,4 +85,7 @@ pip3.5 install --upgrade termcolor pip3.5 install keras_applications==1.0.2 pip3.5 install keras_preprocessing==1.0.1 +# Install last working version of setuptools. +pip3.5 install --upgrade setuptools==39.1.0 + # LINT.ThenChange(//tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh) diff --git a/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh b/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh index 5635977731..323b30f48e 100755 --- a/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh +++ b/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh @@ -49,7 +49,6 @@ cd Python-3.6.1 make altinstall ln -s /usr/local/bin/pip3.6 /usr/local/bin/pip3 -pip3 install --upgrade setuptools pip3 install --upgrade pip pip3 install --upgrade virtualenv @@ -101,4 +100,8 @@ pip3 install --upgrade termcolor # Keras pip3.5 install keras_applications==1.0.2 pip3.5 install keras_preprocessing==1.0.1 + +# Install last working version of setuptools. +pip3 install --upgrade setuptools==39.1.0 + # LINT.ThenChange(//tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh) diff --git a/tensorflow/tools/ci_build/linux/mkl/basic-mkl-test.sh b/tensorflow/tools/ci_build/linux/mkl/basic-mkl-test.sh new file mode 100755 index 0000000000..10a09a415a --- /dev/null +++ b/tensorflow/tools/ci_build/linux/mkl/basic-mkl-test.sh @@ -0,0 +1,29 @@ +#!/usr/bin/env bash +# Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +# +# Usage: basic_mkl_test.sh + +# Helper function to traverse directories up until given file is found. +function upsearch () { + test / == "$PWD" && return || \ + test -e "$1" && echo "$PWD" && return || \ + cd .. && upsearch "$1" +} + +# Set up WORKSPACE. +WORKSPACE="${WORKSPACE:-$(upsearch WORKSPACE)}" + +BUILD_TAG=mkl-ci-test CI_BUILD_USER_FORCE_BADNAME=yes ${WORKSPACE}/tensorflow/tools/ci_build/ci_build.sh cpu tensorflow/tools/ci_build/linux/cpu/run_mkl.sh diff --git a/tensorflow/tools/ci_build/pi/build_raspberry_pi.sh b/tensorflow/tools/ci_build/pi/build_raspberry_pi.sh index 1bd1852ffc..b8bce57c87 100755 --- a/tensorflow/tools/ci_build/pi/build_raspberry_pi.sh +++ b/tensorflow/tools/ci_build/pi/build_raspberry_pi.sh @@ -79,6 +79,7 @@ if [[ $1 == "PI_ONE" ]]; then --linkopt=-L${OPENBLAS_INSTALL_PATH}/lib/ --linkopt=-l:libopenblas.a" echo "Building for the Pi One/Zero, with no NEON support" + WHEEL_ARCH=linux_armv6l else PI_COPTS='--copt=-march=armv7-a --copt=-mfpu=neon-vfpv4 --copt=-std=gnu11 --copt=-DS_IREAD=S_IRUSR --copt=-DS_IWRITE=S_IWUSR @@ -86,6 +87,7 @@ else --copt=-U__GCC_HAVE_SYNC_COMPARE_AND_SWAP_1 --copt=-U__GCC_HAVE_SYNC_COMPARE_AND_SWAP_2 --copt=-U__GCC_HAVE_SYNC_COMPARE_AND_SWAP_8' + WHEEL_ARCH=linux_armv7l echo "Building for the Pi Two/Three, with NEON acceleration" fi @@ -100,6 +102,8 @@ bazel build -c opt ${PI_COPTS} \ --copt=-fomit-frame-pointer --cpu=armeabi \ --crosstool_top=@local_config_arm_compiler//:toolchain \ --verbose_failures \ + //tensorflow:libtensorflow.so \ + //tensorflow:libtensorflow_framework.so \ //tensorflow/tools/benchmark:benchmark_model \ //tensorflow/tools/pip_package:build_pip_package @@ -112,10 +116,12 @@ BDIST_OPTS="--universal" \ bazel-bin/tensorflow/tools/pip_package/build_pip_package "${OUTDIR}" OLD_FN=$(ls "${OUTDIR}" | grep -m 1 \.whl) -SUB='s/tensorflow-([^-]+)-([^-]+)-.*/tensorflow-\1-\2-none-any.whl/; print' +SUB='s/tensorflow-([^-]+)-([^-]+)-.*/tensorflow-\1-\2-none-'${WHEEL_ARCH}'.whl/; print' NEW_FN=$(echo "${OLD_FN}" | perl -ne "${SUB}") mv "${OUTDIR}/${OLD_FN}" "${OUTDIR}/${NEW_FN}" cp bazel-bin/tensorflow/tools/benchmark/benchmark_model "${OUTDIR}" +cp bazel-bin/tensorflow/libtensorflow.so "${OUTDIR}" +cp bazel-bin/tensorflow/libtensorflow_framework.so "${OUTDIR}" echo "Output can be found here:" find "${OUTDIR}" diff --git a/tensorflow/tools/def_file_filter/def_file_filter_configure.bzl b/tensorflow/tools/def_file_filter/def_file_filter_configure.bzl index 47539b2423..f8f63e276c 100644 --- a/tensorflow/tools/def_file_filter/def_file_filter_configure.bzl +++ b/tensorflow/tools/def_file_filter/def_file_filter_configure.bzl @@ -31,7 +31,11 @@ def _def_file_filter_configure_impl(repository_ctx): vc_path = find_vc_path(repository_ctx) if vc_path == "visual-studio-not-found": auto_configure_fail("Visual C++ build tools not found on your machine") - undname_bin_path = find_msvc_tool(repository_ctx, vc_path, "undname.exe").replace("\\", "\\\\") + + undname = find_msvc_tool(repository_ctx, vc_path, "undname.exe") + if undname == None: + auto_configure_fail("Couldn't find undname.exe under %s, please check your VC installation and set BAZEL_VC environment variable correctly." % vc_path) + undname_bin_path = undname.replace("\\", "\\\\") repository_ctx.template( "def_file_filter.py", diff --git a/tensorflow/tools/dist_test/local_test.sh b/tensorflow/tools/dist_test/local_test.sh index 06c2b997cb..b0114721bd 100755 --- a/tensorflow/tools/dist_test/local_test.sh +++ b/tensorflow/tools/dist_test/local_test.sh @@ -64,9 +64,6 @@ die() { # Configurations DOCKER_IMG_NAME="tensorflow/tf-dist-test-local-cluster" -# Use TensorFlow v1.5.0 for Python 2.7 and CPU only as we set num_gpus to 0 in the below -DEFAULT_WHL_FILE_LOCATION="https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp27-none-linux_x86_64.whl" - # Parse input arguments LEAVE_CONTAINER_RUNNING=0 MODEL_NAME="" @@ -77,8 +74,7 @@ SYNC_REPLICAS_FLAG="" WHL_FILE_LOCATION=${1} if [[ -z "${WHL_FILE_LOCATION}" ]]; then - WHL_FILE_LOCATION=${DEFAULT_WHL_FILE_LOCATION} - echo "use default whl file location" + echo "WARNING: No wheel url passed. Will use latest tf-nightly cpu p2 wheel." fi while true; do @@ -131,7 +127,11 @@ echo "Building in temporary directory: ${BUILD_DIR}" cp -r ${DIR}/* "${BUILD_DIR}"/ || \ die "Failed to copy files to ${BUILD_DIR}" -if [[ $WHL_FILE_LOCATION =~ 'http://' || $WHL_FILE_LOCATION =~ 'https://' ]]; then +# Download whl file into the build context directory. +if [[ -z "${WHL_FILE_LOCATION}" ]]; then + pip2 download --no-deps tf-nightly + cp tf-nightly-*.whl "${BUILD_DIR}"/tensorflow-none-any.whl +elif [[ $WHL_FILE_LOCATION =~ 'http://' || $WHL_FILE_LOCATION =~ 'https://' ]]; then # Download whl file into the build context directory. wget -P "${BUILD_DIR}" "${WHL_FILE_LOCATION}" || \ die "Failed to download tensorflow whl file from URL: ${WHL_FILE_LOCATION}" diff --git a/tensorflow/tools/dist_test/remote_test.sh b/tensorflow/tools/dist_test/remote_test.sh index 935535312d..e188c88c8f 100755 --- a/tensorflow/tools/dist_test/remote_test.sh +++ b/tensorflow/tools/dist_test/remote_test.sh @@ -108,7 +108,7 @@ fi # Parse command-line arguments. WHL_URL=${1} if [[ -z "${WHL_URL}" ]]; then - die "whl URL is not specified" + echo "WARNING: No wheel url passed. Will use latest tf-nightly cpu p2 wheel." fi # Create docker build context directory. @@ -121,8 +121,13 @@ cp -r ${DIR}/* ${BUILD_DIR}/ || \ die "Failed to copy files to ${BUILD_DIR}" # Download whl file into the build context directory. -wget -P "${BUILD_DIR}" ${WHL_URL} || \ - die "Failed to download tensorflow whl file from URL: ${WHL_URL}" +if [[ -z "${WHL_URL}" ]]; then + pip2 download --no-deps tf-nightly + cp tf-nightly-*.whl "${BUILD_DIR}"/tensorflow-none-any.whl +else + wget -P "${BUILD_DIR}" ${WHL_URL} || \ + die "Failed to download tensorflow whl file from URL: ${WHL_URL}" +fi # Build docker image for test. docker build ${NO_CACHE_FLAG} \ diff --git a/tensorflow/tools/docker/Dockerfile.devel b/tensorflow/tools/docker/Dockerfile.devel index 406d134699..57a491255e 100644 --- a/tensorflow/tools/docker/Dockerfile.devel +++ b/tensorflow/tools/docker/Dockerfile.devel @@ -76,7 +76,7 @@ RUN mkdir /bazel && \ # Download and build TensorFlow. WORKDIR /tensorflow -RUN git clone --branch=r1.8 --depth=1 https://github.com/tensorflow/tensorflow.git . +RUN git clone --branch=r1.9 --depth=1 https://github.com/tensorflow/tensorflow.git . # TODO(craigcitro): Don't install the pip package, since it makes it # more difficult to experiment with local changes. Instead, just add diff --git a/tensorflow/tools/docker/Dockerfile.devel-cpu-mkl b/tensorflow/tools/docker/Dockerfile.devel-cpu-mkl index a6cd44ced1..6796ad70e5 100644 --- a/tensorflow/tools/docker/Dockerfile.devel-cpu-mkl +++ b/tensorflow/tools/docker/Dockerfile.devel-cpu-mkl @@ -3,7 +3,7 @@ FROM tensorflow/tensorflow:latest-devel LABEL maintainer="Clayne Robison" # These arguments are parameterized. Use --build-args to override. -ARG TF_BRANCH=r1.8 +ARG TF_BRANCH=r1.9 ARG WHL_DIR=/whl RUN apt-get update && apt-get install -y --no-install-recommends \ diff --git a/tensorflow/tools/docker/Dockerfile.devel-gpu b/tensorflow/tools/docker/Dockerfile.devel-gpu index 2fe47f3356..204b5b4dba 100644 --- a/tensorflow/tools/docker/Dockerfile.devel-gpu +++ b/tensorflow/tools/docker/Dockerfile.devel-gpu @@ -13,8 +13,8 @@ RUN apt-get update && apt-get install -y --no-install-recommends \ cuda-cusparse-dev-9-0 \ curl \ git \ - libcudnn7=7.0.5.15-1+cuda9.0 \ - libcudnn7-dev=7.0.5.15-1+cuda9.0 \ + libcudnn7=7.1.4.18-1+cuda9.0 \ + libcudnn7-dev=7.1.4.18-1+cuda9.0 \ libcurl3-dev \ libfreetype6-dev \ libhdf5-serial-dev \ @@ -85,7 +85,7 @@ RUN mkdir /bazel && \ # Download and build TensorFlow. WORKDIR /tensorflow -RUN git clone --branch=r1.8 --depth=1 https://github.com/tensorflow/tensorflow.git . +RUN git clone --branch=r1.9 --depth=1 https://github.com/tensorflow/tensorflow.git . # Configure the build for our CUDA configuration. ENV CI_BUILD_PYTHON python diff --git a/tensorflow/tools/docker/Dockerfile.gpu b/tensorflow/tools/docker/Dockerfile.gpu index bff4a20392..9197651ff4 100644 --- a/tensorflow/tools/docker/Dockerfile.gpu +++ b/tensorflow/tools/docker/Dockerfile.gpu @@ -12,7 +12,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \ cuda-cusolver-9-0 \ cuda-cusparse-9-0 \ curl \ - libcudnn7=7.0.5.15-1+cuda9.0 \ + libcudnn7=7.1.4.18-1+cuda9.0 \ libfreetype6-dev \ libhdf5-serial-dev \ libpng12-dev \ diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD index 5910f0625e..620fef9363 100644 --- a/tensorflow/tools/pip_package/BUILD +++ b/tensorflow/tools/pip_package/BUILD @@ -61,6 +61,7 @@ COMMON_PIP_DEPS = [ "//tensorflow/contrib/autograph/core:core", "//tensorflow/contrib/autograph/impl:impl", "//tensorflow/contrib/autograph/lang:lang", + "//tensorflow/contrib/autograph/operators:operators", "//tensorflow/contrib/autograph/pyct:pyct", "//tensorflow/contrib/autograph/pyct/static_analysis:static_analysis", "//tensorflow/contrib/boosted_trees:boosted_trees_pip", diff --git a/tensorflow/tools/pip_package/build_pip_package.sh b/tensorflow/tools/pip_package/build_pip_package.sh index 0c4065bc77..f7e42ce536 100755 --- a/tensorflow/tools/pip_package/build_pip_package.sh +++ b/tensorflow/tools/pip_package/build_pip_package.sh @@ -41,51 +41,15 @@ function is_windows() { fi } -function main() { +function prepare_src() { if [ $# -lt 1 ] ; then echo "No destination dir provided" exit 1 fi - DEST=$(real_path $1) - TMPDIR=$(mktemp -d -t tmp.XXXXXXXXXX) - - PKG_NAME_FLAG="" - GPU_BUILD=0 - NIGHTLY_BUILD=0 - PROJECT_NAME="" - while true; do - if [[ "$1" == "--nightly_flag" ]]; then - NIGHTLY_BUILD=1 - elif [[ "$1" == "--gpu" ]]; then - GPU_BUILD=1 - elif [[ "$1" == "--gpudirect" ]]; then - PKG_NAME_FLAG="--project_name tensorflow_gpudirect" - elif [[ "$1" == "--project_name" ]]; then - shift - if [[ -z "$1" ]]; then - break - fi - PROJECT_NAME="$1" - fi - shift - - if [[ -z "$1" ]]; then - break - fi - done - - if [[ -n ${PROJECT_NAME} ]]; then - PKG_NAME_FLAG="--project_name ${PROJECT_NAME}" - elif [[ ${NIGHTLY_BUILD} == "1" && ${GPU_BUILD} == "1" ]]; then - PKG_NAME_FLAG="--project_name tf_nightly_gpu" - elif [[ ${NIGHTLY_BUILD} == "1" ]]; then - PKG_NAME_FLAG="--project_name tf_nightly" - elif [[ ${GPU_BUILD} == "1" ]]; then - PKG_NAME_FLAG="--project_name tensorflow_gpu" - fi - - echo $(date) : "=== Using tmpdir: ${TMPDIR}" + TMPDIR="$1" + mkdir -p "$TMPDIR" + echo $(date) : "=== Preparing sources in dir: ${TMPDIR}" if [ ! -d bazel-bin/tensorflow ]; then echo "Could not find bazel-bin. Did you run from the root of the build tree?" @@ -155,17 +119,28 @@ function main() { # over so user defined ops can be compiled. mkdir -p ${TMPDIR}/google mkdir -p ${TMPDIR}/third_party - pushd ${RUNFILES%org_tensorflow} + pushd ${RUNFILES%org_tensorflow} > /dev/null for header in $(find protobuf_archive -name \*.h); do mkdir -p "${TMPDIR}/google/$(dirname ${header})" cp "$header" "${TMPDIR}/google/$(dirname ${header})/" done - popd + popd > /dev/null cp -R $RUNFILES/third_party/eigen3 ${TMPDIR}/third_party cp tensorflow/tools/pip_package/MANIFEST.in ${TMPDIR} cp tensorflow/tools/pip_package/README ${TMPDIR} cp tensorflow/tools/pip_package/setup.py ${TMPDIR} +} + +function build_wheel() { + if [ $# -lt 2 ] ; then + echo "No src and dest dir provided" + exit 1 + fi + + TMPDIR="$1" + DEST="$2" + PKG_NAME_FLAG="$3" # Before we leave the top-level directory, make sure we know how to # call python. @@ -173,15 +148,110 @@ function main() { source tools/python_bin_path.sh fi - pushd ${TMPDIR} + pushd ${TMPDIR} > /dev/null rm -f MANIFEST echo $(date) : "=== Building wheel" "${PYTHON_BIN_PATH:-python}" setup.py bdist_wheel ${PKG_NAME_FLAG} >/dev/null mkdir -p ${DEST} cp dist/* ${DEST} - popd - rm -rf ${TMPDIR} + popd > /dev/null echo $(date) : "=== Output wheel file is in: ${DEST}" } +function usage() { + echo "Usage:" + echo "$0 [--src srcdir] [--dst dstdir] [options]" + echo "$0 dstdir [options]" + echo "" + echo " --src prepare sources in srcdir" + echo " will use temporary dir if not specified" + echo "" + echo " --dst build wheel in dstdir" + echo " if dstdir is not set do not build, only prepare sources" + echo "" + echo " Options:" + echo " --project_name set project name to name" + echo " --gpu build tensorflow_gpu" + echo " --gpudirect build tensorflow_gpudirect" + echo " --nightly_flag build tensorflow nightly" + echo "" + exit 1 +} + +function main() { + PKG_NAME_FLAG="" + PROJECT_NAME="" + GPU_BUILD=0 + NIGHTLY_BUILD=0 + SRCDIR="" + DSTDIR="" + CLEANSRC=1 + while true; do + if [[ "$1" == "--help" ]]; then + usage + exit 1 + elif [[ "$1" == "--nightly_flag" ]]; then + NIGHTLY_BUILD=1 + elif [[ "$1" == "--gpu" ]]; then + GPU_BUILD=1 + elif [[ "$1" == "--gpudirect" ]]; then + PKG_NAME_FLAG="--project_name tensorflow_gpudirect" + elif [[ "$1" == "--project_name" ]]; then + shift + if [[ -z "$1" ]]; then + break + fi + PROJECT_NAME="$1" + elif [[ "$1" == "--src" ]]; then + shift + SRCDIR="$(real_path $1)" + CLEANSRC=0 + elif [[ "$1" == "--dst" ]]; then + shift + DSTDIR="$(real_path $1)" + else + DSTDIR="$(real_path $1)" + fi + shift + + if [[ -z "$1" ]]; then + break + fi + done + + if [[ -z "$DSTDIR" ]] && [[ -z "$SRCDIR" ]]; then + echo "No destination dir provided" + usage + exit 1 + fi + + if [[ -z "$SRCDIR" ]]; then + # make temp srcdir if none set + SRCDIR="$(mktemp -d -t tmp.XXXXXXXXXX)" + fi + + prepare_src "$SRCDIR" + + if [[ -z "$DSTDIR" ]]; then + # only want to prepare sources + exit + fi + + if [[ -n ${PROJECT_NAME} ]]; then + PKG_NAME_FLAG="--project_name ${PROJECT_NAME}" + elif [[ ${NIGHTLY_BUILD} == "1" && ${GPU_BUILD} == "1" ]]; then + PKG_NAME_FLAG="--project_name tf_nightly_gpu" + elif [[ ${NIGHTLY_BUILD} == "1" ]]; then + PKG_NAME_FLAG="--project_name tf_nightly" + elif [[ ${GPU_BUILD} == "1" ]]; then + PKG_NAME_FLAG="--project_name tensorflow_gpu" + fi + + build_wheel "$SRCDIR" "$DSTDIR" "$PKG_NAME_FLAG" + + if [[ $CLEANSRC -ne 0 ]]; then + rm -rf "${TMPDIR}" + fi +} + main "$@" diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py index d25a9e77b1..97f625e7e9 100644 --- a/tensorflow/tools/pip_package/setup.py +++ b/tensorflow/tools/pip_package/setup.py @@ -45,7 +45,7 @@ DOCLINES = __doc__.split('\n') # This version string is semver compatible, but incompatible with pip. # For pip, we will remove all '-' characters from this string, and use the # result for pip. -_VERSION = '1.8.0' +_VERSION = '1.9.0-rc0' REQUIRED_PACKAGES = [ 'absl-py >= 0.1.6', @@ -54,6 +54,7 @@ REQUIRED_PACKAGES = [ 'numpy >= 1.13.3', 'six >= 1.10.0', 'protobuf >= 3.4.0', + 'setuptools <= 39.1.0', 'tensorboard >= 1.8.0, < 1.9.0', 'termcolor >= 1.1.0', ] diff --git a/tensorflow/tools/proto_text/gen_proto_text_functions_lib.cc b/tensorflow/tools/proto_text/gen_proto_text_functions_lib.cc index 29add6d5ea..15d7c70281 100644 --- a/tensorflow/tools/proto_text/gen_proto_text_functions_lib.cc +++ b/tensorflow/tools/proto_text/gen_proto_text_functions_lib.cc @@ -814,6 +814,9 @@ void Generator::Generate(const FileDescriptor& fd) { // Add header to cc file. SetOutput(&cc_); Print("// GENERATED FILE - DO NOT MODIFY"); + Print(); + Print("#include "); // for `std::stable_sort()` + Print(); headers = {GetProtoTextHeaderName(fd, true /* impl */)}; AddHeadersToCurrentSection(headers); Print(); diff --git a/tensorflow/tools/quantization/quantize_graph_test.py b/tensorflow/tools/quantization/quantize_graph_test.py index df71840b64..92bb5127da 100644 --- a/tensorflow/tools/quantization/quantize_graph_test.py +++ b/tensorflow/tools/quantization/quantize_graph_test.py @@ -119,8 +119,8 @@ def are_tensors_near(a, b, tolerance): flat_a = a.flatten() flat_b = b.flatten() if len(flat_a) != len(flat_b): - print("Tensors are different sizes: " + str(len(flat_a)) + " vs " + str( - len(flat_b))) + tf_logging.info("Tensors are different sizes: " + str(len(flat_a)) + " vs " + + str(len(flat_b))) return False value_count = len(flat_a) how_many_different = 0 @@ -140,10 +140,10 @@ def are_tensors_near(a, b, tolerance): if how_many_different == 0: return True else: - print("Tensors have {0} different values ({1}%), with mean difference" - " {2} and mean absolute difference {3}".format( - how_many_different, proportion_different * 100, mean_difference, - mean_abs_difference)) + tf_logging.info("Tensors have {0} different values ({1}%), with mean" + " difference {2} and mean absolute difference {3}".format( + how_many_different, proportion_different * 100, + mean_difference, mean_abs_difference)) return False diff --git a/tensorflow/tools/test/upload_test_benchmarks.py b/tensorflow/tools/test/upload_test_benchmarks.py index 9c45359ee1..c030575109 100644 --- a/tensorflow/tools/test/upload_test_benchmarks.py +++ b/tensorflow/tools/test/upload_test_benchmarks.py @@ -89,7 +89,6 @@ import shutil from six import text_type from google.cloud import datastore -from six import text_type def is_real_file(dirpath, fname): diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index dbec66216a..4f3df570a5 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -50,31 +50,31 @@ def tf_workspace(path_prefix="", tf_repo_name=""): mkl_repository( name = "mkl_linux", urls = [ - "https://mirror.bazel.build/github.com/intel/mkl-dnn/releases/download/v0.13/mklml_lnx_2018.0.2.20180127.tgz", - "https://github.com/intel/mkl-dnn/releases/download/v0.13/mklml_lnx_2018.0.2.20180127.tgz", + "https://mirror.bazel.build/github.com/intel/mkl-dnn/releases/download/v0.14/mklml_lnx_2018.0.3.20180406.tgz", + "https://github.com/intel/mkl-dnn/releases/download/v0.14/mklml_lnx_2018.0.3.20180406.tgz" ], - sha256 = "74844bd77294742bf2396ff040369d1aa4cdd9e826fcd38cf8398ae83564d146", - strip_prefix = "mklml_lnx_2018.0.2.20180127", + sha256 = "d2305244fdc9b87db7426ed4496e87a4b3977ad3374d73b8000e8b7a5b7aa725", + strip_prefix = "mklml_lnx_2018.0.3.20180406", build_file = clean_dep("//third_party/mkl:mkl.BUILD") ) mkl_repository( name = "mkl_windows", urls = [ - "https://mirror.bazel.build/github.com/intel/mkl-dnn/releases/download/v0.13/mklml_win_2018.0.2.20180127.zip", - "https://github.com/intel/mkl-dnn/releases/download/v0.13/mklml_win_2018.0.2.20180127.zip" + "https://mirror.bazel.build/github.com/intel/mkl-dnn/releases/download/v0.14/mklml_win_2018.0.3.20180406.zip", + "https://github.com/intel/mkl-dnn/releases/download/v0.14/mklml_win_2018.0.3.20180406.zip" ], - sha256 = "d8fbf0faa0684bffa3548005d05fe5cfe56ff9dbc0e15e7612d7ac01055a6ded", - strip_prefix = "mklml_win_2018.0.2.20180127", + sha256 = "a584a5bf1c8d2ad70b90d12b52652030e9a338217719064fdb84b7ad0d693694", + strip_prefix = "mklml_win_2018.0.3.20180406", build_file = clean_dep("//third_party/mkl:mkl.BUILD") ) mkl_repository( name = "mkl_darwin", urls = [ - "https://mirror.bazel.build/github.com/intel/mkl-dnn/releases/download/v0.13/mklml_mac_2018.0.2.20180127.tgz", - "https://github.com/intel/mkl-dnn/releases/download/v0.13/mklml_mac_2018.0.2.20180127.tgz" + "https://mirror.bazel.build/github.com/intel/mkl-dnn/releases/download/v0.14/mklml_mac_2018.0.3.20180406.tgz", + "https://github.com/intel/mkl-dnn/releases/download/v0.14/mklml_mac_2018.0.3.20180406.tgz" ], - sha256 = "aa740d71e14562bfea56e6829e6dc186e7487cbcf6748a88dec73826b7ec1943", - strip_prefix = "mklml_mac_2018.0.2.20180127", + sha256 = "094e3dfd61c816136dc8d12a45cc611ce26c5f4828176a3644cd0b0efa15a25b", + strip_prefix = "mklml_mac_2018.0.3.20180406", build_file = clean_dep("//third_party/mkl:mkl.BUILD") ) @@ -85,11 +85,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "mkl_dnn", urls = [ - "https://mirror.bazel.build/github.com/intel/mkl-dnn/archive/v0.13.tar.gz", - "https://github.com/intel/mkl-dnn/archive/v0.13.tar.gz", + "https://mirror.bazel.build/github.com/intel/mkl-dnn/archive/v0.14.tar.gz", + "https://github.com/intel/mkl-dnn/archive/v0.14.tar.gz", ], - sha256 = "d2cfd93a70cfe86ebe054477c530c9b5c1218b70f75856eb6d1956c68ee89e8f", - strip_prefix = "mkl-dnn-0.13", + sha256 = "efebc53882856afec86457a2da644693f5d59c68772d41d640d6b60a8efc4eb0", + strip_prefix = "mkl-dnn-0.14", build_file = clean_dep("//third_party/mkl_dnn:mkldnn.BUILD"), ) @@ -187,11 +187,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "highwayhash", urls = [ - "https://mirror.bazel.build/github.com/google/highwayhash/archive/dfcb97ca4fe9277bf9dc1802dd979b071896453b.tar.gz", - "https://github.com/google/highwayhash/archive/dfcb97ca4fe9277bf9dc1802dd979b071896453b.tar.gz", + "http://mirror.bazel.build/github.com/google/highwayhash/archive/fd3d9af80465e4383162e4a7c5e2f406e82dd968.tar.gz", + "https://github.com/google/highwayhash/archive/fd3d9af80465e4383162e4a7c5e2f406e82dd968.tar.gz", ], - sha256 = "0f30a15b1566d93f146c8d149878a06e91d9bb7ec2cfd76906df62a82be4aac9", - strip_prefix = "highwayhash-dfcb97ca4fe9277bf9dc1802dd979b071896453b", + sha256 = "9c3e0e87d581feeb0c18d814d98f170ff23e62967a2bd6855847f0b2fe598a37", + strip_prefix = "highwayhash-fd3d9af80465e4383162e4a7c5e2f406e82dd968", build_file = clean_dep("//third_party:highwayhash.BUILD"), ) diff --git a/third_party/eigen.BUILD b/third_party/eigen.BUILD index 07bb6645eb..e54c1a4501 100644 --- a/third_party/eigen.BUILD +++ b/third_party/eigen.BUILD @@ -64,6 +64,7 @@ cc_library( # This define (mostly) guarantees we don't link any problematic # code. We use it, but we do not rely on it, as evidenced above. "EIGEN_MPL2_ONLY", + "EIGEN_MAX_ALIGN_BYTES=64", ], includes = ["."], visibility = ["//visibility:public"], diff --git a/third_party/highwayhash.BUILD b/third_party/highwayhash.BUILD index 1b8e40765e..08cb84ea2c 100644 --- a/third_party/highwayhash.BUILD +++ b/third_party/highwayhash.BUILD @@ -10,6 +10,7 @@ cc_library( srcs = ["highwayhash/sip_hash.cc"], hdrs = [ "highwayhash/sip_hash.h", + "highwayhash/endianess.h", "highwayhash/state_helpers.h", ], visibility = ["//visibility:public"], diff --git a/third_party/jpeg/jpeg.BUILD b/third_party/jpeg/jpeg.BUILD index 4418ac32fc..663a218733 100644 --- a/third_party/jpeg/jpeg.BUILD +++ b/third_party/jpeg/jpeg.BUILD @@ -291,8 +291,10 @@ cc_library( "jchuff.h", "jconfig.h", "jdct.h", + "jerror.h", "jinclude.h", "jmorecfg.h", + "jpegint.h", "jpeglib.h", "jsimd.h", "jsimddct.h", diff --git a/third_party/png.BUILD b/third_party/png.BUILD index 76ab32d69c..17c5449cc0 100644 --- a/third_party/png.BUILD +++ b/third_party/png.BUILD @@ -28,7 +28,14 @@ cc_library( "pngwrite.c", "pngwtran.c", "pngwutil.c", - ], + ] + select({ + "@org_tensorflow//tensorflow:linux_ppc64le": [ + "powerpc/powerpc_init.c", + "powerpc/filter_vsx_intrinsics.c", + ], + "//conditions:default": [ + ], + }), hdrs = [ "png.h", "pngconf.h", diff --git a/third_party/py/python_configure.bzl b/third_party/py/python_configure.bzl index 954f21f5f8..3c7e5c8469 100644 --- a/third_party/py/python_configure.bzl +++ b/third_party/py/python_configure.bzl @@ -6,6 +6,7 @@ * `PYTHON_LIB_PATH`: Location of python libraries. """ +_BAZEL_SH = "BAZEL_SH" _PYTHON_BIN_PATH = "PYTHON_BIN_PATH" _PYTHON_LIB_PATH = "PYTHON_LIB_PATH" _TF_PYTHON_CONFIG_REPO = "TF_PYTHON_CONFIG_REPO" @@ -152,6 +153,22 @@ def _get_python_bin(repository_ctx): _PYTHON_BIN_PATH, repository_ctx.os.environ.get("PATH", ""))) +def _get_bash_bin(repository_ctx): + """Gets the bash bin path.""" + bash_bin = repository_ctx.os.environ.get(_BAZEL_SH) + if bash_bin != None: + return bash_bin + else: + bash_bin_path = repository_ctx.which("bash") + if bash_bin_path != None: + return str(bash_bin_path) + else: + _fail("Cannot find bash in PATH, please make sure " + + "bash is installed and add its directory in PATH, or --define " + + "%s='/path/to/bash'.\nPATH=%s" % ( + _BAZEL_SH, repository_ctx.os.environ.get("PATH", ""))) + + def _get_python_lib(repository_ctx, python_bin): """Gets the python lib path.""" python_lib = repository_ctx.os.environ.get(_PYTHON_LIB_PATH) @@ -184,14 +201,14 @@ def _get_python_lib(repository_ctx, python_bin): " print(paths[0])\n" + "END") cmd = '%s - %s' % (python_bin, print_lib) - result = repository_ctx.execute(["bash", "-c", cmd]) + result = repository_ctx.execute([_get_bash_bin(repository_ctx), "-c", cmd]) return result.stdout.strip('\n') def _check_python_lib(repository_ctx, python_lib): """Checks the python lib path.""" cmd = 'test -d "%s" -a -x "%s"' % (python_lib, python_lib) - result = repository_ctx.execute(["bash", "-c", cmd]) + result = repository_ctx.execute([_get_bash_bin(repository_ctx), "-c", cmd]) if result.return_code == 1: _fail("Invalid python library path: %s" % python_lib) @@ -199,7 +216,7 @@ def _check_python_lib(repository_ctx, python_lib): def _check_python_bin(repository_ctx, python_bin): """Checks the python bin path.""" cmd = '[[ -x "%s" ]] && [[ ! -d "%s" ]]' % (python_bin, python_bin) - result = repository_ctx.execute(["bash", "-c", cmd]) + result = repository_ctx.execute([_get_bash_bin(repository_ctx), "-c", cmd]) if result.return_code == 1: _fail("--define %s='%s' is not executable. Is it the python binary?" % ( _PYTHON_BIN_PATH, python_bin)) @@ -294,6 +311,7 @@ def _python_autoconf_impl(repository_ctx): python_configure = repository_rule( implementation = _python_autoconf_impl, environ = [ + _BAZEL_SH, _PYTHON_BIN_PATH, _PYTHON_LIB_PATH, _TF_PYTHON_CONFIG_REPO, diff --git a/third_party/repo.bzl b/third_party/repo.bzl index 36f5aa5bde..cb67d3e961 100644 --- a/third_party/repo.bzl +++ b/third_party/repo.bzl @@ -17,7 +17,6 @@ _SINGLE_URL_WHITELIST = depset([ "arm_compiler", "ortools_archive", - "gemmlowp", ]) def _is_windows(ctx): @@ -88,7 +87,9 @@ def _tf_http_archive(ctx): if ctx.attr.patch_file != None: _apply_patch(ctx, ctx.attr.patch_file) if ctx.attr.build_file != None: - ctx.template("BUILD", ctx.attr.build_file, { + # Use BUILD.bazel to avoid conflict with third party projects with + # BUILD or build (directory) underneath. + ctx.template("BUILD.bazel", ctx.attr.build_file, { "%prefix%": ".." if _repos_are_siblings() else "external", }, False) -- GitLab From 4b87c3bea1764667071a78ead2d282f1098881d5 Mon Sep 17 00:00:00 2001 From: Allen Lavoie Date: Mon, 18 Jun 2018 10:15:52 -0700 Subject: [PATCH 594/816] Don't add to the global losses collection from tf.losses.* when executing eagerly Fixes #20062. RELNOTES: tf.losses.* do not add to the global collection when executing eagerly (avoids leaking memory). PiperOrigin-RevId: 201015215 --- tensorflow/python/kernel_tests/losses_test.py | 16 ++++++ tensorflow/python/ops/losses/losses_impl.py | 55 +++++++++++++++++++ tensorflow/python/ops/losses/util.py | 6 +- 3 files changed, 76 insertions(+), 1 deletion(-) diff --git a/tensorflow/python/kernel_tests/losses_test.py b/tensorflow/python/kernel_tests/losses_test.py index 1123c20a16..87fc715783 100644 --- a/tensorflow/python/kernel_tests/losses_test.py +++ b/tensorflow/python/kernel_tests/losses_test.py @@ -26,6 +26,7 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors_impl from tensorflow.python.framework import ops from tensorflow.python.framework import random_seed +from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import math_ops @@ -118,6 +119,14 @@ class AbsoluteDifferenceLossTest(test.TestCase): with self.test_session(): self.assertAlmostEqual(0.0, loss.eval(), 3) + @test_util.assert_no_new_pyobjects_executing_eagerly + def testEagerNoMemoryLeaked(self): + # This is a somewhat convoluted way of testing that nothing gets added to + # a global collection. + predictions = constant_op.constant([4, 8, 12, 8, 1, 3], shape=(2, 3)) + labels = constant_op.constant([1, 9, 2, -5, -2, 6], shape=(2, 3)) + losses.absolute_difference(labels, predictions) + class SoftmaxCrossEntropyLossTest(test.TestCase): @@ -246,6 +255,13 @@ class SparseSoftmaxCrossEntropyLossTest(test.TestCase): self.assertEquals(loss.op.name, 'sparse_softmax_cross_entropy_loss/value') self.assertAlmostEqual(loss.eval(), 0.0, 3) + @test_util.assert_no_new_pyobjects_executing_eagerly + def testEagerNoMemoryLeaked(self): + logits = constant_op.constant([[10.0, 0.0, 0.0], [0.0, 10.0, 0.0], + [0.0, 0.0, 10.0]]) + labels = constant_op.constant([[0], [1], [2]], dtype=dtypes.int32) + losses.sparse_softmax_cross_entropy(labels, logits) + def testAllCorrectInt64Labels(self): with self.test_session(): logits = constant_op.constant([[10.0, 0.0, 0.0], [0.0, 10.0, 0.0], diff --git a/tensorflow/python/ops/losses/losses_impl.py b/tensorflow/python/ops/losses/losses_impl.py index de9b3c6909..9ba91772f5 100644 --- a/tensorflow/python/ops/losses/losses_impl.py +++ b/tensorflow/python/ops/losses/losses_impl.py @@ -192,6 +192,11 @@ def compute_weighted_loss( on some model parameters but you do not want this to affect the loss gradient, you need to apply @{tf.stop_gradient} to `weights` before passing them to `compute_weighted_loss`. + + @compatbility(eager) + The `loss_collection` argument is ignored when executing eagerly. Consider + holding on to the return value or collecting losses via a `tf.keras.Model`. + @end_compatibility """ Reduction.validate(reduction) with ops.name_scope(scope, "weighted_loss", (losses, weights)): @@ -260,6 +265,11 @@ def absolute_difference( ValueError: If the shape of `predictions` doesn't match that of `labels` or if the shape of `weights` is invalid or if `labels` or `predictions` is None. + + @compatbility(eager) + The `loss_collection` argument is ignored when executing eagerly. Consider + holding on to the return value or collecting losses via a `tf.keras.Model`. + @end_compatibility """ if labels is None: raise ValueError("labels must not be None.") @@ -306,6 +316,11 @@ def cosine_distance( Raises: ValueError: If `predictions` shape doesn't match `labels` shape, or `axis`, `labels`, `predictions` or `weights` is `None`. + + @compatbility(eager) + The `loss_collection` argument is ignored when executing eagerly. Consider + holding on to the return value or collecting losses via a `tf.keras.Model`. + @end_compatibility """ axis = deprecated_argument_lookup("axis", axis, "dim", dim) if axis is None: @@ -353,6 +368,11 @@ def hinge_loss(labels, logits, weights=1.0, scope=None, Raises: ValueError: If the shapes of `logits` and `labels` don't match or if `labels` or `logits` is None. + + @compatbility(eager) + The `loss_collection` argument is ignored when executing eagerly. Consider + holding on to the return value or collecting losses via a `tf.keras.Model`. + @end_compatibility """ if labels is None: raise ValueError("labels must not be None.") @@ -416,6 +436,11 @@ def huber_loss(labels, predictions, weights=1.0, delta=1.0, scope=None, ValueError: If the shape of `predictions` doesn't match that of `labels` or if the shape of `weights` is invalid. Also if `labels` or `predictions` is None. + + @compatbility(eager) + The `loss_collection` argument is ignored when executing eagerly. Consider + holding on to the return value or collecting losses via a `tf.keras.Model`. + @end_compatibility """ if labels is None: raise ValueError("labels must not be None.") @@ -477,6 +502,11 @@ def log_loss(labels, predictions, weights=1.0, epsilon=1e-7, scope=None, ValueError: If the shape of `predictions` doesn't match that of `labels` or if the shape of `weights` is invalid. Also if `labels` or `predictions` is None. + + @compatbility(eager) + The `loss_collection` argument is ignored when executing eagerly. Consider + holding on to the return value or collecting losses via a `tf.keras.Model`. + @end_compatibility """ if labels is None: raise ValueError("labels must not be None.") @@ -540,6 +570,11 @@ def mean_pairwise_squared_error( ValueError: If the shape of `predictions` doesn't match that of `labels` or if the shape of `weights` is invalid. Also if `labels` or `predictions` is None. + + @compatbility(eager) + The `loss_collection` argument is ignored when executing eagerly. Consider + holding on to the return value or collecting losses via a `tf.keras.Model`. + @end_compatibility """ if labels is None: raise ValueError("labels must not be None.") @@ -618,6 +653,11 @@ def mean_squared_error( ValueError: If the shape of `predictions` doesn't match that of `labels` or if the shape of `weights` is invalid. Also if `labels` or `predictions` is None. + + @compatbility(eager) + The `loss_collection` argument is ignored when executing eagerly. Consider + holding on to the return value or collecting losses via a `tf.keras.Model`. + @end_compatibility """ if labels is None: raise ValueError("labels must not be None.") @@ -670,6 +710,11 @@ def sigmoid_cross_entropy( ValueError: If the shape of `logits` doesn't match that of `multi_class_labels` or if the shape of `weights` is invalid, or if `weights` is None. Also if `multi_class_labels` or `logits` is None. + + @compatbility(eager) + The `loss_collection` argument is ignored when executing eagerly. Consider + holding on to the return value or collecting losses via a `tf.keras.Model`. + @end_compatibility """ if multi_class_labels is None: raise ValueError("multi_class_labels must not be None.") @@ -731,6 +776,11 @@ def softmax_cross_entropy( ValueError: If the shape of `logits` doesn't match that of `onehot_labels` or if the shape of `weights` is invalid or if `weights` is None. Also if `onehot_labels` or `logits` is None. + + @compatbility(eager) + The `loss_collection` argument is ignored when executing eagerly. Consider + holding on to the return value or collecting losses via a `tf.keras.Model`. + @end_compatibility """ if onehot_labels is None: raise ValueError("onehot_labels must not be None.") @@ -842,6 +892,11 @@ def sparse_softmax_cross_entropy( Raises: ValueError: If the shapes of `logits`, `labels`, and `weights` are incompatible, or if any of them are None. + + @compatbility(eager) + The `loss_collection` argument is ignored when executing eagerly. Consider + holding on to the return value or collecting losses via a `tf.keras.Model`. + @end_compatibility """ if labels is None: raise ValueError("labels must not be None.") diff --git a/tensorflow/python/ops/losses/util.py b/tensorflow/python/ops/losses/util.py index 10646af8a9..97bba46661 100644 --- a/tensorflow/python/ops/losses/util.py +++ b/tensorflow/python/ops/losses/util.py @@ -18,6 +18,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from tensorflow.python.eager import context from tensorflow.python.framework import constant_op from tensorflow.python.framework import ops from tensorflow.python.ops import math_ops @@ -32,7 +33,10 @@ def add_loss(loss, loss_collection=ops.GraphKeys.LOSSES): loss: A loss `Tensor`. loss_collection: Optional collection to add the loss to. """ - if loss_collection: + # Since we have no way of figuring out when a training iteration starts or + # ends, holding on to a loss when executing eagerly is indistingishable from + # leaking memory. We instead leave the collection empty. + if loss_collection and not context.executing_eagerly(): ops.add_to_collection(loss_collection, loss) -- GitLab From a1ed9bb7d1d8d071e98c3696b61be211c67c8231 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 18 Jun 2018 10:20:01 -0700 Subject: [PATCH 595/816] Go: Update generated wrapper functions for TensorFlow ops. PiperOrigin-RevId: 201015958 --- tensorflow/go/op/wrappers.go | 1570 +++++++++++++++++----------------- 1 file changed, 785 insertions(+), 785 deletions(-) diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go index a5224fbda0..a443879df2 100644 --- a/tensorflow/go/op/wrappers.go +++ b/tensorflow/go/op/wrappers.go @@ -2990,6 +2990,31 @@ func Split(scope *Scope, axis tf.Output, value tf.Output, num_split int64) (outp return output } +// Concatenates tensors along one dimension. +// +// Arguments: +// concat_dim: 0-D. The dimension along which to concatenate. Must be in the +// range [0, rank(values)). +// values: The `N` Tensors to concatenate. Their ranks and types must match, +// and their sizes must match in all dimensions except `concat_dim`. +// +// Returns A `Tensor` with the concatenation of values stacked along the +// `concat_dim` dimension. This tensor's shape matches that of `values` except +// in `concat_dim` where it has the sum of the sizes. +func Concat(scope *Scope, concat_dim tf.Output, values []tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Concat", + Input: []tf.Input{ + concat_dim, tf.OutputList(values), + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Creates a sequence of numbers. // // This operation creates a sequence of numbers that begins at `start` and @@ -8367,157 +8392,124 @@ func BoostedTreesUpdateEnsemble(scope *Scope, tree_ensemble_handle tf.Output, fe return scope.AddOperation(opspec) } -// ResourceSparseApplyFtrlAttr is an optional argument to ResourceSparseApplyFtrl. -type ResourceSparseApplyFtrlAttr func(optionalAttr) +// EncodeJpegAttr is an optional argument to EncodeJpeg. +type EncodeJpegAttr func(optionalAttr) -// ResourceSparseApplyFtrlUseLocking sets the optional use_locking attribute to value. +// EncodeJpegFormat sets the optional format attribute to value. // -// value: If `True`, updating of the var and accum tensors will be protected -// by a lock; otherwise the behavior is undefined, but may exhibit less -// contention. -// If not specified, defaults to false -func ResourceSparseApplyFtrlUseLocking(value bool) ResourceSparseApplyFtrlAttr { +// value: Per pixel image format. +// If not specified, defaults to "" +func EncodeJpegFormat(value string) EncodeJpegAttr { return func(m optionalAttr) { - m["use_locking"] = value + m["format"] = value } } -// Update relevant entries in '*var' according to the Ftrl-proximal scheme. -// -// That is for rows we have grad for, we update var, accum and linear as follows: -// accum_new = accum + grad * grad -// linear += grad + (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var -// quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 -// var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 -// accum = accum_new -// -// Arguments: -// var_: Should be from a Variable(). -// accum: Should be from a Variable(). -// linear: Should be from a Variable(). -// grad: The gradient. -// indices: A vector of indices into the first dimension of var and accum. -// lr: Scaling factor. Must be a scalar. -// l1: L1 regularization. Must be a scalar. -// l2: L2 regularization. Must be a scalar. -// lr_power: Scaling factor. Must be a scalar. +// EncodeJpegQuality sets the optional quality attribute to value. // -// Returns the created operation. -func ResourceSparseApplyFtrl(scope *Scope, var_ tf.Output, accum tf.Output, linear tf.Output, grad tf.Output, indices tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, lr_power tf.Output, optional ...ResourceSparseApplyFtrlAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ResourceSparseApplyFtrl", - Input: []tf.Input{ - var_, accum, linear, grad, indices, lr, l1, l2, lr_power, - }, - Attrs: attrs, +// value: Quality of the compression from 0 to 100 (higher is better and slower). +// If not specified, defaults to 95 +func EncodeJpegQuality(value int64) EncodeJpegAttr { + return func(m optionalAttr) { + m["quality"] = value } - return scope.AddOperation(opspec) } -// Returns which elements of x are Inf. +// EncodeJpegProgressive sets the optional progressive attribute to value. // -// @compatibility(numpy) -// Equivalent to np.isinf -// @end_compatibility -func IsInf(scope *Scope, x tf.Output) (y tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "IsInf", - Input: []tf.Input{ - x, - }, +// value: If True, create a JPEG that loads progressively (coarse to fine). +// If not specified, defaults to false +func EncodeJpegProgressive(value bool) EncodeJpegAttr { + return func(m optionalAttr) { + m["progressive"] = value } - op := scope.AddOperation(opspec) - return op.Output(0) } -// Computes the sum along sparse segments of a tensor divided by the sqrt of N. -// -// N is the size of the segment being reduced. -// -// Read @{$math_ops#Segmentation$the section on segmentation} for an explanation of -// segments. -// -// Arguments: -// -// indices: A 1-D tensor. Has same rank as `segment_ids`. -// segment_ids: A 1-D tensor. Values should be sorted and can be repeated. +// EncodeJpegOptimizeSize sets the optional optimize_size attribute to value. // -// Returns Has same shape as data, except for dimension 0 which -// has size `k`, the number of segments. -func SparseSegmentSqrtN(scope *Scope, data tf.Output, indices tf.Output, segment_ids tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SparseSegmentSqrtN", - Input: []tf.Input{ - data, indices, segment_ids, - }, +// value: If True, spend CPU/RAM to reduce size with no quality change. +// If not specified, defaults to false +func EncodeJpegOptimizeSize(value bool) EncodeJpegAttr { + return func(m optionalAttr) { + m["optimize_size"] = value } - op := scope.AddOperation(opspec) - return op.Output(0) } -// Adds up a `SparseTensor` and a dense `Tensor`, producing a dense `Tensor`. +// EncodeJpegChromaDownsampling sets the optional chroma_downsampling attribute to value. // -// This Op does not require `a_indices` be sorted in standard lexicographic order. +// value: See http://en.wikipedia.org/wiki/Chroma_subsampling. +// If not specified, defaults to true +func EncodeJpegChromaDownsampling(value bool) EncodeJpegAttr { + return func(m optionalAttr) { + m["chroma_downsampling"] = value + } +} + +// EncodeJpegDensityUnit sets the optional density_unit attribute to value. // -// Arguments: -// a_indices: 2-D. The `indices` of the `SparseTensor`, with shape `[nnz, ndims]`. -// a_values: 1-D. The `values` of the `SparseTensor`, with shape `[nnz]`. -// a_shape: 1-D. The `shape` of the `SparseTensor`, with shape `[ndims]`. -// b: `ndims`-D Tensor. With shape `a_shape`. -func SparseTensorDenseAdd(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b tf.Output) (output tf.Output) { - if scope.Err() != nil { - return +// value: Unit used to specify `x_density` and `y_density`: +// pixels per inch (`'in'`) or centimeter (`'cm'`). +// If not specified, defaults to "in" +func EncodeJpegDensityUnit(value string) EncodeJpegAttr { + return func(m optionalAttr) { + m["density_unit"] = value } - opspec := tf.OpSpec{ - Type: "SparseTensorDenseAdd", - Input: []tf.Input{ - a_indices, a_values, a_shape, b, - }, +} + +// EncodeJpegXDensity sets the optional x_density attribute to value. +// +// value: Horizontal pixels per density unit. +// If not specified, defaults to 300 +func EncodeJpegXDensity(value int64) EncodeJpegAttr { + return func(m optionalAttr) { + m["x_density"] = value } - op := scope.AddOperation(opspec) - return op.Output(0) } -// StatelessTruncatedNormalAttr is an optional argument to StatelessTruncatedNormal. -type StatelessTruncatedNormalAttr func(optionalAttr) +// EncodeJpegYDensity sets the optional y_density attribute to value. +// +// value: Vertical pixels per density unit. +// If not specified, defaults to 300 +func EncodeJpegYDensity(value int64) EncodeJpegAttr { + return func(m optionalAttr) { + m["y_density"] = value + } +} -// StatelessTruncatedNormalDtype sets the optional dtype attribute to value. +// EncodeJpegXmpMetadata sets the optional xmp_metadata attribute to value. // -// value: The type of the output. -// If not specified, defaults to DT_FLOAT -func StatelessTruncatedNormalDtype(value tf.DataType) StatelessTruncatedNormalAttr { +// value: If not empty, embed this XMP metadata in the image header. +// If not specified, defaults to "" +func EncodeJpegXmpMetadata(value string) EncodeJpegAttr { return func(m optionalAttr) { - m["dtype"] = value + m["xmp_metadata"] = value } } -// Outputs deterministic pseudorandom values from a truncated normal distribution. +// JPEG-encode an image. // -// The generated values follow a normal distribution with mean 0 and standard -// deviation 1, except that values whose magnitude is more than 2 standard -// deviations from the mean are dropped and re-picked. +// `image` is a 3-D uint8 Tensor of shape `[height, width, channels]`. // -// The outputs are a deterministic function of `shape` and `seed`. +// The attr `format` can be used to override the color format of the encoded +// output. Values can be: +// +// * `''`: Use a default format based on the number of channels in the image. +// * `grayscale`: Output a grayscale JPEG image. The `channels` dimension +// of `image` must be 1. +// * `rgb`: Output an RGB JPEG image. The `channels` dimension +// of `image` must be 3. +// +// If `format` is not specified or is the empty string, a default format is picked +// in function of the number of channels in `image`: +// +// * 1: Output a grayscale image. +// * 3: Output an RGB image. // // Arguments: -// shape: The shape of the output tensor. -// seed: 2 seeds (shape [2]). +// image: 3-D with shape `[height, width, channels]`. // -// Returns Random values with specified shape. -func StatelessTruncatedNormal(scope *Scope, shape tf.Output, seed tf.Output, optional ...StatelessTruncatedNormalAttr) (output tf.Output) { +// Returns 0-D. JPEG-encoded image. +func EncodeJpeg(scope *Scope, image tf.Output, optional ...EncodeJpegAttr) (contents tf.Output) { if scope.Err() != nil { return } @@ -8526,9 +8518,9 @@ func StatelessTruncatedNormal(scope *Scope, shape tf.Output, seed tf.Output, opt a(attrs) } opspec := tf.OpSpec{ - Type: "StatelessTruncatedNormal", + Type: "EncodeJpeg", Input: []tf.Input{ - shape, seed, + image, }, Attrs: attrs, } @@ -8536,32 +8528,307 @@ func StatelessTruncatedNormal(scope *Scope, shape tf.Output, seed tf.Output, opt return op.Output(0) } -// RestoreSliceAttr is an optional argument to RestoreSlice. -type RestoreSliceAttr func(optionalAttr) +// MultinomialAttr is an optional argument to Multinomial. +type MultinomialAttr func(optionalAttr) -// RestoreSlicePreferredShard sets the optional preferred_shard attribute to value. +// MultinomialSeed sets the optional seed attribute to value. // -// value: Index of file to open first if multiple files match -// `file_pattern`. See the documentation for `Restore`. -// If not specified, defaults to -1 -func RestoreSlicePreferredShard(value int64) RestoreSliceAttr { +// value: If either seed or seed2 is set to be non-zero, the internal random number +// generator is seeded by the given seed. Otherwise, a random seed is used. +// If not specified, defaults to 0 +func MultinomialSeed(value int64) MultinomialAttr { return func(m optionalAttr) { - m["preferred_shard"] = value + m["seed"] = value } } -// Restores a tensor from checkpoint files. -// -// This is like `Restore` except that restored tensor can be listed as filling -// only a slice of a larger tensor. `shape_and_slice` specifies the shape of the -// larger tensor and the slice that the restored tensor covers. -// -// The `shape_and_slice` input has the same format as the -// elements of the `shapes_and_slices` input of the `SaveSlices` op. +// MultinomialSeed2 sets the optional seed2 attribute to value. // -// Arguments: -// file_pattern: Must have a single element. The pattern of the files from -// which we read the tensor. +// value: A second seed to avoid seed collision. +// If not specified, defaults to 0 +func MultinomialSeed2(value int64) MultinomialAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// MultinomialOutputDtype sets the optional output_dtype attribute to value. +// If not specified, defaults to DT_INT64 +func MultinomialOutputDtype(value tf.DataType) MultinomialAttr { + return func(m optionalAttr) { + m["output_dtype"] = value + } +} + +// Draws samples from a multinomial distribution. +// +// Arguments: +// logits: 2-D Tensor with shape `[batch_size, num_classes]`. Each slice `[i, :]` +// represents the unnormalized log probabilities for all classes. +// num_samples: 0-D. Number of independent samples to draw for each row slice. +// +// Returns 2-D Tensor with shape `[batch_size, num_samples]`. Each slice `[i, :]` +// contains the drawn class labels with range `[0, num_classes)`. +func Multinomial(scope *Scope, logits tf.Output, num_samples tf.Output, optional ...MultinomialAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Multinomial", + Input: []tf.Input{ + logits, num_samples, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResourceSparseApplyAdagradDAAttr is an optional argument to ResourceSparseApplyAdagradDA. +type ResourceSparseApplyAdagradDAAttr func(optionalAttr) + +// ResourceSparseApplyAdagradDAUseLocking sets the optional use_locking attribute to value. +// +// value: If True, updating of the var and accum tensors will be protected by +// a lock; otherwise the behavior is undefined, but may exhibit less contention. +// If not specified, defaults to false +func ResourceSparseApplyAdagradDAUseLocking(value bool) ResourceSparseApplyAdagradDAAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Update entries in '*var' and '*accum' according to the proximal adagrad scheme. +// +// Arguments: +// var_: Should be from a Variable(). +// gradient_accumulator: Should be from a Variable(). +// gradient_squared_accumulator: Should be from a Variable(). +// grad: The gradient. +// indices: A vector of indices into the first dimension of var and accum. +// lr: Learning rate. Must be a scalar. +// l1: L1 regularization. Must be a scalar. +// l2: L2 regularization. Must be a scalar. +// global_step: Training step number. Must be a scalar. +// +// Returns the created operation. +func ResourceSparseApplyAdagradDA(scope *Scope, var_ tf.Output, gradient_accumulator tf.Output, gradient_squared_accumulator tf.Output, grad tf.Output, indices tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, global_step tf.Output, optional ...ResourceSparseApplyAdagradDAAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceSparseApplyAdagradDA", + Input: []tf.Input{ + var_, gradient_accumulator, gradient_squared_accumulator, grad, indices, lr, l1, l2, global_step, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// ResourceSparseApplyFtrlAttr is an optional argument to ResourceSparseApplyFtrl. +type ResourceSparseApplyFtrlAttr func(optionalAttr) + +// ResourceSparseApplyFtrlUseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, updating of the var and accum tensors will be protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceSparseApplyFtrlUseLocking(value bool) ResourceSparseApplyFtrlAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Update relevant entries in '*var' according to the Ftrl-proximal scheme. +// +// That is for rows we have grad for, we update var, accum and linear as follows: +// accum_new = accum + grad * grad +// linear += grad + (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var +// quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 +// var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 +// accum = accum_new +// +// Arguments: +// var_: Should be from a Variable(). +// accum: Should be from a Variable(). +// linear: Should be from a Variable(). +// grad: The gradient. +// indices: A vector of indices into the first dimension of var and accum. +// lr: Scaling factor. Must be a scalar. +// l1: L1 regularization. Must be a scalar. +// l2: L2 regularization. Must be a scalar. +// lr_power: Scaling factor. Must be a scalar. +// +// Returns the created operation. +func ResourceSparseApplyFtrl(scope *Scope, var_ tf.Output, accum tf.Output, linear tf.Output, grad tf.Output, indices tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, lr_power tf.Output, optional ...ResourceSparseApplyFtrlAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceSparseApplyFtrl", + Input: []tf.Input{ + var_, accum, linear, grad, indices, lr, l1, l2, lr_power, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Returns which elements of x are Inf. +// +// @compatibility(numpy) +// Equivalent to np.isinf +// @end_compatibility +func IsInf(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "IsInf", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the sum along sparse segments of a tensor divided by the sqrt of N. +// +// N is the size of the segment being reduced. +// +// Read @{$math_ops#Segmentation$the section on segmentation} for an explanation of +// segments. +// +// Arguments: +// +// indices: A 1-D tensor. Has same rank as `segment_ids`. +// segment_ids: A 1-D tensor. Values should be sorted and can be repeated. +// +// Returns Has same shape as data, except for dimension 0 which +// has size `k`, the number of segments. +func SparseSegmentSqrtN(scope *Scope, data tf.Output, indices tf.Output, segment_ids tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseSegmentSqrtN", + Input: []tf.Input{ + data, indices, segment_ids, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Adds up a `SparseTensor` and a dense `Tensor`, producing a dense `Tensor`. +// +// This Op does not require `a_indices` be sorted in standard lexicographic order. +// +// Arguments: +// a_indices: 2-D. The `indices` of the `SparseTensor`, with shape `[nnz, ndims]`. +// a_values: 1-D. The `values` of the `SparseTensor`, with shape `[nnz]`. +// a_shape: 1-D. The `shape` of the `SparseTensor`, with shape `[ndims]`. +// b: `ndims`-D Tensor. With shape `a_shape`. +func SparseTensorDenseAdd(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseTensorDenseAdd", + Input: []tf.Input{ + a_indices, a_values, a_shape, b, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// StatelessTruncatedNormalAttr is an optional argument to StatelessTruncatedNormal. +type StatelessTruncatedNormalAttr func(optionalAttr) + +// StatelessTruncatedNormalDtype sets the optional dtype attribute to value. +// +// value: The type of the output. +// If not specified, defaults to DT_FLOAT +func StatelessTruncatedNormalDtype(value tf.DataType) StatelessTruncatedNormalAttr { + return func(m optionalAttr) { + m["dtype"] = value + } +} + +// Outputs deterministic pseudorandom values from a truncated normal distribution. +// +// The generated values follow a normal distribution with mean 0 and standard +// deviation 1, except that values whose magnitude is more than 2 standard +// deviations from the mean are dropped and re-picked. +// +// The outputs are a deterministic function of `shape` and `seed`. +// +// Arguments: +// shape: The shape of the output tensor. +// seed: 2 seeds (shape [2]). +// +// Returns Random values with specified shape. +func StatelessTruncatedNormal(scope *Scope, shape tf.Output, seed tf.Output, optional ...StatelessTruncatedNormalAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StatelessTruncatedNormal", + Input: []tf.Input{ + shape, seed, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RestoreSliceAttr is an optional argument to RestoreSlice. +type RestoreSliceAttr func(optionalAttr) + +// RestoreSlicePreferredShard sets the optional preferred_shard attribute to value. +// +// value: Index of file to open first if multiple files match +// `file_pattern`. See the documentation for `Restore`. +// If not specified, defaults to -1 +func RestoreSlicePreferredShard(value int64) RestoreSliceAttr { + return func(m optionalAttr) { + m["preferred_shard"] = value + } +} + +// Restores a tensor from checkpoint files. +// +// This is like `Restore` except that restored tensor can be listed as filling +// only a slice of a larger tensor. `shape_and_slice` specifies the shape of the +// larger tensor and the slice that the restored tensor covers. +// +// The `shape_and_slice` input has the same format as the +// elements of the `shapes_and_slices` input of the `SaveSlices` op. +// +// Arguments: +// file_pattern: Must have a single element. The pattern of the files from +// which we read the tensor. // tensor_name: Must have a single element. The name of the tensor to be // restored. // shape_and_slice: Scalar. The shapes and slice specifications to use when @@ -8689,6 +8956,186 @@ func StatelessRandomNormal(scope *Scope, shape tf.Output, seed tf.Output, option return op.Output(0) } +// MaxPoolAttr is an optional argument to MaxPool. +type MaxPoolAttr func(optionalAttr) + +// MaxPoolDataFormat sets the optional data_format attribute to value. +// +// value: Specify the data format of the input and output data. With the +// default format "NHWC", the data is stored in the order of: +// [batch, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCHW", the data storage order of: +// [batch, in_channels, in_height, in_width]. +// If not specified, defaults to "NHWC" +func MaxPoolDataFormat(value string) MaxPoolAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// Performs max pooling on the input. +// +// Arguments: +// input: 4-D input to pool over. +// ksize: The size of the window for each dimension of the input tensor. +// strides: The stride of the sliding window for each dimension of the +// input tensor. +// padding: The type of padding algorithm to use. +// +// Returns The max pooled output tensor. +func MaxPool(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MaxPool", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// SparseMatMulAttr is an optional argument to SparseMatMul. +type SparseMatMulAttr func(optionalAttr) + +// SparseMatMulTransposeA sets the optional transpose_a attribute to value. +// If not specified, defaults to false +func SparseMatMulTransposeA(value bool) SparseMatMulAttr { + return func(m optionalAttr) { + m["transpose_a"] = value + } +} + +// SparseMatMulTransposeB sets the optional transpose_b attribute to value. +// If not specified, defaults to false +func SparseMatMulTransposeB(value bool) SparseMatMulAttr { + return func(m optionalAttr) { + m["transpose_b"] = value + } +} + +// SparseMatMulAIsSparse sets the optional a_is_sparse attribute to value. +// If not specified, defaults to false +func SparseMatMulAIsSparse(value bool) SparseMatMulAttr { + return func(m optionalAttr) { + m["a_is_sparse"] = value + } +} + +// SparseMatMulBIsSparse sets the optional b_is_sparse attribute to value. +// If not specified, defaults to false +func SparseMatMulBIsSparse(value bool) SparseMatMulAttr { + return func(m optionalAttr) { + m["b_is_sparse"] = value + } +} + +// Multiply matrix "a" by matrix "b". +// +// The inputs must be two-dimensional matrices and the inner dimension of "a" must +// match the outer dimension of "b". This op is optimized for the case where at +// least one of "a" or "b" is sparse. The breakeven for using this versus a dense +// matrix multiply on one platform was 30% zero values in the sparse matrix. +// +// The gradient computation of this operation will only take advantage of sparsity +// in the input gradient when that gradient comes from a Relu. +func SparseMatMul(scope *Scope, a tf.Output, b tf.Output, optional ...SparseMatMulAttr) (product tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SparseMatMul", + Input: []tf.Input{ + a, b, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Concatenates quantized tensors along one dimension. +// +// Arguments: +// concat_dim: 0-D. The dimension along which to concatenate. Must be in the +// range [0, rank(values)). +// values: The `N` Tensors to concatenate. Their ranks and types must match, +// and their sizes must match in all dimensions except `concat_dim`. +// input_mins: The minimum scalar values for each of the input tensors. +// input_maxes: The maximum scalar values for each of the input tensors. +// +// Returns A `Tensor` with the concatenation of values stacked along the +// `concat_dim` dimension. This tensor's shape matches that of `values` except +// in `concat_dim` where it has the sum of the sizes.The float value that the minimum quantized output value represents.The float value that the maximum quantized output value represents. +func QuantizedConcat(scope *Scope, concat_dim tf.Output, values []tf.Output, input_mins []tf.Output, input_maxes []tf.Output) (output tf.Output, output_min tf.Output, output_max tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "QuantizedConcat", + Input: []tf.Input{ + concat_dim, tf.OutputList(values), tf.OutputList(input_mins), tf.OutputList(input_maxes), + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Slice a `SparseTensor` based on the `start` and `size`. +// +// For example, if the input is +// +// input_tensor = shape = [2, 7] +// [ a d e ] +// [b c ] +// +// Graphically the output tensors are: +// +// sparse_slice([0, 0], [2, 4]) = shape = [2, 4] +// [ a ] +// [b c ] +// +// sparse_slice([0, 4], [2, 3]) = shape = [2, 3] +// [ d e ] +// [ ] +// +// Arguments: +// indices: 2-D tensor represents the indices of the sparse tensor. +// values: 1-D tensor represents the values of the sparse tensor. +// shape: 1-D. tensor represents the shape of the sparse tensor. +// start: 1-D. tensor represents the start of the slice. +// size: 1-D. tensor represents the size of the slice. +// output indices: A list of 1-D tensors represents the indices of the output +// sparse tensors. +// +// Returns A list of 1-D tensors represents the values of the output sparse +// tensors.A list of 1-D tensors represents the shape of the output sparse +// tensors. +func SparseSlice(scope *Scope, indices tf.Output, values tf.Output, shape tf.Output, start tf.Output, size tf.Output) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseSlice", + Input: []tf.Input{ + indices, values, shape, start, size, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + // Reduces sparse updates into the variable referenced by `resource` using the `min` operation. // // This operation computes @@ -10745,86 +11192,13 @@ func IFFT(scope *Scope, input tf.Output) (output tf.Output) { return } opspec := tf.OpSpec{ - Type: "IFFT", - Input: []tf.Input{ - input, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Generates values in an interval. -// -// A sequence of `num` evenly-spaced values are generated beginning at `start`. -// If `num > 1`, the values in the sequence increase by `stop - start / num - 1`, -// so that the last one is exactly `stop`. -// -// For example: -// -// ``` -// tf.linspace(10.0, 12.0, 3, name="linspace") => [ 10.0 11.0 12.0] -// ``` -// -// Arguments: -// start: First entry in the range. -// stop: Last entry in the range. -// num: Number of values to generate. -// -// Returns 1-D. The generated values. -func LinSpace(scope *Scope, start tf.Output, stop tf.Output, num tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "LinSpace", - Input: []tf.Input{ - start, stop, num, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// DestroyResourceOpAttr is an optional argument to DestroyResourceOp. -type DestroyResourceOpAttr func(optionalAttr) - -// DestroyResourceOpIgnoreLookupError sets the optional ignore_lookup_error attribute to value. -// -// value: whether to ignore the error when the resource -// doesn't exist. -// If not specified, defaults to true -func DestroyResourceOpIgnoreLookupError(value bool) DestroyResourceOpAttr { - return func(m optionalAttr) { - m["ignore_lookup_error"] = value - } -} - -// Deletes the resource specified by the handle. -// -// All subsequent operations using the resource will result in a NotFound -// error status. -// -// Arguments: -// resource: handle to the resource to delete. -// -// Returns the created operation. -func DestroyResourceOp(scope *Scope, resource tf.Output, optional ...DestroyResourceOpAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "DestroyResourceOp", + Type: "IFFT", Input: []tf.Input{ - resource, + input, }, - Attrs: attrs, } - return scope.AddOperation(opspec) + op := scope.AddOperation(opspec) + return op.Output(0) } // ResourceSparseApplyRMSPropAttr is an optional argument to ResourceSparseApplyRMSProp. @@ -10955,7 +11329,7 @@ func SampleDistortedBoundingBoxAspectRatioRange(value []float32) SampleDistorted // SampleDistortedBoundingBoxAreaRange sets the optional area_range attribute to value. // // value: The cropped area of the image must contain a fraction of the -// supplied image within this range. +// supplied image within in this range. // If not specified, defaults to func SampleDistortedBoundingBoxAreaRange(value []float32) SampleDistortedBoundingBoxAttr { return func(m optionalAttr) { @@ -12364,278 +12738,36 @@ func MaxPoolWithArgmaxTargmax(value tf.DataType) MaxPoolWithArgmaxAttr { // `[b, y, x, c]` becomes flattened index // `((b * height + y) * width + x) * channels + c`. // -// The indices returned are always in `[0, height) x [0, width)` before flattening, -// even if padding is involved and the mathematically correct answer is outside -// (either negative or too large). This is a bug, but fixing it is difficult to do -// in a safe backwards compatible way, especially due to flattening. -// -// Arguments: -// input: 4-D with shape `[batch, height, width, channels]`. Input to pool over. -// ksize: The size of the window for each dimension of the input tensor. -// strides: The stride of the sliding window for each dimension of the -// input tensor. -// padding: The type of padding algorithm to use. -// -// Returns The max pooled output tensor.4-D. The flattened indices of the max values chosen for each output. -func MaxPoolWithArgmax(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolWithArgmaxAttr) (output tf.Output, argmax tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "MaxPoolWithArgmax", - Input: []tf.Input{ - input, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) -} - -// ResourceSparseApplyAdagradDAAttr is an optional argument to ResourceSparseApplyAdagradDA. -type ResourceSparseApplyAdagradDAAttr func(optionalAttr) - -// ResourceSparseApplyAdagradDAUseLocking sets the optional use_locking attribute to value. -// -// value: If True, updating of the var and accum tensors will be protected by -// a lock; otherwise the behavior is undefined, but may exhibit less contention. -// If not specified, defaults to false -func ResourceSparseApplyAdagradDAUseLocking(value bool) ResourceSparseApplyAdagradDAAttr { - return func(m optionalAttr) { - m["use_locking"] = value - } -} - -// Update entries in '*var' and '*accum' according to the proximal adagrad scheme. -// -// Arguments: -// var_: Should be from a Variable(). -// gradient_accumulator: Should be from a Variable(). -// gradient_squared_accumulator: Should be from a Variable(). -// grad: The gradient. -// indices: A vector of indices into the first dimension of var and accum. -// lr: Learning rate. Must be a scalar. -// l1: L1 regularization. Must be a scalar. -// l2: L2 regularization. Must be a scalar. -// global_step: Training step number. Must be a scalar. -// -// Returns the created operation. -func ResourceSparseApplyAdagradDA(scope *Scope, var_ tf.Output, gradient_accumulator tf.Output, gradient_squared_accumulator tf.Output, grad tf.Output, indices tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, global_step tf.Output, optional ...ResourceSparseApplyAdagradDAAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ResourceSparseApplyAdagradDA", - Input: []tf.Input{ - var_, gradient_accumulator, gradient_squared_accumulator, grad, indices, lr, l1, l2, global_step, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// EncodeJpegAttr is an optional argument to EncodeJpeg. -type EncodeJpegAttr func(optionalAttr) - -// EncodeJpegFormat sets the optional format attribute to value. -// -// value: Per pixel image format. -// If not specified, defaults to "" -func EncodeJpegFormat(value string) EncodeJpegAttr { - return func(m optionalAttr) { - m["format"] = value - } -} - -// EncodeJpegQuality sets the optional quality attribute to value. -// -// value: Quality of the compression from 0 to 100 (higher is better and slower). -// If not specified, defaults to 95 -func EncodeJpegQuality(value int64) EncodeJpegAttr { - return func(m optionalAttr) { - m["quality"] = value - } -} - -// EncodeJpegProgressive sets the optional progressive attribute to value. -// -// value: If True, create a JPEG that loads progressively (coarse to fine). -// If not specified, defaults to false -func EncodeJpegProgressive(value bool) EncodeJpegAttr { - return func(m optionalAttr) { - m["progressive"] = value - } -} - -// EncodeJpegOptimizeSize sets the optional optimize_size attribute to value. -// -// value: If True, spend CPU/RAM to reduce size with no quality change. -// If not specified, defaults to false -func EncodeJpegOptimizeSize(value bool) EncodeJpegAttr { - return func(m optionalAttr) { - m["optimize_size"] = value - } -} - -// EncodeJpegChromaDownsampling sets the optional chroma_downsampling attribute to value. -// -// value: See http://en.wikipedia.org/wiki/Chroma_subsampling. -// If not specified, defaults to true -func EncodeJpegChromaDownsampling(value bool) EncodeJpegAttr { - return func(m optionalAttr) { - m["chroma_downsampling"] = value - } -} - -// EncodeJpegDensityUnit sets the optional density_unit attribute to value. -// -// value: Unit used to specify `x_density` and `y_density`: -// pixels per inch (`'in'`) or centimeter (`'cm'`). -// If not specified, defaults to "in" -func EncodeJpegDensityUnit(value string) EncodeJpegAttr { - return func(m optionalAttr) { - m["density_unit"] = value - } -} - -// EncodeJpegXDensity sets the optional x_density attribute to value. -// -// value: Horizontal pixels per density unit. -// If not specified, defaults to 300 -func EncodeJpegXDensity(value int64) EncodeJpegAttr { - return func(m optionalAttr) { - m["x_density"] = value - } -} - -// EncodeJpegYDensity sets the optional y_density attribute to value. -// -// value: Vertical pixels per density unit. -// If not specified, defaults to 300 -func EncodeJpegYDensity(value int64) EncodeJpegAttr { - return func(m optionalAttr) { - m["y_density"] = value - } -} - -// EncodeJpegXmpMetadata sets the optional xmp_metadata attribute to value. -// -// value: If not empty, embed this XMP metadata in the image header. -// If not specified, defaults to "" -func EncodeJpegXmpMetadata(value string) EncodeJpegAttr { - return func(m optionalAttr) { - m["xmp_metadata"] = value - } -} - -// JPEG-encode an image. -// -// `image` is a 3-D uint8 Tensor of shape `[height, width, channels]`. -// -// The attr `format` can be used to override the color format of the encoded -// output. Values can be: -// -// * `''`: Use a default format based on the number of channels in the image. -// * `grayscale`: Output a grayscale JPEG image. The `channels` dimension -// of `image` must be 1. -// * `rgb`: Output an RGB JPEG image. The `channels` dimension -// of `image` must be 3. -// -// If `format` is not specified or is the empty string, a default format is picked -// in function of the number of channels in `image`: -// -// * 1: Output a grayscale image. -// * 3: Output an RGB image. -// -// Arguments: -// image: 3-D with shape `[height, width, channels]`. -// -// Returns 0-D. JPEG-encoded image. -func EncodeJpeg(scope *Scope, image tf.Output, optional ...EncodeJpegAttr) (contents tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "EncodeJpeg", - Input: []tf.Input{ - image, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// MultinomialAttr is an optional argument to Multinomial. -type MultinomialAttr func(optionalAttr) - -// MultinomialSeed sets the optional seed attribute to value. -// -// value: If either seed or seed2 is set to be non-zero, the internal random number -// generator is seeded by the given seed. Otherwise, a random seed is used. -// If not specified, defaults to 0 -func MultinomialSeed(value int64) MultinomialAttr { - return func(m optionalAttr) { - m["seed"] = value - } -} - -// MultinomialSeed2 sets the optional seed2 attribute to value. -// -// value: A second seed to avoid seed collision. -// If not specified, defaults to 0 -func MultinomialSeed2(value int64) MultinomialAttr { - return func(m optionalAttr) { - m["seed2"] = value - } -} - -// MultinomialOutputDtype sets the optional output_dtype attribute to value. -// If not specified, defaults to DT_INT64 -func MultinomialOutputDtype(value tf.DataType) MultinomialAttr { - return func(m optionalAttr) { - m["output_dtype"] = value - } -} - -// Draws samples from a multinomial distribution. -// +// The indices returned are always in `[0, height) x [0, width)` before flattening, +// even if padding is involved and the mathematically correct answer is outside +// (either negative or too large). This is a bug, but fixing it is difficult to do +// in a safe backwards compatible way, especially due to flattening. +// // Arguments: -// logits: 2-D Tensor with shape `[batch_size, num_classes]`. Each slice `[i, :]` -// represents the unnormalized log probabilities for all classes. -// num_samples: 0-D. Number of independent samples to draw for each row slice. +// input: 4-D with shape `[batch, height, width, channels]`. Input to pool over. +// ksize: The size of the window for each dimension of the input tensor. +// strides: The stride of the sliding window for each dimension of the +// input tensor. +// padding: The type of padding algorithm to use. // -// Returns 2-D Tensor with shape `[batch_size, num_samples]`. Each slice `[i, :]` -// contains the drawn class labels with range `[0, num_classes)`. -func Multinomial(scope *Scope, logits tf.Output, num_samples tf.Output, optional ...MultinomialAttr) (output tf.Output) { +// Returns The max pooled output tensor.4-D. The flattened indices of the max values chosen for each output. +func MaxPoolWithArgmax(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolWithArgmaxAttr) (output tf.Output, argmax tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "Multinomial", + Type: "MaxPoolWithArgmax", Input: []tf.Input{ - logits, num_samples, + input, }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0) + return op.Output(0), op.Output(1) } // Returns the truth value of NOT x element-wise. @@ -13157,62 +13289,6 @@ func ResourceScatterSub(scope *Scope, resource tf.Output, indices tf.Output, upd return scope.AddOperation(opspec) } -// Inverse 2D fast Fourier transform. -// -// Computes the inverse 2-dimensional discrete Fourier transform over the -// inner-most 2 dimensions of `input`. -// -// Arguments: -// input: A complex64 tensor. -// -// Returns A complex64 tensor of the same shape as `input`. The inner-most 2 -// dimensions of `input` are replaced with their inverse 2D Fourier transform. -// -// @compatibility(numpy) -// Equivalent to np.fft.ifft2 -// @end_compatibility -func IFFT2D(scope *Scope, input tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "IFFT2D", - Input: []tf.Input{ - input, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// 2D fast Fourier transform. -// -// Computes the 2-dimensional discrete Fourier transform over the inner-most -// 2 dimensions of `input`. -// -// Arguments: -// input: A complex64 tensor. -// -// Returns A complex64 tensor of the same shape as `input`. The inner-most 2 -// dimensions of `input` are replaced with their 2D Fourier transform. -// -// @compatibility(numpy) -// Equivalent to np.fft.fft2 -// @end_compatibility -func FFT2D(scope *Scope, input tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "FFT2D", - Input: []tf.Input{ - input, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // ResourceApplyProximalGradientDescentAttr is an optional argument to ResourceApplyProximalGradientDescent. type ResourceApplyProximalGradientDescentAttr func(optionalAttr) @@ -15324,31 +15400,6 @@ func BoostedTreesEnsembleResourceHandleOp(scope *Scope, optional ...BoostedTrees return op.Output(0) } -// Concatenates tensors along one dimension. -// -// Arguments: -// concat_dim: 0-D. The dimension along which to concatenate. Must be in the -// range [0, rank(values)). -// values: The `N` Tensors to concatenate. Their ranks and types must match, -// and their sizes must match in all dimensions except `concat_dim`. -// -// Returns A `Tensor` with the concatenation of values stacked along the -// `concat_dim` dimension. This tensor's shape matches that of `values` except -// in `concat_dim` where it has the sum of the sizes. -func Concat(scope *Scope, concat_dim tf.Output, values []tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Concat", - Input: []tf.Input{ - concat_dim, tf.OutputList(values), - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // ResourceApplyMomentumAttr is an optional argument to ResourceApplyMomentum. type ResourceApplyMomentumAttr func(optionalAttr) @@ -16267,6 +16318,62 @@ func MutableDenseHashTableV2(scope *Scope, empty_key tf.Output, value_dtype tf.D return op.Output(0) } +// 2D fast Fourier transform. +// +// Computes the 2-dimensional discrete Fourier transform over the inner-most +// 2 dimensions of `input`. +// +// Arguments: +// input: A complex64 tensor. +// +// Returns A complex64 tensor of the same shape as `input`. The inner-most 2 +// dimensions of `input` are replaced with their 2D Fourier transform. +// +// @compatibility(numpy) +// Equivalent to np.fft.fft2 +// @end_compatibility +func FFT2D(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "FFT2D", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Inverse 2D fast Fourier transform. +// +// Computes the inverse 2-dimensional discrete Fourier transform over the +// inner-most 2 dimensions of `input`. +// +// Arguments: +// input: A complex64 tensor. +// +// Returns A complex64 tensor of the same shape as `input`. The inner-most 2 +// dimensions of `input` are replaced with their inverse 2D Fourier transform. +// +// @compatibility(numpy) +// Equivalent to np.fft.ifft2 +// @end_compatibility +func IFFT2D(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "IFFT2D", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // ResourceApplyRMSPropAttr is an optional argument to ResourceApplyRMSProp. type ResourceApplyRMSPropAttr func(optionalAttr) @@ -17712,137 +17819,66 @@ func SparseConcat(scope *Scope, indices []tf.Output, values []tf.Output, shapes // representing features of one feature column. It outputs a 2D `SparseTensor` with // the batchwise crosses of these features. // -// For example, if the inputs are -// -// inputs[0]: SparseTensor with shape = [2, 2] -// [0, 0]: "a" -// [1, 0]: "b" -// [1, 1]: "c" -// -// inputs[1]: SparseTensor with shape = [2, 1] -// [0, 0]: "d" -// [1, 0]: "e" -// -// inputs[2]: Tensor [["f"], ["g"]] -// -// then the output will be -// -// shape = [2, 2] -// [0, 0]: "a_X_d_X_f" -// [1, 0]: "b_X_e_X_g" -// [1, 1]: "c_X_e_X_g" -// -// if hashed_output=true then the output will be -// -// shape = [2, 2] -// [0, 0]: FingerprintCat64( -// Fingerprint64("f"), FingerprintCat64( -// Fingerprint64("d"), Fingerprint64("a"))) -// [1, 0]: FingerprintCat64( -// Fingerprint64("g"), FingerprintCat64( -// Fingerprint64("e"), Fingerprint64("b"))) -// [1, 1]: FingerprintCat64( -// Fingerprint64("g"), FingerprintCat64( -// Fingerprint64("e"), Fingerprint64("c"))) -// -// Arguments: -// indices: 2-D. Indices of each input `SparseTensor`. -// values: 1-D. values of each `SparseTensor`. -// shapes: 1-D. Shapes of each `SparseTensor`. -// dense_inputs: 2-D. Columns represented by dense `Tensor`. -// hashed_output: If true, returns the hash of the cross instead of the string. -// This will allow us avoiding string manipulations. -// num_buckets: It is used if hashed_output is true. -// output = hashed_value%num_buckets if num_buckets > 0 else hashed_value. -// hash_key: Specify the hash_key that will be used by the `FingerprintCat64` -// function to combine the crosses fingerprints. -// -// -// -// Returns 2-D. Indices of the concatenated `SparseTensor`.1-D. Non-empty values of the concatenated or hashed -// `SparseTensor`.1-D. Shape of the concatenated `SparseTensor`. -func SparseCross(scope *Scope, indices []tf.Output, values []tf.Output, shapes []tf.Output, dense_inputs []tf.Output, hashed_output bool, num_buckets int64, hash_key int64, out_type tf.DataType, internal_type tf.DataType) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"hashed_output": hashed_output, "num_buckets": num_buckets, "hash_key": hash_key, "out_type": out_type, "internal_type": internal_type} - opspec := tf.OpSpec{ - Type: "SparseCross", - Input: []tf.Input{ - tf.OutputList(indices), tf.OutputList(values), tf.OutputList(shapes), tf.OutputList(dense_inputs), - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// Concatenates quantized tensors along one dimension. -// -// Arguments: -// concat_dim: 0-D. The dimension along which to concatenate. Must be in the -// range [0, rank(values)). -// values: The `N` Tensors to concatenate. Their ranks and types must match, -// and their sizes must match in all dimensions except `concat_dim`. -// input_mins: The minimum scalar values for each of the input tensors. -// input_maxes: The maximum scalar values for each of the input tensors. -// -// Returns A `Tensor` with the concatenation of values stacked along the -// `concat_dim` dimension. This tensor's shape matches that of `values` except -// in `concat_dim` where it has the sum of the sizes.The float value that the minimum quantized output value represents.The float value that the maximum quantized output value represents. -func QuantizedConcat(scope *Scope, concat_dim tf.Output, values []tf.Output, input_mins []tf.Output, input_maxes []tf.Output) (output tf.Output, output_min tf.Output, output_max tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "QuantizedConcat", - Input: []tf.Input{ - concat_dim, tf.OutputList(values), tf.OutputList(input_mins), tf.OutputList(input_maxes), - }, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// Slice a `SparseTensor` based on the `start` and `size`. +// For example, if the inputs are // -// For example, if the input is +// inputs[0]: SparseTensor with shape = [2, 2] +// [0, 0]: "a" +// [1, 0]: "b" +// [1, 1]: "c" // -// input_tensor = shape = [2, 7] -// [ a d e ] -// [b c ] +// inputs[1]: SparseTensor with shape = [2, 1] +// [0, 0]: "d" +// [1, 0]: "e" // -// Graphically the output tensors are: +// inputs[2]: Tensor [["f"], ["g"]] // -// sparse_slice([0, 0], [2, 4]) = shape = [2, 4] -// [ a ] -// [b c ] +// then the output will be // -// sparse_slice([0, 4], [2, 3]) = shape = [2, 3] -// [ d e ] -// [ ] +// shape = [2, 2] +// [0, 0]: "a_X_d_X_f" +// [1, 0]: "b_X_e_X_g" +// [1, 1]: "c_X_e_X_g" +// +// if hashed_output=true then the output will be +// +// shape = [2, 2] +// [0, 0]: FingerprintCat64( +// Fingerprint64("f"), FingerprintCat64( +// Fingerprint64("d"), Fingerprint64("a"))) +// [1, 0]: FingerprintCat64( +// Fingerprint64("g"), FingerprintCat64( +// Fingerprint64("e"), Fingerprint64("b"))) +// [1, 1]: FingerprintCat64( +// Fingerprint64("g"), FingerprintCat64( +// Fingerprint64("e"), Fingerprint64("c"))) // // Arguments: -// indices: 2-D tensor represents the indices of the sparse tensor. -// values: 1-D tensor represents the values of the sparse tensor. -// shape: 1-D. tensor represents the shape of the sparse tensor. -// start: 1-D. tensor represents the start of the slice. -// size: 1-D. tensor represents the size of the slice. -// output indices: A list of 1-D tensors represents the indices of the output -// sparse tensors. +// indices: 2-D. Indices of each input `SparseTensor`. +// values: 1-D. values of each `SparseTensor`. +// shapes: 1-D. Shapes of each `SparseTensor`. +// dense_inputs: 2-D. Columns represented by dense `Tensor`. +// hashed_output: If true, returns the hash of the cross instead of the string. +// This will allow us avoiding string manipulations. +// num_buckets: It is used if hashed_output is true. +// output = hashed_value%num_buckets if num_buckets > 0 else hashed_value. +// hash_key: Specify the hash_key that will be used by the `FingerprintCat64` +// function to combine the crosses fingerprints. // -// Returns A list of 1-D tensors represents the values of the output sparse -// tensors.A list of 1-D tensors represents the shape of the output sparse -// tensors. -func SparseSlice(scope *Scope, indices tf.Output, values tf.Output, shape tf.Output, start tf.Output, size tf.Output) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) { +// +// +// Returns 2-D. Indices of the concatenated `SparseTensor`.1-D. Non-empty values of the concatenated or hashed +// `SparseTensor`.1-D. Shape of the concatenated `SparseTensor`. +func SparseCross(scope *Scope, indices []tf.Output, values []tf.Output, shapes []tf.Output, dense_inputs []tf.Output, hashed_output bool, num_buckets int64, hash_key int64, out_type tf.DataType, internal_type tf.DataType) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) { if scope.Err() != nil { return } + attrs := map[string]interface{}{"hashed_output": hashed_output, "num_buckets": num_buckets, "hash_key": hash_key, "out_type": out_type, "internal_type": internal_type} opspec := tf.OpSpec{ - Type: "SparseSlice", + Type: "SparseCross", Input: []tf.Input{ - indices, values, shape, start, size, + tf.OutputList(indices), tf.OutputList(values), tf.OutputList(shapes), tf.OutputList(dense_inputs), }, + Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0), op.Output(1), op.Output(2) @@ -17978,52 +18014,6 @@ func TakeManySparseFromTensorsMap(scope *Scope, sparse_handles tf.Output, dtype return op.Output(0), op.Output(1), op.Output(2) } -// MaxPoolAttr is an optional argument to MaxPool. -type MaxPoolAttr func(optionalAttr) - -// MaxPoolDataFormat sets the optional data_format attribute to value. -// -// value: Specify the data format of the input and output data. With the -// default format "NHWC", the data is stored in the order of: -// [batch, in_height, in_width, in_channels]. -// Alternatively, the format could be "NCHW", the data storage order of: -// [batch, in_channels, in_height, in_width]. -// If not specified, defaults to "NHWC" -func MaxPoolDataFormat(value string) MaxPoolAttr { - return func(m optionalAttr) { - m["data_format"] = value - } -} - -// Performs max pooling on the input. -// -// Arguments: -// input: 4-D input to pool over. -// ksize: The size of the window for each dimension of the input tensor. -// strides: The stride of the sliding window for each dimension of the -// input tensor. -// padding: The type of padding algorithm to use. -// -// Returns The max pooled output tensor. -func MaxPool(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "MaxPool", - Input: []tf.Input{ - input, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // Assigns a new value to a variable. // // Any ReadVariableOp with a control dependency on this op is guaranteed to return @@ -18098,9 +18088,8 @@ func SparseFillEmptyRowsGrad(scope *Scope, reverse_index_map tf.Output, grad_val } // Computes scaled exponential linear: `scale * alpha * (exp(features) - 1)` -// if < 0, `scale * features` otherwise. // -// Assumes weights to have zero mean and variance 1.0 / fan_in. +// if < 0, `scale * features` otherwise. // // See [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515) func Selu(scope *Scope, features tf.Output) (activations tf.Output) { @@ -18606,69 +18595,6 @@ func SdcaOptimizer(scope *Scope, sparse_example_indices []tf.Output, sparse_feat return out_example_state_data, out_delta_sparse_weights, out_delta_dense_weights } -// SparseMatMulAttr is an optional argument to SparseMatMul. -type SparseMatMulAttr func(optionalAttr) - -// SparseMatMulTransposeA sets the optional transpose_a attribute to value. -// If not specified, defaults to false -func SparseMatMulTransposeA(value bool) SparseMatMulAttr { - return func(m optionalAttr) { - m["transpose_a"] = value - } -} - -// SparseMatMulTransposeB sets the optional transpose_b attribute to value. -// If not specified, defaults to false -func SparseMatMulTransposeB(value bool) SparseMatMulAttr { - return func(m optionalAttr) { - m["transpose_b"] = value - } -} - -// SparseMatMulAIsSparse sets the optional a_is_sparse attribute to value. -// If not specified, defaults to false -func SparseMatMulAIsSparse(value bool) SparseMatMulAttr { - return func(m optionalAttr) { - m["a_is_sparse"] = value - } -} - -// SparseMatMulBIsSparse sets the optional b_is_sparse attribute to value. -// If not specified, defaults to false -func SparseMatMulBIsSparse(value bool) SparseMatMulAttr { - return func(m optionalAttr) { - m["b_is_sparse"] = value - } -} - -// Multiply matrix "a" by matrix "b". -// -// The inputs must be two-dimensional matrices and the inner dimension of "a" must -// match the outer dimension of "b". This op is optimized for the case where at -// least one of "a" or "b" is sparse. The breakeven for using this versus a dense -// matrix multiply on one platform was 30% zero values in the sparse matrix. -// -// The gradient computation of this operation will only take advantage of sparsity -// in the input gradient when that gradient comes from a Relu. -func SparseMatMul(scope *Scope, a tf.Output, b tf.Output, optional ...SparseMatMulAttr) (product tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "SparseMatMul", - Input: []tf.Input{ - a, b, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // ShapeAttr is an optional argument to Shape. type ShapeAttr func(optionalAttr) @@ -19514,6 +19440,79 @@ func OrderedMapIncompleteSize(scope *Scope, dtypes []tf.DataType, optional ...Or return op.Output(0) } +// DestroyResourceOpAttr is an optional argument to DestroyResourceOp. +type DestroyResourceOpAttr func(optionalAttr) + +// DestroyResourceOpIgnoreLookupError sets the optional ignore_lookup_error attribute to value. +// +// value: whether to ignore the error when the resource +// doesn't exist. +// If not specified, defaults to true +func DestroyResourceOpIgnoreLookupError(value bool) DestroyResourceOpAttr { + return func(m optionalAttr) { + m["ignore_lookup_error"] = value + } +} + +// Deletes the resource specified by the handle. +// +// All subsequent operations using the resource will result in a NotFound +// error status. +// +// Arguments: +// resource: handle to the resource to delete. +// +// Returns the created operation. +func DestroyResourceOp(scope *Scope, resource tf.Output, optional ...DestroyResourceOpAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DestroyResourceOp", + Input: []tf.Input{ + resource, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Generates values in an interval. +// +// A sequence of `num` evenly-spaced values are generated beginning at `start`. +// If `num > 1`, the values in the sequence increase by `stop - start / num - 1`, +// so that the last one is exactly `stop`. +// +// For example: +// +// ``` +// tf.linspace(10.0, 12.0, 3, name="linspace") => [ 10.0 11.0 12.0] +// ``` +// +// Arguments: +// start: First entry in the range. +// stop: Last entry in the range. +// num: Number of values to generate. +// +// Returns 1-D. The generated values. +func LinSpace(scope *Scope, start tf.Output, stop tf.Output, num tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "LinSpace", + Input: []tf.Input{ + start, stop, num, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // ComplexAttr is an optional argument to Complex. type ComplexAttr func(optionalAttr) @@ -21626,7 +21625,7 @@ func ImageSummaryBadColor(value tf.Tensor) ImageSummaryAttr { // generated sequentially as '*tag*/image/0', '*tag*/image/1', etc. // // The `bad_color` argument is the color to use in the generated images for -// non-finite input values. It is a `uint8` 1-D tensor of length `channels`. +// non-finite input values. It is a `unit8` 1-D tensor of length `channels`. // Each element must be in the range `[0, 255]` (It represents the value of a // pixel in the output image). Non-finite values in the input tensor are // replaced by this tensor in the output image. The default value is the color @@ -24019,7 +24018,7 @@ func SampleDistortedBoundingBoxV2AspectRatioRange(value []float32) SampleDistort // SampleDistortedBoundingBoxV2AreaRange sets the optional area_range attribute to value. // // value: The cropped area of the image must contain a fraction of the -// supplied image within this range. +// supplied image within in this range. // If not specified, defaults to func SampleDistortedBoundingBoxV2AreaRange(value []float32) SampleDistortedBoundingBoxV2Attr { return func(m optionalAttr) { @@ -24715,7 +24714,8 @@ type DecodeProtoV2Attr func(optionalAttr) // If not specified, defaults to "local://" func DecodeProtoV2DescriptorSource(value string) DecodeProtoV2Attr { return func(m optionalAttr) { - m["descriptor_source"] = value } + m["descriptor_source"] = value + } } // DecodeProtoV2MessageFormat sets the optional message_format attribute to value. -- GitLab From 78cef7962be702532cb1998b291c6624f803aa3f Mon Sep 17 00:00:00 2001 From: Sami Kama Date: Mon, 18 Jun 2018 10:47:18 -0700 Subject: [PATCH 596/816] Fix Py3 issue and device placement --- .../contrib/tensorrt/convert/convert_graph.cc | 17 ++- .../contrib/tensorrt/test/test_tftrt.py | 109 +++++++++++++++--- 2 files changed, 111 insertions(+), 15 deletions(-) diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.cc b/tensorflow/contrib/tensorrt/convert/convert_graph.cc index 20abef6806..f19a8cd4bd 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_graph.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_graph.cc @@ -256,6 +256,13 @@ EngineInfo GetEngineInfo( auto node_device = node->requested_device(); if (!node_device.empty()) { segment_devices.insert(node_device); + } else { + if (node->has_assigned_device_name()) { + segment_devices.insert(node->assigned_device_name()); + } else { + VLOG(2) << "Node " << node->name() + << " neither have requested device nor assigned device"; + } } int node_id = node->id(); subgraph_node_ids.push_back(node_id); @@ -315,11 +322,15 @@ EngineInfo GetEngineInfo( &info.engine_name); info.engine_type = EngineInfo::EngineType::TRTStatic; // TODO(sami): This should not happen once segmenter is updated. - if (segment_devices.size() > 1) { + if (segment_devices.size() == 1) { + info.device = *segment_devices.begin(); + } else if (segment_devices.size() > 1) { LOG(WARNING) << "Detected multiple(" << segment_devices.size() << ") devices for the segment. Picking first one to continue " << "but this shouldn't have happened"; info.device = *segment_devices.begin(); + } else { + VLOG(1) << "Segment devices size is 0"; } return info; } @@ -653,8 +664,12 @@ std::pair GetDeviceAndAllocator( dev_allocator = pm->GetGPUAllocator(gpuoptions, tf_gpu_id, 1); VLOG(1) << "Got an allocator for device tf_device=" << tf_gpu_id.value() << " cuda device= " << cuda_device_id << " at " << dev_allocator; + } else { + LOG(WARNING) << "Cluster is set but device " << engine.device + << " is not found in the cluster"; } } else { // cluster not found, possibly a python call + VLOG(1) << "Cluster is not set, probably called from python"; int found_device = 0; bool try_gpu_ids = true; // if device is set, try to find the device. Might be a problem for multi diff --git a/tensorflow/contrib/tensorrt/test/test_tftrt.py b/tensorflow/contrib/tensorrt/test/test_tftrt.py index 748b4ad23c..85f37aa899 100644 --- a/tensorflow/contrib/tensorrt/test/test_tftrt.py +++ b/tensorflow/contrib/tensorrt/test/test_tftrt.py @@ -20,6 +20,7 @@ from __future__ import print_function import argparse import numpy as np +import six as _six # normally we should do import tensorflow as tf and then # tf.placeholder, tf.constant, tf.nn.conv2d etc but @@ -39,6 +40,71 @@ from tensorflow.python.ops import nn as nn from tensorflow.python.ops import nn_ops as nn_ops +def py2bytes(inp): + return inp + + +def py3bytes(inp): + return inp.encode("utf-8", errors="surrogateescape") + + +def py2string(inp): + return inp + + +def py3string(inp): + return inp.decode("utf-8") + + +if _six.PY2: + to_bytes = py2bytes + to_string = py2string +else: + to_bytes = py3bytes + to_string = py3string + + +def get_multi_engine_graph_def(mode="FP32"): + """Create a simple graph and return its graph_def.""" + dtype = dtypes.float32 + if mode.upper() == "FP16": + dtype = dtypes.float16 + else: + pass + + g = ops.Graph() + with g.as_default(): + x = aops.placeholder(shape=[None, 3, 7, 5], name="input", dtype=dtype) + with g.name_scope("Global_scope") as scope: + with g.name_scope("first_scope"): + e = cop.constant( + np.random.randn(3, 2, 3, 4), name="weights", dtype=dtype) + conv = nn.conv2d( + input=x, + filter=e, + data_format="NCHW", + strides=[1, 1, 1, 1], + padding="VALID", + name="conv") + b = cop.constant(np.random.randn(1, 4, 1, 1), name="bias1", dtype=dtype) + t = conv * b + + b = cop.constant(np.random.randn(1, 4, 1, 1), name="bias2", dtype=dtype) + q = conv / b + c = cop.constant(np.random.randn(1, 4, 1, 1), name="bias3", dtype=dtype) + edge = mops.sin(q) + edge1 = mops.cos(conv) + with g.name_scope("test_scope"): + de = edge + edge1 + t = t - edge1 + q = q * edge + t = t + q + t = t - de + k = aops.squeeze(t, name="output") + print(k.dtype) + return g.as_graph_def() + + def get_simple_graph_def(): """Create a simple graph and return its graph_def.""" g = ops.Graph() @@ -66,7 +132,7 @@ def execute_graph(gdef, dumm_inp): """Run given graphdef once.""" print("executing") gpu_options = None - if (trt.trt_convert.get_linked_tensorrt_version()[0] == 3): + if trt.trt_convert.get_linked_tensorrt_version()[0] == 3: gpu_options = cpb2.GPUOptions(per_process_gpu_memory_fraction=0.50) sessconfig = cpb2.ConfigProto(gpu_options=gpu_options) ops.reset_default_graph() @@ -86,7 +152,7 @@ def execute_graph(gdef, dumm_inp): def execute_calibration(gdef, dumm_inp): """Run given calibration graph multiple times.""" gpu_options = None - if (trt.trt_convert.get_linked_tensorrt_version()[0] == 3): + if trt.trt_convert.get_linked_tensorrt_version()[0] == 3: gpu_options = cpb2.GPUOptions(per_process_gpu_memory_fraction=0.50) ops.reset_default_graph() g = ops.Graph() @@ -104,12 +170,17 @@ def execute_calibration(gdef, dumm_inp): return val -def user(run_graph=execute_graph, run_calibration=execute_calibration): +def user(multi_engine, + run_graph=execute_graph, + run_calibration=execute_calibration): """Example function that converts a graph to TFTRT graph.""" - - inp_dims = (100, 24, 24, 2) + if multi_engine: + inp_dims = (2, 3, 7, 5) + orig_graph = get_multi_engine_graph_def() + else: + inp_dims = (100, 24, 24, 2) + orig_graph = get_simple_graph_def() # use a frozen graph for inference dummy_input = np.random.random_sample(inp_dims) - orig_graph = get_simple_graph_def() # use a frozen graph for inference # Get optimized graph trt_graph = trt.create_inference_graph( input_graph_def=orig_graph, @@ -155,22 +226,26 @@ def user(run_graph=execute_graph, run_calibration=execute_calibration): print("Pass") -def auto(): +def auto(multi_engine): """Run the conversion as an optimization pass.""" - inp_dims = (100, 24, 24, 2) + if multi_engine: + inp_dims = (2, 3, 7, 5) + orig_graph = get_multi_engine_graph_def() + else: + inp_dims = (100, 24, 24, 2) + orig_graph = get_simple_graph_def() # use a frozen graph for inference dummy_input = np.random.random_sample(inp_dims) - orig_graph = get_simple_graph_def() opt_config = rwpb2.RewriterConfig() opt_config.optimizers.extend(["constfold", "layout"]) custom_op = opt_config.custom_optimizers.add() custom_op.name = "TensorRTOptimizer" custom_op.parameter_map["minimum_segment_size"].i = 3 - custom_op.parameter_map["precision_mode"].s = "FP32" + custom_op.parameter_map["precision_mode"].s = to_bytes("FP32") custom_op.parameter_map["max_batch_size"].i = inp_dims[0] custom_op.parameter_map["max_workspace_size_bytes"].i = 1 << 25 print(custom_op) gpu_options = None - if (trt.trt_convert.get_linked_tensorrt_version()[0] == 3): + if trt.trt_convert.get_linked_tensorrt_version()[0] == 3: gpu_options = cpb2.GPUOptions(per_process_gpu_memory_fraction=0.50) graph_options = cpb2.GraphOptions(rewrite_options=opt_config) sessconfig = cpb2.ConfigProto( @@ -180,7 +255,7 @@ def auto(): ops.reset_default_graph() with g.as_default(): inp, out = importer.import_graph_def( - graph_def=orig_graph, return_elements=["input", "output"]) + graph_def=orig_graph, return_elements=["input", "output"], name="") inp = inp.outputs[0] out = out.outputs[0] with csess.Session(config=sessconfig, graph=g) as sess: @@ -198,8 +273,14 @@ if "__main__" in __name__: action="store_true", help="Do TRT conversion automatically", default=False) + P.add_argument( + "--multi-engine", + "-m", + action="store_true", + help="Use a graph that will result in 2 engines", + default=False) flags, unparsed = P.parse_known_args() if flags.automatic: - auto() + auto(flags.multi_engine) else: - user() + user(flags.multi_engine) -- GitLab From 9ac856f65798d008da2fc2ca6c9041748474ccfe Mon Sep 17 00:00:00 2001 From: "William D. Irons" Date: Mon, 18 Jun 2018 13:08:29 -0500 Subject: [PATCH 597/816] cpu and gpu Dockerfiles for ppc64le Adding Dockerfile.cpu.ppc64le and Dockerfile.gpu.ppc64le to enable the ability to do builds using docker on ppc64le. Also enables the ability to run ci_sanity.sh (from ci_build.sh) on ppc64le. Modified ci_build.sh and ci_parameterized_build.sh to accept container types that start with cpu or gpu. Added install_bazel_from_source.sh and install_buildifier_from_source.sh install scripts to avoid installing x86 versions of the binaries. These scripts could be used by other platforms in the future. --- .../tools/ci_build/Dockerfile.cpu.ppc64le | 19 +++++++++ .../tools/ci_build/Dockerfile.gpu.ppc64le | 27 +++++++++++++ tensorflow/tools/ci_build/ci_build.sh | 4 +- .../tools/ci_build/ci_parameterized_build.sh | 8 ++-- .../install/install_bazel_from_source.sh | 40 +++++++++++++++++++ .../install/install_buildifier_from_source.sh | 30 ++++++++++++++ .../install/install_golang_ppc64el.sh | 22 ++++++++++ 7 files changed, 144 insertions(+), 6 deletions(-) create mode 100644 tensorflow/tools/ci_build/Dockerfile.cpu.ppc64le create mode 100644 tensorflow/tools/ci_build/Dockerfile.gpu.ppc64le create mode 100755 tensorflow/tools/ci_build/install/install_bazel_from_source.sh create mode 100755 tensorflow/tools/ci_build/install/install_buildifier_from_source.sh create mode 100755 tensorflow/tools/ci_build/install/install_golang_ppc64el.sh diff --git a/tensorflow/tools/ci_build/Dockerfile.cpu.ppc64le b/tensorflow/tools/ci_build/Dockerfile.cpu.ppc64le new file mode 100644 index 0000000000..4aa2ef5eba --- /dev/null +++ b/tensorflow/tools/ci_build/Dockerfile.cpu.ppc64le @@ -0,0 +1,19 @@ +FROM ubuntu:16.04 + +LABEL maintainer="William Irons " + +# Copy and run the install scripts. +COPY install/*.sh /install/ +RUN /install/install_bootstrap_deb_packages.sh +RUN add-apt-repository -y ppa:openjdk-r/ppa +RUN /install/install_deb_packages.sh +RUN apt-get update && apt-get install -y libopenblas-dev +RUN /install/install_pip_packages.sh +RUN /install/install_bazel_from_source.sh +RUN /install/install_proto3.sh +RUN /install/install_buildifier_from_source.sh +RUN /install/install_auditwheel.sh +RUN /install/install_golang_ppc64el.sh + +# Set up the master bazelrc configuration file. +COPY install/.bazelrc /etc/bazel.bazelrc diff --git a/tensorflow/tools/ci_build/Dockerfile.gpu.ppc64le b/tensorflow/tools/ci_build/Dockerfile.gpu.ppc64le new file mode 100644 index 0000000000..9ec6ae6ef4 --- /dev/null +++ b/tensorflow/tools/ci_build/Dockerfile.gpu.ppc64le @@ -0,0 +1,27 @@ +FROM nvidia/cuda-ppc64le:9.0-cudnn7-devel-ubuntu16.04 + +LABEL maintainer="William Irons " + +# In the Ubuntu 16.04 images, cudnn is placed in system paths. Move them to +# /usr/local/cuda +RUN cp -P /usr/include/cudnn.h /usr/local/cuda/include +RUN cp -P /usr/lib/powerpc64le-linux-gnu/libcudnn* /usr/local/cuda/lib64 + +# Copy and run the install scripts. +COPY install/*.sh /install/ +ARG DEBIAN_FRONTEND=noninteractive +RUN /install/install_bootstrap_deb_packages.sh +RUN add-apt-repository -y ppa:openjdk-r/ppa +RUN /install/install_deb_packages.sh +RUN apt-get update && apt-get install -y libopenblas-dev +RUN /install/install_pip_packages.sh +RUN /install/install_bazel_from_source.sh +RUN /install/install_golang_ppc64el.sh + +# Set up the master bazelrc configuration file. +COPY install/.bazelrc /etc/bazel.bazelrc +ENV LD_LIBRARY_PATH /usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH + +# Configure the build for our CUDA configuration. +ENV TF_NEED_CUDA 1 +ENV TF_CUDA_COMPUTE_CAPABILITIES 3.0 diff --git a/tensorflow/tools/ci_build/ci_build.sh b/tensorflow/tools/ci_build/ci_build.sh index 1f0fd0387a..f6a50d3d4c 100755 --- a/tensorflow/tools/ci_build/ci_build.sh +++ b/tensorflow/tools/ci_build/ci_build.sh @@ -79,7 +79,7 @@ if [[ "${CONTAINER_TYPE}" == "cmake" ]]; then fi # Use nvidia-docker if the container is GPU. -if [[ "${CONTAINER_TYPE}" == "gpu" ]]; then +if [[ "${CONTAINER_TYPE}" == gpu* ]]; then DOCKER_BINARY="nvidia-docker" else DOCKER_BINARY="docker" @@ -99,7 +99,7 @@ BUILD_TAG="${BUILD_TAG:-tf_ci}" # Add extra params for cuda devices and libraries for GPU container. # And clear them if we are not building for GPU. -if [[ "${CONTAINER_TYPE}" != "gpu" ]]; then +if [[ "${CONTAINER_TYPE}" != gpu* ]]; then GPU_EXTRA_PARAMS="" fi diff --git a/tensorflow/tools/ci_build/ci_parameterized_build.sh b/tensorflow/tools/ci_build/ci_parameterized_build.sh index 90bd8bc3d0..300ba8ea0b 100755 --- a/tensorflow/tools/ci_build/ci_parameterized_build.sh +++ b/tensorflow/tools/ci_build/ci_parameterized_build.sh @@ -258,9 +258,9 @@ function set_script_variable() { # Process container type -if [[ ${CTYPE} == "cpu" ]] || [[ ${CTYPE} == "debian.jessie.cpu" ]]; then +if [[ ${CTYPE} == cpu* ]] || [[ ${CTYPE} == "debian.jessie.cpu" ]]; then : -elif [[ ${CTYPE} == "gpu" ]]; then +elif [[ ${CTYPE} == gpu* ]]; then set_script_variable TF_NEED_CUDA 1 if [[ $TF_CUDA_CLANG == "1" ]]; then @@ -418,12 +418,12 @@ if [[ ${TF_BUILD_IS_PIP} == "no_pip" ]] || BAZEL_TARGET=${TF_BUILD_BAZEL_TARGET} fi - if [[ ${CTYPE} == "cpu" ]] || \ + if [[ ${CTYPE} == cpu* ]] || \ [[ ${CTYPE} == "debian.jessie.cpu" ]]; then # CPU only command, fully parallel. NO_PIP_MAIN_CMD="${MAIN_CMD} ${BAZEL_CMD} ${OPT_FLAG} ${EXTRA_ARGS} -- "\ "${BAZEL_TARGET}" - elif [[ ${CTYPE} == "gpu" ]]; then + elif [[ ${CTYPE} == gpu* ]]; then # GPU only command, run as many jobs as the GPU count only. NO_PIP_MAIN_CMD="${BAZEL_CMD} ${OPT_FLAG} "\ "--local_test_jobs=${TF_GPU_COUNT} "\ diff --git a/tensorflow/tools/ci_build/install/install_bazel_from_source.sh b/tensorflow/tools/ci_build/install/install_bazel_from_source.sh new file mode 100755 index 0000000000..ddad00c5f0 --- /dev/null +++ b/tensorflow/tools/ci_build/install/install_bazel_from_source.sh @@ -0,0 +1,40 @@ +#!/usr/bin/env bash +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +# This script is to be used to install bzel on non x86_64 systems +# It will compile bazel from source and install it in /usr/local/bin + +# Select bazel version. +BAZEL_VERSION="0.11.0" + +set +e +local_bazel_ver=$(bazel version 2>&1 | grep -i label | awk '{print $3}') + +if [[ "$local_bazel_ver" == "$BAZEL_VERSION" ]]; then + exit 0 +fi + +set -e + +# Compile bazel from source +mkdir -p /bazel +cd /bazel + +curl -fSsL -O https://github.com/bazelbuild/bazel/releases/download/$BAZEL_VERSION/bazel-$BAZEL_VERSION-dist.zip +unzip bazel-$BAZEL_VERSION-dist.zip +bash ./compile.sh +cp output/bazel /usr/local/bin/ +rm -rf /bazel diff --git a/tensorflow/tools/ci_build/install/install_buildifier_from_source.sh b/tensorflow/tools/ci_build/install/install_buildifier_from_source.sh new file mode 100755 index 0000000000..a93c258fad --- /dev/null +++ b/tensorflow/tools/ci_build/install/install_buildifier_from_source.sh @@ -0,0 +1,30 @@ +#!/usr/bin/env bash +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +set -e +BUILDTOOLS_VERSION="0.11.1" + +# Clone buildtools +git clone -b $BUILDTOOLS_VERSION https://github.com/bazelbuild/buildtools +cd buildtools + +# Build buildifier +bazel build //buildifier +sudo mv bazel-bin/buildifier/linux*stripped/buildifier /usr/local/bin + +# Build buildozer +bazel build //buildozer +sudo mv bazel-bin/buildozer/linux*stripped/buildozer /usr/local/bin diff --git a/tensorflow/tools/ci_build/install/install_golang_ppc64el.sh b/tensorflow/tools/ci_build/install/install_golang_ppc64el.sh new file mode 100755 index 0000000000..47d23a59b3 --- /dev/null +++ b/tensorflow/tools/ci_build/install/install_golang_ppc64el.sh @@ -0,0 +1,22 @@ +#!/usr/bin/env bash +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +set -ex + +GOLANG_URL="https://storage.googleapis.com/golang/go1.10.linux-ppc64le.tar.gz" + +sudo mkdir -p /usr/local +wget -q -O - "${GOLANG_URL}" | sudo tar -C /usr/local -xz -- GitLab From fc03fbff3dd7a58fa4f16226df4ada1f21f8b53f Mon Sep 17 00:00:00 2001 From: Peter Hawkins Date: Mon, 18 Jun 2018 11:47:31 -0700 Subject: [PATCH 598/816] Include the name of the resource in error messages about cross-device resource access. PiperOrigin-RevId: 201032994 --- tensorflow/core/framework/resource_mgr.cc | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/core/framework/resource_mgr.cc b/tensorflow/core/framework/resource_mgr.cc index 21fc6c1bd5..0a19861efd 100644 --- a/tensorflow/core/framework/resource_mgr.cc +++ b/tensorflow/core/framework/resource_mgr.cc @@ -60,8 +60,8 @@ namespace internal { Status ValidateDevice(OpKernelContext* ctx, const ResourceHandle& p) { if (ctx->device()->attributes().name() != p.device()) { return errors::InvalidArgument( - "Trying to access resource located in device ", p.device(), - " from device ", ctx->device()->attributes().name()); + "Trying to access resource ", p.name(), " located in device ", + p.device(), " from device ", ctx->device()->attributes().name()); } return Status::OK(); } -- GitLab From 148b4381fd0259cae441e459ec8ebe2c5d557722 Mon Sep 17 00:00:00 2001 From: Akshay Modi Date: Mon, 18 Jun 2018 11:48:36 -0700 Subject: [PATCH 599/816] Automated g4 rollback of changelist 201011811 PiperOrigin-RevId: 201033171 --- CONTRIBUTING.md | 2 +- README.md | 1 - RELEASE.md | 67 +-- configure.py | 5 - tensorflow/BUILD | 4 +- tensorflow/c/generate-pc.sh | 11 +- tensorflow/cc/gradients/math_grad.cc | 1 - tensorflow/cc/gradients/nn_grad.cc | 47 -- tensorflow/cc/gradients/nn_grad_test.cc | 84 +--- tensorflow/compiler/aot/codegen_test_h.golden | 4 +- .../compiler/aot/embedded_protocol_buffers.h | 2 +- tensorflow/compiler/aot/runtime.h | 4 +- tensorflow/compiler/aot/runtime_test.cc | 16 +- tensorflow/compiler/xla/service/cpu/BUILD | 18 +- .../compiler/xla/service/cpu/cpu_runtime.cc | 2 - .../compiler/xla/service/cpu/cpu_runtime.h | 1 - .../compiler/xla/service/cpu/ir_emitter.cc | 8 +- .../xla/service/cpu/runtime_fft_impl.h | 20 +- .../cpu/runtime_single_threaded_fft.cc | 32 -- .../service/cpu/runtime_single_threaded_fft.h | 31 -- .../xla/service/cpu/simple_orc_jit.cc | 2 - .../compiler/xla/service/pattern_matcher.h | 2 +- .../compiler/xla/service/tuple_simplifier.cc | 7 - .../compiler/xla/service/tuple_simplifier.h | 9 +- .../xla/service/tuple_simplifier_test.cc | 77 ---- tensorflow/contrib/autograph/__init__.py | 3 - tensorflow/contrib/cmake/tf_c.cmake | 22 +- tensorflow/contrib/cmake/tf_cc_ops.cmake | 2 +- tensorflow/contrib/cmake/tf_python.cmake | 3 +- .../contrib/cmake/tools/create_def_file.py | 9 +- .../bijectors/sinh_arcsinh_bijector_test.py | 28 +- tensorflow/contrib/eager/python/datasets.py | 3 +- .../examples/notebooks/4_high_level.ipynb | 4 +- .../feature_column/sequence_feature_column.py | 22 +- .../sequence_feature_column_test.py | 41 -- tensorflow/contrib/ffmpeg/__init__.py | 1 + tensorflow/contrib/ffmpeg/ffmpeg_ops.py | 1 + tensorflow/contrib/framework/__init__.py | 3 +- .../fused_conv2d_bias_activation_op_test.py | 11 +- .../src_impl/hexagon_controller.c | 2 +- .../contrib/lite/download_dependencies.sh | 4 +- .../contrib/lite/examples/minimal/minimal.cc | 2 +- .../lite/g3doc/tf_ops_compatibility.md | 14 +- tensorflow/contrib/lite/java/ovic/README.md | 4 +- .../internal/reference/reference_ops.h | 4 +- tensorflow/contrib/lite/python/interpreter.py | 2 +- .../interpreter_wrapper.cc | 9 +- .../interpreter_wrapper/interpreter_wrapper.h | 3 +- tensorflow/contrib/lite/python/lite.py | 11 - .../contrib/lite/toco/import_tensorflow.cc | 2 +- tensorflow/contrib/lite/toco/toco_port.cc | 6 - tensorflow/contrib/lite/toco/toco_port.h | 18 - tensorflow/contrib/makefile/compile_nsync.sh | 2 +- .../contrib/makefile/download_dependencies.sh | 4 +- .../contrib/metrics/python/ops/metric_ops.py | 2 +- .../contrib/mpi_collectives/kernels/ring.h | 2 +- .../opt/python/training/adamax_test.py | 6 +- .../training/model_average_optimizer.py | 2 +- tensorflow/contrib/periodic_resample/BUILD | 20 +- .../kernels/periodic_resample_op.cc | 5 - .../kernels/periodic_resample_op.h | 415 +++++------------- .../periodic_resample/ops/array_ops.cc | 53 +-- .../periodic_resample/ops/array_ops_test.cc | 41 -- .../kernel_tests/periodic_resample_op_test.py | 27 +- .../python/ops/periodic_resample_op.py | 8 +- .../predictor/contrib_estimator_predictor.py | 5 +- .../predictor/core_estimator_predictor.py | 5 +- .../contrib/predictor/predictor_factories.py | 24 +- .../predictor/predictor_factories_test.py | 19 - .../predictor/saved_model_predictor.py | 6 +- tensorflow/contrib/quantize/README.md | 2 +- .../slim/python/slim/evaluation_test.py | 25 +- tensorflow/contrib/summary/summary.py | 5 +- .../tensor_forest/client/eval_metrics.py | 45 +- .../tensor_forest/python/tensor_forest.py | 34 +- .../python/tensor_forest_test.py | 45 -- .../contrib/tensorrt/convert/convert_graph.cc | 66 ++- .../contrib/tensorrt/convert/convert_nodes.cc | 97 ++-- tensorflow/contrib/tpu/python/tpu/datasets.py | 16 +- .../contrib/tpu/python/tpu/datasets_test.py | 26 -- tensorflow/core/BUILD | 9 +- .../core/api_def/base_api/api_def_Selu.pbtxt | 4 - .../base_api/api_def_StringSplitV2.pbtxt | 48 -- .../python_api/api_def_StringSplitV2.pbtxt | 4 - .../core/common_runtime/bfc_allocator.cc | 8 +- .../core/common_runtime/bfc_allocator.h | 3 +- ...direct_session_with_tracking_alloc_test.cc | 16 - .../mkl_threadpool_device_test.cc | 53 --- .../core/common_runtime/process_util.cc | 11 +- .../core/common_runtime/threadpool_device.cc | 25 +- .../rpc/grpc_master_service_impl.cc | 4 +- .../distributed_runtime/rpc/grpc_testlib.cc | 10 +- tensorflow/core/framework/allocator.h | 5 + tensorflow/core/framework/op_gen_lib.cc | 1 - .../remote_fused_graph_execute_info.proto | 2 +- tensorflow/core/framework/tensor_test.cc | 24 +- tensorflow/core/graph/mkl_layout_pass.cc | 148 +------ tensorflow/core/graph/mkl_layout_pass_test.cc | 31 -- .../core/grappler/costs/graph_properties.cc | 1 + tensorflow/core/grappler/optimizers/BUILD | 2 +- .../core/grappler/optimizers/remapper.cc | 4 +- tensorflow/core/kernels/as_string_op.cc | 2 - tensorflow/core/kernels/cwise_op_clip.cc | 43 +- .../kernels/dense_update_functor_gpu.cu.cc | 1 - tensorflow/core/kernels/gather_functor.cc | 1 - .../core/kernels/gather_functor_gpu.cu.cc | 1 - tensorflow/core/kernels/gather_nd_op.cc | 4 - .../core/kernels/gather_nd_op_gpu.cu.cc | 2 - tensorflow/core/kernels/gather_op.cc | 1 - tensorflow/core/kernels/mkl_concat_op.cc | 213 +++------ .../core/kernels/mkl_conv_grad_bias_ops.cc | 2 - .../core/kernels/mkl_pooling_ops_common.h | 6 +- tensorflow/core/kernels/scatter_nd_op.cc | 4 - .../core/kernels/scatter_nd_op_gpu.cu.cc | 1 - .../core/kernels/scoped_allocator_ops_test.cc | 9 +- .../core/kernels/segment_reduction_ops.h | 10 +- tensorflow/core/kernels/sparse_matmul_op.cc | 2 +- tensorflow/core/kernels/string_split_op.cc | 130 ------ tensorflow/core/ops/candidate_sampling_ops.cc | 5 +- tensorflow/core/ops/dataset_ops.cc | 24 +- tensorflow/core/ops/image_ops.cc | 4 +- tensorflow/core/ops/math_ops.cc | 2 +- tensorflow/core/ops/nn_ops.cc | 1 - tensorflow/core/ops/string_ops.cc | 20 +- tensorflow/core/platform/cpu_info.cc | 23 - tensorflow/core/platform/cpu_info.h | 7 - .../core/platform/default/build_config.bzl | 2 - .../platform/hadoop/hadoop_file_system.cc | 21 +- tensorflow/core/platform/posix/port.cc | 5 - tensorflow/core/public/version.h | 4 +- tensorflow/core/util/mkl_util.h | 50 +-- tensorflow/docs_src/community/groups.md | 29 +- tensorflow/docs_src/get_started/eager.md | 2 +- tensorflow/docs_src/get_started/index.md | 4 +- tensorflow/docs_src/install/install_c.md | 2 +- tensorflow/docs_src/install/install_go.md | 2 +- tensorflow/docs_src/install/install_java.md | 24 +- tensorflow/docs_src/install/install_linux.md | 24 +- tensorflow/docs_src/install/install_mac.md | 10 +- .../docs_src/install/install_sources.md | 17 +- tensorflow/docs_src/mobile/linking_libs.md | 2 +- tensorflow/docs_src/mobile/prepare_models.md | 4 +- .../docs_src/performance/quantization.md | 2 +- .../docs_src/programmers_guide/estimators.md | 19 +- .../programmers_guide/feature_columns.md | 4 +- tensorflow/examples/learn/iris.py | 7 +- tensorflow/java/src/gen/cc/op_generator.cc | 11 +- tensorflow/java/src/gen/cc/op_specs.cc | 1 - tensorflow/python/eager/backprop.py | 4 +- tensorflow/python/estimator/BUILD | 5 +- tensorflow/python/estimator/exporter.py | 4 +- .../python/estimator/inputs/numpy_io.py | 8 +- .../python/estimator/inputs/numpy_io_test.py | 5 +- .../python/estimator/inputs/pandas_io.py | 7 +- .../python/estimator/inputs/pandas_io_test.py | 5 +- .../inputs/queues/feeding_functions.py | 2 +- tensorflow/python/estimator/keras.py | 4 +- tensorflow/python/estimator/keras_test.py | 14 +- .../python/grappler/layout_optimizer_test.py | 4 +- tensorflow/python/keras/activations.py | 2 - tensorflow/python/keras/callbacks.py | 21 +- tensorflow/python/keras/callbacks_test.py | 2 - tensorflow/python/keras/engine/network.py | 2 +- tensorflow/python/keras/engine/saving_test.py | 4 +- tensorflow/python/keras/engine/training.py | 7 +- .../python/keras/engine/training_eager.py | 2 +- tensorflow/python/keras/initializers_test.py | 26 +- tensorflow/python/keras/layers/core.py | 26 +- tensorflow/python/keras/models_test.py | 14 - .../python/kernel_tests/as_string_op_test.py | 10 - .../python/kernel_tests/betainc_op_test.py | 4 +- .../python/kernel_tests/clip_ops_test.py | 13 - .../python/kernel_tests/conv_ops_test.py | 32 +- .../python/kernel_tests/gather_nd_op_test.py | 32 +- .../python/kernel_tests/gather_op_test.py | 20 +- .../python/kernel_tests/init_ops_test.py | 27 -- .../python/kernel_tests/pooling_ops_test.py | 4 +- .../python/kernel_tests/py_func_test.py | 31 +- .../kernel_tests/scatter_nd_ops_test.py | 6 +- .../python/kernel_tests/scatter_ops_test.py | 14 +- .../segment_reduction_ops_test.py | 4 +- .../kernel_tests/string_split_op_test.py | 96 ---- tensorflow/python/ops/array_ops.py | 4 - tensorflow/python/ops/gradient_checker.py | 8 +- tensorflow/python/ops/image_ops_impl.py | 74 ++-- tensorflow/python/ops/image_ops_test.py | 261 ++--------- tensorflow/python/ops/init_ops.py | 3 +- tensorflow/python/ops/logging_ops.py | 5 +- tensorflow/python/ops/math_ops.py | 28 +- tensorflow/python/ops/nn_impl.py | 5 +- tensorflow/python/ops/nn_ops.py | 4 +- tensorflow/python/ops/nn_test.py | 10 - tensorflow/python/ops/script_ops.py | 35 +- tensorflow/python/ops/sparse_ops.py | 4 - tensorflow/python/ops/string_ops.py | 53 --- tensorflow/python/ops/variable_scope.py | 21 +- .../python/tools/import_pb_to_tensorboard.py | 0 tensorflow/tensorflow.bzl | 2 +- .../tools/api/generator/create_python_api.py | 8 +- .../tools/api/golden/tensorflow.image.pbtxt | 2 +- tensorflow/tools/api/golden/tensorflow.pbtxt | 4 - .../tools/api/golden/tensorflow.strings.pbtxt | 4 - tensorflow/tools/ci_build/builds/pip.sh | 4 - .../tools/ci_build/builds/with_the_same_user | 2 +- tensorflow/tools/ci_build/ci_build.sh | 7 - tensorflow/tools/ci_build/copy_binary.py | 3 +- .../ci_build/install/install_pip_packages.sh | 4 - .../install/install_python3.5_pip_packages.sh | 4 +- .../install/install_python3.6_pip_packages.sh | 5 +- .../ci_build/linux/mkl/basic-mkl-test.sh | 29 -- .../tools/ci_build/pi/build_raspberry_pi.sh | 8 +- .../def_file_filter_configure.bzl | 6 +- tensorflow/tools/dist_test/local_test.sh | 12 +- tensorflow/tools/dist_test/remote_test.sh | 11 +- tensorflow/tools/docker/Dockerfile.devel | 2 +- .../tools/docker/Dockerfile.devel-cpu-mkl | 2 +- tensorflow/tools/docker/Dockerfile.devel-gpu | 6 +- tensorflow/tools/docker/Dockerfile.gpu | 2 +- tensorflow/tools/pip_package/BUILD | 1 - .../tools/pip_package/build_pip_package.sh | 160 ++----- tensorflow/tools/pip_package/setup.py | 3 +- .../gen_proto_text_functions_lib.cc | 3 - .../tools/quantization/quantize_graph_test.py | 12 +- .../tools/test/upload_test_benchmarks.py | 1 + tensorflow/workspace.bzl | 40 +- third_party/eigen.BUILD | 1 - third_party/highwayhash.BUILD | 1 - third_party/jpeg/jpeg.BUILD | 2 - third_party/png.BUILD | 9 +- third_party/py/python_configure.bzl | 24 +- third_party/repo.bzl | 5 +- 231 files changed, 903 insertions(+), 3337 deletions(-) delete mode 100644 tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.cc delete mode 100644 tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.h delete mode 100644 tensorflow/contrib/periodic_resample/ops/array_ops_test.cc delete mode 100644 tensorflow/core/api_def/base_api/api_def_StringSplitV2.pbtxt delete mode 100644 tensorflow/core/api_def/python_api/api_def_StringSplitV2.pbtxt delete mode 100644 tensorflow/core/common_runtime/mkl_threadpool_device_test.cc mode change 100644 => 100755 tensorflow/python/tools/import_pb_to_tensorboard.py delete mode 100755 tensorflow/tools/ci_build/linux/mkl/basic-mkl-test.sh diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index db4b1581ae..8669c25c45 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -90,7 +90,7 @@ Bazel BUILD files also need to include a license section, e.g., Changes to TensorFlow C++ code should conform to [Google C++ Style Guide](https://google.github.io/styleguide/cppguide.html). -Use `clang-tidy` to check your C/C++ changes. To install `clang-tidy` on ubuntu:16.04, do: +Use `clang-tidy` to check your C/C++ changes. To install clang-tidy on ubuntu:16.04, do: ```bash apt-get install -y clang-tidy diff --git a/README.md b/README.md index 63853137cf..6fb4486d0d 100644 --- a/README.md +++ b/README.md @@ -56,7 +56,6 @@ $ python 42 >>> sess.close() ``` -Learn more examples about how to do specific tasks in TensorFlow at the [tutorials page of tensorflow.org](https://www.tensorflow.org/tutorials/). ## Contribution guidelines diff --git a/RELEASE.md b/RELEASE.md index e09e9c6190..84d9d52868 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -1,62 +1,3 @@ -# Release 1.9.0 - -## Major Features And Improvements -* Update tf.keras to the Keras 2.1.6 API. -* `tfe.Network` is deprecated. Please inherit from `tf.keras.Model`. -* Adding support of core feature columns and losses to gradient boosted trees estimators. -* The distributions.Bijector API supports broadcasting for Bijectors with new API changes. See [here](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/distributions/bijectors/Bijector) for more details. -* Layered variable names have changed in the following conditions: - * Using `tf.keras.layers` with custom variable scopes. - * Using `tf.layers` in a subclassed `tf.keras.Model` class. See [here](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/layers) for more details - -## Breaking Chances - * If you're opening empty variable scopes; replace `variable_scope`('', ...) by `variable_scope`(`tf.get_variable_scope()`, ...). - -## Bug Fixes and Other Changes -* `tf.data`: - * The `DatasetBase::DebugString()` method is now `const`. - * Added the `tf.contrib.data.sample_from_datasets()` API for randomly sampling from multiple datasets. -* Eager Execution: -* `tf.keras`: - * Move Keras code out of _impl folder and remove API files. - * `tf.keras.Model.save_weights` now saves in TensorFlow format by default. - * Enable dataset iterators to be passed to `tf.keras.Model` training/eval methods. -* Accelerated Linear Algebra (XLA): -* TensorFlow Debugger (tfdbg): fix an issue in which the TensorBoard Debugger Plugin could not handle total source file size exceeding gRPC message size limit (4 MB). -* `tf.contrib`: - * Add `tf.contrib.data.choose_from_datasets()`. - * `tf.contrib.data.make_csv_dataset()` now supports line breaks in quoted strings. Two arguments were removed from `make_csv_dataset`. - * `tf.contrib.framework.zero_initializer` supports ResourceVariable. - * Adding "constrained_optimization" to tensorflow/contrib. -* Other: - * Add GCS Configuration Ops. - * Changing signature of `MakeIterator` to enable propagating error status. - * KL divergence for two Dirichlet distributions. - * More consistent GcsFileSystem behavior for certain reads past EOF. - * Update benchmark for tf.scan to match ranges across eager and graph modes. - * Fixed bug in `tf.reduce_prod gradient` for complex dtypes. - * Add optional `args` argument to `Dataset.from_generator()`. - * Allow the use of '.' in variables (e.g. "hparams.parse('a.b=1.0')"), which would previously raise an error. This will correspond to an attribute name with an embedded '.' symbol (e.g. 'a.b'), which can only be accessed indirectly (e.g. through getattr and setattr). To set this up the user will first need to explicitly add the variable to the hparam object (e.g. "hparams.add_hparam(name='a.b', value=0.0)"). - * Benchmark for tf.scan in graph and eager modes. - * Added complex128 support to FFT, FFT2D, FFT3D, IFFT, IFFT2D, and IFFT3D. - * Making ids unique in `nn.embedding_lookup_sparse`. This helps to reduce RPC calls for looking up the embeddings when there are repeated ids in the batch. - * Support indicator column in boosted trees. - * Prevent `tf.gradients()` from backpropagating through integer tensors. - * LinearOperator[1D,2D,3D]Circulant added to `tensorflow.linalg`. - * Conv3D, Conv3DBackpropInput, Conv3DBackpropFilter now supports arbitrary. - * Added `tf.train.Checkpoint` for reading/writing object-based checkpoints. - * `Dataset.list_files()` now produces determinstic results when `shuffle=False` or a `seed` is passed. - * Added LinearOperatorKronecker, a dense-free implementation of the Kronecker Product. - * Allow LinearOperator to broadcast. - * SavedModelBuilder will now deduplicate asset names that point to files with the same basename and the same contents. Note that this may result in new asset files included in SavedModels in cases where assets with the same name but different contents were previously overwriting each other. - - -## Thanks to our Contributors - -This release contains contributions from many people at Google, as well as: - -Abdullah Alrasheed, Achal Shah, Ad-530, ADiegoCAlonso, Aditya Yogi, Ag Ramesh, akindyakov, Andy Kernahan, Anya Petrova, Aurelien Geron, Ben, Ben Barsdell, Bhavani-Subramanian, braincodercn, Brett Koonce, Brian Nemsick, Brian Zier, Bryan Heden, candy.dc, cclauss, Clayne Robison, ctiijima, Dalmo Cirne, David Norman, David T.H. Kao, DosLin, ekelsen, Elson Rodriguez, Erik Smistad, Felix Abecassis, Fergal Cotter, fo40225, foo0x29a, Freedom" Koan-Sin Tan, FréDéRic Branchaud-Charron, gdh1995, Geoffrey Irving, Giuseppe, gracehoney, Guido Zuidhof, Guillaume Klein, Guozhong Zhuang, Haggai, Harald Husum, imsheridan, Ivan Zhang, Jan Zikes, Jayaram Bobba, Jesse Benson, Jesse Gumz, Jiajia Li, Jie, jinghuangintel, Jingwen, jjsjann123, Joe Yearsley, Joel Hestness, Joel Shor, josephyearsley, Junpeng Lao, Karol M. Langner, Kb Sriram, krantideep95, Krish Ravindranath, Letian Feng, Loo Rong Jie, Lukas Geiger, Maciej, Mahmoud Abuzaina, ManHyuk, Mark Ryan, mbhuiyan, Michal Turek, Mostafa Alaa, Myungsung Kwak, Nand Dalal, Nehal J Wani, Neil Tenenholtz, ngc92, Nicholas Nadeau, P.Eng., Avs, Niranjan Hasabnis, P-Hidringer, Paul Van Eck, Peng Yu, Qing Zhao, Qingying Chen, Quanlong, Rajendra Arora, Rholais Lii, rmanyari, Robin Richtsfeld, Russell Klopfer, Sagi, Sam Sendelbach, Sandeep N Gupta, Sandip Giri, Sarah Edkins, Scott Tseng, Sdalbsoo, Sergii Khomenko, Seungwoo Choi (Biggie), Seyed Majid Azimi, Shaoning Zeng, shengfuintel, Siu Kei, Muk, Smit Shilu, soonson, Stefan Schweter, Sukhwan Kim, Sunitha Kambhampati, Taehoon Lee, tamimaddari82, Tang, Wenyi, Ted Chang, u2takey, Utkarsh Upadhyay, Vadim Markovtsev, voegtlel, Wai Hon Law, wangsiyu, Wenhao Hu, wenhao.hu, William D. Irons, Yan Facai (颜发才), Yanbo Liang, Yihong Wang, Yilei (Dolee) Yang, Yong Tang, Yuan (Terry) Tang - # Release 1.8.0 ## Major Features And Improvements @@ -463,6 +404,14 @@ answered questions, and were part of inspiring discussions. # Release 1.4.0 +## Major Features And Improvements +* `tf.keras` is now part of the core TensorFlow API. +* [`tf.data`](http://tensorflow.org/programmers_guide/datasets) is now part of + the core TensorFlow API. + * The API is now subject to backwards compatibility guarantees. + +# Release 1.4.0 + ## Major Features And Improvements * `tf.keras` is now part of the core TensorFlow API. * [`tf.data`](http://tensorflow.org/programmers_guide/datasets) is now part of diff --git a/configure.py b/configure.py index ada342a50a..bde7af8c0e 100644 --- a/configure.py +++ b/configure.py @@ -1397,10 +1397,6 @@ def set_grpc_build_flags(): write_to_bazelrc('build --define grpc_no_ares=true') -def set_build_strip_flag(): - write_to_bazelrc('build --strip=always') - - def set_windows_build_flags(): if is_windows(): # The non-monolithic build is not supported yet @@ -1523,7 +1519,6 @@ def main(): set_grpc_build_flags() set_cc_opt_flags(environ_cp) - set_build_strip_flag() set_windows_build_flags() if get_var( diff --git a/tensorflow/BUILD b/tensorflow/BUILD index 6d134dbb80..a73c4ca3aa 100644 --- a/tensorflow/BUILD +++ b/tensorflow/BUILD @@ -475,7 +475,7 @@ tf_cc_shared_object( # excludes all but a subset of function names. # On MacOS, the linker does not support version_script, but has an # an "-exported_symbols_list" command. -z defs disallows undefined -# symbols in object files. +# symbols in object files and -s strips the output. tf_cc_shared_object( name = "libtensorflow.so", @@ -489,6 +489,7 @@ tf_cc_shared_object( "//tensorflow:windows_msvc": [], "//conditions:default": [ "-z defs", + "-s", "-Wl,--version-script", # This line must be directly followed by the version_script.lds file "$(location //tensorflow/c:version_script.lds)", ], @@ -514,6 +515,7 @@ tf_cc_shared_object( "//tensorflow:windows_msvc": [], "//conditions:default": [ "-z defs", + "-s", "-Wl,--version-script", # This line must be directly followed by the version_script.lds file "$(location //tensorflow:tf_version_script.lds)", ], diff --git a/tensorflow/c/generate-pc.sh b/tensorflow/c/generate-pc.sh index 7184ad68fb..02a6a58b61 100755 --- a/tensorflow/c/generate-pc.sh +++ b/tensorflow/c/generate-pc.sh @@ -15,12 +15,10 @@ # ============================================================================== TF_PREFIX='/usr/local' -LIBDIR='lib' usage() { echo "Usage: $0 OPTIONS" echo -e "-p, --prefix\tset installation prefix (default: /usr/local)" - echo -e "-l, --libdir\tset lib directory (default: lib)" echo -e "-v, --version\tset TensorFlow version" echo -e "-h, --help\tdisplay this message" } @@ -28,7 +26,7 @@ usage() { [ $# == 0 ] && usage && exit 0 # read the options -ARGS=$(getopt -o p:l:v:h --long prefix:,libdir:,version:,help -n $0 -- "$@") +ARGS=$(getopt -o p:v:h --long prefix:,version:,help -n $0 -- "$@") eval set -- "$ARGS" # extract options and their arguments into variables. @@ -40,11 +38,6 @@ while true ; do "") shift 2 ;; *) TF_PREFIX=$2 ; shift 2 ;; esac ;; - -l|--libdir) - case "$2" in - "") shift 2 ;; - *) LIBDIR=$2 ; shift 2 ;; - esac ;; -v|--version) case "$2" in "") shift 2 ;; @@ -62,7 +55,7 @@ echo "Generating pkgconfig file for TensorFlow $TF_VERSION in $TF_PREFIX" cat << EOF > tensorflow.pc prefix=${TF_PREFIX} exec_prefix=\${prefix} -libdir=\${exec_prefix}/${LIBDIR} +libdir=\${exec_prefix}/lib includedir=\${prefix}/include Name: TensorFlow diff --git a/tensorflow/cc/gradients/math_grad.cc b/tensorflow/cc/gradients/math_grad.cc index 35a01e0341..52c177212a 100644 --- a/tensorflow/cc/gradients/math_grad.cc +++ b/tensorflow/cc/gradients/math_grad.cc @@ -38,7 +38,6 @@ REGISTER_NO_GRADIENT_OP("NotEqual"); REGISTER_NO_GRADIENT_OP("LogicalAnd"); REGISTER_NO_GRADIENT_OP("LogicalOr"); REGISTER_NO_GRADIENT_OP("LogicalNot"); -REGISTER_NO_GRADIENT_OP("Floor"); // Conjugate helper function returns the conjugate of an Output if it // is complex valued. diff --git a/tensorflow/cc/gradients/nn_grad.cc b/tensorflow/cc/gradients/nn_grad.cc index c73482d5f4..0cb3132e94 100644 --- a/tensorflow/cc/gradients/nn_grad.cc +++ b/tensorflow/cc/gradients/nn_grad.cc @@ -255,53 +255,6 @@ Status LRNGradHelper(const Scope& scope, const Operation& op, } REGISTER_GRADIENT_OP("LRN", LRNGradHelper); -Status SoftplusGradHelper(const Scope& scope, const Operation& op, - const std::vector& grad_inputs, - std::vector* grad_outputs) { - auto dx = internal::SoftplusGrad(scope, grad_inputs[0], op.input(0)); - grad_outputs->push_back(dx); - return scope.status(); -} -REGISTER_GRADIENT_OP("Softplus", SoftplusGradHelper); - -Status SoftsignGradHelper(const Scope& scope, const Operation& op, - const std::vector& grad_inputs, - std::vector* grad_outputs) { - auto dx = internal::SoftsignGrad(scope, grad_inputs[0], op.input(0)); - grad_outputs->push_back(dx); - return scope.status(); -} -REGISTER_GRADIENT_OP("Softsign", SoftsignGradHelper); - -Status FractionalAvgPoolGradHelper(const Scope& scope, const Operation& op, - const std::vector& grad_inputs, - std::vector* grad_outputs) { - bool overlapping; - TF_RETURN_IF_ERROR( - GetNodeAttr(op.output(0).node()->attrs(), "overlapping", &overlapping)); - auto dx = internal::FractionalAvgPoolGrad( - scope, Shape(scope, op.input(0), Shape::OutType(DT_INT64)), - grad_inputs[0], op.output(1), op.output(2), - internal::FractionalAvgPoolGrad::Overlapping(overlapping)); - grad_outputs->push_back(dx); - return scope.status(); -} -REGISTER_GRADIENT_OP("FractionalAvgPool", FractionalAvgPoolGradHelper); - -Status FractionalMaxPoolGradHelper(const Scope& scope, const Operation& op, - const std::vector& grad_inputs, - std::vector* grad_outputs) { - bool overlapping; - TF_RETURN_IF_ERROR( - GetNodeAttr(op.output(0).node()->attrs(), "overlapping", &overlapping)); - auto dx = internal::FractionalMaxPoolGrad( - scope, op.input(0), op.output(0), grad_inputs[0], op.output(1), - op.output(2), internal::FractionalMaxPoolGrad::Overlapping(overlapping)); - grad_outputs->push_back(dx); - return scope.status(); -} -REGISTER_GRADIENT_OP("FractionalMaxPool", FractionalMaxPoolGradHelper); - } // anonymous namespace } // namespace ops } // namespace tensorflow diff --git a/tensorflow/cc/gradients/nn_grad_test.cc b/tensorflow/cc/gradients/nn_grad_test.cc index b4d457a9d1..c4eba7ecb0 100644 --- a/tensorflow/cc/gradients/nn_grad_test.cc +++ b/tensorflow/cc/gradients/nn_grad_test.cc @@ -28,8 +28,6 @@ namespace { using ops::BiasAdd; using ops::Conv2D; using ops::Elu; -using ops::FractionalAvgPool; -using ops::FractionalMaxPool; using ops::L2Loss; using ops::LogSoftmax; using ops::LRN; @@ -43,8 +41,6 @@ using ops::Relu; using ops::Relu6; using ops::Selu; using ops::Softmax; -using ops::Softplus; -using ops::Softsign; class NNGradTest : public ::testing::Test { protected: @@ -75,30 +71,22 @@ class NNGradTest : public ::testing::Test { EXPECT_LT(max_error, 1e-3); } - // Sets tensor with random values, ensuring that every pair of elements are at - // least a reasonable amount apart. - // This is an issue for max pooling operations, in which perturbations by the - // numeric gradient computation in the gradient checker can change the max - // value if a pool has values that are too close together. + // Sets tensor with random values, ensuring that the max value is largest by + // a reasonable amount. + // This is an issue for MaxPool, MaxPoolV2 and MaxPool3D, in which + // perturbations by the numeric gradient computation in the gradient checker + // can change the max value if values are too close together. template - void SetRandomValuesForMaxPooling(Tensor* tensor) { + void SetRandomValuesWithBumpedMax(Tensor* tensor) { auto tensor_flat = tensor->flat(); - // First set the array to an increasing sequence of values spaced - // a reasonable amount apart - T cur = 0; - for (size_t i = 0; i < tensor->NumElements(); i++) { - tensor_flat(i) = cur; - cur += 5e-2; - } - // Fischer-Yates shuffle the array - for (size_t i = tensor->NumElements() - 1; i >= 1; i--) { - // j <- random integer 0 <= j <= i - size_t j = random::New64() % (i + 1); - // swap values at i, j - T tmp = tensor_flat(i); - tensor_flat(i) = tensor_flat(j); - tensor_flat(j) = tmp; + tensor_flat.setRandom(); + int32 max_index = 0; + for (size_t i = 1; i < tensor->NumElements(); i++) { + if (tensor_flat(i) > tensor_flat(max_index)) { + max_index = i; + } } + tensor_flat(max_index) += 1e-2; } Scope scope_; @@ -201,7 +189,7 @@ TEST_F(NNGradTest, MaxPoolGradHelper) { const std::vector strides{1, 2, 2, 1}; auto y = MaxPool(scope_, x, ksize, strides, "VALID"); Tensor x_init_value = Tensor(DT_FLOAT, x_shape); - SetRandomValuesForMaxPooling(&x_init_value); + SetRandomValuesWithBumpedMax(&x_init_value); RunTest(x, x_init_value, y, y_shape); } @@ -214,7 +202,7 @@ TEST_F(NNGradTest, MaxPoolGradV2Helper) { Tensor strides = test::AsTensor({1, 2, 2, 1}, {4}); auto y = MaxPoolV2(scope_, x, ksize, strides, "VALID"); Tensor x_init_value = Tensor(DT_FLOAT, x_shape); - SetRandomValuesForMaxPooling(&x_init_value); + SetRandomValuesWithBumpedMax(&x_init_value); RunTest(x, x_init_value, y, y_shape); } @@ -227,7 +215,7 @@ TEST_F(NNGradTest, MaxPool3DGradHelper) { const std::vector strides{1, 3, 3, 3, 1}; auto y = MaxPool3D(scope_, x, ksize, strides, "VALID"); Tensor x_init_value = Tensor(DT_FLOAT, x_shape); - SetRandomValuesForMaxPooling(&x_init_value); + SetRandomValuesWithBumpedMax(&x_init_value); RunTest(x, x_init_value, y, y_shape); } @@ -260,45 +248,5 @@ TEST_F(NNGradTest, LRN){ RunTest(x, x_shape, y, x_shape); } -TEST_F(NNGradTest, SoftplusGrad) { - TensorShape shape({3, 7}); - auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape)); - auto y = Softplus(scope_, x); - RunTest(x, shape, y, shape); -} - -TEST_F(NNGradTest, SoftsignGrad) { - TensorShape shape({3, 7}); - auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape)); - auto y = Softsign(scope_, x); - RunTest(x, shape, y, shape); -} - -TEST_F(NNGradTest, FractionalAvgPoolGradHelper) { - TensorShape x_shape({1, 3, 7, 1}); - auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape)); - // Force consistent pooling regions for unit testing. - auto y = FractionalAvgPool( - scope_, x, {1, 1.2, 1.9, 1}, - FractionalAvgPool::Deterministic(true).Overlapping(true).Seed(1).Seed2( - 2)); - TensorShape y_shape({1, 2, 3, 1}); - RunTest(x, x_shape, y.output, y_shape); -} - -TEST_F(NNGradTest, FractionalMaxPoolGradHelper) { - TensorShape x_shape({1, 3, 7, 1}); - auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape)); - // Force consistent pooling regions for unit testing. - auto y = FractionalMaxPool( - scope_, x, {1, 1.2, 1.9, 1}, - FractionalMaxPool::Deterministic(true).Overlapping(true).Seed(1).Seed2( - 2)); - Tensor x_init_value = Tensor(DT_FLOAT, x_shape); - SetRandomValuesForMaxPooling(&x_init_value); - TensorShape y_shape({1, 2, 3, 1}); - RunTest(x, x_init_value, y.output, y_shape); -} - } // namespace } // namespace tensorflow diff --git a/tensorflow/compiler/aot/codegen_test_h.golden b/tensorflow/compiler/aot/codegen_test_h.golden index 6641d45e83..6e050cf564 100644 --- a/tensorflow/compiler/aot/codegen_test_h.golden +++ b/tensorflow/compiler/aot/codegen_test_h.golden @@ -56,9 +56,9 @@ namespace bar { // // Memory stats: // arg bytes total: 104 -// arg bytes aligned: 192 +// arg bytes aligned: 128 // temp bytes total: 126 -// temp bytes aligned: 320 +// temp bytes aligned: 224 class MyClass : public tensorflow::XlaCompiledCpuFunction { public: // Number of input arguments for the compiled computation. diff --git a/tensorflow/compiler/aot/embedded_protocol_buffers.h b/tensorflow/compiler/aot/embedded_protocol_buffers.h index 4e194a6aba..ebfe4806c2 100644 --- a/tensorflow/compiler/aot/embedded_protocol_buffers.h +++ b/tensorflow/compiler/aot/embedded_protocol_buffers.h @@ -71,7 +71,7 @@ struct ProtobufToEmbed { const ::tensorflow::protobuf::MessageLite* message; }; -// Embeds a sequence of protocol buffers into an object file. +// Embeds a a sequence of protocol buffers into an object file. // // `target_triple` is the target triple for the target architecture for the // generated object file. diff --git a/tensorflow/compiler/aot/runtime.h b/tensorflow/compiler/aot/runtime.h index d1a669ceb1..d085864f00 100644 --- a/tensorflow/compiler/aot/runtime.h +++ b/tensorflow/compiler/aot/runtime.h @@ -25,8 +25,8 @@ namespace tensorflow { namespace tfcompile { namespace runtime { -// Align to 64-bytes, to mimic tensorflow::Allocator::kAllocatorAlignment. -static constexpr size_t kAlign = 64; +// Align to 32-bytes, to mimic tensorflow::Allocator::kAllocatorAlignment. +static constexpr size_t kAlign = 32; // aligned_buffer_bytes returns the sum of each size in `sizes`, skipping -1 // values. There are `n` entries in `sizes`. Each buffer is aligned to kAlign diff --git a/tensorflow/compiler/aot/runtime_test.cc b/tensorflow/compiler/aot/runtime_test.cc index 06ec623eb2..6d603a02eb 100644 --- a/tensorflow/compiler/aot/runtime_test.cc +++ b/tensorflow/compiler/aot/runtime_test.cc @@ -24,7 +24,7 @@ namespace runtime { namespace { TEST(Runtime, AlignmentValue) { - // We've chosen 64 byte alignment for the tfcompile runtime to mimic the + // We've chosen 32 byte alignment for the tfcompile runtime to mimic the // regular tensorflow allocator, which was chosen to play nicely with Eigen. // The tfcompile runtime also has a requirement that comes from the xla // generated code, on the relation: buffer_size >= 16 ? 2 * sizeof(void*) : 8 @@ -39,13 +39,13 @@ TEST(Runtime, AlignedBufferBytes) { EXPECT_EQ(aligned_buffer_bytes(sizesA, 1), 0); static constexpr intptr_t sizesB[1] = {3}; - EXPECT_EQ(aligned_buffer_bytes(sizesB, 1), 64); + EXPECT_EQ(aligned_buffer_bytes(sizesB, 1), 32); static constexpr intptr_t sizesC[1] = {32}; - EXPECT_EQ(aligned_buffer_bytes(sizesC, 1), 64); + EXPECT_EQ(aligned_buffer_bytes(sizesC, 1), 32); static constexpr intptr_t sizesD[7] = {1, -1, 32, -1, 64, 2, 3}; - EXPECT_EQ(aligned_buffer_bytes(sizesD, 7), 320); + EXPECT_EQ(aligned_buffer_bytes(sizesD, 7), 192); } void* add_ptr(void* base, uintptr_t delta) { @@ -101,11 +101,11 @@ TEST(Runtime, MallocFreeContiguousBuffers) { EXPECT_NE(base, nullptr); EXPECT_EQ(bufD[0], add_ptr(base, 0)); EXPECT_EQ(bufD[1], nullptr); - EXPECT_EQ(bufD[2], add_ptr(base, 64)); + EXPECT_EQ(bufD[2], add_ptr(base, 32)); EXPECT_EQ(bufD[3], nullptr); - EXPECT_EQ(bufD[4], add_ptr(base, 128)); - EXPECT_EQ(bufD[5], add_ptr(base, 192)); - EXPECT_EQ(bufD[6], add_ptr(base, 256)); + EXPECT_EQ(bufD[4], add_ptr(base, 64)); + EXPECT_EQ(bufD[5], add_ptr(base, 128)); + EXPECT_EQ(bufD[6], add_ptr(base, 160)); for (int i = 0; i < 7; ++i) { const intptr_t size = sizesD[i]; if (size != -1) { diff --git a/tensorflow/compiler/xla/service/cpu/BUILD b/tensorflow/compiler/xla/service/cpu/BUILD index 1067b38f93..d82922a359 100644 --- a/tensorflow/compiler/xla/service/cpu/BUILD +++ b/tensorflow/compiler/xla/service/cpu/BUILD @@ -178,7 +178,6 @@ cc_library( ":runtime_matmul", ":runtime_matmul_mkl", ":runtime_single_threaded_conv2d", - ":runtime_single_threaded_fft", ":runtime_single_threaded_matmul", "@llvm//:execution_engine", "@llvm//:core", @@ -517,6 +516,7 @@ cc_library( deps = [ "//tensorflow/compiler/xla:executable_run_options", "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/core:framework", "//tensorflow/core:framework_lite", "//third_party/eigen3", ], @@ -578,22 +578,6 @@ cc_library( ], ) -cc_library( - name = "runtime_single_threaded_fft", - srcs = [ - "runtime_fft_impl.h", - "runtime_single_threaded_fft.cc", - ], - hdrs = ["runtime_single_threaded_fft.h"], - copts = runtime_copts(), - visibility = ["//visibility:public"], - deps = [ - "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/core:framework_lite", - "//third_party/eigen3", - ], -) - cc_library( name = "runtime_single_threaded_matmul", srcs = ["runtime_single_threaded_matmul.cc"], diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc b/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc index 54c52bc08f..215405f680 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc @@ -51,8 +51,6 @@ extern const char* const kEigenConvF16SymbolName = extern const char* const kEigenConvF32SymbolName = "__xla_cpu_runtime_EigenConvF32"; extern const char* const kEigenFftSymbolName = "__xla_cpu_runtime_EigenFft"; -extern const char* const kEigenSingleThreadedFftSymbolName = - "__xla_cpu_runtime_EigenSingleThreadedFft"; extern const char* const kEigenSingleThreadedMatMulF16SymbolName = "__xla_cpu_runtime_EigenSingleThreadedMatMulF16"; extern const char* const kEigenSingleThreadedMatMulF32SymbolName = diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime.h b/tensorflow/compiler/xla/service/cpu/cpu_runtime.h index aa0e967123..1dce6efa5c 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_runtime.h +++ b/tensorflow/compiler/xla/service/cpu/cpu_runtime.h @@ -52,7 +52,6 @@ extern const char* const kMKLSingleThreadedMatMulF64SymbolName; extern const char* const kEigenConvF16SymbolName; extern const char* const kEigenConvF32SymbolName; extern const char* const kEigenFftSymbolName; -extern const char* const kEigenSingleThreadedFftSymbolName; extern const char* const kEigenSingleThreadedMatMulF16SymbolName; extern const char* const kEigenSingleThreadedMatMulF32SymbolName; extern const char* const kEigenSingleThreadedMatMulF64SymbolName; diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc index 758b8c62b4..2c20be155f 100644 --- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc +++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc @@ -1172,13 +1172,7 @@ Status IrEmitter::HandleFft(HloInstruction* fft) { {int8_ptr_type, int8_ptr_type, int8_ptr_type, int32_type, int32_type, int64_type, int64_type, int64_type, int64_type}, /*isVarArg=*/false); - - bool multi_threaded_eigen = - hlo_module_config_.debug_options().xla_cpu_multi_thread_eigen(); - const char* fn_name = multi_threaded_eigen - ? runtime::kEigenFftSymbolName - : runtime::kEigenSingleThreadedFftSymbolName; - + const char* fn_name = runtime::kEigenFftSymbolName; llvm::Function* fft_func = llvm::cast( module_->getOrInsertFunction(fn_name, fft_type)); fft_func->setCallingConv(llvm::CallingConv::C); diff --git a/tensorflow/compiler/xla/service/cpu/runtime_fft_impl.h b/tensorflow/compiler/xla/service/cpu/runtime_fft_impl.h index 0bf693edd0..984cb0616e 100644 --- a/tensorflow/compiler/xla/service/cpu/runtime_fft_impl.h +++ b/tensorflow/compiler/xla/service/cpu/runtime_fft_impl.h @@ -21,6 +21,8 @@ limitations under the License. #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/framework/numeric_types.h" +#include "tensorflow/core/framework/tensor.h" +#include "tensorflow/core/framework/tensor_shape.h" #include "tensorflow/core/platform/types.h" // 'tensorflow' namespace is used so that int64 and other types don't require @@ -69,9 +71,11 @@ void EigenFftR2C(const EigenDevice& device, complex64* out, float* operand, in_dims[0] = input_batch; Eigen::DSizes out_dims; out_dims[0] = input_batch; + TensorShape temp_shape{input_batch}; for (int i = 0; i < FFTRank; i++) { in_dims[i + 1] = fft_shape[i]; out_dims[i + 1] = i == FFTRank - 1 ? fft_shape[i] / 2 + 1 : fft_shape[i]; + temp_shape.AddDim(fft_shape[i]); } const Eigen::TensorMap, Eigen::Aligned> @@ -84,8 +88,8 @@ void EigenFftR2C(const EigenDevice& device, complex64* out, float* operand, const auto axes = Eigen::ArrayXi::LinSpaced(FFTRank, 1, FFTRank); // Compute the full FFT using a temporary tensor. - Eigen::Tensor full_fft(in_dims); - + Tensor temp(DataTypeToEnum::v(), temp_shape); + auto full_fft = temp.flat_inner_dims(); const Eigen::DSizes zero_start_indices; full_fft.device(device) = input.template fft(axes); @@ -108,9 +112,11 @@ void EigenFftC2R(const EigenDevice& device, float* out, complex64* operand, in_dims[0] = input_batch; Eigen::DSizes out_dims; out_dims[0] = input_batch; + TensorShape temp_shape{input_batch}; for (int i = 0; i < FFTRank; i++) { in_dims[i + 1] = i == FFTRank - 1 ? fft_shape[i] / 2 + 1 : fft_shape[i]; out_dims[i + 1] = fft_shape[i]; + temp_shape.AddDim(fft_shape[i]); } const Eigen::TensorMap, Eigen::Aligned> @@ -123,7 +129,8 @@ void EigenFftC2R(const EigenDevice& device, float* out, complex64* operand, // region we will slice from input given fft_shape. We slice input to // fft_shape on its inner-most dimensions, except the last (which we // slice to fft_shape[-1] / 2 + 1). - Eigen::Tensor full_fft(out_dims); + Tensor temp(DataTypeToEnum::v(), temp_shape); + auto full_fft = temp.flat_inner_dims(); // Calculate the starting point and range of the source of // negative frequency part. @@ -172,6 +179,7 @@ template void EigenFftWithRank(const EigenDevice& device, void* out, void* operand, int32 fft_type, int64 input_batch, int64 fft_length0, int64 fft_length1, int64 fft_length2) { + CHECK(::xla::FftType_IsValid(fft_type)) << fft_type; switch (fft_type) { case ::xla::FftType::FFT: EigenFftC2C( @@ -196,8 +204,7 @@ void EigenFftWithRank(const EigenDevice& device, void* out, void* operand, input_batch, fft_length0, fft_length1, fft_length2); break; default: - // Unsupported FFT type - abort(); + LOG(FATAL) << "Unsupported FFT type: " << fft_type; } } @@ -223,8 +230,7 @@ void EigenFftImpl(const EigenDevice& device, void* out, void* operand, fft_length1, fft_length2); break; default: - // Unsupported FFT rank - abort(); + LOG(FATAL) << "Unsupported FFT rank " << fft_rank; } } diff --git a/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.cc b/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.cc deleted file mode 100644 index 2613ddb127..0000000000 --- a/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.cc +++ /dev/null @@ -1,32 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.h" - -#include "tensorflow/compiler/xla/service/cpu/runtime_fft_impl.h" -#include "tensorflow/core/platform/dynamic_annotations.h" -#include "tensorflow/core/platform/types.h" - -using tensorflow::int32; -using tensorflow::int64; - -TF_ATTRIBUTE_NO_SANITIZE_MEMORY void __xla_cpu_runtime_EigenSingleThreadedFft( - const void* run_options_ptr, void* out, void* operand, int32 fft_type, - int32 fft_rank, int64 input_batch, int64 fft_length0, int64 fft_length1, - int64 fft_length2) { - tensorflow::xla::EigenFftImpl(Eigen::DefaultDevice(), out, operand, fft_type, - fft_rank, input_batch, fft_length0, fft_length1, - fft_length2); -} diff --git a/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.h b/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.h deleted file mode 100644 index dcd133d012..0000000000 --- a/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.h +++ /dev/null @@ -1,31 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_CPU_RUNTIME_SINGLE_THREADED_FFT_H_ -#define TENSORFLOW_COMPILER_XLA_SERVICE_CPU_RUNTIME_SINGLE_THREADED_FFT_H_ - -#include "tensorflow/core/platform/types.h" - -extern "C" { - -extern void __xla_cpu_runtime_EigenSingleThreadedFft( - const void* /* xla::ExecutableRunOptions* */ run_options_ptr, void* out, - void* operand, tensorflow::int32 fft_type, tensorflow::int32 fft_rank, - tensorflow::int64 input_batch, tensorflow::int64 fft_length0, - tensorflow::int64 fft_length1, tensorflow::int64 fft_length2); - -} // extern "C" - -#endif // TENSORFLOW_COMPILER_XLA_SERVICE_CPU_RUNTIME_SINGLE_THREADED_FFT_H_ diff --git a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc index c4c90515ac..8d8c5e4c44 100644 --- a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc +++ b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc @@ -38,7 +38,6 @@ limitations under the License. #include "tensorflow/compiler/xla/service/cpu/runtime_matmul.h" #include "tensorflow/compiler/xla/service/cpu/runtime_matmul_mkl.h" #include "tensorflow/compiler/xla/service/cpu/runtime_single_threaded_conv2d.h" -#include "tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.h" #include "tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.h" #include "tensorflow/compiler/xla/service/cpu/windows_compatibility.h" #include "tensorflow/compiler/xla/types.h" @@ -203,7 +202,6 @@ bool RegisterKnownJITSymbols() { REGISTER_CPU_RUNTIME_SYMBOL(MKLSingleThreadedMatMulF64); REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedConvF16); REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedConvF32); - REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedFft); REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedMatMulF16); REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedMatMulF32); REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedMatMulF64); diff --git a/tensorflow/compiler/xla/service/pattern_matcher.h b/tensorflow/compiler/xla/service/pattern_matcher.h index 2515222cf2..d3bc47e61e 100644 --- a/tensorflow/compiler/xla/service/pattern_matcher.h +++ b/tensorflow/compiler/xla/service/pattern_matcher.h @@ -204,7 +204,7 @@ class LayoutPattern { // Modifies the pattern to match only if the layout equals the given proto. // The layout must outlive the returned pattern. constexpr LayoutPattern> EqualTo( - const ::xla::Layout* layout) const { + const Layout* layout) const { return LayoutPattern>( LayoutPatternEqualImpl(impl_, layout), matched_layout_); } diff --git a/tensorflow/compiler/xla/service/tuple_simplifier.cc b/tensorflow/compiler/xla/service/tuple_simplifier.cc index 77bdcc9de0..e536c8afbf 100644 --- a/tensorflow/compiler/xla/service/tuple_simplifier.cc +++ b/tensorflow/compiler/xla/service/tuple_simplifier.cc @@ -30,17 +30,10 @@ limitations under the License. namespace xla { -TupleSimplifier::TupleSimplifier(bool exclude_entry_computation) : - exclude_entry_computation_(exclude_entry_computation) {} - StatusOr TupleSimplifier::Run(HloModule* module) { // Initially add all GTE and Tuple instructions to the worklist. std::queue worklist; for (auto* computation : module->computations()) { - if (exclude_entry_computation_ && - computation == module->entry_computation()) { - continue; - } for (auto* instruction : computation->instructions()) { if (instruction->opcode() == HloOpcode::kTuple || instruction->opcode() == HloOpcode::kGetTupleElement) { diff --git a/tensorflow/compiler/xla/service/tuple_simplifier.h b/tensorflow/compiler/xla/service/tuple_simplifier.h index 7509501883..e5e9b10b5b 100644 --- a/tensorflow/compiler/xla/service/tuple_simplifier.h +++ b/tensorflow/compiler/xla/service/tuple_simplifier.h @@ -27,20 +27,13 @@ namespace xla { // the module. class TupleSimplifier : public HloPassInterface { public: - TupleSimplifier() : TupleSimplifier(/*exclude_entry_computation=*/false) {} - explicit TupleSimplifier(bool exclude_entry_computation); + TupleSimplifier() {} ~TupleSimplifier() override {} tensorflow::StringPiece name() const override { return "tuple-simplifier"; } // Run tuple simplification on the given computation. Returns whether the // computation was changed. StatusOr Run(HloModule* module) override; - - private: - // When set, this pipeline stage will perform optimization of all computations - // apart from the module's entry computation. This is used by Graphcore's - // backend. - bool exclude_entry_computation_; }; } // namespace xla diff --git a/tensorflow/compiler/xla/service/tuple_simplifier_test.cc b/tensorflow/compiler/xla/service/tuple_simplifier_test.cc index d3635eae81..ca9ae91281 100644 --- a/tensorflow/compiler/xla/service/tuple_simplifier_test.cc +++ b/tensorflow/compiler/xla/service/tuple_simplifier_test.cc @@ -42,12 +42,6 @@ class TupleSimplifierTest : public HloTestBase { TF_ASSERT_OK(changed_status.status()); EXPECT_EQ(change_expected, changed_status.ValueOrDie()); } - void Run(HloModule* module, bool change_expected, bool exclude_entry) { - TupleSimplifier simplifier(exclude_entry); - auto changed_status = simplifier.Run(module); - TF_ASSERT_OK(changed_status.status()); - EXPECT_EQ(change_expected, changed_status.ValueOrDie()); - } const Shape scalar_shape_ = ShapeUtil::MakeShape(F32, {}); const Shape tuple_shape_ = ShapeUtil::MakeTupleShape( @@ -217,76 +211,5 @@ TEST_F(TupleSimplifierTest, IncompatibleTuples) { EXPECT_THAT(computation->root_instruction(), tuple); } -TEST_F(TupleSimplifierTest, CanExcludeEntryComputation) { - // Verify that the root computation can be excluded - auto module = CreateNewModule(); - - HloInstruction* p0; - HloInstruction* p1; - HloComputation* c0; - HloComputation* c1; - HloComputation* entry; - - { - HloComputation::Builder builder(TestName() + "_1"); - p0 = builder.AddInstruction( - HloInstruction::CreateParameter(0, tuple_shape_, "param")); - HloInstruction* gte0 = builder.AddInstruction( - HloInstruction::CreateGetTupleElement(scalar_shape_, p0, 0)); - HloInstruction* gte1 = builder.AddInstruction( - HloInstruction::CreateGetTupleElement(scalar_shape_, p0, 1)); - HloInstruction* gte2 = builder.AddInstruction( - HloInstruction::CreateGetTupleElement(scalar_shape_, p0, 2)); - - builder.AddInstruction(HloInstruction::CreateTuple({gte0, gte1, gte2})); - - c0 = module->AddEmbeddedComputation(builder.Build()); - } - { - HloComputation::Builder builder(TestName() + "_2"); - p1 = builder.AddInstruction( - HloInstruction::CreateParameter(0, tuple_shape_, "param")); - HloInstruction* gte0 = builder.AddInstruction( - HloInstruction::CreateGetTupleElement(scalar_shape_, p1, 0)); - HloInstruction* gte1 = builder.AddInstruction( - HloInstruction::CreateGetTupleElement(scalar_shape_, p1, 1)); - HloInstruction* gte2 = builder.AddInstruction( - HloInstruction::CreateGetTupleElement(scalar_shape_, p1, 2)); - - builder.AddInstruction(HloInstruction::CreateTuple({gte0, gte1, gte2})); - - c1 = module->AddEmbeddedComputation(builder.Build()); - } - { - HloComputation::Builder builder(TestName() + "_Entry"); - HloInstruction* tuple_param = builder.AddInstruction( - HloInstruction::CreateParameter(0, tuple_shape_, "param")); - HloInstruction* call0 = builder.AddInstruction( - HloInstruction::CreateCall(tuple_shape_, {tuple_param}, c0)); - HloInstruction* call1 = builder.AddInstruction( - HloInstruction::CreateCall(tuple_shape_, {tuple_param}, c1)); - HloInstruction* gte0 = builder.AddInstruction( - HloInstruction::CreateGetTupleElement(scalar_shape_, call0, 0)); - HloInstruction* gte1 = builder.AddInstruction( - HloInstruction::CreateGetTupleElement(scalar_shape_, call1, 1)); - HloInstruction* tuple0 = - builder.AddInstruction(HloInstruction::CreateTuple({gte0, gte1})); - HloInstruction* gte2 = builder.AddInstruction( - HloInstruction::CreateGetTupleElement(scalar_shape_, tuple0, 0)); - HloInstruction* gte3 = builder.AddInstruction( - HloInstruction::CreateGetTupleElement(scalar_shape_, tuple0, 1)); - - builder.AddInstruction(HloInstruction::CreateTuple({gte2, gte3})); - - entry = module->AddEntryComputation(builder.Build()); - } - - Run(module.get(), /*change_expected=*/true, /*exclude_entry=*/ true); - - EXPECT_THAT(c0->root_instruction(), p0); - EXPECT_THAT(c1->root_instruction(), p1); - EXPECT_THAT(entry->instruction_count(), 9); -} - } // namespace } // namespace xla diff --git a/tensorflow/contrib/autograph/__init__.py b/tensorflow/contrib/autograph/__init__.py index dbdbad8f4c..637e49c082 100644 --- a/tensorflow/contrib/autograph/__init__.py +++ b/tensorflow/contrib/autograph/__init__.py @@ -23,7 +23,6 @@ from __future__ import print_function # TODO(mdan): Bring only the relevant symbols to the top level. from tensorflow.contrib.autograph import utils -from tensorflow.contrib.autograph import operators from tensorflow.contrib.autograph.impl.api import convert from tensorflow.contrib.autograph.impl.api import converted_call from tensorflow.contrib.autograph.impl.api import do_not_convert @@ -44,8 +43,6 @@ _allowed_symbols = [ 'do_not_convert', 'to_code', 'to_graph', - # Overloaded operators - 'operators', # Special functions and directives 'set_element_type', 'set_loop_options', diff --git a/tensorflow/contrib/cmake/tf_c.cmake b/tensorflow/contrib/cmake/tf_c.cmake index 2e0a2fcef4..bda5e26f43 100644 --- a/tensorflow/contrib/cmake/tf_c.cmake +++ b/tensorflow/contrib/cmake/tf_c.cmake @@ -37,15 +37,13 @@ add_dependencies( tf_core_lib tf_protos_cc) -if(tensorflow_BUILD_PYTHON_BINDINGS) - add_library(tf_c_python_api OBJECT - "${tensorflow_source_dir}/tensorflow/c/python_api.cc" - "${tensorflow_source_dir}/tensorflow/c/python_api.h" - ) - add_dependencies( - tf_c_python_api - tf_c - tf_core_lib - tf_core_framework - tf_protos_cc) -endif() +add_library(tf_c_python_api OBJECT + "${tensorflow_source_dir}/tensorflow/c/python_api.cc" + "${tensorflow_source_dir}/tensorflow/c/python_api.h" +) +add_dependencies( + tf_c_python_api + tf_c + tf_core_lib + tf_core_framework + tf_protos_cc) diff --git a/tensorflow/contrib/cmake/tf_cc_ops.cmake b/tensorflow/contrib/cmake/tf_cc_ops.cmake index 6c90cf398c..f73da0b8ab 100644 --- a/tensorflow/contrib/cmake/tf_cc_ops.cmake +++ b/tensorflow/contrib/cmake/tf_cc_ops.cmake @@ -155,7 +155,7 @@ if (WIN32) set (pywrap_tensorflow_lib "${CMAKE_CURRENT_BINARY_DIR}/pywrap_tensorflow_internal.lib") endif() else (WIN32) - set (pywrap_tensorflow_lib "${CMAKE_CURRENT_BINARY_DIR}/libpywrap_tensorflow_internal${CMAKE_SHARED_LIBRARY_SUFFIX}") + set (pywrap_tensorflow_lib "${CMAKE_CURRENT_BINARY_DIR}/libpywrap_tensorflow_internal.so") endif (WIN32) add_custom_target(tf_extension_ops) diff --git a/tensorflow/contrib/cmake/tf_python.cmake b/tensorflow/contrib/cmake/tf_python.cmake index 9244604489..a0c3ddd28b 100755 --- a/tensorflow/contrib/cmake/tf_python.cmake +++ b/tensorflow/contrib/cmake/tf_python.cmake @@ -715,7 +715,7 @@ if(WIN32) endif() else() add_custom_command(TARGET pywrap_tensorflow_internal POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_BINARY_DIR}/libpywrap_tensorflow_internal${CMAKE_SHARED_LIBRARY_SUFFIX} + COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_BINARY_DIR}/libpywrap_tensorflow_internal.so ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/python/_pywrap_tensorflow_internal.so) endif() @@ -832,6 +832,7 @@ add_custom_command(TARGET tf_python_build_pip_package POST_BUILD add_custom_command(TARGET tf_python_copy_scripts_to_destination PRE_BUILD COMMAND ${CMAKE_COMMAND} -E copy ${tensorflow_source_dir}/tensorflow/contrib/testing/python/framework/util_test.py ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/testing/python/framework/) + add_custom_command(TARGET tf_python_build_pip_package POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy ${tensorflow_source_dir}/tensorflow/tools/pip_package/README ${CMAKE_CURRENT_BINARY_DIR}/tf_python/) diff --git a/tensorflow/contrib/cmake/tools/create_def_file.py b/tensorflow/contrib/cmake/tools/create_def_file.py index 4f957f1e0b..cffe069aa3 100644 --- a/tensorflow/contrib/cmake/tools/create_def_file.py +++ b/tensorflow/contrib/cmake/tools/create_def_file.py @@ -44,8 +44,7 @@ UNDNAME = "undname.exe" DUMPBIN = "dumpbin.exe" # Exclude if matched -EXCLUDE_RE = re.compile(r"RTTI|deleting destructor|::internal::|Internal|" - r"python_op_gen_internal|grappler") +EXCLUDE_RE = re.compile(r"RTTI|deleting destructor|::internal::") # Include if matched before exclude INCLUDEPRE_RE = re.compile(r"google::protobuf::internal::ExplicitlyConstructed|" @@ -57,10 +56,6 @@ INCLUDEPRE_RE = re.compile(r"google::protobuf::internal::ExplicitlyConstructed|" r"tensorflow::ops::internal::Enter|" r"tensorflow::strings::internal::AppendPieces|" r"tensorflow::strings::internal::CatPieces|" - r"tensorflow::errors::Internal|" - r"tensorflow::Tensor::CopyFromInternal|" - r"tensorflow::kernel_factory::" - r"OpKernelRegistrar::InitInternal|" r"tensorflow::io::internal::JoinPathImpl") # Include if matched after exclude @@ -69,7 +64,7 @@ INCLUDE_RE = re.compile(r"^(TF_\w*)$|" r"tensorflow::|" r"functor::|" r"\?nsync_|" - r"stream_executor::") + r"perftools::gputools") # We want to identify data members explicitly in the DEF file, so that no one # can implicitly link against the DLL if they use one of the variables exported diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/sinh_arcsinh_bijector_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/sinh_arcsinh_bijector_test.py index 795f1993ba..45760a29ee 100644 --- a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/sinh_arcsinh_bijector_test.py +++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/sinh_arcsinh_bijector_test.py @@ -151,24 +151,16 @@ class SinhArcsinhBijectorTest(test.TestCase): self.assertAllClose(y, bijector.forward(x).eval(), rtol=1e-4, atol=0.) self.assertAllClose(x, bijector.inverse(y).eval(), rtol=1e-4, atol=0.) - # On IBM PPC systems, longdouble (np.float128) is same as double except that it can have more precision. - # Type double being of 8 bytes, can't hold square of max of float64 (which is also 8 bytes) and - # below test fails due to overflow error giving inf. So this check avoids that error by skipping square - # calculation and corresponding assert. - - if np.amax(y) <= np.sqrt(np.finfo(np.float128).max) and \ - np.fabs(np.amin(y)) <= np.sqrt(np.fabs(np.finfo(np.float128).min)): - - # Do the numpy calculation in float128 to avoid inf/nan. - y_float128 = np.float128(y) - self.assertAllClose( - np.log(np.cosh( - np.arcsinh(y_float128) / tailweight - skewness) / np.sqrt( - y_float128**2 + 1)) - - np.log(tailweight), - bijector.inverse_log_det_jacobian(y, event_ndims=0).eval(), - rtol=1e-4, - atol=0.) + # Do the numpy calculation in float128 to avoid inf/nan. + y_float128 = np.float128(y) + self.assertAllClose( + np.log(np.cosh( + np.arcsinh(y_float128) / tailweight - skewness) / np.sqrt( + y_float128**2 + 1)) - + np.log(tailweight), + bijector.inverse_log_det_jacobian(y, event_ndims=0).eval(), + rtol=1e-4, + atol=0.) self.assertAllClose( -bijector.inverse_log_det_jacobian(y, event_ndims=0).eval(), bijector.forward_log_det_jacobian(x, event_ndims=0).eval(), diff --git a/tensorflow/contrib/eager/python/datasets.py b/tensorflow/contrib/eager/python/datasets.py index adf92c27ea..d7909dd5a2 100644 --- a/tensorflow/contrib/eager/python/datasets.py +++ b/tensorflow/contrib/eager/python/datasets.py @@ -106,8 +106,7 @@ class Iterator(iterator_ops.EagerIterator, checkpointable.CheckpointableBase): target_device=target, buffer_size=10, container="", - shared_name=_generate_shared_name( - "contrib_eager_iterator_function_buffer_resource")) + shared_name=_generate_shared_name("function_buffer_resource")) self._buffer_resource_deleter = resource_variable_ops.EagerResourceDeleter( # pylint: disable=line-too-long handle=self._buffer_resource_handle, handle_device=self._device) diff --git a/tensorflow/contrib/eager/python/examples/notebooks/4_high_level.ipynb b/tensorflow/contrib/eager/python/examples/notebooks/4_high_level.ipynb index 5749f22ac5..4fe3a0e3f3 100644 --- a/tensorflow/contrib/eager/python/examples/notebooks/4_high_level.ipynb +++ b/tensorflow/contrib/eager/python/examples/notebooks/4_high_level.ipynb @@ -68,7 +68,7 @@ "# simply construct the object. Most layers take as a first argument the number\n", "# of output dimensions / channels.\n", "layer = tf.keras.layers.Dense(100)\n", - "# The number of input dimensions is often unnecessary, as it can be inferred\n", + "# The number of input dimensionss is often unnecessary, as it can be inferred\n", "# the first time the layer is used, but it can be provided if you want to \n", "# specify it manually, which is useful in some complex models.\n", "layer = tf.keras.layers.Dense(10, input_shape=(None, 5))" @@ -267,7 +267,7 @@ " * `build`, where you know the shapes of the input tensors and can do the rest of the initialization\n", " * `call`, where you do the forward computation\n", "\n", - "Note that you don't have to wait until `build` is called to create your variables, you can also create them in `__init__`. However, the advantage of creating them in `build` is that it enables late variable creation based on the shape of the inputs the layer will operate on. On the other hand, creating variables in `__init__` would mean that shapes required to create the variables will need to be explicitly specified." + "Note that you don't have to wait until `build` is called to create your variables, you can also create them in `__init__`. However, the advantage of creating them in `build` is that it enables late variable creation based on the shape of the inputs the layer will operate on. On the other hand, creating variables in `__init__` would mean that shapes requires to create the variables will need to be explicitly specified." ] }, { diff --git a/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py index 05bcdac2ca..84a413c791 100644 --- a/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py +++ b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py @@ -346,8 +346,7 @@ def sequence_numeric_column( key, shape=(1,), default_value=0., - dtype=dtypes.float32, - normalizer_fn=None): + dtype=dtypes.float32): """Returns a feature column that represents sequences of numeric data. Example: @@ -371,12 +370,6 @@ def sequence_numeric_column( default_value: A single value compatible with `dtype` that is used for padding the sparse data into a dense `Tensor`. dtype: The type of values. - normalizer_fn: If not `None`, a function that can be used to normalize the - value of the tensor after `default_value` is applied for parsing. - Normalizer function takes the input `Tensor` as its argument, and returns - the output `Tensor`. (e.g. lambda x: (x - 3.0) / 4.2). Please note that - even though the most common use case of this function is normalization, it - can be used for any kind of Tensorflow transformations. Returns: A `_SequenceNumericColumn`. @@ -390,16 +383,12 @@ def sequence_numeric_column( if not (dtype.is_integer or dtype.is_floating): raise ValueError('dtype must be convertible to float. ' 'dtype: {}, key: {}'.format(dtype, key)) - if normalizer_fn is not None and not callable(normalizer_fn): - raise TypeError( - 'normalizer_fn must be a callable. Given: {}'.format(normalizer_fn)) return _SequenceNumericColumn( key, shape=shape, default_value=default_value, - dtype=dtype, - normalizer_fn=normalizer_fn) + dtype=dtype) def _assert_all_equal_and_return(tensors, name=None): @@ -418,7 +407,7 @@ class _SequenceNumericColumn( fc._SequenceDenseColumn, collections.namedtuple( '_SequenceNumericColumn', - ['key', 'shape', 'default_value', 'dtype', 'normalizer_fn'])): + ['key', 'shape', 'default_value', 'dtype'])): """Represents sequences of numeric data.""" @property @@ -430,10 +419,7 @@ class _SequenceNumericColumn( return {self.key: parsing_ops.VarLenFeature(self.dtype)} def _transform_feature(self, inputs): - input_tensor = inputs.get(self.key) - if self.normalizer_fn is not None: - input_tensor = self.normalizer_fn(input_tensor) - return input_tensor + return inputs.get(self.key) @property def _variable_shape(self): diff --git a/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py index 45d7b74046..ee74cf56dc 100644 --- a/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py +++ b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py @@ -28,7 +28,6 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor -from tensorflow.python.ops import sparse_ops from tensorflow.python.platform import test from tensorflow.python.training import monitored_session @@ -948,7 +947,6 @@ class SequenceNumericColumnTest(test.TestCase): self.assertEqual((1,), a.shape) self.assertEqual(0., a.default_value) self.assertEqual(dtypes.float32, a.dtype) - self.assertIsNone(a.normalizer_fn) def test_shape_saved_as_tuple(self): a = sfc.sequence_numeric_column('aaa', shape=[1, 2]) @@ -967,10 +965,6 @@ class SequenceNumericColumnTest(test.TestCase): ValueError, 'dtype must be convertible to float'): sfc.sequence_numeric_column('aaa', dtype=dtypes.string) - def test_normalizer_fn_must_be_callable(self): - with self.assertRaisesRegexp(TypeError, 'must be a callable'): - sfc.sequence_numeric_column('aaa', normalizer_fn='NotACallable') - def test_get_sequence_dense_tensor(self): sparse_input = sparse_tensor.SparseTensorValue( # example 0, values [[0.], [1]] @@ -991,41 +985,6 @@ class SequenceNumericColumnTest(test.TestCase): self.assertAllEqual( expected_dense_tensor, dense_tensor.eval(session=sess)) - def test_get_sequence_dense_tensor_with_normalizer_fn(self): - - def _increment_two(input_sparse_tensor): - return sparse_ops.sparse_add( - input_sparse_tensor, - sparse_tensor.SparseTensor(((0, 0), (1, 1)), (2.0, 2.0), (2, 2)) - ) - - sparse_input = sparse_tensor.SparseTensorValue( - # example 0, values [[0.], [1]] - # example 1, [[10.]] - indices=((0, 0), (0, 1), (1, 0)), - values=(0., 1., 10.), - dense_shape=(2, 2)) - - # Before _increment_two: - # [[0.], [1.]], - # [[10.], [0.]], - # After _increment_two: - # [[2.], [1.]], - # [[10.], [2.]], - expected_dense_tensor = [ - [[2.], [1.]], - [[10.], [2.]], - ] - numeric_column = sfc.sequence_numeric_column( - 'aaa', normalizer_fn=_increment_two) - - dense_tensor, _ = numeric_column._get_sequence_dense_tensor( - _LazyBuilder({'aaa': sparse_input})) - - with monitored_session.MonitoredSession() as sess: - self.assertAllEqual( - expected_dense_tensor, dense_tensor.eval(session=sess)) - def test_get_sequence_dense_tensor_with_shape(self): """Tests get_sequence_dense_tensor with shape !=(1,).""" sparse_input = sparse_tensor.SparseTensorValue( diff --git a/tensorflow/contrib/ffmpeg/__init__.py b/tensorflow/contrib/ffmpeg/__init__.py index 484ffee3e7..daba965a98 100644 --- a/tensorflow/contrib/ffmpeg/__init__.py +++ b/tensorflow/contrib/ffmpeg/__init__.py @@ -28,6 +28,7 @@ from __future__ import print_function from tensorflow.contrib.ffmpeg.ffmpeg_ops import decode_audio from tensorflow.contrib.ffmpeg.ffmpeg_ops import decode_video from tensorflow.contrib.ffmpeg.ffmpeg_ops import encode_audio +from tensorflow.contrib.ffmpeg.ffmpeg_ops import decode_video from tensorflow.python.util.all_util import remove_undocumented diff --git a/tensorflow/contrib/ffmpeg/ffmpeg_ops.py b/tensorflow/contrib/ffmpeg/ffmpeg_ops.py index b1b5126d9e..020b5c99c6 100644 --- a/tensorflow/contrib/ffmpeg/ffmpeg_ops.py +++ b/tensorflow/contrib/ffmpeg/ffmpeg_ops.py @@ -21,6 +21,7 @@ from __future__ import print_function from tensorflow.contrib.ffmpeg.ops import gen_decode_audio_op_py from tensorflow.contrib.ffmpeg.ops import gen_decode_video_op_py from tensorflow.contrib.ffmpeg.ops import gen_encode_audio_op_py +from tensorflow.contrib.ffmpeg.ops import gen_decode_video_op_py from tensorflow.contrib.util import loader from tensorflow.python.framework import ops from tensorflow.python.platform import resource_loader diff --git a/tensorflow/contrib/framework/__init__.py b/tensorflow/contrib/framework/__init__.py index dc49383c5c..10d1ecc738 100644 --- a/tensorflow/contrib/framework/__init__.py +++ b/tensorflow/contrib/framework/__init__.py @@ -119,13 +119,14 @@ from tensorflow.python.framework.smart_cond import smart_cond from tensorflow.python.framework.smart_cond import smart_constant_value from tensorflow.python.framework.tensor_spec import BoundedTensorSpec from tensorflow.python.framework.tensor_spec import TensorSpec +from tensorflow.python.ops.array_ops import broadcast_to from tensorflow.python.ops.init_ops import convolutional_delta_orthogonal from tensorflow.python.ops.init_ops import convolutional_orthogonal_1d from tensorflow.python.ops.init_ops import convolutional_orthogonal_2d from tensorflow.python.ops.init_ops import convolutional_orthogonal_3d from tensorflow.python.util.all_util import remove_undocumented -_allowed_symbols = ['nest'] +_allowed_symbols = ['nest', 'broadcast_to'] _nest_allowed_symbols = [ 'assert_same_structure', 'is_sequence', diff --git a/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op_test.py b/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op_test.py index a955e21b72..65cb94b5a4 100644 --- a/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op_test.py +++ b/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op_test.py @@ -301,8 +301,8 @@ class FusedConv2DBiasActivationTest(test.TestCase): conv = tensors[i] value = values[i] ref_value = ref_values[i] - tf_logging.info("expected = ", ref_value) - tf_logging.info("actual = ", value) + print("expected = ", ref_value) + print("actual = ", value) tol = 1e-5 if value.dtype == np.float16: tol = 1e-3 @@ -843,8 +843,7 @@ class FusedConvInt8Tests(test.TestCase): vertical_stride, padding_type) output_width = CalculateConvolvedOutputDim(input_width, filter_width, horizontal_stride, padding_type) - tf_logging.info("output_height=", output_height, ", output_width=", - output_width) + print("output_height=", output_height, ", output_width=", output_width) side_input, _, _ = gen_array_ops.quantize_v2( random_ops.random_uniform( @@ -881,8 +880,8 @@ class FusedConvInt8Tests(test.TestCase): with self.test_session( use_gpu=True, config=NoMemoryOptimizationConfig()) as sess: actual_y, expected_y = sess.run([actual, expected]) - tf_logging.info("actual_y = ", actual_y) - tf_logging.info("expected_y = ", expected_y) + print("actual_y = ", actual_y) + print("expected_y = ", expected_y) self.assertTrue(np.array_equal(actual_y, expected_y)) def testFusedConvInt8(self): diff --git a/tensorflow/contrib/hvx/hexagon_controller/src_impl/hexagon_controller.c b/tensorflow/contrib/hvx/hexagon_controller/src_impl/hexagon_controller.c index 2e5c84704f..6a5d982dc8 100644 --- a/tensorflow/contrib/hvx/hexagon_controller/src_impl/hexagon_controller.c +++ b/tensorflow/contrib/hvx/hexagon_controller/src_impl/hexagon_controller.c @@ -19,7 +19,7 @@ limitations under the License. #include "hexagon_controller.h" -#include +#include #include #include "adspmsgd.h" diff --git a/tensorflow/contrib/lite/download_dependencies.sh b/tensorflow/contrib/lite/download_dependencies.sh index 840015a7fa..436c3e1d4c 100755 --- a/tensorflow/contrib/lite/download_dependencies.sh +++ b/tensorflow/contrib/lite/download_dependencies.sh @@ -30,7 +30,9 @@ if [ ! -f $BZL_FILE_PATH ]; then fi EIGEN_URL="$(grep -o 'http.*bitbucket.org/eigen/eigen/get/.*tar\.gz' "${BZL_FILE_PATH}" | grep -v mirror.bazel | head -n1)" -GEMMLOWP_URL="$(grep -o 'https://mirror.bazel.build/github.com/google/gemmlowp/.*zip' "${BZL_FILE_PATH}" | head -n1)" +# TODO (yongtang): Replace the following with 'https://mirror.bazel.build/github.com/google/gemmlowp/.*zip' once +# the archive has been propagated in mirror.bazel.build. +GEMMLOWP_URL="$(grep -o 'https://github.com/google/gemmlowp/.*zip' "${BZL_FILE_PATH}" | head -n1)" GOOGLETEST_URL="https://github.com/google/googletest/archive/release-1.8.0.tar.gz" ABSL_URL="$(grep -o 'https://github.com/abseil/abseil-cpp/.*tar.gz' "${BZL_FILE_PATH}" | head -n1)" NEON_2_SSE_URL="https://github.com/intel/ARM_NEON_2_x86_SSE/archive/master.zip" diff --git a/tensorflow/contrib/lite/examples/minimal/minimal.cc b/tensorflow/contrib/lite/examples/minimal/minimal.cc index 8b0ace96cc..106e3b0270 100644 --- a/tensorflow/contrib/lite/examples/minimal/minimal.cc +++ b/tensorflow/contrib/lite/examples/minimal/minimal.cc @@ -38,7 +38,7 @@ using namespace tflite; int main(int argc, char *argv[]) { if(argc != 2) { - fprintf(stderr, "minimal \n"); + fprintf(stderr, "Usage: %s \n"); return 1; } const char* filename = argv[1]; diff --git a/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md b/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md index 965273f0f0..bb2e615eac 100644 --- a/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md +++ b/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md @@ -128,6 +128,7 @@ TensorFlow operation not listed above are likely unsupported. Notably, the following common ops are not supported at the moment: * [tf.depth_to_space](https://www.tensorflow.org/api_docs/python/tf/depth_to_space) +* [tf.gather](https://www.tensorflow.org/api_docs/python/tf/gather) * [tf.image.resize_bilinear](https://www.tensorflow.org/api_docs/python/tf/image/resize_bilinear) * [tf.tanh](https://www.tensorflow.org/api_docs/python/tf/tanh) @@ -305,19 +306,6 @@ Options { } ``` -**GATHER** - -``` -Inputs { - 0: params tensor - 1: indices tensor - 2: axis tensor (optional) -} -Outputs { - 0: a tensor with same type as the params tensor. -} -``` - **GREATER** ``` diff --git a/tensorflow/contrib/lite/java/ovic/README.md b/tensorflow/contrib/lite/java/ovic/README.md index 26349347fa..5efa70987e 100644 --- a/tensorflow/contrib/lite/java/ovic/README.md +++ b/tensorflow/contrib/lite/java/ovic/README.md @@ -2,7 +2,7 @@ This folder contains building code for track one of the [Low Power ImageNet Recognition Challenge workshop at CVPR 2018.](https://rebootingcomputing.ieee.org/home/sitemap/14-lpirc/80-low-power-image-recognition-challenge-lpirc-2018) -## Pre-requisite +## Pre-requesits Follow the steps [here](https://www.tensorflow.org/mobile/tflite/demo_android) to install Tensorflow, Bazel, and the Android NDK and SDK. @@ -49,7 +49,7 @@ Once you have a submission that follows the instructions from the [competition s You can call the validator binary below to verify that your model fits the format requirements. This often helps you to catch size mismatches (e.g. output should be [1, 1001] instead of [1,1,1,1001]). Let say the submission file is located at `/path/to/my_model.lite`, then call: ```sh -bazel build --cxxopt=--std=c++11 //tensorflow/contrib/lite/java/ovic:ovic_validator --cxxopt=-Wno-all +bazel build --cxxopt--std=c++11 //tensorflow/contrib/lite/java/ovic:ovic_validator --cxxopt=-Wno-all bazel-bin/tensorflow/contrib/lite/java/ovic/ovic_validator /path/to/my_model.lite ``` diff --git a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h index 1908f7fa6c..a2f192bbc2 100644 --- a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h @@ -1934,7 +1934,7 @@ inline void LstmCell(const float* input_data, const Dims<4>& input_dims, // The quantization of the input, output arrays is as follows: // - The input activations are quantized as uint8 on the interval // [-1, 127/128]. -// The rationale for that is that is the natural interval for output +// The rationale for that is that that is the natural interval for output // activations (see next point) and these need to be concatenated together. // We could accommodate different ranges by re-scaling, but we empirically // found that setting the input activations range to be [-1, 127/128] in the @@ -1999,7 +1999,7 @@ inline void LstmCell(const float* input_data, const Dims<4>& input_dims, // However, for a fixed-point implementation in 16-bit integers, using 5 // integer bits to represent the [-16, 16] range would leave only 11 // fractional bits, giving an increment of 2^-11 = 4.9e-4 between consecutive -// representable values. Notice that is higher than the +// representable values. Notice that that is higher than the // worst-case clamping error with clamping to [-8, 8]: 3.4e-4 for Logistic. // Using [-8, 8] thus seems like the better compromise overall, enjoying // an increment of 2.4e-4 between representable values and a worst-case diff --git a/tensorflow/contrib/lite/python/interpreter.py b/tensorflow/contrib/lite/python/interpreter.py index fd90823425..9400e757b9 100644 --- a/tensorflow/contrib/lite/python/interpreter.py +++ b/tensorflow/contrib/lite/python/interpreter.py @@ -55,7 +55,7 @@ class Interpreter(object): elif model_content and not model_path: self._interpreter = ( _interpreter_wrapper.InterpreterWrapper_CreateWrapperCPPFromBuffer( - model_content)) + model_content, len(model_content))) if not self._interpreter: raise ValueError( 'Failed to create model from {} bytes'.format(len(model_content))) diff --git a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.cc b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.cc index b283551c45..f705551fcb 100644 --- a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.cc +++ b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.cc @@ -397,14 +397,9 @@ InterpreterWrapper* InterpreterWrapper::CreateWrapperCPPFromFile( } InterpreterWrapper* InterpreterWrapper::CreateWrapperCPPFromBuffer( - PyObject* data) { - char * buf = nullptr; - Py_ssize_t length; - if (PY_TO_CPPSTRING(data, &buf, &length) == -1) { - return nullptr; - } + const char* data, size_t len) { std::unique_ptr model = - tflite::FlatBufferModel::BuildFromBuffer(buf, length); + tflite::FlatBufferModel::BuildFromBuffer(data, len); return model ? new InterpreterWrapper(std::move(model)) : nullptr; } diff --git a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h index cbeb53bee7..b0ed7c4559 100644 --- a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h +++ b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h @@ -40,7 +40,8 @@ class InterpreterWrapper { static InterpreterWrapper* CreateWrapperCPPFromFile(const char* model_path); // SWIG caller takes ownership of pointer. - static InterpreterWrapper* CreateWrapperCPPFromBuffer(PyObject* data); + static InterpreterWrapper* CreateWrapperCPPFromBuffer(const char* data, + size_t len); ~InterpreterWrapper(); bool AllocateTensors(); diff --git a/tensorflow/contrib/lite/python/lite.py b/tensorflow/contrib/lite/python/lite.py index 88dda7290b..0913cd2c5c 100644 --- a/tensorflow/contrib/lite/python/lite.py +++ b/tensorflow/contrib/lite/python/lite.py @@ -34,8 +34,6 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from six import PY3 - from google.protobuf import text_format as _text_format from google.protobuf.message import DecodeError from tensorflow.contrib.lite.python import lite_constants as constants @@ -56,7 +54,6 @@ from tensorflow.python.framework.importer import import_graph_def from tensorflow.python.ops.variables import global_variables_initializer from tensorflow.python.saved_model import signature_constants from tensorflow.python.saved_model import tag_constants -# from tensorflow.python.util.all_util import remove_undocumented class TocoConverter(object): @@ -206,12 +203,6 @@ class TocoConverter(object): except (_text_format.ParseError, DecodeError): try: print("Ignore 'tcmalloc: large alloc' warnings.") - - if not isinstance(file_content, str): - if PY3: - file_content = file_content.decode('utf-8') - else: - file_content = file_content.encode('utf-8') _text_format.Merge(file_content, graph_def) except (_text_format.ParseError, DecodeError): raise ValueError( @@ -391,5 +382,3 @@ def _freeze_graph(sess, output_tensors): output_arrays) else: return sess.graph_def - -# remove_undocumented(__name__) diff --git a/tensorflow/contrib/lite/toco/import_tensorflow.cc b/tensorflow/contrib/lite/toco/import_tensorflow.cc index 5c7fa09891..e33b430937 100644 --- a/tensorflow/contrib/lite/toco/import_tensorflow.cc +++ b/tensorflow/contrib/lite/toco/import_tensorflow.cc @@ -178,7 +178,7 @@ ArrayDataType ConvertDataType(tensorflow::DataType dtype) { else if (dtype == DT_STRING) return ArrayDataType::kString; else - LOG(INFO) << "Unsupported data type in placeholder op: " << dtype; + LOG(INFO) << "Unsupported data type in placehoder op: " << dtype; return ArrayDataType::kNone; } diff --git a/tensorflow/contrib/lite/toco/toco_port.cc b/tensorflow/contrib/lite/toco/toco_port.cc index de76fd4032..1b21c8bc60 100644 --- a/tensorflow/contrib/lite/toco/toco_port.cc +++ b/tensorflow/contrib/lite/toco/toco_port.cc @@ -20,12 +20,6 @@ limitations under the License. #include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/platform/logging.h" -#if defined(__ANDROID__) && defined(__ARM_ARCH_7A__) -namespace std { -double round(double x) { return ::round(x); } -} // namespace std -#endif - namespace toco { namespace port { void CopyToBuffer(const string& src, char* dest) { diff --git a/tensorflow/contrib/lite/toco/toco_port.h b/tensorflow/contrib/lite/toco/toco_port.h index 17f82b9dd7..5c019cb2bf 100644 --- a/tensorflow/contrib/lite/toco/toco_port.h +++ b/tensorflow/contrib/lite/toco/toco_port.h @@ -34,24 +34,6 @@ limitations under the License. #define TFLITE_PROTO_NS google::protobuf #endif -#ifdef __ANDROID__ -#include -namespace std { - -template -std::string to_string(T value) -{ - std::ostringstream os ; - os << value ; - return os.str() ; -} - -#ifdef __ARM_ARCH_7A__ -double round(double x); -#endif -} -#endif - namespace toco { namespace port { diff --git a/tensorflow/contrib/makefile/compile_nsync.sh b/tensorflow/contrib/makefile/compile_nsync.sh index a28fc3a87f..e8c6edd7ba 100755 --- a/tensorflow/contrib/makefile/compile_nsync.sh +++ b/tensorflow/contrib/makefile/compile_nsync.sh @@ -270,7 +270,7 @@ for arch in $archs; do PLATFORM_LDFLAGS=-pthread MKDEP=${CC} -M -std=c++11 PLATFORM_C=../../platform/c++11/src/nsync_semaphore_mutex.cc \ - ../../platform/posix/src/per_thread_waiter.c \ + ../../platform/c++11/src/per_thread_waiter.cc \ ../../platform/c++11/src/yield.cc \ ../../platform/c++11/src/time_rep_timespec.cc \ ../../platform/c++11/src/nsync_panic.cc diff --git a/tensorflow/contrib/makefile/download_dependencies.sh b/tensorflow/contrib/makefile/download_dependencies.sh index 48953e2e38..eff9081e35 100755 --- a/tensorflow/contrib/makefile/download_dependencies.sh +++ b/tensorflow/contrib/makefile/download_dependencies.sh @@ -27,7 +27,9 @@ if [ ! -f $BZL_FILE_PATH ]; then fi EIGEN_URL="$(grep -o 'http.*bitbucket.org/eigen/eigen/get/.*tar\.gz' "${BZL_FILE_PATH}" | grep -v mirror.bazel | head -n1)" -GEMMLOWP_URL="$(grep -o 'https://mirror.bazel.build/github.com/google/gemmlowp/.*zip' "${BZL_FILE_PATH}" | head -n1)" +# TODO (yongtang): Replace the following with 'https://mirror.bazel.build/github.com/google/gemmlowp/.*zip' once +# the archive has been propagated in mirror.bazel.build. +GEMMLOWP_URL="$(grep -o 'https://github.com/google/gemmlowp/.*zip' "${BZL_FILE_PATH}" | head -n1)" GOOGLETEST_URL="https://github.com/google/googletest/archive/release-1.8.0.tar.gz" NSYNC_URL="$(grep -o 'https://mirror.bazel.build/github.com/google/nsync/.*tar\.gz' "${BZL_FILE_PATH}" | head -n1)" PROTOBUF_URL="$(grep -o 'https://mirror.bazel.build/github.com/google/protobuf/.*tar\.gz' "${BZL_FILE_PATH}" | head -n1)" diff --git a/tensorflow/contrib/metrics/python/ops/metric_ops.py b/tensorflow/contrib/metrics/python/ops/metric_ops.py index a6be2084aa..2ed99d50a4 100644 --- a/tensorflow/contrib/metrics/python/ops/metric_ops.py +++ b/tensorflow/contrib/metrics/python/ops/metric_ops.py @@ -2503,7 +2503,7 @@ def _compute_recall_at_precision(tp, fp, fn, precision, name): name: An optional variable_scope name. Returns: - The recall at a given `precision`. + The recall at a the given `precision`. """ precisions = math_ops.div(tp, tp + fp + _EPSILON) tf_index = math_ops.argmin( diff --git a/tensorflow/contrib/mpi_collectives/kernels/ring.h b/tensorflow/contrib/mpi_collectives/kernels/ring.h index c001615d3f..1d56d588bc 100644 --- a/tensorflow/contrib/mpi_collectives/kernels/ring.h +++ b/tensorflow/contrib/mpi_collectives/kernels/ring.h @@ -129,7 +129,7 @@ cudaStream_t CudaStreamForMPI(); * has the fully accumulated Segment 1; and so on. The scatter-reduce is * complete. * - * Next, the allgather distributes these fully accumulated chunks across all + * Next, the allgather distributes these fully accumululated chunks across all * nodes. Communication proceeds in the same ring, once again in N-1 steps. At * the ith step, node j will send chunk (j - i + 1) and receive chunk (j - i). * For example, at the first iteration, the following transfers will occur: diff --git a/tensorflow/contrib/opt/python/training/adamax_test.py b/tensorflow/contrib/opt/python/training/adamax_test.py index 915e6504e1..21bf3f5313 100644 --- a/tensorflow/contrib/opt/python/training/adamax_test.py +++ b/tensorflow/contrib/opt/python/training/adamax_test.py @@ -224,10 +224,8 @@ class AdaMaxOptimizerTest(test.TestCase): var1_np, m1, v1 = adamax_update_numpy(var1_np, grads1_np, t, m1, v1) # Validate updated params - self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0), - rtol=1e-2) - self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1), - rtol=1e-2) + self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0)) + self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1)) if use_resource: self.assertEqual("var0_%d/AdaMax:0" % (i,), opt.get_slot(var=var0, name="m").name) diff --git a/tensorflow/contrib/opt/python/training/model_average_optimizer.py b/tensorflow/contrib/opt/python/training/model_average_optimizer.py index b6b10e500b..a7c97a1da2 100644 --- a/tensorflow/contrib/opt/python/training/model_average_optimizer.py +++ b/tensorflow/contrib/opt/python/training/model_average_optimizer.py @@ -62,7 +62,7 @@ class ModelAverageCustomGetter(object): """ def __init__(self, worker_device): - """Create a new `ModelAverageCustomGetter`. + """Create a new `ElasticAverageCustomGetter`. Args: worker_device: String. Name of the `worker` job. diff --git a/tensorflow/contrib/periodic_resample/BUILD b/tensorflow/contrib/periodic_resample/BUILD index aad1ca04c5..6ca7fe8b6e 100644 --- a/tensorflow/contrib/periodic_resample/BUILD +++ b/tensorflow/contrib/periodic_resample/BUILD @@ -6,13 +6,12 @@ exports_files(["LICENSE"]) load( "//tensorflow:tensorflow.bzl", - "tf_cc_test", + "py_test", "tf_gen_op_libs", "tf_custom_op_library", "tf_custom_op_py_library", "tf_gen_op_wrapper_py", ) -load("//tensorflow:tensorflow.bzl", "py_test") cc_library( name = "all_ops", @@ -85,23 +84,6 @@ py_test( ":init_py", "//tensorflow/contrib/util:util_py", "//tensorflow/python:framework_test_lib", - "//tensorflow/python:gradient_checker", - ], -) - -tf_cc_test( - name = "periodic_resample_op_cc_test", - size = "small", - srcs = [ - "ops/array_ops_test.cc", - ], - deps = [ - ":all_ops", - "//tensorflow/core:framework", - "//tensorflow/core:protos_all_proto", - "//tensorflow/core:test", - "//tensorflow/core:test_main", - "//tensorflow/core:testlib", ], ) diff --git a/tensorflow/contrib/periodic_resample/kernels/periodic_resample_op.cc b/tensorflow/contrib/periodic_resample/kernels/periodic_resample_op.cc index 514689cf45..e18923c8aa 100644 --- a/tensorflow/contrib/periodic_resample/kernels/periodic_resample_op.cc +++ b/tensorflow/contrib/periodic_resample/kernels/periodic_resample_op.cc @@ -22,9 +22,4 @@ namespace tensorflow { REGISTER_KERNEL_BUILDER(Name("PeriodicResample").Device(DEVICE_CPU), PeriodicResampleOp); - -REGISTER_KERNEL_BUILDER(Name("PeriodicResampleOpGrad") - .Device(DEVICE_CPU), - PeriodicResampleOpGrad); - } // namespace tensorflow diff --git a/tensorflow/contrib/periodic_resample/kernels/periodic_resample_op.h b/tensorflow/contrib/periodic_resample/kernels/periodic_resample_op.h index 42fba81a5c..3ab588c458 100644 --- a/tensorflow/contrib/periodic_resample/kernels/periodic_resample_op.h +++ b/tensorflow/contrib/periodic_resample/kernels/periodic_resample_op.h @@ -25,202 +25,92 @@ #include "tensorflow/core/framework/shape_inference.h" #include "tensorflow/core/framework/tensor_shape.h" #include "tensorflow/core/lib/core/status.h" -#include "tensorflow/core/util/work_sharder.h" namespace { -// Computes input tensor index for given output index during forward -// propagation through periodic_resample operation. -class InputIndexer { - public: - InputIndexer(const std::vector& output_dimensions, - const tensorflow::TensorShape& input_shape, - int adjustable_dimension) - : output_dimensions_(output_dimensions), - adjustable_dimension_(adjustable_dimension), - rank_(input_shape.dims()), - linear_output_index_(0), - linear_input_index_(0), - adjustable_dimension_carriage_sum_(0) { - auto input_dimensions = TensorShapeToVector(input_shape); - // factors by which input_dimensions increases/decreases w.r.t. - // output_dimensions - dimension_ceiling_ = - ComputeDimensionCeiling(output_dimensions, input_dimensions); - cumulative_dimensions_ = ComputeCumulativeDimensions(); - - output_indices_.resize(output_dimensions_.size()); - input_indices_.resize(output_dimensions_.size()); - - // Compute index_factors - index_factors_.resize(rank_); - tensorflow::int64 last_index_factor = 1; - for (auto r = rank_ - 1; r >= 0; --r) { - index_factors_[r] = last_index_factor; - last_index_factor *= input_dimensions[r]; - } - } - - tensorflow::int64 linear_input_index() const { return linear_input_index_; } - - void MoveToOutputIndex(tensorflow::int64 output_index); - void IncrementOutputIndex(); - - private: - void RecomputeInputAdjustableDimensionIndex() { - tensorflow::int64 index = adjustable_dimension_carriage_sum_; - index *= output_dimensions_[adjustable_dimension_]; - index += output_indices_[adjustable_dimension_]; - input_indices_[adjustable_dimension_] = index; - } - - std::vector TensorShapeToVector( - const tensorflow::TensorShape& tensor_shape); - - std::vector ComputeDimensionCeiling( - const std::vector& output_dimensions, - const std::vector& input_dimensions); - - std::vector ComputeCumulativeDimensions(); - - const std::vector output_dimensions_; - std::vector dimension_ceiling_; - std::vector index_factors_; - std::vector cumulative_dimensions_; - std::vector output_indices_; - std::vector input_indices_; - - const int adjustable_dimension_; - const int rank_; - tensorflow::int64 linear_output_index_; - tensorflow::int64 linear_input_index_; - tensorflow::int64 adjustable_dimension_carriage_sum_; -}; - -void InputIndexer::MoveToOutputIndex(tensorflow::int64 output_index) { - linear_output_index_ = output_index; - linear_input_index_ = 0; +template +IndexT compute_input_index( + IndexVecT* target_dimensions, const IndexT& output_index, + const IndexVecT& original_dimensions, const int& adjustable_dimension, + const std::vector& dimension_ceiling, + const std::vector& cumulative_dimensions, IndexT* result, + std::vector* output_indices, const int& rank) { + *result = 0; + output_indices->clear(); // un-rasterize the output index auto last_reduced_i = output_index; - for (auto r = rank_ - 1; r >= 0; --r) { - output_indices_[r] = last_reduced_i % output_dimensions_[r]; + for (auto r = rank - 1; r >= 0; --r) { + (*output_indices)[r] = last_reduced_i % (*target_dimensions)[r]; last_reduced_i = - (last_reduced_i - output_indices_[r]) / output_dimensions_[r]; + (last_reduced_i - (*output_indices)[r]) / (*target_dimensions)[r]; } - tensorflow::int64 carriage_sum = 0; - for (int qi = 0; qi < rank_; ++qi) { - if (qi == adjustable_dimension_) continue; - carriage_sum += cumulative_dimensions_[qi] * - (output_indices_[qi] % dimension_ceiling_[qi]); - } - adjustable_dimension_carriage_sum_ = carriage_sum; - // rasterize the input index - for (auto r = rank_ - 1; r >= 0; --r) { - if (r != adjustable_dimension_) { - input_indices_[r] = output_indices_[r] / dimension_ceiling_[r]; - } else { - RecomputeInputAdjustableDimensionIndex(); - } - } - for (auto r = rank_ - 1; r >= 0; --r) { - linear_input_index_ += index_factors_[r] * input_indices_[r]; - } -} - -void InputIndexer::IncrementOutputIndex() { - linear_output_index_++; - for (auto r = rank_ - 1; r >= 0; --r) { - auto old_carriage_sum_increment = - cumulative_dimensions_[r] * - (output_indices_[r] % dimension_ceiling_[r]); - output_indices_[r] = (output_indices_[r] + 1) % output_dimensions_[r]; - if (r != adjustable_dimension_) { - auto new_input_index = output_indices_[r] / dimension_ceiling_[r]; - linear_input_index_ += - (new_input_index - input_indices_[r]) * index_factors_[r]; - - input_indices_[r] = new_input_index; - - auto new_carriage_sum_increment = - cumulative_dimensions_[r] * - (output_indices_[r] % dimension_ceiling_[r]); - - adjustable_dimension_carriage_sum_ = adjustable_dimension_carriage_sum_ - - old_carriage_sum_increment + - new_carriage_sum_increment; - } - - if (output_indices_[r] != 0) { - // No more carries to higher indices. - break; + IndexT last_index_factor = 1; + for (auto r = rank - 1; r >= 0; --r) { + IndexT index = 0; + if (r != adjustable_dimension) + index = (*output_indices)[r] / dimension_ceiling[r]; + else { + for (int qi = 0; qi < rank; ++qi) { + if (qi == adjustable_dimension) continue; + index += cumulative_dimensions[qi] * + ((*output_indices)[qi] % dimension_ceiling[qi]); + } + index *= (*target_dimensions)[adjustable_dimension]; + index += (*output_indices)[r]; } + *result += last_index_factor * index; + last_index_factor *= original_dimensions[r]; } - auto old_adjustable_dimension_input_index = - input_indices_[adjustable_dimension_]; - RecomputeInputAdjustableDimensionIndex(); - linear_input_index_ += (input_indices_[adjustable_dimension_] - - old_adjustable_dimension_input_index) * - index_factors_[adjustable_dimension_]; -} -std::vector InputIndexer::TensorShapeToVector( - const tensorflow::TensorShape& tensor_shape) { - std::vector result(tensor_shape.dims()); - int count = 0; - for (const auto dim_info : tensor_shape) { - result[count] = dim_info.size; - ++count; - } - return result; + return *result; } -std::vector InputIndexer::ComputeDimensionCeiling( - const std::vector& output_dimensions, - const std::vector& input_dimensions) { - std::vector dimension_ceiling(input_dimensions.size()); - for (size_t i = 0; i < input_dimensions.size(); ++i) { - dimension_ceiling[i] = (output_dimensions[i] + input_dimensions[i] - 1) / - input_dimensions[i]; - } - return dimension_ceiling; -} +template // both types are needed here b/c IndexVecT and + // InputDataT are not related + void + fill_periodic_tensor( + tensorflow::OpKernelContext* context, + const IndexVecT& desired_shape, + const tensorflow::Tensor& input_tensor) { + // input is a strided array (last index is fastest, C-ordered) + auto input = input_tensor.flat(); + const int rank = input_tensor.dims(); + // original and target dimensions + std::vector original_dimensions(rank), + target_dimensions(rank); + tensorflow::int64 total_size(input_tensor.NumElements()), new_sliced_size(1); + // factors by which original_dimensions increases/decreases w.r.t. + // target_dimensions + std::vector dimension_ceiling(rank), + cumulative_dimensions(rank); + // index of adjustable dimension + int adjustable_dimension; + tensorflow::TensorShape output_shape; -std::vector InputIndexer::ComputeCumulativeDimensions() { - std::vector cumulative_dimensions(rank_); - int count = 0; - for (int i = 0; i < rank_; ++i) { - if (count == 0) { - cumulative_dimensions[count] = 1; - } else { - cumulative_dimensions[count] = - cumulative_dimensions[count - 1] * dimension_ceiling_[count - 1]; - } - ++count; - } - return cumulative_dimensions; -} + // requires that the rank of the input tensor and length of the desired shape + // are equal + OP_REQUIRES(context, rank == desired_shape.size(), + tensorflow::errors::InvalidArgument( + "periodic_resample expects the rank of the input tensor, ", + rank, ", to be the same as the length of the desired shape, ", + desired_shape.size(), ".")); -template -void process_desired_shape(tensorflow::OpKernelContext* context, - const tensorflow::TensorShape& input_tensor_shape, - const IndexVecT& desired_shape, - int* adjustable_dimension, - std::vector* target_dimensions, - tensorflow::int64* output_size) { - tensorflow::int64 new_sliced_size = 1; bool found = false; - const int rank = input_tensor_shape.dims(); + const auto& input_tensor_shape = input_tensor.shape(); + for (int i = 0; i < rank; ++i) { + // if (desired_shape(i) < 1) { if (desired_shape[i] < 1) { // only one index can be adjustable OP_REQUIRES(context, !found, tensorflow::errors::InvalidArgument( "periodic_resample expects only " "one index to be marked as adjustable.")); - *adjustable_dimension = i; + adjustable_dimension = i; found = true; } else { OP_REQUIRES( @@ -232,8 +122,9 @@ void process_desired_shape(tensorflow::OpKernelContext* context, i, " input tensor has size ", input_tensor_shape.dim_size(i), ", desired shape has size ", desired_shape[i], ".")); - (*target_dimensions)[i] = desired_shape[i]; - new_sliced_size *= (*target_dimensions)[i]; + // target_dimensions[i] = desired_shape(i); + target_dimensions[i] = desired_shape[i]; + new_sliced_size *= target_dimensions[i]; } } // at least one index needs to be adjustable @@ -241,50 +132,26 @@ void process_desired_shape(tensorflow::OpKernelContext* context, tensorflow::errors::InvalidArgument( "periodic_resample expects at least " "one index to be marked as adjustable.")); - (*target_dimensions)[*adjustable_dimension] = - input_tensor_shape.num_elements() / new_sliced_size; - - *output_size = new_sliced_size * (*target_dimensions)[*adjustable_dimension]; -} - -// Heuristic number based on measurements on -// Intel(R) Core(TM) i7-4930K CPU @ 3.40GHz -const tensorflow::int64 costPerFillIndex = 35; -enum class Mode { - kForward, - kGradient -}; - -// Computes either periodic_resample operation output or gradients for it, -// depending on |mode|. -// |original_shape| is always shape of input to periodic_resample operation. -// |source_tensor| is either source for periodic_resample (for forward mode) -// or gradients tensor. -// |desired_shape| is always shape, provided by user, to which forward -// propagation attempts resample input tensor. -template -void -do_periodic_resample_op(tensorflow::OpKernelContext* context, - const tensorflow::TensorShape& original_shape, - const tensorflow::PartialTensorShape& desired_shape, - const tensorflow::Tensor& source_tensor) { - const int rank = source_tensor.dims(); + int count = 0; + for (const auto dim_info : input_tensor.shape()) { + original_dimensions[count] = dim_info.size; + ++count; + } - // requires that the rank of the input tensor and length of the desired shape - // are equal - OP_REQUIRES(context, rank == desired_shape.dims(), - tensorflow::errors::InvalidArgument( - "periodic_resample expects the rank of the input tensor, ", - rank, ", to be the same as the length of the desired shape, ", - desired_shape.dims(), ".")); + target_dimensions[adjustable_dimension] = total_size / new_sliced_size; - std::vector target_dimensions(rank); - tensorflow::int64 new_size = 0; - // index of adjustable dimension - int adjustable_dimension = 0; - process_desired_shape(context, original_shape, desired_shape.dim_sizes(), - &adjustable_dimension, &target_dimensions, &new_size); + count = 0; + for (int i = 0; i < input_tensor.shape().dims(); ++i) { + dimension_ceiling[count] = tensorflow::int64(std::ceil( + float(target_dimensions[count]) / float(original_dimensions[count]))); + if (count == 0) + cumulative_dimensions[count] = 1; + else + cumulative_dimensions[count] = + cumulative_dimensions[count - 1] * dimension_ceiling[count - 1]; + ++count; + } // ensure that the new dimension is greater than zero OP_REQUIRES(context, target_dimensions[adjustable_dimension] > 0, @@ -293,14 +160,11 @@ do_periodic_resample_op(tensorflow::OpKernelContext* context, "adjustable dimension, ", adjustable_dimension, ", isn't greater than zero, ", target_dimensions[adjustable_dimension], ".")); - tensorflow::TensorShape output_shape; - if (mode == Mode::kForward) { - for (int i = 0; i < rank; ++i) { - output_shape.AddDim(target_dimensions[i]); - } - } else { - output_shape = original_shape; + for (int i = 0; i < rank; ++i) { + output_shape.AddDim(target_dimensions[i]); } + const auto new_size = + new_sliced_size * target_dimensions[adjustable_dimension]; // Create an output tensor and attach it to the current context tensorflow::Tensor* output_tensor = nullptr; @@ -308,73 +172,47 @@ do_periodic_resample_op(tensorflow::OpKernelContext* context, context->allocate_output(0, output_shape, &output_tensor)); auto output = output_tensor->flat(); - // input is a strided array (last index is fastest, C-ordered) - auto input = source_tensor.flat(); + // memory is allocated for these variables outside the inner loop for + // efficiency (although, I could create a separate class scope for + // this purpose instead) + tensorflow::int64 result = 0; + std::vector output_indices(target_dimensions.size()); // Fill output tensor with periodically resampled input tensor values - InputIndexer input_indexer(target_dimensions, original_shape, - adjustable_dimension); - - auto worker_threads = *(context->device()->tensorflow_cpu_worker_threads()); - auto fill_output_tensor = [&input_indexer, &output, &input]( - tensorflow::int64 start, tensorflow::int64 limit) { - InputIndexer local_indexer(input_indexer); - local_indexer.MoveToOutputIndex(start); - for (tensorflow::int64 output_index = start; output_index < limit; - ++output_index) { - if (mode == Mode::kForward) { - output(output_index) = input(local_indexer.linear_input_index()); - } else { - output(local_indexer.linear_input_index()) = input(output_index); - } - local_indexer.IncrementOutputIndex(); - } - }; - ::tensorflow::Shard(worker_threads.num_threads, worker_threads.workers, - new_size, costPerFillIndex, fill_output_tensor); -} - -#define DATA_TYPE_SWITCH(data_type, context, CASE) \ - switch (data_type) { \ - CASE(float) \ - CASE(double) \ - CASE(tensorflow::int32) \ - CASE(tensorflow::int64) \ - default: \ - context->CtxFailure(__FILE__, __LINE__, \ - tensorflow::errors::InvalidArgument( \ - "Unsuppored tensor elements type")); \ - break; \ + for (tensorflow::int64 output_index = 0; output_index < new_size; + ++output_index) { + output(output_index) = input(compute_input_index( + &target_dimensions, output_index, original_dimensions, + adjustable_dimension, dimension_ceiling, cumulative_dimensions, &result, + &output_indices, rank)); } +} void create_output_tensor( tensorflow::OpKernelContext* context, const tensorflow::Tensor& input_tensor, const tensorflow::DataType& input_tensor_type, - const tensorflow::PartialTensorShape& desired_shape) { -#define CASE(type) \ - case tensorflow::DataTypeToEnum::value: \ - do_periodic_resample_op( \ - context, input_tensor.shape(), desired_shape, input_tensor); \ - break; + const tensorflow::PartialTensorShape& desired_shape_tensor) { + auto desired_shape = desired_shape_tensor.dim_sizes(); - DATA_TYPE_SWITCH(input_tensor_type, context, CASE); -#undef CASE -} - -void create_grad_tensor(tensorflow::OpKernelContext* context, - const tensorflow::Tensor& grad_tensor, - const tensorflow::DataType& grad_tensor_type, - const tensorflow::TensorShape& original_shape, - const tensorflow::PartialTensorShape& desired_shape) { -#define CASE(type) \ - case tensorflow::DataTypeToEnum::value: \ - do_periodic_resample_op( \ - context, original_shape, desired_shape, grad_tensor); \ + // obligatory type switch + switch (input_tensor_type) { + case tensorflow::DataTypeToEnum::value: + fill_periodic_tensor(context, desired_shape, input_tensor); break; - - DATA_TYPE_SWITCH(grad_tensor_type, context, CASE); -#undef CASE + case tensorflow::DataTypeToEnum::value: + fill_periodic_tensor(context, desired_shape, input_tensor); + break; + case tensorflow::DataTypeToEnum::value: + fill_periodic_tensor(context, desired_shape, + input_tensor); + break; + case tensorflow::DataTypeToEnum::value: + fill_periodic_tensor(context, desired_shape, + input_tensor); + break; + default:; + } } } // namespace @@ -400,25 +238,4 @@ class PeriodicResampleOp : public tensorflow::OpKernel { tensorflow::PartialTensorShape desired_shape; }; -class PeriodicResampleOpGrad : public tensorflow::OpKernel { - public: - explicit PeriodicResampleOpGrad(tensorflow::OpKernelConstruction* context) - : tensorflow::OpKernel(context) { - OP_REQUIRES_OK(context, - context->GetAttr("original_shape", &original_shape)); - OP_REQUIRES_OK(context, context->GetAttr("desired_shape", &desired_shape)); - } - - void Compute(tensorflow::OpKernelContext* context) override { - const tensorflow::Tensor& grad_tensor = context->input(0); - const tensorflow::DataType grad_tensor_type = context->input_dtype(0); - create_grad_tensor(context, grad_tensor, grad_tensor_type, original_shape, - desired_shape); - } - - private: - tensorflow::TensorShape original_shape; - tensorflow::PartialTensorShape desired_shape; -}; - #endif // TENSORFLOW_KERNELS_PERIODICRESAMPLE_OP_H_ diff --git a/tensorflow/contrib/periodic_resample/ops/array_ops.cc b/tensorflow/contrib/periodic_resample/ops/array_ops.cc index fd38cd09b4..82bd796956 100644 --- a/tensorflow/contrib/periodic_resample/ops/array_ops.cc +++ b/tensorflow/contrib/periodic_resample/ops/array_ops.cc @@ -26,42 +26,7 @@ REGISTER_OP("PeriodicResample") .Input("values: T") .Attr("shape: shape") .Output("output: T") - .SetShapeFn([](shape_inference::InferenceContext* c) { - tensorflow::PartialTensorShape desired_shape; - TF_RETURN_IF_ERROR(c->GetAttr("shape", &desired_shape)); - shape_inference::ShapeHandle input_tensor_shape = c->input(0); - shape_inference::DimensionHandle num_input_elements = - c->NumElements(input_tensor_shape); - shape_inference::ShapeHandle result_shape_handle; - if (!shape_inference::InferenceContext::ValueKnown(num_input_elements)) { - TF_RETURN_IF_ERROR(c->MakeShapeFromPartialTensorShape( - desired_shape, &result_shape_handle)); - } else { - const int rank = c->Rank(input_tensor_shape); - std::vector target_dimensions(rank); - tensorflow::int64 new_sliced_size = 1; - int adjustable_dimension = 0; - for (int i = 0; i < rank; ++i) { - if (desired_shape.dim_size(i) < 1) { - adjustable_dimension = i; - } else { - target_dimensions[i] = desired_shape.dim_size(i); - new_sliced_size *= target_dimensions[i]; - } - } - target_dimensions[adjustable_dimension] = - shape_inference::InferenceContext::Value( - num_input_elements) / new_sliced_size; - tensorflow::TensorShape result_shape; - for (int i = 0; i < rank; ++i) { - result_shape.AddDim(target_dimensions[i]); - } - TF_RETURN_IF_ERROR(c->MakeShapeFromTensorShape( - result_shape, &result_shape_handle)); - } - c->set_output(0, result_shape_handle); - return Status::OK(); - }) + .SetShapeFn(shape_inference::ExplicitShape) .Doc(R"doc( Periodically resample elements of a tensor to conform to `shape`. @@ -136,20 +101,4 @@ output: Periodically resampled tensor that has dimensions specified as in )doc"); - -REGISTER_OP("PeriodicResampleOpGrad") - .Attr("T: numbertype") - .Input("grad: T") - .Attr("original_shape: shape") - .Attr("desired_shape: shape") - .Output("grad_values: T") - .SetShapeFn([](shape_inference::InferenceContext* c) { - tensorflow::TensorShape original_shape; - TF_RETURN_IF_ERROR(c->GetAttr("original_shape", &original_shape)); - shape_inference::ShapeHandle s; - TF_RETURN_IF_ERROR(c->MakeShapeFromTensorShape(original_shape, &s)); - c->set_output(0, s); - return Status::OK(); -}); - } // namespace tensorflow diff --git a/tensorflow/contrib/periodic_resample/ops/array_ops_test.cc b/tensorflow/contrib/periodic_resample/ops/array_ops_test.cc deleted file mode 100644 index 43b7c1799f..0000000000 --- a/tensorflow/contrib/periodic_resample/ops/array_ops_test.cc +++ /dev/null @@ -1,41 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/core/framework/node_def_builder.h" -#include "tensorflow/core/framework/shape_inference_testutil.h" -#include "tensorflow/core/framework/tensor_shape.pb.h" -#include "tensorflow/core/framework/tensor_testutil.h" -#include "tensorflow/core/lib/core/status_test_util.h" -#include "tensorflow/core/platform/test.h" - -namespace tensorflow { - -TEST(ArrayOpsTest, PeriodicResample_ShapeFn) { - ShapeInferenceTestOp op("PeriodicResample"); - // Case 1: output shape can be fully inferreed. - PartialTensorShape shape({4, 4, -1}); - TensorShapeProto shape_proto; - shape.AsProto(&shape_proto); - - TF_ASSERT_OK(NodeDefBuilder("test", "PeriodicResample") - .Input({"values", 0, DT_INT32}) - .Attr("shape", shape_proto) - .Finalize(&op.node_def)); - INFER_OK(op, "[2,2,4]", "[4,4,1]"); - // Case 2: output shape can not be inferred - report desired shape. - INFER_OK(op, "[2,2,?]", "[4,4,?]"); -} - -} // end namespace tensorflow diff --git a/tensorflow/contrib/periodic_resample/python/kernel_tests/periodic_resample_op_test.py b/tensorflow/contrib/periodic_resample/python/kernel_tests/periodic_resample_op_test.py index 31a6fe1d94..a25de55e18 100644 --- a/tensorflow/contrib/periodic_resample/python/kernel_tests/periodic_resample_op_test.py +++ b/tensorflow/contrib/periodic_resample/python/kernel_tests/periodic_resample_op_test.py @@ -21,11 +21,8 @@ from __future__ import print_function import numpy from tensorflow.contrib.periodic_resample import periodic_resample -from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors_impl from tensorflow.python.framework import test_util -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import gradient_checker from tensorflow.python.ops import variables from tensorflow.python.platform import googletest @@ -96,6 +93,7 @@ class PeriodicResampleTest(test_util.TensorFlowTestCase): def testPeriodicResampleErrors(self): input_tensor = numpy.zeros(shape=[1, 2, 2, 4]) with self.test_session(): + variables.global_variables_initializer().run() with self.assertRaisesWithPredicateMatch( errors_impl.InvalidArgumentError, 'Dimension 3 input tensor has size 4, desired shape has size 1'): @@ -105,29 +103,6 @@ class PeriodicResampleTest(test_util.TensorFlowTestCase): '4, to be the same as the length of the desired shape, 3'): periodic_resample(input_tensor, [None, 4, 4]).eval() - def testPeriodicResampleGradient(self): - desired_shape = numpy.array([4, 4, None]) - result_shape = (4, 4, 1) - input_shape = (2, 2, 4) - with self.test_session() as sess: - x = array_ops.placeholder(dtypes.float32, shape=input_shape) - output = periodic_resample(x, desired_shape) - error = gradient_checker.compute_gradient_error( - x, input_shape, output, result_shape) - self.assertLess(error, 1e-4) - - def testPeriodicResampleShapeInference(self): - with self.test_session() as sess: - # Case 1: output shape can be fully inferreed. - x = array_ops.placeholder(dtypes.float32, shape=(2, 2, 4)) - output = periodic_resample(x, [4, 4, None]) - self.assertEqual(output.shape, [4, 4, 1]) - # Case 2: output shape can not be inferred - report desired shape. - x = array_ops.placeholder(dtypes.float32, shape=(2, 2, None)) - output = periodic_resample(x, [4, 4, None]) - self.assertTrue(output.shape.is_compatible_with([4, 4, None])) - self.assertEqual(output.shape[2].value, None) - if __name__ == '__main__': googletest.main() diff --git a/tensorflow/contrib/periodic_resample/python/ops/periodic_resample_op.py b/tensorflow/contrib/periodic_resample/python/ops/periodic_resample_op.py index 470e300ccb..348623d8f8 100644 --- a/tensorflow/contrib/periodic_resample/python/ops/periodic_resample_op.py +++ b/tensorflow/contrib/periodic_resample/python/ops/periodic_resample_op.py @@ -21,17 +21,11 @@ from __future__ import print_function # pylint: disable=unused-import from tensorflow.contrib.periodic_resample.python.ops import gen_periodic_resample_op -from tensorflow.contrib.periodic_resample.python.ops.gen_periodic_resample_op import periodic_resample, periodic_resample_op_grad +from tensorflow.contrib.periodic_resample.python.ops.gen_periodic_resample_op import periodic_resample from tensorflow.contrib.util import loader -from tensorflow.python.framework import ops from tensorflow.python.platform import resource_loader # pylint: enable=unused-import _periodic_resample_op = loader.load_op_library( resource_loader.get_path_to_datafile('_periodic_resample_op.so')) - -@ops.RegisterGradient("PeriodicResample") -def _periodic_resample_grad_cc(op, grad): - return periodic_resample_op_grad( - grad, op.inputs[0].shape, op.get_attr('shape')) diff --git a/tensorflow/contrib/predictor/contrib_estimator_predictor.py b/tensorflow/contrib/predictor/contrib_estimator_predictor.py index af3b2ad1b5..b7a98c68e2 100644 --- a/tensorflow/contrib/predictor/contrib_estimator_predictor.py +++ b/tensorflow/contrib/predictor/contrib_estimator_predictor.py @@ -34,8 +34,7 @@ class ContribEstimatorPredictor(predictor.Predictor): prediction_input_fn, input_alternative_key=None, output_alternative_key=None, - graph=None, - config=None): + graph=None): """Initialize a `ContribEstimatorPredictor`. Args: @@ -49,7 +48,6 @@ class ContribEstimatorPredictor(predictor.Predictor): multi-headed models. graph: Optional. The Tensorflow `graph` in which prediction should be done. - config: `ConfigProto` proto used to configure the session. """ self._graph = graph or ops.Graph() with self._graph.as_default(): @@ -60,7 +58,6 @@ class ContribEstimatorPredictor(predictor.Predictor): checkpoint_path = saver.latest_checkpoint(estimator.model_dir) self._session = monitored_session.MonitoredSession( session_creator=monitored_session.ChiefSessionCreator( - config=config, checkpoint_filename_with_path=checkpoint_path)) input_alternative_key = ( diff --git a/tensorflow/contrib/predictor/core_estimator_predictor.py b/tensorflow/contrib/predictor/core_estimator_predictor.py index a725072e72..d78d94c269 100644 --- a/tensorflow/contrib/predictor/core_estimator_predictor.py +++ b/tensorflow/contrib/predictor/core_estimator_predictor.py @@ -51,8 +51,7 @@ class CoreEstimatorPredictor(predictor.Predictor): estimator, serving_input_receiver_fn, output_key=None, - graph=None, - config=None): + graph=None): """Initialize a `CoreEstimatorPredictor`. Args: @@ -63,7 +62,6 @@ class CoreEstimatorPredictor(predictor.Predictor): `None`, then `DEFAULT_SERVING_SIGNATURE_DEF_KEY` is used. graph: Optional. The Tensorflow `graph` in which prediction should be done. - config: `ConfigProto` proto used to configure the session. """ self._graph = graph or ops.Graph() with self._graph.as_default(): @@ -73,7 +71,6 @@ class CoreEstimatorPredictor(predictor.Predictor): checkpoint_dir = estimator.model_dir self._session = monitored_session.MonitoredSession( session_creator=monitored_session.ChiefSessionCreator( - config=config, checkpoint_dir=checkpoint_dir)) feed_tensor_info = signature_def.inputs diff --git a/tensorflow/contrib/predictor/predictor_factories.py b/tensorflow/contrib/predictor/predictor_factories.py index f275bc15ad..6e77e934fe 100644 --- a/tensorflow/contrib/predictor/predictor_factories.py +++ b/tensorflow/contrib/predictor/predictor_factories.py @@ -30,8 +30,7 @@ def from_contrib_estimator(estimator, prediction_input_fn, input_alternative_key=None, output_alternative_key=None, - graph=None, - config=None): + graph=None): """Constructs a `Predictor` from a `tf.contrib.learn.Estimator`. Args: @@ -45,7 +44,6 @@ def from_contrib_estimator(estimator, multi-headed models. graph: Optional. The Tensorflow `graph` in which prediction should be done. - config: `ConfigProto` proto used to configure the session. Returns: An initialized `Predictor`. @@ -64,15 +62,13 @@ def from_contrib_estimator(estimator, prediction_input_fn, input_alternative_key=input_alternative_key, output_alternative_key=output_alternative_key, - graph=graph, - config=config) + graph=graph) def from_estimator(estimator, serving_input_receiver_fn, output_key=None, - graph=None, - config=None): + graph=None): """Constructs a `Predictor` from a `tf.python.estimator.Estimator`. Args: @@ -83,7 +79,6 @@ def from_estimator(estimator, `None`, then `DEFAULT_SERVING_SIGNATURE_DEF_KEY` is used. graph: Optional. The Tensorflow `graph` in which prediction should be done. - config: `ConfigProto` proto used to configure the session. Returns: An initialized `Predictor`. @@ -98,19 +93,14 @@ def from_estimator(estimator, 'tf.contrib.learn.Estimator. You likely want to call ' 'from_contrib_estimator.') return core_estimator_predictor.CoreEstimatorPredictor( - estimator, - serving_input_receiver_fn, - output_key=output_key, - graph=graph, - config=config) + estimator, serving_input_receiver_fn, output_key=output_key, graph=graph) def from_saved_model(export_dir, signature_def_key=None, signature_def=None, tags=None, - graph=None, - config=None): + graph=None): """Constructs a `Predictor` from a `SavedModel` on disk. Args: @@ -125,7 +115,6 @@ def from_saved_model(export_dir, `SignatureDef`. Defaults to `DEFAULT_TAGS`. graph: Optional. The Tensorflow `graph` in which prediction should be done. - config: `ConfigProto` proto used to configure the session. Returns: An initialized `Predictor`. @@ -139,5 +128,4 @@ def from_saved_model(export_dir, signature_def_key=signature_def_key, signature_def=signature_def, tags=tags, - graph=graph, - config=config) + graph=graph) diff --git a/tensorflow/contrib/predictor/predictor_factories_test.py b/tensorflow/contrib/predictor/predictor_factories_test.py index a2ef1dc3af..578d9424b2 100644 --- a/tensorflow/contrib/predictor/predictor_factories_test.py +++ b/tensorflow/contrib/predictor/predictor_factories_test.py @@ -20,7 +20,6 @@ from __future__ import print_function from tensorflow.contrib.predictor import predictor_factories from tensorflow.contrib.predictor import testing_common -from tensorflow.core.protobuf import config_pb2 from tensorflow.python.platform import test MODEL_DIR_NAME = 'contrib/predictor/test_export_dir' @@ -42,11 +41,6 @@ class PredictorFactoriesTest(test.TestCase): """Test loading from_saved_model with tags.""" predictor_factories.from_saved_model(self._export_dir, tags='serve') - def testFromSavedModelWithSessionConfig(self): - """Test loading from_saved_model with session config.""" - predictor_factories.from_saved_model( - self._export_dir, config=config_pb2.ConfigProto()) - def testFromSavedModelWithBadTags(self): """Test that loading fails for bad tags.""" bad_tags_regex = ('.*? could not be found in SavedModel') @@ -59,13 +53,6 @@ class PredictorFactoriesTest(test.TestCase): predictor_factories.from_contrib_estimator( estimator, input_fn, output_alternative_key='sum') - def testFromContribEstimatorWithSessionConfig(self): - estimator = testing_common.get_arithmetic_estimator(core=False) - input_fn = testing_common.get_arithmetic_input_fn(core=False) - predictor_factories.from_contrib_estimator( - estimator, input_fn, output_alternative_key='sum', - config=config_pb2.ConfigProto()) - def testFromContribEstimatorWithCoreEstimatorRaises(self): estimator = testing_common.get_arithmetic_estimator(core=True) input_fn = testing_common.get_arithmetic_input_fn(core=True) @@ -77,12 +64,6 @@ class PredictorFactoriesTest(test.TestCase): input_fn = testing_common.get_arithmetic_input_fn(core=True) predictor_factories.from_estimator(estimator, input_fn) - def testFromCoreEstimatorWithSessionConfig(self): - estimator = testing_common.get_arithmetic_estimator(core=True) - input_fn = testing_common.get_arithmetic_input_fn(core=True) - predictor_factories.from_estimator( - estimator, input_fn, config=config_pb2.ConfigProto()) - def testFromCoreEstimatorWithContribEstimatorRaises(self): estimator = testing_common.get_arithmetic_estimator(core=False) input_fn = testing_common.get_arithmetic_input_fn(core=False) diff --git a/tensorflow/contrib/predictor/saved_model_predictor.py b/tensorflow/contrib/predictor/saved_model_predictor.py index 95da6d04ed..0dbca0f813 100644 --- a/tensorflow/contrib/predictor/saved_model_predictor.py +++ b/tensorflow/contrib/predictor/saved_model_predictor.py @@ -121,8 +121,7 @@ class SavedModelPredictor(predictor.Predictor): input_names=None, output_names=None, tags=None, - graph=None, - config=None): + graph=None): """Initialize a `CoreEstimatorPredictor`. Args: @@ -143,7 +142,6 @@ class SavedModelPredictor(predictor.Predictor): the correct `SignatureDef`. Defaults to `DEFAULT_TAGS`. graph: Optional. The Tensorflow `graph` in which prediction should be done. - config: `ConfigProto` proto used to configure the session. Raises: ValueError: If more than one of signature_def_key OR signature_def OR (input_names AND output_names) is specified. @@ -154,7 +152,7 @@ class SavedModelPredictor(predictor.Predictor): self._graph = graph or ops.Graph() with self._graph.as_default(): - self._session = session.Session(config=config) + self._session = session.Session() loader.load(self._session, tags.split(','), export_dir) if input_names is None: diff --git a/tensorflow/contrib/quantize/README.md b/tensorflow/contrib/quantize/README.md index 27a933c0f9..c83623ec94 100644 --- a/tensorflow/contrib/quantize/README.md +++ b/tensorflow/contrib/quantize/README.md @@ -6,7 +6,7 @@ inference. The details of the transformation implemented in this package is described here [1]. This is done using the -[fake quantization op](https://www.tensorflow.org/api_guides/python/array_ops#Fake_quantization). +[fake quantization op](https://www.tensorflow.org/versions/r0.12/api_docs/python/array_ops/fake_quantization). Literature has shown that fixed point networks provide comparable performance to floating point networks [2]. This is achieved by modeling the quantization diff --git a/tensorflow/contrib/slim/python/slim/evaluation_test.py b/tensorflow/contrib/slim/python/slim/evaluation_test.py index 3d0308aaf3..94fc12ca81 100644 --- a/tensorflow/contrib/slim/python/slim/evaluation_test.py +++ b/tensorflow/contrib/slim/python/slim/evaluation_test.py @@ -26,6 +26,7 @@ import time import numpy as np from tensorflow.contrib.framework.python.ops import variables as variables_lib +from tensorflow.contrib.metrics.python.ops import metric_ops from tensorflow.contrib.slim.python.slim import evaluation from tensorflow.contrib.training.python.training import evaluation as evaluation_lib from tensorflow.core.protobuf import saver_pb2 @@ -36,7 +37,6 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops -from tensorflow.python.ops import metrics from tensorflow.python.ops import variables from tensorflow.python.platform import flags from tensorflow.python.platform import gfile @@ -89,8 +89,8 @@ class EvaluationTest(test.TestCase): self._predictions, self._scale = TestModel(self._inputs) def testFinalOpsOnEvaluationLoop(self): - value_op, update_op = metrics.accuracy( - labels=self._labels, predictions=self._predictions) + value_op, update_op = metric_ops.streaming_accuracy(self._predictions, + self._labels) init_op = control_flow_ops.group(variables.global_variables_initializer(), variables.local_variables_initializer()) # Create checkpoint and log directories: @@ -136,10 +136,9 @@ class EvaluationTest(test.TestCase): self.assertTrue(obj.hook_was_run) def _create_names_to_metrics(self, predictions, labels): - accuracy0, update_op0 = metrics.accuracy( - labels=labels, predictions=predictions) - accuracy1, update_op1 = metrics.accuracy( - labels=labels, predictions=predictions + 1) + accuracy0, update_op0 = metric_ops.streaming_accuracy(predictions, labels) + accuracy1, update_op1 = metric_ops.streaming_accuracy(predictions + 1, + labels) names_to_values = {'Accuracy': accuracy0, 'Another_accuracy': accuracy1} names_to_updates = {'Accuracy': update_op0, 'Another_accuracy': update_op1} @@ -199,8 +198,8 @@ class EvaluationTest(test.TestCase): predictions_limited = input.limit_epochs(self._predictions, num_epochs=1) labels_limited = input.limit_epochs(self._labels, num_epochs=1) - value_op, update_op = metrics.accuracy( - labels=labels_limited, predictions=predictions_limited) + value_op, update_op = metric_ops.streaming_accuracy( + predictions_limited, labels_limited) init_op = control_flow_ops.group(variables.global_variables_initializer(), variables.local_variables_initializer()) @@ -261,8 +260,8 @@ class SingleEvaluationTest(test.TestCase): self._prepareCheckpoint(checkpoint_path) # Next, determine the metric to evaluate: - value_op, update_op = metrics.accuracy( - labels=self._labels, predictions=self._predictions) + value_op, update_op = metric_ops.streaming_accuracy(self._predictions, + self._labels) # Run the evaluation and verify the results: accuracy_value = evaluation.evaluate_once( @@ -277,8 +276,8 @@ class SingleEvaluationTest(test.TestCase): self._prepareCheckpoint(checkpoint_path) # Next, determine the metric to evaluate: - value_op, update_op = metrics.accuracy( - labels=self._labels, predictions=self._predictions) + value_op, update_op = metric_ops.streaming_accuracy(self._predictions, + self._labels) dumping_root = os.path.join(self.get_temp_dir(), 'tfdbg_dump_dir') dumping_hook = hooks.DumpingDebugHook(dumping_root, log_usage=False) diff --git a/tensorflow/contrib/summary/summary.py b/tensorflow/contrib/summary/summary.py index d22b80ac88..99ced53e11 100644 --- a/tensorflow/contrib/summary/summary.py +++ b/tensorflow/contrib/summary/summary.py @@ -21,7 +21,6 @@ from @{tf.summary.merge_all} to @{tf.summary.FileWriter}. To use with eager execution enabled, write your code as follows: -```python global_step = tf.train.get_or_create_global_step() summary_writer = tf.contrib.summary.create_file_writer( train_dir, flush_millis=10000) @@ -31,11 +30,9 @@ with summary_writer.as_default(), tf.contrib.summary.always_record_summaries(): tf.contrib.summary.scalar("loss", my_loss) # In this case every call to tf.contrib.summary.scalar will generate a record # ... -``` To use it with graph execution, write your code as follows: -```python global_step = tf.train.get_or_create_global_step() summary_writer = tf.contrib.summary.create_file_writer( train_dir, flush_millis=10000) @@ -56,7 +53,7 @@ with tf.Session(...) as sess: while not_done_training: sess.run([train_op, tf.contrib.summary.all_summary_ops()]) # ... -``` + """ from __future__ import absolute_import diff --git a/tensorflow/contrib/tensor_forest/client/eval_metrics.py b/tensorflow/contrib/tensor_forest/client/eval_metrics.py index d8236a0a6f..e893e1d1c8 100644 --- a/tensorflow/contrib/tensor_forest/client/eval_metrics.py +++ b/tensorflow/contrib/tensor_forest/client/eval_metrics.py @@ -21,10 +21,10 @@ import numpy as np from tensorflow.contrib import losses from tensorflow.contrib.learn.python.learn.estimators import prediction_key +from tensorflow.contrib.metrics.python.ops import metric_ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops -from tensorflow.python.ops import metrics from tensorflow.python.ops import nn INFERENCE_PROB_NAME = prediction_key.PredictionKey.PROBABILITIES @@ -38,13 +38,12 @@ def _top_k_generator(k): targets = math_ops.to_int32(targets) if targets.get_shape().ndims > 1: targets = array_ops.squeeze(targets, axis=[1]) - return metrics.mean(nn.in_top_k(probabilities, targets, k)) + return metric_ops.streaming_mean(nn.in_top_k(probabilities, targets, k)) return _top_k def _accuracy(predictions, targets, weights=None): - return metrics.accuracy( - labels=targets, predictions=predictions, weights=weights) + return metric_ops.streaming_accuracy(predictions, targets, weights=weights) def _r2(probabilities, targets, weights=None): @@ -54,7 +53,7 @@ def _r2(probabilities, targets, weights=None): squares_residuals = math_ops.reduce_sum( math_ops.square(targets - probabilities), 0) score = 1 - math_ops.reduce_sum(squares_residuals / squares_total) - return metrics.mean(score, weights=weights) + return metric_ops.streaming_mean(score, weights=weights) def _squeeze_and_onehot(targets, depth): @@ -63,7 +62,7 @@ def _squeeze_and_onehot(targets, depth): def _sigmoid_entropy(probabilities, targets, weights=None): - return metrics.mean( + return metric_ops.streaming_mean( losses.sigmoid_cross_entropy(probabilities, _squeeze_and_onehot( targets, @@ -72,7 +71,7 @@ def _sigmoid_entropy(probabilities, targets, weights=None): def _softmax_entropy(probabilities, targets, weights=None): - return metrics.mean( + return metric_ops.streaming_mean( losses.sparse_softmax_cross_entropy(probabilities, math_ops.to_int32(targets)), weights=weights) @@ -83,7 +82,7 @@ def _predictions(predictions, unused_targets, **unused_kwargs): def _class_log_loss(probabilities, targets, weights=None): - return metrics.mean( + return metric_ops.streaming_mean( losses.log_loss(probabilities, _squeeze_and_onehot(targets, array_ops.shape(probabilities)[1])), @@ -91,36 +90,34 @@ def _class_log_loss(probabilities, targets, weights=None): def _precision(predictions, targets, weights=None): - return metrics.precision( - labels=targets, predictions=predictions, weights=weights) + return metric_ops.streaming_precision(predictions, targets, weights=weights) def _precision_at_thresholds(predictions, targets, weights=None): - return metrics.precision_at_thresholds( - labels=targets, - predictions=array_ops.slice(predictions, [0, 1], [-1, 1]), - thresholds=np.arange(0, 1, 0.01, dtype=np.float32), + return metric_ops.streaming_precision_at_thresholds( + array_ops.slice(predictions, [0, 1], [-1, 1]), + targets, + np.arange( + 0, 1, 0.01, dtype=np.float32), weights=weights) def _recall(predictions, targets, weights=None): - return metrics.recall( - labels=targets, predictions=predictions, weights=weights) + return metric_ops.streaming_recall(predictions, targets, weights=weights) def _recall_at_thresholds(predictions, targets, weights=None): - return metrics.recall_at_thresholds( - labels=targets, - predictions=array_ops.slice(predictions, [0, 1], [-1, 1]), - thresholds=np.arange(0, 1, 0.01, dtype=np.float32), + return metric_ops.streaming_recall_at_thresholds( + array_ops.slice(predictions, [0, 1], [-1, 1]), + targets, + np.arange( + 0, 1, 0.01, dtype=np.float32), weights=weights) def _auc(probs, targets, weights=None): - return metrics.auc( - labels=targets, - predictions=array_ops.slice(probs, [0, 1], [-1, 1]), - weights=weights) + return metric_ops.streaming_auc(array_ops.slice(probs, [0, 1], [-1, 1]), + targets, weights=weights) _EVAL_METRICS = { diff --git a/tensorflow/contrib/tensor_forest/python/tensor_forest.py b/tensorflow/contrib/tensor_forest/python/tensor_forest.py index 6f62cd11a9..7a35a70bbe 100644 --- a/tensorflow/contrib/tensor_forest/python/tensor_forest.py +++ b/tensorflow/contrib/tensor_forest/python/tensor_forest.py @@ -295,7 +295,7 @@ def get_epoch_variable(): # A simple container to hold the training variables for a single tree. -class TreeVariables(object): +class TreeTrainingVariables(object): """Stores tf.Variables for training a single random tree. Uses tf.get_variable to get tree-specific names so that this can be used @@ -303,7 +303,7 @@ class TreeVariables(object): then relies on restoring that model to evaluate). """ - def __init__(self, params, tree_num, training, tree_config='', tree_stat=''): + def __init__(self, params, tree_num, training): if (not hasattr(params, 'params_proto') or not isinstance(params.params_proto, _params_proto.TensorForestParams)): @@ -315,28 +315,27 @@ class TreeVariables(object): # TODO(gilberth): Manually shard this to be able to fit it on # multiple machines. self.stats = stats_ops.fertile_stats_variable( - params, tree_stat, self.get_tree_name('stats', tree_num)) + params, '', self.get_tree_name('stats', tree_num)) self.tree = model_ops.tree_variable( - params, tree_config, self.stats, self.get_tree_name('tree', tree_num)) + params, '', self.stats, self.get_tree_name('tree', tree_num)) def get_tree_name(self, name, num): return '{0}-{1}'.format(name, num) -class ForestVariables(object): +class ForestTrainingVariables(object): """A container for a forests training data, consisting of multiple trees. - Instantiates a TreeVariables object for each tree. We override the + Instantiates a TreeTrainingVariables object for each tree. We override the __getitem__ and __setitem__ function so that usage looks like this: - forest_variables = ForestVariables(params) + forest_variables = ForestTrainingVariables(params) ... forest_variables.tree ... """ def __init__(self, params, device_assigner, training=True, - tree_variables_class=TreeVariables, - tree_configs=None, tree_stats=None): + tree_variables_class=TreeTrainingVariables): self.variables = [] # Set up some scalar variables to run through the device assigner, then # we can use those to colocate everything related to a tree. @@ -348,13 +347,7 @@ class ForestVariables(object): for i in range(params.num_trees): with ops.device(self.device_dummies[i].device): - kwargs = {} - if tree_configs is not None: - kwargs.update(dict(tree_config=tree_configs[i])) - if tree_stats is not None: - kwargs.update(dict(tree_stat=tree_stats[i])) - self.variables.append(tree_variables_class( - params, i, training, **kwargs)) + self.variables.append(tree_variables_class(params, i, training)) def __setitem__(self, t, val): self.variables[t] = val @@ -368,11 +361,9 @@ class RandomForestGraphs(object): def __init__(self, params, - tree_configs=None, - tree_stats=None, device_assigner=None, variables=None, - tree_variables_class=TreeVariables, + tree_variables_class=TreeTrainingVariables, tree_graphs=None, training=True): self.params = params @@ -380,10 +371,9 @@ class RandomForestGraphs(object): device_assigner or framework_variables.VariableDeviceChooser()) logging.info('Constructing forest with params = ') logging.info(self.params.__dict__) - self.variables = variables or ForestVariables( + self.variables = variables or ForestTrainingVariables( self.params, device_assigner=self.device_assigner, training=training, - tree_variables_class=tree_variables_class, - tree_configs=tree_configs, tree_stats=tree_stats) + tree_variables_class=tree_variables_class) tree_graph_class = tree_graphs or RandomTreeGraphs self.trees = [ tree_graph_class(self.variables[i], self.params, i) diff --git a/tensorflow/contrib/tensor_forest/python/tensor_forest_test.py b/tensorflow/contrib/tensor_forest/python/tensor_forest_test.py index 1c9c81827e..bbe627b157 100644 --- a/tensorflow/contrib/tensor_forest/python/tensor_forest_test.py +++ b/tensorflow/contrib/tensor_forest/python/tensor_forest_test.py @@ -18,14 +18,10 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from google.protobuf.json_format import ParseDict -from tensorflow.contrib.decision_trees.proto import generic_tree_model_pb2 as _tree_proto from tensorflow.contrib.tensor_forest.python import tensor_forest from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor from tensorflow.python.framework import test_util -from tensorflow.python.ops import resources -from tensorflow.python.ops import variables from tensorflow.python.platform import googletest @@ -114,47 +110,6 @@ class TensorForestTest(test_util.TensorFlowTestCase): self.assertTrue(isinstance(paths, ops.Tensor)) self.assertTrue(isinstance(var, ops.Tensor)) - def testInfrenceFromRestoredModel(self): - input_data = [[-1., 0.], [-1., 2.], # node 1 - [1., 0.], [1., -2.]] # node 2 - expected_prediction = [[0.0, 1.0], [0.0, 1.0], - [0.0, 1.0], [0.0, 1.0]] - hparams = tensor_forest.ForestHParams( - num_classes=2, - num_features=2, - num_trees=1, - max_nodes=1000, - split_after_samples=25).fill() - tree_weight = {'decisionTree': - {'nodes': - [{'binaryNode': - {'rightChildId': 2, - 'leftChildId': 1, - 'inequalityLeftChildTest': - {'featureId': {'id': '0'}, - 'threshold': {'floatValue': 0}}}}, - {'leaf': {'vector': - {'value': [{'floatValue': 0.0}, - {'floatValue': 1.0}]}}, - 'nodeId': 1}, - {'leaf': {'vector': - {'value': [{'floatValue': 0.0}, - {'floatValue': 1.0}]}}, - 'nodeId': 2}]}} - restored_tree_param = ParseDict(tree_weight, - _tree_proto.Model()).SerializeToString() - graph_builder = tensor_forest.RandomForestGraphs(hparams, - [restored_tree_param]) - probs, paths, var = graph_builder.inference_graph(input_data) - self.assertTrue(isinstance(probs, ops.Tensor)) - self.assertTrue(isinstance(paths, ops.Tensor)) - self.assertTrue(isinstance(var, ops.Tensor)) - with self.test_session(): - variables.global_variables_initializer().run() - resources.initialize_resources(resources.shared_resources()).run() - self.assertEquals(probs.eval().shape, (4, 2)) - self.assertEquals(probs.eval().tolist(), expected_prediction) - def testTrainingConstructionClassificationSparse(self): input_data = sparse_tensor.SparseTensor( indices=[[0, 0], [0, 3], [1, 0], [1, 7], [2, 1], [3, 9]], diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.cc b/tensorflow/contrib/tensorrt/convert/convert_graph.cc index da4dd5a14c..b7b26cfb1c 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_graph.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_graph.cc @@ -91,11 +91,8 @@ void GetSubGraphIncomingEdges(const tensorflow::Graph& graph, if (!subgraph_node_ids.count(edge->src()->id()) && !edge->src()->IsSource() && !edge->IsControlEdge()) { incoming_edges->insert(edge); - VLOG(2) << "INCOMING " << edge->src()->name() << " -> " << node->name() - << " Y, "; } else { - VLOG(2) << "INCOMING " << edge->src()->name() << " -> " << node->name() - << " N, "; + VLOG(2) << node->name() << " -> " << edge->src()->name() << " N, "; } } } @@ -109,12 +106,10 @@ void GetSubGraphOutgoingEdges(const tensorflow::Graph& graph, for (const tensorflow::Edge* edge : node->out_edges()) { if (!subgraph_node_ids.count(edge->dst()->id()) && !edge->dst()->IsSink() && !edge->IsControlEdge()) { - VLOG(2) << "OUTGOING " << node->name() << " -> " << edge->dst()->name() - << " Y, "; + VLOG(2) << node->name() << " -> " << edge->dst()->name() << " Y, "; outgoing_edges->insert(edge); } else { - VLOG(2) << "OUTGOING " << node->name() << " -> " << edge->dst()->name() - << " N, "; + VLOG(2) << node->name() << " -> " << edge->dst()->name() << " N, "; } } } @@ -186,27 +181,29 @@ struct ConvertGraphParams { static tensorflow::Status FillSubGraphEdgeSets(ConvertGraphParams* p) { GetSubGraphIncomingEdges(p->graph, p->subgraph_node_ids, &p->subgraph_incoming_edges); - - std::set> unique_tensors; - // Add only unique input source nodes. If output of an outside node is shared - // between multiple nodes inside the engine, only one edge should be created for (const tensorflow::Edge* edge : p->subgraph_incoming_edges) { - unique_tensors.insert({edge->src()->id(), edge->src_output()}); + p->subgraph_inputs.push_back({edge->src()->id(), edge->src_output()}); + } + auto output_name_to_index_map = BuildTensorNameMap(p->output_names); + std::set> subgraph_outputs_set; + // Collect outputs referenced from output_names + for (int node_id : p->subgraph_node_ids) { + tensorflow::Node* node = p->graph.FindNodeId(node_id); + if (output_name_to_index_map.count(node->name())) { + for (int index : output_name_to_index_map.at(node->name())) { + subgraph_outputs_set.insert({node_id, index}); + } + } } - p->subgraph_inputs.insert(p->subgraph_inputs.begin(), unique_tensors.begin(), - unique_tensors.end()); GetSubGraphOutgoingEdges(p->graph, p->subgraph_node_ids, &p->subgraph_outgoing_edges); - unique_tensors.clear(); - // Similar to above, if multiple ouside nodes are sharing the output of an - // internal node only one output port should be created and shared between - // outputs for (const tensorflow::Edge* edge : p->subgraph_outgoing_edges) { - unique_tensors.insert({edge->src()->id(), edge->src_output()}); + subgraph_outputs_set.insert({edge->src()->id(), edge->src_output()}); } - p->subgraph_outputs.reserve(unique_tensors.size()); + p->subgraph_outputs.reserve(subgraph_outputs_set.size()); p->subgraph_outputs.insert(p->subgraph_outputs.begin(), - unique_tensors.begin(), unique_tensors.end()); + subgraph_outputs_set.begin(), + subgraph_outputs_set.end()); return tensorflow::Status::OK(); } @@ -228,6 +225,7 @@ tensorflow::Status GetCalibNode(ConvertGraphParams* params) { for (auto in_edge : params->subgraph_incoming_edges) { // loop over incoming edges and // attach them to calib node + // tensorflow::Node* src_node = in_edge->src(); auto src_output = in_edge->src_output(); auto dst_node = in_edge->dst(); auto dst_input = in_edge->dst_input(); @@ -259,24 +257,19 @@ tensorflow::Status ConvertSubGraphToTensorRT(ConvertGraphParams* params) { for (size_t i = 0; i < params->subgraph_inputs.size(); ++i) { subgraph_edge_to_input_map.insert({params->subgraph_inputs.at(i), i}); } - std::set> unique_tensors; for (const tensorflow::Edge* edge : params->subgraph_incoming_edges) { std::pair old_src = {edge->src()->id(), edge->src_output()}; - if (unique_tensors.count(old_src)) continue; - unique_tensors.insert(old_src); int new_src_output = subgraph_edge_to_input_map.at(old_src); params->graph.AddEdge(edge->src(), edge->src_output(), trt_node, new_src_output); - VLOG(1) << "Wire " << edge->src()->name() << ":" << edge->src_output() - << " -> " << trt_node->name() << ":" << new_src_output; params->graph.RemoveEdge(edge); } - if (VLOG_IS_ON(2)) { - VLOG(2) << "new edge count: " << trt_node->in_edges().size(); - for (const tensorflow::Edge* edge : trt_node->in_edges()) { - VLOG(2) << edge->src()->name() << " port: " << edge->src_output(); - } + + VLOG(2) << "new wiring edges: " << trt_node->in_edges().size(); + for (const tensorflow::Edge* edge : trt_node->in_edges()) { + VLOG(2) << edge->src()->name() << " port: " << edge->src_output(); } + TF_RETURN_IF_ERROR(status); // Re-map outgoing edges to use the new TRT node instead of the orig subgraph @@ -290,8 +283,6 @@ tensorflow::Status ConvertSubGraphToTensorRT(ConvertGraphParams* params) { int new_src_output = subgraph_edge_to_output_map.at(old_src); TF_RETURN_IF_ERROR(params->graph.UpdateEdge( trt_node, new_src_output, edge->dst(), edge->dst_input())); - VLOG(1) << "Wire " << trt_node->name() << ":" << new_src_output << " -> " - << edge->dst()->name() << ":" << edge->dst_input(); } // Remove the original subgraph for (int node_id : params->subgraph_node_ids) { @@ -326,12 +317,9 @@ tensorflow::Status ConvertCalibGraphToInferGraph( tensorflow::GraphConstructorOptions(), graph_def, &graph)); // get calib nodes std::vector calib_nodes; - std::vector topo_order; - tensorflow::GetPostOrder(graph, &topo_order); - for (auto rit = topo_order.rbegin(); rit != topo_order.rend(); ++rit) { - auto node = *rit; + for (auto node : graph.op_nodes()) { if (node->type_string() == "TRTCalibOp") { - VLOG(1) << "Found Calib Node " << node->name(); + VLOG(1) << "Found Calib Node"; calib_nodes.push_back(node); } } diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index 4e4d295538..96e0700862 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -362,11 +362,10 @@ void ReorderCKtoKC(const TRT_ShapedWeights& iweights, break; } case tensorflow::DataType::DT_HALF: { - Reorder2( - {k, c}, static_cast(iweights.GetValues()), - istrides, - static_cast(const_cast(oweights->GetValues())), - ostrides); + Reorder2({k, c}, static_cast(iweights.GetValues()), + istrides, static_cast( + const_cast(oweights->GetValues())), + ostrides); break; } default: @@ -1180,9 +1179,9 @@ tensorflow::Status BinaryTensorOpTensor( CHECK_EQ_TYPE(tensor_r->getType(), dtype); auto op_pair = ops.find(node_def.op()); if (op_pair == ops.end()) - return tensorflow::errors::Unimplemented( - "binary op: " + node_def.op() + - " not supported at: " + node_def.name()); + return tensorflow::errors::Unimplemented("binary op: " + node_def.op() + + " not supported at: " + + node_def.name()); nvinfer1::IElementWiseLayer* layer = ctx.network()->addElementWise( *const_cast(tensor_l), @@ -2139,7 +2138,9 @@ void Converter::register_op_converters() { } } // namespace - +tensorflow::Status GetTensorRTGraph(tensorrt::convert::SubGraphParams& s) { + return tensorflow::errors::Unimplemented("Not implemented yet"); +} tensorflow::Status ConvertCalibrationNodeToEngineNode( tensorflow::Graph& graph, tensorflow::Node* c_node) { const auto ndef = c_node->def(); @@ -2163,23 +2164,9 @@ tensorflow::Status ConvertCalibrationNodeToEngineNode( for (auto n : graph.op_nodes()) { node_maps.insert({n->name(), n}); } - std::set subgraph_ids; - for (const auto internal_node : segment_nodes) { - subgraph_ids.insert(node_maps.at(internal_node)->id()); - } - if (VLOG_IS_ON(2)) { - string node_names = StrCat(c_node->name(), " segment nodes= "); - - for (const auto& node_name : segment_nodes) { - StrAppend(&node_names, node_name, ", "); - } - VLOG(2) << node_names; - } - VLOG(1) << "Output Nodes:"; std::vector out_types; std::vector out_edges; - for (auto& i : output_nodes) { auto node_port = tensorflow::str_util::Split(i, ":"); VLOG(1) << " " << i << " in graph " << node_maps.count(i); @@ -2199,24 +2186,18 @@ tensorflow::Status ConvertCalibrationNodeToEngineNode( out_types.push_back(out_node->output_type(0)); } for (auto out_edge : out_node->out_edges()) { - if (subgraph_ids.count(out_edge->dst()->id())) - continue; // skip internal edges; if (out_edge->src_output() == port) { out_edges.push_back(out_edge); - VLOG(1) << "OUTPUT EDGE " << out_edge->src()->name() << ":" - << out_edge->src_output() << " -> " << out_edge->dst()->name() - << ":" << out_edge->dst_input(); + break; } } } else { LOG(WARNING) << " couldn't find output node " << out_node_name; } } - if (VLOG_IS_ON(1)) { - VLOG(1) << c_node->name() << " Input Nodes:"; - for (auto& i : input_names) { - VLOG(1) << " Input " << i << " in graph " << node_maps.count(i); - } + VLOG(1) << "Input Nodes:"; + for (auto& i : input_names) { + VLOG(1) << " " << i << " in graph " << node_maps.count(i); } auto trt_rm = tensorflow::tensorrt::TRTResourceManager::instance(); auto resmgr = trt_rm->getManager("TRTCalibOps"); @@ -2250,24 +2231,14 @@ tensorflow::Status ConvertCalibrationNodeToEngineNode( calib_res->builder_ = nullptr; tensorflow::NodeDefBuilder op_builder(engine_name, "TRTEngineOp"); std::vector income_edges; - income_edges.resize(c_node->num_inputs()); for (const auto in_edge : c_node->in_edges()) { auto src = in_edge->src(); int dest_port = in_edge->dst_input(); - VLOG(1) << "Incoming connection " << src->name() << ":" - << in_edge->src_output() << " -> " << c_node->name() << ":" - << dest_port; - income_edges.at(dest_port) = {src->name(), in_edge->src_output(), - c_node->input_type(dest_port)}; + income_edges.emplace_back(src->name(), in_edge->src_output(), + c_node->input_type(dest_port)); } tensorflow::gtl::ArraySlice input_list( income_edges); - if (VLOG_IS_ON(2)) { - for (const auto& inp : input_list) { - VLOG(2) << " Input from inputlist " << inp.node << ":" << inp.index << " " - << tensorflow::DataTypeString(inp.data_type); - } - } op_builder.Input(input_list); tensorflow::NodeDef engine_node; const char* engine_plan_data = static_cast(engine_plan->data()); @@ -2284,26 +2255,13 @@ tensorflow::Status ConvertCalibrationNodeToEngineNode( } auto trt_engine_node = graph.AddNode(engine_node, &status); TF_RETURN_IF_ERROR(status); - std::map port_map; - for (size_t t = 0; t < output_nodes.size(); t++) { - port_map.insert({output_nodes.at(t), t}); - } - for (auto& i : out_edges) { - string s(i->src()->name()); - if (i->src_output()) StrAppend(&s, ":", i->src_output()); - int out_port = port_map.at(s); - VLOG(1) << "Connecting " << trt_engine_node->name() << ":" << out_port - << " -> " << i->dst()->name() << ":" << i->dst_input(); - TF_RETURN_IF_ERROR( - graph.UpdateEdge(trt_engine_node, out_port, i->dst(), i->dst_input())); - } - for (const auto ed : trt_engine_node->in_edges()) { - VLOG(1) << "In Edge " << ed->src()->name() << ":" << ed->src_output() - << " -> " << ed->dst()->name() << ":" << ed->dst_input(); - } - for (const auto ed : trt_engine_node->out_edges()) { - VLOG(1) << "Out Edge " << ed->src()->name() << ":" << ed->src_output() - << " -> " << ed->dst()->name() << ":" << ed->dst_input(); + for (size_t i = 0; i < out_edges.size(); i++) { + VLOG(1) << "Connecting trt_engine_node output " << i << " with " + << out_edges.at(i)->dst()->name() << " port " + << out_edges.at(i)->dst_input(); + TF_RETURN_IF_ERROR(graph.UpdateEdge(trt_engine_node, i, + out_edges.at(i)->dst(), + out_edges.at(i)->dst_input())); } VLOG(1) << "Segment nodes:"; for (auto& i : segment_nodes) { @@ -2374,7 +2332,6 @@ tensorflow::Status ConvertSubgraph( std::vector* output_names, std::vector* output_dtypes, const string& engine_name) { - std::set added_tensors; for (const std::pair& input : s.input_inds) { VLOG(2) << "parsing input. Node id= " << input.first; int node_id = input.first; @@ -2417,6 +2374,7 @@ tensorflow::Status ConvertSubgraph( auto op_info = op_info_vec.at(shape_inference_output_idx); tensorflow::DataType tf_dtype = op_info.dtype(); + input_dtypes->push_back(tf_dtype); nvinfer1::DataType dtype(nvinfer1::DataType::kFLOAT); auto type_status = ConvertDType(tf_dtype, &dtype); @@ -2452,10 +2410,8 @@ tensorflow::Status ConvertSubgraph( if (output_idx != 0) { input_tensor_name = StrCat(node_name, ":", output_idx); } - if (added_tensors.count(input_tensor_name)) continue; - added_tensors.insert(input_tensor_name); + input_names->push_back(input_tensor_name); - input_dtypes->push_back(tf_dtype); nvinfer1::ITensor* input_tensor = converter.network()->addInput( input_tensor_name.c_str(), dtype, input_dim_pseudo_chw); @@ -2479,7 +2435,6 @@ tensorflow::Status ConvertSubgraph( // Gather output metadata int trt_engine_op_output_idx = 0; - added_tensors.clear(); for (const std::pair& output : s.output_inds) { int node_id = output.first; int output_idx = output.second; @@ -2496,8 +2451,6 @@ tensorflow::Status ConvertSubgraph( if (output_idx != 0) tensorflow::strings::StrAppend(&tensor_name, ":", output_idx); VLOG(2) << "Output tensor name: " << tensor_name; - if (added_tensors.count(tensor_name)) continue; - added_tensors.insert(tensor_name); output_names->push_back(tensor_name); auto tensor_or_weights = converter.get_tensor(tensor_name); if (!tensor_or_weights.is_tensor()) { diff --git a/tensorflow/contrib/tpu/python/tpu/datasets.py b/tensorflow/contrib/tpu/python/tpu/datasets.py index d879170b68..2e472a2805 100644 --- a/tensorflow/contrib/tpu/python/tpu/datasets.py +++ b/tensorflow/contrib/tpu/python/tpu/datasets.py @@ -166,21 +166,11 @@ def StreamingFilesDataset(files, return remote_iterator.get_next() def MapFn(unused_input): - if isinstance(source_dataset.output_types, dtypes.DType): - output_types = [source_dataset.output_types] - elif isinstance(source_dataset.output_types, (list, tuple)): - output_types = source_dataset.output_types - else: - raise ValueError('source dataset has invalid output types') - remote_calls = functional_ops.remote_call( + return functional_ops.remote_call( args=[source_handle], - Tout=output_types, + Tout=[dtypes.string], f=LoadingFunc, - target='/job:%s/replica:0/task:0/cpu:0' % file_reader_job) - if len(remote_calls) == 1: - return remote_calls[0] - else: - return remote_calls + target='/job:%s/replica:0/task:0/cpu:0' % file_reader_job)[0] with ops.device('/job:%s' % worker_job): output_dataset = dataset_ops.Dataset.range(2).repeat().map( diff --git a/tensorflow/contrib/tpu/python/tpu/datasets_test.py b/tensorflow/contrib/tpu/python/tpu/datasets_test.py index b58d05eac5..918cf0ed8e 100644 --- a/tensorflow/contrib/tpu/python/tpu/datasets_test.py +++ b/tensorflow/contrib/tpu/python/tpu/datasets_test.py @@ -26,8 +26,6 @@ from tensorflow.core.protobuf import config_pb2 from tensorflow.python.client import session from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.ops import readers -from tensorflow.python.framework import dtypes -from tensorflow.python.framework import tensor_shape from tensorflow.python.lib.io import python_io from tensorflow.python.platform import test from tensorflow.python.training import server_lib @@ -164,30 +162,6 @@ class DatasetsTest(test.TestCase): self.assertEqual(set(all_contents), set(retrieved_values)) - def testArbitraryReaderFuncFromDatasetGenerator(self): - - def my_generator(): - yield (1, [1] * 10) - - def gen_dataset(dummy): - return dataset_ops.Dataset.from_generator( - my_generator, (dtypes.int64, dtypes.int64), - (tensor_shape.TensorShape([]), tensor_shape.TensorShape([10]))) - - dataset = datasets.StreamingFilesDataset( - dataset_ops.Dataset.range(10), filetype=gen_dataset) - - iterator = dataset.make_initializable_iterator() - self._sess.run(iterator.initializer) - get_next = iterator.get_next() - - retrieved_values = self._sess.run(get_next) - - self.assertIsInstance(retrieved_values, (list, tuple)) - self.assertEqual(len(retrieved_values), 2) - self.assertEqual(retrieved_values[0], 1) - self.assertItemsEqual(retrieved_values[1], [1] * 10) - def testUnexpectedFiletypeString(self): with self.assertRaises(ValueError): datasets.StreamingFilesDataset( diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index b1c224a345..d89633199d 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -699,9 +699,7 @@ cc_library( srcs = ["platform/stacktrace_handler.cc"], hdrs = ["platform/stacktrace_handler.h"], deps = [ - ":abi", ":lib_platform", - ":stacktrace", ], ) @@ -3091,8 +3089,6 @@ cc_library( # we now need at least "str_util". ":lib", ":lib_platform", - ":stacktrace_handler", - ":test_lite", "//tensorflow/core/platform/default/build_config:test_lite_main", ], alwayslink = 1, @@ -3573,10 +3569,7 @@ tf_cc_tests_gpu( tf_cc_test_mkl( name = "mkl_runtime_tests", size = "small", - srcs = [ - "common_runtime/mkl_cpu_allocator_test.cc", - "common_runtime/mkl_threadpool_device_test.cc", - ], + srcs = ["common_runtime/mkl_cpu_allocator_test.cc"], linkstatic = 1, deps = [ ":core", diff --git a/tensorflow/core/api_def/base_api/api_def_Selu.pbtxt b/tensorflow/core/api_def/base_api/api_def_Selu.pbtxt index 985f09312f..cbe76de415 100644 --- a/tensorflow/core/api_def/base_api/api_def_Selu.pbtxt +++ b/tensorflow/core/api_def/base_api/api_def_Selu.pbtxt @@ -4,10 +4,6 @@ op { description: < 0`, limit of the split of the result. -END - } - summary: "Split elements of `source` based on `sep` into a `SparseTensor`." - description: <2<><>3"` and -sep of `"<>"` returns `["1", "2", "", "3"]`. If `sep` is None or an empty -string, consecutive whitespace are regarded as a single separator, and the -result will contain no empty strings at the startor end if the string has -leading or trailing whitespace. - -Note that the above mentioned behavior matches python's str.split. -END -} diff --git a/tensorflow/core/api_def/python_api/api_def_StringSplitV2.pbtxt b/tensorflow/core/api_def/python_api/api_def_StringSplitV2.pbtxt deleted file mode 100644 index 0e8576fb01..0000000000 --- a/tensorflow/core/api_def/python_api/api_def_StringSplitV2.pbtxt +++ /dev/null @@ -1,4 +0,0 @@ -op { - graph_op_name: "StringSplitV2" - visibility: HIDDEN -} diff --git a/tensorflow/core/common_runtime/bfc_allocator.cc b/tensorflow/core/common_runtime/bfc_allocator.cc index 9cda17867b..8f2a419756 100644 --- a/tensorflow/core/common_runtime/bfc_allocator.cc +++ b/tensorflow/core/common_runtime/bfc_allocator.cc @@ -86,7 +86,7 @@ BFCAllocator::Chunk* BFCAllocator::ChunkFromHandle(ChunkHandle h) { return &(chunks_[h]); } -bool BFCAllocator::Extend(size_t alignment, size_t rounded_bytes) { +bool BFCAllocator::Extend(size_t rounded_bytes) { size_t available_bytes = memory_limit_ - total_region_allocated_bytes_; // Rounds available_bytes down to the nearest multiple of kMinAllocationSize. available_bytes = (available_bytes / kMinAllocationSize) * kMinAllocationSize; @@ -108,7 +108,7 @@ bool BFCAllocator::Extend(size_t alignment, size_t rounded_bytes) { // Try allocating. size_t bytes = std::min(curr_region_allocation_bytes_, available_bytes); - void* mem_addr = suballocator_->Alloc(alignment, bytes); + void* mem_addr = suballocator_->Alloc(32, bytes); if (mem_addr == nullptr && !started_backpedal_) { // Only backpedal once. started_backpedal_ = true; @@ -119,7 +119,7 @@ bool BFCAllocator::Extend(size_t alignment, size_t rounded_bytes) { while (mem_addr == nullptr) { bytes = RoundedBytes(bytes * kBackpedalFactor); if (bytes < rounded_bytes) break; - mem_addr = suballocator_->Alloc(alignment, bytes); + mem_addr = suballocator_->Alloc(32, bytes); } } @@ -261,7 +261,7 @@ void* BFCAllocator::AllocateRawInternal(size_t unused_alignment, } // Try to extend - if (Extend(unused_alignment, rounded_bytes)) { + if (Extend(rounded_bytes)) { ptr = FindChunkPtr(bin_num, rounded_bytes, num_bytes); if (ptr != nullptr) { return ptr; diff --git a/tensorflow/core/common_runtime/bfc_allocator.h b/tensorflow/core/common_runtime/bfc_allocator.h index 52aedb1e9c..ba5a3eea3a 100644 --- a/tensorflow/core/common_runtime/bfc_allocator.h +++ b/tensorflow/core/common_runtime/bfc_allocator.h @@ -305,8 +305,7 @@ class BFCAllocator : public VisitableAllocator { // Try to add a new memory region that can satisfy an allocation of // 'rounded_bytes' bytes. Returns true on success and false on // failure. - bool Extend(size_t alignment, size_t rounded_bytes) - EXCLUSIVE_LOCKS_REQUIRED(lock_); + bool Extend(size_t rounded_bytes) EXCLUSIVE_LOCKS_REQUIRED(lock_); // Returns a pointer to an underlying allocated chunk of size // 'rounded_bytes'. diff --git a/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc b/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc index 9028e6298c..c21a1ea9f2 100644 --- a/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc +++ b/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc @@ -102,25 +102,9 @@ TEST(DirectSessionWithTrackingAllocTest, CostModelTest) { EXPECT_EQ(2, shape.dim(0).size()); EXPECT_EQ(1, shape.dim(1).size()); if (node->name() == y->name()) { -#ifdef INTEL_MKL - // if MKL is used, it goes through various additional - // graph rewrite pass. In TF, everytime a graph pass - // happens, "constant" nodes are allocated - // and deallocated. Each allocation calls the - // (FindChunkPtr of BFCAllocator), - // which increments the value of AllocationId. - // Thus AllocationId becomes more than 3 and 4 if - // MKL is used. Now they are 9 and 10 for MKL. - EXPECT_EQ(19, cm->AllocationId(node, 0)); -#else EXPECT_EQ(21, cm->AllocationId(node, 0)); -#endif } else { -#ifdef INTEL_MKL - EXPECT_EQ(20, cm->AllocationId(node, 0)); -#else EXPECT_EQ(22, cm->AllocationId(node, 0)); -#endif } } EXPECT_LE(0, cm->MaxExecutionTime(node)); diff --git a/tensorflow/core/common_runtime/mkl_threadpool_device_test.cc b/tensorflow/core/common_runtime/mkl_threadpool_device_test.cc deleted file mode 100644 index 5d583a8360..0000000000 --- a/tensorflow/core/common_runtime/mkl_threadpool_device_test.cc +++ /dev/null @@ -1,53 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifdef INTEL_MKL - -#include "tensorflow/core/common_runtime/threadpool_device.h" - -#include "tensorflow/core/lib/core/status_test_util.h" -#include "tensorflow/core/platform/cpu_info.h" -#include "tensorflow/core/platform/logging.h" -#include "tensorflow/core/platform/test.h" -#include "tensorflow/core/public/session_options.h" - -namespace tensorflow { - -#ifdef _OPENMP -TEST(MKLThreadPoolDeviceTest, TestOmpDefaults) { - SessionOptions options; - unsetenv("OMP_NUM_THREADS"); - - ThreadPoolDevice* tp = new ThreadPoolDevice( - options, "/device:CPU:0", Bytes(256), DeviceLocality(), cpu_allocator()); - - const int ht = port::NumHyperthreadsPerCore(); - EXPECT_EQ(omp_get_max_threads(), (port::NumSchedulableCPUs() + ht - 1) / ht); -} - -TEST(MKLThreadPoolDeviceTest, TestOmpPreSets) { - SessionOptions options; - setenv("OMP_NUM_THREADS", "314", 1); - - ThreadPoolDevice* tp = new ThreadPoolDevice( - options, "/device:CPU:0", Bytes(256), DeviceLocality(), cpu_allocator()); - - EXPECT_EQ(omp_get_max_threads(), 314); -} -#endif // _OPENMP - -} // namespace tensorflow - -#endif // INTEL_MKL diff --git a/tensorflow/core/common_runtime/process_util.cc b/tensorflow/core/common_runtime/process_util.cc index a5d31b75c7..21912236d0 100644 --- a/tensorflow/core/common_runtime/process_util.cc +++ b/tensorflow/core/common_runtime/process_util.cc @@ -16,10 +16,8 @@ limitations under the License. #include "tensorflow/core/common_runtime/process_util.h" #ifdef INTEL_MKL -#ifdef _OPENMP #include -#endif // _OPENMP -#endif // INTEL_MKL +#endif #include #include "tensorflow/core/lib/core/threadpool.h" @@ -59,10 +57,7 @@ int32 NumInterOpThreadsFromSessionOptions(const SessionOptions& options) { // MKL library executes ops in parallel using OMP threads // Set inter_op conservatively to avoid thread oversubscription that could // lead to severe perf degradations and OMP resource exhaustion - int mkl_intra_op = 1; -#ifdef _OPENMP - mkl_intra_op = omp_get_max_threads(); -#endif // _OPENMP + const int mkl_intra_op = omp_get_max_threads(); CHECK_GE(mkl_intra_op, 1); const int32 mkl_inter_op = std::max( (port::NumSchedulableCPUs() + mkl_intra_op - 1) / mkl_intra_op, 2); @@ -73,7 +68,7 @@ int32 NumInterOpThreadsFromSessionOptions(const SessionOptions& options) { #else // Default to using the number of cores available in the process. return port::NumSchedulableCPUs(); -#endif // INTEL_MKL +#endif } thread::ThreadPool* NewThreadPoolFromSessionOptions( diff --git a/tensorflow/core/common_runtime/threadpool_device.cc b/tensorflow/core/common_runtime/threadpool_device.cc index 74a87215e1..f7a07fe503 100644 --- a/tensorflow/core/common_runtime/threadpool_device.cc +++ b/tensorflow/core/common_runtime/threadpool_device.cc @@ -31,11 +31,7 @@ limitations under the License. #include "tensorflow/core/public/session_options.h" #ifdef INTEL_MKL -#ifdef _OPENMP -#include -#endif #include "tensorflow/core/common_runtime/mkl_cpu_allocator.h" -#include "tensorflow/core/platform/cpu_info.h" #endif namespace tensorflow { @@ -47,26 +43,7 @@ ThreadPoolDevice::ThreadPoolDevice(const SessionOptions& options, : LocalDevice(options, Device::BuildDeviceAttributes( name, DEVICE_CPU, memory_limit, locality)), allocator_(allocator), - scoped_allocator_mgr_(new ScopedAllocatorMgr(name)) { -#ifdef INTEL_MKL -#ifdef _OPENMP - const char* user_omp_threads = getenv("OMP_NUM_THREADS"); - if (user_omp_threads == nullptr) { - // OMP_NUM_THREADS controls MKL's intra-op parallelization - // Default to available physical cores - const int mkl_intra_op = port::NumSchedulableCPUs(); - const int ht = port::NumHyperthreadsPerCore(); - omp_set_num_threads((mkl_intra_op + ht - 1) / ht); - } else { - uint64 user_val = 0; - if (strings::safe_strtou64(user_omp_threads, &user_val)) { - // Superflous but triggers OpenMP loading - omp_set_num_threads(user_val); - } - } -#endif // _OPENMP -#endif // INTEL_MKL -} + scoped_allocator_mgr_(new ScopedAllocatorMgr(name)) {} ThreadPoolDevice::~ThreadPoolDevice() {} diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_master_service_impl.cc b/tensorflow/core/distributed_runtime/rpc/grpc_master_service_impl.cc index 770a0fcf14..1cea1b1462 100644 --- a/tensorflow/core/distributed_runtime/rpc/grpc_master_service_impl.cc +++ b/tensorflow/core/distributed_runtime/rpc/grpc_master_service_impl.cc @@ -147,9 +147,7 @@ MasterService::Stub::Stub( } MasterService::AsyncService::AsyncService() { - int method_len = sizeof(grpcMasterService_method_names) / - sizeof(grpcMasterService_method_names[0]); - for (int i = 0; i < method_len; ++i) { + for (int i = 0; i < 10; ++i) { AddMethod(new ::grpc::internal::RpcServiceMethod( grpcMasterService_method_names[i], ::grpc::internal::RpcMethod::NORMAL_RPC, nullptr)); diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_testlib.cc b/tensorflow/core/distributed_runtime/rpc/grpc_testlib.cc index a8508d2d4f..89f83f9f24 100644 --- a/tensorflow/core/distributed_runtime/rpc/grpc_testlib.cc +++ b/tensorflow/core/distributed_runtime/rpc/grpc_testlib.cc @@ -17,7 +17,6 @@ limitations under the License. #include "tensorflow/core/distributed_runtime/rpc/grpc_session.h" #include "tensorflow/core/lib/strings/str_util.h" -#include "tensorflow/core/platform/env.h" #include "tensorflow/core/util/device_name_utils.h" namespace tensorflow { @@ -51,14 +50,9 @@ Status TestCluster::MakeTestCluster(const SessionOptions& options, int n, } for (int i = 0; i < n; ++i) { - string server_file = - strings::StrCat(testing::TensorFlowSrcRoot(), - "/core/distributed_runtime/rpc/grpc_testlib_server"); - if (!options.env->FileExists(server_file).ok()) { - return errors::Internal("Could not find grpc_testlib_server"); - } const std::vector argv( - {server_file, + {strings::StrCat(testing::TensorFlowSrcRoot(), + "/core/distributed_runtime/rpc/grpc_testlib_server"), /* see grpc_testlib_server.cc for flags */ tf_jobs, "--tf_job=localhost", strings::StrCat("--tf_task=", i), strings::StrCat("--num_cpus=", num_cpus), diff --git a/tensorflow/core/framework/allocator.h b/tensorflow/core/framework/allocator.h index 2bb4d32d57..2c87156dca 100644 --- a/tensorflow/core/framework/allocator.h +++ b/tensorflow/core/framework/allocator.h @@ -67,8 +67,13 @@ struct AllocatorStats { // device memory. class Allocator { public: +#ifdef EIGEN_VECTORIZE_AVX512 // Align to 64 byte boundary. static constexpr size_t kAllocatorAlignment = 64; +#else + // Align to 32 byte boundary. + static constexpr size_t kAllocatorAlignment = 32; +#endif virtual ~Allocator(); diff --git a/tensorflow/core/framework/op_gen_lib.cc b/tensorflow/core/framework/op_gen_lib.cc index 4b56d807df..3d7920a6e2 100644 --- a/tensorflow/core/framework/op_gen_lib.cc +++ b/tensorflow/core/framework/op_gen_lib.cc @@ -15,7 +15,6 @@ limitations under the License. #include "tensorflow/core/framework/op_gen_lib.h" -#include #include #include "tensorflow/core/framework/attr_value.pb.h" #include "tensorflow/core/lib/core/errors.h" diff --git a/tensorflow/core/framework/remote_fused_graph_execute_info.proto b/tensorflow/core/framework/remote_fused_graph_execute_info.proto index 10072724d2..eb689ec1e6 100644 --- a/tensorflow/core/framework/remote_fused_graph_execute_info.proto +++ b/tensorflow/core/framework/remote_fused_graph_execute_info.proto @@ -5,7 +5,7 @@ option cc_enable_arenas = true; option java_outer_classname = "RemoteFusedGraphExecuteInfoProto"; option java_multiple_files = true; option java_package = "org.tensorflow.framework"; -option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework"; +//add go_package externally import "tensorflow/core/framework/graph.proto"; import "tensorflow/core/framework/tensor_shape.proto"; import "tensorflow/core/framework/types.proto"; diff --git a/tensorflow/core/framework/tensor_test.cc b/tensorflow/core/framework/tensor_test.cc index 80e168df97..b613effd18 100644 --- a/tensorflow/core/framework/tensor_test.cc +++ b/tensorflow/core/framework/tensor_test.cc @@ -1147,29 +1147,29 @@ TEST(Tensor, FailureToAllocate) { // On the alignment. // -// As of 2018/5, tensorflow::Tensor allocates its buffer with 64-byte +// As of 2015/8, tensorflow::Tensor allocates its buffer with 32-byte // alignment. Tensor::tensor/flat/vec/matrix methods requires the // buffer satisfies Eigen::Aligned (e.g., 16-bytes aligned usually, -// 32-bytes for AVX, and 64-bytes for AVX512). Tensor::Slice requires -// the caller to ensure its result is aligned if the caller intends -// to use those methods. In this test case, we simply make sure each -// slice is 64-byte aligned: sizeof(float) * 4 * 36 = 576. 576 % 64 = 0. +// and 32-bytes for AVX). Tensor::Slice requires the caller to ensure +// its result is aligned if the caller intends to use those methods. +// In this test case, we simply make sure each slice is 32-byte +// aligned: sizeof(float) * 4 * 2 = 32. TEST(Tensor, Slice_Basic) { Tensor saved; { // General - Tensor x(DT_FLOAT, TensorShape({10, 4, 36})); + Tensor x(DT_FLOAT, TensorShape({10, 4, 34})); // Fills in known values. for (int i = 0; i < 10; ++i) { x.Slice(i, i + 1).flat().setConstant(i * 1.f); } // A simple slice along dim0. Tensor y = x.Slice(4, 8); - EXPECT_TRUE(y.shape().IsSameSize(TensorShape({4, 4, 36}))); + EXPECT_TRUE(y.shape().IsSameSize(TensorShape({4, 4, 34}))); auto tx = x.tensor(); auto ty = y.tensor(); for (int i = 0; i < 4; ++i) { for (int j = 0; j < 4; ++j) { - for (int k = 0; k < 36; ++k) { + for (int k = 0; k < 34; ++k) { EXPECT_EQ(ty(i, j, k), 4.0 + i); EXPECT_EQ(&tx(4 + i, j, k), &ty(i, j, k)); } @@ -1186,7 +1186,7 @@ TEST(Tensor, Slice_Basic) { auto tz = z.tensor(); EXPECT_EQ(1, z.dim_size(0)); for (int j = 0; j < 4; ++j) { - for (int k = 0; k < 36; ++k) { + for (int k = 0; k < 34; ++k) { EXPECT_EQ(tz(0, j, k), 6.0); } } @@ -1198,16 +1198,16 @@ TEST(Tensor, Slice_Basic) { EXPECT_EQ(1, saved.dim_size(0)); auto tsaved = saved.tensor(); for (int j = 0; j < 4; ++j) { - for (int k = 0; k < 36; ++k) { + for (int k = 0; k < 34; ++k) { EXPECT_EQ(tsaved(0, j, k), 6.0); } } } { // Empty - Tensor x(DT_FLOAT, TensorShape({10, 0, 36})); + Tensor x(DT_FLOAT, TensorShape({10, 0, 34})); x.flat().setRandom(); Tensor y = x.Slice(4, 8); - EXPECT_TRUE(y.shape().IsSameSize(TensorShape({4, 0, 36}))); + EXPECT_TRUE(y.shape().IsSameSize(TensorShape({4, 0, 34}))); } { diff --git a/tensorflow/core/graph/mkl_layout_pass.cc b/tensorflow/core/graph/mkl_layout_pass.cc index b9667998d6..72a13d4da7 100644 --- a/tensorflow/core/graph/mkl_layout_pass.cc +++ b/tensorflow/core/graph/mkl_layout_pass.cc @@ -2691,14 +2691,14 @@ class MklLayoutRewritePass : public GraphOptimizationPass { // If Op has been specifically assigned to a non-CPU device, then No. if (!n->assigned_device_name().empty() && - !str_util::StrContains(n->assigned_device_name(), kCPUDeviceSubStr)) { + !str_util::StrContains(n->assigned_device_name(),kCPUDeviceSubStr)) { result = false; reason = "Op has been assigned a runtime device that is not CPU."; } // If user has specifically assigned this op to a non-CPU device, then No. if (!n->def().device().empty() && - !str_util::StrContains(n->def().device(), kCPUDeviceSubStr)) { + !str_util::StrContains(n->def().device(),kCPUDeviceSubStr)) { result = false; reason = "User has assigned a device that is not CPU."; } @@ -2865,9 +2865,9 @@ class MklLayoutRewritePass : public GraphOptimizationPass { return false; } - // If the depth_radius of LRN is not 2, then MKL DNN takes unoptimized - // path. The unoptimized path is slow. Thus we dont rewrite the node - // and use default Eigen. But for depth_radius=2, MKL DNN optimized + // If the depth_radius of LRN is not 2, then MKL DNN takes unoptimized + // path. The unoptimized path is slow. Thus we dont rewrite the node + // and use default Eigen. But for depth_radius=2, MKL DNN optimized // path is taken, i.e., eigen node is rewritten by MKl DNN node. static bool LrnRewrite(const Node* n) { CHECK_NOTNULL(n); @@ -2876,13 +2876,13 @@ class MklLayoutRewritePass : public GraphOptimizationPass { CHECK_EQ(GetNodeAttr(n->def(), "depth_radius", &depth_radius).ok(), true); // if the depth_radius of LRN is not 2, don't rewrite the node by MKL DNN - // and use eigen node instead + // and use eigen node instead if (depth_radius == 2) { return true; } VLOG(1) << "LrnRewrite: The model sets depth_radius as not 2 which" << "case is not optimized by Intel MKL, thus using Eigen op" - << "for LRN "; + << "for LRN " ; return false; } @@ -3015,35 +3015,6 @@ class MklLayoutRewritePass : public GraphOptimizationPass { std::vector* ws_tensors, bool* are_ws_tensors_added); - // Helper function used by FixMklMetaDataEdges. Fixes the metadata edge - // pointed by 'e_metadata' corresponding to the data edge 'e_data' in graph - // 'g'. Returns true is fixup was done; otherwise, it returns false. - bool FixMklMetaDataEdgeIfNeeded(std::unique_ptr* g, - const Edge* e_data, const Edge* e_metadata); - - // Are the input Mkl metadata edges for node 'n' in graph 'g' correctly - // connected? If not, then fix them. This is needed because a graph may have - // some input Mkl metadata edges incorrectly setup after node merge and - // rewrite passes. This could happen because GetReversePostOrder function may - // not provide topologically sorted order if a graph contains cycles. The - // function returns true if at least one Mkl metadata edge for node 'n' was - // fixed. Otherwise, it returns false. - // - // Example: - // - // X = MklConv2D(_, _, _) - // Y = MklConv2DWithBias(_, _, _, _, _, _) - // Z = MklAdd(X, Y, DummyMklTensor, Y:1) - // - // For a graph such as shown above, note that 3rd argument of MklAdd contains - // DummyMklTensor. Actually, it should be getting the Mkl metadata from - // MklConv2D op (specifically, X:2). This incorrect plumbing could be possible - // (although rare) if the Mkl NodeMerge + NodeRewrite passes visit Z before X - // (possible if X, Y, Z are part of a loop.) This function fixes the Mkl - // metadata edges only - it does not rewrite nodes nor does it modify the Mkl - // data edges (1st and 2nd arguments of MklAdd). - bool FixMklMetaDataEdges(std::unique_ptr* g, Node* n); - // Functions specific to operators to copy attributes // We need operator-specific function to copy attributes because the framework // does not provide any generic function for it. @@ -4270,92 +4241,6 @@ MklLayoutRewritePass::CheckForNodeRewrite(const Node* n) const { return nullptr; } -/////////////////////////////////////////////////////////////////////////////// -// Post-rewrite Mkl metadata fixup pass -/////////////////////////////////////////////////////////////////////////////// -bool MklLayoutRewritePass::FixMklMetaDataEdgeIfNeeded(std::unique_ptr* g, - const Edge* e_data, const Edge* e_metadata) { - if (g == nullptr || e_data == nullptr || e_metadata == nullptr) { - return false; - } - - Node* n_data = e_data->src(); - int n_data_op_slot = e_data->src_output(); - int n_metadata_op_slot = GetTensorMetaDataIndex(n_data_op_slot, - n_data->num_outputs()); - - // If the source of meta edge is a constant node (producing dummy Mkl metadata - // tensor), then we will need to fix. - if (IsConstant(e_metadata->src())) { - Node* e_metadata_dst = e_metadata->dst(); - int e_metadata_in_slot = e_metadata->dst_input(); - CHECK_NOTNULL((*g)->AddEdge(n_data, n_metadata_op_slot, - e_metadata_dst, e_metadata_in_slot)); - - (*g)->RemoveEdge(e_metadata); - return true; - } - - return false; -} - -bool MklLayoutRewritePass::FixMklMetaDataEdges(std::unique_ptr* g, - Node* n) { - bool result = false; - - // If graph node is not Mkl node, then return. - DataType T = DT_INVALID; - if (!GetNodeAttr(n->def(), "T", &T).ok() || - !mkl_op_registry::IsMklOp(n->type_string(), T)) { - return result; - } - - // If it is Mkl node, then check if the input edges to this node that carry - // Mkl metadata are linked up correctly with the source node. - - // For Mkl nodes, we generate twice the number of input tensors (n for Mkl - // data tensors + n for Mkl metadata tensors). We need to check for correct - // connection of n metadata tensors only. - int num_data_inputs = n->num_inputs() / 2; - for (int idx = 0; idx < num_data_inputs; idx++) { - // Get the edge connecting input slot with index (idx). - const Edge* e = nullptr; - TF_CHECK_OK(n->input_edge(idx, &e)); - - // If e is control edge, then skip. - if (e->IsControlEdge()) { - continue; - } - - // Check that the source node for edge 'e' is Mkl node. If it is not an Mkl - // node, then we don't need to do anything. - Node* e_src = e->src(); - if (GetNodeAttr(e_src->def(), "T", &T).ok() && - mkl_op_registry::IsMklOp(e_src->type_string(), T)) { - // Source node for edge 'e' is Mkl node. - // Destination node and destination input slot of e is node 'n' and 'idx' - // resp. - CHECK_EQ(e->dst(), n); - CHECK_EQ(e->dst_input(), idx); - - // Let's get edge that carries Mkl metadata corresponding to Mkl data edge - // 'e'. For that, let's first get the input slot of 'n' where the meta - // edge will feed the value. - int e_meta_in_slot = GetTensorMetaDataIndex(e->dst_input(), - n->num_inputs()); - const Edge* e_meta = nullptr; - TF_CHECK_OK(n->input_edge(e_meta_in_slot, &e_meta)); - - // Let's check if we need to fix this meta edge. - if (FixMklMetaDataEdgeIfNeeded(g, e, e_meta)) { - result = true; - } - } - } - - return result; -} - /////////////////////////////////////////////////////////////////////////////// // Run function for the pass /////////////////////////////////////////////////////////////////////////////// @@ -4422,25 +4307,6 @@ bool MklLayoutRewritePass::RunPass(std::unique_ptr* g) { DumpGraph("After running MklLayoutRewritePass(NodeMerge+Rewrite)", &**g); - order.clear(); - GetReversePostOrder(**g, &order); // This will give us topological sort. - for (Node* n : order) { - // If node is not an op or it cannot run on CPU device, then skip. - if (!n->IsOp() || !CanOpRunOnCPUDevice(n)) { - continue; - } - if (FixMklMetaDataEdges(g, n)) { - string node_name = n->name(); - string op_name = n->type_string(); - - VLOG(1) << "MklLayoutRewritePass: fixed metadata edges for node " - << node_name << " with op " << op_name; - result = true; - } - } - DumpGraph("After running MklLayoutRewritePass(NodeMerge+Rewrite+Fixup)", - &**g); - return result; } diff --git a/tensorflow/core/graph/mkl_layout_pass_test.cc b/tensorflow/core/graph/mkl_layout_pass_test.cc index 7645b4a7f0..029cdcf94a 100644 --- a/tensorflow/core/graph/mkl_layout_pass_test.cc +++ b/tensorflow/core/graph/mkl_layout_pass_test.cc @@ -3518,37 +3518,6 @@ TEST_F(MklLayoutPassTest, NodeMerge_Conv2DWithBias_DeviceTest) { "B->C:1;C->E;D->E:1;E->Z;M->C:2;N->C:3;Y->Z:1"); } -///////////////////////////////////////////////////////////////////// -// Post-rewrite fixup pass test - -TEST_F(MklLayoutPassTest, PostRewriteFixUpPass) { - InitGraph( - "node { name: 'A' op: 'Input'}" - "node { name: 'B' op: 'Input'}" - "node { name: 'M' op: '_MklInput'}" - "node { name: 'N' op: '_MklInput'}" - "node { name: 'C' op: '_MklConv2D'" - " attr { key: 'T' value { type: DT_FLOAT } }" - " attr { key: 'data_format' value { s: 'NCHW' } }" - " attr { key: 'use_cudnn_on_gpu' value { b: false } }" - " attr { key: 'strides' value { list: {i: 1, i:1, i:1, i:1} } }" - " attr { key: 'padding' value { s: 'SAME' } }" - " attr { key: 'dilations' value { list: {i: 1, i:1, i:1, i:1} } }" - " input: ['A', 'B', 'M', 'N']}" - "node { name: 'D' op: 'Const' " - " attr { key: 'dtype' value { type: DT_UINT8 } }" - " attr { key: 'value' value { " - " tensor { dtype: DT_UINT8 tensor_shape { dim { size: 1 } } " - " int_val: 0 } } } }" - "node { name: 'E' op: '_MklAdd'" - " attr {key: 'T' value { type: DT_FLOAT } }" - " input: ['C', 'A', 'D', 'D']}"); - EXPECT_EQ(DoMklLayoutOptimizationPass(), - "A(Input);B(Input);C(_MklConv2D);D(Const);E(_MklAdd);" - "M(_MklInput);N(_MklInput)|A->C;A->E:1;B->C:1;C->E;C:2->E:2;" - "D->E:3;M->C:2;N->C:3"); -} - ///////////////////////////////////////////////////////////////////// static void BM_MklLayoutRewritePass(int iters, int op_nodes) { diff --git a/tensorflow/core/grappler/costs/graph_properties.cc b/tensorflow/core/grappler/costs/graph_properties.cc index 0c02876ac5..6749a7c571 100644 --- a/tensorflow/core/grappler/costs/graph_properties.cc +++ b/tensorflow/core/grappler/costs/graph_properties.cc @@ -610,6 +610,7 @@ class SymbolicShapeRefiner { } }; + // Compute the shape of the tensors outputed by node 'node' at output port // 'port_index' as the union of shape1 and shape2. ShapeHandle OutputAsUnion(const NodeDef* node, int port_index, ShapeHandle shape1, ShapeHandle shape2) { diff --git a/tensorflow/core/grappler/optimizers/BUILD b/tensorflow/core/grappler/optimizers/BUILD index 8ca726df0b..1b18087cdf 100644 --- a/tensorflow/core/grappler/optimizers/BUILD +++ b/tensorflow/core/grappler/optimizers/BUILD @@ -679,7 +679,6 @@ cc_library( deps = [ ":constant_folding", ":graph_optimizer", - "//tensorflow/core:lib", "//tensorflow/core:protos_all_cc", "//tensorflow/core/grappler:graph_view", "//tensorflow/core/grappler:grappler_item", @@ -781,6 +780,7 @@ cc_library( "//tensorflow/core:lib", "//tensorflow/core:lib_internal", "//tensorflow/core:protos_all_cc", + "//tensorflow/core:scoped_allocator_ops_op_lib", "//tensorflow/core/grappler:grappler_item", "//tensorflow/core/grappler:op_types", "//tensorflow/core/grappler:utils", diff --git a/tensorflow/core/grappler/optimizers/remapper.cc b/tensorflow/core/grappler/optimizers/remapper.cc index 03e36a7b9c..4dde7ed1b4 100644 --- a/tensorflow/core/grappler/optimizers/remapper.cc +++ b/tensorflow/core/grappler/optimizers/remapper.cc @@ -22,7 +22,6 @@ limitations under the License. #include "tensorflow/core/grappler/op_types.h" #include "tensorflow/core/grappler/optimizers/constant_folding.h" #include "tensorflow/core/grappler/utils.h" -#include "tensorflow/core/platform/logging.h" namespace tensorflow { namespace grappler { @@ -201,7 +200,8 @@ Status Remapper::Optimize(Cluster* /*cluster*/, const GrapplerItem& item, } } if (optimizable) { - VLOG(1) << "Optimizing fused batch norm node " << node.DebugString(); + VLOG(2) << "Optimizing fused batch norm node " << node.DebugString() + << std::endl; AddBatchNormNodes(optimized_graph, node); continue; } diff --git a/tensorflow/core/kernels/as_string_op.cc b/tensorflow/core/kernels/as_string_op.cc index a7757d1361..66c4aff3e3 100644 --- a/tensorflow/core/kernels/as_string_op.cc +++ b/tensorflow/core/kernels/as_string_op.cc @@ -73,7 +73,6 @@ class AsStringOp : public OpKernel { } switch (dtype) { case DT_INT8: - case DT_INT16: case DT_INT32: strings::Appendf(&format_, "d"); break; @@ -130,7 +129,6 @@ class AsStringOp : public OpKernel { ENCODE_TYPE(DT_FLOAT, float, format_); ENCODE_TYPE(DT_DOUBLE, double, format_); ENCODE_TYPE(DT_INT8, int8, format_); - ENCODE_TYPE(DT_INT16, int16, format_); case (DT_BOOL): { const auto& input_flat = input_tensor->flat(); for (int i = 0; i < input_flat.size(); ++i) { diff --git a/tensorflow/core/kernels/cwise_op_clip.cc b/tensorflow/core/kernels/cwise_op_clip.cc index 49b90e855b..14d889e8e3 100644 --- a/tensorflow/core/kernels/cwise_op_clip.cc +++ b/tensorflow/core/kernels/cwise_op_clip.cc @@ -33,41 +33,52 @@ class ClipOp : public OpKernel { const Tensor& in0 = ctx->input(0); const Tensor& in1 = ctx->input(1); const Tensor& in2 = ctx->input(2); - OP_REQUIRES(ctx, (in0.shape() == in1.shape() || - TensorShapeUtils::IsScalar(in1.shape())) && - (in0.shape() == in2.shape() || - TensorShapeUtils::IsScalar(in2.shape())), - errors::InvalidArgument( - "clip_value_min and clip_value_max must be either of " - "the same shape as input, or a scalar. ", - "input shape: ", in0.shape().DebugString(), - "clip_value_min shape: ", in1.shape().DebugString(), - "clip_value_max shape: ", in2.shape().DebugString())); - - Tensor* out = nullptr; - OP_REQUIRES_OK( - ctx, ctx->forward_input_or_allocate_output({0}, 0, in0.shape(), &out)); - if (out->NumElements() == 0) return; // Nothing to do for empty output auto in0_flat = in0.flat(); auto in1_flat = in1.flat(); auto in2_flat = in2.flat(); - auto out_flat = out->flat(); const Device& d = ctx->eigen_device(); + Tensor* out = nullptr; + OP_REQUIRES_OK( + ctx, ctx->forward_input_or_allocate_output({0}, 0, in0.shape(), &out)); + auto out_flat = out->flat(); if (in1.shape() == in2.shape()) { if (in0.shape() == in1.shape()) { functor::TernaryClipOp()(d, in0_flat, in1_flat, in2_flat, out_flat); } else { + OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(in1.shape()), + errors::InvalidArgument( + "clip_value_min and clip_value_max must be either of " + "the same shape as input, or a scalar. ", + "input shape: ", in0.shape().DebugString(), + "clip_value_min shape: ", in1.shape().DebugString(), + "clip_value_max shape: ", in2.shape().DebugString())); functor::UnaryClipOp()(d, in0_flat, in1_flat, in2_flat, out_flat); } } else { if (in0.shape() == in1.shape()) { + OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(in2.shape()), + errors::InvalidArgument( + "clip_value_min and clip_value_max must be either of " + "the same shape as input, or a scalar. ", + "input shape: ", in0.shape().DebugString(), + "clip_value_min shape: ", in1.shape().DebugString(), + "clip_value_max shape: ", in2.shape().DebugString())); functor::BinaryLeftClipOp()(d, in0_flat, in1_flat, in2_flat, out_flat); } else { + OP_REQUIRES(ctx, + (in0.shape() == in2.shape() && + TensorShapeUtils::IsScalar(in1.shape())), + errors::InvalidArgument( + "clip_value_min and clip_value_max must be either of " + "the same shape as input, or a scalar. ", + "input shape: ", in0.shape().DebugString(), + "clip_value_min shape: ", in1.shape().DebugString(), + "clip_value_max shape: ", in2.shape().DebugString())); functor::BinaryRightClipOp()(d, in0_flat, in1_flat, in2_flat, out_flat); } diff --git a/tensorflow/core/kernels/dense_update_functor_gpu.cu.cc b/tensorflow/core/kernels/dense_update_functor_gpu.cu.cc index 17a85d9773..9a3b2303a3 100644 --- a/tensorflow/core/kernels/dense_update_functor_gpu.cu.cc +++ b/tensorflow/core/kernels/dense_update_functor_gpu.cu.cc @@ -57,7 +57,6 @@ struct DenseUpdate { template struct functor::DenseUpdate; \ template struct functor::DenseUpdate; TF_CALL_GPU_NUMBER_TYPES(DEFINE_GPU_KERNELS); -TF_CALL_int32(DEFINE_GPU_KERNELS); TF_CALL_int64(DEFINE_GPU_KERNELS); #undef DEFINE_GPU_KERNELS diff --git a/tensorflow/core/kernels/gather_functor.cc b/tensorflow/core/kernels/gather_functor.cc index 5cd8e04927..e6fefe643b 100644 --- a/tensorflow/core/kernels/gather_functor.cc +++ b/tensorflow/core/kernels/gather_functor.cc @@ -37,7 +37,6 @@ namespace functor { DECLARE_GPU_SPECS_INDEX(T, int32); \ DECLARE_GPU_SPECS_INDEX(T, int64) -TF_CALL_int64(DECLARE_GPU_SPECS); TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_SPECS); TF_CALL_complex64(DECLARE_GPU_SPECS); TF_CALL_complex128(DECLARE_GPU_SPECS); diff --git a/tensorflow/core/kernels/gather_functor_gpu.cu.cc b/tensorflow/core/kernels/gather_functor_gpu.cu.cc index 4563fc6353..39b6924d74 100644 --- a/tensorflow/core/kernels/gather_functor_gpu.cu.cc +++ b/tensorflow/core/kernels/gather_functor_gpu.cu.cc @@ -31,7 +31,6 @@ typedef Eigen::GpuDevice GPUDevice; DEFINE_GPU_SPECS_INDEX(T, int32); \ DEFINE_GPU_SPECS_INDEX(T, int64); -TF_CALL_int64(DEFINE_GPU_SPECS); TF_CALL_GPU_NUMBER_TYPES(DEFINE_GPU_SPECS); TF_CALL_complex64(DEFINE_GPU_SPECS); TF_CALL_complex128(DEFINE_GPU_SPECS); diff --git a/tensorflow/core/kernels/gather_nd_op.cc b/tensorflow/core/kernels/gather_nd_op.cc index 4e53291b7f..7e5a9e1ec5 100644 --- a/tensorflow/core/kernels/gather_nd_op.cc +++ b/tensorflow/core/kernels/gather_nd_op.cc @@ -228,8 +228,6 @@ namespace functor { DECLARE_GPU_SPECS_INDEX(T, int32); \ DECLARE_GPU_SPECS_INDEX(T, int64) -TF_CALL_int32(DECLARE_GPU_SPECS); -TF_CALL_int64(DECLARE_GPU_SPECS); TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_SPECS); TF_CALL_complex64(DECLARE_GPU_SPECS); TF_CALL_complex128(DECLARE_GPU_SPECS); @@ -241,8 +239,6 @@ TF_CALL_complex128(DECLARE_GPU_SPECS); // Registration of the GPU implementations. #define REGISTER_GATHER_ND_GPU(type) REGISTER_GATHER_ND_ALL_INDICES(GPU, type) -TF_CALL_int32(REGISTER_GATHER_ND_GPU); -TF_CALL_int64(REGISTER_GATHER_ND_GPU); TF_CALL_GPU_NUMBER_TYPES(REGISTER_GATHER_ND_GPU); TF_CALL_complex64(REGISTER_GATHER_ND_GPU); TF_CALL_complex128(REGISTER_GATHER_ND_GPU); diff --git a/tensorflow/core/kernels/gather_nd_op_gpu.cu.cc b/tensorflow/core/kernels/gather_nd_op_gpu.cu.cc index da8d2e9e3c..b03efc684f 100644 --- a/tensorflow/core/kernels/gather_nd_op_gpu.cu.cc +++ b/tensorflow/core/kernels/gather_nd_op_gpu.cu.cc @@ -119,8 +119,6 @@ struct GatherNdSlice { DEFINE_GPU_SPECS_INDEX(T, int32); \ DEFINE_GPU_SPECS_INDEX(T, int64); -TF_CALL_int32(DEFINE_GPU_SPECS); -TF_CALL_int64(DEFINE_GPU_SPECS); TF_CALL_GPU_NUMBER_TYPES(DEFINE_GPU_SPECS); TF_CALL_complex64(DEFINE_GPU_SPECS); TF_CALL_complex128(DEFINE_GPU_SPECS); diff --git a/tensorflow/core/kernels/gather_op.cc b/tensorflow/core/kernels/gather_op.cc index 094504d6b9..ef332ebee3 100644 --- a/tensorflow/core/kernels/gather_op.cc +++ b/tensorflow/core/kernels/gather_op.cc @@ -153,7 +153,6 @@ TF_CALL_uint64(REGISTER_GATHER_CPU); // Registration of the GPU implementations. #define REGISTER_GATHER_GPU(type) REGISTER_GATHER_ALL_INDICES(GPU, type) -TF_CALL_int64(REGISTER_GATHER_GPU); TF_CALL_GPU_NUMBER_TYPES(REGISTER_GATHER_GPU); TF_CALL_complex64(REGISTER_GATHER_GPU); TF_CALL_complex128(REGISTER_GATHER_GPU); diff --git a/tensorflow/core/kernels/mkl_concat_op.cc b/tensorflow/core/kernels/mkl_concat_op.cc index 31d1b949ef..5eeb23d810 100644 --- a/tensorflow/core/kernels/mkl_concat_op.cc +++ b/tensorflow/core/kernels/mkl_concat_op.cc @@ -14,7 +14,6 @@ limitations under the License. #include #include -#include #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/core/framework/op_kernel.h" @@ -591,8 +590,8 @@ class MklConcatOp : public OpKernel { const int N = input_tensors.size(); // Get Tensor shapes. - std::vector mkl_input_shapes(N); - GetMklShapeList(context, "values", &mkl_input_shapes); + std::vector input_shapes(N); + GetMklShapeList(context, "values", &input_shapes); const Tensor& concat_dim_tensor = (AxisArgName == NAME_IS_CONCAT_DIM) ? MklGetInput(context, 0) @@ -611,14 +610,19 @@ class MklConcatOp : public OpKernel { int i = 0; bool invoke_eigen = false; bool are_all_mkl_inputs = true, are_all_tf_inputs = true; - const TensorShape expected_shape = mkl_input_shapes[0].IsMklTensor() - ? mkl_input_shapes[0].GetTfShape() - : input_tensors[0].shape(); + const TensorShape expected_shape = input_shapes[0].IsMklTensor() + ? input_shapes[0].GetTfShape() + : input_tensors[0].shape(); size_t expected_dims = expected_shape.dims(); if (concat_dim < 0) concat_dim = expected_dims + concat_dim; - for (auto& s : mkl_input_shapes) { + for (auto& s : input_shapes) { + if (s == expected_shape) { + ++i; + continue; + } + TensorShape s_shape = s.IsMklTensor() ? s.GetTfShape() : input_tensors[i].shape(); size_t s_dims = s_shape.dims(); @@ -661,14 +665,21 @@ class MklConcatOp : public OpKernel { // Call Eigen library if (invoke_eigen) { - CallEigenVersion(context, input_tensors, mkl_input_shapes); + TensorShapeList tf_input_shapes; + i = 0; + for (auto& s : input_shapes) { + TensorShape s_shape = + s.IsMklTensor() ? s.GetTfShape() : input_tensors[i].shape(); + tf_input_shapes.push_back(s_shape); + ++i; + } + CallEigenVersion(context, input_tensors, tf_input_shapes); return; } memory::dims dst_dims; - if (are_all_mkl_inputs) - dst_dims = TFShapeToMklDnnDims(mkl_input_shapes[0].GetTfShape()); + dst_dims = TFShapeToMklDnnDims(input_shapes[0].GetTfShape()); else // When all the inputs are in Tensorflow format, we don't know // what is the input data format. In that case, we just use @@ -678,61 +689,26 @@ class MklConcatOp : public OpKernel { std::vector srcs_pd; std::vector> srcs(N, MklDnnData(&cpu_engine)); int64 dst_concat_dim_size = 0; - - bool isMklReorderNeeded = false; - memory::format mkl_common_format = memory::format::any; - if (are_all_mkl_inputs) { - mkl_common_format = - FindMklCommonFormat(mkl_input_shapes, concat_dim, - &isMklReorderNeeded, &dst_concat_dim_size); - - if (!isMklReorderNeeded) { - // All MKL tensors have a same format. Reorder is not needed. - for (int k = 0; k < N; k++) { - if (input_tensors[k].NumElements() == 0) - continue; - - auto src_md = mkl_input_shapes[k].GetMklLayout(); - srcs[k].SetUsrMem(src_md, &input_tensors[k]); - auto src_mpd = srcs[k].GetUsrMemPrimDesc(); - srcs_pd.push_back(src_mpd); - } - } else { - // MKL tensors have different formats. - // Reorder them to most common format. - for (int k = 0; k < N; k++) { - if (input_tensors[k].NumElements() == 0) - continue; - - auto src_dims = TFShapeToMklDnnDims( - mkl_input_shapes[k].GetTfShape()); - auto src_md = mkl_input_shapes[k].GetMklLayout(); - srcs[k].SetUsrMem(src_md, &input_tensors[k]); - - if (src_md.data.format != mkl_common_format) - src_md = memory::desc(src_dims, MklDnnType(), - mkl_common_format); - - srcs_pd.push_back(memory::primitive_desc(src_md, cpu_engine)); - } - } - } else { // All TF inputs - for (int k = 0; k < N; k++) { - if (input_tensors[k].NumElements() == 0) - continue; - - memory::dims src_dims = TFShapeToMklDnnDims(input_tensors[k].shape()); - dst_concat_dim_size += src_dims[concat_dim]; - - // It does not matter what data format to be used (NHWC versus NCHW). - // We just need to ensure that output uses same data format as inputs. - auto src_md = - memory::desc(src_dims, MklDnnType(), memory::format::nchw); - - srcs[k].SetUsrMem(src_md, &input_tensors[k]); - auto src_mpd = srcs[k].GetUsrMemPrimDesc(); - srcs_pd.push_back(src_mpd); - } + for (int k = 0; k < N; k++) { + bool is_mkl_tensor = input_shapes[k].IsMklTensor(); + memory::dims src_dims; + + // Same comment as dst_dims for src_dims. + src_dims = (is_mkl_tensor) + ? TFShapeToMklDnnDims(input_shapes[k].GetTfShape()) + : TFShapeToMklDnnDims(input_tensors[k].shape()); + + dst_concat_dim_size += src_dims[concat_dim]; + auto src_md = + is_mkl_tensor ? input_shapes[k].GetMklLayout() : + // It does not matter what data format we use here + // (NHWC or NCHW). We just need to ensure that output + // of Concat uses same data format as input. + memory::desc(src_dims, MklDnnType(), memory::format::nchw); + + srcs[k].SetUsrMem(src_md, &input_tensors[k]); + auto src_mpd = srcs[k].GetUsrMemPrimDesc(); + srcs_pd.push_back(src_mpd); } dst_dims[concat_dim] = dst_concat_dim_size; @@ -742,33 +718,25 @@ class MklConcatOp : public OpKernel { if (are_all_mkl_inputs) { // Since we are passing a specific format for destination, // we need to have dst_dims in MklDnn order (NCHW). - auto orig_tf_format = mkl_input_shapes[0].GetTfDataFormat(); + auto orig_tf_format = input_shapes[0].GetTfDataFormat(); dst_dims_in_nchw = MklDnnDimsInNCHW( dst_dims, MklDnnDataFormatToTFDataFormat(orig_tf_format)); - // Set the output format same as the most common format of inputs - // to avoid layout conversions. + // We will set the output in the same format as input to avoid layout + // conversions. + // Currently we are setting dst format same as input format. + // See if we can make this choice in a better way. dst_md = memory::desc( - dst_dims_in_nchw, MklDnnType(), mkl_common_format); + dst_dims_in_nchw, MklDnnType(), + (memory::format)input_shapes[0].GetMklLayout().data.format); } else { - // All inputs are TF tensors. - // Set the output format same as input format (nchw). + // Again, format does not matter here. We just need to make it same as + // input format. dst_md = memory::desc(dst_dims, MklDnnType(), memory::format::nchw); } std::vector inputs; - std::vector net; - if (isMklReorderNeeded) { - for (int k = 0; k < input_tensors.size(); k++) { - if (input_tensors[k].NumElements() > 0) { - srcs[k].CheckReorderToOpMem(srcs_pd[k], &net); - } - } - } - for (int k = 0; k < input_tensors.size(); k++) { - if (input_tensors[k].NumElements() > 0) { - inputs.push_back(srcs[k].GetOpMem()); - } - } + for (int k = 0; k < input_tensors.size(); k++) + inputs.push_back(srcs[k].GetOpMem()); // If all inputs are in MKL format, then meaning of concat_dim needs to // change. Value of concat_dim is tied to input Tensorflow data format @@ -777,8 +745,7 @@ class MklConcatOp : public OpKernel { // But ifinput tensors are in NHWC order, then semantics need to change. // E.g., if we are concatinating over Channel (dimension 3 for NHWC), // then since MklDnn order is NCHW, concat_dim needs to be 1. - if (are_all_mkl_inputs) - concat_dim = mkl_input_shapes[0].TfDimIdx(concat_dim); + if (are_all_mkl_inputs) concat_dim = input_shapes[0].TfDimIdx(concat_dim); auto concat_pd = concat::primitive_desc(dst_md, concat_dim, srcs_pd); @@ -791,7 +758,7 @@ class MklConcatOp : public OpKernel { dnn_shape_dst.SetMklLayout(&dst_pd); dnn_shape_dst.SetElemType(MklDnnType()); dnn_shape_dst.SetTfLayout(dst_dims.size(), dst_dims_in_nchw, - mkl_input_shapes[0].GetTfDataFormat()); + input_shapes[0].GetTfDataFormat()); tf_shape_dst.AddDim((dst_pd.get_size() / sizeof(T))); } else { dnn_shape_dst.SetMklTensor(false); @@ -806,6 +773,7 @@ class MklConcatOp : public OpKernel { dst.SetUsrMem(dst_md, dst_tensor); auto concat_op = concat(concat_pd, inputs, dst.GetOpMem()); + std::vector net; net.push_back(concat_op); stream(stream::kind::eager).submit(net).wait(); } catch (mkldnn::error& e) { @@ -819,27 +787,15 @@ class MklConcatOp : public OpKernel { } void CallEigenVersion(OpKernelContext* context, const OpInputList& values, - const MklDnnShapeList& mkl_input_shapes) { - CHECK_EQ(values.size(), mkl_input_shapes.size()); + const TensorShapeList& input_shapes) { + CHECK_EQ(values.size(), input_shapes.size()); std::vector converted_values; - TensorShapeList tf_input_shapes; - for (int i = 0; i < mkl_input_shapes.size(); i++) { - if (mkl_input_shapes[i].IsMklTensor()) { - // do conversion from MKL to TF - Tensor tmp_tensor = - ConvertMklToTF(context, values[i], mkl_input_shapes[i]); - converted_values.push_back(tmp_tensor); - tf_input_shapes.push_back(mkl_input_shapes[i].GetTfShape()); - } else { - // no conversion since it is TF tensor already - converted_values.push_back(values[i]); - tf_input_shapes.push_back(values[i].shape()); - } - } + for (int i = 0; i < input_shapes.size(); i++) + converted_values.push_back(values[i]); // Call Eigen concat. - eigen_concat_op_.Compute(context, converted_values, tf_input_shapes); + eigen_concat_op_.Compute(context, converted_values, input_shapes); // Set output Mkl tensor for this op. MklDnnShape dnn_shape_output; @@ -856,55 +812,6 @@ class MklConcatOp : public OpKernel { output_tensor->flat().data(), output_tensor->flat().size() * sizeof(uint8)); } - - // This method finds the most commom format accross all MKL inputs - // Inputs: - // 1. input_shapes: shapes of input (MKL) tensors. - // 2. concat_dim: concat dimension. - // Outputs: - // 1. is_reorder_needed is set to true if inputs have difference formats - // It is set to false otherwise. - // 2. concat_dim_size is the size of concat_dim. - // Return: - // return the common MKL format. - memory::format FindMklCommonFormat(const MklDnnShapeList& input_shapes, - int concat_dim, bool* is_reorder_needed, int64* concat_dim_size) { - *is_reorder_needed = false; - *concat_dim_size = 0; - std::unordered_map occurrence_map; - if (input_shapes.size() == 0) - return memory::format::any; - - // Compute ocurrences of each format of all inputs. - for (int k=0; k ( - input_shapes[k].GetMklLayout().data.format); - occurrence_map[fmt] += 1; - } - - if (occurrence_map.size() == 1) { - // this means that all inputs have a same format - // return it with is_reorder_needed set false. - return static_cast( - input_shapes[0].GetMklLayout().data.format); - } - - // Input tensors have different formats. Thus, reorder is needed. - // We pick up the most common format to minimize the total - // number of input reorder. - memory::format commonest_format = memory::format::any; - int max_occurrence = 0; - *is_reorder_needed = true; - for (auto item : occurrence_map) { - if (item.second > max_occurrence) { - commonest_format = static_cast(item.first); - max_occurrence = item.second; - } - } - return commonest_format; - } }; #endif diff --git a/tensorflow/core/kernels/mkl_conv_grad_bias_ops.cc b/tensorflow/core/kernels/mkl_conv_grad_bias_ops.cc index f857be6c32..c1da0ded1d 100644 --- a/tensorflow/core/kernels/mkl_conv_grad_bias_ops.cc +++ b/tensorflow/core/kernels/mkl_conv_grad_bias_ops.cc @@ -18,7 +18,6 @@ limitations under the License. // bias. #ifdef INTEL_MKL -#ifdef INTEL_MKL_ML #define USE_EIGEN_TENSOR #define EIGEN_USE_THREADS @@ -265,5 +264,4 @@ class MklConv2DCustomBackpropBiasOp : public OpKernel { TF_CALL_float(REGISTER_CPU_KERNELS); #undef REGISTER_CPU_KERNELS } /* namespace tensorflow */ -#endif /* INTEL_MKL_ML */ #endif /* INTEL_MKL */ diff --git a/tensorflow/core/kernels/mkl_pooling_ops_common.h b/tensorflow/core/kernels/mkl_pooling_ops_common.h index c0dfed7d7d..279167aba2 100644 --- a/tensorflow/core/kernels/mkl_pooling_ops_common.h +++ b/tensorflow/core/kernels/mkl_pooling_ops_common.h @@ -199,15 +199,13 @@ class MklPoolingForwardOpBase : public MklPoolingOpBase { CHECK_NOTNULL(pool_params); CHECK_NOTNULL(dnn_data_input); TensorShape input_tensor_shape = input_tensor.shape(); - if (input_tensor.NumElements() != 0) { - memory::desc input_md = + memory::desc input_md = input_mkl_shape.IsMklTensor() ? input_mkl_shape.GetMklLayout() : memory::desc(TFShapeToMklDnnDimsInNCHW(input_tensor_shape, this->data_format_tf_), MklDnnType(), this->data_format_mkldnn_); - dnn_data_input->SetUsrMem(input_md, &input_tensor); - } + dnn_data_input->SetUsrMem(input_md, &input_tensor); this->InitMklPoolParameters(context, pool_params, input_mkl_shape, input_tensor_shape); } diff --git a/tensorflow/core/kernels/scatter_nd_op.cc b/tensorflow/core/kernels/scatter_nd_op.cc index e1fc2ea128..43c5b29509 100644 --- a/tensorflow/core/kernels/scatter_nd_op.cc +++ b/tensorflow/core/kernels/scatter_nd_op.cc @@ -292,7 +292,6 @@ TF_CALL_string(REGISTER_SCATTER_ND_CPU); REGISTER_SCATTER_ND_UPDATE_GPU(type); \ REGISTER_SCATTER_ND_GPU(type); -TF_CALL_int32(REGISTER_SCATTER_ND_ALL_GPU); // TODO(b/66916790): Support half types in ScatterNd. TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SCATTER_ND_ALL_GPU); TF_CALL_complex64(REGISTER_SCATTER_ND_ALL_GPU); @@ -307,8 +306,6 @@ TF_CALL_complex128(REGISTER_SCATTER_ND_ALL_GPU); #define REGISTER_SCATTER_ND_UPDATE_SYCL(type) \ REGISTER_SCATTER_ND_UPDATE(type, SYCL); -TF_CALL_int32(REGISTER_SCATTER_ND_ADD_SUB_SYCL); -TF_CALL_int32(REGISTER_SCATTER_ND_UPDATE_SYCL); TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SCATTER_ND_ADD_SUB_SYCL); TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SCATTER_ND_UPDATE_SYCL); #undef REGISTER_SCATTER_ND_ADD_SUB_SYCL @@ -579,7 +576,6 @@ namespace functor { DECLARE_GPU_SPECS_INDEX(T, int32); \ DECLARE_GPU_SPECS_INDEX(T, int64) -TF_CALL_int32(DECLARE_GPU_SPECS); // TODO(b/66916790): Support half types in ScatterNd. TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_SPECS); TF_CALL_complex64(DECLARE_GPU_SPECS); diff --git a/tensorflow/core/kernels/scatter_nd_op_gpu.cu.cc b/tensorflow/core/kernels/scatter_nd_op_gpu.cu.cc index 08b657f4c3..a3c21edc15 100644 --- a/tensorflow/core/kernels/scatter_nd_op_gpu.cu.cc +++ b/tensorflow/core/kernels/scatter_nd_op_gpu.cu.cc @@ -170,7 +170,6 @@ struct ScatterNdFunctor { DECLARE_GPU_SPECS_INDEX(T, int32); \ DECLARE_GPU_SPECS_INDEX(T, int64) -TF_CALL_int32(DECLARE_GPU_SPECS); TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_SPECS); TF_CALL_complex64(DECLARE_GPU_SPECS); TF_CALL_complex128(DECLARE_GPU_SPECS); diff --git a/tensorflow/core/kernels/scoped_allocator_ops_test.cc b/tensorflow/core/kernels/scoped_allocator_ops_test.cc index 634f9ba887..bb0129fa6f 100644 --- a/tensorflow/core/kernels/scoped_allocator_ops_test.cc +++ b/tensorflow/core/kernels/scoped_allocator_ops_test.cc @@ -216,13 +216,8 @@ TEST_F(ScopedAllocatorConcatOpTest, Success3) { } TEST_F(ScopedAllocatorConcatOpTest, Reshape) { - MakeOp({2, 2, 4}, DT_DOUBLE, true, "test", 120, 2); - - // The elements of the third parameter to ExecOp must be multiples of - // Allocator::kAllocatorAlignment in size. If they are not, the backing - // tensor allocated by PrepOp will have too many elements and reshaping - // will fail. - ExecOp(DT_DOUBLE, 120, {{2, 4}, {2, 4}}); + MakeOp({2, 2, 2}, DT_DOUBLE, true, "test", 120, 2); + ExecOp(DT_DOUBLE, 120, {{2, 2}, {2, 2}}); } TEST_F(ScopedAllocatorConcatOpTest, NoReshapeAttr) { diff --git a/tensorflow/core/kernels/segment_reduction_ops.h b/tensorflow/core/kernels/segment_reduction_ops.h index d65692a552..7796bf3587 100644 --- a/tensorflow/core/kernels/segment_reduction_ops.h +++ b/tensorflow/core/kernels/segment_reduction_ops.h @@ -16,14 +16,6 @@ limitations under the License. #ifndef TENSORFLOW_CORE_KERNELS_SEGMENT_REDUCTION_OPS_H_ #define TENSORFLOW_CORE_KERNELS_SEGMENT_REDUCTION_OPS_H_ - -// This file requires the following include because it uses CudaAtomicMax: -// #include "tensorflow/core/util/cuda_kernel_helper.h" - -// Unfortunately we can't add the #include, since it breaks compilation for -// non-GPU targets. This only breaks in clang, because it's more strict for -// template code and CudaAtomicMax is used in template context. - // This file requires the following include because it uses CudaAtomicMax: // #include "tensorflow/core/util/cuda_kernel_helper.h" @@ -138,4 +130,4 @@ struct Highest { } // namespace functor } // namespace tensorflow -#endif // TENSORFLOW_CORE_KERNELS_SEGMENT_REDUCTION_OPS_H_ +#endif // THIRD_PARTY_TENSORFLOW_CORE_KERNELS_SEGMENT_REDUCTION_OPS_H_ diff --git a/tensorflow/core/kernels/sparse_matmul_op.cc b/tensorflow/core/kernels/sparse_matmul_op.cc index 866c5dcd52..a1f9667b78 100644 --- a/tensorflow/core/kernels/sparse_matmul_op.cc +++ b/tensorflow/core/kernels/sparse_matmul_op.cc @@ -1490,7 +1490,7 @@ inline void LibxsmmSparseMatMul::Compute( #endif // TENSORFLOW_USE_LIBXSMM -// Here is an overview of the SparseMatMul code. Note that we assume that the +// Here is a an overview of the SparseMatMul code. Note that we assume that the // left matrix is sparse. // // The matrix "left" is divided into a grid with blocksize of (M, KL). Each diff --git a/tensorflow/core/kernels/string_split_op.cc b/tensorflow/core/kernels/string_split_op.cc index 26ab72f12e..4c2b312c34 100644 --- a/tensorflow/core/kernels/string_split_op.cc +++ b/tensorflow/core/kernels/string_split_op.cc @@ -22,7 +22,6 @@ limitations under the License. #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/lib/core/status.h" -#include "tensorflow/core/lib/core/stringpiece.h" #include "tensorflow/core/lib/strings/str_util.h" namespace tensorflow { @@ -44,63 +43,6 @@ std::vector Split(const string& str, const string& delimiter, return char_vector; } -std::vector SplitV2(const string& str, StringPiece sep, int maxsplit) { - // This SplitV2 method matches the behavior of python's str.split: - // If sep is given, consecutive delimiters are not grouped together - // and are deemed to delimit empty strings (for example, '1,,2'.split(',') - // returns ['1', '', '2']). The sep argument may consist of multiple - // characters (for example, '1<>2<>3'.split('<>') returns ['1', '2', '3']). - // Splitting an empty string with a specified separator returns ['']. - // - // If sep is not specified or is None, a different splitting algorithm is - // applied: runs of consecutive whitespace are regarded as a single - // separator, and the result will contain no empty strings at the start or - // end if the string has leading or trailing whitespace. Consequently, - // splitting an empty string or a string consisting of just whitespace - // with a None separator returns []. - - std::vector result; - - StringPiece text(str); - if (maxsplit == 0) { - result.emplace_back(std::string(text)); - return result; - } - - if (sep.empty()) { - StringPiece token; - // Remove leading whitespaces. - str_util::RemoveLeadingWhitespace(&text); - int split = 0; - while (str_util::ConsumeNonWhitespace(&text, &token)) { - result.emplace_back(std::string(token)); - str_util::RemoveLeadingWhitespace(&text); - ++split; - if (maxsplit > 0 && split == maxsplit) { - result.emplace_back(std::string(text)); - return result; - } - } - return result; - } - auto p = std::search(text.begin(), text.end(), sep.begin(), sep.end()); - int split = 0; - while (p != text.end()) { - StringPiece token = text.substr(0, p - text.begin()); - result.emplace_back(std::string(token)); - text.remove_prefix(token.size()); - text.remove_prefix(sep.size()); - ++split; - if (maxsplit > 0 && split == maxsplit) { - result.emplace_back(std::string(text)); - return result; - } - p = std::search(text.begin(), text.end(), sep.begin(), sep.end()); - } - result.emplace_back(std::string(text)); - return result; -} - } // namespace class StringSplitOp : public OpKernel { @@ -180,78 +122,6 @@ class StringSplitOp : public OpKernel { bool skip_empty_; }; -class StringSplitV2Op : public OpKernel { - public: - explicit StringSplitV2Op(OpKernelConstruction* context) - : OpKernel(context), maxsplit_(-1) { - OP_REQUIRES_OK(context, context->GetAttr("maxsplit", &maxsplit_)); - } - - void Compute(OpKernelContext* ctx) override { - const Tensor* input_tensor; - OP_REQUIRES_OK(ctx, ctx->input("input", &input_tensor)); - OP_REQUIRES(ctx, TensorShapeUtils::IsVector(input_tensor->shape()), - errors::InvalidArgument("input must be a vector, got shape: ", - input_tensor->shape().DebugString())); - - const auto input_vec = input_tensor->vec(); - const int64 batch_size = input_vec.dimension(0); - - const Tensor* sep_tensor; - OP_REQUIRES_OK(ctx, ctx->input("sep", &sep_tensor)); - OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(sep_tensor->shape()), - errors::InvalidArgument("sep must be a scalar, got shape: ", - sep_tensor->shape().DebugString())); - const auto sep_vec = sep_tensor->flat(); - StringPiece sep(sep_vec(0)); - std::vector tokens; - // Guess that we'll be unpacking a handful of tokens per example. - static constexpr int kReserveSize = 4; - tokens.reserve(batch_size * kReserveSize); - - int64 output_size = 0; - int64 max_num_entries = 0; - std::vector num_indices(batch_size); - for (int64 i = 0; i < batch_size; ++i) { - std::vector parts = SplitV2(input_vec(i), sep, maxsplit_); - int64 n_entries = parts.size(); - num_indices[i] = n_entries; - output_size += n_entries; - max_num_entries = std::max(max_num_entries, n_entries); - tokens.insert(tokens.end(), parts.begin(), parts.end()); - } - - Tensor* sp_indices_t; - OP_REQUIRES_OK(ctx, ctx->allocate_output(0, TensorShape({output_size, 2}), - &sp_indices_t)); - Tensor* sp_tokens_t; - OP_REQUIRES_OK( - ctx, ctx->allocate_output(1, TensorShape({output_size}), &sp_tokens_t)); - Tensor* sp_shape_t; - OP_REQUIRES_OK(ctx, ctx->allocate_output(2, TensorShape({2}), &sp_shape_t)); - - auto sp_indices = sp_indices_t->matrix(); - auto sp_tokens = sp_tokens_t->vec(); - auto sp_shape = sp_shape_t->vec(); - sp_shape(0) = batch_size; - sp_shape(1) = max_num_entries; - size_t c = 0; - for (size_t i = 0; i < batch_size; ++i) { - for (size_t j = 0; j < num_indices[i]; ++j) { - sp_indices(c, 0) = i; - sp_indices(c, 1) = j; - sp_tokens(c) = tokens[c]; - ++c; - } - } - } - - private: - int maxsplit_; -}; - REGISTER_KERNEL_BUILDER(Name("StringSplit").Device(DEVICE_CPU), StringSplitOp); -REGISTER_KERNEL_BUILDER(Name("StringSplitV2").Device(DEVICE_CPU), - StringSplitV2Op); } // namespace tensorflow diff --git a/tensorflow/core/ops/candidate_sampling_ops.cc b/tensorflow/core/ops/candidate_sampling_ops.cc index 6e589c8d1c..6e4d100b04 100644 --- a/tensorflow/core/ops/candidate_sampling_ops.cc +++ b/tensorflow/core/ops/candidate_sampling_ops.cc @@ -145,15 +145,12 @@ REGISTER_OP("ComputeAccidentalHits") int64 num_true; TF_RETURN_IF_ERROR(c->GetAttr("num_true", &num_true)); - // Validate true_classes, must be a matrix. + // Validate true_classes. ShapeHandle true_classes; TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 2, &true_classes)); DimensionHandle unused; TF_RETURN_IF_ERROR( c->WithValue(c->Dim(true_classes, 1), num_true, &unused)); - // Validate sampled_candidates, must be a vector. - ShapeHandle sampled_candidates; - TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &sampled_candidates)); // All three outputs are the same shape. ShapeHandle v = c->Vector(InferenceContext::kUnknownDim); diff --git a/tensorflow/core/ops/dataset_ops.cc b/tensorflow/core/ops/dataset_ops.cc index 9dca5f53ce..15e0ca8af9 100644 --- a/tensorflow/core/ops/dataset_ops.cc +++ b/tensorflow/core/ops/dataset_ops.cc @@ -218,17 +218,7 @@ REGISTER_OP("MapAndBatchDataset") .Attr("Targuments: list(type) >= 0") .Attr("output_types: list(type) >= 1") .Attr("output_shapes: list(shape) >= 1") - .SetShapeFn([](shape_inference::InferenceContext* c) { - // Use index from the end to retrieve the Input shapes, - // so that to avoid guessing the length of "other_arguments". - // batch_size, num_parallel_batches, and drop_remainder are 0-D scalars. - shape_inference::ShapeHandle unused; - TF_RETURN_IF_ERROR(c->WithRank(c->input(c->num_inputs() - 3), 0, &unused)); - TF_RETURN_IF_ERROR(c->WithRank(c->input(c->num_inputs() - 2), 0, &unused)); - TF_RETURN_IF_ERROR(c->WithRank(c->input(c->num_inputs() - 1), 0, &unused)); - - return shape_inference::ScalarShape(c); - }); + .SetShapeFn(shape_inference::ScalarShape); REGISTER_OP("MapAndBatchDatasetV2") .Input("input_dataset: variant") @@ -241,17 +231,7 @@ REGISTER_OP("MapAndBatchDatasetV2") .Attr("Targuments: list(type) >= 0") .Attr("output_types: list(type) >= 1") .Attr("output_shapes: list(shape) >= 1") - .SetShapeFn([](shape_inference::InferenceContext* c) { - // Use index from the end to retrieve the Input shapes, - // so that to avoid guessing the length of "other_arguments". - // batch_size, num_parallel_calls, and drop_remainder are 0-D scalars. - shape_inference::ShapeHandle unused; - TF_RETURN_IF_ERROR(c->WithRank(c->input(c->num_inputs() - 3), 0, &unused)); - TF_RETURN_IF_ERROR(c->WithRank(c->input(c->num_inputs() - 2), 0, &unused)); - TF_RETURN_IF_ERROR(c->WithRank(c->input(c->num_inputs() - 1), 0, &unused)); - - return shape_inference::ScalarShape(c); - }); + .SetShapeFn(shape_inference::ScalarShape); REGISTER_OP("PrefetchDataset") .Input("input_dataset: variant") diff --git a/tensorflow/core/ops/image_ops.cc b/tensorflow/core/ops/image_ops.cc index 87f4991134..d949e70c66 100644 --- a/tensorflow/core/ops/image_ops.cc +++ b/tensorflow/core/ops/image_ops.cc @@ -454,9 +454,7 @@ REGISTER_OP("DrawBoundingBoxes") DimensionHandle unused; TF_RETURN_IF_ERROR(c->WithValue(c->Dim(boxes, 2), 4, &unused)); - // The rank of the input image (rank = 4) has already been restricted - // above, and the output is of the same shape as the input. - return shape_inference::UnchangedShape(c); + return shape_inference::UnchangedShapeWithRankAtLeast(c, 3); }); // -------------------------------------------------------------------------- diff --git a/tensorflow/core/ops/math_ops.cc b/tensorflow/core/ops/math_ops.cc index b3487122e2..1740fa152c 100644 --- a/tensorflow/core/ops/math_ops.cc +++ b/tensorflow/core/ops/math_ops.cc @@ -1084,7 +1084,7 @@ REGISTER_OP("UnsortedSegmentProd") .Input("segment_ids: Tindices") .Input("num_segments: Tnumsegments") .Output("output: T") - .Attr("T: numbertype") + .Attr("T: realnumbertype") .Attr("Tindices: {int32,int64}") .Attr("Tnumsegments: {int32,int64} = DT_INT32") .SetShapeFn(UnsortedSegmentReductionShapeFn); diff --git a/tensorflow/core/ops/nn_ops.cc b/tensorflow/core/ops/nn_ops.cc index 41efa49ce3..fc60e807b9 100644 --- a/tensorflow/core/ops/nn_ops.cc +++ b/tensorflow/core/ops/nn_ops.cc @@ -1453,7 +1453,6 @@ REGISTER_OP("QuantizedReluX") ShapeHandle unused; TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused)); TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused)); - TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 0, &unused)); c->set_output(1, c->Scalar()); c->set_output(2, c->Scalar()); return Status::OK(); diff --git a/tensorflow/core/ops/string_ops.cc b/tensorflow/core/ops/string_ops.cc index 4423062362..1d5c743a56 100644 --- a/tensorflow/core/ops/string_ops.cc +++ b/tensorflow/core/ops/string_ops.cc @@ -78,7 +78,7 @@ REGISTER_OP("ReduceJoin") REGISTER_OP("AsString") .Input("input: T") .Output("output: string") - .Attr("T: {int8, int16, int32, int64, complex64, float, double, bool}") + .Attr("T: {int32, int64, complex64, float, double, bool, int8}") .Attr("precision: int = -1") .Attr("scientific: bool = false") .Attr("shortest: bool = false") @@ -134,24 +134,6 @@ REGISTER_OP("StringSplit") return Status::OK(); }); -REGISTER_OP("StringSplitV2") - .Input("input: string") - .Input("sep: string") - .Output("indices: int64") - .Output("values: string") - .Output("shape: int64") - .Attr("maxsplit: int = -1") - .SetShapeFn([](InferenceContext* c) { - ShapeHandle unused; - TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 1, &unused)); - TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused)); - - c->set_output(0, c->Matrix(InferenceContext::kUnknownDim, 2)); - c->set_output(1, c->Vector(InferenceContext::kUnknownDim)); - c->set_output(2, c->Vector(2)); - return Status::OK(); - }); - REGISTER_OP("StringStrip") .Input("input: string") .Output("output: string") diff --git a/tensorflow/core/platform/cpu_info.cc b/tensorflow/core/platform/cpu_info.cc index e9da3d8e32..99de364042 100644 --- a/tensorflow/core/platform/cpu_info.cc +++ b/tensorflow/core/platform/cpu_info.cc @@ -344,28 +344,5 @@ int CPUModelNum() { #endif } -int CPUIDNumSMT() { -#ifdef PLATFORM_IS_X86 - // https://software.intel.com/en-us/articles/intel-64-architecture-processor-topology-enumeration - // https://software.intel.com/en-us/articles/intel-sdm (Vol 3A) - // Section: Detecting Hardware Multi-threads Support and Topology - // Uses CPUID Leaf 11 to enumerate system topology on Intel x86 architectures - // Other cases not supported - uint32 eax, ebx, ecx, edx; - // Check if system supports Leaf 11 - GETCPUID(eax, ebx, ecx, edx, 0, 0); - if (eax >= 11) { - // 1) Leaf 11 available? CPUID.(EAX=11, ECX=0):EBX != 0 - // 2) SMT_Mask_Width = CPUID.(EAX=11, ECX=0):EAX[4:0] if CPUID.(EAX=11, - // ECX=0):ECX[15:8] is 1 - GETCPUID(eax, ebx, ecx, edx, 11, 0); - if (ebx != 0 && ((ecx & 0xff00) >> 8) == 1) { - return 1 << (eax & 0x1f); // 2 ^ SMT_Mask_Width - } - } -#endif // PLATFORM_IS_X86 - return 0; -} - } // namespace port } // namespace tensorflow diff --git a/tensorflow/core/platform/cpu_info.h b/tensorflow/core/platform/cpu_info.h index 175c9ae8b1..b5be7e8b54 100644 --- a/tensorflow/core/platform/cpu_info.h +++ b/tensorflow/core/platform/cpu_info.h @@ -35,10 +35,6 @@ namespace port { // software can change it dynamically. int NumSchedulableCPUs(); -// Returns an estimate of the number of hyperthreads per physical core -// on the CPU -int NumHyperthreadsPerCore(); - // Mostly ISA related features that we care about enum CPUFeature { // Do not change numeric assignments. @@ -111,9 +107,6 @@ int CPUModelNum(); // Returns nominal core processor cycles per second of each processor. double NominalCPUFrequency(); -// Returns num of hyperthreads per physical core -int CPUIDNumSMT(); - } // namespace port } // namespace tensorflow diff --git a/tensorflow/core/platform/default/build_config.bzl b/tensorflow/core/platform/default/build_config.bzl index a319ccbdbe..ae81f9b5b3 100644 --- a/tensorflow/core/platform/default/build_config.bzl +++ b/tensorflow/core/platform/default/build_config.bzl @@ -71,8 +71,6 @@ def pyx_library( name = filename + "_cython_translation", srcs = [filename], outs = [filename.split(".")[0] + ".cpp"], - # Optionally use PYTHON_BIN_PATH on Linux platforms so that python 3 - # works. Windows has issues with cython_binary so skip PYTHON_BIN_PATH. cmd = "PYTHONHASHSEED=0 $(location @cython//:cython_binary) --cplus $(SRCS) --output-file $(OUTS)", tools = ["@cython//:cython_binary"] + pxd_srcs, ) diff --git a/tensorflow/core/platform/hadoop/hadoop_file_system.cc b/tensorflow/core/platform/hadoop/hadoop_file_system.cc index ff4b4436bb..72c12318ca 100644 --- a/tensorflow/core/platform/hadoop/hadoop_file_system.cc +++ b/tensorflow/core/platform/hadoop/hadoop_file_system.cc @@ -115,17 +115,18 @@ class LibHDFS { const char* kLibHdfsDso = "libhdfs.so"; #endif char* hdfs_home = getenv("HADOOP_HDFS_HOME"); - if (hdfs_home != nullptr) { - string path = io::JoinPath(hdfs_home, "lib", "native", kLibHdfsDso); - status_ = TryLoadAndBind(path.c_str(), &handle_); - if (status_.ok()) { - return; - } + if (hdfs_home == nullptr) { + status_ = errors::FailedPrecondition( + "Environment variable HADOOP_HDFS_HOME not set"); + return; + } + string path = io::JoinPath(hdfs_home, "lib", "native", kLibHdfsDso); + status_ = TryLoadAndBind(path.c_str(), &handle_); + if (!status_.ok()) { + // try load libhdfs.so using dynamic loader's search path in case + // libhdfs.so is installed in non-standard location + status_ = TryLoadAndBind(kLibHdfsDso, &handle_); } - - // Try to load the library dynamically in case it has been installed - // to a in non-standard location. - status_ = TryLoadAndBind(kLibHdfsDso, &handle_); } Status status_; diff --git a/tensorflow/core/platform/posix/port.cc b/tensorflow/core/platform/posix/port.cc index 708f32ba80..8e316472fe 100644 --- a/tensorflow/core/platform/posix/port.cc +++ b/tensorflow/core/platform/posix/port.cc @@ -74,11 +74,6 @@ int NumSchedulableCPUs() { return kDefaultCores; } -int NumHyperthreadsPerCore() { - static const int ht_per_core = tensorflow::port::CPUIDNumSMT(); - return (ht_per_core > 0) ? ht_per_core : 1; -} - void* AlignedMalloc(size_t size, int minimum_alignment) { #if defined(__ANDROID__) return memalign(minimum_alignment, size); diff --git a/tensorflow/core/public/version.h b/tensorflow/core/public/version.h index cb1fd09dbb..522a9d84fd 100644 --- a/tensorflow/core/public/version.h +++ b/tensorflow/core/public/version.h @@ -19,12 +19,12 @@ limitations under the License. // TensorFlow uses semantic versioning, see http://semver.org/. #define TF_MAJOR_VERSION 1 -#define TF_MINOR_VERSION 9 +#define TF_MINOR_VERSION 8 #define TF_PATCH_VERSION 0 // TF_VERSION_SUFFIX is non-empty for pre-releases (e.g. "-alpha", "-alpha.1", // "-beta", "-rc", "-rc.1") -#define TF_VERSION_SUFFIX "-rc0" +#define TF_VERSION_SUFFIX "" #define TF_STR_HELPER(x) #x #define TF_STR(x) TF_STR_HELPER(x) diff --git a/tensorflow/core/util/mkl_util.h b/tensorflow/core/util/mkl_util.h index 90b6533690..dffc965b14 100644 --- a/tensorflow/core/util/mkl_util.h +++ b/tensorflow/core/util/mkl_util.h @@ -42,7 +42,6 @@ limitations under the License. #ifndef INTEL_MKL_ML #include "mkldnn.hpp" -#include "tensorflow/core/lib/core/stringpiece.h" using mkldnn::engine; using mkldnn::memory; @@ -713,48 +712,15 @@ inline Tensor ConvertMklToTF(OpKernelContext* context, const Tensor& mkl_tensor, return output_tensor; } #else -using mkldnn::stream; -template class MklDnnData; - template inline Tensor ConvertMklToTF(OpKernelContext* context, const Tensor& mkl_tensor, const MklDnnShape& mkl_shape) { Tensor output_tensor; - try { - if (!mkl_shape.IsMklTensor()) - return mkl_tensor; // return input since it is already TF tensor - - TensorShape output_shape = mkl_shape.GetTfShape();; - - // Allocate output tensor. - context->allocate_temp(DataTypeToEnum::v(), - output_shape, &output_tensor); - - auto cpu_engine = engine(engine::cpu, 0); - MklDnnData input(&cpu_engine); - - // Get Mkl layout of input tensor. - auto input_mkl_md = mkl_shape.GetMklLayout(); - auto output_tf_md = mkl_shape.GetTfLayout(); - auto output_tf_pd = memory::primitive_desc(output_tf_md, cpu_engine); - input.SetUsrMem(input_mkl_md, &mkl_tensor); - - // reorder - if (input.IsReorderNeeded(output_tf_pd)) { - std::vector net; - CHECK_EQ(input.CheckReorderToOpMem(output_tf_pd, &output_tensor, &net), - true); - stream(stream::kind::eager).submit(net).wait(); - } else { - // If not, just forward input tensor to output tensor. - CHECK(output_tensor.CopyFrom(mkl_tensor, output_shape)); - } - } catch (mkldnn::error& e) { - string error_msg = "Status: " + std::to_string(e.status) + - ", message: " + string(e.message) + ", in file " + - string(__FILE__) + ":" + std::to_string(__LINE__); - LOG(FATAL) << "Operation received an exception: " << error_msg; - } + TensorShape output_shape; + + TF_CHECK_OK( + Status(error::Code::UNIMPLEMENTED, "Unimplemented conversion function")); + return output_tensor; } #endif @@ -1877,7 +1843,7 @@ class FactoryKeyCreator { template void AddAsKey(const T data) { auto buffer = reinterpret_cast(&data); - Append(StringPiece(buffer, sizeof(T))); + Append(absl::string_view(buffer, sizeof(T))); } std::string GetKey() { @@ -1888,8 +1854,8 @@ class FactoryKeyCreator { string key_; const char delimiter = 'x'; const int kMaxKeyLength = 256; - void Append(StringPiece s) { - key_.append(s.ToString()); + void Append(absl::string_view s) { + key_.append(string(s)); key_.append(1, delimiter); } }; diff --git a/tensorflow/docs_src/community/groups.md b/tensorflow/docs_src/community/groups.md index 0b07d413da..d92f5775fa 100644 --- a/tensorflow/docs_src/community/groups.md +++ b/tensorflow/docs_src/community/groups.md @@ -1,38 +1,17 @@ # User Groups -TensorFlow has communities around the world. [Submit your community!](https://docs.google.com/forms/d/e/1FAIpQLSc_RQIUYtVgLLihzATaO_WUXkEyBDE_OoRoOXYDPmBEvHuEBA/viewform) +TensorFlow has communities around the world. ## Asia -* [TensorFlow China community](https://www.tensorflowers.cn) -* [TensorFlow Korea (TF-KR) User Group](https://www.facebook.com/groups/TensorFlowKR/) -* [TensorFlow User Group Tokyo](https://tfug-tokyo.connpass.com/) -* [Soleil Data Dojo](https://soleildatadojo.connpass.com/) +* [TensorFlow Korea (TF-KR) User Group](https://www.facebook.com/groups/TensorFlowKR/) _(Korean language)_ +* [TensorFlow User Group Tokyo](https://tfug-tokyo.connpass.com/) _(Japanese Language)_ +* [Soleil Data Dojo](https://soleildatadojo.connpass.com/) _(Japanese language)_ * [TensorFlow User Group Utsunomiya](https://tfug-utsunomiya.connpass.com/) -* [TensorFlow Philippines Community](https://www.facebook.com/groups/TensorFlowPH/) -* [TensorFlow and Deep Learning Singapore](https://www.meetup.com/TensorFlow-and-Deep-Learning-Singapore/) -* [TensorFlow India](https://www.facebook.com/tensorflowindia) ## Europe * [TensorFlow Barcelona](https://www.meetup.com/Barcelona-Machine-Learning-Meetup/) * [TensorFlow Madrid](https://www.meetup.com/TensorFlow-Madrid/) -* [Tensorflow Belgium](https://www.meetup.com/TensorFlow-Belgium) -* [TensorFlow x Rome Meetup](https://www.meetup.com/it-IT/TensorFlow-x-Rome-Meetup) -* [TensorFlow London](https://www.meetup.com/TensorFlow-London/) -* [TensorFlow Edinburgh](https://www.meetup.com/tensorflow-edinburgh/) - -## America - -* [TensorFlow Buenos Aires](https://www.meetup.com/TensorFlow-Buenos-Aires/) - - -## Oceania -* [Melbourne TensorFlow Meetup](https://www.meetup.com/Melbourne-TensorFlow-Meetup) - - -## Africa - -* [TensorFlow Tunis Meetup](https://www.meetup.com/fr-FR/TensorFlow-Tunis-Meetup/) diff --git a/tensorflow/docs_src/get_started/eager.md b/tensorflow/docs_src/get_started/eager.md index bbb25e20c6..f08ac74425 100644 --- a/tensorflow/docs_src/get_started/eager.md +++ b/tensorflow/docs_src/get_started/eager.md @@ -1,3 +1,3 @@ # Get Started with Eager Execution -[Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/r1.9.0/samples/core/get_started/eager.ipynb) +[Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/r1.8.0/samples/core/get_started/eager.ipynb) diff --git a/tensorflow/docs_src/get_started/index.md b/tensorflow/docs_src/get_started/index.md index 232d2f1547..55579d52fb 100644 --- a/tensorflow/docs_src/get_started/index.md +++ b/tensorflow/docs_src/get_started/index.md @@ -10,9 +10,9 @@ course prior to diving into TensorFlow documentation: TensorFlow is a tool for machine learning. While it contains a wide range of functionality, TensorFlow is mainly designed for deep neural network models. -The easiest way to get started with TensorFlow is by using Eager Execution. +The easiest way to get started with TensorFlow is using Eager Execution. - * @{$get_started/eager}, is for anyone new to machine learning or TensorFlow. + * @{$get_started/eager}, is for anyone new to machine learning or TensorFlow. TensorFlow provides many APIs. The remainder of this section focuses on the Estimator API which provide scalable, high-performance models. See the diff --git a/tensorflow/docs_src/install/install_c.md b/tensorflow/docs_src/install/install_c.md index 2901848745..1abd840ab3 100644 --- a/tensorflow/docs_src/install/install_c.md +++ b/tensorflow/docs_src/install/install_c.md @@ -38,7 +38,7 @@ enable TensorFlow for C: OS="linux" # Change to "darwin" for macOS TARGET_DIRECTORY="/usr/local" curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.9.0-rc0.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.8.0.tar.gz" | sudo tar -C $TARGET_DIRECTORY -xz The `tar` command extracts the TensorFlow C library into the `lib` diff --git a/tensorflow/docs_src/install/install_go.md b/tensorflow/docs_src/install/install_go.md index 55bc0f64e7..52a2a3f8a6 100644 --- a/tensorflow/docs_src/install/install_go.md +++ b/tensorflow/docs_src/install/install_go.md @@ -38,7 +38,7 @@ steps to install this library and enable TensorFlow for Go: TF_TYPE="cpu" # Change to "gpu" for GPU support TARGET_DIRECTORY='/usr/local' curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.9.0-rc0.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.8.0.tar.gz" | sudo tar -C $TARGET_DIRECTORY -xz The `tar` command extracts the TensorFlow C library into the `lib` diff --git a/tensorflow/docs_src/install/install_java.md b/tensorflow/docs_src/install/install_java.md index 637231da12..1256fb99c4 100644 --- a/tensorflow/docs_src/install/install_java.md +++ b/tensorflow/docs_src/install/install_java.md @@ -36,7 +36,7 @@ following to the project's `pom.xml` to use the TensorFlow Java APIs: org.tensorflow tensorflow - 1.9.0-rc0 + 1.8.0 ``` @@ -65,7 +65,7 @@ As an example, these steps will create a Maven project that uses TensorFlow: org.tensorflow tensorflow - 1.9.0-rc0 + 1.8.0 @@ -124,12 +124,12 @@ instead: org.tensorflow libtensorflow - 1.9.0-rc0 + 1.8.0 org.tensorflow libtensorflow_jni_gpu - 1.9.0-rc0 + 1.8.0 ``` @@ -148,7 +148,7 @@ refer to the simpler instructions above instead. Take the following steps to install TensorFlow for Java on Linux or macOS: 1. Download - [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.9.0-rc0.jar), + [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.8.0.jar), which is the TensorFlow Java Archive (JAR). 2. Decide whether you will run TensorFlow for Java on CPU(s) only or with @@ -167,7 +167,7 @@ Take the following steps to install TensorFlow for Java on Linux or macOS: OS=$(uname -s | tr '[:upper:]' '[:lower:]') mkdir -p ./jni curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.9.0-rc0.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.8.0.tar.gz" | tar -xz -C ./jni ### Install on Windows @@ -175,13 +175,13 @@ Take the following steps to install TensorFlow for Java on Linux or macOS: Take the following steps to install TensorFlow for Java on Windows: 1. Download - [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.9.0-rc0.jar), + [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.8.0.jar), which is the TensorFlow Java Archive (JAR). 2. Download the following Java Native Interface (JNI) file appropriate for - [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.9.0-rc0.zip). + [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.8.0.zip). 3. Extract this .zip file. -__Note__: The native library (`tensorflow_jni.dll`) requires `msvcp140.dll` at runtime, which is included in the [Visual C++ 2015 Redistributable](https://www.microsoft.com/en-us/download/details.aspx?id=48145) package. + ### Validate the installation @@ -227,7 +227,7 @@ must be part of your `classpath`. For example, you can include the downloaded `.jar` in your `classpath` by using the `-cp` compilation flag as follows: -
javac -cp libtensorflow-1.9.0-rc0.jar HelloTF.java
+
javac -cp libtensorflow-1.8.0.jar HelloTF.java
### Running @@ -241,11 +241,11 @@ two files are available to the JVM: For example, the following command line executes the `HelloTF` program on Linux and macOS X: -
java -cp libtensorflow-1.9.0-rc0.jar:. -Djava.library.path=./jni HelloTF
+
java -cp libtensorflow-1.8.0.jar:. -Djava.library.path=./jni HelloTF
And the following command line executes the `HelloTF` program on Windows: -
java -cp libtensorflow-1.9.0-rc0.jar;. -Djava.library.path=jni HelloTF
+
java -cp libtensorflow-1.8.0.jar;. -Djava.library.path=jni HelloTF
If the program prints Hello from version, you've successfully installed TensorFlow for Java and are ready to use the API. If the program diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md index c8d706cf3c..0ed8160027 100644 --- a/tensorflow/docs_src/install/install_linux.md +++ b/tensorflow/docs_src/install/install_linux.md @@ -339,7 +339,9 @@ Docker will download the TensorFlow binary image the first time you launch it. #### GPU support -To launch a Docker container with NVidia GPU support, enter a command of the following format (this [does not require any local CUDA installation](https://github.com/nvidia/nvidia-docker/wiki/CUDA#requirements)): +Prior to installing TensorFlow with GPU support, ensure that your system meets all +[NVIDIA software requirements](#NVIDIARequirements). To launch a Docker container +with NVidia GPU support, enter a command of the following format: 
 $ nvidia-docker run -it -p hostPort:containerPort TensorFlowGPUImage
@@ -436,7 +438,7 @@ Take the following steps to install TensorFlow in an Anaconda environment:
 
      
      (tensorflow)$ pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp34-cp34m-linux_x86_64.whl

+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0-cp34-cp34m-linux_x86_64.whl
## Validate your installation @@ -515,7 +517,7 @@ on your system: from source. To use the TensorFlow binaries, version 3.5 or higher is required. See the [NVIDIA documentation](https://developer.nvidia.com/cuda-gpus) for a list of supported GPU cards. -* [GPU drivers](http://nvidia.com/drivers) that support your version of the CUDA +* [GPU drivers](http://nvidia.com/driver) that support your version of the CUDA Toolkit. * The `libcupti-dev` library is the NVIDIA CUDA Profile Tools Interface. This library provides advanced profiling support. To install this library, @@ -682,14 +684,14 @@ This section documents the relevant values for Linux installations. CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0-cp27-none-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.8.0-cp27-none-linux_x86_64.whl
Note that GPU support requires the NVIDIA hardware and software described in @@ -701,14 +703,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0-cp34-cp34m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.8.0-cp34-cp34m-linux_x86_64.whl
Note that GPU support requires the NVIDIA hardware and software described in @@ -720,14 +722,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0-cp35-cp35m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.8.0-cp35-cp35m-linux_x86_64.whl
@@ -739,14 +741,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0-cp36-cp36m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.8.0-cp36-cp36m-linux_x86_64.whl
diff --git a/tensorflow/docs_src/install/install_mac.md b/tensorflow/docs_src/install/install_mac.md index 9d01271c5a..29a867a9e3 100644 --- a/tensorflow/docs_src/install/install_mac.md +++ b/tensorflow/docs_src/install/install_mac.md @@ -119,7 +119,7 @@ Take the following steps to install TensorFlow with Virtualenv: TensorFlow in the active Virtualenv is as follows: 
 $ pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py3-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0-py3-none-any.whl If you encounter installation problems, see [Common Installation Problems](#common-installation-problems). @@ -242,7 +242,7 @@ take the following steps: issue the following command: 
 $ sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py3-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0-py3-none-any.whl If the preceding command fails, see [installation problems](#common-installation-problems). @@ -350,7 +350,7 @@ Take the following steps to install TensorFlow in an Anaconda environment: TensorFlow for Python 2.7: 
 (targetDirectory)$ pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py2-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0-py2-none-any.whl @@ -522,7 +522,7 @@ The value you specify depends on your Python version. 
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py2-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0-py2-none-any.whl
@@ -530,5 +530,5 @@ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py2-none-a 
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py3-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0-py3-none-any.whl
diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md index dc6c1e36fc..5ba522b436 100644 --- a/tensorflow/docs_src/install/install_sources.md +++ b/tensorflow/docs_src/install/install_sources.md @@ -81,7 +81,7 @@ or [macOS](#PrepareMac) - + ## Prepare environment for Linux Before building TensorFlow on Linux, install the following build @@ -328,10 +328,10 @@ Invoke `pip install` to install that pip package. The filename of the `.whl` file depends on your platform. For example, the following command will install the pip package -for TensorFlow 1.9.0rc0 on Linux: +for TensorFlow 1.8.0 on Linux: 
-$ sudo pip install /tmp/tensorflow_pkg/tensorflow-1.9.0rc0-py2-none-any.whl
+$ sudo pip install /tmp/tensorflow_pkg/tensorflow-1.8.0-py2-none-any.whl
## Validate your installation @@ -373,9 +373,9 @@ The build and installation problems you encounter typically depend on the operating system. See the "Common installation problems" section of one of the following guides: - * @{$install_linux#common_installation_problems$Installing TensorFlow on Linux} - * @{$install_mac#common_installation_problems$Installing TensorFlow on Mac OS} - * @{$install_windows#common_installation_problems$Installing TensorFlow on Windows} + * @{$install_linux#CommonInstallationProblems$Installing TensorFlow on Linux} + * @{$install_mac#CommonInstallationProblems$Installing TensorFlow on Mac OS} + * @{$install_windows#CommonInstallationProblems$Installing TensorFlow on Windows} Beyond the errors documented in those two guides, the following table notes additional errors specific to building TensorFlow. Note that we @@ -433,8 +433,6 @@ Stack Overflow and specify the `tensorflow` tag. **Linux** - - @@ -458,7 +456,6 @@ Stack Overflow and specify the `tensorflow` tag. **Mac**
Version:CPU/GPU:Python Version:Compiler:Build Tools:cuDNN:CUDA:
tensorflow-1.9.0CPU2.7, 3.3-3.6GCC 4.8Bazel 0.11.0N/AN/A
tensorflow_gpu-1.9.0GPU2.7, 3.3-3.6GCC 4.8Bazel 0.11.079
tensorflow-1.8.0CPU2.7, 3.3-3.6GCC 4.8Bazel 0.10.0N/AN/A
tensorflow_gpu-1.8.0GPU2.7, 3.3-3.6GCC 4.8Bazel 0.9.079
tensorflow-1.7.0CPU2.7, 3.3-3.6GCC 4.8Bazel 0.10.0N/AN/A
- @@ -475,8 +472,6 @@ Stack Overflow and specify the `tensorflow` tag. **Windows**
Version:CPU/GPU:Python Version:Compiler:Build Tools:cuDNN:CUDA:
tensorflow-1.9.0CPU2.7, 3.3-3.6Clang from xcodeBazel 0.11.0N/AN/A
tensorflow-1.8.0CPU2.7, 3.3-3.6Clang from xcodeBazel 0.10.1N/AN/A
tensorflow-1.7.0CPU2.7, 3.3-3.6Clang from xcodeBazel 0.10.1N/AN/A
tensorflow-1.6.0CPU2.7, 3.3-3.6Clang from xcodeBazel 0.8.1N/AN/A
- - diff --git a/tensorflow/docs_src/mobile/linking_libs.md b/tensorflow/docs_src/mobile/linking_libs.md index efef5dd0da..cf0db59021 100644 --- a/tensorflow/docs_src/mobile/linking_libs.md +++ b/tensorflow/docs_src/mobile/linking_libs.md @@ -27,7 +27,7 @@ called `libandroid_tensorflow_inference_java.jar`. There are three ways to include this functionality in your program: 1. Include the jcenter AAR which contains it, as in this - [example app](https://github.com/googlecodelabs/tensorflow-for-poets-2/blob/master/android/tfmobile/build.gradle#L59-L65) + [example app](https://github.com/googlecodelabs/tensorflow-for-poets-2/blob/master/android/build.gradle#L59-L65) 2. Download the nightly precompiled version from [ci.tensorflow.org](http://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/). diff --git a/tensorflow/docs_src/mobile/prepare_models.md b/tensorflow/docs_src/mobile/prepare_models.md index 2b84dbb973..8b22c04d87 100644 --- a/tensorflow/docs_src/mobile/prepare_models.md +++ b/tensorflow/docs_src/mobile/prepare_models.md @@ -105,8 +105,8 @@ inline constants so everything’s in one file. To handle the conversion, you need the `freeze_graph.py` script, that’s held in [`tensorflow/python/tools/freeze_graph.py`](https://www.tensorflow.org/code/tensorflow/python/tools/freeze_graph.py). You’ll run it like this: - bazel build tensorflow/python/tools:freeze_graph - bazel-bin/tensorflow/python/tools/freeze_graph \ + bazel build tensorflow/tools:freeze_graph + bazel-bin/tensorflow/tools/freeze_graph \ --input_graph=/tmp/model/my_graph.pb \ --input_checkpoint=/tmp/model/model.ckpt-1000 \ --output_graph=/tmp/frozen_graph.pb \ diff --git a/tensorflow/docs_src/performance/quantization.md b/tensorflow/docs_src/performance/quantization.md index c97f74139c..2fea02d861 100644 --- a/tensorflow/docs_src/performance/quantization.md +++ b/tensorflow/docs_src/performance/quantization.md @@ -227,8 +227,8 @@ of 30.0f, and an 8-bit array, the quantized values represent the following:
Version:CPU/GPU:Python Version:Compiler:Build Tools:cuDNN:CUDA:
tensorflow-1.9.0CPU3.5-3.6MSVC 2015 update 3Cmake v3.6.3N/AN/A
tensorflow_gpu-1.9.0GPU3.5-3.6MSVC 2015 update 3Cmake v3.6.379
tensorflow-1.8.0CPU3.5-3.6MSVC 2015 update 3Cmake v3.6.3N/AN/A
tensorflow_gpu-1.8.0GPU3.5-3.6MSVC 2015 update 3Cmake v3.6.379
tensorflow-1.7.0CPU3.5-3.6MSVC 2015 update 3Cmake v3.6.3N/AN/A
- +
QuantizedFloat
0-10.0
12810.0
25530.0
12810.0
Table 2: Example quantized value range diff --git a/tensorflow/docs_src/programmers_guide/estimators.md b/tensorflow/docs_src/programmers_guide/estimators.md index b13b47184d..c4aae1d9d6 100644 --- a/tensorflow/docs_src/programmers_guide/estimators.md +++ b/tensorflow/docs_src/programmers_guide/estimators.md @@ -21,17 +21,18 @@ Note: TensorFlow also includes a deprecated `Estimator` class at Estimators provide the following benefits: -* You can run Estimator-based models on a local host or on a +* You can run Estimators-based models on a local host or on a distributed multi-server environment without changing your model. - Furthermore, you can run Estimator-based models on CPUs, GPUs, + Furthermore, you can run Estimators-based models on CPUs, GPUs, or TPUs without recoding your model. * Estimators simplify sharing implementations between model developers. -* You can develop a state of the art model with high-level intuitive code. +* You can develop a state of the art model with high-level intuitive code, In short, it is generally much easier to create models with Estimators than with the low-level TensorFlow APIs. -* Estimators are themselves built on @{tf.layers}, which +* Estimators are themselves built on tf.layers, which simplifies customization. -* Estimators build the graph for you. +* Estimators build the graph for you. In other words, you don't have to + build the graph. * Estimators provide a safe distributed training loop that controls how and when to: * build the graph @@ -56,7 +57,7 @@ the "plumbing" for you. That is, pre-made Estimators create and manage pre-made Estimators let you experiment with different model architectures by making only minimal code changes. @{tf.estimator.DNNClassifier$`DNNClassifier`}, for example, is a pre-made Estimator class that trains classification models -based on dense, feed-forward neural networks. +through dense, feed-forward neural networks. ### Structure of a pre-made Estimators program @@ -78,7 +79,7 @@ of the following four steps: an input function: def input_fn(dataset): - ... # manipulate dataset, extracting the feature dict and the label + ... # manipulate dataset, extracting feature names and the label return feature_dict, label (See @{$programmers_guide/datasets} for full details.) @@ -95,13 +96,13 @@ of the following four steps: population = tf.feature_column.numeric_column('population') crime_rate = tf.feature_column.numeric_column('crime_rate') median_education = tf.feature_column.numeric_column('median_education', - normalizer_fn=lambda x: x - global_education_mean) + normalizer_fn='lambda x: x - global_education_mean') 3. **Instantiate the relevant pre-made Estimator.** For example, here's a sample instantiation of a pre-made Estimator named `LinearClassifier`: # Instantiate an estimator, passing the feature columns. - estimator = tf.estimator.LinearClassifier( + estimator = tf.estimator.Estimator.LinearClassifier( feature_columns=[population, crime_rate, median_education], ) diff --git a/tensorflow/docs_src/programmers_guide/feature_columns.md b/tensorflow/docs_src/programmers_guide/feature_columns.md index 90f5c53a17..845194fe0e 100644 --- a/tensorflow/docs_src/programmers_guide/feature_columns.md +++ b/tensorflow/docs_src/programmers_guide/feature_columns.md @@ -528,10 +528,10 @@ suggested by the following snippet: categorical_column = ... # Create any categorical column # Represent the categorical column as an embedding column. -# This means creating an embedding vector lookup table with one element for each category. +# This means creating a one-hot vector with one element for each category. embedding_column = tf.feature_column.embedding_column( categorical_column=categorical_column, - dimension=embedding_dimensions) + dimension=dimension_of_embedding_vector) ``` @{$programmers_guide/embedding$Embeddings} is a significant topic within machine diff --git a/tensorflow/examples/learn/iris.py b/tensorflow/examples/learn/iris.py index 86f5204ec3..03e60972aa 100644 --- a/tensorflow/examples/learn/iris.py +++ b/tensorflow/examples/learn/iris.py @@ -21,8 +21,7 @@ from __future__ import division from __future__ import print_function import os - -from six.moves.urllib.request import urlretrieve +import urllib import tensorflow as tf @@ -39,7 +38,9 @@ FEATURE_KEYS = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width'] def maybe_download_iris_data(file_name, download_url): """Downloads the file and returns the number of data.""" if not os.path.exists(file_name): - urlretrieve(download_url, file_name) + raw = urllib.urlopen(download_url).read() + with open(file_name, 'w') as f: + f.write(raw) # The first line is a comma-separated string. The first one is the number of # total data in the file. diff --git a/tensorflow/java/src/gen/cc/op_generator.cc b/tensorflow/java/src/gen/cc/op_generator.cc index 9b171f66ec..debd95fc62 100644 --- a/tensorflow/java/src/gen/cc/op_generator.cc +++ b/tensorflow/java/src/gen/cc/op_generator.cc @@ -376,6 +376,9 @@ void GenerateOp(const OpSpec& op, const EndpointSpec& endpoint, } } // op annotations + op_class.add_annotation( + Annotation::Create("Generated", "javax.annotation") + .attributes("value = \"TensorFlow Java Op Generator\"")); if (endpoint.deprecated()) { op_class.add_annotation(Annotation::Create("Deprecated")); string explanation; @@ -412,12 +415,8 @@ void GenerateOp(const OpSpec& op, const EndpointSpec& endpoint, SourceFileWriter writer(op_file.get()); std::list dependencies; CollectOpDependencies(op, mode, &dependencies); - writer.Write(kLicense) - .EndLine() - .Write("// This class has been generated, DO NOT EDIT!") - .EndLine() - .EndLine() - .BeginType(op_class, PUBLIC | FINAL, &dependencies, &op_javadoc); + writer.Write(kLicense).EndLine().BeginType(op_class, PUBLIC | FINAL, + &dependencies, &op_javadoc); if (!op.optional_attributes().empty()) { RenderOptionsClass(op, op_class, &writer); } diff --git a/tensorflow/java/src/gen/cc/op_specs.cc b/tensorflow/java/src/gen/cc/op_specs.cc index 941ab2699c..181fd4c5e3 100644 --- a/tensorflow/java/src/gen/cc/op_specs.cc +++ b/tensorflow/java/src/gen/cc/op_specs.cc @@ -96,7 +96,6 @@ Type TypeResolver::TypeOf(const OpDef_ArgDef& arg_def, bool* iterable_out) { *iterable_out = true; visited_attrs_.insert(std::make_pair(arg_def.number_attr(), Type::Int())); } - Type type = Type::Wildcard(); if (arg_def.type() != DataType::DT_INVALID) { // resolve type from DataType diff --git a/tensorflow/python/eager/backprop.py b/tensorflow/python/eager/backprop.py index bd97b181ff..b2e6c60021 100644 --- a/tensorflow/python/eager/backprop.py +++ b/tensorflow/python/eager/backprop.py @@ -196,11 +196,11 @@ def implicit_val_and_grad(f): # TODO(cais): Remove calls to tf.constant() once the gradients functions # accept lists and np.ndarrays. - def grad_fn(*args, **kwds): + def grad_fn(*args): """Computes the gradient of the wrapped function.""" this_tape = tape.push_new_tape() try: - end_node = f(*args, **kwds) + end_node = f(*args) if end_node is None: raise ValueError("Cannot differentiate a function that returns None; " "did you forget to return a value from {}?".format( diff --git a/tensorflow/python/estimator/BUILD b/tensorflow/python/estimator/BUILD index 20522098b0..9cd17e0407 100644 --- a/tensorflow/python/estimator/BUILD +++ b/tensorflow/python/estimator/BUILD @@ -978,10 +978,7 @@ py_test( size = "large", srcs = ["keras_test.py"], srcs_version = "PY2AND3", - tags = [ - "no_windows", - "notsan", - ], + tags = ["notsan"], deps = [ ":keras", "//tensorflow/core:protos_all_py", diff --git a/tensorflow/python/estimator/exporter.py b/tensorflow/python/estimator/exporter.py index b18212cfcd..7cdf840c97 100644 --- a/tensorflow/python/estimator/exporter.py +++ b/tensorflow/python/estimator/exporter.py @@ -156,7 +156,7 @@ def _loss_smaller(best_eval_result, current_eval_result): return best_eval_result[default_key] > current_eval_result[default_key] -def _verify_compare_fn_args(compare_fn): +def _verify_compre_fn_args(compare_fn): """Verifies compare_fn arguments.""" args = set(util.fn_args(compare_fn)) if 'best_eval_result' not in args: @@ -265,7 +265,7 @@ class BestExporter(Exporter): self._compare_fn = compare_fn if self._compare_fn is None: raise ValueError('`compare_fn` must not be None.') - _verify_compare_fn_args(self._compare_fn) + _verify_compre_fn_args(self._compare_fn) self._saved_model_exporter = _SavedModelExporter( name, serving_input_receiver_fn, assets_extra, as_text) diff --git a/tensorflow/python/estimator/inputs/numpy_io.py b/tensorflow/python/estimator/inputs/numpy_io.py index a6cefdece2..035c7c148c 100644 --- a/tensorflow/python/estimator/inputs/numpy_io.py +++ b/tensorflow/python/estimator/inputs/numpy_io.py @@ -136,13 +136,11 @@ def numpy_input_fn(x, values in `x` have same shape). ValueError: if duplicate keys are in both `x` and `y` when `y` is a dict. ValueError: if x or y is an empty dict. - TypeError: `x` is not a dict or array. - ValueError: if 'shuffle' is not provided or a bool. + TypeError: `x` is not a dict or array, or if `shuffle` is not bool. """ if not isinstance(shuffle, bool): - raise ValueError('shuffle must be provided and explicitly set as boolean ' - '(it is recommended to set it as True for training); ' - 'got {}'.format(shuffle)) + raise TypeError('shuffle must be explicitly set as boolean; ' + 'got {}'.format(shuffle)) def input_fn(): """Numpy input function.""" diff --git a/tensorflow/python/estimator/inputs/numpy_io_test.py b/tensorflow/python/estimator/inputs/numpy_io_test.py index 81b201cc5c..92d057e25d 100644 --- a/tensorflow/python/estimator/inputs/numpy_io_test.py +++ b/tensorflow/python/estimator/inputs/numpy_io_test.py @@ -286,9 +286,8 @@ class NumpyIoTest(test.TestCase): x = np.arange(32, 36) y = np.arange(4) with self.test_session(): - with self.assertRaisesRegexp(ValueError, - 'shuffle must be provided and explicitly ' - 'set as boolean'): + with self.assertRaisesRegexp(TypeError, + 'shuffle must be explicitly set as boolean'): # Default shuffle is None. numpy_io.numpy_input_fn(x, y) diff --git a/tensorflow/python/estimator/inputs/pandas_io.py b/tensorflow/python/estimator/inputs/pandas_io.py index 57f8e5fd6a..938e244fb3 100644 --- a/tensorflow/python/estimator/inputs/pandas_io.py +++ b/tensorflow/python/estimator/inputs/pandas_io.py @@ -68,16 +68,15 @@ def pandas_input_fn(x, Raises: ValueError: if `x` already contains a column with the same name as `y`, or if the indexes of `x` and `y` don't match. - ValueError: if 'shuffle' is not provided or a bool. + TypeError: `shuffle` is not bool. """ if not HAS_PANDAS: raise TypeError( 'pandas_input_fn should not be called without pandas installed') if not isinstance(shuffle, bool): - raise ValueError('shuffle must be provided and explicitly set as boolean ' - '(it is recommended to set it as True for training); ' - 'got {}'.format(shuffle)) + raise TypeError('shuffle must be explicitly set as boolean; ' + 'got {}'.format(shuffle)) x = x.copy() if y is not None: diff --git a/tensorflow/python/estimator/inputs/pandas_io_test.py b/tensorflow/python/estimator/inputs/pandas_io_test.py index dcecf6dd61..e5912a3b28 100644 --- a/tensorflow/python/estimator/inputs/pandas_io_test.py +++ b/tensorflow/python/estimator/inputs/pandas_io_test.py @@ -70,9 +70,8 @@ class PandasIoTest(test.TestCase): return x, _ = self.makeTestDataFrame() y_noindex = pd.Series(np.arange(-32, -28)) - with self.assertRaisesRegexp(ValueError, - 'shuffle must be provided and explicitly ' - 'set as boolean'): + with self.assertRaisesRegexp(TypeError, + 'shuffle must be explicitly set as boolean'): # Default shuffle is None pandas_io.pandas_input_fn(x, y_noindex) diff --git a/tensorflow/python/estimator/inputs/queues/feeding_functions.py b/tensorflow/python/estimator/inputs/queues/feeding_functions.py index 51a61adb21..8e2ec83020 100644 --- a/tensorflow/python/estimator/inputs/queues/feeding_functions.py +++ b/tensorflow/python/estimator/inputs/queues/feeding_functions.py @@ -250,7 +250,7 @@ class _PandasFeedFn(object): num_epochs=None): if len(placeholders) != len(dataframe.columns) + 1: raise ValueError("Expected {} placeholders; got {}.".format( - len(dataframe.columns) + 1, len(placeholders))) + len(dataframe.columns), len(placeholders))) self._index_placeholder = placeholders[0] self._col_placeholders = placeholders[1:] self._dataframe = dataframe diff --git a/tensorflow/python/estimator/keras.py b/tensorflow/python/estimator/keras.py index 2f439f765e..c80af08fba 100644 --- a/tensorflow/python/estimator/keras.py +++ b/tensorflow/python/estimator/keras.py @@ -70,7 +70,7 @@ def _convert_tensor(x): return x -def _any_variable_initialized(): +def _any_variable_initalized(): """Check if any variable has been initialized in the Keras model. Returns: @@ -511,7 +511,7 @@ def model_to_estimator(keras_model=None, keras_model_fn, model_dir=model_dir, config=config) # Check if we need to call get_weights: - if _any_variable_initialized(): + if _any_variable_initalized(): keras_weights = keras_model.get_weights() # Warn if config passed to estimator tries to update GPUOptions. If a # session has already been created, the GPUOptions passed to the first diff --git a/tensorflow/python/estimator/keras_test.py b/tensorflow/python/estimator/keras_test.py index 5e094ae92b..6688a84130 100644 --- a/tensorflow/python/estimator/keras_test.py +++ b/tensorflow/python/estimator/keras_test.py @@ -31,10 +31,10 @@ from tensorflow.python.estimator import run_config as run_config_lib from tensorflow.python.estimator.inputs import numpy_io from tensorflow.python.framework import ops from tensorflow.python.framework import test_util +from tensorflow.python.keras import backend as K from tensorflow.python.keras import testing_utils from tensorflow.python.keras.applications import mobilenet from tensorflow.python.keras.optimizers import SGD -from tensorflow.python.ops.parsing_ops import gen_parsing_ops from tensorflow.python.platform import gfile from tensorflow.python.platform import test from tensorflow.python.summary.writer import writer_cache @@ -146,13 +146,13 @@ def randomize_io_type(array, name): def multi_inputs_multi_outputs_model(): a = keras.layers.Input(shape=(16,), name='input_a') b = keras.layers.Input(shape=(16,), name='input_b') - m = keras.layers.Input(shape=(8,), dtype='string', name='input_m') + m = keras.layers.Input(shape=(8,), dtype='bool', name='input_m') dense = keras.layers.Dense(8, name='dense_1') a_2 = dense(a) - # Read m - m_2 = keras.layers.Lambda(gen_parsing_ops.string_to_number)(m) - s_2 = keras.layers.Lambda(lambda k: k[0] * k[1])([m_2, a_2]) + # Apply a mask + s_2 = keras.layers.Lambda(lambda k: + K.switch(k[0], k[1], K.zeros_like(k[1])))([m, a_2]) b_2 = dense(b) merged = keras.layers.concatenate([s_2, b_2], name='merge') c = keras.layers.Dense(3, activation='softmax', name='dense_2')(merged) @@ -372,13 +372,13 @@ class TestKerasEstimator(test_util.TensorFlowTestCase): def train_input_fn(): input_dict = {'input_a': a_train, 'input_b': b_train, - 'input_m': input_m_train.astype(np.str)} + 'input_m': input_m_train > 0} output_dict = {'dense_2': c_train, 'dense_3': d_train} return input_dict, output_dict def eval_input_fn(): input_dict = {'input_a': a_test, 'input_b': b_test, - 'input_m': input_m_test.astype(np.str)} + 'input_m': input_m_test > 0} output_dict = {'dense_2': c_test, 'dense_3': d_test} return input_dict, output_dict diff --git a/tensorflow/python/grappler/layout_optimizer_test.py b/tensorflow/python/grappler/layout_optimizer_test.py index af5d709f7e..2d6925d1a8 100644 --- a/tensorflow/python/grappler/layout_optimizer_test.py +++ b/tensorflow/python/grappler/layout_optimizer_test.py @@ -1389,7 +1389,7 @@ class LayoutOptimizerTest(test.TestCase): expected_num_transposes = 3 self.assertEqual(expected_num_transposes, num_transposes) self._assert_trans_nhwc_to_nchw('map/while/Conv2D-0', nodes) - self._assert_trans_nchw_to_nhwc('map/while/Add_1-0-2', nodes) + self._assert_trans_nchw_to_nhwc('map/while/Add-0-2', nodes) self.assertAllClose(output_val_ref, output_val, atol=1e-3) def testLoopWithVecAnd4D(self): @@ -1413,7 +1413,7 @@ class LayoutOptimizerTest(test.TestCase): expected_num_transposes = 2 self.assertEqual(expected_num_transposes, num_transposes) self._assert_trans_nhwc_to_nchw('map/while/Conv2D-0', nodes) - self._assert_trans_nchw_to_nhwc('map/while/Add_1-0-2', nodes) + self._assert_trans_nchw_to_nhwc('map/while/Add-0-2', nodes) self.assertAllClose(output_val_ref, output_val, atol=1e-3) def testBinaryOpSecondPort(self): diff --git a/tensorflow/python/keras/activations.py b/tensorflow/python/keras/activations.py index f608dea430..e487f583be 100644 --- a/tensorflow/python/keras/activations.py +++ b/tensorflow/python/keras/activations.py @@ -93,8 +93,6 @@ def selu(x): - To be used together with the initialization "lecun_normal". - To be used together with the dropout variant "AlphaDropout". - References: - - [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515) """ alpha = 1.6732632423543772848170429916717 scale = 1.0507009873554804934193349852946 diff --git a/tensorflow/python/keras/callbacks.py b/tensorflow/python/keras/callbacks.py index 9f91368e5b..70b6a8431a 100644 --- a/tensorflow/python/keras/callbacks.py +++ b/tensorflow/python/keras/callbacks.py @@ -724,6 +724,15 @@ class TensorBoard(Callback): for weight in layer.weights: mapped_weight_name = weight.name.replace(':', '_') tf_summary.histogram(mapped_weight_name, weight) + if self.write_grads: + grads = model.optimizer.get_gradients(model.total_loss, weight) + + def is_indexed_slices(grad): + return type(grad).__name__ == 'IndexedSlices' + + grads = [grad.values if is_indexed_slices(grad) else grad + for grad in grads] + tf_summary.histogram('{}_grad'.format(mapped_weight_name), grads) if self.write_images: w_img = array_ops.squeeze(weight) shape = K.int_shape(w_img) @@ -750,18 +759,6 @@ class TensorBoard(Callback): assert len(shape) == 4 and shape[-1] in [1, 3, 4] tf_summary.image(mapped_weight_name, w_img) - if self.write_grads: - for weight in layer.trainable_weights: - mapped_weight_name = weight.name.replace(':', '_') - grads = model.optimizer.get_gradients(model.total_loss, weight) - - def is_indexed_slices(grad): - return type(grad).__name__ == 'IndexedSlices' - - grads = [grad.values if is_indexed_slices(grad) else grad - for grad in grads] - tf_summary.histogram('{}_grad'.format(mapped_weight_name), grads) - if hasattr(layer, 'output'): tf_summary.histogram('{}_out'.format(layer.name), layer.output) self.merged = tf_summary.merge_all() diff --git a/tensorflow/python/keras/callbacks_test.py b/tensorflow/python/keras/callbacks_test.py index 5062a26580..b355f4a269 100644 --- a/tensorflow/python/keras/callbacks_test.py +++ b/tensorflow/python/keras/callbacks_test.py @@ -653,8 +653,6 @@ class KerasCallbacksTest(test.TestCase): model.add( keras.layers.Dense( NUM_HIDDEN, input_dim=INPUT_DIM, activation='relu')) - # non_trainable_weights: moving_variance, moving_mean - model.add(keras.layers.BatchNormalization()) model.add(keras.layers.Dense(NUM_CLASSES, activation='softmax')) model.compile( loss='categorical_crossentropy', diff --git a/tensorflow/python/keras/engine/network.py b/tensorflow/python/keras/engine/network.py index 1c9135982e..a4cd017d60 100644 --- a/tensorflow/python/keras/engine/network.py +++ b/tensorflow/python/keras/engine/network.py @@ -123,7 +123,7 @@ class Network(base_layer.Layer): # Entries are unique. Includes input and output layers. self._layers = [] - # Used in symbolic mode only, only in conjunction with graph-networks + # Used in symbolic mode only, only in conjonction with graph-networks self._outbound_nodes = [] self._inbound_nodes = [] diff --git a/tensorflow/python/keras/engine/saving_test.py b/tensorflow/python/keras/engine/saving_test.py index 7e82db028b..6a94986b9c 100644 --- a/tensorflow/python/keras/engine/saving_test.py +++ b/tensorflow/python/keras/engine/saving_test.py @@ -482,7 +482,7 @@ class TestWholeModelSaving(test.TestCase): with h5py.File(fname, 'r') as h5file: num_names_arrays = len([attr for attr in h5file['model_weights'].attrs if attr.startswith('layer_names')]) - # The chunking of layer names array should have happened. + # The chunking of layer names array should have happend. self.assertGreater(num_names_arrays, 0) out2 = model.predict(x) self.assertAllClose(out, out2, atol=1e-05) @@ -527,7 +527,7 @@ class TestWholeModelSaving(test.TestCase): num_weight_arrays = len( [attr for attr in h5file['model_weights']['nested_model'].attrs if attr.startswith('weight_names')]) - # The chunking of layer names array should have happened. + # The chunking of layer names array should have happend. self.assertGreater(num_weight_arrays, 0) out2 = model.predict(x) self.assertAllClose(out, out2, atol=1e-05) diff --git a/tensorflow/python/keras/engine/training.py b/tensorflow/python/keras/engine/training.py index fce6cbdb7a..89c1f1a40f 100644 --- a/tensorflow/python/keras/engine/training.py +++ b/tensorflow/python/keras/engine/training.py @@ -24,7 +24,6 @@ import numpy as np from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.ops import iterator_ops from tensorflow.python.eager import context -from tensorflow.python.framework import constant_op from tensorflow.python.framework import errors from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_util @@ -410,13 +409,11 @@ class Model(Network): else: if sample_weight_mode == 'temporal': sample_weights.append(array_ops.placeholder_with_default( - constant_op.constant([[1.]], dtype=K.floatx()), - shape=[None, None], name=name + '_sample_weights')) + [[1.]], shape=[None, None], name=name + '_sample_weights')) sample_weight_modes.append('temporal') else: sample_weights.append(array_ops.placeholder_with_default( - constant_op.constant([1.], dtype=K.floatx()), - shape=[None], name=name + '_sample_weights')) + [1.], shape=[None], name=name + '_sample_weights')) sample_weight_modes.append(None) self.sample_weight_modes = sample_weight_modes self._feed_sample_weight_modes = [] diff --git a/tensorflow/python/keras/engine/training_eager.py b/tensorflow/python/keras/engine/training_eager.py index e8838cd3bc..2ecbff3a1c 100644 --- a/tensorflow/python/keras/engine/training_eager.py +++ b/tensorflow/python/keras/engine/training_eager.py @@ -732,7 +732,7 @@ def slice_arrays(arrays, indices, contiguous=True): """Slices batches out of provided arrays (workaround for eager tensors). Unfortunately eager tensors don't have the same slicing behavior as - Numpy arrays (they follow the same slicing behavior as symbolic TF tensors), + Numpy arrays (they folow the same slicing behavior as symbolic TF tensors), hence we cannot use `generic_utils.slice_arrays` directly and we have to implement this workaround based on `concat`. This has a performance cost. diff --git a/tensorflow/python/keras/initializers_test.py b/tensorflow/python/keras/initializers_test.py index c519e194bd..a54d6da839 100644 --- a/tensorflow/python/keras/initializers_test.py +++ b/tensorflow/python/keras/initializers_test.py @@ -71,7 +71,7 @@ class KerasInitializersTest(test.TestCase): stddev=1, seed=126), tensor_shape, - target_mean=0., target_max=2, target_min=-2) + target_mean=0., target_std=None, target_max=2) def test_constant(self): tensor_shape = (5, 6, 4) @@ -83,49 +83,49 @@ class KerasInitializersTest(test.TestCase): tensor_shape = (5, 6, 4, 2) with self.test_session(): fan_in, _ = init_ops._compute_fans(tensor_shape) - std = np.sqrt(1. / fan_in) + scale = np.sqrt(3. / fan_in) self._runner(keras.initializers.lecun_uniform(seed=123), tensor_shape, - target_mean=0., target_std=std) + target_mean=0., target_max=scale, target_min=-scale) def test_glorot_uniform(self): tensor_shape = (5, 6, 4, 2) with self.test_session(): fan_in, fan_out = init_ops._compute_fans(tensor_shape) - std = np.sqrt(2. / (fan_in + fan_out)) + scale = np.sqrt(6. / (fan_in + fan_out)) self._runner(keras.initializers.glorot_uniform(seed=123), tensor_shape, - target_mean=0., target_std=std) + target_mean=0., target_max=scale, target_min=-scale) def test_he_uniform(self): tensor_shape = (5, 6, 4, 2) with self.test_session(): fan_in, _ = init_ops._compute_fans(tensor_shape) - std = np.sqrt(2. / fan_in) + scale = np.sqrt(6. / fan_in) self._runner(keras.initializers.he_uniform(seed=123), tensor_shape, - target_mean=0., target_std=std) + target_mean=0., target_max=scale, target_min=-scale) def test_lecun_normal(self): tensor_shape = (5, 6, 4, 2) with self.test_session(): fan_in, _ = init_ops._compute_fans(tensor_shape) - std = np.sqrt(1. / fan_in) + scale = np.sqrt(1. / fan_in) self._runner(keras.initializers.lecun_normal(seed=123), tensor_shape, - target_mean=0., target_std=std) + target_mean=0., target_std=None, target_max=2 * scale) def test_glorot_normal(self): tensor_shape = (5, 6, 4, 2) with self.test_session(): fan_in, fan_out = init_ops._compute_fans(tensor_shape) - std = np.sqrt(2. / (fan_in + fan_out)) + scale = np.sqrt(2. / (fan_in + fan_out)) self._runner(keras.initializers.glorot_normal(seed=123), tensor_shape, - target_mean=0., target_std=std) + target_mean=0., target_std=None, target_max=2 * scale) def test_he_normal(self): tensor_shape = (5, 6, 4, 2) with self.test_session(): fan_in, _ = init_ops._compute_fans(tensor_shape) - std = np.sqrt(2. / fan_in) + scale = np.sqrt(2. / fan_in) self._runner(keras.initializers.he_normal(seed=123), tensor_shape, - target_mean=0., target_std=std) + target_mean=0., target_std=None, target_max=2 * scale) def test_orthogonal(self): tensor_shape = (20, 20) diff --git a/tensorflow/python/keras/layers/core.py b/tensorflow/python/keras/layers/core.py index f60064ed63..5061825d38 100644 --- a/tensorflow/python/keras/layers/core.py +++ b/tensorflow/python/keras/layers/core.py @@ -19,9 +19,7 @@ from __future__ import division from __future__ import print_function import copy -import sys import types as python_types -import warnings import numpy as np @@ -716,7 +714,6 @@ class Lambda(Layer): return self.mask def get_config(self): - module = self.function.__module__ if isinstance(self.function, python_types.LambdaType): function = generic_utils.func_dump(self.function) function_type = 'lambda' @@ -724,26 +721,21 @@ class Lambda(Layer): function = self.function.__name__ function_type = 'function' - output_shape_module = None if isinstance(self._output_shape, python_types.LambdaType): output_shape = generic_utils.func_dump(self._output_shape) output_shape_type = 'lambda' - output_shape_module = self._output_shape.__module__ elif callable(self._output_shape): output_shape = self._output_shape.__name__ output_shape_type = 'function' - output_shape_module = self._output_shape.__module__ else: output_shape = self._output_shape output_shape_type = 'raw' config = { 'function': function, - 'module': module, 'function_type': function_type, 'output_shape': output_shape, 'output_shape_type': output_shape_type, - 'output_shape_module': output_shape_module, 'arguments': self.arguments } base_config = super(Lambda, self).get_config() @@ -753,16 +745,8 @@ class Lambda(Layer): def from_config(cls, config, custom_objects=None): config = config.copy() globs = globals() - module = config.pop('module', None) - if module in sys.modules: - globs.update(sys.modules[module].__dict__) - elif module is not None: - # Note: we don't know the name of the function if it's a lambda. - warnings.warn('{} is not loaded, but a Lambda layer uses it. ' - 'It may cause errors.'.format(module) - , UserWarning) if custom_objects: - globs.update(custom_objects) + globs = dict(list(globs.items()) + list(custom_objects.items())) function_type = config.pop('function_type') if function_type == 'function': # Simple lookup in custom objects @@ -776,14 +760,6 @@ class Lambda(Layer): else: raise TypeError('Unknown function type:', function_type) - output_shape_module = config.pop('output_shape_module', None) - if output_shape_module in sys.modules: - globs.update(sys.modules[output_shape_module].__dict__) - elif output_shape_module is not None: - # Note: we don't know the name of the function if it's a lambda. - warnings.warn('{} is not loaded, but a Lambda layer uses it. ' - 'It may cause errors.'.format(output_shape_module) - , UserWarning) output_shape_type = config.pop('output_shape_type') if output_shape_type == 'function': # Simple lookup in custom objects diff --git a/tensorflow/python/keras/models_test.py b/tensorflow/python/keras/models_test.py index e6e45902a8..c616d8f24f 100644 --- a/tensorflow/python/keras/models_test.py +++ b/tensorflow/python/keras/models_test.py @@ -144,19 +144,5 @@ class CheckpointingTests(test.TestCase): model.load_weights(save_prefix) self.assertEqual(12., self.evaluate(beta1_power)) -class TestModelBackend(test.TestCase): - - def test_model_backend_float64_use_cases(self): - # Test case for GitHub issue 19318 - floatx = keras.backend.floatx() - keras.backend.set_floatx('float64') - - x = keras.Input((5,)) - y = keras.layers.Dense(1)(x) - model = keras.models.Model(x, y) - model.compile('rmsprop', 'mse') - - keras.backend.set_floatx(floatx) - if __name__ == '__main__': test.main() diff --git a/tensorflow/python/kernel_tests/as_string_op_test.py b/tensorflow/python/kernel_tests/as_string_op_test.py index 94ed8ebd31..9d54add264 100644 --- a/tensorflow/python/kernel_tests/as_string_op_test.py +++ b/tensorflow/python/kernel_tests/as_string_op_test.py @@ -130,16 +130,6 @@ class AsStringOpTest(test.TestCase): result = output.eval(feed_dict={input_: int_inputs_}) self.assertAllEqual(s(result), ["%d" % x for x in int_inputs_]) - def testHalfInt(self): - s = lambda strs: [x.decode("ascii") for x in strs] - - with self.test_session(): - input_ = array_ops.placeholder(dtypes.int16) - int_inputs_ = [np.iinfo(np.int16).min, np.iinfo(np.int16).max] - output = string_ops.as_string(input_) - result = output.eval(feed_dict={input_: int_inputs_}) - self.assertAllEqual(s(result), ["%d" % x for x in int_inputs_]) - def testBool(self): bool_inputs_ = [False, True] s = lambda strs: [x.decode("ascii") for x in strs] diff --git a/tensorflow/python/kernel_tests/betainc_op_test.py b/tensorflow/python/kernel_tests/betainc_op_test.py index 16fdedac41..08b03f8518 100644 --- a/tensorflow/python/kernel_tests/betainc_op_test.py +++ b/tensorflow/python/kernel_tests/betainc_op_test.py @@ -172,7 +172,7 @@ class BetaincTest(test.TestCase): tf_gout_t = math_ops.betainc(tf_ga_s, tf_gb_s, tf_gx_s) err = gradient_checker.compute_gradient_error( [tf_gx_s], [gx_s.shape], tf_gout_t, gx_s.shape) - tf_logging.info("betainc gradient err = %g " % err) + print("betainc gradient err = %g " % err) self.assertLess(err, err_tolerance) # Test broadcast gradient @@ -181,7 +181,7 @@ class BetaincTest(test.TestCase): tf_gout_t = math_ops.betainc(tf_ga_s, tf_gb_s, tf_gx_s) err = gradient_checker.compute_gradient_error( [tf_gx_s], [()], tf_gout_t, ga_s.shape) - tf_logging.info("betainc gradient err = %g " % err) + print("betainc gradient err = %g " % err) self.assertLess(err, err_tolerance) diff --git a/tensorflow/python/kernel_tests/clip_ops_test.py b/tensorflow/python/kernel_tests/clip_ops_test.py index fb52d10475..e08123b041 100644 --- a/tensorflow/python/kernel_tests/clip_ops_test.py +++ b/tensorflow/python/kernel_tests/clip_ops_test.py @@ -18,12 +18,9 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import numpy as np - from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops -from tensorflow.python.ops import array_ops from tensorflow.python.ops import clip_ops from tensorflow.python.ops import gradient_checker from tensorflow.python.platform import test @@ -417,16 +414,6 @@ class ClipTest(test.TestCase): self.assertAllClose(np_ans, tf_ans) - def testClipByValueEmptyTensor(self): - # Test case for GitHub issue 19337 - zero = array_ops.placeholder(dtype=dtypes.float32, shape=None) - x = clip_ops.clip_by_value(zero, zero, zero) - y = clip_ops.clip_by_value(zero, 1.0, 1.0) - z = clip_ops.clip_by_value(zero, zero, 1.0) - w = clip_ops.clip_by_value(zero, 1.0, zero) - with self.test_session(use_gpu=True) as sess: - sess.run([x, y, z, w], feed_dict={zero: np.zeros((7, 0))}) - if __name__ == '__main__': test.main() diff --git a/tensorflow/python/kernel_tests/conv_ops_test.py b/tensorflow/python/kernel_tests/conv_ops_test.py index 80ba7dafc9..8699fd5b25 100644 --- a/tensorflow/python/kernel_tests/conv_ops_test.py +++ b/tensorflow/python/kernel_tests/conv_ops_test.py @@ -312,8 +312,8 @@ class Conv2DTest(test.TestCase): expected_values = self.evaluate(expected_results) computed_values = self.evaluate(computed_results) for e_value, c_value in zip(expected_values, computed_values): - tf_logging.info("expected = ", e_value) - tf_logging.info("actual = ", c_value) + print("expected = ", e_value) + print("actual = ", c_value) self.assertAllClose( e_value.flatten(), c_value.flatten(), atol=tolerance, rtol=1e-4) @@ -337,8 +337,8 @@ class Conv2DTest(test.TestCase): for i in range(len(tensors)): conv = tensors[i] value = values[i] - tf_logging.info("expected = ", expected) - tf_logging.info("actual = ", value) + print("expected = ", expected) + print("actual = ", value) tol = 1e-5 if value.dtype == np.float16: tol = 1e-3 @@ -547,8 +547,8 @@ class Conv2DTest(test.TestCase): # "values" consists of two tensors for two backprops value = self.evaluate(conv) self.assertShapeEqual(value, conv) - tf_logging.info("expected = ", expected) - tf_logging.info("actual = ", value) + print("expected = ", expected) + print("actual = ", value) self.assertArrayNear(expected, value.flatten(), err) def _CompareBackpropInput(self, input_sizes, filter_sizes, output_sizes, @@ -723,8 +723,8 @@ class Conv2DTest(test.TestCase): data_format=data_format) value = self.evaluate(conv) self.assertShapeEqual(value, conv) - tf_logging.info("expected = ", expected) - tf_logging.info("actual = ", value) + print("expected = ", expected) + print("actual = ", value) self.assertArrayNear(expected, value.flatten(), 1e-5) def _CompareBackFilter(self, input_sizes, filter_sizes, output_sizes, @@ -912,8 +912,8 @@ class Conv2DTest(test.TestCase): value_2 = sess.run(conv_2) self.assertShapeEqual(value, conv) self.assertShapeEqual(value_2, conv_2) - tf_logging.info("expected = ", value_2) - tf_logging.info("actual = ", value) + print("expected = ", value_2) + print("actual = ", value) self.assertArrayNear(value_2.flatten(), value.flatten(), err) # Testing for backprops @@ -965,8 +965,8 @@ class Conv2DTest(test.TestCase): value_2 = sess.run(conv_2) self.assertShapeEqual(value, conv) self.assertShapeEqual(value_2, conv_2) - tf_logging.info("expected = ", value_2) - tf_logging.info("actual = ", value) + print("expected = ", value_2) + print("actual = ", value) self.assertArrayNear(value_2.flatten(), value.flatten(), err) def testConv2D2x2Depth3ValidBackpropFilterStride1x1Dilation2x1(self): @@ -1178,7 +1178,7 @@ class Conv2DTest(test.TestCase): # since fp16 numerical gradients are too imprecise. err = np.fabs(jacob_t - reference_jacob_t).max() - tf_logging.info("conv_2d gradient error = ", err) + print("conv_2d gradient error = ", err) self.assertLess(err, 0.002) def testInputGradientValidPaddingStrideOne(self): @@ -1546,7 +1546,7 @@ class DepthwiseConv2DTest(test.TestCase): conv = nn_impl.depthwise_conv2d( t1, t2, strides=[1, stride, stride, 1], padding=padding) value = sess.run(conv) - tf_logging.info("value = ", value) + print("value = ", value) self.assertArrayNear(expected, np.ravel(value), 1e-5) self.assertShapeEqual(value, conv) @@ -1668,7 +1668,7 @@ class SeparableConv2DTest(test.TestCase): conv = array_ops.transpose(conv, [0, 2, 3, 1]) value = sess.run(conv) - tf_logging.info("value = ", value) + print("value = ", value) self.assertArrayNear(expected, np.ravel(value), 1e-5) self.assertShapeEqual(value, conv) @@ -1826,7 +1826,7 @@ class Conv2DBenchmark(test.Benchmark): wall_time = time.time() - start self.report_benchmark( name="conv_stack_iter_%d" % iter_index, wall_time=wall_time) - tf_logging.info("conv_stack_iter_%d: %.4f" % (iter_index, wall_time)) + print("conv_stack_iter_%d: %.4f" % (iter_index, wall_time)) def GetInceptionFwdTest(input_size, filter_size, stride, padding, diff --git a/tensorflow/python/kernel_tests/gather_nd_op_test.py b/tensorflow/python/kernel_tests/gather_nd_op_test.py index 58e2a8ac2a..91ebe8de99 100644 --- a/tensorflow/python/kernel_tests/gather_nd_op_test.py +++ b/tensorflow/python/kernel_tests/gather_nd_op_test.py @@ -197,21 +197,7 @@ class GatherNdTest(test.TestCase): self.assertEqual(None, shape.ndims) self.assertEqual(None, shape[0].value) - def testBadIndicesCPU(self): - with self.test_session(use_gpu=False): - params = [0, 1, 2] - indices = [[[0], [7]]] # Make this one higher rank - gather_nd = array_ops.gather_nd(params, indices) - with self.assertRaisesOpError( - r"flat indices\[1, :\] = \[7\] does not index into param " - r"\(shape: \[3\]\)"): - gather_nd.eval() - - def _disabledTestBadIndicesGPU(self): - # TODO disabled due to different behavior on GPU and CPU - # On GPU the bad indices do not raise error but fetch 0 values - if not test.is_gpu_available(): - return + def testBadIndices(self): with self.test_session(use_gpu=True): params = [0, 1, 2] indices = [[[0], [7]]] # Make this one higher rank @@ -221,21 +207,7 @@ class GatherNdTest(test.TestCase): r"\(shape: \[3\]\)"): gather_nd.eval() - def testBadIndicesWithSlicesCPU(self): - with self.test_session(use_gpu=False): - params = [[0, 1, 2]] - indices = [[[0], [0], [1]]] # Make this one higher rank - gather_nd = array_ops.gather_nd(params, indices) - with self.assertRaisesOpError( - r"flat indices\[2, :\] = \[1\] does not index into param " - r"\(shape: \[1,3\]\)"): - gather_nd.eval() - - def _disabledTestBadIndicesWithSlicesGPU(self): - # TODO disabled due to different behavior on GPU and CPU - # On GPU the bad indices do not raise error but fetch 0 values - if not test.is_gpu_available(): - return + def testBadIndicesWithSlices(self): with self.test_session(use_gpu=True): params = [[0, 1, 2]] indices = [[[0], [0], [1]]] # Make this one higher rank diff --git a/tensorflow/python/kernel_tests/gather_op_test.py b/tensorflow/python/kernel_tests/gather_op_test.py index 033fa95935..a2fcd751df 100644 --- a/tensorflow/python/kernel_tests/gather_op_test.py +++ b/tensorflow/python/kernel_tests/gather_op_test.py @@ -27,8 +27,7 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import gradients_impl from tensorflow.python.platform import test -_TEST_TYPES = (dtypes.int64, dtypes.float32, - dtypes.complex64, dtypes.complex128) +_TEST_TYPES = (dtypes.float32, dtypes.complex64, dtypes.complex128) class GatherTest(test.TestCase): @@ -123,9 +122,6 @@ class GatherTest(test.TestCase): gather, [tf_params, tf_indices, tf_axis], gather_grad) self.assertEqual(indices_grad, None) self.assertEqual(axis_grad, None) - if dtype.is_integer: - self.assertEqual(params_grad, None) - continue # For axis 0, we are able to create an efficient IndexedSlices for # the gradient. if axis == 0: @@ -181,19 +177,7 @@ class GatherTest(test.TestCase): gather_t = array_ops.gather(params, indices, axis=axis) self.assertEqual(None, gather_t.shape) - def testBadIndicesCPU(self): - with self.test_session(use_gpu=False): - params = [[0, 1, 2], [3, 4, 5]] - with self.assertRaisesOpError(r"indices\[0,0\] = 7 is not in \[0, 2\)"): - array_ops.gather(params, [[7]], axis=0).eval() - with self.assertRaisesOpError(r"indices\[0,0\] = 7 is not in \[0, 3\)"): - array_ops.gather(params, [[7]], axis=1).eval() - - def _disabledTestBadIndicesGPU(self): - # TODO disabled due to different behavior on GPU and CPU - # On GPU the bad indices do not raise error but fetch 0 values - if not test.is_gpu_available(): - return + def testBadIndices(self): with self.test_session(use_gpu=True): params = [[0, 1, 2], [3, 4, 5]] with self.assertRaisesOpError(r"indices\[0,0\] = 7 is not in \[0, 2\)"): diff --git a/tensorflow/python/kernel_tests/init_ops_test.py b/tensorflow/python/kernel_tests/init_ops_test.py index 795aa67248..a9b55854f1 100644 --- a/tensorflow/python/kernel_tests/init_ops_test.py +++ b/tensorflow/python/kernel_tests/init_ops_test.py @@ -362,33 +362,6 @@ class UniformUnitScalingInitializationTest(test.TestCase): dtype=dtypes.string) -class VarianceScalingInitializationTest(test.TestCase): - - def testNormalDistribution(self): - shape = [100, 100] - expect_mean = 0. - expect_var = 1. / shape[0] - init = init_ops.variance_scaling_initializer(distribution='normal') - - with self.test_session(use_gpu=True): - x = init(shape).eval() - - self.assertNear(np.mean(x), expect_mean, err=1e-2) - self.assertNear(np.var(x), expect_var, err=1e-2) - - def testUniformDistribution(self): - shape = [100, 100] - expect_mean = 0. - expect_var = 1. / shape[0] - init = init_ops.variance_scaling_initializer(distribution='uniform') - - with self.test_session(use_gpu=True): - x = init(shape).eval() - - self.assertNear(np.mean(x), expect_mean, err=1e-2) - self.assertNear(np.var(x), expect_var, err=1e-2) - - # TODO(vrv): move to sequence_ops_test? class RangeTest(test.TestCase): diff --git a/tensorflow/python/kernel_tests/pooling_ops_test.py b/tensorflow/python/kernel_tests/pooling_ops_test.py index e95c729715..a0c372db7d 100644 --- a/tensorflow/python/kernel_tests/pooling_ops_test.py +++ b/tensorflow/python/kernel_tests/pooling_ops_test.py @@ -947,7 +947,7 @@ class PoolingTest(test.TestCase): output_sizes, x_init_value=x_init_value, delta=1e-2) - tf_logging.info("%s gradient error = " % func_name, err) + print("%s gradient error = " % func_name, err) self.assertLess(err, err_tolerance) def _ConstructAndTestSecondGradient(self, @@ -1024,7 +1024,7 @@ class PoolingTest(test.TestCase): input_sizes, x_init_value=x_init_value, delta=1e-2) - tf_logging.info("%s second-order gradient error = " % func_name, err) + print("%s second-order gradient error = " % func_name, err) self.assertLess(err, err_tolerance) def _testMaxPoolGradValidPadding1_1(self, data_format, use_gpu): diff --git a/tensorflow/python/kernel_tests/py_func_test.py b/tensorflow/python/kernel_tests/py_func_test.py index 253e43920b..677253946e 100644 --- a/tensorflow/python/kernel_tests/py_func_test.py +++ b/tensorflow/python/kernel_tests/py_func_test.py @@ -19,7 +19,6 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import gc import re import numpy as np @@ -435,29 +434,13 @@ class PyFuncTest(test.TestCase): # ----- Tests shared by py_func and eager_py_func ----- def testCleanup(self): - # Delete everything created by previous tests to avoid side effects. - ops.reset_default_graph() - gc.collect() - initial_size = script_ops._py_funcs.size() - # Encapsulate the graph generation, so locals can be deleted. - def make_graphs(): - for _ in xrange(1000): - g = ops.Graph() - with g.as_default(): - c = constant_op.constant([1.], dtypes.float32) - _ = script_ops.py_func(lambda x: x + 1, [c], [dtypes.float32]) - _ = script_ops.eager_py_func(lambda x: x + 1, [c], [dtypes.float32]) - # These ops have a reference to 'c' which has a reference to the graph. - # Checks if the functions are being deleted though the graph is referenced from them. - # (see #18292) - _ = script_ops.py_func(lambda x: x + c.shape[0], [c], [dtypes.float32]) - _ = script_ops.eager_py_func(lambda x: x + c.shape[0], [c], [dtypes.float32]) - - # Call garbage collector to enforce deletion. - make_graphs() - ops.reset_default_graph() - gc.collect() - self.assertEqual(initial_size, script_ops._py_funcs.size()) + for _ in xrange(1000): + g = ops.Graph() + with g.as_default(): + c = constant_op.constant([1.], dtypes.float32) + _ = script_ops.py_func(lambda x: x + 1, [c], [dtypes.float32]) + _ = script_ops.eager_py_func(lambda x: x + 1, [c], [dtypes.float32]) + self.assertLess(script_ops._py_funcs.size(), 100) # ----- Tests for eager_py_func ----- @test_util.run_in_graph_and_eager_modes() diff --git a/tensorflow/python/kernel_tests/scatter_nd_ops_test.py b/tensorflow/python/kernel_tests/scatter_nd_ops_test.py index faa4b49a8d..79fe927b8a 100644 --- a/tensorflow/python/kernel_tests/scatter_nd_ops_test.py +++ b/tensorflow/python/kernel_tests/scatter_nd_ops_test.py @@ -144,9 +144,7 @@ class StatefulScatterNdTest(test.TestCase): self.assertAllClose(new, ref_var.eval()) def _VariableRankTests(self, np_scatter, tf_scatter): - for vtype in (np.int32, - np.float32, np.float64, - np.complex64, np.complex128): + for vtype in (np.float32, np.float64, np.complex64, np.complex128): for itype in (np.int32, np.int64): self._VariableRankTest(np_scatter, tf_scatter, vtype, itype) @@ -223,7 +221,7 @@ class StatefulScatterNdTest(test.TestCase): # self._VariableRankTests(_NumpyDiv, state_ops.scatter_nd_div) def _ScatterRepeatIndicesTest(self, np_scatter, tf_scatter): - for vtype in (np.int32, np.float32, np.float64): + for vtype in (np.float32, np.float64): for itype in (np.int32, np.int64): self._VariableRankTest( np_scatter, tf_scatter, vtype, itype, repeat_indices=True) diff --git a/tensorflow/python/kernel_tests/scatter_ops_test.py b/tensorflow/python/kernel_tests/scatter_ops_test.py index 1a0fa744ae..c70a4ffce7 100644 --- a/tensorflow/python/kernel_tests/scatter_ops_test.py +++ b/tensorflow/python/kernel_tests/scatter_ops_test.py @@ -159,13 +159,7 @@ class ScatterTest(test.TestCase): # Clips small values to avoid division by zero. def clip_small_values(x): - threshold = 1e-4 - sign = np.sign(x) - - if isinstance(x, np.int32): - threshold = 1 - sign = np.random.choice([-1, 1]) - return threshold * sign if np.abs(x) < threshold else x + return 1e-4 * np.sign(x) if np.abs(x) < 1e-4 else x updates = np.vectorize(clip_small_values)(updates) old = _AsType(np.random.randn(*((first_dim,) + extra_shape)), vtype) @@ -187,11 +181,7 @@ class ScatterTest(test.TestCase): tf_scatter, repeat_indices=False, updates_are_scalar=False): - vtypes = [np.float32, np.float64] - if tf_scatter != state_ops.scatter_div: - vtypes.append(np.int32) - - for vtype in vtypes: + for vtype in (np.float32, np.float64): for itype in (np.int32, np.int64): self._VariableRankTest(tf_scatter, vtype, itype, repeat_indices, updates_are_scalar) diff --git a/tensorflow/python/kernel_tests/segment_reduction_ops_test.py b/tensorflow/python/kernel_tests/segment_reduction_ops_test.py index a82855dfeb..794be096b7 100644 --- a/tensorflow/python/kernel_tests/segment_reduction_ops_test.py +++ b/tensorflow/python/kernel_tests/segment_reduction_ops_test.py @@ -264,9 +264,7 @@ class UnsortedSegmentTest(SegmentReductionHelper): # A subset of ops has been enabled for complex numbers self.complex_ops_list = [(np.add, None, - math_ops.unsorted_segment_sum, lambda t: 0), - (np.ndarray.__mul__, None, - math_ops.unsorted_segment_prod, lambda t: 1)] + math_ops.unsorted_segment_sum, lambda t: 0)] self.differentiable_dtypes = [dtypes_lib.float16, dtypes_lib.float32, dtypes_lib.float64] self.all_dtypes = (self.differentiable_dtypes + diff --git a/tensorflow/python/kernel_tests/string_split_op_test.py b/tensorflow/python/kernel_tests/string_split_op_test.py index e20daccb28..a5bd1b6ee0 100644 --- a/tensorflow/python/kernel_tests/string_split_op_test.py +++ b/tensorflow/python/kernel_tests/string_split_op_test.py @@ -146,101 +146,5 @@ class StringSplitOpTest(test.TestCase): self.assertAllEqual(shape, [3, 1]) -class StringSplitV2OpTest(test.TestCase): - - def testSplitV2(self): - strings = ["pigs on the wing", "animals"] - - with self.test_session() as sess: - tokens = string_ops.string_split_v2(strings) - indices, values, shape = sess.run(tokens) - self.assertAllEqual(indices, [[0, 0], [0, 1], [0, 2], [0, 3], [1, 0]]) - self.assertAllEqual(values, [b"pigs", b"on", b"the", b"wing", b"animals"]) - self.assertAllEqual(shape, [2, 4]) - - def testSplitV2MultiCharSeparator(self): - # Match Python behavior: - # >>> '1<>2<>3'.split('<>') - # ['1', '2', '3'] - # >>> "<><>4<>5<><>6<>".split("<>") - # ['', '', '4', '5', '', '6', ''] - strings = ["1<>2<>3", "<><>4<>5<><>6<>"] - - with self.test_session() as sess: - tokens = string_ops.string_split_v2(strings, sep="<>") - indices, values, shape = sess.run(tokens) - self.assertAllEqual( - indices, [[0, 0], [0, 1], [0, 2], - [1, 0], [1, 1], [1, 2], [1, 3], [1, 4], [1, 5], [1, 6]]) - self.assertAllEqual(values, [b"1", b"2", b"3", - b"", b"", b"4", b"5", b"", b"6", b""]) - self.assertAllEqual(shape, [2, 7]) - - def testSplitV2SimpleSeparator(self): - # Match Python behavior: - # >>> '1,2,3'.split(',') - # ['1', '2', '3'] - # >>> '1,2,,3,'.split(',') - # ['1', '2', '', '3', ''] - strings = ["1,2,3", "4,5,,6,"] - - with self.test_session() as sess: - tokens = string_ops.string_split_v2(strings, sep=',') - indices, values, shape = sess.run(tokens) - self.assertAllEqual(indices, [[0, 0], [0, 1], [0, 2], - [1, 0], [1, 1], [1, 2], [1, 3], [1, 4]]) - self.assertAllEqual(values, [b"1", b"2", b"3", - b"4", b"5", b"", b"6", b""]) - self.assertAllEqual(shape, [2, 5]) - - def testSplitV2EmptySeparator(self): - # Match Python behavior: - # >>> '1 2 3'.split() - # ['1', '2', '3'] - #>>> ' 1 2 3 '.split() - #['1', '2', '3'] - strings = ["1 2 3", " 4 5 6 "] - - with self.test_session() as sess: - tokens = string_ops.string_split_v2(strings) - indices, values, shape = sess.run(tokens) - self.assertAllEqual(indices, [[0, 0], [0, 1], [0, 2], - [1, 0], [1, 1], [1, 2]]) - self.assertAllEqual(values, [b"1", b"2", b"3", b"4", b"5", b"6"]) - self.assertAllEqual(shape, [2, 3]) - - def testSplitV2SimpleSeparatorMaxSplit(self): - # Match Python behavior: - # >>> '1,2,3'.split(',', maxsplit=1) - # ['1', '2,3'] - # >>> '4,5,,6,'.split(',', maxsplit=1) - # ['4', '5,,6,'] - strings = ["1,2,3", "4,5,,6,"] - - with self.test_session() as sess: - tokens = string_ops.string_split_v2(strings, sep=',', maxsplit=1) - indices, values, shape = sess.run(tokens) - self.assertAllEqual(indices, [[0, 0], [0, 1], - [1, 0], [1, 1]]) - self.assertAllEqual(values, [b"1", b"2,3", b"4", b"5,,6,"]) - self.assertAllEqual(shape, [2, 2]) - - def testSplitV2EmptySeparatorMaxSplit(self): - # Match Python behavior: - # '1 2 3'.split(maxsplit=1) - # ['1', '2 3'] - # >>> " 4 5 6 ".split(maxsplit=1) - # ['4', '5 6 '] - strings = ["1 2 3", " 4 5 6 "] - - with self.test_session() as sess: - tokens = string_ops.string_split_v2(strings, maxsplit=1) - indices, values, shape = sess.run(tokens) - self.assertAllEqual(indices, [[0, 0], [0, 1], - [1, 0], [1, 1]]) - self.assertAllEqual(values, [b"1", b"2 3", b"4", b"5 6 "]) - self.assertAllEqual(shape, [2, 2]) - - if __name__ == "__main__": test.main() diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py index fae63b1132..8129334703 100644 --- a/tensorflow/python/ops/array_ops.py +++ b/tensorflow/python/ops/array_ops.py @@ -2619,10 +2619,6 @@ reverse.__doc__ = gen_array_ops.reverse_v2.__doc__ # pylint: disable=redefined-builtin @tf_export("reverse_sequence") -@deprecation.deprecated_args( - None, "seq_dim is deprecated, use seq_axis instead", "seq_dim") -@deprecation.deprecated_args( - None, "batch_dim is deprecated, use batch_axis instead", "batch_dim") def reverse_sequence(input, seq_lengths, seq_axis=None, diff --git a/tensorflow/python/ops/gradient_checker.py b/tensorflow/python/ops/gradient_checker.py index 94c8d79335..12afcd0b51 100644 --- a/tensorflow/python/ops/gradient_checker.py +++ b/tensorflow/python/ops/gradient_checker.py @@ -283,10 +283,10 @@ def compute_gradient(x, numbers. For example, if `x` is complex with shape `[m]` and `y` is complex with shape `[n]`, each Jacobian `J` will have shape `[m * 2, n * 2]` with - J[::2, ::2] = d(Re y)/d(Re x) - J[::2, 1::2] = d(Im y)/d(Re x) - J[1::2, ::2] = d(Re y)/d(Im x) - J[1::2, 1::2] = d(Im y)/d(Im x) + J[:m, :n] = d(Re y)/d(Re x) + J[:m, n:] = d(Im y)/d(Re x) + J[m:, :n] = d(Re y)/d(Im x) + J[m:, n:] = d(Im y)/d(Im x) Args: x: a tensor or list of tensors diff --git a/tensorflow/python/ops/image_ops_impl.py b/tensorflow/python/ops/image_ops_impl.py index f27d9224c1..bdcf420980 100644 --- a/tensorflow/python/ops/image_ops_impl.py +++ b/tensorflow/python/ops/image_ops_impl.py @@ -28,7 +28,6 @@ from tensorflow.python.framework import tensor_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import check_ops from tensorflow.python.ops import control_flow_ops -from tensorflow.python.ops import functional_ops from tensorflow.python.ops import gen_image_ops from tensorflow.python.ops import gen_nn_ops from tensorflow.python.ops import math_ops @@ -259,14 +258,14 @@ def random_flip_up_down(image, seed=None): dimension, which is `height`. Otherwise output the image as-is. Args: - image: 4-D Tensor of shape `[batch, height, width, channels]` or - 3-D Tensor of shape `[height, width, channels]`. + image: A 3-D tensor of shape `[height, width, channels].` seed: A Python integer. Used to create a random seed. See @{tf.set_random_seed} for behavior. Returns: - A tensor of the same type and shape as `image`. + A 3-D tensor of the same type and shape as `image`. + Raises: ValueError: if the shape of `image` not supported. """ @@ -281,14 +280,13 @@ def random_flip_left_right(image, seed=None): second dimension, which is `width`. Otherwise output the image as-is. Args: - image: 4-D Tensor of shape `[batch, height, width, channels]` or - 3-D Tensor of shape `[height, width, channels]`. + image: A 3-D tensor of shape `[height, width, channels].` seed: A Python integer. Used to create a random seed. See @{tf.set_random_seed} for behavior. Returns: - A tensor of the same type and shape as `image`. + A 3-D tensor of the same type and shape as `image`. Raises: ValueError: if the shape of `image` not supported. @@ -299,8 +297,7 @@ def random_flip_left_right(image, seed=None): def _random_flip(image, flip_index, seed, scope_name): """Randomly (50% chance) flip an image along axis `flip_index`. Args: - image: 4-D Tensor of shape `[batch, height, width, channels]` or - 3-D Tensor of shape `[height, width, channels]`. + image: A 3-D tensor of shape `[height, width, channels].` flip_index: The dimension along which to flip the image. Vertical: 0, Horizontal: 1 seed: A Python integer. Used to create a random seed. See @@ -309,37 +306,22 @@ def _random_flip(image, flip_index, seed, scope_name): scope_name: Name of the scope in which the ops are added. Returns: - A tensor of the same type and shape as `image`. + A 3-D tensor of the same type and shape as `image`. Raises: ValueError: if the shape of `image` not supported. """ with ops.name_scope(None, scope_name, [image]) as scope: image = ops.convert_to_tensor(image, name='image') - image = _AssertAtLeast3DImage(image) - shape = image.get_shape() - if shape.ndims == 3 or shape.ndims is None: - uniform_random = random_ops.random_uniform([], 0, 1.0, seed=seed) - mirror_cond = math_ops.less(uniform_random, .5) - result = control_flow_ops.cond( - mirror_cond, - lambda: array_ops.reverse(image, [flip_index]), - lambda: image, - name=scope - ) - return fix_image_flip_shape(image, result) - elif shape.ndims == 4: - uniform_random = random_ops.random_uniform( - [array_ops.shape(image)[0]], 0, 1.0, seed=seed - ) - mirror_cond = math_ops.less(uniform_random, .5) - return array_ops.where( - mirror_cond, - image, - functional_ops.map_fn(lambda x: array_ops.reverse(x, [flip_index]), image, dtype=image.dtype) - ) - else: - raise ValueError('\'image\' must have either 3 or 4 dimensions.') + image = _Assert3DImage(image) + uniform_random = random_ops.random_uniform([], 0, 1.0, seed=seed) + mirror_cond = math_ops.less(uniform_random, .5) + result = control_flow_ops.cond( + mirror_cond, + lambda: array_ops.reverse(image, [flip_index]), + lambda: image, + name=scope) + return fix_image_flip_shape(image, result) @tf_export('image.flip_left_right') @@ -1652,13 +1634,13 @@ def is_jpeg(contents, name=None): @tf_export('image.decode_image') -def decode_image(contents, channels=None, dtype=dtypes.uint8, name=None): +def decode_image(contents, channels=None, name=None): """Convenience function for `decode_bmp`, `decode_gif`, `decode_jpeg`, and `decode_png`. Detects whether an image is a BMP, GIF, JPEG, or PNG, and performs the - appropriate operation to convert the input bytes `string` into a `Tensor` - of type `dtype`. + appropriate operation to convert the input bytes `string` into a `Tensor` of + type `uint8`. Note: `decode_gif` returns a 4-D array `[num_frames, height, width, 3]`, as opposed to `decode_bmp`, `decode_jpeg` and `decode_png`, which return 3-D @@ -1670,11 +1652,10 @@ def decode_image(contents, channels=None, dtype=dtypes.uint8, name=None): contents: 0-D `string`. The encoded image bytes. channels: An optional `int`. Defaults to `0`. Number of color channels for the decoded image. - dtype: The desired DType of the returned `Tensor`. name: A name for the operation (optional) Returns: - `Tensor` with type `dtype` and shape `[height, width, num_channels]` for + `Tensor` with type `uint8` with shape `[height, width, num_channels]` for BMP, JPEG, and PNG images and shape `[num_frames, height, width, 3]` for GIF images. @@ -1698,7 +1679,7 @@ def decode_image(contents, channels=None, dtype=dtypes.uint8, name=None): channels_msg = 'Channels must be in (None, 0, 3) when decoding BMP images' assert_channels = control_flow_ops.Assert(good_channels, [channels_msg]) with ops.control_dependencies([assert_decode, assert_channels]): - return convert_image_dtype(gen_image_ops.decode_bmp(contents), dtype) + return gen_image_ops.decode_bmp(contents) def _gif(): # Create assert to make sure that channels is not set to 1 @@ -1711,7 +1692,7 @@ def decode_image(contents, channels=None, dtype=dtypes.uint8, name=None): channels_msg = 'Channels must be in (None, 0, 3) when decoding GIF images' assert_channels = control_flow_ops.Assert(good_channels, [channels_msg]) with ops.control_dependencies([assert_channels]): - return convert_image_dtype(gen_image_ops.decode_gif(contents), dtype) + return gen_image_ops.decode_gif(contents) def check_gif(): # Create assert op to check that bytes are GIF decodable @@ -1720,11 +1701,7 @@ def decode_image(contents, channels=None, dtype=dtypes.uint8, name=None): def _png(): """Decodes a PNG image.""" - return convert_image_dtype( - gen_image_ops.decode_png(contents, channels, - dtype=dtypes.uint8 - if dtype == dtypes.uint8 - else dtypes.uint16), dtype) + return gen_image_ops.decode_png(contents, channels) def check_png(): """Checks if an image is PNG.""" @@ -1740,8 +1717,7 @@ def decode_image(contents, channels=None, dtype=dtypes.uint8, name=None): 'images') assert_channels = control_flow_ops.Assert(good_channels, [channels_msg]) with ops.control_dependencies([assert_channels]): - return convert_image_dtype( - gen_image_ops.decode_jpeg(contents, channels), dtype) + return gen_image_ops.decode_jpeg(contents, channels) # Decode normal JPEG images (start with \xff\xd8\xff\xe0) # as well as JPEG images with EXIF data (start with \xff\xd8\xff\xe1). @@ -1902,7 +1878,7 @@ def sample_distorted_bounding_box(image_size, width / height within this range. area_range: An optional list of `floats`. Defaults to `[0.05, 1]`. The cropped area of the image must contain a fraction of the - supplied image within this range. + supplied image within in this range. max_attempts: An optional `int`. Defaults to `100`. Number of attempts at generating a cropped region of the image of the specified constraints. After `max_attempts` failures, return the diff --git a/tensorflow/python/ops/image_ops_test.py b/tensorflow/python/ops/image_ops_test.py index 2a6ab26e96..45499dcce0 100644 --- a/tensorflow/python/ops/image_ops_test.py +++ b/tensorflow/python/ops/image_ops_test.py @@ -533,37 +533,6 @@ class FlipImageBenchmark(test.Benchmark): iters=benchmark_rounds, wall_time=step_time) - def _benchmarkBatchedRandomFlipLeftRight(self, device, cpu_count): - image_shape = [16, 299, 299, 3] - warmup_rounds = 100 - benchmark_rounds = 1000 - config = config_pb2.ConfigProto() - if cpu_count is not None: - config.inter_op_parallelism_threads = 1 - config.intra_op_parallelism_threads = cpu_count - with session.Session("", graph=ops.Graph(), config=config) as sess: - with ops.device(device): - inputs = variables.Variable( - random_ops.random_uniform(image_shape, dtype=dtypes.float32) * 255, - trainable=False, - dtype=dtypes.float32) - run_op = image_ops.random_flip_left_right(inputs) - sess.run(variables.global_variables_initializer()) - for i in xrange(warmup_rounds + benchmark_rounds): - if i == warmup_rounds: - start = time.time() - sess.run(run_op) - end = time.time() - step_time = (end - start) / benchmark_rounds - tag = device + "_%s" % (cpu_count if cpu_count is not None else "_all") - print("benchmarkBatchedRandomFlipLeftRight_16_299_299_3_%s step_time: " - "%.2f us" % - (tag, step_time * 1e6)) - self.report_benchmark( - name="benchmarkBatchedRandomFlipLeftRight_16_299_299_3_%s" % (tag), - iters=benchmark_rounds, - wall_time=step_time) - def benchmarkFlipLeftRightCpu1(self): self._benchmarkFlipLeftRight("/cpu:0", 1) @@ -582,15 +551,6 @@ class FlipImageBenchmark(test.Benchmark): def benchmarkRandomFlipLeftRightGpu(self): self._benchmarkRandomFlipLeftRight(test.gpu_device_name(), None) - def benchmarkBatchedRandomFlipLeftRightCpu1(self): - self._benchmarkBatchedRandomFlipLeftRight("/cpu:0", 1) - - def benchmarkBatchedRandomFlipLeftRightCpuAll(self): - self._benchmarkBatchedRandomFlipLeftRight("/cpu:0", None) - - def benchmarkBatchedRandomFlipLeftRightGpu(self): - self._benchmarkBatchedRandomFlipLeftRight(test.gpu_device_name(), None) - class AdjustHueBenchmark(test.Benchmark): @@ -1027,7 +987,7 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): with self.test_session(use_gpu=True): x_tf = constant_op.constant(x_np, shape=x_np.shape) - y = image_ops.random_flip_left_right(x_tf, seed=seed) + y = image_ops.random_flip_left_right(x_tf) self.assertTrue(y.op.name.startswith("random_flip_left_right")) count_flipped = 0 @@ -1048,50 +1008,6 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): self.assertGreaterEqual(count_flipped, 20) self.assertGreaterEqual(count_unflipped, 20) - def testRandomFlipLeftRightWithBatch(self): - batch_size = 16 - seed = 42 - - # create single item of test data - x_np_raw = np.array( - [[1, 2, 3], [1, 2, 3]], dtype=np.uint8 - ).reshape([1, 2, 3, 1]) - y_np_raw = np.array( - [[3, 2, 1], [3, 2, 1]], dtype=np.uint8 - ).reshape([1, 2, 3, 1]) - - # create batched test data - x_np = np.vstack([x_np_raw for _ in range(batch_size)]) - y_np = np.vstack([y_np_raw for _ in range(batch_size)]) - - with self.test_session(use_gpu=True): - x_tf = constant_op.constant(x_np, shape=x_np.shape) - y = image_ops.random_flip_left_right(x_tf, seed=seed) - self.assertTrue(y.op.name.startswith("random_flip_left_right")) - - count_flipped = 0 - count_unflipped = 0 - for _ in range(100): - y_tf = y.eval() - - # check every element of the batch - for i in range(batch_size): - if y_tf[i][0][0] == 1: - self.assertAllEqual(y_tf[i], x_np[i]) - count_unflipped += 1 - else: - self.assertAllEqual(y_tf[i], y_np[i]) - count_flipped += 1 - - # 100 trials, each containing batch_size elements - # Mean: 50 * batch_size - # Std Dev: ~5 * sqrt(batch_size) - # Six Sigma: 50 * batch_size - (5 * 6 * sqrt(batch_size)) - # = 50 * batch_size - 30 * sqrt(batch_size) = 800 - 30 * 4 = 680 - six_sigma = 50 * batch_size - 30 * np.sqrt(batch_size) - self.assertGreaterEqual(count_flipped, six_sigma) - self.assertGreaterEqual(count_unflipped, six_sigma) - def testInvolutionUpDown(self): x_np = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.uint8).reshape([2, 3, 1]) @@ -1141,11 +1057,9 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): x_np = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.uint8).reshape([2, 3, 1]) y_np = np.array([[4, 5, 6], [1, 2, 3]], dtype=np.uint8).reshape([2, 3, 1]) - seed = 42 - with self.test_session(use_gpu=True): x_tf = constant_op.constant(x_np, shape=x_np.shape) - y = image_ops.random_flip_up_down(x_tf, seed=seed) + y = image_ops.random_flip_up_down(x_tf, seed=42) self.assertTrue(y.op.name.startswith("random_flip_up_down")) count_flipped = 0 count_unflipped = 0 @@ -1165,50 +1079,6 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): self.assertGreaterEqual(count_flipped, 20) self.assertGreaterEqual(count_unflipped, 20) - def testRandomFlipUpDownWithBatch(self): - batch_size = 16 - seed = 42 - - # create single item of test data - x_np_raw = np.array( - [[1, 2, 3], [4, 5, 6]], dtype=np.uint8 - ).reshape([1, 2, 3, 1]) - y_np_raw = np.array( - [[4, 5, 6], [1, 2, 3]], dtype=np.uint8 - ).reshape([1, 2, 3, 1]) - - # create batched test data - x_np = np.vstack([x_np_raw for _ in range(batch_size)]) - y_np = np.vstack([y_np_raw for _ in range(batch_size)]) - - with self.test_session(use_gpu=True): - x_tf = constant_op.constant(x_np, shape=x_np.shape) - y = image_ops.random_flip_up_down(x_tf, seed=seed) - self.assertTrue(y.op.name.startswith("random_flip_up_down")) - - count_flipped = 0 - count_unflipped = 0 - for _ in range(100): - y_tf = y.eval() - - # check every element of the batch - for i in range(batch_size): - if y_tf[i][0][0] == 1: - self.assertAllEqual(y_tf[i], x_np[i]) - count_unflipped += 1 - else: - self.assertAllEqual(y_tf[i], y_np[i]) - count_flipped += 1 - - # 100 trials, each containing batch_size elements - # Mean: 50 * batch_size - # Std Dev: ~5 * sqrt(batch_size) - # Six Sigma: 50 * batch_size - (5 * 6 * sqrt(batch_size)) - # = 50 * batch_size - 30 * sqrt(batch_size) = 800 - 30 * 4 = 680 - six_sigma = 50 * batch_size - 30 * np.sqrt(batch_size) - self.assertGreaterEqual(count_flipped, six_sigma) - self.assertGreaterEqual(count_unflipped, six_sigma) - def testInvolutionTranspose(self): x_np = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.uint8).reshape([2, 3, 1]) @@ -1286,7 +1156,6 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): #Ops that support 4D input for op in [ image_ops.flip_left_right, image_ops.flip_up_down, - image_ops.random_flip_left_right, image_ops.random_flip_up_down, image_ops.transpose_image, image_ops.rot90 ]: transformed_unknown_dims_4 = op(p_unknown_dims_4) @@ -1297,6 +1166,14 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): "must be at least three-dimensional"): op(p_wrong_rank) + for op in [ + image_ops.random_flip_left_right, + image_ops.random_flip_up_down, + ]: + with self.assertRaisesRegexp(ValueError, "must be three-dimensional"): + op(p_wrong_rank) + + def testRot90GroupOrder(self): image = np.arange(24, dtype=np.uint8).reshape([2, 4, 3]) with self.test_session(use_gpu=True): @@ -1331,6 +1208,41 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): y_np = np.rot90(image, k=k, axes=(1, 2)) self.assertAllEqual(y_np, y_tf.eval({k_placeholder: k})) +class RandomFlipTest(test_util.TensorFlowTestCase): + + def testRandomLeftRight(self): + x_np = np.array([0, 1], dtype=np.uint8).reshape([1, 2, 1]) + num_iterations = 500 + + hist = [0, 0] + with self.test_session(use_gpu=True): + x_tf = constant_op.constant(x_np, shape=x_np.shape) + y = image_ops.random_flip_left_right(x_tf) + for _ in xrange(num_iterations): + y_np = y.eval().flatten()[0] + hist[y_np] += 1 + + # Ensure that each entry is observed within 4 standard deviations. + four_stddev = 4.0 * np.sqrt(num_iterations / 2.0) + self.assertAllClose(hist, [num_iterations / 2.0] * 2, atol=four_stddev) + + def testRandomUpDown(self): + x_np = np.array([0, 1], dtype=np.uint8).reshape([2, 1, 1]) + num_iterations = 500 + + hist = [0, 0] + with self.test_session(use_gpu=True): + x_tf = constant_op.constant(x_np, shape=x_np.shape) + y = image_ops.random_flip_up_down(x_tf) + for _ in xrange(num_iterations): + y_np = y.eval().flatten()[0] + hist[y_np] += 1 + + # Ensure that each entry is observed within 4 standard deviations. + four_stddev = 4.0 * np.sqrt(num_iterations / 2.0) + self.assertAllClose(hist, [num_iterations / 2.0] * 2, atol=four_stddev) + + class AdjustContrastTest(test_util.TensorFlowTestCase): def _testContrast(self, x_np, y_np, contrast_factor): @@ -3968,88 +3880,5 @@ class SobelEdgesTest(test_util.TensorFlowTestCase): self.assertAllClose(expected_batch, actual_sobel) -class DecodeImageTest(test_util.TensorFlowTestCase): - - def testJpegUint16(self): - with self.test_session(use_gpu=True) as sess: - base = "tensorflow/core/lib/jpeg/testdata" - jpeg0 = io_ops.read_file(os.path.join(base, "jpeg_merge_test1.jpg")) - image0 = image_ops.decode_image(jpeg0, dtype=dtypes.uint16) - image1 = image_ops.convert_image_dtype(image_ops.decode_jpeg(jpeg0), - dtypes.uint16) - image0, image1 = sess.run([image0, image1]) - self.assertAllEqual(image0, image1) - - def testPngUint16(self): - with self.test_session(use_gpu=True) as sess: - base = "tensorflow/core/lib/png/testdata" - png0 = io_ops.read_file(os.path.join(base, "lena_rgba.png")) - image0 = image_ops.decode_image(png0, dtype=dtypes.uint16) - image1 = image_ops.convert_image_dtype( - image_ops.decode_png(png0, dtype=dtypes.uint16), dtypes.uint16) - image0, image1 = sess.run([image0, image1]) - self.assertAllEqual(image0, image1) - - def testGifUint16(self): - with self.test_session(use_gpu=True) as sess: - base = "tensorflow/core/lib/gif/testdata" - gif0 = io_ops.read_file(os.path.join(base, "scan.gif")) - image0 = image_ops.decode_image(gif0, dtype=dtypes.uint16) - image1 = image_ops.convert_image_dtype(image_ops.decode_gif(gif0), - dtypes.uint16) - image0, image1 = sess.run([image0, image1]) - self.assertAllEqual(image0, image1) - - def testBmpUint16(self): - with self.test_session(use_gpu=True) as sess: - base = "tensorflow/core/lib/bmp/testdata" - bmp0 = io_ops.read_file(os.path.join(base, "lena.bmp")) - image0 = image_ops.decode_image(bmp0, dtype=dtypes.uint16) - image1 = image_ops.convert_image_dtype(image_ops.decode_bmp(bmp0), - dtypes.uint16) - image0, image1 = sess.run([image0, image1]) - self.assertAllEqual(image0, image1) - - def testJpegFloat32(self): - with self.test_session(use_gpu=True) as sess: - base = "tensorflow/core/lib/jpeg/testdata" - jpeg0 = io_ops.read_file(os.path.join(base, "jpeg_merge_test1.jpg")) - image0 = image_ops.decode_image(jpeg0, dtype=dtypes.float32) - image1 = image_ops.convert_image_dtype(image_ops.decode_jpeg(jpeg0), - dtypes.float32) - image0, image1 = sess.run([image0, image1]) - self.assertAllEqual(image0, image1) - - def testPngFloat32(self): - with self.test_session(use_gpu=True) as sess: - base = "tensorflow/core/lib/png/testdata" - png0 = io_ops.read_file(os.path.join(base, "lena_rgba.png")) - image0 = image_ops.decode_image(png0, dtype=dtypes.float32) - image1 = image_ops.convert_image_dtype( - image_ops.decode_png(png0, dtype=dtypes.uint16), dtypes.float32) - image0, image1 = sess.run([image0, image1]) - self.assertAllEqual(image0, image1) - - def testGifFloat32(self): - with self.test_session(use_gpu=True) as sess: - base = "tensorflow/core/lib/gif/testdata" - gif0 = io_ops.read_file(os.path.join(base, "scan.gif")) - image0 = image_ops.decode_image(gif0, dtype=dtypes.float32) - image1 = image_ops.convert_image_dtype(image_ops.decode_gif(gif0), - dtypes.float32) - image0, image1 = sess.run([image0, image1]) - self.assertAllEqual(image0, image1) - - def testBmpFloat32(self): - with self.test_session(use_gpu=True) as sess: - base = "tensorflow/core/lib/bmp/testdata" - bmp0 = io_ops.read_file(os.path.join(base, "lena.bmp")) - image0 = image_ops.decode_image(bmp0, dtype=dtypes.float32) - image1 = image_ops.convert_image_dtype(image_ops.decode_bmp(bmp0), - dtypes.float32) - image0, image1 = sess.run([image0, image1]) - self.assertAllEqual(image0, image1) - - if __name__ == "__main__": googletest.main() diff --git a/tensorflow/python/ops/init_ops.py b/tensorflow/python/ops/init_ops.py index 724fcc39cd..2df230d470 100644 --- a/tensorflow/python/ops/init_ops.py +++ b/tensorflow/python/ops/init_ops.py @@ -467,8 +467,7 @@ class VarianceScaling(Initializer): else: scale /= max(1., (fan_in + fan_out) / 2.) if self.distribution == "normal": - # constant taken from scipy.stats.truncnorm.std(a=-2, b=2, loc=0., scale=1.) - stddev = math.sqrt(scale) / .87962566103423978 + stddev = math.sqrt(scale) return random_ops.truncated_normal( shape, 0.0, stddev, dtype, seed=self.seed) else: diff --git a/tensorflow/python/ops/logging_ops.py b/tensorflow/python/ops/logging_ops.py index 8276047cb6..222b8ebc9d 100644 --- a/tensorflow/python/ops/logging_ops.py +++ b/tensorflow/python/ops/logging_ops.py @@ -35,9 +35,8 @@ from tensorflow.python.util.tf_export import tf_export # Assert and Print are special symbols in python, so we must -# have an upper-case version of them. For users with Python 3 or Python 2.7 -# with `from __future__ import print_function`, we also allow lowercase. -@tf_export("Print", "print") +# use an upper-case version of them. +@tf_export("Print") def Print(input_, data, message=None, first_n=None, summarize=None, name=None): """Prints a list of tensors. diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py index 466d0dadc8..e40481f3a7 100644 --- a/tensorflow/python/ops/math_ops.py +++ b/tensorflow/python/ops/math_ops.py @@ -125,8 +125,8 @@ def abs(x, name=None): # pylint: disable=redefined-builtin ``` Args: - x: A `Tensor` or `SparseTensor` of type `float16`, `float32`, `float64`, - `int32`, `int64`, `complex64` or `complex128`. + x: A `Tensor` or `SparseTensor` of type `float32`, `float64`, `int32`, + `int64`, `complex64` or `complex128`. name: A name for the operation (optional). Returns: @@ -430,10 +430,10 @@ def pow(x, y, name=None): # pylint: disable=redefined-builtin ``` Args: - x: A `Tensor` of type `float16`, `float32`, `float64`, `int32`, `int64`, - `complex64`, or `complex128`. - y: A `Tensor` of type `float16`, `float32`, `float64`, `int32`, `int64`, - `complex64`, or `complex128`. + x: A `Tensor` of type `float32`, `float64`, `int32`, `int64`, `complex64`, + or `complex128`. + y: A `Tensor` of type `float32`, `float64`, `int32`, `int64`, `complex64`, + or `complex128`. name: A name for the operation (optional). Returns: @@ -600,7 +600,7 @@ def round(x, name=None): # pylint: disable=redefined-builtin ``` Args: - x: A `Tensor` of type `float16`, `float32`, `float64`, `int32`, or `int64`. + x: A `Tensor` of type `float32` or `float64`. name: A name for the operation (optional). Returns: @@ -1257,7 +1257,7 @@ def reduce_sum(input_tensor, entry in `axis`. If `keepdims` is true, the reduced dimensions are retained with length 1. - If `axis` is None, all dimensions are reduced, and a + If `axis` has no entries, all dimensions are reduced, and a tensor with a single element is returned. For example: @@ -1397,7 +1397,7 @@ def reduce_mean(input_tensor, entry in `axis`. If `keepdims` is true, the reduced dimensions are retained with length 1. - If `axis` is None, all dimensions are reduced, and a + If `axis` has no entries, all dimensions are reduced, and a tensor with a single element is returned. For example: @@ -1469,7 +1469,7 @@ def reduce_prod(input_tensor, entry in `axis`. If `keepdims` is true, the reduced dimensions are retained with length 1. - If `axis` is None, all dimensions are reduced, and a + If `axis` has no entries, all dimensions are reduced, and a tensor with a single element is returned. Args: @@ -1519,7 +1519,7 @@ def reduce_min(input_tensor, entry in `axis`. If `keepdims` is true, the reduced dimensions are retained with length 1. - If `axis` is None, all dimensions are reduced, and a + If `axis` has no entries, all dimensions are reduced, and a tensor with a single element is returned. Args: @@ -1568,7 +1568,7 @@ def reduce_max(input_tensor, entry in `axis`. If `keepdims` is true, the reduced dimensions are retained with length 1. - If `axis` is None, all dimensions are reduced, and a + If `axis` has no entries, all dimensions are reduced, and a tensor with a single element is returned. Args: @@ -1617,7 +1617,7 @@ def reduce_all(input_tensor, entry in `axis`. If `keepdims` is true, the reduced dimensions are retained with length 1. - If `axis` is None, all dimensions are reduced, and a + If `axis` has no entries, all dimensions are reduced, and a tensor with a single element is returned. For example: @@ -1675,7 +1675,7 @@ def reduce_any(input_tensor, entry in `axis`. If `keepdims` is true, the reduced dimensions are retained with length 1. - If `axis` is None, all dimensions are reduced, and a + If `axis` has no entries, all dimensions are reduced, and a tensor with a single element is returned. For example: diff --git a/tensorflow/python/ops/nn_impl.py b/tensorflow/python/ops/nn_impl.py index f47f38e29e..783d485892 100644 --- a/tensorflow/python/ops/nn_impl.py +++ b/tensorflow/python/ops/nn_impl.py @@ -621,7 +621,7 @@ def normalize_moments(counts, mean_ss, variance_ss, shift, name=None): """Calculate the mean and variance of based on the sufficient statistics. Args: - counts: A `Tensor` containing the total count of the data (one value). + counts: A `Tensor` containing a the total count of the data (one value). mean_ss: A `Tensor` containing the mean sufficient statistics: the (possibly shifted) sum of the elements to average over. variance_ss: A `Tensor` containing the variance sufficient statistics: the @@ -689,9 +689,6 @@ def moments( # Compute true mean while keeping the dims for proper broadcasting. mean = math_ops.reduce_mean(y, axes, keepdims=True, name="mean") # sample variance, not unbiased variance - # Note: stop_gradient does not change the gradient that gets - # backpropagated to the mean from the variance calculation, - # because that gradient is zero variance = math_ops.reduce_mean( math_ops.squared_difference(y, array_ops.stop_gradient(mean)), axes, diff --git a/tensorflow/python/ops/nn_ops.py b/tensorflow/python/ops/nn_ops.py index 0c2f5b06c4..a0b55eb077 100644 --- a/tensorflow/python/ops/nn_ops.py +++ b/tensorflow/python/ops/nn_ops.py @@ -1596,12 +1596,12 @@ def leaky_relu(features, alpha=0.2, name=None): Returns: The activation value. """ - with ops.name_scope(name, "LeakyRelu", [features, alpha]) as name: + with ops.name_scope(name, "LeakyRelu", [features, alpha]): features = ops.convert_to_tensor(features, name="features") if features.dtype.is_integer: features = math_ops.to_float(features) alpha = ops.convert_to_tensor(alpha, dtype=features.dtype, name="alpha") - return math_ops.maximum(alpha * features, features, name=name) + return math_ops.maximum(alpha * features, features) def _flatten_outer_dims(logits): diff --git a/tensorflow/python/ops/nn_test.py b/tensorflow/python/ops/nn_test.py index 035b4735af..46a5f4fae6 100644 --- a/tensorflow/python/ops/nn_test.py +++ b/tensorflow/python/ops/nn_test.py @@ -962,16 +962,6 @@ class LeakyReluTest(test_lib.TestCase): self.assertAllClose( outputs, [-0.4, -0.2, 0.0, 1.0, 2.0], rtol=tol, atol=tol) - def testName(self): - np_values = np.array([-2, -1, 0, 1, 2], dtype=np.float64) - outputs_with_name_set = nn_ops.leaky_relu( - constant_op.constant(np_values), - name='test_relu_op') - self.assertEqual(outputs_with_name_set.name, 'test_relu_op:0') - outputs_without_name_set = nn_ops.leaky_relu( - constant_op.constant(np_values)) - self.assertEqual(outputs_without_name_set.name, 'LeakyRelu:0') - class SwishTest(test_lib.TestCase): diff --git a/tensorflow/python/ops/script_ops.py b/tensorflow/python/ops/script_ops.py index 219562de5d..f8676ccb5f 100644 --- a/tensorflow/python/ops/script_ops.py +++ b/tensorflow/python/ops/script_ops.py @@ -23,7 +23,6 @@ import threading # Used by py_util.cc to get tracebacks. import traceback # pylint: disable=unused-import -import weakref import numpy as np import six @@ -130,14 +129,11 @@ class FuncRegistry(object): def __init__(self): self._lock = threading.Lock() self._unique_id = 0 # GUARDED_BY(self._lock) - # Only store weakrefs to the funtions. The strong reference is stored in - # the graph. - self._funcs = weakref.WeakValueDictionary() + self._funcs = {} def insert(self, func): """Registers `func` and returns a unique token for this entry.""" token = self._next_unique_token() - # Store a weakref to the function self._funcs[token] = func return token @@ -190,7 +186,7 @@ class FuncRegistry(object): Raises: ValueError: if no function is registered for `token`. """ - func = self._funcs.get(token, None) + func = self._funcs[token] if func is None: raise ValueError("callback %s is not found" % token) if isinstance(func, EagerFunc): @@ -232,6 +228,19 @@ _py_funcs = FuncRegistry() pywrap_tensorflow.InitializePyTrampoline(_py_funcs) +class CleanupFunc(object): + """A helper class to remove a registered function from _py_funcs.""" + + def __init__(self, token): + self._token = token + + def __del__(self): + if _py_funcs is not None: + # If _py_funcs is None, the program is most likely in shutdown, and the + # _py_funcs object has been destroyed already. + _py_funcs.remove(self._token) + + def _internal_py_func(func, inp, Tout, @@ -261,15 +270,17 @@ def _internal_py_func(func, # bound to that of the outer graph instead. graph = graph._outer_graph + cleanup = CleanupFunc(token) + # TODO(zhifengc): Consider adding a Graph method to collect # `cleanup` objects in one of its member. - if not hasattr(graph, "_py_funcs_used_in_graph"): - graph._py_funcs_used_in_graph = [] + if not hasattr(graph, "_cleanup_py_funcs_used_in_graph"): + graph._cleanup_py_funcs_used_in_graph = [] - # Store a reference to the function in the graph to ensure it stays alive - # as long as the graph lives. When the graph is destroyed, the function - # is left to the garbage collector for destruction as well. - graph._py_funcs_used_in_graph.append(func) + # When `graph` is destroyed, elements in _cleanup_py_funcs_used_in_graph + # will be destroyed and their __del__ will remove the 'token' from + # the funcs registry. + graph._cleanup_py_funcs_used_in_graph.append(cleanup) # pylint: enable=protected-access if eager: diff --git a/tensorflow/python/ops/sparse_ops.py b/tensorflow/python/ops/sparse_ops.py index c3b16a7bd5..0130233746 100644 --- a/tensorflow/python/ops/sparse_ops.py +++ b/tensorflow/python/ops/sparse_ops.py @@ -84,8 +84,6 @@ def _convert_to_sparse_tensors(sp_inputs): # pylint: disable=protected-access @tf_export("sparse_concat") -@deprecation.deprecated_args( - None, "concat_dim is deprecated, use axis instead", "concat_dim") def sparse_concat(axis, sp_inputs, name=None, @@ -599,8 +597,6 @@ class KeywordRequired(object): @tf_export("sparse_split") -@deprecation.deprecated_args( - None, "split_dim is deprecated, use axis instead", "split_dim") def sparse_split(keyword_required=KeywordRequired(), sp_input=None, num_split=None, diff --git a/tensorflow/python/ops/string_ops.py b/tensorflow/python/ops/string_ops.py index 0280c89c10..ae79c01949 100644 --- a/tensorflow/python/ops/string_ops.py +++ b/tensorflow/python/ops/string_ops.py @@ -91,59 +91,6 @@ def string_split(source, delimiter=" ", skip_empty=True): # pylint: disable=inv shape.set_shape([2]) return sparse_tensor.SparseTensor(indices, values, shape) -@tf_export("strings.split") -def string_split_v2(source, sep=None, maxsplit=-1): - """Split elements of `source` based on `sep` into a `SparseTensor`. - - Let N be the size of source (typically N will be the batch size). Split each - element of `source` based on `sep` and return a `SparseTensor` - containing the split tokens. Empty tokens are ignored. - - For example, N = 2, source[0] is 'hello world' and source[1] is 'a b c', - then the output will be - - st.indices = [0, 0; - 0, 1; - 1, 0; - 1, 1; - 1, 2] - st.shape = [2, 3] - st.values = ['hello', 'world', 'a', 'b', 'c'] - - If `sep` is given, consecutive delimiters are not grouped together and are - deemed to delimit empty strings. For example, source of `"1<>2<><>3"` and - sep of `"<>"` returns `["1", "2", "", "3"]`. If `sep` is None or an empty - string, consecutive whitespace are regarded as a single separator, and the - result will contain no empty strings at the startor end if the string has - leading or trailing whitespace. - - Note that the above mentioned behavior matches python's str.split. - - Args: - source: `1-D` string `Tensor`, the strings to split. - sep: `0-D` string `Tensor`, the delimiter character. - maxsplit: An `int`. If `maxsplit > 0`, limit of the split of the result. - - Raises: - ValueError: If sep is not a string. - - Returns: - A `SparseTensor` of rank `2`, the strings split according to the delimiter. - The first column of the indices corresponds to the row in `source` and the - second column corresponds to the index of the split component in this row. - """ - if sep is None: - sep = '' - sep = ops.convert_to_tensor(sep, dtype=dtypes.string) - source = ops.convert_to_tensor(source, dtype=dtypes.string) - - indices, values, shape = gen_string_ops.string_split_v2( - source, sep=sep, maxsplit=maxsplit) - indices.set_shape([None, 2]) - values.set_shape([None]) - shape.set_shape([2]) - return sparse_tensor.SparseTensor(indices, values, shape) - def _reduce_join_reduction_dims(x, axis, reduction_indices): """Returns range(rank(x) - 1, 0, -1) if reduction_indices is None.""" diff --git a/tensorflow/python/ops/variable_scope.py b/tensorflow/python/ops/variable_scope.py index 47414c28af..f49e2d314d 100644 --- a/tensorflow/python/ops/variable_scope.py +++ b/tensorflow/python/ops/variable_scope.py @@ -1786,23 +1786,6 @@ class variable_scope(object): assert v.name == "foo/bar/v:0" ``` - Simple example of how to reenter a premade variable scope safely: - - ```python - with tf.variable_scope("foo") as vs: - pass - - # Re-enter the variable scope. - with tf.variable_scope(vs, - auxiliary_name_scope=False) as vs1: - # Restore the original name_scope. - with tf.name_scope(vs1.original_name_scope): - v = tf.get_variable("v", [1]) - assert v.name == "foo/v:0" - c = tf.constant([1], name="c") - assert c.name == "foo/c:0" - ``` - Basic example of sharing a variable AUTO_REUSE: ```python @@ -1941,9 +1924,7 @@ class variable_scope(object): (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. auxiliary_name_scope: If `True`, we create an auxiliary name scope with - the scope. If `False`, we don't create it. Note that the argument is - not inherited, and it only takes effect for once when creating. You - should only use it for re-entering a premade variable scope. + the scope. If `False`, we don't touch name scope. Returns: A scope that can be captured and reused. diff --git a/tensorflow/python/tools/import_pb_to_tensorboard.py b/tensorflow/python/tools/import_pb_to_tensorboard.py old mode 100644 new mode 100755 diff --git a/tensorflow/tensorflow.bzl b/tensorflow/tensorflow.bzl index b59f8e1f98..522965990b 100644 --- a/tensorflow/tensorflow.bzl +++ b/tensorflow/tensorflow.bzl @@ -1719,7 +1719,7 @@ def tf_py_build_info_genrule(): name="py_build_info_gen", outs=["platform/build_info.py"], cmd= - "$(location //tensorflow/tools/build_info:gen_build_info.py) --raw_generate \"$@\" --build_config " + if_cuda("cuda", "cpu"), + "$(location //tensorflow/tools/build_info:gen_build_info.py) --raw_generate \"$@\" --build_config " + if_cuda("cuda", "cpu"), local=1, tools=[clean_dep("//tensorflow/tools/build_info:gen_build_info.py")],) diff --git a/tensorflow/tools/api/generator/create_python_api.py b/tensorflow/tools/api/generator/create_python_api.py index 671b7e387e..bca9fa49eb 100644 --- a/tensorflow/tools/api/generator/create_python_api.py +++ b/tensorflow/tools/api/generator/create_python_api.py @@ -41,11 +41,7 @@ _GENERATED_FILE_HEADER = """# This file is MACHINE GENERATED! Do not edit. # Generated by: tensorflow/tools/api/generator/create_python_api.py script. \"\"\"%s \"\"\" - -from __future__ import print_function - """ -_GENERATED_FILE_FOOTER = "\n\ndel print_function\n" class SymbolExposedTwiceError(Exception): @@ -153,7 +149,6 @@ class _ModuleInitCodeBuilder(object): _names_with_underscore = [%s] __all__ = [_s for _s in dir() if not _s.startswith('_')] __all__.extend([_s for _s in _names_with_underscore]) -__all__.remove('print_function') ''' % underscore_names_str return module_text_map @@ -338,8 +333,7 @@ def create_api_files( if module or not root_init_template: contents = ( _GENERATED_FILE_HEADER % - get_module_docstring(module, package, api_name) + - text + _GENERATED_FILE_FOOTER) + get_module_docstring(module, package, api_name) + text) else: # Read base init file with open(root_init_template, 'r') as root_init_template_file: diff --git a/tensorflow/tools/api/golden/tensorflow.image.pbtxt b/tensorflow/tools/api/golden/tensorflow.image.pbtxt index 10171b3d60..5bb3b3c444 100644 --- a/tensorflow/tools/api/golden/tensorflow.image.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.image.pbtxt @@ -58,7 +58,7 @@ tf_module { } member_method { name: "decode_image" - argspec: "args=[\'contents\', \'channels\', \'dtype\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \"\", \'None\'], " + argspec: "args=[\'contents\', \'channels\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " } member_method { name: "decode_jpeg" diff --git a/tensorflow/tools/api/golden/tensorflow.pbtxt b/tensorflow/tools/api/golden/tensorflow.pbtxt index 3051c4437e..dc2bd40096 100644 --- a/tensorflow/tools/api/golden/tensorflow.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.pbtxt @@ -1532,10 +1532,6 @@ tf_module { name: "pow" argspec: "args=[\'x\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } - member_method { - name: "print" - argspec: "args=[\'input_\', \'data\', \'message\', \'first_n\', \'summarize\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " - } member_method { name: "py_func" argspec: "args=[\'func\', \'inp\', \'Tout\', \'stateful\', \'name\'], varargs=None, keywords=None, defaults=[\'True\', \'None\'], " diff --git a/tensorflow/tools/api/golden/tensorflow.strings.pbtxt b/tensorflow/tools/api/golden/tensorflow.strings.pbtxt index b641c39feb..a3fbe95bba 100644 --- a/tensorflow/tools/api/golden/tensorflow.strings.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.strings.pbtxt @@ -4,8 +4,4 @@ tf_module { name: "regex_full_match" argspec: "args=[\'input\', \'pattern\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } - member_method { - name: "split" - argspec: "args=[\'source\', \'sep\', \'maxsplit\'], varargs=None, keywords=None, defaults=[\'None\', \'-1\'], " - } } diff --git a/tensorflow/tools/ci_build/builds/pip.sh b/tensorflow/tools/ci_build/builds/pip.sh index 883bb93647..5fa75e1d61 100755 --- a/tensorflow/tools/ci_build/builds/pip.sh +++ b/tensorflow/tools/ci_build/builds/pip.sh @@ -322,10 +322,6 @@ create_activate_virtualenv_and_install_tensorflow() { pip install -v ${PIP_FLAGS} ${WHL_PATH} || \ die "pip install (forcing to reinstall tensorflow) FAILED" echo "Successfully installed pip package ${TF_WHEEL_PATH}" - - # Force downgrade setuptools. - pip install --upgrade setuptools==39.1.0 - } ################################################################################ diff --git a/tensorflow/tools/ci_build/builds/with_the_same_user b/tensorflow/tools/ci_build/builds/with_the_same_user index b216e3549f..d4bf546d40 100755 --- a/tensorflow/tools/ci_build/builds/with_the_same_user +++ b/tensorflow/tools/ci_build/builds/with_the_same_user @@ -40,7 +40,7 @@ if [ -n "${CI_BUILD_USER_FORCE_BADNAME}" ]; then ADDUSER_OPTS="--force-badname" fi -getent group "${CI_BUILD_GID}" || addgroup ${ADDUSER_OPTS} --gid "${CI_BUILD_GID}" "${CI_BUILD_GROUP}" +getent group "${CI_BUILD_GID}" || addgroup --gid "${CI_BUILD_GID}" "${CI_BUILD_GROUP}" getent passwd "${CI_BUILD_UID}" || adduser ${ADDUSER_OPTS} \ --gid "${CI_BUILD_GID}" --uid "${CI_BUILD_UID}" \ --gecos "${CI_BUILD_USER} (generated by with_the_same_user script)" \ diff --git a/tensorflow/tools/ci_build/ci_build.sh b/tensorflow/tools/ci_build/ci_build.sh index 1f0fd0387a..072dd6ab99 100755 --- a/tensorflow/tools/ci_build/ci_build.sh +++ b/tensorflow/tools/ci_build/ci_build.sh @@ -134,12 +134,6 @@ if [[ $? != "0" ]]; then die "ERROR: docker build failed. Dockerfile is at ${DOCKERFILE_PATH}" fi -# If caller wants the with_the_same_user script to allow bad usernames, -# pass the var to the docker environment -if [ -n "${CI_BUILD_USER_FORCE_BADNAME}" ]; then - CI_BUILD_USER_FORCE_BADNAME_ENV="-e CI_BUILD_USER_FORCE_BADNAME=yes" -fi - # Run the command inside the container. echo "Running '${COMMAND[*]}' inside ${DOCKER_IMG_NAME}..." mkdir -p ${WORKSPACE}/bazel-ci_build-cache @@ -154,7 +148,6 @@ ${DOCKER_BINARY} run --rm --pid=host \ -e "CI_BUILD_GROUP=$(id -g -n)" \ -e "CI_BUILD_GID=$(id -g)" \ -e "CI_TENSORFLOW_SUBMODULE_PATH=${CI_TENSORFLOW_SUBMODULE_PATH}" \ - ${CI_BUILD_USER_FORCE_BADNAME_ENV} \ -v ${WORKSPACE}:/workspace \ -w /workspace \ ${GPU_EXTRA_PARAMS} \ diff --git a/tensorflow/tools/ci_build/copy_binary.py b/tensorflow/tools/ci_build/copy_binary.py index 148526492d..420d390d2b 100755 --- a/tensorflow/tools/ci_build/copy_binary.py +++ b/tensorflow/tools/ci_build/copy_binary.py @@ -32,8 +32,7 @@ import shutil import tempfile import zipfile -TF_NIGHTLY_REGEX = (r"(.+)tf_nightly(|_gpu)-(\d\.[\d]{1,2}" - "\.\d.dev[\d]{0,8})-(.+)\.whl") +TF_NIGHTLY_REGEX = r"(.+)tf_nightly(|_gpu)-(\d\.\d\.\d.dev[\d]{0,8})-(.+)\.whl" BINARY_STRING_TEMPLATE = "%s-%s-%s.whl" diff --git a/tensorflow/tools/ci_build/install/install_pip_packages.sh b/tensorflow/tools/ci_build/install/install_pip_packages.sh index 88f1d04193..60290df833 100755 --- a/tensorflow/tools/ci_build/install/install_pip_packages.sh +++ b/tensorflow/tools/ci_build/install/install_pip_packages.sh @@ -115,7 +115,3 @@ pip2 install keras_applications==1.0.2 pip3 install keras_applications==1.0.2 pip2 install keras_preprocessing==1.0.1 pip3 install keras_preprocessing==1.0.1 - -# Install last working version of setuptools. -pip2 install --upgrade setuptools==39.1.0 -pip3 install --upgrade setuptools==39.1.0 diff --git a/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh b/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh index acd69ef346..edb9d4b929 100755 --- a/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh +++ b/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh @@ -39,6 +39,7 @@ if [[ -z $pip35_version ]]; then fi set -e +pip3.5 install --upgrade setuptools pip3.5 install --upgrade pip pip3.5 install --upgrade virtualenv @@ -85,7 +86,4 @@ pip3.5 install --upgrade termcolor pip3.5 install keras_applications==1.0.2 pip3.5 install keras_preprocessing==1.0.1 -# Install last working version of setuptools. -pip3.5 install --upgrade setuptools==39.1.0 - # LINT.ThenChange(//tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh) diff --git a/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh b/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh index 323b30f48e..5635977731 100755 --- a/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh +++ b/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh @@ -49,6 +49,7 @@ cd Python-3.6.1 make altinstall ln -s /usr/local/bin/pip3.6 /usr/local/bin/pip3 +pip3 install --upgrade setuptools pip3 install --upgrade pip pip3 install --upgrade virtualenv @@ -100,8 +101,4 @@ pip3 install --upgrade termcolor # Keras pip3.5 install keras_applications==1.0.2 pip3.5 install keras_preprocessing==1.0.1 - -# Install last working version of setuptools. -pip3 install --upgrade setuptools==39.1.0 - # LINT.ThenChange(//tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh) diff --git a/tensorflow/tools/ci_build/linux/mkl/basic-mkl-test.sh b/tensorflow/tools/ci_build/linux/mkl/basic-mkl-test.sh deleted file mode 100755 index 10a09a415a..0000000000 --- a/tensorflow/tools/ci_build/linux/mkl/basic-mkl-test.sh +++ /dev/null @@ -1,29 +0,0 @@ -#!/usr/bin/env bash -# Copyright 2015 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -# -# Usage: basic_mkl_test.sh - -# Helper function to traverse directories up until given file is found. -function upsearch () { - test / == "$PWD" && return || \ - test -e "$1" && echo "$PWD" && return || \ - cd .. && upsearch "$1" -} - -# Set up WORKSPACE. -WORKSPACE="${WORKSPACE:-$(upsearch WORKSPACE)}" - -BUILD_TAG=mkl-ci-test CI_BUILD_USER_FORCE_BADNAME=yes ${WORKSPACE}/tensorflow/tools/ci_build/ci_build.sh cpu tensorflow/tools/ci_build/linux/cpu/run_mkl.sh diff --git a/tensorflow/tools/ci_build/pi/build_raspberry_pi.sh b/tensorflow/tools/ci_build/pi/build_raspberry_pi.sh index b8bce57c87..1bd1852ffc 100755 --- a/tensorflow/tools/ci_build/pi/build_raspberry_pi.sh +++ b/tensorflow/tools/ci_build/pi/build_raspberry_pi.sh @@ -79,7 +79,6 @@ if [[ $1 == "PI_ONE" ]]; then --linkopt=-L${OPENBLAS_INSTALL_PATH}/lib/ --linkopt=-l:libopenblas.a" echo "Building for the Pi One/Zero, with no NEON support" - WHEEL_ARCH=linux_armv6l else PI_COPTS='--copt=-march=armv7-a --copt=-mfpu=neon-vfpv4 --copt=-std=gnu11 --copt=-DS_IREAD=S_IRUSR --copt=-DS_IWRITE=S_IWUSR @@ -87,7 +86,6 @@ else --copt=-U__GCC_HAVE_SYNC_COMPARE_AND_SWAP_1 --copt=-U__GCC_HAVE_SYNC_COMPARE_AND_SWAP_2 --copt=-U__GCC_HAVE_SYNC_COMPARE_AND_SWAP_8' - WHEEL_ARCH=linux_armv7l echo "Building for the Pi Two/Three, with NEON acceleration" fi @@ -102,8 +100,6 @@ bazel build -c opt ${PI_COPTS} \ --copt=-fomit-frame-pointer --cpu=armeabi \ --crosstool_top=@local_config_arm_compiler//:toolchain \ --verbose_failures \ - //tensorflow:libtensorflow.so \ - //tensorflow:libtensorflow_framework.so \ //tensorflow/tools/benchmark:benchmark_model \ //tensorflow/tools/pip_package:build_pip_package @@ -116,12 +112,10 @@ BDIST_OPTS="--universal" \ bazel-bin/tensorflow/tools/pip_package/build_pip_package "${OUTDIR}" OLD_FN=$(ls "${OUTDIR}" | grep -m 1 \.whl) -SUB='s/tensorflow-([^-]+)-([^-]+)-.*/tensorflow-\1-\2-none-'${WHEEL_ARCH}'.whl/; print' +SUB='s/tensorflow-([^-]+)-([^-]+)-.*/tensorflow-\1-\2-none-any.whl/; print' NEW_FN=$(echo "${OLD_FN}" | perl -ne "${SUB}") mv "${OUTDIR}/${OLD_FN}" "${OUTDIR}/${NEW_FN}" cp bazel-bin/tensorflow/tools/benchmark/benchmark_model "${OUTDIR}" -cp bazel-bin/tensorflow/libtensorflow.so "${OUTDIR}" -cp bazel-bin/tensorflow/libtensorflow_framework.so "${OUTDIR}" echo "Output can be found here:" find "${OUTDIR}" diff --git a/tensorflow/tools/def_file_filter/def_file_filter_configure.bzl b/tensorflow/tools/def_file_filter/def_file_filter_configure.bzl index f8f63e276c..47539b2423 100644 --- a/tensorflow/tools/def_file_filter/def_file_filter_configure.bzl +++ b/tensorflow/tools/def_file_filter/def_file_filter_configure.bzl @@ -31,11 +31,7 @@ def _def_file_filter_configure_impl(repository_ctx): vc_path = find_vc_path(repository_ctx) if vc_path == "visual-studio-not-found": auto_configure_fail("Visual C++ build tools not found on your machine") - - undname = find_msvc_tool(repository_ctx, vc_path, "undname.exe") - if undname == None: - auto_configure_fail("Couldn't find undname.exe under %s, please check your VC installation and set BAZEL_VC environment variable correctly." % vc_path) - undname_bin_path = undname.replace("\\", "\\\\") + undname_bin_path = find_msvc_tool(repository_ctx, vc_path, "undname.exe").replace("\\", "\\\\") repository_ctx.template( "def_file_filter.py", diff --git a/tensorflow/tools/dist_test/local_test.sh b/tensorflow/tools/dist_test/local_test.sh index b0114721bd..06c2b997cb 100755 --- a/tensorflow/tools/dist_test/local_test.sh +++ b/tensorflow/tools/dist_test/local_test.sh @@ -64,6 +64,9 @@ die() { # Configurations DOCKER_IMG_NAME="tensorflow/tf-dist-test-local-cluster" +# Use TensorFlow v1.5.0 for Python 2.7 and CPU only as we set num_gpus to 0 in the below +DEFAULT_WHL_FILE_LOCATION="https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp27-none-linux_x86_64.whl" + # Parse input arguments LEAVE_CONTAINER_RUNNING=0 MODEL_NAME="" @@ -74,7 +77,8 @@ SYNC_REPLICAS_FLAG="" WHL_FILE_LOCATION=${1} if [[ -z "${WHL_FILE_LOCATION}" ]]; then - echo "WARNING: No wheel url passed. Will use latest tf-nightly cpu p2 wheel." + WHL_FILE_LOCATION=${DEFAULT_WHL_FILE_LOCATION} + echo "use default whl file location" fi while true; do @@ -127,11 +131,7 @@ echo "Building in temporary directory: ${BUILD_DIR}" cp -r ${DIR}/* "${BUILD_DIR}"/ || \ die "Failed to copy files to ${BUILD_DIR}" -# Download whl file into the build context directory. -if [[ -z "${WHL_FILE_LOCATION}" ]]; then - pip2 download --no-deps tf-nightly - cp tf-nightly-*.whl "${BUILD_DIR}"/tensorflow-none-any.whl -elif [[ $WHL_FILE_LOCATION =~ 'http://' || $WHL_FILE_LOCATION =~ 'https://' ]]; then +if [[ $WHL_FILE_LOCATION =~ 'http://' || $WHL_FILE_LOCATION =~ 'https://' ]]; then # Download whl file into the build context directory. wget -P "${BUILD_DIR}" "${WHL_FILE_LOCATION}" || \ die "Failed to download tensorflow whl file from URL: ${WHL_FILE_LOCATION}" diff --git a/tensorflow/tools/dist_test/remote_test.sh b/tensorflow/tools/dist_test/remote_test.sh index e188c88c8f..935535312d 100755 --- a/tensorflow/tools/dist_test/remote_test.sh +++ b/tensorflow/tools/dist_test/remote_test.sh @@ -108,7 +108,7 @@ fi # Parse command-line arguments. WHL_URL=${1} if [[ -z "${WHL_URL}" ]]; then - echo "WARNING: No wheel url passed. Will use latest tf-nightly cpu p2 wheel." + die "whl URL is not specified" fi # Create docker build context directory. @@ -121,13 +121,8 @@ cp -r ${DIR}/* ${BUILD_DIR}/ || \ die "Failed to copy files to ${BUILD_DIR}" # Download whl file into the build context directory. -if [[ -z "${WHL_URL}" ]]; then - pip2 download --no-deps tf-nightly - cp tf-nightly-*.whl "${BUILD_DIR}"/tensorflow-none-any.whl -else - wget -P "${BUILD_DIR}" ${WHL_URL} || \ - die "Failed to download tensorflow whl file from URL: ${WHL_URL}" -fi +wget -P "${BUILD_DIR}" ${WHL_URL} || \ + die "Failed to download tensorflow whl file from URL: ${WHL_URL}" # Build docker image for test. docker build ${NO_CACHE_FLAG} \ diff --git a/tensorflow/tools/docker/Dockerfile.devel b/tensorflow/tools/docker/Dockerfile.devel index 57a491255e..406d134699 100644 --- a/tensorflow/tools/docker/Dockerfile.devel +++ b/tensorflow/tools/docker/Dockerfile.devel @@ -76,7 +76,7 @@ RUN mkdir /bazel && \ # Download and build TensorFlow. WORKDIR /tensorflow -RUN git clone --branch=r1.9 --depth=1 https://github.com/tensorflow/tensorflow.git . +RUN git clone --branch=r1.8 --depth=1 https://github.com/tensorflow/tensorflow.git . # TODO(craigcitro): Don't install the pip package, since it makes it # more difficult to experiment with local changes. Instead, just add diff --git a/tensorflow/tools/docker/Dockerfile.devel-cpu-mkl b/tensorflow/tools/docker/Dockerfile.devel-cpu-mkl index 6796ad70e5..a6cd44ced1 100644 --- a/tensorflow/tools/docker/Dockerfile.devel-cpu-mkl +++ b/tensorflow/tools/docker/Dockerfile.devel-cpu-mkl @@ -3,7 +3,7 @@ FROM tensorflow/tensorflow:latest-devel LABEL maintainer="Clayne Robison" # These arguments are parameterized. Use --build-args to override. -ARG TF_BRANCH=r1.9 +ARG TF_BRANCH=r1.8 ARG WHL_DIR=/whl RUN apt-get update && apt-get install -y --no-install-recommends \ diff --git a/tensorflow/tools/docker/Dockerfile.devel-gpu b/tensorflow/tools/docker/Dockerfile.devel-gpu index 204b5b4dba..2fe47f3356 100644 --- a/tensorflow/tools/docker/Dockerfile.devel-gpu +++ b/tensorflow/tools/docker/Dockerfile.devel-gpu @@ -13,8 +13,8 @@ RUN apt-get update && apt-get install -y --no-install-recommends \ cuda-cusparse-dev-9-0 \ curl \ git \ - libcudnn7=7.1.4.18-1+cuda9.0 \ - libcudnn7-dev=7.1.4.18-1+cuda9.0 \ + libcudnn7=7.0.5.15-1+cuda9.0 \ + libcudnn7-dev=7.0.5.15-1+cuda9.0 \ libcurl3-dev \ libfreetype6-dev \ libhdf5-serial-dev \ @@ -85,7 +85,7 @@ RUN mkdir /bazel && \ # Download and build TensorFlow. WORKDIR /tensorflow -RUN git clone --branch=r1.9 --depth=1 https://github.com/tensorflow/tensorflow.git . +RUN git clone --branch=r1.8 --depth=1 https://github.com/tensorflow/tensorflow.git . # Configure the build for our CUDA configuration. ENV CI_BUILD_PYTHON python diff --git a/tensorflow/tools/docker/Dockerfile.gpu b/tensorflow/tools/docker/Dockerfile.gpu index 9197651ff4..bff4a20392 100644 --- a/tensorflow/tools/docker/Dockerfile.gpu +++ b/tensorflow/tools/docker/Dockerfile.gpu @@ -12,7 +12,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \ cuda-cusolver-9-0 \ cuda-cusparse-9-0 \ curl \ - libcudnn7=7.1.4.18-1+cuda9.0 \ + libcudnn7=7.0.5.15-1+cuda9.0 \ libfreetype6-dev \ libhdf5-serial-dev \ libpng12-dev \ diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD index 620fef9363..5910f0625e 100644 --- a/tensorflow/tools/pip_package/BUILD +++ b/tensorflow/tools/pip_package/BUILD @@ -61,7 +61,6 @@ COMMON_PIP_DEPS = [ "//tensorflow/contrib/autograph/core:core", "//tensorflow/contrib/autograph/impl:impl", "//tensorflow/contrib/autograph/lang:lang", - "//tensorflow/contrib/autograph/operators:operators", "//tensorflow/contrib/autograph/pyct:pyct", "//tensorflow/contrib/autograph/pyct/static_analysis:static_analysis", "//tensorflow/contrib/boosted_trees:boosted_trees_pip", diff --git a/tensorflow/tools/pip_package/build_pip_package.sh b/tensorflow/tools/pip_package/build_pip_package.sh index f7e42ce536..0c4065bc77 100755 --- a/tensorflow/tools/pip_package/build_pip_package.sh +++ b/tensorflow/tools/pip_package/build_pip_package.sh @@ -41,15 +41,51 @@ function is_windows() { fi } -function prepare_src() { +function main() { if [ $# -lt 1 ] ; then echo "No destination dir provided" exit 1 fi - TMPDIR="$1" - mkdir -p "$TMPDIR" - echo $(date) : "=== Preparing sources in dir: ${TMPDIR}" + DEST=$(real_path $1) + TMPDIR=$(mktemp -d -t tmp.XXXXXXXXXX) + + PKG_NAME_FLAG="" + GPU_BUILD=0 + NIGHTLY_BUILD=0 + PROJECT_NAME="" + while true; do + if [[ "$1" == "--nightly_flag" ]]; then + NIGHTLY_BUILD=1 + elif [[ "$1" == "--gpu" ]]; then + GPU_BUILD=1 + elif [[ "$1" == "--gpudirect" ]]; then + PKG_NAME_FLAG="--project_name tensorflow_gpudirect" + elif [[ "$1" == "--project_name" ]]; then + shift + if [[ -z "$1" ]]; then + break + fi + PROJECT_NAME="$1" + fi + shift + + if [[ -z "$1" ]]; then + break + fi + done + + if [[ -n ${PROJECT_NAME} ]]; then + PKG_NAME_FLAG="--project_name ${PROJECT_NAME}" + elif [[ ${NIGHTLY_BUILD} == "1" && ${GPU_BUILD} == "1" ]]; then + PKG_NAME_FLAG="--project_name tf_nightly_gpu" + elif [[ ${NIGHTLY_BUILD} == "1" ]]; then + PKG_NAME_FLAG="--project_name tf_nightly" + elif [[ ${GPU_BUILD} == "1" ]]; then + PKG_NAME_FLAG="--project_name tensorflow_gpu" + fi + + echo $(date) : "=== Using tmpdir: ${TMPDIR}" if [ ! -d bazel-bin/tensorflow ]; then echo "Could not find bazel-bin. Did you run from the root of the build tree?" @@ -119,28 +155,17 @@ function prepare_src() { # over so user defined ops can be compiled. mkdir -p ${TMPDIR}/google mkdir -p ${TMPDIR}/third_party - pushd ${RUNFILES%org_tensorflow} > /dev/null + pushd ${RUNFILES%org_tensorflow} for header in $(find protobuf_archive -name \*.h); do mkdir -p "${TMPDIR}/google/$(dirname ${header})" cp "$header" "${TMPDIR}/google/$(dirname ${header})/" done - popd > /dev/null + popd cp -R $RUNFILES/third_party/eigen3 ${TMPDIR}/third_party cp tensorflow/tools/pip_package/MANIFEST.in ${TMPDIR} cp tensorflow/tools/pip_package/README ${TMPDIR} cp tensorflow/tools/pip_package/setup.py ${TMPDIR} -} - -function build_wheel() { - if [ $# -lt 2 ] ; then - echo "No src and dest dir provided" - exit 1 - fi - - TMPDIR="$1" - DEST="$2" - PKG_NAME_FLAG="$3" # Before we leave the top-level directory, make sure we know how to # call python. @@ -148,110 +173,15 @@ function build_wheel() { source tools/python_bin_path.sh fi - pushd ${TMPDIR} > /dev/null + pushd ${TMPDIR} rm -f MANIFEST echo $(date) : "=== Building wheel" "${PYTHON_BIN_PATH:-python}" setup.py bdist_wheel ${PKG_NAME_FLAG} >/dev/null mkdir -p ${DEST} cp dist/* ${DEST} - popd > /dev/null + popd + rm -rf ${TMPDIR} echo $(date) : "=== Output wheel file is in: ${DEST}" } -function usage() { - echo "Usage:" - echo "$0 [--src srcdir] [--dst dstdir] [options]" - echo "$0 dstdir [options]" - echo "" - echo " --src prepare sources in srcdir" - echo " will use temporary dir if not specified" - echo "" - echo " --dst build wheel in dstdir" - echo " if dstdir is not set do not build, only prepare sources" - echo "" - echo " Options:" - echo " --project_name set project name to name" - echo " --gpu build tensorflow_gpu" - echo " --gpudirect build tensorflow_gpudirect" - echo " --nightly_flag build tensorflow nightly" - echo "" - exit 1 -} - -function main() { - PKG_NAME_FLAG="" - PROJECT_NAME="" - GPU_BUILD=0 - NIGHTLY_BUILD=0 - SRCDIR="" - DSTDIR="" - CLEANSRC=1 - while true; do - if [[ "$1" == "--help" ]]; then - usage - exit 1 - elif [[ "$1" == "--nightly_flag" ]]; then - NIGHTLY_BUILD=1 - elif [[ "$1" == "--gpu" ]]; then - GPU_BUILD=1 - elif [[ "$1" == "--gpudirect" ]]; then - PKG_NAME_FLAG="--project_name tensorflow_gpudirect" - elif [[ "$1" == "--project_name" ]]; then - shift - if [[ -z "$1" ]]; then - break - fi - PROJECT_NAME="$1" - elif [[ "$1" == "--src" ]]; then - shift - SRCDIR="$(real_path $1)" - CLEANSRC=0 - elif [[ "$1" == "--dst" ]]; then - shift - DSTDIR="$(real_path $1)" - else - DSTDIR="$(real_path $1)" - fi - shift - - if [[ -z "$1" ]]; then - break - fi - done - - if [[ -z "$DSTDIR" ]] && [[ -z "$SRCDIR" ]]; then - echo "No destination dir provided" - usage - exit 1 - fi - - if [[ -z "$SRCDIR" ]]; then - # make temp srcdir if none set - SRCDIR="$(mktemp -d -t tmp.XXXXXXXXXX)" - fi - - prepare_src "$SRCDIR" - - if [[ -z "$DSTDIR" ]]; then - # only want to prepare sources - exit - fi - - if [[ -n ${PROJECT_NAME} ]]; then - PKG_NAME_FLAG="--project_name ${PROJECT_NAME}" - elif [[ ${NIGHTLY_BUILD} == "1" && ${GPU_BUILD} == "1" ]]; then - PKG_NAME_FLAG="--project_name tf_nightly_gpu" - elif [[ ${NIGHTLY_BUILD} == "1" ]]; then - PKG_NAME_FLAG="--project_name tf_nightly" - elif [[ ${GPU_BUILD} == "1" ]]; then - PKG_NAME_FLAG="--project_name tensorflow_gpu" - fi - - build_wheel "$SRCDIR" "$DSTDIR" "$PKG_NAME_FLAG" - - if [[ $CLEANSRC -ne 0 ]]; then - rm -rf "${TMPDIR}" - fi -} - main "$@" diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py index 97f625e7e9..d25a9e77b1 100644 --- a/tensorflow/tools/pip_package/setup.py +++ b/tensorflow/tools/pip_package/setup.py @@ -45,7 +45,7 @@ DOCLINES = __doc__.split('\n') # This version string is semver compatible, but incompatible with pip. # For pip, we will remove all '-' characters from this string, and use the # result for pip. -_VERSION = '1.9.0-rc0' +_VERSION = '1.8.0' REQUIRED_PACKAGES = [ 'absl-py >= 0.1.6', @@ -54,7 +54,6 @@ REQUIRED_PACKAGES = [ 'numpy >= 1.13.3', 'six >= 1.10.0', 'protobuf >= 3.4.0', - 'setuptools <= 39.1.0', 'tensorboard >= 1.8.0, < 1.9.0', 'termcolor >= 1.1.0', ] diff --git a/tensorflow/tools/proto_text/gen_proto_text_functions_lib.cc b/tensorflow/tools/proto_text/gen_proto_text_functions_lib.cc index 15d7c70281..29add6d5ea 100644 --- a/tensorflow/tools/proto_text/gen_proto_text_functions_lib.cc +++ b/tensorflow/tools/proto_text/gen_proto_text_functions_lib.cc @@ -814,9 +814,6 @@ void Generator::Generate(const FileDescriptor& fd) { // Add header to cc file. SetOutput(&cc_); Print("// GENERATED FILE - DO NOT MODIFY"); - Print(); - Print("#include "); // for `std::stable_sort()` - Print(); headers = {GetProtoTextHeaderName(fd, true /* impl */)}; AddHeadersToCurrentSection(headers); Print(); diff --git a/tensorflow/tools/quantization/quantize_graph_test.py b/tensorflow/tools/quantization/quantize_graph_test.py index 92bb5127da..df71840b64 100644 --- a/tensorflow/tools/quantization/quantize_graph_test.py +++ b/tensorflow/tools/quantization/quantize_graph_test.py @@ -119,8 +119,8 @@ def are_tensors_near(a, b, tolerance): flat_a = a.flatten() flat_b = b.flatten() if len(flat_a) != len(flat_b): - tf_logging.info("Tensors are different sizes: " + str(len(flat_a)) + " vs " - + str(len(flat_b))) + print("Tensors are different sizes: " + str(len(flat_a)) + " vs " + str( + len(flat_b))) return False value_count = len(flat_a) how_many_different = 0 @@ -140,10 +140,10 @@ def are_tensors_near(a, b, tolerance): if how_many_different == 0: return True else: - tf_logging.info("Tensors have {0} different values ({1}%), with mean" - " difference {2} and mean absolute difference {3}".format( - how_many_different, proportion_different * 100, - mean_difference, mean_abs_difference)) + print("Tensors have {0} different values ({1}%), with mean difference" + " {2} and mean absolute difference {3}".format( + how_many_different, proportion_different * 100, mean_difference, + mean_abs_difference)) return False diff --git a/tensorflow/tools/test/upload_test_benchmarks.py b/tensorflow/tools/test/upload_test_benchmarks.py index c030575109..9c45359ee1 100644 --- a/tensorflow/tools/test/upload_test_benchmarks.py +++ b/tensorflow/tools/test/upload_test_benchmarks.py @@ -89,6 +89,7 @@ import shutil from six import text_type from google.cloud import datastore +from six import text_type def is_real_file(dirpath, fname): diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index 4f3df570a5..dbec66216a 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -50,31 +50,31 @@ def tf_workspace(path_prefix="", tf_repo_name=""): mkl_repository( name = "mkl_linux", urls = [ - "https://mirror.bazel.build/github.com/intel/mkl-dnn/releases/download/v0.14/mklml_lnx_2018.0.3.20180406.tgz", - "https://github.com/intel/mkl-dnn/releases/download/v0.14/mklml_lnx_2018.0.3.20180406.tgz" + "https://mirror.bazel.build/github.com/intel/mkl-dnn/releases/download/v0.13/mklml_lnx_2018.0.2.20180127.tgz", + "https://github.com/intel/mkl-dnn/releases/download/v0.13/mklml_lnx_2018.0.2.20180127.tgz", ], - sha256 = "d2305244fdc9b87db7426ed4496e87a4b3977ad3374d73b8000e8b7a5b7aa725", - strip_prefix = "mklml_lnx_2018.0.3.20180406", + sha256 = "74844bd77294742bf2396ff040369d1aa4cdd9e826fcd38cf8398ae83564d146", + strip_prefix = "mklml_lnx_2018.0.2.20180127", build_file = clean_dep("//third_party/mkl:mkl.BUILD") ) mkl_repository( name = "mkl_windows", urls = [ - "https://mirror.bazel.build/github.com/intel/mkl-dnn/releases/download/v0.14/mklml_win_2018.0.3.20180406.zip", - "https://github.com/intel/mkl-dnn/releases/download/v0.14/mklml_win_2018.0.3.20180406.zip" + "https://mirror.bazel.build/github.com/intel/mkl-dnn/releases/download/v0.13/mklml_win_2018.0.2.20180127.zip", + "https://github.com/intel/mkl-dnn/releases/download/v0.13/mklml_win_2018.0.2.20180127.zip" ], - sha256 = "a584a5bf1c8d2ad70b90d12b52652030e9a338217719064fdb84b7ad0d693694", - strip_prefix = "mklml_win_2018.0.3.20180406", + sha256 = "d8fbf0faa0684bffa3548005d05fe5cfe56ff9dbc0e15e7612d7ac01055a6ded", + strip_prefix = "mklml_win_2018.0.2.20180127", build_file = clean_dep("//third_party/mkl:mkl.BUILD") ) mkl_repository( name = "mkl_darwin", urls = [ - "https://mirror.bazel.build/github.com/intel/mkl-dnn/releases/download/v0.14/mklml_mac_2018.0.3.20180406.tgz", - "https://github.com/intel/mkl-dnn/releases/download/v0.14/mklml_mac_2018.0.3.20180406.tgz" + "https://mirror.bazel.build/github.com/intel/mkl-dnn/releases/download/v0.13/mklml_mac_2018.0.2.20180127.tgz", + "https://github.com/intel/mkl-dnn/releases/download/v0.13/mklml_mac_2018.0.2.20180127.tgz" ], - sha256 = "094e3dfd61c816136dc8d12a45cc611ce26c5f4828176a3644cd0b0efa15a25b", - strip_prefix = "mklml_mac_2018.0.3.20180406", + sha256 = "aa740d71e14562bfea56e6829e6dc186e7487cbcf6748a88dec73826b7ec1943", + strip_prefix = "mklml_mac_2018.0.2.20180127", build_file = clean_dep("//third_party/mkl:mkl.BUILD") ) @@ -85,11 +85,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "mkl_dnn", urls = [ - "https://mirror.bazel.build/github.com/intel/mkl-dnn/archive/v0.14.tar.gz", - "https://github.com/intel/mkl-dnn/archive/v0.14.tar.gz", + "https://mirror.bazel.build/github.com/intel/mkl-dnn/archive/v0.13.tar.gz", + "https://github.com/intel/mkl-dnn/archive/v0.13.tar.gz", ], - sha256 = "efebc53882856afec86457a2da644693f5d59c68772d41d640d6b60a8efc4eb0", - strip_prefix = "mkl-dnn-0.14", + sha256 = "d2cfd93a70cfe86ebe054477c530c9b5c1218b70f75856eb6d1956c68ee89e8f", + strip_prefix = "mkl-dnn-0.13", build_file = clean_dep("//third_party/mkl_dnn:mkldnn.BUILD"), ) @@ -187,11 +187,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "highwayhash", urls = [ - "http://mirror.bazel.build/github.com/google/highwayhash/archive/fd3d9af80465e4383162e4a7c5e2f406e82dd968.tar.gz", - "https://github.com/google/highwayhash/archive/fd3d9af80465e4383162e4a7c5e2f406e82dd968.tar.gz", + "https://mirror.bazel.build/github.com/google/highwayhash/archive/dfcb97ca4fe9277bf9dc1802dd979b071896453b.tar.gz", + "https://github.com/google/highwayhash/archive/dfcb97ca4fe9277bf9dc1802dd979b071896453b.tar.gz", ], - sha256 = "9c3e0e87d581feeb0c18d814d98f170ff23e62967a2bd6855847f0b2fe598a37", - strip_prefix = "highwayhash-fd3d9af80465e4383162e4a7c5e2f406e82dd968", + sha256 = "0f30a15b1566d93f146c8d149878a06e91d9bb7ec2cfd76906df62a82be4aac9", + strip_prefix = "highwayhash-dfcb97ca4fe9277bf9dc1802dd979b071896453b", build_file = clean_dep("//third_party:highwayhash.BUILD"), ) diff --git a/third_party/eigen.BUILD b/third_party/eigen.BUILD index e54c1a4501..07bb6645eb 100644 --- a/third_party/eigen.BUILD +++ b/third_party/eigen.BUILD @@ -64,7 +64,6 @@ cc_library( # This define (mostly) guarantees we don't link any problematic # code. We use it, but we do not rely on it, as evidenced above. "EIGEN_MPL2_ONLY", - "EIGEN_MAX_ALIGN_BYTES=64", ], includes = ["."], visibility = ["//visibility:public"], diff --git a/third_party/highwayhash.BUILD b/third_party/highwayhash.BUILD index 08cb84ea2c..1b8e40765e 100644 --- a/third_party/highwayhash.BUILD +++ b/third_party/highwayhash.BUILD @@ -10,7 +10,6 @@ cc_library( srcs = ["highwayhash/sip_hash.cc"], hdrs = [ "highwayhash/sip_hash.h", - "highwayhash/endianess.h", "highwayhash/state_helpers.h", ], visibility = ["//visibility:public"], diff --git a/third_party/jpeg/jpeg.BUILD b/third_party/jpeg/jpeg.BUILD index 663a218733..4418ac32fc 100644 --- a/third_party/jpeg/jpeg.BUILD +++ b/third_party/jpeg/jpeg.BUILD @@ -291,10 +291,8 @@ cc_library( "jchuff.h", "jconfig.h", "jdct.h", - "jerror.h", "jinclude.h", "jmorecfg.h", - "jpegint.h", "jpeglib.h", "jsimd.h", "jsimddct.h", diff --git a/third_party/png.BUILD b/third_party/png.BUILD index 17c5449cc0..76ab32d69c 100644 --- a/third_party/png.BUILD +++ b/third_party/png.BUILD @@ -28,14 +28,7 @@ cc_library( "pngwrite.c", "pngwtran.c", "pngwutil.c", - ] + select({ - "@org_tensorflow//tensorflow:linux_ppc64le": [ - "powerpc/powerpc_init.c", - "powerpc/filter_vsx_intrinsics.c", - ], - "//conditions:default": [ - ], - }), + ], hdrs = [ "png.h", "pngconf.h", diff --git a/third_party/py/python_configure.bzl b/third_party/py/python_configure.bzl index 3c7e5c8469..954f21f5f8 100644 --- a/third_party/py/python_configure.bzl +++ b/third_party/py/python_configure.bzl @@ -6,7 +6,6 @@ * `PYTHON_LIB_PATH`: Location of python libraries. """ -_BAZEL_SH = "BAZEL_SH" _PYTHON_BIN_PATH = "PYTHON_BIN_PATH" _PYTHON_LIB_PATH = "PYTHON_LIB_PATH" _TF_PYTHON_CONFIG_REPO = "TF_PYTHON_CONFIG_REPO" @@ -153,22 +152,6 @@ def _get_python_bin(repository_ctx): _PYTHON_BIN_PATH, repository_ctx.os.environ.get("PATH", ""))) -def _get_bash_bin(repository_ctx): - """Gets the bash bin path.""" - bash_bin = repository_ctx.os.environ.get(_BAZEL_SH) - if bash_bin != None: - return bash_bin - else: - bash_bin_path = repository_ctx.which("bash") - if bash_bin_path != None: - return str(bash_bin_path) - else: - _fail("Cannot find bash in PATH, please make sure " + - "bash is installed and add its directory in PATH, or --define " + - "%s='/path/to/bash'.\nPATH=%s" % ( - _BAZEL_SH, repository_ctx.os.environ.get("PATH", ""))) - - def _get_python_lib(repository_ctx, python_bin): """Gets the python lib path.""" python_lib = repository_ctx.os.environ.get(_PYTHON_LIB_PATH) @@ -201,14 +184,14 @@ def _get_python_lib(repository_ctx, python_bin): " print(paths[0])\n" + "END") cmd = '%s - %s' % (python_bin, print_lib) - result = repository_ctx.execute([_get_bash_bin(repository_ctx), "-c", cmd]) + result = repository_ctx.execute(["bash", "-c", cmd]) return result.stdout.strip('\n') def _check_python_lib(repository_ctx, python_lib): """Checks the python lib path.""" cmd = 'test -d "%s" -a -x "%s"' % (python_lib, python_lib) - result = repository_ctx.execute([_get_bash_bin(repository_ctx), "-c", cmd]) + result = repository_ctx.execute(["bash", "-c", cmd]) if result.return_code == 1: _fail("Invalid python library path: %s" % python_lib) @@ -216,7 +199,7 @@ def _check_python_lib(repository_ctx, python_lib): def _check_python_bin(repository_ctx, python_bin): """Checks the python bin path.""" cmd = '[[ -x "%s" ]] && [[ ! -d "%s" ]]' % (python_bin, python_bin) - result = repository_ctx.execute([_get_bash_bin(repository_ctx), "-c", cmd]) + result = repository_ctx.execute(["bash", "-c", cmd]) if result.return_code == 1: _fail("--define %s='%s' is not executable. Is it the python binary?" % ( _PYTHON_BIN_PATH, python_bin)) @@ -311,7 +294,6 @@ def _python_autoconf_impl(repository_ctx): python_configure = repository_rule( implementation = _python_autoconf_impl, environ = [ - _BAZEL_SH, _PYTHON_BIN_PATH, _PYTHON_LIB_PATH, _TF_PYTHON_CONFIG_REPO, diff --git a/third_party/repo.bzl b/third_party/repo.bzl index cb67d3e961..36f5aa5bde 100644 --- a/third_party/repo.bzl +++ b/third_party/repo.bzl @@ -17,6 +17,7 @@ _SINGLE_URL_WHITELIST = depset([ "arm_compiler", "ortools_archive", + "gemmlowp", ]) def _is_windows(ctx): @@ -87,9 +88,7 @@ def _tf_http_archive(ctx): if ctx.attr.patch_file != None: _apply_patch(ctx, ctx.attr.patch_file) if ctx.attr.build_file != None: - # Use BUILD.bazel to avoid conflict with third party projects with - # BUILD or build (directory) underneath. - ctx.template("BUILD.bazel", ctx.attr.build_file, { + ctx.template("BUILD", ctx.attr.build_file, { "%prefix%": ".." if _repos_are_siblings() else "external", }, False) -- GitLab From 82dfc698e32e89a3bdb1d09b20ee92e3e718dc19 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 18 Jun 2018 12:17:01 -0700 Subject: [PATCH 600/816] Go: Update generated wrapper functions for TensorFlow ops. PiperOrigin-RevId: 201037916 --- tensorflow/go/op/wrappers.go | 1526 +++++++++++++++++----------------- 1 file changed, 763 insertions(+), 763 deletions(-) diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go index a443879df2..5602775b62 100644 --- a/tensorflow/go/op/wrappers.go +++ b/tensorflow/go/op/wrappers.go @@ -2990,31 +2990,6 @@ func Split(scope *Scope, axis tf.Output, value tf.Output, num_split int64) (outp return output } -// Concatenates tensors along one dimension. -// -// Arguments: -// concat_dim: 0-D. The dimension along which to concatenate. Must be in the -// range [0, rank(values)). -// values: The `N` Tensors to concatenate. Their ranks and types must match, -// and their sizes must match in all dimensions except `concat_dim`. -// -// Returns A `Tensor` with the concatenation of values stacked along the -// `concat_dim` dimension. This tensor's shape matches that of `values` except -// in `concat_dim` where it has the sum of the sizes. -func Concat(scope *Scope, concat_dim tf.Output, values []tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Concat", - Input: []tf.Input{ - concat_dim, tf.OutputList(values), - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // Creates a sequence of numbers. // // This operation creates a sequence of numbers that begins at `start` and @@ -8392,124 +8367,157 @@ func BoostedTreesUpdateEnsemble(scope *Scope, tree_ensemble_handle tf.Output, fe return scope.AddOperation(opspec) } -// EncodeJpegAttr is an optional argument to EncodeJpeg. -type EncodeJpegAttr func(optionalAttr) +// ResourceSparseApplyFtrlAttr is an optional argument to ResourceSparseApplyFtrl. +type ResourceSparseApplyFtrlAttr func(optionalAttr) -// EncodeJpegFormat sets the optional format attribute to value. +// ResourceSparseApplyFtrlUseLocking sets the optional use_locking attribute to value. // -// value: Per pixel image format. -// If not specified, defaults to "" -func EncodeJpegFormat(value string) EncodeJpegAttr { +// value: If `True`, updating of the var and accum tensors will be protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceSparseApplyFtrlUseLocking(value bool) ResourceSparseApplyFtrlAttr { return func(m optionalAttr) { - m["format"] = value + m["use_locking"] = value } } -// EncodeJpegQuality sets the optional quality attribute to value. +// Update relevant entries in '*var' according to the Ftrl-proximal scheme. // -// value: Quality of the compression from 0 to 100 (higher is better and slower). -// If not specified, defaults to 95 -func EncodeJpegQuality(value int64) EncodeJpegAttr { - return func(m optionalAttr) { - m["quality"] = value - } -} - -// EncodeJpegProgressive sets the optional progressive attribute to value. +// That is for rows we have grad for, we update var, accum and linear as follows: +// accum_new = accum + grad * grad +// linear += grad + (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var +// quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 +// var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 +// accum = accum_new // -// value: If True, create a JPEG that loads progressively (coarse to fine). -// If not specified, defaults to false -func EncodeJpegProgressive(value bool) EncodeJpegAttr { - return func(m optionalAttr) { - m["progressive"] = value - } -} - -// EncodeJpegOptimizeSize sets the optional optimize_size attribute to value. +// Arguments: +// var_: Should be from a Variable(). +// accum: Should be from a Variable(). +// linear: Should be from a Variable(). +// grad: The gradient. +// indices: A vector of indices into the first dimension of var and accum. +// lr: Scaling factor. Must be a scalar. +// l1: L1 regularization. Must be a scalar. +// l2: L2 regularization. Must be a scalar. +// lr_power: Scaling factor. Must be a scalar. // -// value: If True, spend CPU/RAM to reduce size with no quality change. -// If not specified, defaults to false -func EncodeJpegOptimizeSize(value bool) EncodeJpegAttr { - return func(m optionalAttr) { - m["optimize_size"] = value +// Returns the created operation. +func ResourceSparseApplyFtrl(scope *Scope, var_ tf.Output, accum tf.Output, linear tf.Output, grad tf.Output, indices tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, lr_power tf.Output, optional ...ResourceSparseApplyFtrlAttr) (o *tf.Operation) { + if scope.Err() != nil { + return } -} - -// EncodeJpegChromaDownsampling sets the optional chroma_downsampling attribute to value. -// -// value: See http://en.wikipedia.org/wiki/Chroma_subsampling. -// If not specified, defaults to true -func EncodeJpegChromaDownsampling(value bool) EncodeJpegAttr { - return func(m optionalAttr) { - m["chroma_downsampling"] = value + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceSparseApplyFtrl", + Input: []tf.Input{ + var_, accum, linear, grad, indices, lr, l1, l2, lr_power, + }, + Attrs: attrs, } + return scope.AddOperation(opspec) } -// EncodeJpegDensityUnit sets the optional density_unit attribute to value. +// Returns which elements of x are Inf. // -// value: Unit used to specify `x_density` and `y_density`: -// pixels per inch (`'in'`) or centimeter (`'cm'`). -// If not specified, defaults to "in" -func EncodeJpegDensityUnit(value string) EncodeJpegAttr { - return func(m optionalAttr) { - m["density_unit"] = value +// @compatibility(numpy) +// Equivalent to np.isinf +// @end_compatibility +func IsInf(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return } + opspec := tf.OpSpec{ + Type: "IsInf", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) } -// EncodeJpegXDensity sets the optional x_density attribute to value. +// Computes the sum along sparse segments of a tensor divided by the sqrt of N. // -// value: Horizontal pixels per density unit. -// If not specified, defaults to 300 -func EncodeJpegXDensity(value int64) EncodeJpegAttr { - return func(m optionalAttr) { - m["x_density"] = value +// N is the size of the segment being reduced. +// +// Read @{$math_ops#Segmentation$the section on segmentation} for an explanation of +// segments. +// +// Arguments: +// +// indices: A 1-D tensor. Has same rank as `segment_ids`. +// segment_ids: A 1-D tensor. Values should be sorted and can be repeated. +// +// Returns Has same shape as data, except for dimension 0 which +// has size `k`, the number of segments. +func SparseSegmentSqrtN(scope *Scope, data tf.Output, indices tf.Output, segment_ids tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseSegmentSqrtN", + Input: []tf.Input{ + data, indices, segment_ids, + }, } + op := scope.AddOperation(opspec) + return op.Output(0) } -// EncodeJpegYDensity sets the optional y_density attribute to value. +// Adds up a `SparseTensor` and a dense `Tensor`, producing a dense `Tensor`. // -// value: Vertical pixels per density unit. -// If not specified, defaults to 300 -func EncodeJpegYDensity(value int64) EncodeJpegAttr { - return func(m optionalAttr) { - m["y_density"] = value +// This Op does not require `a_indices` be sorted in standard lexicographic order. +// +// Arguments: +// a_indices: 2-D. The `indices` of the `SparseTensor`, with shape `[nnz, ndims]`. +// a_values: 1-D. The `values` of the `SparseTensor`, with shape `[nnz]`. +// a_shape: 1-D. The `shape` of the `SparseTensor`, with shape `[ndims]`. +// b: `ndims`-D Tensor. With shape `a_shape`. +func SparseTensorDenseAdd(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseTensorDenseAdd", + Input: []tf.Input{ + a_indices, a_values, a_shape, b, + }, } + op := scope.AddOperation(opspec) + return op.Output(0) } -// EncodeJpegXmpMetadata sets the optional xmp_metadata attribute to value. +// StatelessTruncatedNormalAttr is an optional argument to StatelessTruncatedNormal. +type StatelessTruncatedNormalAttr func(optionalAttr) + +// StatelessTruncatedNormalDtype sets the optional dtype attribute to value. // -// value: If not empty, embed this XMP metadata in the image header. -// If not specified, defaults to "" -func EncodeJpegXmpMetadata(value string) EncodeJpegAttr { +// value: The type of the output. +// If not specified, defaults to DT_FLOAT +func StatelessTruncatedNormalDtype(value tf.DataType) StatelessTruncatedNormalAttr { return func(m optionalAttr) { - m["xmp_metadata"] = value + m["dtype"] = value } } -// JPEG-encode an image. -// -// `image` is a 3-D uint8 Tensor of shape `[height, width, channels]`. -// -// The attr `format` can be used to override the color format of the encoded -// output. Values can be: -// -// * `''`: Use a default format based on the number of channels in the image. -// * `grayscale`: Output a grayscale JPEG image. The `channels` dimension -// of `image` must be 1. -// * `rgb`: Output an RGB JPEG image. The `channels` dimension -// of `image` must be 3. +// Outputs deterministic pseudorandom values from a truncated normal distribution. // -// If `format` is not specified or is the empty string, a default format is picked -// in function of the number of channels in `image`: +// The generated values follow a normal distribution with mean 0 and standard +// deviation 1, except that values whose magnitude is more than 2 standard +// deviations from the mean are dropped and re-picked. // -// * 1: Output a grayscale image. -// * 3: Output an RGB image. +// The outputs are a deterministic function of `shape` and `seed`. // // Arguments: -// image: 3-D with shape `[height, width, channels]`. +// shape: The shape of the output tensor. +// seed: 2 seeds (shape [2]). // -// Returns 0-D. JPEG-encoded image. -func EncodeJpeg(scope *Scope, image tf.Output, optional ...EncodeJpegAttr) (contents tf.Output) { +// Returns Random values with specified shape. +func StatelessTruncatedNormal(scope *Scope, shape tf.Output, seed tf.Output, optional ...StatelessTruncatedNormalAttr) (output tf.Output) { if scope.Err() != nil { return } @@ -8518,9 +8526,9 @@ func EncodeJpeg(scope *Scope, image tf.Output, optional ...EncodeJpegAttr) (cont a(attrs) } opspec := tf.OpSpec{ - Type: "EncodeJpeg", + Type: "StatelessTruncatedNormal", Input: []tf.Input{ - image, + shape, seed, }, Attrs: attrs, } @@ -8528,296 +8536,21 @@ func EncodeJpeg(scope *Scope, image tf.Output, optional ...EncodeJpegAttr) (cont return op.Output(0) } -// MultinomialAttr is an optional argument to Multinomial. -type MultinomialAttr func(optionalAttr) +// RestoreSliceAttr is an optional argument to RestoreSlice. +type RestoreSliceAttr func(optionalAttr) -// MultinomialSeed sets the optional seed attribute to value. +// RestoreSlicePreferredShard sets the optional preferred_shard attribute to value. // -// value: If either seed or seed2 is set to be non-zero, the internal random number -// generator is seeded by the given seed. Otherwise, a random seed is used. -// If not specified, defaults to 0 -func MultinomialSeed(value int64) MultinomialAttr { +// value: Index of file to open first if multiple files match +// `file_pattern`. See the documentation for `Restore`. +// If not specified, defaults to -1 +func RestoreSlicePreferredShard(value int64) RestoreSliceAttr { return func(m optionalAttr) { - m["seed"] = value + m["preferred_shard"] = value } } -// MultinomialSeed2 sets the optional seed2 attribute to value. -// -// value: A second seed to avoid seed collision. -// If not specified, defaults to 0 -func MultinomialSeed2(value int64) MultinomialAttr { - return func(m optionalAttr) { - m["seed2"] = value - } -} - -// MultinomialOutputDtype sets the optional output_dtype attribute to value. -// If not specified, defaults to DT_INT64 -func MultinomialOutputDtype(value tf.DataType) MultinomialAttr { - return func(m optionalAttr) { - m["output_dtype"] = value - } -} - -// Draws samples from a multinomial distribution. -// -// Arguments: -// logits: 2-D Tensor with shape `[batch_size, num_classes]`. Each slice `[i, :]` -// represents the unnormalized log probabilities for all classes. -// num_samples: 0-D. Number of independent samples to draw for each row slice. -// -// Returns 2-D Tensor with shape `[batch_size, num_samples]`. Each slice `[i, :]` -// contains the drawn class labels with range `[0, num_classes)`. -func Multinomial(scope *Scope, logits tf.Output, num_samples tf.Output, optional ...MultinomialAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "Multinomial", - Input: []tf.Input{ - logits, num_samples, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// ResourceSparseApplyAdagradDAAttr is an optional argument to ResourceSparseApplyAdagradDA. -type ResourceSparseApplyAdagradDAAttr func(optionalAttr) - -// ResourceSparseApplyAdagradDAUseLocking sets the optional use_locking attribute to value. -// -// value: If True, updating of the var and accum tensors will be protected by -// a lock; otherwise the behavior is undefined, but may exhibit less contention. -// If not specified, defaults to false -func ResourceSparseApplyAdagradDAUseLocking(value bool) ResourceSparseApplyAdagradDAAttr { - return func(m optionalAttr) { - m["use_locking"] = value - } -} - -// Update entries in '*var' and '*accum' according to the proximal adagrad scheme. -// -// Arguments: -// var_: Should be from a Variable(). -// gradient_accumulator: Should be from a Variable(). -// gradient_squared_accumulator: Should be from a Variable(). -// grad: The gradient. -// indices: A vector of indices into the first dimension of var and accum. -// lr: Learning rate. Must be a scalar. -// l1: L1 regularization. Must be a scalar. -// l2: L2 regularization. Must be a scalar. -// global_step: Training step number. Must be a scalar. -// -// Returns the created operation. -func ResourceSparseApplyAdagradDA(scope *Scope, var_ tf.Output, gradient_accumulator tf.Output, gradient_squared_accumulator tf.Output, grad tf.Output, indices tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, global_step tf.Output, optional ...ResourceSparseApplyAdagradDAAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ResourceSparseApplyAdagradDA", - Input: []tf.Input{ - var_, gradient_accumulator, gradient_squared_accumulator, grad, indices, lr, l1, l2, global_step, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// ResourceSparseApplyFtrlAttr is an optional argument to ResourceSparseApplyFtrl. -type ResourceSparseApplyFtrlAttr func(optionalAttr) - -// ResourceSparseApplyFtrlUseLocking sets the optional use_locking attribute to value. -// -// value: If `True`, updating of the var and accum tensors will be protected -// by a lock; otherwise the behavior is undefined, but may exhibit less -// contention. -// If not specified, defaults to false -func ResourceSparseApplyFtrlUseLocking(value bool) ResourceSparseApplyFtrlAttr { - return func(m optionalAttr) { - m["use_locking"] = value - } -} - -// Update relevant entries in '*var' according to the Ftrl-proximal scheme. -// -// That is for rows we have grad for, we update var, accum and linear as follows: -// accum_new = accum + grad * grad -// linear += grad + (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var -// quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 -// var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 -// accum = accum_new -// -// Arguments: -// var_: Should be from a Variable(). -// accum: Should be from a Variable(). -// linear: Should be from a Variable(). -// grad: The gradient. -// indices: A vector of indices into the first dimension of var and accum. -// lr: Scaling factor. Must be a scalar. -// l1: L1 regularization. Must be a scalar. -// l2: L2 regularization. Must be a scalar. -// lr_power: Scaling factor. Must be a scalar. -// -// Returns the created operation. -func ResourceSparseApplyFtrl(scope *Scope, var_ tf.Output, accum tf.Output, linear tf.Output, grad tf.Output, indices tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, lr_power tf.Output, optional ...ResourceSparseApplyFtrlAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ResourceSparseApplyFtrl", - Input: []tf.Input{ - var_, accum, linear, grad, indices, lr, l1, l2, lr_power, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// Returns which elements of x are Inf. -// -// @compatibility(numpy) -// Equivalent to np.isinf -// @end_compatibility -func IsInf(scope *Scope, x tf.Output) (y tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "IsInf", - Input: []tf.Input{ - x, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Computes the sum along sparse segments of a tensor divided by the sqrt of N. -// -// N is the size of the segment being reduced. -// -// Read @{$math_ops#Segmentation$the section on segmentation} for an explanation of -// segments. -// -// Arguments: -// -// indices: A 1-D tensor. Has same rank as `segment_ids`. -// segment_ids: A 1-D tensor. Values should be sorted and can be repeated. -// -// Returns Has same shape as data, except for dimension 0 which -// has size `k`, the number of segments. -func SparseSegmentSqrtN(scope *Scope, data tf.Output, indices tf.Output, segment_ids tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SparseSegmentSqrtN", - Input: []tf.Input{ - data, indices, segment_ids, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Adds up a `SparseTensor` and a dense `Tensor`, producing a dense `Tensor`. -// -// This Op does not require `a_indices` be sorted in standard lexicographic order. -// -// Arguments: -// a_indices: 2-D. The `indices` of the `SparseTensor`, with shape `[nnz, ndims]`. -// a_values: 1-D. The `values` of the `SparseTensor`, with shape `[nnz]`. -// a_shape: 1-D. The `shape` of the `SparseTensor`, with shape `[ndims]`. -// b: `ndims`-D Tensor. With shape `a_shape`. -func SparseTensorDenseAdd(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SparseTensorDenseAdd", - Input: []tf.Input{ - a_indices, a_values, a_shape, b, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// StatelessTruncatedNormalAttr is an optional argument to StatelessTruncatedNormal. -type StatelessTruncatedNormalAttr func(optionalAttr) - -// StatelessTruncatedNormalDtype sets the optional dtype attribute to value. -// -// value: The type of the output. -// If not specified, defaults to DT_FLOAT -func StatelessTruncatedNormalDtype(value tf.DataType) StatelessTruncatedNormalAttr { - return func(m optionalAttr) { - m["dtype"] = value - } -} - -// Outputs deterministic pseudorandom values from a truncated normal distribution. -// -// The generated values follow a normal distribution with mean 0 and standard -// deviation 1, except that values whose magnitude is more than 2 standard -// deviations from the mean are dropped and re-picked. -// -// The outputs are a deterministic function of `shape` and `seed`. -// -// Arguments: -// shape: The shape of the output tensor. -// seed: 2 seeds (shape [2]). -// -// Returns Random values with specified shape. -func StatelessTruncatedNormal(scope *Scope, shape tf.Output, seed tf.Output, optional ...StatelessTruncatedNormalAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "StatelessTruncatedNormal", - Input: []tf.Input{ - shape, seed, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// RestoreSliceAttr is an optional argument to RestoreSlice. -type RestoreSliceAttr func(optionalAttr) - -// RestoreSlicePreferredShard sets the optional preferred_shard attribute to value. -// -// value: Index of file to open first if multiple files match -// `file_pattern`. See the documentation for `Restore`. -// If not specified, defaults to -1 -func RestoreSlicePreferredShard(value int64) RestoreSliceAttr { - return func(m optionalAttr) { - m["preferred_shard"] = value - } -} - -// Restores a tensor from checkpoint files. +// Restores a tensor from checkpoint files. // // This is like `Restore` except that restored tensor can be listed as filling // only a slice of a larger tensor. `shape_and_slice` specifies the shape of the @@ -8956,186 +8689,6 @@ func StatelessRandomNormal(scope *Scope, shape tf.Output, seed tf.Output, option return op.Output(0) } -// MaxPoolAttr is an optional argument to MaxPool. -type MaxPoolAttr func(optionalAttr) - -// MaxPoolDataFormat sets the optional data_format attribute to value. -// -// value: Specify the data format of the input and output data. With the -// default format "NHWC", the data is stored in the order of: -// [batch, in_height, in_width, in_channels]. -// Alternatively, the format could be "NCHW", the data storage order of: -// [batch, in_channels, in_height, in_width]. -// If not specified, defaults to "NHWC" -func MaxPoolDataFormat(value string) MaxPoolAttr { - return func(m optionalAttr) { - m["data_format"] = value - } -} - -// Performs max pooling on the input. -// -// Arguments: -// input: 4-D input to pool over. -// ksize: The size of the window for each dimension of the input tensor. -// strides: The stride of the sliding window for each dimension of the -// input tensor. -// padding: The type of padding algorithm to use. -// -// Returns The max pooled output tensor. -func MaxPool(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "MaxPool", - Input: []tf.Input{ - input, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// SparseMatMulAttr is an optional argument to SparseMatMul. -type SparseMatMulAttr func(optionalAttr) - -// SparseMatMulTransposeA sets the optional transpose_a attribute to value. -// If not specified, defaults to false -func SparseMatMulTransposeA(value bool) SparseMatMulAttr { - return func(m optionalAttr) { - m["transpose_a"] = value - } -} - -// SparseMatMulTransposeB sets the optional transpose_b attribute to value. -// If not specified, defaults to false -func SparseMatMulTransposeB(value bool) SparseMatMulAttr { - return func(m optionalAttr) { - m["transpose_b"] = value - } -} - -// SparseMatMulAIsSparse sets the optional a_is_sparse attribute to value. -// If not specified, defaults to false -func SparseMatMulAIsSparse(value bool) SparseMatMulAttr { - return func(m optionalAttr) { - m["a_is_sparse"] = value - } -} - -// SparseMatMulBIsSparse sets the optional b_is_sparse attribute to value. -// If not specified, defaults to false -func SparseMatMulBIsSparse(value bool) SparseMatMulAttr { - return func(m optionalAttr) { - m["b_is_sparse"] = value - } -} - -// Multiply matrix "a" by matrix "b". -// -// The inputs must be two-dimensional matrices and the inner dimension of "a" must -// match the outer dimension of "b". This op is optimized for the case where at -// least one of "a" or "b" is sparse. The breakeven for using this versus a dense -// matrix multiply on one platform was 30% zero values in the sparse matrix. -// -// The gradient computation of this operation will only take advantage of sparsity -// in the input gradient when that gradient comes from a Relu. -func SparseMatMul(scope *Scope, a tf.Output, b tf.Output, optional ...SparseMatMulAttr) (product tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "SparseMatMul", - Input: []tf.Input{ - a, b, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Concatenates quantized tensors along one dimension. -// -// Arguments: -// concat_dim: 0-D. The dimension along which to concatenate. Must be in the -// range [0, rank(values)). -// values: The `N` Tensors to concatenate. Their ranks and types must match, -// and their sizes must match in all dimensions except `concat_dim`. -// input_mins: The minimum scalar values for each of the input tensors. -// input_maxes: The maximum scalar values for each of the input tensors. -// -// Returns A `Tensor` with the concatenation of values stacked along the -// `concat_dim` dimension. This tensor's shape matches that of `values` except -// in `concat_dim` where it has the sum of the sizes.The float value that the minimum quantized output value represents.The float value that the maximum quantized output value represents. -func QuantizedConcat(scope *Scope, concat_dim tf.Output, values []tf.Output, input_mins []tf.Output, input_maxes []tf.Output) (output tf.Output, output_min tf.Output, output_max tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "QuantizedConcat", - Input: []tf.Input{ - concat_dim, tf.OutputList(values), tf.OutputList(input_mins), tf.OutputList(input_maxes), - }, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// Slice a `SparseTensor` based on the `start` and `size`. -// -// For example, if the input is -// -// input_tensor = shape = [2, 7] -// [ a d e ] -// [b c ] -// -// Graphically the output tensors are: -// -// sparse_slice([0, 0], [2, 4]) = shape = [2, 4] -// [ a ] -// [b c ] -// -// sparse_slice([0, 4], [2, 3]) = shape = [2, 3] -// [ d e ] -// [ ] -// -// Arguments: -// indices: 2-D tensor represents the indices of the sparse tensor. -// values: 1-D tensor represents the values of the sparse tensor. -// shape: 1-D. tensor represents the shape of the sparse tensor. -// start: 1-D. tensor represents the start of the slice. -// size: 1-D. tensor represents the size of the slice. -// output indices: A list of 1-D tensors represents the indices of the output -// sparse tensors. -// -// Returns A list of 1-D tensors represents the values of the output sparse -// tensors.A list of 1-D tensors represents the shape of the output sparse -// tensors. -func SparseSlice(scope *Scope, indices tf.Output, values tf.Output, shape tf.Output, start tf.Output, size tf.Output) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SparseSlice", - Input: []tf.Input{ - indices, values, shape, start, size, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - // Reduces sparse updates into the variable referenced by `resource` using the `min` operation. // // This operation computes @@ -11170,35 +10723,108 @@ func OrderedMapPeek(scope *Scope, key tf.Output, indices tf.Output, dtypes []tf. scope.UpdateErr("OrderedMapPeek", err) return } - return values + return values +} + +// Inverse fast Fourier transform. +// +// Computes the inverse 1-dimensional discrete Fourier transform over the +// inner-most dimension of `input`. +// +// Arguments: +// input: A complex64 tensor. +// +// Returns A complex64 tensor of the same shape as `input`. The inner-most +// dimension of `input` is replaced with its inverse 1D Fourier transform. +// +// @compatibility(numpy) +// Equivalent to np.fft.ifft +// @end_compatibility +func IFFT(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "IFFT", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Generates values in an interval. +// +// A sequence of `num` evenly-spaced values are generated beginning at `start`. +// If `num > 1`, the values in the sequence increase by `stop - start / num - 1`, +// so that the last one is exactly `stop`. +// +// For example: +// +// ``` +// tf.linspace(10.0, 12.0, 3, name="linspace") => [ 10.0 11.0 12.0] +// ``` +// +// Arguments: +// start: First entry in the range. +// stop: Last entry in the range. +// num: Number of values to generate. +// +// Returns 1-D. The generated values. +func LinSpace(scope *Scope, start tf.Output, stop tf.Output, num tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "LinSpace", + Input: []tf.Input{ + start, stop, num, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) } -// Inverse fast Fourier transform. +// DestroyResourceOpAttr is an optional argument to DestroyResourceOp. +type DestroyResourceOpAttr func(optionalAttr) + +// DestroyResourceOpIgnoreLookupError sets the optional ignore_lookup_error attribute to value. // -// Computes the inverse 1-dimensional discrete Fourier transform over the -// inner-most dimension of `input`. +// value: whether to ignore the error when the resource +// doesn't exist. +// If not specified, defaults to true +func DestroyResourceOpIgnoreLookupError(value bool) DestroyResourceOpAttr { + return func(m optionalAttr) { + m["ignore_lookup_error"] = value + } +} + +// Deletes the resource specified by the handle. // -// Arguments: -// input: A complex64 tensor. +// All subsequent operations using the resource will result in a NotFound +// error status. // -// Returns A complex64 tensor of the same shape as `input`. The inner-most -// dimension of `input` is replaced with its inverse 1D Fourier transform. +// Arguments: +// resource: handle to the resource to delete. // -// @compatibility(numpy) -// Equivalent to np.fft.ifft -// @end_compatibility -func IFFT(scope *Scope, input tf.Output) (output tf.Output) { +// Returns the created operation. +func DestroyResourceOp(scope *Scope, resource tf.Output, optional ...DestroyResourceOpAttr) (o *tf.Operation) { if scope.Err() != nil { return } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "IFFT", + Type: "DestroyResourceOp", Input: []tf.Input{ - input, + resource, }, + Attrs: attrs, } - op := scope.AddOperation(opspec) - return op.Output(0) + return scope.AddOperation(opspec) } // ResourceSparseApplyRMSPropAttr is an optional argument to ResourceSparseApplyRMSProp. @@ -12687,33 +12313,264 @@ func FFT(scope *Scope, input tf.Output) (output tf.Output) { if scope.Err() != nil { return } - opspec := tf.OpSpec{ - Type: "FFT", - Input: []tf.Input{ - input, - }, + opspec := tf.OpSpec{ + Type: "FFT", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Transforms a serialized tensorflow.TensorProto proto into a Tensor. +// +// Arguments: +// serialized: A scalar string containing a serialized TensorProto proto. +// out_type: The type of the serialized tensor. The provided type must match the +// type of the serialized tensor and no implicit conversion will take place. +// +// Returns A Tensor of type `out_type`. +func ParseTensor(scope *Scope, serialized tf.Output, out_type tf.DataType) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"out_type": out_type} + opspec := tf.OpSpec{ + Type: "ParseTensor", + Input: []tf.Input{ + serialized, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// MaxPoolWithArgmaxAttr is an optional argument to MaxPoolWithArgmax. +type MaxPoolWithArgmaxAttr func(optionalAttr) + +// MaxPoolWithArgmaxTargmax sets the optional Targmax attribute to value. +// If not specified, defaults to DT_INT64 +func MaxPoolWithArgmaxTargmax(value tf.DataType) MaxPoolWithArgmaxAttr { + return func(m optionalAttr) { + m["Targmax"] = value + } +} + +// Performs max pooling on the input and outputs both max values and indices. +// +// The indices in `argmax` are flattened, so that a maximum value at position +// `[b, y, x, c]` becomes flattened index +// `((b * height + y) * width + x) * channels + c`. +// +// The indices returned are always in `[0, height) x [0, width)` before flattening, +// even if padding is involved and the mathematically correct answer is outside +// (either negative or too large). This is a bug, but fixing it is difficult to do +// in a safe backwards compatible way, especially due to flattening. +// +// Arguments: +// input: 4-D with shape `[batch, height, width, channels]`. Input to pool over. +// ksize: The size of the window for each dimension of the input tensor. +// strides: The stride of the sliding window for each dimension of the +// input tensor. +// padding: The type of padding algorithm to use. +// +// Returns The max pooled output tensor.4-D. The flattened indices of the max values chosen for each output. +func MaxPoolWithArgmax(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolWithArgmaxAttr) (output tf.Output, argmax tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MaxPoolWithArgmax", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// ResourceSparseApplyAdagradDAAttr is an optional argument to ResourceSparseApplyAdagradDA. +type ResourceSparseApplyAdagradDAAttr func(optionalAttr) + +// ResourceSparseApplyAdagradDAUseLocking sets the optional use_locking attribute to value. +// +// value: If True, updating of the var and accum tensors will be protected by +// a lock; otherwise the behavior is undefined, but may exhibit less contention. +// If not specified, defaults to false +func ResourceSparseApplyAdagradDAUseLocking(value bool) ResourceSparseApplyAdagradDAAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Update entries in '*var' and '*accum' according to the proximal adagrad scheme. +// +// Arguments: +// var_: Should be from a Variable(). +// gradient_accumulator: Should be from a Variable(). +// gradient_squared_accumulator: Should be from a Variable(). +// grad: The gradient. +// indices: A vector of indices into the first dimension of var and accum. +// lr: Learning rate. Must be a scalar. +// l1: L1 regularization. Must be a scalar. +// l2: L2 regularization. Must be a scalar. +// global_step: Training step number. Must be a scalar. +// +// Returns the created operation. +func ResourceSparseApplyAdagradDA(scope *Scope, var_ tf.Output, gradient_accumulator tf.Output, gradient_squared_accumulator tf.Output, grad tf.Output, indices tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, global_step tf.Output, optional ...ResourceSparseApplyAdagradDAAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceSparseApplyAdagradDA", + Input: []tf.Input{ + var_, gradient_accumulator, gradient_squared_accumulator, grad, indices, lr, l1, l2, global_step, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// EncodeJpegAttr is an optional argument to EncodeJpeg. +type EncodeJpegAttr func(optionalAttr) + +// EncodeJpegFormat sets the optional format attribute to value. +// +// value: Per pixel image format. +// If not specified, defaults to "" +func EncodeJpegFormat(value string) EncodeJpegAttr { + return func(m optionalAttr) { + m["format"] = value + } +} + +// EncodeJpegQuality sets the optional quality attribute to value. +// +// value: Quality of the compression from 0 to 100 (higher is better and slower). +// If not specified, defaults to 95 +func EncodeJpegQuality(value int64) EncodeJpegAttr { + return func(m optionalAttr) { + m["quality"] = value + } +} + +// EncodeJpegProgressive sets the optional progressive attribute to value. +// +// value: If True, create a JPEG that loads progressively (coarse to fine). +// If not specified, defaults to false +func EncodeJpegProgressive(value bool) EncodeJpegAttr { + return func(m optionalAttr) { + m["progressive"] = value + } +} + +// EncodeJpegOptimizeSize sets the optional optimize_size attribute to value. +// +// value: If True, spend CPU/RAM to reduce size with no quality change. +// If not specified, defaults to false +func EncodeJpegOptimizeSize(value bool) EncodeJpegAttr { + return func(m optionalAttr) { + m["optimize_size"] = value + } +} + +// EncodeJpegChromaDownsampling sets the optional chroma_downsampling attribute to value. +// +// value: See http://en.wikipedia.org/wiki/Chroma_subsampling. +// If not specified, defaults to true +func EncodeJpegChromaDownsampling(value bool) EncodeJpegAttr { + return func(m optionalAttr) { + m["chroma_downsampling"] = value + } +} + +// EncodeJpegDensityUnit sets the optional density_unit attribute to value. +// +// value: Unit used to specify `x_density` and `y_density`: +// pixels per inch (`'in'`) or centimeter (`'cm'`). +// If not specified, defaults to "in" +func EncodeJpegDensityUnit(value string) EncodeJpegAttr { + return func(m optionalAttr) { + m["density_unit"] = value + } +} + +// EncodeJpegXDensity sets the optional x_density attribute to value. +// +// value: Horizontal pixels per density unit. +// If not specified, defaults to 300 +func EncodeJpegXDensity(value int64) EncodeJpegAttr { + return func(m optionalAttr) { + m["x_density"] = value + } +} + +// EncodeJpegYDensity sets the optional y_density attribute to value. +// +// value: Vertical pixels per density unit. +// If not specified, defaults to 300 +func EncodeJpegYDensity(value int64) EncodeJpegAttr { + return func(m optionalAttr) { + m["y_density"] = value + } +} + +// EncodeJpegXmpMetadata sets the optional xmp_metadata attribute to value. +// +// value: If not empty, embed this XMP metadata in the image header. +// If not specified, defaults to "" +func EncodeJpegXmpMetadata(value string) EncodeJpegAttr { + return func(m optionalAttr) { + m["xmp_metadata"] = value } - op := scope.AddOperation(opspec) - return op.Output(0) } -// Transforms a serialized tensorflow.TensorProto proto into a Tensor. +// JPEG-encode an image. +// +// `image` is a 3-D uint8 Tensor of shape `[height, width, channels]`. +// +// The attr `format` can be used to override the color format of the encoded +// output. Values can be: +// +// * `''`: Use a default format based on the number of channels in the image. +// * `grayscale`: Output a grayscale JPEG image. The `channels` dimension +// of `image` must be 1. +// * `rgb`: Output an RGB JPEG image. The `channels` dimension +// of `image` must be 3. +// +// If `format` is not specified or is the empty string, a default format is picked +// in function of the number of channels in `image`: +// +// * 1: Output a grayscale image. +// * 3: Output an RGB image. // // Arguments: -// serialized: A scalar string containing a serialized TensorProto proto. -// out_type: The type of the serialized tensor. The provided type must match the -// type of the serialized tensor and no implicit conversion will take place. +// image: 3-D with shape `[height, width, channels]`. // -// Returns A Tensor of type `out_type`. -func ParseTensor(scope *Scope, serialized tf.Output, out_type tf.DataType) (output tf.Output) { +// Returns 0-D. JPEG-encoded image. +func EncodeJpeg(scope *Scope, image tf.Output, optional ...EncodeJpegAttr) (contents tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"out_type": out_type} + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "ParseTensor", + Type: "EncodeJpeg", Input: []tf.Input{ - serialized, + image, }, Attrs: attrs, } @@ -12721,53 +12578,64 @@ func ParseTensor(scope *Scope, serialized tf.Output, out_type tf.DataType) (outp return op.Output(0) } -// MaxPoolWithArgmaxAttr is an optional argument to MaxPoolWithArgmax. -type MaxPoolWithArgmaxAttr func(optionalAttr) +// MultinomialAttr is an optional argument to Multinomial. +type MultinomialAttr func(optionalAttr) -// MaxPoolWithArgmaxTargmax sets the optional Targmax attribute to value. -// If not specified, defaults to DT_INT64 -func MaxPoolWithArgmaxTargmax(value tf.DataType) MaxPoolWithArgmaxAttr { +// MultinomialSeed sets the optional seed attribute to value. +// +// value: If either seed or seed2 is set to be non-zero, the internal random number +// generator is seeded by the given seed. Otherwise, a random seed is used. +// If not specified, defaults to 0 +func MultinomialSeed(value int64) MultinomialAttr { return func(m optionalAttr) { - m["Targmax"] = value + m["seed"] = value } } -// Performs max pooling on the input and outputs both max values and indices. -// -// The indices in `argmax` are flattened, so that a maximum value at position -// `[b, y, x, c]` becomes flattened index -// `((b * height + y) * width + x) * channels + c`. +// MultinomialSeed2 sets the optional seed2 attribute to value. // -// The indices returned are always in `[0, height) x [0, width)` before flattening, -// even if padding is involved and the mathematically correct answer is outside -// (either negative or too large). This is a bug, but fixing it is difficult to do -// in a safe backwards compatible way, especially due to flattening. +// value: A second seed to avoid seed collision. +// If not specified, defaults to 0 +func MultinomialSeed2(value int64) MultinomialAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// MultinomialOutputDtype sets the optional output_dtype attribute to value. +// If not specified, defaults to DT_INT64 +func MultinomialOutputDtype(value tf.DataType) MultinomialAttr { + return func(m optionalAttr) { + m["output_dtype"] = value + } +} + +// Draws samples from a multinomial distribution. // // Arguments: -// input: 4-D with shape `[batch, height, width, channels]`. Input to pool over. -// ksize: The size of the window for each dimension of the input tensor. -// strides: The stride of the sliding window for each dimension of the -// input tensor. -// padding: The type of padding algorithm to use. +// logits: 2-D Tensor with shape `[batch_size, num_classes]`. Each slice `[i, :]` +// represents the unnormalized log probabilities for all classes. +// num_samples: 0-D. Number of independent samples to draw for each row slice. // -// Returns The max pooled output tensor.4-D. The flattened indices of the max values chosen for each output. -func MaxPoolWithArgmax(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolWithArgmaxAttr) (output tf.Output, argmax tf.Output) { +// Returns 2-D Tensor with shape `[batch_size, num_samples]`. Each slice `[i, :]` +// contains the drawn class labels with range `[0, num_classes)`. +func Multinomial(scope *Scope, logits tf.Output, num_samples tf.Output, optional ...MultinomialAttr) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} + attrs := map[string]interface{}{} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "MaxPoolWithArgmax", + Type: "Multinomial", Input: []tf.Input{ - input, + logits, num_samples, }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) + return op.Output(0) } // Returns the truth value of NOT x element-wise. @@ -13289,6 +13157,62 @@ func ResourceScatterSub(scope *Scope, resource tf.Output, indices tf.Output, upd return scope.AddOperation(opspec) } +// Inverse 2D fast Fourier transform. +// +// Computes the inverse 2-dimensional discrete Fourier transform over the +// inner-most 2 dimensions of `input`. +// +// Arguments: +// input: A complex64 tensor. +// +// Returns A complex64 tensor of the same shape as `input`. The inner-most 2 +// dimensions of `input` are replaced with their inverse 2D Fourier transform. +// +// @compatibility(numpy) +// Equivalent to np.fft.ifft2 +// @end_compatibility +func IFFT2D(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "IFFT2D", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// 2D fast Fourier transform. +// +// Computes the 2-dimensional discrete Fourier transform over the inner-most +// 2 dimensions of `input`. +// +// Arguments: +// input: A complex64 tensor. +// +// Returns A complex64 tensor of the same shape as `input`. The inner-most 2 +// dimensions of `input` are replaced with their 2D Fourier transform. +// +// @compatibility(numpy) +// Equivalent to np.fft.fft2 +// @end_compatibility +func FFT2D(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "FFT2D", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // ResourceApplyProximalGradientDescentAttr is an optional argument to ResourceApplyProximalGradientDescent. type ResourceApplyProximalGradientDescentAttr func(optionalAttr) @@ -15400,6 +15324,31 @@ func BoostedTreesEnsembleResourceHandleOp(scope *Scope, optional ...BoostedTrees return op.Output(0) } +// Concatenates tensors along one dimension. +// +// Arguments: +// concat_dim: 0-D. The dimension along which to concatenate. Must be in the +// range [0, rank(values)). +// values: The `N` Tensors to concatenate. Their ranks and types must match, +// and their sizes must match in all dimensions except `concat_dim`. +// +// Returns A `Tensor` with the concatenation of values stacked along the +// `concat_dim` dimension. This tensor's shape matches that of `values` except +// in `concat_dim` where it has the sum of the sizes. +func Concat(scope *Scope, concat_dim tf.Output, values []tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Concat", + Input: []tf.Input{ + concat_dim, tf.OutputList(values), + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // ResourceApplyMomentumAttr is an optional argument to ResourceApplyMomentum. type ResourceApplyMomentumAttr func(optionalAttr) @@ -16310,65 +16259,9 @@ func MutableDenseHashTableV2(scope *Scope, empty_key tf.Output, value_dtype tf.D opspec := tf.OpSpec{ Type: "MutableDenseHashTableV2", Input: []tf.Input{ - empty_key, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// 2D fast Fourier transform. -// -// Computes the 2-dimensional discrete Fourier transform over the inner-most -// 2 dimensions of `input`. -// -// Arguments: -// input: A complex64 tensor. -// -// Returns A complex64 tensor of the same shape as `input`. The inner-most 2 -// dimensions of `input` are replaced with their 2D Fourier transform. -// -// @compatibility(numpy) -// Equivalent to np.fft.fft2 -// @end_compatibility -func FFT2D(scope *Scope, input tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "FFT2D", - Input: []tf.Input{ - input, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Inverse 2D fast Fourier transform. -// -// Computes the inverse 2-dimensional discrete Fourier transform over the -// inner-most 2 dimensions of `input`. -// -// Arguments: -// input: A complex64 tensor. -// -// Returns A complex64 tensor of the same shape as `input`. The inner-most 2 -// dimensions of `input` are replaced with their inverse 2D Fourier transform. -// -// @compatibility(numpy) -// Equivalent to np.fft.ifft2 -// @end_compatibility -func IFFT2D(scope *Scope, input tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "IFFT2D", - Input: []tf.Input{ - input, + empty_key, }, + Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) @@ -17884,6 +17777,77 @@ func SparseCross(scope *Scope, indices []tf.Output, values []tf.Output, shapes [ return op.Output(0), op.Output(1), op.Output(2) } +// Concatenates quantized tensors along one dimension. +// +// Arguments: +// concat_dim: 0-D. The dimension along which to concatenate. Must be in the +// range [0, rank(values)). +// values: The `N` Tensors to concatenate. Their ranks and types must match, +// and their sizes must match in all dimensions except `concat_dim`. +// input_mins: The minimum scalar values for each of the input tensors. +// input_maxes: The maximum scalar values for each of the input tensors. +// +// Returns A `Tensor` with the concatenation of values stacked along the +// `concat_dim` dimension. This tensor's shape matches that of `values` except +// in `concat_dim` where it has the sum of the sizes.The float value that the minimum quantized output value represents.The float value that the maximum quantized output value represents. +func QuantizedConcat(scope *Scope, concat_dim tf.Output, values []tf.Output, input_mins []tf.Output, input_maxes []tf.Output) (output tf.Output, output_min tf.Output, output_max tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "QuantizedConcat", + Input: []tf.Input{ + concat_dim, tf.OutputList(values), tf.OutputList(input_mins), tf.OutputList(input_maxes), + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Slice a `SparseTensor` based on the `start` and `size`. +// +// For example, if the input is +// +// input_tensor = shape = [2, 7] +// [ a d e ] +// [b c ] +// +// Graphically the output tensors are: +// +// sparse_slice([0, 0], [2, 4]) = shape = [2, 4] +// [ a ] +// [b c ] +// +// sparse_slice([0, 4], [2, 3]) = shape = [2, 3] +// [ d e ] +// [ ] +// +// Arguments: +// indices: 2-D tensor represents the indices of the sparse tensor. +// values: 1-D tensor represents the values of the sparse tensor. +// shape: 1-D. tensor represents the shape of the sparse tensor. +// start: 1-D. tensor represents the start of the slice. +// size: 1-D. tensor represents the size of the slice. +// output indices: A list of 1-D tensors represents the indices of the output +// sparse tensors. +// +// Returns A list of 1-D tensors represents the values of the output sparse +// tensors.A list of 1-D tensors represents the shape of the output sparse +// tensors. +func SparseSlice(scope *Scope, indices tf.Output, values tf.Output, shape tf.Output, start tf.Output, size tf.Output) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseSlice", + Input: []tf.Input{ + indices, values, shape, start, size, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + // Returns the element-wise min of two SparseTensors. // // Assumes the two SparseTensors have the same shape, i.e., no broadcasting. @@ -18014,6 +17978,52 @@ func TakeManySparseFromTensorsMap(scope *Scope, sparse_handles tf.Output, dtype return op.Output(0), op.Output(1), op.Output(2) } +// MaxPoolAttr is an optional argument to MaxPool. +type MaxPoolAttr func(optionalAttr) + +// MaxPoolDataFormat sets the optional data_format attribute to value. +// +// value: Specify the data format of the input and output data. With the +// default format "NHWC", the data is stored in the order of: +// [batch, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCHW", the data storage order of: +// [batch, in_channels, in_height, in_width]. +// If not specified, defaults to "NHWC" +func MaxPoolDataFormat(value string) MaxPoolAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// Performs max pooling on the input. +// +// Arguments: +// input: 4-D input to pool over. +// ksize: The size of the window for each dimension of the input tensor. +// strides: The stride of the sliding window for each dimension of the +// input tensor. +// padding: The type of padding algorithm to use. +// +// Returns The max pooled output tensor. +func MaxPool(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MaxPool", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Assigns a new value to a variable. // // Any ReadVariableOp with a control dependency on this op is guaranteed to return @@ -18595,6 +18605,69 @@ func SdcaOptimizer(scope *Scope, sparse_example_indices []tf.Output, sparse_feat return out_example_state_data, out_delta_sparse_weights, out_delta_dense_weights } +// SparseMatMulAttr is an optional argument to SparseMatMul. +type SparseMatMulAttr func(optionalAttr) + +// SparseMatMulTransposeA sets the optional transpose_a attribute to value. +// If not specified, defaults to false +func SparseMatMulTransposeA(value bool) SparseMatMulAttr { + return func(m optionalAttr) { + m["transpose_a"] = value + } +} + +// SparseMatMulTransposeB sets the optional transpose_b attribute to value. +// If not specified, defaults to false +func SparseMatMulTransposeB(value bool) SparseMatMulAttr { + return func(m optionalAttr) { + m["transpose_b"] = value + } +} + +// SparseMatMulAIsSparse sets the optional a_is_sparse attribute to value. +// If not specified, defaults to false +func SparseMatMulAIsSparse(value bool) SparseMatMulAttr { + return func(m optionalAttr) { + m["a_is_sparse"] = value + } +} + +// SparseMatMulBIsSparse sets the optional b_is_sparse attribute to value. +// If not specified, defaults to false +func SparseMatMulBIsSparse(value bool) SparseMatMulAttr { + return func(m optionalAttr) { + m["b_is_sparse"] = value + } +} + +// Multiply matrix "a" by matrix "b". +// +// The inputs must be two-dimensional matrices and the inner dimension of "a" must +// match the outer dimension of "b". This op is optimized for the case where at +// least one of "a" or "b" is sparse. The breakeven for using this versus a dense +// matrix multiply on one platform was 30% zero values in the sparse matrix. +// +// The gradient computation of this operation will only take advantage of sparsity +// in the input gradient when that gradient comes from a Relu. +func SparseMatMul(scope *Scope, a tf.Output, b tf.Output, optional ...SparseMatMulAttr) (product tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SparseMatMul", + Input: []tf.Input{ + a, b, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // ShapeAttr is an optional argument to Shape. type ShapeAttr func(optionalAttr) @@ -19440,79 +19513,6 @@ func OrderedMapIncompleteSize(scope *Scope, dtypes []tf.DataType, optional ...Or return op.Output(0) } -// DestroyResourceOpAttr is an optional argument to DestroyResourceOp. -type DestroyResourceOpAttr func(optionalAttr) - -// DestroyResourceOpIgnoreLookupError sets the optional ignore_lookup_error attribute to value. -// -// value: whether to ignore the error when the resource -// doesn't exist. -// If not specified, defaults to true -func DestroyResourceOpIgnoreLookupError(value bool) DestroyResourceOpAttr { - return func(m optionalAttr) { - m["ignore_lookup_error"] = value - } -} - -// Deletes the resource specified by the handle. -// -// All subsequent operations using the resource will result in a NotFound -// error status. -// -// Arguments: -// resource: handle to the resource to delete. -// -// Returns the created operation. -func DestroyResourceOp(scope *Scope, resource tf.Output, optional ...DestroyResourceOpAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "DestroyResourceOp", - Input: []tf.Input{ - resource, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// Generates values in an interval. -// -// A sequence of `num` evenly-spaced values are generated beginning at `start`. -// If `num > 1`, the values in the sequence increase by `stop - start / num - 1`, -// so that the last one is exactly `stop`. -// -// For example: -// -// ``` -// tf.linspace(10.0, 12.0, 3, name="linspace") => [ 10.0 11.0 12.0] -// ``` -// -// Arguments: -// start: First entry in the range. -// stop: Last entry in the range. -// num: Number of values to generate. -// -// Returns 1-D. The generated values. -func LinSpace(scope *Scope, start tf.Output, stop tf.Output, num tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "LinSpace", - Input: []tf.Input{ - start, stop, num, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // ComplexAttr is an optional argument to Complex. type ComplexAttr func(optionalAttr) -- GitLab From 339477aa8ad9abe17190a978dcfa2f0aaf8b3de5 Mon Sep 17 00:00:00 2001 From: "William D. Irons" Date: Mon, 18 Jun 2018 14:28:09 -0500 Subject: [PATCH 601/816] Fix golang_ppc64le filename Had used the old style ppc64el in the original filename --- tensorflow/tools/ci_build/Dockerfile.cpu.ppc64le | 2 +- tensorflow/tools/ci_build/Dockerfile.gpu.ppc64le | 2 +- .../{install_golang_ppc64el.sh => install_golang_ppc64le.sh} | 0 3 files changed, 2 insertions(+), 2 deletions(-) rename tensorflow/tools/ci_build/install/{install_golang_ppc64el.sh => install_golang_ppc64le.sh} (100%) diff --git a/tensorflow/tools/ci_build/Dockerfile.cpu.ppc64le b/tensorflow/tools/ci_build/Dockerfile.cpu.ppc64le index 4aa2ef5eba..f496ac59b6 100644 --- a/tensorflow/tools/ci_build/Dockerfile.cpu.ppc64le +++ b/tensorflow/tools/ci_build/Dockerfile.cpu.ppc64le @@ -13,7 +13,7 @@ RUN /install/install_bazel_from_source.sh RUN /install/install_proto3.sh RUN /install/install_buildifier_from_source.sh RUN /install/install_auditwheel.sh -RUN /install/install_golang_ppc64el.sh +RUN /install/install_golang_ppc64le.sh # Set up the master bazelrc configuration file. COPY install/.bazelrc /etc/bazel.bazelrc diff --git a/tensorflow/tools/ci_build/Dockerfile.gpu.ppc64le b/tensorflow/tools/ci_build/Dockerfile.gpu.ppc64le index 9ec6ae6ef4..3eddc56550 100644 --- a/tensorflow/tools/ci_build/Dockerfile.gpu.ppc64le +++ b/tensorflow/tools/ci_build/Dockerfile.gpu.ppc64le @@ -16,7 +16,7 @@ RUN /install/install_deb_packages.sh RUN apt-get update && apt-get install -y libopenblas-dev RUN /install/install_pip_packages.sh RUN /install/install_bazel_from_source.sh -RUN /install/install_golang_ppc64el.sh +RUN /install/install_golang_ppc64le.sh # Set up the master bazelrc configuration file. COPY install/.bazelrc /etc/bazel.bazelrc diff --git a/tensorflow/tools/ci_build/install/install_golang_ppc64el.sh b/tensorflow/tools/ci_build/install/install_golang_ppc64le.sh similarity index 100% rename from tensorflow/tools/ci_build/install/install_golang_ppc64el.sh rename to tensorflow/tools/ci_build/install/install_golang_ppc64le.sh -- GitLab From 34c45c23e21929bd13b6a9cb92c62c1e7cbba8a5 Mon Sep 17 00:00:00 2001 From: David Majnemer Date: Mon, 18 Jun 2018 12:32:26 -0700 Subject: [PATCH 602/816] [XLA] Simplify, add additional testing for TruncatedNormal PiperOrigin-RevId: 201039966 --- tensorflow/compiler/tests/BUILD | 5 +- tensorflow/compiler/tests/random_ops_test.py | 46 +++++++++++++++++-- .../compiler/tf2xla/kernels/random_ops.cc | 11 ++--- 3 files changed, 50 insertions(+), 12 deletions(-) diff --git a/tensorflow/compiler/tests/BUILD b/tensorflow/compiler/tests/BUILD index af760b5416..9ec6b6b749 100644 --- a/tensorflow/compiler/tests/BUILD +++ b/tensorflow/compiler/tests/BUILD @@ -548,8 +548,11 @@ tf_xla_py_test( name = "random_ops_test", size = "small", srcs = ["random_ops_test.py"], - # TODO(b/31361304): enable RNG ops on GPU when parallelized. disabled_backends = [ + # TODO(b/110300529): RngNormal doesn't return values with the expected variance + "cpu", + "cpu_ondemand", + # TODO(b/31361304): enable RNG ops on GPU when parallelized. "gpu", ], deps = [ diff --git a/tensorflow/compiler/tests/random_ops_test.py b/tensorflow/compiler/tests/random_ops_test.py index f13dff9620..8c6366faa6 100644 --- a/tensorflow/compiler/tests/random_ops_test.py +++ b/tensorflow/compiler/tests/random_ops_test.py @@ -18,6 +18,8 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import math + import numpy as np from tensorflow.compiler.tests.xla_test import XLATestCase @@ -25,6 +27,7 @@ from tensorflow.python.framework import dtypes from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import random_ops +from tensorflow.python.ops.distributions import special_math from tensorflow.python.platform import googletest @@ -87,15 +90,52 @@ class RandomOpsTest(XLATestCase): self._testRngIsNotConstant(rng, dtypes.float32) def testTruncatedNormalIsInRange(self): - count = 10000 + count = 10000000 # TODO(b/34339814): implement inverse erf support for non-F32 types. for dtype in [dtypes.float32]: with self.test_session() as sess: with self.test_scope(): x = random_ops.truncated_normal(shape=[count], dtype=dtype, seed=42) y = sess.run(x) - self.assertTrue((y >= -2).sum() == count) - self.assertTrue((y <= 2).sum() == count) + + def normal_cdf(x): + return .5 * math.erfc(-x / math.sqrt(2)) + + def normal_pdf(x): + return math.exp(-(x**2) / 2.) / math.sqrt(2 * math.pi) + + def probit(x, sess=sess): + return sess.run(special_math.ndtri(x)) + + a = -2. + b = 2. + mu = 0. + sigma = 1. + + alpha = (a - mu) / sigma + beta = (b - mu) / sigma + z = normal_cdf(beta) - normal_cdf(alpha) + + self.assertTrue((y >= a).sum() == count) + self.assertTrue((y <= b).sum() == count) + + # For more information on these calculations, see: + # Burkardt, John. "The Truncated Normal Distribution". + # Department of Scientific Computing website. Florida State University. + expected_mean = mu + (normal_pdf(alpha) - normal_pdf(beta)) / z * sigma + actual_mean = np.mean(y) + self.assertAllClose(actual_mean, expected_mean, atol=3e-4) + + expected_median = mu + probit( + (normal_cdf(alpha) + normal_cdf(beta)) / 2.) * sigma + actual_median = np.median(y) + self.assertAllClose(actual_median, expected_median, atol=8e-4) + + expected_variance = sigma**2 * (1 + ( + (alpha * normal_pdf(alpha) - beta * normal_pdf(beta)) / z) - ( + (normal_pdf(alpha) - normal_pdf(beta)) / z)**2) + actual_variance = np.var(y) + self.assertAllClose(actual_variance, expected_variance, rtol=3e-4) def testShuffle1d(self): with self.test_session() as sess: diff --git a/tensorflow/compiler/tf2xla/kernels/random_ops.cc b/tensorflow/compiler/tf2xla/kernels/random_ops.cc index 105be38fe2..a08654b12b 100644 --- a/tensorflow/compiler/tf2xla/kernels/random_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/random_ops.cc @@ -205,14 +205,9 @@ class TruncatedNormalOp : public XlaOpKernel { xla::XlaBuilder* b = ctx->builder(); - auto two_sd = [dtype](bool negate, xla::XlaBuilder* b) { - return XlaHelpers::FloatLiteral(b, dtype, negate ? -2.0 : 2.0); - }; - auto out_of_range_mask = [two_sd](xla::XlaOp candidate, - xla::XlaBuilder* b) { - xla::XlaOp too_large = b->Gt(candidate, two_sd(false, b)); - xla::XlaOp too_small = b->Lt(candidate, two_sd(true, b)); - return b->Or(too_large, too_small); + auto out_of_range_mask = [dtype](xla::XlaOp candidate, xla::XlaBuilder* b) { + xla::XlaOp two_sd = XlaHelpers::FloatLiteral(b, dtype, 2.0); + return b->Gt(b->Abs(candidate), two_sd); }; // The algorithm we're using is roughly: -- GitLab From 07359dda7ff03d8a7b0d62f75e6c93fb22151a18 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 18 Jun 2018 12:36:14 -0700 Subject: [PATCH 603/816] fix ReadTensor not reading the full contents of reader PiperOrigin-RevId: 201040414 --- tensorflow/go/tensor.go | 6 +---- tensorflow/go/tensor_test.go | 49 ++++++++++++++++++++++++++++++++++++ 2 files changed, 50 insertions(+), 5 deletions(-) diff --git a/tensorflow/go/tensor.go b/tensorflow/go/tensor.go index 2d25c04dc9..f3338f6595 100644 --- a/tensorflow/go/tensor.go +++ b/tensorflow/go/tensor.go @@ -131,13 +131,9 @@ func ReadTensor(dataType DataType, shape []int64, r io.Reader) (*Tensor, error) } runtime.SetFinalizer(t, (*Tensor).finalize) raw := tensorData(t.c) - n, err := r.Read(raw) - if err != nil { + if _, err := io.ReadFull(r, raw); err != nil { return nil, err } - if uintptr(n) != nbytes { - return nil, fmt.Errorf("expected serialized tensor to be %v bytes, read %v", nbytes, n) - } return t, nil } diff --git a/tensorflow/go/tensor_test.go b/tensorflow/go/tensor_test.go index 793c36dd4d..dc533cd3e1 100644 --- a/tensorflow/go/tensor_test.go +++ b/tensorflow/go/tensor_test.go @@ -18,6 +18,7 @@ package tensorflow import ( "bytes" + "io" "reflect" "testing" ) @@ -226,6 +227,54 @@ func TestTensorSerializationErrors(t *testing.T) { } } +func TestReadTensorReadAll(t *testing.T) { + // Get the bytes of a tensor. + a := []float32{1.1, 1.2, 1.3} + ats, err := NewTensor(a) + if err != nil { + t.Fatal(err) + } + abuf := new(bytes.Buffer) + if _, err := ats.WriteContentsTo(abuf); err != nil { + t.Fatal(err) + } + + // Get the bytes of another tensor. + b := []float32{1.1, 1.2, 1.3} + bts, err := NewTensor(b) + if err != nil { + t.Fatal(err) + } + bbuf := new(bytes.Buffer) + if _, err := bts.WriteContentsTo(bbuf); err != nil { + t.Fatal(err) + } + + // Check that ReadTensor reads all bytes of both tensors, when the situation + // requires one than reads. + abbuf := io.MultiReader(abuf, bbuf) + abts, err := ReadTensor(Float, []int64{2, 3}, abbuf) + if err != nil { + t.Fatal(err) + } + abtsf32 := abts.Value().([][]float32) + expected := [][]float32{a, b} + + if len(abtsf32) != 2 { + t.Fatalf("first dimension %d is not 2", len(abtsf32)) + } + for i := 0; i < 2; i++ { + if len(abtsf32[i]) != 3 { + t.Fatalf("second dimension %d is not 3", len(abtsf32[i])) + } + for j := 0; j < 3; j++ { + if abtsf32[i][j] != expected[i][j] { + t.Errorf("value at %d %d not equal %f %f", i, j, abtsf32[i][j], expected[i][j]) + } + } + } +} + func benchmarkNewTensor(b *testing.B, v interface{}) { for i := 0; i < b.N; i++ { if t, err := NewTensor(v); err != nil || t == nil { -- GitLab From 33e5fac1a13d358e997c2e75ddb55cfe8610e9c3 Mon Sep 17 00:00:00 2001 From: Yash Katariya Date: Mon, 18 Jun 2018 19:40:05 +0000 Subject: [PATCH 604/816] Made the changes requested --- .../NMT_with_Attention.ipynb | 81 ++++++++++--------- 1 file changed, 45 insertions(+), 36 deletions(-) diff --git a/tensorflow/contrib/eager/python/examples/nmt_with_attention/NMT_with_Attention.ipynb b/tensorflow/contrib/eager/python/examples/nmt_with_attention/NMT_with_Attention.ipynb index a616a67956..5983b04da2 100644 --- a/tensorflow/contrib/eager/python/examples/nmt_with_attention/NMT_with_Attention.ipynb +++ b/tensorflow/contrib/eager/python/examples/nmt_with_attention/NMT_with_Attention.ipynb @@ -3,7 +3,7 @@ "nbformat_minor": 0, "metadata": { "colab": { - "name": "NMT with Attention.ipynb", + "name": "NMT_with_Attention.ipynb", "version": "0.3.2", "views": {}, "default_view": {}, @@ -209,7 +209,7 @@ }, "cell_type": "code", "source": [ - "# first we remove the pronumciations\n", + "# first we remove the pronunciations\n", "# second we clean the sentences\n", "# and third we return word pairs in [ENGLISH, SPANISH] format\n", "def create_dataset(path, num_examples):\n", @@ -251,9 +251,12 @@ " self.vocab.update(phrase.split(' '))\n", " \n", " self.vocab = sorted(self.vocab)\n", - "\n", + " \n", + " self.word2idx[''] = 0\n", " for index, word in enumerate(self.vocab):\n", - " self.word2idx[word] = index\n", + " self.word2idx[word] = index + 1\n", + " \n", + " for word, index in self.word2idx.items():\n", " self.idx2word[index] = word" ], "execution_count": 0, @@ -404,8 +407,8 @@ "BATCH_SIZE = 64\n", "embedding_dim = 256\n", "units = 1024\n", - "vocab_inp_size = len(inp_lang.vocab)\n", - "vocab_tar_size = len(targ_lang.vocab)" + "vocab_inp_size = len(inp_lang.word2idx)\n", + "vocab_tar_size = len(targ_lang.word2idx)" ], "execution_count": 0, "outputs": [] @@ -471,6 +474,37 @@ " " ] }, + { + "metadata": { + "id": "avyJ_4VIUoHb", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "def gru(units):\n", + " # If you have a GPU, we recommend using CuDNNGRU(provides a 3x speedup than GRU)\n", + " # the code automatically does that.\n", + " if tf.test.is_gpu_available():\n", + " return tf.keras.layers.CuDNNGRU(units, \n", + " return_sequences=True, \n", + " return_state=True, \n", + " recurrent_initializer='glorot_uniform')\n", + " else:\n", + " return tf.keras.layers.GRU(units, \n", + " return_sequences=True, \n", + " return_state=True, \n", + " recurrent_activation='sigmoid', \n", + " recurrent_initializer='glorot_uniform')" + ], + "execution_count": 0, + "outputs": [] + }, { "metadata": { "id": "nZ2rI24i3jFg", @@ -490,21 +524,8 @@ " self.batch_sz = batch_sz\n", " self.enc_units = enc_units\n", " self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)\n", + " self.gru = gru(self.enc_units)\n", " \n", - " # If you have a GPU, we recommend using CuDNNGRU(provides a 3x speedup than GRU)\n", - " # the code automatically does that.\n", - " if tf.test.is_gpu_available():\n", - " self.gru = tf.keras.layers.CuDNNGRU(self.enc_units, \n", - " return_sequences=True, \n", - " return_state=True, \n", - " recurrent_initializer='glorot_uniform')\n", - " else:\n", - " self.gru = tf.keras.layers.GRU(self.enc_units, \n", - " return_sequences=True, \n", - " return_state=True, \n", - " recurrent_activation='sigmoid', \n", - " recurrent_initializer='glorot_uniform')\n", - "\n", " def call(self, x, hidden):\n", " x = self.embedding(x)\n", " output, state = self.gru(x, initial_state = hidden) \n", @@ -535,21 +556,7 @@ " self.batch_sz = batch_sz\n", " self.dec_units = dec_units\n", " self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)\n", - " \n", - " # If you have a GPU, we recommend using CuDNNGRU(provides a 3x speedup than GRU)\n", - " # the code automatically does that.\n", - " if tf.test.is_gpu_available():\n", - " self.gru = tf.keras.layers.CuDNNGRU(self.dec_units, \n", - " return_sequences=True,\n", - " return_state=True, \n", - " recurrent_initializer='glorot_uniform')\n", - " else:\n", - " self.gru = tf.keras.layers.GRU(self.dec_units, \n", - " return_sequences=True,\n", - " return_state=True, \n", - " recurrent_activation='sigmoid', \n", - " recurrent_initializer='glorot_uniform')\n", - " \n", + " self.gru = gru(self.dec_units)\n", " self.fc = tf.keras.layers.Dense(vocab_size)\n", " \n", " # used for attention\n", @@ -660,7 +667,9 @@ "cell_type": "code", "source": [ "def loss_function(real, pred):\n", - " return tf.losses.sparse_softmax_cross_entropy(labels=real, logits=pred)" + " mask = 1 - np.equal(real, 0)\n", + " loss_ = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=real, logits=pred) * mask\n", + " return tf.reduce_mean(loss_)" ], "execution_count": 0, "outputs": [] -- GitLab From 75b99747801cba87362c6943d0254f3638a3f1d4 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 18 Jun 2018 12:41:40 -0700 Subject: [PATCH 605/816] Have TensorFlow use latest version of nsync. There is no significant change for popular platforms, and most users will not notice. Some unpopular platforms have better support for atomics. PiperOrigin-RevId: 201040944 --- tensorflow/contrib/cmake/external/nsync.cmake | 2 +- tensorflow/workspace.bzl | 8 ++++---- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/tensorflow/contrib/cmake/external/nsync.cmake b/tensorflow/contrib/cmake/external/nsync.cmake index b9d1dd88d4..6d50a4956b 100644 --- a/tensorflow/contrib/cmake/external/nsync.cmake +++ b/tensorflow/contrib/cmake/external/nsync.cmake @@ -16,7 +16,7 @@ include (ExternalProject) set(nsync_INCLUDE_DIR ${CMAKE_CURRENT_BINARY_DIR}/external/nsync/public) set(nsync_URL https://github.com/google/nsync) -set(nsync_TAG 0559ce013feac8db639ee1bf776aca0325d28777) +set(nsync_TAG 5e8b19a81e5729922629dd505daa651f6ffdf107) set(nsync_BUILD ${CMAKE_CURRENT_BINARY_DIR}/nsync/src/nsync) set(nsync_INSTALL ${CMAKE_CURRENT_BINARY_DIR}/nsync/install) diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index dbec66216a..161d1dbd06 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -363,11 +363,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "nsync", urls = [ - "https://mirror.bazel.build/github.com/google/nsync/archive/0559ce013feac8db639ee1bf776aca0325d28777.tar.gz", - "https://github.com/google/nsync/archive/0559ce013feac8db639ee1bf776aca0325d28777.tar.gz", + "https://mirror.bazel.build/github.com/google/nsync/archive/5e8b19a81e5729922629dd505daa651f6ffdf107.tar.gz", + "https://github.com/google/nsync/archive/5e8b19a81e5729922629dd505daa651f6ffdf107.tar.gz", ], - sha256 = "6284454c5cd8b1dae2eeb8cf5eb63004de930b5427ed5f6b1aa793513df6b361", - strip_prefix = "nsync-0559ce013feac8db639ee1bf776aca0325d28777", + sha256 = "2723e6db509779fcf05bd01556e51f2e5179197e2c864cd8010f6b7100a5b1e1", + strip_prefix = "nsync-5e8b19a81e5729922629dd505daa651f6ffdf107", ) tf_http_archive( -- GitLab From ce74f7362ee5161976f7c30777b88637be1d02b5 Mon Sep 17 00:00:00 2001 From: Yash Katariya Date: Mon, 18 Jun 2018 19:59:45 +0000 Subject: [PATCH 606/816] Added colab links --- .../nmt_with_attention/NMT_with_Attention.ipynb | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/tensorflow/contrib/eager/python/examples/nmt_with_attention/NMT_with_Attention.ipynb b/tensorflow/contrib/eager/python/examples/nmt_with_attention/NMT_with_Attention.ipynb index 5983b04da2..e23f9e719b 100644 --- a/tensorflow/contrib/eager/python/examples/nmt_with_attention/NMT_with_Attention.ipynb +++ b/tensorflow/contrib/eager/python/examples/nmt_with_attention/NMT_with_Attention.ipynb @@ -40,6 +40,20 @@ "\n", "# Neural Machine Translation with Attention\n", "\n", + "
\n", + "\n", + " Run in Google Colab \n", + "\n", + "View source on Github
" + ] + }, + { + "metadata": { + "id": "CiwtNgENbx2g", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ "This notebook trains a sequence to sequence (seq2seq) model for Spanish to English translation using [tf.keras](https://www.tensorflow.org/programmers_guide/keras) and [eager execution](https://www.tensorflow.org/programmers_guide/eager). This is an advanced example for readers with prior background in sequence to sequence models.\n", "\n", "Here's an example output you'll see after running this notebook. After training the model, we'll translate the Spanish sentence \"¿todavia estan en casa?\", and we'll see the output \"are you still at home ?\". \n", -- GitLab From 2863cd7f72d69cdbb94af7673873d1c83ac91a6a Mon Sep 17 00:00:00 2001 From: Dan Osipov Date: Mon, 18 Jun 2018 16:25:06 -0400 Subject: [PATCH 607/816] Update docstring for accuracy --- tensorflow/python/ops/image_ops_impl.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tensorflow/python/ops/image_ops_impl.py b/tensorflow/python/ops/image_ops_impl.py index e132a00865..2c7751f792 100644 --- a/tensorflow/python/ops/image_ops_impl.py +++ b/tensorflow/python/ops/image_ops_impl.py @@ -1078,7 +1078,8 @@ def resize_image_with_pad(image, target_height, target_width, Resizes an image to a target width and height by keeping the aspect ratio the same without distortion. If the target dimensions don't match the image dimensions, the image - is padded with zeroes prior to resizing. + is resized and then padded with zeroes to match requested + dimensions. Args: image: 4-D Tensor of shape `[batch, height, width, channels]` or -- GitLab From 3d3196f34173e5c6e1f9297e2fcd4c316fe903fd Mon Sep 17 00:00:00 2001 From: Gunhan Gulsoy Date: Mon, 18 Jun 2018 13:29:07 -0700 Subject: [PATCH 608/816] Disable large tests in fastbuild mode. PiperOrigin-RevId: 201048439 --- .../contrib/distributions/python/kernel_tests/util/BUILD | 5 ++++- tensorflow/contrib/recurrent/BUILD | 5 ++++- 2 files changed, 8 insertions(+), 2 deletions(-) diff --git a/tensorflow/contrib/distributions/python/kernel_tests/util/BUILD b/tensorflow/contrib/distributions/python/kernel_tests/util/BUILD index 03e26b198e..42ecea034d 100644 --- a/tensorflow/contrib/distributions/python/kernel_tests/util/BUILD +++ b/tensorflow/contrib/distributions/python/kernel_tests/util/BUILD @@ -34,7 +34,10 @@ py_test( name = "correlation_matrix_volumes_test", size = "medium", srcs = ["correlation_matrix_volumes_test.py"], - tags = ["no_pip"], + tags = [ + "no_pip", + "optonly", + ], deps = [ ":correlation_matrix_volumes_py", # For statistical testing diff --git a/tensorflow/contrib/recurrent/BUILD b/tensorflow/contrib/recurrent/BUILD index b3cb04ce26..f9827f766d 100644 --- a/tensorflow/contrib/recurrent/BUILD +++ b/tensorflow/contrib/recurrent/BUILD @@ -102,5 +102,8 @@ cuda_py_tests( "//tensorflow/python:variable_scope", "//tensorflow/python:variables", ], - tags = ["nopip"], + tags = [ + "nopip", + "optonly", + ], ) -- GitLab From ab251a0ec66a3c8b88ca467e49bfc68d18a2a8e9 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 18 Jun 2018 13:36:36 -0700 Subject: [PATCH 609/816] Enables `If` operator lowering in cond_v2 when XLA is disabled. Lowering allows cond_v2 to avoid some of the limitations of Functions, allowing users to specify devices & colocation inside of cond_v2 branches, and enabling non-strict evaluation & partial pruning of branches. This brings cond_v2 closer to feature parity with tf.cond. However, we do not lower `If` in the XLA context because it is easier for XLA to apply its own optimizations when dealing with un-lowered `If` operators than with lowered switch/merge control flow. Also adds a toggleable flag in for InlineFunctionBody in function.cc that prevents the function caller device from overriding the devices of function body nodes. This is necessary for cond_v2 branches to support explicitly-specified devices. Adds several tests to make sure that: - lowering is usually enabled - lowering is disabled for XLA - node colocation inside of cond_v2 branches works - explicit device placement inside of cond_v2 branches works PiperOrigin-RevId: 201049850 --- tensorflow/core/common_runtime/function.cc | 12 +- tensorflow/core/common_runtime/function.h | 6 +- tensorflow/core/common_runtime/lower_if_op.cc | 2 +- .../python/kernel_tests/cond_v2_test.py | 113 +++++++++++++++++- tensorflow/python/ops/cond_v2_impl.py | 18 +++ 5 files changed, 143 insertions(+), 8 deletions(-) diff --git a/tensorflow/core/common_runtime/function.cc b/tensorflow/core/common_runtime/function.cc index 68d37ddbcd..1200dcc1fe 100644 --- a/tensorflow/core/common_runtime/function.cc +++ b/tensorflow/core/common_runtime/function.cc @@ -1188,11 +1188,13 @@ static bool ValidateInlining(const Node* node, const FunctionBody* fbody) { return true; } -// Given a "caller" in "graph", which is a function call of a function +// Given a "caller" in graph "g", which is a function call of a function // to "fbody". Replaces the "caller" with fbody->graph and connects -// edges properly. +// edges properly. "override_device" specifies whether inlining should replace +// explicitly specified devices inside fbody with the callee's device. void InlineFunctionBody(const FunctionLibraryDefinition& flib_def, Graph* g, - Node* caller, const FunctionBody* fbody) { + Node* caller, const FunctionBody* fbody, + bool override_device) { if (!ValidateInlining(caller, fbody)) { LOG(WARNING) << "Inlining mismatch: " << caller->DebugString() << " vs. " << DebugString(fbody->graph); @@ -1227,7 +1229,9 @@ void InlineFunctionBody(const FunctionLibraryDefinition& flib_def, Graph* g, for (Node* n : fbody->graph->op_nodes()) { NodeDef ndef = n->def(); ndef.set_name(strings::StrCat(caller->name(), "/", ndef.name())); - ndef.set_device(caller->def().device()); + if (override_device || ndef.device().empty()) { + ndef.set_device(caller->def().device()); + } Node* clone = g->AddNode(ndef, &s); TF_CHECK_OK(s); node_map[n->id()] = clone; diff --git a/tensorflow/core/common_runtime/function.h b/tensorflow/core/common_runtime/function.h index a0f9fcae0a..a274f1ef51 100644 --- a/tensorflow/core/common_runtime/function.h +++ b/tensorflow/core/common_runtime/function.h @@ -155,9 +155,11 @@ FunctionBody* SymbolicGradient(const FunctionBody& f); // Given a "caller" in graph "g", which is a function call of a function // to "fbody". Replaces the "caller" with fbody->graph and connects -// edges properly. +// edges properly. "override_device" specifies whether inlining should replace +// explicitly specified devices inside fbody with the callee's device. void InlineFunctionBody(const FunctionLibraryDefinition& flib_def, Graph* g, - Node* caller, const FunctionBody* fbody); + Node* caller, const FunctionBody* fbody, + bool override_device = true); // Instantiates FunctionDef into a graph. Set *fbody to point to the // FunctionBody that holds the instantiated FunctionDef. diff --git a/tensorflow/core/common_runtime/lower_if_op.cc b/tensorflow/core/common_runtime/lower_if_op.cc index 567c81870c..dfce7c23e7 100644 --- a/tensorflow/core/common_runtime/lower_if_op.cc +++ b/tensorflow/core/common_runtime/lower_if_op.cc @@ -206,7 +206,7 @@ Status InlineCallInGraph(Node* n, Graph* g) { &fbody)); // TODO(jpienaar): Improve this interface to make the need to delete it // explicit. - InlineFunctionBody(g->flib_def(), g, n, fbody); + InlineFunctionBody(g->flib_def(), g, n, fbody, false); delete fbody; return Status::OK(); } diff --git a/tensorflow/python/kernel_tests/cond_v2_test.py b/tensorflow/python/kernel_tests/cond_v2_test.py index 76bbd61604..759db5d5f4 100644 --- a/tensorflow/python/kernel_tests/cond_v2_test.py +++ b/tensorflow/python/kernel_tests/cond_v2_test.py @@ -19,6 +19,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from tensorflow.core.protobuf import config_pb2 from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops @@ -100,7 +101,7 @@ class NewCondTest(test.TestCase): self.assertEqual(sess.run(out, {pred: False}), [2.0]) def _createCond(self, name): - pred = array_ops.placeholder(dtypes.bool, name="pred") + pred = constant_op.constant(True, name="pred") x = constant_op.constant(1.0, name="x") def true_fn(): @@ -200,6 +201,65 @@ class NewCondTest(test.TestCase): # d2[x]/dx2 = 0 self.assertEqual(false_val, [0.0]) + def testLowering(self): + with ops.Graph().as_default() as g: + with self.test_session(graph=g) as sess: + out_cond = self._createCond("cond") + + run_options = config_pb2.RunOptions(output_partition_graphs=True) + run_metadata = config_pb2.RunMetadata() + sess.run(out_cond, options=run_options, run_metadata=run_metadata) + + # If lowering was enabled, there should be a `Switch` node + switch_found = any( + any(node.op == "Switch" for node in graph.node) + for graph in run_metadata.partition_graphs + ) + + self.assertTrue(switch_found, + "A `Switch` op should exist if the graph was lowered.") + + # If lowering was enabled, there should be no `If` node + if_found = any( + any(node.op == "If" for node in graph.node) + for graph in run_metadata.partition_graphs + ) + + self.assertFalse(if_found, + "An `If` op was found, but it should be lowered.") + + def testLoweringDisabledInXLA(self): + with self.test_session(graph=ops.Graph()) as sess: + # Build the cond_v2 in an XLA context + xla_context = control_flow_ops.XLAControlFlowContext() + xla_context.Enter() + out_cond = self._createCond("cond") + xla_context.Exit() + + run_options = config_pb2.RunOptions(output_partition_graphs=True) + run_metadata = config_pb2.RunMetadata() + sess.run(out_cond, options=run_options, run_metadata=run_metadata) + + # Lowering disabled in XLA, there should be no `Switch` node + switch_found = any( + any(node.op == "Switch" for node in graph.node) + for graph in run_metadata.partition_graphs + ) + + self.assertFalse( + switch_found, + "A `Switch` op exists, but the graph should not be lowered.") + + # Lowering disabled in XLA, there should still be an `If` node + if_found = any( + any(node.op == "If" for node in graph.node) + for graph in run_metadata.partition_graphs + ) + + self.assertTrue( + if_found, + "An `If` op was not found, but the graph should not be lowered.") + class CondV2CollectionTest(test.TestCase): @@ -387,6 +447,34 @@ class CondV2ColocationGroupAndDeviceTest(test.TestCase): d = constant_op.constant([2.0], name="d") self.assertEqual([b"loc:@a"], d.op.colocation_groups()) + def testColocateWithInCondGraphPartitioning(self): + with ops.Graph().as_default() as g: + with self.test_session( + graph=g, + config=config_pb2.ConfigProto(device_count={"CPU": 2}) + ) as sess: + + with ops.device("/device:CPU:0"): + a = constant_op.constant([2.0], name="a") + with ops.device("/device:CPU:1"): + b = constant_op.constant([2.0], name="b") + + def fn(): + with ops.colocate_with(b.op): + c = math_ops.add(a, a, name="c") + return c + out_cond_2 = cond_v2.cond_v2(True, fn, fn)[0] + + run_options = config_pb2.RunOptions(output_partition_graphs=True) + run_metadata = config_pb2.RunMetadata() + sess.run(out_cond_2, options=run_options, run_metadata=run_metadata) + + # We expect there to be two partitions because of the + # colocate_with. We are only running the cond, which has a data + # dependency on `a` but not on `b`. So, without the colocate_with + # we would expect execution on just one device. + self.assertTrue(len(run_metadata.partition_graphs) >= 2) + def testDeviceBeforeCond(self): with ops.Graph().as_default() as g: with self.test_session(graph=g): @@ -421,5 +509,28 @@ class CondV2ColocationGroupAndDeviceTest(test.TestCase): d = constant_op.constant(4.0) self.assertEqual("/device:CPU:0", d.op.device) + def testDeviceInCondGraphPartitioning(self): + with ops.Graph().as_default() as g: + with self.test_session( + graph=g, + config=config_pb2.ConfigProto(device_count={"CPU": 2}) + ) as sess: + + def fn(): + with ops.device("/device:CPU:1"): + c = math_ops.add(a, a, name="c") + return c + + with ops.device("/device:CPU:0"): + a = constant_op.constant([2.0], name="a") + out_cond_2 = cond_v2.cond_v2(True, fn, fn)[0] + + run_options = config_pb2.RunOptions(output_partition_graphs=True) + run_metadata = config_pb2.RunMetadata() + sess.run(out_cond_2, options=run_options, run_metadata=run_metadata) + + self.assertTrue(len(run_metadata.partition_graphs) >= 2) + + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/ops/cond_v2_impl.py b/tensorflow/python/ops/cond_v2_impl.py index d827df7742..d310f83dca 100644 --- a/tensorflow/python/ops/cond_v2_impl.py +++ b/tensorflow/python/ops/cond_v2_impl.py @@ -27,10 +27,12 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from tensorflow.core.framework import attr_value_pb2 from tensorflow.python import pywrap_tensorflow as c_api from tensorflow.python.framework import c_api_util from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops +from tensorflow.python.ops import control_flow_util from tensorflow.python.ops import gen_functional_ops from tensorflow.python.util import compat @@ -110,6 +112,22 @@ def cond_v2(pred, true_fn, false_fn, name="cond"): _create_new_tf_function(false_graph), name=scope) + # Set the flag to enable lowering on the `if` op if necessary + # Lowering allows cond_v2 to avoid some of the limitations of Functions, + # allowing users to specify devices & colocation inside of cond_v2 branches, + # and enabling non-strict evaluation & partial pruning of cond_v2 branches. + # This brings cond_v2 closer to feature parity with tf.cond. + # + # However, we do not lower `If` in the XLA context because it is easier for + # XLA to apply its own optimizations when dealing with un-lowered `If` + # operators than with lowered switch/merge control flow. + # + # TODO(b/110167197) this approach requires cond_v2 to have at least 1 output + if_op = tensors[0].op + if not control_flow_util.IsInXLAContext(if_op): + if_op._set_attr("_lower_using_switch_merge", + attr_value_pb2.AttrValue(b=True)) + return tensors[:num_cond_outputs] -- GitLab From 1d118e769486a7f2a093d1cdcf828dd37c00667a Mon Sep 17 00:00:00 2001 From: Benjamin Kramer Date: Mon, 18 Jun 2018 13:38:26 -0700 Subject: [PATCH 610/816] [XLA:GPU] Un-unimplement gather emission We already have elemental code for doing this in the fused case, this just enables it in the unfused case. PiperOrigin-RevId: 201050143 --- tensorflow/compiler/xla/service/gpu/BUILD | 2 -- tensorflow/compiler/xla/service/gpu/gpu_compiler.cc | 4 ---- tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc | 5 ----- tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h | 1 - 4 files changed, 12 deletions(-) diff --git a/tensorflow/compiler/xla/service/gpu/BUILD b/tensorflow/compiler/xla/service/gpu/BUILD index 541a5275a3..af6d298589 100644 --- a/tensorflow/compiler/xla/service/gpu/BUILD +++ b/tensorflow/compiler/xla/service/gpu/BUILD @@ -583,7 +583,6 @@ cc_library( "//tensorflow/compiler/xla/service:dot_decomposer", "//tensorflow/compiler/xla/service:executable", "//tensorflow/compiler/xla/service:flatten_call_graph", - "//tensorflow/compiler/xla/service:gather_expander", "//tensorflow/compiler/xla/service:hlo", "//tensorflow/compiler/xla/service:hlo_constant_folding", "//tensorflow/compiler/xla/service:hlo_cse", @@ -613,7 +612,6 @@ cc_library( "//tensorflow/core:regexp_internal", "//tensorflow/core:stream_executor_no_cuda", "@llvm//:core", - "@llvm//:support", ], alwayslink = True, # Contains compiler registration ) diff --git a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc index 9d66648a40..a040e6b681 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc +++ b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc @@ -36,7 +36,6 @@ limitations under the License. #include "tensorflow/compiler/xla/service/conditional_simplifier.h" #include "tensorflow/compiler/xla/service/dot_decomposer.h" #include "tensorflow/compiler/xla/service/flatten_call_graph.h" -#include "tensorflow/compiler/xla/service/gather_expander.h" #include "tensorflow/compiler/xla/service/gpu/cudnn_batchnorm_rewriter.h" #include "tensorflow/compiler/xla/service/gpu/cudnn_convolution_algorithm_picker.h" #include "tensorflow/compiler/xla/service/gpu/cudnn_convolution_rewriter.h" @@ -165,9 +164,6 @@ Status OptimizeHloModule(HloModule* hlo_module, se::StreamExecutor* stream_exec, /*rewrite_inference_op=*/true, /*rewrite_grad_op=*/true); - // Rewrite gather ops into smaller ones. - pass.AddPass(); - // BatchNormExpander can create zero-sized ops, so zero-sized HLO // elimination has to come after that pass. pipeline.AddPass(); diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc index 71e0562e40..4a013a7f53 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc @@ -2334,11 +2334,6 @@ GetHloBufferSlices(const HloInstruction* hlo, return slices; } -Status IrEmitterUnnested::HandleGather(HloInstruction* gather) { - // TODO(b/72710576): Gather is not implemented on GPUs - return Unimplemented("Gather is not implemented on GPUs."); -} - std::unique_ptr IrEmitterUnnested::BuildKernelThunk( const HloInstruction* inst, int unroll_factor) { const BufferAssignment& buffer_assn = diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h index d228be81d4..279a5c386a 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h @@ -67,7 +67,6 @@ class IrEmitterUnnested : public IrEmitter { Status HandleDot(HloInstruction* dot) override; Status HandleFft(HloInstruction* fft) override; Status HandleFusion(HloInstruction* fusion) override; - Status HandleGather(HloInstruction* gather) override; Status HandleGetTupleElement(HloInstruction* get_tuple_element) override; Status HandleReduce(HloInstruction* reduce) override; Status HandleSelectAndScatter(HloInstruction* instruction) override; -- GitLab From b0a1fb804240d8454f4af66d74df7e1a46f4db8a Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 18 Jun 2018 13:51:08 -0700 Subject: [PATCH 611/816] Migrate to android skylark rules PiperOrigin-RevId: 201052263 --- tensorflow/contrib/android/BUILD | 2 ++ tensorflow/contrib/lite/examples/android/BUILD | 2 ++ tensorflow/contrib/lite/java/demo/app/src/main/BUILD | 2 ++ tensorflow/contrib/lite/java/ovic/BUILD | 2 ++ tensorflow/contrib/lite/java/ovic/demo/app/BUILD | 2 ++ .../lite/java/src/testhelper/java/org/tensorflow/lite/BUILD | 2 ++ .../contrib/lite/models/smartreply/demo/app/src/main/BUILD | 2 ++ tensorflow/examples/android/BUILD | 2 ++ 8 files changed, 16 insertions(+) diff --git a/tensorflow/contrib/android/BUILD b/tensorflow/contrib/android/BUILD index c10179ba8b..f0b1c92cf7 100644 --- a/tensorflow/contrib/android/BUILD +++ b/tensorflow/contrib/android/BUILD @@ -1,6 +1,8 @@ # Description: # JNI-based Java inference interface for TensorFlow. +load("@build_bazel_rules_android//android:rules.bzl", "android_library") + package(default_visibility = ["//visibility:public"]) licenses(["notice"]) # Apache 2.0 diff --git a/tensorflow/contrib/lite/examples/android/BUILD b/tensorflow/contrib/lite/examples/android/BUILD index 5700007256..3e3b4db7d3 100644 --- a/tensorflow/contrib/lite/examples/android/BUILD +++ b/tensorflow/contrib/lite/examples/android/BUILD @@ -1,6 +1,8 @@ # Description: # TensorFlow camera demo app for Android. +load("@build_bazel_rules_android//android:rules.bzl", "android_binary") + package(default_visibility = ["//visibility:public"]) licenses(["notice"]) # Apache 2.0 diff --git a/tensorflow/contrib/lite/java/demo/app/src/main/BUILD b/tensorflow/contrib/lite/java/demo/app/src/main/BUILD index d6fbef9cc9..220d6c2159 100644 --- a/tensorflow/contrib/lite/java/demo/app/src/main/BUILD +++ b/tensorflow/contrib/lite/java/demo/app/src/main/BUILD @@ -1,3 +1,5 @@ +load("@build_bazel_rules_android//android:rules.bzl", "android_binary") + package(default_visibility = ["//visibility:private"]) licenses(["notice"]) # Apache 2.0 diff --git a/tensorflow/contrib/lite/java/ovic/BUILD b/tensorflow/contrib/lite/java/ovic/BUILD index 362d93636f..f232b00045 100644 --- a/tensorflow/contrib/lite/java/ovic/BUILD +++ b/tensorflow/contrib/lite/java/ovic/BUILD @@ -1,6 +1,8 @@ # Description: # OVIC Benchmarker Java API. +load("@build_bazel_rules_android//android:rules.bzl", "android_library") + package(default_visibility = ["//visibility:public"]) licenses(["notice"]) # Apache 2.0 diff --git a/tensorflow/contrib/lite/java/ovic/demo/app/BUILD b/tensorflow/contrib/lite/java/ovic/demo/app/BUILD index 83974f4b33..a8d751ade2 100644 --- a/tensorflow/contrib/lite/java/ovic/demo/app/BUILD +++ b/tensorflow/contrib/lite/java/ovic/demo/app/BUILD @@ -1,3 +1,5 @@ +load("@build_bazel_rules_android//android:rules.bzl", "android_binary") + # Sample app for OVIC benchmarking. licenses(["notice"]) # Apache 2.0 diff --git a/tensorflow/contrib/lite/java/src/testhelper/java/org/tensorflow/lite/BUILD b/tensorflow/contrib/lite/java/src/testhelper/java/org/tensorflow/lite/BUILD index b524246d43..af1d99ef41 100644 --- a/tensorflow/contrib/lite/java/src/testhelper/java/org/tensorflow/lite/BUILD +++ b/tensorflow/contrib/lite/java/src/testhelper/java/org/tensorflow/lite/BUILD @@ -1,6 +1,8 @@ # Description: # Internal helper function to test TF Lite API. +load("@build_bazel_rules_android//android:rules.bzl", "android_library") + package(default_visibility = ["//visibility:public"]) licenses(["notice"]) # Apache 2.0 diff --git a/tensorflow/contrib/lite/models/smartreply/demo/app/src/main/BUILD b/tensorflow/contrib/lite/models/smartreply/demo/app/src/main/BUILD index f8767b443a..f18a2ca07a 100644 --- a/tensorflow/contrib/lite/models/smartreply/demo/app/src/main/BUILD +++ b/tensorflow/contrib/lite/models/smartreply/demo/app/src/main/BUILD @@ -1,3 +1,5 @@ +load("@build_bazel_rules_android//android:rules.bzl", "android_binary") + package(default_visibility = ["//visibility:public"]) licenses(["notice"]) # Apache 2.0 diff --git a/tensorflow/examples/android/BUILD b/tensorflow/examples/android/BUILD index 07f096418f..f327b645f5 100644 --- a/tensorflow/examples/android/BUILD +++ b/tensorflow/examples/android/BUILD @@ -1,6 +1,8 @@ # Description: # TensorFlow camera demo app for Android. +load("@build_bazel_rules_android//android:rules.bzl", "android_binary") + package(default_visibility = ["//visibility:public"]) licenses(["notice"]) # Apache 2.0 -- GitLab From 586d2d510eb5722464911a38b4f22b4b344d8689 Mon Sep 17 00:00:00 2001 From: Dan Moldovan Date: Mon, 18 Jun 2018 13:55:36 -0700 Subject: [PATCH 612/816] Broad refactoring (part 3): reorganize the code so that the dependency graph is cleaner and better separates AutoGraph logic from the general purpose SCT code (concentrated in pyct). The new module structure is described in CONTRIBUTING.md. Summary of changes: * the new lang and core modules now replace their old counterparts * CONTRIBUTING.md now has a short paragraph on developer info * the lang APIs are exposed into the main autograph interface * the old implementations for converter_test_base.py, config.py, directives.py, naming.py, special_functions.py and their tests are now removed * all converters now inherit converter.Base instead of transformer.Base * all converter tests now inherit converter_testing.TestCase instead of converter_test_base.TestCase * converter interfaces now all share a common signature: .transform(node, context) * the decorator module now actually imports dependencies requires for existing decorators, which was previously just a TODO * decorator_test now runs an additional test that was previously disabled * the implementation of conversion.node_to_graph is now simpler and more consistent; ConversionMap is removed * type_info.py now creates a separate "definition" annotation for all symbols * transformer.py no longer has any mention to AutoGraph specific implementations * other no-op code simplifications, doc and comment updates PiperOrigin-RevId: 201053048 --- tensorflow/contrib/autograph/BUILD | 4 +- tensorflow/contrib/autograph/CONTRIBUTING.md | 49 ++++- tensorflow/contrib/autograph/__init__.py | 8 +- tensorflow/contrib/autograph/converters/BUILD | 64 +++--- .../contrib/autograph/converters/asserts.py | 8 +- .../autograph/converters/asserts_test.py | 4 +- .../autograph/converters/break_statements.py | 12 +- .../converters/break_statements_test.py | 4 +- .../autograph/converters/builtin_functions.py | 8 +- .../converters/builtin_functions_test.py | 4 +- .../autograph/converters/call_trees.py | 53 +++-- .../autograph/converters/call_trees_test.py | 30 +-- .../converters/continue_statements.py | 10 +- .../converters/continue_statements_test.py | 4 +- .../autograph/converters/control_flow.py | 36 ++-- .../autograph/converters/control_flow_test.py | 4 +- .../converters/converter_test_base.py | 136 ------------ .../autograph/converters/decorators.py | 75 ++++--- .../autograph/converters/decorators_test.py | 72 ++++--- .../contrib/autograph/converters/ifexp.py | 12 +- .../autograph/converters/ifexp_test.py | 4 +- .../converters/list_comprehension.py | 11 +- .../converters/list_comprehension_test.py | 4 +- .../contrib/autograph/converters/lists.py | 10 +- .../autograph/converters/lists_test.py | 4 +- .../converters/logical_expressions.py | 12 +- .../converters/logical_expressions_test.py | 4 +- .../autograph/converters/name_scopes.py | 8 +- .../autograph/converters/name_scopes_test.py | 4 +- .../converters/side_effect_guards.py | 17 +- .../converters/side_effect_guards_test.py | 4 +- .../autograph/converters/single_return.py | 28 +-- .../converters/single_return_test.py | 4 +- .../contrib/autograph/converters/slices.py | 8 +- .../autograph/converters/slices_test.py | 4 +- .../contrib/autograph/core/converter.py | 29 ++- .../autograph/core/converter_testing.py | 2 +- tensorflow/contrib/autograph/impl/BUILD | 27 +-- tensorflow/contrib/autograph/impl/api.py | 35 ++- tensorflow/contrib/autograph/impl/api_test.py | 2 +- tensorflow/contrib/autograph/impl/config.py | 49 ----- .../contrib/autograph/impl/conversion.py | 204 +++++------------- .../contrib/autograph/impl/conversion_test.py | 78 +++---- .../contrib/autograph/impl/directives.py | 68 ------ tensorflow/contrib/autograph/impl/naming.py | 130 ----------- .../contrib/autograph/impl/naming_test.py | 77 ------- .../autograph/impl/special_functions.py | 48 ----- .../autograph/impl/special_functions_test.py | 50 ----- tensorflow/contrib/autograph/operators/BUILD | 8 + tensorflow/contrib/autograph/pyct/BUILD | 3 +- tensorflow/contrib/autograph/pyct/context.py | 49 ----- .../autograph/pyct/static_analysis/BUILD | 1 + .../pyct/static_analysis/activity_test.py | 12 +- .../autograph/pyct/static_analysis/cfg.py | 25 +-- .../pyct/static_analysis/cfg_test.py | 29 ++- .../pyct/static_analysis/live_values.py | 10 +- .../pyct/static_analysis/live_values_test.py | 17 +- .../pyct/static_analysis/type_info.py | 55 ++--- .../pyct/static_analysis/type_info_test.py | 68 +----- .../contrib/autograph/pyct/transformer.py | 57 +++-- .../autograph/pyct/transformer_test.py | 17 +- tensorflow/tools/pip_package/BUILD | 2 +- 62 files changed, 606 insertions(+), 1269 deletions(-) delete mode 100644 tensorflow/contrib/autograph/converters/converter_test_base.py delete mode 100644 tensorflow/contrib/autograph/impl/config.py delete mode 100644 tensorflow/contrib/autograph/impl/directives.py delete mode 100644 tensorflow/contrib/autograph/impl/naming.py delete mode 100644 tensorflow/contrib/autograph/impl/naming_test.py delete mode 100644 tensorflow/contrib/autograph/impl/special_functions.py delete mode 100644 tensorflow/contrib/autograph/impl/special_functions_test.py delete mode 100644 tensorflow/contrib/autograph/pyct/context.py diff --git a/tensorflow/contrib/autograph/BUILD b/tensorflow/contrib/autograph/BUILD index 30dd846893..ad700ac4a0 100644 --- a/tensorflow/contrib/autograph/BUILD +++ b/tensorflow/contrib/autograph/BUILD @@ -23,9 +23,9 @@ py_library( visibility = ["//visibility:public"], deps = [ "//tensorflow/contrib/autograph/impl", + "//tensorflow/contrib/autograph/lang", "//tensorflow/contrib/autograph/pyct", "//tensorflow/contrib/autograph/utils", - "@gast_archive//:gast", - "@six_archive//:six", + "//tensorflow/python:util", ], ) diff --git a/tensorflow/contrib/autograph/CONTRIBUTING.md b/tensorflow/contrib/autograph/CONTRIBUTING.md index a4aec8c74a..06fb7b03d5 100644 --- a/tensorflow/contrib/autograph/CONTRIBUTING.md +++ b/tensorflow/contrib/autograph/CONTRIBUTING.md @@ -1,4 +1,4 @@ -# How to Contribute +# How to contribute We'd love to have your patches and contributions! Here are some guidelines. In general, we follow the [TensorFlow contributing guidelines](../../CONTRIBUTING.md), but have some [AutoGraph-specific style guidelines](STYLE_GUIDE.md). More details below. @@ -46,3 +46,50 @@ bazel test --config=opt --copt=-O3 --copt=-march=native \ ``` from the root of the `tensorflow` repository. For more details see the [main TensorFlow Contributing File](../../CONTRIBUTING.md) + +## Developer info + +### Module structure + +The graph below describes the dependencies between AutoGraph modules (not to be mistaken with the directory structure for these modules, which is flat): + +```dot +digraph d_modules { + autograph [style=filled]; + converters; + core; + impl; + lang; + operators; + + autograph -> impl + autograph -> lang + + impl -> converters + impl -> core + impl -> operators + + lang -> operators + + converters -> core + converters -> lang +} +``` + +`autograph` is the sole user-visible module. + +A short description of the modules: + + * `autograph`: the main module imported by the user and by the generated code; only contains declarations + * `impl`: high level code and the implementation of the api frontend + * `core`: base classes for the AutoGraph source code transformation logic; see in particular `converter.py` + * `lang`: special user-visible functions that serve as extensions to the Python language + * `converters`: collection of source code transformation modules specialized for particular AutoGraph features + * `operators`: collection of operators that AutoGraph overloads; these correspond to Python operators as well as Python syntactic structures, like control flow + +There are two additional modules, `pyct` and `utils`. These are independent of AutoGraph: + + * `pyct`: a general purpose Python source code transformation library + * `utils`: the kitchen sync; deprecated + +Note: we have a long term plan to factor out an implementation of `impl` and `converters` that is independent of autograph, into a general purpose Python operator overloading library. diff --git a/tensorflow/contrib/autograph/__init__.py b/tensorflow/contrib/autograph/__init__.py index 637e49c082..8fd83ef376 100644 --- a/tensorflow/contrib/autograph/__init__.py +++ b/tensorflow/contrib/autograph/__init__.py @@ -29,9 +29,9 @@ from tensorflow.contrib.autograph.impl.api import do_not_convert from tensorflow.contrib.autograph.impl.api import RunMode from tensorflow.contrib.autograph.impl.api import to_code from tensorflow.contrib.autograph.impl.api import to_graph -from tensorflow.contrib.autograph.impl.directives import set_element_type -from tensorflow.contrib.autograph.impl.directives import set_loop_options -from tensorflow.contrib.autograph.impl.special_functions import stack +from tensorflow.contrib.autograph.lang.directives import set_element_type +from tensorflow.contrib.autograph.lang.directives import set_loop_options +from tensorflow.contrib.autograph.lang.special_functions import stack from tensorflow.contrib.autograph.pyct.transformer import AutographParseError from tensorflow.python.util.all_util import remove_undocumented @@ -43,7 +43,7 @@ _allowed_symbols = [ 'do_not_convert', 'to_code', 'to_graph', - # Special functions and directives + # Python language "extensions" 'set_element_type', 'set_loop_options', 'stack', diff --git a/tensorflow/contrib/autograph/converters/BUILD b/tensorflow/contrib/autograph/converters/BUILD index 284ad84be5..94e465066f 100644 --- a/tensorflow/contrib/autograph/converters/BUILD +++ b/tensorflow/contrib/autograph/converters/BUILD @@ -36,25 +36,12 @@ py_library( srcs_version = "PY2AND3", visibility = ["//tensorflow:__subpackages__"], deps = [ - "@gast_archive//:gast", - ], -) - -py_library( - name = "test_lib", - srcs = [ - "converter_test_base.py", - ], - srcs_version = "PY2AND3", - visibility = ["//tensorflow:__subpackages__"], - deps = [ - ":converters", - "//tensorflow/contrib/autograph/operators", + "//tensorflow/contrib/autograph/core", + "//tensorflow/contrib/autograph/lang", "//tensorflow/contrib/autograph/pyct", "//tensorflow/contrib/autograph/pyct/static_analysis", - "//tensorflow/contrib/autograph/utils", + "//tensorflow/python:util", "@gast_archive//:gast", - "@six_archive//:six", ], ) @@ -64,7 +51,8 @@ py_test( srcs_version = "PY2AND3", tags = ["no_windows"], deps = [ - ":test_lib", + ":converters", + "//tensorflow/contrib/autograph/core:test_lib", "//tensorflow/python:client_testlib", ], ) @@ -74,7 +62,8 @@ py_test( srcs = ["break_statements_test.py"], srcs_version = "PY2AND3", deps = [ - ":test_lib", + ":converters", + "//tensorflow/contrib/autograph/core:test_lib", "//tensorflow/python:client_testlib", ], ) @@ -85,7 +74,8 @@ py_test( srcs_version = "PY2AND3", tags = ["no_windows"], deps = [ - ":test_lib", + ":converters", + "//tensorflow/contrib/autograph/core:test_lib", "//tensorflow/python:client_testlib", ], ) @@ -97,7 +87,8 @@ py_test( srcs_version = "PY2AND3", tags = ["no_windows"], deps = [ - ":test_lib", + ":converters", + "//tensorflow/contrib/autograph/core:test_lib", "//tensorflow/contrib/autograph/impl", "//tensorflow/python:client_testlib", ], @@ -108,7 +99,8 @@ py_test( srcs = ["continue_statements_test.py"], srcs_version = "PY2AND3", deps = [ - ":test_lib", + ":converters", + "//tensorflow/contrib/autograph/core:test_lib", "//tensorflow/python:client_testlib", ], ) @@ -118,7 +110,8 @@ py_test( srcs = ["control_flow_test.py"], srcs_version = "PY2AND3", deps = [ - ":test_lib", + ":converters", + "//tensorflow/contrib/autograph/core:test_lib", "//tensorflow/python:client_testlib", ], ) @@ -128,7 +121,8 @@ py_test( srcs = ["decorators_test.py"], srcs_version = "PY2AND3", deps = [ - ":test_lib", + ":converters", + "//tensorflow/contrib/autograph/core:test_lib", "//tensorflow/python:client_testlib", ], ) @@ -137,7 +131,8 @@ py_test( name = "name_scopes_test", srcs = ["name_scopes_test.py"], deps = [ - ":test_lib", + ":converters", + "//tensorflow/contrib/autograph/core:test_lib", "//tensorflow/contrib/autograph/pyct", "//tensorflow/python:client_testlib", ], @@ -148,7 +143,8 @@ py_test( srcs = ["list_comprehension_test.py"], srcs_version = "PY2AND3", deps = [ - ":test_lib", + ":converters", + "//tensorflow/contrib/autograph/core:test_lib", "//tensorflow/python:client_testlib", ], ) @@ -158,7 +154,8 @@ py_test( srcs = ["lists_test.py"], srcs_version = "PY2AND3", deps = [ - ":test_lib", + ":converters", + "//tensorflow/contrib/autograph/core:test_lib", "//tensorflow/python:client_testlib", ], ) @@ -168,7 +165,8 @@ py_test( srcs = ["logical_expressions_test.py"], srcs_version = "PY2AND3", deps = [ - ":test_lib", + ":converters", + "//tensorflow/contrib/autograph/core:test_lib", "//tensorflow/python:client_testlib", ], ) @@ -183,7 +181,8 @@ py_test( "notap", ], deps = [ - ":test_lib", + ":converters", + "//tensorflow/contrib/autograph/core:test_lib", "//tensorflow/python:client_testlib", ], ) @@ -193,7 +192,8 @@ py_test( srcs = ["single_return_test.py"], srcs_version = "PY2AND3", deps = [ - ":test_lib", + ":converters", + "//tensorflow/contrib/autograph/core:test_lib", "//tensorflow/contrib/autograph/pyct", "//tensorflow/python:client_testlib", ], @@ -204,7 +204,8 @@ py_test( srcs = ["ifexp_test.py"], srcs_version = "PY2AND3", deps = [ - ":test_lib", + ":converters", + "//tensorflow/contrib/autograph/core:test_lib", "//tensorflow/contrib/autograph/pyct", "//tensorflow/python:client_testlib", ], @@ -215,7 +216,8 @@ py_test( srcs = ["slices_test.py"], srcs_version = "PY2AND3", deps = [ - ":test_lib", + ":converters", + "//tensorflow/contrib/autograph/core:test_lib", "//tensorflow/contrib/autograph/pyct", "//tensorflow/python:client_testlib", ], diff --git a/tensorflow/contrib/autograph/converters/asserts.py b/tensorflow/contrib/autograph/converters/asserts.py index 3b0db677ce..e664a403a5 100644 --- a/tensorflow/contrib/autograph/converters/asserts.py +++ b/tensorflow/contrib/autograph/converters/asserts.py @@ -20,11 +20,11 @@ from __future__ import print_function import gast +from tensorflow.contrib.autograph.core import converter from tensorflow.contrib.autograph.pyct import templates -from tensorflow.contrib.autograph.pyct import transformer -class AssertsTransformer(transformer.Base): +class AssertsTransformer(converter.Base): """Transforms Print nodes to Call so they can be handled as functions.""" def visit_Assert(self, node): @@ -45,5 +45,5 @@ class AssertsTransformer(transformer.Base): raise NotImplementedError('can only convert string messages for now.') -def transform(node, context): - return AssertsTransformer(context).visit(node) +def transform(node, ctx): + return AssertsTransformer(ctx).visit(node) diff --git a/tensorflow/contrib/autograph/converters/asserts_test.py b/tensorflow/contrib/autograph/converters/asserts_test.py index cc913febe8..2cd0e626bc 100644 --- a/tensorflow/contrib/autograph/converters/asserts_test.py +++ b/tensorflow/contrib/autograph/converters/asserts_test.py @@ -21,11 +21,11 @@ from __future__ import print_function import gast from tensorflow.contrib.autograph.converters import asserts -from tensorflow.contrib.autograph.converters import converter_test_base +from tensorflow.contrib.autograph.core import converter_testing from tensorflow.python.platform import test -class AssertsTest(converter_test_base.TestCase): +class AssertsTest(converter_testing.TestCase): def test_transform(self): diff --git a/tensorflow/contrib/autograph/converters/break_statements.py b/tensorflow/contrib/autograph/converters/break_statements.py index 775d92c1d9..a990e359a2 100644 --- a/tensorflow/contrib/autograph/converters/break_statements.py +++ b/tensorflow/contrib/autograph/converters/break_statements.py @@ -18,9 +18,9 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from tensorflow.contrib.autograph.core import converter from tensorflow.contrib.autograph.pyct import anno from tensorflow.contrib.autograph.pyct import templates -from tensorflow.contrib.autograph.pyct import transformer from tensorflow.contrib.autograph.pyct.static_analysis.annos import NodeAnno @@ -29,7 +29,7 @@ BREAK_USED = 'break_used' CONTROL_VAR_NAME = 'control_var_name' -class BreakStatementTransformer(transformer.Base): +class BreakStatementTransformer(converter.Base): """Canonicalizes break statements into additional conditionals.""" def visit_Break(self, node): @@ -67,7 +67,7 @@ class BreakStatementTransformer(transformer.Base): def visit_While(self, node): scope = anno.getanno(node, NodeAnno.BODY_SCOPE) - break_var = self.context.namer.new_symbol('break_', scope.referenced) + break_var = self.ctx.namer.new_symbol('break_', scope.referenced) node.test = self.visit(node.test) node.body, break_used = self._track_body(node.body, break_var) @@ -97,7 +97,7 @@ class BreakStatementTransformer(transformer.Base): def visit_For(self, node): scope = anno.getanno(node, NodeAnno.BODY_SCOPE) - break_var = self.context.namer.new_symbol('break_', scope.referenced) + break_var = self.ctx.namer.new_symbol('break_', scope.referenced) node.target = self.visit(node.target) node.iter = self.visit(node.iter) @@ -137,5 +137,5 @@ class BreakStatementTransformer(transformer.Base): return node -def transform(node, context): - return BreakStatementTransformer(context).visit(node) +def transform(node, ctx): + return BreakStatementTransformer(ctx).visit(node) diff --git a/tensorflow/contrib/autograph/converters/break_statements_test.py b/tensorflow/contrib/autograph/converters/break_statements_test.py index 1af59e9b52..dcff1c54c2 100644 --- a/tensorflow/contrib/autograph/converters/break_statements_test.py +++ b/tensorflow/contrib/autograph/converters/break_statements_test.py @@ -19,11 +19,11 @@ from __future__ import division from __future__ import print_function from tensorflow.contrib.autograph.converters import break_statements -from tensorflow.contrib.autograph.converters import converter_test_base +from tensorflow.contrib.autograph.core import converter_testing from tensorflow.python.platform import test -class BreakCanonicalizationTest(converter_test_base.TestCase): +class BreakCanonicalizationTest(converter_testing.TestCase): def test_basic_while(self): diff --git a/tensorflow/contrib/autograph/converters/builtin_functions.py b/tensorflow/contrib/autograph/converters/builtin_functions.py index 231e4ee35a..b26c52294c 100644 --- a/tensorflow/contrib/autograph/converters/builtin_functions.py +++ b/tensorflow/contrib/autograph/converters/builtin_functions.py @@ -20,11 +20,11 @@ from __future__ import print_function import gast +from tensorflow.contrib.autograph.core import converter from tensorflow.contrib.autograph.pyct import templates -from tensorflow.contrib.autograph.pyct import transformer -class BuiltinFunctionTransformer(transformer.Base): +class BuiltinFunctionTransformer(converter.Base): """Handles builtin functions. This transformer only covers functions that are translated into a @@ -68,5 +68,5 @@ class BuiltinFunctionTransformer(transformer.Base): return self.visit(function_call) -def transform(node, context): - return BuiltinFunctionTransformer(context).visit(node) +def transform(node, ctx): + return BuiltinFunctionTransformer(ctx).visit(node) diff --git a/tensorflow/contrib/autograph/converters/builtin_functions_test.py b/tensorflow/contrib/autograph/converters/builtin_functions_test.py index 30272409df..e9000e518c 100644 --- a/tensorflow/contrib/autograph/converters/builtin_functions_test.py +++ b/tensorflow/contrib/autograph/converters/builtin_functions_test.py @@ -23,13 +23,13 @@ import sys import six from tensorflow.contrib.autograph.converters import builtin_functions -from tensorflow.contrib.autograph.converters import converter_test_base +from tensorflow.contrib.autograph.core import converter_testing from tensorflow.python.framework import constant_op from tensorflow.python.ops import array_ops from tensorflow.python.platform import test -class BuiltinFunctionsTest(converter_test_base.TestCase): +class BuiltinFunctionsTest(converter_testing.TestCase): def test_len(self): diff --git a/tensorflow/contrib/autograph/converters/call_trees.py b/tensorflow/contrib/autograph/converters/call_trees.py index b6ecdcb780..a36b3d77a9 100644 --- a/tensorflow/contrib/autograph/converters/call_trees.py +++ b/tensorflow/contrib/autograph/converters/call_trees.py @@ -26,12 +26,12 @@ from collections import namedtuple import gast +from tensorflow.contrib.autograph.core import converter from tensorflow.contrib.autograph.pyct import anno from tensorflow.contrib.autograph.pyct import ast_util from tensorflow.contrib.autograph.pyct import inspect_utils from tensorflow.contrib.autograph.pyct import parser from tensorflow.contrib.autograph.pyct import templates -from tensorflow.contrib.autograph.pyct import transformer from tensorflow.python.util import tf_inspect @@ -45,6 +45,9 @@ KNOWN_NUMPY_FUNCTIONS = { } +# TODO(mdan): Get rid of these interfaces. Can now depend directly on Namer. + + class FunctionNamer(object): """Describes the interface for CallTreeTransformer's namer.""" @@ -76,20 +79,18 @@ class FunctionNamer(object): raise NotImplementedError() -class CallTreeTransformer(transformer.Base): - """Transforms the call tree by renaming transformed symbols.""" +# TODO(mdan): Rename to CallsTransformer. - def __init__(self, context, uncompiled_modules, nocompile_decorators): - super(CallTreeTransformer, self).__init__(context) - self.uncompiled_modules = uncompiled_modules - self.nocompile_decorators = nocompile_decorators + +class CallTreeTransformer(converter.Base): + """Transforms the call tree by renaming transformed symbols.""" def _resolve_name(self, node): """Used to resolve decorator info.""" if isinstance(node, gast.Call): return self._resolve_name(node.func) if isinstance(node, gast.Name): - return self.context.namespace.get(node.id) + return self.ctx.namespace.get(node.id) if isinstance(node, gast.Attribute): parent = self._resolve_name(node.value) if parent is not None: @@ -119,12 +120,12 @@ class CallTreeTransformer(transformer.Base): """Determines whether an entity should be compiled in the context.""" # TODO(mdan): Needs cleanup. We should remove the use of fqn altogether. module_name = fqn[0] - for mod in self.uncompiled_modules: + for mod in self.ctx.program.uncompiled_modules: if module_name.startswith(mod[0] + '.'): return False for i in range(1, len(fqn)): - if fqn[:i] in self.uncompiled_modules: + if fqn[:i] in self.ctx.program.uncompiled_modules: return False # Check for local decorations @@ -140,7 +141,7 @@ class CallTreeTransformer(transformer.Base): if hasattr(target_entity, '__pyct_is_compile_decorator'): return False - if target_entity in self.nocompile_decorators: + if target_entity in self.ctx.program.autograph_decorators: return False # Inspect the target function decorators. If any include a @convert @@ -159,7 +160,7 @@ class CallTreeTransformer(transformer.Base): for dec in target_node.decorator_list: decorator_fn = self._resolve_name(dec) if (decorator_fn is not None and - decorator_fn in self.nocompile_decorators): + decorator_fn in self.ctx.program.autograph_decorators): return False return True @@ -174,7 +175,7 @@ class CallTreeTransformer(transformer.Base): return node if anno.hasanno(node, 'is_constructor'): - new_name = self.context.namer.compiled_class_name( + new_name = self.ctx.namer.compiled_class_name( target_fqn, live_entity=target_entity) do_rename = True else: @@ -183,7 +184,7 @@ class CallTreeTransformer(transformer.Base): else: # Fallback - not reliable. owner_type = inspect_utils.getmethodclass(target_entity) - new_name, do_rename = self.context.namer.compiled_function_name( + new_name, do_rename = self.ctx.namer.compiled_function_name( target_fqn, live_entity=target_entity, owner_type=owner_type) if do_rename: @@ -264,15 +265,16 @@ class CallTreeTransformer(transformer.Base): return node def visit_Call(self, node): - # If the function is wrapped by one of the marker decorators, + # If the function call is wrapped by one of the marker decorators, # consider it graph ready. if anno.hasanno(node.func, 'live_val'): target_entity = anno.getanno(node.func, 'live_val') - if target_entity in self.nocompile_decorators: + if target_entity in self.ctx.program.autograph_decorators: if len(node.args) < 1: raise ValueError( 'Found call to decorator function "%s", but it had no arguments. ' - 'A decorator needs at least an argument.') + 'A decorator needs at least one positional argument.' % + target_entity) anno.setanno(node.args[0], 'graph_ready', True) self.generic_visit(node) @@ -309,27 +311,20 @@ class CallTreeTransformer(transformer.Base): # ensure that they return the correct value. return node - if self.context.recursive: + if self.ctx.program.recursive: node = self._insert_dynamic_conversion(node) return node -def transform(node, context, uncompiled_modules, nocompile_decorators): +def transform(node, ctx): """Transform function call to the compiled counterparts. Args: - node: AST to transform. - context: An EntityContext object. - uncompiled_modules: set of string tuples, each tuple represents the fully - qualified name of a package containing functions that will not be - compiled. - nocompile_decorators: A tuple containing decorators to be stripped from - functions during conversion. + node: AST + ctx: EntityContext Returns: A tuple (node, new_names): node: The transformed AST new_names: set(string), containing any newly-generated names """ - t = CallTreeTransformer(context, uncompiled_modules, nocompile_decorators) - node = t.visit(node) - return node + return CallTreeTransformer(ctx).visit(node) diff --git a/tensorflow/contrib/autograph/converters/call_trees_test.py b/tensorflow/contrib/autograph/converters/call_trees_test.py index 303dd54a4e..27d8281b85 100644 --- a/tensorflow/contrib/autograph/converters/call_trees_test.py +++ b/tensorflow/contrib/autograph/converters/call_trees_test.py @@ -21,7 +21,7 @@ from __future__ import print_function import numpy as np from tensorflow.contrib.autograph.converters import call_trees -from tensorflow.contrib.autograph.converters import converter_test_base +from tensorflow.contrib.autograph.core import converter_testing from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops @@ -29,7 +29,7 @@ from tensorflow.python.ops import math_ops from tensorflow.python.platform import test -class CallTreesTest(converter_test_base.TestCase): +class CallTreesTest(converter_testing.TestCase): def test_basic(self): @@ -43,7 +43,7 @@ class CallTreesTest(converter_test_base.TestCase): return test_fn_1(a) + 1 node = self.parse_and_analyze(test_fn_2, {'test_fn_1': test_fn_1}) - node = call_trees.transform(node, self.ctx, (), ()) + node = call_trees.transform(node, self.ctx) with self.compiled(node) as result: # Only test_fn_2 is transformed, so we'll insert renamed_test_fn_1 @@ -60,7 +60,7 @@ class CallTreesTest(converter_test_base.TestCase): return f() + 3 node = self.parse_and_analyze(test_fn_2, {}) - node = call_trees.transform(node, self.ctx, (), ()) + node = call_trees.transform(node, self.ctx) with self.compiled(node) as result: # 10 = 7 (from the mock) + 3 (from test_fn_2) @@ -78,9 +78,9 @@ class CallTreesTest(converter_test_base.TestCase): node = self.parse_and_analyze( TestClass.test_fn_2, {'TestClass': TestClass}, - namer=converter_test_base.FakeNoRenameNamer(), + namer=converter_testing.FakeNoRenameNamer(), arg_types={'self': (TestClass.__name__, TestClass)}) - node = call_trees.transform(node, self.ctx, (), ()) + node = call_trees.transform(node, self.ctx) with self.compiled(node) as result: tc = TestClass() @@ -92,7 +92,7 @@ class CallTreesTest(converter_test_base.TestCase): setattr(a, 'foo', 'bar') node = self.parse_and_analyze(test_fn, {'setattr': setattr}) - node = call_trees.transform(node, self.ctx, (), ()) + node = call_trees.transform(node, self.ctx) with self.compiled(node) as result: with self.test_session() as sess: @@ -115,7 +115,7 @@ class CallTreesTest(converter_test_base.TestCase): return np.random.binomial(2, 0.5) node = self.parse_and_analyze(test_fn, {'np': np}) - node = call_trees.transform(node, self.ctx, (), ()) + node = call_trees.transform(node, self.ctx) with self.compiled(node, dtypes.int64) as result: result.np = np @@ -130,13 +130,13 @@ class CallTreesTest(converter_test_base.TestCase): a = math_ops.add(a, constant_op.constant(1)) return a - node = self.parse_and_analyze(test_fn, { - 'math_ops': math_ops, - 'constant_op': constant_op - }) - node = call_trees.transform(node, self.ctx, - set(((math_ops.__name__,), - (constant_op.__name__,))), ()) + node = self.parse_and_analyze( + test_fn, { + 'math_ops': math_ops, + 'constant_op': constant_op + }, + arg_types=set(((math_ops.__name__,), (constant_op.__name__,)))) + node = call_trees.transform(node, self.ctx) with self.compiled(node) as result: result.math_ops = math_ops diff --git a/tensorflow/contrib/autograph/converters/continue_statements.py b/tensorflow/contrib/autograph/converters/continue_statements.py index 0417817a77..958bde0a58 100644 --- a/tensorflow/contrib/autograph/converters/continue_statements.py +++ b/tensorflow/contrib/autograph/converters/continue_statements.py @@ -18,9 +18,9 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from tensorflow.contrib.autograph.core import converter from tensorflow.contrib.autograph.pyct import anno from tensorflow.contrib.autograph.pyct import templates -from tensorflow.contrib.autograph.pyct import transformer from tensorflow.contrib.autograph.pyct.static_analysis.annos import NodeAnno @@ -31,7 +31,7 @@ GUARD_CREATED = 'guard_created' CREATE_GUARD_NEXT = 'create_guard_next' -class ContinueCanonicalizationTransformer(transformer.Base): +class ContinueCanonicalizationTransformer(converter.Base): """Canonicalizes continue statements into additional conditionals.""" def visit_Continue(self, node): @@ -85,7 +85,7 @@ class ContinueCanonicalizationTransformer(transformer.Base): def _visit_loop_body(self, node, nodes): self.enter_local_scope() scope = anno.getanno(node, NodeAnno.BODY_SCOPE) - continue_var = self.context.namer.new_symbol('continue_', scope.referenced) + continue_var = self.ctx.namer.new_symbol('continue_', scope.referenced) self.set_local(CONTROL_VAR_NAME, continue_var) nodes = self.visit_block(nodes, after_visit=self._postprocess_statement) @@ -135,5 +135,5 @@ class ContinueCanonicalizationTransformer(transformer.Base): return node -def transform(node, namer): - return ContinueCanonicalizationTransformer(namer).visit(node) +def transform(node, ctx): + return ContinueCanonicalizationTransformer(ctx).visit(node) diff --git a/tensorflow/contrib/autograph/converters/continue_statements_test.py b/tensorflow/contrib/autograph/converters/continue_statements_test.py index bcbb316d74..2ce1837972 100644 --- a/tensorflow/contrib/autograph/converters/continue_statements_test.py +++ b/tensorflow/contrib/autograph/converters/continue_statements_test.py @@ -19,11 +19,11 @@ from __future__ import division from __future__ import print_function from tensorflow.contrib.autograph.converters import continue_statements -from tensorflow.contrib.autograph.converters import converter_test_base +from tensorflow.contrib.autograph.core import converter_testing from tensorflow.python.platform import test -class ContinueCanonicalizationTest(converter_test_base.TestCase): +class ContinueCanonicalizationTest(converter_testing.TestCase): def test_basic_continue(self): diff --git a/tensorflow/contrib/autograph/converters/control_flow.py b/tensorflow/contrib/autograph/converters/control_flow.py index d7ddbe8a04..22a671262c 100644 --- a/tensorflow/contrib/autograph/converters/control_flow.py +++ b/tensorflow/contrib/autograph/converters/control_flow.py @@ -20,11 +20,11 @@ from __future__ import print_function import gast +from tensorflow.contrib.autograph.core import converter from tensorflow.contrib.autograph.pyct import anno from tensorflow.contrib.autograph.pyct import ast_util from tensorflow.contrib.autograph.pyct import parser from tensorflow.contrib.autograph.pyct import templates -from tensorflow.contrib.autograph.pyct import transformer from tensorflow.contrib.autograph.pyct.static_analysis import cfg from tensorflow.contrib.autograph.pyct.static_analysis.annos import NodeAnno @@ -45,7 +45,7 @@ class SymbolNamer(object): raise NotImplementedError() -class ControlFlowTransformer(transformer.Base): +class ControlFlowTransformer(converter.Base): """Transforms control flow structures like loops an conditionals.""" def _create_cond_branch(self, body_name, aliased_orig_names, @@ -141,10 +141,10 @@ class ControlFlowTransformer(transformer.Base): aliased_orelse_orig_names = tuple(orelse_scope.modified - orelse_scope.created) aliased_body_new_names = tuple( - self.context.namer.new_symbol(s.ssf(), body_scope.referenced) + self.ctx.namer.new_symbol(s.ssf(), body_scope.referenced) for s in aliased_body_orig_names) aliased_orelse_new_names = tuple( - self.context.namer.new_symbol(s.ssf(), orelse_scope.referenced) + self.ctx.namer.new_symbol(s.ssf(), orelse_scope.referenced) for s in aliased_orelse_orig_names) alias_body_map = dict(zip(aliased_body_orig_names, aliased_body_new_names)) @@ -165,9 +165,8 @@ class ControlFlowTransformer(transformer.Base): else: results = gast.Tuple([s.ast() for s in modified], None) - body_name = self.context.namer.new_symbol('if_true', body_scope.referenced) - orelse_name = self.context.namer.new_symbol('if_false', - orelse_scope.referenced) + body_name = self.ctx.namer.new_symbol('if_true', body_scope.referenced) + orelse_name = self.ctx.namer.new_symbol('if_false', orelse_scope.referenced) if modified: def build_returns(aliased_names, alias_map, scope): @@ -235,7 +234,7 @@ class ControlFlowTransformer(transformer.Base): raise ValueError('cannot convert while loop: no outputs') state_ssf = [ - self.context.namer.new_symbol(s.ssf(), all_referenced) for s in state + self.ctx.namer.new_symbol(s.ssf(), all_referenced) for s in state ] ssf_map = { name: ssf @@ -267,11 +266,9 @@ class ControlFlowTransformer(transformer.Base): state=state, state_ssf=state_ssf, state_ast_tuple=state_ast_tuple, - test_name=self.context.namer.new_symbol('loop_test', - body_scope.referenced), + test_name=self.ctx.namer.new_symbol('loop_test', body_scope.referenced), test=test, - body_name=self.context.namer.new_symbol('loop_body', - body_scope.referenced), + body_name=self.ctx.namer.new_symbol('loop_body', body_scope.referenced), body=node_body, extra_deps=tuple(s.ast() for s in cond_closure), ) @@ -288,7 +285,7 @@ class ControlFlowTransformer(transformer.Base): state = list(body_closure) state_ssf = [ - self.context.namer.new_symbol(s.ssf(), all_referenced) for s in state + self.ctx.namer.new_symbol(s.ssf(), all_referenced) for s in state ] ssf_map = { name: ssf @@ -326,17 +323,16 @@ class ControlFlowTransformer(transformer.Base): state_ast_tuple=state_ast_tuple, iter_=node.iter, iterate=node.target, - extra_test_name=self.context.namer.new_symbol('extra_test', - all_referenced), + extra_test_name=self.ctx.namer.new_symbol('extra_test', all_referenced), extra_test_expr=extra_test, - body_name=self.context.namer.new_symbol('loop_body', all_referenced), + body_name=self.ctx.namer.new_symbol('loop_body', all_referenced), body=node_body) return node -def transform(node, context): - cfg.run_analyses(node, cfg.Liveness(context)) - cfg.run_analyses(node, cfg.Defined(context)) - node = ControlFlowTransformer(context).visit(node) +def transform(node, ctx): + cfg.run_analyses(node, cfg.Liveness(ctx.info)) + cfg.run_analyses(node, cfg.Defined(ctx.info)) + node = ControlFlowTransformer(ctx).visit(node) return node diff --git a/tensorflow/contrib/autograph/converters/control_flow_test.py b/tensorflow/contrib/autograph/converters/control_flow_test.py index 9d23d9b5b7..735eb92a0d 100644 --- a/tensorflow/contrib/autograph/converters/control_flow_test.py +++ b/tensorflow/contrib/autograph/converters/control_flow_test.py @@ -19,7 +19,7 @@ from __future__ import division from __future__ import print_function from tensorflow.contrib.autograph.converters import control_flow -from tensorflow.contrib.autograph.converters import converter_test_base +from tensorflow.contrib.autograph.core import converter_testing from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.ops import array_ops @@ -27,7 +27,7 @@ from tensorflow.python.ops import control_flow_ops from tensorflow.python.platform import test -class ControlFlowTest(converter_test_base.TestCase): +class ControlFlowTest(converter_testing.TestCase): def test_simple_while(self): diff --git a/tensorflow/contrib/autograph/converters/converter_test_base.py b/tensorflow/contrib/autograph/converters/converter_test_base.py deleted file mode 100644 index 41c2e71702..0000000000 --- a/tensorflow/contrib/autograph/converters/converter_test_base.py +++ /dev/null @@ -1,136 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Base class for tests in this module.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import contextlib -import imp - -from tensorflow.contrib.autograph import operators -from tensorflow.contrib.autograph import utils -from tensorflow.contrib.autograph.pyct import compiler -from tensorflow.contrib.autograph.pyct import context -from tensorflow.contrib.autograph.pyct import parser -from tensorflow.contrib.autograph.pyct import pretty_printer -from tensorflow.contrib.autograph.pyct import qual_names -from tensorflow.contrib.autograph.pyct.static_analysis import activity -from tensorflow.contrib.autograph.pyct.static_analysis import live_values -from tensorflow.contrib.autograph.pyct.static_analysis import type_info -from tensorflow.python.platform import test - - -class FakeNamer(object): - """A fake namer that uses a global counter to generate unique names.""" - - def __init__(self): - self.i = 0 - - def new_symbol(self, name_root, used): - while True: - self.i += 1 - name = '%s%d' % (name_root, self.i) - if name not in used: - return name - - def compiled_function_name(self, - original_fqn, - live_entity=None, - owner_type=None): - del live_entity - if owner_type is not None: - return None, False - return ('renamed_%s' % '_'.join(original_fqn)), True - - -class FakeNoRenameNamer(FakeNamer): - - def compiled_function_name(self, original_fqn, **_): - return str(original_fqn), False - - -class TestCase(test.TestCase): - """Base class for unit tests in this module. Contains relevant utilities.""" - - @contextlib.contextmanager - def compiled(self, node, *symbols): - source = None - - self.dynamic_calls = [] - def converted_call(*args): - """Mock version of api.converted_call.""" - self.dynamic_calls.append(args) - return 7 - - try: - result, source = compiler.ast_to_object(node) - result.tf = self.make_fake_mod('fake_tf', *symbols) - fake_ag = self.make_fake_mod('fake_ag', converted_call) - fake_ag.__dict__.update(operators.__dict__) - fake_ag.__dict__['utils'] = utils - result.__dict__['ag__'] = fake_ag - yield result - except Exception: # pylint:disable=broad-except - if source is None: - print('Offending AST:\n%s' % pretty_printer.fmt(node, color=False)) - else: - print('Offending compiled code:\n%s' % source) - raise - - def make_fake_mod(self, name, *symbols): - fake_mod = imp.new_module(name) - for s in symbols: - if hasattr(s, '__name__'): - setattr(fake_mod, s.__name__, s) - elif hasattr(s, 'name'): - # This is a bit of a hack, but works for things like tf.int32 - setattr(fake_mod, s.name, s) - else: - raise ValueError('can not attach %s - what should be its name?' % s) - return fake_mod - - def attach_namespace(self, module, **ns): - for k, v in ns.items(): - setattr(module, k, v) - - def parse_and_analyze(self, - test_fn, - namespace, - namer=None, - arg_types=None, - include_type_analysis=True, - owner_type=None, - recursive=True): - node, source = parser.parse_entity(test_fn) - ctx = context.EntityContext( - namer=namer or FakeNamer(), - source_code=source, - source_file=None, - namespace=namespace, - arg_values=None, - arg_types=arg_types, - owner_type=owner_type, - recursive=recursive, - type_annotation_func=utils.set_element_type) - node = qual_names.resolve(node) - node = activity.resolve(node, ctx) - node = live_values.resolve(node, ctx, {}) - if include_type_analysis: - node = type_info.resolve(node, ctx) - node = live_values.resolve(node, ctx, {}) - self.ctx = ctx - return node diff --git a/tensorflow/contrib/autograph/converters/decorators.py b/tensorflow/contrib/autograph/converters/decorators.py index 92445f3174..3471bd11d6 100644 --- a/tensorflow/contrib/autograph/converters/decorators.py +++ b/tensorflow/contrib/autograph/converters/decorators.py @@ -24,19 +24,14 @@ from __future__ import print_function import gast +from tensorflow.contrib.autograph.core import converter from tensorflow.contrib.autograph.pyct import anno -from tensorflow.contrib.autograph.pyct import pretty_printer +from tensorflow.python.util import tf_inspect -class DecoratorsTransformer(gast.NodeTransformer): +class DecoratorsTransformer(converter.Base): """Converts or removes decorators.""" - def __init__(self, remove_decorators): - self.remove_decorators = remove_decorators - self.additional_dependencies = set() - - # pylint:disable=invalid-name - def visit_FunctionDef(self, node): self.generic_visit(node) kept_decorators = [] @@ -58,31 +53,53 @@ class DecoratorsTransformer(gast.NodeTransformer): # This is currently verified by tests. continue - if not anno.hasanno(dec_func, 'live_val'): - raise ValueError( - 'Could not resolve decorator: %s' % pretty_printer.fmt(dec_func)) - + original_dec = anno.getanno(dec_func, anno.Basic.QN) dec_value = anno.getanno(dec_func, 'live_val') - if dec_value not in self.remove_decorators: - kept_decorators.append((dec, dec_value)) - for _, dec_value in kept_decorators: - if dec_value.__module__ == '__main__': + if dec_value in self.ctx.program.autograph_decorators: + # AutoGraph decorators do not need to be preserved. + continue + + # When using foo.bar.baz, we only really need to grab foo and import + # that. + dec_support_node = dec_func + while isinstance(dec_support_node, gast.Attribute): + dec_support_node = dec_support_node.value + + if not anno.hasanno(dec_support_node, 'live_val'): raise ValueError( - 'decorator "%s" was not allowed because it is declared ' - 'in the module "%s". To fix this, declare it in a separate ' - 'module that we can import it from.' % (dec_value, - dec_value.__module__)) + 'could not resolve symbol "%s" when looking up decorator "%s"' % + (anno.getanno(dec_support_node, anno.Basic.QN), original_dec)) + + dec_support = anno.getanno(dec_support_node, 'live_val') + # The tuple contains: + # * the AST that represents the decorator + # * the entity supporting the decorator (i.e., what we need to import) + # * the name of the module that needs to be imported for this decorator + # to properly resolve. + # Examples: + # for foo.bar, the tuple is (, , 'foo') + # for baz, the tuple is (, , 'baz') + kept_decorators.append((dec, dec_support, + anno.getanno(dec_support_node, anno.Basic.QN))) + + for _, dec_support, name in kept_decorators: + if tf_inspect.ismodule(dec_support): + self.ctx.program.additional_imports.add( + 'import %s as %s' % (dec_support.__name__, name)) else: - self.additional_dependencies.add(dec_value) - - node.decorator_list = [dec for dec, _ in kept_decorators] + if dec_support.__module__ == '__main__': + raise ValueError( + 'decorator "%s" was not allowed because it is declared ' + 'in the module "%s". To fix this, declare it in a separate ' + 'module that we can import it from.' % (dec_support, + dec_support.__module__)) + self.ctx.program.additional_imports.add( + 'from %s import %s' % (dec_support.__module__, name)) + + node.decorator_list = [dec for dec, _, _ in kept_decorators] return node - # pylint:enable=invalid-name - -def transform(node, remove_decorators): - transformer = DecoratorsTransformer(remove_decorators) - node = transformer.visit(node) - return node, transformer.additional_dependencies +def transform(node, ctx): + return DecoratorsTransformer(ctx).visit(node) diff --git a/tensorflow/contrib/autograph/converters/decorators_test.py b/tensorflow/contrib/autograph/converters/decorators_test.py index 9c01f68912..d41c7fde24 100644 --- a/tensorflow/contrib/autograph/converters/decorators_test.py +++ b/tensorflow/contrib/autograph/converters/decorators_test.py @@ -20,9 +20,10 @@ from __future__ import print_function from functools import wraps -from tensorflow.contrib.autograph.converters import converter_test_base from tensorflow.contrib.autograph.converters import decorators +from tensorflow.contrib.autograph.core import converter_testing from tensorflow.contrib.autograph.pyct import compiler +from tensorflow.contrib.autograph.pyct import transformer from tensorflow.python.platform import test @@ -39,28 +40,35 @@ def simple_decorator(f): return lambda a: f(a) + 1 -def self_removing_decorator(removing_wrapper): +def self_transform_decorator(transform): + def decorator(f): @wraps(f) def wrapper(*args): # This removing wrapper is defined in the test below. This setup is so - # intricate just to simulate how we use the transformer in practice. - transformed_f = removing_wrapper(f, (self_removing_decorator,)) + # intricate in order to simulate how we use the transformer in practice. + transformed_f = transform(f, (self_transform_decorator,)) return transformed_f(*args) + 1 return wrapper return decorator -class DecoratorsTest(converter_test_base.TestCase): +class DecoratorsTest(converter_testing.TestCase): - def _remover_wrapper(self, f, remove_decorators): + def _transform(self, f, autograph_decorators): namespace = { - 'self_removing_decorator': self_removing_decorator, - 'simple_decorator': simple_decorator + 'self_transform_decorator': self_transform_decorator, + 'simple_decorator': simple_decorator, + 'converter_testing': converter_testing, } - node = self.parse_and_analyze(f, namespace) - node, _ = decorators.transform(node, remove_decorators=remove_decorators) - result, _ = compiler.ast_to_object(node) + node = self.parse_and_analyze( + f, + namespace, + recursive=False, + autograph_decorators=autograph_decorators) + node = decorators.transform(node, self.ctx) + import_line = '\n'.join(self.ctx.program.additional_imports) + result, _ = compiler.ast_to_object(node, source_prefix=import_line) return getattr(result, f.__name__) def test_noop(self): @@ -69,15 +77,14 @@ class DecoratorsTest(converter_test_base.TestCase): return a node = self.parse_and_analyze(test_fn, {}) - node, deps = decorators.transform(node, remove_decorators=()) + node = decorators.transform(node, self.ctx) result, _ = compiler.ast_to_object(node) - self.assertFalse(deps) self.assertEqual(1, result.test_fn(1)) def test_function(self): - @self_removing_decorator(self._remover_wrapper) + @self_transform_decorator(self._transform) def test_fn(a): return a @@ -88,7 +95,7 @@ class DecoratorsTest(converter_test_base.TestCase): class TestClass(object): - @self_removing_decorator(self._remover_wrapper) + @self_transform_decorator(self._transform) def test_fn(self, a): return a @@ -101,38 +108,39 @@ class DecoratorsTest(converter_test_base.TestCase): # Note that reversing the order of this two doesn't work. @classmethod - @self_removing_decorator(self._remover_wrapper) + @self_transform_decorator(self._transform) def test_fn(cls, a): return a # 2 = 1 (a) + 1 (decorator applied exactly once) self.assertEqual(2, TestClass.test_fn(1)) - def test_nested_decorators(self): + def test_nested_decorators_local(self): - @self_removing_decorator(self._remover_wrapper) + @self_transform_decorator(self._transform) def test_fn(a): @simple_decorator def inner_fn(b): return b + 11 return inner_fn(a) - with self.assertRaises(ValueError): + # Expected to fail because simple_decorator cannot be imported. + with self.assertRaises(transformer.AutographParseError): test_fn(1) - # TODO(mdan): Uncomment this test once converter_test_base is updated. - # (can't do it now because it has unrelated pending changes) - # def test_nested_decorators(self): - # - # @self_removing_decorator(self._remover_wrapper) - # def test_fn(a): - # @imported_decorator - # def inner_fn(b): - # return b + 11 - # return inner_fn(a) - # - # # 14 = 1 (a) + 1 (simple_decorator) + 11 (inner_fn) - # self.assertEqual(14, test_fn(1)) + def test_nested_decorators_imported(self): + + @self_transform_decorator(self._transform) + def test_fn(a): + + @converter_testing.imported_decorator + def inner_fn(b): + return b + 11 + + return inner_fn(a) + + # 14 = 1 (a) + 1 (simple_decorator) + 11 (inner_fn) + self.assertEqual(14, test_fn(1)) if __name__ == '__main__': diff --git a/tensorflow/contrib/autograph/converters/ifexp.py b/tensorflow/contrib/autograph/converters/ifexp.py index 616d222762..e996138498 100644 --- a/tensorflow/contrib/autograph/converters/ifexp.py +++ b/tensorflow/contrib/autograph/converters/ifexp.py @@ -18,11 +18,11 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from tensorflow.contrib.autograph.core import converter from tensorflow.contrib.autograph.pyct import templates -from tensorflow.contrib.autograph.pyct import transformer -class IfExp(transformer.Base): +class IfExp(converter.Base): """Canonicalizes all IfExp nodes into plain conditionals.""" def visit_IfExp(self, node): @@ -34,16 +34,16 @@ class IfExp(transformer.Base): return desugared_ifexp -def transform(node, context): +def transform(node, ctx): """Desugar IfExp nodes into plain conditionals. Args: - node: an AST node to transform - context: a context object + node: ast.AST, the node to transform + ctx: converter.EntityContext Returns: new_node: an AST with no IfExp nodes, only conditionals. """ - node = IfExp(context).visit(node) + node = IfExp(ctx).visit(node) return node diff --git a/tensorflow/contrib/autograph/converters/ifexp_test.py b/tensorflow/contrib/autograph/converters/ifexp_test.py index ac6849dcb4..cdd5a2f591 100644 --- a/tensorflow/contrib/autograph/converters/ifexp_test.py +++ b/tensorflow/contrib/autograph/converters/ifexp_test.py @@ -19,12 +19,12 @@ from __future__ import division from __future__ import print_function from tensorflow.contrib.autograph import utils -from tensorflow.contrib.autograph.converters import converter_test_base from tensorflow.contrib.autograph.converters import ifexp +from tensorflow.contrib.autograph.core import converter_testing from tensorflow.python.platform import test -class IfExpTest(converter_test_base.TestCase): +class IfExpTest(converter_testing.TestCase): def compiled_fn(self, test_fn, *args): node = self.parse_and_analyze(test_fn, {}) diff --git a/tensorflow/contrib/autograph/converters/list_comprehension.py b/tensorflow/contrib/autograph/converters/list_comprehension.py index d7f2920151..c4a13ee822 100644 --- a/tensorflow/contrib/autograph/converters/list_comprehension.py +++ b/tensorflow/contrib/autograph/converters/list_comprehension.py @@ -31,17 +31,14 @@ from __future__ import print_function import gast +from tensorflow.contrib.autograph.core import converter from tensorflow.contrib.autograph.pyct import parser from tensorflow.contrib.autograph.pyct import templates -from tensorflow.contrib.autograph.pyct import transformer -class ListCompCanonicalizationTransformer(transformer.Base): +class ListCompCanonicalizationTransformer(converter.Base): """NodeTransformer to canonicalize list comprehensions.""" - def __init__(self, context): - super(ListCompCanonicalizationTransformer, self).__init__(context) - def make_update_list_node(self, list_, elt): return templates.replace('list_.append(elt)', list_=list_, elt=elt)[0] @@ -76,5 +73,5 @@ class ListCompCanonicalizationTransformer(transformer.Base): return make_list + loop_body -def transform(node, context): - return ListCompCanonicalizationTransformer(context).visit(node) +def transform(node, ctx): + return ListCompCanonicalizationTransformer(ctx).visit(node) diff --git a/tensorflow/contrib/autograph/converters/list_comprehension_test.py b/tensorflow/contrib/autograph/converters/list_comprehension_test.py index 4758671f5e..2bbee93412 100644 --- a/tensorflow/contrib/autograph/converters/list_comprehension_test.py +++ b/tensorflow/contrib/autograph/converters/list_comprehension_test.py @@ -18,12 +18,12 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.autograph.converters import converter_test_base from tensorflow.contrib.autograph.converters import list_comprehension +from tensorflow.contrib.autograph.core import converter_testing from tensorflow.python.platform import test -class ListCompTest(converter_test_base.TestCase): +class ListCompTest(converter_testing.TestCase): def test_basic(self): diff --git a/tensorflow/contrib/autograph/converters/lists.py b/tensorflow/contrib/autograph/converters/lists.py index c15dfff9e8..d77a044798 100644 --- a/tensorflow/contrib/autograph/converters/lists.py +++ b/tensorflow/contrib/autograph/converters/lists.py @@ -32,10 +32,10 @@ from __future__ import print_function import gast +from tensorflow.contrib.autograph.core import converter from tensorflow.contrib.autograph.pyct import anno from tensorflow.contrib.autograph.pyct import parser from tensorflow.contrib.autograph.pyct import templates -from tensorflow.contrib.autograph.pyct import transformer from tensorflow.contrib.autograph.pyct.static_analysis.annos import NodeAnno @@ -43,7 +43,7 @@ from tensorflow.contrib.autograph.pyct.static_analysis.annos import NodeAnno POP_USES = 'pop_uses' -class ListTransformer(transformer.Base): +class ListTransformer(converter.Base): """Converts lists and related operations to their TF counterpart.""" def visit_List(self, node): @@ -94,7 +94,7 @@ class ListTransformer(transformer.Base): target_name = anno.getanno(target_node, anno.Basic.QN).ssf() else: target_name = 'list' - pop_var_name = self.context.namer.new_symbol(target_name, scope.referenced) + pop_var_name = self.ctx.namer.new_symbol(target_name, scope.referenced) pop_uses = self.get_local(POP_USES, []) pop_uses.append((node, pop_var_name)) @@ -223,5 +223,5 @@ class ListTransformer(transformer.Base): return node -def transform(node, context): - return ListTransformer(context).visit(node) +def transform(node, ctx): + return ListTransformer(ctx).visit(node) diff --git a/tensorflow/contrib/autograph/converters/lists_test.py b/tensorflow/contrib/autograph/converters/lists_test.py index 9f18ab9f44..ea04097b28 100644 --- a/tensorflow/contrib/autograph/converters/lists_test.py +++ b/tensorflow/contrib/autograph/converters/lists_test.py @@ -19,8 +19,8 @@ from __future__ import division from __future__ import print_function from tensorflow.contrib.autograph import utils -from tensorflow.contrib.autograph.converters import converter_test_base from tensorflow.contrib.autograph.converters import lists +from tensorflow.contrib.autograph.core import converter_testing from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops @@ -28,7 +28,7 @@ from tensorflow.python.ops import list_ops from tensorflow.python.platform import test -class ListTest(converter_test_base.TestCase): +class ListTest(converter_testing.TestCase): def test_empty_list(self): diff --git a/tensorflow/contrib/autograph/converters/logical_expressions.py b/tensorflow/contrib/autograph/converters/logical_expressions.py index 3a795a315a..16eb1f0e3f 100644 --- a/tensorflow/contrib/autograph/converters/logical_expressions.py +++ b/tensorflow/contrib/autograph/converters/logical_expressions.py @@ -23,10 +23,10 @@ from __future__ import print_function import gast +from tensorflow.contrib.autograph.core import converter from tensorflow.contrib.autograph.pyct import anno from tensorflow.contrib.autograph.pyct import parser from tensorflow.contrib.autograph.pyct import templates -from tensorflow.contrib.autograph.pyct import transformer # TODO(mdan): Properly extrack boolean ops according to lazy eval rules. @@ -39,11 +39,11 @@ from tensorflow.contrib.autograph.pyct import transformer SAFE_BOOLEAN_OPERAND = 'SAFE_BOOLEAN_OPERAND' -class LogicalExpressionTransformer(transformer.Base): +class LogicalExpressionTransformer(converter.Base): """Converts logical expressions to corresponding TF calls.""" - def __init__(self, context): - super(LogicalExpressionTransformer, self).__init__(context) + def __init__(self, ctx): + super(LogicalExpressionTransformer, self).__init__(ctx) # TODO(mdan): Look into replacing with bitwise operators instead. # TODO(mdan): Skip replacing if the function is trivial. self.op_mapping = { @@ -128,5 +128,5 @@ class LogicalExpressionTransformer(transformer.Base): return right -def transform(node, context): - return LogicalExpressionTransformer(context).visit(node) +def transform(node, ctx): + return LogicalExpressionTransformer(ctx).visit(node) diff --git a/tensorflow/contrib/autograph/converters/logical_expressions_test.py b/tensorflow/contrib/autograph/converters/logical_expressions_test.py index 2814060c4d..48186024a9 100644 --- a/tensorflow/contrib/autograph/converters/logical_expressions_test.py +++ b/tensorflow/contrib/autograph/converters/logical_expressions_test.py @@ -18,13 +18,13 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.autograph.converters import converter_test_base from tensorflow.contrib.autograph.converters import logical_expressions +from tensorflow.contrib.autograph.core import converter_testing from tensorflow.python.ops import math_ops from tensorflow.python.platform import test -class GradientsFunctionTest(converter_test_base.TestCase): +class GradientsFunctionTest(converter_testing.TestCase): def test_equals(self): diff --git a/tensorflow/contrib/autograph/converters/name_scopes.py b/tensorflow/contrib/autograph/converters/name_scopes.py index dfee529aba..dd6c6bf960 100644 --- a/tensorflow/contrib/autograph/converters/name_scopes.py +++ b/tensorflow/contrib/autograph/converters/name_scopes.py @@ -20,11 +20,11 @@ from __future__ import print_function import gast +from tensorflow.contrib.autograph.core import converter from tensorflow.contrib.autograph.pyct import templates -from tensorflow.contrib.autograph.pyct import transformer -class FunctionNameScopeTransformer(transformer.Base): +class FunctionNameScopeTransformer(converter.Base): """Wrap a function body with a `name_scope` of the function name.""" def _name_for_current_scope(self): @@ -70,5 +70,5 @@ class FunctionNameScopeTransformer(transformer.Base): return node -def transform(node, context): - return FunctionNameScopeTransformer(context).visit(node) +def transform(node, ctx): + return FunctionNameScopeTransformer(ctx).visit(node) diff --git a/tensorflow/contrib/autograph/converters/name_scopes_test.py b/tensorflow/contrib/autograph/converters/name_scopes_test.py index 17692cbd88..444d0bcd46 100644 --- a/tensorflow/contrib/autograph/converters/name_scopes_test.py +++ b/tensorflow/contrib/autograph/converters/name_scopes_test.py @@ -18,14 +18,14 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.autograph.converters import converter_test_base from tensorflow.contrib.autograph.converters import name_scopes +from tensorflow.contrib.autograph.core import converter_testing from tensorflow.python.framework import constant_op from tensorflow.python.framework import ops from tensorflow.python.platform import test -class FunctionNameScopeTransformer(converter_test_base.TestCase): +class FunctionNameScopeTransformer(converter_testing.TestCase): def test_basic(self): diff --git a/tensorflow/contrib/autograph/converters/side_effect_guards.py b/tensorflow/contrib/autograph/converters/side_effect_guards.py index 3bcb2d3c42..b808604f0a 100644 --- a/tensorflow/contrib/autograph/converters/side_effect_guards.py +++ b/tensorflow/contrib/autograph/converters/side_effect_guards.py @@ -36,11 +36,11 @@ from __future__ import print_function import gast +from tensorflow.contrib.autograph.core import converter from tensorflow.contrib.autograph.pyct import anno from tensorflow.contrib.autograph.pyct import ast_util from tensorflow.contrib.autograph.pyct import qual_names from tensorflow.contrib.autograph.pyct import templates -from tensorflow.contrib.autograph.pyct import transformer from tensorflow.contrib.autograph.pyct.static_analysis.annos import NodeAnno @@ -59,14 +59,9 @@ class SymbolNamer(object): raise NotImplementedError() -class SideEffectGuardTransformer(transformer.Base): +class SideEffectGuardTransformer(converter.Base): """Adds control dependencies to functions with side effects.""" - def __init__(self, context): - super(SideEffectGuardTransformer, self).__init__(context) - - # pylint:disable=invalid-name - def _visit_and_reindent(self, nodes): new_nodes = [] current_dest = new_nodes @@ -149,7 +144,7 @@ class SideEffectGuardTransformer(transformer.Base): s for s in guarded_args if s not in args_scope.parent.modified) aliased_new_names = tuple( qual_names.QN( - self.context.namer.new_symbol( + self.ctx.namer.new_symbol( s.ssf(), args_scope.parent.referenced)) for s in need_alias) alias_map = dict(zip(need_alias, aliased_new_names)) if len(guarded_args) == 1: @@ -183,8 +178,6 @@ class SideEffectGuardTransformer(transformer.Base): (node.body, alias_map)) return node - # pylint:enable=invalid-name - -def transform(node, context): - return SideEffectGuardTransformer(context).visit(node) +def transform(node, ctx): + return SideEffectGuardTransformer(ctx).visit(node) diff --git a/tensorflow/contrib/autograph/converters/side_effect_guards_test.py b/tensorflow/contrib/autograph/converters/side_effect_guards_test.py index ce0ce33243..a7ad8efed4 100644 --- a/tensorflow/contrib/autograph/converters/side_effect_guards_test.py +++ b/tensorflow/contrib/autograph/converters/side_effect_guards_test.py @@ -18,8 +18,8 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.autograph.converters import converter_test_base from tensorflow.contrib.autograph.converters import side_effect_guards +from tensorflow.contrib.autograph.core import converter_testing from tensorflow.python.framework import constant_op from tensorflow.python.framework import errors_impl from tensorflow.python.framework import ops @@ -29,7 +29,7 @@ from tensorflow.python.ops import variables from tensorflow.python.platform import test -class SideEffectGuardsTest(converter_test_base.TestCase): +class SideEffectGuardsTest(converter_testing.TestCase): def test_side_effect_on_return_only_variable(self): diff --git a/tensorflow/contrib/autograph/converters/single_return.py b/tensorflow/contrib/autograph/converters/single_return.py index bcc9ca9dfe..a351cd81b8 100644 --- a/tensorflow/contrib/autograph/converters/single_return.py +++ b/tensorflow/contrib/autograph/converters/single_return.py @@ -20,21 +20,21 @@ from __future__ import print_function import gast +from tensorflow.contrib.autograph.core import converter from tensorflow.contrib.autograph.pyct import anno from tensorflow.contrib.autograph.pyct import ast_util from tensorflow.contrib.autograph.pyct import templates -from tensorflow.contrib.autograph.pyct import transformer from tensorflow.contrib.autograph.pyct.static_analysis.annos import NodeAnno # TODO(mdan): Move this logic into transformer_base. -class BodyVisitor(transformer.Base): +class BodyVisitor(converter.Base): """Walks breadth- or depth-first the list-of-nodes bodies of AST nodes.""" - def __init__(self, context, depth_first=False): + def __init__(self, ctx, depth_first=False): + super(BodyVisitor, self).__init__(ctx) self.depth_first = depth_first self.changes_made = False - super(BodyVisitor, self).__init__(context) def visit_nodelist(self, nodelist): for node in nodelist: @@ -144,13 +144,13 @@ def contains_return(node): return False -class LiftReturn(transformer.Base): +class LiftReturn(converter.Base): """Move return statements out of If and With blocks.""" - def __init__(self, context): + def __init__(self, ctx): + super(LiftReturn, self).__init__(ctx) self.changes_made = False self.common_return_name = None - super(LiftReturn, self).__init__(context) def visit_If(self, node): # Depth-first traversal of if statements @@ -195,8 +195,8 @@ class LiftReturn(transformer.Base): last_return_name = self.common_return_name body_scope = anno.getanno(node, NodeAnno.BODY_SCOPE) referenced_names = body_scope.referenced - self.common_return_name = self.context.namer.new_symbol( - 'return_', referenced_names) + self.common_return_name = self.ctx.namer.new_symbol('return_', + referenced_names) node = self.generic_visit(node) self.common_return_name = last_return_name return node @@ -265,7 +265,7 @@ class DetectReturnInFunctionDef(gast.NodeVisitor): 'Each function definition should contain at least one return.') -def transform(node, context): +def transform(node, ctx): """Ensure a function has only a single return. This transforms an AST node with multiple returns successively into containing @@ -280,8 +280,8 @@ def transform(node, context): this is an error. Args: - node: an AST node to transform - context: a context object + node: ast.AST + ctx: converter.EntityContext Returns: new_node: an AST with a single return value @@ -301,10 +301,10 @@ def transform(node, context): while True: # Try to lift all returns out of if statements and with blocks - lr = LiftReturn(context) + lr = LiftReturn(ctx) node = lr.visit(node) changes_made = lr.changes_made - fe = FoldElse(context) + fe = FoldElse(ctx) node = fe.visit(node) changes_made = changes_made or fe.changes_made diff --git a/tensorflow/contrib/autograph/converters/single_return_test.py b/tensorflow/contrib/autograph/converters/single_return_test.py index d483005a09..1f0de4310e 100644 --- a/tensorflow/contrib/autograph/converters/single_return_test.py +++ b/tensorflow/contrib/autograph/converters/single_return_test.py @@ -18,13 +18,13 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.autograph.converters import converter_test_base from tensorflow.contrib.autograph.converters import single_return +from tensorflow.contrib.autograph.core import converter_testing from tensorflow.python.framework.ops import name_scope from tensorflow.python.platform import test -class SingleReturnTest(converter_test_base.TestCase): +class SingleReturnTest(converter_testing.TestCase): def compiled_fn(self, test_fn, *args): node = self.parse_and_analyze(test_fn, {}) diff --git a/tensorflow/contrib/autograph/converters/slices.py b/tensorflow/contrib/autograph/converters/slices.py index 85aeda9c41..3f5fc57125 100644 --- a/tensorflow/contrib/autograph/converters/slices.py +++ b/tensorflow/contrib/autograph/converters/slices.py @@ -20,12 +20,12 @@ from __future__ import print_function import gast +from tensorflow.contrib.autograph.core import converter from tensorflow.contrib.autograph.pyct import anno from tensorflow.contrib.autograph.pyct import templates -from tensorflow.contrib.autograph.pyct import transformer -class SliceTransformer(transformer.Base): +class SliceTransformer(converter.Base): """Converts slicing operations to their TF counterpart. Currently, relying on the default slice operator that Tensor uses is @@ -79,5 +79,5 @@ class SliceTransformer(transformer.Base): template, target=node.value, key=node.slice, dtype=dtype) -def transform(node, context): - return SliceTransformer(context).visit(node) +def transform(node, ctx): + return SliceTransformer(ctx).visit(node) diff --git a/tensorflow/contrib/autograph/converters/slices_test.py b/tensorflow/contrib/autograph/converters/slices_test.py index 6c2d7e1ea1..df9a4c8bab 100644 --- a/tensorflow/contrib/autograph/converters/slices_test.py +++ b/tensorflow/contrib/autograph/converters/slices_test.py @@ -19,15 +19,15 @@ from __future__ import division from __future__ import print_function from tensorflow.contrib.autograph import utils -from tensorflow.contrib.autograph.converters import converter_test_base from tensorflow.contrib.autograph.converters import slices +from tensorflow.contrib.autograph.core import converter_testing from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.ops import list_ops from tensorflow.python.platform import test -class SliceTest(converter_test_base.TestCase): +class SliceTest(converter_testing.TestCase): def test_index_access(self): diff --git a/tensorflow/contrib/autograph/core/converter.py b/tensorflow/contrib/autograph/core/converter.py index 5f26e0e1fc..54e6aa0f3b 100644 --- a/tensorflow/contrib/autograph/core/converter.py +++ b/tensorflow/contrib/autograph/core/converter.py @@ -53,6 +53,10 @@ Below is the overal flow at conversion: entity = converter.visit(entity) + +Note that pyct contains a small number of transformers used for static analysis. +These implement transformer.Base, rather than converter.Base, to avoid a +dependency on AutoGraph. """ from __future__ import absolute_import @@ -87,7 +91,7 @@ class ProgramContext(object): in the generated code name_map: Dict[str, str], map of original entity name to the name of their converted counterparts - ag_module: Module, a reference to the autograph module. This + autograph_module: Module, a reference to the autograph module. This needs to be specified by the caller to avoid circular dependencies. uncompiled_modules: Set[Tuple[str, ...]], with each tuple representing the fully qualified name of a package containing functions that will not be @@ -97,19 +101,18 @@ class ProgramContext(object): to the closures of each entity, which are attached dynamically. """ - # TODO(mdan): Rename ag_module to autograph_module? def __init__( self, recursive, autograph_decorators, partial_types, - ag_module, + autograph_module, uncompiled_modules, ): self.recursive = recursive self.autograph_decorators = autograph_decorators self.partial_types = partial_types if partial_types else () - self.ag_module = ag_module + self.autograph_module = autograph_module self.uncompiled_modules = uncompiled_modules # Required to output dependencies in discovery order, which should match @@ -189,11 +192,19 @@ class Base(transformer.Base): def __init__(self, ctx): super(Base, self).__init__(ctx.info) - self._used = False self.ctx = ctx # Keeping this short because it's used frequently. + self._used = False + self._ast_depth = 0 + def visit(self, node): - if self._used: - raise ValueError('visit may only be called once') - self._used = True - super(Base, self).visit(node) + if not self._ast_depth: + if self._used: + raise ValueError('converter objects cannot be reused') + self._used = True + + self._ast_depth += 1 + try: + return super(Base, self).visit(node) + finally: + self._ast_depth -= 1 diff --git a/tensorflow/contrib/autograph/core/converter_testing.py b/tensorflow/contrib/autograph/core/converter_testing.py index eee51c1f6f..0e46aacc12 100644 --- a/tensorflow/contrib/autograph/core/converter_testing.py +++ b/tensorflow/contrib/autograph/core/converter_testing.py @@ -131,7 +131,7 @@ class TestCase(test.TestCase): recursive=recursive, autograph_decorators=autograph_decorators, partial_types=None, - ag_module=None, + autograph_module=None, uncompiled_modules=config.DEFAULT_UNCOMPILED_MODULES) entity_info = transformer.EntityInfo( source_code=source, diff --git a/tensorflow/contrib/autograph/impl/BUILD b/tensorflow/contrib/autograph/impl/BUILD index 02f16ae187..a5438592c3 100644 --- a/tensorflow/contrib/autograph/impl/BUILD +++ b/tensorflow/contrib/autograph/impl/BUILD @@ -18,20 +18,19 @@ py_library( name = "impl", srcs = [ "api.py", - "config.py", "conversion.py", - "directives.py", - "naming.py", - "special_functions.py", ], srcs_version = "PY2AND3", visibility = ["//tensorflow:__subpackages__"], deps = [ "//tensorflow/contrib/autograph/converters", + "//tensorflow/contrib/autograph/core", "//tensorflow/contrib/autograph/operators", "//tensorflow/contrib/autograph/pyct", "//tensorflow/contrib/autograph/pyct/static_analysis", "//tensorflow/contrib/autograph/utils", + "//tensorflow/python:platform", + "//tensorflow/python:util", "@gast_archive//:gast", "@six_archive//:six", ], @@ -61,23 +60,3 @@ py_test( "@gast_archive//:gast", ], ) - -py_test( - name = "naming_test", - srcs = ["naming_test.py"], - srcs_version = "PY2AND3", - deps = [ - ":impl", - "//tensorflow/python:client_testlib", - ], -) - -py_test( - name = "special_functions_test", - srcs = ["special_functions_test.py"], - srcs_version = "PY2AND3", - deps = [ - ":impl", - "//tensorflow/python:client_testlib", - ], -) diff --git a/tensorflow/contrib/autograph/impl/api.py b/tensorflow/contrib/autograph/impl/api.py index 24f87b2c14..209e494ac2 100644 --- a/tensorflow/contrib/autograph/impl/api.py +++ b/tensorflow/contrib/autograph/impl/api.py @@ -27,11 +27,11 @@ import gast import six # pylint:enable=g-bad-import-order -from tensorflow.contrib.autograph.impl import config +from tensorflow.contrib.autograph.core import config +from tensorflow.contrib.autograph.core import converter from tensorflow.contrib.autograph.impl import conversion from tensorflow.contrib.autograph.pyct import compiler from tensorflow.contrib.autograph.pyct import inspect_utils -from tensorflow.contrib.autograph.pyct import parser from tensorflow.contrib.autograph.utils import builtins from tensorflow.contrib.autograph.utils import py_func from tensorflow.python.platform import tf_logging as logging @@ -230,20 +230,20 @@ def to_graph(e, A function with a signature identical to `o`, but which when executed it creates TF a graph that has the same functionality as the original entity. """ - conversion_map = conversion.ConversionMap( + program_ctx = converter.ProgramContext( recursive=recursive, - nocompile_decorators=(convert, do_not_convert, converted_call), + autograph_decorators=(convert, do_not_convert, converted_call), partial_types=partial_types, - api_module=tf_inspect.getmodule(to_graph)) - _, name, namespace = conversion.entity_to_graph(e, conversion_map, arg_values, + autograph_module=tf_inspect.getmodule(to_graph), + uncompiled_modules=config.DEFAULT_UNCOMPILED_MODULES) + _, name, namespace = conversion.entity_to_graph(e, program_ctx, arg_values, arg_types) module = gast.Module([]) - for import_line in config.COMPILED_IMPORT_STATEMENTS: - module.body.extend(parser.parse_str(import_line).body) - for dep in reversed(conversion_map.dependency_cache.values()): + for dep in reversed(program_ctx.dependency_cache.values()): module.body.append(dep) - compiled_node, compiled_src = compiler.ast_to_object(module) + compiled_node, compiled_src = compiler.ast_to_object( + module, source_prefix=program_ctx.required_imports) # The compiled code should see everything the entry entity saw. # TODO(mdan): This might not work well if the call tree spans modules? @@ -280,17 +280,16 @@ def to_code(e, Returns: String. """ - conversion_map = conversion.ConversionMap( + program_ctx = converter.ProgramContext( recursive=recursive, - nocompile_decorators=(convert, do_not_convert, converted_call), + autograph_decorators=(convert, do_not_convert, converted_call), partial_types=partial_types, - api_module=tf_inspect.getmodule(to_graph)) - conversion.entity_to_graph(e, conversion_map, arg_values, arg_types) + autograph_module=tf_inspect.getmodule(to_graph), + uncompiled_modules=config.DEFAULT_UNCOMPILED_MODULES) + conversion.entity_to_graph(e, program_ctx, arg_values, arg_types) - imports = '\n'.join(config.COMPILED_IMPORT_STATEMENTS) code = '\n'.join( compiler.ast_to_source(dep, indentation) - for dep in reversed(tuple( - six.itervalues(conversion_map.dependency_cache)))) + for dep in reversed(tuple(six.itervalues(program_ctx.dependency_cache)))) - return imports + '\n\n' + code + return program_ctx.required_imports + '\n\n' + code diff --git a/tensorflow/contrib/autograph/impl/api_test.py b/tensorflow/contrib/autograph/impl/api_test.py index a7737b7f44..ed9fbdd230 100644 --- a/tensorflow/contrib/autograph/impl/api_test.py +++ b/tensorflow/contrib/autograph/impl/api_test.py @@ -21,8 +21,8 @@ from __future__ import print_function import numpy as np from tensorflow.contrib.autograph import utils +from tensorflow.contrib.autograph.core import config from tensorflow.contrib.autograph.impl import api -from tensorflow.contrib.autograph.impl import config from tensorflow.contrib.autograph.pyct import parser from tensorflow.contrib.autograph.utils import py_func from tensorflow.python.framework import constant_op diff --git a/tensorflow/contrib/autograph/impl/config.py b/tensorflow/contrib/autograph/impl/config.py deleted file mode 100644 index 878bb7e12f..0000000000 --- a/tensorflow/contrib/autograph/impl/config.py +++ /dev/null @@ -1,49 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Global configuration.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from tensorflow.contrib.autograph import utils - - -PYTHON_LITERALS = { - 'None': None, - 'False': False, - 'True': True, - 'float': float, -} - -DEFAULT_UNCOMPILED_MODULES = set(( - ('tensorflow',), - (utils.__name__,), - - # All of tensorflow's subpackages. Unlike the root tf module, they don't - # have well-known names. Not referring to the module directly to avoid - # circular imports. - ( - utils.__name__[:-len('.contrib.autograph.utils')],), -)) - -NO_SIDE_EFFECT_CONSTRUCTORS = set(('tensorflow',)) - -# TODO(mdan): Also allow controlling the generated names. -# TODO(mdan); Consolidate all internal imports into a single __ag module. -COMPILED_IMPORT_STATEMENTS = ( - 'from __future__ import print_function', - 'import tensorflow as tf', -) diff --git a/tensorflow/contrib/autograph/impl/conversion.py b/tensorflow/contrib/autograph/impl/conversion.py index 7802bbbe27..776d19f672 100644 --- a/tensorflow/contrib/autograph/impl/conversion.py +++ b/tensorflow/contrib/autograph/impl/conversion.py @@ -12,13 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""High level conversion support.""" +"""Core conversion logic, serves as main point of access.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function -import collections import imp import gast @@ -39,77 +38,22 @@ from tensorflow.contrib.autograph.converters import name_scopes from tensorflow.contrib.autograph.converters import side_effect_guards from tensorflow.contrib.autograph.converters import single_return from tensorflow.contrib.autograph.converters import slices -from tensorflow.contrib.autograph.impl import config -from tensorflow.contrib.autograph.impl import naming +from tensorflow.contrib.autograph.core import config +from tensorflow.contrib.autograph.core import converter from tensorflow.contrib.autograph.pyct import ast_util -from tensorflow.contrib.autograph.pyct import context from tensorflow.contrib.autograph.pyct import inspect_utils from tensorflow.contrib.autograph.pyct import parser from tensorflow.contrib.autograph.pyct import qual_names +from tensorflow.contrib.autograph.pyct import transformer from tensorflow.contrib.autograph.pyct.static_analysis import activity from tensorflow.contrib.autograph.pyct.static_analysis import live_values from tensorflow.contrib.autograph.pyct.static_analysis import type_info -from tensorflow.contrib.autograph.utils import type_hints from tensorflow.python.util import tf_inspect # TODO(mdan): Might we not need any renaming at all? -class ConversionMap(object): - """ConversionMap keeps track of converting function hierarchies. - - This object is mutable, and is updated as functions are converted. - - Attributes: - recursive: Whether to recursively convert any functions that the decorator - function may call. - nocompile_decorators: tuple of decorator functions that toggle compilation - off. - dependency_cache: dict[object]: ast; maps original entities to their - converted AST - additional_imports: set(object); additional entities which for any reason - cannot be attached after loading and need to be explicitly imported - in the generated code - name_map: dict[string]: string; maps original entities to the name of - their converted counterparts - api_module: A reference to the api module. The reference needs to be passed - to avoid circular dependencies. - """ - - # TODO(mdan): Rename to ConversionContext, and pull in additional flags. - - def __init__(self, recursive, nocompile_decorators, partial_types, - api_module): - self.recursive = recursive - self.nocompile_decorators = nocompile_decorators - self.partial_types = partial_types if partial_types else () - # Required to output dependencies in discovery order, which should match - # the reverse dependency order. - self.dependency_cache = collections.OrderedDict() - self.additional_imports = set() - self.name_map = {} - self.api_module = api_module - - def new_namer(self, namespace): - return naming.Namer(namespace, self.recursive, self.name_map, - self.partial_types) - - def update_name_map(self, namer): - for o, name in namer.renamed_calls.items(): - if o in self.name_map: - if self.name_map[o] != name: - raise ValueError( - 'Calls to %s were converted using multiple names (%s). This is ' - 'possible when an entity with one of these names already ' - 'existed. To fix, avoid using any of these names.') - else: - self.name_map[o] = name - - def add_to_cache(self, original_entity, converted_ast): - self.dependency_cache[original_entity] = converted_ast - - def is_whitelisted_for_graph(o): """Check whether an entity is whitelisted for use in graph mode. @@ -128,7 +72,7 @@ def is_whitelisted_for_graph(o): return False -def entity_to_graph(o, conversion_map, arg_values, arg_types): +def entity_to_graph(o, program_ctx, arg_values, arg_types): """Compile a Python entity into equivalent TensorFlow. The function will also recursively compile all the entities that `o` @@ -139,7 +83,7 @@ def entity_to_graph(o, conversion_map, arg_values, arg_types): Args: o: A Python entity. - conversion_map: A ConversionMap object. + program_ctx: A ProgramContext object. arg_values: A dict containing value hints for symbols like function parameters. arg_types: A dict containing type hints for symbols like function @@ -157,7 +101,7 @@ def entity_to_graph(o, conversion_map, arg_values, arg_types): ValueError: if the entity type is not supported. """ if tf_inspect.isclass(o): - node, name, ns = class_to_graph(o, conversion_map) + node, name, ns = class_to_graph(o, program_ctx) elif tf_inspect.isfunction(o): # TODO(mdan): This is not a reliable mechanism. # The most reliable way is to check the source code, the AST will contain @@ -167,36 +111,35 @@ def entity_to_graph(o, conversion_map, arg_values, arg_types): 'lambda functions are not yet supported; declare the function' ' using def instead: %s' % o) else: - node, name, ns = function_to_graph(o, conversion_map, arg_values, - arg_types) + node, name, ns = function_to_graph(o, program_ctx, arg_values, arg_types) elif tf_inspect.ismethod(o): - node, name, ns = function_to_graph(o, conversion_map, arg_values, arg_types) + node, name, ns = function_to_graph(o, program_ctx, arg_values, arg_types) else: raise ValueError( 'Entity "%s" has unsupported type "%s". Only functions and classes are ' 'supported for now.' % (o, type(o))) - conversion_map.add_to_cache(o, node) - if conversion_map.recursive: + program_ctx.add_to_cache(o, node) + if program_ctx.recursive: while True: candidate = None - for obj in conversion_map.name_map.keys(): - if obj not in conversion_map.dependency_cache: + for obj in program_ctx.name_map.keys(): + if obj not in program_ctx.dependency_cache: candidate = obj break if candidate is None: break if (hasattr(candidate, 'im_class') and - getattr(candidate, 'im_class') not in conversion_map.partial_types): + getattr(candidate, 'im_class') not in program_ctx.partial_types): # Class members are converted with their objects, unless they're # only converted partially. continue - entity_to_graph(candidate, conversion_map, {}, {}) + entity_to_graph(candidate, program_ctx, {}, {}) return node, name, ns -def class_to_graph(c, conversion_map): +def class_to_graph(c, program_ctx): """Specialization of `entity_to_graph` for classes.""" converted_members = {} method_filter = lambda m: tf_inspect.isfunction(m) or tf_inspect.ismethod(m) @@ -211,7 +154,7 @@ def class_to_graph(c, conversion_map): continue node, _, namespace = function_to_graph( m, - conversion_map=conversion_map, + program_ctx=program_ctx, arg_values={}, arg_types={'self': (c.__name__, c)}, owner_type=c) @@ -220,14 +163,14 @@ def class_to_graph(c, conversion_map): else: class_namespace.update(namespace) converted_members[m] = node - namer = conversion_map.new_namer(class_namespace) + namer = program_ctx.new_namer(class_namespace) class_name = namer.compiled_class_name(c.__name__, c) # TODO(mdan): This needs to be explained more thoroughly. # Process any base classes: if the sueprclass if of a whitelisted type, an # absolute import line is generated. Otherwise, it is marked for conversion # (as a side effect of the call to namer.compiled_class_name() followed by - # conversion_map.update_name_map(namer)). + # program_ctx.update_name_map(namer)). output_nodes = [] renames = {} bases = [] @@ -247,7 +190,7 @@ def class_to_graph(c, conversion_map): alias = namer.compiled_class_name(base.__name__, base) bases.append(alias) renames[qual_names.QN(base.__name__)] = qual_names.QN(alias) - conversion_map.update_name_map(namer) + program_ctx.update_name_map(namer) # Generate the definition of the converted class. output_nodes.append( @@ -279,14 +222,14 @@ def _add_reserved_symbol(namespace, name, entity): ag_internal = None -def _add_self_references(namespace, api_module): +def _add_self_references(namespace, autograph_module): """Adds namespace references to the module that exposes the api itself.""" global ag_internal if ag_internal is None: # Craft a module that exposes parts of the external API as well as certain # internal modules. ag_internal = imp.new_module('autograph') - ag_internal.converted_call = api_module.converted_call + ag_internal.converted_call = autograph_module.converted_call ag_internal.utils = utils # TODO(mdan): Add safeguards against name clashes. # We don't want to create a submodule because we want the operators to be @@ -296,27 +239,24 @@ def _add_self_references(namespace, api_module): _add_reserved_symbol(namespace, 'ag__', ag_internal) -def function_to_graph(f, conversion_map, arg_values, arg_types, - owner_type=None): +def function_to_graph(f, program_ctx, arg_values, arg_types, owner_type=None): """Specialization of `entity_to_graph` for callable functions.""" node, source = parser.parse_entity(f) node = node.body[0] namespace = inspect_utils.getnamespace(f) - _add_self_references(namespace, conversion_map.api_module) - namer = conversion_map.new_namer(namespace) + _add_self_references(namespace, program_ctx.autograph_module) + namer = program_ctx.new_namer(namespace) - ctx = context.EntityContext( - namer=namer, + entity_info = transformer.EntityInfo( source_code=source, source_file='', namespace=namespace, arg_values=arg_values, arg_types=arg_types, - owner_type=owner_type, - recursive=conversion_map.recursive, - type_annotation_func=type_hints.set_element_type) - node, deps = node_to_graph(node, ctx, conversion_map.nocompile_decorators) + owner_type=owner_type) + context = converter.EntityContext(namer, entity_info, program_ctx) + node = node_to_graph(node, context) # TODO(mdan): This somewhat duplicates the call rename logic in call_treest.py new_name, did_rename = namer.compiled_function_name(f.__name__, f, owner_type) @@ -326,29 +266,28 @@ def function_to_graph(f, conversion_map, arg_values, arg_types, raise NotImplementedError('Strange corner case. Send us offending code!') node.name = new_name - conversion_map.update_name_map(namer) + program_ctx.update_name_map(namer) # TODO(mdan): Use this at compilation. - conversion_map.additional_imports.update(deps) return node, new_name, namespace -def _static_analysis_pass(node, ctx): +def _apply_transformer(node, context, converter_module): + # TODO(mdan): Clear static analysis here. node = qual_names.resolve(node) - node = activity.resolve(node, ctx, None) - node = live_values.resolve(node, ctx, config.PYTHON_LITERALS) - node = type_info.resolve(node, ctx) + node = activity.resolve(node, context.info, None) + node = live_values.resolve(node, context.info, config.PYTHON_LITERALS) + node = type_info.resolve(node, context.info) + node = converter_module.transform(node, context) return node -def node_to_graph(node, ctx, nocompile_decorators): +def node_to_graph(node, context): """Convert Python code to equivalent TF graph mode code. Args: - node: A Python AST node representing the code to convert. - ctx: An EntityContext object. - nocompile_decorators: A tuple containing decorators to be stripped from - functions during conversion. + node: AST, the code to convert. + context: converter.EntityContext Returns: A tuple (node, deps): @@ -358,57 +297,26 @@ def node_to_graph(node, ctx, nocompile_decorators): """ # TODO(mdan): Verify arguments for correctness. - # TODO(mdan): Factor out common elements. - # These include: - # * code move between blocks - # * visiting blocks in transformers - - # Certain steps, especially canonicalization, insert new symbols into the - # tree, which must be accounted. Although less efficient, it is most robust - # to re-run the analysis. - - node = _static_analysis_pass(node, ctx) - - # TODO(mdan): Clean this up. - # Some intermediate analyses are not required, and some comments got orphaned. - - # TODO(mdan): We may assume all converters require analysis to be re-done. - + node = _apply_transformer(node, context, ifexp) # Past this point, line numbers are no longer accurate so we ignore the # source. # TODO(mdan): Is it feasible to reconstruct intermediate source code? - ctx.source_code = None - node = ifexp.transform(node, ctx) - node, deps = decorators.transform(node, nocompile_decorators) - node = break_statements.transform(node, ctx) - node = _static_analysis_pass(node, ctx) - - node = asserts.transform(node, ctx) - + context.info.source_code = None + node = _apply_transformer(node, context, decorators) + node = _apply_transformer(node, context, break_statements) + node = _apply_transformer(node, context, asserts) # Note: sequencing continue canonicalization before for loop one avoids # dealing with the extra loop increment operation that the for # canonicalization creates. - node = continue_statements.transform(node, ctx) - ctx.namespace['len'] = len - - node = _static_analysis_pass(node, ctx) - node = single_return.transform(node, ctx) - - node = _static_analysis_pass(node, ctx) - node = lists.transform(node, ctx) - node = _static_analysis_pass(node, ctx) - node = slices.transform(node, ctx) - node = builtin_functions.transform(node, ctx) - - node = _static_analysis_pass(node, ctx) - node = call_trees.transform(node, ctx, config.DEFAULT_UNCOMPILED_MODULES, - nocompile_decorators) - node = control_flow.transform(node, ctx) - - # control_flow may create new symbols and change scopes. - node = _static_analysis_pass(node, ctx) - node = logical_expressions.transform(node, ctx) - node = side_effect_guards.transform(node, ctx) - node = name_scopes.transform(node, ctx) - - return node, deps + node = _apply_transformer(node, context, continue_statements) + context.info.namespace['len'] = len + node = _apply_transformer(node, context, single_return) + node = _apply_transformer(node, context, lists) + node = _apply_transformer(node, context, slices) + node = _apply_transformer(node, context, builtin_functions) + node = _apply_transformer(node, context, call_trees) + node = _apply_transformer(node, context, control_flow) + node = _apply_transformer(node, context, logical_expressions) + node = _apply_transformer(node, context, side_effect_guards) + node = _apply_transformer(node, context, name_scopes) + return node diff --git a/tensorflow/contrib/autograph/impl/conversion_test.py b/tensorflow/contrib/autograph/impl/conversion_test.py index bc61498b54..f5279298af 100644 --- a/tensorflow/contrib/autograph/impl/conversion_test.py +++ b/tensorflow/contrib/autograph/impl/conversion_test.py @@ -21,6 +21,8 @@ from __future__ import print_function import gast from tensorflow.contrib.autograph import utils +from tensorflow.contrib.autograph.core import config +from tensorflow.contrib.autograph.core import converter from tensorflow.contrib.autograph.impl import api from tensorflow.contrib.autograph.impl import conversion from tensorflow.python.framework import constant_op @@ -30,8 +32,13 @@ from tensorflow.python.platform import test class ConversionTest(test.TestCase): - def _simple_conversion_map(self): - return conversion.ConversionMap(True, (), (), api) + def _simple_program_ctx(self): + return converter.ProgramContext( + recursive=True, + autograph_decorators=(), + partial_types=(), + autograph_module=api, + uncompiled_modules=config.DEFAULT_UNCOMPILED_MODULES) def test_is_whitelisted_for_graph(self): @@ -44,16 +51,16 @@ class ConversionTest(test.TestCase): def test_entity_to_graph_unsupported_types(self): with self.assertRaises(ValueError): - conversion_map = self._simple_conversion_map() - conversion.entity_to_graph('dummy', conversion_map, None, None) + program_ctx = self._simple_program_ctx() + conversion.entity_to_graph('dummy', program_ctx, None, None) def test_entity_to_graph_callable(self): b = 2 def f(a): return a + b - conversion_map = self._simple_conversion_map() - ast, name, ns = conversion.entity_to_graph(f, conversion_map, None, None) + program_ctx = self._simple_program_ctx() + ast, name, ns = conversion.entity_to_graph(f, program_ctx, None, None) self.assertTrue(isinstance(ast, gast.FunctionDef), ast) self.assertEqual('tf__f', name) self.assertTrue(ns['b'] is b) @@ -66,18 +73,17 @@ class ConversionTest(test.TestCase): def f(a): return g(a) - conversion_map = self._simple_conversion_map() - conversion.entity_to_graph(f, conversion_map, None, None) + program_ctx = self._simple_program_ctx() + conversion.entity_to_graph(f, program_ctx, None, None) - self.assertTrue(f in conversion_map.dependency_cache) - self.assertTrue(g in conversion_map.dependency_cache) - self.assertEqual('tf__f', conversion_map.dependency_cache[f].name) + self.assertTrue(f in program_ctx.dependency_cache) + self.assertTrue(g in program_ctx.dependency_cache) + self.assertEqual('tf__f', program_ctx.dependency_cache[f].name) # need the extra .body[0] in order to step past the with tf.name_scope('f') # that is added automatically self.assertEqual( - 'tf__g', - conversion_map.dependency_cache[f].body[0].body[0].value.func.id) - self.assertEqual('tf__g', conversion_map.dependency_cache[g].name) + 'tf__g', program_ctx.dependency_cache[f].body[0].body[0].value.func.id) + self.assertEqual('tf__g', program_ctx.dependency_cache[g].name) def test_entity_to_graph_class_hierarchy(self): @@ -104,16 +110,15 @@ class ConversionTest(test.TestCase): def baz(self): return self.y - conversion_map = self._simple_conversion_map() - conversion.entity_to_graph(TestSubclass, conversion_map, None, None) + program_ctx = self._simple_program_ctx() + conversion.entity_to_graph(TestSubclass, program_ctx, None, None) - self.assertTrue(TestBase in conversion_map.dependency_cache) - self.assertTrue(TestSubclass in conversion_map.dependency_cache) + self.assertTrue(TestBase in program_ctx.dependency_cache) + self.assertTrue(TestSubclass in program_ctx.dependency_cache) self.assertEqual('TfTestBase', - conversion_map.dependency_cache[TestBase].body[-1].name) - self.assertEqual( - 'TfTestSubclass', - conversion_map.dependency_cache[TestSubclass].body[-1].name) + program_ctx.dependency_cache[TestBase].body[-1].name) + self.assertEqual('TfTestSubclass', + program_ctx.dependency_cache[TestSubclass].body[-1].name) def test_entity_to_graph_class_hierarchy_whitelisted(self): @@ -126,24 +131,23 @@ class ConversionTest(test.TestCase): def call(self, x): return 3 * x - conversion_map = self._simple_conversion_map() - conversion.entity_to_graph(TestSubclass, conversion_map, None, None) + program_ctx = self._simple_program_ctx() + conversion.entity_to_graph(TestSubclass, program_ctx, None, None) - self.assertTrue(TestSubclass in conversion_map.dependency_cache) - self.assertFalse(training.Model in conversion_map.dependency_cache) + self.assertTrue(TestSubclass in program_ctx.dependency_cache) + self.assertFalse(training.Model in program_ctx.dependency_cache) self.assertEqual( 'Model', - conversion_map.dependency_cache[TestSubclass].body[0].names[0].name) - self.assertEqual( - 'TfTestSubclass', - conversion_map.dependency_cache[TestSubclass].body[-1].name) + program_ctx.dependency_cache[TestSubclass].body[0].names[0].name) + self.assertEqual('TfTestSubclass', + program_ctx.dependency_cache[TestSubclass].body[-1].name) def test_entity_to_graph_lambda(self): f = lambda a: a with self.assertRaises(NotImplementedError): - conversion_map = self._simple_conversion_map() - conversion.entity_to_graph(f, conversion_map, None, None) + program_ctx = self._simple_program_ctx() + conversion.entity_to_graph(f, program_ctx, None, None) def test_ag_module_cached(self): def callee(): @@ -152,11 +156,11 @@ class ConversionTest(test.TestCase): def caller(a): return a() - conversion_map = self._simple_conversion_map() - _, _, callee_ns = conversion.entity_to_graph( - callee, conversion_map, None, None) - _, _, caller_ns = conversion.entity_to_graph( - caller, conversion_map, None, None) + program_ctx = self._simple_program_ctx() + _, _, callee_ns = conversion.entity_to_graph(callee, program_ctx, None, + None) + _, _, caller_ns = conversion.entity_to_graph(caller, program_ctx, None, + None) self.assertTrue(callee_ns['ag__'] is caller_ns['ag__']) diff --git a/tensorflow/contrib/autograph/impl/directives.py b/tensorflow/contrib/autograph/impl/directives.py deleted file mode 100644 index aabe5d9939..0000000000 --- a/tensorflow/contrib/autograph/impl/directives.py +++ /dev/null @@ -1,68 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Directives are special no-op functions that serve as compilation markers. - -They provide static information like type hints, compilation and TensorFlow -overrides. - -These serve as annotations in the compiled code, allowing the user some control -over the compilation process. They have no functional role at runtime. -""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - - -UNSPECIFIED = object() - - -def set_element_type(entity, dtype, shape=UNSPECIFIED): - """Indicates that the entity is expected hold items of specified type/shape. - - The staged TensorFlow ops will reflect and assert this data type. Ignored - otherwise. - - Args: - entity: The entity to annotate. - dtype: TensorFlow dtype value to assert for entity. - shape: Optional shape to assert for entity. - """ - del entity - del dtype - del shape - - -def set_loop_options( - parallel_iterations=UNSPECIFIED, - back_prop=UNSPECIFIED, - swap_memory=UNSPECIFIED, - maximum_iterations=UNSPECIFIED): - """Specifies additional arguments to be passed to the enclosing while_loop. - - The parameters apply to and only to the immediately enclosing loop. It only - has effect if the loop is staged as a TF while_loop; otherwise the parameters - have no effect. - - Args: - parallel_iterations: See tf.while_loop. - back_prop: See tf.while_loop. - swap_memory: See tf.while_loop. - maximum_iterations: See tf.while_loop. - """ - del parallel_iterations - del back_prop - del swap_memory - del maximum_iterations diff --git a/tensorflow/contrib/autograph/impl/naming.py b/tensorflow/contrib/autograph/impl/naming.py deleted file mode 100644 index b1d3f76be7..0000000000 --- a/tensorflow/contrib/autograph/impl/naming.py +++ /dev/null @@ -1,130 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Symbol naming utilities.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from tensorflow.contrib.autograph.pyct import qual_names - - -class Namer(object): - """Implementation of the namer interfaces required by various converters. - - This implementation performs additional tasks like keeping track of the - function calls that have been encountered and replaced with calls to their - corresponding compiled counterparts. - - Interfaces currently implemented: - * call_trees.FunctionNamer - * control_flow.SymbolNamer - * side_effect_guards.SymbolNamer - """ - - def __init__(self, global_namespace, recursive, name_map, partial_types): - self.global_namespace = global_namespace - self.recursive = recursive - self.partial_types = partial_types - - self.renamed_calls = {} - if name_map is not None: - self.renamed_calls.update(name_map) - - self.generated_names = set() - - def compiled_class_name(self, original_fqn, live_entity=None): - """See call_trees.FunctionNamer.compiled_class_name.""" - if live_entity is not None and live_entity in self.renamed_calls: - return self.renamed_calls[live_entity] - - if isinstance(original_fqn, tuple): - original_name = '__'.join(original_fqn) - else: - original_name = original_fqn - - new_name_root = 'Tf%s' % original_name - new_name = new_name_root - n = 0 - while new_name in self.global_namespace: - n += 1 - new_name = '%s_%d' % (new_name_root, n) - - self.generated_names.add(new_name) - if live_entity is not None: - self.renamed_calls[live_entity] = new_name - return new_name - - def compiled_function_name(self, - original_fqn, - live_entity=None, - owner_type=None): - """See call_trees.FunctionNamer.compiled_function_name.""" - - if not self.recursive: - return None, False - - if owner_type is not None and owner_type not in self.partial_types: - # Members are not renamed when part of an entire converted class. - return None, False - - if isinstance(original_fqn, tuple): - original_name = '__'.join(original_fqn) - else: - original_name = original_fqn - - if live_entity is not None and live_entity in self.renamed_calls: - return self.renamed_calls[live_entity], True - - new_name_root = 'tf__%s' % original_name - new_name = new_name_root - n = 0 - while new_name in self.global_namespace: - n += 1 - new_name = '%s_%d' % (new_name_root, n) - - if live_entity is not None: - self.renamed_calls[live_entity] = new_name - self.generated_names.add(new_name) - - return new_name, True - - def new_symbol(self, name_root, reserved_locals): - """See control_flow.SymbolNamer.new_symbol.""" - # reserved_locals may contain QNs. - all_reserved_locals = set() - for s in reserved_locals: - if isinstance(s, qual_names.QN): - all_reserved_locals.update(s.qn) - elif isinstance(s, str): - all_reserved_locals.add(s) - else: - raise ValueError('Unexpected symbol type "%s"' % type(s)) - - pieces = name_root.split('_') - if pieces[-1].isdigit(): - name_root = '_'.join(pieces[:-1]) - n = int(pieces[-1]) - else: - n = 0 - new_name = name_root - - while (new_name in self.global_namespace or - new_name in all_reserved_locals or new_name in self.generated_names): - n += 1 - new_name = '%s_%d' % (name_root, n) - - self.generated_names.add(new_name) - return new_name diff --git a/tensorflow/contrib/autograph/impl/naming_test.py b/tensorflow/contrib/autograph/impl/naming_test.py deleted file mode 100644 index 73fc089465..0000000000 --- a/tensorflow/contrib/autograph/impl/naming_test.py +++ /dev/null @@ -1,77 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for naming module.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from tensorflow.contrib.autograph.impl import naming -from tensorflow.python.platform import test - - -class NamerTest(test.TestCase): - - def test_compiled_function_name_tracks_names(self): - def bar(): - pass - - namer = naming.Namer({}, True, None, ()) - self.assertEqual(('tf__foo', True), namer.compiled_function_name('foo')) - self.assertEqual(('tf__bar', True), namer.compiled_function_name( - 'bar', bar)) - self.assertEqual({bar: 'tf__bar'}, namer.renamed_calls) - self.assertItemsEqual(('tf__bar', 'tf__foo'), namer.generated_names) - - def test_compiled_function_name_consistent(self): - def foo(): - pass - - namer = naming.Namer({}, True, None, ()) - self.assertEqual(('tf__foo', True), namer.compiled_function_name( - 'foo', foo)) - self.assertEqual(('tf__foo', True), namer.compiled_function_name( - 'foo', foo)) - - def test_compiled_function_name_avoids_global_conflicts(self): - def foo(): - pass - - namer = naming.Namer({'tf__foo': 1}, True, None, ()) - self.assertEqual(('tf__foo_1', True), - namer.compiled_function_name('foo', foo)) - - def test_new_symbol_tracks_names(self): - namer = naming.Namer({}, True, None, ()) - self.assertEqual('temp', namer.new_symbol('temp', set())) - self.assertItemsEqual(('temp',), namer.generated_names) - - def test_new_symbol_avoids_duplicates(self): - namer = naming.Namer({}, True, None, ()) - self.assertEqual('temp', namer.new_symbol('temp', set())) - self.assertEqual('temp_1', namer.new_symbol('temp', set())) - self.assertItemsEqual(('temp', 'temp_1'), namer.generated_names) - - def test_new_symbol_avoids_conflicts(self): - namer = naming.Namer({'temp': 1}, True, None, ()) - # temp is reserved in the global namespace - self.assertEqual('temp_1', namer.new_symbol('temp', set())) - # temp_2 is reserved in the local namespace - self.assertEqual('temp_3', namer.new_symbol('temp', set(('temp_2',)))) - self.assertItemsEqual(('temp_1', 'temp_3'), namer.generated_names) - - -if __name__ == '__main__': - test.main() diff --git a/tensorflow/contrib/autograph/impl/special_functions.py b/tensorflow/contrib/autograph/impl/special_functions.py deleted file mode 100644 index b7a8177c44..0000000000 --- a/tensorflow/contrib/autograph/impl/special_functions.py +++ /dev/null @@ -1,48 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Special functions that only make sense for AutoGraph. - -These functions are meant to ensure feature parity between Python and AutoGraph, -so that the exact same code works in both modes. In general, AutoGraph will -replace these calls. -""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from tensorflow.contrib.autograph.operators import data_structures - - -def stack(list_or_tensor, element_dtype=None): - """Stacks the input, if it admits the notion of stacking. No-op otherwise. - - For example, a list of tensors can be stacked into a larger tensor. This - function is similar to tf.stack, but it accepts non-lists and lists of - non-tensors as arguments. In the latter case, the function does nothing. - - Args: - list_or_tensor: Any entity. - element_dtype: Optional dtype for the elements in the list. Required if the - input is stackable, and the list is untyped. - - Returns: - If the input is stackable, a new object representing the stacked inputs. - Otherwise it returns list_or_tensor unchanged. - """ - return data_structures.list_stack( - list_or_tensor, - data_structures.ListStackOpts( - element_dtype=element_dtype, original_call=lambda x: x)) diff --git a/tensorflow/contrib/autograph/impl/special_functions_test.py b/tensorflow/contrib/autograph/impl/special_functions_test.py deleted file mode 100644 index 9b52d2a59b..0000000000 --- a/tensorflow/contrib/autograph/impl/special_functions_test.py +++ /dev/null @@ -1,50 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for special_functions module.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from tensorflow.contrib.autograph.impl import special_functions -from tensorflow.python.framework import constant_op -from tensorflow.python.framework import dtypes -from tensorflow.python.framework import tensor_util -from tensorflow.python.ops import list_ops -from tensorflow.python.platform import test - - -class SpecialFunctionsTest(test.TestCase): - - def test_basic(self): - self.assertEqual(special_functions.stack(1), 1) - self.assertListEqual(special_functions.stack([1, 2, 3]), [1, 2, 3]) - # TODO(mdan): This should probably forward to tf.stack. - self.assertTrue( - isinstance( - special_functions.stack( - [constant_op.constant(1), - constant_op.constant(2)]), list)) - - t = constant_op.constant([1.0, 2.0]) - l = list_ops.tensor_list_from_tensor( - t, element_shape=constant_op.constant([], dtype=dtypes.int32)) - self.assertTrue( - tensor_util.is_tensor( - special_functions.stack(l, element_dtype=dtypes.float32))) - - -if __name__ == '__main__': - test.main() diff --git a/tensorflow/contrib/autograph/operators/BUILD b/tensorflow/contrib/autograph/operators/BUILD index 0c6ab65505..332d5dab19 100644 --- a/tensorflow/contrib/autograph/operators/BUILD +++ b/tensorflow/contrib/autograph/operators/BUILD @@ -28,7 +28,15 @@ py_library( visibility = ["//tensorflow:__subpackages__"], deps = [ "//tensorflow/contrib/autograph/utils", + "//tensorflow/python:array_ops", + "//tensorflow/python:constant_op", + "//tensorflow/python:control_flow_ops", + "//tensorflow/python:dtypes", + "//tensorflow/python:framework_ops", + "//tensorflow/python:list_ops", "//tensorflow/python:tensor_array_ops", + "//tensorflow/python:tensor_util", + "//tensorflow/python:variables", "//tensorflow/python/data/ops:dataset_ops", ], ) diff --git a/tensorflow/contrib/autograph/pyct/BUILD b/tensorflow/contrib/autograph/pyct/BUILD index 989b821e53..8f09689fe9 100644 --- a/tensorflow/contrib/autograph/pyct/BUILD +++ b/tensorflow/contrib/autograph/pyct/BUILD @@ -23,7 +23,6 @@ py_library( "anno.py", "ast_util.py", "compiler.py", - "context.py", "inspect_utils.py", "parser.py", "pretty_printer.py", @@ -38,6 +37,8 @@ py_library( "@gast_archive//:gast", "@six_archive//:six", "@termcolor_archive//:termcolor", + # TODO(mdan): Remove this dependency. + "//tensorflow/python:util", ], ) diff --git a/tensorflow/contrib/autograph/pyct/context.py b/tensorflow/contrib/autograph/pyct/context.py deleted file mode 100644 index b34015cfd2..0000000000 --- a/tensorflow/contrib/autograph/pyct/context.py +++ /dev/null @@ -1,49 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Conversion context containers.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - - -class EntityContext(object): - """Contains information about an entity, like source code. - - In general, objects of this class should be considered immutable. - - Attributes: - namer: Namer that matches the contract of all converters. - source_code: The entity's source code. - source_file: The entity's source file. - namespace: Dict[str->*], containing symbols visible to the entity - (excluding parameters). - arg_values: Dict[str->*], containing parameter values, if known. - arg_types: Dict[str->*], containing parameter types, if known. - owner_type: The surrounding class type of the function, if present. - """ - - # TODO(mdan): Remove the default and update tests. - def __init__(self, namer, source_code, source_file, namespace, arg_values, - arg_types, owner_type, recursive, type_annotation_func=None): - self.namer = namer - self.source_code = source_code - self.source_file = source_file - self.namespace = namespace - self.arg_values = {} if arg_values is None else arg_values - self.arg_types = {} if arg_types is None else arg_types - self.owner_type = owner_type - self.recursive = recursive - self.type_annotation_func = type_annotation_func diff --git a/tensorflow/contrib/autograph/pyct/static_analysis/BUILD b/tensorflow/contrib/autograph/pyct/static_analysis/BUILD index 8064a967cd..bcf2dacec2 100644 --- a/tensorflow/contrib/autograph/pyct/static_analysis/BUILD +++ b/tensorflow/contrib/autograph/pyct/static_analysis/BUILD @@ -27,6 +27,7 @@ py_library( visibility = ["//visibility:public"], deps = [ "//tensorflow/contrib/autograph/pyct", + "//tensorflow/contrib/autograph/utils", "@gast_archive//:gast", ], ) diff --git a/tensorflow/contrib/autograph/pyct/static_analysis/activity_test.py b/tensorflow/contrib/autograph/pyct/static_analysis/activity_test.py index fdbd349af9..bc22be0a27 100644 --- a/tensorflow/contrib/autograph/pyct/static_analysis/activity_test.py +++ b/tensorflow/contrib/autograph/pyct/static_analysis/activity_test.py @@ -21,9 +21,9 @@ from __future__ import print_function import gast from tensorflow.contrib.autograph.pyct import anno -from tensorflow.contrib.autograph.pyct import context from tensorflow.contrib.autograph.pyct import parser from tensorflow.contrib.autograph.pyct import qual_names +from tensorflow.contrib.autograph.pyct import transformer from tensorflow.contrib.autograph.pyct.qual_names import QN from tensorflow.contrib.autograph.pyct.static_analysis import activity from tensorflow.contrib.autograph.pyct.static_analysis.annos import NodeAnno @@ -112,18 +112,16 @@ class ActivityAnalyzerTest(test.TestCase): def _parse_and_analyze(self, test_fn): node, source = parser.parse_entity(test_fn) - ctx = context.EntityContext( - namer=None, + entity_info = transformer.EntityInfo( source_code=source, source_file=None, namespace={}, arg_values=None, arg_types=None, - owner_type=None, - recursive=True) + owner_type=None) node = qual_names.resolve(node) - node = activity.resolve(node, ctx) - return node, ctx + node = activity.resolve(node, entity_info) + return node, entity_info def test_local_markers(self): diff --git a/tensorflow/contrib/autograph/pyct/static_analysis/cfg.py b/tensorflow/contrib/autograph/pyct/static_analysis/cfg.py index ad97fdfa8e..358d56ce20 100644 --- a/tensorflow/contrib/autograph/pyct/static_analysis/cfg.py +++ b/tensorflow/contrib/autograph/pyct/static_analysis/cfg.py @@ -276,9 +276,9 @@ class Forward(object): taken). """ - def __init__(self, label, context, transfer_fn=operator.or_): + def __init__(self, label, source_info, transfer_fn=operator.or_): self.transfer_fn = transfer_fn - self.context = context + self.source_info = source_info self.out_label = label + '_out' self.in_label = label + '_in' self.gen_label = label + '_gen' @@ -399,18 +399,18 @@ class Liveness(Backward): later in the program. """ - def __init__(self, context): - super(Liveness, self).__init__('live', context) + def __init__(self, source_info): + super(Liveness, self).__init__('live', source_info) def get_gen_kill(self, node, _): # A variable's parents are live if it is live # e.g. x is live if x.y is live. This means gen needs to return # all parents of a variable (if it's an Attribute or Subscript). # This doesn't apply to kill (e.g. del x.y doesn't affect liveness of x) - gen = activity.get_read(node.value, self.context) + gen = activity.get_read(node.value, self.source_info) gen = functools.reduce(lambda left, right: left | right.support_set, gen, gen) - kill = activity.get_updated(node.value, self.context) + kill = activity.get_updated(node.value, self.source_info) return gen, kill @@ -420,11 +420,11 @@ class ReachingDefinitions(Forward): Each statement is annotated with a set of (variable, definition) pairs. """ - def __init__(self, context): - super(ReachingDefinitions, self).__init__('definitions', context) + def __init__(self, source_info): + super(ReachingDefinitions, self).__init__('definitions', source_info) def get_gen_kill(self, node, incoming): - definitions = activity.get_updated(node.value, self.context) + definitions = activity.get_updated(node.value, self.source_info) gen = frozenset((id_, node.value) for id_ in definitions) kill = frozenset(def_ for def_ in incoming if def_[0] in definitions) return gen, kill @@ -437,9 +437,10 @@ class Defined(Forward): be defined at that point. """ - def __init__(self, context): - super(Defined, self).__init__('defined', context, transfer_fn=operator.and_) + def __init__(self, source_info): + super(Defined, self).__init__( + 'defined', source_info, transfer_fn=operator.and_) def get_gen_kill(self, node, _): - gen = activity.get_updated(node.value, self.context) + gen = activity.get_updated(node.value, self.source_info) return gen, frozenset() diff --git a/tensorflow/contrib/autograph/pyct/static_analysis/cfg_test.py b/tensorflow/contrib/autograph/pyct/static_analysis/cfg_test.py index fc07fa3447..428ebbedca 100644 --- a/tensorflow/contrib/autograph/pyct/static_analysis/cfg_test.py +++ b/tensorflow/contrib/autograph/pyct/static_analysis/cfg_test.py @@ -23,29 +23,26 @@ import functools import gast from tensorflow.contrib.autograph.pyct import anno -from tensorflow.contrib.autograph.pyct import context from tensorflow.contrib.autograph.pyct import parser from tensorflow.contrib.autograph.pyct import qual_names +from tensorflow.contrib.autograph.pyct import transformer from tensorflow.contrib.autograph.pyct.static_analysis import cfg from tensorflow.python.platform import test class CFGTest(test.TestCase): - def _parse_and_analyze(self, test_fn, namespace, arg_types=None): - arg_types = arg_types or {} + def _parse_and_analyze(self, test_fn): node, source = parser.parse_entity(test_fn) - ctx = context.EntityContext( - namer=None, + entity_info = transformer.EntityInfo( source_code=source, source_file=None, - namespace=namespace, + namespace={}, arg_values=None, - arg_types=arg_types, - owner_type=None, - recursive=True) + arg_types=None, + owner_type=None) node = qual_names.resolve(node) - return node, ctx + return node, entity_info def _check_anno_matches(self, node, anno_name, var_names): if isinstance(var_names, str): @@ -73,7 +70,7 @@ class CFGTest(test.TestCase): x = x return x - node, ctx = self._parse_and_analyze(f, {}) + node, ctx = self._parse_and_analyze(f) cfg.run_analyses(node, cfg.ReachingDefinitions(ctx)) body = node.body[0].body # Only the argument reaches the expression @@ -106,7 +103,7 @@ class CFGTest(test.TestCase): y = 2 # pylint: disable=unused-variable return x - node, ctx = self._parse_and_analyze(f, {}) + node, ctx = self._parse_and_analyze(f) cfg.run_analyses(node, cfg.Defined(ctx)) body = node.body[0].body # only x is for sure defined at the end @@ -116,7 +113,7 @@ class CFGTest(test.TestCase): self._check_anno_matches(if_body[0], 'defined_out', ('x', 'y')) def _get_live_annotated_fnbody(self, f): - node, ctx = self._parse_and_analyze(f, {}) + node, ctx = self._parse_and_analyze(f) cfg.run_analyses(node, cfg.Liveness(ctx)) body = node.body[0].body return body @@ -226,7 +223,7 @@ class CFGTest(test.TestCase): return g(x) - node, ctx = self._parse_and_analyze(f, {}) + node, ctx = self._parse_and_analyze(f) cfg.run_analyses(node, cfg.Defined(ctx)) body = node.body[0].body @@ -253,7 +250,7 @@ class CFGTest(test.TestCase): return g() # y is not defined here - node, ctx = self._parse_and_analyze(f, {}) + node, ctx = self._parse_and_analyze(f) cfg.run_analyses(node, cfg.Defined(ctx)) body = node.body[0].body self.assertEqual( @@ -282,7 +279,7 @@ class CFGTest(test.TestCase): return x, y for f in (for_orelse, while_orelse): - node, ctx = self._parse_and_analyze(f, {}) + node, ctx = self._parse_and_analyze(f) cfg.run_analyses(node, cfg.ReachingDefinitions(ctx)) body = node.body[0].body return_node = body[-1] diff --git a/tensorflow/contrib/autograph/pyct/static_analysis/live_values.py b/tensorflow/contrib/autograph/pyct/static_analysis/live_values.py index 53ae154590..9ccb98f79a 100644 --- a/tensorflow/contrib/autograph/pyct/static_analysis/live_values.py +++ b/tensorflow/contrib/autograph/pyct/static_analysis/live_values.py @@ -39,7 +39,7 @@ class LiveValueResolver(transformer.Base): def visit_ClassDef(self, node): self.generic_visit(node) - anno.setanno(node, 'live_val', self.context.namespace[node.name]) + anno.setanno(node, 'live_val', self.entity_info.namespace[node.name]) return node def visit_Name(self, node): @@ -55,8 +55,8 @@ class LiveValueResolver(transformer.Base): if not symbol_is_local and not symbol_is_param: if node.id in self.literals: anno.setanno(node, 'live_val', self.literals[node.id]) - elif node.id in self.context.namespace: - obj = self.context.namespace[node.id] + elif node.id in self.entity_info.namespace: + obj = self.entity_info.namespace[node.id] anno.setanno(node, 'live_val', obj) if hasattr(obj, '__name__'): anno.setanno(node, 'fqn', (obj.__name__,)) @@ -80,8 +80,8 @@ class LiveValueResolver(transformer.Base): # TODO(mdan): Use type annotations as fallback. if not symbol_is_modified: - if node.id in self.context.arg_values: - obj = self.context.arg_values[node.id] + if node.id in self.entity_info.arg_values: + obj = self.entity_info.arg_values[node.id] anno.setanno(node, 'live_val', obj) anno.setanno(node, 'fqn', (obj.__class__.__name__,)) return node diff --git a/tensorflow/contrib/autograph/pyct/static_analysis/live_values_test.py b/tensorflow/contrib/autograph/pyct/static_analysis/live_values_test.py index 69e428bde1..38af792777 100644 --- a/tensorflow/contrib/autograph/pyct/static_analysis/live_values_test.py +++ b/tensorflow/contrib/autograph/pyct/static_analysis/live_values_test.py @@ -21,9 +21,9 @@ from __future__ import print_function import six from tensorflow.contrib.autograph.pyct import anno -from tensorflow.contrib.autograph.pyct import context from tensorflow.contrib.autograph.pyct import parser from tensorflow.contrib.autograph.pyct import qual_names +from tensorflow.contrib.autograph.pyct import transformer from tensorflow.contrib.autograph.pyct.static_analysis import activity from tensorflow.contrib.autograph.pyct.static_analysis import live_values from tensorflow.contrib.autograph.pyct.static_analysis import type_info @@ -39,22 +39,19 @@ class LiveValuesResolverTest(test.TestCase): literals=None, arg_types=None): literals = literals or {} - arg_types = arg_types or {} node, source = parser.parse_entity(test_fn) - ctx = context.EntityContext( - namer=None, + entity_info = transformer.EntityInfo( source_code=source, source_file=None, namespace=namespace, arg_values=None, arg_types=arg_types, - owner_type=None, - recursive=True) + owner_type=None) node = qual_names.resolve(node) - node = activity.resolve(node, ctx) - node = live_values.resolve(node, ctx, literals) - node = type_info.resolve(node, ctx) - node = live_values.resolve(node, ctx, literals) + node = activity.resolve(node, entity_info) + node = live_values.resolve(node, entity_info, literals) + node = type_info.resolve(node, entity_info) + node = live_values.resolve(node, entity_info, literals) return node def test_literals(self): diff --git a/tensorflow/contrib/autograph/pyct/static_analysis/type_info.py b/tensorflow/contrib/autograph/pyct/static_analysis/type_info.py index 7d1e65c958..a229c288a8 100644 --- a/tensorflow/contrib/autograph/pyct/static_analysis/type_info.py +++ b/tensorflow/contrib/autograph/pyct/static_analysis/type_info.py @@ -43,6 +43,7 @@ from __future__ import print_function import gast +from tensorflow.contrib.autograph import utils from tensorflow.contrib.autograph.pyct import anno from tensorflow.contrib.autograph.pyct import parser from tensorflow.contrib.autograph.pyct import transformer @@ -52,6 +53,7 @@ from tensorflow.python.util import tf_inspect # TODO(mdan): Remove the duplication between this and activity.py. # In particular, the symbol definitions we track here could as well be tracked # there because they follow the same rules for visibility. +# TODO(mdan): Use a CFG based Defined analysis instead. class Scope(object): """Tracks symbol value references. @@ -135,35 +137,40 @@ class TypeInfoResolver(transformer.Base): node.orelse = self._visit_block(node.orelse) return node - def _process_function_arg(self, arg_name): - str_name = str(arg_name) - type_holder = arg_name.ast() - self.scope.setval(arg_name, type_holder) - if len(self.enclosing_entities) == 1 and str_name in self.context.arg_types: + def _process_function_arg(self, arg_node): + qn = anno.getanno(arg_node, anno.Basic.QN) + arg_name = str(qn) + self.scope.setval(qn, arg_node) + if (len(self.enclosing_entities) == 1 and + arg_name in self.entity_info.arg_types): # Forge a node to hold the type information, so that method calls on # it can resolve the type. - type_string, type_obj = self.context.arg_types[str_name] - anno.setanno(type_holder, 'type', type_obj) - anno.setanno(type_holder, 'type_fqn', tuple(type_string.split('.'))) + type_string, type_obj = self.entity_info.arg_types[arg_name] + anno.setanno(arg_node, 'type', type_obj) + anno.setanno(arg_node, 'type_fqn', tuple(type_string.split('.'))) def visit_arg(self, node): - self._process_function_arg(anno.getanno(node.arg, anno.Basic.QN)) + self._process_function_arg(node.arg) return node def visit_Name(self, node): self.generic_visit(node) - qn = anno.getanno(node, anno.Basic.QN) if isinstance(node.ctx, gast.Param): - self._process_function_arg(qn) - elif isinstance(node.ctx, gast.Load) and self.scope.hasval(qn): - # E.g. if we had - # a = b - # then for future references to `a` we should have definition = `b` - definition = self.scope.getval(qn) - anno.copyanno(definition, node, 'type') - anno.copyanno(definition, node, 'type_fqn') - anno.copyanno(definition, node, 'element_type') - anno.copyanno(definition, node, 'element_shape') + self._process_function_arg(node) + elif isinstance(node.ctx, gast.Load): + qn = anno.getanno(node, anno.Basic.QN) + if self.scope.hasval(qn): + # E.g. if we had + # a = b + # then for future references to `a` we should have definition = `b` + definition = self.scope.getval(qn) + anno.copyanno(definition, node, 'type') + anno.copyanno(definition, node, 'type_fqn') + anno.setanno(node, 'definition', definition) + + # TODO(mdan): Remove this when the directives module is in. + anno.copyanno(definition, node, 'element_type') + anno.copyanno(definition, node, 'element_shape') return node def _process_variable_assignment(self, target, value): @@ -203,12 +210,12 @@ class TypeInfoResolver(transformer.Base): node.targets, node.value, self._process_variable_assignment) return node + # TODO(mdan): Remove as soon as the new directives module is ready. def visit_Call(self, node): if anno.hasanno(node.func, 'live_val'): # Symbols targeted by the "set_type" marker function are assigned the data # type that it specified. - if (anno.getanno(node.func, 'live_val') is - self.context.type_annotation_func): + if anno.getanno(node.func, 'live_val') is utils.set_element_type: if len(node.args) < 2 or len(node.args) > 3: raise ValueError('"%s" must have either two or three parameters' @@ -219,8 +226,8 @@ class TypeInfoResolver(transformer.Base): else: target_arg, type_arg, shape_arg = node.args if not anno.hasanno(target_arg, anno.Basic.QN): - raise ValueError('the first argument of "%s" must by a symbol' - % self.context.type_annotation_func) + raise ValueError('the first argument of "%s" must by a symbol' % + utils.set_element_type) # TODO(mdan): This is vulnerable to symbol renaming. element_type = type_arg element_shape = shape_arg diff --git a/tensorflow/contrib/autograph/pyct/static_analysis/type_info_test.py b/tensorflow/contrib/autograph/pyct/static_analysis/type_info_test.py index 484562f294..32b1148ab2 100644 --- a/tensorflow/contrib/autograph/pyct/static_analysis/type_info_test.py +++ b/tensorflow/contrib/autograph/pyct/static_analysis/type_info_test.py @@ -18,11 +18,10 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.autograph import utils from tensorflow.contrib.autograph.pyct import anno -from tensorflow.contrib.autograph.pyct import context from tensorflow.contrib.autograph.pyct import parser from tensorflow.contrib.autograph.pyct import qual_names +from tensorflow.contrib.autograph.pyct import transformer from tensorflow.contrib.autograph.pyct.static_analysis import activity from tensorflow.contrib.autograph.pyct.static_analysis import live_values from tensorflow.contrib.autograph.pyct.static_analysis import type_info @@ -62,21 +61,18 @@ class TypeInfoResolverTest(test.TestCase): namespace, arg_types=None): node, source = parser.parse_entity(test_fn) - ctx = context.EntityContext( - namer=None, + entity_info = transformer.EntityInfo( source_code=source, source_file=None, namespace=namespace, arg_values=None, arg_types=arg_types, - owner_type=None, - recursive=True, - type_annotation_func=utils.set_element_type) + owner_type=None) node = qual_names.resolve(node) - node = activity.resolve(node, ctx) - node = live_values.resolve(node, ctx, {}) - node = type_info.resolve(node, ctx) - node = live_values.resolve(node, ctx, {}) + node = activity.resolve(node, entity_info) + node = live_values.resolve(node, entity_info, {}) + node = type_info.resolve(node, entity_info) + node = live_values.resolve(node, entity_info, {}) return node def test_constructor_detection(self): @@ -147,7 +143,7 @@ class TypeInfoResolverTest(test.TestCase): opt.minimize(0) node = self._parse_and_analyze( - test_fn, {'training': training}, + test_fn, {}, arg_types={ 'opt': (training.GradientDescentOptimizer.__name__, training.GradientDescentOptimizer) @@ -180,35 +176,6 @@ class TypeInfoResolverTest(test.TestCase): method_call = node.body[0].body[1].value.func self.assertFalse(anno.hasanno(method_call, 'live_val')) - def test_type_annotation(self): - - class Foo(object): - pass - - def test_fn(): - f = [] - f = utils.set_element_type(f, Foo, (1, 2, 3)) - return f - - node = self._parse_and_analyze(test_fn, {'Foo': Foo, 'utils': utils}) - f_def = node.body[0].body[0].value - self.assertEqual(anno.getanno(f_def, 'element_type').id, 'Foo') - f_ref = node.body[0].body[1].value - self.assertEqual(anno.getanno(f_ref, 'element_type').id, 'Foo') - - def test_type_annotation_args(self): - - class Foo(object): - pass - - def test_fn(f): - utils.set_element_type(f, Foo) - return f - - node = self._parse_and_analyze(test_fn, {'Foo': Foo, 'utils': utils}) - f_ref = node.body[0].body[1].value - self.assertEqual(anno.getanno(f_ref, 'element_type').id, 'Foo') - def test_nested_unpacking(self): class Foo(object): @@ -230,25 +197,6 @@ class TypeInfoResolverTest(test.TestCase): self.assertFalse(anno.hasanno(b, 'live_val')) self.assertFalse(anno.hasanno(c, 'live_val')) - def test_inner_scope(self): - - def test_fn(): - a = [] - utils.set_element_type(a, 1) - for _ in a: - b = [] - utils.set_element_type(b, 2) - return a, b - - node = self._parse_and_analyze(test_fn, {'utils': utils}) - a, b = node.body[0].body[2].body[2].value.elts - self.assertEquals(anno.getanno(a, 'element_type').n, 1) - self.assertEquals(anno.getanno(b, 'element_type').n, 2) - self.assertFalse(anno.hasanno(a, 'type')) - self.assertFalse(anno.hasanno(b, 'type')) - self.assertFalse(anno.hasanno(a, 'live_val')) - self.assertFalse(anno.hasanno(b, 'live_val')) - if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/autograph/pyct/transformer.py b/tensorflow/contrib/autograph/pyct/transformer.py index 60bca8b38d..3328dde7aa 100644 --- a/tensorflow/contrib/autograph/pyct/transformer.py +++ b/tensorflow/contrib/autograph/pyct/transformer.py @@ -32,15 +32,40 @@ class AutographParseError(SyntaxError): pass -def try_ast_to_source(node): - try: - return compiler.ast_to_source(node) - except AssertionError: - return '' +# TODO(mdan): Use namedtuple. +class EntityInfo(object): + """Contains information about a Python entity. Immutable. + + Examples of entities include functions and classes. + + Attributes: + source_code: The entity's source code. + source_file: The entity's source file. + namespace: Dict[str, ], containing symbols visible to the entity + (excluding parameters). + arg_values: dict[str->*], containing parameter values, if known. + arg_types: dict[str->*], containing parameter types, if known. + owner_type: The surrounding class type of the function, if present. + """ + + # TODO(mdan): Remove the default and update tests. + def __init__(self, source_code, source_file, namespace, arg_values, arg_types, + owner_type): + self.source_code = source_code + self.source_file = source_file + self.namespace = namespace + self.arg_values = {} if arg_values is None else arg_values + self.arg_types = {} if arg_types is None else arg_types + self.owner_type = owner_type class Base(gast.NodeTransformer): - """Base class for specialized transformers. + """Base class for general-purpose code transformers transformers. + + This is an extension of ast.NodeTransformer that provides a few additional + functions, like state tracking within the scope of arbitrary node, helpers + for processing code blocks, debugging, mapping of transformed code to + original code, and others. Scope-local state tracking: to keep state across nodes, at the level of (possibly nested) scopes, use enter/exit_local_scope and set/get_local. @@ -48,15 +73,17 @@ class Base(gast.NodeTransformer): when they are not properly paired. """ - def __init__(self, context): + # TODO(mdan): Document all extra features. + + def __init__(self, entity_info): """Initialize the transformer. Subclasses should call this. Args: - context: An EntityContext. + entity_info: An EntityInfo object. """ self._lineno = 0 self._col_offset = 0 - self.context = context + self.entity_info = entity_info self._enclosing_entities = [] # A stack that allows keeping mutable, scope-local state where scopes may be @@ -237,9 +264,15 @@ class Base(gast.NodeTransformer): # TODO(mdan): Look into allowing to rewrite the AST here. apply_fn(target, values) + def _get_source(self, node): + try: + return compiler.ast_to_source(node) + except AssertionError: + return '' + def visit(self, node): - source_code = self.context.source_code - source_file = self.context.source_file + source_code = self.entity_info.source_code + source_file = self.entity_info.source_file did_enter_function = False local_scope_size_at_entry = len(self._local_scope_state) @@ -275,7 +308,7 @@ class Base(gast.NodeTransformer): except (ValueError, AttributeError, KeyError, NotImplementedError) as e: msg = '%s: %s\nOffending source:\n%s\n\nOccurred at node:\n%s' % ( - e.__class__.__name__, str(e), try_ast_to_source(node), + e.__class__.__name__, str(e), self._get_source(node), pretty_printer.fmt(node, color=False)) if source_code: line = source_code.splitlines()[self._lineno - 1] diff --git a/tensorflow/contrib/autograph/pyct/transformer_test.py b/tensorflow/contrib/autograph/pyct/transformer_test.py index f110e79605..baf04653ae 100644 --- a/tensorflow/contrib/autograph/pyct/transformer_test.py +++ b/tensorflow/contrib/autograph/pyct/transformer_test.py @@ -21,7 +21,6 @@ from __future__ import print_function import gast from tensorflow.contrib.autograph.pyct import anno -from tensorflow.contrib.autograph.pyct import context from tensorflow.contrib.autograph.pyct import parser from tensorflow.contrib.autograph.pyct import transformer from tensorflow.python.platform import test @@ -29,16 +28,14 @@ from tensorflow.python.platform import test class TransformerTest(test.TestCase): - def _context_for_testing(self): - return context.EntityContext( - namer=None, + def _simple_source_info(self): + return transformer.EntityInfo( source_code=None, source_file=None, namespace=None, arg_values=None, arg_types=None, - owner_type=None, - recursive=False) + owner_type=None) def test_entity_scope_tracking(self): @@ -55,7 +52,7 @@ class TransformerTest(test.TestCase): anno.setanno(node, 'enclosing_entities', self.enclosing_entities) return self.generic_visit(node) - tr = TestTransformer(self._context_for_testing()) + tr = TestTransformer(self._simple_source_info()) def test_function(): a = 0 @@ -118,7 +115,7 @@ class TransformerTest(test.TestCase): def visit_For(self, node): return self._annotate_result(node) - tr = TestTransformer(self._context_for_testing()) + tr = TestTransformer(self._simple_source_info()) def test_function(a): """Docstring.""" @@ -157,7 +154,7 @@ class TransformerTest(test.TestCase): self.exit_local_scope() return node - tr = TestTransformer(self._context_for_testing()) + tr = TestTransformer(self._simple_source_info()) def no_exit(a): if a > 0: @@ -196,7 +193,7 @@ class TransformerTest(test.TestCase): z = y return z - tr = TestTransformer(self._context_for_testing()) + tr = TestTransformer(self._simple_source_info()) node, _ = parser.parse_entity(test_function) node = tr.visit(node) diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD index 5910f0625e..d0fd0fae97 100644 --- a/tensorflow/tools/pip_package/BUILD +++ b/tensorflow/tools/pip_package/BUILD @@ -57,8 +57,8 @@ COMMON_PIP_DEPS = [ "//tensorflow:tensorflow_py", "//tensorflow/contrib/autograph:autograph", "//tensorflow/contrib/autograph/converters:converters", - "//tensorflow/contrib/autograph/converters:test_lib", "//tensorflow/contrib/autograph/core:core", + "//tensorflow/contrib/autograph/core:test_lib", "//tensorflow/contrib/autograph/impl:impl", "//tensorflow/contrib/autograph/lang:lang", "//tensorflow/contrib/autograph/pyct:pyct", -- GitLab From 3550ef89bc66d03b6e2db8e47bf7b038d9f4ceff Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 18 Jun 2018 14:14:12 -0700 Subject: [PATCH 613/816] Convert CheckInputsSize to return a Status instead of CHECK-failing, and convert existing callsites to TF_QCHECK_OK the call. This moves us towards the goal of returning Statuses instead of check-failing in ImportTensorFlowNode(). PiperOrigin-RevId: 201056489 --- .../contrib/lite/toco/import_tensorflow.cc | 99 ++++++++++--------- 1 file changed, 51 insertions(+), 48 deletions(-) diff --git a/tensorflow/contrib/lite/toco/import_tensorflow.cc b/tensorflow/contrib/lite/toco/import_tensorflow.cc index e33b430937..4465f953ba 100644 --- a/tensorflow/contrib/lite/toco/import_tensorflow.cc +++ b/tensorflow/contrib/lite/toco/import_tensorflow.cc @@ -426,18 +426,19 @@ int GetInputsCount(const NodeDef& node, return i; } } - return node.input_size(); - } else { - return node.input_size(); } + return node.input_size(); } -void CheckInputsCount(const NodeDef& node, - const TensorFlowImportFlags& tf_import_flags, - int expected_input_count) { - QCHECK_EQ(GetInputsCount(node, tf_import_flags), expected_input_count) - << node.op() << " node expects " << expected_input_count - << " input(s) other than control dependencies: " << node.DebugString(); +tensorflow::Status CheckInputsCount( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + int expected_input_count) { + if (GetInputsCount(node, tf_import_flags) != expected_input_count) { + return tensorflow::errors::FailedPrecondition( + node.op(), " node expects ", expected_input_count, + " input(s) other than control dependencies: ", node.DebugString()); + } + return tensorflow::Status::OK(); } template @@ -504,7 +505,7 @@ tensorflow::Status ConvertConvOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { CHECK_EQ(node.op(), "Conv2D"); - CheckInputsCount(node, tf_import_flags, 2); + TF_RETURN_IF_ERROR(CheckInputsCount(node, tf_import_flags, 2)); // We only support NHWC, which is the default data_format. // So if data_format is not defined, we're all good. @@ -578,7 +579,7 @@ tensorflow::Status ConvertDepthwiseConvOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { CHECK_EQ(node.op(), "DepthwiseConv2dNative"); - CheckInputsCount(node, tf_import_flags, 2); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); // We only support NHWC, which is the default data_format. // So if data_format is not defined, we're all good. @@ -632,7 +633,7 @@ tensorflow::Status ConvertDepthToSpaceOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { CHECK_EQ(node.op(), "DepthToSpace"); - CheckInputsCount(node, tf_import_flags, 1); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 1)); CHECK_EQ(GetDataTypeAttr(node, "T"), DT_FLOAT); auto* op = new DepthToSpaceOperator; @@ -648,7 +649,7 @@ tensorflow::Status ConvertSpaceToDepthOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { CHECK_EQ(node.op(), "SpaceToDepth"); - CheckInputsCount(node, tf_import_flags, 1); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 1)); tensorflow::DataType dtype = GetDataTypeAttr(node, "T"); if (dtype != DT_FLOAT && dtype != DT_UINT8 && dtype != DT_INT32 && @@ -671,7 +672,7 @@ tensorflow::Status ConvertBiasAddOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { CHECK_EQ(node.op(), "BiasAdd"); - CheckInputsCount(node, tf_import_flags, 2); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); const auto& input_name = node.input(0); const auto& bias_name = node.input(1); @@ -688,7 +689,7 @@ tensorflow::Status ConvertRandomUniform( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { CHECK_EQ(node.op(), "RandomUniform"); - CheckInputsCount(node, tf_import_flags, 1); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 1)); CHECK_EQ(GetDataTypeAttr(node, "T"), DT_INT32); auto op = absl::make_unique(); @@ -728,7 +729,7 @@ tensorflow::Status ConvertFakeQuantWithMinMaxArgs( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { CHECK_EQ(node.op(), "FakeQuantWithMinMaxArgs"); - CheckInputsCount(node, tf_import_flags, 1); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 1)); auto* op = new FakeQuantOperator; op->inputs.push_back(node.input(0)); op->minmax.reset(new MinMax); @@ -765,7 +766,7 @@ tensorflow::Status ConvertSqueezeOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { CHECK_EQ(node.op(), "Squeeze"); - CheckInputsCount(node, tf_import_flags, 1); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 1)); auto* op = new SqueezeOperator; op->inputs.push_back(node.input(0)); op->outputs.push_back(node.name()); @@ -786,7 +787,7 @@ tensorflow::Status ConvertSumOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { CHECK_EQ(node.op(), "Sum"); - CheckInputsCount(node, tf_import_flags, 2); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); auto* op = new TensorFlowSumOperator; op->inputs.push_back(node.input(0)); op->inputs.push_back(node.input(1)); @@ -802,7 +803,7 @@ tensorflow::Status ConvertSplitOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { CHECK_EQ(node.op(), "Split"); - CheckInputsCount(node, tf_import_flags, 2); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); auto* op = new TensorFlowSplitOperator; op->inputs.push_back(node.input(0)); op->inputs.push_back(node.input(1)); @@ -820,7 +821,7 @@ tensorflow::Status ConvertSwitchOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { CHECK_EQ(node.op(), "Switch"); - CheckInputsCount(node, tf_import_flags, 2); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); auto* op = new TensorFlowSwitchOperator; op->inputs.push_back(node.input(0)); op->inputs.push_back(node.input(1)); @@ -835,7 +836,7 @@ tensorflow::Status ConvertSoftmaxOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { CHECK_EQ(node.op(), "Softmax"); - CheckInputsCount(node, tf_import_flags, 1); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 1)); const auto& input_name = node.input(0); auto* softmax = new SoftmaxOperator; softmax->inputs.push_back(input_name); @@ -851,7 +852,7 @@ tensorflow::Status ConvertLRNOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { CHECK_EQ(node.op(), "LRN"); - CheckInputsCount(node, tf_import_flags, 1); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 1)); const auto& input_name = node.input(0); auto* lrn = new LocalResponseNormalizationOperator; lrn->inputs.push_back(input_name); @@ -868,7 +869,7 @@ tensorflow::Status ConvertMaxPoolOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { CHECK_EQ(node.op(), "MaxPool"); - CheckInputsCount(node, tf_import_flags, 1); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 1)); const auto& input_name = node.input(0); // We only support NHWC, which is the default data_format. // So if data_format is not defined, we're all good. @@ -911,7 +912,7 @@ tensorflow::Status ConvertAvgPoolOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { CHECK_EQ(node.op(), "AvgPool"); - CheckInputsCount(node, tf_import_flags, 1); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 1)); const auto& input_name = node.input(0); // We only support NHWC, which is the default data_format. // So if data_format is not defined, we're all good. @@ -949,7 +950,7 @@ tensorflow::Status ConvertAvgPoolOperator( tensorflow::Status ConvertBatchMatMulOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { - CheckInputsCount(node, tf_import_flags, 2); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); // https://www.tensorflow.org/versions/r0.12/api_docs/python/math_ops/matrix_math_functions CHECK(!HasAttr(node, "adj_a") || (GetBoolAttr(node, "adj_a") == false)); @@ -965,7 +966,7 @@ tensorflow::Status ConvertBatchMatMulOperator( tensorflow::Status ConvertMatMulOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { - CheckInputsCount(node, tf_import_flags, 2); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); // Transpose flags should be easy to support, but we don't have a // GraphDef with them to test on at the moment. @@ -1030,7 +1031,7 @@ template tensorflow::Status ConvertSimpleOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { - CheckInputsCount(node, tf_import_flags, NumInputs); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, NumInputs)); return ConvertSimpleOperator(node, tf_import_flags, model); } @@ -1038,7 +1039,7 @@ tensorflow::Status ConvertMaxOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { CHECK_EQ(node.op(), "Max"); - CheckInputsCount(node, tf_import_flags, 2); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); auto* op = new TensorFlowMaxOperator; op->inputs.push_back(node.input(0)); op->inputs.push_back(node.input(1)); @@ -1054,7 +1055,7 @@ tensorflow::Status ConvertMinOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { CHECK_EQ(node.op(), "Min"); - CheckInputsCount(node, tf_import_flags, 2); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); auto* op = new TensorFlowMinOperator; op->inputs.push_back(node.input(0)); op->inputs.push_back(node.input(1)); @@ -1100,7 +1101,7 @@ tensorflow::Status ConvertStridedSliceOperator( CHECK_EQ(node.op(), "StridedSlice"); // TODO(soroosh): The 4th input (strides) should be e optional, to be // consistent with TF. - CheckInputsCount(node, tf_import_flags, 4); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 4)); auto* op = new StridedSliceOperator; for (const auto& input : node.input()) { @@ -1128,7 +1129,7 @@ tensorflow::Status ConvertPlaceholderOperator( Model* model) { CHECK(node.op() == "Placeholder" || node.op() == "LegacyFedInput"); if (node.op() == "Placeholder") { - CheckInputsCount(node, tf_import_flags, 0); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 0)); } auto& array = model->GetOrCreateArray(node.name()); if (node.attr().count("dtype")) { @@ -1166,7 +1167,7 @@ tensorflow::Status ConvertCastOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { CHECK_EQ(node.op(), "Cast"); - CheckInputsCount(node, tf_import_flags, 1); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 1)); const auto tf_src_dtype = GetDataTypeAttr(node, "SrcT"); const auto tf_dst_dtype = GetDataTypeAttr(node, "DstT"); auto* op = new CastOperator; @@ -1182,7 +1183,7 @@ tensorflow::Status ConvertFloorOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { CHECK_EQ(node.op(), "Floor"); - CheckInputsCount(node, tf_import_flags, 1); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 1)); const auto data_type = GetDataTypeAttr(node, "T"); CHECK(data_type == DT_FLOAT); auto* op = new FloorOperator; @@ -1196,8 +1197,10 @@ tensorflow::Status ConvertGatherOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { CHECK(node.op() == "Gather" || node.op() == "GatherV2"); - if (node.op() == "Gather") CheckInputsCount(node, tf_import_flags, 2); - if (node.op() == "GatherV2") CheckInputsCount(node, tf_import_flags, 3); + if (node.op() == "Gather") + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); + if (node.op() == "GatherV2") + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 3)); const auto indices_data_type = GetDataTypeAttr(node, "Tindices"); CHECK(indices_data_type == DT_INT32 || indices_data_type == DT_INT64); auto* op = new GatherOperator; @@ -1214,7 +1217,7 @@ tensorflow::Status ConvertArgMaxOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { CHECK_EQ(node.op(), "ArgMax"); - CheckInputsCount(node, tf_import_flags, 2); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); const auto axis_data_type = HasAttr(node, "Tidx") ? GetDataTypeAttr(node, "Tidx") : DT_INT32; const auto output_type = HasAttr(node, "output_type") @@ -1235,7 +1238,7 @@ tensorflow::Status ConvertResizeBilinearOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { CHECK_EQ(node.op(), "ResizeBilinear"); - CheckInputsCount(node, tf_import_flags, 2); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); auto* op = new ResizeBilinearOperator; op->align_corners = false; @@ -1254,7 +1257,7 @@ tensorflow::Status ConvertBatchNormWithGlobalNormalizationOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { CHECK_EQ(node.op(), "BatchNormWithGlobalNormalization"); - CheckInputsCount(node, tf_import_flags, 5); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 5)); // TODO(ahentz): to really match tensorflow we need to add variance_epsilon // to the input, before feeding it into TensorFlowRsqrtOperator. @@ -1304,7 +1307,7 @@ tensorflow::Status ConvertFusedBatchNormOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { CHECK_EQ(node.op(), "FusedBatchNorm"); - CheckInputsCount(node, tf_import_flags, 5); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 5)); // Declare shortcuts for the inputs. const string& gamma_input = node.input(1); @@ -1357,7 +1360,7 @@ tensorflow::Status ConvertSpaceToBatchNDOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { CHECK_EQ(node.op(), "SpaceToBatchND"); - CheckInputsCount(node, tf_import_flags, 3); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 3)); CHECK_EQ(GetDataTypeAttr(node, "Tblock_shape"), DT_INT32); CHECK_EQ(GetDataTypeAttr(node, "Tpaddings"), DT_INT32); auto* op = new SpaceToBatchNDOperator; @@ -1373,7 +1376,7 @@ tensorflow::Status ConvertBatchToSpaceNDOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { CHECK_EQ(node.op(), "BatchToSpaceND"); - CheckInputsCount(node, tf_import_flags, 3); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 3)); CHECK_EQ(GetDataTypeAttr(node, "Tblock_shape"), DT_INT32); CHECK_EQ(GetDataTypeAttr(node, "Tcrops"), DT_INT32); auto* op = new BatchToSpaceNDOperator; @@ -1389,7 +1392,7 @@ tensorflow::Status ConvertMeanOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { CHECK_EQ(node.op(), "Mean"); - CheckInputsCount(node, tf_import_flags, 2); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); auto* op = new MeanOperator; op->inputs.push_back(node.input(0)); op->inputs.push_back(node.input(1)); @@ -1436,7 +1439,7 @@ tensorflow::Status ConvertTransposeConvOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { CHECK_EQ(node.op(), "Conv2DBackpropInput"); - CheckInputsCount(node, tf_import_flags, 3); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 3)); auto* op = new TransposeConvOperator; op->inputs.push_back(node.input(0)); op->inputs.push_back(node.input(1)); @@ -1507,7 +1510,7 @@ tensorflow::Status ConvertRangeOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { CHECK_EQ(node.op(), "Range"); - CheckInputsCount(node, tf_import_flags, 3); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 3)); auto* op = new RangeOperator; if (HasAttr(node, "Tidx")) { const auto dtype = toco::GetDataTypeAttr(node, "Tidx"); @@ -1722,7 +1725,7 @@ tensorflow::Status ConvertTopKV2Operator( model, node.name() + "k", {static_cast(GetIntAttr(node, "k"))}); op->inputs.push_back(k_array); } else { - CheckInputsCount(node, tf_import_flags, 2); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); op->inputs.push_back(node.input(1)); } // The op has two outputs. @@ -1738,7 +1741,7 @@ tensorflow::Status ConvertDynamicPartitionOperator( auto op = absl::make_unique(); CHECK(HasAttr(node, "num_partitions")); op->num_partitions = GetIntAttr(node, "num_partitions"); - CheckInputsCount(node, tf_import_flags, 2); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); op->inputs.push_back(node.input(0)); op->inputs.push_back(node.input(1)); CHECK_GT(op->num_partitions, 1); @@ -1760,7 +1763,7 @@ tensorflow::Status ConvertDynamicStitchOperator( CHECK(HasAttr(node, "N")); op->num_partitions = GetIntAttr(node, "N"); // Expect all ID partitions + all value partitions. - CheckInputsCount(node, tf_import_flags, op->num_partitions * 2); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, op->num_partitions * 2)); for (int i = 0; i < op->num_partitions * 2; ++i) { op->inputs.push_back(node.input(i)); } @@ -1773,7 +1776,7 @@ tensorflow::Status ConvertSparseToDenseOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { CHECK_EQ(node.op(), "SparseToDense"); - CheckInputsCount(node, tf_import_flags, 4); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 4)); auto* op = new SparseToDenseOperator; for (const string& input : node.input()) { -- GitLab From 7e45987850406049aa673fdfcff9bb762f3a7b24 Mon Sep 17 00:00:00 2001 From: Yash Katariya Date: Mon, 18 Jun 2018 21:21:25 +0000 Subject: [PATCH 614/816] Changing the colab link to the right one --- .../python/examples/nmt_with_attention/NMT_with_Attention.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/eager/python/examples/nmt_with_attention/NMT_with_Attention.ipynb b/tensorflow/contrib/eager/python/examples/nmt_with_attention/NMT_with_Attention.ipynb index e23f9e719b..5382d4b940 100644 --- a/tensorflow/contrib/eager/python/examples/nmt_with_attention/NMT_with_Attention.ipynb +++ b/tensorflow/contrib/eager/python/examples/nmt_with_attention/NMT_with_Attention.ipynb @@ -41,7 +41,7 @@ "# Neural Machine Translation with Attention\n", "\n", "
\n", - "\n", + "\n", " Run in Google Colab \n", "\n", "View source on Github
" -- GitLab From 3fa0009cbdb8ef95593ffaf63d97e05bf1835cb8 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 18 Jun 2018 14:27:49 -0700 Subject: [PATCH 615/816] Replace distribution_util.assert_close with tf.assert_near. PiperOrigin-RevId: 201058937 --- .../python/ops/onehot_categorical.py | 2 +- .../python/ops/relaxed_onehot_categorical.py | 2 +- .../kernel_tests/distributions/util_test.py | 59 ------------------- .../python/ops/distributions/dirichlet.py | 6 +- tensorflow/python/ops/distributions/util.py | 45 ++------------ 5 files changed, 10 insertions(+), 104 deletions(-) diff --git a/tensorflow/contrib/distributions/python/ops/onehot_categorical.py b/tensorflow/contrib/distributions/python/ops/onehot_categorical.py index 0c762f17c9..214c6dca4a 100644 --- a/tensorflow/contrib/distributions/python/ops/onehot_categorical.py +++ b/tensorflow/contrib/distributions/python/ops/onehot_categorical.py @@ -235,7 +235,7 @@ class OneHotCategorical(distribution.Distribution): return x return control_flow_ops.with_dependencies([ check_ops.assert_non_positive(x), - distribution_util.assert_close( + check_ops.assert_near( array_ops.zeros([], dtype=self.dtype), math_ops.reduce_logsumexp(x, axis=[-1])), ], x) diff --git a/tensorflow/contrib/distributions/python/ops/relaxed_onehot_categorical.py b/tensorflow/contrib/distributions/python/ops/relaxed_onehot_categorical.py index 9b5bd7576f..25aaac379a 100644 --- a/tensorflow/contrib/distributions/python/ops/relaxed_onehot_categorical.py +++ b/tensorflow/contrib/distributions/python/ops/relaxed_onehot_categorical.py @@ -299,7 +299,7 @@ class ExpRelaxedOneHotCategorical(distribution.Distribution): return x return control_flow_ops.with_dependencies([ check_ops.assert_non_positive(x), - distribution_util.assert_close( + check_ops.assert_near( array_ops.zeros([], dtype=self.dtype), math_ops.reduce_logsumexp(x, axis=[-1])), ], x) diff --git a/tensorflow/python/kernel_tests/distributions/util_test.py b/tensorflow/python/kernel_tests/distributions/util_test.py index 2f256d3e8b..08fb21e976 100644 --- a/tensorflow/python/kernel_tests/distributions/util_test.py +++ b/tensorflow/python/kernel_tests/distributions/util_test.py @@ -59,65 +59,6 @@ def _logit(x): class AssertCloseTest(test.TestCase): - def testAssertCloseIntegerDtype(self): - x = array_ops.placeholder(dtypes.int32) - y = x - z = array_ops.placeholder(dtypes.int32) - feed_dict = {x: [1, 5, 10, 15, 20], z: [2, 5, 10, 15, 20]} - with self.test_session(): - with ops.control_dependencies([du.assert_close(x, y)]): - array_ops.identity(x).eval(feed_dict=feed_dict) - - with ops.control_dependencies([du.assert_close(y, x)]): - array_ops.identity(x).eval(feed_dict=feed_dict) - - with self.assertRaisesOpError("Condition x ~= y"): - with ops.control_dependencies([du.assert_close(x, z)]): - array_ops.identity(x).eval(feed_dict=feed_dict) - - with self.assertRaisesOpError("Condition x ~= y"): - with ops.control_dependencies([du.assert_close(y, z)]): - array_ops.identity(y).eval(feed_dict=feed_dict) - - def testAssertCloseNonIntegerDtype(self): - x = array_ops.placeholder(dtypes.float32) - y = x + 1e-8 - z = array_ops.placeholder(dtypes.float32) - feed_dict = {x: [1., 5, 10, 15, 20], z: [2., 5, 10, 15, 20]} - with self.test_session(): - with ops.control_dependencies([du.assert_close(x, y)]): - array_ops.identity(x).eval(feed_dict=feed_dict) - - with ops.control_dependencies([du.assert_close(y, x)]): - array_ops.identity(x).eval(feed_dict=feed_dict) - - with self.assertRaisesOpError("Condition x ~= y"): - with ops.control_dependencies([du.assert_close(x, z)]): - array_ops.identity(x).eval(feed_dict=feed_dict) - - with self.assertRaisesOpError("Condition x ~= y"): - with ops.control_dependencies([du.assert_close(y, z)]): - array_ops.identity(y).eval(feed_dict=feed_dict) - - @test_util.run_in_graph_and_eager_modes() - def testAssertCloseEpsilon(self): - x = [0., 5, 10, 15, 20] - # x != y - y = [0.1, 5, 10, 15, 20] - # x = z - z = [1e-8, 5, 10, 15, 20] - with self.test_session(): - with ops.control_dependencies([du.assert_close(x, z)]): - self.evaluate(array_ops.identity(x)) - - with self.assertRaisesOpError("Condition x ~= y"): - with ops.control_dependencies([du.assert_close(x, y)]): - self.evaluate(array_ops.identity(x)) - - with self.assertRaisesOpError("Condition x ~= y"): - with ops.control_dependencies([du.assert_close(y, z)]): - self.evaluate(array_ops.identity(y)) - def testAssertIntegerForm(self): # This should only be detected as an integer. x = array_ops.placeholder(dtypes.float32) diff --git a/tensorflow/python/ops/distributions/dirichlet.py b/tensorflow/python/ops/distributions/dirichlet.py index 72567e62f7..2dba61d43b 100644 --- a/tensorflow/python/ops/distributions/dirichlet.py +++ b/tensorflow/python/ops/distributions/dirichlet.py @@ -290,10 +290,8 @@ class Dirichlet(distribution.Distribution): if not self.validate_args: return x return control_flow_ops.with_dependencies([ - check_ops.assert_positive( - x, - message="samples must be positive"), - distribution_util.assert_close( + check_ops.assert_positive(x, message="samples must be positive"), + check_ops.assert_near( array_ops.ones([], dtype=self.dtype), math_ops.reduce_sum(x, -1), message="sample last-dimension must sum to `1`"), diff --git a/tensorflow/python/ops/distributions/util.py b/tensorflow/python/ops/distributions/util.py index 401676bf84..3e480a79f5 100644 --- a/tensorflow/python/ops/distributions/util.py +++ b/tensorflow/python/ops/distributions/util.py @@ -36,43 +36,6 @@ from tensorflow.python.ops import nn from tensorflow.python.util import tf_inspect -def assert_close( - x, y, data=None, summarize=None, message=None, name="assert_close"): - """Assert that x and y are within machine epsilon of each other. - - Args: - x: Floating-point `Tensor` - y: Floating-point `Tensor` - data: The tensors to print out if the condition is `False`. Defaults to - error message and first few entries of `x` and `y`. - summarize: Print this many entries of each tensor. - message: A string to prefix to the default message. - name: A name for this operation (optional). - - Returns: - Op raising `InvalidArgumentError` if |x - y| > machine epsilon. - """ - message = message or "" - x = ops.convert_to_tensor(x, name="x") - y = ops.convert_to_tensor(y, name="y") - - if data is None: - data = [ - message, - "Condition x ~= y did not hold element-wise: x = ", x, "y = ", y - ] - - if x.dtype.is_integer: - return check_ops.assert_equal( - x, y, data=data, summarize=summarize, message=message, name=name) - - with ops.name_scope(name, "assert_close", [x, y, data]): - tol = np.finfo(x.dtype.as_numpy_dtype).eps - condition = math_ops.reduce_all(math_ops.less_equal(math_ops.abs(x-y), tol)) - return control_flow_ops.Assert( - condition, data, summarize=summarize) - - def assert_integer_form( x, data=None, summarize=None, message=None, int_dtype=None, name="assert_integer_form"): @@ -241,8 +204,12 @@ def get_logits_and_probs(logits=None, dependencies = [check_ops.assert_non_negative(probs)] if multidimensional: probs = embed_check_categorical_event_shape(probs) - dependencies += [assert_close(math_ops.reduce_sum(probs, -1), one, - message="probs does not sum to 1.")] + dependencies += [ + check_ops.assert_near( + math_ops.reduce_sum(probs, -1), + one, + message="probs does not sum to 1.") + ] else: dependencies += [check_ops.assert_less_equal( probs, one, message="probs has components greater than 1.")] -- GitLab From 24e9804217a450fc0f8e8f2c4a98e1a593aa77f8 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 18 Jun 2018 14:53:21 -0700 Subject: [PATCH 616/816] This is an initial submission of GGT to tensorflow contrib. Paper link: https://arxiv.org/pdf/1806.02958.pdf PiperOrigin-RevId: 201063723 --- tensorflow/contrib/opt/BUILD | 22 ++ tensorflow/contrib/opt/__init__.py | 4 +- tensorflow/contrib/opt/python/training/ggt.py | 312 ++++++++++++++++++ .../contrib/opt/python/training/ggt_test.py | 183 ++++++++++ 4 files changed, 520 insertions(+), 1 deletion(-) create mode 100644 tensorflow/contrib/opt/python/training/ggt.py create mode 100644 tensorflow/contrib/opt/python/training/ggt_test.py diff --git a/tensorflow/contrib/opt/BUILD b/tensorflow/contrib/opt/BUILD index 13aa1d7e7a..4f35de4e5d 100644 --- a/tensorflow/contrib/opt/BUILD +++ b/tensorflow/contrib/opt/BUILD @@ -19,6 +19,7 @@ py_library( "python/training/drop_stale_gradient_optimizer.py", "python/training/elastic_average_optimizer.py", "python/training/external_optimizer.py", + "python/training/ggt.py", "python/training/lazy_adam_optimizer.py", "python/training/model_average_optimizer.py", "python/training/moving_average_optimizer.py", @@ -31,12 +32,15 @@ py_library( ], srcs_version = "PY2AND3", deps = [ + "//tensorflow/contrib/optimizer_v2:optimizer_v2_py", "//tensorflow/python:array_ops", "//tensorflow/python:clip_ops", "//tensorflow/python:control_flow_ops", "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:framework_ops", "//tensorflow/python:gradients", "//tensorflow/python:init_ops", + "//tensorflow/python:linalg_ops", "//tensorflow/python:math_ops", "//tensorflow/python:platform", "//tensorflow/python:state_ops", @@ -302,3 +306,21 @@ py_test( "//third_party/py/numpy", ], ) + +py_test( + name = "ggt_test", + srcs = ["python/training/ggt_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":opt_py", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework", + "//tensorflow/python:math_ops", + "//tensorflow/python:platform", + "//tensorflow/python:platform_test", + "//tensorflow/python:resource_variable_ops", + "//tensorflow/python:variables", + "//third_party/py/numpy", + ], +) diff --git a/tensorflow/contrib/opt/__init__.py b/tensorflow/contrib/opt/__init__.py index 4c13c8e247..b41148329d 100644 --- a/tensorflow/contrib/opt/__init__.py +++ b/tensorflow/contrib/opt/__init__.py @@ -31,6 +31,7 @@ from tensorflow.contrib.opt.python.training.powersign import * from tensorflow.contrib.opt.python.training.variable_clipping_optimizer import * from tensorflow.contrib.opt.python.training.elastic_average_optimizer import * from tensorflow.contrib.opt.python.training.model_average_optimizer import * +from tensorflow.contrib.opt.python.training.ggt import * # pylint: enable=wildcard-import from tensorflow.python.util.all_util import remove_undocumented @@ -53,7 +54,8 @@ _allowed_symbols = [ 'ElasticAverageOptimizer', 'ElasticAverageCustomGetter', 'ModelAverageOptimizer', - 'ModelAverageCustomGetter' + 'ModelAverageCustomGetter', + 'GGTOptimizer', ] remove_undocumented(__name__, _allowed_symbols) diff --git a/tensorflow/contrib/opt/python/training/ggt.py b/tensorflow/contrib/opt/python/training/ggt.py new file mode 100644 index 0000000000..928c453517 --- /dev/null +++ b/tensorflow/contrib/opt/python/training/ggt.py @@ -0,0 +1,312 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""GGT for Tensorflow.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import collections +import numpy as np +from tensorflow.contrib.optimizer_v2 import optimizer_v2 +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import linalg_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import state_ops + + +class GGTOptimizer(optimizer_v2.OptimizerV2): + """Optimizer that implements the GGT algorithm. + + GGT has an advantage over sgd and adam on large models with poor conditioning, + for example language models and CNNs, + see [ABCHSZZ 2018]([pdf](https://arxiv.org/pdf/1806.02958.pdf)). + """ + + def __init__(self, + learning_rate=0.001, + beta1=0.9, + use_locking=False, + name="GGT", + window=10, + eps=1e-4, + svd_eps=1e-6, + sigma_eps=1e-2): + """Construct a new GGT optimizer. + + Initialization: + + ``` + t <- 0 (Initialize timestep) + grad_buffer <- 0 (Initialize buffer for keeping past gradients) + flat_grad <- 0 (Initialize flattened gradient that contains gradients of all + variables) + m_0 <- 0 (Initialize 1st moment vector) + ``` + + Suppose all variables and their gradients are concatenated into vectors + `flat_vars` and `flat_grad`. The update rule for `flat_vars` + uses an optimization described at the beginning of section 2 of the paper: + + ``` + t <- t + 1 + + m_t <- beta1 * m_{t-1} + (1 - beta1) * flat_grad + grad_buffer[(t-1) % window, :] <- m_t + + M <- grad_buffer^T / sqrt(min(t, window)) + U, sigma, _ <- SVD(M^TM + I * svd_eps) + + sigma_sqrt_inv <- (sqrt(sigma) + sigma_eps)^(-3) + sigma_sqrt_min <- min(sqrt(sigma)) + + if sigma_sqrt_min > eps: + new_step <- M U diag(sigma_sqrt_inv) U^T M^T m_t + + (m_t - M U diag(1/sigma) U^T M^T m_t) / sigma_sqrt_min + else: + new_step <- M U diag(sigma_sqrt_inv) U^T M^T m_t + + flat_vars <- flat_vars - learning_rate * new_step + ``` + + GGT provides the power of full-matrix adaptive regularization at a cost not + much larger than SGD. As a result it is suited for large models where the + gradient covariance matrix has a poor condition number that slows down first + order methods. + GGT uses the preconditioner from full-matrix AdaGrad, with gradient history + attenuated exponentially as in Adam, and truncated to a window parameter. + It has provable guarantees even for non-convex optimization that is never + significantly worse than SGD and in some cases better. + + Args: + learning_rate: A float hyperparameter. The learning rate. + beta1: A float hyperparameter. The exponential decay rate for the 1st + moment estimates. + use_locking: If True use locks for update operations. + name: Optional name for the operations created when applying gradients. + Defaults to "GGT". + window: An integer hyperparameter. The number of first moments to keep in + computing the adaptive preconditioner. + eps: A float hyperparameter. Used to truncate small eigenvalues of the + gradient covariance matrix. + svd_eps: A float hyperparameter. Used to stabilize SVD. + sigma_eps: A float hyperparameter. Used to regularize matrix inversion. + """ + super(GGTOptimizer, self).__init__(use_locking, name) + self._set_hyper("lr", learning_rate) + self._set_hyper("beta1", beta1) + self._set_hyper("window", window) + self._set_hyper("eps", eps) + self._set_hyper("svd_eps", svd_eps) + self._set_hyper("sigma_eps", sigma_eps) + + self.index_dict = {} + self.shape_dict = {} + + def _create_vars(self, var_list, state): + # Construct ordered dictionary for variable dimensions, sorted by name. + shape_dict = {} + for v in var_list: + shape_dict[v.name] = np.prod(v.get_shape()).value + self.shape_dict = collections.OrderedDict( + sorted(shape_dict.items(), key=lambda t: t[0])) + + # Assign each variable its location in flat_grad. The locations are based on + # the order of sorted names. + idx = 0 + for v_name, v_dim in self.shape_dict.items(): + self.index_dict[v_name] = idx + idx += v_dim + + state.create_non_slot( + initial_value=math_ops.cast(0., dtype=var_list[0].dtype.base_dtype), + name="global_step") + + # Buffer for keeping past gradients. + window = state.get_hyper("window") + grad_buffer_init = array_ops.zeros( + [window, idx], dtype=var_list[0].dtype.base_dtype) + state.create_non_slot(initial_value=grad_buffer_init, name="grad_buffer") + + state.create_non_slot( + initial_value=array_ops.zeros( + (idx,), dtype=var_list[0].dtype.base_dtype), + name="moment1") + + # Flattened gradient that contains gradients for all variables in the model. + state.create_non_slot( + initial_value=array_ops.zeros( + (idx,), dtype=var_list[0].dtype.base_dtype), + name="flat_grad") + + def _get_global_step(self, state=None): + if state is None: + state = self._get_per_graph_state() + return state.get_non_slot("global_step") + + def _get_moment1(self, state=None): + if state is None: + state = self._get_per_graph_state() + return state.get_non_slot("moment1") + + def _get_grad_buffer(self, state=None): + if state is None: + state = self._get_per_graph_state() + return state.get_non_slot("grad_buffer") + + def _get_flat_grad(self, state=None): + if state is None: + state = self._get_per_graph_state() + return state.get_non_slot("flat_grad") + + def _apply_sparse(self, grad, var): + raise NotImplementedError("Sparse gradient updates are not supported.") + + def _prepare(self, state): + self._variables = [] + + def _apply_dense(self, grad, var, state): + self._variables.append(var) + dim = self.shape_dict[var.name] + start_index = self.index_dict[var.name] + end_index = start_index + dim + + # Update flat_gradient at the index associated with the variable. + flat_grad = self._get_flat_grad(state) + new_flat_grad = array_ops.reshape(grad, [-1]) + flat_grad_updated = state_ops.scatter_update( + flat_grad, math_ops.range(start_index, end_index), new_flat_grad) + + return flat_grad_updated + + def _resource_apply_dense(self, grad, var, state): + self._variables.append(var) + dim = self.shape_dict[var.name] + start_index = self.index_dict[var.name] + end_index = start_index + dim + + # Update flat_gradient at the index associated with the variable. + flat_grad = self._get_flat_grad(state) + new_flat_grad = array_ops.reshape(grad, [-1]) + flat_grad_updated = state_ops.scatter_update( + flat_grad, math_ops.range(start_index, end_index), new_flat_grad) + + return flat_grad_updated + + def _finish(self, state): + var_dtype = self._variables[0].dtype.base_dtype + # Update global step. + global_step = self._get_global_step(state) + update_global_step = state_ops.assign_add(global_step, 1.) + + # Update the first moment estimate. + beta1 = state.get_hyper("beta1", dtype=var_dtype) + moment1 = self._get_moment1(state) + flat_grad = self._get_flat_grad(state) + # moment1_t := beta1 * moment1_{t-1} + (1 - beta1) * flat_grad_t + update_moment1 = moment1.assign(beta1 * moment1 + (1. - beta1) * flat_grad) + + # Update the gradient buffer. + window = state.get_hyper("window") + grad_buffer = self._get_grad_buffer(state) + next_grad_index = math_ops.floormod( + math_ops.to_int32(update_global_step - 1.), window) + # grad_buffer[(t-1) % window] := moment1_t + update_grad_buffer = state_ops.scatter_update(grad_buffer, next_grad_index, + update_moment1) + + # Compute the update step. + eps = state.get_hyper("eps", dtype=var_dtype) + svd_eps = state.get_hyper("svd_eps", dtype=var_dtype) + sigma_eps = state.get_hyper("sigma_eps", dtype=var_dtype) + lr = state.get_hyper("lr", dtype=var_dtype) + denom = math_ops.sqrt( + math_ops.minimum( + ops.convert_to_tensor(update_global_step), + ops.convert_to_tensor(math_ops.cast(window, dtype=var_dtype)))) + moment1_2d = array_ops.expand_dims(update_moment1, -1) + + # m = grad_buffer^T / sqrt(min(t, window)) + # m has shape [model dimension, window], where model dimension is the sum + # of the dimensions of the flattened variables. + m = array_ops.transpose(math_ops.divide(update_grad_buffer, denom)) + + # sigma, u, _ = SVD(m^Tm + I * svd_eps) + mm = math_ops.matmul(m, m, transpose_a=True) + damping = math_ops.cast(linalg_ops.eye(window), dtype=var_dtype) * svd_eps + sigma, u, _ = linalg_ops.svd(mm + damping) + sigma_sqrt = math_ops.sqrt(sigma) + sigma_sqrt_min = math_ops.reduce_min(sigma_sqrt) + + # sigma_sqrt_inv = 1 / (\sqrt{sigma} + sigma_eps) ^ 3 + # We add sigma_eps to alleviate numerical instability. + # Note that (m^Tm)^(-3/2) = u diag(sigma_sqrt_inv) u^T. + sigma_sqrt_inv = math_ops.divide( + math_ops.cast(1.0, dtype=var_dtype), + math_ops.pow(sigma_sqrt + sigma_eps, 3)) + + # In full matrix AdaGrad, the update step computes (mm^T)^(-1/2)g, where the + # inversion of a model dimension by model dimension matrix is needed. To + # speed up this computation we calculate the following instead: + # m(m^Tm)^(-3/2)m^T moment1 = m u diag(sigma_sqrt_inv) u^T m^T moment1. + new_step = array_ops.expand_dims( + array_ops.zeros(flat_grad.get_shape(), dtype=var_dtype), -1) + head = math_ops.matmul( + m, + math_ops.matmul( + u, + math_ops.matmul( + array_ops.diag(sigma_sqrt_inv), + math_ops.matmul( + u, + math_ops.matmul(m, moment1_2d, transpose_a=True), + transpose_a=True)))) + + # When inverting (mm^t)^(1/2), we also add epsilon * I regularization for + # degenerate cases. We expand ((mm^t)^(1/2) + epsilon * I)^(-1) using + # Woodbury's identity. + # For full derivation please see paper at + # https://arxiv.org/pdf/1806.02958.pdf + tail = moment1_2d - math_ops.matmul( + m, + math_ops.matmul( + u, + math_ops.matmul( + array_ops.diag( + math_ops.divide(math_ops.cast(1.0, dtype=var_dtype), + sigma)), + math_ops.matmul( + u, + math_ops.matmul(m, moment1_2d, transpose_a=True), + transpose_a=True)))) + scaled_tail = math_ops.divide(tail, sigma_sqrt_min) + + update_new_step = control_flow_ops.cond( + sigma_sqrt_min > eps, lambda: math_ops.add(head, scaled_tail), + lambda: math_ops.add(new_step, head)) + + # Update each variable. + update_step = [] + for var in self._variables: + dim = self.shape_dict[var.name] + start_index = self.index_dict[var.name] + end_index = start_index + dim + var_update_correct_shape = array_ops.reshape( + update_new_step[start_index:end_index], var.get_shape()) + var_updated = state_ops.assign_sub(var, lr * var_update_correct_shape) + update_step.append(var_updated) + + return control_flow_ops.group(update_step) diff --git a/tensorflow/contrib/opt/python/training/ggt_test.py b/tensorflow/contrib/opt/python/training/ggt_test.py new file mode 100644 index 0000000000..42162960b0 --- /dev/null +++ b/tensorflow/contrib/opt/python/training/ggt_test.py @@ -0,0 +1,183 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for GGTOptimizer.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +from tensorflow.contrib.opt.python.training.ggt import GGTOptimizer +from tensorflow.python.eager import context +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.framework import test_util +from tensorflow.python.ops import resource_variable_ops +from tensorflow.python.ops import variables +from tensorflow.python.platform import test + + +def ggt_update_numpy(param, + g_t, + lr, + grad_buffer, + m, + window, + t, + beta1=0.9, + eps=1e-4, + svd_eps=1e-6, + sigma_eps=1e-2): + """Tests the correctness of one step of GGT.""" + m_t = m * beta1 + (1 - beta1) * g_t + grad_buffer[((t - 1) % window), :] = m_t + m_matrix = np.transpose(grad_buffer / np.sqrt(np.minimum(t, window))) + mm = np.dot(np.transpose(m_matrix), m_matrix) + damping = np.eye(window) * svd_eps + u, sigma, _ = np.linalg.svd(mm + damping) + + sigma_sqrt_inv = np.power(np.sqrt(sigma) + sigma_eps, -3) + new_step = np.linalg.multi_dot([ + m_matrix, u, + np.diag(sigma_sqrt_inv), + np.transpose(u), + np.transpose(m_matrix), m_t + ]) + + sigma_sqrt_min = np.sqrt(sigma).min() + + if sigma_sqrt_min > eps: + new_step += (m_t - np.linalg.multi_dot([ + m_matrix, u, + np.diag(1.0 / sigma), + np.transpose(u), + np.transpose(m_matrix), m_t + ])) * (1.0 / sigma_sqrt_min) + + param_t = param - lr * new_step + return param_t, m_t, grad_buffer + + +class GGTOptimizerTest(test.TestCase): + + def doTestBasic(self, use_resource=False): + # SVD does not support float16 + for i, dtype in enumerate([dtypes.float32, dtypes.float64]): + with self.test_session(graph=ops.Graph()): + # Initialize variables for numpy implementation. + m0 = 0.0 + window = 3 + grad_buffer = np.zeros((window, 4), dtype=dtype.as_numpy_dtype) + lr = 0.001 + var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype) + grads0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype) + var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype) + grads1_np = np.array([0.01, 0.01], dtype=dtype.as_numpy_dtype) + + if use_resource: + var0 = resource_variable_ops.ResourceVariable( + var0_np, name="var0_%d" % i) + var1 = resource_variable_ops.ResourceVariable( + var1_np, name="var1_%d" % i) + else: + var0 = variables.Variable(var0_np, name="var0") + var1 = variables.Variable(var1_np, name="var1") + grads0 = constant_op.constant(grads0_np) + grads1 = constant_op.constant(grads1_np) + + opt = GGTOptimizer(learning_rate=lr, window=window) + update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) + opt_variables = opt.variables() + + m_t = opt._get_moment1() + grad_buffer_t = opt._get_grad_buffer() + g_t = opt._get_flat_grad() + self.assertTrue(m_t is not None) + self.assertTrue(grad_buffer_t is not None) + self.assertTrue(g_t is not None) + self.assertIn(m_t, opt_variables) + self.assertIn(grad_buffer_t, opt_variables) + self.assertIn(g_t, opt_variables) + + with ops.Graph().as_default(): + # Shouldn't return non-slot variables from other graphs. + self.assertEqual(0, len(opt.variables())) + + if not context.executing_eagerly(): + self.evaluate(variables.global_variables_initializer()) + # Fetch params to validate initial values + self.assertAllClose([1.0, 2.0], self.evaluate(var0)) + self.assertAllClose([3.0, 4.0], self.evaluate(var1)) + + m_t = opt._get_moment1() + grad_buffer_t = opt._get_grad_buffer() + g_t = opt._get_flat_grad() + + # Run 3 steps of GGT + for t in range(1, 4): + if not context.executing_eagerly(): + self.evaluate(update) + elif t > 1: + opt.apply_gradients(zip([grads0, grads1], [var0, var1])) + + if t == 1: + self.assertAllCloseAccordingToType( + np.array([0.01, 0.01, 0.001, 0.001]), self.evaluate(m_t)) + self.assertAllCloseAccordingToType( + np.array([[0.01, 0.01, 0.001, 0.001], [0., 0., 0., 0.], + [0., 0., 0., 0.]]), self.evaluate(grad_buffer_t)) + elif t == 2: + self.assertAllCloseAccordingToType( + np.array([0.019, 0.019, 0.0019, 0.0019]), self.evaluate(m_t)) + self.assertAllCloseAccordingToType( + np.array([[0.01, 0.01, 0.001, 0.001], + [0.019, 0.019, 0.0019, 0.0019], [0., 0., 0., 0.]]), + self.evaluate(grad_buffer_t)) + else: + self.assertAllCloseAccordingToType( + np.array([0.0271, 0.0271, 0.00271, 0.00271]), + self.evaluate(m_t)) + self.assertAllCloseAccordingToType( + np.array([[0.01, 0.01, 0.001, + 0.001], [0.019, 0.019, 0.0019, 0.0019], + [0.0271, 0.0271, 0.00271, 0.00271]]), + self.evaluate(grad_buffer_t)) + + self.assertAllCloseAccordingToType([0.1, 0.1, 0.01, 0.01], + self.evaluate(g_t)) + + var_np = np.append(var0_np, var1_np) + grads_np = np.append(grads0_np, grads1_np) + var_np, m0, grad_buffer = ggt_update_numpy(var_np, grads_np, lr, + grad_buffer, m0, window, t) + + var0_np = var_np[:2] + var1_np = var_np[2:] + # Validate updated params + self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0)) + self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1)) + + def testBasic(self): + with self.test_session(): + self.doTestBasic(use_resource=False) + + @test_util.run_in_graph_and_eager_modes(reset_test=True) + def testResourceBasic(self): + self.doTestBasic(use_resource=True) + + +if __name__ == "__main__": + test.main() -- GitLab From c26ba8f104cd6efd16080ada5f6414baa1f4e372 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 18 Jun 2018 15:13:21 -0700 Subject: [PATCH 617/816] Support rsqrt for graphdef export. PiperOrigin-RevId: 201067685 --- .../contrib/lite/toco/export_tensorflow.cc | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/tensorflow/contrib/lite/toco/export_tensorflow.cc b/tensorflow/contrib/lite/toco/export_tensorflow.cc index 6e5e0d0137..afc6d5df20 100644 --- a/tensorflow/contrib/lite/toco/export_tensorflow.cc +++ b/tensorflow/contrib/lite/toco/export_tensorflow.cc @@ -1047,6 +1047,18 @@ void ConvertSqrtOperator(const TensorFlowSqrtOperator& src_op, (*sqrt_op->mutable_attr())["T"].set_type(DT_FLOAT); } +void ConvertRsqrtOperator(const Model& model, + const TensorFlowRsqrtOperator& src_op, + GraphDef* tensorflow_graph) { + auto* rsqrt_op = tensorflow_graph->add_node(); + rsqrt_op->set_op("Rsqrt"); + rsqrt_op->set_name(src_op.outputs[0]); + CHECK_EQ(src_op.inputs.size(), 1); + *rsqrt_op->add_input() = src_op.inputs[0]; + const auto data_type = GetTensorFlowDataType(model, src_op.inputs[0]); + (*rsqrt_op->mutable_attr())["T"].set_type(data_type); +} + void ConvertSplitOperator(const Model& model, const TensorFlowSplitOperator& src_op, GraphDef* tensorflow_graph) { @@ -1856,6 +1868,10 @@ void ConvertOperator(const Model& model, const Operator& src_op, } else if (src_op.type == OperatorType::kTensorFlowSqrt) { ConvertSqrtOperator(static_cast(src_op), tensorflow_graph); + } else if (src_op.type == OperatorType::kTensorFlowRsqrt) { + ConvertRsqrtOperator(model, + static_cast(src_op), + tensorflow_graph); } else if (src_op.type == OperatorType::kTensorFlowSplit) { ConvertSplitOperator(model, static_cast(src_op), -- GitLab From 209662bac4a3e04ae359939f67ab892456453b92 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 18 Jun 2018 15:23:36 -0700 Subject: [PATCH 618/816] Fix bug in RemoveIdempotent optimizer stage. Minor cleanup in RemoveIdentityTranspose. PiperOrigin-RevId: 201069367 --- tensorflow/core/grappler/op_types.cc | 3 +- .../optimizers/arithmetic_optimizer.cc | 45 +++++++++---------- .../optimizers/arithmetic_optimizer_test.cc | 26 +++-------- 3 files changed, 30 insertions(+), 44 deletions(-) diff --git a/tensorflow/core/grappler/op_types.cc b/tensorflow/core/grappler/op_types.cc index b4ddd61c29..bdeb5c66fc 100644 --- a/tensorflow/core/grappler/op_types.cc +++ b/tensorflow/core/grappler/op_types.cc @@ -629,7 +629,8 @@ bool HasOpDef(const NodeDef& node) { } bool IsIdempotent(const NodeDef& node) { - return IsValueAndOrderAndShapePreserving(node) && IsFreeOfSideEffect(node); + return IsValueAndOrderAndShapePreserving(node) && IsFreeOfSideEffect(node) && + !ModifiesFrameInfo(node); } } // namespace grappler diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc index d518685216..0d69e0dde3 100644 --- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc +++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc @@ -1083,14 +1083,6 @@ class RemoveIdentityTranspose : public ArithmeticOptimizerStage { Status TrySimplify(NodeDef* node, string* simplified_node_name) override { TF_RETURN_IF_ERROR(EnsureNodeIsSupported(node)); - NodeDef* tail = node; - // TODO(rmlarsen): Enable after debugging breakage in Bayesflow. - if (ctx().opt_level == RewriterConfig::AGGRESSIVE) { - tail = GetTailOfIdempotentChain(*tail, *ctx().node_map, - *ctx().nodes_to_preserve); - } - NodeDef* first_transpose; - TF_RETURN_IF_ERROR(GetInputNode(tail->input(0), &first_transpose)); NodeDef* node_perm; TF_RETURN_IF_ERROR(GetInputNode(node->input(1), &node_perm)); @@ -1099,7 +1091,21 @@ class RemoveIdentityTranspose : public ArithmeticOptimizerStage { } std::vector node_perm_values; TF_RETURN_IF_ERROR(GetPermutation(*node_perm, &node_perm_values)); - if (first_transpose->op() == node->op()) { + + // Remove simple identity transposes. + if (IsIdentityPermutation(node_perm_values)) { + *simplified_node_name = node->input(0); + return Status::OK(); + } + + NodeDef* tail = node; + tail = GetTailOfIdempotentChain(*tail, *ctx().node_map, + *ctx().nodes_to_preserve); + NodeDef* first_transpose; + TF_RETURN_IF_ERROR(GetInputNode(tail->input(0), &first_transpose)); + + if (first_transpose->op() == node->op() && + NumNonControlOutputs(*first_transpose, *ctx().node_map) == 1) { // Remove pairs of transposes that cancel each other. NodeDef* first_transpose_perm; TF_RETURN_IF_ERROR( @@ -1124,11 +1130,6 @@ class RemoveIdentityTranspose : public ArithmeticOptimizerStage { *simplified_node_name = node->input(0); } } - } else { - // Remove simple identity transposes. - if (IsIdentityPermutation(node_perm_values)) { - *simplified_node_name = node->input(0); - } } return Status::OK(); } @@ -1722,19 +1723,15 @@ class RemoveIdempotentStage : public ArithmeticOptimizerStage { ~RemoveIdempotentStage() override = default; bool IsSupported(const NodeDef* node) const override { - return IsIdempotent(*node) && !IsInPreserveSet(*node); + return node->input_size() == 1 && IsIdempotent(*node) && + !IsInPreserveSet(*node); } Status TrySimplify(NodeDef* node, string* simplified_node_name) override { NodeDef* input; TF_RETURN_IF_ERROR(GetInputNode(node->input(0), &input)); - auto root_scope_and_name = ParseNodeScopeAndName(node->name()); - const string new_name = OptimizedNodeName(root_scope_and_name); - if (input->op() == node->op() && input->device() == node->device() && - IsIdempotent(*input) && !ctx().node_map->NodeExists(new_name)) { - NodeDef* new_input_node = AddCopyNode(new_name, input); - ForwardControlDependencies(new_input_node, {node}); - *simplified_node_name = new_input_node->name(); + if (input->op() == node->op() && input->device() == node->device()) { + *simplified_node_name = node->input(0); } return Status::OK(); } @@ -2901,7 +2898,7 @@ Status ArithmeticOptimizer::SimplifyArithmeticOps(bool can_use_shapes) { pipeline.AddStage(ctx, ctx_ext); if (options_.minimize_broadcasts && can_use_shapes) pipeline.AddStage(ctx, ctx_ext); - if (options_.remove_identity_transpose && can_use_shapes) + if (options_.remove_identity_transpose) pipeline.AddStage(ctx, ctx_ext); if (options_.remove_involution) pipeline.AddStage(ctx, ctx_ext); @@ -2909,7 +2906,7 @@ Status ArithmeticOptimizer::SimplifyArithmeticOps(bool can_use_shapes) { pipeline.AddStage(ctx, ctx_ext); if (options_.remove_redundant_cast) pipeline.AddStage(ctx, ctx_ext); - if (options_.remove_redundant_reshape) + if (options_.remove_redundant_reshape && can_use_shapes) pipeline.AddStage(ctx, ctx_ext); if (options_.remove_negation) pipeline.AddStage(ctx, ctx_ext); diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc b/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc index e1d55cdf5f..d0e6b04679 100644 --- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc +++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc @@ -2976,12 +2976,8 @@ TEST_F(ArithmeticOptimizerTest, HoistCWiseUnaryIntoSplit) { TEST_F(ArithmeticOptimizerTest, RemoveIdempotent) { tensorflow::Scope s = tensorflow::Scope::NewRootScope(); Output a = ops::Const(s.WithOpName("a"), 3.14f, {32}); - Output ctrl1 = ops::Const(s.WithOpName("ctrl1"), 1, {}); - Output ctrl2 = ops::Const(s.WithOpName("ctrl2"), 2, {}); - Output sn1 = - ops::Snapshot(s.WithOpName("sn1").WithControlDependencies(ctrl1), a); - Output sn2 = - ops::Snapshot(s.WithOpName("sn2").WithControlDependencies(ctrl2), sn1); + Output sn1 = ops::Snapshot(s.WithOpName("sn1"), a); + Output sn2 = ops::Snapshot(s.WithOpName("sn2"), sn1); Output out1 = ops::Identity(s.WithOpName("out1"), sn2); Output id1 = ops::Identity(s.WithOpName("id1"), a); Output id2 = ops::Identity(s.WithOpName("id2"), id1); @@ -2997,32 +2993,24 @@ TEST_F(ArithmeticOptimizerTest, RemoveIdempotent) { EnableOnlyRemoveIdempotent(&optimizer); OptimizeTwice(&optimizer, &item, &output); - EXPECT_EQ(11, output.node_size()); + EXPECT_EQ(7, output.node_size()); int found = 0; for (const NodeDef& node : output.node()) { if (node.name() == "out1") { EXPECT_EQ(1, node.input_size()); - EXPECT_EQ("ArithmeticOptimizer/RemoveIdempotent_sn2", node.input(0)); - found++; - } else if (node.name() == "ArithmeticOptimizer/RemoveIdempotent_sn2") { - EXPECT_EQ(3, node.input_size()); - EXPECT_EQ("Snapshot", node.op()); - EXPECT_EQ("a", node.input(0)); - EXPECT_EQ("^ctrl1", node.input(1)); - EXPECT_EQ("^ctrl2", node.input(2)); + EXPECT_EQ("sn1", node.input(0)); found++; } else if (node.name() == "out2") { EXPECT_EQ(1, node.input_size()); - EXPECT_EQ("ArithmeticOptimizer/RemoveIdempotent_id2", node.input(0)); + EXPECT_EQ("id1", node.input(0)); found++; - } else if (node.name() == "ArithmeticOptimizer/RemoveIdempotent_id2") { - EXPECT_EQ("Identity", node.op()); + } else if (node.name() == "sn1") { EXPECT_EQ(1, node.input_size()); EXPECT_EQ("a", node.input(0)); found++; } } - EXPECT_EQ(4, found); + EXPECT_EQ(3, found); auto tensors = EvaluateNodes(output, item.fetch); EXPECT_EQ(tensors.size(), tensors_expected.size()); -- GitLab From 205fe2dbb8e00ebe25e5e9a480a24a49f0d87646 Mon Sep 17 00:00:00 2001 From: Youlong Cheng Date: Mon, 18 Jun 2018 15:32:53 -0700 Subject: [PATCH 619/816] Fix input_batch_size for PER_HOST_V2 when model parallelism is enabled. PiperOrigin-RevId: 201070853 --- tensorflow/contrib/tpu/python/tpu/tpu_context.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_context.py b/tensorflow/contrib/tpu/python/tpu/tpu_context.py index ffd7b43c31..c4c69902f9 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_context.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_context.py @@ -384,9 +384,7 @@ class _InternalTPUContext(object): # On TPU if self.is_input_sharded_per_core() or ( self.is_input_per_host_with_iterators()): - # We prohibit per core input sharding for the model parallelism case, - # therefore it is safe to use num_cores here. - return global_batch_size // self.num_cores + return global_batch_size // self.num_replicas else: return global_batch_size // self.num_hosts -- GitLab From ae377d44a9796a2b226306aeade57888d2f2df03 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 18 Jun 2018 15:32:55 -0700 Subject: [PATCH 620/816] Enable the natural layouts of the entry computation to flow into the parameters and result layouts of the entry ComputationLayout. If the arguments shapes passed in to the servie.cc API do not have a layout, it is assumed the caller is willing to accept the natural layout propagated by the XLA compiler. Similarly, if the ExecutionOptions has a shape for the result, but no layout is set in such shape, it is assumed the caller is willing to accept the natural layout propagated by the XLA compiler. Same thing for the ExecutableBuildOptions result_layout(). PiperOrigin-RevId: 201070858 --- .../compiler/xla/service/layout_assignment.cc | 41 ++++++++----------- .../compiler/xla/service/layout_assignment.h | 5 +++ .../compiler/xla/service/local_service.cc | 12 ++++-- tensorflow/compiler/xla/service/service.cc | 40 +++++++++--------- tensorflow/compiler/xla/service/service.h | 6 +-- 5 files changed, 52 insertions(+), 52 deletions(-) diff --git a/tensorflow/compiler/xla/service/layout_assignment.cc b/tensorflow/compiler/xla/service/layout_assignment.cc index eb469e77a0..b319518421 100644 --- a/tensorflow/compiler/xla/service/layout_assignment.cc +++ b/tensorflow/compiler/xla/service/layout_assignment.cc @@ -175,41 +175,32 @@ Status LayoutConstraints::SetBufferLayout(const Layout& layout, TF_RETURN_IF_ERROR( LayoutUtil::ValidateLayoutForShape(layout, buffer.shape())); - const BufferLayoutConstraint* curr_constraint = - GetBufferLayoutConstraint(buffer); - if (curr_constraint != nullptr) { - if (LayoutUtil::Equal(curr_constraint->layout(), layout)) { + auto iter = buffer_constraints_.find(&buffer); + if (iter != buffer_constraints_.end()) { + const BufferLayoutConstraint& curr_constraint = iter->second; + if (LayoutUtil::Equal(curr_constraint.layout(), layout)) { // New constraint matches existing constraint. Nothing to do. return Status::OK(); } - if (curr_constraint->mandatory()) { + if (curr_constraint.mandatory()) { return FailedPrecondition( "Buffer %s already has the layout constraint %s, cannot add " "incompatible constraint %s", buffer.ToString().c_str(), - LayoutUtil::HumanString(curr_constraint->layout()).c_str(), + LayoutUtil::HumanString(curr_constraint.layout()).c_str(), LayoutUtil::HumanString(layout).c_str()); } - } - - auto iter = buffer_constraints_.find(&buffer); - bool overwrite = iter != buffer_constraints_.end(); - if (!overwrite) { + iter->second = BufferLayoutConstraint(layout, buffer, mandatory, dfs); + } else { + TF_RET_CHECK(unconstrained_buffer_ids_.erase(buffer.id()) == 1) + << buffer.ToString(); iter = buffer_constraints_ .insert(std::make_pair( &buffer, BufferLayoutConstraint(layout, buffer, mandatory, dfs))) .first; - } else { - iter->second = BufferLayoutConstraint(layout, buffer, mandatory, dfs); } added_constraints_.push_back(&iter->second); - - // Remove buffer from the set of unconstrained buffers. - TF_RET_CHECK(unconstrained_buffer_ids_.count(buffer.id()) == - static_cast(!overwrite)); - unconstrained_buffer_ids_.erase(buffer.id()); - return Status::OK(); } @@ -716,7 +707,8 @@ Status CheckParameterLayout(HloInstruction* parameter, const ComputationLayout& computation_layout) { const ShapeLayout& parameter_layout = computation_layout.parameter_layout(parameter->parameter_number()); - if (!parameter_layout.MatchesLayoutInShape(parameter->shape())) { + if (parameter_layout.LayoutIsSet() && + !parameter_layout.MatchesLayoutInShape(parameter->shape())) { return InternalError( "parameter instruction %s does not match layout of computation " "shape: %s", @@ -936,6 +928,7 @@ LayoutAssignment::LayoutAssignment( ComputationLayout* entry_computation_layout, ChannelLayoutConstraints* channel_constraints) : entry_computation_layout_(entry_computation_layout), + saved_entry_computation_layout_(*entry_computation_layout), channel_layout_constraints_(channel_constraints) { if (channel_layout_constraints_ != nullptr) { // Save a copy of the input ChannelLayoutConstraints so that we can reset it @@ -944,11 +937,6 @@ LayoutAssignment::LayoutAssignment( } VLOG(1) << "Entry computation layout given to layout assignment: " << entry_computation_layout_->ToString(); - // Layouts of all parameter instructions must be set. - for (const ShapeLayout& parameter_layout : - entry_computation_layout_->parameter_layouts()) { - CHECK(parameter_layout.LayoutIsSet()); - } } std::unique_ptr LayoutAssignment::ChooseOperandLayoutFromOutputLayout( @@ -1728,6 +1716,7 @@ StatusOr LayoutAssignment::Run(HloModule* module) { // root, we also fix up the eventually inconsistent ComputationLayout, which // will be then made mandatory by the second pass. for (int64 i = 0; i < 2; ++i) { + VLOG(5) << "Running " << (i == 0 ? "un" : "") << "constrained pass"; TF_RETURN_IF_ERROR(ClearPreviousPassSideEffects(module)); TF_ASSIGN_OR_RETURN(auto points_to_analysis, TuplePointsToAnalysis::Run(module)); @@ -1765,10 +1754,12 @@ StatusOr LayoutAssignment::Run(HloModule* module) { Status LayoutAssignment::Init() { computation_layouts_.clear(); + *entry_computation_layout_ = saved_entry_computation_layout_; return Status::OK(); } Status LayoutAssignment::ClearPreviousPassSideEffects(HloModule* module) { + VLOG(5) << "Clearing previous side effects"; // Clear all the copies which have been added, and all the related // instructions (like GTE and tuples). int64 removed_copies = 0; diff --git a/tensorflow/compiler/xla/service/layout_assignment.h b/tensorflow/compiler/xla/service/layout_assignment.h index eb4cd5936b..0d7dde9c55 100644 --- a/tensorflow/compiler/xla/service/layout_assignment.h +++ b/tensorflow/compiler/xla/service/layout_assignment.h @@ -432,8 +432,13 @@ class LayoutAssignment : public HloPassInterface { Status PropagateComputationLayouts(HloComputation* computation, ComputationLayout* computation_layout); + // The pointer to the ComputationLayout passed as constructor parameter. ComputationLayout* entry_computation_layout_; + // A copy of entry_computation_layout_ used to reset it to the initial values + // during the multiple passes done by the layout assignment operation. + ComputationLayout saved_entry_computation_layout_; + protected: // Sets up the copy instruction according to the characteristic (sharding, // metadata, ...) of the reference instruction. The index argument is used diff --git a/tensorflow/compiler/xla/service/local_service.cc b/tensorflow/compiler/xla/service/local_service.cc index 296d04d436..a6aa8bf82c 100644 --- a/tensorflow/compiler/xla/service/local_service.cc +++ b/tensorflow/compiler/xla/service/local_service.cc @@ -154,7 +154,8 @@ StatusOr> LocalService::CompileExecutable( for (int i = 0; i < argument_layouts.size(); ++i) { const Shape& argument_shape = *argument_layouts[i]; - TF_RETURN_IF_ERROR(ShapeUtil::ValidateShape(argument_shape)); + TF_RETURN_IF_ERROR( + ShapeUtil::ValidateShapeWithOptionalLayout(argument_shape)); if (!ShapeUtil::Compatible(argument_shape, program_shape.parameters(i))) { tensorflow::gtl::optional metadata = ParameterMetadata(computation, /*parameter_number=*/i); @@ -178,8 +179,8 @@ StatusOr> LocalService::CompileExecutable( } } if (build_options.result_layout() != nullptr) { - TF_RETURN_IF_ERROR(ValidateResultShapeWithLayout( - *build_options.result_layout(), program_shape.result())); + TF_RETURN_IF_ERROR(ValidateResultShape(*build_options.result_layout(), + program_shape.result())); } ExecutionOptions execution_options = @@ -189,6 +190,11 @@ StatusOr> LocalService::CompileExecutable( std::unique_ptr module_config, CreateModuleConfig(program_shape, argument_layouts, &execution_options)); + VLOG(3) << "Host Computation Layout: " + << module_config->host_entry_computation_layout().ToString(); + VLOG(3) << "Device Computation Layout: " + << module_config->device_entry_computation_layout().ToString(); + TF_ASSIGN_OR_RETURN( se::StreamExecutor * executor, execute_backend_->stream_executor(build_options.device_ordinal())); diff --git a/tensorflow/compiler/xla/service/service.cc b/tensorflow/compiler/xla/service/service.cc index 961158e677..ff68d65fbc 100644 --- a/tensorflow/compiler/xla/service/service.cc +++ b/tensorflow/compiler/xla/service/service.cc @@ -191,21 +191,17 @@ Status Service::DeconstructTuple(const DeconstructTupleRequest* arg, return Status::OK(); } -Status Service::ValidateResultShapeWithLayout(const Shape& shape_with_layout, - const Shape& result_shape) const { - if (!ShapeUtil::Compatible(shape_with_layout, result_shape)) { +Status Service::ValidateResultShape(const Shape& client_shape, + const Shape& result_shape) const { + TF_RETURN_IF_ERROR(ShapeUtil::ValidateShapeWithOptionalLayout(client_shape)); + if (!ShapeUtil::Compatible(client_shape, result_shape)) { return InvalidArgument( "Shape used to set computation result layout %s is not compatible " "with result shape %s", - ShapeUtil::HumanStringWithLayout(shape_with_layout).c_str(), + ShapeUtil::HumanStringWithLayout(client_shape).c_str(), ShapeUtil::HumanString(result_shape).c_str()); } - if (!LayoutUtil::HasLayout(shape_with_layout)) { - return InvalidArgument( - "Shape used to set computation result layout %s does not have layout", - ShapeUtil::HumanStringWithLayout(shape_with_layout).c_str()); - } - return ShapeUtil::ValidateShape(shape_with_layout); + return Status::OK(); } StatusOr>> @@ -277,8 +273,8 @@ StatusOr> Service::CreateModuleConfig( execution_options->has_shape_with_output_layout()) { const auto& shape_with_output_layout = execution_options->shape_with_output_layout(); - TF_RETURN_IF_ERROR(ValidateResultShapeWithLayout(shape_with_output_layout, - program_shape.result())); + TF_RETURN_IF_ERROR( + ValidateResultShape(shape_with_output_layout, program_shape.result())); TF_RETURN_IF_ERROR( host_computation_layout->mutable_result_layout()->CopyLayoutFromShape( shape_with_output_layout)); @@ -382,18 +378,20 @@ StatusOr>> Service::BuildExecutables( } Status Service::ValidateEntryComputationLayout(HloModule* module) { + const ComputationLayout& on_host = module->host_entry_computation_layout(); const ComputationLayout& on_device = module->device_entry_computation_layout(); for (int64 i = 0; i < on_device.parameter_count(); ++i) { - TF_RET_CHECK(ShapeUtil::Equal( - on_device.parameter_shape(i), - execute_backend_->transfer_manager()->HostShapeToDeviceShape( - module->host_entry_computation_layout().parameter_shape(i)))); - } - TF_RET_CHECK(ShapeUtil::Equal( - module->device_entry_computation_layout().result_shape(), - execute_backend_->transfer_manager()->HostShapeToDeviceShape( - module->host_entry_computation_layout().result_shape()))); + TF_RET_CHECK(ShapeUtil::Compatible(on_device.parameter_shape(i), + on_host.parameter_shape(i))) + << ShapeUtil::HumanStringWithLayout(on_device.parameter_shape(i)) + << " vs " + << ShapeUtil::HumanStringWithLayout(on_host.parameter_shape(i)); + } + TF_RET_CHECK( + ShapeUtil::Compatible(on_device.result_shape(), on_host.result_shape())) + << ShapeUtil::HumanStringWithLayout(on_device.result_shape()) << " vs " + << ShapeUtil::HumanStringWithLayout(on_host.result_shape()); return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/service.h b/tensorflow/compiler/xla/service/service.h index 8748a4c144..7960429084 100644 --- a/tensorflow/compiler/xla/service/service.h +++ b/tensorflow/compiler/xla/service/service.h @@ -266,11 +266,11 @@ class Service : public ServiceInterface { // will be the result of this computation. Status ExecuteOneToN(const ExecuteGraphRequest* arg, ExecuteResponse* result); - // Convenience function which checks whether the given shape_with_layout + // Convenience function which checks whether the given client_shape // (presumably passed by the client to set the result layout) is valid for the // given computation result shape. - Status ValidateResultShapeWithLayout(const Shape& shape_with_layout, - const Shape& result_shape) const; + Status ValidateResultShape(const Shape& client_shape, + const Shape& result_shape) const; // Returns the stream executors assigned to the replicas represented by the // given device handle. Each device_handle is a virtual replicated device that -- GitLab From 23feb3b06e2ea992f24314679c0aae4d0650f0d8 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 18 Jun 2018 15:32:56 -0700 Subject: [PATCH 621/816] Make sure CRS is fully deserializable from an HLO TXT and Proto POV. PiperOrigin-RevId: 201070859 --- tensorflow/compiler/xla/service/hlo.proto | 6 +++++- tensorflow/compiler/xla/service/hlo_instruction.cc | 7 ++++++- tensorflow/compiler/xla/service/hlo_instructions.cc | 10 +++++++--- tensorflow/compiler/xla/service/hlo_parser.cc | 9 ++++++--- 4 files changed, 24 insertions(+), 8 deletions(-) diff --git a/tensorflow/compiler/xla/service/hlo.proto b/tensorflow/compiler/xla/service/hlo.proto index e201359d3d..d241791060 100644 --- a/tensorflow/compiler/xla/service/hlo.proto +++ b/tensorflow/compiler/xla/service/hlo.proto @@ -145,12 +145,16 @@ message HloInstructionProto { repeated int64 operand_ids = 36; repeated int64 control_predecessor_ids = 37; repeated int64 called_computation_ids = 38; - repeated int64 replica_group_ids = 44; xla.OpSharding sharding = 40; // Backend configuration for the instruction. Has backend-specific meaning. string backend_config = 43; + + // Cross Replica Sum fields. + repeated int64 replica_group_ids = 44; + int64 all_reduce_id = 45; + string cross_replica_sum_barrier = 46; } // Serialization of HloComputation. diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc index 8bedd2a865..8f89b6f255 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction.cc +++ b/tensorflow/compiler/xla/service/hlo_instruction.cc @@ -261,12 +261,17 @@ StatusOr> HloInstruction::CreateFromProto( [&instruction_map](int64 operand_id) { return instruction_map.at(operand_id); }); + tensorflow::gtl::optional all_reduce_id; + if (proto.all_reduce_id() > 0) { + all_reduce_id = proto.all_reduce_id(); + } instruction = CreateCrossReplicaSum( proto.shape(), all_operands, computations(0), /*replica_group_ids=*/ std::vector(proto.replica_group_ids().begin(), proto.replica_group_ids().end()), - /*barrier=*/""); + /*barrier=*/proto.cross_replica_sum_barrier(), + /*all_reduce_id=*/all_reduce_id); break; } default: { diff --git a/tensorflow/compiler/xla/service/hlo_instructions.cc b/tensorflow/compiler/xla/service/hlo_instructions.cc index 5871a6605f..1ebc4c936a 100644 --- a/tensorflow/compiler/xla/service/hlo_instructions.cc +++ b/tensorflow/compiler/xla/service/hlo_instructions.cc @@ -280,7 +280,7 @@ HloAllReduceInstruction::HloAllReduceInstruction( cross_replica_sum_barrier_(barrier.begin(), barrier.end()), all_reduce_id_(all_reduce_id) { // TODO(b/79737069): Remove the CHECK when supported. - CHECK(!all_reduce_id_.has_value()); + CHECK(!all_reduce_id_); for (auto operand : operands) { AppendOperand(operand); } @@ -292,7 +292,11 @@ HloInstructionProto HloAllReduceInstruction::ToProto() const { for (int64 i : replica_group_ids_) { proto.add_replica_group_ids(i); } - // TODO(b/79737069): handle barrier and all_reduce_id. + // Proto3 is so sad. + if (all_reduce_id_) { + proto.set_all_reduce_id(*all_reduce_id_); + } + proto.set_cross_replica_sum_barrier(cross_replica_sum_barrier_); return proto; } @@ -303,7 +307,7 @@ std::vector HloAllReduceInstruction::ExtraAttributesToStringImpl( if (!cross_replica_sum_barrier().empty()) { result.push_back(StrCat("barrier=\"", cross_replica_sum_barrier(), "\"")); } - if (all_reduce_id_.has_value()) { + if (all_reduce_id_) { result.push_back(StrCat("all_reduce_id=", *all_reduce_id_)); } return result; diff --git a/tensorflow/compiler/xla/service/hlo_parser.cc b/tensorflow/compiler/xla/service/hlo_parser.cc index fef475380c..daa3bc4232 100644 --- a/tensorflow/compiler/xla/service/hlo_parser.cc +++ b/tensorflow/compiler/xla/service/hlo_parser.cc @@ -590,24 +590,27 @@ bool HloParser::ParseInstruction(HloComputation::Builder* builder, optional to_apply; optional> replica_group_ids; optional barrier; + optional all_reduce_id; attrs["to_apply"] = {/*required=*/true, AttrTy::kHloComputation, &to_apply}; attrs["replica_group_ids"] = { /*required=*/false, AttrTy::kBracedInt64List, &replica_group_ids}; attrs["barrier"] = {/*required=*/false, AttrTy::kString, &barrier}; + attrs["all_reduce_id"] = {/*required=*/false, AttrTy::kInt64, + &all_reduce_id}; if (!ParseOperands(&operands) || !ParseAttributes(attrs)) { return false; } - if (replica_group_ids) { instruction = builder->AddInstruction(HloInstruction::CreateCrossReplicaSum( shape, operands, *to_apply, *replica_group_ids, - barrier ? *barrier : "")); + barrier ? *barrier : "", all_reduce_id)); } else { instruction = builder->AddInstruction(HloInstruction::CreateCrossReplicaSum( - shape, operands, *to_apply, {}, barrier ? *barrier : "")); + shape, operands, *to_apply, {}, barrier ? *barrier : "", + all_reduce_id)); } break; } -- GitLab From 19ba09066cfc1be9afa795a31743cbc63e6742d1 Mon Sep 17 00:00:00 2001 From: Yash Katariya Date: Mon, 18 Jun 2018 22:41:50 +0000 Subject: [PATCH 622/816] Removed TOC --- .../examples/nmt_with_attention/NMT_with_Attention.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tensorflow/contrib/eager/python/examples/nmt_with_attention/NMT_with_Attention.ipynb b/tensorflow/contrib/eager/python/examples/nmt_with_attention/NMT_with_Attention.ipynb index 5382d4b940..8d044c5705 100644 --- a/tensorflow/contrib/eager/python/examples/nmt_with_attention/NMT_with_Attention.ipynb +++ b/tensorflow/contrib/eager/python/examples/nmt_with_attention/NMT_with_Attention.ipynb @@ -646,7 +646,7 @@ }, "cell_type": "markdown", "source": [ - "## Step 5: Define the optimizers and the loss function" + "## Define the optimizers and the loss function" ] }, { @@ -695,7 +695,7 @@ }, "cell_type": "markdown", "source": [ - "## Step 6: Training\n", + "## Training\n", "\n", "* Here we pass the input through the encoder which return *encoder output* and the *encoder hidden state*.\n", "* The encoder output, encoder hidden state and the decoder input (which is the \"start\" token) is passed to the decoder.\n", @@ -790,7 +790,7 @@ }, "cell_type": "markdown", "source": [ - "## Step 7: Translate\n", + "## Translate\n", "\n", "* The evaluate function is similar to the training loop. The only change is that we don't use teacher forcing here. The input to the decoder at each time step is its previous predictions along with the hidden state and the encoder output.\n", "* We stop predicting when the model predicts the *'end' token*.\n", -- GitLab From c4f0f9a8f74bf9dba4fd261ab1970592ba6a9668 Mon Sep 17 00:00:00 2001 From: Asim Shankar Date: Mon, 18 Jun 2018 15:36:12 -0700 Subject: [PATCH 623/816] Java: Release 1.9.0-rc1 PiperOrigin-RevId: 201071358 --- tensorflow/java/maven/libtensorflow/pom.xml | 2 +- tensorflow/java/maven/libtensorflow_jni/pom.xml | 2 +- tensorflow/java/maven/libtensorflow_jni_gpu/pom.xml | 2 +- tensorflow/java/maven/pom.xml | 2 +- tensorflow/java/maven/proto/pom.xml | 2 +- tensorflow/java/maven/tensorflow/pom.xml | 2 +- 6 files changed, 6 insertions(+), 6 deletions(-) diff --git a/tensorflow/java/maven/libtensorflow/pom.xml b/tensorflow/java/maven/libtensorflow/pom.xml index 38e87b1639..a7fa9ea5cc 100644 --- a/tensorflow/java/maven/libtensorflow/pom.xml +++ b/tensorflow/java/maven/libtensorflow/pom.xml @@ -6,7 +6,7 @@ org.tensorflow parentpom - 1.9.0-rc0 + 1.9.0-rc1 ../ libtensorflow diff --git a/tensorflow/java/maven/libtensorflow_jni/pom.xml b/tensorflow/java/maven/libtensorflow_jni/pom.xml index 36c984e280..83aae29f1e 100644 --- a/tensorflow/java/maven/libtensorflow_jni/pom.xml +++ b/tensorflow/java/maven/libtensorflow_jni/pom.xml @@ -6,7 +6,7 @@ org.tensorflow parentpom - 1.9.0-rc0 + 1.9.0-rc1 ../ libtensorflow_jni diff --git a/tensorflow/java/maven/libtensorflow_jni_gpu/pom.xml b/tensorflow/java/maven/libtensorflow_jni_gpu/pom.xml index 4c846de05a..50bd8ee5f9 100644 --- a/tensorflow/java/maven/libtensorflow_jni_gpu/pom.xml +++ b/tensorflow/java/maven/libtensorflow_jni_gpu/pom.xml @@ -6,7 +6,7 @@ org.tensorflow parentpom - 1.9.0-rc0 + 1.9.0-rc1 ../ libtensorflow_jni_gpu diff --git a/tensorflow/java/maven/pom.xml b/tensorflow/java/maven/pom.xml index f2a0a97eae..3890f3fcaa 100644 --- a/tensorflow/java/maven/pom.xml +++ b/tensorflow/java/maven/pom.xml @@ -6,7 +6,7 @@ 4.0.0 org.tensorflow parentpom - 1.9.0-rc0 + 1.9.0-rc1 pom https://www.tensorflow.org diff --git a/tensorflow/java/maven/proto/pom.xml b/tensorflow/java/maven/proto/pom.xml index eb0a952c7d..618a2a124c 100644 --- a/tensorflow/java/maven/proto/pom.xml +++ b/tensorflow/java/maven/proto/pom.xml @@ -6,7 +6,7 @@ org.tensorflow parentpom - 1.9.0-rc0 + 1.9.0-rc1 ../ proto diff --git a/tensorflow/java/maven/tensorflow/pom.xml b/tensorflow/java/maven/tensorflow/pom.xml index 48668a47f2..157c4b8e82 100644 --- a/tensorflow/java/maven/tensorflow/pom.xml +++ b/tensorflow/java/maven/tensorflow/pom.xml @@ -6,7 +6,7 @@ org.tensorflow parentpom - 1.9.0-rc0 + 1.9.0-rc1 ../ tensorflow -- GitLab From d22fa07e2b86ceb2a0b5de484fc1fd9c2bf5a5b9 Mon Sep 17 00:00:00 2001 From: Igor Ganichev Date: Mon, 18 Jun 2018 15:36:41 -0700 Subject: [PATCH 624/816] Default to compiling functions running on TPU. PiperOrigin-RevId: 201071433 --- .../compiler/jit/create_xla_launch_op.cc | 22 ++++++++++++++++++- tensorflow/compiler/tests/eager_test.py | 16 +++++++------- .../core/common_runtime/eager/execute.cc | 14 ++++++++++++ 3 files changed, 43 insertions(+), 9 deletions(-) diff --git a/tensorflow/compiler/jit/create_xla_launch_op.cc b/tensorflow/compiler/jit/create_xla_launch_op.cc index 731b8ebfdc..a2e6285339 100644 --- a/tensorflow/compiler/jit/create_xla_launch_op.cc +++ b/tensorflow/compiler/jit/create_xla_launch_op.cc @@ -66,8 +66,28 @@ class SinglePassSearch { Status CompilationRequested(const FunctionLibraryRuntime& flr, const NodeDef& node_def) { + const FunctionDef* function_def = + flr.GetFunctionLibraryDefinition()->Find(node_def.name()); + if (function_def == nullptr) { + // The node def is not calling a function. Individual ops can be + // run directly using on-demand mode, no need to create XlaLaunch + // kernel for them. + // TODO(b/110359382): Make custom kernel creation return a bool instead of + // status. + // We don't set error messages here to avoid unnecessary string copy. + // Similarly below. + return Status(error::INVALID_ARGUMENT, ""); + } + + // If kXlaCompileAttr is set on the node_def, use its value. + const auto& it = node_def.attr().find(kXlaCompileAttr); + if (it != node_def.attr().end()) { + return it->second.b() ? Status::OK() : Status(error::INVALID_ARGUMENT, ""); + } + + // kXlaCompileAttr is not set on node_def, check if it is set on + // FunctionDef. bool xla_compile = false; - // Check if op is marked _XlaCompile=true. Status status = flr.GetFunctionLibraryDefinition()->GetAttr( node_def, kXlaCompileAttr, &xla_compile); if (!status.ok() || !xla_compile) { diff --git a/tensorflow/compiler/tests/eager_test.py b/tensorflow/compiler/tests/eager_test.py index 3bb3049e87..e438832a23 100644 --- a/tensorflow/compiler/tests/eager_test.py +++ b/tensorflow/compiler/tests/eager_test.py @@ -290,7 +290,7 @@ class EagerFunctionTest(XLATestCase): def testBasic(self): with self.test_scope(): - matmul = function.defun(math_ops.matmul, compiled=True) + matmul = function.defun(math_ops.matmul) t = constant_op.constant([[1.0, 2.0], [3.0, 4.0]]) sq = matmul(t, t, transpose_a=True) self.assertAllEqual(sq.numpy().reshape(-1), [10, 14, 14, 20]) @@ -312,7 +312,7 @@ class EagerFunctionTest(XLATestCase): def model(x): x = conv(x) return pool(x) - model = function.defun(model, compiled=True) + model = function.defun(model) x = array_ops.ones([1, 4, 4, 1]) y = model(x) @@ -322,7 +322,7 @@ class EagerFunctionTest(XLATestCase): with self.test_scope(): v = resource_variable_ops.ResourceVariable(1.0) - @function.defun(compiled=True) + @function.defun def f(): return v.read_value() @@ -337,7 +337,7 @@ class EagerFunctionTest(XLATestCase): v.assign_add(1.0) return v - f = function.defun(f, compiled=True) + f = function.defun(f) var = f(v) self.assertEqual(2.0, var.numpy()) @@ -365,7 +365,7 @@ class EagerFunctionTest(XLATestCase): d = r2 * v2 return a, b, c, d - foo = function.defun(foo, compiled=True) + foo = function.defun(foo) c1 = [0, 0] c2 = array_ops.ones([2], dtype=dtypes.int32) @@ -387,7 +387,7 @@ class EagerFunctionTest(XLATestCase): with self.test_scope(): v0 = resource_variable_ops.ResourceVariable(5.0) - @function.defun(compiled=True) + @function.defun def f(x): x = v0 * v0 * x return x @@ -450,7 +450,7 @@ class ExcessivePaddingTest(XLATestCase): def testAsFunctionInput(self): with self.test_scope(): - @function.defun(compiled=True) + @function.defun def f(x): return math_ops.reduce_sum(x, axis=2) @@ -461,7 +461,7 @@ class ExcessivePaddingTest(XLATestCase): def testAsFunctionOutput(self): with self.test_scope(): - @function.defun(compiled=True) + @function.defun def f(x): return x * constant_op.constant(100 * [[[10.0, 2.0]]]) diff --git a/tensorflow/core/common_runtime/eager/execute.cc b/tensorflow/core/common_runtime/eager/execute.cc index c619857b78..08abded4e4 100644 --- a/tensorflow/core/common_runtime/eager/execute.cc +++ b/tensorflow/core/common_runtime/eager/execute.cc @@ -39,6 +39,11 @@ namespace tensorflow { namespace { +// Copy of the definition in third_party/tensorflow/compiler/jit/defs.h +// Copied here because we don't currently compile XLA on windows. So, can't +// depend on it directly. +const char* const kXlaCompileAttr = "_XlaCompile"; + // Initializes the step stats if needed. void MaybeInitializeStepStats(StepStats* step_stats, EagerContext* ctx) { // Lazily initialize the RunMetadata with information about all devices if @@ -472,6 +477,15 @@ Status EagerLocalExecute(EagerOperation* op, device == nullptr ? "unspecified" : device->name()); KernelAndDevice* kernel = ctx->GetCachedKernel(cache_key); if (kernel == nullptr) { + // If we are running a function on explicitly requested TPU, + // compile it with XLA. + // Note that it is not ideal, but currently ok, to set this + // attribute after computing the kernel cache key above. + if (op->is_function() && device != nullptr && + device->device_type() == "TPU") { + op->MutableAttrs()->Set(kXlaCompileAttr, true); + } + const NodeDef& ndef = op->MutableAttrs()->BuildNodeDef(); if (device == nullptr) { status = SelectDevice(ndef, ctx, &device); -- GitLab From e52a3dc15820da0b0be271336384efeba7b241bb Mon Sep 17 00:00:00 2001 From: Yash Katariya Date: Mon, 18 Jun 2018 22:49:43 +0000 Subject: [PATCH 625/816] Removed numbers from text --- .../nmt_with_attention/NMT_with_Attention.ipynb | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/tensorflow/contrib/eager/python/examples/nmt_with_attention/NMT_with_Attention.ipynb b/tensorflow/contrib/eager/python/examples/nmt_with_attention/NMT_with_Attention.ipynb index 8d044c5705..db6f91de73 100644 --- a/tensorflow/contrib/eager/python/examples/nmt_with_attention/NMT_with_Attention.ipynb +++ b/tensorflow/contrib/eager/python/examples/nmt_with_attention/NMT_with_Attention.ipynb @@ -476,12 +476,12 @@ "\n", "Pseudo-code:\n", "\n", - " 1. *score = FC(tanh(FC(EO) + FC(H)))*\n", - " 2. *attention weights = softmax(score, axis = 1)*. Softmax by default is applied on the last axis but here we want to apply it on the *1st axis*, since the shape of score is *(batch_size, max_length, hidden_size)*. Max_length is the length of our input. Since we are trying to assign a weight to each input, softmax should be applied on that axis.\n", - " 3. *context vector = sum(attention weights * EO, axis = 1)*. Same reason as above for choosing axis as 1.\n", - " 4. *embedding output = The input to the decoder X is passed through an embedding layer.*\n", - " 5. *merged vector = concat(embedding output, context vector)*\n", - " 6. *This merged vector is then given to the GRU*\n", + " * score = FC(tanh(FC(EO) + FC(H)))*\n", + " * attention weights = softmax(score, axis = 1)*. Softmax by default is applied on the last axis but here we want to apply it on the *1st axis*, since the shape of score is *(batch_size, max_length, hidden_size)*. Max_length is the length of our input. Since we are trying to assign a weight to each input, softmax should be applied on that axis.\n", + " * context vector = sum(attention weights * EO, axis = 1)*. Same reason as above for choosing axis as 1.\n", + " * embedding output = The input to the decoder X is passed through an embedding layer.*\n", + " * merged vector = concat(embedding output, context vector)*\n", + " * This merged vector is then given to the GRU*\n", " \n", "The shapes of all the vectors at each step have been specified in the comments in the code.\n", " \n", -- GitLab From 3029a930c4f6e2ca3eadfb75bf25068645e055aa Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 18 Jun 2018 15:36:47 -0700 Subject: [PATCH 626/816] Extract tf_record_test.py from reader_ops_test.py PiperOrigin-RevId: 201071448 --- tensorflow/python/BUILD | 13 + .../python/kernel_tests/reader_ops_test.py | 224 ------------ tensorflow/python/lib/io/tf_record_test.py | 322 ++++++++++++++++++ 3 files changed, 335 insertions(+), 224 deletions(-) create mode 100644 tensorflow/python/lib/io/tf_record_test.py diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD index f3a848b7df..cf4eac5328 100644 --- a/tensorflow/python/BUILD +++ b/tensorflow/python/BUILD @@ -4076,6 +4076,19 @@ py_test( ], ) +py_test( + name = "tf_record_test", + size = "small", + srcs = ["lib/io/tf_record_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":client_testlib", + ":errors", + ":lib", + ":util", + ], +) + cuda_py_test( name = "adam_test", size = "small", diff --git a/tensorflow/python/kernel_tests/reader_ops_test.py b/tensorflow/python/kernel_tests/reader_ops_test.py index 7be473a5e7..8e06e1abfb 100644 --- a/tensorflow/python/kernel_tests/reader_ops_test.py +++ b/tensorflow/python/kernel_tests/reader_ops_test.py @@ -25,8 +25,6 @@ import shutil import threading import zlib -import six - from tensorflow.core.protobuf import config_pb2 from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors_impl @@ -703,228 +701,6 @@ class TFRecordReaderTest(TFCompressionTestCase): self.assertAllEqual(self._Record(i, j), v) -class TFRecordWriterTest(TFCompressionTestCase): - - def setUp(self): - super(TFRecordWriterTest, self).setUp() - - def _AssertFilesEqual(self, a, b, equal): - for an, bn in zip(a, b): - with open(an, "rb") as af, open(bn, "rb") as bf: - if equal: - self.assertEqual(af.read(), bf.read()) - else: - self.assertNotEqual(af.read(), bf.read()) - - def testWriteReadZLibFiles(self): - # Write uncompressed then compress manually. - options = tf_record.TFRecordOptions(TFRecordCompressionType.NONE) - files = self._CreateFiles(options, prefix="uncompressed") - zlib_files = [ - self._ZlibCompressFile(fn, "tfrecord_%s.z" % i) - for i, fn in enumerate(files) - ] - self._AssertFilesEqual(files, zlib_files, False) - - # Now write compressd and verify same. - options = tf_record.TFRecordOptions(TFRecordCompressionType.ZLIB) - compressed_files = self._CreateFiles(options, prefix="compressed") - self._AssertFilesEqual(compressed_files, zlib_files, True) - - # Decompress compress and verify same. - uncompressed_files = [ - self._ZlibDecompressFile(fn, "tfrecord_%s.z" % i) - for i, fn in enumerate(compressed_files) - ] - self._AssertFilesEqual(uncompressed_files, files, True) - - def testWriteReadGzipFiles(self): - # Write uncompressed then compress manually. - options = tf_record.TFRecordOptions(TFRecordCompressionType.NONE) - files = self._CreateFiles(options, prefix="uncompressed") - gzip_files = [ - self._GzipCompressFile(fn, "tfrecord_%s.gz" % i) - for i, fn in enumerate(files) - ] - self._AssertFilesEqual(files, gzip_files, False) - - # Now write compressd and verify same. - options = tf_record.TFRecordOptions(TFRecordCompressionType.GZIP) - compressed_files = self._CreateFiles(options, prefix="compressed") - - # Note: Gzips written by TFRecordWriter add 'tfrecord_0' so - # compressed_files can't be compared with gzip_files - - # Decompress compress and verify same. - uncompressed_files = [ - self._GzipDecompressFile(fn, "tfrecord_%s.gz" % i) - for i, fn in enumerate(compressed_files) - ] - self._AssertFilesEqual(uncompressed_files, files, True) - - -class TFRecordWriterZlibTest(TFCompressionTestCase): - - def testOneEpoch(self): - options = tf_record.TFRecordOptions(TFRecordCompressionType.ZLIB) - files = self._CreateFiles(options) - with self.test_session() as sess: - reader = io_ops.TFRecordReader(name="test_reader", options=options) - queue = data_flow_ops.FIFOQueue(99, [dtypes.string], shapes=()) - key, value = reader.read(queue) - - queue.enqueue_many([files]).run() - queue.close().run() - for i in range(self._num_files): - for j in range(self._num_records): - k, v = sess.run([key, value]) - self.assertTrue(compat.as_text(k).startswith("%s:" % files[i])) - self.assertAllEqual(self._Record(i, j), v) - - with self.assertRaisesOpError("is closed and has insufficient elements " - "\\(requested 1, current size 0\\)"): - k, v = sess.run([key, value]) - - def testZLibFlushRecord(self): - fn = self._WriteRecordsToFile([b"small record"], "small_record") - with open(fn, "rb") as h: - buff = h.read() - - # creating more blocks and trailing blocks shouldn't break reads - compressor = zlib.compressobj(9, zlib.DEFLATED, zlib.MAX_WBITS) - - output = b"" - for c in buff: - if isinstance(c, int): - c = six.int2byte(c) - output += compressor.compress(c) - output += compressor.flush(zlib.Z_FULL_FLUSH) - - output += compressor.flush(zlib.Z_FULL_FLUSH) - output += compressor.flush(zlib.Z_FULL_FLUSH) - output += compressor.flush(zlib.Z_FINISH) - - # overwrite the original file with the compressed data - with open(fn, "wb") as h: - h.write(output) - - with self.test_session() as sess: - options = tf_record.TFRecordOptions(TFRecordCompressionType.ZLIB) - reader = io_ops.TFRecordReader(name="test_reader", options=options) - queue = data_flow_ops.FIFOQueue(1, [dtypes.string], shapes=()) - key, value = reader.read(queue) - queue.enqueue(fn).run() - queue.close().run() - k, v = sess.run([key, value]) - self.assertTrue(compat.as_text(k).startswith("%s:" % fn)) - self.assertAllEqual(b"small record", v) - - def testZlibReadWrite(self): - """Verify that files produced are zlib compatible.""" - original = [b"foo", b"bar"] - fn = self._WriteRecordsToFile(original, "zlib_read_write.tfrecord") - zfn = self._ZlibCompressFile(fn, "zlib_read_write.tfrecord.z") - - # read the compressed contents and verify. - actual = [] - for r in tf_record.tf_record_iterator( - zfn, options=tf_record.TFRecordOptions(TFRecordCompressionType.ZLIB)): - actual.append(r) - self.assertEqual(actual, original) - - def testZlibReadWriteLarge(self): - """Verify that writing large contents also works.""" - - # Make it large (about 5MB) - original = [_TEXT * 10240] - fn = self._WriteRecordsToFile(original, "zlib_read_write_large.tfrecord") - zfn = self._ZlibCompressFile(fn, "zlib_read_write_large.tfrecord.z") - - actual = [] - for r in tf_record.tf_record_iterator( - zfn, options=tf_record.TFRecordOptions(TFRecordCompressionType.ZLIB)): - actual.append(r) - self.assertEqual(actual, original) - - def testGzipReadWrite(self): - """Verify that files produced are gzip compatible.""" - original = [b"foo", b"bar"] - fn = self._WriteRecordsToFile(original, "gzip_read_write.tfrecord") - gzfn = self._GzipCompressFile(fn, "tfrecord.gz") - - actual = [] - for r in tf_record.tf_record_iterator( - gzfn, options=tf_record.TFRecordOptions(TFRecordCompressionType.GZIP)): - actual.append(r) - self.assertEqual(actual, original) - - -class TFRecordIteratorTest(TFCompressionTestCase): - - def setUp(self): - super(TFRecordIteratorTest, self).setUp() - self._num_records = 7 - - def testIterator(self): - records = [self._Record(0, i) for i in range(self._num_records)] - options = tf_record.TFRecordOptions(TFRecordCompressionType.ZLIB) - fn = self._WriteRecordsToFile(records, "compressed_records", options) - - reader = tf_record.tf_record_iterator(fn, options) - for expected in records: - record = next(reader) - self.assertAllEqual(expected, record) - with self.assertRaises(StopIteration): - record = next(reader) - - def testWriteZlibRead(self): - """Verify compression with TFRecordWriter is zlib library compatible.""" - original = [b"foo", b"bar"] - options = tf_record.TFRecordOptions(TFRecordCompressionType.ZLIB) - fn = self._WriteRecordsToFile(original, "write_zlib_read.tfrecord.z", - options) - - zfn = self._ZlibDecompressFile(fn, "write_zlib_read.tfrecord") - actual = list(tf_record.tf_record_iterator(zfn)) - self.assertEqual(actual, original) - - def testWriteZlibReadLarge(self): - """Verify compression for large records is zlib library compatible.""" - # Make it large (about 5MB) - original = [_TEXT * 10240] - options = tf_record.TFRecordOptions(TFRecordCompressionType.ZLIB) - fn = self._WriteRecordsToFile(original, "write_zlib_read_large.tfrecord.z", - options) - zfn = self._ZlibDecompressFile(fn, "write_zlib_read_large.tfrecord") - actual = list(tf_record.tf_record_iterator(zfn)) - self.assertEqual(actual, original) - - def testWriteGzipRead(self): - original = [b"foo", b"bar"] - options = tf_record.TFRecordOptions(TFRecordCompressionType.GZIP) - fn = self._WriteRecordsToFile(original, "write_gzip_read.tfrecord.gz", - options) - - gzfn = self._GzipDecompressFile(fn, "write_gzip_read.tfrecord") - actual = list(tf_record.tf_record_iterator(gzfn)) - self.assertEqual(actual, original) - - def testBadFile(self): - """Verify that tf_record_iterator throws an exception on bad TFRecords.""" - fn = os.path.join(self.get_temp_dir(), "bad_file") - with tf_record.TFRecordWriter(fn) as writer: - writer.write(b"123") - fn_truncated = os.path.join(self.get_temp_dir(), "bad_file_truncated") - with open(fn, "rb") as f: - with open(fn_truncated, "wb") as f2: - # DataLossError requires that we've written the header, so this must - # be at least 12 bytes. - f2.write(f.read(14)) - with self.assertRaises(errors_impl.DataLossError): - for _ in tf_record.tf_record_iterator(fn_truncated): - pass - - class AsyncReaderTest(test.TestCase): def testNoDeadlockFromQueue(self): diff --git a/tensorflow/python/lib/io/tf_record_test.py b/tensorflow/python/lib/io/tf_record_test.py new file mode 100644 index 0000000000..dcc1a25f42 --- /dev/null +++ b/tensorflow/python/lib/io/tf_record_test.py @@ -0,0 +1,322 @@ +# Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for tf_record.TFRecordWriter and tf_record.tf_record_iterator.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import gzip +import os +import zlib + +import six + +from tensorflow.python.framework import errors_impl +from tensorflow.python.lib.io import tf_record +from tensorflow.python.platform import test +from tensorflow.python.util import compat + +prefix_path = "third_party/tensorflow/core/lib" + +# pylint: disable=invalid-name +TFRecordCompressionType = tf_record.TFRecordCompressionType +# pylint: enable=invalid-name + +# Edgar Allan Poe's 'Eldorado' +_TEXT = b"""Gaily bedight, + A gallant knight, + In sunshine and in shadow, + Had journeyed long, + Singing a song, + In search of Eldorado. + + But he grew old + This knight so bold + And o'er his heart a shadow + Fell as he found + No spot of ground + That looked like Eldorado. + + And, as his strength + Failed him at length, + He met a pilgrim shadow + 'Shadow,' said he, + 'Where can it be + This land of Eldorado?' + + 'Over the Mountains + Of the Moon' + Down the Valley of the Shadow, + Ride, boldly ride,' + The shade replied, + 'If you seek for Eldorado!' + """ + + +class TFCompressionTestCase(test.TestCase): + + def setUp(self): + super(TFCompressionTestCase, self).setUp() + self._num_files = 2 + self._num_records = 7 + + def _Record(self, f, r): + return compat.as_bytes("Record %d of file %d" % (r, f)) + + def _CreateFiles(self, options=None, prefix=""): + filenames = [] + for i in range(self._num_files): + name = prefix + "tfrecord.%d.txt" % i + records = [self._Record(i, j) for j in range(self._num_records)] + fn = self._WriteRecordsToFile(records, name, options) + filenames.append(fn) + return filenames + + def _WriteRecordsToFile(self, records, name="tfrecord", options=None): + fn = os.path.join(self.get_temp_dir(), name) + with tf_record.TFRecordWriter(fn, options=options) as writer: + for r in records: + writer.write(r) + return fn + + def _ZlibCompressFile(self, infile, name="tfrecord.z"): + # zlib compress the file and write compressed contents to file. + with open(infile, "rb") as f: + cdata = zlib.compress(f.read()) + + zfn = os.path.join(self.get_temp_dir(), name) + with open(zfn, "wb") as f: + f.write(cdata) + return zfn + + def _GzipCompressFile(self, infile, name="tfrecord.gz"): + # gzip compress the file and write compressed contents to file. + with open(infile, "rb") as f: + cdata = f.read() + + gzfn = os.path.join(self.get_temp_dir(), name) + with gzip.GzipFile(gzfn, "wb") as f: + f.write(cdata) + return gzfn + + def _ZlibDecompressFile(self, infile, name="tfrecord"): + with open(infile, "rb") as f: + cdata = zlib.decompress(f.read()) + fn = os.path.join(self.get_temp_dir(), name) + with open(fn, "wb") as f: + f.write(cdata) + return fn + + def _GzipDecompressFile(self, infile, name="tfrecord"): + with gzip.GzipFile(infile, "rb") as f: + cdata = f.read() + fn = os.path.join(self.get_temp_dir(), name) + with open(fn, "wb") as f: + f.write(cdata) + return fn + + +class TFRecordWriterTest(TFCompressionTestCase): + + def setUp(self): + super(TFRecordWriterTest, self).setUp() + + def _AssertFilesEqual(self, a, b, equal): + for an, bn in zip(a, b): + with open(an, "rb") as af, open(bn, "rb") as bf: + if equal: + self.assertEqual(af.read(), bf.read()) + else: + self.assertNotEqual(af.read(), bf.read()) + + def testWriteReadZLibFiles(self): + # Write uncompressed then compress manually. + options = tf_record.TFRecordOptions(TFRecordCompressionType.NONE) + files = self._CreateFiles(options, prefix="uncompressed") + zlib_files = [ + self._ZlibCompressFile(fn, "tfrecord_%s.z" % i) + for i, fn in enumerate(files) + ] + self._AssertFilesEqual(files, zlib_files, False) + + # Now write compressd and verify same. + options = tf_record.TFRecordOptions(TFRecordCompressionType.ZLIB) + compressed_files = self._CreateFiles(options, prefix="compressed") + self._AssertFilesEqual(compressed_files, zlib_files, True) + + # Decompress compress and verify same. + uncompressed_files = [ + self._ZlibDecompressFile(fn, "tfrecord_%s.z" % i) + for i, fn in enumerate(compressed_files) + ] + self._AssertFilesEqual(uncompressed_files, files, True) + + def testWriteReadGzipFiles(self): + # Write uncompressed then compress manually. + options = tf_record.TFRecordOptions(TFRecordCompressionType.NONE) + files = self._CreateFiles(options, prefix="uncompressed") + gzip_files = [ + self._GzipCompressFile(fn, "tfrecord_%s.gz" % i) + for i, fn in enumerate(files) + ] + self._AssertFilesEqual(files, gzip_files, False) + + # Now write compressd and verify same. + options = tf_record.TFRecordOptions(TFRecordCompressionType.GZIP) + compressed_files = self._CreateFiles(options, prefix="compressed") + + # Note: Gzips written by TFRecordWriter add 'tfrecord_0' so + # compressed_files can't be compared with gzip_files + + # Decompress compress and verify same. + uncompressed_files = [ + self._GzipDecompressFile(fn, "tfrecord_%s.gz" % i) + for i, fn in enumerate(compressed_files) + ] + self._AssertFilesEqual(uncompressed_files, files, True) + + +class TFRecordWriterZlibTest(TFCompressionTestCase): + + def testZLibFlushRecord(self): + original = [b"small record"] + fn = self._WriteRecordsToFile(original, "small_record") + with open(fn, "rb") as h: + buff = h.read() + + # creating more blocks and trailing blocks shouldn't break reads + compressor = zlib.compressobj(9, zlib.DEFLATED, zlib.MAX_WBITS) + + output = b"" + for c in buff: + if isinstance(c, int): + c = six.int2byte(c) + output += compressor.compress(c) + output += compressor.flush(zlib.Z_FULL_FLUSH) + + output += compressor.flush(zlib.Z_FULL_FLUSH) + output += compressor.flush(zlib.Z_FULL_FLUSH) + output += compressor.flush(zlib.Z_FINISH) + + # overwrite the original file with the compressed data + with open(fn, "wb") as h: + h.write(output) + + options = tf_record.TFRecordOptions(TFRecordCompressionType.ZLIB) + actual = list(tf_record.tf_record_iterator(fn, options=options)) + self.assertEqual(actual, original) + + def testZlibReadWrite(self): + """Verify that files produced are zlib compatible.""" + original = [b"foo", b"bar"] + fn = self._WriteRecordsToFile(original, "zlib_read_write.tfrecord") + zfn = self._ZlibCompressFile(fn, "zlib_read_write.tfrecord.z") + + # read the compressed contents and verify. + options = tf_record.TFRecordOptions(TFRecordCompressionType.ZLIB) + actual = list(tf_record.tf_record_iterator(zfn, options=options)) + self.assertEqual(actual, original) + + def testZlibReadWriteLarge(self): + """Verify that writing large contents also works.""" + + # Make it large (about 5MB) + original = [_TEXT * 10240] + fn = self._WriteRecordsToFile(original, "zlib_read_write_large.tfrecord") + zfn = self._ZlibCompressFile(fn, "zlib_read_write_large.tfrecord.z") + + options = tf_record.TFRecordOptions(TFRecordCompressionType.ZLIB) + actual = list(tf_record.tf_record_iterator(zfn, options=options)) + self.assertEqual(actual, original) + + def testGzipReadWrite(self): + """Verify that files produced are gzip compatible.""" + original = [b"foo", b"bar"] + fn = self._WriteRecordsToFile(original, "gzip_read_write.tfrecord") + gzfn = self._GzipCompressFile(fn, "tfrecord.gz") + + options = tf_record.TFRecordOptions(TFRecordCompressionType.GZIP) + actual = list(tf_record.tf_record_iterator(gzfn, options=options)) + self.assertEqual(actual, original) + + +class TFRecordIteratorTest(TFCompressionTestCase): + + def setUp(self): + super(TFRecordIteratorTest, self).setUp() + self._num_records = 7 + + def testIterator(self): + records = [self._Record(0, i) for i in range(self._num_records)] + options = tf_record.TFRecordOptions(TFRecordCompressionType.ZLIB) + fn = self._WriteRecordsToFile(records, "compressed_records", options) + + reader = tf_record.tf_record_iterator(fn, options) + for expected in records: + record = next(reader) + self.assertAllEqual(expected, record) + with self.assertRaises(StopIteration): + record = next(reader) + + def testWriteZlibRead(self): + """Verify compression with TFRecordWriter is zlib library compatible.""" + original = [b"foo", b"bar"] + options = tf_record.TFRecordOptions(TFRecordCompressionType.ZLIB) + fn = self._WriteRecordsToFile(original, "write_zlib_read.tfrecord.z", + options) + + zfn = self._ZlibDecompressFile(fn, "write_zlib_read.tfrecord") + actual = list(tf_record.tf_record_iterator(zfn)) + self.assertEqual(actual, original) + + def testWriteZlibReadLarge(self): + """Verify compression for large records is zlib library compatible.""" + # Make it large (about 5MB) + original = [_TEXT * 10240] + options = tf_record.TFRecordOptions(TFRecordCompressionType.ZLIB) + fn = self._WriteRecordsToFile(original, "write_zlib_read_large.tfrecord.z", + options) + zfn = self._ZlibDecompressFile(fn, "write_zlib_read_large.tfrecord") + actual = list(tf_record.tf_record_iterator(zfn)) + self.assertEqual(actual, original) + + def testWriteGzipRead(self): + original = [b"foo", b"bar"] + options = tf_record.TFRecordOptions(TFRecordCompressionType.GZIP) + fn = self._WriteRecordsToFile(original, "write_gzip_read.tfrecord.gz", + options) + + gzfn = self._GzipDecompressFile(fn, "write_gzip_read.tfrecord") + actual = list(tf_record.tf_record_iterator(gzfn)) + self.assertEqual(actual, original) + + def testBadFile(self): + """Verify that tf_record_iterator throws an exception on bad TFRecords.""" + fn = os.path.join(self.get_temp_dir(), "bad_file") + with tf_record.TFRecordWriter(fn) as writer: + writer.write(b"123") + fn_truncated = os.path.join(self.get_temp_dir(), "bad_file_truncated") + with open(fn, "rb") as f: + with open(fn_truncated, "wb") as f2: + # DataLossError requires that we've written the header, so this must + # be at least 12 bytes. + f2.write(f.read(14)) + with self.assertRaises(errors_impl.DataLossError): + for _ in tf_record.tf_record_iterator(fn_truncated): + pass + +if __name__ == "__main__": + test.main() -- GitLab From f91b5b0896e3ed2b57a32b5a21068b9b5c55899e Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 18 Jun 2018 15:52:14 -0700 Subject: [PATCH 627/816] Internal change. PiperOrigin-RevId: 201073792 --- tensorflow/core/BUILD | 1 + tensorflow/tensorflow.bzl | 4 ++++ 2 files changed, 5 insertions(+) diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index d89633199d..c72ba2daff 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -89,6 +89,7 @@ load( "tf_generate_proto_text_sources", "tf_genrule_cmd_append_to_srcs", "tf_opts_nortti_if_android", + "tf_features_nomodules_if_android", ) load("//tensorflow:tensorflow.bzl", "tf_cc_test_mkl") load("//tensorflow:tensorflow.bzl", "tf_cc_test_gpu") diff --git a/tensorflow/tensorflow.bzl b/tensorflow/tensorflow.bzl index 522965990b..1f9fbad0b4 100644 --- a/tensorflow/tensorflow.bzl +++ b/tensorflow/tensorflow.bzl @@ -241,6 +241,9 @@ def tf_opts_nortti_if_android(): # LINT.ThenChange(//tensorflow/contrib/android/cmake/CMakeLists.txt) +def tf_features_nomodules_if_android(): + return if_android(["-use_header_modules"]) + # Given a list of "op_lib_names" (a list of files in the ops directory # without their .cc extensions), generate a library for that file. def tf_gen_op_libs(op_lib_names, deps=None, is_external=True): @@ -959,6 +962,7 @@ def tf_cuda_library(deps=None, cuda_deps=None, copts=tf_copts(), **kwargs): if not cuda_deps: cuda_deps = [] + kwargs["features"] = kwargs.get("features", []) + ["-use_header_modules"] native.cc_library( deps=deps + if_cuda(cuda_deps + [ clean_dep("//tensorflow/core:cuda"), -- GitLab From 323b59706dbef01b1700002e1e211bcb117c0f50 Mon Sep 17 00:00:00 2001 From: Yash Katariya Date: Mon, 18 Jun 2018 22:55:56 +0000 Subject: [PATCH 628/816] TOC visible --- .../examples/nmt_with_attention/NMT_with_Attention.ipynb | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/eager/python/examples/nmt_with_attention/NMT_with_Attention.ipynb b/tensorflow/contrib/eager/python/examples/nmt_with_attention/NMT_with_Attention.ipynb index db6f91de73..d40dbfe63b 100644 --- a/tensorflow/contrib/eager/python/examples/nmt_with_attention/NMT_with_Attention.ipynb +++ b/tensorflow/contrib/eager/python/examples/nmt_with_attention/NMT_with_Attention.ipynb @@ -18,7 +18,8 @@ } ], "private_outputs": true, - "collapsed_sections": [] + "collapsed_sections": [], + "toc_visible": true }, "kernelspec": { "name": "python3", -- GitLab From 8798ad3dcc1c7d5e0b50288908ca5245576165ed Mon Sep 17 00:00:00 2001 From: Nick Felt Date: Mon, 18 Jun 2018 16:14:27 -0700 Subject: [PATCH 629/816] Update tb-nightly dep to >= 1.10.0a0, < 1.11.0a0 --- tensorflow/tools/pip_package/setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py index 97f625e7e9..55cd4f37c6 100644 --- a/tensorflow/tools/pip_package/setup.py +++ b/tensorflow/tools/pip_package/setup.py @@ -84,7 +84,7 @@ else: if 'tf_nightly' in project_name: for i, pkg in enumerate(REQUIRED_PACKAGES): if 'tensorboard' in pkg: - REQUIRED_PACKAGES[i] = 'tb-nightly >= 1.9.0a0, < 1.10.0a0' + REQUIRED_PACKAGES[i] = 'tb-nightly >= 1.10.0a0, < 1.11.0a0' break # weakref.finalize and enum were introduced in Python 3.4 -- GitLab From 3edb609926f2521c726737fc1efeae1572dc6581 Mon Sep 17 00:00:00 2001 From: Mustafa Ispir Date: Mon, 18 Jun 2018 17:04:18 -0700 Subject: [PATCH 630/816] Improving local run behavior in estimator.train_and_evaluate. Current behavior is unintuitive (depends on throttle_secs) and leads to frequent checkpoint than desired. This CL makes evaluation synchronized with checkpointing. It also makes the behavior more closer to distributed setting in following ways: * in distributed setting we do create input_pipeline only once, in current behavior of local run we do recreate input pipeline in a loop. This cl creates training input pipeline only once. * in distributed setting evaluator job waits for checkpoints which are dumped by training job. In current behavior of local run evaluator controls the checkpoint schedule. In this cl, we give back the control to trainer. PiperOrigin-RevId: 201085814 --- tensorflow/python/estimator/training.py | 160 +++++---- tensorflow/python/estimator/training_test.py | 322 +++++++++---------- 2 files changed, 231 insertions(+), 251 deletions(-) diff --git a/tensorflow/python/estimator/training.py b/tensorflow/python/estimator/training.py index 1572af579b..37b123217a 100644 --- a/tensorflow/python/estimator/training.py +++ b/tensorflow/python/estimator/training.py @@ -470,6 +470,61 @@ class _StopAtSecsHook(session_run_hook.SessionRunHook): run_context.request_stop() +class _NewCheckpointListenerForEvaluate( + basic_session_run_hooks.CheckpointSaverListener): + """A saver listener to run evaluate with every checkpoint.""" + + def __init__(self, evaluator, eval_throttle_secs, continuous_eval_listener): + self._evaluator = evaluator + self._eval_throttle_secs = eval_throttle_secs + self._continuous_eval_listener = continuous_eval_listener + self.eval_result, self.export_results = None, None + + def begin(self): + self._timer = basic_session_run_hooks.SecondOrStepTimer( + every_secs=self._eval_throttle_secs) + self._is_first_run = True + + def after_save(self, session, global_step_value): + del session # unused; required by signature. + # skip first run model is not trained yet. + if self._is_first_run: + self._is_first_run = False + return + + if not self._continuous_eval_listener.before_eval(): + logging.info('Exiting training and evaluation loop, as requested by ' + '_ContinuousEvalListener.before_eval.') + return True + if self._timer.should_trigger_for_step(global_step_value): + self._evaluate(global_step_value) # updates self.eval_result + if not self._continuous_eval_listener.after_eval(self.eval_result): + logging.info('Exiting evaluation, as requested by ' + '_ContinuousEvalListener.after_eval.') + return True + else: + # TODO(ispir): add remaining time in the log. + logging.info('Skip the current checkpoint eval due to throttle secs ' + '({} secs).'.format(self._eval_throttle_secs)) + + def end(self, session, global_step_value): + # Evaluate if the last step has not been evaluated, yet. + if global_step_value != self._timer.last_triggered_step(): + if self._continuous_eval_listener.before_eval(): + self._evaluate(global_step_value) + self._continuous_eval_listener.after_eval(self.eval_result) + + def _evaluate(self, global_step_value): + self._timer.update_last_triggered_step(global_step_value) + self.eval_result, self.export_results = ( + self._evaluator.evaluate_and_export()) + if self.eval_result.status != _EvalStatus.EVALUATED: + # This is unexpected; should never happen. + # Training should always end with a new checkpoint. + raise RuntimeError('There was no new checkpoint after the training. ' + 'Eval status: {}'.format(self.eval_result.status)) + + class _TrainingExecutor(object): """The executor to run `Estimator` training and evaluation. @@ -576,28 +631,6 @@ class _TrainingExecutor(object): def run_master(self): """Runs task master.""" - - class NewCheckpointListener( - basic_session_run_hooks.CheckpointSaverListener): - - def __init__(self, evaluator, eval_throttle_secs): - self._evaluator = evaluator - self._eval_throttle_secs = eval_throttle_secs - - def begin(self): - self._timer = basic_session_run_hooks.SecondOrStepTimer( - every_secs=self._eval_throttle_secs) - - def after_save(self, session, global_step_value): - del session # unused; required by signature. - - if self._timer.should_trigger_for_step(global_step_value): - self._timer.update_last_triggered_step(global_step_value) - self._evaluator.evaluate_and_export() - else: - logging.info('Skip the current checkpoint eval due to throttle secs ' - '({} secs).'.format(self._eval_throttle_secs)) - _assert_eval_spec(self._eval_spec) # Final export signal: For any eval result with global_step >= train @@ -617,16 +650,12 @@ class _TrainingExecutor(object): # When the underlying `Estimator` object saves a new checkpoint, we would # like this callback to be called so that evaluation and export can trigger. saving_listeners = [ - NewCheckpointListener(evaluator, self._eval_spec.throttle_secs) + _NewCheckpointListenerForEvaluate(evaluator, + self._eval_spec.throttle_secs, + _ContinuousEvalListener()) ] self._start_distributed_training(saving_listeners=saving_listeners) - if not evaluator.is_final_export_triggered: - logging.info('Training has already ended. But the last eval is skipped ' - 'due to eval throttle_secs. Now evaluating the final ' - 'checkpoint.') - evaluator.evaluate_and_export() - def run_evaluator(self): """Runs task evaluator.""" # TODO(xiejw): To allow execution framework to add continuous eval listener. @@ -640,68 +669,33 @@ class _TrainingExecutor(object): def run_local(self): """Runs training and evaluation locally (non-distributed).""" - - def _should_stop_local_train(global_step): - if self._train_spec.max_steps is None: - return False - if global_step >= self._train_spec.max_steps: - return True - return False - _assert_eval_spec(self._eval_spec) - if self._eval_spec.throttle_secs <= 0: - raise ValueError('eval_spec.throttle_secs should be positive, given: {}.' - 'It is used do determine how long each training ' - 'iteration should go when train and evaluate ' - 'locally.'.format(self._eval_spec.throttle_secs)) - - stop_hook = _StopAtSecsHook(self._eval_spec.throttle_secs) - train_hooks = ( - list(self._train_spec.hooks) + [stop_hook] + list(self._train_hooks)) + train_hooks = list(self._train_spec.hooks) + list(self._train_hooks) logging.info('Start train and evaluate loop. The evaluate will happen ' - 'after {} secs (eval_spec.throttle_secs) or training is ' - 'finished.'.format(self._eval_spec.throttle_secs)) + 'after every checkpoint. Checkpoint frequency is determined ' + 'based on RunConfig arguments: save_checkpoints_steps {} or ' + 'save_checkpoints_secs {}.'.format( + self._estimator.config.save_checkpoints_steps, + self._estimator.config.save_checkpoints_secs)) evaluator = _TrainingExecutor._Evaluator(self._estimator, self._eval_spec, self._train_spec.max_steps) - eval_result = _EvalResult(status=_EvalStatus.MISSING_CHECKPOINT) - export_results = [] - - while True: - self._estimator.train( - input_fn=self._train_spec.input_fn, - max_steps=self._train_spec.max_steps, - hooks=train_hooks) - - if not self._continuous_eval_listener.before_eval(): - logging.info('Exiting training and evaluation loop, as requested by ' - '_ContinuousEvalListener.before_eval.') - break - - # Final export signal: For any eval result with global_step >= train - # max_steps, the evaluator will send the final export signal. The - # _should_stop_local_train will then end the while True as the stopping - # condition is satisfied (both checks use the same global_step value, - # i.e., no race condition) - eval_result, export_results = evaluator.evaluate_and_export() - - if eval_result.status != _EvalStatus.EVALUATED: - # This is unexpected; should never happen. - # Training should always end with a new checkpoint. - raise RuntimeError('There was no new checkpoint after the training. ' - 'Eval status: {}'.format(eval_result.status)) - - if not self._continuous_eval_listener.after_eval(eval_result): - logging.info('Exiting evaluation, as requested by ' - '_ContinuousEvalListener.after_eval.') - break + listener_for_eval = _NewCheckpointListenerForEvaluate( + evaluator, self._eval_spec.throttle_secs, + self._continuous_eval_listener) + saving_listeners = [listener_for_eval] + + self._estimator.train( + input_fn=self._train_spec.input_fn, + max_steps=self._train_spec.max_steps, + hooks=train_hooks, + saving_listeners=saving_listeners) - if _should_stop_local_train( - eval_result.metrics[ops.GraphKeys.GLOBAL_STEP]): - break - return eval_result.metrics, export_results + eval_result = listener_for_eval.eval_result or _EvalResult( + status=_EvalStatus.MISSING_CHECKPOINT) + return eval_result.metrics, listener_for_eval.export_results def _start_std_server(self, config): """Creates, starts, and returns a server_lib.Server.""" diff --git a/tensorflow/python/estimator/training_test.py b/tensorflow/python/estimator/training_test.py index 2c838db7a4..6bee7cbe83 100644 --- a/tensorflow/python/estimator/training_test.py +++ b/tensorflow/python/estimator/training_test.py @@ -29,17 +29,21 @@ import time import numpy as np +from tensorflow.python.data.ops import dataset_ops from tensorflow.python.estimator import estimator as estimator_lib from tensorflow.python.estimator import exporter as exporter_lib +from tensorflow.python.estimator import model_fn as model_fn_lib from tensorflow.python.estimator import run_config as run_config_lib from tensorflow.python.estimator import training from tensorflow.python.estimator.canned import dnn from tensorflow.python.estimator.canned import prediction_keys from tensorflow.python.estimator.export import export as export_lib -from tensorflow.python.estimator.inputs import numpy_io from tensorflow.python.feature_column import feature_column +from tensorflow.python.framework import constant_op from tensorflow.python.framework import ops from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import metrics as metrics_lib +from tensorflow.python.ops import state_ops from tensorflow.python.platform import gfile from tensorflow.python.platform import test from tensorflow.python.platform import tf_logging as logging @@ -49,6 +53,7 @@ from tensorflow.python.training import basic_session_run_hooks from tensorflow.python.training import monitored_session from tensorflow.python.training import server_lib from tensorflow.python.training import session_run_hook +from tensorflow.python.training import training_util from tensorflow.python.util import compat _DEFAULT_EVAL_STEPS = 100 @@ -885,7 +890,8 @@ class TrainingExecutorRunMasterTest(test.TestCase): # `after_save`. del args, kwargs saving_listeners[0].begin() - saving_listeners[0].after_save(session=None, global_step_value=None) + saving_listeners[0].after_save(session=None, global_step_value=0) + saving_listeners[0].after_save(session=None, global_step_value=10) mock_est = test.mock.Mock( spec=estimator_lib.Estimator, model_dir='path/', train=estimator_train) @@ -930,7 +936,10 @@ class TrainingExecutorRunMasterTest(test.TestCase): del args, kwargs saving_listeners[0].begin() - # Call three times. + # Call four times. + mock_timer.should_trigger_for_step.return_value = True + saving_listeners[0].after_save(session=None, global_step_value=None) + mock_timer.should_trigger_for_step.return_value = True saving_listeners[0].after_save(session=None, global_step_value=None) @@ -979,14 +988,19 @@ class TrainingExecutorRunMasterTest(test.TestCase): del args, kwargs saving_listeners[0].begin() - # Call two times. + # Call tree times (one for first saving). mock_timer.should_trigger_for_step.return_value = True - saving_listeners[0].after_save(session=None, global_step_value=None) + saving_listeners[0].after_save(session=None, global_step_value=0) + + mock_timer.should_trigger_for_step.return_value = True + saving_listeners[0].after_save(session=None, global_step_value=125) - # The final ckpt is skipped by the timer. It will be picked up the final - # export check in the code. mock_timer.should_trigger_for_step.return_value = False - saving_listeners[0].after_save(session=None, global_step_value=None) + saving_listeners[0].after_save(session=None, global_step_value=250) + + # At the end evaluate should be called even if throttle secs prevents it. + mock_timer.should_trigger_for_step.return_value = False + saving_listeners[0].end(session=None, global_step_value=300) mock_est.train = estimator_train mock_est.latest_checkpoint.side_effect = ['ckpt1', 'ckpt2'] @@ -1566,28 +1580,31 @@ class StopAtSecsHookTest(test.TestCase): class TrainingExecutorRunLocalTest(test.TestCase): """Tests run_local of _TrainingExecutor.""" + def _model_fn(self, features, labels, mode): + del labels + with ops.control_dependencies([features]): + train_op = state_ops.assign_add(training_util.get_global_step(), 1) + return model_fn_lib.EstimatorSpec( + mode, + loss=constant_op.constant(0.), + train_op=train_op, + predictions=constant_op.constant([[10.]]), + eval_metric_ops={'mean_of_features': metrics_lib.mean(features)}) + + def _input_fn(self, repeat=True): + ds = dataset_ops.Dataset.from_tensors([1]) + if repeat: + return ds.repeat() + return ds + def unique_checkpoint_every_time_fn(self): return 'checkpoint_path_%s/' % random.random() - def test_send_stop_at_secs_to_train(self): - mock_est = test.mock.Mock(spec=estimator_lib.Estimator, model_dir='path/') - mock_est.latest_checkpoint = self.unique_checkpoint_every_time_fn - train_spec = training.TrainSpec( - input_fn=lambda: 1, max_steps=2, hooks=[_FakeHook()]) - eval_spec = training.EvalSpec( - input_fn=lambda: 1, hooks=[_FakeHook()], throttle_secs=100) - mock_est.evaluate.return_value = {_GLOBAL_STEP_KEY: train_spec.max_steps} - - executor = training._TrainingExecutor(mock_est, train_spec, eval_spec) - executor.run_local() - - stop_hook = mock_est.train.call_args[1]['hooks'][-1] - self.assertIsInstance(stop_hook, training._StopAtSecsHook) - self.assertEqual(eval_spec.throttle_secs, stop_hook._stop_after_secs) - - def test_runs_in_a_loop_until_max_steps(self): - mock_est = test.mock.Mock(spec=estimator_lib.Estimator, model_dir='path/') - mock_est.latest_checkpoint = self.unique_checkpoint_every_time_fn + def test_runs_evaluate_with_every_new_checkpoint(self): + est = estimator_lib.Estimator( + model_fn=self._model_fn, + config=run_config_lib.RunConfig(save_checkpoints_steps=10)) + mock_est = test.mock.Mock(spec=estimator_lib.Estimator, wraps=est) mock_est.times_export_was_called = 0 mock_est.times_final_export_was_called = 0 @@ -1604,42 +1621,30 @@ class TrainingExecutorRunLocalTest(test.TestCase): exporter.name = 'see_how_many_times_export_is_called' exporter.export = export - train_spec = training.TrainSpec( - input_fn=lambda: 1, max_steps=300, hooks=[_FakeHook()]) + train_spec = training.TrainSpec(input_fn=self._input_fn, max_steps=22) eval_spec = training.EvalSpec( - input_fn=lambda: 1, - hooks=[_FakeHook()], - throttle_secs=100, + input_fn=lambda: self._input_fn(repeat=False), + throttle_secs=0, exporters=exporter) - # should be called 3 times. - mock_est.evaluate.side_effect = [{ - _GLOBAL_STEP_KEY: train_spec.max_steps - 100 - }, { - _GLOBAL_STEP_KEY: train_spec.max_steps - 50 - }, { - _GLOBAL_STEP_KEY: train_spec.max_steps - }] executor = training._TrainingExecutor(mock_est, train_spec, eval_spec) executor.run_local() - self.assertEqual(3, mock_est.train.call_count) + self.assertEqual(1, mock_est.train.call_count) self.assertEqual(3, mock_est.evaluate.call_count) self.assertEqual(3, mock_est.times_export_was_called) self.assertEqual(1, mock_est.times_final_export_was_called) def test_runs_with_eval_listener_before_eval(self): - mock_est = test.mock.Mock(spec=estimator_lib.Estimator, model_dir='path/') + est = estimator_lib.Estimator( + model_fn=self._model_fn, + config=run_config_lib.RunConfig(save_checkpoints_steps=10)) + mock_est = test.mock.Mock(spec=estimator_lib.Estimator, wraps=est) mock_est.latest_checkpoint = self.unique_checkpoint_every_time_fn - train_spec = training.TrainSpec(input_fn=lambda: 1, max_steps=300) - eval_spec = training.EvalSpec(input_fn=lambda: 1, throttle_secs=100) - # should be called 2 times without the evallistener - mock_est.evaluate.side_effect = [{ - _GLOBAL_STEP_KEY: train_spec.max_steps - 50 - }, { - _GLOBAL_STEP_KEY: train_spec.max_steps - }] + train_spec = training.TrainSpec(input_fn=self._input_fn, max_steps=12) + eval_spec = training.EvalSpec(input_fn=lambda: self._input_fn(repeat=False)) + mock_est.evaluate.side_effect = [{_GLOBAL_STEP_KEY: train_spec.max_steps}] class _Listener(training._ContinuousEvalListener): @@ -1658,67 +1663,61 @@ class TrainingExecutorRunLocalTest(test.TestCase): self.assertEqual(1, mock_est.train.call_count) self.assertEqual(0, mock_est.evaluate.call_count) - self.assertEqual(1, listener.call_count) def test_runs_with_eval_listener_after_eval(self): - mock_est = test.mock.Mock(spec=estimator_lib.Estimator, model_dir='path/') - mock_est.latest_checkpoint = self.unique_checkpoint_every_time_fn + est = estimator_lib.Estimator( + model_fn=self._model_fn, + config=run_config_lib.RunConfig(save_checkpoints_steps=10)) + mock_est = test.mock.Mock(spec=estimator_lib.Estimator, wraps=est) - train_spec = training.TrainSpec(input_fn=lambda: 1, max_steps=300) - eval_spec = training.EvalSpec(input_fn=lambda: 1, throttle_secs=100) - # should be called 2 times without the evallistener - mock_est.evaluate.side_effect = [{ - _GLOBAL_STEP_KEY: train_spec.max_steps - 50 - }, { - _GLOBAL_STEP_KEY: train_spec.max_steps - }] + train_spec = training.TrainSpec(input_fn=self._input_fn, max_steps=3000) + eval_spec = training.EvalSpec( + input_fn=lambda: self._input_fn(repeat=False), throttle_secs=0) class _Listener(training._ContinuousEvalListener): - def __init__(self, test_case): + def __init__(self): self.call_count = 0 - self._test_case = test_case def after_eval(self, eval_result): self.call_count += 1 - self._test_case.assertEqual( - train_spec.max_steps - 50, eval_result.metrics[_GLOBAL_STEP_KEY]) return False # Will stop the run_local after first eval. - listener = _Listener(test_case=self) + listener = _Listener() executor = training._TrainingExecutor( mock_est, train_spec, eval_spec, continuous_eval_listener=listener) - executor.run_local() + metrics, _ = executor.run_local() # pylint: disable=assignment-from-no-return self.assertEqual(1, mock_est.train.call_count) self.assertEqual(1, mock_est.evaluate.call_count) self.assertEqual(1, listener.call_count) + # Should be less than max_steps since listener did early stopping. + self.assertLess(metrics[_GLOBAL_STEP_KEY], train_spec.max_steps) def test_handles_no_new_checkpoint_found(self): - mock_est = test.mock.Mock(spec=estimator_lib.Estimator, model_dir='path/') - mock_est.latest_checkpoint.return_value = ( - 'no_new_checkpoints_after_the_first_train_step') + est = estimator_lib.Estimator( + model_fn=self._model_fn, + # disable saving checkpoint + config=run_config_lib.RunConfig( + save_checkpoints_steps=None, save_checkpoints_secs=None)) train_spec = training.TrainSpec( - input_fn=lambda: 1, max_steps=300, hooks=[_FakeHook()]) + input_fn=self._input_fn, max_steps=300, hooks=[_FakeHook()]) eval_spec = training.EvalSpec( - input_fn=lambda: 1, hooks=[_FakeHook()], throttle_secs=100) - # It was going to be called 3 times. - mock_est.evaluate.side_effect = [{ - _GLOBAL_STEP_KEY: train_spec.max_steps - 100 - }, { - _GLOBAL_STEP_KEY: train_spec.max_steps - 50 - }, { - _GLOBAL_STEP_KEY: train_spec.max_steps - }] + input_fn=lambda: self._input_fn(repeat=False), + hooks=[_FakeHook()], + throttle_secs=100) - executor = training._TrainingExecutor(mock_est, train_spec, eval_spec) - with self.assertRaisesRegexp(RuntimeError, _STALE_CHECKPOINT_MSG): + executor = training._TrainingExecutor(est, train_spec, eval_spec) + with self.assertRaisesRegexp(ValueError, + 'There should be a CheckpointSaverHook'): executor.run_local() def test_final_export_is_true_in_the_end(self): - mock_est = test.mock.Mock(spec=estimator_lib.Estimator, model_dir='path/') - mock_est.latest_checkpoint = self.unique_checkpoint_every_time_fn + est = estimator_lib.Estimator( + model_fn=self._model_fn, + config=run_config_lib.RunConfig(save_checkpoints_steps=10)) + mock_est = test.mock.Mock(spec=estimator_lib.Estimator, wraps=est) mock_est.times_export_fn_was_called = 0 mock_est.times_the_final_export_was_true = 0 @@ -1734,37 +1733,29 @@ class TrainingExecutorRunLocalTest(test.TestCase): exporter.export = export train_spec = training.TrainSpec( - input_fn=lambda: 1, max_steps=300, hooks=[_FakeHook()]) + input_fn=self._input_fn, max_steps=12, hooks=[_FakeHook()]) eval_spec = training.EvalSpec( - input_fn=lambda: 1, - hooks=[_FakeHook()], - throttle_secs=100, + input_fn=lambda: self._input_fn(repeat=False), + throttle_secs=0, exporters=exporter) - # should be called 3 times. - mock_est.evaluate.side_effect = [{ - _GLOBAL_STEP_KEY: train_spec.max_steps - 100 - }, { - _GLOBAL_STEP_KEY: train_spec.max_steps - 50 - }, { - _GLOBAL_STEP_KEY: train_spec.max_steps - }] - executor = training._TrainingExecutor(mock_est, train_spec, eval_spec) executor.run_local() - self.assertEqual(3, mock_est.train.call_count) - self.assertEqual(3, mock_est.evaluate.call_count) - self.assertEqual(3, mock_est.times_export_fn_was_called) + self.assertEqual(1, mock_est.train.call_count) + self.assertEqual(2, mock_est.evaluate.call_count) + self.assertEqual(2, mock_est.times_export_fn_was_called) self.assertEqual(1, mock_est.times_the_final_export_was_true) def test_train_and_evaluate_args(self): - mock_est = test.mock.Mock(spec=estimator_lib.Estimator, model_dir='path/') - mock_est.latest_checkpoint.return_value = 'checkpoint_path/' + est = estimator_lib.Estimator(model_fn=self._model_fn) + mock_est = test.mock.Mock(spec=estimator_lib.Estimator, wraps=est) train_spec = training.TrainSpec( - input_fn=lambda: 1, max_steps=300, hooks=[_FakeHook()]) + input_fn=self._input_fn, max_steps=300, hooks=[_FakeHook()]) eval_spec = training.EvalSpec( - input_fn=lambda: 1, steps=2, hooks=[_FakeHook()], name='local_eval') - mock_est.evaluate.return_value = {_GLOBAL_STEP_KEY: train_spec.max_steps} + input_fn=lambda: self._input_fn(repeat=False), + steps=2, + hooks=[_FakeHook()], + name='local_eval') executor = training._TrainingExecutor(mock_est, train_spec, eval_spec) executor.run_local() @@ -1773,11 +1764,11 @@ class TrainingExecutorRunLocalTest(test.TestCase): name=eval_spec.name, input_fn=eval_spec.input_fn, steps=eval_spec.steps, - checkpoint_path='checkpoint_path/', + checkpoint_path=est.latest_checkpoint(), hooks=eval_spec.hooks) train_args = mock_est.train.call_args[1] - self.assertEqual(list(train_spec.hooks), list(train_args['hooks'][:-1])) + self.assertEqual(list(train_spec.hooks), list(train_args['hooks'])) self.assertEqual(train_spec.input_fn, train_args['input_fn']) self.assertEqual(train_spec.max_steps, train_args['max_steps']) @@ -1812,25 +1803,11 @@ class TrainingExecutorRunLocalTest(test.TestCase): if not isinstance(h, training._StopAtSecsHook) ]) - def test_errors_out_if_throttle_secs_is_zero(self): - mock_est = test.mock.Mock(spec=estimator_lib.Estimator) - train_spec = training.TrainSpec(input_fn=lambda: 1) - eval_spec = training.EvalSpec(input_fn=lambda: 1, throttle_secs=0) - - executor = training._TrainingExecutor(mock_est, train_spec, eval_spec) - with self.assertRaisesRegexp(ValueError, 'throttle_secs'): - executor.run_local() - def test_that_export_is_called_with_run_local(self): - mock_est = test.mock.Mock(spec=estimator_lib.Estimator) - mock_train_spec = test.mock.Mock(spec=training.TrainSpec) - mock_train_spec.max_steps = 200 - mock_est.evaluate.return_value = { - _GLOBAL_STEP_KEY: mock_train_spec.max_steps - } - # _validate_hooks would have made sure that train_spec.hooks is [], when - # None were passed. - mock_train_spec.hooks = [] + est = estimator_lib.Estimator(model_fn=self._model_fn) + mock_est = test.mock.Mock(spec=estimator_lib.Estimator, wraps=est) + train_spec = training.TrainSpec(input_fn=self._input_fn, max_steps=12) + mock_est.evaluate.return_value = {_GLOBAL_STEP_KEY: train_spec.max_steps} def export(estimator, *args, **kwargs): del args, kwargs @@ -1842,13 +1819,13 @@ class TrainingExecutorRunLocalTest(test.TestCase): exporter.export = export eval_spec = training.EvalSpec( - input_fn=lambda: 1, + input_fn=lambda: self._input_fn(repeat=False), steps=2, start_delay_secs=0, throttle_secs=213, exporters=exporter) - executor = training._TrainingExecutor(mock_est, mock_train_spec, eval_spec) + executor = training._TrainingExecutor(mock_est, train_spec, eval_spec) # pylint: disable=assignment-from-no-return _, export_results = executor.run_local() # pylint: enable=assignment-from-no-return @@ -1857,9 +1834,13 @@ class TrainingExecutorRunLocalTest(test.TestCase): self.assertEqual(export_results, ['path_to_export']) def test_errors_out_if_evaluate_returns_empty_dict(self): - mock_est = test.mock.Mock(spec=estimator_lib.Estimator) - train_spec = training.TrainSpec(input_fn=lambda: 1) - eval_spec = training.EvalSpec(input_fn=(lambda: 1), throttle_secs=123) + est = estimator_lib.Estimator( + model_fn=self._model_fn, + config=run_config_lib.RunConfig(save_checkpoints_steps=2)) + mock_est = test.mock.Mock(spec=estimator_lib.Estimator, wraps=est) + train_spec = training.TrainSpec(input_fn=self._input_fn) + eval_spec = training.EvalSpec( + input_fn=lambda: self._input_fn(repeat=False), throttle_secs=0) mock_est.evaluate.return_value = {} executor = training._TrainingExecutor(mock_est, train_spec, eval_spec) @@ -1867,18 +1848,26 @@ class TrainingExecutorRunLocalTest(test.TestCase): executor.run_local() def test_errors_out_if_evaluate_returns_non_dict(self): - mock_est = test.mock.Mock(spec=estimator_lib.Estimator) - train_spec = training.TrainSpec(input_fn=lambda: 1) - eval_spec = training.EvalSpec(input_fn=(lambda: 1), throttle_secs=123) + est = estimator_lib.Estimator( + model_fn=self._model_fn, + config=run_config_lib.RunConfig(save_checkpoints_steps=2)) + mock_est = test.mock.Mock(spec=estimator_lib.Estimator, wraps=est) + train_spec = training.TrainSpec(input_fn=self._input_fn) + eval_spec = training.EvalSpec( + input_fn=lambda: self._input_fn(repeat=False), throttle_secs=0) mock_est.evaluate.return_value = 123 executor = training._TrainingExecutor(mock_est, train_spec, eval_spec) with self.assertRaisesRegexp(TypeError, _INVALID_EVAL_RESULT_TYPE_ERR): executor.run_local() def test_errors_out_if_evaluate_returns_dict_without_global_step(self): - mock_est = test.mock.Mock(spec=estimator_lib.Estimator) - train_spec = training.TrainSpec(input_fn=lambda: 1) - eval_spec = training.EvalSpec(input_fn=(lambda: 1), throttle_secs=123) + est = estimator_lib.Estimator( + model_fn=self._model_fn, + config=run_config_lib.RunConfig(save_checkpoints_steps=2)) + mock_est = test.mock.Mock(spec=estimator_lib.Estimator, wraps=est) + train_spec = training.TrainSpec(input_fn=self._input_fn) + eval_spec = training.EvalSpec( + input_fn=lambda: self._input_fn(repeat=False), throttle_secs=0) mock_est.evaluate.return_value = {'loss': 123} executor = training._TrainingExecutor(mock_est, train_spec, eval_spec) @@ -1887,19 +1876,21 @@ class TrainingExecutorRunLocalTest(test.TestCase): executor.run_local() def test_train_and_evaluate_return_metrics(self): - mock_est = test.mock.Mock(spec=estimator_lib.Estimator, model_dir='path/') - mock_est.latest_checkpoint.return_value = 'checkpoint_path/' + est = estimator_lib.Estimator(model_fn=self._model_fn) + mock_est = test.mock.Mock(spec=estimator_lib.Estimator, wraps=est) train_spec = training.TrainSpec( - input_fn=lambda: 1, max_steps=300, hooks=[_FakeHook()]) + input_fn=self._input_fn, max_steps=12, hooks=[_FakeHook()]) eval_spec = training.EvalSpec( - input_fn=lambda: 1, steps=2, hooks=[_FakeHook()], name='local_eval') - mock_est.evaluate.return_value = {_GLOBAL_STEP_KEY: train_spec.max_steps} + input_fn=lambda: self._input_fn(repeat=False), + steps=2, + hooks=[_FakeHook()], + name='local_eval') executor = training._TrainingExecutor(mock_est, train_spec, eval_spec) # pylint: disable=assignment-from-no-return metrics, _ = executor.run_local() # pylint: enable=assignment-from-no-return - self.assertEqual(metrics['global_step'], 300) + self.assertEqual(metrics['global_step'], 12) class TrainAndEvaluateRunTest(test.TestCase): @@ -2096,7 +2087,7 @@ class TrainAndEvaluateIntegrationTest(test.TestCase): # max_steps should be larger than save_summary_steps max_steps = 10 - save_summary_steps = 2 + save_summary_steps = 9 data = np.linspace( 0., n_classes - 1., batch_size * input_dimension, dtype=np.float32) @@ -2104,24 +2095,20 @@ class TrainAndEvaluateIntegrationTest(test.TestCase): y_data = np.reshape(self._as_label(data[:batch_size]), (batch_size, 1)) # learn y = x - train_input_fn = numpy_io.numpy_input_fn( - x={'x': x_data}, - y=y_data, - batch_size=batch_size, - num_epochs=None, - shuffle=True) - - eval_input_fn = numpy_io.numpy_input_fn( - x={'x': x_data}, - y=y_data, - batch_size=batch_size, - num_epochs=1, - shuffle=False) - - predict_input_fn = numpy_io.numpy_input_fn( - x={'x': x_data}, - batch_size=batch_size, - shuffle=False) + def train_input_fn(): + return dataset_ops.Dataset.from_tensor_slices(({ + 'x': x_data + }, y_data)).batch(batch_size).repeat().shuffle(1000) + + def eval_input_fn(): + return dataset_ops.Dataset.from_tensor_slices(({ + 'x': x_data + }, y_data)).batch(batch_size) + + def predict_input_fn(): + return dataset_ops.Dataset.from_tensor_slices({ + 'x': x_data + }).batch(batch_size) feature_columns = [ feature_column.numeric_column('x', shape=(input_dimension,))] @@ -2137,9 +2124,11 @@ class TrainAndEvaluateIntegrationTest(test.TestCase): max_steps=max_steps) eval_spec = training.EvalSpec( - name=eval_name, input_fn=eval_input_fn, steps=None, + name=eval_name, + input_fn=eval_input_fn, + steps=None, exporters=self._get_exporter(exporter_name, feature_columns), - throttle_secs=2) + throttle_secs=0) training.train_and_evaluate(est, train_spec, eval_spec) @@ -2148,15 +2137,12 @@ class TrainAndEvaluateIntegrationTest(test.TestCase): # Examine the training events. Use a range to check global step to avoid # flakyness due to global step race condition. - training_loss, training_global_step = self._extract_loss_and_global_step( - est.model_dir) + training_loss, _ = self._extract_loss_and_global_step(est.model_dir) self.assertIsNotNone(training_loss) - self.assertTrue( - max_steps - save_summary_steps < training_global_step <= max_steps) # Examine the eval events. The global step should be accurate. eval_loss, eval_global_step = self._extract_loss_and_global_step( - event_folder=os.path.join(est.model_dir, 'eval_' + eval_name)) + event_folder=est.eval_dir(eval_name)) self.assertIsNotNone(eval_loss) self.assertEqual(max_steps, eval_global_step) -- GitLab From ca24a3e823884e6a1929ca5afc09b77677dd67c3 Mon Sep 17 00:00:00 2001 From: Shashi Shekhar Date: Mon, 18 Jun 2018 17:05:00 -0700 Subject: [PATCH 631/816] Add an iOS benchmarking app. PiperOrigin-RevId: 201085939 --- .../contrib/lite/build_ios_universal_lib.sh | 37 +- .../lite/tools/benchmark/ios/README.md | 43 ++ .../TFLiteBenchmark.xcodeproj/project.pbxproj | 381 ++++++++++++++++++ .../TFLiteBenchmark/AppDelegate.h | 22 + .../TFLiteBenchmark/AppDelegate.m | 27 ++ .../AppIcon.appiconset/Contents.json | 98 +++++ .../Assets.xcassets/Contents.json | 6 + .../Base.lproj/LaunchScreen.storyboard | 25 ++ .../Base.lproj/Main.storyboard | 60 +++ .../TFLiteBenchmark/BenchmarkViewController.h | 21 + .../BenchmarkViewController.mm | 125 ++++++ .../TFLiteBenchmark/Info.plist | 43 ++ .../benchmark_data/benchmark_params.json | 10 + .../TFLiteBenchmark/TFLiteBenchmark/main.m | 23 ++ 14 files changed, 903 insertions(+), 18 deletions(-) create mode 100644 tensorflow/contrib/lite/tools/benchmark/ios/README.md create mode 100644 tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark.xcodeproj/project.pbxproj create mode 100644 tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/AppDelegate.h create mode 100644 tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/AppDelegate.m create mode 100644 tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/Assets.xcassets/AppIcon.appiconset/Contents.json create mode 100644 tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/Assets.xcassets/Contents.json create mode 100644 tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/Base.lproj/LaunchScreen.storyboard create mode 100644 tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/Base.lproj/Main.storyboard create mode 100644 tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/BenchmarkViewController.h create mode 100644 tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/BenchmarkViewController.mm create mode 100644 tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/Info.plist create mode 100644 tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/benchmark_data/benchmark_params.json create mode 100644 tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/main.m diff --git a/tensorflow/contrib/lite/build_ios_universal_lib.sh b/tensorflow/contrib/lite/build_ios_universal_lib.sh index 9f398f4a9f..e9531aef19 100755 --- a/tensorflow/contrib/lite/build_ios_universal_lib.sh +++ b/tensorflow/contrib/lite/build_ios_universal_lib.sh @@ -19,22 +19,23 @@ set -e SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" cd "$SCRIPT_DIR/../../.." -make -f tensorflow/contrib/lite/Makefile TARGET=IOS IOS_ARCH=x86_64 -j 8 \ -$SCRIPT_DIR/gen/lib/ios_x86_64/libtensorflow-lite.a -make -f tensorflow/contrib/lite/Makefile TARGET=IOS IOS_ARCH=i386 -j 8 \ -$SCRIPT_DIR/gen/lib/ios_i386/libtensorflow-lite.a -make -f tensorflow/contrib/lite/Makefile TARGET=IOS IOS_ARCH=armv7 -j 8 \ -$SCRIPT_DIR/gen/lib/ios_armv7/libtensorflow-lite.a -make -f tensorflow/contrib/lite/Makefile TARGET=IOS IOS_ARCH=armv7s -j 8 \ -$SCRIPT_DIR/gen/lib/ios_armv7s/libtensorflow-lite.a -make -f tensorflow/contrib/lite/Makefile TARGET=IOS IOS_ARCH=arm64 -j 8 \ -$SCRIPT_DIR/gen/lib/ios_arm64/libtensorflow-lite.a +# Build library for supported architectures and packs them in a fat binary. +make_library() { + for arch in x86_64 i386 armv7 armv7s arm64 + do + make -f tensorflow/contrib/lite/Makefile TARGET=IOS IOS_ARCH=${arch} \ + -j 8 \ + $SCRIPT_DIR/gen/lib/ios_${arch}/${1} + done + lipo \ + tensorflow/contrib/lite/gen/lib/ios_x86_64/${1} \ + tensorflow/contrib/lite/gen/lib/ios_i386/${1} \ + tensorflow/contrib/lite/gen/lib/ios_armv7/${1} \ + tensorflow/contrib/lite/gen/lib/ios_armv7s/${1} \ + tensorflow/contrib/lite/gen/lib/ios_arm64/${1} \ + -create \ + -output tensorflow/contrib/lite/gen/lib/${1} +} -lipo \ -tensorflow/contrib/lite/gen/lib/ios_x86_64/libtensorflow-lite.a \ -tensorflow/contrib/lite/gen/lib/ios_i386/libtensorflow-lite.a \ -tensorflow/contrib/lite/gen/lib/ios_armv7/libtensorflow-lite.a \ -tensorflow/contrib/lite/gen/lib/ios_armv7s/libtensorflow-lite.a \ -tensorflow/contrib/lite/gen/lib/ios_arm64/libtensorflow-lite.a \ --create \ --output tensorflow/contrib/lite/gen/lib/libtensorflow-lite.a +make_library libtensorflow-lite.a +make_library benchmark-lib.a diff --git a/tensorflow/contrib/lite/tools/benchmark/ios/README.md b/tensorflow/contrib/lite/tools/benchmark/ios/README.md new file mode 100644 index 0000000000..c8d3307e29 --- /dev/null +++ b/tensorflow/contrib/lite/tools/benchmark/ios/README.md @@ -0,0 +1,43 @@ +# TFLite iOS benchmark app. + +## Description + +An iOS app to benchmark TFLite models. + +The app reads benchmark parameters from a JSON file named `benchmark_params.json` +in its `benchmark_data` directory. Any downloaded models for benchmarking should +also be placed in `benchmark_data` directory. + +The JSON file specifies the name of the model file and other benchmarking +parameters like inputs to the model, type of inputs, number of iterations, +number of threads. The default values in the JSON file are for the +Mobilenet_1.0_224 model +([paper](https://arxiv.org/pdf/1704.04861.pdf), +[tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224.tgz)) + +## To build/install/run + +- Follow instructions at [iOS build for TFLite] +(https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/lite/g3doc/ios.md) +to build TFLite. + +Running + +```bash +tensorflow/contrib/lite/build_ios_universal_lib.sh +``` +will also build `tensorflow/contrib/lite/gen/lib/benchmark-lib.a` . + +- Now copy the downloaded model file to `benchmark_data` directory. + +- Modify `benchmark_params.json` change the `input_layer`, `input_layer_shape` +and other benchmark parameters. + +- Change `Build Phases -> Copy Bundle Resources` and add the model file to the +resources that need to be copied. + +- Ensure that `Build Phases -> Link Binary With Library` contains the +`Accelerate framework` and `tensorflow/contrib/lite/gen/lib/benchmark-lib.a`. + +- Now try running the app. The app has a single button that runs the benchmark + on the model and displays results in a text view below. diff --git a/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark.xcodeproj/project.pbxproj b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark.xcodeproj/project.pbxproj new file mode 100644 index 0000000000..b908f733d4 --- /dev/null +++ b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark.xcodeproj/project.pbxproj @@ -0,0 +1,381 @@ +// !$*UTF8*$! +{ + archiveVersion = 1; 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All Rights Reserved. + +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#import + +@interface AppDelegate : UIResponder + +@property(strong, nonatomic) UIWindow *window; + +@end diff --git a/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/AppDelegate.m b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/AppDelegate.m new file mode 100644 index 0000000000..b1165940e9 --- /dev/null +++ b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/AppDelegate.m @@ -0,0 +1,27 @@ +// Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#import "AppDelegate.h" + +@interface AppDelegate () + +@end + +@implementation AppDelegate +- (BOOL)application:(UIApplication *)application + didFinishLaunchingWithOptions:(NSDictionary *)launchOptions { + return YES; +} +@end diff --git a/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/Assets.xcassets/AppIcon.appiconset/Contents.json b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/Assets.xcassets/AppIcon.appiconset/Contents.json new file mode 100644 index 0000000000..d8db8d65fd --- /dev/null +++ b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/Assets.xcassets/AppIcon.appiconset/Contents.json @@ -0,0 +1,98 @@ +{ + "images" : [ + { + "idiom" : "iphone", + "size" : "20x20", + "scale" : "2x" + }, + { + "idiom" : "iphone", + "size" : "20x20", + "scale" : "3x" + }, + { + "idiom" : "iphone", + "size" : "29x29", + "scale" : "2x" + }, + { + "idiom" : "iphone", + "size" : "29x29", + "scale" : "3x" + }, + { + "idiom" : "iphone", + "size" : "40x40", + "scale" : "2x" + }, + { + "idiom" : "iphone", + "size" : "40x40", + "scale" : "3x" + }, + { + "idiom" : "iphone", + "size" : "60x60", + "scale" : "2x" + }, + { + "idiom" : "iphone", + "size" : "60x60", + "scale" : "3x" + }, + { + "idiom" : "ipad", + "size" : "20x20", + "scale" : "1x" + }, + { + "idiom" : "ipad", + "size" : "20x20", + "scale" : "2x" + }, + { + "idiom" : "ipad", + "size" : "29x29", + "scale" : "1x" + }, + { + "idiom" : "ipad", + "size" : "29x29", + "scale" : "2x" + }, + { + "idiom" : "ipad", + "size" : "40x40", + "scale" : "1x" + }, + { + "idiom" : "ipad", + "size" : "40x40", + "scale" : "2x" + }, + { + "idiom" : "ipad", + "size" : "76x76", + "scale" : "1x" + }, + { + "idiom" : "ipad", + "size" : "76x76", + "scale" : "2x" + }, + { + "idiom" : "ipad", + "size" : "83.5x83.5", + "scale" : "2x" + }, + { + "idiom" : "ios-marketing", + "size" : "1024x1024", + "scale" : "1x" + } + ], + "info" : { + "version" : 1, + "author" : "xcode" + } +} \ No newline at end of file diff --git a/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/Assets.xcassets/Contents.json b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/Assets.xcassets/Contents.json new file mode 100644 index 0000000000..da4a164c91 --- /dev/null +++ b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/Assets.xcassets/Contents.json @@ -0,0 +1,6 @@ +{ + "info" : { + "version" : 1, + "author" : "xcode" + } +} \ No newline at end of file diff --git a/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/Base.lproj/LaunchScreen.storyboard b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/Base.lproj/LaunchScreen.storyboard new file mode 100644 index 0000000000..bfa3612941 --- /dev/null +++ b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/Base.lproj/LaunchScreen.storyboard @@ -0,0 +1,25 @@ + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/Base.lproj/Main.storyboard b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/Base.lproj/Main.storyboard new file mode 100644 index 0000000000..adcfe1ef4e --- /dev/null +++ b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/Base.lproj/Main.storyboard @@ -0,0 +1,60 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/BenchmarkViewController.h b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/BenchmarkViewController.h new file mode 100644 index 0000000000..ec6dea0546 --- /dev/null +++ b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/BenchmarkViewController.h @@ -0,0 +1,21 @@ +// Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#import + +@interface BenchmarkViewController : UIViewController +@property(weak, nonatomic) IBOutlet UITextView *resultsView; + +@end diff --git a/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/BenchmarkViewController.mm b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/BenchmarkViewController.mm new file mode 100644 index 0000000000..356d5b0e17 --- /dev/null +++ b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/BenchmarkViewController.mm @@ -0,0 +1,125 @@ +// Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#import "BenchmarkViewController.h" +#import +#import +#import +#import +#import "tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.h" +#import "tensorflow/contrib/lite/tools/benchmark/logging.h" + +namespace { +NSString* FilePathForResourceName(NSString* filename) { + NSString* name = [filename stringByDeletingPathExtension]; + NSString* extension = [filename pathExtension]; + NSString* file_path = [[NSBundle mainBundle] pathForResource:name ofType:extension]; + if (file_path == NULL) { + TFLITE_LOG(FATAL) << "Couldn't find '" << [name UTF8String] << "." << [extension UTF8String] + << "' in bundle."; + } + return file_path; +} + +NSDictionary* ParseJson() { + NSString* params_json_path = FilePathForResourceName(@"benchmark_params.json"); + NSData* data = [NSData dataWithContentsOfFile:params_json_path]; + return [NSJSONSerialization JSONObjectWithData:data options:kNilOptions error:nil]; +} + +std::string FormatCommandLineParam(NSString* key, NSString* value) { + std::ostringstream stream; + stream << "--" << [key UTF8String] << "=" << [value UTF8String]; + return stream.str(); +} + +// Reads the |benchmark_params.json| to read command line parameters and returns them as a vector of +// strings. +void ReadCommandLineParameters(std::vector* params) { + NSDictionary* param_dict = ParseJson(); + for (NSString* key in param_dict) { + NSString* value = param_dict[key]; + if ([key isEqualToString:@"graph"]) { + value = FilePathForResourceName(value); + } + params->push_back(FormatCommandLineParam(key, value)); + } +} +std::vector StringVecToCharPtrVec(const std::vector& str_vec) { + std::vector charptr_vec; + std::transform(str_vec.begin(), str_vec.end(), std::back_inserter(charptr_vec), + [](const std::string& s) -> char* { return const_cast(s.c_str()); }); + return charptr_vec; +} + +class ResultsListener : public tflite::benchmark::BenchmarkListener { + public: + void OnBenchmarkEnd(const tflite::benchmark::BenchmarkResults& results) override; + std::string Results() { return results_; } + + private: + std::string results_; +}; + +void OutputMicrosecondsStatToStream(const tensorflow::Stat& time_us, + const std::string& prefix, std::ostringstream* stream) { + *stream << prefix << "Num runs: " << time_us.count() << "\n"; + + *stream << prefix << "Average: " << time_us.avg() / 1e3 << " ms\n"; + *stream << prefix << "Min: " << time_us.min() / 1e3 << " ms \n"; + *stream << prefix << "Max: " << time_us.max() / 1e3 << " ms \n"; + *stream << prefix << "Std deviation: " << time_us.std_deviation() / 1e3 << " ms\n"; +} + +void ResultsListener::OnBenchmarkEnd(const tflite::benchmark::BenchmarkResults& results) { + std::ostringstream stream; + const std::string prefix = " - "; + stream << "Startup latency: "; + stream << results.startup_latency_us() / 1e3 << " ms\n"; + stream << "\nInference:\n"; + OutputMicrosecondsStatToStream(results.inference_time_us(), prefix, &stream); + stream << "\nWarmup:\n"; + OutputMicrosecondsStatToStream(results.warmup_time_us(), prefix, &stream); + + results_ = stream.str(); +} + +std::string RunBenchmark() { + ResultsListener listener; + tflite::benchmark::BenchmarkTfLiteModel benchmark; + benchmark.AddListener(&listener); + // TODO(shashishekhar): Passing arguments like this is brittle, refactor the BenchmarkParams + // so that it contains arguments for BenchmarkTfLiteModel and set parameters using BenchmarkParams + std::vector command_line_params; + // Benchmark model expects first arg to be program name. + // push a string for name of program. + command_line_params.push_back("benchmark_tflite_model"); + ReadCommandLineParameters(&command_line_params); + std::vector argv = StringVecToCharPtrVec(command_line_params); + int argc = static_cast(argv.size()); + benchmark.Run(argc, argv.data()); + return listener.Results(); +} +} // namespace + +@interface BenchmarkViewController () +@end + +@implementation BenchmarkViewController +- (IBAction)onBenchmarkModel:(UIButton*)sender { + std::string results = RunBenchmark(); + [_resultsView setText:[NSString stringWithUTF8String:results.c_str()]]; +} +@end diff --git a/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/Info.plist b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/Info.plist new file mode 100644 index 0000000000..96051cf08f --- /dev/null +++ b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/Info.plist @@ -0,0 +1,43 @@ + + + + + UILaunchStoryboardName + Main + CFBundleDevelopmentRegion + $(DEVELOPMENT_LANGUAGE) + CFBundleExecutable + $(EXECUTABLE_NAME) + CFBundleIdentifier + $(PRODUCT_BUNDLE_IDENTIFIER) + CFBundleInfoDictionaryVersion + 6.0 + CFBundleName + $(PRODUCT_NAME) + CFBundlePackageType + APPL + CFBundleShortVersionString + 1.0 + CFBundleVersion + 1 + LSRequiresIPhoneOS + + UIMainStoryboardFile + Main + UIRequiredDeviceCapabilities + + armv7 + + UISupportedInterfaceOrientations + + UIInterfaceOrientationPortrait + + UISupportedInterfaceOrientations~ipad + + UIInterfaceOrientationPortrait + UIInterfaceOrientationPortraitUpsideDown + UIInterfaceOrientationLandscapeLeft + UIInterfaceOrientationLandscapeRight + + + diff --git a/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/benchmark_data/benchmark_params.json b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/benchmark_data/benchmark_params.json new file mode 100644 index 0000000000..d344a7a5ef --- /dev/null +++ b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/benchmark_data/benchmark_params.json @@ -0,0 +1,10 @@ +{ + "benchmark_name" : "mobile_net_benchmark", + "num_threads" : "4", + "num_runs" : "20", + "warmup_runs" : "1", + "graph" : "mobilenet_v1_1.0_224.tflite", + "input_layer" : "input", + "input_layer_shape" : "1,224,224,3", + "run_delay" : "-1" +} diff --git a/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/main.m b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/main.m new file mode 100644 index 0000000000..1e70b9cd1d --- /dev/null +++ b/tensorflow/contrib/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/main.m @@ -0,0 +1,23 @@ +// Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#import +#import "AppDelegate.h" + +int main(int argc, char* argv[]) { + @autoreleasepool { + return UIApplicationMain(argc, argv, nil, NSStringFromClass([AppDelegate class])); + } +} -- GitLab From c70b8e73af3423d1e50dfade2c92e3d553a534d9 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 18 Jun 2018 17:05:03 -0700 Subject: [PATCH 632/816] The pretrained text embedding models in tf.hub expect a string input. If I pass dtype as tf.string in tf.keras.layers.InputLayer, it fails in a numpy array conversion as numpy doesn't recognize tf string type. I have added a check for that and if the input is a string, then the dtype passed to np.asarray is object. PiperOrigin-RevId: 201085946 --- tensorflow/python/keras/backend.py | 5 ++++- tensorflow/python/keras/backend_test.py | 8 ++++++++ 2 files changed, 12 insertions(+), 1 deletion(-) diff --git a/tensorflow/python/keras/backend.py b/tensorflow/python/keras/backend.py index 84821918bf..c55a756bcc 100644 --- a/tensorflow/python/keras/backend.py +++ b/tensorflow/python/keras/backend.py @@ -2880,7 +2880,10 @@ class Function(object): feed_arrays.append(tensor) # We need to do array conversion and type casting at this level, since # `callable_fn` only supports exact matches. - array_vals.append(np.asarray(value, dtype=tensor.dtype.base_dtype.name)) + tensor_type = dtypes_module.as_dtype(tensor.dtype) + array_vals.append(np.asarray(value, + dtype=tensor_type.as_numpy_dtype)) + if self.feed_dict: for key in sorted(self.feed_dict.keys()): array_vals.append( diff --git a/tensorflow/python/keras/backend_test.py b/tensorflow/python/keras/backend_test.py index 53e30e0e4a..98f36ad87f 100644 --- a/tensorflow/python/keras/backend_test.py +++ b/tensorflow/python/keras/backend_test.py @@ -21,6 +21,7 @@ import numpy as np import scipy.sparse from tensorflow.python import keras +from tensorflow.python.framework import dtypes from tensorflow.python.framework import sparse_tensor from tensorflow.python.ops import variables from tensorflow.python.platform import test @@ -1212,6 +1213,13 @@ class TestRandomOps(test.TestCase): self.assertAllClose(np.max(y), 2., atol=0.1) self.assertAllClose(np.min(y), -2., atol=0.1) + def test_string_input(self): + seq = keras.Sequential([ + keras.layers.InputLayer(input_shape=(1,), dtype=dtypes.string), + keras.layers.Lambda(lambda x: x[0]) + ]) + preds = seq.predict([['tensorflow eager']]) + self.assertEqual(preds.shape, (1,)) if __name__ == '__main__': test.main() -- GitLab From aecd8fecf17e8b5215372e92147846b474936f3f Mon Sep 17 00:00:00 2001 From: Xuechen Li Date: Mon, 18 Jun 2018 17:32:53 -0700 Subject: [PATCH 633/816] Make learning decay functions return functions that return the learning rate tensor. This enables proper learning rate schedules in eager mode. PiperOrigin-RevId: 201089859 --- .../python/training/learning_rate_decay.py | 302 ++++++++---- .../training/learning_rate_decay_test.py | 460 +++++++++--------- 2 files changed, 429 insertions(+), 333 deletions(-) diff --git a/tensorflow/python/training/learning_rate_decay.py b/tensorflow/python/training/learning_rate_decay.py index 10ab4c1137..a585aee5bb 100644 --- a/tensorflow/python/training/learning_rate_decay.py +++ b/tensorflow/python/training/learning_rate_decay.py @@ -19,6 +19,7 @@ from __future__ import print_function import math +from tensorflow.python.eager import context from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops @@ -87,6 +88,12 @@ def exponential_decay(learning_rate, Raises: ValueError: if `global_step` is not supplied. + + @compatibility(eager) + When eager execution is enabled, this function returns a function which in + turn returns the decayed learning rate Tensor. This can be useful for changing + the learning rate value across different invocations of optimizer functions. + @end_compatibility """ if global_step is None: raise ValueError("global_step is required for exponential_decay.") @@ -95,14 +102,22 @@ def exponential_decay(learning_rate, [learning_rate, global_step, decay_steps, decay_rate]) as name: learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") dtype = learning_rate.dtype - global_step = math_ops.cast(global_step, dtype) decay_steps = math_ops.cast(decay_steps, dtype) decay_rate = math_ops.cast(decay_rate, dtype) - p = global_step / decay_steps - if staircase: - p = math_ops.floor(p) - return math_ops.multiply( - learning_rate, math_ops.pow(decay_rate, p), name=name) + + def decayed_lr(): + """Helper to recompute learning rate; most helpful in eager-mode.""" + global_step_recomp = math_ops.cast(global_step, dtype) + p = global_step_recomp / decay_steps + if staircase: + p = math_ops.floor(p) + return math_ops.multiply( + learning_rate, math_ops.pow(decay_rate, p), name=name) + + if not context.executing_eagerly(): + decayed_lr = decayed_lr() + + return decayed_lr @tf_export("train.piecewise_constant") @@ -263,6 +278,12 @@ def polynomial_decay(learning_rate, Raises: ValueError: if `global_step` is not supplied. + + @compatibility(eager) + When eager execution is enabled, this function returns a function which in + turn returns the decayed learning rate Tensor. This can be useful for changing + the learning rate value across different invocations of optimizer functions. + @end_compatibility """ if global_step is None: raise ValueError("global_step is required for polynomial_decay.") @@ -272,27 +293,35 @@ def polynomial_decay(learning_rate, ]) as name: learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") dtype = learning_rate.dtype - global_step = math_ops.cast(global_step, dtype) - decay_steps = math_ops.cast(decay_steps, dtype) end_learning_rate = math_ops.cast(end_learning_rate, dtype) power = math_ops.cast(power, dtype) - if cycle: - # Find the first multiple of decay_steps that is bigger than global_step. - # If global_step is zero set the multiplier to 1 - multiplier = control_flow_ops.cond( - math_ops.equal(global_step, 0), lambda: 1.0, - lambda: math_ops.ceil(global_step / decay_steps)) - decay_steps = math_ops.multiply(decay_steps, multiplier) - else: - # Make sure that the global_step used is not bigger than decay_steps. - global_step = math_ops.minimum(global_step, decay_steps) - - p = math_ops.div(global_step, decay_steps) - return math_ops.add( - math_ops.multiply(learning_rate - end_learning_rate, - math_ops.pow(1 - p, power)), - end_learning_rate, - name=name) + + def decayed_lr(): + """Helper to recompute learning rate; most helpful in eager-mode.""" + global_step_recomp = math_ops.cast(global_step, dtype) + decay_steps_recomp = math_ops.cast(decay_steps, dtype) + if cycle: + # Find the first multiple of decay_steps that is bigger than + # global_step. If global_step is zero set the multiplier to 1 + multiplier = control_flow_ops.cond( + math_ops.equal(global_step_recomp, 0), lambda: 1.0, + lambda: math_ops.ceil(global_step_recomp / decay_steps)) + decay_steps_recomp = math_ops.multiply(decay_steps_recomp, multiplier) + else: + # Make sure that the global_step used is not bigger than decay_steps. + global_step_recomp = math_ops.minimum(global_step_recomp, decay_steps) + + p = math_ops.div(global_step_recomp, decay_steps_recomp) + return math_ops.add( + math_ops.multiply(learning_rate - end_learning_rate, + math_ops.pow(1 - p, power)), + end_learning_rate, + name=name) + + if not context.executing_eagerly(): + decayed_lr = decayed_lr() + + return decayed_lr @tf_export("train.natural_exp_decay") @@ -350,6 +379,12 @@ def natural_exp_decay(learning_rate, Raises: ValueError: if `global_step` is not supplied. + + @compatibility(eager) + When eager execution is enabled, this function returns a function which in + turn returns the decayed learning rate Tensor. This can be useful for changing + the learning rate value across different invocations of optimizer functions. + @end_compatibility """ if global_step is None: raise ValueError("global_step is required for natural_exp_decay.") @@ -357,14 +392,23 @@ def natural_exp_decay(learning_rate, [learning_rate, global_step, decay_rate]) as name: learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") dtype = learning_rate.dtype - global_step = math_ops.cast(global_step, dtype) decay_steps = math_ops.cast(decay_steps, dtype) decay_rate = math_ops.cast(decay_rate, dtype) - p = global_step / decay_steps - if staircase: - p = math_ops.floor(p) - exponent = math_ops.exp(math_ops.multiply(math_ops.negative(decay_rate), p)) - return math_ops.multiply(learning_rate, exponent, name=name) + + def decayed_lr(): + """Helper to recompute learning rate; most helpful in eager-mode.""" + global_step_recomp = math_ops.cast(global_step, dtype) + p = global_step_recomp / decay_steps + if staircase: + p = math_ops.floor(p) + exponent = math_ops.exp( + math_ops.multiply(math_ops.negative(decay_rate), p)) + return math_ops.multiply(learning_rate, exponent, name=name) + + if not context.executing_eagerly(): + decayed_lr = decayed_lr() + + return decayed_lr @tf_export("train.inverse_time_decay") @@ -432,6 +476,12 @@ def inverse_time_decay(learning_rate, Raises: ValueError: if `global_step` is not supplied. + + @compatibility(eager) + When eager execution is enabled, this function returns a function which in + turn returns the decayed learning rate Tensor. This can be useful for changing + the learning rate value across different invocations of optimizer functions. + @end_compatibility """ if global_step is None: raise ValueError("global_step is required for inverse_time_decay.") @@ -439,15 +489,23 @@ def inverse_time_decay(learning_rate, [learning_rate, global_step, decay_rate]) as name: learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") dtype = learning_rate.dtype - global_step = math_ops.cast(global_step, dtype) decay_steps = math_ops.cast(decay_steps, dtype) decay_rate = math_ops.cast(decay_rate, dtype) - p = global_step / decay_steps - if staircase: - p = math_ops.floor(p) - const = math_ops.cast(constant_op.constant(1), learning_rate.dtype) - denom = math_ops.add(const, math_ops.multiply(decay_rate, p)) - return math_ops.div(learning_rate, denom, name=name) + + def decayed_lr(): + """Helper to recompute learning rate; most helpful in eager-mode.""" + global_step_recomp = math_ops.cast(global_step, dtype) + p = global_step_recomp / decay_steps + if staircase: + p = math_ops.floor(p) + const = math_ops.cast(constant_op.constant(1), dtype) + denom = math_ops.add(const, math_ops.multiply(decay_rate, p)) + return math_ops.div(learning_rate, denom, name=name) + + if not context.executing_eagerly(): + decayed_lr = decayed_lr() + + return decayed_lr @tf_export("train.cosine_decay") @@ -492,6 +550,12 @@ def cosine_decay(learning_rate, global_step, decay_steps, alpha=0.0, name=None): learning rate. Raises: ValueError: if `global_step` is not supplied. + + @compatibility(eager) + When eager execution is enabled, this function returns a function which in + turn returns the decayed learning rate Tensor. This can be useful for changing + the learning rate value across different invocations of optimizer functions. + @end_compatibility """ if global_step is None: raise ValueError("cosine decay requires global_step") @@ -499,15 +563,23 @@ def cosine_decay(learning_rate, global_step, decay_steps, alpha=0.0, name=None): [learning_rate, global_step]) as name: learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") dtype = learning_rate.dtype - global_step = math_ops.cast(global_step, dtype) decay_steps = math_ops.cast(decay_steps, dtype) - global_step = math_ops.minimum(global_step, decay_steps) - completed_fraction = global_step / decay_steps - cosine_decayed = 0.5 * ( - 1.0 + math_ops.cos(constant_op.constant(math.pi) * completed_fraction)) - decayed = (1 - alpha) * cosine_decayed + alpha - return math_ops.multiply(learning_rate, decayed) + def decayed_lr(): + """Helper to recompute learning rate; most helpful in eager-mode.""" + global_step_recomp = math_ops.cast(global_step, dtype) + global_step_recomp = math_ops.minimum(global_step_recomp, decay_steps) + completed_fraction = global_step_recomp / decay_steps + cosine_decayed = 0.5 * (1.0 + math_ops.cos( + constant_op.constant(math.pi) * completed_fraction)) + + decayed = (1 - alpha) * cosine_decayed + alpha + return math_ops.multiply(learning_rate, decayed) + + if not context.executing_eagerly(): + decayed_lr = decayed_lr() + + return decayed_lr @tf_export("train.cosine_decay_restarts") @@ -561,6 +633,12 @@ def cosine_decay_restarts(learning_rate, learning rate. Raises: ValueError: if `global_step` is not supplied. + + @compatibility(eager) + When eager execution is enabled, this function returns a function which in + turn returns the decayed learning rate Tensor. This can be useful for changing + the learning rate value across different invocations of optimizer functions. + @end_compatibility """ if global_step is None: raise ValueError("cosine decay restarts requires global_step") @@ -568,40 +646,48 @@ def cosine_decay_restarts(learning_rate, learning_rate = ops.convert_to_tensor( learning_rate, name="initial_learning_rate") dtype = learning_rate.dtype - global_step = math_ops.cast(global_step, dtype) first_decay_steps = math_ops.cast(first_decay_steps, dtype) alpha = math_ops.cast(alpha, dtype) t_mul = math_ops.cast(t_mul, dtype) m_mul = math_ops.cast(m_mul, dtype) - completed_fraction = global_step / first_decay_steps + def decayed_lr(): + """Helper to recompute learning rate; most helpful in eager-mode.""" + global_step_recomp = math_ops.cast(global_step, dtype) + completed_fraction = global_step_recomp / first_decay_steps - def compute_step(completed_fraction, geometric=False): - if geometric: - i_restart = math_ops.floor( - math_ops.log(1.0 - completed_fraction * (1.0 - t_mul)) / - math_ops.log(t_mul)) + def compute_step(completed_fraction, geometric=False): + """Helper for `cond` operation.""" + if geometric: + i_restart = math_ops.floor( + math_ops.log(1.0 - completed_fraction * (1.0 - t_mul)) / + math_ops.log(t_mul)) - sum_r = (1.0 - t_mul**i_restart) / (1.0 - t_mul) - completed_fraction = (completed_fraction - sum_r) / t_mul**i_restart + sum_r = (1.0 - t_mul**i_restart) / (1.0 - t_mul) + completed_fraction = (completed_fraction - sum_r) / t_mul**i_restart - else: - i_restart = math_ops.floor(completed_fraction) - completed_fraction = completed_fraction - i_restart + else: + i_restart = math_ops.floor(completed_fraction) + completed_fraction -= i_restart + + return i_restart, completed_fraction + + i_restart, completed_fraction = control_flow_ops.cond( + math_ops.equal(t_mul, 1.0), + lambda: compute_step(completed_fraction, geometric=False), + lambda: compute_step(completed_fraction, geometric=True)) - return i_restart, completed_fraction + m_fac = m_mul**i_restart + cosine_decayed = 0.5 * m_fac * (1.0 + math_ops.cos( + constant_op.constant(math.pi) * completed_fraction)) + decayed = (1 - alpha) * cosine_decayed + alpha - i_restart, completed_fraction = control_flow_ops.cond( - math_ops.equal(t_mul, 1.0), - lambda: compute_step(completed_fraction, geometric=False), - lambda: compute_step(completed_fraction, geometric=True)) + return math_ops.multiply(learning_rate, decayed, name=name) - m_fac = m_mul**i_restart - cosine_decayed = 0.5 * m_fac * ( - 1.0 + math_ops.cos(constant_op.constant(math.pi) * completed_fraction)) - decayed = (1 - alpha) * cosine_decayed + alpha + if not context.executing_eagerly(): + decayed_lr = decayed_lr() - return math_ops.multiply(learning_rate, decayed, name=name) + return decayed_lr @tf_export("train.linear_cosine_decay") @@ -664,6 +750,12 @@ def linear_cosine_decay(learning_rate, learning rate. Raises: ValueError: if `global_step` is not supplied. + + @compatibility(eager) + When eager execution is enabled, this function returns a function which in + turn returns the decayed learning rate Tensor. This can be useful for changing + the learning rate value across different invocations of optimizer functions. + @end_compatibility """ if global_step is None: raise ValueError("linear cosine decay requires global_step") @@ -671,21 +763,28 @@ def linear_cosine_decay(learning_rate, [learning_rate, global_step]) as name: learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") dtype = learning_rate.dtype - global_step = math_ops.cast(global_step, dtype) decay_steps = math_ops.cast(decay_steps, dtype) num_periods = math_ops.cast(num_periods, dtype) - global_step = math_ops.minimum(global_step, decay_steps) alpha = math_ops.cast(alpha, dtype) beta = math_ops.cast(beta, dtype) - linear_decayed = (decay_steps - global_step) / decay_steps - completed_fraction = global_step / decay_steps - fraction = 2.0 * num_periods * completed_fraction - cosine_decayed = 0.5 * ( - 1.0 + math_ops.cos(constant_op.constant(math.pi) * fraction)) + def decayed_lr(): + """Helper to recompute learning rate; most helpful in eager-mode.""" + global_step_recomp = math_ops.cast(global_step, dtype) + global_step_recomp = math_ops.minimum(global_step_recomp, decay_steps) + linear_decayed = (decay_steps - global_step_recomp) / decay_steps + completed_fraction = global_step_recomp / decay_steps + fraction = 2.0 * num_periods * completed_fraction + cosine_decayed = 0.5 * ( + 1.0 + math_ops.cos(constant_op.constant(math.pi) * fraction)) + + linear_cosine_decayed = (alpha + linear_decayed) * cosine_decayed + beta + return math_ops.multiply(learning_rate, linear_cosine_decayed, name=name) - linear_cosine_decayed = (alpha + linear_decayed) * cosine_decayed + beta - return math_ops.multiply(learning_rate, linear_cosine_decayed, name=name) + if not context.executing_eagerly(): + decayed_lr = decayed_lr() + + return decayed_lr @tf_export("train.noisy_linear_cosine_decay") @@ -756,6 +855,12 @@ def noisy_linear_cosine_decay(learning_rate, learning rate. Raises: ValueError: if `global_step` is not supplied. + + @compatibility(eager) + When eager execution is enabled, this function returns a function which in + turn returns the decayed learning rate Tensor. This can be useful for changing + the learning rate value across different invocations of optimizer functions. + @end_compatibility """ if global_step is None: raise ValueError("noisy linear cosine decay requires global_step") @@ -763,29 +868,36 @@ def noisy_linear_cosine_decay(learning_rate, [learning_rate, global_step]) as name: learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") dtype = learning_rate.dtype - global_step = math_ops.cast(global_step, dtype) decay_steps = math_ops.cast(decay_steps, dtype) - global_step = math_ops.minimum(global_step, decay_steps) initial_variance = math_ops.cast(initial_variance, dtype) variance_decay = math_ops.cast(variance_decay, dtype) num_periods = math_ops.cast(num_periods, dtype) alpha = math_ops.cast(alpha, dtype) beta = math_ops.cast(beta, dtype) - linear_decayed = (decay_steps - global_step) / decay_steps - variance = initial_variance / ( - math_ops.pow(1.0 + global_step, variance_decay)) - std = math_ops.sqrt(variance) - noisy_linear_decayed = ( - linear_decayed + - random_ops.random_normal(linear_decayed.shape, stddev=std)) - - completed_fraction = global_step / decay_steps - fraction = 2.0 * num_periods * completed_fraction - cosine_decayed = 0.5 * ( - 1.0 + math_ops.cos(constant_op.constant(math.pi) * fraction)) - noisy_linear_cosine_decayed = ( - (alpha + noisy_linear_decayed) * cosine_decayed + beta) - - return math_ops.multiply( - learning_rate, noisy_linear_cosine_decayed, name=name) + def decayed_lr(): + """Helper to recompute learning rate; most helpful in eager-mode.""" + global_step_recomp = math_ops.cast(global_step, dtype) + global_step_recomp = math_ops.minimum(global_step_recomp, decay_steps) + linear_decayed = (decay_steps - global_step_recomp) / decay_steps + variance = initial_variance / ( + math_ops.pow(1.0 + global_step_recomp, variance_decay)) + std = math_ops.sqrt(variance) + noisy_linear_decayed = ( + linear_decayed + random_ops.random_normal( + linear_decayed.shape, stddev=std)) + + completed_fraction = global_step_recomp / decay_steps + fraction = 2.0 * num_periods * completed_fraction + cosine_decayed = 0.5 * ( + 1.0 + math_ops.cos(constant_op.constant(math.pi) * fraction)) + noisy_linear_cosine_decayed = ( + (alpha + noisy_linear_decayed) * cosine_decayed + beta) + + return math_ops.multiply( + learning_rate, noisy_linear_cosine_decayed, name=name) + + if not context.executing_eagerly(): + decayed_lr = decayed_lr() + + return decayed_lr diff --git a/tensorflow/python/training/learning_rate_decay_test.py b/tensorflow/python/training/learning_rate_decay_test.py index 60306e4f12..d55a28b233 100644 --- a/tensorflow/python/training/learning_rate_decay_test.py +++ b/tensorflow/python/training/learning_rate_decay_test.py @@ -21,12 +21,9 @@ from __future__ import print_function import math from tensorflow.python.eager import context -from tensorflow.python.framework import dtypes from tensorflow.python.framework import test_util -from tensorflow.python.ops import gen_state_ops # Import resource_variable_ops for the variables-to-tensor implicit conversion. from tensorflow.python.ops import resource_variable_ops # pylint: disable=unused-import -from tensorflow.python.ops import state_ops from tensorflow.python.ops import variables from tensorflow.python.platform import googletest from tensorflow.python.training import learning_rate_decay @@ -34,31 +31,35 @@ from tensorflow.python.training import learning_rate_decay class LRDecayTest(test_util.TensorFlowTestCase): + @test_util.run_in_graph_and_eager_modes() def testContinuous(self): - with self.test_session(): - step = 5 - decayed_lr = learning_rate_decay.exponential_decay(0.05, step, 10, 0.96) - expected = .05 * 0.96 ** (5.0 / 10.0) - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + self.evaluate(variables.global_variables_initializer()) + step = 5 + decayed_lr = learning_rate_decay.exponential_decay(0.05, step, 10, 0.96) + expected = .05 * 0.96**(5.0 / 10.0) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + @test_util.run_in_graph_and_eager_modes() def testStaircase(self): - with self.test_session(): - step = gen_state_ops.variable(shape=[], dtype=dtypes.int32, - name="step", container="", shared_name="") - assign_100 = state_ops.assign(step, 100) - assign_1 = state_ops.assign(step, 1) - assign_2 = state_ops.assign(step, 2) - decayed_lr = learning_rate_decay.exponential_decay(.1, step, 3, 0.96, - staircase=True) - # No change to learning rate - assign_1.op.run() - self.assertAllClose(decayed_lr.eval(), .1, 1e-6) - assign_2.op.run() - self.assertAllClose(decayed_lr.eval(), .1, 1e-6) + if context.executing_eagerly(): + step = resource_variable_ops.ResourceVariable(0) + self.evaluate(variables.global_variables_initializer()) + decayed_lr = learning_rate_decay.exponential_decay( + .1, step, 3, 0.96, staircase=True) + + # No change to learning rate due to staircase + expected = .1 + self.evaluate(step.assign(1)) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + + expected = .1 + self.evaluate(step.assign(2)) + self.assertAllClose(self.evaluate(decayed_lr), .1, 1e-6) + # Decayed learning rate - assign_100.op.run() expected = .1 * 0.96 ** (100 // 3) - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + self.evaluate(step.assign(100)) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) def testVariables(self): with self.test_session(): @@ -140,204 +141,188 @@ class LRDecayTest(test_util.TensorFlowTestCase): class LinearDecayTest(test_util.TensorFlowTestCase): + @test_util.run_in_graph_and_eager_modes() def testHalfWay(self): - with self.test_session(): - step = 5 - lr = 0.05 - end_lr = 0.0 - decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr) - expected = lr * 0.5 - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + step = 5 + lr = 0.05 + end_lr = 0.0 + decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr) + expected = lr * 0.5 + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + @test_util.run_in_graph_and_eager_modes() def testEnd(self): - with self.test_session(): - step = 10 - lr = 0.05 - end_lr = 0.001 - decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr) - expected = end_lr - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + step = 10 + lr = 0.05 + end_lr = 0.001 + decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr) + expected = end_lr + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + @test_util.run_in_graph_and_eager_modes() def testHalfWayWithEnd(self): - with self.test_session(): - step = 5 - lr = 0.05 - end_lr = 0.001 - decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr) - expected = (lr + end_lr) * 0.5 - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + step = 5 + lr = 0.05 + end_lr = 0.001 + decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr) + expected = (lr + end_lr) * 0.5 + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + @test_util.run_in_graph_and_eager_modes() def testBeyondEnd(self): - with self.test_session(): - step = 15 - lr = 0.05 - end_lr = 0.001 - decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr) - expected = end_lr - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + step = 15 + lr = 0.05 + end_lr = 0.001 + decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr) + expected = end_lr + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + @test_util.run_in_graph_and_eager_modes() def testBeyondEndWithCycle(self): - with self.test_session(): - step = 15 - lr = 0.05 - end_lr = 0.001 - decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr, - cycle=True) - expected = (lr - end_lr) * 0.25 + end_lr - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + step = 15 + lr = 0.05 + end_lr = 0.001 + decayed_lr = learning_rate_decay.polynomial_decay( + lr, step, 10, end_lr, cycle=True) + expected = (lr - end_lr) * 0.25 + end_lr + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) class SqrtDecayTest(test_util.TensorFlowTestCase): + @test_util.run_in_graph_and_eager_modes() def testHalfWay(self): - with self.test_session(): - step = 5 - lr = 0.05 - end_lr = 0.0 - power = 0.5 - decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr, - power=power) - expected = lr * 0.5 ** power - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + step = 5 + lr = 0.05 + end_lr = 0.0 + power = 0.5 + decayed_lr = learning_rate_decay.polynomial_decay( + lr, step, 10, end_lr, power=power) + expected = lr * 0.5**power + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + @test_util.run_in_graph_and_eager_modes() def testEnd(self): - with self.test_session(): - step = 10 - lr = 0.05 - end_lr = 0.001 - power = 0.5 - decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr, - power=power) - expected = end_lr - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + step = 10 + lr = 0.05 + end_lr = 0.001 + power = 0.5 + decayed_lr = learning_rate_decay.polynomial_decay( + lr, step, 10, end_lr, power=power) + expected = end_lr + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + @test_util.run_in_graph_and_eager_modes() def testHalfWayWithEnd(self): - with self.test_session(): - step = 5 - lr = 0.05 - end_lr = 0.001 - power = 0.5 - decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr, - power=power) - expected = (lr - end_lr) * 0.5 ** power + end_lr - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + step = 5 + lr = 0.05 + end_lr = 0.001 + power = 0.5 + decayed_lr = learning_rate_decay.polynomial_decay( + lr, step, 10, end_lr, power=power) + expected = (lr - end_lr) * 0.5**power + end_lr + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + @test_util.run_in_graph_and_eager_modes() def testBeyondEnd(self): - with self.test_session(): - step = 15 - lr = 0.05 - end_lr = 0.001 - power = 0.5 - decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr, - power=power) - expected = end_lr - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + step = 15 + lr = 0.05 + end_lr = 0.001 + power = 0.5 + decayed_lr = learning_rate_decay.polynomial_decay( + lr, step, 10, end_lr, power=power) + expected = end_lr + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + @test_util.run_in_graph_and_eager_modes() def testBeyondEndWithCycle(self): - with self.test_session(): - step = 15 - lr = 0.05 - end_lr = 0.001 - power = 0.5 - decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr, - power=power, cycle=True) - expected = (lr - end_lr) * 0.25 ** power + end_lr - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + step = 15 + lr = 0.05 + end_lr = 0.001 + power = 0.5 + decayed_lr = learning_rate_decay.polynomial_decay( + lr, step, 10, end_lr, power=power, cycle=True) + expected = (lr - end_lr) * 0.25**power + end_lr + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) class PolynomialDecayTest(test_util.TensorFlowTestCase): + @test_util.run_in_graph_and_eager_modes() def testBeginWithCycle(self): - with self.test_session(): - lr = 0.001 - decay_steps = 10 - step = 0 - decayed_lr = learning_rate_decay.polynomial_decay(lr, step, - decay_steps, cycle=True) - expected = lr - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + lr = 0.001 + decay_steps = 10 + step = 0 + decayed_lr = learning_rate_decay.polynomial_decay( + lr, step, decay_steps, cycle=True) + expected = lr + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) class ExponentialDecayTest(test_util.TensorFlowTestCase): + @test_util.run_in_graph_and_eager_modes() def testDecay(self): initial_lr = 0.1 k = 10 decay_rate = 0.96 - step = gen_state_ops.variable( - shape=[], dtype=dtypes.int32, name="step", container="", shared_name="") - assign_step = state_ops.assign(step, 0) - increment_step = state_ops.assign_add(step, 1) - decayed_lr = learning_rate_decay.natural_exp_decay(initial_lr, step, - k, decay_rate) - with self.test_session(): - assign_step.op.run() - for i in range(k+1): - expected = initial_lr * math.exp(-i / k * decay_rate) - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) - increment_step.op.run() + step = resource_variable_ops.ResourceVariable(0) + decayed_lr = learning_rate_decay.natural_exp_decay(initial_lr, step, k, + decay_rate) + + self.evaluate(variables.global_variables_initializer()) + for i in range(k + 1): + expected = initial_lr * math.exp(-i / k * decay_rate) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + self.evaluate(step.assign_add(1)) + @test_util.run_in_graph_and_eager_modes() def testStaircase(self): initial_lr = 0.1 k = 10 decay_rate = 0.96 - step = gen_state_ops.variable( - shape=[], dtype=dtypes.int32, name="step", container="", shared_name="") - assign_step = state_ops.assign(step, 0) - increment_step = state_ops.assign_add(step, 1) - decayed_lr = learning_rate_decay.natural_exp_decay(initial_lr, - step, - k, - decay_rate, - staircase=True) - with self.test_session(): - assign_step.op.run() - for i in range(k+1): - expected = initial_lr * math.exp(-decay_rate * (i // k)) - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) - increment_step.op.run() + step = resource_variable_ops.ResourceVariable(0) + decayed_lr = learning_rate_decay.natural_exp_decay( + initial_lr, step, k, decay_rate, staircase=True) + + self.evaluate(variables.global_variables_initializer()) + for i in range(k + 1): + expected = initial_lr * math.exp(-decay_rate * (i // k)) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + self.evaluate(step.assign_add(1)) class InverseDecayTest(test_util.TensorFlowTestCase): + @test_util.run_in_graph_and_eager_modes() def testDecay(self): initial_lr = 0.1 k = 10 decay_rate = 0.96 - step = gen_state_ops.variable( - shape=[], dtype=dtypes.int32, name="step", container="", shared_name="") - assign_step = state_ops.assign(step, 0) - increment_step = state_ops.assign_add(step, 1) - decayed_lr = learning_rate_decay.inverse_time_decay(initial_lr, - step, - k, + step = resource_variable_ops.ResourceVariable(0) + decayed_lr = learning_rate_decay.inverse_time_decay(initial_lr, step, k, decay_rate) - with self.test_session(): - assign_step.op.run() - for i in range(k+1): - expected = initial_lr / (1 + i / k * decay_rate) - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) - increment_step.op.run() + self.evaluate(variables.global_variables_initializer()) + for i in range(k + 1): + expected = initial_lr / (1 + i / k * decay_rate) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + self.evaluate(step.assign_add(1)) + + @test_util.run_in_graph_and_eager_modes() def testStaircase(self): initial_lr = 0.1 k = 10 decay_rate = 0.96 - step = gen_state_ops.variable( - shape=[], dtype=dtypes.int32, name="step", container="", shared_name="") - assign_step = state_ops.assign(step, 0) - increment_step = state_ops.assign_add(step, 1) - decayed_lr = learning_rate_decay.inverse_time_decay(initial_lr, - step, - k, - decay_rate, - staircase=True) - with self.test_session(): - assign_step.op.run() - for i in range(k+1): - expected = initial_lr / (1 + decay_rate * (i // k)) - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) - increment_step.op.run() + step = resource_variable_ops.ResourceVariable(0) + decayed_lr = learning_rate_decay.inverse_time_decay( + initial_lr, step, k, decay_rate, staircase=True) + + self.evaluate(variables.global_variables_initializer()) + for i in range(k + 1): + expected = initial_lr / (1 + decay_rate * (i // k)) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + self.evaluate(step.assign_add(1)) class CosineDecayTest(test_util.TensorFlowTestCase): @@ -348,34 +333,35 @@ class CosineDecayTest(test_util.TensorFlowTestCase): decay = 0.5 * (1.0 + math.cos(math.pi * completed_fraction)) return (1.0 - alpha) * decay + alpha + @test_util.run_in_graph_and_eager_modes() def testDecay(self): num_training_steps = 1000 initial_lr = 1.0 for step in range(0, 1500, 250): - with self.test_session(): - decayed_lr = learning_rate_decay.cosine_decay( - initial_lr, step, num_training_steps) - expected = self.np_cosine_decay(step, num_training_steps) - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + decayed_lr = learning_rate_decay.cosine_decay(initial_lr, step, + num_training_steps) + expected = self.np_cosine_decay(step, num_training_steps) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + @test_util.run_in_graph_and_eager_modes() def testAlpha(self): num_training_steps = 1000 initial_lr = 1.0 alpha = 0.1 for step in range(0, 1500, 250): - with self.test_session(): - decayed_lr = learning_rate_decay.cosine_decay( - initial_lr, step, num_training_steps, alpha) - expected = self.np_cosine_decay(step, num_training_steps, alpha) - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + decayed_lr = learning_rate_decay.cosine_decay(initial_lr, step, + num_training_steps, alpha) + expected = self.np_cosine_decay(step, num_training_steps, alpha) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) class CosineDecayRestartsTest(test_util.TensorFlowTestCase): + def np_cosine_decay_restarts(self, step, decay_steps, t_mul=2.0, m_mul=1.0, alpha=0.0): fac = 1.0 while step >= decay_steps: - step = step - decay_steps + step -= decay_steps decay_steps *= t_mul fac *= m_mul @@ -383,51 +369,51 @@ class CosineDecayRestartsTest(test_util.TensorFlowTestCase): decay = fac * 0.5 * (1.0 + math.cos(math.pi * completed_fraction)) return (1.0 - alpha) * decay + alpha + @test_util.run_in_graph_and_eager_modes() def testDecay(self): num_training_steps = 1000 initial_lr = 1.0 for step in range(0, 1500, 250): - with self.test_session(): - decayed_lr = learning_rate_decay.cosine_decay_restarts( - initial_lr, step, num_training_steps) - expected = self.np_cosine_decay_restarts(step, num_training_steps) - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + decayed_lr = learning_rate_decay.cosine_decay_restarts( + initial_lr, step, num_training_steps) + expected = self.np_cosine_decay_restarts(step, num_training_steps) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + @test_util.run_in_graph_and_eager_modes() def testAlpha(self): num_training_steps = 1000 initial_lr = 1.0 alpha = 0.1 for step in range(0, 1500, 250): - with self.test_session(): - decayed_lr = learning_rate_decay.cosine_decay_restarts( - initial_lr, step, num_training_steps, alpha=alpha) - expected = self.np_cosine_decay_restarts(step, num_training_steps, - alpha=alpha) - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + decayed_lr = learning_rate_decay.cosine_decay_restarts( + initial_lr, step, num_training_steps, alpha=alpha) + expected = self.np_cosine_decay_restarts( + step, num_training_steps, alpha=alpha) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + @test_util.run_in_graph_and_eager_modes() def testMMul(self): num_training_steps = 1000 initial_lr = 1.0 m_mul = 0.9 for step in range(0, 1500, 250): - with self.test_session(): - decayed_lr = learning_rate_decay.cosine_decay_restarts( - initial_lr, step, num_training_steps, m_mul=m_mul) - expected = self.np_cosine_decay_restarts(step, num_training_steps, - m_mul=m_mul) - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + decayed_lr = learning_rate_decay.cosine_decay_restarts( + initial_lr, step, num_training_steps, m_mul=m_mul) + expected = self.np_cosine_decay_restarts( + step, num_training_steps, m_mul=m_mul) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + @test_util.run_in_graph_and_eager_modes() def testTMul(self): num_training_steps = 1000 initial_lr = 1.0 t_mul = 1.0 for step in range(0, 1500, 250): - with self.test_session(): - decayed_lr = learning_rate_decay.cosine_decay_restarts( - initial_lr, step, num_training_steps, t_mul=t_mul) - expected = self.np_cosine_decay_restarts(step, num_training_steps, - t_mul=t_mul) - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + decayed_lr = learning_rate_decay.cosine_decay_restarts( + initial_lr, step, num_training_steps, t_mul=t_mul) + expected = self.np_cosine_decay_restarts( + step, num_training_steps, t_mul=t_mul) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) class LinearCosineDecayTest(test_util.TensorFlowTestCase): @@ -444,65 +430,63 @@ class LinearCosineDecayTest(test_util.TensorFlowTestCase): cosine_decayed = 0.5 * (1.0 + math.cos(math.pi * fraction)) return (alpha + linear_decayed) * cosine_decayed + beta + @test_util.run_in_graph_and_eager_modes() def testDefaultDecay(self): num_training_steps = 1000 initial_lr = 1.0 for step in range(0, 1500, 250): - with self.test_session(): - decayed_lr = learning_rate_decay.linear_cosine_decay( - initial_lr, step, num_training_steps) - expected = self.np_linear_cosine_decay(step, num_training_steps) - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + decayed_lr = learning_rate_decay.linear_cosine_decay( + initial_lr, step, num_training_steps) + expected = self.np_linear_cosine_decay(step, num_training_steps) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + @test_util.run_in_graph_and_eager_modes() def testNonDefaultDecay(self): num_training_steps = 1000 initial_lr = 1.0 for step in range(0, 1500, 250): - with self.test_session(): - decayed_lr = learning_rate_decay.linear_cosine_decay( - initial_lr, - step, - num_training_steps, - alpha=0.1, - beta=1e-4, - num_periods=5) - expected = self.np_linear_cosine_decay( - step, - num_training_steps, - alpha=0.1, - beta=1e-4, - num_periods=5) - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + decayed_lr = learning_rate_decay.linear_cosine_decay( + initial_lr, + step, + num_training_steps, + alpha=0.1, + beta=1e-4, + num_periods=5) + expected = self.np_linear_cosine_decay( + step, num_training_steps, alpha=0.1, beta=1e-4, num_periods=5) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) class NoisyLinearCosineDecayTest(test_util.TensorFlowTestCase): + @test_util.run_in_graph_and_eager_modes() def testDefaultNoisyLinearCosine(self): num_training_steps = 1000 initial_lr = 1.0 for step in range(0, 1500, 250): - with self.test_session(): - # No numerical check because of noise - decayed_lr = learning_rate_decay.noisy_linear_cosine_decay( - initial_lr, step, num_training_steps) - decayed_lr.eval() + # No numerical check because of noise + decayed_lr = learning_rate_decay.noisy_linear_cosine_decay( + initial_lr, step, num_training_steps) + # Cannot be deterministically tested + self.evaluate(decayed_lr) + @test_util.run_in_graph_and_eager_modes() def testNonDefaultNoisyLinearCosine(self): num_training_steps = 1000 initial_lr = 1.0 for step in range(0, 1500, 250): - with self.test_session(): - # No numerical check because of noise - decayed_lr = learning_rate_decay.noisy_linear_cosine_decay( - initial_lr, - step, - num_training_steps, - initial_variance=0.5, - variance_decay=0.1, - alpha=0.1, - beta=1e-4, - num_periods=5) - decayed_lr.eval() + # No numerical check because of noise + decayed_lr = learning_rate_decay.noisy_linear_cosine_decay( + initial_lr, + step, + num_training_steps, + initial_variance=0.5, + variance_decay=0.1, + alpha=0.1, + beta=1e-4, + num_periods=5) + # Cannot be deterministically tested + self.evaluate(decayed_lr) if __name__ == "__main__": -- GitLab From 3c0c74e0147ef284a6f2cc5533bea8777af1e740 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 18 Jun 2018 17:34:45 -0700 Subject: [PATCH 634/816] Make NNAPI delegation support more ops. PiperOrigin-RevId: 201090056 --- .../lite/delegates/nnapi/nnapi_delegate.cc | 253 +++++++-- .../delegates/nnapi/nnapi_delegate_test.cc | 533 ++++++++++++++++++ 2 files changed, 745 insertions(+), 41 deletions(-) diff --git a/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate.cc b/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate.cc index 0731d14419..e96ee92376 100644 --- a/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate.cc +++ b/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate.cc @@ -26,6 +26,10 @@ limitations under the License. #include "tensorflow/contrib/lite/kernels/kernel_util.h" #include "tensorflow/contrib/lite/nnapi/NeuralNetworksShim.h" +#ifdef __ANDROID__ +#include +#endif + namespace tflite { namespace { @@ -37,6 +41,29 @@ namespace { return kTfLiteError; \ } +namespace { +int32_t GetAndroidSdkVersion() { +#ifdef __ANDROID__ + const char* sdkProp = "ro.build.version.sdk"; + char sdkVersion[PROP_VALUE_MAX]; + int length = __system_property_get(sdkProp, sdkVersion); + if (length != 0) { + for (int i = 0; i < length; ++i) { + int digit = sdkVersion[i] - '0'; + if (digit < 0 || digit > 9) { + // Non-numeric SDK version, assume it's higher then expected; + return std::numeric_limits::max(); + } + } + return atoi(sdkVersion); + } +#endif // __ANDROID__ + return 0; +} + +static const int32_t kAndroidSdkVersion = GetAndroidSdkVersion(); +} // namespace + // RAII NN API Model Destructor for use with std::unique_ptr struct NNFreeModel { void operator()(ANeuralNetworksModel* model) { @@ -71,7 +98,7 @@ class OperandMapping { // Add a new mapping from `tflite_index` and return the NN API tensor index. int add_new_ann_tensor_index(int tflite_index) { if (tflite_index >= lite_tensor_to_ann_tensor_.size()) { - lite_tensor_to_ann_tensor_.resize(tflite_index + 1); + lite_tensor_to_ann_tensor_.resize(tflite_index + 1, -1); } int new_tensor_index = next_ann_tensor_index_++; lite_tensor_to_ann_tensor_[tflite_index] = new_tensor_index; @@ -98,14 +125,22 @@ class NNAPIOpBuilder { operand_mapping_(tensor_mapping), nn_model_(nn_model) {} - TfLiteStatus AddScalarInt32Operand(int value) { - ANeuralNetworksOperandType operand_type{.type = ANEURALNETWORKS_INT32}; - CHECK_NN(context_, - ANeuralNetworksModel_addOperand(nn_model_, &operand_type)); - int ann_operand = operand_mapping_->add_new_non_tensor_operand(); - CHECK_NN(context_, ANeuralNetworksModel_setOperandValue( - nn_model_, ann_operand, &value, sizeof(int32_t))); - augmented_inputs_.push_back(ann_operand); + TfLiteStatus AddScalarInt32Operand(int32_t value) { + return AddScalarOperand(value, ANEURALNETWORKS_INT32); + } + + TfLiteStatus AddScalarFloat32Operand(float value) { + return AddScalarOperand(value, ANEURALNETWORKS_FLOAT32); + } + + TfLiteStatus AddPoolingParams(void* data) { + auto builtin = reinterpret_cast(data); + AddScalarInt32Operand(builtin->padding); + AddScalarInt32Operand(builtin->stride_width); + AddScalarInt32Operand(builtin->stride_height); + AddScalarInt32Operand(builtin->filter_width); + AddScalarInt32Operand(builtin->filter_height); + AddScalarInt32Operand(builtin->activation); return kTfLiteOk; } @@ -149,7 +184,6 @@ class NNAPIOpBuilder { return kTfLiteOk; case kTfLiteFloat32: nn_type = ANEURALNETWORKS_TENSOR_FLOAT32; - scale = 0.f; break; case kTfLiteUInt8: nn_type = ANEURALNETWORKS_TENSOR_QUANT8_ASYMM; @@ -158,8 +192,8 @@ class NNAPIOpBuilder { break; case kTfLiteInt32: nn_type = ANEURALNETWORKS_TENSOR_INT32; - scale = 0.f; - zeroPoint = 0; + scale = tensor->params.scale; + zeroPoint = tensor->params.zero_point; break; default: context_->ReportError(context_, "Logic error in NN API Delegate.\n"); @@ -192,12 +226,24 @@ class NNAPIOpBuilder { augmented_inputs_.data(), static_cast(augmented_outputs_.size()), augmented_outputs_.data())); - augmented_outputs_.clear(); + augmented_inputs_.clear(); augmented_outputs_.clear(); return kTfLiteOk; } private: + template + TfLiteStatus AddScalarOperand(T value, int32_t nn_type) { + ANeuralNetworksOperandType operand_type{.type = nn_type}; + CHECK_NN(context_, + ANeuralNetworksModel_addOperand(nn_model_, &operand_type)); + int ann_operand = operand_mapping_->add_new_non_tensor_operand(); + CHECK_NN(context_, ANeuralNetworksModel_setOperandValue( + nn_model_, ann_operand, &value, sizeof(T))); + augmented_inputs_.push_back(ann_operand); + return kTfLiteOk; + } + // TfLiteContext for error handling. Must be named context for macros to // work. TfLiteContext* context_; @@ -227,29 +273,143 @@ class NNAPIDelegateKernel { // Return a function that knows how to translate a node into its operands // when called. You can use this function to see if a node is supported // (i.e. that MappingFn is not nullptr). - MappingFn Map(TfLiteContext* context, int builtin_code, TfLiteNode* node) { + MappingFn Map(TfLiteContext* context, int builtin_code, int version, + TfLiteNode* node) { switch (builtin_code) { case kTfLiteBuiltinAdd: - return [](TfLiteContext* context, NNAPIOpBuilder* builder, - TfLiteNode* node) -> ANeuralNetworksOperationType { - auto builtin = reinterpret_cast(node->builtin_data); - builder->AddScalarInt32Operand(builtin->activation); - return ANEURALNETWORKS_ADD; - }; + if (version == 1) { + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + auto builtin = + reinterpret_cast(node->builtin_data); + builder->AddScalarInt32Operand(builtin->activation); + return ANEURALNETWORKS_ADD; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinMul: + if (version == 1) { + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + auto builtin = + reinterpret_cast(node->builtin_data); + builder->AddScalarInt32Operand(builtin->activation); + return ANEURALNETWORKS_MUL; + }; + } else { + return nullptr; + } break; case kTfLiteBuiltinAveragePool2d: - return [](TfLiteContext* context, NNAPIOpBuilder* builder, - TfLiteNode* node) -> ANeuralNetworksOperationType { + if (version == 1) { + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + builder->AddPoolingParams(node->builtin_data); + return ANEURALNETWORKS_AVERAGE_POOL_2D; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinMaxPool2d: + if (version == 1) { + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + builder->AddPoolingParams(node->builtin_data); + return ANEURALNETWORKS_MAX_POOL_2D; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinL2Pool2d: + if (version == 1) { + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + builder->AddPoolingParams(node->builtin_data); + return ANEURALNETWORKS_L2_POOL_2D; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinConv2d: + if (version == 1) { auto builtin = - reinterpret_cast(node->builtin_data); - builder->AddScalarInt32Operand(builtin->padding); - builder->AddScalarInt32Operand(builtin->stride_width); - builder->AddScalarInt32Operand(builtin->stride_height); - builder->AddScalarInt32Operand(builtin->filter_width); - builder->AddScalarInt32Operand(builtin->filter_height); - builder->AddScalarInt32Operand(builtin->activation); - return ANEURALNETWORKS_AVERAGE_POOL_2D; - }; + reinterpret_cast(node->builtin_data); + if (builtin->dilation_width_factor != 1 || + builtin->dilation_height_factor != 1 || node->inputs->size != 3) { + // NNAPI does not support dilated Conv2D. + return nullptr; + } + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + auto builtin = + reinterpret_cast(node->builtin_data); + builder->AddScalarInt32Operand(builtin->padding); + builder->AddScalarInt32Operand(builtin->stride_width); + builder->AddScalarInt32Operand(builtin->stride_height); + builder->AddScalarInt32Operand(builtin->activation); + return ANEURALNETWORKS_CONV_2D; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinDepthwiseConv2d: + if (version == 1) { + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + auto builtin = reinterpret_cast( + node->builtin_data); + builder->AddScalarInt32Operand(builtin->padding); + builder->AddScalarInt32Operand(builtin->stride_width); + builder->AddScalarInt32Operand(builtin->stride_height); + builder->AddScalarInt32Operand(builtin->depth_multiplier); + builder->AddScalarInt32Operand(builtin->activation); + return ANEURALNETWORKS_DEPTHWISE_CONV_2D; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinFullyConnected: + if (version == 1) { + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + auto builtin = reinterpret_cast( + node->builtin_data); + builder->AddScalarInt32Operand(builtin->activation); + return ANEURALNETWORKS_FULLY_CONNECTED; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinSoftmax: + if (version == 1) { + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + auto builtin = + reinterpret_cast(node->builtin_data); + builder->AddScalarFloat32Operand(builtin->beta); + return ANEURALNETWORKS_SOFTMAX; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinReshape: + if (version == 1) { + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + return ANEURALNETWORKS_RESHAPE; + }; + } else { + return nullptr; + } break; default: return nullptr; @@ -292,10 +452,14 @@ class NNAPIDelegateKernel { int relative_input_index = 0; for (auto absolute_input_index : TfLiteIntArrayView(node->inputs)) { TfLiteTensor* tensor = &context->tensors[absolute_input_index]; - CHECK_NN(context, ANeuralNetworksExecution_setInput( - execution, relative_input_index, nullptr, - tensor->data.raw, tensor->bytes)); - relative_input_index++; + // TODO(miaowang): make sure the delegation works with dequantized weights + // as intermediate tensors. + if (tensor->allocation_type != kTfLiteMmapRo) { + CHECK_NN(context, ANeuralNetworksExecution_setInput( + execution, relative_input_index, nullptr, + tensor->data.raw, tensor->bytes)); + relative_input_index++; + } } // Set the output tensor buffers. @@ -345,8 +509,8 @@ class NNAPIDelegateKernel { TF_LITE_ENSURE_STATUS(builder.AddTensorInput(input_index)); } // Get op type and operands - int nn_op_type = - Map(context, reg->builtin_code, node)(context, &builder, node); + int nn_op_type = Map(context, reg->builtin_code, reg->version, node)( + context, &builder, node); // Map outputs to NN API tensor indices. for (auto output_index : TfLiteIntArrayView(node->outputs)) { TF_LITE_ENSURE_STATUS(builder.AddTensorOutput(output_index)); @@ -368,8 +532,12 @@ class NNAPIDelegateKernel { std::vector outputs; outputs.reserve(output_tensors->size); // Make the TensorFlow lite inputs and outputs to ann_indices. - for (int i : TfLiteIntArrayView(input_tensors)) - inputs.push_back(operand_mapping_.lite_index_to_ann(i)); + for (int i : TfLiteIntArrayView(input_tensors)) { + // Constant tensors are not NNAPI inputs. + if (context->tensors[i].allocation_type != kTfLiteMmapRo) { + inputs.push_back(operand_mapping_.lite_index_to_ann(i)); + } + } for (int i : TfLiteIntArrayView(output_tensors)) outputs.push_back(operand_mapping_.lite_index_to_ann(i)); // Tell ANN to declare inputs/outputs @@ -392,7 +560,8 @@ TfLiteDelegate* NnApiDelegate() { .Prepare = [](TfLiteContext* context, TfLiteDelegate* delegate) -> TfLiteStatus { // Do not check nodes_ if NN API is unavailable. - if (!NNAPIExists()) return kTfLiteOk; + // NN API is only available since Android O-MR1 (API 27). + if (kAndroidSdkVersion < 27 || !NNAPIExists()) return kTfLiteOk; std::vector supported_nodes(1); // We don't care about all nodes_, we only care about ones in the @@ -400,6 +569,7 @@ TfLiteDelegate* NnApiDelegate() { TfLiteIntArray* plan; TF_LITE_ENSURE_STATUS(context->GetExecutionPlan(context, &plan)); int total_supported_nodes = 0; + // Check for every node if it is supported // TODO(b/80625235): Fix this to do more careful checking of versioning. for (int node_index : TfLiteIntArrayView(plan)) { @@ -408,7 +578,8 @@ TfLiteDelegate* NnApiDelegate() { TF_LITE_ENSURE_STATUS(context->GetNodeAndRegistration( context, node_index, &node, ®istration)); NNAPIDelegateKernel dummy_kernel; - if (dummy_kernel.Map(context, registration->builtin_code, node)) { + if (dummy_kernel.Map(context, registration->builtin_code, + registration->version, node)) { supported_nodes.push_back(node_index); } total_supported_nodes += 1; diff --git a/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate_test.cc b/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate_test.cc index ff2e721423..799e3efe0b 100644 --- a/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate_test.cc +++ b/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate_test.cc @@ -21,8 +21,12 @@ limitations under the License. namespace tflite { namespace { +using ::testing::ElementsAre; using ::testing::ElementsAreArray; +// TODO(b/110368244): figure out how to share the existing tests in kernels/ but +// with the delegation on. Also, add more unit tests to improve code coverage. + class FloatAddOpModel : public SingleOpModel { public: FloatAddOpModel(const TensorData& input1, const TensorData& input2, @@ -72,6 +76,535 @@ TEST(NNAPIDelegate, AddWithRelu) { EXPECT_THAT(m.GetOutput(), ElementsAreArray({0.0, 0.4, 1.0, 1.3})); } +class FloatMulOpModel : public SingleOpModel { + public: + FloatMulOpModel(const TensorData& input1, const TensorData& input2, + const TensorData& output, + ActivationFunctionType activation_type) { + this->SetApplyDelegate([](Interpreter* interpreter) { + interpreter->ModifyGraphWithDelegate(NnApiDelegate()); + }); + input1_ = AddInput(input1); + input2_ = AddInput(input2); + output_ = AddOutput(output); + SetBuiltinOp(BuiltinOperator_MUL, BuiltinOptions_MulOptions, + CreateMulOptions(builder_, activation_type).Union()); + BuildInterpreter({GetShape(input1_), GetShape(input2_)}); + } + + int input1() { return input1_; } + int input2() { return input2_; } + + std::vector GetOutput() { return ExtractVector(output_); } + + protected: + int input1_; + int input2_; + int output_; +}; + +TEST(NNAPIDelegate, MulWithNoActivation) { + FloatMulOpModel m({TensorType_FLOAT32, {1, 2, 2, 1}}, + {TensorType_FLOAT32, {1, 2, 2, 1}}, + {TensorType_FLOAT32, {}}, ActivationFunctionType_NONE); + m.PopulateTensor(m.input1(), {-2.0, 0.2, 0.7, 0.8}); + m.PopulateTensor(m.input2(), {0.1, 0.2, 0.3, 0.5}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray(ArrayFloatNear({-0.2, 0.04, 0.21, 0.4}))); +} + +class FloatPoolingOpModel : public SingleOpModel { + public: + FloatPoolingOpModel(BuiltinOperator type, const TensorData& input, + int filter_width, int filter_height, + const TensorData& output) { + this->SetApplyDelegate([](Interpreter* interpreter) { + interpreter->ModifyGraphWithDelegate(NnApiDelegate()); + }); + + input_ = AddInput(input); + output_ = AddOutput(output); + + SetBuiltinOp( + type, BuiltinOptions_Pool2DOptions, + CreatePool2DOptions(builder_, Padding_VALID, 2, 2, filter_width, + filter_height, ActivationFunctionType_NONE) + .Union()); + + BuildInterpreter({GetShape(input_)}); + } + + void SetInput(std::initializer_list data) { + PopulateTensor(input_, data); + } + + std::vector GetOutput() { return ExtractVector(output_); } + + protected: + int input_; + int output_; +}; + +TEST(NNAPIDelegate, AveragePoolWithNoActivation) { + FloatPoolingOpModel m(BuiltinOperator_AVERAGE_POOL_2D, + /*input=*/{TensorType_FLOAT32, {1, 2, 4, 1}}, + /*filter_width=*/2, /*filter_height=*/2, + /*output=*/{TensorType_FLOAT32, {}}); + m.SetInput({ + 0, 6, 2, 4, // + 3, 2, 10, 7, // + }); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({2.75, 5.75})); +} + +TEST(NNAPIDelegate, MaxPoolWithNoActivation) { + FloatPoolingOpModel m(BuiltinOperator_MAX_POOL_2D, + /*input=*/{TensorType_FLOAT32, {1, 2, 4, 1}}, + /*filter_width=*/2, /*filter_height=*/2, + /*output=*/{TensorType_FLOAT32, {}}); + m.SetInput({ + 0, 6, 2, 4, // + 3, 2, 10, 7, // + }); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({6, 10})); +} + +TEST(NNAPIDelegate, L2PoolWithNoActivation) { + FloatPoolingOpModel m(BuiltinOperator_L2_POOL_2D, + /*input=*/{TensorType_FLOAT32, {1, 2, 4, 1}}, + /*filter_width=*/2, /*filter_height=*/2, + /*output=*/{TensorType_FLOAT32, {}}); + m.SetInput({ + 0, 6, 2, 4, // + 3, 2, 10, 7, // + }); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({3.5, 6.5})); +} + +class BaseConvolutionOpModel : public SingleOpModel { + public: + BaseConvolutionOpModel( + const TensorData& input, const TensorData& filter, + const TensorData& output, int stride_width = 2, int stride_height = 2, + enum Padding padding = Padding_VALID, + enum ActivationFunctionType activation = ActivationFunctionType_NONE, + int dilation_width_factor = 1, int dilation_height_factor = 1) { + this->SetApplyDelegate([](Interpreter* interpreter) { + interpreter->ModifyGraphWithDelegate(NnApiDelegate()); + }); + + input_ = AddInput(input); + filter_ = AddInput(filter); + + int bias_size = GetShape(filter_)[0]; + if (input.type == TensorType_FLOAT32) { + bias_ = AddInput({TensorType_FLOAT32, {bias_size}}); + } else { + // This is a quantized version. The scale of 'bias' depends on the scales + // of input and filter. Supposedly this is correctly set during quantized + // training. + auto bias_scale = GetScale(input_) * GetScale(filter_); + TensorData bias{TensorType_INT32, {bias_size}, 0, 0, bias_scale}; + bias_ = AddInput(bias); + } + + output_ = AddOutput(output); + if (input.type != TensorType_FLOAT32) { + // The following is required by quantized inference. It is the unittest's + // responsibility to make sure the output scale falls into the correct + // range. + CHECK_LT(GetScale(input_) * GetScale(filter_), GetScale(output_)); + } + + SetBuiltinOp(BuiltinOperator_CONV_2D, BuiltinOptions_Conv2DOptions, + CreateConv2DOptions( + builder_, padding, stride_width, stride_height, activation, + dilation_width_factor, dilation_height_factor) + .Union()); + + BuildInterpreter({GetShape(input_), GetShape(filter_), GetShape(bias_)}); + } + + protected: + int input_; + int filter_; + int bias_; + int output_; +}; + +class ConvolutionOpModel : public BaseConvolutionOpModel { + public: + using BaseConvolutionOpModel::BaseConvolutionOpModel; + + void SetFilter(std::initializer_list f) { PopulateTensor(filter_, f); } + + void SetBias(std::initializer_list f) { PopulateTensor(bias_, f); } + + void SetInput(std::initializer_list data) { + PopulateTensor(input_, data); + } + std::vector GetOutput() { return ExtractVector(output_); } +}; + +class QuantizedConvolutionOpModel : public BaseConvolutionOpModel { + public: + using BaseConvolutionOpModel::BaseConvolutionOpModel; + + void SetInput(std::initializer_list data) { + QuantizeAndPopulate(input_, data); + } + + void SetFilter(std::initializer_list data) { + QuantizeAndPopulate(filter_, data); + } + + void SetBias(std::initializer_list data) { + QuantizeAndPopulate(bias_, data); + } + + std::vector GetOutput() { return ExtractVector(output_); } + std::vector GetDequantizedOutput() { + return Dequantize(ExtractVector(output_), + GetScale(output_), GetZeroPoint(output_)); + } +}; + +// In this tests we set the input and output scales so that the results +// match exactly the 'non-quantized' version. +TEST(NNAPIDelegate, SimpleTestQuantized) { + QuantizedConvolutionOpModel m({TensorType_UINT8, {2, 2, 4, 1}, -63.5, 64}, + {TensorType_UINT8, {3, 2, 2, 1}, -63.5, 64}, + {TensorType_UINT8, {}, -127, 128}); + m.SetInput({ + // First batch + 1, 1, 1, 1, // row = 1 + 2, 2, 2, 2, // row = 2 + // Second batch + 1, 2, 3, 4, // row = 1 + 1, 2, 3, 4, // row = 2 + }); + m.SetFilter({ + 1, 2, 3, 4, // first 2x2 filter + -1, 1, -1, 1, // second 2x2 filter + -1, -1, 1, 1, // third 2x2 filter + }); + m.SetBias({1, 2, 3}); + + m.Invoke(); + + EXPECT_THAT(m.GetDequantizedOutput(), + ElementsAreArray(ArrayFloatNear( + { + 18, 2, 5, // first batch, left + 18, 2, 5, // first batch, right + 17, 4, 3, // second batch, left + 37, 4, 3, // second batch, right + }, + 1e-5))); + // For good measure, let's also verify the quantized values: + EXPECT_THAT(m.GetOutput(), ElementsAreArray({ + 145, 129, 132, // + 145, 129, 132, // + 144, 131, 130, // + 164, 131, 130, // + })); +} + +TEST(NNAPIDelegate, Conv2DWithNoActivation) { + ConvolutionOpModel m({TensorType_FLOAT32, {2, 2, 4, 1}}, + {TensorType_FLOAT32, {3, 2, 2, 1}}, + {TensorType_FLOAT32, {}}); + + m.SetInput({ + // First batch + 1, 1, 1, 1, // row = 1 + 2, 2, 2, 2, // row = 2 + // Second batch + 1, 2, 3, 4, // row = 1 + 1, 2, 3, 4, // row = 2 + }); + m.SetFilter({ + 1, 2, 3, 4, // first 2x2 filter + -1, 1, -1, 1, // second 2x2 filter + -1, -1, 1, 1, // third 2x2 filter + }); + m.SetBias({1, 2, 3}); + + m.Invoke(); + + EXPECT_THAT(m.GetOutput(), ElementsAreArray({ + 18, 2, 5, // first batch, left + 18, 2, 5, // first batch, right + 17, 4, 3, // second batch, left + 37, 4, 3, // second batch, right + })); +} + +class DepthwiseConvolutionOpModel : public SingleOpModel { + public: + DepthwiseConvolutionOpModel(const TensorData& input, const TensorData& filter, + const TensorData& output) { + this->SetApplyDelegate([](Interpreter* interpreter) { + interpreter->ModifyGraphWithDelegate(NnApiDelegate()); + }); + + input_ = AddInput(input); + filter_ = AddInput(filter); + + int bias_size = GetShape(filter_)[3]; + if (input.type == TensorType_FLOAT32) { + bias_ = AddInput({TensorType_FLOAT32, {bias_size}}); + } else { + // This is a quantized version. The scale of 'bias' depends on the scales + // of input and filter. Supposedly this is correctly set during quantized + // training. + auto bias_scale = GetScale(input_) * GetScale(filter_); + TensorData bias{TensorType_INT32, {bias_size}, 0, 0, bias_scale}; + bias_ = AddInput(bias); + } + + output_ = AddOutput(output); + + int input_depth = GetShape(input_)[3]; + int output_depth = GetShape(filter_)[3]; + int depth_mul = output_depth / input_depth; + + SetBuiltinOp( + BuiltinOperator_DEPTHWISE_CONV_2D, + BuiltinOptions_DepthwiseConv2DOptions, + CreateDepthwiseConv2DOptions(builder_, Padding_VALID, 1, 1, depth_mul, + ActivationFunctionType_NONE) + .Union()); + + BuildInterpreter({GetShape(input_), GetShape(filter_), GetShape(bias_)}); + } + + void SetFilter(std::initializer_list f) { PopulateTensor(filter_, f); } + + void SetBias(std::initializer_list f) { PopulateTensor(bias_, f); } + + void SetInput(std::initializer_list data) { + PopulateTensor(input_, data); + } + + std::vector GetOutput() { return ExtractVector(output_); } + + protected: + int input_; + int filter_; + int bias_; + int output_; +}; + +TEST(NNAPIDelegate, DepthwiseConv2DWithNoActivation) { + DepthwiseConvolutionOpModel m({TensorType_FLOAT32, {1, 3, 2, 2}}, + {TensorType_FLOAT32, {1, 2, 2, 4}}, + {TensorType_FLOAT32, {}}); + + m.SetInput({ + 1, 2, 7, 8, // column 1 + 3, 4, 9, 10, // column 2 + 5, 6, 11, 12, // column 3 + }); + m.SetFilter({ + 1, 2, 3, 4, // + -9, 10, -11, 12, // + 5, 6, 7, 8, // + 13, -14, 15, -16, // + }); + m.SetBias({1, 2, 3, 4}); + + m.Invoke(); + + EXPECT_THAT(m.GetOutput(), ElementsAreArray({ + 71, -34, 99, -20, // + 91, -26, 127, -4, // + })); +} + +class FloatFullyConnectedOpModel : public SingleOpModel { + public: + FloatFullyConnectedOpModel(int units, int batches, const TensorData& input, + const TensorData& output = {TensorType_FLOAT32}) + : batches_(batches), units_(units) { + this->SetApplyDelegate([](Interpreter* interpreter) { + interpreter->ModifyGraphWithDelegate(NnApiDelegate()); + }); + + int total_input_size = 1; + for (int i = 0; i < input.shape.size(); ++i) { + total_input_size *= input.shape[i]; + } + input_size_ = total_input_size / batches_; + + input_ = AddInput(input); + weights_ = + AddInput({input.type, {units_, input_size_}, input.min, input.max}); + + if (input.type == TensorType_FLOAT32) { + bias_ = AddInput({TensorType_FLOAT32, {units_}}); + } else { + // This is a quantized version. The scale of 'bias' depends on the scales + // of input and filter. Supposedly this is correctly set during quantized + // training. + auto bias_scale = GetScale(input_) * GetScale(weights_); + TensorData bias{TensorType_INT32, {units_}, 0, 0, bias_scale}; + bias_ = AddInput(bias); + } + + output_ = AddOutput(output); + + SetBuiltinOp( + BuiltinOperator_FULLY_CONNECTED, BuiltinOptions_FullyConnectedOptions, + CreateFullyConnectedOptions(builder_, ActivationFunctionType_RELU) + .Union()); + BuildInterpreter({GetShape(input_), GetShape(weights_), GetShape(bias_)}); + } + + int input_size() { return input_size_; } + int num_units() { return units_; } + int num_batches() { return batches_; } + + void SetBias(std::initializer_list f) { PopulateTensor(bias_, f); } + + void SetWeights(std::initializer_list f) { + PopulateTensor(weights_, f); + } + + void SetInput(std::initializer_list data) { + PopulateTensor(input_, data); + } + void SetInput(int offset, float* begin, float* end) { + PopulateTensor(input_, offset, begin, end); + } + + std::vector GetOutput() { return ExtractVector(output_); } + + protected: + int input_; + int weights_; + int bias_; + int output_; + + int batches_; + int units_; + int input_size_; +}; + +TEST(NNAPIDelegate, FullyConnectedSimpleTest) { + FloatFullyConnectedOpModel m(/*units=*/3, /*batches=*/2, + /*input=*/{TensorType_FLOAT32, {2, 10}}); + m.SetWeights({ + 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, // u = 0 + 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, // u = 1 + 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, // u = 1 + }); + m.SetBias({1, 2, 3}); + + m.SetInput({ + 1, 2, 3, 4, 5, 6, 7, 8, -9, -10, // b = 0 + 1, 2, 3, 4, 5, 6, 7, -8, 9, -10, // b = 1 + }); + + m.Invoke(); + + EXPECT_THAT(m.GetOutput(), ElementsAre(24, 25, 26, 58, 59, 60)); +} + +class SoftmaxOpModel : public SingleOpModel { + public: + SoftmaxOpModel(int batches, int size, float beta) + : batches_(batches), input_size_(size), beta_(beta) { + this->SetApplyDelegate([](Interpreter* interpreter) { + interpreter->ModifyGraphWithDelegate(NnApiDelegate()); + }); + + input_ = AddInput(TensorType_FLOAT32); + output_ = AddOutput(TensorType_FLOAT32); + SetBuiltinOp(BuiltinOperator_SOFTMAX, BuiltinOptions_SoftmaxOptions, + CreateSoftmaxOptions(builder_, beta_).Union()); + BuildInterpreter({{batches_, input_size_}}); + } + + void SetInput(std::initializer_list data) { + PopulateTensor(input_, data); + } + + void SetInput(int offset, float* begin, float* end) { + PopulateTensor(input_, offset, begin, end); + } + + std::vector GetOutput() { return ExtractVector(output_); } + + private: + int input_; + int output_; + + int batches_; + int input_size_; + float beta_; +}; + +TEST(NNAPIDelegate, SoftmaxSimpleTest) { + SoftmaxOpModel m(/*batches=*/2, /*size=*/5, /*beta=*/1.0); + m.SetInput({ + 1.0, 2.0, 3.0, 4.0, 5.0, // b = 0 + -1.0, -2.0, -3.0, -4.0, -5.0, // b = 0 + }); + + m.Invoke(); + + EXPECT_THAT( + m.GetOutput(), + ElementsAreArray(ArrayFloatNear( + {0.011656231, 0.031684921, 0.086128544, 0.234121657, 0.636408647, + 0.636408647, 0.234121657, 0.086128544, 0.031684921, 0.011656231}, + 1e-6))); +} + +class ReshapeOpModel : public SingleOpModel { + public: + ReshapeOpModel(std::initializer_list input_shape, + std::initializer_list new_shape) { + this->SetApplyDelegate([](Interpreter* interpreter) { + interpreter->ModifyGraphWithDelegate(NnApiDelegate()); + }); + + input_ = AddInput(TensorType_FLOAT32); + new_shape_ = AddInput(TensorType_INT32); + output_ = AddOutput(TensorType_FLOAT32); + SetBuiltinOp( + BuiltinOperator_RESHAPE, BuiltinOptions_ReshapeOptions, + CreateReshapeOptions(builder_, builder_.CreateVector(new_shape)) + .Union()); + BuildInterpreter({input_shape, {static_cast(new_shape.size())}}); + PopulateTensor(new_shape_, new_shape); + } + + void SetInput(std::initializer_list data) { + PopulateTensor(input_, data); + } + std::vector GetOutput() { return ExtractVector(output_); } + std::vector GetOutputShape() { return GetTensorShape(output_); } + + private: + int input_; + int new_shape_; + int output_; +}; + +TEST(NNAPIDelegate, ReshapeSimpleTest) { + ReshapeOpModel m({1, 2, 4, 1}, {2, 2, 2}); + m.SetInput({1, 2, 3, 4, 5, 6, 7, 8}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({1, 2, 3, 4, 5, 6, 7, 8})); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2, 2, 2})); +} + } // namespace } // namespace tflite -- GitLab From 84a1f27d79f444cd865b6c46787bc650c6ff90ec Mon Sep 17 00:00:00 2001 From: Sami Kama Date: Mon, 18 Jun 2018 17:54:49 -0700 Subject: [PATCH 635/816] Workaround Grappler funcdef optimization issue --- tensorflow/contrib/tensorrt/convert/convert_graph.cc | 4 +++- .../tensorrt/convert/trt_optimization_pass.cc | 12 ++++++++++++ tensorflow/contrib/tensorrt/test/test_tftrt.py | 1 + 3 files changed, 16 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.cc b/tensorflow/contrib/tensorrt/convert/convert_graph.cc index f19a8cd4bd..c17ef5fdab 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_graph.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_graph.cc @@ -479,7 +479,7 @@ tensorflow::Status CreateTRTNode(tensorflow::Graph* graph, node_builder.Device(info.device); } if (VLOG_IS_ON(1)) { - string ins(info.engine_name); + string ins=StrCat(info.engine_name," inputs= "); for (const auto& ii : inputs) { StrAppend(&ins, ii.node, ":", ii.index, " "); } @@ -623,6 +623,7 @@ tensorflow::Status RegisterSegmentFunctionToFunctionLibrary( VLOG(7) << name << " Function_Def "; VLOG(7) << native_segment->DebugString(); } + VLOG(1)<<"Adding funcdef to graphlib"; TF_RETURN_IF_ERROR(graph->AddFunctionLibrary(fdeflib)); return tensorflow::Status::OK(); } @@ -813,6 +814,7 @@ tensorflow::Status ConvertAfterShapes(ConversionParams& params) { cudaSetDevice(old_cuda_device); graph.ToGraphDef(params.output_graph_def); for (auto tn : trt_nodes) delete tn; + VLOG(1)<<"Returning from conversion"; return tensorflow::Status::OK(); } diff --git a/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc b/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc index 6d0fd7a44b..ec9dbfa13b 100644 --- a/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc +++ b/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc @@ -191,6 +191,17 @@ tensorflow::Status TRTOptimizationPass::Optimize( if (VLOG_IS_ON(1)) { PrintDebugInfo(cluster, item); } + // This is a hack to workaround optimizer issue. MetaOptimizer calls + // optimization passes on function objects as well, we should not modify + // generated funcdefs! This is fragile but we don't have any other option + // until framework fixes it. + if (item.id != "tf_graph") { + LOG(WARNING) << name_ + << " is probably called on funcdef! This optimizer must *NOT* " + "be called on function objects."; + *optimized_graph = item.graph; + return tensorflow::Status::OK(); + } int max_dim = -1; if (item.feed.size()) { for (const auto& f : item.feed) { @@ -235,6 +246,7 @@ tensorflow::Status TRTOptimizationPass::Optimize( cp.max_cached_engines = max_cached_batches_; auto status = tensorflow::tensorrt::convert::ConvertAfterShapes(cp); VLOG(2) << optimized_graph->DebugString(); + VLOG(1) << "Returning from " << name_; return status; } diff --git a/tensorflow/contrib/tensorrt/test/test_tftrt.py b/tensorflow/contrib/tensorrt/test/test_tftrt.py index 85f37aa899..12e84f7d3c 100644 --- a/tensorflow/contrib/tensorrt/test/test_tftrt.py +++ b/tensorflow/contrib/tensorrt/test/test_tftrt.py @@ -236,6 +236,7 @@ def auto(multi_engine): orig_graph = get_simple_graph_def() # use a frozen graph for inference dummy_input = np.random.random_sample(inp_dims) opt_config = rwpb2.RewriterConfig() + opt_config.meta_optimizer_iterations=opt_config.ONE opt_config.optimizers.extend(["constfold", "layout"]) custom_op = opt_config.custom_optimizers.add() custom_op.name = "TensorRTOptimizer" -- GitLab From eeeb666fd9f2af1e4f55d88b813934bb5e79a098 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 18 Jun 2018 18:02:48 -0700 Subject: [PATCH 636/816] Split out opcodes with window as subclasses from HloInstruction (kConvolution, kReduceWindow, kSelectAndScatter, kCustomCall). PiperOrigin-RevId: 201093426 --- .../compiler/xla/service/hlo_instruction.cc | 290 ++++++------------ .../compiler/xla/service/hlo_instruction.h | 125 ++++---- .../compiler/xla/service/hlo_instructions.cc | 257 ++++++++++++++++ .../compiler/xla/service/hlo_instructions.h | 162 ++++++++++ 4 files changed, 575 insertions(+), 259 deletions(-) diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc index 8f89b6f255..58a33f5229 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction.cc +++ b/tensorflow/compiler/xla/service/hlo_instruction.cc @@ -35,7 +35,6 @@ limitations under the License. #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/util.h" -#include "tensorflow/compiler/xla/window_util.h" #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/lib/gtl/flatmap.h" #include "tensorflow/core/lib/gtl/flatset.h" @@ -274,6 +273,48 @@ StatusOr> HloInstruction::CreateFromProto( /*all_reduce_id=*/all_reduce_id); break; } + case HloOpcode::kConvolution: + CHECK_EQ(proto.operand_ids_size(), 2); + CHECK(proto.has_window()); + CHECK(proto.has_convolution_dimension_numbers()); + instruction = + CreateConvolve(proto.shape(), operands(0), operands(1), + proto.window(), proto.convolution_dimension_numbers()); + break; + case HloOpcode::kReduceWindow: + CHECK_EQ(proto.operand_ids_size(), 2); + CHECK_EQ(proto.called_computation_ids_size(), 1); + instruction = CreateReduceWindow(proto.shape(), operands(0), operands(1), + proto.window(), computations(0)); + break; + case HloOpcode::kSelectAndScatter: + CHECK_EQ(proto.operand_ids_size(), 3); + CHECK_EQ(proto.called_computation_ids_size(), 2); + instruction = CreateSelectAndScatter( + proto.shape(), operands(0), computations(0), proto.window(), + operands(1), operands(2), computations(1)); + break; + case HloOpcode::kCustomCall: { + std::vector custom_call_operands( + proto.operand_ids_size()); + std::transform(proto.operand_ids().begin(), proto.operand_ids().end(), + custom_call_operands.begin(), + [&instruction_map](int64 operand_id) { + return instruction_map.at(operand_id); + }); + instruction = CreateCustomCall(proto.shape(), custom_call_operands, + proto.custom_call_target()); + if (proto.has_window()) { + static_cast(instruction.get()) + ->set_window(proto.window()); + } + if (proto.has_convolution_dimension_numbers()) { + static_cast(instruction.get()) + ->set_convolution_dimension_numbers( + proto.convolution_dimension_numbers()); + } + break; + } default: { instruction = WrapUnique(new HloInstruction(opcode, proto.shape())); for (const int64 operand_id : proto.operand_ids()) { @@ -304,14 +345,6 @@ StatusOr> HloInstruction::CreateFromProto( instruction->metadata_ = proto.metadata(); instruction->backend_config_ = proto.backend_config(); - if (proto.has_window()) { - instruction->window_ = MakeUnique(proto.window()); - } - if (proto.has_convolution_dimension_numbers()) { - instruction->convolution_dimension_numbers_ = - MakeUnique( - proto.convolution_dimension_numbers()); - } if (proto.has_dot_dimension_numbers()) { instruction->dot_dimension_numbers_ = MakeUnique(proto.dot_dimension_numbers()); @@ -324,7 +357,6 @@ StatusOr> HloInstruction::CreateFromProto( instruction->padding_config_ = MakeUnique(proto.padding_config()); } - instruction->custom_call_target_ = proto.custom_call_target(); if (proto.has_sharding()) { TF_ASSIGN_OR_RETURN(const auto& sharding, @@ -493,20 +525,8 @@ HloInstruction::CreateGetTupleElement(const Shape& shape, const Shape& shape, HloInstruction* lhs, HloInstruction* rhs, const Window& window, const ConvolutionDimensionNumbers& dimension_numbers) { - auto instruction = - WrapUnique(new HloInstruction(HloOpcode::kConvolution, shape)); - if (window_util::HasBaseDilation(window)) { - instruction->name_ = instruction->name() + "-base-dilated"; - } - if (window_util::HasWindowDilation(window)) { - instruction->name_ = instruction->name() + "-window-dilated"; - } - instruction->AppendOperand(lhs); - instruction->AppendOperand(rhs); - instruction->window_ = MakeUnique(window); - instruction->convolution_dimension_numbers_ = - MakeUnique(dimension_numbers); - return instruction; + return MakeUnique(shape, lhs, rhs, window, + dimension_numbers); } /* static */ std::unique_ptr HloInstruction::CreateFft( @@ -710,13 +730,8 @@ HloInstruction::CreateBitcastConvert(const Shape& shape, /* static */ std::unique_ptr HloInstruction::CreateReduceWindow( const Shape& shape, HloInstruction* operand, HloInstruction* init_value, const Window& window, HloComputation* reduce_computation) { - auto instruction = - WrapUnique(new HloInstruction(HloOpcode::kReduceWindow, shape)); - instruction->AppendOperand(operand); - instruction->AppendOperand(init_value); - instruction->called_computations_.push_back(reduce_computation); - instruction->window_ = MakeUnique(window); - return instruction; + return MakeUnique(shape, operand, init_value, + window, reduce_computation); } /* static */ std::unique_ptr @@ -754,16 +769,8 @@ HloInstruction::CreateSelectAndScatter( const Shape& shape, HloInstruction* operand, HloComputation* select, const Window& window, HloInstruction* source, HloInstruction* init_value, HloComputation* scatter) { - auto instruction = - WrapUnique(new HloInstruction(HloOpcode::kSelectAndScatter, shape)); - instruction->AppendOperand(operand); - instruction->AppendOperand(source); - instruction->AppendOperand(init_value); - // Select comes before scatter in the vector. - instruction->called_computations_.push_back(select); - instruction->called_computations_.push_back(scatter); - instruction->window_ = MakeUnique(window); - return instruction; + return MakeUnique( + shape, operand, select, window, source, init_value, scatter); } /* static */ std::unique_ptr HloInstruction::CreateBroadcast( @@ -929,13 +936,8 @@ bool HloInstruction::HasSideEffect() const { /* static */ std::unique_ptr HloInstruction::CreateCustomCall( const Shape& shape, tensorflow::gtl::ArraySlice operands, tensorflow::StringPiece custom_call_target) { - std::unique_ptr instruction = - WrapUnique(new HloInstruction(HloOpcode::kCustomCall, shape)); - for (auto operand : operands) { - instruction->AppendOperand(operand); - } - instruction->custom_call_target_ = std::string(custom_call_target); - return instruction; + return MakeUnique(shape, operands, + custom_call_target); } /* static */ std::unique_ptr HloInstruction::CreateHostCompute( @@ -1048,6 +1050,10 @@ std::unique_ptr HloInstruction::CloneWithNewOperands( case HloOpcode::kCrossReplicaSum: case HloOpcode::kInfeed: case HloOpcode::kOutfeed: + case HloOpcode::kConvolution: + case HloOpcode::kCustomCall: + case HloOpcode::kReduceWindow: + case HloOpcode::kSelectAndScatter: clone = CloneWithNewOperandsImpl(shape, new_operands, context); break; // Unary ops. @@ -1111,17 +1117,6 @@ std::unique_ptr HloInstruction::CloneWithNewOperands( case HloOpcode::kCall: clone = CreateCall(shape, new_operands, to_apply()); break; - case HloOpcode::kCustomCall: - clone = CreateCustomCall(shape, new_operands, custom_call_target_); - if (window_ != nullptr) { - clone->window_ = MakeUnique(*window_); - } - if (convolution_dimension_numbers_ != nullptr) { - clone->convolution_dimension_numbers_ = - MakeUnique( - *convolution_dimension_numbers_); - } - break; case HloOpcode::kHostCompute: clone = CreateHostCompute(shape, new_operands, channel_name_, cost_estimate_ns_); @@ -1134,11 +1129,6 @@ std::unique_ptr HloInstruction::CloneWithNewOperands( CHECK_EQ(new_operands.size(), 1); clone = CreateBitcastConvert(shape, new_operands[0]); break; - case HloOpcode::kConvolution: - CHECK_EQ(new_operands.size(), 2); - clone = CreateConvolve(shape, new_operands[0], new_operands[1], *window_, - *convolution_dimension_numbers_); - break; case HloOpcode::kDot: CHECK_EQ(new_operands.size(), 2); clone = CreateDot(shape, new_operands[0], new_operands[1], @@ -1149,17 +1139,6 @@ std::unique_ptr HloInstruction::CloneWithNewOperands( clone = CreatePad(shape, new_operands[0], new_operands[1], *padding_config_); break; - case HloOpcode::kReduceWindow: - CHECK_EQ(new_operands.size(), 2); - clone = CreateReduceWindow(shape, new_operands[0], new_operands[1], - *window_, to_apply()); - break; - case HloOpcode::kSelectAndScatter: - CHECK_EQ(new_operands.size(), 3); - clone = - CreateSelectAndScatter(shape, new_operands[0], select(), *window_, - new_operands[1], new_operands[2], scatter()); - break; case HloOpcode::kReshape: CHECK_EQ(new_operands.size(), 1); clone = CreateReshape(shape, new_operands[0]); @@ -1466,12 +1445,6 @@ bool HloInstruction::IdenticalSlowPath( case HloOpcode::kGenerateToken: return false; - // Convolution has a window and dimensions. - case HloOpcode::kConvolution: - return protobuf_util::ProtobufEquals(window(), other.window()) && - protobuf_util::ProtobufEquals( - convolution_dimension_numbers(), - other.convolution_dimension_numbers()); // Check dot dimension numbers. case HloOpcode::kDot: return protobuf_util::ProtobufEquals(dot_dimension_numbers(), @@ -1482,37 +1455,11 @@ bool HloInstruction::IdenticalSlowPath( other.gather_dimension_numbers()) && gather_window_bounds() == other.gather_window_bounds(); - case HloOpcode::kReduceWindow: - return eq_computations(to_apply(), other.to_apply()) && - protobuf_util::ProtobufEquals(window(), other.window()); - - // SelectAndScatter is determined by both select and scatter - // computation as well as the window configuration. - case HloOpcode::kSelectAndScatter: - return eq_computations(select(), other.select()) && - eq_computations(scatter(), other.scatter()) && - protobuf_util::ProtobufEquals(window(), other.window()); - // Remaining instructions with special values. case HloOpcode::kPad: return protobuf_util::ProtobufEquals(padding_config(), other.padding_config()); case HloOpcode::kCall: - case HloOpcode::kCustomCall: - if ((window_ == nullptr) != (other.window_ == nullptr) || - (window_ != nullptr && - !protobuf_util::ProtobufEquals(window(), other.window()))) { - return false; - } - if ((convolution_dimension_numbers_ == nullptr) != - (other.convolution_dimension_numbers_ == nullptr) || - (convolution_dimension_numbers_ != nullptr && - !protobuf_util::ProtobufEquals( - convolution_dimension_numbers(), - other.convolution_dimension_numbers()))) { - return false; - } - return custom_call_target_ == other.custom_call_target_; case HloOpcode::kConditional: return eq_computations(true_computation(), other.true_computation()) && eq_computations(false_computation(), other.false_computation()); @@ -1549,6 +1496,10 @@ bool HloInstruction::IdenticalSlowPath( case HloOpcode::kInfeed: case HloOpcode::kOutfeed: case HloOpcode::kCrossReplicaSum: + case HloOpcode::kConvolution: + case HloOpcode::kCustomCall: + case HloOpcode::kReduceWindow: + case HloOpcode::kSelectAndScatter: LOG(FATAL) << "Base class impl called for opcode with subclass: " << opcode(); } @@ -1669,11 +1620,6 @@ void HloInstruction::set_to_apply(HloComputation* computation) { } } -const string& HloInstruction::custom_call_target() const { - CHECK_EQ(opcode_, HloOpcode::kCustomCall); - return custom_call_target_; -} - HloComputation* HloInstruction::while_condition() const { CHECK_EQ(HloOpcode::kWhile, opcode_); return called_computations_[kConditionComputationIndex]; @@ -1700,32 +1646,6 @@ void HloInstruction::set_while_body(HloComputation* computation) { called_computations_[kBodyComputationIndex] = computation; } -HloComputation* HloInstruction::select() const { - CHECK_EQ(HloOpcode::kSelectAndScatter, opcode_); - return called_computations_[kSelectComputationIndex]; -} - -HloComputation* HloInstruction::scatter() const { - CHECK_EQ(HloOpcode::kSelectAndScatter, opcode_); - return called_computations_[kScatterComputationIndex]; -} - -void HloInstruction::set_select(HloComputation* computation) { - // Don't allow changing the computation for fused instructions so we don't - // have to recompute called_instructions for the entire fusion instruction. - CHECK(!IsFused()); - CHECK_EQ(HloOpcode::kSelectAndScatter, opcode_); - called_computations_[kSelectComputationIndex] = computation; -} - -void HloInstruction::set_scatter(HloComputation* computation) { - // Don't allow changing the computation for fused instructions so we don't - // have to recompute called_instructions for the entire fusion instruction. - CHECK(!IsFused()); - CHECK_EQ(HloOpcode::kSelectAndScatter, opcode_); - called_computations_[kScatterComputationIndex] = computation; -} - HloComputation* HloInstruction::true_computation() const { CHECK_EQ(HloOpcode::kConditional, opcode_); return called_computations_[kTrueComputationIndex]; @@ -1926,9 +1846,6 @@ string HloInstruction::OperandsToStringWithCanonicalNameMap( std::vector HloInstruction::ExtraAttributesToString( const HloPrintOptions& options) const { std::vector extra = ExtraAttributesToStringImpl(options); - if (window_ != nullptr && window_->dimensions_size() != 0) { - extra.push_back(StrCat("window={", window_util::ToString(*window_), "}")); - } if (padding_config_ != nullptr) { extra.push_back( StrCat("padding=", xla::PaddingConfigToString(*padding_config_))); @@ -1939,11 +1856,6 @@ std::vector HloInstruction::ExtraAttributesToString( StrCat("dynamic_slice_sizes={", Join(dynamic_slice_sizes(), ","), "}")); } - if (convolution_dimension_numbers_ != nullptr) { - extra.push_back(StrCat( - "dim_labels=", - ConvolutionDimensionNumbersToString(*convolution_dimension_numbers_))); - } if (dot_dimension_numbers_ != nullptr) { extra.push_back(DotDimensionNumbersToString()); } @@ -2042,14 +1954,6 @@ std::vector HloInstruction::ExtraAttributesToString( ", exit=", user_side_metadata_->ToString(), "}")); } - // By contract, we print the custom call target even if - // options.print_subcomputation_mode() == kOff, because the call target is not - // an HloComputation. - if (opcode() == HloOpcode::kCustomCall) { - extra.push_back( - StrCat("custom_call_target=\"", CEscape(custom_call_target_), "\"")); - } - return extra; } @@ -2086,13 +1990,6 @@ HloInstructionProto HloInstruction::ToProto() const { } } - if (window_ != nullptr) { - *proto.mutable_window() = *window_; - } - if (convolution_dimension_numbers_ != nullptr) { - *proto.mutable_convolution_dimension_numbers() = - *convolution_dimension_numbers_; - } if (dot_dimension_numbers_ != nullptr) { *proto.mutable_dot_dimension_numbers() = *dot_dimension_numbers_; } @@ -2111,7 +2008,6 @@ HloInstructionProto HloInstruction::ToProto() const { if (padding_config_ != nullptr) { *proto.mutable_padding_config() = *padding_config_; } - proto.set_custom_call_target(custom_call_target_); if (has_sharding()) { *proto.mutable_sharding() = sharding().ToProto(); @@ -2129,35 +2025,6 @@ string HloInstruction::ToCategory() const { return "data formatting"; } - if (opcode() == HloOpcode::kConvolution) { - string category = "convolution"; - if (window_util::HasBaseDilation(window())) { - category += " base-dilated"; - } - if (window_util::HasWindowDilation(window())) { - category += " window-dilated"; - } - return category; - } - - // Give transpose-dot and backwards-conv fusions the categories "dot" and - // "convolution" so they match the categories of proper kDot and kConvolution - // ops. These fusion categories are really just a way of expressing a - // particular kind of dot or conv, so they should have the same category as a - // vanilla dot/conv. - if (opcode() == HloOpcode::kFusion) { - switch (fusion_kind()) { - case FusionKind::kLoop: - return "loop fusion"; - case FusionKind::kInput: - return "input fusion"; - case FusionKind::kOutput: - return "output fusion"; - case FusionKind::kCustom: - return "custom fusion"; - } - } - if (IsElementwise()) { return "non-fusion elementwise"; } @@ -3176,4 +3043,45 @@ tensorflow::gtl::optional HloInstruction::all_reduce_id() const { return Cast(this)->all_reduce_id(); } +const ConvolutionDimensionNumbers& +HloInstruction::convolution_dimension_numbers() const { + if (auto convolution = DynCast(this)) { + return convolution->convolution_dimension_numbers(); + } + if (auto custom_call = DynCast(this)) { + return custom_call->convolution_dimension_numbers(); + } + LOG(FATAL) << "Unimplemented method."; +} + +void HloInstruction::set_convolution_dimension_numbers( + const ConvolutionDimensionNumbers& dnums) { + if (auto convolution = DynCast(this)) { + convolution->set_convolution_dimension_numbers(dnums); + } else if (auto custom_call = DynCast(this)) { + custom_call->set_convolution_dimension_numbers(dnums); + } else { + LOG(FATAL) << "Unimplemented method."; + } +} + +HloComputation* HloInstruction::select() const { + return Cast(this)->select(); +} + +HloComputation* HloInstruction::scatter() const { + return Cast(this)->scatter(); +} + +void HloInstruction::set_select(HloComputation* computation) { + return Cast(this)->set_select(computation); +} + +void HloInstruction::set_scatter(HloComputation* computation) { + return Cast(this)->set_scatter(computation); +} + +const string& HloInstruction::custom_call_target() const { + return Cast(this)->custom_call_target(); +} } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h index 8a0ffc21cd..3f9cf513bd 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction.h +++ b/tensorflow/compiler/xla/service/hlo_instruction.h @@ -896,10 +896,6 @@ class HloInstruction { HloComputation* to_apply() const; void set_to_apply(HloComputation* to_apply); - // Returns the custom_call_target for CustomCall. - // Precondition: opcode() == HloOpcode::kCustomCall - const string& custom_call_target() const; - // Gets/sets the while_condition or while_body HloComputation for While. The // setters should only be called by HloModule or HloComputation methods. // @@ -909,15 +905,6 @@ class HloInstruction { void set_while_condition(HloComputation* while_condition); void set_while_body(HloComputation* while_body); - // Gets/sets the select or scatter HloComputation for SelectAndScatter. The - // setters should only be called by HloModule or HloComputation methods. - // - // Precondition: opcode() == HloOpcode::kSelectAndScatter. - HloComputation* select() const; - HloComputation* scatter() const; - void set_select(HloComputation* select); - void set_scatter(HloComputation* scatter); - // Gets/sets the true and false HloComputation for Conditional. The setters // should only be called by HloModule or HloComputation methods. // @@ -959,7 +946,7 @@ class HloInstruction { // Returns a category for the HLO. This could be something like "convolution" // or "elementwise". - string ToCategory() const; + virtual string ToCategory() const; // Returns a logging instruction, if the output of this instruction is logged. // @@ -1065,18 +1052,6 @@ class HloInstruction { return dynamic_slice_sizes_; } - // Returns data on the window in a windowed operation such as - // convolution. - const Window& window() const { - CHECK(window_ != nullptr); - return *window_; - } - - // Sets the window data in a windowed operation such as convolution. - void set_window(const Window& window) { - window_ = MakeUnique(window); - } - // Returns the padding configuration for a pad node. // // Precondition: opcode() == HloOpcode::kPad @@ -1085,23 +1060,6 @@ class HloInstruction { return *padding_config_; } - // Returns data on the dimension numbers used for a convolution operation, - // which may be a kConvolution instruction or a kCustomCall that implements a - // convolution. - const ConvolutionDimensionNumbers& convolution_dimension_numbers() const { - CHECK(convolution_dimension_numbers_ != nullptr); - return *convolution_dimension_numbers_; - } - - // Sets the convolution dimension numbers on this instruction. In general you - // shouldn't need to call this; instead, specify the convolution dimension - // numbers when you create the instruction. - void set_convolution_dimension_numbers( - const ConvolutionDimensionNumbers& dnums) { - convolution_dimension_numbers_ = - MakeUnique(dnums); - } - // Returns data on the dimension numbers used for a dot operation. const DotDimensionNumbers& dot_dimension_numbers() const { CHECK(dot_dimension_numbers_ != nullptr); @@ -1441,6 +1399,43 @@ class HloInstruction { // Delegates to HloAllReduceInstruction::all_reduce_id. tensorflow::gtl::optional all_reduce_id() const; + + // Returns data on the window in a windowed operation such as + // convolution. + virtual const Window& window() const { + LOG(FATAL) << "Unimplemented method."; + } + + // Sets the window data in a windowed operation such as convolution. + virtual void set_window(const Window& window) { + LOG(FATAL) << "Unimplemented method."; + } + + // Returns data on the dimension numbers used for a convolution operation, + // which may be a kConvolution instruction or a kCustomCall that implements a + // convolution. + const ConvolutionDimensionNumbers& convolution_dimension_numbers() const; + + // Sets the convolution dimension numbers on this instruction. In general you + // shouldn't need to call this; instead, specify the convolution dimension + // numbers when you create the instruction. + void set_convolution_dimension_numbers( + const ConvolutionDimensionNumbers& dnums); + + // Delegates to HloSelectAndScatterInstruction::select. + HloComputation* select() const; + + // Delegates to HloSelectAndScatterInstruction::scatter. + HloComputation* scatter() const; + + // Delegates to HloSelectAndScatterInstruction::set_select. + void set_select(HloComputation* computation); + + // Delegates to HloSelectAndScatterInstruction::set_scatter. + void set_scatter(HloComputation* computation); + + // Delegates to HloCustomCallInstruction::custom_call_target. + const string& custom_call_target() const; // Old methods kept for smooth subclassing transition END. protected: @@ -1466,6 +1461,25 @@ class HloInstruction { void DetachFrom(HloInstruction* usee) { usee->RemoveUser(this); } + void set_called_computation(int index, HloComputation* computation) { + called_computations_[index] = computation; + } + // Indices of computations in called_computations_ for instructions which call + // multiple computations. + enum { + // kWhile computations. + kBodyComputationIndex = 0, + kConditionComputationIndex = 1, + + // kSelectAndScatter computations. + kSelectComputationIndex = 0, + kScatterComputationIndex = 1, + + // kConditional computations. + kTrueComputationIndex = 0, + kFalseComputationIndex = 1, + }; + private: // Implementation for non-common logic of CloneWithNewOperands. virtual std::unique_ptr CloneWithNewOperandsImpl( @@ -1558,12 +1572,6 @@ class HloInstruction { // Result shape of this instruction. Shape shape_; - // Describes the window in a windowed operation such as convolution. - std::unique_ptr window_; - - // Describes the dimension numbers used for a convolution. - std::unique_ptr convolution_dimension_numbers_; - // Describes the dimension numbers used for a dot. std::unique_ptr dot_dimension_numbers_; @@ -1588,9 +1596,6 @@ class HloInstruction { std::unique_ptr operand_side_metadata_; std::unique_ptr user_side_metadata_; - // Name of a global symbol to call, only present for kCustomCall. - string custom_call_target_; - // Name to use for host send/recv channels, only present for kHostCompute. string channel_name_; @@ -1600,22 +1605,6 @@ class HloInstruction { // Computations called by this instruction. std::vector called_computations_; - // Indices of computations in called_computations_ for instructions which call - // multiple computations. - enum { - // kWhile computations. - kBodyComputationIndex = 0, - kConditionComputationIndex = 1, - - // kSelectAndScatter computations. - kSelectComputationIndex = 0, - kScatterComputationIndex = 1, - - // kConditional computations. - kTrueComputationIndex = 0, - kFalseComputationIndex = 1, - }; - // A trace instruction that consumes this instruction. // // Invariant: if trace_instruction_ != nullptr, trace_instruction has this as diff --git a/tensorflow/compiler/xla/service/hlo_instructions.cc b/tensorflow/compiler/xla/service/hlo_instructions.cc index 1ebc4c936a..5098a4beeb 100644 --- a/tensorflow/compiler/xla/service/hlo_instructions.cc +++ b/tensorflow/compiler/xla/service/hlo_instructions.cc @@ -20,6 +20,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_casting_utils.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_module.h" +#include "tensorflow/compiler/xla/window_util.h" namespace xla { namespace { @@ -806,6 +807,19 @@ HloFusionInstruction::HloFusionInstruction( fusion_computation->SetFusionInstruction(this); } +string HloFusionInstruction::ToCategory() const { + switch (fusion_kind()) { + case FusionKind::kLoop: + return "loop fusion"; + case FusionKind::kInput: + return "input fusion"; + case FusionKind::kOutput: + return "output fusion"; + case FusionKind::kCustom: + return "custom fusion"; + } +} + HloInstructionProto HloFusionInstruction::ToProto() const { HloInstructionProto proto = HloInstruction::ToProto(); proto.set_fusion_kind(xla::ToString(fusion_kind())); @@ -1433,4 +1447,247 @@ std::unique_ptr HloOutfeedInstruction::CloneWithNewOperandsImpl( outfeed_config()); } +HloConvolutionInstruction::HloConvolutionInstruction( + const Shape& shape, HloInstruction* lhs, HloInstruction* rhs, + const Window& window, const ConvolutionDimensionNumbers& dimension_numbers) + : HloInstruction(HloOpcode::kConvolution, shape), + window_(window), + convolution_dimension_numbers_(dimension_numbers) { + if (window_util::HasBaseDilation(window)) { + SetAndSanitizeName(StrCat(name(), "-base-dilated")); + } + if (window_util::HasWindowDilation(window)) { + SetAndSanitizeName(StrCat(name(), "-window-dilated")); + } + AppendOperand(lhs); + AppendOperand(rhs); +} + +string HloConvolutionInstruction::ToCategory() const { + string category = "convolution"; + if (window_util::HasBaseDilation(window())) { + category += " base-dilated"; + } + if (window_util::HasWindowDilation(window())) { + category += " window-dilated"; + } + return category; +} + +HloInstructionProto HloConvolutionInstruction::ToProto() const { + HloInstructionProto proto = HloInstruction::ToProto(); + *proto.mutable_window() = window_; + *proto.mutable_convolution_dimension_numbers() = + convolution_dimension_numbers_; + return proto; +} + +std::vector HloConvolutionInstruction::ExtraAttributesToStringImpl( + const HloPrintOptions& options) const { + std::vector extra; + if (window_.dimensions_size() != 0) { + extra.push_back(StrCat("window={", window_util::ToString(window()), "}")); + } + extra.push_back(StrCat("dim_labels=", ConvolutionDimensionNumbersToString( + convolution_dimension_numbers_))); + return extra; +} + +bool HloConvolutionInstruction::IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const { + const auto& casted_other = + static_cast(other); + return protobuf_util::ProtobufEquals(window(), casted_other.window()) && + protobuf_util::ProtobufEquals( + convolution_dimension_numbers(), + casted_other.convolution_dimension_numbers()); +} + +std::unique_ptr +HloConvolutionInstruction::CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const { + CHECK_EQ(new_operands.size(), 2); + return MakeUnique(shape, new_operands[0], + new_operands[1], window(), + convolution_dimension_numbers_); +} + +HloReduceWindowInstruction::HloReduceWindowInstruction( + const Shape& shape, HloInstruction* operand, HloInstruction* init_value, + const Window& window, HloComputation* reduce_computation) + : HloInstruction(HloOpcode::kReduceWindow, shape), window_(window) { + AppendOperand(operand); + AppendOperand(init_value); + AppendComputation(reduce_computation); +} + +HloInstructionProto HloReduceWindowInstruction::ToProto() const { + HloInstructionProto proto = HloInstruction::ToProto(); + *proto.mutable_window() = window_; + return proto; +} + +std::vector HloReduceWindowInstruction::ExtraAttributesToStringImpl( + const HloPrintOptions& options) const { + std::vector extra; + if (window_.dimensions_size() != 0) { + extra.push_back(StrCat("window={", window_util::ToString(window()), "}")); + } + return extra; +} + +bool HloReduceWindowInstruction::IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const { + const auto& casted_other = + static_cast(other); + return eq_computations(to_apply(), casted_other.to_apply()) && + protobuf_util::ProtobufEquals(window(), casted_other.window()); +} + +std::unique_ptr +HloReduceWindowInstruction::CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const { + CHECK_EQ(new_operands.size(), 2); + return MakeUnique( + shape, new_operands[0], new_operands[1], window(), to_apply()); +} + +HloSelectAndScatterInstruction::HloSelectAndScatterInstruction( + const Shape& shape, HloInstruction* operand, HloComputation* select, + const Window& window, HloInstruction* source, HloInstruction* init_value, + HloComputation* scatter) + : HloInstruction(HloOpcode::kSelectAndScatter, shape), window_(window) { + AppendOperand(operand); + AppendOperand(source); + AppendOperand(init_value); + // Select comes before scatter in the vector. + AppendComputation(select); + AppendComputation(scatter); +} + +HloInstructionProto HloSelectAndScatterInstruction::ToProto() const { + HloInstructionProto proto = HloInstruction::ToProto(); + *proto.mutable_window() = window_; + return proto; +} + +std::vector HloSelectAndScatterInstruction::ExtraAttributesToStringImpl( + const HloPrintOptions& options) const { + std::vector extra; + if (window_.dimensions_size() != 0) { + extra.push_back(StrCat("window={", window_util::ToString(window()), "}")); + } + return extra; +} + +bool HloSelectAndScatterInstruction::IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const { + const auto& casted_other = + static_cast(other); + return eq_computations(select(), casted_other.select()) && + eq_computations(scatter(), casted_other.scatter()) && + protobuf_util::ProtobufEquals(window(), casted_other.window()); +} + +std::unique_ptr +HloSelectAndScatterInstruction::CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const { + CHECK_EQ(new_operands.size(), 3); + return MakeUnique( + shape, new_operands[0], select(), window(), new_operands[1], + new_operands[2], scatter()); +} + +HloCustomCallInstruction::HloCustomCallInstruction( + const Shape& shape, tensorflow::gtl::ArraySlice operands, + tensorflow::StringPiece custom_call_target) + : HloInstruction(HloOpcode::kCustomCall, shape), + custom_call_target_(custom_call_target.begin(), + custom_call_target.end()) { + for (auto operand : operands) { + AppendOperand(operand); + } +} + +HloInstructionProto HloCustomCallInstruction::ToProto() const { + HloInstructionProto proto = HloInstruction::ToProto(); + if (window_ != nullptr) { + *proto.mutable_window() = *window_; + } + if (convolution_dimension_numbers_ != nullptr) { + *proto.mutable_convolution_dimension_numbers() = + *convolution_dimension_numbers_; + } + proto.set_custom_call_target(custom_call_target_); + return proto; +} + +std::vector HloCustomCallInstruction::ExtraAttributesToStringImpl( + const HloPrintOptions& options) const { + std::vector extra; + if (window_ != nullptr && window_->dimensions_size() != 0) { + extra.push_back(StrCat("window={", window_util::ToString(*window_), "}")); + } + if (convolution_dimension_numbers_ != nullptr) { + extra.push_back(StrCat( + "dim_labels=", + ConvolutionDimensionNumbersToString(*convolution_dimension_numbers_))); + } + // By contract, we print the custom call target even if + // options.print_subcomputation_mode() == kOff, because the call target is not + // an HloComputation. + extra.push_back( + StrCat("custom_call_target=\"", CEscape(custom_call_target_), "\"")); + return extra; +} + +bool HloCustomCallInstruction::IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const { + const auto& casted_other = + static_cast(other); + if ((window_ == nullptr) != (casted_other.window_ == nullptr) || + (window_ != nullptr && + !protobuf_util::ProtobufEquals(*window_, *casted_other.window_))) { + return false; + } + if ((convolution_dimension_numbers_ == nullptr) != + (casted_other.convolution_dimension_numbers_ == nullptr) || + (convolution_dimension_numbers_ != nullptr && + !protobuf_util::ProtobufEquals( + convolution_dimension_numbers(), + casted_other.convolution_dimension_numbers()))) { + return false; + } + return custom_call_target_ == casted_other.custom_call_target_; +} + +std::unique_ptr +HloCustomCallInstruction::CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const { + auto cloned = MakeUnique(shape, new_operands, + custom_call_target()); + if (window_ != nullptr) { + cloned->set_window(*window_); + } + if (convolution_dimension_numbers_ != nullptr) { + cloned->set_convolution_dimension_numbers(*convolution_dimension_numbers_); + } + return std::move(cloned); +} } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_instructions.h b/tensorflow/compiler/xla/service/hlo_instructions.h index 04df2d860e..d310c88995 100644 --- a/tensorflow/compiler/xla/service/hlo_instructions.h +++ b/tensorflow/compiler/xla/service/hlo_instructions.h @@ -557,6 +557,7 @@ class HloFusionInstruction : public HloInstruction { tensorflow::gtl::ArraySlice operands, HloComputation* fusion_computation); + string ToCategory() const override; // Returns a serialized representation of this instruction. HloInstructionProto ToProto() const override; @@ -842,6 +843,167 @@ class HloOutfeedInstruction : public HloInstruction { // Outfeed configuration information, only present for kOutfeed. string outfeed_config_; }; + +class HloConvolutionInstruction : public HloInstruction { + public: + explicit HloConvolutionInstruction( + const Shape& shape, HloInstruction* lhs, HloInstruction* rhs, + const Window& window, + const ConvolutionDimensionNumbers& dimension_numbers); + const Window& window() const override { return window_; } + void set_window(const Window& window) override { window_ = window; } + const ConvolutionDimensionNumbers& convolution_dimension_numbers() const { + return convolution_dimension_numbers_; + } + void set_convolution_dimension_numbers( + const ConvolutionDimensionNumbers& dnums) { + convolution_dimension_numbers_ = dnums; + } + string ToCategory() const override; + // Returns a serialized representation of this instruction. + HloInstructionProto ToProto() const override; + + private: + std::vector ExtraAttributesToStringImpl( + const HloPrintOptions& options) const override; + bool IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const override; + // Implementation for non-common logic of CloneWithNewOperands. + std::unique_ptr CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const override; + Window window_; + // Describes the dimension numbers used for a convolution. + ConvolutionDimensionNumbers convolution_dimension_numbers_; +}; + +class HloReduceWindowInstruction : public HloInstruction { + public: + explicit HloReduceWindowInstruction(const Shape& shape, + HloInstruction* operand, + HloInstruction* init_value, + const Window& window, + HloComputation* reduce_computation); + const Window& window() const override { return window_; } + void set_window(const Window& window) override { window_ = window; } + // Returns a serialized representation of this instruction. + HloInstructionProto ToProto() const override; + + private: + std::vector ExtraAttributesToStringImpl( + const HloPrintOptions& options) const override; + bool IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const override; + // Implementation for non-common logic of CloneWithNewOperands. + std::unique_ptr CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const override; + Window window_; +}; + +class HloSelectAndScatterInstruction : public HloInstruction { + public: + explicit HloSelectAndScatterInstruction( + const Shape& shape, HloInstruction* operand, HloComputation* select, + const Window& window, HloInstruction* source, HloInstruction* init_value, + HloComputation* scatter); + const Window& window() const override { return window_; } + void set_window(const Window& window) override { window_ = window; } + // Gets/sets the select or scatter HloComputation for SelectAndScatter. The + // setters should only be called by HloModule or HloComputation methods. + HloComputation* select() const { + return called_computations()[kSelectComputationIndex]; + } + + HloComputation* scatter() const { + return called_computations()[kScatterComputationIndex]; + } + + void set_select(HloComputation* computation) { + // Don't allow changing the computation for fused instructions so we don't + // have to recompute called_instructions for the entire fusion instruction. + CHECK(!IsFused()); + set_called_computation(kSelectComputationIndex, computation); + } + + void set_scatter(HloComputation* computation) { + // Don't allow changing the computation for fused instructions so we don't + // have to recompute called_instructions for the entire fusion instruction. + CHECK(!IsFused()); + set_called_computation(kScatterComputationIndex, computation); + } + // Returns a serialized representation of this instruction. + HloInstructionProto ToProto() const override; + + private: + std::vector ExtraAttributesToStringImpl( + const HloPrintOptions& options) const override; + bool IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const override; + // Implementation for non-common logic of CloneWithNewOperands. + std::unique_ptr CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const override; + Window window_; +}; + +class HloCustomCallInstruction : public HloInstruction { + public: + explicit HloCustomCallInstruction( + const Shape& shape, tensorflow::gtl::ArraySlice operands, + tensorflow::StringPiece custom_call_target); + const Window& window() const override { + CHECK(window_ != nullptr); + return *window_; + } + + void set_window(const Window& window) override { + window_ = MakeUnique(window); + } + + const ConvolutionDimensionNumbers& convolution_dimension_numbers() const { + CHECK(convolution_dimension_numbers_ != nullptr); + return *convolution_dimension_numbers_; + } + + void set_convolution_dimension_numbers( + const ConvolutionDimensionNumbers& dnums) { + convolution_dimension_numbers_ = + MakeUnique(dnums); + } + const string& custom_call_target() const { return custom_call_target_; } + // Returns a serialized representation of this instruction. + HloInstructionProto ToProto() const override; + + private: + std::vector ExtraAttributesToStringImpl( + const HloPrintOptions& options) const override; + bool IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const override; + // Implementation for non-common logic of CloneWithNewOperands. + std::unique_ptr CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const override; + // Name of a global symbol to call, only present for kCustomCall. + string custom_call_target_; + // Describes the window in a windowed operation such as convolution. + std::unique_ptr window_; + // Describes the dimension numbers used for a convolution. + std::unique_ptr convolution_dimension_numbers_; +}; + } // namespace xla #endif // TENSORFLOW_COMPILER_XLA_SERVICE_HLO_INSTRUCTIONS_H_ -- GitLab From 91d98f5403145ad5899ecdaa8a6564da9bd111c9 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 18 Jun 2018 18:04:44 -0700 Subject: [PATCH 637/816] Migration to python 3 for estimator.predict. PiperOrigin-RevId: 201093768 --- tensorflow/contrib/tpu/python/tpu/tpu_estimator.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py index 2131969e8f..85ea4d3df3 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py @@ -3105,7 +3105,7 @@ class _SignalsHelper(object): def __init__(self, signals): self._signal_keys = [] - for key in sorted(signals.iterkeys()): + for key in sorted(iter(signals.keys())): self._signal_keys.append(key) @property @@ -3117,7 +3117,7 @@ class _SignalsHelper(object): @staticmethod def as_tensor_list(signals): - return [signals[key] for key in sorted(signals.iterkeys())] + return [signals[key] for key in sorted(iter(signals.keys()))] def _verify_cross_hosts_transfer_size(tensor_dict, message): -- GitLab From 27acbe0b4c7f13d52762419d2d819b11c1d9f54b Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 18 Jun 2018 18:22:04 -0700 Subject: [PATCH 638/816] Reduce Grappler overhead by skipping optimizers when the graph is tiny. PiperOrigin-RevId: 201095811 --- .../signal/python/kernel_tests/test_util.py | 1 + ...direct_session_with_tracking_alloc_test.cc | 3 +++ .../grappler/optimizers/meta_optimizer.cc | 27 +++++++++++++++---- .../optimizers/meta_optimizer_test.cc | 3 +++ .../core/protobuf/rewriter_config.proto | 6 +++++ .../lib/debug_graph_reconstruction_test.py | 3 ++- .../python/grappler/layout_optimizer_test.py | 7 +++-- .../python/grappler/memory_optimizer_test.py | 6 ++++- .../python/grappler/tf_optimizer_test.py | 3 +++ .../python/profiler/model_analyzer_test.py | 4 ++- 10 files changed, 53 insertions(+), 10 deletions(-) diff --git a/tensorflow/contrib/signal/python/kernel_tests/test_util.py b/tensorflow/contrib/signal/python/kernel_tests/test_util.py index 9a3603b6a9..7d6289532a 100644 --- a/tensorflow/contrib/signal/python/kernel_tests/test_util.py +++ b/tensorflow/contrib/signal/python/kernel_tests/test_util.py @@ -39,6 +39,7 @@ def grappler_optimize(graph, fetches=None, rewriter_config=None): """ if rewriter_config is None: rewriter_config = rewriter_config_pb2.RewriterConfig() + rewriter_config.min_graph_nodes = -1 if fetches is not None: for fetch in fetches: graph.add_to_collection('train_op', fetch) diff --git a/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc b/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc index c21a1ea9f2..6e08e33f8e 100644 --- a/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc +++ b/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc @@ -74,6 +74,9 @@ TEST(DirectSessionWithTrackingAllocTest, CostModelTest) { options.config.mutable_graph_options() ->mutable_rewrite_options() ->set_constant_folding(RewriterConfig::OFF); + options.config.mutable_graph_options() + ->mutable_rewrite_options() + ->set_min_graph_nodes(-1); std::unique_ptr session(NewSession(options)); TF_ASSERT_OK(session->Create(def)); std::vector> inputs; diff --git a/tensorflow/core/grappler/optimizers/meta_optimizer.cc b/tensorflow/core/grappler/optimizers/meta_optimizer.cc index 143d9dc1c6..b1f31ad0d0 100644 --- a/tensorflow/core/grappler/optimizers/meta_optimizer.cc +++ b/tensorflow/core/grappler/optimizers/meta_optimizer.cc @@ -42,6 +42,7 @@ namespace grappler { namespace { constexpr int kDefaultNumberOfIterations = 2; +constexpr int kDefaultMinGraphNodes = 4; int64 NumEdges(const GraphDef& graph) { int64 num_edges = 0; @@ -194,6 +195,15 @@ Status MetaOptimizer::InitializeOptimizersByName( Status MetaOptimizer::OptimizeGraph(Cluster* cluster, const GrapplerItem& item, GraphDef* optimized_graph) { + int min_graph_nodes = cfg_.min_graph_nodes() == 0 ? kDefaultMinGraphNodes + : cfg_.min_graph_nodes(); + if (item.graph.node_size() < min_graph_nodes) { + VLOG(3) << "Skipping optimization, graph has less than " << min_graph_nodes + << " nodes."; + *optimized_graph = item.graph; + return Status::OK(); + } + std::vector> optimizers; if (cfg_.optimizers().empty() && cfg_.custom_optimizers().empty()) { TF_RETURN_IF_ERROR(InitializeOptimizers(&optimizers)); @@ -202,10 +212,11 @@ Status MetaOptimizer::OptimizeGraph(Cluster* cluster, const GrapplerItem& item, } VLOG(2) << "Optimize GrapplerItem: item.id=" << item.id - << " num_optimizers=" << optimizers.size(); + << " num_optimizers=" << optimizers.size() + << ", num nodes = " << item.graph.node_size(); if (optimizers.empty()) { - VLOG(3) << "Skip graph optimization, no optimizers registered"; + VLOG(3) << "Skipping graph optimization, no optimizers registered"; *optimized_graph = item.graph; return Status::OK(); } @@ -221,8 +232,15 @@ Status MetaOptimizer::OptimizeGraph(Cluster* cluster, const GrapplerItem& item, GraphOptimizer* sa_optimizer = nullptr; for (int iteration = 0; iteration < NumIterations(cfg_); ++iteration) { - VLOG(4) << "Starting optimization iteration " << iteration + 1; + // Don't bother optimizing further if the graph is already tiny. + if (optimized_graph->node_size() < min_graph_nodes) { + VLOG(3) << "Stopping after iteration " << iteration + << ", graph is tiny (#nodes = " << optimized_graph->node_size() + << " < " << min_graph_nodes << ")"; + break; + } + VLOG(4) << "Starting optimization iteration " << iteration; for (const auto& optimizer : optimizers) { // Some optimizers can run only once. if (iteration > 0 && IsRunOnceOptimizer(optimizer->name())) continue; @@ -235,7 +253,6 @@ Status MetaOptimizer::OptimizeGraph(Cluster* cluster, const GrapplerItem& item, if (fusion_optimizer == nullptr) fusion_optimizer = optimizer.get(); continue; } - Status status = RunOptimizer(optimizer.get(), cluster, &optimized_item, optimized_graph, &optimization_result); if (status.ok()) is_optimized = true; @@ -297,7 +314,7 @@ Status MetaOptimizer::RunOptimizer( PrintSizesBeforeAfter(optimized_item->graph, *optimized_graph), ", time = ", duration_ms, "ms."); } - VLOG(4) << optimizer->name() << ": " << result; + VLOG(1) << optimizer->name() << ": " << result; OptimizerResult optimizer_result{optimizer->name(), result}; optimization_result->results.push_back(optimizer_result); diff --git a/tensorflow/core/grappler/optimizers/meta_optimizer_test.cc b/tensorflow/core/grappler/optimizers/meta_optimizer_test.cc index 8247cce339..9a03c7dfef 100644 --- a/tensorflow/core/grappler/optimizers/meta_optimizer_test.cc +++ b/tensorflow/core/grappler/optimizers/meta_optimizer_test.cc @@ -74,6 +74,7 @@ TEST_F(MetaOptimizerTest, RunsCustomOptimizer) { TestOptimizer::SetOptimized(false); RewriterConfig rewriter_config; rewriter_config.add_optimizers("TestOptimizer"); + rewriter_config.set_min_graph_nodes(-1); MetaOptimizer optimizer(nullptr, rewriter_config); GraphDef output; @@ -89,6 +90,7 @@ TEST_F(MetaOptimizerTest, RunOptimizersTwice) { RewriterConfig rewriter_config; rewriter_config.set_meta_optimizer_iterations(RewriterConfig::TWO); + rewriter_config.set_min_graph_nodes(-1); MetaOptimizer optimizer(nullptr, rewriter_config); GraphDef output; @@ -104,6 +106,7 @@ TEST_F(MetaOptimizerTest, OptimizeFunctionLibrary) { rewriter_config.set_meta_optimizer_iterations(RewriterConfig::TWO); rewriter_config.set_function_optimization(RewriterConfig::ON); rewriter_config.add_optimizers("function"); + rewriter_config.set_min_graph_nodes(-1); MetaOptimizer optimizer(nullptr, rewriter_config); diff --git a/tensorflow/core/protobuf/rewriter_config.proto b/tensorflow/core/protobuf/rewriter_config.proto index bbb25d6f3f..07f984ceea 100644 --- a/tensorflow/core/protobuf/rewriter_config.proto +++ b/tensorflow/core/protobuf/rewriter_config.proto @@ -80,6 +80,12 @@ message RewriterConfig { // is once). NumIterationsType meta_optimizer_iterations = 12; + // The minimum number of nodes in a graph to optimizer. For smaller graphs, + // optimization is skipped. + // 0 means the system picks an appropriate number. + // < 0 means do not skip optimization. + int32 min_graph_nodes = 17; + enum MemOptType { // The default setting (SCHEDULING and SWAPPING HEURISTICS only) DEFAULT_MEM_OPT = 0; diff --git a/tensorflow/python/debug/lib/debug_graph_reconstruction_test.py b/tensorflow/python/debug/lib/debug_graph_reconstruction_test.py index bd00f73861..676097fde9 100644 --- a/tensorflow/python/debug/lib/debug_graph_reconstruction_test.py +++ b/tensorflow/python/debug/lib/debug_graph_reconstruction_test.py @@ -44,7 +44,8 @@ class ReconstructNonDebugGraphTest(test_util.TensorFlowTestCase): def _no_rewrite_session_config(self): rewriter_config = rewriter_config_pb2.RewriterConfig( - dependency_optimization=rewriter_config_pb2.RewriterConfig.OFF) + dependency_optimization=rewriter_config_pb2.RewriterConfig.OFF, + min_graph_nodes=-1) graph_options = config_pb2.GraphOptions(rewrite_options=rewriter_config) return config_pb2.ConfigProto(graph_options=graph_options) diff --git a/tensorflow/python/grappler/layout_optimizer_test.py b/tensorflow/python/grappler/layout_optimizer_test.py index 2d6925d1a8..2c9f391d01 100644 --- a/tensorflow/python/grappler/layout_optimizer_test.py +++ b/tensorflow/python/grappler/layout_optimizer_test.py @@ -158,6 +158,7 @@ def _get_config(layout_optimizer=True): layout_optimizer=rewriter_config_pb2.RewriterConfig.OFF, # do not remove duplicated nodes arithmetic_optimization=rewriter_config_pb2.RewriterConfig.OFF) + rewrite_options.min_graph_nodes = -1 graph_options = config_pb2.GraphOptions( rewrite_options=rewrite_options, build_cost_model=1) config = config_pb2.ConfigProto(graph_options=graph_options) @@ -1443,7 +1444,8 @@ class LayoutOptimizerTest(test.TestCase): def testGradient(self): meta_graph = _simple_metagraph() rewrite_options = rewriter_config_pb2.RewriterConfig( - layout_optimizer=rewriter_config_pb2.RewriterConfig.ON) + layout_optimizer=rewriter_config_pb2.RewriterConfig.ON, + min_graph_nodes=-1) optimized_graph = tf_optimizer.OptimizeGraph( rewrite_options, meta_graph, cluster=_get_cluster()) @@ -1457,7 +1459,8 @@ class LayoutOptimizerTest(test.TestCase): def testDepthwise(self): meta_graph = _simple_metagraph(depthwise=True) rewrite_options = rewriter_config_pb2.RewriterConfig( - layout_optimizer=rewriter_config_pb2.RewriterConfig.ON) + layout_optimizer=rewriter_config_pb2.RewriterConfig.ON, + min_graph_nodes=-1) optimized_graph = tf_optimizer.OptimizeGraph( rewrite_options, meta_graph, cluster=_get_cluster()) diff --git a/tensorflow/python/grappler/memory_optimizer_test.py b/tensorflow/python/grappler/memory_optimizer_test.py index 7ed4b128e4..b658edff2d 100644 --- a/tensorflow/python/grappler/memory_optimizer_test.py +++ b/tensorflow/python/grappler/memory_optimizer_test.py @@ -76,7 +76,8 @@ class MemoryOptimizerSwapTest(test.TestCase): disable_model_pruning=True, meta_optimizer_iterations=rewriter_config_pb2.RewriterConfig.ONE, constant_folding=rewriter_config_pb2.RewriterConfig.OFF, - memory_optimization=rewriter_config_pb2.RewriterConfig.MANUAL) + memory_optimization=rewriter_config_pb2.RewriterConfig.MANUAL, + min_graph_nodes=-1) graph = tf_optimizer.OptimizeGraph(rewriter_config, mg) self.assertEqual(len(graph.node), graph_size + 2) @@ -133,6 +134,7 @@ class MemoryOptimizerRecomputeTest(test.TestCase): dependency_optimization=rewriter_config_pb2.RewriterConfig.OFF, layout_optimizer=rewriter_config_pb2.RewriterConfig.OFF, arithmetic_optimization=rewriter_config_pb2.RewriterConfig.OFF, + min_graph_nodes=-1, memory_optimization=rewriter_config_pb2.RewriterConfig. RECOMPUTATION_HEURISTICS), original_metagraph) self.assertGreater( @@ -158,6 +160,7 @@ class MemoryOptimizerRecomputeTest(test.TestCase): dependency_optimization=rewriter_config_pb2.RewriterConfig.OFF, layout_optimizer=rewriter_config_pb2.RewriterConfig.OFF, arithmetic_optimization=rewriter_config_pb2.RewriterConfig.OFF, + min_graph_nodes=-1, memory_optimization=rewriter_config_pb2.RewriterConfig. RECOMPUTATION_HEURISTICS, # Checks that name scope "gradients/" also match sub-scope. @@ -297,6 +300,7 @@ class MemoryOptimizerRecomputeTest(test.TestCase): if 'Recomputed/' in node.name])) rewritten_graph_def = tf_optimizer.OptimizeGraph( rewriter_config_pb2.RewriterConfig( + min_graph_nodes=-1, memory_optimization=rewriter_config_pb2.RewriterConfig.MANUAL), metagraph) self.assertEqual( diff --git a/tensorflow/python/grappler/tf_optimizer_test.py b/tensorflow/python/grappler/tf_optimizer_test.py index 1c0f072dd3..5a9afe7257 100644 --- a/tensorflow/python/grappler/tf_optimizer_test.py +++ b/tensorflow/python/grappler/tf_optimizer_test.py @@ -47,6 +47,7 @@ class PyWrapOptimizeGraphTest(test.TestCase): rewriter_config = rewriter_config_pb2.RewriterConfig() rewriter_config.optimizers.append('constfold') + rewriter_config.min_graph_nodes = -1 graph = tf_optimizer.OptimizeGraph(rewriter_config, mg) @@ -68,6 +69,7 @@ class PyWrapOptimizeGraphTest(test.TestCase): # Optimize the graph. mg = meta_graph.create_meta_graph_def(graph=g) rewriter_config = rewriter_config_pb2.RewriterConfig() + rewriter_config.min_graph_nodes = -1 optimized_graph = tf_optimizer.OptimizeGraph(rewriter_config, mg) # Check that the nodes referenced in various collections have been preserved @@ -109,6 +111,7 @@ class PyWrapOptimizeGraphTest(test.TestCase): # Optimize the graph. mg = meta_graph.create_meta_graph_def(graph=g) rewriter_config = rewriter_config_pb2.RewriterConfig() + rewriter_config.min_graph_nodes = -1 optimized_graph = tf_optimizer.OptimizeGraph(rewriter_config, mg) mg.graph_def.CopyFrom(optimized_graph) diff --git a/tensorflow/python/profiler/model_analyzer_test.py b/tensorflow/python/profiler/model_analyzer_test.py index 9e49188c1e..f9891f3b1e 100644 --- a/tensorflow/python/profiler/model_analyzer_test.py +++ b/tensorflow/python/profiler/model_analyzer_test.py @@ -707,8 +707,10 @@ class PrintModelAnalysisTest(test.TestCase): a = array_ops.constant(np.ones((100, 100))) b = array_ops.constant(np.ones((100, 100))) c = a * b + config = config_pb2.ConfigProto() + config.graph_options.rewrite_options.min_graph_nodes = -1 - with session.Session() as sess: + with session.Session(config=config) as sess: run_options = config_pb2.RunOptions( trace_level=config_pb2.RunOptions.FULL_TRACE) run_metadata = config_pb2.RunMetadata() -- GitLab From 3423d28a53fa0abdec6f9f83b15571f3b07a10cf Mon Sep 17 00:00:00 2001 From: Gunhan Gulsoy Date: Mon, 18 Jun 2018 18:23:37 -0700 Subject: [PATCH 639/816] Add missing numpy header dependency to pywrap_tfe_lib PiperOrigin-RevId: 201095991 --- tensorflow/python/eager/BUILD | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/python/eager/BUILD b/tensorflow/python/eager/BUILD index e8a7904a88..6ede8e4f4d 100644 --- a/tensorflow/python/eager/BUILD +++ b/tensorflow/python/eager/BUILD @@ -32,6 +32,7 @@ cc_library( "//tensorflow/python:numpy_lib", "//tensorflow/python:py_seq_tensor", "//tensorflow/python:safe_ptr", + "//third_party/py/numpy:headers", "//third_party/python_runtime:headers", ], ) -- GitLab From 36bf4a43248077fd5635b13e2def636be299e435 Mon Sep 17 00:00:00 2001 From: Peter Hawkins Date: Mon, 18 Jun 2018 19:07:24 -0700 Subject: [PATCH 640/816] [TF:XLA] Implement TopKV2 for bfloat16 types by packing into a float32 PiperOrigin-RevId: 201100290 --- tensorflow/compiler/tests/sort_ops_test.py | 57 ++++++++- tensorflow/compiler/tf2xla/kernels/BUILD | 1 + tensorflow/compiler/tf2xla/kernels/topk_op.cc | 111 ++++++++++++++++++ 3 files changed, 165 insertions(+), 4 deletions(-) create mode 100644 tensorflow/compiler/tf2xla/kernels/topk_op.cc diff --git a/tensorflow/compiler/tests/sort_ops_test.py b/tensorflow/compiler/tests/sort_ops_test.py index 5ff40edaa5..370085c1e2 100644 --- a/tensorflow/compiler/tests/sort_ops_test.py +++ b/tensorflow/compiler/tests/sort_ops_test.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Tests for XlaSort.""" +"""Tests for sorting operators.""" from __future__ import absolute_import from __future__ import division @@ -23,7 +23,9 @@ import numpy as np from tensorflow.compiler.tests import xla_test from tensorflow.compiler.tf2xla.python import xla from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops +from tensorflow.python.ops import nn_ops from tensorflow.python.platform import test @@ -38,19 +40,66 @@ class XlaSortOpTest(xla_test.XLATestCase): ] feeds = {placeholders[i]: args[i] for i in range(0, len(args))} output = op(*placeholders) - result = session.run(output, feeds) - self.assertAllClose(result, expected, rtol=1e-3) + if isinstance(output, ops.Tensor): + output = [output] + + results = session.run(output, feeds) + for result, v in zip(results, expected): + self.assertAllClose(v, result, rtol=1e-3) def testSort(self): # TODO(b/26783907): The Sort HLO is not implemented on CPU or GPU. if self.device in ["XLA_CPU", "XLA_GPU"]: return + supported_types = set([dtypes.bfloat16.as_numpy_dtype, np.float32]) for dtype in supported_types.intersection(self.numeric_types): x = np.arange(101, dtype=dtype) np.random.shuffle(x) self._assertOpOutputMatchesExpected( - xla.sort, [x], expected=np.arange(101, dtype=dtype)) + xla.sort, [x], expected=[np.arange(101, dtype=dtype)]) + + def testTopK(self): + # TODO(b/26783907): The Sort HLO is not implemented on CPU or GPU. + if self.device in ["XLA_CPU", "XLA_GPU"]: + return + + # Only bfloat16 is implemented. + bfloat16 = dtypes.bfloat16.as_numpy_dtype + if bfloat16 in self.numeric_types: + for x in [np.arange(20)]: + np.random.shuffle(x) + for k in [0, 1, 2, 10, 20]: + indices = x.argsort()[::-1][:k] + + def topk(v, k=k): + return nn_ops.top_k(v, k=k, sorted=True) + + self._assertOpOutputMatchesExpected( + topk, [x.astype(bfloat16)], + expected=[x[indices].astype(bfloat16), indices]) + + def testTopKZeros(self): + """Tests that positive and negative zeros sort correctly.""" + # Requires Sort HLO, which is not implemented on CPU or GPU. + if self.device in ["XLA_CPU", "XLA_GPU"]: + return + + # Only bfloat16 is implemented. + bfloat16 = dtypes.bfloat16.as_numpy_dtype + if bfloat16 not in self.numeric_types: + return + + with self.test_session() as sess: + p = array_ops.placeholder(dtypes.bfloat16) + with self.test_scope(): + topk = nn_ops.top_k(p, k=4) + results = sess.run( + topk, + {p: np.array([0., -0., 0., 3., -0., -4., 0., -0.], dtype=bfloat16)}) + self.assertAllEqual( + np.array([3., 0., 0., 0.], dtype=bfloat16), results[0]) + self.assertEqual(set([0, 2, 3, 6]), set(results[1])) if __name__ == "__main__": diff --git a/tensorflow/compiler/tf2xla/kernels/BUILD b/tensorflow/compiler/tf2xla/kernels/BUILD index e86b333e4b..c431a4b9cf 100644 --- a/tensorflow/compiler/tf2xla/kernels/BUILD +++ b/tensorflow/compiler/tf2xla/kernels/BUILD @@ -88,6 +88,7 @@ tf_kernel_library( "strided_slice_op.cc", "tensor_array_ops.cc", "tile_ops.cc", + "topk_op.cc", "training_ops.cc", "transpose_op.cc", "unary_ops.cc", diff --git a/tensorflow/compiler/tf2xla/kernels/topk_op.cc b/tensorflow/compiler/tf2xla/kernels/topk_op.cc new file mode 100644 index 0000000000..703e13e089 --- /dev/null +++ b/tensorflow/compiler/tf2xla/kernels/topk_op.cc @@ -0,0 +1,111 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/tf2xla/xla_helpers.h" +#include "tensorflow/compiler/tf2xla/xla_op_kernel.h" +#include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/core/framework/kernel_def_builder.h" +#include "tensorflow/core/framework/types.h" +#include "tensorflow/core/kernels/no_op.h" + +namespace tensorflow { +namespace { + +class TopKOp : public XlaOpKernel { + public: + explicit TopKOp(OpKernelConstruction* context) : XlaOpKernel(context) { + OP_REQUIRES_OK(context, context->GetAttr("sorted", &sorted_)); + } + + void Compile(XlaOpKernelContext* context) override { + int64 k; + OP_REQUIRES_OK(context, context->ConstantInputAsIntScalar(1, &k)); + OP_REQUIRES(context, k >= 0, + errors::InvalidArgument("Need k >= 0, got ", k)); + const TensorShape input_shape = context->InputShape(0); + OP_REQUIRES(context, input_shape.dims() >= 1, + errors::InvalidArgument("input must be >= 1-D, got shape ", + input_shape.DebugString())); + OP_REQUIRES( + context, input_shape.dim_size(input_shape.dims() - 1) >= k, + errors::InvalidArgument("input must have at least k columns. Had ", + input_shape.dim_size(input_shape.dims() - 1), + ", needed ", k)); + + OP_REQUIRES( + context, input_shape.dims() == 1, + errors::Unimplemented("TopK is implemented for 1-D inputs, got shape ", + input_shape.DebugString())); + + const int64 n = input_shape.dim_size(0); + OP_REQUIRES(context, n < (1 << 16), + errors::Unimplemented( + "TopK is implemented for sizes up to 2**16, got shape ", + input_shape.DebugString())); + + xla::XlaBuilder* const b = context->builder(); + if (input_shape.dim_size(0) < k) { + k = input_shape.dim_size(0); + } + const xla::XlaOp input = context->Input(0); + xla::XlaOp iota; + OP_REQUIRES_OK(context, XlaHelpers::Iota(b, DT_INT32, n, &iota)); + + // TODO(b/73891930): add a key-value sort to HLO, rather than using + // bit-packing tricks here. + // TODO(b/73891930): this implementation will convert Infs to NaNs. A + // key-value sort would avoid this; for now, it is no worse than, say, the + // CPU backend in fast-math mode. + + // Pack elements as: + // * upper 16 bits are the value + // * lower 16 bits are the index. + xla::XlaOp packed = b->BitcastConvertType( + b->Or(b->BitcastConvertType(b->ConvertElementType(input, xla::F32), + xla::S32), + iota), + xla::F32); + + // TODO(phawkins): use a more efficient algorithm that does not require a + // full sort. + xla::XlaOp sorted = b->Slice(b->Rev(b->Sort(packed), {0}), + /*start_indices=*/{0}, + /*limit_indices=*/{k}, + /*strides=*/{1}); + + // Unpack the value/index + xla::XlaOp x = b->BitcastConvertType(sorted, xla::S32); + xla::XlaOp indices = b->And(x, b->ConstantR0(0x0000FFFF)); + xla::XlaOp values = b->ConvertElementType( + b->BitcastConvertType(b->And(x, b->ConstantR0(0xFFFF0000)), + xla::F32), + xla::BF16); + + context->SetOutput(0, values); + context->SetOutput(1, indices); + } + + private: + bool sorted_; +}; + +REGISTER_XLA_OP( + Name("TopKV2").CompileTimeConstInput("k").TypeConstraint("T", DT_BFLOAT16), + TopKOp); + +} // namespace +} // namespace tensorflow -- GitLab From 98a829817c027b9681a728160c746bcc63ad86b9 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 18 Jun 2018 19:14:12 -0700 Subject: [PATCH 641/816] HloInstruction::CreateFromProto should not crash on CHECK, instead needs to return error status. PiperOrigin-RevId: 201100918 --- .../compiler/xla/service/hlo_instruction.cc | 100 +++++++++++++----- 1 file changed, 73 insertions(+), 27 deletions(-) diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc index 58a33f5229..1dd2ce40da 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction.cc +++ b/tensorflow/compiler/xla/service/hlo_instruction.cc @@ -70,25 +70,33 @@ StatusOr> HloInstruction::CreateFromProto( switch (opcode) { // Ops migrated to subclasses. case HloOpcode::kBatchNormTraining: - CHECK_EQ(proto.operand_ids_size(), 3); + TF_RET_CHECK(proto.operand_ids_size() == 3) + << "BatchNormTraining instruction should have 3 operands but sees " + << proto.operand_ids_size(); instruction = CreateBatchNormTraining( proto.shape(), operands(0), operands(1), operands(2), proto.epsilon(), proto.feature_index()); break; case HloOpcode::kBatchNormInference: - CHECK_EQ(proto.operand_ids_size(), 5); + TF_RET_CHECK(proto.operand_ids_size() == 5) + << "BatchNormInference instruction should have 5 operands but sees " + << proto.operand_ids_size(); instruction = CreateBatchNormInference( proto.shape(), operands(0), operands(1), operands(2), operands(3), operands(4), proto.epsilon(), proto.feature_index()); break; case HloOpcode::kBatchNormGrad: - CHECK_EQ(proto.operand_ids_size(), 5); + TF_RET_CHECK(proto.operand_ids_size() == 5) + << "BatchNormGrad instruction should have 5 operands but sees " + << proto.operand_ids_size(); instruction = CreateBatchNormGrad(proto.shape(), operands(0), operands(1), operands(2), operands(3), operands(4), proto.epsilon(), proto.feature_index()); break; case HloOpcode::kFft: { - CHECK_EQ(proto.operand_ids_size(), 1); + TF_RET_CHECK(proto.operand_ids_size() == 1) + << "Fft instruction should have 1 operand but sees " + << proto.operand_ids_size(); std::vector fft_length(proto.fft_length().begin(), proto.fft_length().end()); instruction = CreateFft(proto.shape(), operands(0), proto.fft_type(), @@ -96,30 +104,42 @@ StatusOr> HloInstruction::CreateFromProto( break; } case HloOpcode::kSend: - CHECK_EQ(proto.operand_ids_size(), 1); + TF_RET_CHECK(proto.operand_ids_size() == 1) + << "Send instruction should have 1 operand but sees " + << proto.operand_ids_size(); instruction = CreateSend(operands(0), proto.channel_id()); break; case HloOpcode::kSendDone: - CHECK_EQ(proto.operand_ids_size(), 1); + TF_RET_CHECK(proto.operand_ids_size() == 1) + << "SendDone instruction should have 1 operand but sees " + << proto.operand_ids_size(); instruction = CreateSendDone(operands(0)); break; case HloOpcode::kRecv: - CHECK_EQ(proto.operand_ids_size(), 0); + TF_RET_CHECK(proto.operand_ids_size() == 0) + << "Recv instruction should have 0 operand but sees " + << proto.operand_ids_size(); instruction = CreateRecv(proto.shape().tuple_shapes(0), proto.channel_id()); break; case HloOpcode::kRecvDone: - CHECK_EQ(proto.operand_ids_size(), 1); + TF_RET_CHECK(proto.operand_ids_size() == 1) + << "RecvDone instruction should have 1 operand but sees " + << proto.operand_ids_size(); instruction = CreateRecvDone(operands(0)); break; case HloOpcode::kReverse: - CHECK_EQ(proto.operand_ids_size(), 1); + TF_RET_CHECK(proto.operand_ids_size() == 1) + << "Reverse instruction should have 1 operand but sees " + << proto.operand_ids_size(); instruction = CreateReverse(proto.shape(), operands(0), std::vector(proto.dimensions().begin(), proto.dimensions().end())); break; case HloOpcode::kConcatenate: { - CHECK_EQ(proto.dimensions_size(), 1); + TF_RET_CHECK(proto.dimensions_size() == 1) + << "Concatenate instruction should have 1 dimension but sees " + << proto.dimensions_size(); std::vector concat_operands(proto.operand_ids_size()); std::transform(proto.operand_ids().begin(), proto.operand_ids().end(), concat_operands.begin(), @@ -131,29 +151,39 @@ StatusOr> HloInstruction::CreateFromProto( break; } case HloOpcode::kReduce: - CHECK_EQ(proto.operand_ids_size(), 2); - CHECK_EQ(proto.called_computation_ids_size(), 1); + TF_RET_CHECK(proto.operand_ids_size() == 2) + << "Reduce instruction should have 2 operands but sees " + << proto.operand_ids_size(); + TF_RET_CHECK(proto.called_computation_ids_size() == 1) + << "Reduce instruction should have 1 called computation but sees " + << proto.called_computation_ids_size(); instruction = CreateReduce(proto.shape(), operands(0), operands(1), std::vector(proto.dimensions().begin(), proto.dimensions().end()), computations(0)); break; case HloOpcode::kTranspose: - CHECK_EQ(proto.operand_ids_size(), 1); + TF_RET_CHECK(proto.operand_ids_size() == 1) + << "Transpose instruction should have 1 operand but sees " + << proto.operand_ids_size(); instruction = CreateTranspose(proto.shape(), operands(0), std::vector(proto.dimensions().begin(), proto.dimensions().end())); break; case HloOpcode::kBroadcast: - CHECK_EQ(proto.operand_ids_size(), 1); + TF_RET_CHECK(proto.operand_ids_size() == 1) + << "Broadcast instruction should have 1 operand but sees " + << proto.operand_ids_size(); instruction = CreateBroadcast(proto.shape(), operands(0), std::vector(proto.dimensions().begin(), proto.dimensions().end())); break; case HloOpcode::kMap: { - CHECK_EQ(proto.called_computation_ids_size(), 1); + TF_RET_CHECK(proto.called_computation_ids_size() == 1) + << "Map instruction should have 1 called computation but sees " + << proto.called_computation_ids_size(); std::vector map_operands(proto.operand_ids_size()); std::transform(proto.operand_ids().begin(), proto.operand_ids().end(), map_operands.begin(), @@ -164,7 +194,9 @@ StatusOr> HloInstruction::CreateFromProto( break; } case HloOpcode::kSlice: { - CHECK_EQ(proto.operand_ids_size(), 1); + TF_RET_CHECK(proto.operand_ids_size() == 1) + << "Slice instruction should have 1 operand but sees " + << proto.operand_ids_size(); std::vector slice_starts, slice_limits, slice_strides; for (const HloInstructionProto::SliceDimensions& slice_dimensions : proto.slice_dimensions()) { @@ -191,7 +223,7 @@ StatusOr> HloInstruction::CreateFromProto( TF_RET_CHECK(proto.operand_ids_size() == 1) << "Trace instruction should have 1 operand but sees " << proto.operand_ids_size(); - CHECK(proto.has_literal()); + TF_RET_CHECK(proto.has_literal()); TF_ASSIGN_OR_RETURN(auto literal, Literal::CreateFromProto(proto.literal())); instruction = CreateTrace(literal->GetR1U8AsString(), operands(0)); @@ -207,7 +239,7 @@ StatusOr> HloInstruction::CreateFromProto( // Find the fused computation and set its fusion instruction. TF_RET_CHECK(proto.called_computation_ids_size() == 1) - << "Expect 1 called computation for fusion instruction, but sees " + << "Expect 1 called computation for fusion instruction but sees " << proto.called_computation_ids_size(); const int64 fusion_id = proto.called_computation_ids(0); auto* fused_computation = FindPtrOrNull(computation_map, fusion_id); @@ -237,7 +269,9 @@ StatusOr> HloInstruction::CreateFromProto( proto.name()); break; case HloOpcode::kGetTupleElement: - CHECK_EQ(proto.operand_ids_size(), 1); + TF_RET_CHECK(proto.operand_ids_size() == 1) + << "GetTupleElement instruction should have 1 operand but sees " + << proto.operand_ids_size(); instruction = CreateGetTupleElement(proto.shape(), operands(0), proto.tuple_index()); break; @@ -254,7 +288,9 @@ StatusOr> HloInstruction::CreateFromProto( proto.outfeed_config()); break; case HloOpcode::kCrossReplicaSum: { - CHECK_EQ(proto.called_computation_ids_size(), 1); + TF_RET_CHECK(proto.called_computation_ids_size() == 1) + << "CrossReplicaSum should have 1 called computation but sees " + << proto.called_computation_ids_size(); std::vector all_operands(proto.operand_ids_size()); c_transform(proto.operand_ids(), all_operands.begin(), [&instruction_map](int64 operand_id) { @@ -274,22 +310,32 @@ StatusOr> HloInstruction::CreateFromProto( break; } case HloOpcode::kConvolution: - CHECK_EQ(proto.operand_ids_size(), 2); - CHECK(proto.has_window()); - CHECK(proto.has_convolution_dimension_numbers()); + TF_RET_CHECK(proto.operand_ids_size() == 2) + << "Convolution instruction should have 2 operands but sees " + << proto.operand_ids_size(); + TF_RET_CHECK(proto.has_window()); + TF_RET_CHECK(proto.has_convolution_dimension_numbers()); instruction = CreateConvolve(proto.shape(), operands(0), operands(1), proto.window(), proto.convolution_dimension_numbers()); break; case HloOpcode::kReduceWindow: - CHECK_EQ(proto.operand_ids_size(), 2); - CHECK_EQ(proto.called_computation_ids_size(), 1); + TF_RET_CHECK(proto.operand_ids_size() == 2) + << "ReduceWindow instruction should have 2 operands but sees " + << proto.operand_ids_size(); + TF_RET_CHECK(proto.called_computation_ids_size() == 1) + << "ReduceWindow should have 1 called computation but sees " + << proto.called_computation_ids_size(); instruction = CreateReduceWindow(proto.shape(), operands(0), operands(1), proto.window(), computations(0)); break; case HloOpcode::kSelectAndScatter: - CHECK_EQ(proto.operand_ids_size(), 3); - CHECK_EQ(proto.called_computation_ids_size(), 2); + TF_RET_CHECK(proto.operand_ids_size() == 3) + << "SelectAndScatter instruction should have 3 operands but sees " + << proto.operand_ids_size(); + TF_RET_CHECK(proto.called_computation_ids_size() == 2) + << "SelectAndScatter should have 2 called computations but sees " + << proto.called_computation_ids_size(); instruction = CreateSelectAndScatter( proto.shape(), operands(0), computations(0), proto.window(), operands(1), operands(2), computations(1)); -- GitLab From 183ea7af9f1c3535cfadf0bea51719d4f2b74662 Mon Sep 17 00:00:00 2001 From: Xuechen Li Date: Mon, 18 Jun 2018 19:25:34 -0700 Subject: [PATCH 642/816] Automated g4 rollback of changelist 201089859 PiperOrigin-RevId: 201101839 --- .../python/training/learning_rate_decay.py | 303 ++++-------- .../training/learning_rate_decay_test.py | 457 +++++++++--------- 2 files changed, 333 insertions(+), 427 deletions(-) diff --git a/tensorflow/python/training/learning_rate_decay.py b/tensorflow/python/training/learning_rate_decay.py index a585aee5bb..bae3e51494 100644 --- a/tensorflow/python/training/learning_rate_decay.py +++ b/tensorflow/python/training/learning_rate_decay.py @@ -19,7 +19,6 @@ from __future__ import print_function import math -from tensorflow.python.eager import context from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops @@ -88,12 +87,6 @@ def exponential_decay(learning_rate, Raises: ValueError: if `global_step` is not supplied. - - @compatibility(eager) - When eager execution is enabled, this function returns a function which in - turn returns the decayed learning rate Tensor. This can be useful for changing - the learning rate value across different invocations of optimizer functions. - @end_compatibility """ if global_step is None: raise ValueError("global_step is required for exponential_decay.") @@ -102,22 +95,14 @@ def exponential_decay(learning_rate, [learning_rate, global_step, decay_steps, decay_rate]) as name: learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") dtype = learning_rate.dtype + global_step = math_ops.cast(global_step, dtype) decay_steps = math_ops.cast(decay_steps, dtype) decay_rate = math_ops.cast(decay_rate, dtype) - - def decayed_lr(): - """Helper to recompute learning rate; most helpful in eager-mode.""" - global_step_recomp = math_ops.cast(global_step, dtype) - p = global_step_recomp / decay_steps - if staircase: - p = math_ops.floor(p) - return math_ops.multiply( - learning_rate, math_ops.pow(decay_rate, p), name=name) - - if not context.executing_eagerly(): - decayed_lr = decayed_lr() - - return decayed_lr + p = global_step / decay_steps + if staircase: + p = math_ops.floor(p) + return math_ops.multiply( + learning_rate, math_ops.pow(decay_rate, p), name=name) @tf_export("train.piecewise_constant") @@ -278,12 +263,6 @@ def polynomial_decay(learning_rate, Raises: ValueError: if `global_step` is not supplied. - - @compatibility(eager) - When eager execution is enabled, this function returns a function which in - turn returns the decayed learning rate Tensor. This can be useful for changing - the learning rate value across different invocations of optimizer functions. - @end_compatibility """ if global_step is None: raise ValueError("global_step is required for polynomial_decay.") @@ -293,35 +272,27 @@ def polynomial_decay(learning_rate, ]) as name: learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") dtype = learning_rate.dtype + global_step = math_ops.cast(global_step, dtype) + decay_steps = math_ops.cast(decay_steps, dtype) end_learning_rate = math_ops.cast(end_learning_rate, dtype) power = math_ops.cast(power, dtype) - - def decayed_lr(): - """Helper to recompute learning rate; most helpful in eager-mode.""" - global_step_recomp = math_ops.cast(global_step, dtype) - decay_steps_recomp = math_ops.cast(decay_steps, dtype) - if cycle: - # Find the first multiple of decay_steps that is bigger than - # global_step. If global_step is zero set the multiplier to 1 - multiplier = control_flow_ops.cond( - math_ops.equal(global_step_recomp, 0), lambda: 1.0, - lambda: math_ops.ceil(global_step_recomp / decay_steps)) - decay_steps_recomp = math_ops.multiply(decay_steps_recomp, multiplier) - else: - # Make sure that the global_step used is not bigger than decay_steps. - global_step_recomp = math_ops.minimum(global_step_recomp, decay_steps) - - p = math_ops.div(global_step_recomp, decay_steps_recomp) - return math_ops.add( - math_ops.multiply(learning_rate - end_learning_rate, - math_ops.pow(1 - p, power)), - end_learning_rate, - name=name) - - if not context.executing_eagerly(): - decayed_lr = decayed_lr() - - return decayed_lr + if cycle: + # Find the first multiple of decay_steps that is bigger than global_step. + # If global_step is zero set the multiplier to 1 + multiplier = control_flow_ops.cond( + math_ops.equal(global_step, 0), lambda: 1.0, + lambda: math_ops.ceil(global_step / decay_steps)) + decay_steps = math_ops.multiply(decay_steps, multiplier) + else: + # Make sure that the global_step used is not bigger than decay_steps. + global_step = math_ops.minimum(global_step, decay_steps) + + p = math_ops.div(global_step, decay_steps) + return math_ops.add( + math_ops.multiply(learning_rate - end_learning_rate, + math_ops.pow(1 - p, power)), + end_learning_rate, + name=name) @tf_export("train.natural_exp_decay") @@ -379,12 +350,6 @@ def natural_exp_decay(learning_rate, Raises: ValueError: if `global_step` is not supplied. - - @compatibility(eager) - When eager execution is enabled, this function returns a function which in - turn returns the decayed learning rate Tensor. This can be useful for changing - the learning rate value across different invocations of optimizer functions. - @end_compatibility """ if global_step is None: raise ValueError("global_step is required for natural_exp_decay.") @@ -392,23 +357,14 @@ def natural_exp_decay(learning_rate, [learning_rate, global_step, decay_rate]) as name: learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") dtype = learning_rate.dtype + global_step = math_ops.cast(global_step, dtype) decay_steps = math_ops.cast(decay_steps, dtype) decay_rate = math_ops.cast(decay_rate, dtype) - - def decayed_lr(): - """Helper to recompute learning rate; most helpful in eager-mode.""" - global_step_recomp = math_ops.cast(global_step, dtype) - p = global_step_recomp / decay_steps - if staircase: - p = math_ops.floor(p) - exponent = math_ops.exp( - math_ops.multiply(math_ops.negative(decay_rate), p)) - return math_ops.multiply(learning_rate, exponent, name=name) - - if not context.executing_eagerly(): - decayed_lr = decayed_lr() - - return decayed_lr + p = global_step / decay_steps + if staircase: + p = math_ops.floor(p) + exponent = math_ops.exp(math_ops.multiply(math_ops.negative(decay_rate), p)) + return math_ops.multiply(learning_rate, exponent, name=name) @tf_export("train.inverse_time_decay") @@ -476,12 +432,6 @@ def inverse_time_decay(learning_rate, Raises: ValueError: if `global_step` is not supplied. - - @compatibility(eager) - When eager execution is enabled, this function returns a function which in - turn returns the decayed learning rate Tensor. This can be useful for changing - the learning rate value across different invocations of optimizer functions. - @end_compatibility """ if global_step is None: raise ValueError("global_step is required for inverse_time_decay.") @@ -489,23 +439,15 @@ def inverse_time_decay(learning_rate, [learning_rate, global_step, decay_rate]) as name: learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") dtype = learning_rate.dtype + global_step = math_ops.cast(global_step, dtype) decay_steps = math_ops.cast(decay_steps, dtype) decay_rate = math_ops.cast(decay_rate, dtype) - - def decayed_lr(): - """Helper to recompute learning rate; most helpful in eager-mode.""" - global_step_recomp = math_ops.cast(global_step, dtype) - p = global_step_recomp / decay_steps - if staircase: - p = math_ops.floor(p) - const = math_ops.cast(constant_op.constant(1), dtype) - denom = math_ops.add(const, math_ops.multiply(decay_rate, p)) - return math_ops.div(learning_rate, denom, name=name) - - if not context.executing_eagerly(): - decayed_lr = decayed_lr() - - return decayed_lr + p = global_step / decay_steps + if staircase: + p = math_ops.floor(p) + const = math_ops.cast(constant_op.constant(1), learning_rate.dtype) + denom = math_ops.add(const, math_ops.multiply(decay_rate, p)) + return math_ops.div(learning_rate, denom, name=name) @tf_export("train.cosine_decay") @@ -550,12 +492,6 @@ def cosine_decay(learning_rate, global_step, decay_steps, alpha=0.0, name=None): learning rate. Raises: ValueError: if `global_step` is not supplied. - - @compatibility(eager) - When eager execution is enabled, this function returns a function which in - turn returns the decayed learning rate Tensor. This can be useful for changing - the learning rate value across different invocations of optimizer functions. - @end_compatibility """ if global_step is None: raise ValueError("cosine decay requires global_step") @@ -563,23 +499,15 @@ def cosine_decay(learning_rate, global_step, decay_steps, alpha=0.0, name=None): [learning_rate, global_step]) as name: learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") dtype = learning_rate.dtype + global_step = math_ops.cast(global_step, dtype) decay_steps = math_ops.cast(decay_steps, dtype) + global_step = math_ops.minimum(global_step, decay_steps) + completed_fraction = global_step / decay_steps + cosine_decayed = 0.5 * ( + 1.0 + math_ops.cos(constant_op.constant(math.pi) * completed_fraction)) - def decayed_lr(): - """Helper to recompute learning rate; most helpful in eager-mode.""" - global_step_recomp = math_ops.cast(global_step, dtype) - global_step_recomp = math_ops.minimum(global_step_recomp, decay_steps) - completed_fraction = global_step_recomp / decay_steps - cosine_decayed = 0.5 * (1.0 + math_ops.cos( - constant_op.constant(math.pi) * completed_fraction)) - - decayed = (1 - alpha) * cosine_decayed + alpha - return math_ops.multiply(learning_rate, decayed) - - if not context.executing_eagerly(): - decayed_lr = decayed_lr() - - return decayed_lr + decayed = (1 - alpha) * cosine_decayed + alpha + return math_ops.multiply(learning_rate, decayed) @tf_export("train.cosine_decay_restarts") @@ -633,12 +561,6 @@ def cosine_decay_restarts(learning_rate, learning rate. Raises: ValueError: if `global_step` is not supplied. - - @compatibility(eager) - When eager execution is enabled, this function returns a function which in - turn returns the decayed learning rate Tensor. This can be useful for changing - the learning rate value across different invocations of optimizer functions. - @end_compatibility """ if global_step is None: raise ValueError("cosine decay restarts requires global_step") @@ -646,48 +568,41 @@ def cosine_decay_restarts(learning_rate, learning_rate = ops.convert_to_tensor( learning_rate, name="initial_learning_rate") dtype = learning_rate.dtype + global_step = math_ops.cast(global_step, dtype) first_decay_steps = math_ops.cast(first_decay_steps, dtype) alpha = math_ops.cast(alpha, dtype) t_mul = math_ops.cast(t_mul, dtype) m_mul = math_ops.cast(m_mul, dtype) - def decayed_lr(): - """Helper to recompute learning rate; most helpful in eager-mode.""" - global_step_recomp = math_ops.cast(global_step, dtype) - completed_fraction = global_step_recomp / first_decay_steps + completed_fraction = global_step / first_decay_steps - def compute_step(completed_fraction, geometric=False): - """Helper for `cond` operation.""" - if geometric: - i_restart = math_ops.floor( - math_ops.log(1.0 - completed_fraction * (1.0 - t_mul)) / - math_ops.log(t_mul)) + def compute_step(completed_fraction, geometric=False): + """Compute restart step and completed fraction.""" + if geometric: + i_restart = math_ops.floor( + math_ops.log(1.0 - completed_fraction * (1.0 - t_mul)) / + math_ops.log(t_mul)) - sum_r = (1.0 - t_mul**i_restart) / (1.0 - t_mul) - completed_fraction = (completed_fraction - sum_r) / t_mul**i_restart + sum_r = (1.0 - t_mul**i_restart) / (1.0 - t_mul) + completed_fraction = (completed_fraction - sum_r) / t_mul**i_restart - else: - i_restart = math_ops.floor(completed_fraction) - completed_fraction -= i_restart - - return i_restart, completed_fraction - - i_restart, completed_fraction = control_flow_ops.cond( - math_ops.equal(t_mul, 1.0), - lambda: compute_step(completed_fraction, geometric=False), - lambda: compute_step(completed_fraction, geometric=True)) + else: + i_restart = math_ops.floor(completed_fraction) + completed_fraction -= i_restart - m_fac = m_mul**i_restart - cosine_decayed = 0.5 * m_fac * (1.0 + math_ops.cos( - constant_op.constant(math.pi) * completed_fraction)) - decayed = (1 - alpha) * cosine_decayed + alpha + return i_restart, completed_fraction - return math_ops.multiply(learning_rate, decayed, name=name) + i_restart, completed_fraction = control_flow_ops.cond( + math_ops.equal(t_mul, 1.0), + lambda: compute_step(completed_fraction, geometric=False), + lambda: compute_step(completed_fraction, geometric=True)) - if not context.executing_eagerly(): - decayed_lr = decayed_lr() + m_fac = m_mul**i_restart + cosine_decayed = 0.5 * m_fac * ( + 1.0 + math_ops.cos(constant_op.constant(math.pi) * completed_fraction)) + decayed = (1 - alpha) * cosine_decayed + alpha - return decayed_lr + return math_ops.multiply(learning_rate, decayed, name=name) @tf_export("train.linear_cosine_decay") @@ -750,12 +665,6 @@ def linear_cosine_decay(learning_rate, learning rate. Raises: ValueError: if `global_step` is not supplied. - - @compatibility(eager) - When eager execution is enabled, this function returns a function which in - turn returns the decayed learning rate Tensor. This can be useful for changing - the learning rate value across different invocations of optimizer functions. - @end_compatibility """ if global_step is None: raise ValueError("linear cosine decay requires global_step") @@ -763,28 +672,21 @@ def linear_cosine_decay(learning_rate, [learning_rate, global_step]) as name: learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") dtype = learning_rate.dtype + global_step = math_ops.cast(global_step, dtype) decay_steps = math_ops.cast(decay_steps, dtype) num_periods = math_ops.cast(num_periods, dtype) + global_step = math_ops.minimum(global_step, decay_steps) alpha = math_ops.cast(alpha, dtype) beta = math_ops.cast(beta, dtype) - def decayed_lr(): - """Helper to recompute learning rate; most helpful in eager-mode.""" - global_step_recomp = math_ops.cast(global_step, dtype) - global_step_recomp = math_ops.minimum(global_step_recomp, decay_steps) - linear_decayed = (decay_steps - global_step_recomp) / decay_steps - completed_fraction = global_step_recomp / decay_steps - fraction = 2.0 * num_periods * completed_fraction - cosine_decayed = 0.5 * ( - 1.0 + math_ops.cos(constant_op.constant(math.pi) * fraction)) - - linear_cosine_decayed = (alpha + linear_decayed) * cosine_decayed + beta - return math_ops.multiply(learning_rate, linear_cosine_decayed, name=name) + linear_decayed = (decay_steps - global_step) / decay_steps + completed_fraction = global_step / decay_steps + fraction = 2.0 * num_periods * completed_fraction + cosine_decayed = 0.5 * ( + 1.0 + math_ops.cos(constant_op.constant(math.pi) * fraction)) - if not context.executing_eagerly(): - decayed_lr = decayed_lr() - - return decayed_lr + linear_cosine_decayed = (alpha + linear_decayed) * cosine_decayed + beta + return math_ops.multiply(learning_rate, linear_cosine_decayed, name=name) @tf_export("train.noisy_linear_cosine_decay") @@ -855,12 +757,6 @@ def noisy_linear_cosine_decay(learning_rate, learning rate. Raises: ValueError: if `global_step` is not supplied. - - @compatibility(eager) - When eager execution is enabled, this function returns a function which in - turn returns the decayed learning rate Tensor. This can be useful for changing - the learning rate value across different invocations of optimizer functions. - @end_compatibility """ if global_step is None: raise ValueError("noisy linear cosine decay requires global_step") @@ -868,36 +764,29 @@ def noisy_linear_cosine_decay(learning_rate, [learning_rate, global_step]) as name: learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") dtype = learning_rate.dtype + global_step = math_ops.cast(global_step, dtype) decay_steps = math_ops.cast(decay_steps, dtype) + global_step = math_ops.minimum(global_step, decay_steps) initial_variance = math_ops.cast(initial_variance, dtype) variance_decay = math_ops.cast(variance_decay, dtype) num_periods = math_ops.cast(num_periods, dtype) alpha = math_ops.cast(alpha, dtype) beta = math_ops.cast(beta, dtype) - def decayed_lr(): - """Helper to recompute learning rate; most helpful in eager-mode.""" - global_step_recomp = math_ops.cast(global_step, dtype) - global_step_recomp = math_ops.minimum(global_step_recomp, decay_steps) - linear_decayed = (decay_steps - global_step_recomp) / decay_steps - variance = initial_variance / ( - math_ops.pow(1.0 + global_step_recomp, variance_decay)) - std = math_ops.sqrt(variance) - noisy_linear_decayed = ( - linear_decayed + random_ops.random_normal( - linear_decayed.shape, stddev=std)) - - completed_fraction = global_step_recomp / decay_steps - fraction = 2.0 * num_periods * completed_fraction - cosine_decayed = 0.5 * ( - 1.0 + math_ops.cos(constant_op.constant(math.pi) * fraction)) - noisy_linear_cosine_decayed = ( - (alpha + noisy_linear_decayed) * cosine_decayed + beta) - - return math_ops.multiply( - learning_rate, noisy_linear_cosine_decayed, name=name) - - if not context.executing_eagerly(): - decayed_lr = decayed_lr() - - return decayed_lr + linear_decayed = (decay_steps - global_step) / decay_steps + variance = initial_variance / ( + math_ops.pow(1.0 + global_step, variance_decay)) + std = math_ops.sqrt(variance) + noisy_linear_decayed = ( + linear_decayed + + random_ops.random_normal(linear_decayed.shape, stddev=std)) + + completed_fraction = global_step / decay_steps + fraction = 2.0 * num_periods * completed_fraction + cosine_decayed = 0.5 * ( + 1.0 + math_ops.cos(constant_op.constant(math.pi) * fraction)) + noisy_linear_cosine_decayed = ( + (alpha + noisy_linear_decayed) * cosine_decayed + beta) + + return math_ops.multiply( + learning_rate, noisy_linear_cosine_decayed, name=name) diff --git a/tensorflow/python/training/learning_rate_decay_test.py b/tensorflow/python/training/learning_rate_decay_test.py index d55a28b233..f56f4bb442 100644 --- a/tensorflow/python/training/learning_rate_decay_test.py +++ b/tensorflow/python/training/learning_rate_decay_test.py @@ -21,9 +21,12 @@ from __future__ import print_function import math from tensorflow.python.eager import context +from tensorflow.python.framework import dtypes from tensorflow.python.framework import test_util +from tensorflow.python.ops import gen_state_ops # Import resource_variable_ops for the variables-to-tensor implicit conversion. from tensorflow.python.ops import resource_variable_ops # pylint: disable=unused-import +from tensorflow.python.ops import state_ops from tensorflow.python.ops import variables from tensorflow.python.platform import googletest from tensorflow.python.training import learning_rate_decay @@ -31,35 +34,31 @@ from tensorflow.python.training import learning_rate_decay class LRDecayTest(test_util.TensorFlowTestCase): - @test_util.run_in_graph_and_eager_modes() def testContinuous(self): - self.evaluate(variables.global_variables_initializer()) - step = 5 - decayed_lr = learning_rate_decay.exponential_decay(0.05, step, 10, 0.96) - expected = .05 * 0.96**(5.0 / 10.0) - self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + with self.test_session(): + step = 5 + decayed_lr = learning_rate_decay.exponential_decay(0.05, step, 10, 0.96) + expected = .05 * 0.96 ** (5.0 / 10.0) + self.assertAllClose(decayed_lr.eval(), expected, 1e-6) - @test_util.run_in_graph_and_eager_modes() def testStaircase(self): - if context.executing_eagerly(): - step = resource_variable_ops.ResourceVariable(0) - self.evaluate(variables.global_variables_initializer()) - decayed_lr = learning_rate_decay.exponential_decay( - .1, step, 3, 0.96, staircase=True) - - # No change to learning rate due to staircase - expected = .1 - self.evaluate(step.assign(1)) - self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) - - expected = .1 - self.evaluate(step.assign(2)) - self.assertAllClose(self.evaluate(decayed_lr), .1, 1e-6) - + with self.test_session(): + step = gen_state_ops.variable(shape=[], dtype=dtypes.int32, + name="step", container="", shared_name="") + assign_100 = state_ops.assign(step, 100) + assign_1 = state_ops.assign(step, 1) + assign_2 = state_ops.assign(step, 2) + decayed_lr = learning_rate_decay.exponential_decay(.1, step, 3, 0.96, + staircase=True) + # No change to learning rate + assign_1.op.run() + self.assertAllClose(decayed_lr.eval(), .1, 1e-6) + assign_2.op.run() + self.assertAllClose(decayed_lr.eval(), .1, 1e-6) # Decayed learning rate + assign_100.op.run() expected = .1 * 0.96 ** (100 // 3) - self.evaluate(step.assign(100)) - self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + self.assertAllClose(decayed_lr.eval(), expected, 1e-6) def testVariables(self): with self.test_session(): @@ -141,188 +140,204 @@ class LRDecayTest(test_util.TensorFlowTestCase): class LinearDecayTest(test_util.TensorFlowTestCase): - @test_util.run_in_graph_and_eager_modes() def testHalfWay(self): - step = 5 - lr = 0.05 - end_lr = 0.0 - decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr) - expected = lr * 0.5 - self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + with self.test_session(): + step = 5 + lr = 0.05 + end_lr = 0.0 + decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr) + expected = lr * 0.5 + self.assertAllClose(decayed_lr.eval(), expected, 1e-6) - @test_util.run_in_graph_and_eager_modes() def testEnd(self): - step = 10 - lr = 0.05 - end_lr = 0.001 - decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr) - expected = end_lr - self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + with self.test_session(): + step = 10 + lr = 0.05 + end_lr = 0.001 + decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr) + expected = end_lr + self.assertAllClose(decayed_lr.eval(), expected, 1e-6) - @test_util.run_in_graph_and_eager_modes() def testHalfWayWithEnd(self): - step = 5 - lr = 0.05 - end_lr = 0.001 - decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr) - expected = (lr + end_lr) * 0.5 - self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + with self.test_session(): + step = 5 + lr = 0.05 + end_lr = 0.001 + decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr) + expected = (lr + end_lr) * 0.5 + self.assertAllClose(decayed_lr.eval(), expected, 1e-6) - @test_util.run_in_graph_and_eager_modes() def testBeyondEnd(self): - step = 15 - lr = 0.05 - end_lr = 0.001 - decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr) - expected = end_lr - self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + with self.test_session(): + step = 15 + lr = 0.05 + end_lr = 0.001 + decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr) + expected = end_lr + self.assertAllClose(decayed_lr.eval(), expected, 1e-6) - @test_util.run_in_graph_and_eager_modes() def testBeyondEndWithCycle(self): - step = 15 - lr = 0.05 - end_lr = 0.001 - decayed_lr = learning_rate_decay.polynomial_decay( - lr, step, 10, end_lr, cycle=True) - expected = (lr - end_lr) * 0.25 + end_lr - self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + with self.test_session(): + step = 15 + lr = 0.05 + end_lr = 0.001 + decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr, + cycle=True) + expected = (lr - end_lr) * 0.25 + end_lr + self.assertAllClose(decayed_lr.eval(), expected, 1e-6) class SqrtDecayTest(test_util.TensorFlowTestCase): - @test_util.run_in_graph_and_eager_modes() def testHalfWay(self): - step = 5 - lr = 0.05 - end_lr = 0.0 - power = 0.5 - decayed_lr = learning_rate_decay.polynomial_decay( - lr, step, 10, end_lr, power=power) - expected = lr * 0.5**power - self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + with self.test_session(): + step = 5 + lr = 0.05 + end_lr = 0.0 + power = 0.5 + decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr, + power=power) + expected = lr * 0.5 ** power + self.assertAllClose(decayed_lr.eval(), expected, 1e-6) - @test_util.run_in_graph_and_eager_modes() def testEnd(self): - step = 10 - lr = 0.05 - end_lr = 0.001 - power = 0.5 - decayed_lr = learning_rate_decay.polynomial_decay( - lr, step, 10, end_lr, power=power) - expected = end_lr - self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + with self.test_session(): + step = 10 + lr = 0.05 + end_lr = 0.001 + power = 0.5 + decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr, + power=power) + expected = end_lr + self.assertAllClose(decayed_lr.eval(), expected, 1e-6) - @test_util.run_in_graph_and_eager_modes() def testHalfWayWithEnd(self): - step = 5 - lr = 0.05 - end_lr = 0.001 - power = 0.5 - decayed_lr = learning_rate_decay.polynomial_decay( - lr, step, 10, end_lr, power=power) - expected = (lr - end_lr) * 0.5**power + end_lr - self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + with self.test_session(): + step = 5 + lr = 0.05 + end_lr = 0.001 + power = 0.5 + decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr, + power=power) + expected = (lr - end_lr) * 0.5 ** power + end_lr + self.assertAllClose(decayed_lr.eval(), expected, 1e-6) - @test_util.run_in_graph_and_eager_modes() def testBeyondEnd(self): - step = 15 - lr = 0.05 - end_lr = 0.001 - power = 0.5 - decayed_lr = learning_rate_decay.polynomial_decay( - lr, step, 10, end_lr, power=power) - expected = end_lr - self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + with self.test_session(): + step = 15 + lr = 0.05 + end_lr = 0.001 + power = 0.5 + decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr, + power=power) + expected = end_lr + self.assertAllClose(decayed_lr.eval(), expected, 1e-6) - @test_util.run_in_graph_and_eager_modes() def testBeyondEndWithCycle(self): - step = 15 - lr = 0.05 - end_lr = 0.001 - power = 0.5 - decayed_lr = learning_rate_decay.polynomial_decay( - lr, step, 10, end_lr, power=power, cycle=True) - expected = (lr - end_lr) * 0.25**power + end_lr - self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + with self.test_session(): + step = 15 + lr = 0.05 + end_lr = 0.001 + power = 0.5 + decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr, + power=power, cycle=True) + expected = (lr - end_lr) * 0.25 ** power + end_lr + self.assertAllClose(decayed_lr.eval(), expected, 1e-6) class PolynomialDecayTest(test_util.TensorFlowTestCase): - @test_util.run_in_graph_and_eager_modes() def testBeginWithCycle(self): - lr = 0.001 - decay_steps = 10 - step = 0 - decayed_lr = learning_rate_decay.polynomial_decay( - lr, step, decay_steps, cycle=True) - expected = lr - self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + with self.test_session(): + lr = 0.001 + decay_steps = 10 + step = 0 + decayed_lr = learning_rate_decay.polynomial_decay(lr, step, + decay_steps, cycle=True) + expected = lr + self.assertAllClose(decayed_lr.eval(), expected, 1e-6) class ExponentialDecayTest(test_util.TensorFlowTestCase): - @test_util.run_in_graph_and_eager_modes() def testDecay(self): initial_lr = 0.1 k = 10 decay_rate = 0.96 - step = resource_variable_ops.ResourceVariable(0) - decayed_lr = learning_rate_decay.natural_exp_decay(initial_lr, step, k, - decay_rate) - - self.evaluate(variables.global_variables_initializer()) - for i in range(k + 1): - expected = initial_lr * math.exp(-i / k * decay_rate) - self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) - self.evaluate(step.assign_add(1)) + step = gen_state_ops.variable( + shape=[], dtype=dtypes.int32, name="step", container="", shared_name="") + assign_step = state_ops.assign(step, 0) + increment_step = state_ops.assign_add(step, 1) + decayed_lr = learning_rate_decay.natural_exp_decay(initial_lr, step, + k, decay_rate) + with self.test_session(): + assign_step.op.run() + for i in range(k+1): + expected = initial_lr * math.exp(-i / k * decay_rate) + self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + increment_step.op.run() - @test_util.run_in_graph_and_eager_modes() def testStaircase(self): initial_lr = 0.1 k = 10 decay_rate = 0.96 - step = resource_variable_ops.ResourceVariable(0) - decayed_lr = learning_rate_decay.natural_exp_decay( - initial_lr, step, k, decay_rate, staircase=True) - - self.evaluate(variables.global_variables_initializer()) - for i in range(k + 1): - expected = initial_lr * math.exp(-decay_rate * (i // k)) - self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) - self.evaluate(step.assign_add(1)) + step = gen_state_ops.variable( + shape=[], dtype=dtypes.int32, name="step", container="", shared_name="") + assign_step = state_ops.assign(step, 0) + increment_step = state_ops.assign_add(step, 1) + decayed_lr = learning_rate_decay.natural_exp_decay(initial_lr, + step, + k, + decay_rate, + staircase=True) + with self.test_session(): + assign_step.op.run() + for i in range(k+1): + expected = initial_lr * math.exp(-decay_rate * (i // k)) + self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + increment_step.op.run() class InverseDecayTest(test_util.TensorFlowTestCase): - @test_util.run_in_graph_and_eager_modes() def testDecay(self): initial_lr = 0.1 k = 10 decay_rate = 0.96 - step = resource_variable_ops.ResourceVariable(0) - decayed_lr = learning_rate_decay.inverse_time_decay(initial_lr, step, k, + step = gen_state_ops.variable( + shape=[], dtype=dtypes.int32, name="step", container="", shared_name="") + assign_step = state_ops.assign(step, 0) + increment_step = state_ops.assign_add(step, 1) + decayed_lr = learning_rate_decay.inverse_time_decay(initial_lr, + step, + k, decay_rate) + with self.test_session(): + assign_step.op.run() + for i in range(k+1): + expected = initial_lr / (1 + i / k * decay_rate) + self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + increment_step.op.run() - self.evaluate(variables.global_variables_initializer()) - for i in range(k + 1): - expected = initial_lr / (1 + i / k * decay_rate) - self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) - self.evaluate(step.assign_add(1)) - - @test_util.run_in_graph_and_eager_modes() def testStaircase(self): initial_lr = 0.1 k = 10 decay_rate = 0.96 - step = resource_variable_ops.ResourceVariable(0) - decayed_lr = learning_rate_decay.inverse_time_decay( - initial_lr, step, k, decay_rate, staircase=True) - - self.evaluate(variables.global_variables_initializer()) - for i in range(k + 1): - expected = initial_lr / (1 + decay_rate * (i // k)) - self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) - self.evaluate(step.assign_add(1)) + step = gen_state_ops.variable( + shape=[], dtype=dtypes.int32, name="step", container="", shared_name="") + assign_step = state_ops.assign(step, 0) + increment_step = state_ops.assign_add(step, 1) + decayed_lr = learning_rate_decay.inverse_time_decay(initial_lr, + step, + k, + decay_rate, + staircase=True) + with self.test_session(): + assign_step.op.run() + for i in range(k+1): + expected = initial_lr / (1 + decay_rate * (i // k)) + self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + increment_step.op.run() class CosineDecayTest(test_util.TensorFlowTestCase): @@ -333,26 +348,26 @@ class CosineDecayTest(test_util.TensorFlowTestCase): decay = 0.5 * (1.0 + math.cos(math.pi * completed_fraction)) return (1.0 - alpha) * decay + alpha - @test_util.run_in_graph_and_eager_modes() def testDecay(self): num_training_steps = 1000 initial_lr = 1.0 for step in range(0, 1500, 250): - decayed_lr = learning_rate_decay.cosine_decay(initial_lr, step, - num_training_steps) - expected = self.np_cosine_decay(step, num_training_steps) - self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + with self.test_session(): + decayed_lr = learning_rate_decay.cosine_decay( + initial_lr, step, num_training_steps) + expected = self.np_cosine_decay(step, num_training_steps) + self.assertAllClose(decayed_lr.eval(), expected, 1e-6) - @test_util.run_in_graph_and_eager_modes() def testAlpha(self): num_training_steps = 1000 initial_lr = 1.0 alpha = 0.1 for step in range(0, 1500, 250): - decayed_lr = learning_rate_decay.cosine_decay(initial_lr, step, - num_training_steps, alpha) - expected = self.np_cosine_decay(step, num_training_steps, alpha) - self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + with self.test_session(): + decayed_lr = learning_rate_decay.cosine_decay( + initial_lr, step, num_training_steps, alpha) + expected = self.np_cosine_decay(step, num_training_steps, alpha) + self.assertAllClose(decayed_lr.eval(), expected, 1e-6) class CosineDecayRestartsTest(test_util.TensorFlowTestCase): @@ -369,51 +384,51 @@ class CosineDecayRestartsTest(test_util.TensorFlowTestCase): decay = fac * 0.5 * (1.0 + math.cos(math.pi * completed_fraction)) return (1.0 - alpha) * decay + alpha - @test_util.run_in_graph_and_eager_modes() def testDecay(self): num_training_steps = 1000 initial_lr = 1.0 for step in range(0, 1500, 250): - decayed_lr = learning_rate_decay.cosine_decay_restarts( - initial_lr, step, num_training_steps) - expected = self.np_cosine_decay_restarts(step, num_training_steps) - self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + with self.test_session(): + decayed_lr = learning_rate_decay.cosine_decay_restarts( + initial_lr, step, num_training_steps) + expected = self.np_cosine_decay_restarts(step, num_training_steps) + self.assertAllClose(decayed_lr.eval(), expected, 1e-6) - @test_util.run_in_graph_and_eager_modes() def testAlpha(self): num_training_steps = 1000 initial_lr = 1.0 alpha = 0.1 for step in range(0, 1500, 250): - decayed_lr = learning_rate_decay.cosine_decay_restarts( - initial_lr, step, num_training_steps, alpha=alpha) - expected = self.np_cosine_decay_restarts( - step, num_training_steps, alpha=alpha) - self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + with self.test_session(): + decayed_lr = learning_rate_decay.cosine_decay_restarts( + initial_lr, step, num_training_steps, alpha=alpha) + expected = self.np_cosine_decay_restarts(step, num_training_steps, + alpha=alpha) + self.assertAllClose(decayed_lr.eval(), expected, 1e-6) - @test_util.run_in_graph_and_eager_modes() def testMMul(self): num_training_steps = 1000 initial_lr = 1.0 m_mul = 0.9 for step in range(0, 1500, 250): - decayed_lr = learning_rate_decay.cosine_decay_restarts( - initial_lr, step, num_training_steps, m_mul=m_mul) - expected = self.np_cosine_decay_restarts( - step, num_training_steps, m_mul=m_mul) - self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + with self.test_session(): + decayed_lr = learning_rate_decay.cosine_decay_restarts( + initial_lr, step, num_training_steps, m_mul=m_mul) + expected = self.np_cosine_decay_restarts(step, num_training_steps, + m_mul=m_mul) + self.assertAllClose(decayed_lr.eval(), expected, 1e-6) - @test_util.run_in_graph_and_eager_modes() def testTMul(self): num_training_steps = 1000 initial_lr = 1.0 t_mul = 1.0 for step in range(0, 1500, 250): - decayed_lr = learning_rate_decay.cosine_decay_restarts( - initial_lr, step, num_training_steps, t_mul=t_mul) - expected = self.np_cosine_decay_restarts( - step, num_training_steps, t_mul=t_mul) - self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + with self.test_session(): + decayed_lr = learning_rate_decay.cosine_decay_restarts( + initial_lr, step, num_training_steps, t_mul=t_mul) + expected = self.np_cosine_decay_restarts(step, num_training_steps, + t_mul=t_mul) + self.assertAllClose(decayed_lr.eval(), expected, 1e-6) class LinearCosineDecayTest(test_util.TensorFlowTestCase): @@ -430,63 +445,65 @@ class LinearCosineDecayTest(test_util.TensorFlowTestCase): cosine_decayed = 0.5 * (1.0 + math.cos(math.pi * fraction)) return (alpha + linear_decayed) * cosine_decayed + beta - @test_util.run_in_graph_and_eager_modes() def testDefaultDecay(self): num_training_steps = 1000 initial_lr = 1.0 for step in range(0, 1500, 250): - decayed_lr = learning_rate_decay.linear_cosine_decay( - initial_lr, step, num_training_steps) - expected = self.np_linear_cosine_decay(step, num_training_steps) - self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + with self.test_session(): + decayed_lr = learning_rate_decay.linear_cosine_decay( + initial_lr, step, num_training_steps) + expected = self.np_linear_cosine_decay(step, num_training_steps) + self.assertAllClose(decayed_lr.eval(), expected, 1e-6) - @test_util.run_in_graph_and_eager_modes() def testNonDefaultDecay(self): num_training_steps = 1000 initial_lr = 1.0 for step in range(0, 1500, 250): - decayed_lr = learning_rate_decay.linear_cosine_decay( - initial_lr, - step, - num_training_steps, - alpha=0.1, - beta=1e-4, - num_periods=5) - expected = self.np_linear_cosine_decay( - step, num_training_steps, alpha=0.1, beta=1e-4, num_periods=5) - self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + with self.test_session(): + decayed_lr = learning_rate_decay.linear_cosine_decay( + initial_lr, + step, + num_training_steps, + alpha=0.1, + beta=1e-4, + num_periods=5) + expected = self.np_linear_cosine_decay( + step, + num_training_steps, + alpha=0.1, + beta=1e-4, + num_periods=5) + self.assertAllClose(decayed_lr.eval(), expected, 1e-6) class NoisyLinearCosineDecayTest(test_util.TensorFlowTestCase): - @test_util.run_in_graph_and_eager_modes() def testDefaultNoisyLinearCosine(self): num_training_steps = 1000 initial_lr = 1.0 for step in range(0, 1500, 250): - # No numerical check because of noise - decayed_lr = learning_rate_decay.noisy_linear_cosine_decay( - initial_lr, step, num_training_steps) - # Cannot be deterministically tested - self.evaluate(decayed_lr) + with self.test_session(): + # No numerical check because of noise + decayed_lr = learning_rate_decay.noisy_linear_cosine_decay( + initial_lr, step, num_training_steps) + decayed_lr.eval() - @test_util.run_in_graph_and_eager_modes() def testNonDefaultNoisyLinearCosine(self): num_training_steps = 1000 initial_lr = 1.0 for step in range(0, 1500, 250): - # No numerical check because of noise - decayed_lr = learning_rate_decay.noisy_linear_cosine_decay( - initial_lr, - step, - num_training_steps, - initial_variance=0.5, - variance_decay=0.1, - alpha=0.1, - beta=1e-4, - num_periods=5) - # Cannot be deterministically tested - self.evaluate(decayed_lr) + with self.test_session(): + # No numerical check because of noise + decayed_lr = learning_rate_decay.noisy_linear_cosine_decay( + initial_lr, + step, + num_training_steps, + initial_variance=0.5, + variance_decay=0.1, + alpha=0.1, + beta=1e-4, + num_periods=5) + decayed_lr.eval() if __name__ == "__main__": -- GitLab From e8d37d9d27b59d54fb48e6b379093840bbd54f13 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 18 Jun 2018 20:38:52 -0700 Subject: [PATCH 643/816] Split out HloHostComputeInstruction, HloPadInstruction and HloDynamicSliceInstruction as subclasses from HloInstruction.. PiperOrigin-RevId: 201108336 --- .../compiler/xla/service/hlo_instruction.cc | 188 +++++++----------- .../compiler/xla/service/hlo_instruction.h | 53 ++--- .../compiler/xla/service/hlo_instructions.cc | 113 +++++++++++ .../compiler/xla/service/hlo_instructions.h | 90 +++++++++ 4 files changed, 285 insertions(+), 159 deletions(-) diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc index 1dd2ce40da..f5ba10cede 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction.cc +++ b/tensorflow/compiler/xla/service/hlo_instruction.cc @@ -64,6 +64,14 @@ StatusOr> HloInstruction::CreateFromProto( const auto operands = [&instruction_map, &proto](int index) { return instruction_map.at(proto.operand_ids(index)); }; + const auto all_operands = [&instruction_map, &proto]() { + std::vector result(proto.operand_ids_size()); + std::transform(proto.operand_ids().begin(), proto.operand_ids().end(), + result.begin(), [&instruction_map](int64 operand_id) { + return instruction_map.at(operand_id); + }); + return result; + }; const auto computations = [&computation_map, &proto](int index) { return computation_map.at(proto.called_computation_ids(index)); }; @@ -136,20 +144,13 @@ StatusOr> HloInstruction::CreateFromProto( std::vector(proto.dimensions().begin(), proto.dimensions().end())); break; - case HloOpcode::kConcatenate: { + case HloOpcode::kConcatenate: TF_RET_CHECK(proto.dimensions_size() == 1) << "Concatenate instruction should have 1 dimension but sees " << proto.dimensions_size(); - std::vector concat_operands(proto.operand_ids_size()); - std::transform(proto.operand_ids().begin(), proto.operand_ids().end(), - concat_operands.begin(), - [&instruction_map](int64 operand_id) { - return instruction_map.at(operand_id); - }); - instruction = CreateConcatenate(proto.shape(), concat_operands, - proto.dimensions(0)); + instruction = + CreateConcatenate(proto.shape(), all_operands(), proto.dimensions(0)); break; - } case HloOpcode::kReduce: TF_RET_CHECK(proto.operand_ids_size() == 2) << "Reduce instruction should have 2 operands but sees " @@ -180,19 +181,12 @@ StatusOr> HloInstruction::CreateFromProto( std::vector(proto.dimensions().begin(), proto.dimensions().end())); break; - case HloOpcode::kMap: { + case HloOpcode::kMap: TF_RET_CHECK(proto.called_computation_ids_size() == 1) << "Map instruction should have 1 called computation but sees " << proto.called_computation_ids_size(); - std::vector map_operands(proto.operand_ids_size()); - std::transform(proto.operand_ids().begin(), proto.operand_ids().end(), - map_operands.begin(), - [&instruction_map](int64 operand_id) { - return instruction_map.at(operand_id); - }); - instruction = CreateMap(proto.shape(), map_operands, computations(0)); + instruction = CreateMap(proto.shape(), all_operands(), computations(0)); break; - } case HloOpcode::kSlice: { TF_RET_CHECK(proto.operand_ids_size() == 1) << "Slice instruction should have 1 operand but sees " @@ -245,25 +239,14 @@ StatusOr> HloInstruction::CreateFromProto( auto* fused_computation = FindPtrOrNull(computation_map, fusion_id); TF_RET_CHECK(fused_computation != nullptr) << "No fusion computation with id " << fusion_id; - std::vector fusion_operands(proto.operand_ids_size()); - std::transform(proto.operand_ids().begin(), proto.operand_ids().end(), - fusion_operands.begin(), - [&instruction_map](int64 operand_id) { - return instruction_map.at(operand_id); - }); - instruction = CreateFusion(proto.shape(), fusion_kind, fusion_operands, + instruction = CreateFusion(proto.shape(), fusion_kind, all_operands(), fused_computation); break; } - case HloOpcode::kRng: { - std::vector rng_parms(proto.operand_ids_size()); - std::transform(proto.operand_ids().begin(), proto.operand_ids().end(), - rng_parms.begin(), [&instruction_map](int64 operand_id) { - return instruction_map.at(operand_id); - }); - instruction = CreateRng(proto.shape(), proto.distribution(), rng_parms); + case HloOpcode::kRng: + instruction = + CreateRng(proto.shape(), proto.distribution(), all_operands()); break; - } case HloOpcode::kParameter: instruction = CreateParameter(proto.parameter_number(), proto.shape(), proto.name()); @@ -291,17 +274,12 @@ StatusOr> HloInstruction::CreateFromProto( TF_RET_CHECK(proto.called_computation_ids_size() == 1) << "CrossReplicaSum should have 1 called computation but sees " << proto.called_computation_ids_size(); - std::vector all_operands(proto.operand_ids_size()); - c_transform(proto.operand_ids(), all_operands.begin(), - [&instruction_map](int64 operand_id) { - return instruction_map.at(operand_id); - }); tensorflow::gtl::optional all_reduce_id; if (proto.all_reduce_id() > 0) { all_reduce_id = proto.all_reduce_id(); } instruction = CreateCrossReplicaSum( - proto.shape(), all_operands, computations(0), + proto.shape(), all_operands(), computations(0), /*replica_group_ids=*/ std::vector(proto.replica_group_ids().begin(), proto.replica_group_ids().end()), @@ -340,15 +318,8 @@ StatusOr> HloInstruction::CreateFromProto( proto.shape(), operands(0), computations(0), proto.window(), operands(1), operands(2), computations(1)); break; - case HloOpcode::kCustomCall: { - std::vector custom_call_operands( - proto.operand_ids_size()); - std::transform(proto.operand_ids().begin(), proto.operand_ids().end(), - custom_call_operands.begin(), - [&instruction_map](int64 operand_id) { - return instruction_map.at(operand_id); - }); - instruction = CreateCustomCall(proto.shape(), custom_call_operands, + case HloOpcode::kCustomCall: + instruction = CreateCustomCall(proto.shape(), all_operands(), proto.custom_call_target()); if (proto.has_window()) { static_cast(instruction.get()) @@ -360,6 +331,28 @@ StatusOr> HloInstruction::CreateFromProto( proto.convolution_dimension_numbers()); } break; + case HloOpcode::kHostCompute: + instruction = + CreateHostCompute(proto.shape(), all_operands(), proto.channel_name(), + proto.cost_estimate_ns()); + break; + case HloOpcode::kPad: + TF_RET_CHECK(proto.operand_ids_size() == 2) + << "Pad instruction should have 2 operands but sees " + << proto.operand_ids_size(); + TF_RET_CHECK(proto.has_padding_config()); + instruction = CreatePad(proto.shape(), operands(0), operands(1), + proto.padding_config()); + break; + case HloOpcode::kDynamicSlice: { + TF_RET_CHECK(proto.operand_ids_size() == 2) + << "DynamicSlice instruction should have 2 operands but sees " + << proto.operand_ids_size(); + std::vector slice_sizes(proto.dynamic_slice_sizes_size()); + c_copy(proto.dynamic_slice_sizes(), slice_sizes.begin()); + instruction = CreateDynamicSlice(proto.shape(), operands(0), operands(1), + slice_sizes); + break; } default: { instruction = WrapUnique(new HloInstruction(opcode, proto.shape())); @@ -396,14 +389,6 @@ StatusOr> HloInstruction::CreateFromProto( MakeUnique(proto.dot_dimension_numbers()); } - for (int64 dynamic_slice_size : proto.dynamic_slice_sizes()) { - instruction->dynamic_slice_sizes_.push_back(dynamic_slice_size); - } - if (proto.has_padding_config()) { - instruction->padding_config_ = - MakeUnique(proto.padding_config()); - } - if (proto.has_sharding()) { TF_ASSIGN_OR_RETURN(const auto& sharding, HloSharding::FromProto(proto.sharding())); @@ -417,10 +402,6 @@ StatusOr> HloInstruction::CreateFromProto( for (int64 bound : proto.gather_window_bounds()) { instruction->gather_window_bounds_.push_back(bound); } - - instruction->channel_name_ = proto.channel_name(); - instruction->cost_estimate_ns_ = proto.cost_estimate_ns(); - return std::move(instruction); } @@ -721,13 +702,8 @@ HloInstruction::CreateGenerateToken( /* static */ std::unique_ptr HloInstruction::CreateDynamicSlice( const Shape& shape, HloInstruction* operand, HloInstruction* start_indices, tensorflow::gtl::ArraySlice slice_sizes) { - auto instruction = - WrapUnique(new HloInstruction(HloOpcode::kDynamicSlice, shape)); - instruction->AppendOperand(operand); - instruction->AppendOperand(start_indices); - instruction->dynamic_slice_sizes_.assign(slice_sizes.begin(), - slice_sizes.end()); - return instruction; + return MakeUnique(shape, operand, start_indices, + slice_sizes); } /* static */ std::unique_ptr @@ -881,11 +857,8 @@ HloInstruction::CreateBroadcastSequence( /* static */ std::unique_ptr HloInstruction::CreatePad( const Shape& shape, HloInstruction* operand, HloInstruction* padding_value, const PaddingConfig& padding_config) { - auto instruction = WrapUnique(new HloInstruction(HloOpcode::kPad, shape)); - instruction->AppendOperand(operand); - instruction->AppendOperand(padding_value); - instruction->padding_config_ = MakeUnique(padding_config); - return instruction; + return MakeUnique(shape, operand, padding_value, + padding_config); } /* static */ std::unique_ptr HloInstruction::CreateReshape( @@ -989,14 +962,8 @@ bool HloInstruction::HasSideEffect() const { /* static */ std::unique_ptr HloInstruction::CreateHostCompute( const Shape& shape, tensorflow::gtl::ArraySlice operands, tensorflow::StringPiece channel_name, const int64 cost_estimate_ns) { - std::unique_ptr instruction = - WrapUnique(new HloInstruction(HloOpcode::kHostCompute, shape)); - for (auto operand : operands) { - instruction->AppendOperand(operand); - } - instruction->channel_name_ = std::string(channel_name); - instruction->cost_estimate_ns_ = cost_estimate_ns; - return instruction; + return MakeUnique(shape, operands, channel_name, + cost_estimate_ns); } /* static */ std::unique_ptr HloInstruction::CreateTuple( @@ -1100,6 +1067,9 @@ std::unique_ptr HloInstruction::CloneWithNewOperands( case HloOpcode::kCustomCall: case HloOpcode::kReduceWindow: case HloOpcode::kSelectAndScatter: + case HloOpcode::kHostCompute: + case HloOpcode::kPad: + case HloOpcode::kDynamicSlice: clone = CloneWithNewOperandsImpl(shape, new_operands, context); break; // Unary ops. @@ -1163,10 +1133,6 @@ std::unique_ptr HloInstruction::CloneWithNewOperands( case HloOpcode::kCall: clone = CreateCall(shape, new_operands, to_apply()); break; - case HloOpcode::kHostCompute: - clone = CreateHostCompute(shape, new_operands, channel_name_, - cost_estimate_ns_); - break; case HloOpcode::kConvert: CHECK_EQ(new_operands.size(), 1); clone = CreateConvert(shape, new_operands[0]); @@ -1180,19 +1146,10 @@ std::unique_ptr HloInstruction::CloneWithNewOperands( clone = CreateDot(shape, new_operands[0], new_operands[1], *dot_dimension_numbers_); break; - case HloOpcode::kPad: - CHECK_EQ(new_operands.size(), 2); - clone = - CreatePad(shape, new_operands[0], new_operands[1], *padding_config_); - break; case HloOpcode::kReshape: CHECK_EQ(new_operands.size(), 1); clone = CreateReshape(shape, new_operands[0]); break; - case HloOpcode::kDynamicSlice: - clone = CreateDynamicSlice(shape, new_operands[0], new_operands[1], - dynamic_slice_sizes_); - break; case HloOpcode::kDynamicUpdateSlice: CHECK_EQ(new_operands.size(), 3); clone = CreateDynamicUpdateSlice(shape, new_operands[0], new_operands[1], @@ -1447,7 +1404,6 @@ bool HloInstruction::IdenticalSlowPath( case HloOpcode::kCopy: case HloOpcode::kCos: case HloOpcode::kDivide: - case HloOpcode::kDynamicSlice: case HloOpcode::kDynamicUpdateSlice: case HloOpcode::kEq: case HloOpcode::kExp: @@ -1502,9 +1458,6 @@ bool HloInstruction::IdenticalSlowPath( gather_window_bounds() == other.gather_window_bounds(); // Remaining instructions with special values. - case HloOpcode::kPad: - return protobuf_util::ProtobufEquals(padding_config(), - other.padding_config()); case HloOpcode::kCall: case HloOpcode::kConditional: return eq_computations(true_computation(), other.true_computation()) && @@ -1512,7 +1465,6 @@ bool HloInstruction::IdenticalSlowPath( // These opcodes are not yet supported. case HloOpcode::kSort: - case HloOpcode::kHostCompute: return false; // Ops migrated to subclasses should never come to this line. @@ -1546,6 +1498,9 @@ bool HloInstruction::IdenticalSlowPath( case HloOpcode::kCustomCall: case HloOpcode::kReduceWindow: case HloOpcode::kSelectAndScatter: + case HloOpcode::kHostCompute: + case HloOpcode::kPad: + case HloOpcode::kDynamicSlice: LOG(FATAL) << "Base class impl called for opcode with subclass: " << opcode(); } @@ -1892,15 +1847,6 @@ string HloInstruction::OperandsToStringWithCanonicalNameMap( std::vector HloInstruction::ExtraAttributesToString( const HloPrintOptions& options) const { std::vector extra = ExtraAttributesToStringImpl(options); - if (padding_config_ != nullptr) { - extra.push_back( - StrCat("padding=", xla::PaddingConfigToString(*padding_config_))); - } - - if (opcode() == HloOpcode::kDynamicSlice) { - extra.push_back( - StrCat("dynamic_slice_sizes={", Join(dynamic_slice_sizes(), ","), "}")); - } if (dot_dimension_numbers_ != nullptr) { extra.push_back(DotDimensionNumbersToString()); @@ -2048,20 +1994,10 @@ HloInstructionProto HloInstruction::ToProto() const { } } - for (int64 slice_size : dynamic_slice_sizes_) { - proto.add_dynamic_slice_sizes(slice_size); - } - if (padding_config_ != nullptr) { - *proto.mutable_padding_config() = *padding_config_; - } - if (has_sharding()) { *proto.mutable_sharding() = sharding().ToProto(); } - proto.set_channel_name(channel_name_); - proto.set_cost_estimate_ns(cost_estimate_ns_); - return proto; } @@ -3130,4 +3066,20 @@ void HloInstruction::set_scatter(HloComputation* computation) { const string& HloInstruction::custom_call_target() const { return Cast(this)->custom_call_target(); } + +const string& HloInstruction::channel_name() const { + return Cast(this)->channel_name(); +} + +const PaddingConfig& HloInstruction::padding_config() const { + return Cast(this)->padding_config(); +} + +int64 HloInstruction::slice_sizes(int64 dimension) const { + return Cast(this)->slice_sizes(dimension); +} + +const std::vector& HloInstruction::dynamic_slice_sizes() const { + return Cast(this)->dynamic_slice_sizes(); +} } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h index 3f9cf513bd..8f59e67123 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction.h +++ b/tensorflow/compiler/xla/service/hlo_instruction.h @@ -954,12 +954,6 @@ class HloInstruction { HloInstruction* tracing() const; void set_tracing(HloInstruction* trace_instruction); - // Returns the channel name associated with the instruction. The name is - // used to identify host Send/Recv operations. - // - // Precondition: opcode() == HloOpcode::kHostCompute - string channel_name() const { return channel_name_; } - // Returns true if this instruction is fused, ie contained within a fusion // instruction. bool IsFused() const; @@ -1039,27 +1033,6 @@ class HloInstruction { copy_elision_allowed_ = value; } - // Returns the size of the slice in the given dimension for a dynamic - // slice node. - // - // Precondition: opcode() == HloOpcode::kDynamicSlice - int64 slice_sizes(int64 dimension) const { - CHECK_EQ(HloOpcode::kDynamicSlice, opcode_); - return dynamic_slice_sizes_[dimension]; - } - const std::vector& dynamic_slice_sizes() const { - CHECK_EQ(HloOpcode::kDynamicSlice, opcode_); - return dynamic_slice_sizes_; - } - - // Returns the padding configuration for a pad node. - // - // Precondition: opcode() == HloOpcode::kPad - const PaddingConfig& padding_config() const { - CHECK(padding_config_ != nullptr); - return *padding_config_; - } - // Returns data on the dimension numbers used for a dot operation. const DotDimensionNumbers& dot_dimension_numbers() const { CHECK(dot_dimension_numbers_ != nullptr); @@ -1436,6 +1409,18 @@ class HloInstruction { // Delegates to HloCustomCallInstruction::custom_call_target. const string& custom_call_target() const; + + // Delegates to HloHostComputeInstruction::channel_name. + const string& channel_name() const; + + // Delegates to HloPadInstruction::padding_config. + const PaddingConfig& padding_config() const; + + // Delegates to HloDynamicSliceInstruction::slice_sizes. + int64 slice_sizes(int64 dimension) const; + + // Delegates to HloDynamicSliceInstruction::dynamic_slice_sizes. + const std::vector& dynamic_slice_sizes() const; // Old methods kept for smooth subclassing transition END. protected: @@ -1581,14 +1566,6 @@ class HloInstruction { // Used to tag kCopy instructions that are eligible for copy elision. bool copy_elision_allowed_ = true; - // Describes the [start, start + size) range size for a dynamic slice - // ('start' is specified dynamically in the second operand of the operation). - std::vector dynamic_slice_sizes_; - - // The padding configuration that describes the edge padding and interior - // padding of this pad instruction. Only set for pad instructions. - std::unique_ptr padding_config_; - // The sharding, if one exists. std::unique_ptr sharding_; @@ -1596,12 +1573,6 @@ class HloInstruction { std::unique_ptr operand_side_metadata_; std::unique_ptr user_side_metadata_; - // Name to use for host send/recv channels, only present for kHostCompute. - string channel_name_; - - // Estimate of the duration of a host computation in nanoseconds. - int64 cost_estimate_ns_ = 0; - // Computations called by this instruction. std::vector called_computations_; diff --git a/tensorflow/compiler/xla/service/hlo_instructions.cc b/tensorflow/compiler/xla/service/hlo_instructions.cc index 5098a4beeb..0b4ce71539 100644 --- a/tensorflow/compiler/xla/service/hlo_instructions.cc +++ b/tensorflow/compiler/xla/service/hlo_instructions.cc @@ -1690,4 +1690,117 @@ HloCustomCallInstruction::CloneWithNewOperandsImpl( } return std::move(cloned); } + +HloHostComputeInstruction::HloHostComputeInstruction( + const Shape& shape, tensorflow::gtl::ArraySlice operands, + tensorflow::StringPiece channel_name, const int64 cost_estimate_ns) + : HloInstruction(HloOpcode::kHostCompute, shape), + channel_name_(channel_name.begin(), channel_name.end()), + cost_estimate_ns_(cost_estimate_ns) { + for (auto operand : operands) { + AppendOperand(operand); + } +} + +HloInstructionProto HloHostComputeInstruction::ToProto() const { + HloInstructionProto proto = HloInstruction::ToProto(); + proto.set_channel_name(channel_name_); + proto.set_cost_estimate_ns(cost_estimate_ns_); + return proto; +} + +bool HloHostComputeInstruction::IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const { + // Not yet supported. + return false; +} + +std::unique_ptr +HloHostComputeInstruction::CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const { + return MakeUnique( + shape, new_operands, channel_name_, cost_estimate_ns_); +} + +HloPadInstruction::HloPadInstruction(const Shape& shape, + HloInstruction* operand, + HloInstruction* padding_value, + const PaddingConfig& padding_config) + : HloInstruction(HloOpcode::kPad, shape), padding_config_(padding_config) { + AppendOperand(operand); + AppendOperand(padding_value); +} + +HloInstructionProto HloPadInstruction::ToProto() const { + HloInstructionProto proto = HloInstruction::ToProto(); + *proto.mutable_padding_config() = padding_config_; + return proto; +} + +std::vector HloPadInstruction::ExtraAttributesToStringImpl( + const HloPrintOptions& options) const { + return {StrCat("padding=", xla::PaddingConfigToString(padding_config_))}; +} + +bool HloPadInstruction::IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const { + const auto& casted_other = static_cast(other); + return protobuf_util::ProtobufEquals(padding_config(), + casted_other.padding_config()); +} + +std::unique_ptr HloPadInstruction::CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const { + CHECK_EQ(new_operands.size(), 2); + return MakeUnique(shape, new_operands[0], new_operands[1], + padding_config_); +} + +HloDynamicSliceInstruction::HloDynamicSliceInstruction( + const Shape& shape, HloInstruction* operand, HloInstruction* start_indices, + tensorflow::gtl::ArraySlice slice_sizes) + : HloInstruction(HloOpcode::kDynamicSlice, shape), + dynamic_slice_sizes_(slice_sizes.begin(), slice_sizes.end()) { + AppendOperand(operand); + AppendOperand(start_indices); +} + +HloInstructionProto HloDynamicSliceInstruction::ToProto() const { + HloInstructionProto proto = HloInstruction::ToProto(); + for (int64 slice_size : dynamic_slice_sizes_) { + proto.add_dynamic_slice_sizes(slice_size); + } + return proto; +} + +std::vector HloDynamicSliceInstruction::ExtraAttributesToStringImpl( + const HloPrintOptions& options) const { + return { + StrCat("dynamic_slice_sizes={", Join(dynamic_slice_sizes(), ","), "}")}; +} + +bool HloDynamicSliceInstruction::IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const { + return true; +} + +std::unique_ptr +HloDynamicSliceInstruction::CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const { + CHECK_EQ(new_operands.size(), 2); + return MakeUnique( + shape, new_operands[0], new_operands[1], dynamic_slice_sizes_); +} } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_instructions.h b/tensorflow/compiler/xla/service/hlo_instructions.h index d310c88995..1a2e4ae0a5 100644 --- a/tensorflow/compiler/xla/service/hlo_instructions.h +++ b/tensorflow/compiler/xla/service/hlo_instructions.h @@ -1004,6 +1004,96 @@ class HloCustomCallInstruction : public HloInstruction { std::unique_ptr convolution_dimension_numbers_; }; +class HloHostComputeInstruction : public HloInstruction { + public: + explicit HloHostComputeInstruction( + const Shape& shape, tensorflow::gtl::ArraySlice operands, + tensorflow::StringPiece channel_name, const int64 cost_estimate_ns); + // Returns the channel name associated with the instruction. The name is + // used to identify host Send/Recv operations. + const string& channel_name() const { return channel_name_; } + // Returns a serialized representation of this instruction. + HloInstructionProto ToProto() const override; + + private: + bool IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const override; + // Implementation for non-common logic of CloneWithNewOperands. + std::unique_ptr CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const override; + // Name to use for host send/recv channels. + string channel_name_; + // Estimate of the duration of a host computation in nanoseconds. + int64 cost_estimate_ns_ = 0; +}; + +class HloPadInstruction : public HloInstruction { + public: + explicit HloPadInstruction(const Shape& shape, HloInstruction* operand, + HloInstruction* padding_value, + const PaddingConfig& padding_config); + // Returns the padding configuration for a pad node. + const PaddingConfig& padding_config() const { return padding_config_; } + // Returns a serialized representation of this instruction. + HloInstructionProto ToProto() const override; + + private: + std::vector ExtraAttributesToStringImpl( + const HloPrintOptions& options) const override; + bool IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const override; + // Implementation for non-common logic of CloneWithNewOperands. + std::unique_ptr CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const override; + + // The padding configuration that describes the edge padding and interior + // padding of this pad instruction. + PaddingConfig padding_config_; +}; + +class HloDynamicSliceInstruction : public HloInstruction { + public: + explicit HloDynamicSliceInstruction( + const Shape& shape, HloInstruction* operand, + HloInstruction* start_indices, + tensorflow::gtl::ArraySlice slice_sizes); + // Old methods kept for smooth subclassing transition END. + // Returns the size of the slice in the given dimension for a dynamic + // slice node. + int64 slice_sizes(int64 dimension) const { + return dynamic_slice_sizes_[dimension]; + } + const std::vector& dynamic_slice_sizes() const { + return dynamic_slice_sizes_; + } + // Returns a serialized representation of this instruction. + HloInstructionProto ToProto() const override; + + private: + std::vector ExtraAttributesToStringImpl( + const HloPrintOptions& options) const override; + bool IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const override; + // Implementation for non-common logic of CloneWithNewOperands. + std::unique_ptr CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const override; + + // Describes the [start, start + size) range size for a dynamic slice + // ('start' is specified dynamically in the second operand of the operation). + std::vector dynamic_slice_sizes_; +}; } // namespace xla #endif // TENSORFLOW_COMPILER_XLA_SERVICE_HLO_INSTRUCTIONS_H_ -- GitLab From 60b78d6152e6f8d985f3086930ff986c140c36bf Mon Sep 17 00:00:00 2001 From: Smit Hinsu Date: Mon, 18 Jun 2018 20:51:50 -0700 Subject: [PATCH 644/816] Load NCCL lib on-demand to facilitate default NCCL version upgrade to 2 Change in the default version to NCCL 2 would require all TF users to download the NCCL library without the on-demand loading. With on-demand loading, it will only require users using the nccl ops to download and install the NCCL lib. PiperOrigin-RevId: 201109554 --- tensorflow/contrib/nccl/BUILD | 40 +++++++++++-- .../nccl/python/ops/nccl_dependency_test.py | 59 +++++++++++++++++++ .../contrib/nccl/python/ops/nccl_ops.py | 39 ++++++++---- 3 files changed, 123 insertions(+), 15 deletions(-) create mode 100644 tensorflow/contrib/nccl/python/ops/nccl_dependency_test.py diff --git a/tensorflow/contrib/nccl/BUILD b/tensorflow/contrib/nccl/BUILD index 334e70318d..7cfdf0f607 100644 --- a/tensorflow/contrib/nccl/BUILD +++ b/tensorflow/contrib/nccl/BUILD @@ -97,18 +97,19 @@ tf_gen_op_wrapper_py( deps = [":nccl_ops_op_lib"], ) +# Test only nccl ops lib without dso to test behavior when NCCL lib is not +# installed. See nccl_dependency_test for more details. +# +# Users should use the public nccl_py lib that also adds the dso. tf_custom_op_py_library( - name = "nccl_py", + name = "nccl_ops_lib_without_dso", srcs = [ "__init__.py", "python/ops/nccl_ops.py", ], - dso = [":python/ops/_nccl_ops.so"], kernels = if_cuda([":nccl_kernels"]) + [ ":nccl_ops_op_lib", ], - srcs_version = "PY2AND3", - visibility = ["//visibility:public"], deps = [ ":nccl_ops", "//tensorflow/contrib/util:util_py", @@ -120,6 +121,15 @@ tf_custom_op_py_library( ], ) +tf_custom_op_py_library( + name = "nccl_py", + dso = [":python/ops/_nccl_ops.so"], + visibility = ["//visibility:public"], + deps = [ + ":nccl_ops_lib_without_dso", + ], +) + cuda_py_test( name = "nccl_ops_test", size = "small", @@ -141,3 +151,25 @@ cuda_py_test( "notap", ], ) + +cuda_py_test( + name = "nccl_dependency_test", + size = "small", + srcs = ["python/ops/nccl_dependency_test.py"], + additional_deps = [ + ":nccl_ops_lib_without_dso", + "//tensorflow/python:constant_op", + "//tensorflow/python:errors", + "//tensorflow/python:framework_ops", + "//tensorflow/python:util", + "//tensorflow/python:client_testlib", + "//tensorflow/python:platform_test", + ], + # Disable this test internally as static linking is used internally and only + # run for OSS to verify that NCCL is an optional dynamic dependency. + tags = [ + "manual", + "noguitar", + "notap", + ], +) diff --git a/tensorflow/contrib/nccl/python/ops/nccl_dependency_test.py b/tensorflow/contrib/nccl/python/ops/nccl_dependency_test.py new file mode 100644 index 0000000000..c766080dbe --- /dev/null +++ b/tensorflow/contrib/nccl/python/ops/nccl_dependency_test.py @@ -0,0 +1,59 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Dependency test for nccl to test behavior when NCCL is not installed.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib import nccl +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import errors_impl +from tensorflow.python.framework import ops +from tensorflow.python.platform import test +from tensorflow.python.util import tf_inspect + + +class NcclDependencyTest(test.TestCase): + """Verifies that importing nccl ops lib does not fail even if NCCL is not + installed but nccl ops throws an exception on use if NCCL is not installed. + """ + + def test_nccl_ops(self): + """Tests behavior of nccl ops when NCCL is not installed.""" + + public_methods = [ + m[0] + for m in tf_inspect.getmembers(nccl, tf_inspect.isfunction) + if not m[0].startswith('_') + ] + for method_name in public_methods: + with ops.device('/device:CPU:0'): + tensor = constant_op.constant(1) + + if method_name == 'broadcast': + arg = tensor + else: + arg = [tensor] + + nccl_op = getattr(nccl, method_name) + with ops.device('/device:CPU:0'): + with self.assertRaisesRegexp(errors_impl.NotFoundError, + r'cannot open shared object file'): + nccl_op(arg) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/nccl/python/ops/nccl_ops.py b/tensorflow/contrib/nccl/python/ops/nccl_ops.py index 794372a1f4..029b01412d 100644 --- a/tensorflow/contrib/nccl/python/ops/nccl_ops.py +++ b/tensorflow/contrib/nccl/python/ops/nccl_ops.py @@ -26,8 +26,10 @@ from tensorflow.python.framework import device from tensorflow.python.framework import ops from tensorflow.python.platform import resource_loader -_nccl_ops_so = loader.load_op_library( - resource_loader.get_path_to_datafile('_nccl_ops.so')) + +_nccl_ops_so = None +_module_lock = threading.Lock() +_shared_name_counter = 0 def all_sum(tensors): @@ -180,7 +182,7 @@ def broadcast(tensor): A tensor with the value of `src_tensor`, which can be used as input to ops on other GPU devices. """ - _check_graph_mode() + _validate_and_load_nccl_so() _check_device(tensor) with ops.device(tensor.device): @@ -212,7 +214,7 @@ def _apply_all_reduce(reduction, tensors): """Helper function for all_* functions.""" if not tensors: raise ValueError('Must pass >0 tensors to all reduce operations') - _check_graph_mode() + _validate_and_load_nccl_so() shared_name = _get_shared_name() res = [] @@ -234,7 +236,7 @@ def _apply_reduce(reduction, tensors): """Helper function for reduce_* functions.""" if not tensors: raise ValueError('Must pass >0 tensors to reduce operations') - _check_graph_mode() + _validate_and_load_nccl_so() for t in tensors: _check_device(t) @@ -246,14 +248,10 @@ def _apply_reduce(reduction, tensors): return result -_lock = threading.Lock() -_shared_name_counter = 0 - - def _get_shared_name(): global _shared_name_counter - with _lock: + with _module_lock: val = _shared_name_counter _shared_name_counter += 1 return 'c%s' % val @@ -266,6 +264,25 @@ def _check_device(tensor, expected=None): raise ValueError('Expected device %s, got %s' % (expected, tensor.device)) -def _check_graph_mode(): +def _maybe_load_nccl_ops_so(): + """Loads nccl ops so if it hasn't been loaded already.""" + + with _module_lock: + global _nccl_ops_so + if not _nccl_ops_so: + _nccl_ops_so = loader.load_op_library( + resource_loader.get_path_to_datafile('_nccl_ops.so')) + + +def _validate_and_load_nccl_so(): + """Validates calling context and loads nccl ops so file. + + Raises: + ValueError: Ops are not supported. + errors_impl.NotFoundError: nccl library is not installed. + """ + if context.executing_eagerly(): raise ValueError('Nccl ops are not supported in eager mode') + + _maybe_load_nccl_ops_so() -- GitLab From 6070ae0e148f50dbc8f36e1654f0a3f53b8b067e Mon Sep 17 00:00:00 2001 From: Akshay Modi Date: Mon, 18 Jun 2018 21:00:34 -0700 Subject: [PATCH 645/816] Merge changes from github. PiperOrigin-RevId: 201110240 --- CONTRIBUTING.md | 2 +- README.md | 1 + RELEASE.md | 67 ++- configure.py | 5 + tensorflow/BUILD | 4 +- tensorflow/c/generate-pc.sh | 11 +- tensorflow/cc/gradients/math_grad.cc | 1 + tensorflow/cc/gradients/nn_grad.cc | 47 ++ tensorflow/cc/gradients/nn_grad_test.cc | 84 +++- tensorflow/compiler/aot/codegen_test_h.golden | 4 +- .../compiler/aot/embedded_protocol_buffers.h | 2 +- tensorflow/compiler/aot/runtime.h | 4 +- tensorflow/compiler/aot/runtime_test.cc | 16 +- tensorflow/compiler/xla/service/cpu/BUILD | 18 +- .../compiler/xla/service/cpu/cpu_runtime.cc | 2 + .../compiler/xla/service/cpu/cpu_runtime.h | 1 + .../compiler/xla/service/cpu/ir_emitter.cc | 8 +- .../xla/service/cpu/runtime_fft_impl.h | 20 +- .../cpu/runtime_single_threaded_fft.cc | 32 ++ .../service/cpu/runtime_single_threaded_fft.h | 31 ++ .../xla/service/cpu/simple_orc_jit.cc | 2 + .../compiler/xla/service/pattern_matcher.h | 2 +- .../compiler/xla/service/tuple_simplifier.cc | 7 + .../compiler/xla/service/tuple_simplifier.h | 9 +- .../xla/service/tuple_simplifier_test.cc | 77 ++++ tensorflow/contrib/autograph/__init__.py | 3 + tensorflow/contrib/cmake/tf_c.cmake | 22 +- tensorflow/contrib/cmake/tf_cc_ops.cmake | 2 +- tensorflow/contrib/cmake/tf_python.cmake | 3 +- .../contrib/cmake/tools/create_def_file.py | 9 +- .../bijectors/sinh_arcsinh_bijector_test.py | 28 +- tensorflow/contrib/eager/python/datasets.py | 3 +- .../examples/notebooks/4_high_level.ipynb | 4 +- .../feature_column/sequence_feature_column.py | 22 +- .../sequence_feature_column_test.py | 41 ++ tensorflow/contrib/ffmpeg/__init__.py | 1 - tensorflow/contrib/ffmpeg/ffmpeg_ops.py | 1 - tensorflow/contrib/framework/__init__.py | 3 +- .../fused_conv2d_bias_activation_op_test.py | 11 +- .../src_impl/hexagon_controller.c | 2 +- .../contrib/lite/download_dependencies.sh | 4 +- .../contrib/lite/examples/minimal/minimal.cc | 2 +- .../lite/g3doc/tf_ops_compatibility.md | 14 +- tensorflow/contrib/lite/java/ovic/README.md | 4 +- .../internal/reference/reference_ops.h | 4 +- tensorflow/contrib/lite/python/interpreter.py | 2 +- .../interpreter_wrapper.cc | 9 +- .../interpreter_wrapper/interpreter_wrapper.h | 3 +- tensorflow/contrib/lite/python/lite.py | 11 + .../contrib/lite/toco/import_tensorflow.cc | 2 +- tensorflow/contrib/lite/toco/toco_port.cc | 6 + tensorflow/contrib/lite/toco/toco_port.h | 18 + tensorflow/contrib/makefile/compile_nsync.sh | 2 +- .../contrib/makefile/download_dependencies.sh | 4 +- .../contrib/metrics/python/ops/metric_ops.py | 2 +- .../contrib/mpi_collectives/kernels/ring.h | 2 +- .../opt/python/training/adamax_test.py | 6 +- .../training/model_average_optimizer.py | 2 +- tensorflow/contrib/periodic_resample/BUILD | 19 +- .../kernels/periodic_resample_op.cc | 5 + .../kernels/periodic_resample_op.h | 415 +++++++++++++----- .../periodic_resample/ops/array_ops.cc | 53 ++- .../periodic_resample/ops/array_ops_test.cc | 41 ++ .../kernel_tests/periodic_resample_op_test.py | 27 +- .../python/ops/periodic_resample_op.py | 8 +- .../predictor/contrib_estimator_predictor.py | 5 +- .../predictor/core_estimator_predictor.py | 5 +- .../contrib/predictor/predictor_factories.py | 24 +- .../predictor/predictor_factories_test.py | 19 + .../predictor/saved_model_predictor.py | 6 +- tensorflow/contrib/quantize/README.md | 2 +- .../slim/python/slim/evaluation_test.py | 25 +- tensorflow/contrib/summary/summary.py | 5 +- .../tensor_forest/client/eval_metrics.py | 45 +- .../tensor_forest/python/tensor_forest.py | 34 +- .../python/tensor_forest_test.py | 45 ++ .../contrib/tensorrt/convert/convert_graph.cc | 66 +-- .../contrib/tensorrt/convert/convert_nodes.cc | 97 ++-- tensorflow/contrib/tpu/python/tpu/datasets.py | 16 +- .../contrib/tpu/python/tpu/datasets_test.py | 26 ++ tensorflow/core/BUILD | 9 +- .../core/api_def/base_api/api_def_Selu.pbtxt | 4 + .../base_api/api_def_StringSplitV2.pbtxt | 48 ++ .../python_api/api_def_StringSplitV2.pbtxt | 4 + .../core/common_runtime/bfc_allocator.cc | 8 +- .../core/common_runtime/bfc_allocator.h | 3 +- ...direct_session_with_tracking_alloc_test.cc | 16 + .../mkl_threadpool_device_test.cc | 53 +++ .../core/common_runtime/process_util.cc | 11 +- .../core/common_runtime/threadpool_device.cc | 25 +- .../rpc/grpc_master_service_impl.cc | 4 +- .../distributed_runtime/rpc/grpc_testlib.cc | 10 +- tensorflow/core/framework/allocator.h | 5 - tensorflow/core/framework/op_gen_lib.cc | 1 + .../remote_fused_graph_execute_info.proto | 2 +- tensorflow/core/framework/tensor_test.cc | 24 +- tensorflow/core/graph/mkl_layout_pass.cc | 148 ++++++- tensorflow/core/graph/mkl_layout_pass_test.cc | 31 ++ .../grappler/clusters/single_machine_test.cc | 8 +- .../core/grappler/costs/graph_properties.cc | 1 - tensorflow/core/grappler/optimizers/BUILD | 2 +- .../core/grappler/optimizers/remapper.cc | 4 +- tensorflow/core/kernels/as_string_op.cc | 2 + tensorflow/core/kernels/cwise_op_clip.cc | 43 +- .../kernels/dense_update_functor_gpu.cu.cc | 1 + tensorflow/core/kernels/gather_functor.cc | 1 + .../core/kernels/gather_functor_gpu.cu.cc | 1 + tensorflow/core/kernels/gather_nd_op.cc | 4 + .../core/kernels/gather_nd_op_gpu.cu.cc | 2 + tensorflow/core/kernels/gather_op.cc | 1 + tensorflow/core/kernels/mkl_concat_op.cc | 213 ++++++--- .../core/kernels/mkl_conv_grad_bias_ops.cc | 2 + .../core/kernels/mkl_pooling_ops_common.h | 6 +- tensorflow/core/kernels/scatter_nd_op.cc | 4 + .../core/kernels/scatter_nd_op_gpu.cu.cc | 1 + .../core/kernels/scoped_allocator_ops_test.cc | 9 +- .../core/kernels/segment_reduction_ops.h | 10 +- tensorflow/core/kernels/sparse_matmul_op.cc | 2 +- tensorflow/core/kernels/string_split_op.cc | 130 ++++++ tensorflow/core/ops/candidate_sampling_ops.cc | 5 +- tensorflow/core/ops/dataset_ops.cc | 24 +- tensorflow/core/ops/image_ops.cc | 4 +- tensorflow/core/ops/math_ops.cc | 2 +- tensorflow/core/ops/nn_ops.cc | 1 + tensorflow/core/ops/string_ops.cc | 20 +- tensorflow/core/platform/cpu_info.cc | 23 + tensorflow/core/platform/cpu_info.h | 7 + .../core/platform/default/build_config.bzl | 2 + .../platform/hadoop/hadoop_file_system.cc | 21 +- tensorflow/core/platform/posix/port.cc | 5 + tensorflow/core/public/version.h | 4 +- tensorflow/core/util/mkl_util.h | 50 ++- tensorflow/docs_src/community/groups.md | 29 +- tensorflow/docs_src/get_started/eager.md | 2 +- tensorflow/docs_src/get_started/index.md | 4 +- tensorflow/docs_src/install/install_c.md | 2 +- tensorflow/docs_src/install/install_go.md | 2 +- tensorflow/docs_src/install/install_java.md | 24 +- tensorflow/docs_src/install/install_linux.md | 24 +- tensorflow/docs_src/install/install_mac.md | 10 +- .../docs_src/install/install_sources.md | 17 +- tensorflow/docs_src/mobile/linking_libs.md | 2 +- tensorflow/docs_src/mobile/prepare_models.md | 4 +- .../docs_src/performance/quantization.md | 2 +- .../docs_src/programmers_guide/estimators.md | 19 +- .../programmers_guide/feature_columns.md | 4 +- tensorflow/examples/learn/iris.py | 7 +- tensorflow/java/src/gen/cc/op_generator.cc | 11 +- tensorflow/java/src/gen/cc/op_specs.cc | 1 + tensorflow/python/eager/backprop.py | 4 +- tensorflow/python/estimator/BUILD | 5 +- tensorflow/python/estimator/exporter.py | 4 +- .../python/estimator/inputs/numpy_io.py | 8 +- .../python/estimator/inputs/numpy_io_test.py | 5 +- .../python/estimator/inputs/pandas_io.py | 7 +- .../python/estimator/inputs/pandas_io_test.py | 5 +- .../inputs/queues/feeding_functions.py | 2 +- tensorflow/python/estimator/keras.py | 4 +- tensorflow/python/estimator/keras_test.py | 14 +- tensorflow/python/keras/activations.py | 2 + tensorflow/python/keras/callbacks.py | 21 +- tensorflow/python/keras/callbacks_test.py | 2 + tensorflow/python/keras/engine/network.py | 2 +- tensorflow/python/keras/engine/saving_test.py | 4 +- tensorflow/python/keras/engine/training.py | 7 +- .../python/keras/engine/training_eager.py | 2 +- tensorflow/python/keras/initializers_test.py | 26 +- tensorflow/python/keras/layers/core.py | 26 +- tensorflow/python/keras/models_test.py | 14 + .../python/kernel_tests/as_string_op_test.py | 10 + .../python/kernel_tests/betainc_op_test.py | 4 +- .../python/kernel_tests/clip_ops_test.py | 13 + .../python/kernel_tests/conv_ops_test.py | 32 +- .../python/kernel_tests/gather_nd_op_test.py | 32 +- .../python/kernel_tests/gather_op_test.py | 20 +- .../python/kernel_tests/init_ops_test.py | 27 ++ .../python/kernel_tests/pooling_ops_test.py | 4 +- .../python/kernel_tests/py_func_test.py | 31 +- .../kernel_tests/scatter_nd_ops_test.py | 6 +- .../python/kernel_tests/scatter_ops_test.py | 14 +- .../segment_reduction_ops_test.py | 4 +- .../kernel_tests/string_split_op_test.py | 96 ++++ tensorflow/python/ops/array_ops.py | 4 + tensorflow/python/ops/gradient_checker.py | 8 +- tensorflow/python/ops/image_ops_impl.py | 74 ++-- tensorflow/python/ops/image_ops_test.py | 261 +++++++++-- tensorflow/python/ops/init_ops.py | 3 +- tensorflow/python/ops/logging_ops.py | 5 +- tensorflow/python/ops/math_ops.py | 28 +- tensorflow/python/ops/nn_impl.py | 5 +- tensorflow/python/ops/nn_ops.py | 4 +- tensorflow/python/ops/nn_test.py | 10 + tensorflow/python/ops/script_ops.py | 35 +- tensorflow/python/ops/sparse_ops.py | 4 + tensorflow/python/ops/string_ops.py | 53 +++ tensorflow/python/ops/variable_scope.py | 21 +- .../python/tools/import_pb_to_tensorboard.py | 0 tensorflow/tensorflow.bzl | 2 +- .../tools/api/generator/create_python_api.py | 8 +- .../tools/api/golden/tensorflow.image.pbtxt | 2 +- tensorflow/tools/api/golden/tensorflow.pbtxt | 4 + .../tools/api/golden/tensorflow.strings.pbtxt | 4 + tensorflow/tools/ci_build/builds/pip.sh | 4 + .../tools/ci_build/builds/with_the_same_user | 2 +- tensorflow/tools/ci_build/ci_build.sh | 7 + tensorflow/tools/ci_build/copy_binary.py | 3 +- .../ci_build/install/install_pip_packages.sh | 4 + .../install/install_python3.5_pip_packages.sh | 4 +- .../install/install_python3.6_pip_packages.sh | 5 +- .../ci_build/linux/mkl/basic-mkl-test.sh | 29 ++ .../tools/ci_build/pi/build_raspberry_pi.sh | 8 +- .../def_file_filter_configure.bzl | 6 +- tensorflow/tools/dist_test/local_test.sh | 12 +- tensorflow/tools/dist_test/remote_test.sh | 11 +- tensorflow/tools/docker/Dockerfile.devel | 2 +- .../tools/docker/Dockerfile.devel-cpu-mkl | 2 +- tensorflow/tools/docker/Dockerfile.devel-gpu | 6 +- tensorflow/tools/docker/Dockerfile.gpu | 2 +- tensorflow/tools/pip_package/BUILD | 1 + .../tools/pip_package/build_pip_package.sh | 160 +++++-- tensorflow/tools/pip_package/setup.py | 3 +- .../gen_proto_text_functions_lib.cc | 3 + .../tools/quantization/quantize_graph_test.py | 12 +- .../tools/test/upload_test_benchmarks.py | 1 - tensorflow/workspace.bzl | 40 +- third_party/eigen.BUILD | 1 + third_party/highwayhash.BUILD | 1 + third_party/jpeg/jpeg.BUILD | 2 + third_party/png.BUILD | 9 +- third_party/py/python_configure.bzl | 24 +- third_party/repo.bzl | 5 +- 231 files changed, 3338 insertions(+), 905 deletions(-) create mode 100644 tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.cc create mode 100644 tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.h create mode 100644 tensorflow/contrib/periodic_resample/ops/array_ops_test.cc create mode 100644 tensorflow/core/api_def/base_api/api_def_StringSplitV2.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_StringSplitV2.pbtxt create mode 100644 tensorflow/core/common_runtime/mkl_threadpool_device_test.cc mode change 100755 => 100644 tensorflow/python/tools/import_pb_to_tensorboard.py create mode 100755 tensorflow/tools/ci_build/linux/mkl/basic-mkl-test.sh diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 8669c25c45..db4b1581ae 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -90,7 +90,7 @@ Bazel BUILD files also need to include a license section, e.g., Changes to TensorFlow C++ code should conform to [Google C++ Style Guide](https://google.github.io/styleguide/cppguide.html). -Use `clang-tidy` to check your C/C++ changes. To install clang-tidy on ubuntu:16.04, do: +Use `clang-tidy` to check your C/C++ changes. To install `clang-tidy` on ubuntu:16.04, do: ```bash apt-get install -y clang-tidy diff --git a/README.md b/README.md index 6fb4486d0d..63853137cf 100644 --- a/README.md +++ b/README.md @@ -56,6 +56,7 @@ $ python 42 >>> sess.close() ``` +Learn more examples about how to do specific tasks in TensorFlow at the [tutorials page of tensorflow.org](https://www.tensorflow.org/tutorials/). ## Contribution guidelines diff --git a/RELEASE.md b/RELEASE.md index 84d9d52868..e09e9c6190 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -1,3 +1,62 @@ +# Release 1.9.0 + +## Major Features And Improvements +* Update tf.keras to the Keras 2.1.6 API. +* `tfe.Network` is deprecated. Please inherit from `tf.keras.Model`. +* Adding support of core feature columns and losses to gradient boosted trees estimators. +* The distributions.Bijector API supports broadcasting for Bijectors with new API changes. See [here](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/distributions/bijectors/Bijector) for more details. +* Layered variable names have changed in the following conditions: + * Using `tf.keras.layers` with custom variable scopes. + * Using `tf.layers` in a subclassed `tf.keras.Model` class. See [here](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/layers) for more details + +## Breaking Chances + * If you're opening empty variable scopes; replace `variable_scope`('', ...) by `variable_scope`(`tf.get_variable_scope()`, ...). + +## Bug Fixes and Other Changes +* `tf.data`: + * The `DatasetBase::DebugString()` method is now `const`. + * Added the `tf.contrib.data.sample_from_datasets()` API for randomly sampling from multiple datasets. +* Eager Execution: +* `tf.keras`: + * Move Keras code out of _impl folder and remove API files. + * `tf.keras.Model.save_weights` now saves in TensorFlow format by default. + * Enable dataset iterators to be passed to `tf.keras.Model` training/eval methods. +* Accelerated Linear Algebra (XLA): +* TensorFlow Debugger (tfdbg): fix an issue in which the TensorBoard Debugger Plugin could not handle total source file size exceeding gRPC message size limit (4 MB). +* `tf.contrib`: + * Add `tf.contrib.data.choose_from_datasets()`. + * `tf.contrib.data.make_csv_dataset()` now supports line breaks in quoted strings. Two arguments were removed from `make_csv_dataset`. + * `tf.contrib.framework.zero_initializer` supports ResourceVariable. + * Adding "constrained_optimization" to tensorflow/contrib. +* Other: + * Add GCS Configuration Ops. + * Changing signature of `MakeIterator` to enable propagating error status. + * KL divergence for two Dirichlet distributions. + * More consistent GcsFileSystem behavior for certain reads past EOF. + * Update benchmark for tf.scan to match ranges across eager and graph modes. + * Fixed bug in `tf.reduce_prod gradient` for complex dtypes. + * Add optional `args` argument to `Dataset.from_generator()`. + * Allow the use of '.' in variables (e.g. "hparams.parse('a.b=1.0')"), which would previously raise an error. This will correspond to an attribute name with an embedded '.' symbol (e.g. 'a.b'), which can only be accessed indirectly (e.g. through getattr and setattr). To set this up the user will first need to explicitly add the variable to the hparam object (e.g. "hparams.add_hparam(name='a.b', value=0.0)"). + * Benchmark for tf.scan in graph and eager modes. + * Added complex128 support to FFT, FFT2D, FFT3D, IFFT, IFFT2D, and IFFT3D. + * Making ids unique in `nn.embedding_lookup_sparse`. This helps to reduce RPC calls for looking up the embeddings when there are repeated ids in the batch. + * Support indicator column in boosted trees. + * Prevent `tf.gradients()` from backpropagating through integer tensors. + * LinearOperator[1D,2D,3D]Circulant added to `tensorflow.linalg`. + * Conv3D, Conv3DBackpropInput, Conv3DBackpropFilter now supports arbitrary. + * Added `tf.train.Checkpoint` for reading/writing object-based checkpoints. + * `Dataset.list_files()` now produces determinstic results when `shuffle=False` or a `seed` is passed. + * Added LinearOperatorKronecker, a dense-free implementation of the Kronecker Product. + * Allow LinearOperator to broadcast. + * SavedModelBuilder will now deduplicate asset names that point to files with the same basename and the same contents. Note that this may result in new asset files included in SavedModels in cases where assets with the same name but different contents were previously overwriting each other. + + +## Thanks to our Contributors + +This release contains contributions from many people at Google, as well as: + +Abdullah Alrasheed, Achal Shah, Ad-530, ADiegoCAlonso, Aditya Yogi, Ag Ramesh, akindyakov, Andy Kernahan, Anya Petrova, Aurelien Geron, Ben, Ben Barsdell, Bhavani-Subramanian, braincodercn, Brett Koonce, Brian Nemsick, Brian Zier, Bryan Heden, candy.dc, cclauss, Clayne Robison, ctiijima, Dalmo Cirne, David Norman, David T.H. Kao, DosLin, ekelsen, Elson Rodriguez, Erik Smistad, Felix Abecassis, Fergal Cotter, fo40225, foo0x29a, Freedom" Koan-Sin Tan, FréDéRic Branchaud-Charron, gdh1995, Geoffrey Irving, Giuseppe, gracehoney, Guido Zuidhof, Guillaume Klein, Guozhong Zhuang, Haggai, Harald Husum, imsheridan, Ivan Zhang, Jan Zikes, Jayaram Bobba, Jesse Benson, Jesse Gumz, Jiajia Li, Jie, jinghuangintel, Jingwen, jjsjann123, Joe Yearsley, Joel Hestness, Joel Shor, josephyearsley, Junpeng Lao, Karol M. Langner, Kb Sriram, krantideep95, Krish Ravindranath, Letian Feng, Loo Rong Jie, Lukas Geiger, Maciej, Mahmoud Abuzaina, ManHyuk, Mark Ryan, mbhuiyan, Michal Turek, Mostafa Alaa, Myungsung Kwak, Nand Dalal, Nehal J Wani, Neil Tenenholtz, ngc92, Nicholas Nadeau, P.Eng., Avs, Niranjan Hasabnis, P-Hidringer, Paul Van Eck, Peng Yu, Qing Zhao, Qingying Chen, Quanlong, Rajendra Arora, Rholais Lii, rmanyari, Robin Richtsfeld, Russell Klopfer, Sagi, Sam Sendelbach, Sandeep N Gupta, Sandip Giri, Sarah Edkins, Scott Tseng, Sdalbsoo, Sergii Khomenko, Seungwoo Choi (Biggie), Seyed Majid Azimi, Shaoning Zeng, shengfuintel, Siu Kei, Muk, Smit Shilu, soonson, Stefan Schweter, Sukhwan Kim, Sunitha Kambhampati, Taehoon Lee, tamimaddari82, Tang, Wenyi, Ted Chang, u2takey, Utkarsh Upadhyay, Vadim Markovtsev, voegtlel, Wai Hon Law, wangsiyu, Wenhao Hu, wenhao.hu, William D. Irons, Yan Facai (颜发才), Yanbo Liang, Yihong Wang, Yilei (Dolee) Yang, Yong Tang, Yuan (Terry) Tang + # Release 1.8.0 ## Major Features And Improvements @@ -404,14 +463,6 @@ answered questions, and were part of inspiring discussions. # Release 1.4.0 -## Major Features And Improvements -* `tf.keras` is now part of the core TensorFlow API. -* [`tf.data`](http://tensorflow.org/programmers_guide/datasets) is now part of - the core TensorFlow API. - * The API is now subject to backwards compatibility guarantees. - -# Release 1.4.0 - ## Major Features And Improvements * `tf.keras` is now part of the core TensorFlow API. * [`tf.data`](http://tensorflow.org/programmers_guide/datasets) is now part of diff --git a/configure.py b/configure.py index bde7af8c0e..ada342a50a 100644 --- a/configure.py +++ b/configure.py @@ -1397,6 +1397,10 @@ def set_grpc_build_flags(): write_to_bazelrc('build --define grpc_no_ares=true') +def set_build_strip_flag(): + write_to_bazelrc('build --strip=always') + + def set_windows_build_flags(): if is_windows(): # The non-monolithic build is not supported yet @@ -1519,6 +1523,7 @@ def main(): set_grpc_build_flags() set_cc_opt_flags(environ_cp) + set_build_strip_flag() set_windows_build_flags() if get_var( diff --git a/tensorflow/BUILD b/tensorflow/BUILD index a73c4ca3aa..6d134dbb80 100644 --- a/tensorflow/BUILD +++ b/tensorflow/BUILD @@ -475,7 +475,7 @@ tf_cc_shared_object( # excludes all but a subset of function names. # On MacOS, the linker does not support version_script, but has an # an "-exported_symbols_list" command. -z defs disallows undefined -# symbols in object files and -s strips the output. +# symbols in object files. tf_cc_shared_object( name = "libtensorflow.so", @@ -489,7 +489,6 @@ tf_cc_shared_object( "//tensorflow:windows_msvc": [], "//conditions:default": [ "-z defs", - "-s", "-Wl,--version-script", # This line must be directly followed by the version_script.lds file "$(location //tensorflow/c:version_script.lds)", ], @@ -515,7 +514,6 @@ tf_cc_shared_object( "//tensorflow:windows_msvc": [], "//conditions:default": [ "-z defs", - "-s", "-Wl,--version-script", # This line must be directly followed by the version_script.lds file "$(location //tensorflow:tf_version_script.lds)", ], diff --git a/tensorflow/c/generate-pc.sh b/tensorflow/c/generate-pc.sh index 02a6a58b61..7184ad68fb 100755 --- a/tensorflow/c/generate-pc.sh +++ b/tensorflow/c/generate-pc.sh @@ -15,10 +15,12 @@ # ============================================================================== TF_PREFIX='/usr/local' +LIBDIR='lib' usage() { echo "Usage: $0 OPTIONS" echo -e "-p, --prefix\tset installation prefix (default: /usr/local)" + echo -e "-l, --libdir\tset lib directory (default: lib)" echo -e "-v, --version\tset TensorFlow version" echo -e "-h, --help\tdisplay this message" } @@ -26,7 +28,7 @@ usage() { [ $# == 0 ] && usage && exit 0 # read the options -ARGS=$(getopt -o p:v:h --long prefix:,version:,help -n $0 -- "$@") +ARGS=$(getopt -o p:l:v:h --long prefix:,libdir:,version:,help -n $0 -- "$@") eval set -- "$ARGS" # extract options and their arguments into variables. @@ -38,6 +40,11 @@ while true ; do "") shift 2 ;; *) TF_PREFIX=$2 ; shift 2 ;; esac ;; + -l|--libdir) + case "$2" in + "") shift 2 ;; + *) LIBDIR=$2 ; shift 2 ;; + esac ;; -v|--version) case "$2" in "") shift 2 ;; @@ -55,7 +62,7 @@ echo "Generating pkgconfig file for TensorFlow $TF_VERSION in $TF_PREFIX" cat << EOF > tensorflow.pc prefix=${TF_PREFIX} exec_prefix=\${prefix} -libdir=\${exec_prefix}/lib +libdir=\${exec_prefix}/${LIBDIR} includedir=\${prefix}/include Name: TensorFlow diff --git a/tensorflow/cc/gradients/math_grad.cc b/tensorflow/cc/gradients/math_grad.cc index 52c177212a..35a01e0341 100644 --- a/tensorflow/cc/gradients/math_grad.cc +++ b/tensorflow/cc/gradients/math_grad.cc @@ -38,6 +38,7 @@ REGISTER_NO_GRADIENT_OP("NotEqual"); REGISTER_NO_GRADIENT_OP("LogicalAnd"); REGISTER_NO_GRADIENT_OP("LogicalOr"); REGISTER_NO_GRADIENT_OP("LogicalNot"); +REGISTER_NO_GRADIENT_OP("Floor"); // Conjugate helper function returns the conjugate of an Output if it // is complex valued. diff --git a/tensorflow/cc/gradients/nn_grad.cc b/tensorflow/cc/gradients/nn_grad.cc index 0cb3132e94..c73482d5f4 100644 --- a/tensorflow/cc/gradients/nn_grad.cc +++ b/tensorflow/cc/gradients/nn_grad.cc @@ -255,6 +255,53 @@ Status LRNGradHelper(const Scope& scope, const Operation& op, } REGISTER_GRADIENT_OP("LRN", LRNGradHelper); +Status SoftplusGradHelper(const Scope& scope, const Operation& op, + const std::vector& grad_inputs, + std::vector* grad_outputs) { + auto dx = internal::SoftplusGrad(scope, grad_inputs[0], op.input(0)); + grad_outputs->push_back(dx); + return scope.status(); +} +REGISTER_GRADIENT_OP("Softplus", SoftplusGradHelper); + +Status SoftsignGradHelper(const Scope& scope, const Operation& op, + const std::vector& grad_inputs, + std::vector* grad_outputs) { + auto dx = internal::SoftsignGrad(scope, grad_inputs[0], op.input(0)); + grad_outputs->push_back(dx); + return scope.status(); +} +REGISTER_GRADIENT_OP("Softsign", SoftsignGradHelper); + +Status FractionalAvgPoolGradHelper(const Scope& scope, const Operation& op, + const std::vector& grad_inputs, + std::vector* grad_outputs) { + bool overlapping; + TF_RETURN_IF_ERROR( + GetNodeAttr(op.output(0).node()->attrs(), "overlapping", &overlapping)); + auto dx = internal::FractionalAvgPoolGrad( + scope, Shape(scope, op.input(0), Shape::OutType(DT_INT64)), + grad_inputs[0], op.output(1), op.output(2), + internal::FractionalAvgPoolGrad::Overlapping(overlapping)); + grad_outputs->push_back(dx); + return scope.status(); +} +REGISTER_GRADIENT_OP("FractionalAvgPool", FractionalAvgPoolGradHelper); + +Status FractionalMaxPoolGradHelper(const Scope& scope, const Operation& op, + const std::vector& grad_inputs, + std::vector* grad_outputs) { + bool overlapping; + TF_RETURN_IF_ERROR( + GetNodeAttr(op.output(0).node()->attrs(), "overlapping", &overlapping)); + auto dx = internal::FractionalMaxPoolGrad( + scope, op.input(0), op.output(0), grad_inputs[0], op.output(1), + op.output(2), internal::FractionalMaxPoolGrad::Overlapping(overlapping)); + grad_outputs->push_back(dx); + return scope.status(); +} +REGISTER_GRADIENT_OP("FractionalMaxPool", FractionalMaxPoolGradHelper); + } // anonymous namespace } // namespace ops } // namespace tensorflow diff --git a/tensorflow/cc/gradients/nn_grad_test.cc b/tensorflow/cc/gradients/nn_grad_test.cc index c4eba7ecb0..b4d457a9d1 100644 --- a/tensorflow/cc/gradients/nn_grad_test.cc +++ b/tensorflow/cc/gradients/nn_grad_test.cc @@ -28,6 +28,8 @@ namespace { using ops::BiasAdd; using ops::Conv2D; using ops::Elu; +using ops::FractionalAvgPool; +using ops::FractionalMaxPool; using ops::L2Loss; using ops::LogSoftmax; using ops::LRN; @@ -41,6 +43,8 @@ using ops::Relu; using ops::Relu6; using ops::Selu; using ops::Softmax; +using ops::Softplus; +using ops::Softsign; class NNGradTest : public ::testing::Test { protected: @@ -71,22 +75,30 @@ class NNGradTest : public ::testing::Test { EXPECT_LT(max_error, 1e-3); } - // Sets tensor with random values, ensuring that the max value is largest by - // a reasonable amount. - // This is an issue for MaxPool, MaxPoolV2 and MaxPool3D, in which - // perturbations by the numeric gradient computation in the gradient checker - // can change the max value if values are too close together. + // Sets tensor with random values, ensuring that every pair of elements are at + // least a reasonable amount apart. + // This is an issue for max pooling operations, in which perturbations by the + // numeric gradient computation in the gradient checker can change the max + // value if a pool has values that are too close together. template - void SetRandomValuesWithBumpedMax(Tensor* tensor) { + void SetRandomValuesForMaxPooling(Tensor* tensor) { auto tensor_flat = tensor->flat(); - tensor_flat.setRandom(); - int32 max_index = 0; - for (size_t i = 1; i < tensor->NumElements(); i++) { - if (tensor_flat(i) > tensor_flat(max_index)) { - max_index = i; - } + // First set the array to an increasing sequence of values spaced + // a reasonable amount apart + T cur = 0; + for (size_t i = 0; i < tensor->NumElements(); i++) { + tensor_flat(i) = cur; + cur += 5e-2; + } + // Fischer-Yates shuffle the array + for (size_t i = tensor->NumElements() - 1; i >= 1; i--) { + // j <- random integer 0 <= j <= i + size_t j = random::New64() % (i + 1); + // swap values at i, j + T tmp = tensor_flat(i); + tensor_flat(i) = tensor_flat(j); + tensor_flat(j) = tmp; } - tensor_flat(max_index) += 1e-2; } Scope scope_; @@ -189,7 +201,7 @@ TEST_F(NNGradTest, MaxPoolGradHelper) { const std::vector strides{1, 2, 2, 1}; auto y = MaxPool(scope_, x, ksize, strides, "VALID"); Tensor x_init_value = Tensor(DT_FLOAT, x_shape); - SetRandomValuesWithBumpedMax(&x_init_value); + SetRandomValuesForMaxPooling(&x_init_value); RunTest(x, x_init_value, y, y_shape); } @@ -202,7 +214,7 @@ TEST_F(NNGradTest, MaxPoolGradV2Helper) { Tensor strides = test::AsTensor({1, 2, 2, 1}, {4}); auto y = MaxPoolV2(scope_, x, ksize, strides, "VALID"); Tensor x_init_value = Tensor(DT_FLOAT, x_shape); - SetRandomValuesWithBumpedMax(&x_init_value); + SetRandomValuesForMaxPooling(&x_init_value); RunTest(x, x_init_value, y, y_shape); } @@ -215,7 +227,7 @@ TEST_F(NNGradTest, MaxPool3DGradHelper) { const std::vector strides{1, 3, 3, 3, 1}; auto y = MaxPool3D(scope_, x, ksize, strides, "VALID"); Tensor x_init_value = Tensor(DT_FLOAT, x_shape); - SetRandomValuesWithBumpedMax(&x_init_value); + SetRandomValuesForMaxPooling(&x_init_value); RunTest(x, x_init_value, y, y_shape); } @@ -248,5 +260,45 @@ TEST_F(NNGradTest, LRN){ RunTest(x, x_shape, y, x_shape); } +TEST_F(NNGradTest, SoftplusGrad) { + TensorShape shape({3, 7}); + auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape)); + auto y = Softplus(scope_, x); + RunTest(x, shape, y, shape); +} + +TEST_F(NNGradTest, SoftsignGrad) { + TensorShape shape({3, 7}); + auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape)); + auto y = Softsign(scope_, x); + RunTest(x, shape, y, shape); +} + +TEST_F(NNGradTest, FractionalAvgPoolGradHelper) { + TensorShape x_shape({1, 3, 7, 1}); + auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape)); + // Force consistent pooling regions for unit testing. + auto y = FractionalAvgPool( + scope_, x, {1, 1.2, 1.9, 1}, + FractionalAvgPool::Deterministic(true).Overlapping(true).Seed(1).Seed2( + 2)); + TensorShape y_shape({1, 2, 3, 1}); + RunTest(x, x_shape, y.output, y_shape); +} + +TEST_F(NNGradTest, FractionalMaxPoolGradHelper) { + TensorShape x_shape({1, 3, 7, 1}); + auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape)); + // Force consistent pooling regions for unit testing. + auto y = FractionalMaxPool( + scope_, x, {1, 1.2, 1.9, 1}, + FractionalMaxPool::Deterministic(true).Overlapping(true).Seed(1).Seed2( + 2)); + Tensor x_init_value = Tensor(DT_FLOAT, x_shape); + SetRandomValuesForMaxPooling(&x_init_value); + TensorShape y_shape({1, 2, 3, 1}); + RunTest(x, x_init_value, y.output, y_shape); +} + } // namespace } // namespace tensorflow diff --git a/tensorflow/compiler/aot/codegen_test_h.golden b/tensorflow/compiler/aot/codegen_test_h.golden index 6e050cf564..6641d45e83 100644 --- a/tensorflow/compiler/aot/codegen_test_h.golden +++ b/tensorflow/compiler/aot/codegen_test_h.golden @@ -56,9 +56,9 @@ namespace bar { // // Memory stats: // arg bytes total: 104 -// arg bytes aligned: 128 +// arg bytes aligned: 192 // temp bytes total: 126 -// temp bytes aligned: 224 +// temp bytes aligned: 320 class MyClass : public tensorflow::XlaCompiledCpuFunction { public: // Number of input arguments for the compiled computation. diff --git a/tensorflow/compiler/aot/embedded_protocol_buffers.h b/tensorflow/compiler/aot/embedded_protocol_buffers.h index ebfe4806c2..4e194a6aba 100644 --- a/tensorflow/compiler/aot/embedded_protocol_buffers.h +++ b/tensorflow/compiler/aot/embedded_protocol_buffers.h @@ -71,7 +71,7 @@ struct ProtobufToEmbed { const ::tensorflow::protobuf::MessageLite* message; }; -// Embeds a a sequence of protocol buffers into an object file. +// Embeds a sequence of protocol buffers into an object file. // // `target_triple` is the target triple for the target architecture for the // generated object file. diff --git a/tensorflow/compiler/aot/runtime.h b/tensorflow/compiler/aot/runtime.h index d085864f00..d1a669ceb1 100644 --- a/tensorflow/compiler/aot/runtime.h +++ b/tensorflow/compiler/aot/runtime.h @@ -25,8 +25,8 @@ namespace tensorflow { namespace tfcompile { namespace runtime { -// Align to 32-bytes, to mimic tensorflow::Allocator::kAllocatorAlignment. -static constexpr size_t kAlign = 32; +// Align to 64-bytes, to mimic tensorflow::Allocator::kAllocatorAlignment. +static constexpr size_t kAlign = 64; // aligned_buffer_bytes returns the sum of each size in `sizes`, skipping -1 // values. There are `n` entries in `sizes`. Each buffer is aligned to kAlign diff --git a/tensorflow/compiler/aot/runtime_test.cc b/tensorflow/compiler/aot/runtime_test.cc index 6d603a02eb..06ec623eb2 100644 --- a/tensorflow/compiler/aot/runtime_test.cc +++ b/tensorflow/compiler/aot/runtime_test.cc @@ -24,7 +24,7 @@ namespace runtime { namespace { TEST(Runtime, AlignmentValue) { - // We've chosen 32 byte alignment for the tfcompile runtime to mimic the + // We've chosen 64 byte alignment for the tfcompile runtime to mimic the // regular tensorflow allocator, which was chosen to play nicely with Eigen. // The tfcompile runtime also has a requirement that comes from the xla // generated code, on the relation: buffer_size >= 16 ? 2 * sizeof(void*) : 8 @@ -39,13 +39,13 @@ TEST(Runtime, AlignedBufferBytes) { EXPECT_EQ(aligned_buffer_bytes(sizesA, 1), 0); static constexpr intptr_t sizesB[1] = {3}; - EXPECT_EQ(aligned_buffer_bytes(sizesB, 1), 32); + EXPECT_EQ(aligned_buffer_bytes(sizesB, 1), 64); static constexpr intptr_t sizesC[1] = {32}; - EXPECT_EQ(aligned_buffer_bytes(sizesC, 1), 32); + EXPECT_EQ(aligned_buffer_bytes(sizesC, 1), 64); static constexpr intptr_t sizesD[7] = {1, -1, 32, -1, 64, 2, 3}; - EXPECT_EQ(aligned_buffer_bytes(sizesD, 7), 192); + EXPECT_EQ(aligned_buffer_bytes(sizesD, 7), 320); } void* add_ptr(void* base, uintptr_t delta) { @@ -101,11 +101,11 @@ TEST(Runtime, MallocFreeContiguousBuffers) { EXPECT_NE(base, nullptr); EXPECT_EQ(bufD[0], add_ptr(base, 0)); EXPECT_EQ(bufD[1], nullptr); - EXPECT_EQ(bufD[2], add_ptr(base, 32)); + EXPECT_EQ(bufD[2], add_ptr(base, 64)); EXPECT_EQ(bufD[3], nullptr); - EXPECT_EQ(bufD[4], add_ptr(base, 64)); - EXPECT_EQ(bufD[5], add_ptr(base, 128)); - EXPECT_EQ(bufD[6], add_ptr(base, 160)); + EXPECT_EQ(bufD[4], add_ptr(base, 128)); + EXPECT_EQ(bufD[5], add_ptr(base, 192)); + EXPECT_EQ(bufD[6], add_ptr(base, 256)); for (int i = 0; i < 7; ++i) { const intptr_t size = sizesD[i]; if (size != -1) { diff --git a/tensorflow/compiler/xla/service/cpu/BUILD b/tensorflow/compiler/xla/service/cpu/BUILD index d82922a359..1067b38f93 100644 --- a/tensorflow/compiler/xla/service/cpu/BUILD +++ b/tensorflow/compiler/xla/service/cpu/BUILD @@ -178,6 +178,7 @@ cc_library( ":runtime_matmul", ":runtime_matmul_mkl", ":runtime_single_threaded_conv2d", + ":runtime_single_threaded_fft", ":runtime_single_threaded_matmul", "@llvm//:execution_engine", "@llvm//:core", @@ -516,7 +517,6 @@ cc_library( deps = [ "//tensorflow/compiler/xla:executable_run_options", "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/core:framework", "//tensorflow/core:framework_lite", "//third_party/eigen3", ], @@ -578,6 +578,22 @@ cc_library( ], ) +cc_library( + name = "runtime_single_threaded_fft", + srcs = [ + "runtime_fft_impl.h", + "runtime_single_threaded_fft.cc", + ], + hdrs = ["runtime_single_threaded_fft.h"], + copts = runtime_copts(), + visibility = ["//visibility:public"], + deps = [ + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/core:framework_lite", + "//third_party/eigen3", + ], +) + cc_library( name = "runtime_single_threaded_matmul", srcs = ["runtime_single_threaded_matmul.cc"], diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc b/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc index 215405f680..54c52bc08f 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc @@ -51,6 +51,8 @@ extern const char* const kEigenConvF16SymbolName = extern const char* const kEigenConvF32SymbolName = "__xla_cpu_runtime_EigenConvF32"; extern const char* const kEigenFftSymbolName = "__xla_cpu_runtime_EigenFft"; +extern const char* const kEigenSingleThreadedFftSymbolName = + "__xla_cpu_runtime_EigenSingleThreadedFft"; extern const char* const kEigenSingleThreadedMatMulF16SymbolName = "__xla_cpu_runtime_EigenSingleThreadedMatMulF16"; extern const char* const kEigenSingleThreadedMatMulF32SymbolName = diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime.h b/tensorflow/compiler/xla/service/cpu/cpu_runtime.h index 1dce6efa5c..aa0e967123 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_runtime.h +++ b/tensorflow/compiler/xla/service/cpu/cpu_runtime.h @@ -52,6 +52,7 @@ extern const char* const kMKLSingleThreadedMatMulF64SymbolName; extern const char* const kEigenConvF16SymbolName; extern const char* const kEigenConvF32SymbolName; extern const char* const kEigenFftSymbolName; +extern const char* const kEigenSingleThreadedFftSymbolName; extern const char* const kEigenSingleThreadedMatMulF16SymbolName; extern const char* const kEigenSingleThreadedMatMulF32SymbolName; extern const char* const kEigenSingleThreadedMatMulF64SymbolName; diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc index 2c20be155f..758b8c62b4 100644 --- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc +++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc @@ -1172,7 +1172,13 @@ Status IrEmitter::HandleFft(HloInstruction* fft) { {int8_ptr_type, int8_ptr_type, int8_ptr_type, int32_type, int32_type, int64_type, int64_type, int64_type, int64_type}, /*isVarArg=*/false); - const char* fn_name = runtime::kEigenFftSymbolName; + + bool multi_threaded_eigen = + hlo_module_config_.debug_options().xla_cpu_multi_thread_eigen(); + const char* fn_name = multi_threaded_eigen + ? runtime::kEigenFftSymbolName + : runtime::kEigenSingleThreadedFftSymbolName; + llvm::Function* fft_func = llvm::cast( module_->getOrInsertFunction(fn_name, fft_type)); fft_func->setCallingConv(llvm::CallingConv::C); diff --git a/tensorflow/compiler/xla/service/cpu/runtime_fft_impl.h b/tensorflow/compiler/xla/service/cpu/runtime_fft_impl.h index 984cb0616e..0bf693edd0 100644 --- a/tensorflow/compiler/xla/service/cpu/runtime_fft_impl.h +++ b/tensorflow/compiler/xla/service/cpu/runtime_fft_impl.h @@ -21,8 +21,6 @@ limitations under the License. #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/framework/numeric_types.h" -#include "tensorflow/core/framework/tensor.h" -#include "tensorflow/core/framework/tensor_shape.h" #include "tensorflow/core/platform/types.h" // 'tensorflow' namespace is used so that int64 and other types don't require @@ -71,11 +69,9 @@ void EigenFftR2C(const EigenDevice& device, complex64* out, float* operand, in_dims[0] = input_batch; Eigen::DSizes out_dims; out_dims[0] = input_batch; - TensorShape temp_shape{input_batch}; for (int i = 0; i < FFTRank; i++) { in_dims[i + 1] = fft_shape[i]; out_dims[i + 1] = i == FFTRank - 1 ? fft_shape[i] / 2 + 1 : fft_shape[i]; - temp_shape.AddDim(fft_shape[i]); } const Eigen::TensorMap, Eigen::Aligned> @@ -88,8 +84,8 @@ void EigenFftR2C(const EigenDevice& device, complex64* out, float* operand, const auto axes = Eigen::ArrayXi::LinSpaced(FFTRank, 1, FFTRank); // Compute the full FFT using a temporary tensor. - Tensor temp(DataTypeToEnum::v(), temp_shape); - auto full_fft = temp.flat_inner_dims(); + Eigen::Tensor full_fft(in_dims); + const Eigen::DSizes zero_start_indices; full_fft.device(device) = input.template fft(axes); @@ -112,11 +108,9 @@ void EigenFftC2R(const EigenDevice& device, float* out, complex64* operand, in_dims[0] = input_batch; Eigen::DSizes out_dims; out_dims[0] = input_batch; - TensorShape temp_shape{input_batch}; for (int i = 0; i < FFTRank; i++) { in_dims[i + 1] = i == FFTRank - 1 ? fft_shape[i] / 2 + 1 : fft_shape[i]; out_dims[i + 1] = fft_shape[i]; - temp_shape.AddDim(fft_shape[i]); } const Eigen::TensorMap, Eigen::Aligned> @@ -129,8 +123,7 @@ void EigenFftC2R(const EigenDevice& device, float* out, complex64* operand, // region we will slice from input given fft_shape. We slice input to // fft_shape on its inner-most dimensions, except the last (which we // slice to fft_shape[-1] / 2 + 1). - Tensor temp(DataTypeToEnum::v(), temp_shape); - auto full_fft = temp.flat_inner_dims(); + Eigen::Tensor full_fft(out_dims); // Calculate the starting point and range of the source of // negative frequency part. @@ -179,7 +172,6 @@ template void EigenFftWithRank(const EigenDevice& device, void* out, void* operand, int32 fft_type, int64 input_batch, int64 fft_length0, int64 fft_length1, int64 fft_length2) { - CHECK(::xla::FftType_IsValid(fft_type)) << fft_type; switch (fft_type) { case ::xla::FftType::FFT: EigenFftC2C( @@ -204,7 +196,8 @@ void EigenFftWithRank(const EigenDevice& device, void* out, void* operand, input_batch, fft_length0, fft_length1, fft_length2); break; default: - LOG(FATAL) << "Unsupported FFT type: " << fft_type; + // Unsupported FFT type + abort(); } } @@ -230,7 +223,8 @@ void EigenFftImpl(const EigenDevice& device, void* out, void* operand, fft_length1, fft_length2); break; default: - LOG(FATAL) << "Unsupported FFT rank " << fft_rank; + // Unsupported FFT rank + abort(); } } diff --git a/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.cc b/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.cc new file mode 100644 index 0000000000..2613ddb127 --- /dev/null +++ b/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.cc @@ -0,0 +1,32 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.h" + +#include "tensorflow/compiler/xla/service/cpu/runtime_fft_impl.h" +#include "tensorflow/core/platform/dynamic_annotations.h" +#include "tensorflow/core/platform/types.h" + +using tensorflow::int32; +using tensorflow::int64; + +TF_ATTRIBUTE_NO_SANITIZE_MEMORY void __xla_cpu_runtime_EigenSingleThreadedFft( + const void* run_options_ptr, void* out, void* operand, int32 fft_type, + int32 fft_rank, int64 input_batch, int64 fft_length0, int64 fft_length1, + int64 fft_length2) { + tensorflow::xla::EigenFftImpl(Eigen::DefaultDevice(), out, operand, fft_type, + fft_rank, input_batch, fft_length0, fft_length1, + fft_length2); +} diff --git a/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.h b/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.h new file mode 100644 index 0000000000..dcd133d012 --- /dev/null +++ b/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.h @@ -0,0 +1,31 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_CPU_RUNTIME_SINGLE_THREADED_FFT_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_CPU_RUNTIME_SINGLE_THREADED_FFT_H_ + +#include "tensorflow/core/platform/types.h" + +extern "C" { + +extern void __xla_cpu_runtime_EigenSingleThreadedFft( + const void* /* xla::ExecutableRunOptions* */ run_options_ptr, void* out, + void* operand, tensorflow::int32 fft_type, tensorflow::int32 fft_rank, + tensorflow::int64 input_batch, tensorflow::int64 fft_length0, + tensorflow::int64 fft_length1, tensorflow::int64 fft_length2); + +} // extern "C" + +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_CPU_RUNTIME_SINGLE_THREADED_FFT_H_ diff --git a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc index 8d8c5e4c44..c4c90515ac 100644 --- a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc +++ b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc @@ -38,6 +38,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/cpu/runtime_matmul.h" #include "tensorflow/compiler/xla/service/cpu/runtime_matmul_mkl.h" #include "tensorflow/compiler/xla/service/cpu/runtime_single_threaded_conv2d.h" +#include "tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.h" #include "tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.h" #include "tensorflow/compiler/xla/service/cpu/windows_compatibility.h" #include "tensorflow/compiler/xla/types.h" @@ -202,6 +203,7 @@ bool RegisterKnownJITSymbols() { REGISTER_CPU_RUNTIME_SYMBOL(MKLSingleThreadedMatMulF64); REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedConvF16); REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedConvF32); + REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedFft); REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedMatMulF16); REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedMatMulF32); REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedMatMulF64); diff --git a/tensorflow/compiler/xla/service/pattern_matcher.h b/tensorflow/compiler/xla/service/pattern_matcher.h index d3bc47e61e..2515222cf2 100644 --- a/tensorflow/compiler/xla/service/pattern_matcher.h +++ b/tensorflow/compiler/xla/service/pattern_matcher.h @@ -204,7 +204,7 @@ class LayoutPattern { // Modifies the pattern to match only if the layout equals the given proto. // The layout must outlive the returned pattern. constexpr LayoutPattern> EqualTo( - const Layout* layout) const { + const ::xla::Layout* layout) const { return LayoutPattern>( LayoutPatternEqualImpl(impl_, layout), matched_layout_); } diff --git a/tensorflow/compiler/xla/service/tuple_simplifier.cc b/tensorflow/compiler/xla/service/tuple_simplifier.cc index e536c8afbf..77bdcc9de0 100644 --- a/tensorflow/compiler/xla/service/tuple_simplifier.cc +++ b/tensorflow/compiler/xla/service/tuple_simplifier.cc @@ -30,10 +30,17 @@ limitations under the License. namespace xla { +TupleSimplifier::TupleSimplifier(bool exclude_entry_computation) : + exclude_entry_computation_(exclude_entry_computation) {} + StatusOr TupleSimplifier::Run(HloModule* module) { // Initially add all GTE and Tuple instructions to the worklist. std::queue worklist; for (auto* computation : module->computations()) { + if (exclude_entry_computation_ && + computation == module->entry_computation()) { + continue; + } for (auto* instruction : computation->instructions()) { if (instruction->opcode() == HloOpcode::kTuple || instruction->opcode() == HloOpcode::kGetTupleElement) { diff --git a/tensorflow/compiler/xla/service/tuple_simplifier.h b/tensorflow/compiler/xla/service/tuple_simplifier.h index e5e9b10b5b..7509501883 100644 --- a/tensorflow/compiler/xla/service/tuple_simplifier.h +++ b/tensorflow/compiler/xla/service/tuple_simplifier.h @@ -27,13 +27,20 @@ namespace xla { // the module. class TupleSimplifier : public HloPassInterface { public: - TupleSimplifier() {} + TupleSimplifier() : TupleSimplifier(/*exclude_entry_computation=*/false) {} + explicit TupleSimplifier(bool exclude_entry_computation); ~TupleSimplifier() override {} tensorflow::StringPiece name() const override { return "tuple-simplifier"; } // Run tuple simplification on the given computation. Returns whether the // computation was changed. StatusOr Run(HloModule* module) override; + + private: + // When set, this pipeline stage will perform optimization of all computations + // apart from the module's entry computation. This is used by Graphcore's + // backend. + bool exclude_entry_computation_; }; } // namespace xla diff --git a/tensorflow/compiler/xla/service/tuple_simplifier_test.cc b/tensorflow/compiler/xla/service/tuple_simplifier_test.cc index ca9ae91281..d3635eae81 100644 --- a/tensorflow/compiler/xla/service/tuple_simplifier_test.cc +++ b/tensorflow/compiler/xla/service/tuple_simplifier_test.cc @@ -42,6 +42,12 @@ class TupleSimplifierTest : public HloTestBase { TF_ASSERT_OK(changed_status.status()); EXPECT_EQ(change_expected, changed_status.ValueOrDie()); } + void Run(HloModule* module, bool change_expected, bool exclude_entry) { + TupleSimplifier simplifier(exclude_entry); + auto changed_status = simplifier.Run(module); + TF_ASSERT_OK(changed_status.status()); + EXPECT_EQ(change_expected, changed_status.ValueOrDie()); + } const Shape scalar_shape_ = ShapeUtil::MakeShape(F32, {}); const Shape tuple_shape_ = ShapeUtil::MakeTupleShape( @@ -211,5 +217,76 @@ TEST_F(TupleSimplifierTest, IncompatibleTuples) { EXPECT_THAT(computation->root_instruction(), tuple); } +TEST_F(TupleSimplifierTest, CanExcludeEntryComputation) { + // Verify that the root computation can be excluded + auto module = CreateNewModule(); + + HloInstruction* p0; + HloInstruction* p1; + HloComputation* c0; + HloComputation* c1; + HloComputation* entry; + + { + HloComputation::Builder builder(TestName() + "_1"); + p0 = builder.AddInstruction( + HloInstruction::CreateParameter(0, tuple_shape_, "param")); + HloInstruction* gte0 = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(scalar_shape_, p0, 0)); + HloInstruction* gte1 = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(scalar_shape_, p0, 1)); + HloInstruction* gte2 = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(scalar_shape_, p0, 2)); + + builder.AddInstruction(HloInstruction::CreateTuple({gte0, gte1, gte2})); + + c0 = module->AddEmbeddedComputation(builder.Build()); + } + { + HloComputation::Builder builder(TestName() + "_2"); + p1 = builder.AddInstruction( + HloInstruction::CreateParameter(0, tuple_shape_, "param")); + HloInstruction* gte0 = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(scalar_shape_, p1, 0)); + HloInstruction* gte1 = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(scalar_shape_, p1, 1)); + HloInstruction* gte2 = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(scalar_shape_, p1, 2)); + + builder.AddInstruction(HloInstruction::CreateTuple({gte0, gte1, gte2})); + + c1 = module->AddEmbeddedComputation(builder.Build()); + } + { + HloComputation::Builder builder(TestName() + "_Entry"); + HloInstruction* tuple_param = builder.AddInstruction( + HloInstruction::CreateParameter(0, tuple_shape_, "param")); + HloInstruction* call0 = builder.AddInstruction( + HloInstruction::CreateCall(tuple_shape_, {tuple_param}, c0)); + HloInstruction* call1 = builder.AddInstruction( + HloInstruction::CreateCall(tuple_shape_, {tuple_param}, c1)); + HloInstruction* gte0 = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(scalar_shape_, call0, 0)); + HloInstruction* gte1 = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(scalar_shape_, call1, 1)); + HloInstruction* tuple0 = + builder.AddInstruction(HloInstruction::CreateTuple({gte0, gte1})); + HloInstruction* gte2 = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(scalar_shape_, tuple0, 0)); + HloInstruction* gte3 = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(scalar_shape_, tuple0, 1)); + + builder.AddInstruction(HloInstruction::CreateTuple({gte2, gte3})); + + entry = module->AddEntryComputation(builder.Build()); + } + + Run(module.get(), /*change_expected=*/true, /*exclude_entry=*/ true); + + EXPECT_THAT(c0->root_instruction(), p0); + EXPECT_THAT(c1->root_instruction(), p1); + EXPECT_THAT(entry->instruction_count(), 9); +} + } // namespace } // namespace xla diff --git a/tensorflow/contrib/autograph/__init__.py b/tensorflow/contrib/autograph/__init__.py index 8fd83ef376..361cf2d77c 100644 --- a/tensorflow/contrib/autograph/__init__.py +++ b/tensorflow/contrib/autograph/__init__.py @@ -23,6 +23,7 @@ from __future__ import print_function # TODO(mdan): Bring only the relevant symbols to the top level. from tensorflow.contrib.autograph import utils +from tensorflow.contrib.autograph import operators from tensorflow.contrib.autograph.impl.api import convert from tensorflow.contrib.autograph.impl.api import converted_call from tensorflow.contrib.autograph.impl.api import do_not_convert @@ -43,6 +44,8 @@ _allowed_symbols = [ 'do_not_convert', 'to_code', 'to_graph', + # Overloaded operators + 'operators', # Python language "extensions" 'set_element_type', 'set_loop_options', diff --git a/tensorflow/contrib/cmake/tf_c.cmake b/tensorflow/contrib/cmake/tf_c.cmake index bda5e26f43..2e0a2fcef4 100644 --- a/tensorflow/contrib/cmake/tf_c.cmake +++ b/tensorflow/contrib/cmake/tf_c.cmake @@ -37,13 +37,15 @@ add_dependencies( tf_core_lib tf_protos_cc) -add_library(tf_c_python_api OBJECT - "${tensorflow_source_dir}/tensorflow/c/python_api.cc" - "${tensorflow_source_dir}/tensorflow/c/python_api.h" -) -add_dependencies( - tf_c_python_api - tf_c - tf_core_lib - tf_core_framework - tf_protos_cc) +if(tensorflow_BUILD_PYTHON_BINDINGS) + add_library(tf_c_python_api OBJECT + "${tensorflow_source_dir}/tensorflow/c/python_api.cc" + "${tensorflow_source_dir}/tensorflow/c/python_api.h" + ) + add_dependencies( + tf_c_python_api + tf_c + tf_core_lib + tf_core_framework + tf_protos_cc) +endif() diff --git a/tensorflow/contrib/cmake/tf_cc_ops.cmake b/tensorflow/contrib/cmake/tf_cc_ops.cmake index f73da0b8ab..6c90cf398c 100644 --- a/tensorflow/contrib/cmake/tf_cc_ops.cmake +++ b/tensorflow/contrib/cmake/tf_cc_ops.cmake @@ -155,7 +155,7 @@ if (WIN32) set (pywrap_tensorflow_lib "${CMAKE_CURRENT_BINARY_DIR}/pywrap_tensorflow_internal.lib") endif() else (WIN32) - set (pywrap_tensorflow_lib "${CMAKE_CURRENT_BINARY_DIR}/libpywrap_tensorflow_internal.so") + set (pywrap_tensorflow_lib "${CMAKE_CURRENT_BINARY_DIR}/libpywrap_tensorflow_internal${CMAKE_SHARED_LIBRARY_SUFFIX}") endif (WIN32) add_custom_target(tf_extension_ops) diff --git a/tensorflow/contrib/cmake/tf_python.cmake b/tensorflow/contrib/cmake/tf_python.cmake index a0c3ddd28b..9244604489 100755 --- a/tensorflow/contrib/cmake/tf_python.cmake +++ b/tensorflow/contrib/cmake/tf_python.cmake @@ -715,7 +715,7 @@ if(WIN32) endif() else() add_custom_command(TARGET pywrap_tensorflow_internal POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_BINARY_DIR}/libpywrap_tensorflow_internal.so + COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_BINARY_DIR}/libpywrap_tensorflow_internal${CMAKE_SHARED_LIBRARY_SUFFIX} ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/python/_pywrap_tensorflow_internal.so) endif() @@ -832,7 +832,6 @@ add_custom_command(TARGET tf_python_build_pip_package POST_BUILD add_custom_command(TARGET tf_python_copy_scripts_to_destination PRE_BUILD COMMAND ${CMAKE_COMMAND} -E copy ${tensorflow_source_dir}/tensorflow/contrib/testing/python/framework/util_test.py ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/testing/python/framework/) - add_custom_command(TARGET tf_python_build_pip_package POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy ${tensorflow_source_dir}/tensorflow/tools/pip_package/README ${CMAKE_CURRENT_BINARY_DIR}/tf_python/) diff --git a/tensorflow/contrib/cmake/tools/create_def_file.py b/tensorflow/contrib/cmake/tools/create_def_file.py index cffe069aa3..4f957f1e0b 100644 --- a/tensorflow/contrib/cmake/tools/create_def_file.py +++ b/tensorflow/contrib/cmake/tools/create_def_file.py @@ -44,7 +44,8 @@ UNDNAME = "undname.exe" DUMPBIN = "dumpbin.exe" # Exclude if matched -EXCLUDE_RE = re.compile(r"RTTI|deleting destructor|::internal::") +EXCLUDE_RE = re.compile(r"RTTI|deleting destructor|::internal::|Internal|" + r"python_op_gen_internal|grappler") # Include if matched before exclude INCLUDEPRE_RE = re.compile(r"google::protobuf::internal::ExplicitlyConstructed|" @@ -56,6 +57,10 @@ INCLUDEPRE_RE = re.compile(r"google::protobuf::internal::ExplicitlyConstructed|" r"tensorflow::ops::internal::Enter|" r"tensorflow::strings::internal::AppendPieces|" r"tensorflow::strings::internal::CatPieces|" + r"tensorflow::errors::Internal|" + r"tensorflow::Tensor::CopyFromInternal|" + r"tensorflow::kernel_factory::" + r"OpKernelRegistrar::InitInternal|" r"tensorflow::io::internal::JoinPathImpl") # Include if matched after exclude @@ -64,7 +69,7 @@ INCLUDE_RE = re.compile(r"^(TF_\w*)$|" r"tensorflow::|" r"functor::|" r"\?nsync_|" - r"perftools::gputools") + r"stream_executor::") # We want to identify data members explicitly in the DEF file, so that no one # can implicitly link against the DLL if they use one of the variables exported diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/sinh_arcsinh_bijector_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/sinh_arcsinh_bijector_test.py index 45760a29ee..795f1993ba 100644 --- a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/sinh_arcsinh_bijector_test.py +++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/sinh_arcsinh_bijector_test.py @@ -151,16 +151,24 @@ class SinhArcsinhBijectorTest(test.TestCase): self.assertAllClose(y, bijector.forward(x).eval(), rtol=1e-4, atol=0.) self.assertAllClose(x, bijector.inverse(y).eval(), rtol=1e-4, atol=0.) - # Do the numpy calculation in float128 to avoid inf/nan. - y_float128 = np.float128(y) - self.assertAllClose( - np.log(np.cosh( - np.arcsinh(y_float128) / tailweight - skewness) / np.sqrt( - y_float128**2 + 1)) - - np.log(tailweight), - bijector.inverse_log_det_jacobian(y, event_ndims=0).eval(), - rtol=1e-4, - atol=0.) + # On IBM PPC systems, longdouble (np.float128) is same as double except that it can have more precision. + # Type double being of 8 bytes, can't hold square of max of float64 (which is also 8 bytes) and + # below test fails due to overflow error giving inf. So this check avoids that error by skipping square + # calculation and corresponding assert. + + if np.amax(y) <= np.sqrt(np.finfo(np.float128).max) and \ + np.fabs(np.amin(y)) <= np.sqrt(np.fabs(np.finfo(np.float128).min)): + + # Do the numpy calculation in float128 to avoid inf/nan. + y_float128 = np.float128(y) + self.assertAllClose( + np.log(np.cosh( + np.arcsinh(y_float128) / tailweight - skewness) / np.sqrt( + y_float128**2 + 1)) - + np.log(tailweight), + bijector.inverse_log_det_jacobian(y, event_ndims=0).eval(), + rtol=1e-4, + atol=0.) self.assertAllClose( -bijector.inverse_log_det_jacobian(y, event_ndims=0).eval(), bijector.forward_log_det_jacobian(x, event_ndims=0).eval(), diff --git a/tensorflow/contrib/eager/python/datasets.py b/tensorflow/contrib/eager/python/datasets.py index d7909dd5a2..adf92c27ea 100644 --- a/tensorflow/contrib/eager/python/datasets.py +++ b/tensorflow/contrib/eager/python/datasets.py @@ -106,7 +106,8 @@ class Iterator(iterator_ops.EagerIterator, checkpointable.CheckpointableBase): target_device=target, buffer_size=10, container="", - shared_name=_generate_shared_name("function_buffer_resource")) + shared_name=_generate_shared_name( + "contrib_eager_iterator_function_buffer_resource")) self._buffer_resource_deleter = resource_variable_ops.EagerResourceDeleter( # pylint: disable=line-too-long handle=self._buffer_resource_handle, handle_device=self._device) diff --git a/tensorflow/contrib/eager/python/examples/notebooks/4_high_level.ipynb b/tensorflow/contrib/eager/python/examples/notebooks/4_high_level.ipynb index 4fe3a0e3f3..5749f22ac5 100644 --- a/tensorflow/contrib/eager/python/examples/notebooks/4_high_level.ipynb +++ b/tensorflow/contrib/eager/python/examples/notebooks/4_high_level.ipynb @@ -68,7 +68,7 @@ "# simply construct the object. Most layers take as a first argument the number\n", "# of output dimensions / channels.\n", "layer = tf.keras.layers.Dense(100)\n", - "# The number of input dimensionss is often unnecessary, as it can be inferred\n", + "# The number of input dimensions is often unnecessary, as it can be inferred\n", "# the first time the layer is used, but it can be provided if you want to \n", "# specify it manually, which is useful in some complex models.\n", "layer = tf.keras.layers.Dense(10, input_shape=(None, 5))" @@ -267,7 +267,7 @@ " * `build`, where you know the shapes of the input tensors and can do the rest of the initialization\n", " * `call`, where you do the forward computation\n", "\n", - "Note that you don't have to wait until `build` is called to create your variables, you can also create them in `__init__`. However, the advantage of creating them in `build` is that it enables late variable creation based on the shape of the inputs the layer will operate on. On the other hand, creating variables in `__init__` would mean that shapes requires to create the variables will need to be explicitly specified." + "Note that you don't have to wait until `build` is called to create your variables, you can also create them in `__init__`. However, the advantage of creating them in `build` is that it enables late variable creation based on the shape of the inputs the layer will operate on. On the other hand, creating variables in `__init__` would mean that shapes required to create the variables will need to be explicitly specified." ] }, { diff --git a/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py index 84a413c791..05bcdac2ca 100644 --- a/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py +++ b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py @@ -346,7 +346,8 @@ def sequence_numeric_column( key, shape=(1,), default_value=0., - dtype=dtypes.float32): + dtype=dtypes.float32, + normalizer_fn=None): """Returns a feature column that represents sequences of numeric data. Example: @@ -370,6 +371,12 @@ def sequence_numeric_column( default_value: A single value compatible with `dtype` that is used for padding the sparse data into a dense `Tensor`. dtype: The type of values. + normalizer_fn: If not `None`, a function that can be used to normalize the + value of the tensor after `default_value` is applied for parsing. + Normalizer function takes the input `Tensor` as its argument, and returns + the output `Tensor`. (e.g. lambda x: (x - 3.0) / 4.2). Please note that + even though the most common use case of this function is normalization, it + can be used for any kind of Tensorflow transformations. Returns: A `_SequenceNumericColumn`. @@ -383,12 +390,16 @@ def sequence_numeric_column( if not (dtype.is_integer or dtype.is_floating): raise ValueError('dtype must be convertible to float. ' 'dtype: {}, key: {}'.format(dtype, key)) + if normalizer_fn is not None and not callable(normalizer_fn): + raise TypeError( + 'normalizer_fn must be a callable. Given: {}'.format(normalizer_fn)) return _SequenceNumericColumn( key, shape=shape, default_value=default_value, - dtype=dtype) + dtype=dtype, + normalizer_fn=normalizer_fn) def _assert_all_equal_and_return(tensors, name=None): @@ -407,7 +418,7 @@ class _SequenceNumericColumn( fc._SequenceDenseColumn, collections.namedtuple( '_SequenceNumericColumn', - ['key', 'shape', 'default_value', 'dtype'])): + ['key', 'shape', 'default_value', 'dtype', 'normalizer_fn'])): """Represents sequences of numeric data.""" @property @@ -419,7 +430,10 @@ class _SequenceNumericColumn( return {self.key: parsing_ops.VarLenFeature(self.dtype)} def _transform_feature(self, inputs): - return inputs.get(self.key) + input_tensor = inputs.get(self.key) + if self.normalizer_fn is not None: + input_tensor = self.normalizer_fn(input_tensor) + return input_tensor @property def _variable_shape(self): diff --git a/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py index ee74cf56dc..45d7b74046 100644 --- a/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py +++ b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py @@ -28,6 +28,7 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor +from tensorflow.python.ops import sparse_ops from tensorflow.python.platform import test from tensorflow.python.training import monitored_session @@ -947,6 +948,7 @@ class SequenceNumericColumnTest(test.TestCase): self.assertEqual((1,), a.shape) self.assertEqual(0., a.default_value) self.assertEqual(dtypes.float32, a.dtype) + self.assertIsNone(a.normalizer_fn) def test_shape_saved_as_tuple(self): a = sfc.sequence_numeric_column('aaa', shape=[1, 2]) @@ -965,6 +967,10 @@ class SequenceNumericColumnTest(test.TestCase): ValueError, 'dtype must be convertible to float'): sfc.sequence_numeric_column('aaa', dtype=dtypes.string) + def test_normalizer_fn_must_be_callable(self): + with self.assertRaisesRegexp(TypeError, 'must be a callable'): + sfc.sequence_numeric_column('aaa', normalizer_fn='NotACallable') + def test_get_sequence_dense_tensor(self): sparse_input = sparse_tensor.SparseTensorValue( # example 0, values [[0.], [1]] @@ -985,6 +991,41 @@ class SequenceNumericColumnTest(test.TestCase): self.assertAllEqual( expected_dense_tensor, dense_tensor.eval(session=sess)) + def test_get_sequence_dense_tensor_with_normalizer_fn(self): + + def _increment_two(input_sparse_tensor): + return sparse_ops.sparse_add( + input_sparse_tensor, + sparse_tensor.SparseTensor(((0, 0), (1, 1)), (2.0, 2.0), (2, 2)) + ) + + sparse_input = sparse_tensor.SparseTensorValue( + # example 0, values [[0.], [1]] + # example 1, [[10.]] + indices=((0, 0), (0, 1), (1, 0)), + values=(0., 1., 10.), + dense_shape=(2, 2)) + + # Before _increment_two: + # [[0.], [1.]], + # [[10.], [0.]], + # After _increment_two: + # [[2.], [1.]], + # [[10.], [2.]], + expected_dense_tensor = [ + [[2.], [1.]], + [[10.], [2.]], + ] + numeric_column = sfc.sequence_numeric_column( + 'aaa', normalizer_fn=_increment_two) + + dense_tensor, _ = numeric_column._get_sequence_dense_tensor( + _LazyBuilder({'aaa': sparse_input})) + + with monitored_session.MonitoredSession() as sess: + self.assertAllEqual( + expected_dense_tensor, dense_tensor.eval(session=sess)) + def test_get_sequence_dense_tensor_with_shape(self): """Tests get_sequence_dense_tensor with shape !=(1,).""" sparse_input = sparse_tensor.SparseTensorValue( diff --git a/tensorflow/contrib/ffmpeg/__init__.py b/tensorflow/contrib/ffmpeg/__init__.py index daba965a98..484ffee3e7 100644 --- a/tensorflow/contrib/ffmpeg/__init__.py +++ b/tensorflow/contrib/ffmpeg/__init__.py @@ -28,7 +28,6 @@ from __future__ import print_function from tensorflow.contrib.ffmpeg.ffmpeg_ops import decode_audio from tensorflow.contrib.ffmpeg.ffmpeg_ops import decode_video from tensorflow.contrib.ffmpeg.ffmpeg_ops import encode_audio -from tensorflow.contrib.ffmpeg.ffmpeg_ops import decode_video from tensorflow.python.util.all_util import remove_undocumented diff --git a/tensorflow/contrib/ffmpeg/ffmpeg_ops.py b/tensorflow/contrib/ffmpeg/ffmpeg_ops.py index 020b5c99c6..b1b5126d9e 100644 --- a/tensorflow/contrib/ffmpeg/ffmpeg_ops.py +++ b/tensorflow/contrib/ffmpeg/ffmpeg_ops.py @@ -21,7 +21,6 @@ from __future__ import print_function from tensorflow.contrib.ffmpeg.ops import gen_decode_audio_op_py from tensorflow.contrib.ffmpeg.ops import gen_decode_video_op_py from tensorflow.contrib.ffmpeg.ops import gen_encode_audio_op_py -from tensorflow.contrib.ffmpeg.ops import gen_decode_video_op_py from tensorflow.contrib.util import loader from tensorflow.python.framework import ops from tensorflow.python.platform import resource_loader diff --git a/tensorflow/contrib/framework/__init__.py b/tensorflow/contrib/framework/__init__.py index 10d1ecc738..dc49383c5c 100644 --- a/tensorflow/contrib/framework/__init__.py +++ b/tensorflow/contrib/framework/__init__.py @@ -119,14 +119,13 @@ from tensorflow.python.framework.smart_cond import smart_cond from tensorflow.python.framework.smart_cond import smart_constant_value from tensorflow.python.framework.tensor_spec import BoundedTensorSpec from tensorflow.python.framework.tensor_spec import TensorSpec -from tensorflow.python.ops.array_ops import broadcast_to from tensorflow.python.ops.init_ops import convolutional_delta_orthogonal from tensorflow.python.ops.init_ops import convolutional_orthogonal_1d from tensorflow.python.ops.init_ops import convolutional_orthogonal_2d from tensorflow.python.ops.init_ops import convolutional_orthogonal_3d from tensorflow.python.util.all_util import remove_undocumented -_allowed_symbols = ['nest', 'broadcast_to'] +_allowed_symbols = ['nest'] _nest_allowed_symbols = [ 'assert_same_structure', 'is_sequence', diff --git a/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op_test.py b/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op_test.py index 65cb94b5a4..a955e21b72 100644 --- a/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op_test.py +++ b/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op_test.py @@ -301,8 +301,8 @@ class FusedConv2DBiasActivationTest(test.TestCase): conv = tensors[i] value = values[i] ref_value = ref_values[i] - print("expected = ", ref_value) - print("actual = ", value) + tf_logging.info("expected = ", ref_value) + tf_logging.info("actual = ", value) tol = 1e-5 if value.dtype == np.float16: tol = 1e-3 @@ -843,7 +843,8 @@ class FusedConvInt8Tests(test.TestCase): vertical_stride, padding_type) output_width = CalculateConvolvedOutputDim(input_width, filter_width, horizontal_stride, padding_type) - print("output_height=", output_height, ", output_width=", output_width) + tf_logging.info("output_height=", output_height, ", output_width=", + output_width) side_input, _, _ = gen_array_ops.quantize_v2( random_ops.random_uniform( @@ -880,8 +881,8 @@ class FusedConvInt8Tests(test.TestCase): with self.test_session( use_gpu=True, config=NoMemoryOptimizationConfig()) as sess: actual_y, expected_y = sess.run([actual, expected]) - print("actual_y = ", actual_y) - print("expected_y = ", expected_y) + tf_logging.info("actual_y = ", actual_y) + tf_logging.info("expected_y = ", expected_y) self.assertTrue(np.array_equal(actual_y, expected_y)) def testFusedConvInt8(self): diff --git a/tensorflow/contrib/hvx/hexagon_controller/src_impl/hexagon_controller.c b/tensorflow/contrib/hvx/hexagon_controller/src_impl/hexagon_controller.c index 6a5d982dc8..2e5c84704f 100644 --- a/tensorflow/contrib/hvx/hexagon_controller/src_impl/hexagon_controller.c +++ b/tensorflow/contrib/hvx/hexagon_controller/src_impl/hexagon_controller.c @@ -19,7 +19,7 @@ limitations under the License. #include "hexagon_controller.h" -#include +#include #include #include "adspmsgd.h" diff --git a/tensorflow/contrib/lite/download_dependencies.sh b/tensorflow/contrib/lite/download_dependencies.sh index 436c3e1d4c..840015a7fa 100755 --- a/tensorflow/contrib/lite/download_dependencies.sh +++ b/tensorflow/contrib/lite/download_dependencies.sh @@ -30,9 +30,7 @@ if [ ! -f $BZL_FILE_PATH ]; then fi EIGEN_URL="$(grep -o 'http.*bitbucket.org/eigen/eigen/get/.*tar\.gz' "${BZL_FILE_PATH}" | grep -v mirror.bazel | head -n1)" -# TODO (yongtang): Replace the following with 'https://mirror.bazel.build/github.com/google/gemmlowp/.*zip' once -# the archive has been propagated in mirror.bazel.build. -GEMMLOWP_URL="$(grep -o 'https://github.com/google/gemmlowp/.*zip' "${BZL_FILE_PATH}" | head -n1)" +GEMMLOWP_URL="$(grep -o 'https://mirror.bazel.build/github.com/google/gemmlowp/.*zip' "${BZL_FILE_PATH}" | head -n1)" GOOGLETEST_URL="https://github.com/google/googletest/archive/release-1.8.0.tar.gz" ABSL_URL="$(grep -o 'https://github.com/abseil/abseil-cpp/.*tar.gz' "${BZL_FILE_PATH}" | head -n1)" NEON_2_SSE_URL="https://github.com/intel/ARM_NEON_2_x86_SSE/archive/master.zip" diff --git a/tensorflow/contrib/lite/examples/minimal/minimal.cc b/tensorflow/contrib/lite/examples/minimal/minimal.cc index 106e3b0270..8b0ace96cc 100644 --- a/tensorflow/contrib/lite/examples/minimal/minimal.cc +++ b/tensorflow/contrib/lite/examples/minimal/minimal.cc @@ -38,7 +38,7 @@ using namespace tflite; int main(int argc, char *argv[]) { if(argc != 2) { - fprintf(stderr, "Usage: %s \n"); + fprintf(stderr, "minimal \n"); return 1; } const char* filename = argv[1]; diff --git a/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md b/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md index bb2e615eac..965273f0f0 100644 --- a/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md +++ b/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md @@ -128,7 +128,6 @@ TensorFlow operation not listed above are likely unsupported. Notably, the following common ops are not supported at the moment: * [tf.depth_to_space](https://www.tensorflow.org/api_docs/python/tf/depth_to_space) -* [tf.gather](https://www.tensorflow.org/api_docs/python/tf/gather) * [tf.image.resize_bilinear](https://www.tensorflow.org/api_docs/python/tf/image/resize_bilinear) * [tf.tanh](https://www.tensorflow.org/api_docs/python/tf/tanh) @@ -306,6 +305,19 @@ Options { } ``` +**GATHER** + +``` +Inputs { + 0: params tensor + 1: indices tensor + 2: axis tensor (optional) +} +Outputs { + 0: a tensor with same type as the params tensor. +} +``` + **GREATER** ``` diff --git a/tensorflow/contrib/lite/java/ovic/README.md b/tensorflow/contrib/lite/java/ovic/README.md index 5efa70987e..26349347fa 100644 --- a/tensorflow/contrib/lite/java/ovic/README.md +++ b/tensorflow/contrib/lite/java/ovic/README.md @@ -2,7 +2,7 @@ This folder contains building code for track one of the [Low Power ImageNet Recognition Challenge workshop at CVPR 2018.](https://rebootingcomputing.ieee.org/home/sitemap/14-lpirc/80-low-power-image-recognition-challenge-lpirc-2018) -## Pre-requesits +## Pre-requisite Follow the steps [here](https://www.tensorflow.org/mobile/tflite/demo_android) to install Tensorflow, Bazel, and the Android NDK and SDK. @@ -49,7 +49,7 @@ Once you have a submission that follows the instructions from the [competition s You can call the validator binary below to verify that your model fits the format requirements. This often helps you to catch size mismatches (e.g. output should be [1, 1001] instead of [1,1,1,1001]). Let say the submission file is located at `/path/to/my_model.lite`, then call: ```sh -bazel build --cxxopt--std=c++11 //tensorflow/contrib/lite/java/ovic:ovic_validator --cxxopt=-Wno-all +bazel build --cxxopt=--std=c++11 //tensorflow/contrib/lite/java/ovic:ovic_validator --cxxopt=-Wno-all bazel-bin/tensorflow/contrib/lite/java/ovic/ovic_validator /path/to/my_model.lite ``` diff --git a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h index a2f192bbc2..1908f7fa6c 100644 --- a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h @@ -1934,7 +1934,7 @@ inline void LstmCell(const float* input_data, const Dims<4>& input_dims, // The quantization of the input, output arrays is as follows: // - The input activations are quantized as uint8 on the interval // [-1, 127/128]. -// The rationale for that is that that is the natural interval for output +// The rationale for that is that is the natural interval for output // activations (see next point) and these need to be concatenated together. // We could accommodate different ranges by re-scaling, but we empirically // found that setting the input activations range to be [-1, 127/128] in the @@ -1999,7 +1999,7 @@ inline void LstmCell(const float* input_data, const Dims<4>& input_dims, // However, for a fixed-point implementation in 16-bit integers, using 5 // integer bits to represent the [-16, 16] range would leave only 11 // fractional bits, giving an increment of 2^-11 = 4.9e-4 between consecutive -// representable values. Notice that that is higher than the +// representable values. Notice that is higher than the // worst-case clamping error with clamping to [-8, 8]: 3.4e-4 for Logistic. // Using [-8, 8] thus seems like the better compromise overall, enjoying // an increment of 2.4e-4 between representable values and a worst-case diff --git a/tensorflow/contrib/lite/python/interpreter.py b/tensorflow/contrib/lite/python/interpreter.py index 9400e757b9..fd90823425 100644 --- a/tensorflow/contrib/lite/python/interpreter.py +++ b/tensorflow/contrib/lite/python/interpreter.py @@ -55,7 +55,7 @@ class Interpreter(object): elif model_content and not model_path: self._interpreter = ( _interpreter_wrapper.InterpreterWrapper_CreateWrapperCPPFromBuffer( - model_content, len(model_content))) + model_content)) if not self._interpreter: raise ValueError( 'Failed to create model from {} bytes'.format(len(model_content))) diff --git a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.cc b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.cc index f705551fcb..b283551c45 100644 --- a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.cc +++ b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.cc @@ -397,9 +397,14 @@ InterpreterWrapper* InterpreterWrapper::CreateWrapperCPPFromFile( } InterpreterWrapper* InterpreterWrapper::CreateWrapperCPPFromBuffer( - const char* data, size_t len) { + PyObject* data) { + char * buf = nullptr; + Py_ssize_t length; + if (PY_TO_CPPSTRING(data, &buf, &length) == -1) { + return nullptr; + } std::unique_ptr model = - tflite::FlatBufferModel::BuildFromBuffer(data, len); + tflite::FlatBufferModel::BuildFromBuffer(buf, length); return model ? new InterpreterWrapper(std::move(model)) : nullptr; } diff --git a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h index b0ed7c4559..cbeb53bee7 100644 --- a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h +++ b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h @@ -40,8 +40,7 @@ class InterpreterWrapper { static InterpreterWrapper* CreateWrapperCPPFromFile(const char* model_path); // SWIG caller takes ownership of pointer. - static InterpreterWrapper* CreateWrapperCPPFromBuffer(const char* data, - size_t len); + static InterpreterWrapper* CreateWrapperCPPFromBuffer(PyObject* data); ~InterpreterWrapper(); bool AllocateTensors(); diff --git a/tensorflow/contrib/lite/python/lite.py b/tensorflow/contrib/lite/python/lite.py index 0913cd2c5c..88dda7290b 100644 --- a/tensorflow/contrib/lite/python/lite.py +++ b/tensorflow/contrib/lite/python/lite.py @@ -34,6 +34,8 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from six import PY3 + from google.protobuf import text_format as _text_format from google.protobuf.message import DecodeError from tensorflow.contrib.lite.python import lite_constants as constants @@ -54,6 +56,7 @@ from tensorflow.python.framework.importer import import_graph_def from tensorflow.python.ops.variables import global_variables_initializer from tensorflow.python.saved_model import signature_constants from tensorflow.python.saved_model import tag_constants +# from tensorflow.python.util.all_util import remove_undocumented class TocoConverter(object): @@ -203,6 +206,12 @@ class TocoConverter(object): except (_text_format.ParseError, DecodeError): try: print("Ignore 'tcmalloc: large alloc' warnings.") + + if not isinstance(file_content, str): + if PY3: + file_content = file_content.decode('utf-8') + else: + file_content = file_content.encode('utf-8') _text_format.Merge(file_content, graph_def) except (_text_format.ParseError, DecodeError): raise ValueError( @@ -382,3 +391,5 @@ def _freeze_graph(sess, output_tensors): output_arrays) else: return sess.graph_def + +# remove_undocumented(__name__) diff --git a/tensorflow/contrib/lite/toco/import_tensorflow.cc b/tensorflow/contrib/lite/toco/import_tensorflow.cc index 4465f953ba..caca199d2e 100644 --- a/tensorflow/contrib/lite/toco/import_tensorflow.cc +++ b/tensorflow/contrib/lite/toco/import_tensorflow.cc @@ -178,7 +178,7 @@ ArrayDataType ConvertDataType(tensorflow::DataType dtype) { else if (dtype == DT_STRING) return ArrayDataType::kString; else - LOG(INFO) << "Unsupported data type in placehoder op: " << dtype; + LOG(INFO) << "Unsupported data type in placeholder op: " << dtype; return ArrayDataType::kNone; } diff --git a/tensorflow/contrib/lite/toco/toco_port.cc b/tensorflow/contrib/lite/toco/toco_port.cc index 1b21c8bc60..de76fd4032 100644 --- a/tensorflow/contrib/lite/toco/toco_port.cc +++ b/tensorflow/contrib/lite/toco/toco_port.cc @@ -20,6 +20,12 @@ limitations under the License. #include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/platform/logging.h" +#if defined(__ANDROID__) && defined(__ARM_ARCH_7A__) +namespace std { +double round(double x) { return ::round(x); } +} // namespace std +#endif + namespace toco { namespace port { void CopyToBuffer(const string& src, char* dest) { diff --git a/tensorflow/contrib/lite/toco/toco_port.h b/tensorflow/contrib/lite/toco/toco_port.h index 5c019cb2bf..17f82b9dd7 100644 --- a/tensorflow/contrib/lite/toco/toco_port.h +++ b/tensorflow/contrib/lite/toco/toco_port.h @@ -34,6 +34,24 @@ limitations under the License. #define TFLITE_PROTO_NS google::protobuf #endif +#ifdef __ANDROID__ +#include +namespace std { + +template +std::string to_string(T value) +{ + std::ostringstream os ; + os << value ; + return os.str() ; +} + +#ifdef __ARM_ARCH_7A__ +double round(double x); +#endif +} +#endif + namespace toco { namespace port { diff --git a/tensorflow/contrib/makefile/compile_nsync.sh b/tensorflow/contrib/makefile/compile_nsync.sh index e8c6edd7ba..a28fc3a87f 100755 --- a/tensorflow/contrib/makefile/compile_nsync.sh +++ b/tensorflow/contrib/makefile/compile_nsync.sh @@ -270,7 +270,7 @@ for arch in $archs; do PLATFORM_LDFLAGS=-pthread MKDEP=${CC} -M -std=c++11 PLATFORM_C=../../platform/c++11/src/nsync_semaphore_mutex.cc \ - ../../platform/c++11/src/per_thread_waiter.cc \ + ../../platform/posix/src/per_thread_waiter.c \ ../../platform/c++11/src/yield.cc \ ../../platform/c++11/src/time_rep_timespec.cc \ ../../platform/c++11/src/nsync_panic.cc diff --git a/tensorflow/contrib/makefile/download_dependencies.sh b/tensorflow/contrib/makefile/download_dependencies.sh index eff9081e35..48953e2e38 100755 --- a/tensorflow/contrib/makefile/download_dependencies.sh +++ b/tensorflow/contrib/makefile/download_dependencies.sh @@ -27,9 +27,7 @@ if [ ! -f $BZL_FILE_PATH ]; then fi EIGEN_URL="$(grep -o 'http.*bitbucket.org/eigen/eigen/get/.*tar\.gz' "${BZL_FILE_PATH}" | grep -v mirror.bazel | head -n1)" -# TODO (yongtang): Replace the following with 'https://mirror.bazel.build/github.com/google/gemmlowp/.*zip' once -# the archive has been propagated in mirror.bazel.build. -GEMMLOWP_URL="$(grep -o 'https://github.com/google/gemmlowp/.*zip' "${BZL_FILE_PATH}" | head -n1)" +GEMMLOWP_URL="$(grep -o 'https://mirror.bazel.build/github.com/google/gemmlowp/.*zip' "${BZL_FILE_PATH}" | head -n1)" GOOGLETEST_URL="https://github.com/google/googletest/archive/release-1.8.0.tar.gz" NSYNC_URL="$(grep -o 'https://mirror.bazel.build/github.com/google/nsync/.*tar\.gz' "${BZL_FILE_PATH}" | head -n1)" PROTOBUF_URL="$(grep -o 'https://mirror.bazel.build/github.com/google/protobuf/.*tar\.gz' "${BZL_FILE_PATH}" | head -n1)" diff --git a/tensorflow/contrib/metrics/python/ops/metric_ops.py b/tensorflow/contrib/metrics/python/ops/metric_ops.py index 2ed99d50a4..a6be2084aa 100644 --- a/tensorflow/contrib/metrics/python/ops/metric_ops.py +++ b/tensorflow/contrib/metrics/python/ops/metric_ops.py @@ -2503,7 +2503,7 @@ def _compute_recall_at_precision(tp, fp, fn, precision, name): name: An optional variable_scope name. Returns: - The recall at a the given `precision`. + The recall at a given `precision`. """ precisions = math_ops.div(tp, tp + fp + _EPSILON) tf_index = math_ops.argmin( diff --git a/tensorflow/contrib/mpi_collectives/kernels/ring.h b/tensorflow/contrib/mpi_collectives/kernels/ring.h index 1d56d588bc..c001615d3f 100644 --- a/tensorflow/contrib/mpi_collectives/kernels/ring.h +++ b/tensorflow/contrib/mpi_collectives/kernels/ring.h @@ -129,7 +129,7 @@ cudaStream_t CudaStreamForMPI(); * has the fully accumulated Segment 1; and so on. The scatter-reduce is * complete. * - * Next, the allgather distributes these fully accumululated chunks across all + * Next, the allgather distributes these fully accumulated chunks across all * nodes. Communication proceeds in the same ring, once again in N-1 steps. At * the ith step, node j will send chunk (j - i + 1) and receive chunk (j - i). * For example, at the first iteration, the following transfers will occur: diff --git a/tensorflow/contrib/opt/python/training/adamax_test.py b/tensorflow/contrib/opt/python/training/adamax_test.py index 21bf3f5313..915e6504e1 100644 --- a/tensorflow/contrib/opt/python/training/adamax_test.py +++ b/tensorflow/contrib/opt/python/training/adamax_test.py @@ -224,8 +224,10 @@ class AdaMaxOptimizerTest(test.TestCase): var1_np, m1, v1 = adamax_update_numpy(var1_np, grads1_np, t, m1, v1) # Validate updated params - self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0)) - self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1)) + self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0), + rtol=1e-2) + self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1), + rtol=1e-2) if use_resource: self.assertEqual("var0_%d/AdaMax:0" % (i,), opt.get_slot(var=var0, name="m").name) diff --git a/tensorflow/contrib/opt/python/training/model_average_optimizer.py b/tensorflow/contrib/opt/python/training/model_average_optimizer.py index a7c97a1da2..b6b10e500b 100644 --- a/tensorflow/contrib/opt/python/training/model_average_optimizer.py +++ b/tensorflow/contrib/opt/python/training/model_average_optimizer.py @@ -62,7 +62,7 @@ class ModelAverageCustomGetter(object): """ def __init__(self, worker_device): - """Create a new `ElasticAverageCustomGetter`. + """Create a new `ModelAverageCustomGetter`. Args: worker_device: String. Name of the `worker` job. diff --git a/tensorflow/contrib/periodic_resample/BUILD b/tensorflow/contrib/periodic_resample/BUILD index 6ca7fe8b6e..f2171efc95 100644 --- a/tensorflow/contrib/periodic_resample/BUILD +++ b/tensorflow/contrib/periodic_resample/BUILD @@ -6,12 +6,13 @@ exports_files(["LICENSE"]) load( "//tensorflow:tensorflow.bzl", - "py_test", + "tf_cc_test", "tf_gen_op_libs", "tf_custom_op_library", "tf_custom_op_py_library", "tf_gen_op_wrapper_py", ) +load("//tensorflow:tensorflow.bzl", "py_test") cc_library( name = "all_ops", @@ -84,6 +85,22 @@ py_test( ":init_py", "//tensorflow/contrib/util:util_py", "//tensorflow/python:framework_test_lib", + "//tensorflow/python:gradient_checker", + ], +) + +tf_cc_test( + name = "periodic_resample_op_cc_test", + size = "small", + srcs = [ + "ops/array_ops_test.cc", + ], + deps = [ + ":all_ops", + "//tensorflow/core:framework", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + "//tensorflow/core:testlib", ], ) diff --git a/tensorflow/contrib/periodic_resample/kernels/periodic_resample_op.cc b/tensorflow/contrib/periodic_resample/kernels/periodic_resample_op.cc index e18923c8aa..514689cf45 100644 --- a/tensorflow/contrib/periodic_resample/kernels/periodic_resample_op.cc +++ b/tensorflow/contrib/periodic_resample/kernels/periodic_resample_op.cc @@ -22,4 +22,9 @@ namespace tensorflow { REGISTER_KERNEL_BUILDER(Name("PeriodicResample").Device(DEVICE_CPU), PeriodicResampleOp); + +REGISTER_KERNEL_BUILDER(Name("PeriodicResampleOpGrad") + .Device(DEVICE_CPU), + PeriodicResampleOpGrad); + } // namespace tensorflow diff --git a/tensorflow/contrib/periodic_resample/kernels/periodic_resample_op.h b/tensorflow/contrib/periodic_resample/kernels/periodic_resample_op.h index 3ab588c458..42fba81a5c 100644 --- a/tensorflow/contrib/periodic_resample/kernels/periodic_resample_op.h +++ b/tensorflow/contrib/periodic_resample/kernels/periodic_resample_op.h @@ -25,92 +25,202 @@ #include "tensorflow/core/framework/shape_inference.h" #include "tensorflow/core/framework/tensor_shape.h" #include "tensorflow/core/lib/core/status.h" +#include "tensorflow/core/util/work_sharder.h" namespace { -template -IndexT compute_input_index( - IndexVecT* target_dimensions, const IndexT& output_index, - const IndexVecT& original_dimensions, const int& adjustable_dimension, - const std::vector& dimension_ceiling, - const std::vector& cumulative_dimensions, IndexT* result, - std::vector* output_indices, const int& rank) { - *result = 0; - output_indices->clear(); +// Computes input tensor index for given output index during forward +// propagation through periodic_resample operation. +class InputIndexer { + public: + InputIndexer(const std::vector& output_dimensions, + const tensorflow::TensorShape& input_shape, + int adjustable_dimension) + : output_dimensions_(output_dimensions), + adjustable_dimension_(adjustable_dimension), + rank_(input_shape.dims()), + linear_output_index_(0), + linear_input_index_(0), + adjustable_dimension_carriage_sum_(0) { + auto input_dimensions = TensorShapeToVector(input_shape); + // factors by which input_dimensions increases/decreases w.r.t. + // output_dimensions + dimension_ceiling_ = + ComputeDimensionCeiling(output_dimensions, input_dimensions); + cumulative_dimensions_ = ComputeCumulativeDimensions(); + + output_indices_.resize(output_dimensions_.size()); + input_indices_.resize(output_dimensions_.size()); + + // Compute index_factors + index_factors_.resize(rank_); + tensorflow::int64 last_index_factor = 1; + for (auto r = rank_ - 1; r >= 0; --r) { + index_factors_[r] = last_index_factor; + last_index_factor *= input_dimensions[r]; + } + } + + tensorflow::int64 linear_input_index() const { return linear_input_index_; } + + void MoveToOutputIndex(tensorflow::int64 output_index); + void IncrementOutputIndex(); + + private: + void RecomputeInputAdjustableDimensionIndex() { + tensorflow::int64 index = adjustable_dimension_carriage_sum_; + index *= output_dimensions_[adjustable_dimension_]; + index += output_indices_[adjustable_dimension_]; + input_indices_[adjustable_dimension_] = index; + } + + std::vector TensorShapeToVector( + const tensorflow::TensorShape& tensor_shape); + + std::vector ComputeDimensionCeiling( + const std::vector& output_dimensions, + const std::vector& input_dimensions); + + std::vector ComputeCumulativeDimensions(); + + const std::vector output_dimensions_; + std::vector dimension_ceiling_; + std::vector index_factors_; + std::vector cumulative_dimensions_; + std::vector output_indices_; + std::vector input_indices_; + + const int adjustable_dimension_; + const int rank_; + tensorflow::int64 linear_output_index_; + tensorflow::int64 linear_input_index_; + tensorflow::int64 adjustable_dimension_carriage_sum_; +}; + +void InputIndexer::MoveToOutputIndex(tensorflow::int64 output_index) { + linear_output_index_ = output_index; + linear_input_index_ = 0; // un-rasterize the output index auto last_reduced_i = output_index; - for (auto r = rank - 1; r >= 0; --r) { - (*output_indices)[r] = last_reduced_i % (*target_dimensions)[r]; + for (auto r = rank_ - 1; r >= 0; --r) { + output_indices_[r] = last_reduced_i % output_dimensions_[r]; last_reduced_i = - (last_reduced_i - (*output_indices)[r]) / (*target_dimensions)[r]; + (last_reduced_i - output_indices_[r]) / output_dimensions_[r]; } + tensorflow::int64 carriage_sum = 0; + for (int qi = 0; qi < rank_; ++qi) { + if (qi == adjustable_dimension_) continue; + carriage_sum += cumulative_dimensions_[qi] * + (output_indices_[qi] % dimension_ceiling_[qi]); + } + adjustable_dimension_carriage_sum_ = carriage_sum; + // rasterize the input index - IndexT last_index_factor = 1; - for (auto r = rank - 1; r >= 0; --r) { - IndexT index = 0; - if (r != adjustable_dimension) - index = (*output_indices)[r] / dimension_ceiling[r]; - else { - for (int qi = 0; qi < rank; ++qi) { - if (qi == adjustable_dimension) continue; - index += cumulative_dimensions[qi] * - ((*output_indices)[qi] % dimension_ceiling[qi]); - } - index *= (*target_dimensions)[adjustable_dimension]; - index += (*output_indices)[r]; + for (auto r = rank_ - 1; r >= 0; --r) { + if (r != adjustable_dimension_) { + input_indices_[r] = output_indices_[r] / dimension_ceiling_[r]; + } else { + RecomputeInputAdjustableDimensionIndex(); } - *result += last_index_factor * index; - last_index_factor *= original_dimensions[r]; } + for (auto r = rank_ - 1; r >= 0; --r) { + linear_input_index_ += index_factors_[r] * input_indices_[r]; + } +} + +void InputIndexer::IncrementOutputIndex() { + linear_output_index_++; + for (auto r = rank_ - 1; r >= 0; --r) { + auto old_carriage_sum_increment = + cumulative_dimensions_[r] * + (output_indices_[r] % dimension_ceiling_[r]); + output_indices_[r] = (output_indices_[r] + 1) % output_dimensions_[r]; + if (r != adjustable_dimension_) { + auto new_input_index = output_indices_[r] / dimension_ceiling_[r]; + linear_input_index_ += + (new_input_index - input_indices_[r]) * index_factors_[r]; + + input_indices_[r] = new_input_index; + + auto new_carriage_sum_increment = + cumulative_dimensions_[r] * + (output_indices_[r] % dimension_ceiling_[r]); - return *result; + adjustable_dimension_carriage_sum_ = adjustable_dimension_carriage_sum_ - + old_carriage_sum_increment + + new_carriage_sum_increment; + } + + if (output_indices_[r] != 0) { + // No more carries to higher indices. + break; + } + } + auto old_adjustable_dimension_input_index = + input_indices_[adjustable_dimension_]; + RecomputeInputAdjustableDimensionIndex(); + linear_input_index_ += (input_indices_[adjustable_dimension_] - + old_adjustable_dimension_input_index) * + index_factors_[adjustable_dimension_]; } -template // both types are needed here b/c IndexVecT and - // InputDataT are not related - void - fill_periodic_tensor( - tensorflow::OpKernelContext* context, - const IndexVecT& desired_shape, - const tensorflow::Tensor& input_tensor) { - // input is a strided array (last index is fastest, C-ordered) - auto input = input_tensor.flat(); - const int rank = input_tensor.dims(); - // original and target dimensions - std::vector original_dimensions(rank), - target_dimensions(rank); - tensorflow::int64 total_size(input_tensor.NumElements()), new_sliced_size(1); - // factors by which original_dimensions increases/decreases w.r.t. - // target_dimensions - std::vector dimension_ceiling(rank), - cumulative_dimensions(rank); - // index of adjustable dimension - int adjustable_dimension; - tensorflow::TensorShape output_shape; +std::vector InputIndexer::TensorShapeToVector( + const tensorflow::TensorShape& tensor_shape) { + std::vector result(tensor_shape.dims()); + int count = 0; + for (const auto dim_info : tensor_shape) { + result[count] = dim_info.size; + ++count; + } + return result; +} - // requires that the rank of the input tensor and length of the desired shape - // are equal - OP_REQUIRES(context, rank == desired_shape.size(), - tensorflow::errors::InvalidArgument( - "periodic_resample expects the rank of the input tensor, ", - rank, ", to be the same as the length of the desired shape, ", - desired_shape.size(), ".")); +std::vector InputIndexer::ComputeDimensionCeiling( + const std::vector& output_dimensions, + const std::vector& input_dimensions) { + std::vector dimension_ceiling(input_dimensions.size()); + for (size_t i = 0; i < input_dimensions.size(); ++i) { + dimension_ceiling[i] = (output_dimensions[i] + input_dimensions[i] - 1) / + input_dimensions[i]; + } + return dimension_ceiling; +} - bool found = false; - const auto& input_tensor_shape = input_tensor.shape(); +std::vector InputIndexer::ComputeCumulativeDimensions() { + std::vector cumulative_dimensions(rank_); + int count = 0; + for (int i = 0; i < rank_; ++i) { + if (count == 0) { + cumulative_dimensions[count] = 1; + } else { + cumulative_dimensions[count] = + cumulative_dimensions[count - 1] * dimension_ceiling_[count - 1]; + } + ++count; + } + return cumulative_dimensions; +} +template +void process_desired_shape(tensorflow::OpKernelContext* context, + const tensorflow::TensorShape& input_tensor_shape, + const IndexVecT& desired_shape, + int* adjustable_dimension, + std::vector* target_dimensions, + tensorflow::int64* output_size) { + tensorflow::int64 new_sliced_size = 1; + bool found = false; + const int rank = input_tensor_shape.dims(); for (int i = 0; i < rank; ++i) { - // if (desired_shape(i) < 1) { if (desired_shape[i] < 1) { // only one index can be adjustable OP_REQUIRES(context, !found, tensorflow::errors::InvalidArgument( "periodic_resample expects only " "one index to be marked as adjustable.")); - adjustable_dimension = i; + *adjustable_dimension = i; found = true; } else { OP_REQUIRES( @@ -122,9 +232,8 @@ template +void +do_periodic_resample_op(tensorflow::OpKernelContext* context, + const tensorflow::TensorShape& original_shape, + const tensorflow::PartialTensorShape& desired_shape, + const tensorflow::Tensor& source_tensor) { + const int rank = source_tensor.dims(); + + // requires that the rank of the input tensor and length of the desired shape + // are equal + OP_REQUIRES(context, rank == desired_shape.dims(), + tensorflow::errors::InvalidArgument( + "periodic_resample expects the rank of the input tensor, ", + rank, ", to be the same as the length of the desired shape, ", + desired_shape.dims(), ".")); + + std::vector target_dimensions(rank); + tensorflow::int64 new_size = 0; + // index of adjustable dimension + int adjustable_dimension = 0; + process_desired_shape(context, original_shape, desired_shape.dim_sizes(), + &adjustable_dimension, &target_dimensions, &new_size); // ensure that the new dimension is greater than zero OP_REQUIRES(context, target_dimensions[adjustable_dimension] > 0, @@ -160,11 +293,14 @@ template allocate_output(0, output_shape, &output_tensor)); auto output = output_tensor->flat(); - // memory is allocated for these variables outside the inner loop for - // efficiency (although, I could create a separate class scope for - // this purpose instead) - tensorflow::int64 result = 0; - std::vector output_indices(target_dimensions.size()); + // input is a strided array (last index is fastest, C-ordered) + auto input = source_tensor.flat(); // Fill output tensor with periodically resampled input tensor values - for (tensorflow::int64 output_index = 0; output_index < new_size; - ++output_index) { - output(output_index) = input(compute_input_index( - &target_dimensions, output_index, original_dimensions, - adjustable_dimension, dimension_ceiling, cumulative_dimensions, &result, - &output_indices, rank)); - } + InputIndexer input_indexer(target_dimensions, original_shape, + adjustable_dimension); + + auto worker_threads = *(context->device()->tensorflow_cpu_worker_threads()); + auto fill_output_tensor = [&input_indexer, &output, &input]( + tensorflow::int64 start, tensorflow::int64 limit) { + InputIndexer local_indexer(input_indexer); + local_indexer.MoveToOutputIndex(start); + for (tensorflow::int64 output_index = start; output_index < limit; + ++output_index) { + if (mode == Mode::kForward) { + output(output_index) = input(local_indexer.linear_input_index()); + } else { + output(local_indexer.linear_input_index()) = input(output_index); + } + local_indexer.IncrementOutputIndex(); + } + }; + ::tensorflow::Shard(worker_threads.num_threads, worker_threads.workers, + new_size, costPerFillIndex, fill_output_tensor); } +#define DATA_TYPE_SWITCH(data_type, context, CASE) \ + switch (data_type) { \ + CASE(float) \ + CASE(double) \ + CASE(tensorflow::int32) \ + CASE(tensorflow::int64) \ + default: \ + context->CtxFailure(__FILE__, __LINE__, \ + tensorflow::errors::InvalidArgument( \ + "Unsuppored tensor elements type")); \ + break; \ + } + void create_output_tensor( tensorflow::OpKernelContext* context, const tensorflow::Tensor& input_tensor, const tensorflow::DataType& input_tensor_type, - const tensorflow::PartialTensorShape& desired_shape_tensor) { - auto desired_shape = desired_shape_tensor.dim_sizes(); - - // obligatory type switch - switch (input_tensor_type) { - case tensorflow::DataTypeToEnum::value: - fill_periodic_tensor(context, desired_shape, input_tensor); + const tensorflow::PartialTensorShape& desired_shape) { +#define CASE(type) \ + case tensorflow::DataTypeToEnum::value: \ + do_periodic_resample_op( \ + context, input_tensor.shape(), desired_shape, input_tensor); \ break; - case tensorflow::DataTypeToEnum::value: - fill_periodic_tensor(context, desired_shape, input_tensor); - break; - case tensorflow::DataTypeToEnum::value: - fill_periodic_tensor(context, desired_shape, - input_tensor); - break; - case tensorflow::DataTypeToEnum::value: - fill_periodic_tensor(context, desired_shape, - input_tensor); + + DATA_TYPE_SWITCH(input_tensor_type, context, CASE); +#undef CASE +} + +void create_grad_tensor(tensorflow::OpKernelContext* context, + const tensorflow::Tensor& grad_tensor, + const tensorflow::DataType& grad_tensor_type, + const tensorflow::TensorShape& original_shape, + const tensorflow::PartialTensorShape& desired_shape) { +#define CASE(type) \ + case tensorflow::DataTypeToEnum::value: \ + do_periodic_resample_op( \ + context, original_shape, desired_shape, grad_tensor); \ break; - default:; - } + + DATA_TYPE_SWITCH(grad_tensor_type, context, CASE); +#undef CASE } } // namespace @@ -238,4 +400,25 @@ class PeriodicResampleOp : public tensorflow::OpKernel { tensorflow::PartialTensorShape desired_shape; }; +class PeriodicResampleOpGrad : public tensorflow::OpKernel { + public: + explicit PeriodicResampleOpGrad(tensorflow::OpKernelConstruction* context) + : tensorflow::OpKernel(context) { + OP_REQUIRES_OK(context, + context->GetAttr("original_shape", &original_shape)); + OP_REQUIRES_OK(context, context->GetAttr("desired_shape", &desired_shape)); + } + + void Compute(tensorflow::OpKernelContext* context) override { + const tensorflow::Tensor& grad_tensor = context->input(0); + const tensorflow::DataType grad_tensor_type = context->input_dtype(0); + create_grad_tensor(context, grad_tensor, grad_tensor_type, original_shape, + desired_shape); + } + + private: + tensorflow::TensorShape original_shape; + tensorflow::PartialTensorShape desired_shape; +}; + #endif // TENSORFLOW_KERNELS_PERIODICRESAMPLE_OP_H_ diff --git a/tensorflow/contrib/periodic_resample/ops/array_ops.cc b/tensorflow/contrib/periodic_resample/ops/array_ops.cc index 82bd796956..fd38cd09b4 100644 --- a/tensorflow/contrib/periodic_resample/ops/array_ops.cc +++ b/tensorflow/contrib/periodic_resample/ops/array_ops.cc @@ -26,7 +26,42 @@ REGISTER_OP("PeriodicResample") .Input("values: T") .Attr("shape: shape") .Output("output: T") - .SetShapeFn(shape_inference::ExplicitShape) + .SetShapeFn([](shape_inference::InferenceContext* c) { + tensorflow::PartialTensorShape desired_shape; + TF_RETURN_IF_ERROR(c->GetAttr("shape", &desired_shape)); + shape_inference::ShapeHandle input_tensor_shape = c->input(0); + shape_inference::DimensionHandle num_input_elements = + c->NumElements(input_tensor_shape); + shape_inference::ShapeHandle result_shape_handle; + if (!shape_inference::InferenceContext::ValueKnown(num_input_elements)) { + TF_RETURN_IF_ERROR(c->MakeShapeFromPartialTensorShape( + desired_shape, &result_shape_handle)); + } else { + const int rank = c->Rank(input_tensor_shape); + std::vector target_dimensions(rank); + tensorflow::int64 new_sliced_size = 1; + int adjustable_dimension = 0; + for (int i = 0; i < rank; ++i) { + if (desired_shape.dim_size(i) < 1) { + adjustable_dimension = i; + } else { + target_dimensions[i] = desired_shape.dim_size(i); + new_sliced_size *= target_dimensions[i]; + } + } + target_dimensions[adjustable_dimension] = + shape_inference::InferenceContext::Value( + num_input_elements) / new_sliced_size; + tensorflow::TensorShape result_shape; + for (int i = 0; i < rank; ++i) { + result_shape.AddDim(target_dimensions[i]); + } + TF_RETURN_IF_ERROR(c->MakeShapeFromTensorShape( + result_shape, &result_shape_handle)); + } + c->set_output(0, result_shape_handle); + return Status::OK(); + }) .Doc(R"doc( Periodically resample elements of a tensor to conform to `shape`. @@ -101,4 +136,20 @@ output: Periodically resampled tensor that has dimensions specified as in )doc"); + +REGISTER_OP("PeriodicResampleOpGrad") + .Attr("T: numbertype") + .Input("grad: T") + .Attr("original_shape: shape") + .Attr("desired_shape: shape") + .Output("grad_values: T") + .SetShapeFn([](shape_inference::InferenceContext* c) { + tensorflow::TensorShape original_shape; + TF_RETURN_IF_ERROR(c->GetAttr("original_shape", &original_shape)); + shape_inference::ShapeHandle s; + TF_RETURN_IF_ERROR(c->MakeShapeFromTensorShape(original_shape, &s)); + c->set_output(0, s); + return Status::OK(); +}); + } // namespace tensorflow diff --git a/tensorflow/contrib/periodic_resample/ops/array_ops_test.cc b/tensorflow/contrib/periodic_resample/ops/array_ops_test.cc new file mode 100644 index 0000000000..43b7c1799f --- /dev/null +++ b/tensorflow/contrib/periodic_resample/ops/array_ops_test.cc @@ -0,0 +1,41 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/core/framework/node_def_builder.h" +#include "tensorflow/core/framework/shape_inference_testutil.h" +#include "tensorflow/core/framework/tensor_shape.pb.h" +#include "tensorflow/core/framework/tensor_testutil.h" +#include "tensorflow/core/lib/core/status_test_util.h" +#include "tensorflow/core/platform/test.h" + +namespace tensorflow { + +TEST(ArrayOpsTest, PeriodicResample_ShapeFn) { + ShapeInferenceTestOp op("PeriodicResample"); + // Case 1: output shape can be fully inferreed. + PartialTensorShape shape({4, 4, -1}); + TensorShapeProto shape_proto; + shape.AsProto(&shape_proto); + + TF_ASSERT_OK(NodeDefBuilder("test", "PeriodicResample") + .Input({"values", 0, DT_INT32}) + .Attr("shape", shape_proto) + .Finalize(&op.node_def)); + INFER_OK(op, "[2,2,4]", "[4,4,1]"); + // Case 2: output shape can not be inferred - report desired shape. + INFER_OK(op, "[2,2,?]", "[4,4,?]"); +} + +} // end namespace tensorflow diff --git a/tensorflow/contrib/periodic_resample/python/kernel_tests/periodic_resample_op_test.py b/tensorflow/contrib/periodic_resample/python/kernel_tests/periodic_resample_op_test.py index a25de55e18..31a6fe1d94 100644 --- a/tensorflow/contrib/periodic_resample/python/kernel_tests/periodic_resample_op_test.py +++ b/tensorflow/contrib/periodic_resample/python/kernel_tests/periodic_resample_op_test.py @@ -21,8 +21,11 @@ from __future__ import print_function import numpy from tensorflow.contrib.periodic_resample import periodic_resample +from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors_impl from tensorflow.python.framework import test_util +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import gradient_checker from tensorflow.python.ops import variables from tensorflow.python.platform import googletest @@ -93,7 +96,6 @@ class PeriodicResampleTest(test_util.TensorFlowTestCase): def testPeriodicResampleErrors(self): input_tensor = numpy.zeros(shape=[1, 2, 2, 4]) with self.test_session(): - variables.global_variables_initializer().run() with self.assertRaisesWithPredicateMatch( errors_impl.InvalidArgumentError, 'Dimension 3 input tensor has size 4, desired shape has size 1'): @@ -103,6 +105,29 @@ class PeriodicResampleTest(test_util.TensorFlowTestCase): '4, to be the same as the length of the desired shape, 3'): periodic_resample(input_tensor, [None, 4, 4]).eval() + def testPeriodicResampleGradient(self): + desired_shape = numpy.array([4, 4, None]) + result_shape = (4, 4, 1) + input_shape = (2, 2, 4) + with self.test_session() as sess: + x = array_ops.placeholder(dtypes.float32, shape=input_shape) + output = periodic_resample(x, desired_shape) + error = gradient_checker.compute_gradient_error( + x, input_shape, output, result_shape) + self.assertLess(error, 1e-4) + + def testPeriodicResampleShapeInference(self): + with self.test_session() as sess: + # Case 1: output shape can be fully inferreed. + x = array_ops.placeholder(dtypes.float32, shape=(2, 2, 4)) + output = periodic_resample(x, [4, 4, None]) + self.assertEqual(output.shape, [4, 4, 1]) + # Case 2: output shape can not be inferred - report desired shape. + x = array_ops.placeholder(dtypes.float32, shape=(2, 2, None)) + output = periodic_resample(x, [4, 4, None]) + self.assertTrue(output.shape.is_compatible_with([4, 4, None])) + self.assertEqual(output.shape[2].value, None) + if __name__ == '__main__': googletest.main() diff --git a/tensorflow/contrib/periodic_resample/python/ops/periodic_resample_op.py b/tensorflow/contrib/periodic_resample/python/ops/periodic_resample_op.py index 348623d8f8..470e300ccb 100644 --- a/tensorflow/contrib/periodic_resample/python/ops/periodic_resample_op.py +++ b/tensorflow/contrib/periodic_resample/python/ops/periodic_resample_op.py @@ -21,11 +21,17 @@ from __future__ import print_function # pylint: disable=unused-import from tensorflow.contrib.periodic_resample.python.ops import gen_periodic_resample_op -from tensorflow.contrib.periodic_resample.python.ops.gen_periodic_resample_op import periodic_resample +from tensorflow.contrib.periodic_resample.python.ops.gen_periodic_resample_op import periodic_resample, periodic_resample_op_grad from tensorflow.contrib.util import loader +from tensorflow.python.framework import ops from tensorflow.python.platform import resource_loader # pylint: enable=unused-import _periodic_resample_op = loader.load_op_library( resource_loader.get_path_to_datafile('_periodic_resample_op.so')) + +@ops.RegisterGradient("PeriodicResample") +def _periodic_resample_grad_cc(op, grad): + return periodic_resample_op_grad( + grad, op.inputs[0].shape, op.get_attr('shape')) diff --git a/tensorflow/contrib/predictor/contrib_estimator_predictor.py b/tensorflow/contrib/predictor/contrib_estimator_predictor.py index b7a98c68e2..af3b2ad1b5 100644 --- a/tensorflow/contrib/predictor/contrib_estimator_predictor.py +++ b/tensorflow/contrib/predictor/contrib_estimator_predictor.py @@ -34,7 +34,8 @@ class ContribEstimatorPredictor(predictor.Predictor): prediction_input_fn, input_alternative_key=None, output_alternative_key=None, - graph=None): + graph=None, + config=None): """Initialize a `ContribEstimatorPredictor`. Args: @@ -48,6 +49,7 @@ class ContribEstimatorPredictor(predictor.Predictor): multi-headed models. graph: Optional. The Tensorflow `graph` in which prediction should be done. + config: `ConfigProto` proto used to configure the session. """ self._graph = graph or ops.Graph() with self._graph.as_default(): @@ -58,6 +60,7 @@ class ContribEstimatorPredictor(predictor.Predictor): checkpoint_path = saver.latest_checkpoint(estimator.model_dir) self._session = monitored_session.MonitoredSession( session_creator=monitored_session.ChiefSessionCreator( + config=config, checkpoint_filename_with_path=checkpoint_path)) input_alternative_key = ( diff --git a/tensorflow/contrib/predictor/core_estimator_predictor.py b/tensorflow/contrib/predictor/core_estimator_predictor.py index d78d94c269..a725072e72 100644 --- a/tensorflow/contrib/predictor/core_estimator_predictor.py +++ b/tensorflow/contrib/predictor/core_estimator_predictor.py @@ -51,7 +51,8 @@ class CoreEstimatorPredictor(predictor.Predictor): estimator, serving_input_receiver_fn, output_key=None, - graph=None): + graph=None, + config=None): """Initialize a `CoreEstimatorPredictor`. Args: @@ -62,6 +63,7 @@ class CoreEstimatorPredictor(predictor.Predictor): `None`, then `DEFAULT_SERVING_SIGNATURE_DEF_KEY` is used. graph: Optional. The Tensorflow `graph` in which prediction should be done. + config: `ConfigProto` proto used to configure the session. """ self._graph = graph or ops.Graph() with self._graph.as_default(): @@ -71,6 +73,7 @@ class CoreEstimatorPredictor(predictor.Predictor): checkpoint_dir = estimator.model_dir self._session = monitored_session.MonitoredSession( session_creator=monitored_session.ChiefSessionCreator( + config=config, checkpoint_dir=checkpoint_dir)) feed_tensor_info = signature_def.inputs diff --git a/tensorflow/contrib/predictor/predictor_factories.py b/tensorflow/contrib/predictor/predictor_factories.py index 6e77e934fe..f275bc15ad 100644 --- a/tensorflow/contrib/predictor/predictor_factories.py +++ b/tensorflow/contrib/predictor/predictor_factories.py @@ -30,7 +30,8 @@ def from_contrib_estimator(estimator, prediction_input_fn, input_alternative_key=None, output_alternative_key=None, - graph=None): + graph=None, + config=None): """Constructs a `Predictor` from a `tf.contrib.learn.Estimator`. Args: @@ -44,6 +45,7 @@ def from_contrib_estimator(estimator, multi-headed models. graph: Optional. The Tensorflow `graph` in which prediction should be done. + config: `ConfigProto` proto used to configure the session. Returns: An initialized `Predictor`. @@ -62,13 +64,15 @@ def from_contrib_estimator(estimator, prediction_input_fn, input_alternative_key=input_alternative_key, output_alternative_key=output_alternative_key, - graph=graph) + graph=graph, + config=config) def from_estimator(estimator, serving_input_receiver_fn, output_key=None, - graph=None): + graph=None, + config=None): """Constructs a `Predictor` from a `tf.python.estimator.Estimator`. Args: @@ -79,6 +83,7 @@ def from_estimator(estimator, `None`, then `DEFAULT_SERVING_SIGNATURE_DEF_KEY` is used. graph: Optional. The Tensorflow `graph` in which prediction should be done. + config: `ConfigProto` proto used to configure the session. Returns: An initialized `Predictor`. @@ -93,14 +98,19 @@ def from_estimator(estimator, 'tf.contrib.learn.Estimator. You likely want to call ' 'from_contrib_estimator.') return core_estimator_predictor.CoreEstimatorPredictor( - estimator, serving_input_receiver_fn, output_key=output_key, graph=graph) + estimator, + serving_input_receiver_fn, + output_key=output_key, + graph=graph, + config=config) def from_saved_model(export_dir, signature_def_key=None, signature_def=None, tags=None, - graph=None): + graph=None, + config=None): """Constructs a `Predictor` from a `SavedModel` on disk. Args: @@ -115,6 +125,7 @@ def from_saved_model(export_dir, `SignatureDef`. Defaults to `DEFAULT_TAGS`. graph: Optional. The Tensorflow `graph` in which prediction should be done. + config: `ConfigProto` proto used to configure the session. Returns: An initialized `Predictor`. @@ -128,4 +139,5 @@ def from_saved_model(export_dir, signature_def_key=signature_def_key, signature_def=signature_def, tags=tags, - graph=graph) + graph=graph, + config=config) diff --git a/tensorflow/contrib/predictor/predictor_factories_test.py b/tensorflow/contrib/predictor/predictor_factories_test.py index 578d9424b2..a2ef1dc3af 100644 --- a/tensorflow/contrib/predictor/predictor_factories_test.py +++ b/tensorflow/contrib/predictor/predictor_factories_test.py @@ -20,6 +20,7 @@ from __future__ import print_function from tensorflow.contrib.predictor import predictor_factories from tensorflow.contrib.predictor import testing_common +from tensorflow.core.protobuf import config_pb2 from tensorflow.python.platform import test MODEL_DIR_NAME = 'contrib/predictor/test_export_dir' @@ -41,6 +42,11 @@ class PredictorFactoriesTest(test.TestCase): """Test loading from_saved_model with tags.""" predictor_factories.from_saved_model(self._export_dir, tags='serve') + def testFromSavedModelWithSessionConfig(self): + """Test loading from_saved_model with session config.""" + predictor_factories.from_saved_model( + self._export_dir, config=config_pb2.ConfigProto()) + def testFromSavedModelWithBadTags(self): """Test that loading fails for bad tags.""" bad_tags_regex = ('.*? could not be found in SavedModel') @@ -53,6 +59,13 @@ class PredictorFactoriesTest(test.TestCase): predictor_factories.from_contrib_estimator( estimator, input_fn, output_alternative_key='sum') + def testFromContribEstimatorWithSessionConfig(self): + estimator = testing_common.get_arithmetic_estimator(core=False) + input_fn = testing_common.get_arithmetic_input_fn(core=False) + predictor_factories.from_contrib_estimator( + estimator, input_fn, output_alternative_key='sum', + config=config_pb2.ConfigProto()) + def testFromContribEstimatorWithCoreEstimatorRaises(self): estimator = testing_common.get_arithmetic_estimator(core=True) input_fn = testing_common.get_arithmetic_input_fn(core=True) @@ -64,6 +77,12 @@ class PredictorFactoriesTest(test.TestCase): input_fn = testing_common.get_arithmetic_input_fn(core=True) predictor_factories.from_estimator(estimator, input_fn) + def testFromCoreEstimatorWithSessionConfig(self): + estimator = testing_common.get_arithmetic_estimator(core=True) + input_fn = testing_common.get_arithmetic_input_fn(core=True) + predictor_factories.from_estimator( + estimator, input_fn, config=config_pb2.ConfigProto()) + def testFromCoreEstimatorWithContribEstimatorRaises(self): estimator = testing_common.get_arithmetic_estimator(core=False) input_fn = testing_common.get_arithmetic_input_fn(core=False) diff --git a/tensorflow/contrib/predictor/saved_model_predictor.py b/tensorflow/contrib/predictor/saved_model_predictor.py index 0dbca0f813..95da6d04ed 100644 --- a/tensorflow/contrib/predictor/saved_model_predictor.py +++ b/tensorflow/contrib/predictor/saved_model_predictor.py @@ -121,7 +121,8 @@ class SavedModelPredictor(predictor.Predictor): input_names=None, output_names=None, tags=None, - graph=None): + graph=None, + config=None): """Initialize a `CoreEstimatorPredictor`. Args: @@ -142,6 +143,7 @@ class SavedModelPredictor(predictor.Predictor): the correct `SignatureDef`. Defaults to `DEFAULT_TAGS`. graph: Optional. The Tensorflow `graph` in which prediction should be done. + config: `ConfigProto` proto used to configure the session. Raises: ValueError: If more than one of signature_def_key OR signature_def OR (input_names AND output_names) is specified. @@ -152,7 +154,7 @@ class SavedModelPredictor(predictor.Predictor): self._graph = graph or ops.Graph() with self._graph.as_default(): - self._session = session.Session() + self._session = session.Session(config=config) loader.load(self._session, tags.split(','), export_dir) if input_names is None: diff --git a/tensorflow/contrib/quantize/README.md b/tensorflow/contrib/quantize/README.md index c83623ec94..27a933c0f9 100644 --- a/tensorflow/contrib/quantize/README.md +++ b/tensorflow/contrib/quantize/README.md @@ -6,7 +6,7 @@ inference. The details of the transformation implemented in this package is described here [1]. This is done using the -[fake quantization op](https://www.tensorflow.org/versions/r0.12/api_docs/python/array_ops/fake_quantization). +[fake quantization op](https://www.tensorflow.org/api_guides/python/array_ops#Fake_quantization). Literature has shown that fixed point networks provide comparable performance to floating point networks [2]. This is achieved by modeling the quantization diff --git a/tensorflow/contrib/slim/python/slim/evaluation_test.py b/tensorflow/contrib/slim/python/slim/evaluation_test.py index 94fc12ca81..3d0308aaf3 100644 --- a/tensorflow/contrib/slim/python/slim/evaluation_test.py +++ b/tensorflow/contrib/slim/python/slim/evaluation_test.py @@ -26,7 +26,6 @@ import time import numpy as np from tensorflow.contrib.framework.python.ops import variables as variables_lib -from tensorflow.contrib.metrics.python.ops import metric_ops from tensorflow.contrib.slim.python.slim import evaluation from tensorflow.contrib.training.python.training import evaluation as evaluation_lib from tensorflow.core.protobuf import saver_pb2 @@ -37,6 +36,7 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops +from tensorflow.python.ops import metrics from tensorflow.python.ops import variables from tensorflow.python.platform import flags from tensorflow.python.platform import gfile @@ -89,8 +89,8 @@ class EvaluationTest(test.TestCase): self._predictions, self._scale = TestModel(self._inputs) def testFinalOpsOnEvaluationLoop(self): - value_op, update_op = metric_ops.streaming_accuracy(self._predictions, - self._labels) + value_op, update_op = metrics.accuracy( + labels=self._labels, predictions=self._predictions) init_op = control_flow_ops.group(variables.global_variables_initializer(), variables.local_variables_initializer()) # Create checkpoint and log directories: @@ -136,9 +136,10 @@ class EvaluationTest(test.TestCase): self.assertTrue(obj.hook_was_run) def _create_names_to_metrics(self, predictions, labels): - accuracy0, update_op0 = metric_ops.streaming_accuracy(predictions, labels) - accuracy1, update_op1 = metric_ops.streaming_accuracy(predictions + 1, - labels) + accuracy0, update_op0 = metrics.accuracy( + labels=labels, predictions=predictions) + accuracy1, update_op1 = metrics.accuracy( + labels=labels, predictions=predictions + 1) names_to_values = {'Accuracy': accuracy0, 'Another_accuracy': accuracy1} names_to_updates = {'Accuracy': update_op0, 'Another_accuracy': update_op1} @@ -198,8 +199,8 @@ class EvaluationTest(test.TestCase): predictions_limited = input.limit_epochs(self._predictions, num_epochs=1) labels_limited = input.limit_epochs(self._labels, num_epochs=1) - value_op, update_op = metric_ops.streaming_accuracy( - predictions_limited, labels_limited) + value_op, update_op = metrics.accuracy( + labels=labels_limited, predictions=predictions_limited) init_op = control_flow_ops.group(variables.global_variables_initializer(), variables.local_variables_initializer()) @@ -260,8 +261,8 @@ class SingleEvaluationTest(test.TestCase): self._prepareCheckpoint(checkpoint_path) # Next, determine the metric to evaluate: - value_op, update_op = metric_ops.streaming_accuracy(self._predictions, - self._labels) + value_op, update_op = metrics.accuracy( + labels=self._labels, predictions=self._predictions) # Run the evaluation and verify the results: accuracy_value = evaluation.evaluate_once( @@ -276,8 +277,8 @@ class SingleEvaluationTest(test.TestCase): self._prepareCheckpoint(checkpoint_path) # Next, determine the metric to evaluate: - value_op, update_op = metric_ops.streaming_accuracy(self._predictions, - self._labels) + value_op, update_op = metrics.accuracy( + labels=self._labels, predictions=self._predictions) dumping_root = os.path.join(self.get_temp_dir(), 'tfdbg_dump_dir') dumping_hook = hooks.DumpingDebugHook(dumping_root, log_usage=False) diff --git a/tensorflow/contrib/summary/summary.py b/tensorflow/contrib/summary/summary.py index 99ced53e11..d22b80ac88 100644 --- a/tensorflow/contrib/summary/summary.py +++ b/tensorflow/contrib/summary/summary.py @@ -21,6 +21,7 @@ from @{tf.summary.merge_all} to @{tf.summary.FileWriter}. To use with eager execution enabled, write your code as follows: +```python global_step = tf.train.get_or_create_global_step() summary_writer = tf.contrib.summary.create_file_writer( train_dir, flush_millis=10000) @@ -30,9 +31,11 @@ with summary_writer.as_default(), tf.contrib.summary.always_record_summaries(): tf.contrib.summary.scalar("loss", my_loss) # In this case every call to tf.contrib.summary.scalar will generate a record # ... +``` To use it with graph execution, write your code as follows: +```python global_step = tf.train.get_or_create_global_step() summary_writer = tf.contrib.summary.create_file_writer( train_dir, flush_millis=10000) @@ -53,7 +56,7 @@ with tf.Session(...) as sess: while not_done_training: sess.run([train_op, tf.contrib.summary.all_summary_ops()]) # ... - +``` """ from __future__ import absolute_import diff --git a/tensorflow/contrib/tensor_forest/client/eval_metrics.py b/tensorflow/contrib/tensor_forest/client/eval_metrics.py index e893e1d1c8..d8236a0a6f 100644 --- a/tensorflow/contrib/tensor_forest/client/eval_metrics.py +++ b/tensorflow/contrib/tensor_forest/client/eval_metrics.py @@ -21,10 +21,10 @@ import numpy as np from tensorflow.contrib import losses from tensorflow.contrib.learn.python.learn.estimators import prediction_key -from tensorflow.contrib.metrics.python.ops import metric_ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops +from tensorflow.python.ops import metrics from tensorflow.python.ops import nn INFERENCE_PROB_NAME = prediction_key.PredictionKey.PROBABILITIES @@ -38,12 +38,13 @@ def _top_k_generator(k): targets = math_ops.to_int32(targets) if targets.get_shape().ndims > 1: targets = array_ops.squeeze(targets, axis=[1]) - return metric_ops.streaming_mean(nn.in_top_k(probabilities, targets, k)) + return metrics.mean(nn.in_top_k(probabilities, targets, k)) return _top_k def _accuracy(predictions, targets, weights=None): - return metric_ops.streaming_accuracy(predictions, targets, weights=weights) + return metrics.accuracy( + labels=targets, predictions=predictions, weights=weights) def _r2(probabilities, targets, weights=None): @@ -53,7 +54,7 @@ def _r2(probabilities, targets, weights=None): squares_residuals = math_ops.reduce_sum( math_ops.square(targets - probabilities), 0) score = 1 - math_ops.reduce_sum(squares_residuals / squares_total) - return metric_ops.streaming_mean(score, weights=weights) + return metrics.mean(score, weights=weights) def _squeeze_and_onehot(targets, depth): @@ -62,7 +63,7 @@ def _squeeze_and_onehot(targets, depth): def _sigmoid_entropy(probabilities, targets, weights=None): - return metric_ops.streaming_mean( + return metrics.mean( losses.sigmoid_cross_entropy(probabilities, _squeeze_and_onehot( targets, @@ -71,7 +72,7 @@ def _sigmoid_entropy(probabilities, targets, weights=None): def _softmax_entropy(probabilities, targets, weights=None): - return metric_ops.streaming_mean( + return metrics.mean( losses.sparse_softmax_cross_entropy(probabilities, math_ops.to_int32(targets)), weights=weights) @@ -82,7 +83,7 @@ def _predictions(predictions, unused_targets, **unused_kwargs): def _class_log_loss(probabilities, targets, weights=None): - return metric_ops.streaming_mean( + return metrics.mean( losses.log_loss(probabilities, _squeeze_and_onehot(targets, array_ops.shape(probabilities)[1])), @@ -90,34 +91,36 @@ def _class_log_loss(probabilities, targets, weights=None): def _precision(predictions, targets, weights=None): - return metric_ops.streaming_precision(predictions, targets, weights=weights) + return metrics.precision( + labels=targets, predictions=predictions, weights=weights) def _precision_at_thresholds(predictions, targets, weights=None): - return metric_ops.streaming_precision_at_thresholds( - array_ops.slice(predictions, [0, 1], [-1, 1]), - targets, - np.arange( - 0, 1, 0.01, dtype=np.float32), + return metrics.precision_at_thresholds( + labels=targets, + predictions=array_ops.slice(predictions, [0, 1], [-1, 1]), + thresholds=np.arange(0, 1, 0.01, dtype=np.float32), weights=weights) def _recall(predictions, targets, weights=None): - return metric_ops.streaming_recall(predictions, targets, weights=weights) + return metrics.recall( + labels=targets, predictions=predictions, weights=weights) def _recall_at_thresholds(predictions, targets, weights=None): - return metric_ops.streaming_recall_at_thresholds( - array_ops.slice(predictions, [0, 1], [-1, 1]), - targets, - np.arange( - 0, 1, 0.01, dtype=np.float32), + return metrics.recall_at_thresholds( + labels=targets, + predictions=array_ops.slice(predictions, [0, 1], [-1, 1]), + thresholds=np.arange(0, 1, 0.01, dtype=np.float32), weights=weights) def _auc(probs, targets, weights=None): - return metric_ops.streaming_auc(array_ops.slice(probs, [0, 1], [-1, 1]), - targets, weights=weights) + return metrics.auc( + labels=targets, + predictions=array_ops.slice(probs, [0, 1], [-1, 1]), + weights=weights) _EVAL_METRICS = { diff --git a/tensorflow/contrib/tensor_forest/python/tensor_forest.py b/tensorflow/contrib/tensor_forest/python/tensor_forest.py index 7a35a70bbe..6f62cd11a9 100644 --- a/tensorflow/contrib/tensor_forest/python/tensor_forest.py +++ b/tensorflow/contrib/tensor_forest/python/tensor_forest.py @@ -295,7 +295,7 @@ def get_epoch_variable(): # A simple container to hold the training variables for a single tree. -class TreeTrainingVariables(object): +class TreeVariables(object): """Stores tf.Variables for training a single random tree. Uses tf.get_variable to get tree-specific names so that this can be used @@ -303,7 +303,7 @@ class TreeTrainingVariables(object): then relies on restoring that model to evaluate). """ - def __init__(self, params, tree_num, training): + def __init__(self, params, tree_num, training, tree_config='', tree_stat=''): if (not hasattr(params, 'params_proto') or not isinstance(params.params_proto, _params_proto.TensorForestParams)): @@ -315,27 +315,28 @@ class TreeTrainingVariables(object): # TODO(gilberth): Manually shard this to be able to fit it on # multiple machines. self.stats = stats_ops.fertile_stats_variable( - params, '', self.get_tree_name('stats', tree_num)) + params, tree_stat, self.get_tree_name('stats', tree_num)) self.tree = model_ops.tree_variable( - params, '', self.stats, self.get_tree_name('tree', tree_num)) + params, tree_config, self.stats, self.get_tree_name('tree', tree_num)) def get_tree_name(self, name, num): return '{0}-{1}'.format(name, num) -class ForestTrainingVariables(object): +class ForestVariables(object): """A container for a forests training data, consisting of multiple trees. - Instantiates a TreeTrainingVariables object for each tree. We override the + Instantiates a TreeVariables object for each tree. We override the __getitem__ and __setitem__ function so that usage looks like this: - forest_variables = ForestTrainingVariables(params) + forest_variables = ForestVariables(params) ... forest_variables.tree ... """ def __init__(self, params, device_assigner, training=True, - tree_variables_class=TreeTrainingVariables): + tree_variables_class=TreeVariables, + tree_configs=None, tree_stats=None): self.variables = [] # Set up some scalar variables to run through the device assigner, then # we can use those to colocate everything related to a tree. @@ -347,7 +348,13 @@ class ForestTrainingVariables(object): for i in range(params.num_trees): with ops.device(self.device_dummies[i].device): - self.variables.append(tree_variables_class(params, i, training)) + kwargs = {} + if tree_configs is not None: + kwargs.update(dict(tree_config=tree_configs[i])) + if tree_stats is not None: + kwargs.update(dict(tree_stat=tree_stats[i])) + self.variables.append(tree_variables_class( + params, i, training, **kwargs)) def __setitem__(self, t, val): self.variables[t] = val @@ -361,9 +368,11 @@ class RandomForestGraphs(object): def __init__(self, params, + tree_configs=None, + tree_stats=None, device_assigner=None, variables=None, - tree_variables_class=TreeTrainingVariables, + tree_variables_class=TreeVariables, tree_graphs=None, training=True): self.params = params @@ -371,9 +380,10 @@ class RandomForestGraphs(object): device_assigner or framework_variables.VariableDeviceChooser()) logging.info('Constructing forest with params = ') logging.info(self.params.__dict__) - self.variables = variables or ForestTrainingVariables( + self.variables = variables or ForestVariables( self.params, device_assigner=self.device_assigner, training=training, - tree_variables_class=tree_variables_class) + tree_variables_class=tree_variables_class, + tree_configs=tree_configs, tree_stats=tree_stats) tree_graph_class = tree_graphs or RandomTreeGraphs self.trees = [ tree_graph_class(self.variables[i], self.params, i) diff --git a/tensorflow/contrib/tensor_forest/python/tensor_forest_test.py b/tensorflow/contrib/tensor_forest/python/tensor_forest_test.py index bbe627b157..1c9c81827e 100644 --- a/tensorflow/contrib/tensor_forest/python/tensor_forest_test.py +++ b/tensorflow/contrib/tensor_forest/python/tensor_forest_test.py @@ -18,10 +18,14 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from google.protobuf.json_format import ParseDict +from tensorflow.contrib.decision_trees.proto import generic_tree_model_pb2 as _tree_proto from tensorflow.contrib.tensor_forest.python import tensor_forest from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor from tensorflow.python.framework import test_util +from tensorflow.python.ops import resources +from tensorflow.python.ops import variables from tensorflow.python.platform import googletest @@ -110,6 +114,47 @@ class TensorForestTest(test_util.TensorFlowTestCase): self.assertTrue(isinstance(paths, ops.Tensor)) self.assertTrue(isinstance(var, ops.Tensor)) + def testInfrenceFromRestoredModel(self): + input_data = [[-1., 0.], [-1., 2.], # node 1 + [1., 0.], [1., -2.]] # node 2 + expected_prediction = [[0.0, 1.0], [0.0, 1.0], + [0.0, 1.0], [0.0, 1.0]] + hparams = tensor_forest.ForestHParams( + num_classes=2, + num_features=2, + num_trees=1, + max_nodes=1000, + split_after_samples=25).fill() + tree_weight = {'decisionTree': + {'nodes': + [{'binaryNode': + {'rightChildId': 2, + 'leftChildId': 1, + 'inequalityLeftChildTest': + {'featureId': {'id': '0'}, + 'threshold': {'floatValue': 0}}}}, + {'leaf': {'vector': + {'value': [{'floatValue': 0.0}, + {'floatValue': 1.0}]}}, + 'nodeId': 1}, + {'leaf': {'vector': + {'value': [{'floatValue': 0.0}, + {'floatValue': 1.0}]}}, + 'nodeId': 2}]}} + restored_tree_param = ParseDict(tree_weight, + _tree_proto.Model()).SerializeToString() + graph_builder = tensor_forest.RandomForestGraphs(hparams, + [restored_tree_param]) + probs, paths, var = graph_builder.inference_graph(input_data) + self.assertTrue(isinstance(probs, ops.Tensor)) + self.assertTrue(isinstance(paths, ops.Tensor)) + self.assertTrue(isinstance(var, ops.Tensor)) + with self.test_session(): + variables.global_variables_initializer().run() + resources.initialize_resources(resources.shared_resources()).run() + self.assertEquals(probs.eval().shape, (4, 2)) + self.assertEquals(probs.eval().tolist(), expected_prediction) + def testTrainingConstructionClassificationSparse(self): input_data = sparse_tensor.SparseTensor( indices=[[0, 0], [0, 3], [1, 0], [1, 7], [2, 1], [3, 9]], diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.cc b/tensorflow/contrib/tensorrt/convert/convert_graph.cc index b7b26cfb1c..da4dd5a14c 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_graph.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_graph.cc @@ -91,8 +91,11 @@ void GetSubGraphIncomingEdges(const tensorflow::Graph& graph, if (!subgraph_node_ids.count(edge->src()->id()) && !edge->src()->IsSource() && !edge->IsControlEdge()) { incoming_edges->insert(edge); + VLOG(2) << "INCOMING " << edge->src()->name() << " -> " << node->name() + << " Y, "; } else { - VLOG(2) << node->name() << " -> " << edge->src()->name() << " N, "; + VLOG(2) << "INCOMING " << edge->src()->name() << " -> " << node->name() + << " N, "; } } } @@ -106,10 +109,12 @@ void GetSubGraphOutgoingEdges(const tensorflow::Graph& graph, for (const tensorflow::Edge* edge : node->out_edges()) { if (!subgraph_node_ids.count(edge->dst()->id()) && !edge->dst()->IsSink() && !edge->IsControlEdge()) { - VLOG(2) << node->name() << " -> " << edge->dst()->name() << " Y, "; + VLOG(2) << "OUTGOING " << node->name() << " -> " << edge->dst()->name() + << " Y, "; outgoing_edges->insert(edge); } else { - VLOG(2) << node->name() << " -> " << edge->dst()->name() << " N, "; + VLOG(2) << "OUTGOING " << node->name() << " -> " << edge->dst()->name() + << " N, "; } } } @@ -181,29 +186,27 @@ struct ConvertGraphParams { static tensorflow::Status FillSubGraphEdgeSets(ConvertGraphParams* p) { GetSubGraphIncomingEdges(p->graph, p->subgraph_node_ids, &p->subgraph_incoming_edges); + + std::set> unique_tensors; + // Add only unique input source nodes. If output of an outside node is shared + // between multiple nodes inside the engine, only one edge should be created for (const tensorflow::Edge* edge : p->subgraph_incoming_edges) { - p->subgraph_inputs.push_back({edge->src()->id(), edge->src_output()}); - } - auto output_name_to_index_map = BuildTensorNameMap(p->output_names); - std::set> subgraph_outputs_set; - // Collect outputs referenced from output_names - for (int node_id : p->subgraph_node_ids) { - tensorflow::Node* node = p->graph.FindNodeId(node_id); - if (output_name_to_index_map.count(node->name())) { - for (int index : output_name_to_index_map.at(node->name())) { - subgraph_outputs_set.insert({node_id, index}); - } - } + unique_tensors.insert({edge->src()->id(), edge->src_output()}); } + p->subgraph_inputs.insert(p->subgraph_inputs.begin(), unique_tensors.begin(), + unique_tensors.end()); GetSubGraphOutgoingEdges(p->graph, p->subgraph_node_ids, &p->subgraph_outgoing_edges); + unique_tensors.clear(); + // Similar to above, if multiple ouside nodes are sharing the output of an + // internal node only one output port should be created and shared between + // outputs for (const tensorflow::Edge* edge : p->subgraph_outgoing_edges) { - subgraph_outputs_set.insert({edge->src()->id(), edge->src_output()}); + unique_tensors.insert({edge->src()->id(), edge->src_output()}); } - p->subgraph_outputs.reserve(subgraph_outputs_set.size()); + p->subgraph_outputs.reserve(unique_tensors.size()); p->subgraph_outputs.insert(p->subgraph_outputs.begin(), - subgraph_outputs_set.begin(), - subgraph_outputs_set.end()); + unique_tensors.begin(), unique_tensors.end()); return tensorflow::Status::OK(); } @@ -225,7 +228,6 @@ tensorflow::Status GetCalibNode(ConvertGraphParams* params) { for (auto in_edge : params->subgraph_incoming_edges) { // loop over incoming edges and // attach them to calib node - // tensorflow::Node* src_node = in_edge->src(); auto src_output = in_edge->src_output(); auto dst_node = in_edge->dst(); auto dst_input = in_edge->dst_input(); @@ -257,19 +259,24 @@ tensorflow::Status ConvertSubGraphToTensorRT(ConvertGraphParams* params) { for (size_t i = 0; i < params->subgraph_inputs.size(); ++i) { subgraph_edge_to_input_map.insert({params->subgraph_inputs.at(i), i}); } + std::set> unique_tensors; for (const tensorflow::Edge* edge : params->subgraph_incoming_edges) { std::pair old_src = {edge->src()->id(), edge->src_output()}; + if (unique_tensors.count(old_src)) continue; + unique_tensors.insert(old_src); int new_src_output = subgraph_edge_to_input_map.at(old_src); params->graph.AddEdge(edge->src(), edge->src_output(), trt_node, new_src_output); + VLOG(1) << "Wire " << edge->src()->name() << ":" << edge->src_output() + << " -> " << trt_node->name() << ":" << new_src_output; params->graph.RemoveEdge(edge); } - - VLOG(2) << "new wiring edges: " << trt_node->in_edges().size(); - for (const tensorflow::Edge* edge : trt_node->in_edges()) { - VLOG(2) << edge->src()->name() << " port: " << edge->src_output(); + if (VLOG_IS_ON(2)) { + VLOG(2) << "new edge count: " << trt_node->in_edges().size(); + for (const tensorflow::Edge* edge : trt_node->in_edges()) { + VLOG(2) << edge->src()->name() << " port: " << edge->src_output(); + } } - TF_RETURN_IF_ERROR(status); // Re-map outgoing edges to use the new TRT node instead of the orig subgraph @@ -283,6 +290,8 @@ tensorflow::Status ConvertSubGraphToTensorRT(ConvertGraphParams* params) { int new_src_output = subgraph_edge_to_output_map.at(old_src); TF_RETURN_IF_ERROR(params->graph.UpdateEdge( trt_node, new_src_output, edge->dst(), edge->dst_input())); + VLOG(1) << "Wire " << trt_node->name() << ":" << new_src_output << " -> " + << edge->dst()->name() << ":" << edge->dst_input(); } // Remove the original subgraph for (int node_id : params->subgraph_node_ids) { @@ -317,9 +326,12 @@ tensorflow::Status ConvertCalibGraphToInferGraph( tensorflow::GraphConstructorOptions(), graph_def, &graph)); // get calib nodes std::vector calib_nodes; - for (auto node : graph.op_nodes()) { + std::vector topo_order; + tensorflow::GetPostOrder(graph, &topo_order); + for (auto rit = topo_order.rbegin(); rit != topo_order.rend(); ++rit) { + auto node = *rit; if (node->type_string() == "TRTCalibOp") { - VLOG(1) << "Found Calib Node"; + VLOG(1) << "Found Calib Node " << node->name(); calib_nodes.push_back(node); } } diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index 96e0700862..4e4d295538 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -362,10 +362,11 @@ void ReorderCKtoKC(const TRT_ShapedWeights& iweights, break; } case tensorflow::DataType::DT_HALF: { - Reorder2({k, c}, static_cast(iweights.GetValues()), - istrides, static_cast( - const_cast(oweights->GetValues())), - ostrides); + Reorder2( + {k, c}, static_cast(iweights.GetValues()), + istrides, + static_cast(const_cast(oweights->GetValues())), + ostrides); break; } default: @@ -1179,9 +1180,9 @@ tensorflow::Status BinaryTensorOpTensor( CHECK_EQ_TYPE(tensor_r->getType(), dtype); auto op_pair = ops.find(node_def.op()); if (op_pair == ops.end()) - return tensorflow::errors::Unimplemented("binary op: " + node_def.op() + - " not supported at: " + - node_def.name()); + return tensorflow::errors::Unimplemented( + "binary op: " + node_def.op() + + " not supported at: " + node_def.name()); nvinfer1::IElementWiseLayer* layer = ctx.network()->addElementWise( *const_cast(tensor_l), @@ -2138,9 +2139,7 @@ void Converter::register_op_converters() { } } // namespace -tensorflow::Status GetTensorRTGraph(tensorrt::convert::SubGraphParams& s) { - return tensorflow::errors::Unimplemented("Not implemented yet"); -} + tensorflow::Status ConvertCalibrationNodeToEngineNode( tensorflow::Graph& graph, tensorflow::Node* c_node) { const auto ndef = c_node->def(); @@ -2164,9 +2163,23 @@ tensorflow::Status ConvertCalibrationNodeToEngineNode( for (auto n : graph.op_nodes()) { node_maps.insert({n->name(), n}); } + std::set subgraph_ids; + for (const auto internal_node : segment_nodes) { + subgraph_ids.insert(node_maps.at(internal_node)->id()); + } + if (VLOG_IS_ON(2)) { + string node_names = StrCat(c_node->name(), " segment nodes= "); + + for (const auto& node_name : segment_nodes) { + StrAppend(&node_names, node_name, ", "); + } + VLOG(2) << node_names; + } + VLOG(1) << "Output Nodes:"; std::vector out_types; std::vector out_edges; + for (auto& i : output_nodes) { auto node_port = tensorflow::str_util::Split(i, ":"); VLOG(1) << " " << i << " in graph " << node_maps.count(i); @@ -2186,18 +2199,24 @@ tensorflow::Status ConvertCalibrationNodeToEngineNode( out_types.push_back(out_node->output_type(0)); } for (auto out_edge : out_node->out_edges()) { + if (subgraph_ids.count(out_edge->dst()->id())) + continue; // skip internal edges; if (out_edge->src_output() == port) { out_edges.push_back(out_edge); - break; + VLOG(1) << "OUTPUT EDGE " << out_edge->src()->name() << ":" + << out_edge->src_output() << " -> " << out_edge->dst()->name() + << ":" << out_edge->dst_input(); } } } else { LOG(WARNING) << " couldn't find output node " << out_node_name; } } - VLOG(1) << "Input Nodes:"; - for (auto& i : input_names) { - VLOG(1) << " " << i << " in graph " << node_maps.count(i); + if (VLOG_IS_ON(1)) { + VLOG(1) << c_node->name() << " Input Nodes:"; + for (auto& i : input_names) { + VLOG(1) << " Input " << i << " in graph " << node_maps.count(i); + } } auto trt_rm = tensorflow::tensorrt::TRTResourceManager::instance(); auto resmgr = trt_rm->getManager("TRTCalibOps"); @@ -2231,14 +2250,24 @@ tensorflow::Status ConvertCalibrationNodeToEngineNode( calib_res->builder_ = nullptr; tensorflow::NodeDefBuilder op_builder(engine_name, "TRTEngineOp"); std::vector income_edges; + income_edges.resize(c_node->num_inputs()); for (const auto in_edge : c_node->in_edges()) { auto src = in_edge->src(); int dest_port = in_edge->dst_input(); - income_edges.emplace_back(src->name(), in_edge->src_output(), - c_node->input_type(dest_port)); + VLOG(1) << "Incoming connection " << src->name() << ":" + << in_edge->src_output() << " -> " << c_node->name() << ":" + << dest_port; + income_edges.at(dest_port) = {src->name(), in_edge->src_output(), + c_node->input_type(dest_port)}; } tensorflow::gtl::ArraySlice input_list( income_edges); + if (VLOG_IS_ON(2)) { + for (const auto& inp : input_list) { + VLOG(2) << " Input from inputlist " << inp.node << ":" << inp.index << " " + << tensorflow::DataTypeString(inp.data_type); + } + } op_builder.Input(input_list); tensorflow::NodeDef engine_node; const char* engine_plan_data = static_cast(engine_plan->data()); @@ -2255,13 +2284,26 @@ tensorflow::Status ConvertCalibrationNodeToEngineNode( } auto trt_engine_node = graph.AddNode(engine_node, &status); TF_RETURN_IF_ERROR(status); - for (size_t i = 0; i < out_edges.size(); i++) { - VLOG(1) << "Connecting trt_engine_node output " << i << " with " - << out_edges.at(i)->dst()->name() << " port " - << out_edges.at(i)->dst_input(); - TF_RETURN_IF_ERROR(graph.UpdateEdge(trt_engine_node, i, - out_edges.at(i)->dst(), - out_edges.at(i)->dst_input())); + std::map port_map; + for (size_t t = 0; t < output_nodes.size(); t++) { + port_map.insert({output_nodes.at(t), t}); + } + for (auto& i : out_edges) { + string s(i->src()->name()); + if (i->src_output()) StrAppend(&s, ":", i->src_output()); + int out_port = port_map.at(s); + VLOG(1) << "Connecting " << trt_engine_node->name() << ":" << out_port + << " -> " << i->dst()->name() << ":" << i->dst_input(); + TF_RETURN_IF_ERROR( + graph.UpdateEdge(trt_engine_node, out_port, i->dst(), i->dst_input())); + } + for (const auto ed : trt_engine_node->in_edges()) { + VLOG(1) << "In Edge " << ed->src()->name() << ":" << ed->src_output() + << " -> " << ed->dst()->name() << ":" << ed->dst_input(); + } + for (const auto ed : trt_engine_node->out_edges()) { + VLOG(1) << "Out Edge " << ed->src()->name() << ":" << ed->src_output() + << " -> " << ed->dst()->name() << ":" << ed->dst_input(); } VLOG(1) << "Segment nodes:"; for (auto& i : segment_nodes) { @@ -2332,6 +2374,7 @@ tensorflow::Status ConvertSubgraph( std::vector* output_names, std::vector* output_dtypes, const string& engine_name) { + std::set added_tensors; for (const std::pair& input : s.input_inds) { VLOG(2) << "parsing input. Node id= " << input.first; int node_id = input.first; @@ -2374,7 +2417,6 @@ tensorflow::Status ConvertSubgraph( auto op_info = op_info_vec.at(shape_inference_output_idx); tensorflow::DataType tf_dtype = op_info.dtype(); - input_dtypes->push_back(tf_dtype); nvinfer1::DataType dtype(nvinfer1::DataType::kFLOAT); auto type_status = ConvertDType(tf_dtype, &dtype); @@ -2410,8 +2452,10 @@ tensorflow::Status ConvertSubgraph( if (output_idx != 0) { input_tensor_name = StrCat(node_name, ":", output_idx); } - + if (added_tensors.count(input_tensor_name)) continue; + added_tensors.insert(input_tensor_name); input_names->push_back(input_tensor_name); + input_dtypes->push_back(tf_dtype); nvinfer1::ITensor* input_tensor = converter.network()->addInput( input_tensor_name.c_str(), dtype, input_dim_pseudo_chw); @@ -2435,6 +2479,7 @@ tensorflow::Status ConvertSubgraph( // Gather output metadata int trt_engine_op_output_idx = 0; + added_tensors.clear(); for (const std::pair& output : s.output_inds) { int node_id = output.first; int output_idx = output.second; @@ -2451,6 +2496,8 @@ tensorflow::Status ConvertSubgraph( if (output_idx != 0) tensorflow::strings::StrAppend(&tensor_name, ":", output_idx); VLOG(2) << "Output tensor name: " << tensor_name; + if (added_tensors.count(tensor_name)) continue; + added_tensors.insert(tensor_name); output_names->push_back(tensor_name); auto tensor_or_weights = converter.get_tensor(tensor_name); if (!tensor_or_weights.is_tensor()) { diff --git a/tensorflow/contrib/tpu/python/tpu/datasets.py b/tensorflow/contrib/tpu/python/tpu/datasets.py index 2e472a2805..d879170b68 100644 --- a/tensorflow/contrib/tpu/python/tpu/datasets.py +++ b/tensorflow/contrib/tpu/python/tpu/datasets.py @@ -166,11 +166,21 @@ def StreamingFilesDataset(files, return remote_iterator.get_next() def MapFn(unused_input): - return functional_ops.remote_call( + if isinstance(source_dataset.output_types, dtypes.DType): + output_types = [source_dataset.output_types] + elif isinstance(source_dataset.output_types, (list, tuple)): + output_types = source_dataset.output_types + else: + raise ValueError('source dataset has invalid output types') + remote_calls = functional_ops.remote_call( args=[source_handle], - Tout=[dtypes.string], + Tout=output_types, f=LoadingFunc, - target='/job:%s/replica:0/task:0/cpu:0' % file_reader_job)[0] + target='/job:%s/replica:0/task:0/cpu:0' % file_reader_job) + if len(remote_calls) == 1: + return remote_calls[0] + else: + return remote_calls with ops.device('/job:%s' % worker_job): output_dataset = dataset_ops.Dataset.range(2).repeat().map( diff --git a/tensorflow/contrib/tpu/python/tpu/datasets_test.py b/tensorflow/contrib/tpu/python/tpu/datasets_test.py index 918cf0ed8e..b58d05eac5 100644 --- a/tensorflow/contrib/tpu/python/tpu/datasets_test.py +++ b/tensorflow/contrib/tpu/python/tpu/datasets_test.py @@ -26,6 +26,8 @@ from tensorflow.core.protobuf import config_pb2 from tensorflow.python.client import session from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.ops import readers +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import tensor_shape from tensorflow.python.lib.io import python_io from tensorflow.python.platform import test from tensorflow.python.training import server_lib @@ -162,6 +164,30 @@ class DatasetsTest(test.TestCase): self.assertEqual(set(all_contents), set(retrieved_values)) + def testArbitraryReaderFuncFromDatasetGenerator(self): + + def my_generator(): + yield (1, [1] * 10) + + def gen_dataset(dummy): + return dataset_ops.Dataset.from_generator( + my_generator, (dtypes.int64, dtypes.int64), + (tensor_shape.TensorShape([]), tensor_shape.TensorShape([10]))) + + dataset = datasets.StreamingFilesDataset( + dataset_ops.Dataset.range(10), filetype=gen_dataset) + + iterator = dataset.make_initializable_iterator() + self._sess.run(iterator.initializer) + get_next = iterator.get_next() + + retrieved_values = self._sess.run(get_next) + + self.assertIsInstance(retrieved_values, (list, tuple)) + self.assertEqual(len(retrieved_values), 2) + self.assertEqual(retrieved_values[0], 1) + self.assertItemsEqual(retrieved_values[1], [1] * 10) + def testUnexpectedFiletypeString(self): with self.assertRaises(ValueError): datasets.StreamingFilesDataset( diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index c72ba2daff..a0cf59852b 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -700,7 +700,9 @@ cc_library( srcs = ["platform/stacktrace_handler.cc"], hdrs = ["platform/stacktrace_handler.h"], deps = [ + ":abi", ":lib_platform", + ":stacktrace", ], ) @@ -3090,6 +3092,8 @@ cc_library( # we now need at least "str_util". ":lib", ":lib_platform", + ":stacktrace_handler", + ":test_lite", "//tensorflow/core/platform/default/build_config:test_lite_main", ], alwayslink = 1, @@ -3570,7 +3574,10 @@ tf_cc_tests_gpu( tf_cc_test_mkl( name = "mkl_runtime_tests", size = "small", - srcs = ["common_runtime/mkl_cpu_allocator_test.cc"], + srcs = [ + "common_runtime/mkl_cpu_allocator_test.cc", + "common_runtime/mkl_threadpool_device_test.cc", + ], linkstatic = 1, deps = [ ":core", diff --git a/tensorflow/core/api_def/base_api/api_def_Selu.pbtxt b/tensorflow/core/api_def/base_api/api_def_Selu.pbtxt index cbe76de415..985f09312f 100644 --- a/tensorflow/core/api_def/base_api/api_def_Selu.pbtxt +++ b/tensorflow/core/api_def/base_api/api_def_Selu.pbtxt @@ -4,6 +4,10 @@ op { description: < 0`, limit of the split of the result. +END + } + summary: "Split elements of `source` based on `sep` into a `SparseTensor`." + description: <2<><>3"` and +sep of `"<>"` returns `["1", "2", "", "3"]`. If `sep` is None or an empty +string, consecutive whitespace are regarded as a single separator, and the +result will contain no empty strings at the startor end if the string has +leading or trailing whitespace. + +Note that the above mentioned behavior matches python's str.split. +END +} diff --git a/tensorflow/core/api_def/python_api/api_def_StringSplitV2.pbtxt b/tensorflow/core/api_def/python_api/api_def_StringSplitV2.pbtxt new file mode 100644 index 0000000000..0e8576fb01 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_StringSplitV2.pbtxt @@ -0,0 +1,4 @@ +op { + graph_op_name: "StringSplitV2" + visibility: HIDDEN +} diff --git a/tensorflow/core/common_runtime/bfc_allocator.cc b/tensorflow/core/common_runtime/bfc_allocator.cc index 8f2a419756..9cda17867b 100644 --- a/tensorflow/core/common_runtime/bfc_allocator.cc +++ b/tensorflow/core/common_runtime/bfc_allocator.cc @@ -86,7 +86,7 @@ BFCAllocator::Chunk* BFCAllocator::ChunkFromHandle(ChunkHandle h) { return &(chunks_[h]); } -bool BFCAllocator::Extend(size_t rounded_bytes) { +bool BFCAllocator::Extend(size_t alignment, size_t rounded_bytes) { size_t available_bytes = memory_limit_ - total_region_allocated_bytes_; // Rounds available_bytes down to the nearest multiple of kMinAllocationSize. available_bytes = (available_bytes / kMinAllocationSize) * kMinAllocationSize; @@ -108,7 +108,7 @@ bool BFCAllocator::Extend(size_t rounded_bytes) { // Try allocating. size_t bytes = std::min(curr_region_allocation_bytes_, available_bytes); - void* mem_addr = suballocator_->Alloc(32, bytes); + void* mem_addr = suballocator_->Alloc(alignment, bytes); if (mem_addr == nullptr && !started_backpedal_) { // Only backpedal once. started_backpedal_ = true; @@ -119,7 +119,7 @@ bool BFCAllocator::Extend(size_t rounded_bytes) { while (mem_addr == nullptr) { bytes = RoundedBytes(bytes * kBackpedalFactor); if (bytes < rounded_bytes) break; - mem_addr = suballocator_->Alloc(32, bytes); + mem_addr = suballocator_->Alloc(alignment, bytes); } } @@ -261,7 +261,7 @@ void* BFCAllocator::AllocateRawInternal(size_t unused_alignment, } // Try to extend - if (Extend(rounded_bytes)) { + if (Extend(unused_alignment, rounded_bytes)) { ptr = FindChunkPtr(bin_num, rounded_bytes, num_bytes); if (ptr != nullptr) { return ptr; diff --git a/tensorflow/core/common_runtime/bfc_allocator.h b/tensorflow/core/common_runtime/bfc_allocator.h index ba5a3eea3a..52aedb1e9c 100644 --- a/tensorflow/core/common_runtime/bfc_allocator.h +++ b/tensorflow/core/common_runtime/bfc_allocator.h @@ -305,7 +305,8 @@ class BFCAllocator : public VisitableAllocator { // Try to add a new memory region that can satisfy an allocation of // 'rounded_bytes' bytes. Returns true on success and false on // failure. - bool Extend(size_t rounded_bytes) EXCLUSIVE_LOCKS_REQUIRED(lock_); + bool Extend(size_t alignment, size_t rounded_bytes) + EXCLUSIVE_LOCKS_REQUIRED(lock_); // Returns a pointer to an underlying allocated chunk of size // 'rounded_bytes'. diff --git a/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc b/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc index 6e08e33f8e..486f0be698 100644 --- a/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc +++ b/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc @@ -105,9 +105,25 @@ TEST(DirectSessionWithTrackingAllocTest, CostModelTest) { EXPECT_EQ(2, shape.dim(0).size()); EXPECT_EQ(1, shape.dim(1).size()); if (node->name() == y->name()) { +#ifdef INTEL_MKL + // if MKL is used, it goes through various additional + // graph rewrite pass. In TF, everytime a graph pass + // happens, "constant" nodes are allocated + // and deallocated. Each allocation calls the + // (FindChunkPtr of BFCAllocator), + // which increments the value of AllocationId. + // Thus AllocationId becomes more than 3 and 4 if + // MKL is used. Now they are 9 and 10 for MKL. + EXPECT_EQ(19, cm->AllocationId(node, 0)); +#else EXPECT_EQ(21, cm->AllocationId(node, 0)); +#endif } else { +#ifdef INTEL_MKL + EXPECT_EQ(20, cm->AllocationId(node, 0)); +#else EXPECT_EQ(22, cm->AllocationId(node, 0)); +#endif } } EXPECT_LE(0, cm->MaxExecutionTime(node)); diff --git a/tensorflow/core/common_runtime/mkl_threadpool_device_test.cc b/tensorflow/core/common_runtime/mkl_threadpool_device_test.cc new file mode 100644 index 0000000000..5d583a8360 --- /dev/null +++ b/tensorflow/core/common_runtime/mkl_threadpool_device_test.cc @@ -0,0 +1,53 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifdef INTEL_MKL + +#include "tensorflow/core/common_runtime/threadpool_device.h" + +#include "tensorflow/core/lib/core/status_test_util.h" +#include "tensorflow/core/platform/cpu_info.h" +#include "tensorflow/core/platform/logging.h" +#include "tensorflow/core/platform/test.h" +#include "tensorflow/core/public/session_options.h" + +namespace tensorflow { + +#ifdef _OPENMP +TEST(MKLThreadPoolDeviceTest, TestOmpDefaults) { + SessionOptions options; + unsetenv("OMP_NUM_THREADS"); + + ThreadPoolDevice* tp = new ThreadPoolDevice( + options, "/device:CPU:0", Bytes(256), DeviceLocality(), cpu_allocator()); + + const int ht = port::NumHyperthreadsPerCore(); + EXPECT_EQ(omp_get_max_threads(), (port::NumSchedulableCPUs() + ht - 1) / ht); +} + +TEST(MKLThreadPoolDeviceTest, TestOmpPreSets) { + SessionOptions options; + setenv("OMP_NUM_THREADS", "314", 1); + + ThreadPoolDevice* tp = new ThreadPoolDevice( + options, "/device:CPU:0", Bytes(256), DeviceLocality(), cpu_allocator()); + + EXPECT_EQ(omp_get_max_threads(), 314); +} +#endif // _OPENMP + +} // namespace tensorflow + +#endif // INTEL_MKL diff --git a/tensorflow/core/common_runtime/process_util.cc b/tensorflow/core/common_runtime/process_util.cc index 21912236d0..a5d31b75c7 100644 --- a/tensorflow/core/common_runtime/process_util.cc +++ b/tensorflow/core/common_runtime/process_util.cc @@ -16,8 +16,10 @@ limitations under the License. #include "tensorflow/core/common_runtime/process_util.h" #ifdef INTEL_MKL +#ifdef _OPENMP #include -#endif +#endif // _OPENMP +#endif // INTEL_MKL #include #include "tensorflow/core/lib/core/threadpool.h" @@ -57,7 +59,10 @@ int32 NumInterOpThreadsFromSessionOptions(const SessionOptions& options) { // MKL library executes ops in parallel using OMP threads // Set inter_op conservatively to avoid thread oversubscription that could // lead to severe perf degradations and OMP resource exhaustion - const int mkl_intra_op = omp_get_max_threads(); + int mkl_intra_op = 1; +#ifdef _OPENMP + mkl_intra_op = omp_get_max_threads(); +#endif // _OPENMP CHECK_GE(mkl_intra_op, 1); const int32 mkl_inter_op = std::max( (port::NumSchedulableCPUs() + mkl_intra_op - 1) / mkl_intra_op, 2); @@ -68,7 +73,7 @@ int32 NumInterOpThreadsFromSessionOptions(const SessionOptions& options) { #else // Default to using the number of cores available in the process. return port::NumSchedulableCPUs(); -#endif +#endif // INTEL_MKL } thread::ThreadPool* NewThreadPoolFromSessionOptions( diff --git a/tensorflow/core/common_runtime/threadpool_device.cc b/tensorflow/core/common_runtime/threadpool_device.cc index f7a07fe503..74a87215e1 100644 --- a/tensorflow/core/common_runtime/threadpool_device.cc +++ b/tensorflow/core/common_runtime/threadpool_device.cc @@ -31,7 +31,11 @@ limitations under the License. #include "tensorflow/core/public/session_options.h" #ifdef INTEL_MKL +#ifdef _OPENMP +#include +#endif #include "tensorflow/core/common_runtime/mkl_cpu_allocator.h" +#include "tensorflow/core/platform/cpu_info.h" #endif namespace tensorflow { @@ -43,7 +47,26 @@ ThreadPoolDevice::ThreadPoolDevice(const SessionOptions& options, : LocalDevice(options, Device::BuildDeviceAttributes( name, DEVICE_CPU, memory_limit, locality)), allocator_(allocator), - scoped_allocator_mgr_(new ScopedAllocatorMgr(name)) {} + scoped_allocator_mgr_(new ScopedAllocatorMgr(name)) { +#ifdef INTEL_MKL +#ifdef _OPENMP + const char* user_omp_threads = getenv("OMP_NUM_THREADS"); + if (user_omp_threads == nullptr) { + // OMP_NUM_THREADS controls MKL's intra-op parallelization + // Default to available physical cores + const int mkl_intra_op = port::NumSchedulableCPUs(); + const int ht = port::NumHyperthreadsPerCore(); + omp_set_num_threads((mkl_intra_op + ht - 1) / ht); + } else { + uint64 user_val = 0; + if (strings::safe_strtou64(user_omp_threads, &user_val)) { + // Superflous but triggers OpenMP loading + omp_set_num_threads(user_val); + } + } +#endif // _OPENMP +#endif // INTEL_MKL +} ThreadPoolDevice::~ThreadPoolDevice() {} diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_master_service_impl.cc b/tensorflow/core/distributed_runtime/rpc/grpc_master_service_impl.cc index 1cea1b1462..770a0fcf14 100644 --- a/tensorflow/core/distributed_runtime/rpc/grpc_master_service_impl.cc +++ b/tensorflow/core/distributed_runtime/rpc/grpc_master_service_impl.cc @@ -147,7 +147,9 @@ MasterService::Stub::Stub( } MasterService::AsyncService::AsyncService() { - for (int i = 0; i < 10; ++i) { + int method_len = sizeof(grpcMasterService_method_names) / + sizeof(grpcMasterService_method_names[0]); + for (int i = 0; i < method_len; ++i) { AddMethod(new ::grpc::internal::RpcServiceMethod( grpcMasterService_method_names[i], ::grpc::internal::RpcMethod::NORMAL_RPC, nullptr)); diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_testlib.cc b/tensorflow/core/distributed_runtime/rpc/grpc_testlib.cc index 89f83f9f24..a8508d2d4f 100644 --- a/tensorflow/core/distributed_runtime/rpc/grpc_testlib.cc +++ b/tensorflow/core/distributed_runtime/rpc/grpc_testlib.cc @@ -17,6 +17,7 @@ limitations under the License. #include "tensorflow/core/distributed_runtime/rpc/grpc_session.h" #include "tensorflow/core/lib/strings/str_util.h" +#include "tensorflow/core/platform/env.h" #include "tensorflow/core/util/device_name_utils.h" namespace tensorflow { @@ -50,9 +51,14 @@ Status TestCluster::MakeTestCluster(const SessionOptions& options, int n, } for (int i = 0; i < n; ++i) { + string server_file = + strings::StrCat(testing::TensorFlowSrcRoot(), + "/core/distributed_runtime/rpc/grpc_testlib_server"); + if (!options.env->FileExists(server_file).ok()) { + return errors::Internal("Could not find grpc_testlib_server"); + } const std::vector argv( - {strings::StrCat(testing::TensorFlowSrcRoot(), - "/core/distributed_runtime/rpc/grpc_testlib_server"), + {server_file, /* see grpc_testlib_server.cc for flags */ tf_jobs, "--tf_job=localhost", strings::StrCat("--tf_task=", i), strings::StrCat("--num_cpus=", num_cpus), diff --git a/tensorflow/core/framework/allocator.h b/tensorflow/core/framework/allocator.h index 2c87156dca..2bb4d32d57 100644 --- a/tensorflow/core/framework/allocator.h +++ b/tensorflow/core/framework/allocator.h @@ -67,13 +67,8 @@ struct AllocatorStats { // device memory. class Allocator { public: -#ifdef EIGEN_VECTORIZE_AVX512 // Align to 64 byte boundary. static constexpr size_t kAllocatorAlignment = 64; -#else - // Align to 32 byte boundary. - static constexpr size_t kAllocatorAlignment = 32; -#endif virtual ~Allocator(); diff --git a/tensorflow/core/framework/op_gen_lib.cc b/tensorflow/core/framework/op_gen_lib.cc index 3d7920a6e2..4b56d807df 100644 --- a/tensorflow/core/framework/op_gen_lib.cc +++ b/tensorflow/core/framework/op_gen_lib.cc @@ -15,6 +15,7 @@ limitations under the License. #include "tensorflow/core/framework/op_gen_lib.h" +#include #include #include "tensorflow/core/framework/attr_value.pb.h" #include "tensorflow/core/lib/core/errors.h" diff --git a/tensorflow/core/framework/remote_fused_graph_execute_info.proto b/tensorflow/core/framework/remote_fused_graph_execute_info.proto index eb689ec1e6..10072724d2 100644 --- a/tensorflow/core/framework/remote_fused_graph_execute_info.proto +++ b/tensorflow/core/framework/remote_fused_graph_execute_info.proto @@ -5,7 +5,7 @@ option cc_enable_arenas = true; option java_outer_classname = "RemoteFusedGraphExecuteInfoProto"; option java_multiple_files = true; option java_package = "org.tensorflow.framework"; -//add go_package externally +option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework"; import "tensorflow/core/framework/graph.proto"; import "tensorflow/core/framework/tensor_shape.proto"; import "tensorflow/core/framework/types.proto"; diff --git a/tensorflow/core/framework/tensor_test.cc b/tensorflow/core/framework/tensor_test.cc index b613effd18..80e168df97 100644 --- a/tensorflow/core/framework/tensor_test.cc +++ b/tensorflow/core/framework/tensor_test.cc @@ -1147,29 +1147,29 @@ TEST(Tensor, FailureToAllocate) { // On the alignment. // -// As of 2015/8, tensorflow::Tensor allocates its buffer with 32-byte +// As of 2018/5, tensorflow::Tensor allocates its buffer with 64-byte // alignment. Tensor::tensor/flat/vec/matrix methods requires the // buffer satisfies Eigen::Aligned (e.g., 16-bytes aligned usually, -// and 32-bytes for AVX). Tensor::Slice requires the caller to ensure -// its result is aligned if the caller intends to use those methods. -// In this test case, we simply make sure each slice is 32-byte -// aligned: sizeof(float) * 4 * 2 = 32. +// 32-bytes for AVX, and 64-bytes for AVX512). Tensor::Slice requires +// the caller to ensure its result is aligned if the caller intends +// to use those methods. In this test case, we simply make sure each +// slice is 64-byte aligned: sizeof(float) * 4 * 36 = 576. 576 % 64 = 0. TEST(Tensor, Slice_Basic) { Tensor saved; { // General - Tensor x(DT_FLOAT, TensorShape({10, 4, 34})); + Tensor x(DT_FLOAT, TensorShape({10, 4, 36})); // Fills in known values. for (int i = 0; i < 10; ++i) { x.Slice(i, i + 1).flat().setConstant(i * 1.f); } // A simple slice along dim0. Tensor y = x.Slice(4, 8); - EXPECT_TRUE(y.shape().IsSameSize(TensorShape({4, 4, 34}))); + EXPECT_TRUE(y.shape().IsSameSize(TensorShape({4, 4, 36}))); auto tx = x.tensor(); auto ty = y.tensor(); for (int i = 0; i < 4; ++i) { for (int j = 0; j < 4; ++j) { - for (int k = 0; k < 34; ++k) { + for (int k = 0; k < 36; ++k) { EXPECT_EQ(ty(i, j, k), 4.0 + i); EXPECT_EQ(&tx(4 + i, j, k), &ty(i, j, k)); } @@ -1186,7 +1186,7 @@ TEST(Tensor, Slice_Basic) { auto tz = z.tensor(); EXPECT_EQ(1, z.dim_size(0)); for (int j = 0; j < 4; ++j) { - for (int k = 0; k < 34; ++k) { + for (int k = 0; k < 36; ++k) { EXPECT_EQ(tz(0, j, k), 6.0); } } @@ -1198,16 +1198,16 @@ TEST(Tensor, Slice_Basic) { EXPECT_EQ(1, saved.dim_size(0)); auto tsaved = saved.tensor(); for (int j = 0; j < 4; ++j) { - for (int k = 0; k < 34; ++k) { + for (int k = 0; k < 36; ++k) { EXPECT_EQ(tsaved(0, j, k), 6.0); } } } { // Empty - Tensor x(DT_FLOAT, TensorShape({10, 0, 34})); + Tensor x(DT_FLOAT, TensorShape({10, 0, 36})); x.flat().setRandom(); Tensor y = x.Slice(4, 8); - EXPECT_TRUE(y.shape().IsSameSize(TensorShape({4, 0, 34}))); + EXPECT_TRUE(y.shape().IsSameSize(TensorShape({4, 0, 36}))); } { diff --git a/tensorflow/core/graph/mkl_layout_pass.cc b/tensorflow/core/graph/mkl_layout_pass.cc index 72a13d4da7..b9667998d6 100644 --- a/tensorflow/core/graph/mkl_layout_pass.cc +++ b/tensorflow/core/graph/mkl_layout_pass.cc @@ -2691,14 +2691,14 @@ class MklLayoutRewritePass : public GraphOptimizationPass { // If Op has been specifically assigned to a non-CPU device, then No. if (!n->assigned_device_name().empty() && - !str_util::StrContains(n->assigned_device_name(),kCPUDeviceSubStr)) { + !str_util::StrContains(n->assigned_device_name(), kCPUDeviceSubStr)) { result = false; reason = "Op has been assigned a runtime device that is not CPU."; } // If user has specifically assigned this op to a non-CPU device, then No. if (!n->def().device().empty() && - !str_util::StrContains(n->def().device(),kCPUDeviceSubStr)) { + !str_util::StrContains(n->def().device(), kCPUDeviceSubStr)) { result = false; reason = "User has assigned a device that is not CPU."; } @@ -2865,9 +2865,9 @@ class MklLayoutRewritePass : public GraphOptimizationPass { return false; } - // If the depth_radius of LRN is not 2, then MKL DNN takes unoptimized - // path. The unoptimized path is slow. Thus we dont rewrite the node - // and use default Eigen. But for depth_radius=2, MKL DNN optimized + // If the depth_radius of LRN is not 2, then MKL DNN takes unoptimized + // path. The unoptimized path is slow. Thus we dont rewrite the node + // and use default Eigen. But for depth_radius=2, MKL DNN optimized // path is taken, i.e., eigen node is rewritten by MKl DNN node. static bool LrnRewrite(const Node* n) { CHECK_NOTNULL(n); @@ -2876,13 +2876,13 @@ class MklLayoutRewritePass : public GraphOptimizationPass { CHECK_EQ(GetNodeAttr(n->def(), "depth_radius", &depth_radius).ok(), true); // if the depth_radius of LRN is not 2, don't rewrite the node by MKL DNN - // and use eigen node instead + // and use eigen node instead if (depth_radius == 2) { return true; } VLOG(1) << "LrnRewrite: The model sets depth_radius as not 2 which" << "case is not optimized by Intel MKL, thus using Eigen op" - << "for LRN " ; + << "for LRN "; return false; } @@ -3015,6 +3015,35 @@ class MklLayoutRewritePass : public GraphOptimizationPass { std::vector* ws_tensors, bool* are_ws_tensors_added); + // Helper function used by FixMklMetaDataEdges. Fixes the metadata edge + // pointed by 'e_metadata' corresponding to the data edge 'e_data' in graph + // 'g'. Returns true is fixup was done; otherwise, it returns false. + bool FixMklMetaDataEdgeIfNeeded(std::unique_ptr* g, + const Edge* e_data, const Edge* e_metadata); + + // Are the input Mkl metadata edges for node 'n' in graph 'g' correctly + // connected? If not, then fix them. This is needed because a graph may have + // some input Mkl metadata edges incorrectly setup after node merge and + // rewrite passes. This could happen because GetReversePostOrder function may + // not provide topologically sorted order if a graph contains cycles. The + // function returns true if at least one Mkl metadata edge for node 'n' was + // fixed. Otherwise, it returns false. + // + // Example: + // + // X = MklConv2D(_, _, _) + // Y = MklConv2DWithBias(_, _, _, _, _, _) + // Z = MklAdd(X, Y, DummyMklTensor, Y:1) + // + // For a graph such as shown above, note that 3rd argument of MklAdd contains + // DummyMklTensor. Actually, it should be getting the Mkl metadata from + // MklConv2D op (specifically, X:2). This incorrect plumbing could be possible + // (although rare) if the Mkl NodeMerge + NodeRewrite passes visit Z before X + // (possible if X, Y, Z are part of a loop.) This function fixes the Mkl + // metadata edges only - it does not rewrite nodes nor does it modify the Mkl + // data edges (1st and 2nd arguments of MklAdd). + bool FixMklMetaDataEdges(std::unique_ptr* g, Node* n); + // Functions specific to operators to copy attributes // We need operator-specific function to copy attributes because the framework // does not provide any generic function for it. @@ -4241,6 +4270,92 @@ MklLayoutRewritePass::CheckForNodeRewrite(const Node* n) const { return nullptr; } +/////////////////////////////////////////////////////////////////////////////// +// Post-rewrite Mkl metadata fixup pass +/////////////////////////////////////////////////////////////////////////////// +bool MklLayoutRewritePass::FixMklMetaDataEdgeIfNeeded(std::unique_ptr* g, + const Edge* e_data, const Edge* e_metadata) { + if (g == nullptr || e_data == nullptr || e_metadata == nullptr) { + return false; + } + + Node* n_data = e_data->src(); + int n_data_op_slot = e_data->src_output(); + int n_metadata_op_slot = GetTensorMetaDataIndex(n_data_op_slot, + n_data->num_outputs()); + + // If the source of meta edge is a constant node (producing dummy Mkl metadata + // tensor), then we will need to fix. + if (IsConstant(e_metadata->src())) { + Node* e_metadata_dst = e_metadata->dst(); + int e_metadata_in_slot = e_metadata->dst_input(); + CHECK_NOTNULL((*g)->AddEdge(n_data, n_metadata_op_slot, + e_metadata_dst, e_metadata_in_slot)); + + (*g)->RemoveEdge(e_metadata); + return true; + } + + return false; +} + +bool MklLayoutRewritePass::FixMklMetaDataEdges(std::unique_ptr* g, + Node* n) { + bool result = false; + + // If graph node is not Mkl node, then return. + DataType T = DT_INVALID; + if (!GetNodeAttr(n->def(), "T", &T).ok() || + !mkl_op_registry::IsMklOp(n->type_string(), T)) { + return result; + } + + // If it is Mkl node, then check if the input edges to this node that carry + // Mkl metadata are linked up correctly with the source node. + + // For Mkl nodes, we generate twice the number of input tensors (n for Mkl + // data tensors + n for Mkl metadata tensors). We need to check for correct + // connection of n metadata tensors only. + int num_data_inputs = n->num_inputs() / 2; + for (int idx = 0; idx < num_data_inputs; idx++) { + // Get the edge connecting input slot with index (idx). + const Edge* e = nullptr; + TF_CHECK_OK(n->input_edge(idx, &e)); + + // If e is control edge, then skip. + if (e->IsControlEdge()) { + continue; + } + + // Check that the source node for edge 'e' is Mkl node. If it is not an Mkl + // node, then we don't need to do anything. + Node* e_src = e->src(); + if (GetNodeAttr(e_src->def(), "T", &T).ok() && + mkl_op_registry::IsMklOp(e_src->type_string(), T)) { + // Source node for edge 'e' is Mkl node. + // Destination node and destination input slot of e is node 'n' and 'idx' + // resp. + CHECK_EQ(e->dst(), n); + CHECK_EQ(e->dst_input(), idx); + + // Let's get edge that carries Mkl metadata corresponding to Mkl data edge + // 'e'. For that, let's first get the input slot of 'n' where the meta + // edge will feed the value. + int e_meta_in_slot = GetTensorMetaDataIndex(e->dst_input(), + n->num_inputs()); + const Edge* e_meta = nullptr; + TF_CHECK_OK(n->input_edge(e_meta_in_slot, &e_meta)); + + // Let's check if we need to fix this meta edge. + if (FixMklMetaDataEdgeIfNeeded(g, e, e_meta)) { + result = true; + } + } + } + + return result; +} + /////////////////////////////////////////////////////////////////////////////// // Run function for the pass /////////////////////////////////////////////////////////////////////////////// @@ -4307,6 +4422,25 @@ bool MklLayoutRewritePass::RunPass(std::unique_ptr* g) { DumpGraph("After running MklLayoutRewritePass(NodeMerge+Rewrite)", &**g); + order.clear(); + GetReversePostOrder(**g, &order); // This will give us topological sort. + for (Node* n : order) { + // If node is not an op or it cannot run on CPU device, then skip. + if (!n->IsOp() || !CanOpRunOnCPUDevice(n)) { + continue; + } + if (FixMklMetaDataEdges(g, n)) { + string node_name = n->name(); + string op_name = n->type_string(); + + VLOG(1) << "MklLayoutRewritePass: fixed metadata edges for node " + << node_name << " with op " << op_name; + result = true; + } + } + DumpGraph("After running MklLayoutRewritePass(NodeMerge+Rewrite+Fixup)", + &**g); + return result; } diff --git a/tensorflow/core/graph/mkl_layout_pass_test.cc b/tensorflow/core/graph/mkl_layout_pass_test.cc index 029cdcf94a..7645b4a7f0 100644 --- a/tensorflow/core/graph/mkl_layout_pass_test.cc +++ b/tensorflow/core/graph/mkl_layout_pass_test.cc @@ -3518,6 +3518,37 @@ TEST_F(MklLayoutPassTest, NodeMerge_Conv2DWithBias_DeviceTest) { "B->C:1;C->E;D->E:1;E->Z;M->C:2;N->C:3;Y->Z:1"); } +///////////////////////////////////////////////////////////////////// +// Post-rewrite fixup pass test + +TEST_F(MklLayoutPassTest, PostRewriteFixUpPass) { + InitGraph( + "node { name: 'A' op: 'Input'}" + "node { name: 'B' op: 'Input'}" + "node { name: 'M' op: '_MklInput'}" + "node { name: 'N' op: '_MklInput'}" + "node { name: 'C' op: '_MklConv2D'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'data_format' value { s: 'NCHW' } }" + " attr { key: 'use_cudnn_on_gpu' value { b: false } }" + " attr { key: 'strides' value { list: {i: 1, i:1, i:1, i:1} } }" + " attr { key: 'padding' value { s: 'SAME' } }" + " attr { key: 'dilations' value { list: {i: 1, i:1, i:1, i:1} } }" + " input: ['A', 'B', 'M', 'N']}" + "node { name: 'D' op: 'Const' " + " attr { key: 'dtype' value { type: DT_UINT8 } }" + " attr { key: 'value' value { " + " tensor { dtype: DT_UINT8 tensor_shape { dim { size: 1 } } " + " int_val: 0 } } } }" + "node { name: 'E' op: '_MklAdd'" + " attr {key: 'T' value { type: DT_FLOAT } }" + " input: ['C', 'A', 'D', 'D']}"); + EXPECT_EQ(DoMklLayoutOptimizationPass(), + "A(Input);B(Input);C(_MklConv2D);D(Const);E(_MklAdd);" + "M(_MklInput);N(_MklInput)|A->C;A->E:1;B->C:1;C->E;C:2->E:2;" + "D->E:3;M->C:2;N->C:3"); +} + ///////////////////////////////////////////////////////////////////// static void BM_MklLayoutRewritePass(int iters, int op_nodes) { diff --git a/tensorflow/core/grappler/clusters/single_machine_test.cc b/tensorflow/core/grappler/clusters/single_machine_test.cc index 352f08fede..31b19cfcfd 100644 --- a/tensorflow/core/grappler/clusters/single_machine_test.cc +++ b/tensorflow/core/grappler/clusters/single_machine_test.cc @@ -546,7 +546,7 @@ TEST_F(SingleMachineTest, ReleaseMemoryAfterDestruction) { TF_CHECK_OK(cluster_->GetPeakMemoryUsage(&device_peak_memory_before)); EXPECT_EQ(device_peak_memory_before.size(), 1); // There might be a bit memory used before session's running anything. - EXPECT_LT(device_peak_memory_before.begin()->second, 200); + EXPECT_LT(device_peak_memory_before.begin()->second, 400); RunMetadata metadata; TF_CHECK_OK(cluster_->Run(item.graph, item.feed, item.fetch, &metadata)); @@ -567,8 +567,8 @@ TEST_F(SingleMachineTest, ReleaseMemoryAfterDestruction) { // Check memory used by resources are released after cluster destruction. EXPECT_EQ(device_peak_memory_before.size(), 1); EXPECT_EQ(device_peak_memory_after.size(), 1); - EXPECT_LT(device_peak_memory_before.begin()->second, 200); - EXPECT_LT(device_peak_memory_after.begin()->second, 200); + EXPECT_LT(device_peak_memory_before.begin()->second, 400); + EXPECT_LT(device_peak_memory_after.begin()->second, 400); } TEST_F(SingleMachineTest, PeakMemory) { @@ -597,7 +597,7 @@ TEST_F(SingleMachineTest, PeakMemory) { device_peak_memory.end()); cpu_memory = device_peak_memory["/job:localhost/replica:0/task:0/device:CPU:0"]; - EXPECT_LT(cpu_memory, 100); + EXPECT_LT(cpu_memory, 200); } TEST_F(SingleMachineTest, PeakMemoryStatsNotEnabled) { diff --git a/tensorflow/core/grappler/costs/graph_properties.cc b/tensorflow/core/grappler/costs/graph_properties.cc index 6749a7c571..0c02876ac5 100644 --- a/tensorflow/core/grappler/costs/graph_properties.cc +++ b/tensorflow/core/grappler/costs/graph_properties.cc @@ -610,7 +610,6 @@ class SymbolicShapeRefiner { } }; - // Compute the shape of the tensors outputed by node 'node' at output port // 'port_index' as the union of shape1 and shape2. ShapeHandle OutputAsUnion(const NodeDef* node, int port_index, ShapeHandle shape1, ShapeHandle shape2) { diff --git a/tensorflow/core/grappler/optimizers/BUILD b/tensorflow/core/grappler/optimizers/BUILD index 1b18087cdf..8ca726df0b 100644 --- a/tensorflow/core/grappler/optimizers/BUILD +++ b/tensorflow/core/grappler/optimizers/BUILD @@ -679,6 +679,7 @@ cc_library( deps = [ ":constant_folding", ":graph_optimizer", + "//tensorflow/core:lib", "//tensorflow/core:protos_all_cc", "//tensorflow/core/grappler:graph_view", "//tensorflow/core/grappler:grappler_item", @@ -780,7 +781,6 @@ cc_library( "//tensorflow/core:lib", "//tensorflow/core:lib_internal", "//tensorflow/core:protos_all_cc", - "//tensorflow/core:scoped_allocator_ops_op_lib", "//tensorflow/core/grappler:grappler_item", "//tensorflow/core/grappler:op_types", "//tensorflow/core/grappler:utils", diff --git a/tensorflow/core/grappler/optimizers/remapper.cc b/tensorflow/core/grappler/optimizers/remapper.cc index 4dde7ed1b4..03e36a7b9c 100644 --- a/tensorflow/core/grappler/optimizers/remapper.cc +++ b/tensorflow/core/grappler/optimizers/remapper.cc @@ -22,6 +22,7 @@ limitations under the License. #include "tensorflow/core/grappler/op_types.h" #include "tensorflow/core/grappler/optimizers/constant_folding.h" #include "tensorflow/core/grappler/utils.h" +#include "tensorflow/core/platform/logging.h" namespace tensorflow { namespace grappler { @@ -200,8 +201,7 @@ Status Remapper::Optimize(Cluster* /*cluster*/, const GrapplerItem& item, } } if (optimizable) { - VLOG(2) << "Optimizing fused batch norm node " << node.DebugString() - << std::endl; + VLOG(1) << "Optimizing fused batch norm node " << node.DebugString(); AddBatchNormNodes(optimized_graph, node); continue; } diff --git a/tensorflow/core/kernels/as_string_op.cc b/tensorflow/core/kernels/as_string_op.cc index 66c4aff3e3..a7757d1361 100644 --- a/tensorflow/core/kernels/as_string_op.cc +++ b/tensorflow/core/kernels/as_string_op.cc @@ -73,6 +73,7 @@ class AsStringOp : public OpKernel { } switch (dtype) { case DT_INT8: + case DT_INT16: case DT_INT32: strings::Appendf(&format_, "d"); break; @@ -129,6 +130,7 @@ class AsStringOp : public OpKernel { ENCODE_TYPE(DT_FLOAT, float, format_); ENCODE_TYPE(DT_DOUBLE, double, format_); ENCODE_TYPE(DT_INT8, int8, format_); + ENCODE_TYPE(DT_INT16, int16, format_); case (DT_BOOL): { const auto& input_flat = input_tensor->flat(); for (int i = 0; i < input_flat.size(); ++i) { diff --git a/tensorflow/core/kernels/cwise_op_clip.cc b/tensorflow/core/kernels/cwise_op_clip.cc index 14d889e8e3..49b90e855b 100644 --- a/tensorflow/core/kernels/cwise_op_clip.cc +++ b/tensorflow/core/kernels/cwise_op_clip.cc @@ -33,52 +33,41 @@ class ClipOp : public OpKernel { const Tensor& in0 = ctx->input(0); const Tensor& in1 = ctx->input(1); const Tensor& in2 = ctx->input(2); + OP_REQUIRES(ctx, (in0.shape() == in1.shape() || + TensorShapeUtils::IsScalar(in1.shape())) && + (in0.shape() == in2.shape() || + TensorShapeUtils::IsScalar(in2.shape())), + errors::InvalidArgument( + "clip_value_min and clip_value_max must be either of " + "the same shape as input, or a scalar. ", + "input shape: ", in0.shape().DebugString(), + "clip_value_min shape: ", in1.shape().DebugString(), + "clip_value_max shape: ", in2.shape().DebugString())); + + Tensor* out = nullptr; + OP_REQUIRES_OK( + ctx, ctx->forward_input_or_allocate_output({0}, 0, in0.shape(), &out)); + if (out->NumElements() == 0) return; // Nothing to do for empty output auto in0_flat = in0.flat(); auto in1_flat = in1.flat(); auto in2_flat = in2.flat(); + auto out_flat = out->flat(); const Device& d = ctx->eigen_device(); - Tensor* out = nullptr; - OP_REQUIRES_OK( - ctx, ctx->forward_input_or_allocate_output({0}, 0, in0.shape(), &out)); - auto out_flat = out->flat(); if (in1.shape() == in2.shape()) { if (in0.shape() == in1.shape()) { functor::TernaryClipOp()(d, in0_flat, in1_flat, in2_flat, out_flat); } else { - OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(in1.shape()), - errors::InvalidArgument( - "clip_value_min and clip_value_max must be either of " - "the same shape as input, or a scalar. ", - "input shape: ", in0.shape().DebugString(), - "clip_value_min shape: ", in1.shape().DebugString(), - "clip_value_max shape: ", in2.shape().DebugString())); functor::UnaryClipOp()(d, in0_flat, in1_flat, in2_flat, out_flat); } } else { if (in0.shape() == in1.shape()) { - OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(in2.shape()), - errors::InvalidArgument( - "clip_value_min and clip_value_max must be either of " - "the same shape as input, or a scalar. ", - "input shape: ", in0.shape().DebugString(), - "clip_value_min shape: ", in1.shape().DebugString(), - "clip_value_max shape: ", in2.shape().DebugString())); functor::BinaryLeftClipOp()(d, in0_flat, in1_flat, in2_flat, out_flat); } else { - OP_REQUIRES(ctx, - (in0.shape() == in2.shape() && - TensorShapeUtils::IsScalar(in1.shape())), - errors::InvalidArgument( - "clip_value_min and clip_value_max must be either of " - "the same shape as input, or a scalar. ", - "input shape: ", in0.shape().DebugString(), - "clip_value_min shape: ", in1.shape().DebugString(), - "clip_value_max shape: ", in2.shape().DebugString())); functor::BinaryRightClipOp()(d, in0_flat, in1_flat, in2_flat, out_flat); } diff --git a/tensorflow/core/kernels/dense_update_functor_gpu.cu.cc b/tensorflow/core/kernels/dense_update_functor_gpu.cu.cc index 9a3b2303a3..17a85d9773 100644 --- a/tensorflow/core/kernels/dense_update_functor_gpu.cu.cc +++ b/tensorflow/core/kernels/dense_update_functor_gpu.cu.cc @@ -57,6 +57,7 @@ struct DenseUpdate { template struct functor::DenseUpdate; \ template struct functor::DenseUpdate; TF_CALL_GPU_NUMBER_TYPES(DEFINE_GPU_KERNELS); +TF_CALL_int32(DEFINE_GPU_KERNELS); TF_CALL_int64(DEFINE_GPU_KERNELS); #undef DEFINE_GPU_KERNELS diff --git a/tensorflow/core/kernels/gather_functor.cc b/tensorflow/core/kernels/gather_functor.cc index e6fefe643b..5cd8e04927 100644 --- a/tensorflow/core/kernels/gather_functor.cc +++ b/tensorflow/core/kernels/gather_functor.cc @@ -37,6 +37,7 @@ namespace functor { DECLARE_GPU_SPECS_INDEX(T, int32); \ DECLARE_GPU_SPECS_INDEX(T, int64) +TF_CALL_int64(DECLARE_GPU_SPECS); TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_SPECS); TF_CALL_complex64(DECLARE_GPU_SPECS); TF_CALL_complex128(DECLARE_GPU_SPECS); diff --git a/tensorflow/core/kernels/gather_functor_gpu.cu.cc b/tensorflow/core/kernels/gather_functor_gpu.cu.cc index 39b6924d74..4563fc6353 100644 --- a/tensorflow/core/kernels/gather_functor_gpu.cu.cc +++ b/tensorflow/core/kernels/gather_functor_gpu.cu.cc @@ -31,6 +31,7 @@ typedef Eigen::GpuDevice GPUDevice; DEFINE_GPU_SPECS_INDEX(T, int32); \ DEFINE_GPU_SPECS_INDEX(T, int64); +TF_CALL_int64(DEFINE_GPU_SPECS); TF_CALL_GPU_NUMBER_TYPES(DEFINE_GPU_SPECS); TF_CALL_complex64(DEFINE_GPU_SPECS); TF_CALL_complex128(DEFINE_GPU_SPECS); diff --git a/tensorflow/core/kernels/gather_nd_op.cc b/tensorflow/core/kernels/gather_nd_op.cc index 7e5a9e1ec5..4e53291b7f 100644 --- a/tensorflow/core/kernels/gather_nd_op.cc +++ b/tensorflow/core/kernels/gather_nd_op.cc @@ -228,6 +228,8 @@ namespace functor { DECLARE_GPU_SPECS_INDEX(T, int32); \ DECLARE_GPU_SPECS_INDEX(T, int64) +TF_CALL_int32(DECLARE_GPU_SPECS); +TF_CALL_int64(DECLARE_GPU_SPECS); TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_SPECS); TF_CALL_complex64(DECLARE_GPU_SPECS); TF_CALL_complex128(DECLARE_GPU_SPECS); @@ -239,6 +241,8 @@ TF_CALL_complex128(DECLARE_GPU_SPECS); // Registration of the GPU implementations. #define REGISTER_GATHER_ND_GPU(type) REGISTER_GATHER_ND_ALL_INDICES(GPU, type) +TF_CALL_int32(REGISTER_GATHER_ND_GPU); +TF_CALL_int64(REGISTER_GATHER_ND_GPU); TF_CALL_GPU_NUMBER_TYPES(REGISTER_GATHER_ND_GPU); TF_CALL_complex64(REGISTER_GATHER_ND_GPU); TF_CALL_complex128(REGISTER_GATHER_ND_GPU); diff --git a/tensorflow/core/kernels/gather_nd_op_gpu.cu.cc b/tensorflow/core/kernels/gather_nd_op_gpu.cu.cc index b03efc684f..da8d2e9e3c 100644 --- a/tensorflow/core/kernels/gather_nd_op_gpu.cu.cc +++ b/tensorflow/core/kernels/gather_nd_op_gpu.cu.cc @@ -119,6 +119,8 @@ struct GatherNdSlice { DEFINE_GPU_SPECS_INDEX(T, int32); \ DEFINE_GPU_SPECS_INDEX(T, int64); +TF_CALL_int32(DEFINE_GPU_SPECS); +TF_CALL_int64(DEFINE_GPU_SPECS); TF_CALL_GPU_NUMBER_TYPES(DEFINE_GPU_SPECS); TF_CALL_complex64(DEFINE_GPU_SPECS); TF_CALL_complex128(DEFINE_GPU_SPECS); diff --git a/tensorflow/core/kernels/gather_op.cc b/tensorflow/core/kernels/gather_op.cc index ef332ebee3..094504d6b9 100644 --- a/tensorflow/core/kernels/gather_op.cc +++ b/tensorflow/core/kernels/gather_op.cc @@ -153,6 +153,7 @@ TF_CALL_uint64(REGISTER_GATHER_CPU); // Registration of the GPU implementations. #define REGISTER_GATHER_GPU(type) REGISTER_GATHER_ALL_INDICES(GPU, type) +TF_CALL_int64(REGISTER_GATHER_GPU); TF_CALL_GPU_NUMBER_TYPES(REGISTER_GATHER_GPU); TF_CALL_complex64(REGISTER_GATHER_GPU); TF_CALL_complex128(REGISTER_GATHER_GPU); diff --git a/tensorflow/core/kernels/mkl_concat_op.cc b/tensorflow/core/kernels/mkl_concat_op.cc index 5eeb23d810..31d1b949ef 100644 --- a/tensorflow/core/kernels/mkl_concat_op.cc +++ b/tensorflow/core/kernels/mkl_concat_op.cc @@ -14,6 +14,7 @@ limitations under the License. #include #include +#include #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/core/framework/op_kernel.h" @@ -590,8 +591,8 @@ class MklConcatOp : public OpKernel { const int N = input_tensors.size(); // Get Tensor shapes. - std::vector input_shapes(N); - GetMklShapeList(context, "values", &input_shapes); + std::vector mkl_input_shapes(N); + GetMklShapeList(context, "values", &mkl_input_shapes); const Tensor& concat_dim_tensor = (AxisArgName == NAME_IS_CONCAT_DIM) ? MklGetInput(context, 0) @@ -610,19 +611,14 @@ class MklConcatOp : public OpKernel { int i = 0; bool invoke_eigen = false; bool are_all_mkl_inputs = true, are_all_tf_inputs = true; - const TensorShape expected_shape = input_shapes[0].IsMklTensor() - ? input_shapes[0].GetTfShape() - : input_tensors[0].shape(); + const TensorShape expected_shape = mkl_input_shapes[0].IsMklTensor() + ? mkl_input_shapes[0].GetTfShape() + : input_tensors[0].shape(); size_t expected_dims = expected_shape.dims(); if (concat_dim < 0) concat_dim = expected_dims + concat_dim; - for (auto& s : input_shapes) { - if (s == expected_shape) { - ++i; - continue; - } - + for (auto& s : mkl_input_shapes) { TensorShape s_shape = s.IsMklTensor() ? s.GetTfShape() : input_tensors[i].shape(); size_t s_dims = s_shape.dims(); @@ -665,21 +661,14 @@ class MklConcatOp : public OpKernel { // Call Eigen library if (invoke_eigen) { - TensorShapeList tf_input_shapes; - i = 0; - for (auto& s : input_shapes) { - TensorShape s_shape = - s.IsMklTensor() ? s.GetTfShape() : input_tensors[i].shape(); - tf_input_shapes.push_back(s_shape); - ++i; - } - CallEigenVersion(context, input_tensors, tf_input_shapes); + CallEigenVersion(context, input_tensors, mkl_input_shapes); return; } memory::dims dst_dims; + if (are_all_mkl_inputs) - dst_dims = TFShapeToMklDnnDims(input_shapes[0].GetTfShape()); + dst_dims = TFShapeToMklDnnDims(mkl_input_shapes[0].GetTfShape()); else // When all the inputs are in Tensorflow format, we don't know // what is the input data format. In that case, we just use @@ -689,26 +678,61 @@ class MklConcatOp : public OpKernel { std::vector srcs_pd; std::vector> srcs(N, MklDnnData(&cpu_engine)); int64 dst_concat_dim_size = 0; - for (int k = 0; k < N; k++) { - bool is_mkl_tensor = input_shapes[k].IsMklTensor(); - memory::dims src_dims; - - // Same comment as dst_dims for src_dims. - src_dims = (is_mkl_tensor) - ? TFShapeToMklDnnDims(input_shapes[k].GetTfShape()) - : TFShapeToMklDnnDims(input_tensors[k].shape()); - - dst_concat_dim_size += src_dims[concat_dim]; - auto src_md = - is_mkl_tensor ? input_shapes[k].GetMklLayout() : - // It does not matter what data format we use here - // (NHWC or NCHW). We just need to ensure that output - // of Concat uses same data format as input. - memory::desc(src_dims, MklDnnType(), memory::format::nchw); - - srcs[k].SetUsrMem(src_md, &input_tensors[k]); - auto src_mpd = srcs[k].GetUsrMemPrimDesc(); - srcs_pd.push_back(src_mpd); + + bool isMklReorderNeeded = false; + memory::format mkl_common_format = memory::format::any; + if (are_all_mkl_inputs) { + mkl_common_format = + FindMklCommonFormat(mkl_input_shapes, concat_dim, + &isMklReorderNeeded, &dst_concat_dim_size); + + if (!isMklReorderNeeded) { + // All MKL tensors have a same format. Reorder is not needed. + for (int k = 0; k < N; k++) { + if (input_tensors[k].NumElements() == 0) + continue; + + auto src_md = mkl_input_shapes[k].GetMklLayout(); + srcs[k].SetUsrMem(src_md, &input_tensors[k]); + auto src_mpd = srcs[k].GetUsrMemPrimDesc(); + srcs_pd.push_back(src_mpd); + } + } else { + // MKL tensors have different formats. + // Reorder them to most common format. + for (int k = 0; k < N; k++) { + if (input_tensors[k].NumElements() == 0) + continue; + + auto src_dims = TFShapeToMklDnnDims( + mkl_input_shapes[k].GetTfShape()); + auto src_md = mkl_input_shapes[k].GetMklLayout(); + srcs[k].SetUsrMem(src_md, &input_tensors[k]); + + if (src_md.data.format != mkl_common_format) + src_md = memory::desc(src_dims, MklDnnType(), + mkl_common_format); + + srcs_pd.push_back(memory::primitive_desc(src_md, cpu_engine)); + } + } + } else { // All TF inputs + for (int k = 0; k < N; k++) { + if (input_tensors[k].NumElements() == 0) + continue; + + memory::dims src_dims = TFShapeToMklDnnDims(input_tensors[k].shape()); + dst_concat_dim_size += src_dims[concat_dim]; + + // It does not matter what data format to be used (NHWC versus NCHW). + // We just need to ensure that output uses same data format as inputs. + auto src_md = + memory::desc(src_dims, MklDnnType(), memory::format::nchw); + + srcs[k].SetUsrMem(src_md, &input_tensors[k]); + auto src_mpd = srcs[k].GetUsrMemPrimDesc(); + srcs_pd.push_back(src_mpd); + } } dst_dims[concat_dim] = dst_concat_dim_size; @@ -718,25 +742,33 @@ class MklConcatOp : public OpKernel { if (are_all_mkl_inputs) { // Since we are passing a specific format for destination, // we need to have dst_dims in MklDnn order (NCHW). - auto orig_tf_format = input_shapes[0].GetTfDataFormat(); + auto orig_tf_format = mkl_input_shapes[0].GetTfDataFormat(); dst_dims_in_nchw = MklDnnDimsInNCHW( dst_dims, MklDnnDataFormatToTFDataFormat(orig_tf_format)); - // We will set the output in the same format as input to avoid layout - // conversions. - // Currently we are setting dst format same as input format. - // See if we can make this choice in a better way. + // Set the output format same as the most common format of inputs + // to avoid layout conversions. dst_md = memory::desc( - dst_dims_in_nchw, MklDnnType(), - (memory::format)input_shapes[0].GetMklLayout().data.format); + dst_dims_in_nchw, MklDnnType(), mkl_common_format); } else { - // Again, format does not matter here. We just need to make it same as - // input format. + // All inputs are TF tensors. + // Set the output format same as input format (nchw). dst_md = memory::desc(dst_dims, MklDnnType(), memory::format::nchw); } std::vector inputs; - for (int k = 0; k < input_tensors.size(); k++) - inputs.push_back(srcs[k].GetOpMem()); + std::vector net; + if (isMklReorderNeeded) { + for (int k = 0; k < input_tensors.size(); k++) { + if (input_tensors[k].NumElements() > 0) { + srcs[k].CheckReorderToOpMem(srcs_pd[k], &net); + } + } + } + for (int k = 0; k < input_tensors.size(); k++) { + if (input_tensors[k].NumElements() > 0) { + inputs.push_back(srcs[k].GetOpMem()); + } + } // If all inputs are in MKL format, then meaning of concat_dim needs to // change. Value of concat_dim is tied to input Tensorflow data format @@ -745,7 +777,8 @@ class MklConcatOp : public OpKernel { // But ifinput tensors are in NHWC order, then semantics need to change. // E.g., if we are concatinating over Channel (dimension 3 for NHWC), // then since MklDnn order is NCHW, concat_dim needs to be 1. - if (are_all_mkl_inputs) concat_dim = input_shapes[0].TfDimIdx(concat_dim); + if (are_all_mkl_inputs) + concat_dim = mkl_input_shapes[0].TfDimIdx(concat_dim); auto concat_pd = concat::primitive_desc(dst_md, concat_dim, srcs_pd); @@ -758,7 +791,7 @@ class MklConcatOp : public OpKernel { dnn_shape_dst.SetMklLayout(&dst_pd); dnn_shape_dst.SetElemType(MklDnnType()); dnn_shape_dst.SetTfLayout(dst_dims.size(), dst_dims_in_nchw, - input_shapes[0].GetTfDataFormat()); + mkl_input_shapes[0].GetTfDataFormat()); tf_shape_dst.AddDim((dst_pd.get_size() / sizeof(T))); } else { dnn_shape_dst.SetMklTensor(false); @@ -773,7 +806,6 @@ class MklConcatOp : public OpKernel { dst.SetUsrMem(dst_md, dst_tensor); auto concat_op = concat(concat_pd, inputs, dst.GetOpMem()); - std::vector net; net.push_back(concat_op); stream(stream::kind::eager).submit(net).wait(); } catch (mkldnn::error& e) { @@ -787,15 +819,27 @@ class MklConcatOp : public OpKernel { } void CallEigenVersion(OpKernelContext* context, const OpInputList& values, - const TensorShapeList& input_shapes) { - CHECK_EQ(values.size(), input_shapes.size()); + const MklDnnShapeList& mkl_input_shapes) { + CHECK_EQ(values.size(), mkl_input_shapes.size()); std::vector converted_values; - for (int i = 0; i < input_shapes.size(); i++) - converted_values.push_back(values[i]); + TensorShapeList tf_input_shapes; + for (int i = 0; i < mkl_input_shapes.size(); i++) { + if (mkl_input_shapes[i].IsMklTensor()) { + // do conversion from MKL to TF + Tensor tmp_tensor = + ConvertMklToTF(context, values[i], mkl_input_shapes[i]); + converted_values.push_back(tmp_tensor); + tf_input_shapes.push_back(mkl_input_shapes[i].GetTfShape()); + } else { + // no conversion since it is TF tensor already + converted_values.push_back(values[i]); + tf_input_shapes.push_back(values[i].shape()); + } + } // Call Eigen concat. - eigen_concat_op_.Compute(context, converted_values, input_shapes); + eigen_concat_op_.Compute(context, converted_values, tf_input_shapes); // Set output Mkl tensor for this op. MklDnnShape dnn_shape_output; @@ -812,6 +856,55 @@ class MklConcatOp : public OpKernel { output_tensor->flat().data(), output_tensor->flat().size() * sizeof(uint8)); } + + // This method finds the most commom format accross all MKL inputs + // Inputs: + // 1. input_shapes: shapes of input (MKL) tensors. + // 2. concat_dim: concat dimension. + // Outputs: + // 1. is_reorder_needed is set to true if inputs have difference formats + // It is set to false otherwise. + // 2. concat_dim_size is the size of concat_dim. + // Return: + // return the common MKL format. + memory::format FindMklCommonFormat(const MklDnnShapeList& input_shapes, + int concat_dim, bool* is_reorder_needed, int64* concat_dim_size) { + *is_reorder_needed = false; + *concat_dim_size = 0; + std::unordered_map occurrence_map; + if (input_shapes.size() == 0) + return memory::format::any; + + // Compute ocurrences of each format of all inputs. + for (int k=0; k ( + input_shapes[k].GetMklLayout().data.format); + occurrence_map[fmt] += 1; + } + + if (occurrence_map.size() == 1) { + // this means that all inputs have a same format + // return it with is_reorder_needed set false. + return static_cast( + input_shapes[0].GetMklLayout().data.format); + } + + // Input tensors have different formats. Thus, reorder is needed. + // We pick up the most common format to minimize the total + // number of input reorder. + memory::format commonest_format = memory::format::any; + int max_occurrence = 0; + *is_reorder_needed = true; + for (auto item : occurrence_map) { + if (item.second > max_occurrence) { + commonest_format = static_cast(item.first); + max_occurrence = item.second; + } + } + return commonest_format; + } }; #endif diff --git a/tensorflow/core/kernels/mkl_conv_grad_bias_ops.cc b/tensorflow/core/kernels/mkl_conv_grad_bias_ops.cc index c1da0ded1d..f857be6c32 100644 --- a/tensorflow/core/kernels/mkl_conv_grad_bias_ops.cc +++ b/tensorflow/core/kernels/mkl_conv_grad_bias_ops.cc @@ -18,6 +18,7 @@ limitations under the License. // bias. #ifdef INTEL_MKL +#ifdef INTEL_MKL_ML #define USE_EIGEN_TENSOR #define EIGEN_USE_THREADS @@ -264,4 +265,5 @@ class MklConv2DCustomBackpropBiasOp : public OpKernel { TF_CALL_float(REGISTER_CPU_KERNELS); #undef REGISTER_CPU_KERNELS } /* namespace tensorflow */ +#endif /* INTEL_MKL_ML */ #endif /* INTEL_MKL */ diff --git a/tensorflow/core/kernels/mkl_pooling_ops_common.h b/tensorflow/core/kernels/mkl_pooling_ops_common.h index 279167aba2..c0dfed7d7d 100644 --- a/tensorflow/core/kernels/mkl_pooling_ops_common.h +++ b/tensorflow/core/kernels/mkl_pooling_ops_common.h @@ -199,13 +199,15 @@ class MklPoolingForwardOpBase : public MklPoolingOpBase { CHECK_NOTNULL(pool_params); CHECK_NOTNULL(dnn_data_input); TensorShape input_tensor_shape = input_tensor.shape(); - memory::desc input_md = + if (input_tensor.NumElements() != 0) { + memory::desc input_md = input_mkl_shape.IsMklTensor() ? input_mkl_shape.GetMklLayout() : memory::desc(TFShapeToMklDnnDimsInNCHW(input_tensor_shape, this->data_format_tf_), MklDnnType(), this->data_format_mkldnn_); - dnn_data_input->SetUsrMem(input_md, &input_tensor); + dnn_data_input->SetUsrMem(input_md, &input_tensor); + } this->InitMklPoolParameters(context, pool_params, input_mkl_shape, input_tensor_shape); } diff --git a/tensorflow/core/kernels/scatter_nd_op.cc b/tensorflow/core/kernels/scatter_nd_op.cc index 43c5b29509..e1fc2ea128 100644 --- a/tensorflow/core/kernels/scatter_nd_op.cc +++ b/tensorflow/core/kernels/scatter_nd_op.cc @@ -292,6 +292,7 @@ TF_CALL_string(REGISTER_SCATTER_ND_CPU); REGISTER_SCATTER_ND_UPDATE_GPU(type); \ REGISTER_SCATTER_ND_GPU(type); +TF_CALL_int32(REGISTER_SCATTER_ND_ALL_GPU); // TODO(b/66916790): Support half types in ScatterNd. TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SCATTER_ND_ALL_GPU); TF_CALL_complex64(REGISTER_SCATTER_ND_ALL_GPU); @@ -306,6 +307,8 @@ TF_CALL_complex128(REGISTER_SCATTER_ND_ALL_GPU); #define REGISTER_SCATTER_ND_UPDATE_SYCL(type) \ REGISTER_SCATTER_ND_UPDATE(type, SYCL); +TF_CALL_int32(REGISTER_SCATTER_ND_ADD_SUB_SYCL); +TF_CALL_int32(REGISTER_SCATTER_ND_UPDATE_SYCL); TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SCATTER_ND_ADD_SUB_SYCL); TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SCATTER_ND_UPDATE_SYCL); #undef REGISTER_SCATTER_ND_ADD_SUB_SYCL @@ -576,6 +579,7 @@ namespace functor { DECLARE_GPU_SPECS_INDEX(T, int32); \ DECLARE_GPU_SPECS_INDEX(T, int64) +TF_CALL_int32(DECLARE_GPU_SPECS); // TODO(b/66916790): Support half types in ScatterNd. TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_SPECS); TF_CALL_complex64(DECLARE_GPU_SPECS); diff --git a/tensorflow/core/kernels/scatter_nd_op_gpu.cu.cc b/tensorflow/core/kernels/scatter_nd_op_gpu.cu.cc index a3c21edc15..08b657f4c3 100644 --- a/tensorflow/core/kernels/scatter_nd_op_gpu.cu.cc +++ b/tensorflow/core/kernels/scatter_nd_op_gpu.cu.cc @@ -170,6 +170,7 @@ struct ScatterNdFunctor { DECLARE_GPU_SPECS_INDEX(T, int32); \ DECLARE_GPU_SPECS_INDEX(T, int64) +TF_CALL_int32(DECLARE_GPU_SPECS); TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_SPECS); TF_CALL_complex64(DECLARE_GPU_SPECS); TF_CALL_complex128(DECLARE_GPU_SPECS); diff --git a/tensorflow/core/kernels/scoped_allocator_ops_test.cc b/tensorflow/core/kernels/scoped_allocator_ops_test.cc index bb0129fa6f..634f9ba887 100644 --- a/tensorflow/core/kernels/scoped_allocator_ops_test.cc +++ b/tensorflow/core/kernels/scoped_allocator_ops_test.cc @@ -216,8 +216,13 @@ TEST_F(ScopedAllocatorConcatOpTest, Success3) { } TEST_F(ScopedAllocatorConcatOpTest, Reshape) { - MakeOp({2, 2, 2}, DT_DOUBLE, true, "test", 120, 2); - ExecOp(DT_DOUBLE, 120, {{2, 2}, {2, 2}}); + MakeOp({2, 2, 4}, DT_DOUBLE, true, "test", 120, 2); + + // The elements of the third parameter to ExecOp must be multiples of + // Allocator::kAllocatorAlignment in size. If they are not, the backing + // tensor allocated by PrepOp will have too many elements and reshaping + // will fail. + ExecOp(DT_DOUBLE, 120, {{2, 4}, {2, 4}}); } TEST_F(ScopedAllocatorConcatOpTest, NoReshapeAttr) { diff --git a/tensorflow/core/kernels/segment_reduction_ops.h b/tensorflow/core/kernels/segment_reduction_ops.h index 7796bf3587..d65692a552 100644 --- a/tensorflow/core/kernels/segment_reduction_ops.h +++ b/tensorflow/core/kernels/segment_reduction_ops.h @@ -16,6 +16,14 @@ limitations under the License. #ifndef TENSORFLOW_CORE_KERNELS_SEGMENT_REDUCTION_OPS_H_ #define TENSORFLOW_CORE_KERNELS_SEGMENT_REDUCTION_OPS_H_ + +// This file requires the following include because it uses CudaAtomicMax: +// #include "tensorflow/core/util/cuda_kernel_helper.h" + +// Unfortunately we can't add the #include, since it breaks compilation for +// non-GPU targets. This only breaks in clang, because it's more strict for +// template code and CudaAtomicMax is used in template context. + // This file requires the following include because it uses CudaAtomicMax: // #include "tensorflow/core/util/cuda_kernel_helper.h" @@ -130,4 +138,4 @@ struct Highest { } // namespace functor } // namespace tensorflow -#endif // THIRD_PARTY_TENSORFLOW_CORE_KERNELS_SEGMENT_REDUCTION_OPS_H_ +#endif // TENSORFLOW_CORE_KERNELS_SEGMENT_REDUCTION_OPS_H_ diff --git a/tensorflow/core/kernels/sparse_matmul_op.cc b/tensorflow/core/kernels/sparse_matmul_op.cc index a1f9667b78..866c5dcd52 100644 --- a/tensorflow/core/kernels/sparse_matmul_op.cc +++ b/tensorflow/core/kernels/sparse_matmul_op.cc @@ -1490,7 +1490,7 @@ inline void LibxsmmSparseMatMul::Compute( #endif // TENSORFLOW_USE_LIBXSMM -// Here is a an overview of the SparseMatMul code. Note that we assume that the +// Here is an overview of the SparseMatMul code. Note that we assume that the // left matrix is sparse. // // The matrix "left" is divided into a grid with blocksize of (M, KL). Each diff --git a/tensorflow/core/kernels/string_split_op.cc b/tensorflow/core/kernels/string_split_op.cc index 4c2b312c34..26ab72f12e 100644 --- a/tensorflow/core/kernels/string_split_op.cc +++ b/tensorflow/core/kernels/string_split_op.cc @@ -22,6 +22,7 @@ limitations under the License. #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/lib/core/status.h" +#include "tensorflow/core/lib/core/stringpiece.h" #include "tensorflow/core/lib/strings/str_util.h" namespace tensorflow { @@ -43,6 +44,63 @@ std::vector Split(const string& str, const string& delimiter, return char_vector; } +std::vector SplitV2(const string& str, StringPiece sep, int maxsplit) { + // This SplitV2 method matches the behavior of python's str.split: + // If sep is given, consecutive delimiters are not grouped together + // and are deemed to delimit empty strings (for example, '1,,2'.split(',') + // returns ['1', '', '2']). The sep argument may consist of multiple + // characters (for example, '1<>2<>3'.split('<>') returns ['1', '2', '3']). + // Splitting an empty string with a specified separator returns ['']. + // + // If sep is not specified or is None, a different splitting algorithm is + // applied: runs of consecutive whitespace are regarded as a single + // separator, and the result will contain no empty strings at the start or + // end if the string has leading or trailing whitespace. Consequently, + // splitting an empty string or a string consisting of just whitespace + // with a None separator returns []. + + std::vector result; + + StringPiece text(str); + if (maxsplit == 0) { + result.emplace_back(std::string(text)); + return result; + } + + if (sep.empty()) { + StringPiece token; + // Remove leading whitespaces. + str_util::RemoveLeadingWhitespace(&text); + int split = 0; + while (str_util::ConsumeNonWhitespace(&text, &token)) { + result.emplace_back(std::string(token)); + str_util::RemoveLeadingWhitespace(&text); + ++split; + if (maxsplit > 0 && split == maxsplit) { + result.emplace_back(std::string(text)); + return result; + } + } + return result; + } + auto p = std::search(text.begin(), text.end(), sep.begin(), sep.end()); + int split = 0; + while (p != text.end()) { + StringPiece token = text.substr(0, p - text.begin()); + result.emplace_back(std::string(token)); + text.remove_prefix(token.size()); + text.remove_prefix(sep.size()); + ++split; + if (maxsplit > 0 && split == maxsplit) { + result.emplace_back(std::string(text)); + return result; + } + p = std::search(text.begin(), text.end(), sep.begin(), sep.end()); + } + result.emplace_back(std::string(text)); + return result; +} + } // namespace class StringSplitOp : public OpKernel { @@ -122,6 +180,78 @@ class StringSplitOp : public OpKernel { bool skip_empty_; }; +class StringSplitV2Op : public OpKernel { + public: + explicit StringSplitV2Op(OpKernelConstruction* context) + : OpKernel(context), maxsplit_(-1) { + OP_REQUIRES_OK(context, context->GetAttr("maxsplit", &maxsplit_)); + } + + void Compute(OpKernelContext* ctx) override { + const Tensor* input_tensor; + OP_REQUIRES_OK(ctx, ctx->input("input", &input_tensor)); + OP_REQUIRES(ctx, TensorShapeUtils::IsVector(input_tensor->shape()), + errors::InvalidArgument("input must be a vector, got shape: ", + input_tensor->shape().DebugString())); + + const auto input_vec = input_tensor->vec(); + const int64 batch_size = input_vec.dimension(0); + + const Tensor* sep_tensor; + OP_REQUIRES_OK(ctx, ctx->input("sep", &sep_tensor)); + OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(sep_tensor->shape()), + errors::InvalidArgument("sep must be a scalar, got shape: ", + sep_tensor->shape().DebugString())); + const auto sep_vec = sep_tensor->flat(); + StringPiece sep(sep_vec(0)); + std::vector tokens; + // Guess that we'll be unpacking a handful of tokens per example. + static constexpr int kReserveSize = 4; + tokens.reserve(batch_size * kReserveSize); + + int64 output_size = 0; + int64 max_num_entries = 0; + std::vector num_indices(batch_size); + for (int64 i = 0; i < batch_size; ++i) { + std::vector parts = SplitV2(input_vec(i), sep, maxsplit_); + int64 n_entries = parts.size(); + num_indices[i] = n_entries; + output_size += n_entries; + max_num_entries = std::max(max_num_entries, n_entries); + tokens.insert(tokens.end(), parts.begin(), parts.end()); + } + + Tensor* sp_indices_t; + OP_REQUIRES_OK(ctx, ctx->allocate_output(0, TensorShape({output_size, 2}), + &sp_indices_t)); + Tensor* sp_tokens_t; + OP_REQUIRES_OK( + ctx, ctx->allocate_output(1, TensorShape({output_size}), &sp_tokens_t)); + Tensor* sp_shape_t; + OP_REQUIRES_OK(ctx, ctx->allocate_output(2, TensorShape({2}), &sp_shape_t)); + + auto sp_indices = sp_indices_t->matrix(); + auto sp_tokens = sp_tokens_t->vec(); + auto sp_shape = sp_shape_t->vec(); + sp_shape(0) = batch_size; + sp_shape(1) = max_num_entries; + size_t c = 0; + for (size_t i = 0; i < batch_size; ++i) { + for (size_t j = 0; j < num_indices[i]; ++j) { + sp_indices(c, 0) = i; + sp_indices(c, 1) = j; + sp_tokens(c) = tokens[c]; + ++c; + } + } + } + + private: + int maxsplit_; +}; + REGISTER_KERNEL_BUILDER(Name("StringSplit").Device(DEVICE_CPU), StringSplitOp); +REGISTER_KERNEL_BUILDER(Name("StringSplitV2").Device(DEVICE_CPU), + StringSplitV2Op); } // namespace tensorflow diff --git a/tensorflow/core/ops/candidate_sampling_ops.cc b/tensorflow/core/ops/candidate_sampling_ops.cc index 6e4d100b04..6e589c8d1c 100644 --- a/tensorflow/core/ops/candidate_sampling_ops.cc +++ b/tensorflow/core/ops/candidate_sampling_ops.cc @@ -145,12 +145,15 @@ REGISTER_OP("ComputeAccidentalHits") int64 num_true; TF_RETURN_IF_ERROR(c->GetAttr("num_true", &num_true)); - // Validate true_classes. + // Validate true_classes, must be a matrix. ShapeHandle true_classes; TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 2, &true_classes)); DimensionHandle unused; TF_RETURN_IF_ERROR( c->WithValue(c->Dim(true_classes, 1), num_true, &unused)); + // Validate sampled_candidates, must be a vector. + ShapeHandle sampled_candidates; + TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &sampled_candidates)); // All three outputs are the same shape. ShapeHandle v = c->Vector(InferenceContext::kUnknownDim); diff --git a/tensorflow/core/ops/dataset_ops.cc b/tensorflow/core/ops/dataset_ops.cc index 15e0ca8af9..9dca5f53ce 100644 --- a/tensorflow/core/ops/dataset_ops.cc +++ b/tensorflow/core/ops/dataset_ops.cc @@ -218,7 +218,17 @@ REGISTER_OP("MapAndBatchDataset") .Attr("Targuments: list(type) >= 0") .Attr("output_types: list(type) >= 1") .Attr("output_shapes: list(shape) >= 1") - .SetShapeFn(shape_inference::ScalarShape); + .SetShapeFn([](shape_inference::InferenceContext* c) { + // Use index from the end to retrieve the Input shapes, + // so that to avoid guessing the length of "other_arguments". + // batch_size, num_parallel_batches, and drop_remainder are 0-D scalars. + shape_inference::ShapeHandle unused; + TF_RETURN_IF_ERROR(c->WithRank(c->input(c->num_inputs() - 3), 0, &unused)); + TF_RETURN_IF_ERROR(c->WithRank(c->input(c->num_inputs() - 2), 0, &unused)); + TF_RETURN_IF_ERROR(c->WithRank(c->input(c->num_inputs() - 1), 0, &unused)); + + return shape_inference::ScalarShape(c); + }); REGISTER_OP("MapAndBatchDatasetV2") .Input("input_dataset: variant") @@ -231,7 +241,17 @@ REGISTER_OP("MapAndBatchDatasetV2") .Attr("Targuments: list(type) >= 0") .Attr("output_types: list(type) >= 1") .Attr("output_shapes: list(shape) >= 1") - .SetShapeFn(shape_inference::ScalarShape); + .SetShapeFn([](shape_inference::InferenceContext* c) { + // Use index from the end to retrieve the Input shapes, + // so that to avoid guessing the length of "other_arguments". + // batch_size, num_parallel_calls, and drop_remainder are 0-D scalars. + shape_inference::ShapeHandle unused; + TF_RETURN_IF_ERROR(c->WithRank(c->input(c->num_inputs() - 3), 0, &unused)); + TF_RETURN_IF_ERROR(c->WithRank(c->input(c->num_inputs() - 2), 0, &unused)); + TF_RETURN_IF_ERROR(c->WithRank(c->input(c->num_inputs() - 1), 0, &unused)); + + return shape_inference::ScalarShape(c); + }); REGISTER_OP("PrefetchDataset") .Input("input_dataset: variant") diff --git a/tensorflow/core/ops/image_ops.cc b/tensorflow/core/ops/image_ops.cc index d949e70c66..87f4991134 100644 --- a/tensorflow/core/ops/image_ops.cc +++ b/tensorflow/core/ops/image_ops.cc @@ -454,7 +454,9 @@ REGISTER_OP("DrawBoundingBoxes") DimensionHandle unused; TF_RETURN_IF_ERROR(c->WithValue(c->Dim(boxes, 2), 4, &unused)); - return shape_inference::UnchangedShapeWithRankAtLeast(c, 3); + // The rank of the input image (rank = 4) has already been restricted + // above, and the output is of the same shape as the input. + return shape_inference::UnchangedShape(c); }); // -------------------------------------------------------------------------- diff --git a/tensorflow/core/ops/math_ops.cc b/tensorflow/core/ops/math_ops.cc index 1740fa152c..b3487122e2 100644 --- a/tensorflow/core/ops/math_ops.cc +++ b/tensorflow/core/ops/math_ops.cc @@ -1084,7 +1084,7 @@ REGISTER_OP("UnsortedSegmentProd") .Input("segment_ids: Tindices") .Input("num_segments: Tnumsegments") .Output("output: T") - .Attr("T: realnumbertype") + .Attr("T: numbertype") .Attr("Tindices: {int32,int64}") .Attr("Tnumsegments: {int32,int64} = DT_INT32") .SetShapeFn(UnsortedSegmentReductionShapeFn); diff --git a/tensorflow/core/ops/nn_ops.cc b/tensorflow/core/ops/nn_ops.cc index fc60e807b9..41efa49ce3 100644 --- a/tensorflow/core/ops/nn_ops.cc +++ b/tensorflow/core/ops/nn_ops.cc @@ -1453,6 +1453,7 @@ REGISTER_OP("QuantizedReluX") ShapeHandle unused; TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused)); TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused)); + TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 0, &unused)); c->set_output(1, c->Scalar()); c->set_output(2, c->Scalar()); return Status::OK(); diff --git a/tensorflow/core/ops/string_ops.cc b/tensorflow/core/ops/string_ops.cc index 1d5c743a56..4423062362 100644 --- a/tensorflow/core/ops/string_ops.cc +++ b/tensorflow/core/ops/string_ops.cc @@ -78,7 +78,7 @@ REGISTER_OP("ReduceJoin") REGISTER_OP("AsString") .Input("input: T") .Output("output: string") - .Attr("T: {int32, int64, complex64, float, double, bool, int8}") + .Attr("T: {int8, int16, int32, int64, complex64, float, double, bool}") .Attr("precision: int = -1") .Attr("scientific: bool = false") .Attr("shortest: bool = false") @@ -134,6 +134,24 @@ REGISTER_OP("StringSplit") return Status::OK(); }); +REGISTER_OP("StringSplitV2") + .Input("input: string") + .Input("sep: string") + .Output("indices: int64") + .Output("values: string") + .Output("shape: int64") + .Attr("maxsplit: int = -1") + .SetShapeFn([](InferenceContext* c) { + ShapeHandle unused; + TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 1, &unused)); + TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused)); + + c->set_output(0, c->Matrix(InferenceContext::kUnknownDim, 2)); + c->set_output(1, c->Vector(InferenceContext::kUnknownDim)); + c->set_output(2, c->Vector(2)); + return Status::OK(); + }); + REGISTER_OP("StringStrip") .Input("input: string") .Output("output: string") diff --git a/tensorflow/core/platform/cpu_info.cc b/tensorflow/core/platform/cpu_info.cc index 99de364042..e9da3d8e32 100644 --- a/tensorflow/core/platform/cpu_info.cc +++ b/tensorflow/core/platform/cpu_info.cc @@ -344,5 +344,28 @@ int CPUModelNum() { #endif } +int CPUIDNumSMT() { +#ifdef PLATFORM_IS_X86 + // https://software.intel.com/en-us/articles/intel-64-architecture-processor-topology-enumeration + // https://software.intel.com/en-us/articles/intel-sdm (Vol 3A) + // Section: Detecting Hardware Multi-threads Support and Topology + // Uses CPUID Leaf 11 to enumerate system topology on Intel x86 architectures + // Other cases not supported + uint32 eax, ebx, ecx, edx; + // Check if system supports Leaf 11 + GETCPUID(eax, ebx, ecx, edx, 0, 0); + if (eax >= 11) { + // 1) Leaf 11 available? CPUID.(EAX=11, ECX=0):EBX != 0 + // 2) SMT_Mask_Width = CPUID.(EAX=11, ECX=0):EAX[4:0] if CPUID.(EAX=11, + // ECX=0):ECX[15:8] is 1 + GETCPUID(eax, ebx, ecx, edx, 11, 0); + if (ebx != 0 && ((ecx & 0xff00) >> 8) == 1) { + return 1 << (eax & 0x1f); // 2 ^ SMT_Mask_Width + } + } +#endif // PLATFORM_IS_X86 + return 0; +} + } // namespace port } // namespace tensorflow diff --git a/tensorflow/core/platform/cpu_info.h b/tensorflow/core/platform/cpu_info.h index b5be7e8b54..175c9ae8b1 100644 --- a/tensorflow/core/platform/cpu_info.h +++ b/tensorflow/core/platform/cpu_info.h @@ -35,6 +35,10 @@ namespace port { // software can change it dynamically. int NumSchedulableCPUs(); +// Returns an estimate of the number of hyperthreads per physical core +// on the CPU +int NumHyperthreadsPerCore(); + // Mostly ISA related features that we care about enum CPUFeature { // Do not change numeric assignments. @@ -107,6 +111,9 @@ int CPUModelNum(); // Returns nominal core processor cycles per second of each processor. double NominalCPUFrequency(); +// Returns num of hyperthreads per physical core +int CPUIDNumSMT(); + } // namespace port } // namespace tensorflow diff --git a/tensorflow/core/platform/default/build_config.bzl b/tensorflow/core/platform/default/build_config.bzl index ae81f9b5b3..a319ccbdbe 100644 --- a/tensorflow/core/platform/default/build_config.bzl +++ b/tensorflow/core/platform/default/build_config.bzl @@ -71,6 +71,8 @@ def pyx_library( name = filename + "_cython_translation", srcs = [filename], outs = [filename.split(".")[0] + ".cpp"], + # Optionally use PYTHON_BIN_PATH on Linux platforms so that python 3 + # works. Windows has issues with cython_binary so skip PYTHON_BIN_PATH. cmd = "PYTHONHASHSEED=0 $(location @cython//:cython_binary) --cplus $(SRCS) --output-file $(OUTS)", tools = ["@cython//:cython_binary"] + pxd_srcs, ) diff --git a/tensorflow/core/platform/hadoop/hadoop_file_system.cc b/tensorflow/core/platform/hadoop/hadoop_file_system.cc index 72c12318ca..ff4b4436bb 100644 --- a/tensorflow/core/platform/hadoop/hadoop_file_system.cc +++ b/tensorflow/core/platform/hadoop/hadoop_file_system.cc @@ -115,18 +115,17 @@ class LibHDFS { const char* kLibHdfsDso = "libhdfs.so"; #endif char* hdfs_home = getenv("HADOOP_HDFS_HOME"); - if (hdfs_home == nullptr) { - status_ = errors::FailedPrecondition( - "Environment variable HADOOP_HDFS_HOME not set"); - return; - } - string path = io::JoinPath(hdfs_home, "lib", "native", kLibHdfsDso); - status_ = TryLoadAndBind(path.c_str(), &handle_); - if (!status_.ok()) { - // try load libhdfs.so using dynamic loader's search path in case - // libhdfs.so is installed in non-standard location - status_ = TryLoadAndBind(kLibHdfsDso, &handle_); + if (hdfs_home != nullptr) { + string path = io::JoinPath(hdfs_home, "lib", "native", kLibHdfsDso); + status_ = TryLoadAndBind(path.c_str(), &handle_); + if (status_.ok()) { + return; + } } + + // Try to load the library dynamically in case it has been installed + // to a in non-standard location. + status_ = TryLoadAndBind(kLibHdfsDso, &handle_); } Status status_; diff --git a/tensorflow/core/platform/posix/port.cc b/tensorflow/core/platform/posix/port.cc index 8e316472fe..708f32ba80 100644 --- a/tensorflow/core/platform/posix/port.cc +++ b/tensorflow/core/platform/posix/port.cc @@ -74,6 +74,11 @@ int NumSchedulableCPUs() { return kDefaultCores; } +int NumHyperthreadsPerCore() { + static const int ht_per_core = tensorflow::port::CPUIDNumSMT(); + return (ht_per_core > 0) ? ht_per_core : 1; +} + void* AlignedMalloc(size_t size, int minimum_alignment) { #if defined(__ANDROID__) return memalign(minimum_alignment, size); diff --git a/tensorflow/core/public/version.h b/tensorflow/core/public/version.h index 522a9d84fd..cb1fd09dbb 100644 --- a/tensorflow/core/public/version.h +++ b/tensorflow/core/public/version.h @@ -19,12 +19,12 @@ limitations under the License. // TensorFlow uses semantic versioning, see http://semver.org/. #define TF_MAJOR_VERSION 1 -#define TF_MINOR_VERSION 8 +#define TF_MINOR_VERSION 9 #define TF_PATCH_VERSION 0 // TF_VERSION_SUFFIX is non-empty for pre-releases (e.g. "-alpha", "-alpha.1", // "-beta", "-rc", "-rc.1") -#define TF_VERSION_SUFFIX "" +#define TF_VERSION_SUFFIX "-rc0" #define TF_STR_HELPER(x) #x #define TF_STR(x) TF_STR_HELPER(x) diff --git a/tensorflow/core/util/mkl_util.h b/tensorflow/core/util/mkl_util.h index dffc965b14..90b6533690 100644 --- a/tensorflow/core/util/mkl_util.h +++ b/tensorflow/core/util/mkl_util.h @@ -42,6 +42,7 @@ limitations under the License. #ifndef INTEL_MKL_ML #include "mkldnn.hpp" +#include "tensorflow/core/lib/core/stringpiece.h" using mkldnn::engine; using mkldnn::memory; @@ -712,15 +713,48 @@ inline Tensor ConvertMklToTF(OpKernelContext* context, const Tensor& mkl_tensor, return output_tensor; } #else +using mkldnn::stream; +template class MklDnnData; + template inline Tensor ConvertMklToTF(OpKernelContext* context, const Tensor& mkl_tensor, const MklDnnShape& mkl_shape) { Tensor output_tensor; - TensorShape output_shape; - - TF_CHECK_OK( - Status(error::Code::UNIMPLEMENTED, "Unimplemented conversion function")); - + try { + if (!mkl_shape.IsMklTensor()) + return mkl_tensor; // return input since it is already TF tensor + + TensorShape output_shape = mkl_shape.GetTfShape();; + + // Allocate output tensor. + context->allocate_temp(DataTypeToEnum::v(), + output_shape, &output_tensor); + + auto cpu_engine = engine(engine::cpu, 0); + MklDnnData input(&cpu_engine); + + // Get Mkl layout of input tensor. + auto input_mkl_md = mkl_shape.GetMklLayout(); + auto output_tf_md = mkl_shape.GetTfLayout(); + auto output_tf_pd = memory::primitive_desc(output_tf_md, cpu_engine); + input.SetUsrMem(input_mkl_md, &mkl_tensor); + + // reorder + if (input.IsReorderNeeded(output_tf_pd)) { + std::vector net; + CHECK_EQ(input.CheckReorderToOpMem(output_tf_pd, &output_tensor, &net), + true); + stream(stream::kind::eager).submit(net).wait(); + } else { + // If not, just forward input tensor to output tensor. + CHECK(output_tensor.CopyFrom(mkl_tensor, output_shape)); + } + } catch (mkldnn::error& e) { + string error_msg = "Status: " + std::to_string(e.status) + + ", message: " + string(e.message) + ", in file " + + string(__FILE__) + ":" + std::to_string(__LINE__); + LOG(FATAL) << "Operation received an exception: " << error_msg; + } return output_tensor; } #endif @@ -1843,7 +1877,7 @@ class FactoryKeyCreator { template void AddAsKey(const T data) { auto buffer = reinterpret_cast(&data); - Append(absl::string_view(buffer, sizeof(T))); + Append(StringPiece(buffer, sizeof(T))); } std::string GetKey() { @@ -1854,8 +1888,8 @@ class FactoryKeyCreator { string key_; const char delimiter = 'x'; const int kMaxKeyLength = 256; - void Append(absl::string_view s) { - key_.append(string(s)); + void Append(StringPiece s) { + key_.append(s.ToString()); key_.append(1, delimiter); } }; diff --git a/tensorflow/docs_src/community/groups.md b/tensorflow/docs_src/community/groups.md index d92f5775fa..0b07d413da 100644 --- a/tensorflow/docs_src/community/groups.md +++ b/tensorflow/docs_src/community/groups.md @@ -1,17 +1,38 @@ # User Groups -TensorFlow has communities around the world. +TensorFlow has communities around the world. [Submit your community!](https://docs.google.com/forms/d/e/1FAIpQLSc_RQIUYtVgLLihzATaO_WUXkEyBDE_OoRoOXYDPmBEvHuEBA/viewform) ## Asia -* [TensorFlow Korea (TF-KR) User Group](https://www.facebook.com/groups/TensorFlowKR/) _(Korean language)_ -* [TensorFlow User Group Tokyo](https://tfug-tokyo.connpass.com/) _(Japanese Language)_ -* [Soleil Data Dojo](https://soleildatadojo.connpass.com/) _(Japanese language)_ +* [TensorFlow China community](https://www.tensorflowers.cn) +* [TensorFlow Korea (TF-KR) User Group](https://www.facebook.com/groups/TensorFlowKR/) +* [TensorFlow User Group Tokyo](https://tfug-tokyo.connpass.com/) +* [Soleil Data Dojo](https://soleildatadojo.connpass.com/) * [TensorFlow User Group Utsunomiya](https://tfug-utsunomiya.connpass.com/) +* [TensorFlow Philippines Community](https://www.facebook.com/groups/TensorFlowPH/) +* [TensorFlow and Deep Learning Singapore](https://www.meetup.com/TensorFlow-and-Deep-Learning-Singapore/) +* [TensorFlow India](https://www.facebook.com/tensorflowindia) ## Europe * [TensorFlow Barcelona](https://www.meetup.com/Barcelona-Machine-Learning-Meetup/) * [TensorFlow Madrid](https://www.meetup.com/TensorFlow-Madrid/) +* [Tensorflow Belgium](https://www.meetup.com/TensorFlow-Belgium) +* [TensorFlow x Rome Meetup](https://www.meetup.com/it-IT/TensorFlow-x-Rome-Meetup) +* [TensorFlow London](https://www.meetup.com/TensorFlow-London/) +* [TensorFlow Edinburgh](https://www.meetup.com/tensorflow-edinburgh/) + +## America + +* [TensorFlow Buenos Aires](https://www.meetup.com/TensorFlow-Buenos-Aires/) + + +## Oceania +* [Melbourne TensorFlow Meetup](https://www.meetup.com/Melbourne-TensorFlow-Meetup) + + +## Africa + +* [TensorFlow Tunis Meetup](https://www.meetup.com/fr-FR/TensorFlow-Tunis-Meetup/) diff --git a/tensorflow/docs_src/get_started/eager.md b/tensorflow/docs_src/get_started/eager.md index f08ac74425..bbb25e20c6 100644 --- a/tensorflow/docs_src/get_started/eager.md +++ b/tensorflow/docs_src/get_started/eager.md @@ -1,3 +1,3 @@ # Get Started with Eager Execution -[Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/r1.8.0/samples/core/get_started/eager.ipynb) +[Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/r1.9.0/samples/core/get_started/eager.ipynb) diff --git a/tensorflow/docs_src/get_started/index.md b/tensorflow/docs_src/get_started/index.md index 55579d52fb..232d2f1547 100644 --- a/tensorflow/docs_src/get_started/index.md +++ b/tensorflow/docs_src/get_started/index.md @@ -10,9 +10,9 @@ course prior to diving into TensorFlow documentation: TensorFlow is a tool for machine learning. While it contains a wide range of functionality, TensorFlow is mainly designed for deep neural network models. -The easiest way to get started with TensorFlow is using Eager Execution. +The easiest way to get started with TensorFlow is by using Eager Execution. - * @{$get_started/eager}, is for anyone new to machine learning or TensorFlow. + * @{$get_started/eager}, is for anyone new to machine learning or TensorFlow. TensorFlow provides many APIs. The remainder of this section focuses on the Estimator API which provide scalable, high-performance models. See the diff --git a/tensorflow/docs_src/install/install_c.md b/tensorflow/docs_src/install/install_c.md index 1abd840ab3..2901848745 100644 --- a/tensorflow/docs_src/install/install_c.md +++ b/tensorflow/docs_src/install/install_c.md @@ -38,7 +38,7 @@ enable TensorFlow for C: OS="linux" # Change to "darwin" for macOS TARGET_DIRECTORY="/usr/local" curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.8.0.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.9.0-rc0.tar.gz" | sudo tar -C $TARGET_DIRECTORY -xz The `tar` command extracts the TensorFlow C library into the `lib` diff --git a/tensorflow/docs_src/install/install_go.md b/tensorflow/docs_src/install/install_go.md index 52a2a3f8a6..55bc0f64e7 100644 --- a/tensorflow/docs_src/install/install_go.md +++ b/tensorflow/docs_src/install/install_go.md @@ -38,7 +38,7 @@ steps to install this library and enable TensorFlow for Go: TF_TYPE="cpu" # Change to "gpu" for GPU support TARGET_DIRECTORY='/usr/local' curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.8.0.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.9.0-rc0.tar.gz" | sudo tar -C $TARGET_DIRECTORY -xz The `tar` command extracts the TensorFlow C library into the `lib` diff --git a/tensorflow/docs_src/install/install_java.md b/tensorflow/docs_src/install/install_java.md index 1256fb99c4..637231da12 100644 --- a/tensorflow/docs_src/install/install_java.md +++ b/tensorflow/docs_src/install/install_java.md @@ -36,7 +36,7 @@ following to the project's `pom.xml` to use the TensorFlow Java APIs: org.tensorflow tensorflow - 1.8.0 + 1.9.0-rc0 ``` @@ -65,7 +65,7 @@ As an example, these steps will create a Maven project that uses TensorFlow: org.tensorflow tensorflow - 1.8.0 + 1.9.0-rc0 @@ -124,12 +124,12 @@ instead: org.tensorflow libtensorflow - 1.8.0 + 1.9.0-rc0 org.tensorflow libtensorflow_jni_gpu - 1.8.0 + 1.9.0-rc0 ``` @@ -148,7 +148,7 @@ refer to the simpler instructions above instead. Take the following steps to install TensorFlow for Java on Linux or macOS: 1. Download - [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.8.0.jar), + [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.9.0-rc0.jar), which is the TensorFlow Java Archive (JAR). 2. Decide whether you will run TensorFlow for Java on CPU(s) only or with @@ -167,7 +167,7 @@ Take the following steps to install TensorFlow for Java on Linux or macOS: OS=$(uname -s | tr '[:upper:]' '[:lower:]') mkdir -p ./jni curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.8.0.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.9.0-rc0.tar.gz" | tar -xz -C ./jni ### Install on Windows @@ -175,13 +175,13 @@ Take the following steps to install TensorFlow for Java on Linux or macOS: Take the following steps to install TensorFlow for Java on Windows: 1. Download - [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.8.0.jar), + [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.9.0-rc0.jar), which is the TensorFlow Java Archive (JAR). 2. Download the following Java Native Interface (JNI) file appropriate for - [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.8.0.zip). + [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.9.0-rc0.zip). 3. Extract this .zip file. - +__Note__: The native library (`tensorflow_jni.dll`) requires `msvcp140.dll` at runtime, which is included in the [Visual C++ 2015 Redistributable](https://www.microsoft.com/en-us/download/details.aspx?id=48145) package. ### Validate the installation @@ -227,7 +227,7 @@ must be part of your `classpath`. For example, you can include the downloaded `.jar` in your `classpath` by using the `-cp` compilation flag as follows: -
javac -cp libtensorflow-1.8.0.jar HelloTF.java
+
javac -cp libtensorflow-1.9.0-rc0.jar HelloTF.java
### Running @@ -241,11 +241,11 @@ two files are available to the JVM: For example, the following command line executes the `HelloTF` program on Linux and macOS X: -
java -cp libtensorflow-1.8.0.jar:. -Djava.library.path=./jni HelloTF
+
java -cp libtensorflow-1.9.0-rc0.jar:. -Djava.library.path=./jni HelloTF
And the following command line executes the `HelloTF` program on Windows: -
java -cp libtensorflow-1.8.0.jar;. -Djava.library.path=jni HelloTF
+
java -cp libtensorflow-1.9.0-rc0.jar;. -Djava.library.path=jni HelloTF
If the program prints Hello from version, you've successfully installed TensorFlow for Java and are ready to use the API. If the program diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md index 0ed8160027..c8d706cf3c 100644 --- a/tensorflow/docs_src/install/install_linux.md +++ b/tensorflow/docs_src/install/install_linux.md @@ -339,9 +339,7 @@ Docker will download the TensorFlow binary image the first time you launch it. #### GPU support -Prior to installing TensorFlow with GPU support, ensure that your system meets all -[NVIDIA software requirements](#NVIDIARequirements). To launch a Docker container -with NVidia GPU support, enter a command of the following format: +To launch a Docker container with NVidia GPU support, enter a command of the following format (this [does not require any local CUDA installation](https://github.com/nvidia/nvidia-docker/wiki/CUDA#requirements)): 
 $ nvidia-docker run -it -p hostPort:containerPort TensorFlowGPUImage
@@ -438,7 +436,7 @@ Take the following steps to install TensorFlow in an Anaconda environment:
 
      
      (tensorflow)$ pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0-cp34-cp34m-linux_x86_64.whl

+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp34-cp34m-linux_x86_64.whl
## Validate your installation @@ -517,7 +515,7 @@ on your system: from source. To use the TensorFlow binaries, version 3.5 or higher is required. See the [NVIDIA documentation](https://developer.nvidia.com/cuda-gpus) for a list of supported GPU cards. -* [GPU drivers](http://nvidia.com/driver) that support your version of the CUDA +* [GPU drivers](http://nvidia.com/drivers) that support your version of the CUDA Toolkit. * The `libcupti-dev` library is the NVIDIA CUDA Profile Tools Interface. This library provides advanced profiling support. To install this library, @@ -684,14 +682,14 @@ This section documents the relevant values for Linux installations. CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp27-none-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.8.0-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp27-none-linux_x86_64.whl
Note that GPU support requires the NVIDIA hardware and software described in @@ -703,14 +701,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp34-cp34m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.8.0-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp34-cp34m-linux_x86_64.whl
Note that GPU support requires the NVIDIA hardware and software described in @@ -722,14 +720,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp35-cp35m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.8.0-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp35-cp35m-linux_x86_64.whl
@@ -741,14 +739,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp36-cp36m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.8.0-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp36-cp36m-linux_x86_64.whl
diff --git a/tensorflow/docs_src/install/install_mac.md b/tensorflow/docs_src/install/install_mac.md index 29a867a9e3..9d01271c5a 100644 --- a/tensorflow/docs_src/install/install_mac.md +++ b/tensorflow/docs_src/install/install_mac.md @@ -119,7 +119,7 @@ Take the following steps to install TensorFlow with Virtualenv: TensorFlow in the active Virtualenv is as follows: 
 $ pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0-py3-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py3-none-any.whl If you encounter installation problems, see [Common Installation Problems](#common-installation-problems). @@ -242,7 +242,7 @@ take the following steps: issue the following command: 
 $ sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0-py3-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py3-none-any.whl If the preceding command fails, see [installation problems](#common-installation-problems). @@ -350,7 +350,7 @@ Take the following steps to install TensorFlow in an Anaconda environment: TensorFlow for Python 2.7: 
 (targetDirectory)$ pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0-py2-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py2-none-any.whl @@ -522,7 +522,7 @@ The value you specify depends on your Python version. 
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0-py2-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py2-none-any.whl
@@ -530,5 +530,5 @@ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0-py2-none-any. 
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0-py3-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py3-none-any.whl
diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md index 5ba522b436..dc6c1e36fc 100644 --- a/tensorflow/docs_src/install/install_sources.md +++ b/tensorflow/docs_src/install/install_sources.md @@ -81,7 +81,7 @@ or [macOS](#PrepareMac) - + ## Prepare environment for Linux Before building TensorFlow on Linux, install the following build @@ -328,10 +328,10 @@ Invoke `pip install` to install that pip package. The filename of the `.whl` file depends on your platform. For example, the following command will install the pip package -for TensorFlow 1.8.0 on Linux: +for TensorFlow 1.9.0rc0 on Linux: 
-$ sudo pip install /tmp/tensorflow_pkg/tensorflow-1.8.0-py2-none-any.whl
+$ sudo pip install /tmp/tensorflow_pkg/tensorflow-1.9.0rc0-py2-none-any.whl
## Validate your installation @@ -373,9 +373,9 @@ The build and installation problems you encounter typically depend on the operating system. See the "Common installation problems" section of one of the following guides: - * @{$install_linux#CommonInstallationProblems$Installing TensorFlow on Linux} - * @{$install_mac#CommonInstallationProblems$Installing TensorFlow on Mac OS} - * @{$install_windows#CommonInstallationProblems$Installing TensorFlow on Windows} + * @{$install_linux#common_installation_problems$Installing TensorFlow on Linux} + * @{$install_mac#common_installation_problems$Installing TensorFlow on Mac OS} + * @{$install_windows#common_installation_problems$Installing TensorFlow on Windows} Beyond the errors documented in those two guides, the following table notes additional errors specific to building TensorFlow. Note that we @@ -433,6 +433,8 @@ Stack Overflow and specify the `tensorflow` tag. **Linux** + + @@ -456,6 +458,7 @@ Stack Overflow and specify the `tensorflow` tag. **Mac**
Version:CPU/GPU:Python Version:Compiler:Build Tools:cuDNN:CUDA:
tensorflow-1.9.0CPU2.7, 3.3-3.6GCC 4.8Bazel 0.11.0N/AN/A
tensorflow_gpu-1.9.0GPU2.7, 3.3-3.6GCC 4.8Bazel 0.11.079
tensorflow-1.8.0CPU2.7, 3.3-3.6GCC 4.8Bazel 0.10.0N/AN/A
tensorflow_gpu-1.8.0GPU2.7, 3.3-3.6GCC 4.8Bazel 0.9.079
tensorflow-1.7.0CPU2.7, 3.3-3.6GCC 4.8Bazel 0.10.0N/AN/A
+ @@ -472,6 +475,8 @@ Stack Overflow and specify the `tensorflow` tag. **Windows**
Version:CPU/GPU:Python Version:Compiler:Build Tools:cuDNN:CUDA:
tensorflow-1.9.0CPU2.7, 3.3-3.6Clang from xcodeBazel 0.11.0N/AN/A
tensorflow-1.8.0CPU2.7, 3.3-3.6Clang from xcodeBazel 0.10.1N/AN/A
tensorflow-1.7.0CPU2.7, 3.3-3.6Clang from xcodeBazel 0.10.1N/AN/A
tensorflow-1.6.0CPU2.7, 3.3-3.6Clang from xcodeBazel 0.8.1N/AN/A
+ + diff --git a/tensorflow/docs_src/mobile/linking_libs.md b/tensorflow/docs_src/mobile/linking_libs.md index cf0db59021..efef5dd0da 100644 --- a/tensorflow/docs_src/mobile/linking_libs.md +++ b/tensorflow/docs_src/mobile/linking_libs.md @@ -27,7 +27,7 @@ called `libandroid_tensorflow_inference_java.jar`. There are three ways to include this functionality in your program: 1. Include the jcenter AAR which contains it, as in this - [example app](https://github.com/googlecodelabs/tensorflow-for-poets-2/blob/master/android/build.gradle#L59-L65) + [example app](https://github.com/googlecodelabs/tensorflow-for-poets-2/blob/master/android/tfmobile/build.gradle#L59-L65) 2. Download the nightly precompiled version from [ci.tensorflow.org](http://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/). diff --git a/tensorflow/docs_src/mobile/prepare_models.md b/tensorflow/docs_src/mobile/prepare_models.md index 8b22c04d87..2b84dbb973 100644 --- a/tensorflow/docs_src/mobile/prepare_models.md +++ b/tensorflow/docs_src/mobile/prepare_models.md @@ -105,8 +105,8 @@ inline constants so everything’s in one file. To handle the conversion, you need the `freeze_graph.py` script, that’s held in [`tensorflow/python/tools/freeze_graph.py`](https://www.tensorflow.org/code/tensorflow/python/tools/freeze_graph.py). You’ll run it like this: - bazel build tensorflow/tools:freeze_graph - bazel-bin/tensorflow/tools/freeze_graph \ + bazel build tensorflow/python/tools:freeze_graph + bazel-bin/tensorflow/python/tools/freeze_graph \ --input_graph=/tmp/model/my_graph.pb \ --input_checkpoint=/tmp/model/model.ckpt-1000 \ --output_graph=/tmp/frozen_graph.pb \ diff --git a/tensorflow/docs_src/performance/quantization.md b/tensorflow/docs_src/performance/quantization.md index 2fea02d861..c97f74139c 100644 --- a/tensorflow/docs_src/performance/quantization.md +++ b/tensorflow/docs_src/performance/quantization.md @@ -227,8 +227,8 @@ of 30.0f, and an 8-bit array, the quantized values represent the following:
Version:CPU/GPU:Python Version:Compiler:Build Tools:cuDNN:CUDA:
tensorflow-1.9.0CPU3.5-3.6MSVC 2015 update 3Cmake v3.6.3N/AN/A
tensorflow_gpu-1.9.0GPU3.5-3.6MSVC 2015 update 3Cmake v3.6.379
tensorflow-1.8.0CPU3.5-3.6MSVC 2015 update 3Cmake v3.6.3N/AN/A
tensorflow_gpu-1.8.0GPU3.5-3.6MSVC 2015 update 3Cmake v3.6.379
tensorflow-1.7.0CPU3.5-3.6MSVC 2015 update 3Cmake v3.6.3N/AN/A
- +
QuantizedFloat
0-10.0
25530.0
12810.0
25530.0
Table 2: Example quantized value range diff --git a/tensorflow/docs_src/programmers_guide/estimators.md b/tensorflow/docs_src/programmers_guide/estimators.md index c4aae1d9d6..b13b47184d 100644 --- a/tensorflow/docs_src/programmers_guide/estimators.md +++ b/tensorflow/docs_src/programmers_guide/estimators.md @@ -21,18 +21,17 @@ Note: TensorFlow also includes a deprecated `Estimator` class at Estimators provide the following benefits: -* You can run Estimators-based models on a local host or on a +* You can run Estimator-based models on a local host or on a distributed multi-server environment without changing your model. - Furthermore, you can run Estimators-based models on CPUs, GPUs, + Furthermore, you can run Estimator-based models on CPUs, GPUs, or TPUs without recoding your model. * Estimators simplify sharing implementations between model developers. -* You can develop a state of the art model with high-level intuitive code, +* You can develop a state of the art model with high-level intuitive code. In short, it is generally much easier to create models with Estimators than with the low-level TensorFlow APIs. -* Estimators are themselves built on tf.layers, which +* Estimators are themselves built on @{tf.layers}, which simplifies customization. -* Estimators build the graph for you. In other words, you don't have to - build the graph. +* Estimators build the graph for you. * Estimators provide a safe distributed training loop that controls how and when to: * build the graph @@ -57,7 +56,7 @@ the "plumbing" for you. That is, pre-made Estimators create and manage pre-made Estimators let you experiment with different model architectures by making only minimal code changes. @{tf.estimator.DNNClassifier$`DNNClassifier`}, for example, is a pre-made Estimator class that trains classification models -through dense, feed-forward neural networks. +based on dense, feed-forward neural networks. ### Structure of a pre-made Estimators program @@ -79,7 +78,7 @@ of the following four steps: an input function: def input_fn(dataset): - ... # manipulate dataset, extracting feature names and the label + ... # manipulate dataset, extracting the feature dict and the label return feature_dict, label (See @{$programmers_guide/datasets} for full details.) @@ -96,13 +95,13 @@ of the following four steps: population = tf.feature_column.numeric_column('population') crime_rate = tf.feature_column.numeric_column('crime_rate') median_education = tf.feature_column.numeric_column('median_education', - normalizer_fn='lambda x: x - global_education_mean') + normalizer_fn=lambda x: x - global_education_mean) 3. **Instantiate the relevant pre-made Estimator.** For example, here's a sample instantiation of a pre-made Estimator named `LinearClassifier`: # Instantiate an estimator, passing the feature columns. - estimator = tf.estimator.Estimator.LinearClassifier( + estimator = tf.estimator.LinearClassifier( feature_columns=[population, crime_rate, median_education], ) diff --git a/tensorflow/docs_src/programmers_guide/feature_columns.md b/tensorflow/docs_src/programmers_guide/feature_columns.md index 845194fe0e..90f5c53a17 100644 --- a/tensorflow/docs_src/programmers_guide/feature_columns.md +++ b/tensorflow/docs_src/programmers_guide/feature_columns.md @@ -528,10 +528,10 @@ suggested by the following snippet: categorical_column = ... # Create any categorical column # Represent the categorical column as an embedding column. -# This means creating a one-hot vector with one element for each category. +# This means creating an embedding vector lookup table with one element for each category. embedding_column = tf.feature_column.embedding_column( categorical_column=categorical_column, - dimension=dimension_of_embedding_vector) + dimension=embedding_dimensions) ``` @{$programmers_guide/embedding$Embeddings} is a significant topic within machine diff --git a/tensorflow/examples/learn/iris.py b/tensorflow/examples/learn/iris.py index 03e60972aa..86f5204ec3 100644 --- a/tensorflow/examples/learn/iris.py +++ b/tensorflow/examples/learn/iris.py @@ -21,7 +21,8 @@ from __future__ import division from __future__ import print_function import os -import urllib + +from six.moves.urllib.request import urlretrieve import tensorflow as tf @@ -38,9 +39,7 @@ FEATURE_KEYS = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width'] def maybe_download_iris_data(file_name, download_url): """Downloads the file and returns the number of data.""" if not os.path.exists(file_name): - raw = urllib.urlopen(download_url).read() - with open(file_name, 'w') as f: - f.write(raw) + urlretrieve(download_url, file_name) # The first line is a comma-separated string. The first one is the number of # total data in the file. diff --git a/tensorflow/java/src/gen/cc/op_generator.cc b/tensorflow/java/src/gen/cc/op_generator.cc index debd95fc62..9b171f66ec 100644 --- a/tensorflow/java/src/gen/cc/op_generator.cc +++ b/tensorflow/java/src/gen/cc/op_generator.cc @@ -376,9 +376,6 @@ void GenerateOp(const OpSpec& op, const EndpointSpec& endpoint, } } // op annotations - op_class.add_annotation( - Annotation::Create("Generated", "javax.annotation") - .attributes("value = \"TensorFlow Java Op Generator\"")); if (endpoint.deprecated()) { op_class.add_annotation(Annotation::Create("Deprecated")); string explanation; @@ -415,8 +412,12 @@ void GenerateOp(const OpSpec& op, const EndpointSpec& endpoint, SourceFileWriter writer(op_file.get()); std::list dependencies; CollectOpDependencies(op, mode, &dependencies); - writer.Write(kLicense).EndLine().BeginType(op_class, PUBLIC | FINAL, - &dependencies, &op_javadoc); + writer.Write(kLicense) + .EndLine() + .Write("// This class has been generated, DO NOT EDIT!") + .EndLine() + .EndLine() + .BeginType(op_class, PUBLIC | FINAL, &dependencies, &op_javadoc); if (!op.optional_attributes().empty()) { RenderOptionsClass(op, op_class, &writer); } diff --git a/tensorflow/java/src/gen/cc/op_specs.cc b/tensorflow/java/src/gen/cc/op_specs.cc index 181fd4c5e3..941ab2699c 100644 --- a/tensorflow/java/src/gen/cc/op_specs.cc +++ b/tensorflow/java/src/gen/cc/op_specs.cc @@ -96,6 +96,7 @@ Type TypeResolver::TypeOf(const OpDef_ArgDef& arg_def, bool* iterable_out) { *iterable_out = true; visited_attrs_.insert(std::make_pair(arg_def.number_attr(), Type::Int())); } + Type type = Type::Wildcard(); if (arg_def.type() != DataType::DT_INVALID) { // resolve type from DataType diff --git a/tensorflow/python/eager/backprop.py b/tensorflow/python/eager/backprop.py index b2e6c60021..bd97b181ff 100644 --- a/tensorflow/python/eager/backprop.py +++ b/tensorflow/python/eager/backprop.py @@ -196,11 +196,11 @@ def implicit_val_and_grad(f): # TODO(cais): Remove calls to tf.constant() once the gradients functions # accept lists and np.ndarrays. - def grad_fn(*args): + def grad_fn(*args, **kwds): """Computes the gradient of the wrapped function.""" this_tape = tape.push_new_tape() try: - end_node = f(*args) + end_node = f(*args, **kwds) if end_node is None: raise ValueError("Cannot differentiate a function that returns None; " "did you forget to return a value from {}?".format( diff --git a/tensorflow/python/estimator/BUILD b/tensorflow/python/estimator/BUILD index 9cd17e0407..20522098b0 100644 --- a/tensorflow/python/estimator/BUILD +++ b/tensorflow/python/estimator/BUILD @@ -978,7 +978,10 @@ py_test( size = "large", srcs = ["keras_test.py"], srcs_version = "PY2AND3", - tags = ["notsan"], + tags = [ + "no_windows", + "notsan", + ], deps = [ ":keras", "//tensorflow/core:protos_all_py", diff --git a/tensorflow/python/estimator/exporter.py b/tensorflow/python/estimator/exporter.py index 7cdf840c97..b18212cfcd 100644 --- a/tensorflow/python/estimator/exporter.py +++ b/tensorflow/python/estimator/exporter.py @@ -156,7 +156,7 @@ def _loss_smaller(best_eval_result, current_eval_result): return best_eval_result[default_key] > current_eval_result[default_key] -def _verify_compre_fn_args(compare_fn): +def _verify_compare_fn_args(compare_fn): """Verifies compare_fn arguments.""" args = set(util.fn_args(compare_fn)) if 'best_eval_result' not in args: @@ -265,7 +265,7 @@ class BestExporter(Exporter): self._compare_fn = compare_fn if self._compare_fn is None: raise ValueError('`compare_fn` must not be None.') - _verify_compre_fn_args(self._compare_fn) + _verify_compare_fn_args(self._compare_fn) self._saved_model_exporter = _SavedModelExporter( name, serving_input_receiver_fn, assets_extra, as_text) diff --git a/tensorflow/python/estimator/inputs/numpy_io.py b/tensorflow/python/estimator/inputs/numpy_io.py index 035c7c148c..a6cefdece2 100644 --- a/tensorflow/python/estimator/inputs/numpy_io.py +++ b/tensorflow/python/estimator/inputs/numpy_io.py @@ -136,11 +136,13 @@ def numpy_input_fn(x, values in `x` have same shape). ValueError: if duplicate keys are in both `x` and `y` when `y` is a dict. ValueError: if x or y is an empty dict. - TypeError: `x` is not a dict or array, or if `shuffle` is not bool. + TypeError: `x` is not a dict or array. + ValueError: if 'shuffle' is not provided or a bool. """ if not isinstance(shuffle, bool): - raise TypeError('shuffle must be explicitly set as boolean; ' - 'got {}'.format(shuffle)) + raise ValueError('shuffle must be provided and explicitly set as boolean ' + '(it is recommended to set it as True for training); ' + 'got {}'.format(shuffle)) def input_fn(): """Numpy input function.""" diff --git a/tensorflow/python/estimator/inputs/numpy_io_test.py b/tensorflow/python/estimator/inputs/numpy_io_test.py index 92d057e25d..81b201cc5c 100644 --- a/tensorflow/python/estimator/inputs/numpy_io_test.py +++ b/tensorflow/python/estimator/inputs/numpy_io_test.py @@ -286,8 +286,9 @@ class NumpyIoTest(test.TestCase): x = np.arange(32, 36) y = np.arange(4) with self.test_session(): - with self.assertRaisesRegexp(TypeError, - 'shuffle must be explicitly set as boolean'): + with self.assertRaisesRegexp(ValueError, + 'shuffle must be provided and explicitly ' + 'set as boolean'): # Default shuffle is None. numpy_io.numpy_input_fn(x, y) diff --git a/tensorflow/python/estimator/inputs/pandas_io.py b/tensorflow/python/estimator/inputs/pandas_io.py index 938e244fb3..57f8e5fd6a 100644 --- a/tensorflow/python/estimator/inputs/pandas_io.py +++ b/tensorflow/python/estimator/inputs/pandas_io.py @@ -68,15 +68,16 @@ def pandas_input_fn(x, Raises: ValueError: if `x` already contains a column with the same name as `y`, or if the indexes of `x` and `y` don't match. - TypeError: `shuffle` is not bool. + ValueError: if 'shuffle' is not provided or a bool. """ if not HAS_PANDAS: raise TypeError( 'pandas_input_fn should not be called without pandas installed') if not isinstance(shuffle, bool): - raise TypeError('shuffle must be explicitly set as boolean; ' - 'got {}'.format(shuffle)) + raise ValueError('shuffle must be provided and explicitly set as boolean ' + '(it is recommended to set it as True for training); ' + 'got {}'.format(shuffle)) x = x.copy() if y is not None: diff --git a/tensorflow/python/estimator/inputs/pandas_io_test.py b/tensorflow/python/estimator/inputs/pandas_io_test.py index e5912a3b28..dcecf6dd61 100644 --- a/tensorflow/python/estimator/inputs/pandas_io_test.py +++ b/tensorflow/python/estimator/inputs/pandas_io_test.py @@ -70,8 +70,9 @@ class PandasIoTest(test.TestCase): return x, _ = self.makeTestDataFrame() y_noindex = pd.Series(np.arange(-32, -28)) - with self.assertRaisesRegexp(TypeError, - 'shuffle must be explicitly set as boolean'): + with self.assertRaisesRegexp(ValueError, + 'shuffle must be provided and explicitly ' + 'set as boolean'): # Default shuffle is None pandas_io.pandas_input_fn(x, y_noindex) diff --git a/tensorflow/python/estimator/inputs/queues/feeding_functions.py b/tensorflow/python/estimator/inputs/queues/feeding_functions.py index 8e2ec83020..51a61adb21 100644 --- a/tensorflow/python/estimator/inputs/queues/feeding_functions.py +++ b/tensorflow/python/estimator/inputs/queues/feeding_functions.py @@ -250,7 +250,7 @@ class _PandasFeedFn(object): num_epochs=None): if len(placeholders) != len(dataframe.columns) + 1: raise ValueError("Expected {} placeholders; got {}.".format( - len(dataframe.columns), len(placeholders))) + len(dataframe.columns) + 1, len(placeholders))) self._index_placeholder = placeholders[0] self._col_placeholders = placeholders[1:] self._dataframe = dataframe diff --git a/tensorflow/python/estimator/keras.py b/tensorflow/python/estimator/keras.py index c80af08fba..2f439f765e 100644 --- a/tensorflow/python/estimator/keras.py +++ b/tensorflow/python/estimator/keras.py @@ -70,7 +70,7 @@ def _convert_tensor(x): return x -def _any_variable_initalized(): +def _any_variable_initialized(): """Check if any variable has been initialized in the Keras model. Returns: @@ -511,7 +511,7 @@ def model_to_estimator(keras_model=None, keras_model_fn, model_dir=model_dir, config=config) # Check if we need to call get_weights: - if _any_variable_initalized(): + if _any_variable_initialized(): keras_weights = keras_model.get_weights() # Warn if config passed to estimator tries to update GPUOptions. If a # session has already been created, the GPUOptions passed to the first diff --git a/tensorflow/python/estimator/keras_test.py b/tensorflow/python/estimator/keras_test.py index 6688a84130..5e094ae92b 100644 --- a/tensorflow/python/estimator/keras_test.py +++ b/tensorflow/python/estimator/keras_test.py @@ -31,10 +31,10 @@ from tensorflow.python.estimator import run_config as run_config_lib from tensorflow.python.estimator.inputs import numpy_io from tensorflow.python.framework import ops from tensorflow.python.framework import test_util -from tensorflow.python.keras import backend as K from tensorflow.python.keras import testing_utils from tensorflow.python.keras.applications import mobilenet from tensorflow.python.keras.optimizers import SGD +from tensorflow.python.ops.parsing_ops import gen_parsing_ops from tensorflow.python.platform import gfile from tensorflow.python.platform import test from tensorflow.python.summary.writer import writer_cache @@ -146,13 +146,13 @@ def randomize_io_type(array, name): def multi_inputs_multi_outputs_model(): a = keras.layers.Input(shape=(16,), name='input_a') b = keras.layers.Input(shape=(16,), name='input_b') - m = keras.layers.Input(shape=(8,), dtype='bool', name='input_m') + m = keras.layers.Input(shape=(8,), dtype='string', name='input_m') dense = keras.layers.Dense(8, name='dense_1') a_2 = dense(a) - # Apply a mask - s_2 = keras.layers.Lambda(lambda k: - K.switch(k[0], k[1], K.zeros_like(k[1])))([m, a_2]) + # Read m + m_2 = keras.layers.Lambda(gen_parsing_ops.string_to_number)(m) + s_2 = keras.layers.Lambda(lambda k: k[0] * k[1])([m_2, a_2]) b_2 = dense(b) merged = keras.layers.concatenate([s_2, b_2], name='merge') c = keras.layers.Dense(3, activation='softmax', name='dense_2')(merged) @@ -372,13 +372,13 @@ class TestKerasEstimator(test_util.TensorFlowTestCase): def train_input_fn(): input_dict = {'input_a': a_train, 'input_b': b_train, - 'input_m': input_m_train > 0} + 'input_m': input_m_train.astype(np.str)} output_dict = {'dense_2': c_train, 'dense_3': d_train} return input_dict, output_dict def eval_input_fn(): input_dict = {'input_a': a_test, 'input_b': b_test, - 'input_m': input_m_test > 0} + 'input_m': input_m_test.astype(np.str)} output_dict = {'dense_2': c_test, 'dense_3': d_test} return input_dict, output_dict diff --git a/tensorflow/python/keras/activations.py b/tensorflow/python/keras/activations.py index e487f583be..f608dea430 100644 --- a/tensorflow/python/keras/activations.py +++ b/tensorflow/python/keras/activations.py @@ -93,6 +93,8 @@ def selu(x): - To be used together with the initialization "lecun_normal". - To be used together with the dropout variant "AlphaDropout". + References: + - [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515) """ alpha = 1.6732632423543772848170429916717 scale = 1.0507009873554804934193349852946 diff --git a/tensorflow/python/keras/callbacks.py b/tensorflow/python/keras/callbacks.py index 70b6a8431a..9f91368e5b 100644 --- a/tensorflow/python/keras/callbacks.py +++ b/tensorflow/python/keras/callbacks.py @@ -724,15 +724,6 @@ class TensorBoard(Callback): for weight in layer.weights: mapped_weight_name = weight.name.replace(':', '_') tf_summary.histogram(mapped_weight_name, weight) - if self.write_grads: - grads = model.optimizer.get_gradients(model.total_loss, weight) - - def is_indexed_slices(grad): - return type(grad).__name__ == 'IndexedSlices' - - grads = [grad.values if is_indexed_slices(grad) else grad - for grad in grads] - tf_summary.histogram('{}_grad'.format(mapped_weight_name), grads) if self.write_images: w_img = array_ops.squeeze(weight) shape = K.int_shape(w_img) @@ -759,6 +750,18 @@ class TensorBoard(Callback): assert len(shape) == 4 and shape[-1] in [1, 3, 4] tf_summary.image(mapped_weight_name, w_img) + if self.write_grads: + for weight in layer.trainable_weights: + mapped_weight_name = weight.name.replace(':', '_') + grads = model.optimizer.get_gradients(model.total_loss, weight) + + def is_indexed_slices(grad): + return type(grad).__name__ == 'IndexedSlices' + + grads = [grad.values if is_indexed_slices(grad) else grad + for grad in grads] + tf_summary.histogram('{}_grad'.format(mapped_weight_name), grads) + if hasattr(layer, 'output'): tf_summary.histogram('{}_out'.format(layer.name), layer.output) self.merged = tf_summary.merge_all() diff --git a/tensorflow/python/keras/callbacks_test.py b/tensorflow/python/keras/callbacks_test.py index b355f4a269..5062a26580 100644 --- a/tensorflow/python/keras/callbacks_test.py +++ b/tensorflow/python/keras/callbacks_test.py @@ -653,6 +653,8 @@ class KerasCallbacksTest(test.TestCase): model.add( keras.layers.Dense( NUM_HIDDEN, input_dim=INPUT_DIM, activation='relu')) + # non_trainable_weights: moving_variance, moving_mean + model.add(keras.layers.BatchNormalization()) model.add(keras.layers.Dense(NUM_CLASSES, activation='softmax')) model.compile( loss='categorical_crossentropy', diff --git a/tensorflow/python/keras/engine/network.py b/tensorflow/python/keras/engine/network.py index a4cd017d60..1c9135982e 100644 --- a/tensorflow/python/keras/engine/network.py +++ b/tensorflow/python/keras/engine/network.py @@ -123,7 +123,7 @@ class Network(base_layer.Layer): # Entries are unique. Includes input and output layers. self._layers = [] - # Used in symbolic mode only, only in conjonction with graph-networks + # Used in symbolic mode only, only in conjunction with graph-networks self._outbound_nodes = [] self._inbound_nodes = [] diff --git a/tensorflow/python/keras/engine/saving_test.py b/tensorflow/python/keras/engine/saving_test.py index 6a94986b9c..7e82db028b 100644 --- a/tensorflow/python/keras/engine/saving_test.py +++ b/tensorflow/python/keras/engine/saving_test.py @@ -482,7 +482,7 @@ class TestWholeModelSaving(test.TestCase): with h5py.File(fname, 'r') as h5file: num_names_arrays = len([attr for attr in h5file['model_weights'].attrs if attr.startswith('layer_names')]) - # The chunking of layer names array should have happend. + # The chunking of layer names array should have happened. self.assertGreater(num_names_arrays, 0) out2 = model.predict(x) self.assertAllClose(out, out2, atol=1e-05) @@ -527,7 +527,7 @@ class TestWholeModelSaving(test.TestCase): num_weight_arrays = len( [attr for attr in h5file['model_weights']['nested_model'].attrs if attr.startswith('weight_names')]) - # The chunking of layer names array should have happend. + # The chunking of layer names array should have happened. self.assertGreater(num_weight_arrays, 0) out2 = model.predict(x) self.assertAllClose(out, out2, atol=1e-05) diff --git a/tensorflow/python/keras/engine/training.py b/tensorflow/python/keras/engine/training.py index 89c1f1a40f..fce6cbdb7a 100644 --- a/tensorflow/python/keras/engine/training.py +++ b/tensorflow/python/keras/engine/training.py @@ -24,6 +24,7 @@ import numpy as np from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.ops import iterator_ops from tensorflow.python.eager import context +from tensorflow.python.framework import constant_op from tensorflow.python.framework import errors from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_util @@ -409,11 +410,13 @@ class Model(Network): else: if sample_weight_mode == 'temporal': sample_weights.append(array_ops.placeholder_with_default( - [[1.]], shape=[None, None], name=name + '_sample_weights')) + constant_op.constant([[1.]], dtype=K.floatx()), + shape=[None, None], name=name + '_sample_weights')) sample_weight_modes.append('temporal') else: sample_weights.append(array_ops.placeholder_with_default( - [1.], shape=[None], name=name + '_sample_weights')) + constant_op.constant([1.], dtype=K.floatx()), + shape=[None], name=name + '_sample_weights')) sample_weight_modes.append(None) self.sample_weight_modes = sample_weight_modes self._feed_sample_weight_modes = [] diff --git a/tensorflow/python/keras/engine/training_eager.py b/tensorflow/python/keras/engine/training_eager.py index 2ecbff3a1c..e8838cd3bc 100644 --- a/tensorflow/python/keras/engine/training_eager.py +++ b/tensorflow/python/keras/engine/training_eager.py @@ -732,7 +732,7 @@ def slice_arrays(arrays, indices, contiguous=True): """Slices batches out of provided arrays (workaround for eager tensors). Unfortunately eager tensors don't have the same slicing behavior as - Numpy arrays (they folow the same slicing behavior as symbolic TF tensors), + Numpy arrays (they follow the same slicing behavior as symbolic TF tensors), hence we cannot use `generic_utils.slice_arrays` directly and we have to implement this workaround based on `concat`. This has a performance cost. diff --git a/tensorflow/python/keras/initializers_test.py b/tensorflow/python/keras/initializers_test.py index a54d6da839..c519e194bd 100644 --- a/tensorflow/python/keras/initializers_test.py +++ b/tensorflow/python/keras/initializers_test.py @@ -71,7 +71,7 @@ class KerasInitializersTest(test.TestCase): stddev=1, seed=126), tensor_shape, - target_mean=0., target_std=None, target_max=2) + target_mean=0., target_max=2, target_min=-2) def test_constant(self): tensor_shape = (5, 6, 4) @@ -83,49 +83,49 @@ class KerasInitializersTest(test.TestCase): tensor_shape = (5, 6, 4, 2) with self.test_session(): fan_in, _ = init_ops._compute_fans(tensor_shape) - scale = np.sqrt(3. / fan_in) + std = np.sqrt(1. / fan_in) self._runner(keras.initializers.lecun_uniform(seed=123), tensor_shape, - target_mean=0., target_max=scale, target_min=-scale) + target_mean=0., target_std=std) def test_glorot_uniform(self): tensor_shape = (5, 6, 4, 2) with self.test_session(): fan_in, fan_out = init_ops._compute_fans(tensor_shape) - scale = np.sqrt(6. / (fan_in + fan_out)) + std = np.sqrt(2. / (fan_in + fan_out)) self._runner(keras.initializers.glorot_uniform(seed=123), tensor_shape, - target_mean=0., target_max=scale, target_min=-scale) + target_mean=0., target_std=std) def test_he_uniform(self): tensor_shape = (5, 6, 4, 2) with self.test_session(): fan_in, _ = init_ops._compute_fans(tensor_shape) - scale = np.sqrt(6. / fan_in) + std = np.sqrt(2. / fan_in) self._runner(keras.initializers.he_uniform(seed=123), tensor_shape, - target_mean=0., target_max=scale, target_min=-scale) + target_mean=0., target_std=std) def test_lecun_normal(self): tensor_shape = (5, 6, 4, 2) with self.test_session(): fan_in, _ = init_ops._compute_fans(tensor_shape) - scale = np.sqrt(1. / fan_in) + std = np.sqrt(1. / fan_in) self._runner(keras.initializers.lecun_normal(seed=123), tensor_shape, - target_mean=0., target_std=None, target_max=2 * scale) + target_mean=0., target_std=std) def test_glorot_normal(self): tensor_shape = (5, 6, 4, 2) with self.test_session(): fan_in, fan_out = init_ops._compute_fans(tensor_shape) - scale = np.sqrt(2. / (fan_in + fan_out)) + std = np.sqrt(2. / (fan_in + fan_out)) self._runner(keras.initializers.glorot_normal(seed=123), tensor_shape, - target_mean=0., target_std=None, target_max=2 * scale) + target_mean=0., target_std=std) def test_he_normal(self): tensor_shape = (5, 6, 4, 2) with self.test_session(): fan_in, _ = init_ops._compute_fans(tensor_shape) - scale = np.sqrt(2. / fan_in) + std = np.sqrt(2. / fan_in) self._runner(keras.initializers.he_normal(seed=123), tensor_shape, - target_mean=0., target_std=None, target_max=2 * scale) + target_mean=0., target_std=std) def test_orthogonal(self): tensor_shape = (20, 20) diff --git a/tensorflow/python/keras/layers/core.py b/tensorflow/python/keras/layers/core.py index 5061825d38..f60064ed63 100644 --- a/tensorflow/python/keras/layers/core.py +++ b/tensorflow/python/keras/layers/core.py @@ -19,7 +19,9 @@ from __future__ import division from __future__ import print_function import copy +import sys import types as python_types +import warnings import numpy as np @@ -714,6 +716,7 @@ class Lambda(Layer): return self.mask def get_config(self): + module = self.function.__module__ if isinstance(self.function, python_types.LambdaType): function = generic_utils.func_dump(self.function) function_type = 'lambda' @@ -721,21 +724,26 @@ class Lambda(Layer): function = self.function.__name__ function_type = 'function' + output_shape_module = None if isinstance(self._output_shape, python_types.LambdaType): output_shape = generic_utils.func_dump(self._output_shape) output_shape_type = 'lambda' + output_shape_module = self._output_shape.__module__ elif callable(self._output_shape): output_shape = self._output_shape.__name__ output_shape_type = 'function' + output_shape_module = self._output_shape.__module__ else: output_shape = self._output_shape output_shape_type = 'raw' config = { 'function': function, + 'module': module, 'function_type': function_type, 'output_shape': output_shape, 'output_shape_type': output_shape_type, + 'output_shape_module': output_shape_module, 'arguments': self.arguments } base_config = super(Lambda, self).get_config() @@ -745,8 +753,16 @@ class Lambda(Layer): def from_config(cls, config, custom_objects=None): config = config.copy() globs = globals() + module = config.pop('module', None) + if module in sys.modules: + globs.update(sys.modules[module].__dict__) + elif module is not None: + # Note: we don't know the name of the function if it's a lambda. + warnings.warn('{} is not loaded, but a Lambda layer uses it. ' + 'It may cause errors.'.format(module) + , UserWarning) if custom_objects: - globs = dict(list(globs.items()) + list(custom_objects.items())) + globs.update(custom_objects) function_type = config.pop('function_type') if function_type == 'function': # Simple lookup in custom objects @@ -760,6 +776,14 @@ class Lambda(Layer): else: raise TypeError('Unknown function type:', function_type) + output_shape_module = config.pop('output_shape_module', None) + if output_shape_module in sys.modules: + globs.update(sys.modules[output_shape_module].__dict__) + elif output_shape_module is not None: + # Note: we don't know the name of the function if it's a lambda. + warnings.warn('{} is not loaded, but a Lambda layer uses it. ' + 'It may cause errors.'.format(output_shape_module) + , UserWarning) output_shape_type = config.pop('output_shape_type') if output_shape_type == 'function': # Simple lookup in custom objects diff --git a/tensorflow/python/keras/models_test.py b/tensorflow/python/keras/models_test.py index c616d8f24f..e6e45902a8 100644 --- a/tensorflow/python/keras/models_test.py +++ b/tensorflow/python/keras/models_test.py @@ -144,5 +144,19 @@ class CheckpointingTests(test.TestCase): model.load_weights(save_prefix) self.assertEqual(12., self.evaluate(beta1_power)) +class TestModelBackend(test.TestCase): + + def test_model_backend_float64_use_cases(self): + # Test case for GitHub issue 19318 + floatx = keras.backend.floatx() + keras.backend.set_floatx('float64') + + x = keras.Input((5,)) + y = keras.layers.Dense(1)(x) + model = keras.models.Model(x, y) + model.compile('rmsprop', 'mse') + + keras.backend.set_floatx(floatx) + if __name__ == '__main__': test.main() diff --git a/tensorflow/python/kernel_tests/as_string_op_test.py b/tensorflow/python/kernel_tests/as_string_op_test.py index 9d54add264..94ed8ebd31 100644 --- a/tensorflow/python/kernel_tests/as_string_op_test.py +++ b/tensorflow/python/kernel_tests/as_string_op_test.py @@ -130,6 +130,16 @@ class AsStringOpTest(test.TestCase): result = output.eval(feed_dict={input_: int_inputs_}) self.assertAllEqual(s(result), ["%d" % x for x in int_inputs_]) + def testHalfInt(self): + s = lambda strs: [x.decode("ascii") for x in strs] + + with self.test_session(): + input_ = array_ops.placeholder(dtypes.int16) + int_inputs_ = [np.iinfo(np.int16).min, np.iinfo(np.int16).max] + output = string_ops.as_string(input_) + result = output.eval(feed_dict={input_: int_inputs_}) + self.assertAllEqual(s(result), ["%d" % x for x in int_inputs_]) + def testBool(self): bool_inputs_ = [False, True] s = lambda strs: [x.decode("ascii") for x in strs] diff --git a/tensorflow/python/kernel_tests/betainc_op_test.py b/tensorflow/python/kernel_tests/betainc_op_test.py index 08b03f8518..16fdedac41 100644 --- a/tensorflow/python/kernel_tests/betainc_op_test.py +++ b/tensorflow/python/kernel_tests/betainc_op_test.py @@ -172,7 +172,7 @@ class BetaincTest(test.TestCase): tf_gout_t = math_ops.betainc(tf_ga_s, tf_gb_s, tf_gx_s) err = gradient_checker.compute_gradient_error( [tf_gx_s], [gx_s.shape], tf_gout_t, gx_s.shape) - print("betainc gradient err = %g " % err) + tf_logging.info("betainc gradient err = %g " % err) self.assertLess(err, err_tolerance) # Test broadcast gradient @@ -181,7 +181,7 @@ class BetaincTest(test.TestCase): tf_gout_t = math_ops.betainc(tf_ga_s, tf_gb_s, tf_gx_s) err = gradient_checker.compute_gradient_error( [tf_gx_s], [()], tf_gout_t, ga_s.shape) - print("betainc gradient err = %g " % err) + tf_logging.info("betainc gradient err = %g " % err) self.assertLess(err, err_tolerance) diff --git a/tensorflow/python/kernel_tests/clip_ops_test.py b/tensorflow/python/kernel_tests/clip_ops_test.py index e08123b041..fb52d10475 100644 --- a/tensorflow/python/kernel_tests/clip_ops_test.py +++ b/tensorflow/python/kernel_tests/clip_ops_test.py @@ -18,9 +18,12 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import numpy as np + from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops from tensorflow.python.ops import clip_ops from tensorflow.python.ops import gradient_checker from tensorflow.python.platform import test @@ -414,6 +417,16 @@ class ClipTest(test.TestCase): self.assertAllClose(np_ans, tf_ans) + def testClipByValueEmptyTensor(self): + # Test case for GitHub issue 19337 + zero = array_ops.placeholder(dtype=dtypes.float32, shape=None) + x = clip_ops.clip_by_value(zero, zero, zero) + y = clip_ops.clip_by_value(zero, 1.0, 1.0) + z = clip_ops.clip_by_value(zero, zero, 1.0) + w = clip_ops.clip_by_value(zero, 1.0, zero) + with self.test_session(use_gpu=True) as sess: + sess.run([x, y, z, w], feed_dict={zero: np.zeros((7, 0))}) + if __name__ == '__main__': test.main() diff --git a/tensorflow/python/kernel_tests/conv_ops_test.py b/tensorflow/python/kernel_tests/conv_ops_test.py index 8699fd5b25..80ba7dafc9 100644 --- a/tensorflow/python/kernel_tests/conv_ops_test.py +++ b/tensorflow/python/kernel_tests/conv_ops_test.py @@ -312,8 +312,8 @@ class Conv2DTest(test.TestCase): expected_values = self.evaluate(expected_results) computed_values = self.evaluate(computed_results) for e_value, c_value in zip(expected_values, computed_values): - print("expected = ", e_value) - print("actual = ", c_value) + tf_logging.info("expected = ", e_value) + tf_logging.info("actual = ", c_value) self.assertAllClose( e_value.flatten(), c_value.flatten(), atol=tolerance, rtol=1e-4) @@ -337,8 +337,8 @@ class Conv2DTest(test.TestCase): for i in range(len(tensors)): conv = tensors[i] value = values[i] - print("expected = ", expected) - print("actual = ", value) + tf_logging.info("expected = ", expected) + tf_logging.info("actual = ", value) tol = 1e-5 if value.dtype == np.float16: tol = 1e-3 @@ -547,8 +547,8 @@ class Conv2DTest(test.TestCase): # "values" consists of two tensors for two backprops value = self.evaluate(conv) self.assertShapeEqual(value, conv) - print("expected = ", expected) - print("actual = ", value) + tf_logging.info("expected = ", expected) + tf_logging.info("actual = ", value) self.assertArrayNear(expected, value.flatten(), err) def _CompareBackpropInput(self, input_sizes, filter_sizes, output_sizes, @@ -723,8 +723,8 @@ class Conv2DTest(test.TestCase): data_format=data_format) value = self.evaluate(conv) self.assertShapeEqual(value, conv) - print("expected = ", expected) - print("actual = ", value) + tf_logging.info("expected = ", expected) + tf_logging.info("actual = ", value) self.assertArrayNear(expected, value.flatten(), 1e-5) def _CompareBackFilter(self, input_sizes, filter_sizes, output_sizes, @@ -912,8 +912,8 @@ class Conv2DTest(test.TestCase): value_2 = sess.run(conv_2) self.assertShapeEqual(value, conv) self.assertShapeEqual(value_2, conv_2) - print("expected = ", value_2) - print("actual = ", value) + tf_logging.info("expected = ", value_2) + tf_logging.info("actual = ", value) self.assertArrayNear(value_2.flatten(), value.flatten(), err) # Testing for backprops @@ -965,8 +965,8 @@ class Conv2DTest(test.TestCase): value_2 = sess.run(conv_2) self.assertShapeEqual(value, conv) self.assertShapeEqual(value_2, conv_2) - print("expected = ", value_2) - print("actual = ", value) + tf_logging.info("expected = ", value_2) + tf_logging.info("actual = ", value) self.assertArrayNear(value_2.flatten(), value.flatten(), err) def testConv2D2x2Depth3ValidBackpropFilterStride1x1Dilation2x1(self): @@ -1178,7 +1178,7 @@ class Conv2DTest(test.TestCase): # since fp16 numerical gradients are too imprecise. err = np.fabs(jacob_t - reference_jacob_t).max() - print("conv_2d gradient error = ", err) + tf_logging.info("conv_2d gradient error = ", err) self.assertLess(err, 0.002) def testInputGradientValidPaddingStrideOne(self): @@ -1546,7 +1546,7 @@ class DepthwiseConv2DTest(test.TestCase): conv = nn_impl.depthwise_conv2d( t1, t2, strides=[1, stride, stride, 1], padding=padding) value = sess.run(conv) - print("value = ", value) + tf_logging.info("value = ", value) self.assertArrayNear(expected, np.ravel(value), 1e-5) self.assertShapeEqual(value, conv) @@ -1668,7 +1668,7 @@ class SeparableConv2DTest(test.TestCase): conv = array_ops.transpose(conv, [0, 2, 3, 1]) value = sess.run(conv) - print("value = ", value) + tf_logging.info("value = ", value) self.assertArrayNear(expected, np.ravel(value), 1e-5) self.assertShapeEqual(value, conv) @@ -1826,7 +1826,7 @@ class Conv2DBenchmark(test.Benchmark): wall_time = time.time() - start self.report_benchmark( name="conv_stack_iter_%d" % iter_index, wall_time=wall_time) - print("conv_stack_iter_%d: %.4f" % (iter_index, wall_time)) + tf_logging.info("conv_stack_iter_%d: %.4f" % (iter_index, wall_time)) def GetInceptionFwdTest(input_size, filter_size, stride, padding, diff --git a/tensorflow/python/kernel_tests/gather_nd_op_test.py b/tensorflow/python/kernel_tests/gather_nd_op_test.py index 91ebe8de99..58e2a8ac2a 100644 --- a/tensorflow/python/kernel_tests/gather_nd_op_test.py +++ b/tensorflow/python/kernel_tests/gather_nd_op_test.py @@ -197,7 +197,21 @@ class GatherNdTest(test.TestCase): self.assertEqual(None, shape.ndims) self.assertEqual(None, shape[0].value) - def testBadIndices(self): + def testBadIndicesCPU(self): + with self.test_session(use_gpu=False): + params = [0, 1, 2] + indices = [[[0], [7]]] # Make this one higher rank + gather_nd = array_ops.gather_nd(params, indices) + with self.assertRaisesOpError( + r"flat indices\[1, :\] = \[7\] does not index into param " + r"\(shape: \[3\]\)"): + gather_nd.eval() + + def _disabledTestBadIndicesGPU(self): + # TODO disabled due to different behavior on GPU and CPU + # On GPU the bad indices do not raise error but fetch 0 values + if not test.is_gpu_available(): + return with self.test_session(use_gpu=True): params = [0, 1, 2] indices = [[[0], [7]]] # Make this one higher rank @@ -207,7 +221,21 @@ class GatherNdTest(test.TestCase): r"\(shape: \[3\]\)"): gather_nd.eval() - def testBadIndicesWithSlices(self): + def testBadIndicesWithSlicesCPU(self): + with self.test_session(use_gpu=False): + params = [[0, 1, 2]] + indices = [[[0], [0], [1]]] # Make this one higher rank + gather_nd = array_ops.gather_nd(params, indices) + with self.assertRaisesOpError( + r"flat indices\[2, :\] = \[1\] does not index into param " + r"\(shape: \[1,3\]\)"): + gather_nd.eval() + + def _disabledTestBadIndicesWithSlicesGPU(self): + # TODO disabled due to different behavior on GPU and CPU + # On GPU the bad indices do not raise error but fetch 0 values + if not test.is_gpu_available(): + return with self.test_session(use_gpu=True): params = [[0, 1, 2]] indices = [[[0], [0], [1]]] # Make this one higher rank diff --git a/tensorflow/python/kernel_tests/gather_op_test.py b/tensorflow/python/kernel_tests/gather_op_test.py index a2fcd751df..033fa95935 100644 --- a/tensorflow/python/kernel_tests/gather_op_test.py +++ b/tensorflow/python/kernel_tests/gather_op_test.py @@ -27,7 +27,8 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import gradients_impl from tensorflow.python.platform import test -_TEST_TYPES = (dtypes.float32, dtypes.complex64, dtypes.complex128) +_TEST_TYPES = (dtypes.int64, dtypes.float32, + dtypes.complex64, dtypes.complex128) class GatherTest(test.TestCase): @@ -122,6 +123,9 @@ class GatherTest(test.TestCase): gather, [tf_params, tf_indices, tf_axis], gather_grad) self.assertEqual(indices_grad, None) self.assertEqual(axis_grad, None) + if dtype.is_integer: + self.assertEqual(params_grad, None) + continue # For axis 0, we are able to create an efficient IndexedSlices for # the gradient. if axis == 0: @@ -177,7 +181,19 @@ class GatherTest(test.TestCase): gather_t = array_ops.gather(params, indices, axis=axis) self.assertEqual(None, gather_t.shape) - def testBadIndices(self): + def testBadIndicesCPU(self): + with self.test_session(use_gpu=False): + params = [[0, 1, 2], [3, 4, 5]] + with self.assertRaisesOpError(r"indices\[0,0\] = 7 is not in \[0, 2\)"): + array_ops.gather(params, [[7]], axis=0).eval() + with self.assertRaisesOpError(r"indices\[0,0\] = 7 is not in \[0, 3\)"): + array_ops.gather(params, [[7]], axis=1).eval() + + def _disabledTestBadIndicesGPU(self): + # TODO disabled due to different behavior on GPU and CPU + # On GPU the bad indices do not raise error but fetch 0 values + if not test.is_gpu_available(): + return with self.test_session(use_gpu=True): params = [[0, 1, 2], [3, 4, 5]] with self.assertRaisesOpError(r"indices\[0,0\] = 7 is not in \[0, 2\)"): diff --git a/tensorflow/python/kernel_tests/init_ops_test.py b/tensorflow/python/kernel_tests/init_ops_test.py index a9b55854f1..795aa67248 100644 --- a/tensorflow/python/kernel_tests/init_ops_test.py +++ b/tensorflow/python/kernel_tests/init_ops_test.py @@ -362,6 +362,33 @@ class UniformUnitScalingInitializationTest(test.TestCase): dtype=dtypes.string) +class VarianceScalingInitializationTest(test.TestCase): + + def testNormalDistribution(self): + shape = [100, 100] + expect_mean = 0. + expect_var = 1. / shape[0] + init = init_ops.variance_scaling_initializer(distribution='normal') + + with self.test_session(use_gpu=True): + x = init(shape).eval() + + self.assertNear(np.mean(x), expect_mean, err=1e-2) + self.assertNear(np.var(x), expect_var, err=1e-2) + + def testUniformDistribution(self): + shape = [100, 100] + expect_mean = 0. + expect_var = 1. / shape[0] + init = init_ops.variance_scaling_initializer(distribution='uniform') + + with self.test_session(use_gpu=True): + x = init(shape).eval() + + self.assertNear(np.mean(x), expect_mean, err=1e-2) + self.assertNear(np.var(x), expect_var, err=1e-2) + + # TODO(vrv): move to sequence_ops_test? class RangeTest(test.TestCase): diff --git a/tensorflow/python/kernel_tests/pooling_ops_test.py b/tensorflow/python/kernel_tests/pooling_ops_test.py index a0c372db7d..e95c729715 100644 --- a/tensorflow/python/kernel_tests/pooling_ops_test.py +++ b/tensorflow/python/kernel_tests/pooling_ops_test.py @@ -947,7 +947,7 @@ class PoolingTest(test.TestCase): output_sizes, x_init_value=x_init_value, delta=1e-2) - print("%s gradient error = " % func_name, err) + tf_logging.info("%s gradient error = " % func_name, err) self.assertLess(err, err_tolerance) def _ConstructAndTestSecondGradient(self, @@ -1024,7 +1024,7 @@ class PoolingTest(test.TestCase): input_sizes, x_init_value=x_init_value, delta=1e-2) - print("%s second-order gradient error = " % func_name, err) + tf_logging.info("%s second-order gradient error = " % func_name, err) self.assertLess(err, err_tolerance) def _testMaxPoolGradValidPadding1_1(self, data_format, use_gpu): diff --git a/tensorflow/python/kernel_tests/py_func_test.py b/tensorflow/python/kernel_tests/py_func_test.py index 677253946e..253e43920b 100644 --- a/tensorflow/python/kernel_tests/py_func_test.py +++ b/tensorflow/python/kernel_tests/py_func_test.py @@ -19,6 +19,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import gc import re import numpy as np @@ -434,13 +435,29 @@ class PyFuncTest(test.TestCase): # ----- Tests shared by py_func and eager_py_func ----- def testCleanup(self): - for _ in xrange(1000): - g = ops.Graph() - with g.as_default(): - c = constant_op.constant([1.], dtypes.float32) - _ = script_ops.py_func(lambda x: x + 1, [c], [dtypes.float32]) - _ = script_ops.eager_py_func(lambda x: x + 1, [c], [dtypes.float32]) - self.assertLess(script_ops._py_funcs.size(), 100) + # Delete everything created by previous tests to avoid side effects. + ops.reset_default_graph() + gc.collect() + initial_size = script_ops._py_funcs.size() + # Encapsulate the graph generation, so locals can be deleted. + def make_graphs(): + for _ in xrange(1000): + g = ops.Graph() + with g.as_default(): + c = constant_op.constant([1.], dtypes.float32) + _ = script_ops.py_func(lambda x: x + 1, [c], [dtypes.float32]) + _ = script_ops.eager_py_func(lambda x: x + 1, [c], [dtypes.float32]) + # These ops have a reference to 'c' which has a reference to the graph. + # Checks if the functions are being deleted though the graph is referenced from them. + # (see #18292) + _ = script_ops.py_func(lambda x: x + c.shape[0], [c], [dtypes.float32]) + _ = script_ops.eager_py_func(lambda x: x + c.shape[0], [c], [dtypes.float32]) + + # Call garbage collector to enforce deletion. + make_graphs() + ops.reset_default_graph() + gc.collect() + self.assertEqual(initial_size, script_ops._py_funcs.size()) # ----- Tests for eager_py_func ----- @test_util.run_in_graph_and_eager_modes() diff --git a/tensorflow/python/kernel_tests/scatter_nd_ops_test.py b/tensorflow/python/kernel_tests/scatter_nd_ops_test.py index 79fe927b8a..faa4b49a8d 100644 --- a/tensorflow/python/kernel_tests/scatter_nd_ops_test.py +++ b/tensorflow/python/kernel_tests/scatter_nd_ops_test.py @@ -144,7 +144,9 @@ class StatefulScatterNdTest(test.TestCase): self.assertAllClose(new, ref_var.eval()) def _VariableRankTests(self, np_scatter, tf_scatter): - for vtype in (np.float32, np.float64, np.complex64, np.complex128): + for vtype in (np.int32, + np.float32, np.float64, + np.complex64, np.complex128): for itype in (np.int32, np.int64): self._VariableRankTest(np_scatter, tf_scatter, vtype, itype) @@ -221,7 +223,7 @@ class StatefulScatterNdTest(test.TestCase): # self._VariableRankTests(_NumpyDiv, state_ops.scatter_nd_div) def _ScatterRepeatIndicesTest(self, np_scatter, tf_scatter): - for vtype in (np.float32, np.float64): + for vtype in (np.int32, np.float32, np.float64): for itype in (np.int32, np.int64): self._VariableRankTest( np_scatter, tf_scatter, vtype, itype, repeat_indices=True) diff --git a/tensorflow/python/kernel_tests/scatter_ops_test.py b/tensorflow/python/kernel_tests/scatter_ops_test.py index c70a4ffce7..1a0fa744ae 100644 --- a/tensorflow/python/kernel_tests/scatter_ops_test.py +++ b/tensorflow/python/kernel_tests/scatter_ops_test.py @@ -159,7 +159,13 @@ class ScatterTest(test.TestCase): # Clips small values to avoid division by zero. def clip_small_values(x): - return 1e-4 * np.sign(x) if np.abs(x) < 1e-4 else x + threshold = 1e-4 + sign = np.sign(x) + + if isinstance(x, np.int32): + threshold = 1 + sign = np.random.choice([-1, 1]) + return threshold * sign if np.abs(x) < threshold else x updates = np.vectorize(clip_small_values)(updates) old = _AsType(np.random.randn(*((first_dim,) + extra_shape)), vtype) @@ -181,7 +187,11 @@ class ScatterTest(test.TestCase): tf_scatter, repeat_indices=False, updates_are_scalar=False): - for vtype in (np.float32, np.float64): + vtypes = [np.float32, np.float64] + if tf_scatter != state_ops.scatter_div: + vtypes.append(np.int32) + + for vtype in vtypes: for itype in (np.int32, np.int64): self._VariableRankTest(tf_scatter, vtype, itype, repeat_indices, updates_are_scalar) diff --git a/tensorflow/python/kernel_tests/segment_reduction_ops_test.py b/tensorflow/python/kernel_tests/segment_reduction_ops_test.py index 794be096b7..a82855dfeb 100644 --- a/tensorflow/python/kernel_tests/segment_reduction_ops_test.py +++ b/tensorflow/python/kernel_tests/segment_reduction_ops_test.py @@ -264,7 +264,9 @@ class UnsortedSegmentTest(SegmentReductionHelper): # A subset of ops has been enabled for complex numbers self.complex_ops_list = [(np.add, None, - math_ops.unsorted_segment_sum, lambda t: 0)] + math_ops.unsorted_segment_sum, lambda t: 0), + (np.ndarray.__mul__, None, + math_ops.unsorted_segment_prod, lambda t: 1)] self.differentiable_dtypes = [dtypes_lib.float16, dtypes_lib.float32, dtypes_lib.float64] self.all_dtypes = (self.differentiable_dtypes + diff --git a/tensorflow/python/kernel_tests/string_split_op_test.py b/tensorflow/python/kernel_tests/string_split_op_test.py index a5bd1b6ee0..e20daccb28 100644 --- a/tensorflow/python/kernel_tests/string_split_op_test.py +++ b/tensorflow/python/kernel_tests/string_split_op_test.py @@ -146,5 +146,101 @@ class StringSplitOpTest(test.TestCase): self.assertAllEqual(shape, [3, 1]) +class StringSplitV2OpTest(test.TestCase): + + def testSplitV2(self): + strings = ["pigs on the wing", "animals"] + + with self.test_session() as sess: + tokens = string_ops.string_split_v2(strings) + indices, values, shape = sess.run(tokens) + self.assertAllEqual(indices, [[0, 0], [0, 1], [0, 2], [0, 3], [1, 0]]) + self.assertAllEqual(values, [b"pigs", b"on", b"the", b"wing", b"animals"]) + self.assertAllEqual(shape, [2, 4]) + + def testSplitV2MultiCharSeparator(self): + # Match Python behavior: + # >>> '1<>2<>3'.split('<>') + # ['1', '2', '3'] + # >>> "<><>4<>5<><>6<>".split("<>") + # ['', '', '4', '5', '', '6', ''] + strings = ["1<>2<>3", "<><>4<>5<><>6<>"] + + with self.test_session() as sess: + tokens = string_ops.string_split_v2(strings, sep="<>") + indices, values, shape = sess.run(tokens) + self.assertAllEqual( + indices, [[0, 0], [0, 1], [0, 2], + [1, 0], [1, 1], [1, 2], [1, 3], [1, 4], [1, 5], [1, 6]]) + self.assertAllEqual(values, [b"1", b"2", b"3", + b"", b"", b"4", b"5", b"", b"6", b""]) + self.assertAllEqual(shape, [2, 7]) + + def testSplitV2SimpleSeparator(self): + # Match Python behavior: + # >>> '1,2,3'.split(',') + # ['1', '2', '3'] + # >>> '1,2,,3,'.split(',') + # ['1', '2', '', '3', ''] + strings = ["1,2,3", "4,5,,6,"] + + with self.test_session() as sess: + tokens = string_ops.string_split_v2(strings, sep=',') + indices, values, shape = sess.run(tokens) + self.assertAllEqual(indices, [[0, 0], [0, 1], [0, 2], + [1, 0], [1, 1], [1, 2], [1, 3], [1, 4]]) + self.assertAllEqual(values, [b"1", b"2", b"3", + b"4", b"5", b"", b"6", b""]) + self.assertAllEqual(shape, [2, 5]) + + def testSplitV2EmptySeparator(self): + # Match Python behavior: + # >>> '1 2 3'.split() + # ['1', '2', '3'] + #>>> ' 1 2 3 '.split() + #['1', '2', '3'] + strings = ["1 2 3", " 4 5 6 "] + + with self.test_session() as sess: + tokens = string_ops.string_split_v2(strings) + indices, values, shape = sess.run(tokens) + self.assertAllEqual(indices, [[0, 0], [0, 1], [0, 2], + [1, 0], [1, 1], [1, 2]]) + self.assertAllEqual(values, [b"1", b"2", b"3", b"4", b"5", b"6"]) + self.assertAllEqual(shape, [2, 3]) + + def testSplitV2SimpleSeparatorMaxSplit(self): + # Match Python behavior: + # >>> '1,2,3'.split(',', maxsplit=1) + # ['1', '2,3'] + # >>> '4,5,,6,'.split(',', maxsplit=1) + # ['4', '5,,6,'] + strings = ["1,2,3", "4,5,,6,"] + + with self.test_session() as sess: + tokens = string_ops.string_split_v2(strings, sep=',', maxsplit=1) + indices, values, shape = sess.run(tokens) + self.assertAllEqual(indices, [[0, 0], [0, 1], + [1, 0], [1, 1]]) + self.assertAllEqual(values, [b"1", b"2,3", b"4", b"5,,6,"]) + self.assertAllEqual(shape, [2, 2]) + + def testSplitV2EmptySeparatorMaxSplit(self): + # Match Python behavior: + # '1 2 3'.split(maxsplit=1) + # ['1', '2 3'] + # >>> " 4 5 6 ".split(maxsplit=1) + # ['4', '5 6 '] + strings = ["1 2 3", " 4 5 6 "] + + with self.test_session() as sess: + tokens = string_ops.string_split_v2(strings, maxsplit=1) + indices, values, shape = sess.run(tokens) + self.assertAllEqual(indices, [[0, 0], [0, 1], + [1, 0], [1, 1]]) + self.assertAllEqual(values, [b"1", b"2 3", b"4", b"5 6 "]) + self.assertAllEqual(shape, [2, 2]) + + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py index 8129334703..fae63b1132 100644 --- a/tensorflow/python/ops/array_ops.py +++ b/tensorflow/python/ops/array_ops.py @@ -2619,6 +2619,10 @@ reverse.__doc__ = gen_array_ops.reverse_v2.__doc__ # pylint: disable=redefined-builtin @tf_export("reverse_sequence") +@deprecation.deprecated_args( + None, "seq_dim is deprecated, use seq_axis instead", "seq_dim") +@deprecation.deprecated_args( + None, "batch_dim is deprecated, use batch_axis instead", "batch_dim") def reverse_sequence(input, seq_lengths, seq_axis=None, diff --git a/tensorflow/python/ops/gradient_checker.py b/tensorflow/python/ops/gradient_checker.py index 12afcd0b51..94c8d79335 100644 --- a/tensorflow/python/ops/gradient_checker.py +++ b/tensorflow/python/ops/gradient_checker.py @@ -283,10 +283,10 @@ def compute_gradient(x, numbers. For example, if `x` is complex with shape `[m]` and `y` is complex with shape `[n]`, each Jacobian `J` will have shape `[m * 2, n * 2]` with - J[:m, :n] = d(Re y)/d(Re x) - J[:m, n:] = d(Im y)/d(Re x) - J[m:, :n] = d(Re y)/d(Im x) - J[m:, n:] = d(Im y)/d(Im x) + J[::2, ::2] = d(Re y)/d(Re x) + J[::2, 1::2] = d(Im y)/d(Re x) + J[1::2, ::2] = d(Re y)/d(Im x) + J[1::2, 1::2] = d(Im y)/d(Im x) Args: x: a tensor or list of tensors diff --git a/tensorflow/python/ops/image_ops_impl.py b/tensorflow/python/ops/image_ops_impl.py index bdcf420980..f27d9224c1 100644 --- a/tensorflow/python/ops/image_ops_impl.py +++ b/tensorflow/python/ops/image_ops_impl.py @@ -28,6 +28,7 @@ from tensorflow.python.framework import tensor_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import check_ops from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import functional_ops from tensorflow.python.ops import gen_image_ops from tensorflow.python.ops import gen_nn_ops from tensorflow.python.ops import math_ops @@ -258,14 +259,14 @@ def random_flip_up_down(image, seed=None): dimension, which is `height`. Otherwise output the image as-is. Args: - image: A 3-D tensor of shape `[height, width, channels].` + image: 4-D Tensor of shape `[batch, height, width, channels]` or + 3-D Tensor of shape `[height, width, channels]`. seed: A Python integer. Used to create a random seed. See @{tf.set_random_seed} for behavior. Returns: - A 3-D tensor of the same type and shape as `image`. - + A tensor of the same type and shape as `image`. Raises: ValueError: if the shape of `image` not supported. """ @@ -280,13 +281,14 @@ def random_flip_left_right(image, seed=None): second dimension, which is `width`. Otherwise output the image as-is. Args: - image: A 3-D tensor of shape `[height, width, channels].` + image: 4-D Tensor of shape `[batch, height, width, channels]` or + 3-D Tensor of shape `[height, width, channels]`. seed: A Python integer. Used to create a random seed. See @{tf.set_random_seed} for behavior. Returns: - A 3-D tensor of the same type and shape as `image`. + A tensor of the same type and shape as `image`. Raises: ValueError: if the shape of `image` not supported. @@ -297,7 +299,8 @@ def random_flip_left_right(image, seed=None): def _random_flip(image, flip_index, seed, scope_name): """Randomly (50% chance) flip an image along axis `flip_index`. Args: - image: A 3-D tensor of shape `[height, width, channels].` + image: 4-D Tensor of shape `[batch, height, width, channels]` or + 3-D Tensor of shape `[height, width, channels]`. flip_index: The dimension along which to flip the image. Vertical: 0, Horizontal: 1 seed: A Python integer. Used to create a random seed. See @@ -306,22 +309,37 @@ def _random_flip(image, flip_index, seed, scope_name): scope_name: Name of the scope in which the ops are added. Returns: - A 3-D tensor of the same type and shape as `image`. + A tensor of the same type and shape as `image`. Raises: ValueError: if the shape of `image` not supported. """ with ops.name_scope(None, scope_name, [image]) as scope: image = ops.convert_to_tensor(image, name='image') - image = _Assert3DImage(image) - uniform_random = random_ops.random_uniform([], 0, 1.0, seed=seed) - mirror_cond = math_ops.less(uniform_random, .5) - result = control_flow_ops.cond( - mirror_cond, - lambda: array_ops.reverse(image, [flip_index]), - lambda: image, - name=scope) - return fix_image_flip_shape(image, result) + image = _AssertAtLeast3DImage(image) + shape = image.get_shape() + if shape.ndims == 3 or shape.ndims is None: + uniform_random = random_ops.random_uniform([], 0, 1.0, seed=seed) + mirror_cond = math_ops.less(uniform_random, .5) + result = control_flow_ops.cond( + mirror_cond, + lambda: array_ops.reverse(image, [flip_index]), + lambda: image, + name=scope + ) + return fix_image_flip_shape(image, result) + elif shape.ndims == 4: + uniform_random = random_ops.random_uniform( + [array_ops.shape(image)[0]], 0, 1.0, seed=seed + ) + mirror_cond = math_ops.less(uniform_random, .5) + return array_ops.where( + mirror_cond, + image, + functional_ops.map_fn(lambda x: array_ops.reverse(x, [flip_index]), image, dtype=image.dtype) + ) + else: + raise ValueError('\'image\' must have either 3 or 4 dimensions.') @tf_export('image.flip_left_right') @@ -1634,13 +1652,13 @@ def is_jpeg(contents, name=None): @tf_export('image.decode_image') -def decode_image(contents, channels=None, name=None): +def decode_image(contents, channels=None, dtype=dtypes.uint8, name=None): """Convenience function for `decode_bmp`, `decode_gif`, `decode_jpeg`, and `decode_png`. Detects whether an image is a BMP, GIF, JPEG, or PNG, and performs the - appropriate operation to convert the input bytes `string` into a `Tensor` of - type `uint8`. + appropriate operation to convert the input bytes `string` into a `Tensor` + of type `dtype`. Note: `decode_gif` returns a 4-D array `[num_frames, height, width, 3]`, as opposed to `decode_bmp`, `decode_jpeg` and `decode_png`, which return 3-D @@ -1652,10 +1670,11 @@ def decode_image(contents, channels=None, name=None): contents: 0-D `string`. The encoded image bytes. channels: An optional `int`. Defaults to `0`. Number of color channels for the decoded image. + dtype: The desired DType of the returned `Tensor`. name: A name for the operation (optional) Returns: - `Tensor` with type `uint8` with shape `[height, width, num_channels]` for + `Tensor` with type `dtype` and shape `[height, width, num_channels]` for BMP, JPEG, and PNG images and shape `[num_frames, height, width, 3]` for GIF images. @@ -1679,7 +1698,7 @@ def decode_image(contents, channels=None, name=None): channels_msg = 'Channels must be in (None, 0, 3) when decoding BMP images' assert_channels = control_flow_ops.Assert(good_channels, [channels_msg]) with ops.control_dependencies([assert_decode, assert_channels]): - return gen_image_ops.decode_bmp(contents) + return convert_image_dtype(gen_image_ops.decode_bmp(contents), dtype) def _gif(): # Create assert to make sure that channels is not set to 1 @@ -1692,7 +1711,7 @@ def decode_image(contents, channels=None, name=None): channels_msg = 'Channels must be in (None, 0, 3) when decoding GIF images' assert_channels = control_flow_ops.Assert(good_channels, [channels_msg]) with ops.control_dependencies([assert_channels]): - return gen_image_ops.decode_gif(contents) + return convert_image_dtype(gen_image_ops.decode_gif(contents), dtype) def check_gif(): # Create assert op to check that bytes are GIF decodable @@ -1701,7 +1720,11 @@ def decode_image(contents, channels=None, name=None): def _png(): """Decodes a PNG image.""" - return gen_image_ops.decode_png(contents, channels) + return convert_image_dtype( + gen_image_ops.decode_png(contents, channels, + dtype=dtypes.uint8 + if dtype == dtypes.uint8 + else dtypes.uint16), dtype) def check_png(): """Checks if an image is PNG.""" @@ -1717,7 +1740,8 @@ def decode_image(contents, channels=None, name=None): 'images') assert_channels = control_flow_ops.Assert(good_channels, [channels_msg]) with ops.control_dependencies([assert_channels]): - return gen_image_ops.decode_jpeg(contents, channels) + return convert_image_dtype( + gen_image_ops.decode_jpeg(contents, channels), dtype) # Decode normal JPEG images (start with \xff\xd8\xff\xe0) # as well as JPEG images with EXIF data (start with \xff\xd8\xff\xe1). @@ -1878,7 +1902,7 @@ def sample_distorted_bounding_box(image_size, width / height within this range. area_range: An optional list of `floats`. Defaults to `[0.05, 1]`. The cropped area of the image must contain a fraction of the - supplied image within in this range. + supplied image within this range. max_attempts: An optional `int`. Defaults to `100`. Number of attempts at generating a cropped region of the image of the specified constraints. After `max_attempts` failures, return the diff --git a/tensorflow/python/ops/image_ops_test.py b/tensorflow/python/ops/image_ops_test.py index 45499dcce0..2a6ab26e96 100644 --- a/tensorflow/python/ops/image_ops_test.py +++ b/tensorflow/python/ops/image_ops_test.py @@ -533,6 +533,37 @@ class FlipImageBenchmark(test.Benchmark): iters=benchmark_rounds, wall_time=step_time) + def _benchmarkBatchedRandomFlipLeftRight(self, device, cpu_count): + image_shape = [16, 299, 299, 3] + warmup_rounds = 100 + benchmark_rounds = 1000 + config = config_pb2.ConfigProto() + if cpu_count is not None: + config.inter_op_parallelism_threads = 1 + config.intra_op_parallelism_threads = cpu_count + with session.Session("", graph=ops.Graph(), config=config) as sess: + with ops.device(device): + inputs = variables.Variable( + random_ops.random_uniform(image_shape, dtype=dtypes.float32) * 255, + trainable=False, + dtype=dtypes.float32) + run_op = image_ops.random_flip_left_right(inputs) + sess.run(variables.global_variables_initializer()) + for i in xrange(warmup_rounds + benchmark_rounds): + if i == warmup_rounds: + start = time.time() + sess.run(run_op) + end = time.time() + step_time = (end - start) / benchmark_rounds + tag = device + "_%s" % (cpu_count if cpu_count is not None else "_all") + print("benchmarkBatchedRandomFlipLeftRight_16_299_299_3_%s step_time: " + "%.2f us" % + (tag, step_time * 1e6)) + self.report_benchmark( + name="benchmarkBatchedRandomFlipLeftRight_16_299_299_3_%s" % (tag), + iters=benchmark_rounds, + wall_time=step_time) + def benchmarkFlipLeftRightCpu1(self): self._benchmarkFlipLeftRight("/cpu:0", 1) @@ -551,6 +582,15 @@ class FlipImageBenchmark(test.Benchmark): def benchmarkRandomFlipLeftRightGpu(self): self._benchmarkRandomFlipLeftRight(test.gpu_device_name(), None) + def benchmarkBatchedRandomFlipLeftRightCpu1(self): + self._benchmarkBatchedRandomFlipLeftRight("/cpu:0", 1) + + def benchmarkBatchedRandomFlipLeftRightCpuAll(self): + self._benchmarkBatchedRandomFlipLeftRight("/cpu:0", None) + + def benchmarkBatchedRandomFlipLeftRightGpu(self): + self._benchmarkBatchedRandomFlipLeftRight(test.gpu_device_name(), None) + class AdjustHueBenchmark(test.Benchmark): @@ -987,7 +1027,7 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): with self.test_session(use_gpu=True): x_tf = constant_op.constant(x_np, shape=x_np.shape) - y = image_ops.random_flip_left_right(x_tf) + y = image_ops.random_flip_left_right(x_tf, seed=seed) self.assertTrue(y.op.name.startswith("random_flip_left_right")) count_flipped = 0 @@ -1008,6 +1048,50 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): self.assertGreaterEqual(count_flipped, 20) self.assertGreaterEqual(count_unflipped, 20) + def testRandomFlipLeftRightWithBatch(self): + batch_size = 16 + seed = 42 + + # create single item of test data + x_np_raw = np.array( + [[1, 2, 3], [1, 2, 3]], dtype=np.uint8 + ).reshape([1, 2, 3, 1]) + y_np_raw = np.array( + [[3, 2, 1], [3, 2, 1]], dtype=np.uint8 + ).reshape([1, 2, 3, 1]) + + # create batched test data + x_np = np.vstack([x_np_raw for _ in range(batch_size)]) + y_np = np.vstack([y_np_raw for _ in range(batch_size)]) + + with self.test_session(use_gpu=True): + x_tf = constant_op.constant(x_np, shape=x_np.shape) + y = image_ops.random_flip_left_right(x_tf, seed=seed) + self.assertTrue(y.op.name.startswith("random_flip_left_right")) + + count_flipped = 0 + count_unflipped = 0 + for _ in range(100): + y_tf = y.eval() + + # check every element of the batch + for i in range(batch_size): + if y_tf[i][0][0] == 1: + self.assertAllEqual(y_tf[i], x_np[i]) + count_unflipped += 1 + else: + self.assertAllEqual(y_tf[i], y_np[i]) + count_flipped += 1 + + # 100 trials, each containing batch_size elements + # Mean: 50 * batch_size + # Std Dev: ~5 * sqrt(batch_size) + # Six Sigma: 50 * batch_size - (5 * 6 * sqrt(batch_size)) + # = 50 * batch_size - 30 * sqrt(batch_size) = 800 - 30 * 4 = 680 + six_sigma = 50 * batch_size - 30 * np.sqrt(batch_size) + self.assertGreaterEqual(count_flipped, six_sigma) + self.assertGreaterEqual(count_unflipped, six_sigma) + def testInvolutionUpDown(self): x_np = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.uint8).reshape([2, 3, 1]) @@ -1057,9 +1141,11 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): x_np = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.uint8).reshape([2, 3, 1]) y_np = np.array([[4, 5, 6], [1, 2, 3]], dtype=np.uint8).reshape([2, 3, 1]) + seed = 42 + with self.test_session(use_gpu=True): x_tf = constant_op.constant(x_np, shape=x_np.shape) - y = image_ops.random_flip_up_down(x_tf, seed=42) + y = image_ops.random_flip_up_down(x_tf, seed=seed) self.assertTrue(y.op.name.startswith("random_flip_up_down")) count_flipped = 0 count_unflipped = 0 @@ -1079,6 +1165,50 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): self.assertGreaterEqual(count_flipped, 20) self.assertGreaterEqual(count_unflipped, 20) + def testRandomFlipUpDownWithBatch(self): + batch_size = 16 + seed = 42 + + # create single item of test data + x_np_raw = np.array( + [[1, 2, 3], [4, 5, 6]], dtype=np.uint8 + ).reshape([1, 2, 3, 1]) + y_np_raw = np.array( + [[4, 5, 6], [1, 2, 3]], dtype=np.uint8 + ).reshape([1, 2, 3, 1]) + + # create batched test data + x_np = np.vstack([x_np_raw for _ in range(batch_size)]) + y_np = np.vstack([y_np_raw for _ in range(batch_size)]) + + with self.test_session(use_gpu=True): + x_tf = constant_op.constant(x_np, shape=x_np.shape) + y = image_ops.random_flip_up_down(x_tf, seed=seed) + self.assertTrue(y.op.name.startswith("random_flip_up_down")) + + count_flipped = 0 + count_unflipped = 0 + for _ in range(100): + y_tf = y.eval() + + # check every element of the batch + for i in range(batch_size): + if y_tf[i][0][0] == 1: + self.assertAllEqual(y_tf[i], x_np[i]) + count_unflipped += 1 + else: + self.assertAllEqual(y_tf[i], y_np[i]) + count_flipped += 1 + + # 100 trials, each containing batch_size elements + # Mean: 50 * batch_size + # Std Dev: ~5 * sqrt(batch_size) + # Six Sigma: 50 * batch_size - (5 * 6 * sqrt(batch_size)) + # = 50 * batch_size - 30 * sqrt(batch_size) = 800 - 30 * 4 = 680 + six_sigma = 50 * batch_size - 30 * np.sqrt(batch_size) + self.assertGreaterEqual(count_flipped, six_sigma) + self.assertGreaterEqual(count_unflipped, six_sigma) + def testInvolutionTranspose(self): x_np = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.uint8).reshape([2, 3, 1]) @@ -1156,6 +1286,7 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): #Ops that support 4D input for op in [ image_ops.flip_left_right, image_ops.flip_up_down, + image_ops.random_flip_left_right, image_ops.random_flip_up_down, image_ops.transpose_image, image_ops.rot90 ]: transformed_unknown_dims_4 = op(p_unknown_dims_4) @@ -1166,14 +1297,6 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): "must be at least three-dimensional"): op(p_wrong_rank) - for op in [ - image_ops.random_flip_left_right, - image_ops.random_flip_up_down, - ]: - with self.assertRaisesRegexp(ValueError, "must be three-dimensional"): - op(p_wrong_rank) - - def testRot90GroupOrder(self): image = np.arange(24, dtype=np.uint8).reshape([2, 4, 3]) with self.test_session(use_gpu=True): @@ -1208,41 +1331,6 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): y_np = np.rot90(image, k=k, axes=(1, 2)) self.assertAllEqual(y_np, y_tf.eval({k_placeholder: k})) -class RandomFlipTest(test_util.TensorFlowTestCase): - - def testRandomLeftRight(self): - x_np = np.array([0, 1], dtype=np.uint8).reshape([1, 2, 1]) - num_iterations = 500 - - hist = [0, 0] - with self.test_session(use_gpu=True): - x_tf = constant_op.constant(x_np, shape=x_np.shape) - y = image_ops.random_flip_left_right(x_tf) - for _ in xrange(num_iterations): - y_np = y.eval().flatten()[0] - hist[y_np] += 1 - - # Ensure that each entry is observed within 4 standard deviations. - four_stddev = 4.0 * np.sqrt(num_iterations / 2.0) - self.assertAllClose(hist, [num_iterations / 2.0] * 2, atol=four_stddev) - - def testRandomUpDown(self): - x_np = np.array([0, 1], dtype=np.uint8).reshape([2, 1, 1]) - num_iterations = 500 - - hist = [0, 0] - with self.test_session(use_gpu=True): - x_tf = constant_op.constant(x_np, shape=x_np.shape) - y = image_ops.random_flip_up_down(x_tf) - for _ in xrange(num_iterations): - y_np = y.eval().flatten()[0] - hist[y_np] += 1 - - # Ensure that each entry is observed within 4 standard deviations. - four_stddev = 4.0 * np.sqrt(num_iterations / 2.0) - self.assertAllClose(hist, [num_iterations / 2.0] * 2, atol=four_stddev) - - class AdjustContrastTest(test_util.TensorFlowTestCase): def _testContrast(self, x_np, y_np, contrast_factor): @@ -3880,5 +3968,88 @@ class SobelEdgesTest(test_util.TensorFlowTestCase): self.assertAllClose(expected_batch, actual_sobel) +class DecodeImageTest(test_util.TensorFlowTestCase): + + def testJpegUint16(self): + with self.test_session(use_gpu=True) as sess: + base = "tensorflow/core/lib/jpeg/testdata" + jpeg0 = io_ops.read_file(os.path.join(base, "jpeg_merge_test1.jpg")) + image0 = image_ops.decode_image(jpeg0, dtype=dtypes.uint16) + image1 = image_ops.convert_image_dtype(image_ops.decode_jpeg(jpeg0), + dtypes.uint16) + image0, image1 = sess.run([image0, image1]) + self.assertAllEqual(image0, image1) + + def testPngUint16(self): + with self.test_session(use_gpu=True) as sess: + base = "tensorflow/core/lib/png/testdata" + png0 = io_ops.read_file(os.path.join(base, "lena_rgba.png")) + image0 = image_ops.decode_image(png0, dtype=dtypes.uint16) + image1 = image_ops.convert_image_dtype( + image_ops.decode_png(png0, dtype=dtypes.uint16), dtypes.uint16) + image0, image1 = sess.run([image0, image1]) + self.assertAllEqual(image0, image1) + + def testGifUint16(self): + with self.test_session(use_gpu=True) as sess: + base = "tensorflow/core/lib/gif/testdata" + gif0 = io_ops.read_file(os.path.join(base, "scan.gif")) + image0 = image_ops.decode_image(gif0, dtype=dtypes.uint16) + image1 = image_ops.convert_image_dtype(image_ops.decode_gif(gif0), + dtypes.uint16) + image0, image1 = sess.run([image0, image1]) + self.assertAllEqual(image0, image1) + + def testBmpUint16(self): + with self.test_session(use_gpu=True) as sess: + base = "tensorflow/core/lib/bmp/testdata" + bmp0 = io_ops.read_file(os.path.join(base, "lena.bmp")) + image0 = image_ops.decode_image(bmp0, dtype=dtypes.uint16) + image1 = image_ops.convert_image_dtype(image_ops.decode_bmp(bmp0), + dtypes.uint16) + image0, image1 = sess.run([image0, image1]) + self.assertAllEqual(image0, image1) + + def testJpegFloat32(self): + with self.test_session(use_gpu=True) as sess: + base = "tensorflow/core/lib/jpeg/testdata" + jpeg0 = io_ops.read_file(os.path.join(base, "jpeg_merge_test1.jpg")) + image0 = image_ops.decode_image(jpeg0, dtype=dtypes.float32) + image1 = image_ops.convert_image_dtype(image_ops.decode_jpeg(jpeg0), + dtypes.float32) + image0, image1 = sess.run([image0, image1]) + self.assertAllEqual(image0, image1) + + def testPngFloat32(self): + with self.test_session(use_gpu=True) as sess: + base = "tensorflow/core/lib/png/testdata" + png0 = io_ops.read_file(os.path.join(base, "lena_rgba.png")) + image0 = image_ops.decode_image(png0, dtype=dtypes.float32) + image1 = image_ops.convert_image_dtype( + image_ops.decode_png(png0, dtype=dtypes.uint16), dtypes.float32) + image0, image1 = sess.run([image0, image1]) + self.assertAllEqual(image0, image1) + + def testGifFloat32(self): + with self.test_session(use_gpu=True) as sess: + base = "tensorflow/core/lib/gif/testdata" + gif0 = io_ops.read_file(os.path.join(base, "scan.gif")) + image0 = image_ops.decode_image(gif0, dtype=dtypes.float32) + image1 = image_ops.convert_image_dtype(image_ops.decode_gif(gif0), + dtypes.float32) + image0, image1 = sess.run([image0, image1]) + self.assertAllEqual(image0, image1) + + def testBmpFloat32(self): + with self.test_session(use_gpu=True) as sess: + base = "tensorflow/core/lib/bmp/testdata" + bmp0 = io_ops.read_file(os.path.join(base, "lena.bmp")) + image0 = image_ops.decode_image(bmp0, dtype=dtypes.float32) + image1 = image_ops.convert_image_dtype(image_ops.decode_bmp(bmp0), + dtypes.float32) + image0, image1 = sess.run([image0, image1]) + self.assertAllEqual(image0, image1) + + if __name__ == "__main__": googletest.main() diff --git a/tensorflow/python/ops/init_ops.py b/tensorflow/python/ops/init_ops.py index 2df230d470..724fcc39cd 100644 --- a/tensorflow/python/ops/init_ops.py +++ b/tensorflow/python/ops/init_ops.py @@ -467,7 +467,8 @@ class VarianceScaling(Initializer): else: scale /= max(1., (fan_in + fan_out) / 2.) if self.distribution == "normal": - stddev = math.sqrt(scale) + # constant taken from scipy.stats.truncnorm.std(a=-2, b=2, loc=0., scale=1.) + stddev = math.sqrt(scale) / .87962566103423978 return random_ops.truncated_normal( shape, 0.0, stddev, dtype, seed=self.seed) else: diff --git a/tensorflow/python/ops/logging_ops.py b/tensorflow/python/ops/logging_ops.py index 222b8ebc9d..8276047cb6 100644 --- a/tensorflow/python/ops/logging_ops.py +++ b/tensorflow/python/ops/logging_ops.py @@ -35,8 +35,9 @@ from tensorflow.python.util.tf_export import tf_export # Assert and Print are special symbols in python, so we must -# use an upper-case version of them. -@tf_export("Print") +# have an upper-case version of them. For users with Python 3 or Python 2.7 +# with `from __future__ import print_function`, we also allow lowercase. +@tf_export("Print", "print") def Print(input_, data, message=None, first_n=None, summarize=None, name=None): """Prints a list of tensors. diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py index e40481f3a7..466d0dadc8 100644 --- a/tensorflow/python/ops/math_ops.py +++ b/tensorflow/python/ops/math_ops.py @@ -125,8 +125,8 @@ def abs(x, name=None): # pylint: disable=redefined-builtin ``` Args: - x: A `Tensor` or `SparseTensor` of type `float32`, `float64`, `int32`, - `int64`, `complex64` or `complex128`. + x: A `Tensor` or `SparseTensor` of type `float16`, `float32`, `float64`, + `int32`, `int64`, `complex64` or `complex128`. name: A name for the operation (optional). Returns: @@ -430,10 +430,10 @@ def pow(x, y, name=None): # pylint: disable=redefined-builtin ``` Args: - x: A `Tensor` of type `float32`, `float64`, `int32`, `int64`, `complex64`, - or `complex128`. - y: A `Tensor` of type `float32`, `float64`, `int32`, `int64`, `complex64`, - or `complex128`. + x: A `Tensor` of type `float16`, `float32`, `float64`, `int32`, `int64`, + `complex64`, or `complex128`. + y: A `Tensor` of type `float16`, `float32`, `float64`, `int32`, `int64`, + `complex64`, or `complex128`. name: A name for the operation (optional). Returns: @@ -600,7 +600,7 @@ def round(x, name=None): # pylint: disable=redefined-builtin ``` Args: - x: A `Tensor` of type `float32` or `float64`. + x: A `Tensor` of type `float16`, `float32`, `float64`, `int32`, or `int64`. name: A name for the operation (optional). Returns: @@ -1257,7 +1257,7 @@ def reduce_sum(input_tensor, entry in `axis`. If `keepdims` is true, the reduced dimensions are retained with length 1. - If `axis` has no entries, all dimensions are reduced, and a + If `axis` is None, all dimensions are reduced, and a tensor with a single element is returned. For example: @@ -1397,7 +1397,7 @@ def reduce_mean(input_tensor, entry in `axis`. If `keepdims` is true, the reduced dimensions are retained with length 1. - If `axis` has no entries, all dimensions are reduced, and a + If `axis` is None, all dimensions are reduced, and a tensor with a single element is returned. For example: @@ -1469,7 +1469,7 @@ def reduce_prod(input_tensor, entry in `axis`. If `keepdims` is true, the reduced dimensions are retained with length 1. - If `axis` has no entries, all dimensions are reduced, and a + If `axis` is None, all dimensions are reduced, and a tensor with a single element is returned. Args: @@ -1519,7 +1519,7 @@ def reduce_min(input_tensor, entry in `axis`. If `keepdims` is true, the reduced dimensions are retained with length 1. - If `axis` has no entries, all dimensions are reduced, and a + If `axis` is None, all dimensions are reduced, and a tensor with a single element is returned. Args: @@ -1568,7 +1568,7 @@ def reduce_max(input_tensor, entry in `axis`. If `keepdims` is true, the reduced dimensions are retained with length 1. - If `axis` has no entries, all dimensions are reduced, and a + If `axis` is None, all dimensions are reduced, and a tensor with a single element is returned. Args: @@ -1617,7 +1617,7 @@ def reduce_all(input_tensor, entry in `axis`. If `keepdims` is true, the reduced dimensions are retained with length 1. - If `axis` has no entries, all dimensions are reduced, and a + If `axis` is None, all dimensions are reduced, and a tensor with a single element is returned. For example: @@ -1675,7 +1675,7 @@ def reduce_any(input_tensor, entry in `axis`. If `keepdims` is true, the reduced dimensions are retained with length 1. - If `axis` has no entries, all dimensions are reduced, and a + If `axis` is None, all dimensions are reduced, and a tensor with a single element is returned. For example: diff --git a/tensorflow/python/ops/nn_impl.py b/tensorflow/python/ops/nn_impl.py index 783d485892..f47f38e29e 100644 --- a/tensorflow/python/ops/nn_impl.py +++ b/tensorflow/python/ops/nn_impl.py @@ -621,7 +621,7 @@ def normalize_moments(counts, mean_ss, variance_ss, shift, name=None): """Calculate the mean and variance of based on the sufficient statistics. Args: - counts: A `Tensor` containing a the total count of the data (one value). + counts: A `Tensor` containing the total count of the data (one value). mean_ss: A `Tensor` containing the mean sufficient statistics: the (possibly shifted) sum of the elements to average over. variance_ss: A `Tensor` containing the variance sufficient statistics: the @@ -689,6 +689,9 @@ def moments( # Compute true mean while keeping the dims for proper broadcasting. mean = math_ops.reduce_mean(y, axes, keepdims=True, name="mean") # sample variance, not unbiased variance + # Note: stop_gradient does not change the gradient that gets + # backpropagated to the mean from the variance calculation, + # because that gradient is zero variance = math_ops.reduce_mean( math_ops.squared_difference(y, array_ops.stop_gradient(mean)), axes, diff --git a/tensorflow/python/ops/nn_ops.py b/tensorflow/python/ops/nn_ops.py index a0b55eb077..0c2f5b06c4 100644 --- a/tensorflow/python/ops/nn_ops.py +++ b/tensorflow/python/ops/nn_ops.py @@ -1596,12 +1596,12 @@ def leaky_relu(features, alpha=0.2, name=None): Returns: The activation value. """ - with ops.name_scope(name, "LeakyRelu", [features, alpha]): + with ops.name_scope(name, "LeakyRelu", [features, alpha]) as name: features = ops.convert_to_tensor(features, name="features") if features.dtype.is_integer: features = math_ops.to_float(features) alpha = ops.convert_to_tensor(alpha, dtype=features.dtype, name="alpha") - return math_ops.maximum(alpha * features, features) + return math_ops.maximum(alpha * features, features, name=name) def _flatten_outer_dims(logits): diff --git a/tensorflow/python/ops/nn_test.py b/tensorflow/python/ops/nn_test.py index 46a5f4fae6..035b4735af 100644 --- a/tensorflow/python/ops/nn_test.py +++ b/tensorflow/python/ops/nn_test.py @@ -962,6 +962,16 @@ class LeakyReluTest(test_lib.TestCase): self.assertAllClose( outputs, [-0.4, -0.2, 0.0, 1.0, 2.0], rtol=tol, atol=tol) + def testName(self): + np_values = np.array([-2, -1, 0, 1, 2], dtype=np.float64) + outputs_with_name_set = nn_ops.leaky_relu( + constant_op.constant(np_values), + name='test_relu_op') + self.assertEqual(outputs_with_name_set.name, 'test_relu_op:0') + outputs_without_name_set = nn_ops.leaky_relu( + constant_op.constant(np_values)) + self.assertEqual(outputs_without_name_set.name, 'LeakyRelu:0') + class SwishTest(test_lib.TestCase): diff --git a/tensorflow/python/ops/script_ops.py b/tensorflow/python/ops/script_ops.py index f8676ccb5f..219562de5d 100644 --- a/tensorflow/python/ops/script_ops.py +++ b/tensorflow/python/ops/script_ops.py @@ -23,6 +23,7 @@ import threading # Used by py_util.cc to get tracebacks. import traceback # pylint: disable=unused-import +import weakref import numpy as np import six @@ -129,11 +130,14 @@ class FuncRegistry(object): def __init__(self): self._lock = threading.Lock() self._unique_id = 0 # GUARDED_BY(self._lock) - self._funcs = {} + # Only store weakrefs to the funtions. The strong reference is stored in + # the graph. + self._funcs = weakref.WeakValueDictionary() def insert(self, func): """Registers `func` and returns a unique token for this entry.""" token = self._next_unique_token() + # Store a weakref to the function self._funcs[token] = func return token @@ -186,7 +190,7 @@ class FuncRegistry(object): Raises: ValueError: if no function is registered for `token`. """ - func = self._funcs[token] + func = self._funcs.get(token, None) if func is None: raise ValueError("callback %s is not found" % token) if isinstance(func, EagerFunc): @@ -228,19 +232,6 @@ _py_funcs = FuncRegistry() pywrap_tensorflow.InitializePyTrampoline(_py_funcs) -class CleanupFunc(object): - """A helper class to remove a registered function from _py_funcs.""" - - def __init__(self, token): - self._token = token - - def __del__(self): - if _py_funcs is not None: - # If _py_funcs is None, the program is most likely in shutdown, and the - # _py_funcs object has been destroyed already. - _py_funcs.remove(self._token) - - def _internal_py_func(func, inp, Tout, @@ -270,17 +261,15 @@ def _internal_py_func(func, # bound to that of the outer graph instead. graph = graph._outer_graph - cleanup = CleanupFunc(token) - # TODO(zhifengc): Consider adding a Graph method to collect # `cleanup` objects in one of its member. - if not hasattr(graph, "_cleanup_py_funcs_used_in_graph"): - graph._cleanup_py_funcs_used_in_graph = [] + if not hasattr(graph, "_py_funcs_used_in_graph"): + graph._py_funcs_used_in_graph = [] - # When `graph` is destroyed, elements in _cleanup_py_funcs_used_in_graph - # will be destroyed and their __del__ will remove the 'token' from - # the funcs registry. - graph._cleanup_py_funcs_used_in_graph.append(cleanup) + # Store a reference to the function in the graph to ensure it stays alive + # as long as the graph lives. When the graph is destroyed, the function + # is left to the garbage collector for destruction as well. + graph._py_funcs_used_in_graph.append(func) # pylint: enable=protected-access if eager: diff --git a/tensorflow/python/ops/sparse_ops.py b/tensorflow/python/ops/sparse_ops.py index 0130233746..c3b16a7bd5 100644 --- a/tensorflow/python/ops/sparse_ops.py +++ b/tensorflow/python/ops/sparse_ops.py @@ -84,6 +84,8 @@ def _convert_to_sparse_tensors(sp_inputs): # pylint: disable=protected-access @tf_export("sparse_concat") +@deprecation.deprecated_args( + None, "concat_dim is deprecated, use axis instead", "concat_dim") def sparse_concat(axis, sp_inputs, name=None, @@ -597,6 +599,8 @@ class KeywordRequired(object): @tf_export("sparse_split") +@deprecation.deprecated_args( + None, "split_dim is deprecated, use axis instead", "split_dim") def sparse_split(keyword_required=KeywordRequired(), sp_input=None, num_split=None, diff --git a/tensorflow/python/ops/string_ops.py b/tensorflow/python/ops/string_ops.py index ae79c01949..0280c89c10 100644 --- a/tensorflow/python/ops/string_ops.py +++ b/tensorflow/python/ops/string_ops.py @@ -91,6 +91,59 @@ def string_split(source, delimiter=" ", skip_empty=True): # pylint: disable=inv shape.set_shape([2]) return sparse_tensor.SparseTensor(indices, values, shape) +@tf_export("strings.split") +def string_split_v2(source, sep=None, maxsplit=-1): + """Split elements of `source` based on `sep` into a `SparseTensor`. + + Let N be the size of source (typically N will be the batch size). Split each + element of `source` based on `sep` and return a `SparseTensor` + containing the split tokens. Empty tokens are ignored. + + For example, N = 2, source[0] is 'hello world' and source[1] is 'a b c', + then the output will be + + st.indices = [0, 0; + 0, 1; + 1, 0; + 1, 1; + 1, 2] + st.shape = [2, 3] + st.values = ['hello', 'world', 'a', 'b', 'c'] + + If `sep` is given, consecutive delimiters are not grouped together and are + deemed to delimit empty strings. For example, source of `"1<>2<><>3"` and + sep of `"<>"` returns `["1", "2", "", "3"]`. If `sep` is None or an empty + string, consecutive whitespace are regarded as a single separator, and the + result will contain no empty strings at the startor end if the string has + leading or trailing whitespace. + + Note that the above mentioned behavior matches python's str.split. + + Args: + source: `1-D` string `Tensor`, the strings to split. + sep: `0-D` string `Tensor`, the delimiter character. + maxsplit: An `int`. If `maxsplit > 0`, limit of the split of the result. + + Raises: + ValueError: If sep is not a string. + + Returns: + A `SparseTensor` of rank `2`, the strings split according to the delimiter. + The first column of the indices corresponds to the row in `source` and the + second column corresponds to the index of the split component in this row. + """ + if sep is None: + sep = '' + sep = ops.convert_to_tensor(sep, dtype=dtypes.string) + source = ops.convert_to_tensor(source, dtype=dtypes.string) + + indices, values, shape = gen_string_ops.string_split_v2( + source, sep=sep, maxsplit=maxsplit) + indices.set_shape([None, 2]) + values.set_shape([None]) + shape.set_shape([2]) + return sparse_tensor.SparseTensor(indices, values, shape) + def _reduce_join_reduction_dims(x, axis, reduction_indices): """Returns range(rank(x) - 1, 0, -1) if reduction_indices is None.""" diff --git a/tensorflow/python/ops/variable_scope.py b/tensorflow/python/ops/variable_scope.py index f49e2d314d..47414c28af 100644 --- a/tensorflow/python/ops/variable_scope.py +++ b/tensorflow/python/ops/variable_scope.py @@ -1786,6 +1786,23 @@ class variable_scope(object): assert v.name == "foo/bar/v:0" ``` + Simple example of how to reenter a premade variable scope safely: + + ```python + with tf.variable_scope("foo") as vs: + pass + + # Re-enter the variable scope. + with tf.variable_scope(vs, + auxiliary_name_scope=False) as vs1: + # Restore the original name_scope. + with tf.name_scope(vs1.original_name_scope): + v = tf.get_variable("v", [1]) + assert v.name == "foo/v:0" + c = tf.constant([1], name="c") + assert c.name == "foo/c:0" + ``` + Basic example of sharing a variable AUTO_REUSE: ```python @@ -1924,7 +1941,9 @@ class variable_scope(object): (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. auxiliary_name_scope: If `True`, we create an auxiliary name scope with - the scope. If `False`, we don't touch name scope. + the scope. If `False`, we don't create it. Note that the argument is + not inherited, and it only takes effect for once when creating. You + should only use it for re-entering a premade variable scope. Returns: A scope that can be captured and reused. diff --git a/tensorflow/python/tools/import_pb_to_tensorboard.py b/tensorflow/python/tools/import_pb_to_tensorboard.py old mode 100755 new mode 100644 diff --git a/tensorflow/tensorflow.bzl b/tensorflow/tensorflow.bzl index 1f9fbad0b4..c3bc9ccd45 100644 --- a/tensorflow/tensorflow.bzl +++ b/tensorflow/tensorflow.bzl @@ -1723,7 +1723,7 @@ def tf_py_build_info_genrule(): name="py_build_info_gen", outs=["platform/build_info.py"], cmd= - "$(location //tensorflow/tools/build_info:gen_build_info.py) --raw_generate \"$@\" --build_config " + if_cuda("cuda", "cpu"), + "$(location //tensorflow/tools/build_info:gen_build_info.py) --raw_generate \"$@\" --build_config " + if_cuda("cuda", "cpu"), local=1, tools=[clean_dep("//tensorflow/tools/build_info:gen_build_info.py")],) diff --git a/tensorflow/tools/api/generator/create_python_api.py b/tensorflow/tools/api/generator/create_python_api.py index bca9fa49eb..671b7e387e 100644 --- a/tensorflow/tools/api/generator/create_python_api.py +++ b/tensorflow/tools/api/generator/create_python_api.py @@ -41,7 +41,11 @@ _GENERATED_FILE_HEADER = """# This file is MACHINE GENERATED! Do not edit. # Generated by: tensorflow/tools/api/generator/create_python_api.py script. \"\"\"%s \"\"\" + +from __future__ import print_function + """ +_GENERATED_FILE_FOOTER = "\n\ndel print_function\n" class SymbolExposedTwiceError(Exception): @@ -149,6 +153,7 @@ class _ModuleInitCodeBuilder(object): _names_with_underscore = [%s] __all__ = [_s for _s in dir() if not _s.startswith('_')] __all__.extend([_s for _s in _names_with_underscore]) +__all__.remove('print_function') ''' % underscore_names_str return module_text_map @@ -333,7 +338,8 @@ def create_api_files( if module or not root_init_template: contents = ( _GENERATED_FILE_HEADER % - get_module_docstring(module, package, api_name) + text) + get_module_docstring(module, package, api_name) + + text + _GENERATED_FILE_FOOTER) else: # Read base init file with open(root_init_template, 'r') as root_init_template_file: diff --git a/tensorflow/tools/api/golden/tensorflow.image.pbtxt b/tensorflow/tools/api/golden/tensorflow.image.pbtxt index 5bb3b3c444..10171b3d60 100644 --- a/tensorflow/tools/api/golden/tensorflow.image.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.image.pbtxt @@ -58,7 +58,7 @@ tf_module { } member_method { name: "decode_image" - argspec: "args=[\'contents\', \'channels\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + argspec: "args=[\'contents\', \'channels\', \'dtype\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \"\", \'None\'], " } member_method { name: "decode_jpeg" diff --git a/tensorflow/tools/api/golden/tensorflow.pbtxt b/tensorflow/tools/api/golden/tensorflow.pbtxt index dc2bd40096..3051c4437e 100644 --- a/tensorflow/tools/api/golden/tensorflow.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.pbtxt @@ -1532,6 +1532,10 @@ tf_module { name: "pow" argspec: "args=[\'x\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } + member_method { + name: "print" + argspec: "args=[\'input_\', \'data\', \'message\', \'first_n\', \'summarize\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " + } member_method { name: "py_func" argspec: "args=[\'func\', \'inp\', \'Tout\', \'stateful\', \'name\'], varargs=None, keywords=None, defaults=[\'True\', \'None\'], " diff --git a/tensorflow/tools/api/golden/tensorflow.strings.pbtxt b/tensorflow/tools/api/golden/tensorflow.strings.pbtxt index a3fbe95bba..b641c39feb 100644 --- a/tensorflow/tools/api/golden/tensorflow.strings.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.strings.pbtxt @@ -4,4 +4,8 @@ tf_module { name: "regex_full_match" argspec: "args=[\'input\', \'pattern\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } + member_method { + name: "split" + argspec: "args=[\'source\', \'sep\', \'maxsplit\'], varargs=None, keywords=None, defaults=[\'None\', \'-1\'], " + } } diff --git a/tensorflow/tools/ci_build/builds/pip.sh b/tensorflow/tools/ci_build/builds/pip.sh index 5fa75e1d61..883bb93647 100755 --- a/tensorflow/tools/ci_build/builds/pip.sh +++ b/tensorflow/tools/ci_build/builds/pip.sh @@ -322,6 +322,10 @@ create_activate_virtualenv_and_install_tensorflow() { pip install -v ${PIP_FLAGS} ${WHL_PATH} || \ die "pip install (forcing to reinstall tensorflow) FAILED" echo "Successfully installed pip package ${TF_WHEEL_PATH}" + + # Force downgrade setuptools. + pip install --upgrade setuptools==39.1.0 + } ################################################################################ diff --git a/tensorflow/tools/ci_build/builds/with_the_same_user b/tensorflow/tools/ci_build/builds/with_the_same_user index d4bf546d40..b216e3549f 100755 --- a/tensorflow/tools/ci_build/builds/with_the_same_user +++ b/tensorflow/tools/ci_build/builds/with_the_same_user @@ -40,7 +40,7 @@ if [ -n "${CI_BUILD_USER_FORCE_BADNAME}" ]; then ADDUSER_OPTS="--force-badname" fi -getent group "${CI_BUILD_GID}" || addgroup --gid "${CI_BUILD_GID}" "${CI_BUILD_GROUP}" +getent group "${CI_BUILD_GID}" || addgroup ${ADDUSER_OPTS} --gid "${CI_BUILD_GID}" "${CI_BUILD_GROUP}" getent passwd "${CI_BUILD_UID}" || adduser ${ADDUSER_OPTS} \ --gid "${CI_BUILD_GID}" --uid "${CI_BUILD_UID}" \ --gecos "${CI_BUILD_USER} (generated by with_the_same_user script)" \ diff --git a/tensorflow/tools/ci_build/ci_build.sh b/tensorflow/tools/ci_build/ci_build.sh index 072dd6ab99..1f0fd0387a 100755 --- a/tensorflow/tools/ci_build/ci_build.sh +++ b/tensorflow/tools/ci_build/ci_build.sh @@ -134,6 +134,12 @@ if [[ $? != "0" ]]; then die "ERROR: docker build failed. Dockerfile is at ${DOCKERFILE_PATH}" fi +# If caller wants the with_the_same_user script to allow bad usernames, +# pass the var to the docker environment +if [ -n "${CI_BUILD_USER_FORCE_BADNAME}" ]; then + CI_BUILD_USER_FORCE_BADNAME_ENV="-e CI_BUILD_USER_FORCE_BADNAME=yes" +fi + # Run the command inside the container. echo "Running '${COMMAND[*]}' inside ${DOCKER_IMG_NAME}..." mkdir -p ${WORKSPACE}/bazel-ci_build-cache @@ -148,6 +154,7 @@ ${DOCKER_BINARY} run --rm --pid=host \ -e "CI_BUILD_GROUP=$(id -g -n)" \ -e "CI_BUILD_GID=$(id -g)" \ -e "CI_TENSORFLOW_SUBMODULE_PATH=${CI_TENSORFLOW_SUBMODULE_PATH}" \ + ${CI_BUILD_USER_FORCE_BADNAME_ENV} \ -v ${WORKSPACE}:/workspace \ -w /workspace \ ${GPU_EXTRA_PARAMS} \ diff --git a/tensorflow/tools/ci_build/copy_binary.py b/tensorflow/tools/ci_build/copy_binary.py index 420d390d2b..148526492d 100755 --- a/tensorflow/tools/ci_build/copy_binary.py +++ b/tensorflow/tools/ci_build/copy_binary.py @@ -32,7 +32,8 @@ import shutil import tempfile import zipfile -TF_NIGHTLY_REGEX = r"(.+)tf_nightly(|_gpu)-(\d\.\d\.\d.dev[\d]{0,8})-(.+)\.whl" +TF_NIGHTLY_REGEX = (r"(.+)tf_nightly(|_gpu)-(\d\.[\d]{1,2}" + "\.\d.dev[\d]{0,8})-(.+)\.whl") BINARY_STRING_TEMPLATE = "%s-%s-%s.whl" diff --git a/tensorflow/tools/ci_build/install/install_pip_packages.sh b/tensorflow/tools/ci_build/install/install_pip_packages.sh index 60290df833..88f1d04193 100755 --- a/tensorflow/tools/ci_build/install/install_pip_packages.sh +++ b/tensorflow/tools/ci_build/install/install_pip_packages.sh @@ -115,3 +115,7 @@ pip2 install keras_applications==1.0.2 pip3 install keras_applications==1.0.2 pip2 install keras_preprocessing==1.0.1 pip3 install keras_preprocessing==1.0.1 + +# Install last working version of setuptools. +pip2 install --upgrade setuptools==39.1.0 +pip3 install --upgrade setuptools==39.1.0 diff --git a/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh b/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh index edb9d4b929..acd69ef346 100755 --- a/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh +++ b/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh @@ -39,7 +39,6 @@ if [[ -z $pip35_version ]]; then fi set -e -pip3.5 install --upgrade setuptools pip3.5 install --upgrade pip pip3.5 install --upgrade virtualenv @@ -86,4 +85,7 @@ pip3.5 install --upgrade termcolor pip3.5 install keras_applications==1.0.2 pip3.5 install keras_preprocessing==1.0.1 +# Install last working version of setuptools. +pip3.5 install --upgrade setuptools==39.1.0 + # LINT.ThenChange(//tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh) diff --git a/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh b/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh index 5635977731..323b30f48e 100755 --- a/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh +++ b/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh @@ -49,7 +49,6 @@ cd Python-3.6.1 make altinstall ln -s /usr/local/bin/pip3.6 /usr/local/bin/pip3 -pip3 install --upgrade setuptools pip3 install --upgrade pip pip3 install --upgrade virtualenv @@ -101,4 +100,8 @@ pip3 install --upgrade termcolor # Keras pip3.5 install keras_applications==1.0.2 pip3.5 install keras_preprocessing==1.0.1 + +# Install last working version of setuptools. +pip3 install --upgrade setuptools==39.1.0 + # LINT.ThenChange(//tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh) diff --git a/tensorflow/tools/ci_build/linux/mkl/basic-mkl-test.sh b/tensorflow/tools/ci_build/linux/mkl/basic-mkl-test.sh new file mode 100755 index 0000000000..10a09a415a --- /dev/null +++ b/tensorflow/tools/ci_build/linux/mkl/basic-mkl-test.sh @@ -0,0 +1,29 @@ +#!/usr/bin/env bash +# Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +# +# Usage: basic_mkl_test.sh + +# Helper function to traverse directories up until given file is found. +function upsearch () { + test / == "$PWD" && return || \ + test -e "$1" && echo "$PWD" && return || \ + cd .. && upsearch "$1" +} + +# Set up WORKSPACE. +WORKSPACE="${WORKSPACE:-$(upsearch WORKSPACE)}" + +BUILD_TAG=mkl-ci-test CI_BUILD_USER_FORCE_BADNAME=yes ${WORKSPACE}/tensorflow/tools/ci_build/ci_build.sh cpu tensorflow/tools/ci_build/linux/cpu/run_mkl.sh diff --git a/tensorflow/tools/ci_build/pi/build_raspberry_pi.sh b/tensorflow/tools/ci_build/pi/build_raspberry_pi.sh index 1bd1852ffc..b8bce57c87 100755 --- a/tensorflow/tools/ci_build/pi/build_raspberry_pi.sh +++ b/tensorflow/tools/ci_build/pi/build_raspberry_pi.sh @@ -79,6 +79,7 @@ if [[ $1 == "PI_ONE" ]]; then --linkopt=-L${OPENBLAS_INSTALL_PATH}/lib/ --linkopt=-l:libopenblas.a" echo "Building for the Pi One/Zero, with no NEON support" + WHEEL_ARCH=linux_armv6l else PI_COPTS='--copt=-march=armv7-a --copt=-mfpu=neon-vfpv4 --copt=-std=gnu11 --copt=-DS_IREAD=S_IRUSR --copt=-DS_IWRITE=S_IWUSR @@ -86,6 +87,7 @@ else --copt=-U__GCC_HAVE_SYNC_COMPARE_AND_SWAP_1 --copt=-U__GCC_HAVE_SYNC_COMPARE_AND_SWAP_2 --copt=-U__GCC_HAVE_SYNC_COMPARE_AND_SWAP_8' + WHEEL_ARCH=linux_armv7l echo "Building for the Pi Two/Three, with NEON acceleration" fi @@ -100,6 +102,8 @@ bazel build -c opt ${PI_COPTS} \ --copt=-fomit-frame-pointer --cpu=armeabi \ --crosstool_top=@local_config_arm_compiler//:toolchain \ --verbose_failures \ + //tensorflow:libtensorflow.so \ + //tensorflow:libtensorflow_framework.so \ //tensorflow/tools/benchmark:benchmark_model \ //tensorflow/tools/pip_package:build_pip_package @@ -112,10 +116,12 @@ BDIST_OPTS="--universal" \ bazel-bin/tensorflow/tools/pip_package/build_pip_package "${OUTDIR}" OLD_FN=$(ls "${OUTDIR}" | grep -m 1 \.whl) -SUB='s/tensorflow-([^-]+)-([^-]+)-.*/tensorflow-\1-\2-none-any.whl/; print' +SUB='s/tensorflow-([^-]+)-([^-]+)-.*/tensorflow-\1-\2-none-'${WHEEL_ARCH}'.whl/; print' NEW_FN=$(echo "${OLD_FN}" | perl -ne "${SUB}") mv "${OUTDIR}/${OLD_FN}" "${OUTDIR}/${NEW_FN}" cp bazel-bin/tensorflow/tools/benchmark/benchmark_model "${OUTDIR}" +cp bazel-bin/tensorflow/libtensorflow.so "${OUTDIR}" +cp bazel-bin/tensorflow/libtensorflow_framework.so "${OUTDIR}" echo "Output can be found here:" find "${OUTDIR}" diff --git a/tensorflow/tools/def_file_filter/def_file_filter_configure.bzl b/tensorflow/tools/def_file_filter/def_file_filter_configure.bzl index 47539b2423..f8f63e276c 100644 --- a/tensorflow/tools/def_file_filter/def_file_filter_configure.bzl +++ b/tensorflow/tools/def_file_filter/def_file_filter_configure.bzl @@ -31,7 +31,11 @@ def _def_file_filter_configure_impl(repository_ctx): vc_path = find_vc_path(repository_ctx) if vc_path == "visual-studio-not-found": auto_configure_fail("Visual C++ build tools not found on your machine") - undname_bin_path = find_msvc_tool(repository_ctx, vc_path, "undname.exe").replace("\\", "\\\\") + + undname = find_msvc_tool(repository_ctx, vc_path, "undname.exe") + if undname == None: + auto_configure_fail("Couldn't find undname.exe under %s, please check your VC installation and set BAZEL_VC environment variable correctly." % vc_path) + undname_bin_path = undname.replace("\\", "\\\\") repository_ctx.template( "def_file_filter.py", diff --git a/tensorflow/tools/dist_test/local_test.sh b/tensorflow/tools/dist_test/local_test.sh index 06c2b997cb..b0114721bd 100755 --- a/tensorflow/tools/dist_test/local_test.sh +++ b/tensorflow/tools/dist_test/local_test.sh @@ -64,9 +64,6 @@ die() { # Configurations DOCKER_IMG_NAME="tensorflow/tf-dist-test-local-cluster" -# Use TensorFlow v1.5.0 for Python 2.7 and CPU only as we set num_gpus to 0 in the below -DEFAULT_WHL_FILE_LOCATION="https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp27-none-linux_x86_64.whl" - # Parse input arguments LEAVE_CONTAINER_RUNNING=0 MODEL_NAME="" @@ -77,8 +74,7 @@ SYNC_REPLICAS_FLAG="" WHL_FILE_LOCATION=${1} if [[ -z "${WHL_FILE_LOCATION}" ]]; then - WHL_FILE_LOCATION=${DEFAULT_WHL_FILE_LOCATION} - echo "use default whl file location" + echo "WARNING: No wheel url passed. Will use latest tf-nightly cpu p2 wheel." fi while true; do @@ -131,7 +127,11 @@ echo "Building in temporary directory: ${BUILD_DIR}" cp -r ${DIR}/* "${BUILD_DIR}"/ || \ die "Failed to copy files to ${BUILD_DIR}" -if [[ $WHL_FILE_LOCATION =~ 'http://' || $WHL_FILE_LOCATION =~ 'https://' ]]; then +# Download whl file into the build context directory. +if [[ -z "${WHL_FILE_LOCATION}" ]]; then + pip2 download --no-deps tf-nightly + cp tf-nightly-*.whl "${BUILD_DIR}"/tensorflow-none-any.whl +elif [[ $WHL_FILE_LOCATION =~ 'http://' || $WHL_FILE_LOCATION =~ 'https://' ]]; then # Download whl file into the build context directory. wget -P "${BUILD_DIR}" "${WHL_FILE_LOCATION}" || \ die "Failed to download tensorflow whl file from URL: ${WHL_FILE_LOCATION}" diff --git a/tensorflow/tools/dist_test/remote_test.sh b/tensorflow/tools/dist_test/remote_test.sh index 935535312d..e188c88c8f 100755 --- a/tensorflow/tools/dist_test/remote_test.sh +++ b/tensorflow/tools/dist_test/remote_test.sh @@ -108,7 +108,7 @@ fi # Parse command-line arguments. WHL_URL=${1} if [[ -z "${WHL_URL}" ]]; then - die "whl URL is not specified" + echo "WARNING: No wheel url passed. Will use latest tf-nightly cpu p2 wheel." fi # Create docker build context directory. @@ -121,8 +121,13 @@ cp -r ${DIR}/* ${BUILD_DIR}/ || \ die "Failed to copy files to ${BUILD_DIR}" # Download whl file into the build context directory. -wget -P "${BUILD_DIR}" ${WHL_URL} || \ - die "Failed to download tensorflow whl file from URL: ${WHL_URL}" +if [[ -z "${WHL_URL}" ]]; then + pip2 download --no-deps tf-nightly + cp tf-nightly-*.whl "${BUILD_DIR}"/tensorflow-none-any.whl +else + wget -P "${BUILD_DIR}" ${WHL_URL} || \ + die "Failed to download tensorflow whl file from URL: ${WHL_URL}" +fi # Build docker image for test. docker build ${NO_CACHE_FLAG} \ diff --git a/tensorflow/tools/docker/Dockerfile.devel b/tensorflow/tools/docker/Dockerfile.devel index 406d134699..57a491255e 100644 --- a/tensorflow/tools/docker/Dockerfile.devel +++ b/tensorflow/tools/docker/Dockerfile.devel @@ -76,7 +76,7 @@ RUN mkdir /bazel && \ # Download and build TensorFlow. WORKDIR /tensorflow -RUN git clone --branch=r1.8 --depth=1 https://github.com/tensorflow/tensorflow.git . +RUN git clone --branch=r1.9 --depth=1 https://github.com/tensorflow/tensorflow.git . # TODO(craigcitro): Don't install the pip package, since it makes it # more difficult to experiment with local changes. Instead, just add diff --git a/tensorflow/tools/docker/Dockerfile.devel-cpu-mkl b/tensorflow/tools/docker/Dockerfile.devel-cpu-mkl index a6cd44ced1..6796ad70e5 100644 --- a/tensorflow/tools/docker/Dockerfile.devel-cpu-mkl +++ b/tensorflow/tools/docker/Dockerfile.devel-cpu-mkl @@ -3,7 +3,7 @@ FROM tensorflow/tensorflow:latest-devel LABEL maintainer="Clayne Robison" # These arguments are parameterized. Use --build-args to override. -ARG TF_BRANCH=r1.8 +ARG TF_BRANCH=r1.9 ARG WHL_DIR=/whl RUN apt-get update && apt-get install -y --no-install-recommends \ diff --git a/tensorflow/tools/docker/Dockerfile.devel-gpu b/tensorflow/tools/docker/Dockerfile.devel-gpu index 2fe47f3356..204b5b4dba 100644 --- a/tensorflow/tools/docker/Dockerfile.devel-gpu +++ b/tensorflow/tools/docker/Dockerfile.devel-gpu @@ -13,8 +13,8 @@ RUN apt-get update && apt-get install -y --no-install-recommends \ cuda-cusparse-dev-9-0 \ curl \ git \ - libcudnn7=7.0.5.15-1+cuda9.0 \ - libcudnn7-dev=7.0.5.15-1+cuda9.0 \ + libcudnn7=7.1.4.18-1+cuda9.0 \ + libcudnn7-dev=7.1.4.18-1+cuda9.0 \ libcurl3-dev \ libfreetype6-dev \ libhdf5-serial-dev \ @@ -85,7 +85,7 @@ RUN mkdir /bazel && \ # Download and build TensorFlow. WORKDIR /tensorflow -RUN git clone --branch=r1.8 --depth=1 https://github.com/tensorflow/tensorflow.git . +RUN git clone --branch=r1.9 --depth=1 https://github.com/tensorflow/tensorflow.git . # Configure the build for our CUDA configuration. ENV CI_BUILD_PYTHON python diff --git a/tensorflow/tools/docker/Dockerfile.gpu b/tensorflow/tools/docker/Dockerfile.gpu index bff4a20392..9197651ff4 100644 --- a/tensorflow/tools/docker/Dockerfile.gpu +++ b/tensorflow/tools/docker/Dockerfile.gpu @@ -12,7 +12,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \ cuda-cusolver-9-0 \ cuda-cusparse-9-0 \ curl \ - libcudnn7=7.0.5.15-1+cuda9.0 \ + libcudnn7=7.1.4.18-1+cuda9.0 \ libfreetype6-dev \ libhdf5-serial-dev \ libpng12-dev \ diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD index d0fd0fae97..d149365ac1 100644 --- a/tensorflow/tools/pip_package/BUILD +++ b/tensorflow/tools/pip_package/BUILD @@ -61,6 +61,7 @@ COMMON_PIP_DEPS = [ "//tensorflow/contrib/autograph/core:test_lib", "//tensorflow/contrib/autograph/impl:impl", "//tensorflow/contrib/autograph/lang:lang", + "//tensorflow/contrib/autograph/operators:operators", "//tensorflow/contrib/autograph/pyct:pyct", "//tensorflow/contrib/autograph/pyct/static_analysis:static_analysis", "//tensorflow/contrib/boosted_trees:boosted_trees_pip", diff --git a/tensorflow/tools/pip_package/build_pip_package.sh b/tensorflow/tools/pip_package/build_pip_package.sh index 0c4065bc77..f7e42ce536 100755 --- a/tensorflow/tools/pip_package/build_pip_package.sh +++ b/tensorflow/tools/pip_package/build_pip_package.sh @@ -41,51 +41,15 @@ function is_windows() { fi } -function main() { +function prepare_src() { if [ $# -lt 1 ] ; then echo "No destination dir provided" exit 1 fi - DEST=$(real_path $1) - TMPDIR=$(mktemp -d -t tmp.XXXXXXXXXX) - - PKG_NAME_FLAG="" - GPU_BUILD=0 - NIGHTLY_BUILD=0 - PROJECT_NAME="" - while true; do - if [[ "$1" == "--nightly_flag" ]]; then - NIGHTLY_BUILD=1 - elif [[ "$1" == "--gpu" ]]; then - GPU_BUILD=1 - elif [[ "$1" == "--gpudirect" ]]; then - PKG_NAME_FLAG="--project_name tensorflow_gpudirect" - elif [[ "$1" == "--project_name" ]]; then - shift - if [[ -z "$1" ]]; then - break - fi - PROJECT_NAME="$1" - fi - shift - - if [[ -z "$1" ]]; then - break - fi - done - - if [[ -n ${PROJECT_NAME} ]]; then - PKG_NAME_FLAG="--project_name ${PROJECT_NAME}" - elif [[ ${NIGHTLY_BUILD} == "1" && ${GPU_BUILD} == "1" ]]; then - PKG_NAME_FLAG="--project_name tf_nightly_gpu" - elif [[ ${NIGHTLY_BUILD} == "1" ]]; then - PKG_NAME_FLAG="--project_name tf_nightly" - elif [[ ${GPU_BUILD} == "1" ]]; then - PKG_NAME_FLAG="--project_name tensorflow_gpu" - fi - - echo $(date) : "=== Using tmpdir: ${TMPDIR}" + TMPDIR="$1" + mkdir -p "$TMPDIR" + echo $(date) : "=== Preparing sources in dir: ${TMPDIR}" if [ ! -d bazel-bin/tensorflow ]; then echo "Could not find bazel-bin. Did you run from the root of the build tree?" @@ -155,17 +119,28 @@ function main() { # over so user defined ops can be compiled. mkdir -p ${TMPDIR}/google mkdir -p ${TMPDIR}/third_party - pushd ${RUNFILES%org_tensorflow} + pushd ${RUNFILES%org_tensorflow} > /dev/null for header in $(find protobuf_archive -name \*.h); do mkdir -p "${TMPDIR}/google/$(dirname ${header})" cp "$header" "${TMPDIR}/google/$(dirname ${header})/" done - popd + popd > /dev/null cp -R $RUNFILES/third_party/eigen3 ${TMPDIR}/third_party cp tensorflow/tools/pip_package/MANIFEST.in ${TMPDIR} cp tensorflow/tools/pip_package/README ${TMPDIR} cp tensorflow/tools/pip_package/setup.py ${TMPDIR} +} + +function build_wheel() { + if [ $# -lt 2 ] ; then + echo "No src and dest dir provided" + exit 1 + fi + + TMPDIR="$1" + DEST="$2" + PKG_NAME_FLAG="$3" # Before we leave the top-level directory, make sure we know how to # call python. @@ -173,15 +148,110 @@ function main() { source tools/python_bin_path.sh fi - pushd ${TMPDIR} + pushd ${TMPDIR} > /dev/null rm -f MANIFEST echo $(date) : "=== Building wheel" "${PYTHON_BIN_PATH:-python}" setup.py bdist_wheel ${PKG_NAME_FLAG} >/dev/null mkdir -p ${DEST} cp dist/* ${DEST} - popd - rm -rf ${TMPDIR} + popd > /dev/null echo $(date) : "=== Output wheel file is in: ${DEST}" } +function usage() { + echo "Usage:" + echo "$0 [--src srcdir] [--dst dstdir] [options]" + echo "$0 dstdir [options]" + echo "" + echo " --src prepare sources in srcdir" + echo " will use temporary dir if not specified" + echo "" + echo " --dst build wheel in dstdir" + echo " if dstdir is not set do not build, only prepare sources" + echo "" + echo " Options:" + echo " --project_name set project name to name" + echo " --gpu build tensorflow_gpu" + echo " --gpudirect build tensorflow_gpudirect" + echo " --nightly_flag build tensorflow nightly" + echo "" + exit 1 +} + +function main() { + PKG_NAME_FLAG="" + PROJECT_NAME="" + GPU_BUILD=0 + NIGHTLY_BUILD=0 + SRCDIR="" + DSTDIR="" + CLEANSRC=1 + while true; do + if [[ "$1" == "--help" ]]; then + usage + exit 1 + elif [[ "$1" == "--nightly_flag" ]]; then + NIGHTLY_BUILD=1 + elif [[ "$1" == "--gpu" ]]; then + GPU_BUILD=1 + elif [[ "$1" == "--gpudirect" ]]; then + PKG_NAME_FLAG="--project_name tensorflow_gpudirect" + elif [[ "$1" == "--project_name" ]]; then + shift + if [[ -z "$1" ]]; then + break + fi + PROJECT_NAME="$1" + elif [[ "$1" == "--src" ]]; then + shift + SRCDIR="$(real_path $1)" + CLEANSRC=0 + elif [[ "$1" == "--dst" ]]; then + shift + DSTDIR="$(real_path $1)" + else + DSTDIR="$(real_path $1)" + fi + shift + + if [[ -z "$1" ]]; then + break + fi + done + + if [[ -z "$DSTDIR" ]] && [[ -z "$SRCDIR" ]]; then + echo "No destination dir provided" + usage + exit 1 + fi + + if [[ -z "$SRCDIR" ]]; then + # make temp srcdir if none set + SRCDIR="$(mktemp -d -t tmp.XXXXXXXXXX)" + fi + + prepare_src "$SRCDIR" + + if [[ -z "$DSTDIR" ]]; then + # only want to prepare sources + exit + fi + + if [[ -n ${PROJECT_NAME} ]]; then + PKG_NAME_FLAG="--project_name ${PROJECT_NAME}" + elif [[ ${NIGHTLY_BUILD} == "1" && ${GPU_BUILD} == "1" ]]; then + PKG_NAME_FLAG="--project_name tf_nightly_gpu" + elif [[ ${NIGHTLY_BUILD} == "1" ]]; then + PKG_NAME_FLAG="--project_name tf_nightly" + elif [[ ${GPU_BUILD} == "1" ]]; then + PKG_NAME_FLAG="--project_name tensorflow_gpu" + fi + + build_wheel "$SRCDIR" "$DSTDIR" "$PKG_NAME_FLAG" + + if [[ $CLEANSRC -ne 0 ]]; then + rm -rf "${TMPDIR}" + fi +} + main "$@" diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py index d25a9e77b1..97f625e7e9 100644 --- a/tensorflow/tools/pip_package/setup.py +++ b/tensorflow/tools/pip_package/setup.py @@ -45,7 +45,7 @@ DOCLINES = __doc__.split('\n') # This version string is semver compatible, but incompatible with pip. # For pip, we will remove all '-' characters from this string, and use the # result for pip. -_VERSION = '1.8.0' +_VERSION = '1.9.0-rc0' REQUIRED_PACKAGES = [ 'absl-py >= 0.1.6', @@ -54,6 +54,7 @@ REQUIRED_PACKAGES = [ 'numpy >= 1.13.3', 'six >= 1.10.0', 'protobuf >= 3.4.0', + 'setuptools <= 39.1.0', 'tensorboard >= 1.8.0, < 1.9.0', 'termcolor >= 1.1.0', ] diff --git a/tensorflow/tools/proto_text/gen_proto_text_functions_lib.cc b/tensorflow/tools/proto_text/gen_proto_text_functions_lib.cc index 29add6d5ea..15d7c70281 100644 --- a/tensorflow/tools/proto_text/gen_proto_text_functions_lib.cc +++ b/tensorflow/tools/proto_text/gen_proto_text_functions_lib.cc @@ -814,6 +814,9 @@ void Generator::Generate(const FileDescriptor& fd) { // Add header to cc file. SetOutput(&cc_); Print("// GENERATED FILE - DO NOT MODIFY"); + Print(); + Print("#include "); // for `std::stable_sort()` + Print(); headers = {GetProtoTextHeaderName(fd, true /* impl */)}; AddHeadersToCurrentSection(headers); Print(); diff --git a/tensorflow/tools/quantization/quantize_graph_test.py b/tensorflow/tools/quantization/quantize_graph_test.py index df71840b64..92bb5127da 100644 --- a/tensorflow/tools/quantization/quantize_graph_test.py +++ b/tensorflow/tools/quantization/quantize_graph_test.py @@ -119,8 +119,8 @@ def are_tensors_near(a, b, tolerance): flat_a = a.flatten() flat_b = b.flatten() if len(flat_a) != len(flat_b): - print("Tensors are different sizes: " + str(len(flat_a)) + " vs " + str( - len(flat_b))) + tf_logging.info("Tensors are different sizes: " + str(len(flat_a)) + " vs " + + str(len(flat_b))) return False value_count = len(flat_a) how_many_different = 0 @@ -140,10 +140,10 @@ def are_tensors_near(a, b, tolerance): if how_many_different == 0: return True else: - print("Tensors have {0} different values ({1}%), with mean difference" - " {2} and mean absolute difference {3}".format( - how_many_different, proportion_different * 100, mean_difference, - mean_abs_difference)) + tf_logging.info("Tensors have {0} different values ({1}%), with mean" + " difference {2} and mean absolute difference {3}".format( + how_many_different, proportion_different * 100, + mean_difference, mean_abs_difference)) return False diff --git a/tensorflow/tools/test/upload_test_benchmarks.py b/tensorflow/tools/test/upload_test_benchmarks.py index 9c45359ee1..c030575109 100644 --- a/tensorflow/tools/test/upload_test_benchmarks.py +++ b/tensorflow/tools/test/upload_test_benchmarks.py @@ -89,7 +89,6 @@ import shutil from six import text_type from google.cloud import datastore -from six import text_type def is_real_file(dirpath, fname): diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index 161d1dbd06..b4fbbd6c23 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -50,31 +50,31 @@ def tf_workspace(path_prefix="", tf_repo_name=""): mkl_repository( name = "mkl_linux", urls = [ - "https://mirror.bazel.build/github.com/intel/mkl-dnn/releases/download/v0.13/mklml_lnx_2018.0.2.20180127.tgz", - "https://github.com/intel/mkl-dnn/releases/download/v0.13/mklml_lnx_2018.0.2.20180127.tgz", + "https://mirror.bazel.build/github.com/intel/mkl-dnn/releases/download/v0.14/mklml_lnx_2018.0.3.20180406.tgz", + "https://github.com/intel/mkl-dnn/releases/download/v0.14/mklml_lnx_2018.0.3.20180406.tgz" ], - sha256 = "74844bd77294742bf2396ff040369d1aa4cdd9e826fcd38cf8398ae83564d146", - strip_prefix = "mklml_lnx_2018.0.2.20180127", + sha256 = "d2305244fdc9b87db7426ed4496e87a4b3977ad3374d73b8000e8b7a5b7aa725", + strip_prefix = "mklml_lnx_2018.0.3.20180406", build_file = clean_dep("//third_party/mkl:mkl.BUILD") ) mkl_repository( name = "mkl_windows", urls = [ - "https://mirror.bazel.build/github.com/intel/mkl-dnn/releases/download/v0.13/mklml_win_2018.0.2.20180127.zip", - "https://github.com/intel/mkl-dnn/releases/download/v0.13/mklml_win_2018.0.2.20180127.zip" + "https://mirror.bazel.build/github.com/intel/mkl-dnn/releases/download/v0.14/mklml_win_2018.0.3.20180406.zip", + "https://github.com/intel/mkl-dnn/releases/download/v0.14/mklml_win_2018.0.3.20180406.zip" ], - sha256 = "d8fbf0faa0684bffa3548005d05fe5cfe56ff9dbc0e15e7612d7ac01055a6ded", - strip_prefix = "mklml_win_2018.0.2.20180127", + sha256 = "a584a5bf1c8d2ad70b90d12b52652030e9a338217719064fdb84b7ad0d693694", + strip_prefix = "mklml_win_2018.0.3.20180406", build_file = clean_dep("//third_party/mkl:mkl.BUILD") ) mkl_repository( name = "mkl_darwin", urls = [ - "https://mirror.bazel.build/github.com/intel/mkl-dnn/releases/download/v0.13/mklml_mac_2018.0.2.20180127.tgz", - "https://github.com/intel/mkl-dnn/releases/download/v0.13/mklml_mac_2018.0.2.20180127.tgz" + "https://mirror.bazel.build/github.com/intel/mkl-dnn/releases/download/v0.14/mklml_mac_2018.0.3.20180406.tgz", + "https://github.com/intel/mkl-dnn/releases/download/v0.14/mklml_mac_2018.0.3.20180406.tgz" ], - sha256 = "aa740d71e14562bfea56e6829e6dc186e7487cbcf6748a88dec73826b7ec1943", - strip_prefix = "mklml_mac_2018.0.2.20180127", + sha256 = "094e3dfd61c816136dc8d12a45cc611ce26c5f4828176a3644cd0b0efa15a25b", + strip_prefix = "mklml_mac_2018.0.3.20180406", build_file = clean_dep("//third_party/mkl:mkl.BUILD") ) @@ -85,11 +85,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "mkl_dnn", urls = [ - "https://mirror.bazel.build/github.com/intel/mkl-dnn/archive/v0.13.tar.gz", - "https://github.com/intel/mkl-dnn/archive/v0.13.tar.gz", + "https://mirror.bazel.build/github.com/intel/mkl-dnn/archive/v0.14.tar.gz", + "https://github.com/intel/mkl-dnn/archive/v0.14.tar.gz", ], - sha256 = "d2cfd93a70cfe86ebe054477c530c9b5c1218b70f75856eb6d1956c68ee89e8f", - strip_prefix = "mkl-dnn-0.13", + sha256 = "efebc53882856afec86457a2da644693f5d59c68772d41d640d6b60a8efc4eb0", + strip_prefix = "mkl-dnn-0.14", build_file = clean_dep("//third_party/mkl_dnn:mkldnn.BUILD"), ) @@ -187,11 +187,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "highwayhash", urls = [ - "https://mirror.bazel.build/github.com/google/highwayhash/archive/dfcb97ca4fe9277bf9dc1802dd979b071896453b.tar.gz", - "https://github.com/google/highwayhash/archive/dfcb97ca4fe9277bf9dc1802dd979b071896453b.tar.gz", + "http://mirror.bazel.build/github.com/google/highwayhash/archive/fd3d9af80465e4383162e4a7c5e2f406e82dd968.tar.gz", + "https://github.com/google/highwayhash/archive/fd3d9af80465e4383162e4a7c5e2f406e82dd968.tar.gz", ], - sha256 = "0f30a15b1566d93f146c8d149878a06e91d9bb7ec2cfd76906df62a82be4aac9", - strip_prefix = "highwayhash-dfcb97ca4fe9277bf9dc1802dd979b071896453b", + sha256 = "9c3e0e87d581feeb0c18d814d98f170ff23e62967a2bd6855847f0b2fe598a37", + strip_prefix = "highwayhash-fd3d9af80465e4383162e4a7c5e2f406e82dd968", build_file = clean_dep("//third_party:highwayhash.BUILD"), ) diff --git a/third_party/eigen.BUILD b/third_party/eigen.BUILD index 07bb6645eb..e54c1a4501 100644 --- a/third_party/eigen.BUILD +++ b/third_party/eigen.BUILD @@ -64,6 +64,7 @@ cc_library( # This define (mostly) guarantees we don't link any problematic # code. We use it, but we do not rely on it, as evidenced above. "EIGEN_MPL2_ONLY", + "EIGEN_MAX_ALIGN_BYTES=64", ], includes = ["."], visibility = ["//visibility:public"], diff --git a/third_party/highwayhash.BUILD b/third_party/highwayhash.BUILD index 1b8e40765e..08cb84ea2c 100644 --- a/third_party/highwayhash.BUILD +++ b/third_party/highwayhash.BUILD @@ -10,6 +10,7 @@ cc_library( srcs = ["highwayhash/sip_hash.cc"], hdrs = [ "highwayhash/sip_hash.h", + "highwayhash/endianess.h", "highwayhash/state_helpers.h", ], visibility = ["//visibility:public"], diff --git a/third_party/jpeg/jpeg.BUILD b/third_party/jpeg/jpeg.BUILD index 4418ac32fc..663a218733 100644 --- a/third_party/jpeg/jpeg.BUILD +++ b/third_party/jpeg/jpeg.BUILD @@ -291,8 +291,10 @@ cc_library( "jchuff.h", "jconfig.h", "jdct.h", + "jerror.h", "jinclude.h", "jmorecfg.h", + "jpegint.h", "jpeglib.h", "jsimd.h", "jsimddct.h", diff --git a/third_party/png.BUILD b/third_party/png.BUILD index 76ab32d69c..17c5449cc0 100644 --- a/third_party/png.BUILD +++ b/third_party/png.BUILD @@ -28,7 +28,14 @@ cc_library( "pngwrite.c", "pngwtran.c", "pngwutil.c", - ], + ] + select({ + "@org_tensorflow//tensorflow:linux_ppc64le": [ + "powerpc/powerpc_init.c", + "powerpc/filter_vsx_intrinsics.c", + ], + "//conditions:default": [ + ], + }), hdrs = [ "png.h", "pngconf.h", diff --git a/third_party/py/python_configure.bzl b/third_party/py/python_configure.bzl index 954f21f5f8..3c7e5c8469 100644 --- a/third_party/py/python_configure.bzl +++ b/third_party/py/python_configure.bzl @@ -6,6 +6,7 @@ * `PYTHON_LIB_PATH`: Location of python libraries. """ +_BAZEL_SH = "BAZEL_SH" _PYTHON_BIN_PATH = "PYTHON_BIN_PATH" _PYTHON_LIB_PATH = "PYTHON_LIB_PATH" _TF_PYTHON_CONFIG_REPO = "TF_PYTHON_CONFIG_REPO" @@ -152,6 +153,22 @@ def _get_python_bin(repository_ctx): _PYTHON_BIN_PATH, repository_ctx.os.environ.get("PATH", ""))) +def _get_bash_bin(repository_ctx): + """Gets the bash bin path.""" + bash_bin = repository_ctx.os.environ.get(_BAZEL_SH) + if bash_bin != None: + return bash_bin + else: + bash_bin_path = repository_ctx.which("bash") + if bash_bin_path != None: + return str(bash_bin_path) + else: + _fail("Cannot find bash in PATH, please make sure " + + "bash is installed and add its directory in PATH, or --define " + + "%s='/path/to/bash'.\nPATH=%s" % ( + _BAZEL_SH, repository_ctx.os.environ.get("PATH", ""))) + + def _get_python_lib(repository_ctx, python_bin): """Gets the python lib path.""" python_lib = repository_ctx.os.environ.get(_PYTHON_LIB_PATH) @@ -184,14 +201,14 @@ def _get_python_lib(repository_ctx, python_bin): " print(paths[0])\n" + "END") cmd = '%s - %s' % (python_bin, print_lib) - result = repository_ctx.execute(["bash", "-c", cmd]) + result = repository_ctx.execute([_get_bash_bin(repository_ctx), "-c", cmd]) return result.stdout.strip('\n') def _check_python_lib(repository_ctx, python_lib): """Checks the python lib path.""" cmd = 'test -d "%s" -a -x "%s"' % (python_lib, python_lib) - result = repository_ctx.execute(["bash", "-c", cmd]) + result = repository_ctx.execute([_get_bash_bin(repository_ctx), "-c", cmd]) if result.return_code == 1: _fail("Invalid python library path: %s" % python_lib) @@ -199,7 +216,7 @@ def _check_python_lib(repository_ctx, python_lib): def _check_python_bin(repository_ctx, python_bin): """Checks the python bin path.""" cmd = '[[ -x "%s" ]] && [[ ! -d "%s" ]]' % (python_bin, python_bin) - result = repository_ctx.execute(["bash", "-c", cmd]) + result = repository_ctx.execute([_get_bash_bin(repository_ctx), "-c", cmd]) if result.return_code == 1: _fail("--define %s='%s' is not executable. Is it the python binary?" % ( _PYTHON_BIN_PATH, python_bin)) @@ -294,6 +311,7 @@ def _python_autoconf_impl(repository_ctx): python_configure = repository_rule( implementation = _python_autoconf_impl, environ = [ + _BAZEL_SH, _PYTHON_BIN_PATH, _PYTHON_LIB_PATH, _TF_PYTHON_CONFIG_REPO, diff --git a/third_party/repo.bzl b/third_party/repo.bzl index 36f5aa5bde..cb67d3e961 100644 --- a/third_party/repo.bzl +++ b/third_party/repo.bzl @@ -17,7 +17,6 @@ _SINGLE_URL_WHITELIST = depset([ "arm_compiler", "ortools_archive", - "gemmlowp", ]) def _is_windows(ctx): @@ -88,7 +87,9 @@ def _tf_http_archive(ctx): if ctx.attr.patch_file != None: _apply_patch(ctx, ctx.attr.patch_file) if ctx.attr.build_file != None: - ctx.template("BUILD", ctx.attr.build_file, { + # Use BUILD.bazel to avoid conflict with third party projects with + # BUILD or build (directory) underneath. + ctx.template("BUILD.bazel", ctx.attr.build_file, { "%prefix%": ".." if _repos_are_siblings() else "external", }, False) -- GitLab From a36636e9098fb6e40150d10c4ef65345e06aa788 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 18 Jun 2018 21:18:16 -0700 Subject: [PATCH 646/816] Update ops-related pbtxt files. PiperOrigin-RevId: 201111838 --- .../core/ops/compat/ops_history.v1.pbtxt | 159 ++++++++++++++++++ tensorflow/core/ops/ops.pbtxt | 38 ++++- 2 files changed, 196 insertions(+), 1 deletion(-) diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt index 726bfd63b7..5e260b87c1 100644 --- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt +++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt @@ -6425,6 +6425,68 @@ op { } } } +op { + name: "AsString" + input_arg { + name: "input" + type_attr: "T" + } + output_arg { + name: "output" + type: DT_STRING + } + attr { + name: "T" + type: "type" + allowed_values { + list { + type: DT_INT8 + type: DT_INT16 + type: DT_INT32 + type: DT_INT64 + type: DT_COMPLEX64 + type: DT_FLOAT + type: DT_DOUBLE + type: DT_BOOL + } + } + } + attr { + name: "precision" + type: "int" + default_value { + i: -1 + } + } + attr { + name: "scientific" + type: "bool" + default_value { + b: false + } + } + attr { + name: "shortest" + type: "bool" + default_value { + b: false + } + } + attr { + name: "width" + type: "int" + default_value { + i: -1 + } + } + attr { + name: "fill" + type: "string" + default_value { + s: "" + } + } +} op { name: "Asin" input_arg { @@ -68139,6 +68201,36 @@ op { } } } +op { + name: "StringSplitV2" + input_arg { + name: "input" + type: DT_STRING + } + input_arg { + name: "sep" + type: DT_STRING + } + output_arg { + name: "indices" + type: DT_INT64 + } + output_arg { + name: "values" + type: DT_STRING + } + output_arg { + name: "shape" + type: DT_INT64 + } + attr { + name: "maxsplit" + type: "int" + default_value { + i: -1 + } + } +} op { name: "StringStrip" input_arg { @@ -72670,6 +72762,73 @@ op { } } } +op { + name: "UnsortedSegmentProd" + input_arg { + name: "data" + type_attr: "T" + } + input_arg { + name: "segment_ids" + type_attr: "Tindices" + } + input_arg { + name: "num_segments" + type_attr: "Tnumsegments" + } + output_arg { + name: "output" + type_attr: "T" + } + attr { + name: "T" + type: "type" + allowed_values { + list { + type: DT_FLOAT + type: DT_DOUBLE + type: DT_INT32 + type: DT_UINT8 + type: DT_INT16 + type: DT_INT8 + type: DT_COMPLEX64 + type: DT_INT64 + type: DT_QINT8 + type: DT_QUINT8 + type: DT_QINT32 + type: DT_BFLOAT16 + type: DT_UINT16 + type: DT_COMPLEX128 + type: DT_HALF + type: DT_UINT32 + type: DT_UINT64 + } + } + } + attr { + name: "Tindices" + type: "type" + allowed_values { + list { + type: DT_INT32 + type: DT_INT64 + } + } + } + attr { + name: "Tnumsegments" + type: "type" + default_value { + type: DT_INT32 + } + allowed_values { + list { + type: DT_INT32 + type: DT_INT64 + } + } + } +} op { name: "UnsortedSegmentSum" input_arg { diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt index c609703bcb..94a373e990 100644 --- a/tensorflow/core/ops/ops.pbtxt +++ b/tensorflow/core/ops/ops.pbtxt @@ -1977,13 +1977,14 @@ op { type: "type" allowed_values { list { + type: DT_INT8 + type: DT_INT16 type: DT_INT32 type: DT_INT64 type: DT_COMPLEX64 type: DT_FLOAT type: DT_DOUBLE type: DT_BOOL - type: DT_INT8 } } } @@ -31612,6 +31613,36 @@ op { } } } +op { + name: "StringSplitV2" + input_arg { + name: "input" + type: DT_STRING + } + input_arg { + name: "sep" + type: DT_STRING + } + output_arg { + name: "indices" + type: DT_INT64 + } + output_arg { + name: "values" + type: DT_STRING + } + output_arg { + name: "shape" + type: DT_INT64 + } + attr { + name: "maxsplit" + type: "int" + default_value { + i: -1 + } + } +} op { name: "StringStrip" input_arg { @@ -34534,9 +34565,14 @@ op { type: DT_UINT8 type: DT_INT16 type: DT_INT8 + type: DT_COMPLEX64 type: DT_INT64 + type: DT_QINT8 + type: DT_QUINT8 + type: DT_QINT32 type: DT_BFLOAT16 type: DT_UINT16 + type: DT_COMPLEX128 type: DT_HALF type: DT_UINT32 type: DT_UINT64 -- GitLab From a79d083197fdcc887c2f39d4942e1f0c848234f2 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 18 Jun 2018 21:46:05 -0700 Subject: [PATCH 647/816] Go: Update generated wrapper functions for TensorFlow ops. PiperOrigin-RevId: 201113951 --- tensorflow/go/op/wrappers.go | 5794 +++++++++++++++++----------------- 1 file changed, 2897 insertions(+), 2897 deletions(-) diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go index 5602775b62..a443879df2 100644 --- a/tensorflow/go/op/wrappers.go +++ b/tensorflow/go/op/wrappers.go @@ -2990,6 +2990,31 @@ func Split(scope *Scope, axis tf.Output, value tf.Output, num_split int64) (outp return output } +// Concatenates tensors along one dimension. +// +// Arguments: +// concat_dim: 0-D. The dimension along which to concatenate. Must be in the +// range [0, rank(values)). +// values: The `N` Tensors to concatenate. Their ranks and types must match, +// and their sizes must match in all dimensions except `concat_dim`. +// +// Returns A `Tensor` with the concatenation of values stacked along the +// `concat_dim` dimension. This tensor's shape matches that of `values` except +// in `concat_dim` where it has the sum of the sizes. +func Concat(scope *Scope, concat_dim tf.Output, values []tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Concat", + Input: []tf.Input{ + concat_dim, tf.OutputList(values), + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Creates a sequence of numbers. // // This operation creates a sequence of numbers that begins at `start` and @@ -8367,157 +8392,124 @@ func BoostedTreesUpdateEnsemble(scope *Scope, tree_ensemble_handle tf.Output, fe return scope.AddOperation(opspec) } -// ResourceSparseApplyFtrlAttr is an optional argument to ResourceSparseApplyFtrl. -type ResourceSparseApplyFtrlAttr func(optionalAttr) +// EncodeJpegAttr is an optional argument to EncodeJpeg. +type EncodeJpegAttr func(optionalAttr) -// ResourceSparseApplyFtrlUseLocking sets the optional use_locking attribute to value. +// EncodeJpegFormat sets the optional format attribute to value. // -// value: If `True`, updating of the var and accum tensors will be protected -// by a lock; otherwise the behavior is undefined, but may exhibit less -// contention. -// If not specified, defaults to false -func ResourceSparseApplyFtrlUseLocking(value bool) ResourceSparseApplyFtrlAttr { +// value: Per pixel image format. +// If not specified, defaults to "" +func EncodeJpegFormat(value string) EncodeJpegAttr { return func(m optionalAttr) { - m["use_locking"] = value + m["format"] = value } } -// Update relevant entries in '*var' according to the Ftrl-proximal scheme. -// -// That is for rows we have grad for, we update var, accum and linear as follows: -// accum_new = accum + grad * grad -// linear += grad + (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var -// quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 -// var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 -// accum = accum_new -// -// Arguments: -// var_: Should be from a Variable(). -// accum: Should be from a Variable(). -// linear: Should be from a Variable(). -// grad: The gradient. -// indices: A vector of indices into the first dimension of var and accum. -// lr: Scaling factor. Must be a scalar. -// l1: L1 regularization. Must be a scalar. -// l2: L2 regularization. Must be a scalar. -// lr_power: Scaling factor. Must be a scalar. +// EncodeJpegQuality sets the optional quality attribute to value. // -// Returns the created operation. -func ResourceSparseApplyFtrl(scope *Scope, var_ tf.Output, accum tf.Output, linear tf.Output, grad tf.Output, indices tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, lr_power tf.Output, optional ...ResourceSparseApplyFtrlAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ResourceSparseApplyFtrl", - Input: []tf.Input{ - var_, accum, linear, grad, indices, lr, l1, l2, lr_power, - }, - Attrs: attrs, +// value: Quality of the compression from 0 to 100 (higher is better and slower). +// If not specified, defaults to 95 +func EncodeJpegQuality(value int64) EncodeJpegAttr { + return func(m optionalAttr) { + m["quality"] = value } - return scope.AddOperation(opspec) } -// Returns which elements of x are Inf. +// EncodeJpegProgressive sets the optional progressive attribute to value. // -// @compatibility(numpy) -// Equivalent to np.isinf -// @end_compatibility -func IsInf(scope *Scope, x tf.Output) (y tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "IsInf", - Input: []tf.Input{ - x, - }, +// value: If True, create a JPEG that loads progressively (coarse to fine). +// If not specified, defaults to false +func EncodeJpegProgressive(value bool) EncodeJpegAttr { + return func(m optionalAttr) { + m["progressive"] = value } - op := scope.AddOperation(opspec) - return op.Output(0) } -// Computes the sum along sparse segments of a tensor divided by the sqrt of N. -// -// N is the size of the segment being reduced. -// -// Read @{$math_ops#Segmentation$the section on segmentation} for an explanation of -// segments. -// -// Arguments: -// -// indices: A 1-D tensor. Has same rank as `segment_ids`. -// segment_ids: A 1-D tensor. Values should be sorted and can be repeated. +// EncodeJpegOptimizeSize sets the optional optimize_size attribute to value. // -// Returns Has same shape as data, except for dimension 0 which -// has size `k`, the number of segments. -func SparseSegmentSqrtN(scope *Scope, data tf.Output, indices tf.Output, segment_ids tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SparseSegmentSqrtN", - Input: []tf.Input{ - data, indices, segment_ids, - }, +// value: If True, spend CPU/RAM to reduce size with no quality change. +// If not specified, defaults to false +func EncodeJpegOptimizeSize(value bool) EncodeJpegAttr { + return func(m optionalAttr) { + m["optimize_size"] = value } - op := scope.AddOperation(opspec) - return op.Output(0) } -// Adds up a `SparseTensor` and a dense `Tensor`, producing a dense `Tensor`. +// EncodeJpegChromaDownsampling sets the optional chroma_downsampling attribute to value. // -// This Op does not require `a_indices` be sorted in standard lexicographic order. +// value: See http://en.wikipedia.org/wiki/Chroma_subsampling. +// If not specified, defaults to true +func EncodeJpegChromaDownsampling(value bool) EncodeJpegAttr { + return func(m optionalAttr) { + m["chroma_downsampling"] = value + } +} + +// EncodeJpegDensityUnit sets the optional density_unit attribute to value. // -// Arguments: -// a_indices: 2-D. The `indices` of the `SparseTensor`, with shape `[nnz, ndims]`. -// a_values: 1-D. The `values` of the `SparseTensor`, with shape `[nnz]`. -// a_shape: 1-D. The `shape` of the `SparseTensor`, with shape `[ndims]`. -// b: `ndims`-D Tensor. With shape `a_shape`. -func SparseTensorDenseAdd(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b tf.Output) (output tf.Output) { - if scope.Err() != nil { - return +// value: Unit used to specify `x_density` and `y_density`: +// pixels per inch (`'in'`) or centimeter (`'cm'`). +// If not specified, defaults to "in" +func EncodeJpegDensityUnit(value string) EncodeJpegAttr { + return func(m optionalAttr) { + m["density_unit"] = value } - opspec := tf.OpSpec{ - Type: "SparseTensorDenseAdd", - Input: []tf.Input{ - a_indices, a_values, a_shape, b, - }, +} + +// EncodeJpegXDensity sets the optional x_density attribute to value. +// +// value: Horizontal pixels per density unit. +// If not specified, defaults to 300 +func EncodeJpegXDensity(value int64) EncodeJpegAttr { + return func(m optionalAttr) { + m["x_density"] = value } - op := scope.AddOperation(opspec) - return op.Output(0) } -// StatelessTruncatedNormalAttr is an optional argument to StatelessTruncatedNormal. -type StatelessTruncatedNormalAttr func(optionalAttr) +// EncodeJpegYDensity sets the optional y_density attribute to value. +// +// value: Vertical pixels per density unit. +// If not specified, defaults to 300 +func EncodeJpegYDensity(value int64) EncodeJpegAttr { + return func(m optionalAttr) { + m["y_density"] = value + } +} -// StatelessTruncatedNormalDtype sets the optional dtype attribute to value. +// EncodeJpegXmpMetadata sets the optional xmp_metadata attribute to value. // -// value: The type of the output. -// If not specified, defaults to DT_FLOAT -func StatelessTruncatedNormalDtype(value tf.DataType) StatelessTruncatedNormalAttr { +// value: If not empty, embed this XMP metadata in the image header. +// If not specified, defaults to "" +func EncodeJpegXmpMetadata(value string) EncodeJpegAttr { return func(m optionalAttr) { - m["dtype"] = value + m["xmp_metadata"] = value } } -// Outputs deterministic pseudorandom values from a truncated normal distribution. +// JPEG-encode an image. // -// The generated values follow a normal distribution with mean 0 and standard -// deviation 1, except that values whose magnitude is more than 2 standard -// deviations from the mean are dropped and re-picked. +// `image` is a 3-D uint8 Tensor of shape `[height, width, channels]`. // -// The outputs are a deterministic function of `shape` and `seed`. +// The attr `format` can be used to override the color format of the encoded +// output. Values can be: +// +// * `''`: Use a default format based on the number of channels in the image. +// * `grayscale`: Output a grayscale JPEG image. The `channels` dimension +// of `image` must be 1. +// * `rgb`: Output an RGB JPEG image. The `channels` dimension +// of `image` must be 3. +// +// If `format` is not specified or is the empty string, a default format is picked +// in function of the number of channels in `image`: +// +// * 1: Output a grayscale image. +// * 3: Output an RGB image. // // Arguments: -// shape: The shape of the output tensor. -// seed: 2 seeds (shape [2]). +// image: 3-D with shape `[height, width, channels]`. // -// Returns Random values with specified shape. -func StatelessTruncatedNormal(scope *Scope, shape tf.Output, seed tf.Output, optional ...StatelessTruncatedNormalAttr) (output tf.Output) { +// Returns 0-D. JPEG-encoded image. +func EncodeJpeg(scope *Scope, image tf.Output, optional ...EncodeJpegAttr) (contents tf.Output) { if scope.Err() != nil { return } @@ -8526,9 +8518,9 @@ func StatelessTruncatedNormal(scope *Scope, shape tf.Output, seed tf.Output, opt a(attrs) } opspec := tf.OpSpec{ - Type: "StatelessTruncatedNormal", + Type: "EncodeJpeg", Input: []tf.Input{ - shape, seed, + image, }, Attrs: attrs, } @@ -8536,51 +8528,59 @@ func StatelessTruncatedNormal(scope *Scope, shape tf.Output, seed tf.Output, opt return op.Output(0) } -// RestoreSliceAttr is an optional argument to RestoreSlice. -type RestoreSliceAttr func(optionalAttr) +// MultinomialAttr is an optional argument to Multinomial. +type MultinomialAttr func(optionalAttr) -// RestoreSlicePreferredShard sets the optional preferred_shard attribute to value. +// MultinomialSeed sets the optional seed attribute to value. // -// value: Index of file to open first if multiple files match -// `file_pattern`. See the documentation for `Restore`. -// If not specified, defaults to -1 -func RestoreSlicePreferredShard(value int64) RestoreSliceAttr { +// value: If either seed or seed2 is set to be non-zero, the internal random number +// generator is seeded by the given seed. Otherwise, a random seed is used. +// If not specified, defaults to 0 +func MultinomialSeed(value int64) MultinomialAttr { return func(m optionalAttr) { - m["preferred_shard"] = value + m["seed"] = value } } -// Restores a tensor from checkpoint files. -// -// This is like `Restore` except that restored tensor can be listed as filling -// only a slice of a larger tensor. `shape_and_slice` specifies the shape of the -// larger tensor and the slice that the restored tensor covers. -// -// The `shape_and_slice` input has the same format as the -// elements of the `shapes_and_slices` input of the `SaveSlices` op. +// MultinomialSeed2 sets the optional seed2 attribute to value. // -// Arguments: -// file_pattern: Must have a single element. The pattern of the files from -// which we read the tensor. -// tensor_name: Must have a single element. The name of the tensor to be -// restored. -// shape_and_slice: Scalar. The shapes and slice specifications to use when -// restoring a tensors. -// dt: The type of the tensor to be restored. +// value: A second seed to avoid seed collision. +// If not specified, defaults to 0 +func MultinomialSeed2(value int64) MultinomialAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// MultinomialOutputDtype sets the optional output_dtype attribute to value. +// If not specified, defaults to DT_INT64 +func MultinomialOutputDtype(value tf.DataType) MultinomialAttr { + return func(m optionalAttr) { + m["output_dtype"] = value + } +} + +// Draws samples from a multinomial distribution. // -// Returns The restored tensor. -func RestoreSlice(scope *Scope, file_pattern tf.Output, tensor_name tf.Output, shape_and_slice tf.Output, dt tf.DataType, optional ...RestoreSliceAttr) (tensor tf.Output) { +// Arguments: +// logits: 2-D Tensor with shape `[batch_size, num_classes]`. Each slice `[i, :]` +// represents the unnormalized log probabilities for all classes. +// num_samples: 0-D. Number of independent samples to draw for each row slice. +// +// Returns 2-D Tensor with shape `[batch_size, num_samples]`. Each slice `[i, :]` +// contains the drawn class labels with range `[0, num_classes)`. +func Multinomial(scope *Scope, logits tf.Output, num_samples tf.Output, optional ...MultinomialAttr) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"dt": dt} + attrs := map[string]interface{}{} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "RestoreSlice", + Type: "Multinomial", Input: []tf.Input{ - file_pattern, tensor_name, shape_and_slice, + logits, num_samples, }, Attrs: attrs, } @@ -8588,89 +8588,89 @@ func RestoreSlice(scope *Scope, file_pattern tf.Output, tensor_name tf.Output, s return op.Output(0) } -// Divides sparse updates into the variable referenced by `resource`. -// -// This operation computes -// -// # Scalar indices -// ref[indices, ...] /= updates[...] -// -// # Vector indices (for each i) -// ref[indices[i], ...] /= updates[i, ...] -// -// # High rank indices (for each i, ..., j) -// ref[indices[i, ..., j], ...] /= updates[i, ..., j, ...] -// -// Duplicate entries are handled correctly: if multiple `indices` reference -// the same location, their contributions multiply. -// -// Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`. +// ResourceSparseApplyAdagradDAAttr is an optional argument to ResourceSparseApplyAdagradDA. +type ResourceSparseApplyAdagradDAAttr func(optionalAttr) + +// ResourceSparseApplyAdagradDAUseLocking sets the optional use_locking attribute to value. // -//
-// -//
+// value: If True, updating of the var and accum tensors will be protected by +// a lock; otherwise the behavior is undefined, but may exhibit less contention. +// If not specified, defaults to false +func ResourceSparseApplyAdagradDAUseLocking(value bool) ResourceSparseApplyAdagradDAAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Update entries in '*var' and '*accum' according to the proximal adagrad scheme. // // Arguments: -// resource: Should be from a `Variable` node. -// indices: A tensor of indices into the first dimension of `ref`. -// updates: A tensor of updated values to add to `ref`. +// var_: Should be from a Variable(). +// gradient_accumulator: Should be from a Variable(). +// gradient_squared_accumulator: Should be from a Variable(). +// grad: The gradient. +// indices: A vector of indices into the first dimension of var and accum. +// lr: Learning rate. Must be a scalar. +// l1: L1 regularization. Must be a scalar. +// l2: L2 regularization. Must be a scalar. +// global_step: Training step number. Must be a scalar. // // Returns the created operation. -func ResourceScatterDiv(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) { +func ResourceSparseApplyAdagradDA(scope *Scope, var_ tf.Output, gradient_accumulator tf.Output, gradient_squared_accumulator tf.Output, grad tf.Output, indices tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, global_step tf.Output, optional ...ResourceSparseApplyAdagradDAAttr) (o *tf.Operation) { if scope.Err() != nil { return } - opspec := tf.OpSpec{ - Type: "ResourceScatterDiv", - Input: []tf.Input{ - resource, indices, updates, - }, - } - return scope.AddOperation(opspec) -} - -// Mutually reduces multiple tensors of identical type and shape. -func CollectiveReduce(scope *Scope, input tf.Output, group_size int64, group_key int64, instance_key int64, merge_op string, final_op string, subdiv_offsets []int64) (data tf.Output) { - if scope.Err() != nil { - return + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) } - attrs := map[string]interface{}{"group_size": group_size, "group_key": group_key, "instance_key": instance_key, "merge_op": merge_op, "final_op": final_op, "subdiv_offsets": subdiv_offsets} opspec := tf.OpSpec{ - Type: "CollectiveReduce", + Type: "ResourceSparseApplyAdagradDA", Input: []tf.Input{ - input, + var_, gradient_accumulator, gradient_squared_accumulator, grad, indices, lr, l1, l2, global_step, }, Attrs: attrs, } - op := scope.AddOperation(opspec) - return op.Output(0) + return scope.AddOperation(opspec) } -// StatelessRandomNormalAttr is an optional argument to StatelessRandomNormal. -type StatelessRandomNormalAttr func(optionalAttr) +// ResourceSparseApplyFtrlAttr is an optional argument to ResourceSparseApplyFtrl. +type ResourceSparseApplyFtrlAttr func(optionalAttr) -// StatelessRandomNormalDtype sets the optional dtype attribute to value. +// ResourceSparseApplyFtrlUseLocking sets the optional use_locking attribute to value. // -// value: The type of the output. -// If not specified, defaults to DT_FLOAT -func StatelessRandomNormalDtype(value tf.DataType) StatelessRandomNormalAttr { +// value: If `True`, updating of the var and accum tensors will be protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceSparseApplyFtrlUseLocking(value bool) ResourceSparseApplyFtrlAttr { return func(m optionalAttr) { - m["dtype"] = value + m["use_locking"] = value } } -// Outputs deterministic pseudorandom values from a normal distribution. -// -// The generated values will have mean 0 and standard deviation 1. +// Update relevant entries in '*var' according to the Ftrl-proximal scheme. // -// The outputs are a deterministic function of `shape` and `seed`. +// That is for rows we have grad for, we update var, accum and linear as follows: +// accum_new = accum + grad * grad +// linear += grad + (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var +// quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 +// var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 +// accum = accum_new // // Arguments: -// shape: The shape of the output tensor. -// seed: 2 seeds (shape [2]). +// var_: Should be from a Variable(). +// accum: Should be from a Variable(). +// linear: Should be from a Variable(). +// grad: The gradient. +// indices: A vector of indices into the first dimension of var and accum. +// lr: Scaling factor. Must be a scalar. +// l1: L1 regularization. Must be a scalar. +// l2: L2 regularization. Must be a scalar. +// lr_power: Scaling factor. Must be a scalar. // -// Returns Random values with specified shape. -func StatelessRandomNormal(scope *Scope, shape tf.Output, seed tf.Output, optional ...StatelessRandomNormalAttr) (output tf.Output) { +// Returns the created operation. +func ResourceSparseApplyFtrl(scope *Scope, var_ tf.Output, accum tf.Output, linear tf.Output, grad tf.Output, indices tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, lr_power tf.Output, optional ...ResourceSparseApplyFtrlAttr) (o *tf.Operation) { if scope.Err() != nil { return } @@ -8679,322 +8679,265 @@ func StatelessRandomNormal(scope *Scope, shape tf.Output, seed tf.Output, option a(attrs) } opspec := tf.OpSpec{ - Type: "StatelessRandomNormal", + Type: "ResourceSparseApplyFtrl", Input: []tf.Input{ - shape, seed, + var_, accum, linear, grad, indices, lr, l1, l2, lr_power, }, Attrs: attrs, } - op := scope.AddOperation(opspec) - return op.Output(0) + return scope.AddOperation(opspec) } -// Reduces sparse updates into the variable referenced by `resource` using the `min` operation. -// -// This operation computes -// -// # Scalar indices -// ref[indices, ...] = min(ref[indices, ...], updates[...]) -// -// # Vector indices (for each i) -// ref[indices[i], ...] = min(ref[indices[i], ...], updates[i, ...]) -// -// # High rank indices (for each i, ..., j) -// ref[indices[i, ..., j], ...] = min(ref[indices[i, ..., j], ...], updates[i, ..., j, ...]) -// -// Duplicate entries are handled correctly: if multiple `indices` reference -// the same location, their contributions are combined. -// -// Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`. -// -//
-// -//
-// -// Arguments: -// resource: Should be from a `Variable` node. -// indices: A tensor of indices into the first dimension of `ref`. -// updates: A tensor of updated values to add to `ref`. +// Returns which elements of x are Inf. // -// Returns the created operation. -func ResourceScatterMin(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) { +// @compatibility(numpy) +// Equivalent to np.isinf +// @end_compatibility +func IsInf(scope *Scope, x tf.Output) (y tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "ResourceScatterMin", + Type: "IsInf", Input: []tf.Input{ - resource, indices, updates, + x, }, } - return scope.AddOperation(opspec) + op := scope.AddOperation(opspec) + return op.Output(0) } -// Reshapes a quantized tensor as per the Reshape op. +// Computes the sum along sparse segments of a tensor divided by the sqrt of N. // -// ``` +// N is the size of the segment being reduced. +// +// Read @{$math_ops#Segmentation$the section on segmentation} for an explanation of +// segments. // // Arguments: // -// shape: Defines the shape of the output tensor. -// input_min: The minimum value of the input. -// input_max: The maximum value of the input. +// indices: A 1-D tensor. Has same rank as `segment_ids`. +// segment_ids: A 1-D tensor. Values should be sorted and can be repeated. // -// Returns This value is copied from input_min.This value is copied from input_max. -func QuantizedReshape(scope *Scope, tensor tf.Output, shape tf.Output, input_min tf.Output, input_max tf.Output) (output tf.Output, output_min tf.Output, output_max tf.Output) { +// Returns Has same shape as data, except for dimension 0 which +// has size `k`, the number of segments. +func SparseSegmentSqrtN(scope *Scope, data tf.Output, indices tf.Output, segment_ids tf.Output) (output tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "QuantizedReshape", + Type: "SparseSegmentSqrtN", Input: []tf.Input{ - tensor, shape, input_min, input_max, + data, indices, segment_ids, }, } op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) + return op.Output(0) } -// Returns the truth value of (x != y) element-wise. +// Adds up a `SparseTensor` and a dense `Tensor`, producing a dense `Tensor`. // -// *NOTE*: `NotEqual` supports broadcasting. More about broadcasting -// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) -func NotEqual(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { +// This Op does not require `a_indices` be sorted in standard lexicographic order. +// +// Arguments: +// a_indices: 2-D. The `indices` of the `SparseTensor`, with shape `[nnz, ndims]`. +// a_values: 1-D. The `values` of the `SparseTensor`, with shape `[nnz]`. +// a_shape: 1-D. The `shape` of the `SparseTensor`, with shape `[ndims]`. +// b: `ndims`-D Tensor. With shape `a_shape`. +func SparseTensorDenseAdd(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b tf.Output) (output tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "NotEqual", + Type: "SparseTensorDenseAdd", Input: []tf.Input{ - x, y, + a_indices, a_values, a_shape, b, }, } op := scope.AddOperation(opspec) return op.Output(0) } -// Inverse 3D real-valued fast Fourier transform. +// StatelessTruncatedNormalAttr is an optional argument to StatelessTruncatedNormal. +type StatelessTruncatedNormalAttr func(optionalAttr) + +// StatelessTruncatedNormalDtype sets the optional dtype attribute to value. // -// Computes the inverse 3-dimensional discrete Fourier transform of a real-valued -// signal over the inner-most 3 dimensions of `input`. +// value: The type of the output. +// If not specified, defaults to DT_FLOAT +func StatelessTruncatedNormalDtype(value tf.DataType) StatelessTruncatedNormalAttr { + return func(m optionalAttr) { + m["dtype"] = value + } +} + +// Outputs deterministic pseudorandom values from a truncated normal distribution. // -// The inner-most 3 dimensions of `input` are assumed to be the result of `RFFT3D`: -// The inner-most dimension contains the `fft_length / 2 + 1` unique components of -// the DFT of a real-valued signal. If `fft_length` is not provided, it is computed -// from the size of the inner-most 3 dimensions of `input`. If the FFT length used -// to compute `input` is odd, it should be provided since it cannot be inferred -// properly. +// The generated values follow a normal distribution with mean 0 and standard +// deviation 1, except that values whose magnitude is more than 2 standard +// deviations from the mean are dropped and re-picked. // -// Along each axis `IRFFT3D` is computed on, if `fft_length` (or -// `fft_length / 2 + 1` for the inner-most dimension) is smaller than the -// corresponding dimension of `input`, the dimension is cropped. If it is larger, -// the dimension is padded with zeros. +// The outputs are a deterministic function of `shape` and `seed`. // // Arguments: -// input: A complex64 tensor. -// fft_length: An int32 tensor of shape [3]. The FFT length for each dimension. -// -// Returns A float32 tensor of the same rank as `input`. The inner-most 3 -// dimensions of `input` are replaced with the `fft_length` samples of their -// inverse 3D real Fourier transform. +// shape: The shape of the output tensor. +// seed: 2 seeds (shape [2]). // -// @compatibility(numpy) -// Equivalent to np.irfftn with 3 dimensions. -// @end_compatibility -func IRFFT3D(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) { +// Returns Random values with specified shape. +func StatelessTruncatedNormal(scope *Scope, shape tf.Output, seed tf.Output, optional ...StatelessTruncatedNormalAttr) (output tf.Output) { if scope.Err() != nil { return } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "IRFFT3D", + Type: "StatelessTruncatedNormal", Input: []tf.Input{ - input, fft_length, + shape, seed, }, + Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// StringSplitAttr is an optional argument to StringSplit. -type StringSplitAttr func(optionalAttr) +// RestoreSliceAttr is an optional argument to RestoreSlice. +type RestoreSliceAttr func(optionalAttr) -// StringSplitSkipEmpty sets the optional skip_empty attribute to value. +// RestoreSlicePreferredShard sets the optional preferred_shard attribute to value. // -// value: A `bool`. If `True`, skip the empty strings from the result. -// If not specified, defaults to true -func StringSplitSkipEmpty(value bool) StringSplitAttr { +// value: Index of file to open first if multiple files match +// `file_pattern`. See the documentation for `Restore`. +// If not specified, defaults to -1 +func RestoreSlicePreferredShard(value int64) RestoreSliceAttr { return func(m optionalAttr) { - m["skip_empty"] = value + m["preferred_shard"] = value } } -// Split elements of `input` based on `delimiter` into a `SparseTensor`. -// -// Let N be the size of source (typically N will be the batch size). Split each -// element of `input` based on `delimiter` and return a `SparseTensor` -// containing the splitted tokens. Empty tokens are ignored. -// -// `delimiter` can be empty, or a string of split characters. If `delimiter` is an -// empty string, each element of `input` is split into individual single-byte -// character strings, including splitting of UTF-8 multibyte sequences. Otherwise -// every character of `delimiter` is a potential split point. +// Restores a tensor from checkpoint files. // -// For example: -// N = 2, input[0] is 'hello world' and input[1] is 'a b c', then the output -// will be +// This is like `Restore` except that restored tensor can be listed as filling +// only a slice of a larger tensor. `shape_and_slice` specifies the shape of the +// larger tensor and the slice that the restored tensor covers. // -// indices = [0, 0; -// 0, 1; -// 1, 0; -// 1, 1; -// 1, 2] -// shape = [2, 3] -// values = ['hello', 'world', 'a', 'b', 'c'] +// The `shape_and_slice` input has the same format as the +// elements of the `shapes_and_slices` input of the `SaveSlices` op. // // Arguments: -// input: 1-D. Strings to split. -// delimiter: 0-D. Delimiter characters (bytes), or empty string. +// file_pattern: Must have a single element. The pattern of the files from +// which we read the tensor. +// tensor_name: Must have a single element. The name of the tensor to be +// restored. +// shape_and_slice: Scalar. The shapes and slice specifications to use when +// restoring a tensors. +// dt: The type of the tensor to be restored. // -// Returns A dense matrix of int64 representing the indices of the sparse tensor.A vector of strings corresponding to the splited values.a length-2 vector of int64 representing the shape of the sparse -// tensor, where the first value is N and the second value is the maximum number -// of tokens in a single input entry. -func StringSplit(scope *Scope, input tf.Output, delimiter tf.Output, optional ...StringSplitAttr) (indices tf.Output, values tf.Output, shape tf.Output) { +// Returns The restored tensor. +func RestoreSlice(scope *Scope, file_pattern tf.Output, tensor_name tf.Output, shape_and_slice tf.Output, dt tf.DataType, optional ...RestoreSliceAttr) (tensor tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} + attrs := map[string]interface{}{"dt": dt} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "StringSplit", + Type: "RestoreSlice", Input: []tf.Input{ - input, delimiter, + file_pattern, tensor_name, shape_and_slice, }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) + return op.Output(0) } -// ResourceSparseApplyMomentumAttr is an optional argument to ResourceSparseApplyMomentum. -type ResourceSparseApplyMomentumAttr func(optionalAttr) - -// ResourceSparseApplyMomentumUseLocking sets the optional use_locking attribute to value. +// Divides sparse updates into the variable referenced by `resource`. // -// value: If `True`, updating of the var and accum tensors will be protected -// by a lock; otherwise the behavior is undefined, but may exhibit less -// contention. -// If not specified, defaults to false -func ResourceSparseApplyMomentumUseLocking(value bool) ResourceSparseApplyMomentumAttr { - return func(m optionalAttr) { - m["use_locking"] = value - } -} - -// ResourceSparseApplyMomentumUseNesterov sets the optional use_nesterov attribute to value. +// This operation computes // -// value: If `True`, the tensor passed to compute grad will be -// var - lr * momentum * accum, so in the end, the var you get is actually -// var - lr * momentum * accum. -// If not specified, defaults to false -func ResourceSparseApplyMomentumUseNesterov(value bool) ResourceSparseApplyMomentumAttr { - return func(m optionalAttr) { - m["use_nesterov"] = value - } -} - -// Update relevant entries in '*var' and '*accum' according to the momentum scheme. +// # Scalar indices +// ref[indices, ...] /= updates[...] // -// Set use_nesterov = True if you want to use Nesterov momentum. +// # Vector indices (for each i) +// ref[indices[i], ...] /= updates[i, ...] // -// That is for rows we have grad for, we update var and accum as follows: +// # High rank indices (for each i, ..., j) +// ref[indices[i, ..., j], ...] /= updates[i, ..., j, ...] // -// accum = accum * momentum + grad -// var -= lr * accum +// Duplicate entries are handled correctly: if multiple `indices` reference +// the same location, their contributions multiply. +// +// Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`. +// +//
+// +//
// // Arguments: -// var_: Should be from a Variable(). -// accum: Should be from a Variable(). -// lr: Learning rate. Must be a scalar. -// grad: The gradient. -// indices: A vector of indices into the first dimension of var and accum. -// momentum: Momentum. Must be a scalar. +// resource: Should be from a `Variable` node. +// indices: A tensor of indices into the first dimension of `ref`. +// updates: A tensor of updated values to add to `ref`. // // Returns the created operation. -func ResourceSparseApplyMomentum(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, indices tf.Output, momentum tf.Output, optional ...ResourceSparseApplyMomentumAttr) (o *tf.Operation) { +func ResourceScatterDiv(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } opspec := tf.OpSpec{ - Type: "ResourceSparseApplyMomentum", + Type: "ResourceScatterDiv", Input: []tf.Input{ - var_, accum, lr, grad, indices, momentum, + resource, indices, updates, }, - Attrs: attrs, } return scope.AddOperation(opspec) } -// Returns the complex conjugate of a complex number. -// -// Given a tensor `input` of complex numbers, this operation returns a tensor of -// complex numbers that are the complex conjugate of each element in `input`. The -// complex numbers in `input` must be of the form \\(a + bj\\), where *a* is the -// real part and *b* is the imaginary part. -// -// The complex conjugate returned by this operation is of the form \\(a - bj\\). -// -// For example: -// -// ``` -// # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j] -// tf.conj(input) ==> [-2.25 - 4.75j, 3.25 - 5.75j] -// ``` -func Conj(scope *Scope, input tf.Output) (output tf.Output) { +// Mutually reduces multiple tensors of identical type and shape. +func CollectiveReduce(scope *Scope, input tf.Output, group_size int64, group_key int64, instance_key int64, merge_op string, final_op string, subdiv_offsets []int64) (data tf.Output) { if scope.Err() != nil { return } + attrs := map[string]interface{}{"group_size": group_size, "group_key": group_key, "instance_key": instance_key, "merge_op": merge_op, "final_op": final_op, "subdiv_offsets": subdiv_offsets} opspec := tf.OpSpec{ - Type: "Conj", + Type: "CollectiveReduce", Input: []tf.Input{ input, }, + Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// ResizeBilinearAttr is an optional argument to ResizeBilinear. -type ResizeBilinearAttr func(optionalAttr) +// StatelessRandomNormalAttr is an optional argument to StatelessRandomNormal. +type StatelessRandomNormalAttr func(optionalAttr) -// ResizeBilinearAlignCorners sets the optional align_corners attribute to value. +// StatelessRandomNormalDtype sets the optional dtype attribute to value. // -// value: If true, the centers of the 4 corner pixels of the input and output tensors are -// aligned, preserving the values at the corner pixels. Defaults to false. -// If not specified, defaults to false -func ResizeBilinearAlignCorners(value bool) ResizeBilinearAttr { +// value: The type of the output. +// If not specified, defaults to DT_FLOAT +func StatelessRandomNormalDtype(value tf.DataType) StatelessRandomNormalAttr { return func(m optionalAttr) { - m["align_corners"] = value + m["dtype"] = value } } -// Resize `images` to `size` using bilinear interpolation. +// Outputs deterministic pseudorandom values from a normal distribution. // -// Input images can be of different types but output images are always float. +// The generated values will have mean 0 and standard deviation 1. +// +// The outputs are a deterministic function of `shape` and `seed`. // // Arguments: -// images: 4-D with shape `[batch, height, width, channels]`. -// size: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The -// new size for the images. +// shape: The shape of the output tensor. +// seed: 2 seeds (shape [2]). // -// Returns 4-D with shape -// `[batch, new_height, new_width, channels]`. -func ResizeBilinear(scope *Scope, images tf.Output, size tf.Output, optional ...ResizeBilinearAttr) (resized_images tf.Output) { +// Returns Random values with specified shape. +func StatelessRandomNormal(scope *Scope, shape tf.Output, seed tf.Output, optional ...StatelessRandomNormalAttr) (output tf.Output) { if scope.Err() != nil { return } @@ -9003,9 +8946,9 @@ func ResizeBilinear(scope *Scope, images tf.Output, size tf.Output, optional ... a(attrs) } opspec := tf.OpSpec{ - Type: "ResizeBilinear", + Type: "StatelessRandomNormal", Input: []tf.Input{ - images, size, + shape, seed, }, Attrs: attrs, } @@ -9013,128 +8956,207 @@ func ResizeBilinear(scope *Scope, images tf.Output, size tf.Output, optional ... return op.Output(0) } -// Computes softsign: `features / (abs(features) + 1)`. -func Softsign(scope *Scope, features tf.Output) (activations tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Softsign", - Input: []tf.Input{ - features, - }, +// MaxPoolAttr is an optional argument to MaxPool. +type MaxPoolAttr func(optionalAttr) + +// MaxPoolDataFormat sets the optional data_format attribute to value. +// +// value: Specify the data format of the input and output data. With the +// default format "NHWC", the data is stored in the order of: +// [batch, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCHW", the data storage order of: +// [batch, in_channels, in_height, in_width]. +// If not specified, defaults to "NHWC" +func MaxPoolDataFormat(value string) MaxPoolAttr { + return func(m optionalAttr) { + m["data_format"] = value } - op := scope.AddOperation(opspec) - return op.Output(0) } -// Creates a TensorList which, when stacked, has the value of `tensor`. -// -// Each tensor in the result list corresponds to one row of the input tensor. +// Performs max pooling on the input. // -// tensor: The input tensor. -// output_handle: The list. -func TensorListFromTensor(scope *Scope, tensor tf.Output, element_shape tf.Output) (output_handle tf.Output) { +// Arguments: +// input: 4-D input to pool over. +// ksize: The size of the window for each dimension of the input tensor. +// strides: The stride of the sliding window for each dimension of the +// input tensor. +// padding: The type of padding algorithm to use. +// +// Returns The max pooled output tensor. +func MaxPool(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolAttr) (output tf.Output) { if scope.Err() != nil { return } + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "TensorListFromTensor", + Type: "MaxPool", Input: []tf.Input{ - tensor, element_shape, + input, }, + Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// GenerateVocabRemappingAttr is an optional argument to GenerateVocabRemapping. -type GenerateVocabRemappingAttr func(optionalAttr) +// SparseMatMulAttr is an optional argument to SparseMatMul. +type SparseMatMulAttr func(optionalAttr) -// GenerateVocabRemappingOldVocabSize sets the optional old_vocab_size attribute to value. +// SparseMatMulTransposeA sets the optional transpose_a attribute to value. +// If not specified, defaults to false +func SparseMatMulTransposeA(value bool) SparseMatMulAttr { + return func(m optionalAttr) { + m["transpose_a"] = value + } +} + +// SparseMatMulTransposeB sets the optional transpose_b attribute to value. +// If not specified, defaults to false +func SparseMatMulTransposeB(value bool) SparseMatMulAttr { + return func(m optionalAttr) { + m["transpose_b"] = value + } +} + +// SparseMatMulAIsSparse sets the optional a_is_sparse attribute to value. +// If not specified, defaults to false +func SparseMatMulAIsSparse(value bool) SparseMatMulAttr { + return func(m optionalAttr) { + m["a_is_sparse"] = value + } +} + +// SparseMatMulBIsSparse sets the optional b_is_sparse attribute to value. +// If not specified, defaults to false +func SparseMatMulBIsSparse(value bool) SparseMatMulAttr { + return func(m optionalAttr) { + m["b_is_sparse"] = value + } +} + +// Multiply matrix "a" by matrix "b". // -// value: Number of entries in the old vocab file to consider. If -1, -// use the entire old vocabulary. -// If not specified, defaults to -1 +// The inputs must be two-dimensional matrices and the inner dimension of "a" must +// match the outer dimension of "b". This op is optimized for the case where at +// least one of "a" or "b" is sparse. The breakeven for using this versus a dense +// matrix multiply on one platform was 30% zero values in the sparse matrix. // -// REQUIRES: value >= -1 -func GenerateVocabRemappingOldVocabSize(value int64) GenerateVocabRemappingAttr { - return func(m optionalAttr) { - m["old_vocab_size"] = value +// The gradient computation of this operation will only take advantage of sparsity +// in the input gradient when that gradient comes from a Relu. +func SparseMatMul(scope *Scope, a tf.Output, b tf.Output, optional ...SparseMatMulAttr) (product tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SparseMatMul", + Input: []tf.Input{ + a, b, + }, + Attrs: attrs, } + op := scope.AddOperation(opspec) + return op.Output(0) } -// Given a path to new and old vocabulary files, returns a remapping Tensor of +// Concatenates quantized tensors along one dimension. // -// length `num_new_vocab`, where `remapping[i]` contains the row number in the old -// vocabulary that corresponds to row `i` in the new vocabulary (starting at line -// `new_vocab_offset` and up to `num_new_vocab` entities), or `-1` if entry `i` -// in the new vocabulary is not in the old vocabulary. The old vocabulary is -// constrained to the first `old_vocab_size` entries if `old_vocab_size` is not the -// default value of -1. +// Arguments: +// concat_dim: 0-D. The dimension along which to concatenate. Must be in the +// range [0, rank(values)). +// values: The `N` Tensors to concatenate. Their ranks and types must match, +// and their sizes must match in all dimensions except `concat_dim`. +// input_mins: The minimum scalar values for each of the input tensors. +// input_maxes: The maximum scalar values for each of the input tensors. // -// `num_vocab_offset` enables -// use in the partitioned variable case, and should generally be set through -// examining partitioning info. The format of the files should be a text file, -// with each line containing a single entity within the vocabulary. +// Returns A `Tensor` with the concatenation of values stacked along the +// `concat_dim` dimension. This tensor's shape matches that of `values` except +// in `concat_dim` where it has the sum of the sizes.The float value that the minimum quantized output value represents.The float value that the maximum quantized output value represents. +func QuantizedConcat(scope *Scope, concat_dim tf.Output, values []tf.Output, input_mins []tf.Output, input_maxes []tf.Output) (output tf.Output, output_min tf.Output, output_max tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "QuantizedConcat", + Input: []tf.Input{ + concat_dim, tf.OutputList(values), tf.OutputList(input_mins), tf.OutputList(input_maxes), + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Slice a `SparseTensor` based on the `start` and `size`. // -// For example, with `new_vocab_file` a text file containing each of the following -// elements on a single line: `[f0, f1, f2, f3]`, old_vocab_file = [f1, f0, f3], -// `num_new_vocab = 3, new_vocab_offset = 1`, the returned remapping would be -// `[0, -1, 2]`. +// For example, if the input is // -// The op also returns a count of how many entries in the new vocabulary -// were present in the old vocabulary, which is used to calculate the number of -// values to initialize in a weight matrix remapping +// input_tensor = shape = [2, 7] +// [ a d e ] +// [b c ] // -// This functionality can be used to remap both row vocabularies (typically, -// features) and column vocabularies (typically, classes) from TensorFlow -// checkpoints. Note that the partitioning logic relies on contiguous vocabularies -// corresponding to div-partitioned variables. Moreover, the underlying remapping -// uses an IndexTable (as opposed to an inexact CuckooTable), so client code should -// use the corresponding index_table_from_file() as the FeatureColumn framework -// does (as opposed to tf.feature_to_id(), which uses a CuckooTable). +// Graphically the output tensors are: +// +// sparse_slice([0, 0], [2, 4]) = shape = [2, 4] +// [ a ] +// [b c ] +// +// sparse_slice([0, 4], [2, 3]) = shape = [2, 3] +// [ d e ] +// [ ] // // Arguments: -// new_vocab_file: Path to the new vocab file. -// old_vocab_file: Path to the old vocab file. -// new_vocab_offset: How many entries into the new vocab file to start reading. -// num_new_vocab: Number of entries in the new vocab file to remap. +// indices: 2-D tensor represents the indices of the sparse tensor. +// values: 1-D tensor represents the values of the sparse tensor. +// shape: 1-D. tensor represents the shape of the sparse tensor. +// start: 1-D. tensor represents the start of the slice. +// size: 1-D. tensor represents the size of the slice. +// output indices: A list of 1-D tensors represents the indices of the output +// sparse tensors. // -// Returns A Tensor of length num_new_vocab where the element at index i -// is equal to the old ID that maps to the new ID i. This element is -1 for any -// new ID that is not found in the old vocabulary.Number of new vocab entries found in old vocab. -func GenerateVocabRemapping(scope *Scope, new_vocab_file tf.Output, old_vocab_file tf.Output, new_vocab_offset int64, num_new_vocab int64, optional ...GenerateVocabRemappingAttr) (remapping tf.Output, num_present tf.Output) { +// Returns A list of 1-D tensors represents the values of the output sparse +// tensors.A list of 1-D tensors represents the shape of the output sparse +// tensors. +func SparseSlice(scope *Scope, indices tf.Output, values tf.Output, shape tf.Output, start tf.Output, size tf.Output) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"new_vocab_offset": new_vocab_offset, "num_new_vocab": num_new_vocab} - for _, a := range optional { - a(attrs) - } opspec := tf.OpSpec{ - Type: "GenerateVocabRemapping", + Type: "SparseSlice", Input: []tf.Input{ - new_vocab_file, old_vocab_file, + indices, values, shape, start, size, }, - Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) + return op.Output(0), op.Output(1), op.Output(2) } -// Assigns sparse updates to the variable referenced by `resource`. +// Reduces sparse updates into the variable referenced by `resource` using the `min` operation. // // This operation computes // // # Scalar indices -// ref[indices, ...] = updates[...] +// ref[indices, ...] = min(ref[indices, ...], updates[...]) // // # Vector indices (for each i) -// ref[indices[i], ...] = updates[i, ...] +// ref[indices[i], ...] = min(ref[indices[i], ...], updates[i, ...]) // // # High rank indices (for each i, ..., j) -// ref[indices[i, ..., j], ...] = updates[i, ..., j, ...] +// ref[indices[i, ..., j], ...] = min(ref[indices[i, ..., j], ...], updates[i, ..., j, ...]) +// +// Duplicate entries are handled correctly: if multiple `indices` reference +// the same location, their contributions are combined. +// +// Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`. +// +//
+// +//
// // Arguments: // resource: Should be from a `Variable` node. @@ -9142,12 +9164,12 @@ func GenerateVocabRemapping(scope *Scope, new_vocab_file tf.Output, old_vocab_fi // updates: A tensor of updated values to add to `ref`. // // Returns the created operation. -func ResourceScatterUpdate(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) { +func ResourceScatterMin(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "ResourceScatterUpdate", + Type: "ResourceScatterMin", Input: []tf.Input{ resource, indices, updates, }, @@ -9155,214 +9177,271 @@ func ResourceScatterUpdate(scope *Scope, resource tf.Output, indices tf.Output, return scope.AddOperation(opspec) } -// Creates and returns an empty tensor list. +// Reshapes a quantized tensor as per the Reshape op. // -// All list elements must be tensors of dtype element_dtype and shape compatible -// with element_shape. +// ``` // -// handle: an empty tensor list. -// element_dtype: the type of elements in the list. -// element_shape: a shape compatible with that of elements in the list. -func EmptyTensorList(scope *Scope, element_shape tf.Output, element_dtype tf.DataType) (handle tf.Output) { +// Arguments: +// +// shape: Defines the shape of the output tensor. +// input_min: The minimum value of the input. +// input_max: The maximum value of the input. +// +// Returns This value is copied from input_min.This value is copied from input_max. +func QuantizedReshape(scope *Scope, tensor tf.Output, shape tf.Output, input_min tf.Output, input_max tf.Output) (output tf.Output, output_min tf.Output, output_max tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"element_dtype": element_dtype} opspec := tf.OpSpec{ - Type: "EmptyTensorList", + Type: "QuantizedReshape", Input: []tf.Input{ - element_shape, + tensor, shape, input_min, input_max, }, - Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0) + return op.Output(0), op.Output(1), op.Output(2) } -// AvgPoolGradAttr is an optional argument to AvgPoolGrad. -type AvgPoolGradAttr func(optionalAttr) - -// AvgPoolGradDataFormat sets the optional data_format attribute to value. +// Returns the truth value of (x != y) element-wise. // -// value: Specify the data format of the input and output data. With the -// default format "NHWC", the data is stored in the order of: -// [batch, in_height, in_width, in_channels]. -// Alternatively, the format could be "NCHW", the data storage order of: -// [batch, in_channels, in_height, in_width]. -// If not specified, defaults to "NHWC" -func AvgPoolGradDataFormat(value string) AvgPoolGradAttr { - return func(m optionalAttr) { - m["data_format"] = value +// *NOTE*: `NotEqual` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func NotEqual(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "NotEqual", + Input: []tf.Input{ + x, y, + }, } + op := scope.AddOperation(opspec) + return op.Output(0) } -// Computes gradients of the average pooling function. +// Inverse 3D real-valued fast Fourier transform. // -// Arguments: -// orig_input_shape: 1-D. Shape of the original input to `avg_pool`. -// grad: 4-D with shape `[batch, height, width, channels]`. Gradients w.r.t. -// the output of `avg_pool`. -// ksize: The size of the sliding window for each dimension of the input. -// strides: The stride of the sliding window for each dimension of the input. -// padding: The type of padding algorithm to use. +// Computes the inverse 3-dimensional discrete Fourier transform of a real-valued +// signal over the inner-most 3 dimensions of `input`. // -// Returns 4-D. Gradients w.r.t. the input of `avg_pool`. -func AvgPoolGrad(scope *Scope, orig_input_shape tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...AvgPoolGradAttr) (output tf.Output) { +// The inner-most 3 dimensions of `input` are assumed to be the result of `RFFT3D`: +// The inner-most dimension contains the `fft_length / 2 + 1` unique components of +// the DFT of a real-valued signal. If `fft_length` is not provided, it is computed +// from the size of the inner-most 3 dimensions of `input`. If the FFT length used +// to compute `input` is odd, it should be provided since it cannot be inferred +// properly. +// +// Along each axis `IRFFT3D` is computed on, if `fft_length` (or +// `fft_length / 2 + 1` for the inner-most dimension) is smaller than the +// corresponding dimension of `input`, the dimension is cropped. If it is larger, +// the dimension is padded with zeros. +// +// Arguments: +// input: A complex64 tensor. +// fft_length: An int32 tensor of shape [3]. The FFT length for each dimension. +// +// Returns A float32 tensor of the same rank as `input`. The inner-most 3 +// dimensions of `input` are replaced with the `fft_length` samples of their +// inverse 3D real Fourier transform. +// +// @compatibility(numpy) +// Equivalent to np.irfftn with 3 dimensions. +// @end_compatibility +func IRFFT3D(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} - for _, a := range optional { - a(attrs) - } opspec := tf.OpSpec{ - Type: "AvgPoolGrad", + Type: "IRFFT3D", Input: []tf.Input{ - orig_input_shape, grad, + input, fft_length, }, - Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// StageClearAttr is an optional argument to StageClear. -type StageClearAttr func(optionalAttr) +// StringSplitAttr is an optional argument to StringSplit. +type StringSplitAttr func(optionalAttr) -// StageClearCapacity sets the optional capacity attribute to value. -// If not specified, defaults to 0 +// StringSplitSkipEmpty sets the optional skip_empty attribute to value. // -// REQUIRES: value >= 0 -func StageClearCapacity(value int64) StageClearAttr { +// value: A `bool`. If `True`, skip the empty strings from the result. +// If not specified, defaults to true +func StringSplitSkipEmpty(value bool) StringSplitAttr { return func(m optionalAttr) { - m["capacity"] = value + m["skip_empty"] = value } } -// StageClearMemoryLimit sets the optional memory_limit attribute to value. -// If not specified, defaults to 0 +// Split elements of `input` based on `delimiter` into a `SparseTensor`. // -// REQUIRES: value >= 0 -func StageClearMemoryLimit(value int64) StageClearAttr { - return func(m optionalAttr) { - m["memory_limit"] = value - } -} - -// StageClearContainer sets the optional container attribute to value. -// If not specified, defaults to "" -func StageClearContainer(value string) StageClearAttr { - return func(m optionalAttr) { - m["container"] = value - } -} - -// StageClearSharedName sets the optional shared_name attribute to value. -// If not specified, defaults to "" -func StageClearSharedName(value string) StageClearAttr { - return func(m optionalAttr) { - m["shared_name"] = value - } -} - -// Op removes all elements in the underlying container. +// Let N be the size of source (typically N will be the batch size). Split each +// element of `input` based on `delimiter` and return a `SparseTensor` +// containing the splitted tokens. Empty tokens are ignored. // -// Returns the created operation. -func StageClear(scope *Scope, dtypes []tf.DataType, optional ...StageClearAttr) (o *tf.Operation) { +// `delimiter` can be empty, or a string of split characters. If `delimiter` is an +// empty string, each element of `input` is split into individual single-byte +// character strings, including splitting of UTF-8 multibyte sequences. Otherwise +// every character of `delimiter` is a potential split point. +// +// For example: +// N = 2, input[0] is 'hello world' and input[1] is 'a b c', then the output +// will be +// +// indices = [0, 0; +// 0, 1; +// 1, 0; +// 1, 1; +// 1, 2] +// shape = [2, 3] +// values = ['hello', 'world', 'a', 'b', 'c'] +// +// Arguments: +// input: 1-D. Strings to split. +// delimiter: 0-D. Delimiter characters (bytes), or empty string. +// +// Returns A dense matrix of int64 representing the indices of the sparse tensor.A vector of strings corresponding to the splited values.a length-2 vector of int64 representing the shape of the sparse +// tensor, where the first value is N and the second value is the maximum number +// of tokens in a single input entry. +func StringSplit(scope *Scope, input tf.Output, delimiter tf.Output, optional ...StringSplitAttr) (indices tf.Output, values tf.Output, shape tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"dtypes": dtypes} + attrs := map[string]interface{}{} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "StageClear", - + Type: "StringSplit", + Input: []tf.Input{ + input, delimiter, + }, Attrs: attrs, } - return scope.AddOperation(opspec) + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) } -// ComputeAccidentalHitsAttr is an optional argument to ComputeAccidentalHits. -type ComputeAccidentalHitsAttr func(optionalAttr) +// ResourceSparseApplyMomentumAttr is an optional argument to ResourceSparseApplyMomentum. +type ResourceSparseApplyMomentumAttr func(optionalAttr) -// ComputeAccidentalHitsSeed sets the optional seed attribute to value. +// ResourceSparseApplyMomentumUseLocking sets the optional use_locking attribute to value. // -// value: If either seed or seed2 are set to be non-zero, the random number -// generator is seeded by the given seed. Otherwise, it is seeded by a -// random seed. -// If not specified, defaults to 0 -func ComputeAccidentalHitsSeed(value int64) ComputeAccidentalHitsAttr { +// value: If `True`, updating of the var and accum tensors will be protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceSparseApplyMomentumUseLocking(value bool) ResourceSparseApplyMomentumAttr { return func(m optionalAttr) { - m["seed"] = value + m["use_locking"] = value } } -// ComputeAccidentalHitsSeed2 sets the optional seed2 attribute to value. +// ResourceSparseApplyMomentumUseNesterov sets the optional use_nesterov attribute to value. // -// value: An second seed to avoid seed collision. -// If not specified, defaults to 0 -func ComputeAccidentalHitsSeed2(value int64) ComputeAccidentalHitsAttr { +// value: If `True`, the tensor passed to compute grad will be +// var - lr * momentum * accum, so in the end, the var you get is actually +// var - lr * momentum * accum. +// If not specified, defaults to false +func ResourceSparseApplyMomentumUseNesterov(value bool) ResourceSparseApplyMomentumAttr { return func(m optionalAttr) { - m["seed2"] = value + m["use_nesterov"] = value } } -// Computes the ids of the positions in sampled_candidates that match true_labels. +// Update relevant entries in '*var' and '*accum' according to the momentum scheme. // -// When doing log-odds NCE, the result of this op should be passed through a -// SparseToDense op, then added to the logits of the sampled candidates. This has -// the effect of 'removing' the sampled labels that match the true labels by -// making the classifier sure that they are sampled labels. +// Set use_nesterov = True if you want to use Nesterov momentum. +// +// That is for rows we have grad for, we update var and accum as follows: +// +// accum = accum * momentum + grad +// var -= lr * accum // // Arguments: -// true_classes: The true_classes output of UnpackSparseLabels. -// sampled_candidates: The sampled_candidates output of CandidateSampler. -// num_true: Number of true labels per context. +// var_: Should be from a Variable(). +// accum: Should be from a Variable(). +// lr: Learning rate. Must be a scalar. +// grad: The gradient. +// indices: A vector of indices into the first dimension of var and accum. +// momentum: Momentum. Must be a scalar. // -// Returns A vector of indices corresponding to rows of true_candidates.A vector of IDs of positions in sampled_candidates that match a true_label -// for the row with the corresponding index in indices.A vector of the same length as indices and ids, in which each element -// is -FLOAT_MAX. -func ComputeAccidentalHits(scope *Scope, true_classes tf.Output, sampled_candidates tf.Output, num_true int64, optional ...ComputeAccidentalHitsAttr) (indices tf.Output, ids tf.Output, weights tf.Output) { +// Returns the created operation. +func ResourceSparseApplyMomentum(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, indices tf.Output, momentum tf.Output, optional ...ResourceSparseApplyMomentumAttr) (o *tf.Operation) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"num_true": num_true} + attrs := map[string]interface{}{} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "ComputeAccidentalHits", + Type: "ResourceSparseApplyMomentum", Input: []tf.Input{ - true_classes, sampled_candidates, + var_, accum, lr, grad, indices, momentum, }, Attrs: attrs, } + return scope.AddOperation(opspec) +} + +// Returns the complex conjugate of a complex number. +// +// Given a tensor `input` of complex numbers, this operation returns a tensor of +// complex numbers that are the complex conjugate of each element in `input`. The +// complex numbers in `input` must be of the form \\(a + bj\\), where *a* is the +// real part and *b* is the imaginary part. +// +// The complex conjugate returned by this operation is of the form \\(a - bj\\). +// +// For example: +// +// ``` +// # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j] +// tf.conj(input) ==> [-2.25 - 4.75j, 3.25 - 5.75j] +// ``` +func Conj(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Conj", + Input: []tf.Input{ + input, + }, + } op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) + return op.Output(0) } -// QuantizedRelu6Attr is an optional argument to QuantizedRelu6. -type QuantizedRelu6Attr func(optionalAttr) +// ResizeBilinearAttr is an optional argument to ResizeBilinear. +type ResizeBilinearAttr func(optionalAttr) -// QuantizedRelu6OutType sets the optional out_type attribute to value. -// If not specified, defaults to DT_QUINT8 -func QuantizedRelu6OutType(value tf.DataType) QuantizedRelu6Attr { +// ResizeBilinearAlignCorners sets the optional align_corners attribute to value. +// +// value: If true, the centers of the 4 corner pixels of the input and output tensors are +// aligned, preserving the values at the corner pixels. Defaults to false. +// If not specified, defaults to false +func ResizeBilinearAlignCorners(value bool) ResizeBilinearAttr { return func(m optionalAttr) { - m["out_type"] = value + m["align_corners"] = value } } -// Computes Quantized Rectified Linear 6: `min(max(features, 0), 6)` +// Resize `images` to `size` using bilinear interpolation. // -// Arguments: +// Input images can be of different types but output images are always float. // -// min_features: The float value that the lowest quantized value represents. -// max_features: The float value that the highest quantized value represents. +// Arguments: +// images: 4-D with shape `[batch, height, width, channels]`. +// size: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The +// new size for the images. // -// Returns Has the same output shape as "features".The float value that the lowest quantized value represents.The float value that the highest quantized value represents. -func QuantizedRelu6(scope *Scope, features tf.Output, min_features tf.Output, max_features tf.Output, optional ...QuantizedRelu6Attr) (activations tf.Output, min_activations tf.Output, max_activations tf.Output) { +// Returns 4-D with shape +// `[batch, new_height, new_width, channels]`. +func ResizeBilinear(scope *Scope, images tf.Output, size tf.Output, optional ...ResizeBilinearAttr) (resized_images tf.Output) { if scope.Err() != nil { return } @@ -9371,176 +9450,222 @@ func QuantizedRelu6(scope *Scope, features tf.Output, min_features tf.Output, ma a(attrs) } opspec := tf.OpSpec{ - Type: "QuantizedRelu6", + Type: "ResizeBilinear", Input: []tf.Input{ - features, min_features, max_features, + images, size, }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) + return op.Output(0) } -// FixedLengthRecordReaderV2Attr is an optional argument to FixedLengthRecordReaderV2. -type FixedLengthRecordReaderV2Attr func(optionalAttr) - -// FixedLengthRecordReaderV2HeaderBytes sets the optional header_bytes attribute to value. -// -// value: Number of bytes in the header, defaults to 0. -// If not specified, defaults to 0 -func FixedLengthRecordReaderV2HeaderBytes(value int64) FixedLengthRecordReaderV2Attr { - return func(m optionalAttr) { - m["header_bytes"] = value +// Computes softsign: `features / (abs(features) + 1)`. +func Softsign(scope *Scope, features tf.Output) (activations tf.Output) { + if scope.Err() != nil { + return } -} - -// FixedLengthRecordReaderV2FooterBytes sets the optional footer_bytes attribute to value. -// -// value: Number of bytes in the footer, defaults to 0. -// If not specified, defaults to 0 -func FixedLengthRecordReaderV2FooterBytes(value int64) FixedLengthRecordReaderV2Attr { - return func(m optionalAttr) { - m["footer_bytes"] = value + opspec := tf.OpSpec{ + Type: "Softsign", + Input: []tf.Input{ + features, + }, } + op := scope.AddOperation(opspec) + return op.Output(0) } -// FixedLengthRecordReaderV2HopBytes sets the optional hop_bytes attribute to value. +// Creates a TensorList which, when stacked, has the value of `tensor`. // -// value: Number of bytes to hop before each read. Default of 0 means using -// record_bytes. -// If not specified, defaults to 0 -func FixedLengthRecordReaderV2HopBytes(value int64) FixedLengthRecordReaderV2Attr { - return func(m optionalAttr) { - m["hop_bytes"] = value - } -} - -// FixedLengthRecordReaderV2Container sets the optional container attribute to value. +// Each tensor in the result list corresponds to one row of the input tensor. // -// value: If non-empty, this reader is placed in the given container. -// Otherwise, a default container is used. -// If not specified, defaults to "" -func FixedLengthRecordReaderV2Container(value string) FixedLengthRecordReaderV2Attr { - return func(m optionalAttr) { - m["container"] = value +// tensor: The input tensor. +// output_handle: The list. +func TensorListFromTensor(scope *Scope, tensor tf.Output, element_shape tf.Output) (output_handle tf.Output) { + if scope.Err() != nil { + return } -} - -// FixedLengthRecordReaderV2SharedName sets the optional shared_name attribute to value. -// -// value: If non-empty, this reader is named in the given bucket -// with this shared_name. Otherwise, the node name is used instead. -// If not specified, defaults to "" -func FixedLengthRecordReaderV2SharedName(value string) FixedLengthRecordReaderV2Attr { - return func(m optionalAttr) { - m["shared_name"] = value + opspec := tf.OpSpec{ + Type: "TensorListFromTensor", + Input: []tf.Input{ + tensor, element_shape, + }, } + op := scope.AddOperation(opspec) + return op.Output(0) } -// FixedLengthRecordReaderV2Encoding sets the optional encoding attribute to value. +// GenerateVocabRemappingAttr is an optional argument to GenerateVocabRemapping. +type GenerateVocabRemappingAttr func(optionalAttr) + +// GenerateVocabRemappingOldVocabSize sets the optional old_vocab_size attribute to value. // -// value: The type of encoding for the file. Currently ZLIB and GZIP -// are supported. Defaults to none. -// If not specified, defaults to "" -func FixedLengthRecordReaderV2Encoding(value string) FixedLengthRecordReaderV2Attr { +// value: Number of entries in the old vocab file to consider. If -1, +// use the entire old vocabulary. +// If not specified, defaults to -1 +// +// REQUIRES: value >= -1 +func GenerateVocabRemappingOldVocabSize(value int64) GenerateVocabRemappingAttr { return func(m optionalAttr) { - m["encoding"] = value + m["old_vocab_size"] = value } } -// A Reader that outputs fixed-length records from a file. +// Given a path to new and old vocabulary files, returns a remapping Tensor of +// +// length `num_new_vocab`, where `remapping[i]` contains the row number in the old +// vocabulary that corresponds to row `i` in the new vocabulary (starting at line +// `new_vocab_offset` and up to `num_new_vocab` entities), or `-1` if entry `i` +// in the new vocabulary is not in the old vocabulary. The old vocabulary is +// constrained to the first `old_vocab_size` entries if `old_vocab_size` is not the +// default value of -1. +// +// `num_vocab_offset` enables +// use in the partitioned variable case, and should generally be set through +// examining partitioning info. The format of the files should be a text file, +// with each line containing a single entity within the vocabulary. +// +// For example, with `new_vocab_file` a text file containing each of the following +// elements on a single line: `[f0, f1, f2, f3]`, old_vocab_file = [f1, f0, f3], +// `num_new_vocab = 3, new_vocab_offset = 1`, the returned remapping would be +// `[0, -1, 2]`. +// +// The op also returns a count of how many entries in the new vocabulary +// were present in the old vocabulary, which is used to calculate the number of +// values to initialize in a weight matrix remapping +// +// This functionality can be used to remap both row vocabularies (typically, +// features) and column vocabularies (typically, classes) from TensorFlow +// checkpoints. Note that the partitioning logic relies on contiguous vocabularies +// corresponding to div-partitioned variables. Moreover, the underlying remapping +// uses an IndexTable (as opposed to an inexact CuckooTable), so client code should +// use the corresponding index_table_from_file() as the FeatureColumn framework +// does (as opposed to tf.feature_to_id(), which uses a CuckooTable). // // Arguments: -// record_bytes: Number of bytes in the record. +// new_vocab_file: Path to the new vocab file. +// old_vocab_file: Path to the old vocab file. +// new_vocab_offset: How many entries into the new vocab file to start reading. +// num_new_vocab: Number of entries in the new vocab file to remap. // -// Returns The handle to reference the Reader. -func FixedLengthRecordReaderV2(scope *Scope, record_bytes int64, optional ...FixedLengthRecordReaderV2Attr) (reader_handle tf.Output) { +// Returns A Tensor of length num_new_vocab where the element at index i +// is equal to the old ID that maps to the new ID i. This element is -1 for any +// new ID that is not found in the old vocabulary.Number of new vocab entries found in old vocab. +func GenerateVocabRemapping(scope *Scope, new_vocab_file tf.Output, old_vocab_file tf.Output, new_vocab_offset int64, num_new_vocab int64, optional ...GenerateVocabRemappingAttr) (remapping tf.Output, num_present tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"record_bytes": record_bytes} + attrs := map[string]interface{}{"new_vocab_offset": new_vocab_offset, "num_new_vocab": num_new_vocab} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "FixedLengthRecordReaderV2", - + Type: "GenerateVocabRemapping", + Input: []tf.Input{ + new_vocab_file, old_vocab_file, + }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0) + return op.Output(0), op.Output(1) } -// Converts each string in the input Tensor to its hash mod by a number of buckets. +// Assigns sparse updates to the variable referenced by `resource`. // -// The hash function is deterministic on the content of the string within the -// process. +// This operation computes // -// Note that the hash function may change from time to time. -// This functionality will be deprecated and it's recommended to use -// `tf.string_to_hash_bucket_fast()` or `tf.string_to_hash_bucket_strong()`. +// # Scalar indices +// ref[indices, ...] = updates[...] // -// Arguments: +// # Vector indices (for each i) +// ref[indices[i], ...] = updates[i, ...] // -// num_buckets: The number of buckets. +// # High rank indices (for each i, ..., j) +// ref[indices[i, ..., j], ...] = updates[i, ..., j, ...] // -// Returns A Tensor of the same shape as the input `string_tensor`. -func StringToHashBucket(scope *Scope, string_tensor tf.Output, num_buckets int64) (output tf.Output) { +// Arguments: +// resource: Should be from a `Variable` node. +// indices: A tensor of indices into the first dimension of `ref`. +// updates: A tensor of updated values to add to `ref`. +// +// Returns the created operation. +func ResourceScatterUpdate(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"num_buckets": num_buckets} opspec := tf.OpSpec{ - Type: "StringToHashBucket", + Type: "ResourceScatterUpdate", Input: []tf.Input{ - string_tensor, + resource, indices, updates, }, - Attrs: attrs, } - op := scope.AddOperation(opspec) - return op.Output(0) + return scope.AddOperation(opspec) } -// Computes gradients for the exponential linear (Elu) operation. +// Creates and returns an empty tensor list. // -// Arguments: -// gradients: The backpropagated gradients to the corresponding Elu operation. -// outputs: The outputs of the corresponding Elu operation. +// All list elements must be tensors of dtype element_dtype and shape compatible +// with element_shape. // -// Returns The gradients: `gradients * (outputs + 1)` if outputs < 0, -// `gradients` otherwise. -func EluGrad(scope *Scope, gradients tf.Output, outputs tf.Output) (backprops tf.Output) { +// handle: an empty tensor list. +// element_dtype: the type of elements in the list. +// element_shape: a shape compatible with that of elements in the list. +func EmptyTensorList(scope *Scope, element_shape tf.Output, element_dtype tf.DataType) (handle tf.Output) { if scope.Err() != nil { return } + attrs := map[string]interface{}{"element_dtype": element_dtype} opspec := tf.OpSpec{ - Type: "EluGrad", + Type: "EmptyTensorList", Input: []tf.Input{ - gradients, outputs, + element_shape, }, + Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// Creates a dataset that contains `count` elements from the `input_dataset`. -// -// Arguments: +// AvgPoolGradAttr is an optional argument to AvgPoolGrad. +type AvgPoolGradAttr func(optionalAttr) + +// AvgPoolGradDataFormat sets the optional data_format attribute to value. // -// count: A scalar representing the number of elements from the `input_dataset` -// that should be taken. A value of `-1` indicates that all of `input_dataset` -// is taken. +// value: Specify the data format of the input and output data. With the +// default format "NHWC", the data is stored in the order of: +// [batch, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCHW", the data storage order of: +// [batch, in_channels, in_height, in_width]. +// If not specified, defaults to "NHWC" +func AvgPoolGradDataFormat(value string) AvgPoolGradAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// Computes gradients of the average pooling function. // +// Arguments: +// orig_input_shape: 1-D. Shape of the original input to `avg_pool`. +// grad: 4-D with shape `[batch, height, width, channels]`. Gradients w.r.t. +// the output of `avg_pool`. +// ksize: The size of the sliding window for each dimension of the input. +// strides: The stride of the sliding window for each dimension of the input. +// padding: The type of padding algorithm to use. // -func TakeDataset(scope *Scope, input_dataset tf.Output, count tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { +// Returns 4-D. Gradients w.r.t. the input of `avg_pool`. +func AvgPoolGrad(scope *Scope, orig_input_shape tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...AvgPoolGradAttr) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "TakeDataset", + Type: "AvgPoolGrad", Input: []tf.Input{ - input_dataset, count, + orig_input_shape, grad, }, Attrs: attrs, } @@ -9548,375 +9673,274 @@ func TakeDataset(scope *Scope, input_dataset tf.Output, count tf.Output, output_ return op.Output(0) } -// The gradient operator for the SparseAdd op. +// StageClearAttr is an optional argument to StageClear. +type StageClearAttr func(optionalAttr) + +// StageClearCapacity sets the optional capacity attribute to value. +// If not specified, defaults to 0 // -// The SparseAdd op calculates A + B, where A, B, and the sum are all represented -// as `SparseTensor` objects. This op takes in the upstream gradient w.r.t. -// non-empty values of the sum, and outputs the gradients w.r.t. the non-empty -// values of A and B. +// REQUIRES: value >= 0 +func StageClearCapacity(value int64) StageClearAttr { + return func(m optionalAttr) { + m["capacity"] = value + } +} + +// StageClearMemoryLimit sets the optional memory_limit attribute to value. +// If not specified, defaults to 0 // -// Arguments: -// backprop_val_grad: 1-D with shape `[nnz(sum)]`. The gradient with respect to -// the non-empty values of the sum. -// a_indices: 2-D. The `indices` of the `SparseTensor` A, size `[nnz(A), ndims]`. -// b_indices: 2-D. The `indices` of the `SparseTensor` B, size `[nnz(B), ndims]`. -// sum_indices: 2-D. The `indices` of the sum `SparseTensor`, size -// `[nnz(sum), ndims]`. -// -// Returns 1-D with shape `[nnz(A)]`. The gradient with respect to the -// non-empty values of A.1-D with shape `[nnz(B)]`. The gradient with respect to the -// non-empty values of B. -func SparseAddGrad(scope *Scope, backprop_val_grad tf.Output, a_indices tf.Output, b_indices tf.Output, sum_indices tf.Output) (a_val_grad tf.Output, b_val_grad tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SparseAddGrad", - Input: []tf.Input{ - backprop_val_grad, a_indices, b_indices, sum_indices, - }, +// REQUIRES: value >= 0 +func StageClearMemoryLimit(value int64) StageClearAttr { + return func(m optionalAttr) { + m["memory_limit"] = value } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) } -// Computes atan of x element-wise. -func Atan(scope *Scope, x tf.Output) (y tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Atan", - Input: []tf.Input{ - x, - }, +// StageClearContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func StageClearContainer(value string) StageClearAttr { + return func(m optionalAttr) { + m["container"] = value } - op := scope.AddOperation(opspec) - return op.Output(0) } -// Encode audio data using the WAV file format. -// -// This operation will generate a string suitable to be saved out to create a .wav -// audio file. It will be encoded in the 16-bit PCM format. It takes in float -// values in the range -1.0f to 1.0f, and any outside that value will be clamped to -// that range. -// -// `audio` is a 2-D float Tensor of shape `[length, channels]`. -// `sample_rate` is a scalar Tensor holding the rate to use (e.g. 44100). -// -// Arguments: -// audio: 2-D with shape `[length, channels]`. -// sample_rate: Scalar containing the sample frequency. -// -// Returns 0-D. WAV-encoded file contents. -func EncodeWav(scope *Scope, audio tf.Output, sample_rate tf.Output) (contents tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "EncodeWav", - Input: []tf.Input{ - audio, sample_rate, - }, +// StageClearSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func StageClearSharedName(value string) StageClearAttr { + return func(m optionalAttr) { + m["shared_name"] = value } - op := scope.AddOperation(opspec) - return op.Output(0) } -// Converts each string in the input Tensor to its hash mod by a number of buckets. -// -// The hash function is deterministic on the content of the string within the -// process. The hash function is a keyed hash function, where attribute `key` -// defines the key of the hash function. `key` is an array of 2 elements. -// -// A strong hash is important when inputs may be malicious, e.g. URLs with -// additional components. Adversaries could try to make their inputs hash to the -// same bucket for a denial-of-service attack or to skew the results. A strong -// hash prevents this by making it difficult, if not infeasible, to compute inputs -// that hash to the same bucket. This comes at a cost of roughly 4x higher compute -// time than `tf.string_to_hash_bucket_fast`. -// -// Arguments: -// input: The strings to assign a hash bucket. -// num_buckets: The number of buckets. -// key: The key for the keyed hash function passed as a list of two uint64 -// elements. +// Op removes all elements in the underlying container. // -// Returns A Tensor of the same shape as the input `string_tensor`. -func StringToHashBucketStrong(scope *Scope, input tf.Output, num_buckets int64, key []int64) (output tf.Output) { +// Returns the created operation. +func StageClear(scope *Scope, dtypes []tf.DataType, optional ...StageClearAttr) (o *tf.Operation) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"num_buckets": num_buckets, "key": key} + attrs := map[string]interface{}{"dtypes": dtypes} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "StringToHashBucketStrong", - Input: []tf.Input{ - input, - }, + Type: "StageClear", + Attrs: attrs, } - op := scope.AddOperation(opspec) - return op.Output(0) + return scope.AddOperation(opspec) } -// RegexReplaceAttr is an optional argument to RegexReplace. -type RegexReplaceAttr func(optionalAttr) +// ComputeAccidentalHitsAttr is an optional argument to ComputeAccidentalHits. +type ComputeAccidentalHitsAttr func(optionalAttr) -// RegexReplaceReplaceGlobal sets the optional replace_global attribute to value. +// ComputeAccidentalHitsSeed sets the optional seed attribute to value. // -// value: If True, the replacement is global, otherwise the replacement -// is done only on the first match. -// If not specified, defaults to true -func RegexReplaceReplaceGlobal(value bool) RegexReplaceAttr { +// value: If either seed or seed2 are set to be non-zero, the random number +// generator is seeded by the given seed. Otherwise, it is seeded by a +// random seed. +// If not specified, defaults to 0 +func ComputeAccidentalHitsSeed(value int64) ComputeAccidentalHitsAttr { return func(m optionalAttr) { - m["replace_global"] = value + m["seed"] = value } } -// Replaces the match of pattern in input with rewrite. +// ComputeAccidentalHitsSeed2 sets the optional seed2 attribute to value. // -// It follows the re2 syntax (https://github.com/google/re2/wiki/Syntax) +// value: An second seed to avoid seed collision. +// If not specified, defaults to 0 +func ComputeAccidentalHitsSeed2(value int64) ComputeAccidentalHitsAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// Computes the ids of the positions in sampled_candidates that match true_labels. +// +// When doing log-odds NCE, the result of this op should be passed through a +// SparseToDense op, then added to the logits of the sampled candidates. This has +// the effect of 'removing' the sampled labels that match the true labels by +// making the classifier sure that they are sampled labels. // // Arguments: -// input: The text to be processed. -// pattern: The regular expression to match the input. -// rewrite: The rewrite to be applied to the matched expresion. +// true_classes: The true_classes output of UnpackSparseLabels. +// sampled_candidates: The sampled_candidates output of CandidateSampler. +// num_true: Number of true labels per context. // -// Returns The text after applying pattern and rewrite. -func RegexReplace(scope *Scope, input tf.Output, pattern tf.Output, rewrite tf.Output, optional ...RegexReplaceAttr) (output tf.Output) { +// Returns A vector of indices corresponding to rows of true_candidates.A vector of IDs of positions in sampled_candidates that match a true_label +// for the row with the corresponding index in indices.A vector of the same length as indices and ids, in which each element +// is -FLOAT_MAX. +func ComputeAccidentalHits(scope *Scope, true_classes tf.Output, sampled_candidates tf.Output, num_true int64, optional ...ComputeAccidentalHitsAttr) (indices tf.Output, ids tf.Output, weights tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} + attrs := map[string]interface{}{"num_true": num_true} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "RegexReplace", + Type: "ComputeAccidentalHits", Input: []tf.Input{ - input, pattern, rewrite, + true_classes, sampled_candidates, }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0) + return op.Output(0), op.Output(1), op.Output(2) } -// Computes numerical negative value element-wise. -// -// I.e., \\(y = -x\\). -func Neg(scope *Scope, x tf.Output) (y tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Neg", - Input: []tf.Input{ - x, - }, +// QuantizedRelu6Attr is an optional argument to QuantizedRelu6. +type QuantizedRelu6Attr func(optionalAttr) + +// QuantizedRelu6OutType sets the optional out_type attribute to value. +// If not specified, defaults to DT_QUINT8 +func QuantizedRelu6OutType(value tf.DataType) QuantizedRelu6Attr { + return func(m optionalAttr) { + m["out_type"] = value } - op := scope.AddOperation(opspec) - return op.Output(0) } -// Execute a sub graph on a remote processor. -// -// The graph specifications(such as graph itself, input tensors and output names) -// are stored as a serialized protocol buffer of RemoteFusedGraphExecuteInfo -// as serialized_remote_fused_graph_execute_info. -// The specifications will be passed to a dedicated registered -// remote fused graph executor. The executor will send the graph specifications -// to a remote processor and execute that graph. The execution results -// will be passed to consumer nodes as outputs of this node. +// Computes Quantized Rectified Linear 6: `min(max(features, 0), 6)` // // Arguments: -// inputs: Arbitrary number of tensors with arbitrary data types // -// serialized_remote_fused_graph_execute_info: Serialized protocol buffer -// of RemoteFusedGraphExecuteInfo which contains graph specifications. +// min_features: The float value that the lowest quantized value represents. +// max_features: The float value that the highest quantized value represents. // -// Returns Arbitrary number of tensors with arbitrary data types -func RemoteFusedGraphExecute(scope *Scope, inputs []tf.Output, Toutputs []tf.DataType, serialized_remote_fused_graph_execute_info string) (outputs []tf.Output) { +// Returns Has the same output shape as "features".The float value that the lowest quantized value represents.The float value that the highest quantized value represents. +func QuantizedRelu6(scope *Scope, features tf.Output, min_features tf.Output, max_features tf.Output, optional ...QuantizedRelu6Attr) (activations tf.Output, min_activations tf.Output, max_activations tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"Toutputs": Toutputs, "serialized_remote_fused_graph_execute_info": serialized_remote_fused_graph_execute_info} + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "RemoteFusedGraphExecute", + Type: "QuantizedRelu6", Input: []tf.Input{ - tf.OutputList(inputs), + features, min_features, max_features, }, Attrs: attrs, } op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil { - scope.UpdateErr("RemoteFusedGraphExecute", err) - return - } - return outputs + return op.Output(0), op.Output(1), op.Output(2) } -// MaxPool3DGradGradAttr is an optional argument to MaxPool3DGradGrad. -type MaxPool3DGradGradAttr func(optionalAttr) +// FixedLengthRecordReaderV2Attr is an optional argument to FixedLengthRecordReaderV2. +type FixedLengthRecordReaderV2Attr func(optionalAttr) -// MaxPool3DGradGradDataFormat sets the optional data_format attribute to value. +// FixedLengthRecordReaderV2HeaderBytes sets the optional header_bytes attribute to value. // -// value: The data format of the input and output data. With the -// default format "NDHWC", the data is stored in the order of: -// [batch, in_depth, in_height, in_width, in_channels]. -// Alternatively, the format could be "NCDHW", the data storage order is: -// [batch, in_channels, in_depth, in_height, in_width]. -// If not specified, defaults to "NDHWC" -func MaxPool3DGradGradDataFormat(value string) MaxPool3DGradGradAttr { +// value: Number of bytes in the header, defaults to 0. +// If not specified, defaults to 0 +func FixedLengthRecordReaderV2HeaderBytes(value int64) FixedLengthRecordReaderV2Attr { return func(m optionalAttr) { - m["data_format"] = value + m["header_bytes"] = value } } -// Computes second-order gradients of the maxpooling function. -// -// Arguments: -// orig_input: The original input tensor. -// orig_output: The original output tensor. -// grad: Output backprop of shape `[batch, depth, rows, cols, channels]`. -// ksize: 1-D tensor of length 5. The size of the window for each dimension of -// the input tensor. Must have `ksize[0] = ksize[4] = 1`. -// strides: 1-D tensor of length 5. The stride of the sliding window for each -// dimension of `input`. Must have `strides[0] = strides[4] = 1`. -// padding: The type of padding algorithm to use. +// FixedLengthRecordReaderV2FooterBytes sets the optional footer_bytes attribute to value. // -// Returns Gradients of gradients w.r.t. the input to `max_pool`. -func MaxPool3DGradGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPool3DGradGradAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} - for _, a := range optional { - a(attrs) +// value: Number of bytes in the footer, defaults to 0. +// If not specified, defaults to 0 +func FixedLengthRecordReaderV2FooterBytes(value int64) FixedLengthRecordReaderV2Attr { + return func(m optionalAttr) { + m["footer_bytes"] = value } - opspec := tf.OpSpec{ - Type: "MaxPool3DGradGrad", - Input: []tf.Input{ - orig_input, orig_output, grad, - }, - Attrs: attrs, +} + +// FixedLengthRecordReaderV2HopBytes sets the optional hop_bytes attribute to value. +// +// value: Number of bytes to hop before each read. Default of 0 means using +// record_bytes. +// If not specified, defaults to 0 +func FixedLengthRecordReaderV2HopBytes(value int64) FixedLengthRecordReaderV2Attr { + return func(m optionalAttr) { + m["hop_bytes"] = value } - op := scope.AddOperation(opspec) - return op.Output(0) } -// Conv3DBackpropFilterV2Attr is an optional argument to Conv3DBackpropFilterV2. -type Conv3DBackpropFilterV2Attr func(optionalAttr) +// FixedLengthRecordReaderV2Container sets the optional container attribute to value. +// +// value: If non-empty, this reader is placed in the given container. +// Otherwise, a default container is used. +// If not specified, defaults to "" +func FixedLengthRecordReaderV2Container(value string) FixedLengthRecordReaderV2Attr { + return func(m optionalAttr) { + m["container"] = value + } +} -// Conv3DBackpropFilterV2DataFormat sets the optional data_format attribute to value. +// FixedLengthRecordReaderV2SharedName sets the optional shared_name attribute to value. // -// value: The data format of the input and output data. With the -// default format "NDHWC", the data is stored in the order of: -// [batch, in_depth, in_height, in_width, in_channels]. -// Alternatively, the format could be "NCDHW", the data storage order is: -// [batch, in_channels, in_depth, in_height, in_width]. -// If not specified, defaults to "NDHWC" -func Conv3DBackpropFilterV2DataFormat(value string) Conv3DBackpropFilterV2Attr { +// value: If non-empty, this reader is named in the given bucket +// with this shared_name. Otherwise, the node name is used instead. +// If not specified, defaults to "" +func FixedLengthRecordReaderV2SharedName(value string) FixedLengthRecordReaderV2Attr { return func(m optionalAttr) { - m["data_format"] = value + m["shared_name"] = value } } -// Conv3DBackpropFilterV2Dilations sets the optional dilations attribute to value. +// FixedLengthRecordReaderV2Encoding sets the optional encoding attribute to value. // -// value: 1-D tensor of length 5. The dilation factor for each dimension of -// `input`. If set to k > 1, there will be k-1 skipped cells between each -// filter element on that dimension. The dimension order is determined by the -// value of `data_format`, see above for details. Dilations in the batch and -// depth dimensions must be 1. -// If not specified, defaults to -func Conv3DBackpropFilterV2Dilations(value []int64) Conv3DBackpropFilterV2Attr { +// value: The type of encoding for the file. Currently ZLIB and GZIP +// are supported. Defaults to none. +// If not specified, defaults to "" +func FixedLengthRecordReaderV2Encoding(value string) FixedLengthRecordReaderV2Attr { return func(m optionalAttr) { - m["dilations"] = value + m["encoding"] = value } } -// Computes the gradients of 3-D convolution with respect to the filter. +// A Reader that outputs fixed-length records from a file. // // Arguments: -// input: Shape `[batch, depth, rows, cols, in_channels]`. -// filter_sizes: An integer vector representing the tensor shape of `filter`, -// where `filter` is a 5-D -// `[filter_depth, filter_height, filter_width, in_channels, out_channels]` -// tensor. -// out_backprop: Backprop signal of shape `[batch, out_depth, out_rows, out_cols, -// out_channels]`. -// strides: 1-D tensor of length 5. The stride of the sliding window for each -// dimension of `input`. Must have `strides[0] = strides[4] = 1`. -// padding: The type of padding algorithm to use. -func Conv3DBackpropFilterV2(scope *Scope, input tf.Output, filter_sizes tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...Conv3DBackpropFilterV2Attr) (output tf.Output) { +// record_bytes: Number of bytes in the record. +// +// Returns The handle to reference the Reader. +func FixedLengthRecordReaderV2(scope *Scope, record_bytes int64, optional ...FixedLengthRecordReaderV2Attr) (reader_handle tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"strides": strides, "padding": padding} + attrs := map[string]interface{}{"record_bytes": record_bytes} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "Conv3DBackpropFilterV2", - Input: []tf.Input{ - input, filter_sizes, out_backprop, - }, + Type: "FixedLengthRecordReaderV2", + Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// FakeQuantWithMinMaxVarsAttr is an optional argument to FakeQuantWithMinMaxVars. -type FakeQuantWithMinMaxVarsAttr func(optionalAttr) - -// FakeQuantWithMinMaxVarsNumBits sets the optional num_bits attribute to value. -// If not specified, defaults to 8 -func FakeQuantWithMinMaxVarsNumBits(value int64) FakeQuantWithMinMaxVarsAttr { - return func(m optionalAttr) { - m["num_bits"] = value - } -} - -// FakeQuantWithMinMaxVarsNarrowRange sets the optional narrow_range attribute to value. -// If not specified, defaults to false -func FakeQuantWithMinMaxVarsNarrowRange(value bool) FakeQuantWithMinMaxVarsAttr { - return func(m optionalAttr) { - m["narrow_range"] = value - } -} - -// Fake-quantize the 'inputs' tensor of type float via global float scalars `min` +// Converts each string in the input Tensor to its hash mod by a number of buckets. // -// and `max` to 'outputs' tensor of same shape as `inputs`. +// The hash function is deterministic on the content of the string within the +// process. // -// `[min; max]` define the clamping range for the `inputs` data. -// `inputs` values are quantized into the quantization range (`[0; 2^num_bits - 1]` -// when `narrow_range` is false and `[1; 2^num_bits - 1]` when it is true) and -// then de-quantized and output as floats in `[min; max]` interval. -// `num_bits` is the bitwidth of the quantization; between 2 and 16, inclusive. +// Note that the hash function may change from time to time. +// This functionality will be deprecated and it's recommended to use +// `tf.string_to_hash_bucket_fast()` or `tf.string_to_hash_bucket_strong()`. // -// This operation has a gradient and thus allows for training `min` and `max` -// values. -func FakeQuantWithMinMaxVars(scope *Scope, inputs tf.Output, min tf.Output, max tf.Output, optional ...FakeQuantWithMinMaxVarsAttr) (outputs tf.Output) { +// Arguments: +// +// num_buckets: The number of buckets. +// +// Returns A Tensor of the same shape as the input `string_tensor`. +func StringToHashBucket(scope *Scope, string_tensor tf.Output, num_buckets int64) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } + attrs := map[string]interface{}{"num_buckets": num_buckets} opspec := tf.OpSpec{ - Type: "FakeQuantWithMinMaxVars", + Type: "StringToHashBucket", Input: []tf.Input{ - inputs, min, max, + string_tensor, }, Attrs: attrs, } @@ -9924,229 +9948,158 @@ func FakeQuantWithMinMaxVars(scope *Scope, inputs tf.Output, min tf.Output, max return op.Output(0) } -// Applies softmax to a batched N-D `SparseTensor`. -// -// The inputs represent an N-D SparseTensor with logical shape `[..., B, C]` -// (where `N >= 2`), and with indices sorted in the canonical lexicographic order. -// -// This op is equivalent to applying the normal `tf.nn.softmax()` to each innermost -// logical submatrix with shape `[B, C]`, but with the catch that *the implicitly -// zero elements do not participate*. Specifically, the algorithm is equivalent -// to the following: -// -// (1) Applies `tf.nn.softmax()` to a densified view of each innermost submatrix -// with shape `[B, C]`, along the size-C dimension; -// (2) Masks out the original implicitly-zero locations; -// (3) Renormalizes the remaining elements. -// -// Hence, the `SparseTensor` result has exactly the same non-zero indices and -// shape. +// Computes gradients for the exponential linear (Elu) operation. // // Arguments: -// sp_indices: 2-D. `NNZ x R` matrix with the indices of non-empty values in a -// SparseTensor, in canonical ordering. -// sp_values: 1-D. `NNZ` non-empty values corresponding to `sp_indices`. -// sp_shape: 1-D. Shape of the input SparseTensor. +// gradients: The backpropagated gradients to the corresponding Elu operation. +// outputs: The outputs of the corresponding Elu operation. // -// Returns 1-D. The `NNZ` values for the result `SparseTensor`. -func SparseSoftmax(scope *Scope, sp_indices tf.Output, sp_values tf.Output, sp_shape tf.Output) (output tf.Output) { +// Returns The gradients: `gradients * (outputs + 1)` if outputs < 0, +// `gradients` otherwise. +func EluGrad(scope *Scope, gradients tf.Output, outputs tf.Output) (backprops tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "SparseSoftmax", + Type: "EluGrad", Input: []tf.Input{ - sp_indices, sp_values, sp_shape, + gradients, outputs, }, } op := scope.AddOperation(opspec) return op.Output(0) } -// Partitions `data` into `num_partitions` tensors using indices from `partitions`. -// -// For each index tuple `js` of size `partitions.ndim`, the slice `data[js, ...]` -// becomes part of `outputs[partitions[js]]`. The slices with `partitions[js] = i` -// are placed in `outputs[i]` in lexicographic order of `js`, and the first -// dimension of `outputs[i]` is the number of entries in `partitions` equal to `i`. -// In detail, -// -// ```python -// outputs[i].shape = [sum(partitions == i)] + data.shape[partitions.ndim:] -// -// outputs[i] = pack([data[js, ...] for js if partitions[js] == i]) -// ``` -// -// `data.shape` must start with `partitions.shape`. -// -// For example: -// -// ```python -// # Scalar partitions. -// partitions = 1 -// num_partitions = 2 -// data = [10, 20] -// outputs[0] = [] # Empty with shape [0, 2] -// outputs[1] = [[10, 20]] -// -// # Vector partitions. -// partitions = [0, 0, 1, 1, 0] -// num_partitions = 2 -// data = [10, 20, 30, 40, 50] -// outputs[0] = [10, 20, 50] -// outputs[1] = [30, 40] -// ``` +// Creates a dataset that contains `count` elements from the `input_dataset`. // -// See `dynamic_stitch` for an example on how to merge partitions back. +// Arguments: // -//
-// -//
+// count: A scalar representing the number of elements from the `input_dataset` +// that should be taken. A value of `-1` indicates that all of `input_dataset` +// is taken. // -// Arguments: // -// partitions: Any shape. Indices in the range `[0, num_partitions)`. -// num_partitions: The number of partitions to output. -func DynamicPartition(scope *Scope, data tf.Output, partitions tf.Output, num_partitions int64) (outputs []tf.Output) { +func TakeDataset(scope *Scope, input_dataset tf.Output, count tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"num_partitions": num_partitions} + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} opspec := tf.OpSpec{ - Type: "DynamicPartition", + Type: "TakeDataset", Input: []tf.Input{ - data, partitions, + input_dataset, count, }, Attrs: attrs, } op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil { - scope.UpdateErr("DynamicPartition", err) - return - } - return outputs + return op.Output(0) } -// ResourceApplyAdagradAttr is an optional argument to ResourceApplyAdagrad. -type ResourceApplyAdagradAttr func(optionalAttr) - -// ResourceApplyAdagradUseLocking sets the optional use_locking attribute to value. +// The gradient operator for the SparseAdd op. // -// value: If `True`, updating of the var and accum tensors will be protected -// by a lock; otherwise the behavior is undefined, but may exhibit less -// contention. -// If not specified, defaults to false -func ResourceApplyAdagradUseLocking(value bool) ResourceApplyAdagradAttr { - return func(m optionalAttr) { - m["use_locking"] = value - } -} - -// ResourceApplyAdagradUpdateSlots sets the optional update_slots attribute to value. -// If not specified, defaults to true -func ResourceApplyAdagradUpdateSlots(value bool) ResourceApplyAdagradAttr { - return func(m optionalAttr) { - m["update_slots"] = value - } -} - -// Update '*var' according to the adagrad scheme. -// -// accum += grad * grad -// var -= lr * grad * (1 / sqrt(accum)) +// The SparseAdd op calculates A + B, where A, B, and the sum are all represented +// as `SparseTensor` objects. This op takes in the upstream gradient w.r.t. +// non-empty values of the sum, and outputs the gradients w.r.t. the non-empty +// values of A and B. // // Arguments: -// var_: Should be from a Variable(). -// accum: Should be from a Variable(). -// lr: Scaling factor. Must be a scalar. -// grad: The gradient. +// backprop_val_grad: 1-D with shape `[nnz(sum)]`. The gradient with respect to +// the non-empty values of the sum. +// a_indices: 2-D. The `indices` of the `SparseTensor` A, size `[nnz(A), ndims]`. +// b_indices: 2-D. The `indices` of the `SparseTensor` B, size `[nnz(B), ndims]`. +// sum_indices: 2-D. The `indices` of the sum `SparseTensor`, size +// `[nnz(sum), ndims]`. // -// Returns the created operation. -func ResourceApplyAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, optional ...ResourceApplyAdagradAttr) (o *tf.Operation) { +// Returns 1-D with shape `[nnz(A)]`. The gradient with respect to the +// non-empty values of A.1-D with shape `[nnz(B)]`. The gradient with respect to the +// non-empty values of B. +func SparseAddGrad(scope *Scope, backprop_val_grad tf.Output, a_indices tf.Output, b_indices tf.Output, sum_indices tf.Output) (a_val_grad tf.Output, b_val_grad tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } opspec := tf.OpSpec{ - Type: "ResourceApplyAdagrad", + Type: "SparseAddGrad", Input: []tf.Input{ - var_, accum, lr, grad, + backprop_val_grad, a_indices, b_indices, sum_indices, }, - Attrs: attrs, } - return scope.AddOperation(opspec) + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) } -// Return the shape of s0 op s1 with broadcast. -// -// Given `s0` and `s1`, tensors that represent shapes, compute `r0`, the -// broadcasted shape. `s0`, `s1` and `r0` are all integer vectors. -func BroadcastArgs(scope *Scope, s0 tf.Output, s1 tf.Output) (r0 tf.Output) { +// Computes atan of x element-wise. +func Atan(scope *Scope, x tf.Output) (y tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "BroadcastArgs", + Type: "Atan", Input: []tf.Input{ - s0, s1, + x, }, } op := scope.AddOperation(opspec) return op.Output(0) } -// DataFormatDimMapAttr is an optional argument to DataFormatDimMap. -type DataFormatDimMapAttr func(optionalAttr) - -// DataFormatDimMapSrcFormat sets the optional src_format attribute to value. +// Encode audio data using the WAV file format. // -// value: source data format. -// If not specified, defaults to "NHWC" -func DataFormatDimMapSrcFormat(value string) DataFormatDimMapAttr { - return func(m optionalAttr) { - m["src_format"] = value - } -} - -// DataFormatDimMapDstFormat sets the optional dst_format attribute to value. +// This operation will generate a string suitable to be saved out to create a .wav +// audio file. It will be encoded in the 16-bit PCM format. It takes in float +// values in the range -1.0f to 1.0f, and any outside that value will be clamped to +// that range. // -// value: destination data format. -// If not specified, defaults to "NCHW" -func DataFormatDimMapDstFormat(value string) DataFormatDimMapAttr { - return func(m optionalAttr) { - m["dst_format"] = value +// `audio` is a 2-D float Tensor of shape `[length, channels]`. +// `sample_rate` is a scalar Tensor holding the rate to use (e.g. 44100). +// +// Arguments: +// audio: 2-D with shape `[length, channels]`. +// sample_rate: Scalar containing the sample frequency. +// +// Returns 0-D. WAV-encoded file contents. +func EncodeWav(scope *Scope, audio tf.Output, sample_rate tf.Output) (contents tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "EncodeWav", + Input: []tf.Input{ + audio, sample_rate, + }, } + op := scope.AddOperation(opspec) + return op.Output(0) } -// Returns the dimension index in the destination data format given the one in +// Converts each string in the input Tensor to its hash mod by a number of buckets. // -// the source data format. +// The hash function is deterministic on the content of the string within the +// process. The hash function is a keyed hash function, where attribute `key` +// defines the key of the hash function. `key` is an array of 2 elements. +// +// A strong hash is important when inputs may be malicious, e.g. URLs with +// additional components. Adversaries could try to make their inputs hash to the +// same bucket for a denial-of-service attack or to skew the results. A strong +// hash prevents this by making it difficult, if not infeasible, to compute inputs +// that hash to the same bucket. This comes at a cost of roughly 4x higher compute +// time than `tf.string_to_hash_bucket_fast`. // // Arguments: -// x: A Tensor with each element as a dimension index in source data format. -// Must be in the range [-4, 4). +// input: The strings to assign a hash bucket. +// num_buckets: The number of buckets. +// key: The key for the keyed hash function passed as a list of two uint64 +// elements. // -// Returns A Tensor with each element as a dimension index in destination data format. -func DataFormatDimMap(scope *Scope, x tf.Output, optional ...DataFormatDimMapAttr) (y tf.Output) { +// Returns A Tensor of the same shape as the input `string_tensor`. +func StringToHashBucketStrong(scope *Scope, input tf.Output, num_buckets int64, key []int64) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } + attrs := map[string]interface{}{"num_buckets": num_buckets, "key": key} opspec := tf.OpSpec{ - Type: "DataFormatDimMap", + Type: "StringToHashBucketStrong", Input: []tf.Input{ - x, + input, }, Attrs: attrs, } @@ -10154,38 +10107,31 @@ func DataFormatDimMap(scope *Scope, x tf.Output, optional ...DataFormatDimMapAtt return op.Output(0) } -// ResourceApplyPowerSignAttr is an optional argument to ResourceApplyPowerSign. -type ResourceApplyPowerSignAttr func(optionalAttr) +// RegexReplaceAttr is an optional argument to RegexReplace. +type RegexReplaceAttr func(optionalAttr) -// ResourceApplyPowerSignUseLocking sets the optional use_locking attribute to value. +// RegexReplaceReplaceGlobal sets the optional replace_global attribute to value. // -// value: If `True`, updating of the var and m tensors is -// protected by a lock; otherwise the behavior is undefined, but may exhibit less -// contention. -// If not specified, defaults to false -func ResourceApplyPowerSignUseLocking(value bool) ResourceApplyPowerSignAttr { +// value: If True, the replacement is global, otherwise the replacement +// is done only on the first match. +// If not specified, defaults to true +func RegexReplaceReplaceGlobal(value bool) RegexReplaceAttr { return func(m optionalAttr) { - m["use_locking"] = value + m["replace_global"] = value } } -// Update '*var' according to the AddSign update. +// Replaces the match of pattern in input with rewrite. // -// m_t <- beta1 * m_{t-1} + (1 - beta1) * g -// update <- exp(logbase * sign_decay * sign(g) * sign(m_t)) * g -// variable <- variable - lr_t * update +// It follows the re2 syntax (https://github.com/google/re2/wiki/Syntax) // // Arguments: -// var_: Should be from a Variable(). -// m: Should be from a Variable(). -// lr: Scaling factor. Must be a scalar. -// logbase: Must be a scalar. -// sign_decay: Must be a scalar. -// beta: Must be a scalar. -// grad: The gradient. +// input: The text to be processed. +// pattern: The regular expression to match the input. +// rewrite: The rewrite to be applied to the matched expresion. // -// Returns the created operation. -func ResourceApplyPowerSign(scope *Scope, var_ tf.Output, m tf.Output, lr tf.Output, logbase tf.Output, sign_decay tf.Output, beta tf.Output, grad tf.Output, optional ...ResourceApplyPowerSignAttr) (o *tf.Operation) { +// Returns The text after applying pattern and rewrite. +func RegexReplace(scope *Scope, input tf.Output, pattern tf.Output, rewrite tf.Output, optional ...RegexReplaceAttr) (output tf.Output) { if scope.Err() != nil { return } @@ -10194,161 +10140,219 @@ func ResourceApplyPowerSign(scope *Scope, var_ tf.Output, m tf.Output, lr tf.Out a(attrs) } opspec := tf.OpSpec{ - Type: "ResourceApplyPowerSign", + Type: "RegexReplace", Input: []tf.Input{ - var_, m, lr, logbase, sign_decay, beta, grad, + input, pattern, rewrite, }, Attrs: attrs, } - return scope.AddOperation(opspec) + op := scope.AddOperation(opspec) + return op.Output(0) } -// Locks a mutex resource. The output is the lock. So long as the lock tensor -// -// is alive, any other request to use `MutexLock` with this mutex will wait. -// -// This is particularly useful for creating a critical section when used in -// conjunction with `MutexLockIdentity`: -// -// ```python -// -// mutex = mutex_v2( -// shared_name=handle_name, container=container, name=name) -// -// def execute_in_critical_section(fn, *args, **kwargs): -// lock = gen_resource_variable_ops.mutex_lock(mutex) -// -// with ops.control_dependencies([lock]): -// r = fn(*args, **kwargs) +// Computes numerical negative value element-wise. // -// with ops.control_dependencies(nest.flatten(r)): -// with ops.colocate_with(mutex): -// ensure_lock_exists = mutex_lock_identity(lock) +// I.e., \\(y = -x\\). +func Neg(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Neg", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Execute a sub graph on a remote processor. // -// # Make sure that if any element of r is accessed, all of -// # them are executed together. -// r = nest.map_structure(tf.identity, r) +// The graph specifications(such as graph itself, input tensors and output names) +// are stored as a serialized protocol buffer of RemoteFusedGraphExecuteInfo +// as serialized_remote_fused_graph_execute_info. +// The specifications will be passed to a dedicated registered +// remote fused graph executor. The executor will send the graph specifications +// to a remote processor and execute that graph. The execution results +// will be passed to consumer nodes as outputs of this node. // -// with ops.control_dependencies([ensure_lock_exists]): -// return nest.map_structure(tf.identity, r) -// ``` +// Arguments: +// inputs: Arbitrary number of tensors with arbitrary data types // -// While `fn` is running in the critical section, no other functions which wish to -// use this critical section may run. +// serialized_remote_fused_graph_execute_info: Serialized protocol buffer +// of RemoteFusedGraphExecuteInfo which contains graph specifications. // -// Often the use case is that two executions of the same graph, in parallel, -// wish to run `fn`; and we wish to ensure that only one of them executes -// at a time. This is especially important if `fn` modifies one or more -// variables at a time. +// Returns Arbitrary number of tensors with arbitrary data types +func RemoteFusedGraphExecute(scope *Scope, inputs []tf.Output, Toutputs []tf.DataType, serialized_remote_fused_graph_execute_info string) (outputs []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"Toutputs": Toutputs, "serialized_remote_fused_graph_execute_info": serialized_remote_fused_graph_execute_info} + opspec := tf.OpSpec{ + Type: "RemoteFusedGraphExecute", + Input: []tf.Input{ + tf.OutputList(inputs), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil { + scope.UpdateErr("RemoteFusedGraphExecute", err) + return + } + return outputs +} + +// MaxPool3DGradGradAttr is an optional argument to MaxPool3DGradGrad. +type MaxPool3DGradGradAttr func(optionalAttr) + +// MaxPool3DGradGradDataFormat sets the optional data_format attribute to value. // -// It is also useful if two separate functions must share a resource, but we -// wish to ensure the usage is exclusive. +// value: The data format of the input and output data. With the +// default format "NDHWC", the data is stored in the order of: +// [batch, in_depth, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCDHW", the data storage order is: +// [batch, in_channels, in_depth, in_height, in_width]. +// If not specified, defaults to "NDHWC" +func MaxPool3DGradGradDataFormat(value string) MaxPool3DGradGradAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// Computes second-order gradients of the maxpooling function. // // Arguments: -// mutex: The mutex resource to lock. +// orig_input: The original input tensor. +// orig_output: The original output tensor. +// grad: Output backprop of shape `[batch, depth, rows, cols, channels]`. +// ksize: 1-D tensor of length 5. The size of the window for each dimension of +// the input tensor. Must have `ksize[0] = ksize[4] = 1`. +// strides: 1-D tensor of length 5. The stride of the sliding window for each +// dimension of `input`. Must have `strides[0] = strides[4] = 1`. +// padding: The type of padding algorithm to use. // -// Returns A tensor that keeps a shared pointer to a lock on the mutex; -// when the Tensor is destroyed, the use count on the shared pointer is decreased -// by 1. When it reaches 0, the lock is released. -func MutexLock(scope *Scope, mutex tf.Output) (mutex_lock tf.Output) { +// Returns Gradients of gradients w.r.t. the input to `max_pool`. +func MaxPool3DGradGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPool3DGradGradAttr) (output tf.Output) { if scope.Err() != nil { return } + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "MutexLock", + Type: "MaxPool3DGradGrad", Input: []tf.Input{ - mutex, + orig_input, orig_output, grad, }, + Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// Computes the mean along segments of a tensor. -// -// Read @{$math_ops#Segmentation$the section on segmentation} for an explanation of -// segments. -// -// Computes a tensor such that -// \\(output_i = \frac{\sum_j data_j}{N}\\) where `mean` is -// over `j` such that `segment_ids[j] == i` and `N` is the total number of -// values summed. +// Conv3DBackpropFilterV2Attr is an optional argument to Conv3DBackpropFilterV2. +type Conv3DBackpropFilterV2Attr func(optionalAttr) + +// Conv3DBackpropFilterV2DataFormat sets the optional data_format attribute to value. // -// If the mean is empty for a given segment ID `i`, `output[i] = 0`. +// value: The data format of the input and output data. With the +// default format "NDHWC", the data is stored in the order of: +// [batch, in_depth, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCDHW", the data storage order is: +// [batch, in_channels, in_depth, in_height, in_width]. +// If not specified, defaults to "NDHWC" +func Conv3DBackpropFilterV2DataFormat(value string) Conv3DBackpropFilterV2Attr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// Conv3DBackpropFilterV2Dilations sets the optional dilations attribute to value. // -//
-// -//
+// value: 1-D tensor of length 5. The dilation factor for each dimension of +// `input`. If set to k > 1, there will be k-1 skipped cells between each +// filter element on that dimension. The dimension order is determined by the +// value of `data_format`, see above for details. Dilations in the batch and +// depth dimensions must be 1. +// If not specified, defaults to +func Conv3DBackpropFilterV2Dilations(value []int64) Conv3DBackpropFilterV2Attr { + return func(m optionalAttr) { + m["dilations"] = value + } +} + +// Computes the gradients of 3-D convolution with respect to the filter. // // Arguments: -// -// segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s -// first dimension. Values should be sorted and can be repeated. -// -// Returns Has same shape as data, except for dimension 0 which -// has size `k`, the number of segments. -func SegmentMean(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) { +// input: Shape `[batch, depth, rows, cols, in_channels]`. +// filter_sizes: An integer vector representing the tensor shape of `filter`, +// where `filter` is a 5-D +// `[filter_depth, filter_height, filter_width, in_channels, out_channels]` +// tensor. +// out_backprop: Backprop signal of shape `[batch, out_depth, out_rows, out_cols, +// out_channels]`. +// strides: 1-D tensor of length 5. The stride of the sliding window for each +// dimension of `input`. Must have `strides[0] = strides[4] = 1`. +// padding: The type of padding algorithm to use. +func Conv3DBackpropFilterV2(scope *Scope, input tf.Output, filter_sizes tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...Conv3DBackpropFilterV2Attr) (output tf.Output) { if scope.Err() != nil { return } + attrs := map[string]interface{}{"strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "SegmentMean", + Type: "Conv3DBackpropFilterV2", Input: []tf.Input{ - data, segment_ids, + input, filter_sizes, out_backprop, }, + Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// ResourceSparseApplyCenteredRMSPropAttr is an optional argument to ResourceSparseApplyCenteredRMSProp. -type ResourceSparseApplyCenteredRMSPropAttr func(optionalAttr) +// FakeQuantWithMinMaxVarsAttr is an optional argument to FakeQuantWithMinMaxVars. +type FakeQuantWithMinMaxVarsAttr func(optionalAttr) -// ResourceSparseApplyCenteredRMSPropUseLocking sets the optional use_locking attribute to value. -// -// value: If `True`, updating of the var, mg, ms, and mom tensors is -// protected by a lock; otherwise the behavior is undefined, but may exhibit less -// contention. -// If not specified, defaults to false -func ResourceSparseApplyCenteredRMSPropUseLocking(value bool) ResourceSparseApplyCenteredRMSPropAttr { +// FakeQuantWithMinMaxVarsNumBits sets the optional num_bits attribute to value. +// If not specified, defaults to 8 +func FakeQuantWithMinMaxVarsNumBits(value int64) FakeQuantWithMinMaxVarsAttr { return func(m optionalAttr) { - m["use_locking"] = value + m["num_bits"] = value } } -// Update '*var' according to the centered RMSProp algorithm. -// -// The centered RMSProp algorithm uses an estimate of the centered second moment -// (i.e., the variance) for normalization, as opposed to regular RMSProp, which -// uses the (uncentered) second moment. This often helps with training, but is -// slightly more expensive in terms of computation and memory. -// -// Note that in dense implementation of this algorithm, mg, ms, and mom will -// update even if the grad is zero, but in this sparse implementation, mg, ms, -// and mom will not update in iterations during which the grad is zero. -// -// mean_square = decay * mean_square + (1-decay) * gradient ** 2 -// mean_grad = decay * mean_grad + (1-decay) * gradient -// Delta = learning_rate * gradient / sqrt(mean_square + epsilon - mean_grad ** 2) -// -// ms <- rho * ms_{t-1} + (1-rho) * grad * grad -// mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon) -// var <- var - mom -// -// Arguments: -// var_: Should be from a Variable(). -// mg: Should be from a Variable(). -// ms: Should be from a Variable(). -// mom: Should be from a Variable(). -// lr: Scaling factor. Must be a scalar. -// rho: Decay rate. Must be a scalar. +// FakeQuantWithMinMaxVarsNarrowRange sets the optional narrow_range attribute to value. +// If not specified, defaults to false +func FakeQuantWithMinMaxVarsNarrowRange(value bool) FakeQuantWithMinMaxVarsAttr { + return func(m optionalAttr) { + m["narrow_range"] = value + } +} + +// Fake-quantize the 'inputs' tensor of type float via global float scalars `min` // -// epsilon: Ridge term. Must be a scalar. -// grad: The gradient. -// indices: A vector of indices into the first dimension of var, ms and mom. +// and `max` to 'outputs' tensor of same shape as `inputs`. // -// Returns the created operation. -func ResourceSparseApplyCenteredRMSProp(scope *Scope, var_ tf.Output, mg tf.Output, ms tf.Output, mom tf.Output, lr tf.Output, rho tf.Output, momentum tf.Output, epsilon tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyCenteredRMSPropAttr) (o *tf.Operation) { +// `[min; max]` define the clamping range for the `inputs` data. +// `inputs` values are quantized into the quantization range (`[0; 2^num_bits - 1]` +// when `narrow_range` is false and `[1; 2^num_bits - 1]` when it is true) and +// then de-quantized and output as floats in `[min; max]` interval. +// `num_bits` is the bitwidth of the quantization; between 2 and 16, inclusive. +// +// This operation has a gradient and thus allows for training `min` and `max` +// values. +func FakeQuantWithMinMaxVars(scope *Scope, inputs tf.Output, min tf.Output, max tf.Output, optional ...FakeQuantWithMinMaxVarsAttr) (outputs tf.Output) { if scope.Err() != nil { return } @@ -10357,174 +10361,228 @@ func ResourceSparseApplyCenteredRMSProp(scope *Scope, var_ tf.Output, mg tf.Outp a(attrs) } opspec := tf.OpSpec{ - Type: "ResourceSparseApplyCenteredRMSProp", + Type: "FakeQuantWithMinMaxVars", Input: []tf.Input{ - var_, mg, ms, mom, lr, rho, momentum, epsilon, grad, indices, + inputs, min, max, }, Attrs: attrs, } - return scope.AddOperation(opspec) + op := scope.AddOperation(opspec) + return op.Output(0) } -// Creates a dataset that batches `batch_size` elements from `input_dataset`. +// Applies softmax to a batched N-D `SparseTensor`. // -// Arguments: +// The inputs represent an N-D SparseTensor with logical shape `[..., B, C]` +// (where `N >= 2`), and with indices sorted in the canonical lexicographic order. // -// batch_size: A scalar representing the number of elements to accumulate in a -// batch. +// This op is equivalent to applying the normal `tf.nn.softmax()` to each innermost +// logical submatrix with shape `[B, C]`, but with the catch that *the implicitly +// zero elements do not participate*. Specifically, the algorithm is equivalent +// to the following: +// +// (1) Applies `tf.nn.softmax()` to a densified view of each innermost submatrix +// with shape `[B, C]`, along the size-C dimension; +// (2) Masks out the original implicitly-zero locations; +// (3) Renormalizes the remaining elements. // +// Hence, the `SparseTensor` result has exactly the same non-zero indices and +// shape. // -func BatchDataset(scope *Scope, input_dataset tf.Output, batch_size tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { +// Arguments: +// sp_indices: 2-D. `NNZ x R` matrix with the indices of non-empty values in a +// SparseTensor, in canonical ordering. +// sp_values: 1-D. `NNZ` non-empty values corresponding to `sp_indices`. +// sp_shape: 1-D. Shape of the input SparseTensor. +// +// Returns 1-D. The `NNZ` values for the result `SparseTensor`. +func SparseSoftmax(scope *Scope, sp_indices tf.Output, sp_values tf.Output, sp_shape tf.Output) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} opspec := tf.OpSpec{ - Type: "BatchDataset", + Type: "SparseSoftmax", Input: []tf.Input{ - input_dataset, batch_size, + sp_indices, sp_values, sp_shape, }, - Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// Says whether the targets are in the top `K` predictions. +// Partitions `data` into `num_partitions` tensors using indices from `partitions`. // -// This outputs a `batch_size` bool array, an entry `out[i]` is `true` if the -// prediction for the target class is among the top `k` predictions among -// all predictions for example `i`. Note that the behavior of `InTopK` differs -// from the `TopK` op in its handling of ties; if multiple classes have the -// same prediction value and straddle the top-`k` boundary, all of those -// classes are considered to be in the top `k`. +// For each index tuple `js` of size `partitions.ndim`, the slice `data[js, ...]` +// becomes part of `outputs[partitions[js]]`. The slices with `partitions[js] = i` +// are placed in `outputs[i]` in lexicographic order of `js`, and the first +// dimension of `outputs[i]` is the number of entries in `partitions` equal to `i`. +// In detail, // -// More formally, let +// ```python +// outputs[i].shape = [sum(partitions == i)] + data.shape[partitions.ndim:] // -// \\(predictions_i\\) be the predictions for all classes for example `i`, -// \\(targets_i\\) be the target class for example `i`, -// \\(out_i\\) be the output for example `i`, +// outputs[i] = pack([data[js, ...] for js if partitions[js] == i]) +// ``` // -// $$out_i = predictions_{i, targets_i} \in TopKIncludingTies(predictions_i)$$ +// `data.shape` must start with `partitions.shape`. +// +// For example: +// +// ```python +// # Scalar partitions. +// partitions = 1 +// num_partitions = 2 +// data = [10, 20] +// outputs[0] = [] # Empty with shape [0, 2] +// outputs[1] = [[10, 20]] +// +// # Vector partitions. +// partitions = [0, 0, 1, 1, 0] +// num_partitions = 2 +// data = [10, 20, 30, 40, 50] +// outputs[0] = [10, 20, 50] +// outputs[1] = [30, 40] +// ``` +// +// See `dynamic_stitch` for an example on how to merge partitions back. +// +//
+// +//
// // Arguments: -// predictions: A `batch_size` x `classes` tensor. -// targets: A `batch_size` vector of class ids. -// k: Number of top elements to look at for computing precision. // -// Returns Computed precision at `k` as a `bool Tensor`. -func InTopKV2(scope *Scope, predictions tf.Output, targets tf.Output, k tf.Output) (precision tf.Output) { +// partitions: Any shape. Indices in the range `[0, num_partitions)`. +// num_partitions: The number of partitions to output. +func DynamicPartition(scope *Scope, data tf.Output, partitions tf.Output, num_partitions int64) (outputs []tf.Output) { if scope.Err() != nil { return } + attrs := map[string]interface{}{"num_partitions": num_partitions} opspec := tf.OpSpec{ - Type: "InTopKV2", + Type: "DynamicPartition", Input: []tf.Input{ - predictions, targets, k, + data, partitions, }, + Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0) -} - -// DecodeAndCropJpegAttr is an optional argument to DecodeAndCropJpeg. -type DecodeAndCropJpegAttr func(optionalAttr) - -// DecodeAndCropJpegChannels sets the optional channels attribute to value. -// -// value: Number of color channels for the decoded image. -// If not specified, defaults to 0 -func DecodeAndCropJpegChannels(value int64) DecodeAndCropJpegAttr { - return func(m optionalAttr) { - m["channels"] = value + if scope.Err() != nil { + return } + var idx int + var err error + if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil { + scope.UpdateErr("DynamicPartition", err) + return + } + return outputs } -// DecodeAndCropJpegRatio sets the optional ratio attribute to value. +// ResourceApplyAdagradAttr is an optional argument to ResourceApplyAdagrad. +type ResourceApplyAdagradAttr func(optionalAttr) + +// ResourceApplyAdagradUseLocking sets the optional use_locking attribute to value. // -// value: Downscaling ratio. -// If not specified, defaults to 1 -func DecodeAndCropJpegRatio(value int64) DecodeAndCropJpegAttr { +// value: If `True`, updating of the var and accum tensors will be protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceApplyAdagradUseLocking(value bool) ResourceApplyAdagradAttr { return func(m optionalAttr) { - m["ratio"] = value + m["use_locking"] = value } } -// DecodeAndCropJpegFancyUpscaling sets the optional fancy_upscaling attribute to value. -// -// value: If true use a slower but nicer upscaling of the -// chroma planes (yuv420/422 only). +// ResourceApplyAdagradUpdateSlots sets the optional update_slots attribute to value. // If not specified, defaults to true -func DecodeAndCropJpegFancyUpscaling(value bool) DecodeAndCropJpegAttr { +func ResourceApplyAdagradUpdateSlots(value bool) ResourceApplyAdagradAttr { return func(m optionalAttr) { - m["fancy_upscaling"] = value + m["update_slots"] = value } } -// DecodeAndCropJpegTryRecoverTruncated sets the optional try_recover_truncated attribute to value. +// Update '*var' according to the adagrad scheme. // -// value: If true try to recover an image from truncated input. -// If not specified, defaults to false -func DecodeAndCropJpegTryRecoverTruncated(value bool) DecodeAndCropJpegAttr { - return func(m optionalAttr) { - m["try_recover_truncated"] = value +// accum += grad * grad +// var -= lr * grad * (1 / sqrt(accum)) +// +// Arguments: +// var_: Should be from a Variable(). +// accum: Should be from a Variable(). +// lr: Scaling factor. Must be a scalar. +// grad: The gradient. +// +// Returns the created operation. +func ResourceApplyAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, optional ...ResourceApplyAdagradAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceApplyAdagrad", + Input: []tf.Input{ + var_, accum, lr, grad, + }, + Attrs: attrs, } + return scope.AddOperation(opspec) } -// DecodeAndCropJpegAcceptableFraction sets the optional acceptable_fraction attribute to value. +// Return the shape of s0 op s1 with broadcast. // -// value: The minimum required fraction of lines before a truncated -// input is accepted. -// If not specified, defaults to 1 -func DecodeAndCropJpegAcceptableFraction(value float32) DecodeAndCropJpegAttr { - return func(m optionalAttr) { - m["acceptable_fraction"] = value +// Given `s0` and `s1`, tensors that represent shapes, compute `r0`, the +// broadcasted shape. `s0`, `s1` and `r0` are all integer vectors. +func BroadcastArgs(scope *Scope, s0 tf.Output, s1 tf.Output) (r0 tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "BroadcastArgs", + Input: []tf.Input{ + s0, s1, + }, } + op := scope.AddOperation(opspec) + return op.Output(0) } -// DecodeAndCropJpegDctMethod sets the optional dct_method attribute to value. +// DataFormatDimMapAttr is an optional argument to DataFormatDimMap. +type DataFormatDimMapAttr func(optionalAttr) + +// DataFormatDimMapSrcFormat sets the optional src_format attribute to value. // -// value: string specifying a hint about the algorithm used for -// decompression. Defaults to "" which maps to a system-specific -// default. Currently valid values are ["INTEGER_FAST", -// "INTEGER_ACCURATE"]. The hint may be ignored (e.g., the internal -// jpeg library changes to a version that does not have that specific -// option.) -// If not specified, defaults to "" -func DecodeAndCropJpegDctMethod(value string) DecodeAndCropJpegAttr { +// value: source data format. +// If not specified, defaults to "NHWC" +func DataFormatDimMapSrcFormat(value string) DataFormatDimMapAttr { return func(m optionalAttr) { - m["dct_method"] = value + m["src_format"] = value } } - -// Decode and Crop a JPEG-encoded image to a uint8 tensor. -// -// The attr `channels` indicates the desired number of color channels for the -// decoded image. -// -// Accepted values are: -// -// * 0: Use the number of channels in the JPEG-encoded image. -// * 1: output a grayscale image. -// * 3: output an RGB image. -// -// If needed, the JPEG-encoded image is transformed to match the requested number -// of color channels. -// -// The attr `ratio` allows downscaling the image by an integer factor during -// decoding. Allowed values are: 1, 2, 4, and 8. This is much faster than -// downscaling the image later. + +// DataFormatDimMapDstFormat sets the optional dst_format attribute to value. // +// value: destination data format. +// If not specified, defaults to "NCHW" +func DataFormatDimMapDstFormat(value string) DataFormatDimMapAttr { + return func(m optionalAttr) { + m["dst_format"] = value + } +} + +// Returns the dimension index in the destination data format given the one in // -// It is equivalent to a combination of decode and crop, but much faster by only -// decoding partial jpeg image. +// the source data format. // // Arguments: -// contents: 0-D. The JPEG-encoded image. -// crop_window: 1-D. The crop window: [crop_y, crop_x, crop_height, crop_width]. +// x: A Tensor with each element as a dimension index in source data format. +// Must be in the range [-4, 4). // -// Returns 3-D with shape `[height, width, channels]`.. -func DecodeAndCropJpeg(scope *Scope, contents tf.Output, crop_window tf.Output, optional ...DecodeAndCropJpegAttr) (image tf.Output) { +// Returns A Tensor with each element as a dimension index in destination data format. +func DataFormatDimMap(scope *Scope, x tf.Output, optional ...DataFormatDimMapAttr) (y tf.Output) { if scope.Err() != nil { return } @@ -10533,9 +10591,9 @@ func DecodeAndCropJpeg(scope *Scope, contents tf.Output, crop_window tf.Output, a(attrs) } opspec := tf.OpSpec{ - Type: "DecodeAndCropJpeg", + Type: "DataFormatDimMap", Input: []tf.Input{ - contents, crop_window, + x, }, Attrs: attrs, } @@ -10543,331 +10601,377 @@ func DecodeAndCropJpeg(scope *Scope, contents tf.Output, crop_window tf.Output, return op.Output(0) } -// AllCandidateSamplerAttr is an optional argument to AllCandidateSampler. -type AllCandidateSamplerAttr func(optionalAttr) - -// AllCandidateSamplerSeed sets the optional seed attribute to value. -// -// value: If either seed or seed2 are set to be non-zero, the random number -// generator is seeded by the given seed. Otherwise, it is seeded by a -// random seed. -// If not specified, defaults to 0 -func AllCandidateSamplerSeed(value int64) AllCandidateSamplerAttr { - return func(m optionalAttr) { - m["seed"] = value - } -} +// ResourceApplyPowerSignAttr is an optional argument to ResourceApplyPowerSign. +type ResourceApplyPowerSignAttr func(optionalAttr) -// AllCandidateSamplerSeed2 sets the optional seed2 attribute to value. +// ResourceApplyPowerSignUseLocking sets the optional use_locking attribute to value. // -// value: An second seed to avoid seed collision. -// If not specified, defaults to 0 -func AllCandidateSamplerSeed2(value int64) AllCandidateSamplerAttr { +// value: If `True`, updating of the var and m tensors is +// protected by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceApplyPowerSignUseLocking(value bool) ResourceApplyPowerSignAttr { return func(m optionalAttr) { - m["seed2"] = value + m["use_locking"] = value } } -// Generates labels for candidate sampling with a learned unigram distribution. -// -// See explanations of candidate sampling and the data formats at -// go/candidate-sampling. -// -// For each batch, this op picks a single set of sampled candidate labels. +// Update '*var' according to the AddSign update. // -// The advantages of sampling candidates per-batch are simplicity and the -// possibility of efficient dense matrix multiplication. The disadvantage is that -// the sampled candidates must be chosen independently of the context and of the -// true labels. +// m_t <- beta1 * m_{t-1} + (1 - beta1) * g +// update <- exp(logbase * sign_decay * sign(g) * sign(m_t)) * g +// variable <- variable - lr_t * update // // Arguments: -// true_classes: A batch_size * num_true matrix, in which each row contains the -// IDs of the num_true target_classes in the corresponding original label. -// num_true: Number of true labels per context. -// num_sampled: Number of candidates to produce. -// unique: If unique is true, we sample with rejection, so that all sampled -// candidates in a batch are unique. This requires some approximation to -// estimate the post-rejection sampling probabilities. +// var_: Should be from a Variable(). +// m: Should be from a Variable(). +// lr: Scaling factor. Must be a scalar. +// logbase: Must be a scalar. +// sign_decay: Must be a scalar. +// beta: Must be a scalar. +// grad: The gradient. // -// Returns A vector of length num_sampled, in which each element is -// the ID of a sampled candidate.A batch_size * num_true matrix, representing -// the number of times each candidate is expected to occur in a batch -// of sampled candidates. If unique=true, then this is a probability.A vector of length num_sampled, for each sampled -// candidate representing the number of times the candidate is expected -// to occur in a batch of sampled candidates. If unique=true, then this is a -// probability. -func AllCandidateSampler(scope *Scope, true_classes tf.Output, num_true int64, num_sampled int64, unique bool, optional ...AllCandidateSamplerAttr) (sampled_candidates tf.Output, true_expected_count tf.Output, sampled_expected_count tf.Output) { +// Returns the created operation. +func ResourceApplyPowerSign(scope *Scope, var_ tf.Output, m tf.Output, lr tf.Output, logbase tf.Output, sign_decay tf.Output, beta tf.Output, grad tf.Output, optional ...ResourceApplyPowerSignAttr) (o *tf.Operation) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"num_true": num_true, "num_sampled": num_sampled, "unique": unique} + attrs := map[string]interface{}{} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "AllCandidateSampler", + Type: "ResourceApplyPowerSign", Input: []tf.Input{ - true_classes, + var_, m, lr, logbase, sign_decay, beta, grad, }, Attrs: attrs, } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) + return scope.AddOperation(opspec) } -// Adds two `SparseTensor` objects to produce another `SparseTensor`. +// Locks a mutex resource. The output is the lock. So long as the lock tensor // -// The input `SparseTensor` objects' indices are assumed ordered in standard -// lexicographic order. If this is not the case, before this step run -// `SparseReorder` to restore index ordering. +// is alive, any other request to use `MutexLock` with this mutex will wait. // -// By default, if two values sum to zero at some index, the output `SparseTensor` -// would still include that particular location in its index, storing a zero in the -// corresponding value slot. To override this, callers can specify `thresh`, -// indicating that if the sum has a magnitude strictly smaller than `thresh`, its -// corresponding value and index would then not be included. In particular, -// `thresh == 0` (default) means everything is kept and actual thresholding happens -// only for a positive value. +// This is particularly useful for creating a critical section when used in +// conjunction with `MutexLockIdentity`: // -// In the following shapes, `nnz` is the count after taking `thresh` into account. +// ```python +// +// mutex = mutex_v2( +// shared_name=handle_name, container=container, name=name) +// +// def execute_in_critical_section(fn, *args, **kwargs): +// lock = gen_resource_variable_ops.mutex_lock(mutex) +// +// with ops.control_dependencies([lock]): +// r = fn(*args, **kwargs) +// +// with ops.control_dependencies(nest.flatten(r)): +// with ops.colocate_with(mutex): +// ensure_lock_exists = mutex_lock_identity(lock) +// +// # Make sure that if any element of r is accessed, all of +// # them are executed together. +// r = nest.map_structure(tf.identity, r) +// +// with ops.control_dependencies([ensure_lock_exists]): +// return nest.map_structure(tf.identity, r) +// ``` +// +// While `fn` is running in the critical section, no other functions which wish to +// use this critical section may run. +// +// Often the use case is that two executions of the same graph, in parallel, +// wish to run `fn`; and we wish to ensure that only one of them executes +// at a time. This is especially important if `fn` modifies one or more +// variables at a time. +// +// It is also useful if two separate functions must share a resource, but we +// wish to ensure the usage is exclusive. // // Arguments: -// a_indices: 2-D. The `indices` of the first `SparseTensor`, size `[nnz, ndims]` Matrix. -// a_values: 1-D. The `values` of the first `SparseTensor`, size `[nnz]` Vector. -// a_shape: 1-D. The `shape` of the first `SparseTensor`, size `[ndims]` Vector. -// b_indices: 2-D. The `indices` of the second `SparseTensor`, size `[nnz, ndims]` Matrix. -// b_values: 1-D. The `values` of the second `SparseTensor`, size `[nnz]` Vector. -// b_shape: 1-D. The `shape` of the second `SparseTensor`, size `[ndims]` Vector. -// thresh: 0-D. The magnitude threshold that determines if an output value/index -// pair takes space. -func SparseAdd(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b_indices tf.Output, b_values tf.Output, b_shape tf.Output, thresh tf.Output) (sum_indices tf.Output, sum_values tf.Output, sum_shape tf.Output) { +// mutex: The mutex resource to lock. +// +// Returns A tensor that keeps a shared pointer to a lock on the mutex; +// when the Tensor is destroyed, the use count on the shared pointer is decreased +// by 1. When it reaches 0, the lock is released. +func MutexLock(scope *Scope, mutex tf.Output) (mutex_lock tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "SparseAdd", + Type: "MutexLock", Input: []tf.Input{ - a_indices, a_values, a_shape, b_indices, b_values, b_shape, thresh, + mutex, }, } op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) + return op.Output(0) } -// OrderedMapPeekAttr is an optional argument to OrderedMapPeek. -type OrderedMapPeekAttr func(optionalAttr) - -// OrderedMapPeekCapacity sets the optional capacity attribute to value. -// If not specified, defaults to 0 +// Computes the mean along segments of a tensor. // -// REQUIRES: value >= 0 -func OrderedMapPeekCapacity(value int64) OrderedMapPeekAttr { - return func(m optionalAttr) { - m["capacity"] = value - } -} - -// OrderedMapPeekMemoryLimit sets the optional memory_limit attribute to value. -// If not specified, defaults to 0 +// Read @{$math_ops#Segmentation$the section on segmentation} for an explanation of +// segments. // -// REQUIRES: value >= 0 -func OrderedMapPeekMemoryLimit(value int64) OrderedMapPeekAttr { - return func(m optionalAttr) { - m["memory_limit"] = value +// Computes a tensor such that +// \\(output_i = \frac{\sum_j data_j}{N}\\) where `mean` is +// over `j` such that `segment_ids[j] == i` and `N` is the total number of +// values summed. +// +// If the mean is empty for a given segment ID `i`, `output[i] = 0`. +// +//
+// +//
+// +// Arguments: +// +// segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s +// first dimension. Values should be sorted and can be repeated. +// +// Returns Has same shape as data, except for dimension 0 which +// has size `k`, the number of segments. +func SegmentMean(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) { + if scope.Err() != nil { + return } -} - -// OrderedMapPeekContainer sets the optional container attribute to value. -// If not specified, defaults to "" -func OrderedMapPeekContainer(value string) OrderedMapPeekAttr { - return func(m optionalAttr) { - m["container"] = value + opspec := tf.OpSpec{ + Type: "SegmentMean", + Input: []tf.Input{ + data, segment_ids, + }, } + op := scope.AddOperation(opspec) + return op.Output(0) } -// OrderedMapPeekSharedName sets the optional shared_name attribute to value. -// If not specified, defaults to "" -func OrderedMapPeekSharedName(value string) OrderedMapPeekAttr { +// ResourceSparseApplyCenteredRMSPropAttr is an optional argument to ResourceSparseApplyCenteredRMSProp. +type ResourceSparseApplyCenteredRMSPropAttr func(optionalAttr) + +// ResourceSparseApplyCenteredRMSPropUseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, updating of the var, mg, ms, and mom tensors is +// protected by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceSparseApplyCenteredRMSPropUseLocking(value bool) ResourceSparseApplyCenteredRMSPropAttr { return func(m optionalAttr) { - m["shared_name"] = value + m["use_locking"] = value } } -// Op peeks at the values at the specified key. If the +// Update '*var' according to the centered RMSProp algorithm. +// +// The centered RMSProp algorithm uses an estimate of the centered second moment +// (i.e., the variance) for normalization, as opposed to regular RMSProp, which +// uses the (uncentered) second moment. This often helps with training, but is +// slightly more expensive in terms of computation and memory. +// +// Note that in dense implementation of this algorithm, mg, ms, and mom will +// update even if the grad is zero, but in this sparse implementation, mg, ms, +// and mom will not update in iterations during which the grad is zero. +// +// mean_square = decay * mean_square + (1-decay) * gradient ** 2 +// mean_grad = decay * mean_grad + (1-decay) * gradient +// Delta = learning_rate * gradient / sqrt(mean_square + epsilon - mean_grad ** 2) // -// underlying container does not contain this key -// this op will block until it does. This Op is optimized for -// performance. -func OrderedMapPeek(scope *Scope, key tf.Output, indices tf.Output, dtypes []tf.DataType, optional ...OrderedMapPeekAttr) (values []tf.Output) { +// ms <- rho * ms_{t-1} + (1-rho) * grad * grad +// mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon) +// var <- var - mom +// +// Arguments: +// var_: Should be from a Variable(). +// mg: Should be from a Variable(). +// ms: Should be from a Variable(). +// mom: Should be from a Variable(). +// lr: Scaling factor. Must be a scalar. +// rho: Decay rate. Must be a scalar. +// +// epsilon: Ridge term. Must be a scalar. +// grad: The gradient. +// indices: A vector of indices into the first dimension of var, ms and mom. +// +// Returns the created operation. +func ResourceSparseApplyCenteredRMSProp(scope *Scope, var_ tf.Output, mg tf.Output, ms tf.Output, mom tf.Output, lr tf.Output, rho tf.Output, momentum tf.Output, epsilon tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyCenteredRMSPropAttr) (o *tf.Operation) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"dtypes": dtypes} + attrs := map[string]interface{}{} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "OrderedMapPeek", + Type: "ResourceSparseApplyCenteredRMSProp", Input: []tf.Input{ - key, indices, + var_, mg, ms, mom, lr, rho, momentum, epsilon, grad, indices, }, Attrs: attrs, } - op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - if values, idx, err = makeOutputList(op, idx, "values"); err != nil { - scope.UpdateErr("OrderedMapPeek", err) - return - } - return values + return scope.AddOperation(opspec) } -// Inverse fast Fourier transform. -// -// Computes the inverse 1-dimensional discrete Fourier transform over the -// inner-most dimension of `input`. +// Creates a dataset that batches `batch_size` elements from `input_dataset`. // // Arguments: -// input: A complex64 tensor. // -// Returns A complex64 tensor of the same shape as `input`. The inner-most -// dimension of `input` is replaced with its inverse 1D Fourier transform. +// batch_size: A scalar representing the number of elements to accumulate in a +// batch. // -// @compatibility(numpy) -// Equivalent to np.fft.ifft -// @end_compatibility -func IFFT(scope *Scope, input tf.Output) (output tf.Output) { +// +func BatchDataset(scope *Scope, input_dataset tf.Output, batch_size tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { if scope.Err() != nil { return } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} opspec := tf.OpSpec{ - Type: "IFFT", + Type: "BatchDataset", Input: []tf.Input{ - input, + input_dataset, batch_size, }, + Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// Generates values in an interval. +// Says whether the targets are in the top `K` predictions. // -// A sequence of `num` evenly-spaced values are generated beginning at `start`. -// If `num > 1`, the values in the sequence increase by `stop - start / num - 1`, -// so that the last one is exactly `stop`. +// This outputs a `batch_size` bool array, an entry `out[i]` is `true` if the +// prediction for the target class is among the top `k` predictions among +// all predictions for example `i`. Note that the behavior of `InTopK` differs +// from the `TopK` op in its handling of ties; if multiple classes have the +// same prediction value and straddle the top-`k` boundary, all of those +// classes are considered to be in the top `k`. // -// For example: +// More formally, let // -// ``` -// tf.linspace(10.0, 12.0, 3, name="linspace") => [ 10.0 11.0 12.0] -// ``` +// \\(predictions_i\\) be the predictions for all classes for example `i`, +// \\(targets_i\\) be the target class for example `i`, +// \\(out_i\\) be the output for example `i`, +// +// $$out_i = predictions_{i, targets_i} \in TopKIncludingTies(predictions_i)$$ // // Arguments: -// start: First entry in the range. -// stop: Last entry in the range. -// num: Number of values to generate. +// predictions: A `batch_size` x `classes` tensor. +// targets: A `batch_size` vector of class ids. +// k: Number of top elements to look at for computing precision. // -// Returns 1-D. The generated values. -func LinSpace(scope *Scope, start tf.Output, stop tf.Output, num tf.Output) (output tf.Output) { +// Returns Computed precision at `k` as a `bool Tensor`. +func InTopKV2(scope *Scope, predictions tf.Output, targets tf.Output, k tf.Output) (precision tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "LinSpace", + Type: "InTopKV2", Input: []tf.Input{ - start, stop, num, + predictions, targets, k, }, } op := scope.AddOperation(opspec) return op.Output(0) } -// DestroyResourceOpAttr is an optional argument to DestroyResourceOp. -type DestroyResourceOpAttr func(optionalAttr) +// DecodeAndCropJpegAttr is an optional argument to DecodeAndCropJpeg. +type DecodeAndCropJpegAttr func(optionalAttr) -// DestroyResourceOpIgnoreLookupError sets the optional ignore_lookup_error attribute to value. +// DecodeAndCropJpegChannels sets the optional channels attribute to value. // -// value: whether to ignore the error when the resource -// doesn't exist. -// If not specified, defaults to true -func DestroyResourceOpIgnoreLookupError(value bool) DestroyResourceOpAttr { +// value: Number of color channels for the decoded image. +// If not specified, defaults to 0 +func DecodeAndCropJpegChannels(value int64) DecodeAndCropJpegAttr { return func(m optionalAttr) { - m["ignore_lookup_error"] = value + m["channels"] = value } } -// Deletes the resource specified by the handle. -// -// All subsequent operations using the resource will result in a NotFound -// error status. -// -// Arguments: -// resource: handle to the resource to delete. +// DecodeAndCropJpegRatio sets the optional ratio attribute to value. // -// Returns the created operation. -func DestroyResourceOp(scope *Scope, resource tf.Output, optional ...DestroyResourceOpAttr) (o *tf.Operation) { - if scope.Err() != nil { - return +// value: Downscaling ratio. +// If not specified, defaults to 1 +func DecodeAndCropJpegRatio(value int64) DecodeAndCropJpegAttr { + return func(m optionalAttr) { + m["ratio"] = value } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) +} + +// DecodeAndCropJpegFancyUpscaling sets the optional fancy_upscaling attribute to value. +// +// value: If true use a slower but nicer upscaling of the +// chroma planes (yuv420/422 only). +// If not specified, defaults to true +func DecodeAndCropJpegFancyUpscaling(value bool) DecodeAndCropJpegAttr { + return func(m optionalAttr) { + m["fancy_upscaling"] = value } - opspec := tf.OpSpec{ - Type: "DestroyResourceOp", - Input: []tf.Input{ - resource, - }, - Attrs: attrs, +} + +// DecodeAndCropJpegTryRecoverTruncated sets the optional try_recover_truncated attribute to value. +// +// value: If true try to recover an image from truncated input. +// If not specified, defaults to false +func DecodeAndCropJpegTryRecoverTruncated(value bool) DecodeAndCropJpegAttr { + return func(m optionalAttr) { + m["try_recover_truncated"] = value } - return scope.AddOperation(opspec) } -// ResourceSparseApplyRMSPropAttr is an optional argument to ResourceSparseApplyRMSProp. -type ResourceSparseApplyRMSPropAttr func(optionalAttr) +// DecodeAndCropJpegAcceptableFraction sets the optional acceptable_fraction attribute to value. +// +// value: The minimum required fraction of lines before a truncated +// input is accepted. +// If not specified, defaults to 1 +func DecodeAndCropJpegAcceptableFraction(value float32) DecodeAndCropJpegAttr { + return func(m optionalAttr) { + m["acceptable_fraction"] = value + } +} -// ResourceSparseApplyRMSPropUseLocking sets the optional use_locking attribute to value. +// DecodeAndCropJpegDctMethod sets the optional dct_method attribute to value. // -// value: If `True`, updating of the var, ms, and mom tensors is protected -// by a lock; otherwise the behavior is undefined, but may exhibit less -// contention. -// If not specified, defaults to false -func ResourceSparseApplyRMSPropUseLocking(value bool) ResourceSparseApplyRMSPropAttr { +// value: string specifying a hint about the algorithm used for +// decompression. Defaults to "" which maps to a system-specific +// default. Currently valid values are ["INTEGER_FAST", +// "INTEGER_ACCURATE"]. The hint may be ignored (e.g., the internal +// jpeg library changes to a version that does not have that specific +// option.) +// If not specified, defaults to "" +func DecodeAndCropJpegDctMethod(value string) DecodeAndCropJpegAttr { return func(m optionalAttr) { - m["use_locking"] = value + m["dct_method"] = value } } -// Update '*var' according to the RMSProp algorithm. +// Decode and Crop a JPEG-encoded image to a uint8 tensor. // -// Note that in dense implementation of this algorithm, ms and mom will -// update even if the grad is zero, but in this sparse implementation, ms -// and mom will not update in iterations during which the grad is zero. +// The attr `channels` indicates the desired number of color channels for the +// decoded image. // -// mean_square = decay * mean_square + (1-decay) * gradient ** 2 -// Delta = learning_rate * gradient / sqrt(mean_square + epsilon) +// Accepted values are: // -// ms <- rho * ms_{t-1} + (1-rho) * grad * grad -// mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon) -// var <- var - mom +// * 0: Use the number of channels in the JPEG-encoded image. +// * 1: output a grayscale image. +// * 3: output an RGB image. // -// Arguments: -// var_: Should be from a Variable(). -// ms: Should be from a Variable(). -// mom: Should be from a Variable(). -// lr: Scaling factor. Must be a scalar. -// rho: Decay rate. Must be a scalar. +// If needed, the JPEG-encoded image is transformed to match the requested number +// of color channels. // -// epsilon: Ridge term. Must be a scalar. -// grad: The gradient. -// indices: A vector of indices into the first dimension of var, ms and mom. +// The attr `ratio` allows downscaling the image by an integer factor during +// decoding. Allowed values are: 1, 2, 4, and 8. This is much faster than +// downscaling the image later. // -// Returns the created operation. -func ResourceSparseApplyRMSProp(scope *Scope, var_ tf.Output, ms tf.Output, mom tf.Output, lr tf.Output, rho tf.Output, momentum tf.Output, epsilon tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyRMSPropAttr) (o *tf.Operation) { +// +// It is equivalent to a combination of decode and crop, but much faster by only +// decoding partial jpeg image. +// +// Arguments: +// contents: 0-D. The JPEG-encoded image. +// crop_window: 1-D. The crop window: [crop_y, crop_x, crop_height, crop_width]. +// +// Returns 3-D with shape `[height, width, channels]`.. +func DecodeAndCropJpeg(scope *Scope, contents tf.Output, crop_window tf.Output, optional ...DecodeAndCropJpegAttr) (image tf.Output) { if scope.Err() != nil { return } @@ -10876,319 +10980,268 @@ func ResourceSparseApplyRMSProp(scope *Scope, var_ tf.Output, ms tf.Output, mom a(attrs) } opspec := tf.OpSpec{ - Type: "ResourceSparseApplyRMSProp", + Type: "DecodeAndCropJpeg", Input: []tf.Input{ - var_, ms, mom, lr, rho, momentum, epsilon, grad, indices, + contents, crop_window, }, Attrs: attrs, } - return scope.AddOperation(opspec) -} - -// Returns the truth value of (x > y) element-wise. -// -// *NOTE*: `Greater` supports broadcasting. More about broadcasting -// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) -func Greater(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Greater", - Input: []tf.Input{ - x, y, - }, - } op := scope.AddOperation(opspec) return op.Output(0) } -// SampleDistortedBoundingBoxAttr is an optional argument to SampleDistortedBoundingBox. -type SampleDistortedBoundingBoxAttr func(optionalAttr) +// AllCandidateSamplerAttr is an optional argument to AllCandidateSampler. +type AllCandidateSamplerAttr func(optionalAttr) -// SampleDistortedBoundingBoxSeed sets the optional seed attribute to value. +// AllCandidateSamplerSeed sets the optional seed attribute to value. // -// value: If either `seed` or `seed2` are set to non-zero, the random number -// generator is seeded by the given `seed`. Otherwise, it is seeded by a random -// seed. +// value: If either seed or seed2 are set to be non-zero, the random number +// generator is seeded by the given seed. Otherwise, it is seeded by a +// random seed. // If not specified, defaults to 0 -func SampleDistortedBoundingBoxSeed(value int64) SampleDistortedBoundingBoxAttr { +func AllCandidateSamplerSeed(value int64) AllCandidateSamplerAttr { return func(m optionalAttr) { m["seed"] = value } } -// SampleDistortedBoundingBoxSeed2 sets the optional seed2 attribute to value. +// AllCandidateSamplerSeed2 sets the optional seed2 attribute to value. // -// value: A second seed to avoid seed collision. +// value: An second seed to avoid seed collision. // If not specified, defaults to 0 -func SampleDistortedBoundingBoxSeed2(value int64) SampleDistortedBoundingBoxAttr { +func AllCandidateSamplerSeed2(value int64) AllCandidateSamplerAttr { return func(m optionalAttr) { m["seed2"] = value } } -// SampleDistortedBoundingBoxMinObjectCovered sets the optional min_object_covered attribute to value. +// Generates labels for candidate sampling with a learned unigram distribution. // -// value: The cropped area of the image must contain at least this -// fraction of any bounding box supplied. The value of this parameter should be -// non-negative. In the case of 0, the cropped area does not need to overlap -// any of the bounding boxes supplied. -// If not specified, defaults to 0.1 -func SampleDistortedBoundingBoxMinObjectCovered(value float32) SampleDistortedBoundingBoxAttr { - return func(m optionalAttr) { - m["min_object_covered"] = value - } -} - -// SampleDistortedBoundingBoxAspectRatioRange sets the optional aspect_ratio_range attribute to value. +// See explanations of candidate sampling and the data formats at +// go/candidate-sampling. // -// value: The cropped area of the image must have an aspect ratio = -// width / height within this range. -// If not specified, defaults to -func SampleDistortedBoundingBoxAspectRatioRange(value []float32) SampleDistortedBoundingBoxAttr { - return func(m optionalAttr) { - m["aspect_ratio_range"] = value - } -} - -// SampleDistortedBoundingBoxAreaRange sets the optional area_range attribute to value. +// For each batch, this op picks a single set of sampled candidate labels. // -// value: The cropped area of the image must contain a fraction of the -// supplied image within in this range. -// If not specified, defaults to -func SampleDistortedBoundingBoxAreaRange(value []float32) SampleDistortedBoundingBoxAttr { - return func(m optionalAttr) { - m["area_range"] = value - } -} - -// SampleDistortedBoundingBoxMaxAttempts sets the optional max_attempts attribute to value. +// The advantages of sampling candidates per-batch are simplicity and the +// possibility of efficient dense matrix multiplication. The disadvantage is that +// the sampled candidates must be chosen independently of the context and of the +// true labels. // -// value: Number of attempts at generating a cropped region of the image -// of the specified constraints. After `max_attempts` failures, return the entire -// image. -// If not specified, defaults to 100 -func SampleDistortedBoundingBoxMaxAttempts(value int64) SampleDistortedBoundingBoxAttr { - return func(m optionalAttr) { - m["max_attempts"] = value - } -} - -// SampleDistortedBoundingBoxUseImageIfNoBoundingBoxes sets the optional use_image_if_no_bounding_boxes attribute to value. +// Arguments: +// true_classes: A batch_size * num_true matrix, in which each row contains the +// IDs of the num_true target_classes in the corresponding original label. +// num_true: Number of true labels per context. +// num_sampled: Number of candidates to produce. +// unique: If unique is true, we sample with rejection, so that all sampled +// candidates in a batch are unique. This requires some approximation to +// estimate the post-rejection sampling probabilities. // -// value: Controls behavior if no bounding boxes supplied. -// If true, assume an implicit bounding box covering the whole input. If false, -// raise an error. -// If not specified, defaults to false -func SampleDistortedBoundingBoxUseImageIfNoBoundingBoxes(value bool) SampleDistortedBoundingBoxAttr { - return func(m optionalAttr) { - m["use_image_if_no_bounding_boxes"] = value +// Returns A vector of length num_sampled, in which each element is +// the ID of a sampled candidate.A batch_size * num_true matrix, representing +// the number of times each candidate is expected to occur in a batch +// of sampled candidates. If unique=true, then this is a probability.A vector of length num_sampled, for each sampled +// candidate representing the number of times the candidate is expected +// to occur in a batch of sampled candidates. If unique=true, then this is a +// probability. +func AllCandidateSampler(scope *Scope, true_classes tf.Output, num_true int64, num_sampled int64, unique bool, optional ...AllCandidateSamplerAttr) (sampled_candidates tf.Output, true_expected_count tf.Output, sampled_expected_count tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_true": num_true, "num_sampled": num_sampled, "unique": unique} + for _, a := range optional { + a(attrs) } + opspec := tf.OpSpec{ + Type: "AllCandidateSampler", + Input: []tf.Input{ + true_classes, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) } -// Generate a single randomly distorted bounding box for an image. -// -// Bounding box annotations are often supplied in addition to ground-truth labels -// in image recognition or object localization tasks. A common technique for -// training such a system is to randomly distort an image while preserving -// its content, i.e. *data augmentation*. This Op outputs a randomly distorted -// localization of an object, i.e. bounding box, given an `image_size`, -// `bounding_boxes` and a series of constraints. -// -// The output of this Op is a single bounding box that may be used to crop the -// original image. The output is returned as 3 tensors: `begin`, `size` and -// `bboxes`. The first 2 tensors can be fed directly into `tf.slice` to crop the -// image. The latter may be supplied to `tf.image.draw_bounding_boxes` to visualize -// what the bounding box looks like. -// -// Bounding boxes are supplied and returned as `[y_min, x_min, y_max, x_max]`. The -// bounding box coordinates are floats in `[0.0, 1.0]` relative to the width and -// height of the underlying image. -// -// For example, -// -// ```python -// # Generate a single distorted bounding box. -// begin, size, bbox_for_draw = tf.image.sample_distorted_bounding_box( -// tf.shape(image), -// bounding_boxes=bounding_boxes) +// Adds two `SparseTensor` objects to produce another `SparseTensor`. // -// # Draw the bounding box in an image summary. -// image_with_box = tf.image.draw_bounding_boxes(tf.expand_dims(image, 0), -// bbox_for_draw) -// tf.summary.image('images_with_box', image_with_box) +// The input `SparseTensor` objects' indices are assumed ordered in standard +// lexicographic order. If this is not the case, before this step run +// `SparseReorder` to restore index ordering. // -// # Employ the bounding box to distort the image. -// distorted_image = tf.slice(image, begin, size) -// ``` +// By default, if two values sum to zero at some index, the output `SparseTensor` +// would still include that particular location in its index, storing a zero in the +// corresponding value slot. To override this, callers can specify `thresh`, +// indicating that if the sum has a magnitude strictly smaller than `thresh`, its +// corresponding value and index would then not be included. In particular, +// `thresh == 0` (default) means everything is kept and actual thresholding happens +// only for a positive value. // -// Note that if no bounding box information is available, setting -// `use_image_if_no_bounding_boxes = true` will assume there is a single implicit -// bounding box covering the whole image. If `use_image_if_no_bounding_boxes` is -// false and no bounding boxes are supplied, an error is raised. +// In the following shapes, `nnz` is the count after taking `thresh` into account. // // Arguments: -// image_size: 1-D, containing `[height, width, channels]`. -// bounding_boxes: 3-D with shape `[batch, N, 4]` describing the N bounding boxes -// associated with the image. -// -// Returns 1-D, containing `[offset_height, offset_width, 0]`. Provide as input to -// `tf.slice`.1-D, containing `[target_height, target_width, -1]`. Provide as input to -// `tf.slice`.3-D with shape `[1, 1, 4]` containing the distorted bounding box. -// Provide as input to `tf.image.draw_bounding_boxes`. -func SampleDistortedBoundingBox(scope *Scope, image_size tf.Output, bounding_boxes tf.Output, optional ...SampleDistortedBoundingBoxAttr) (begin tf.Output, size tf.Output, bboxes tf.Output) { +// a_indices: 2-D. The `indices` of the first `SparseTensor`, size `[nnz, ndims]` Matrix. +// a_values: 1-D. The `values` of the first `SparseTensor`, size `[nnz]` Vector. +// a_shape: 1-D. The `shape` of the first `SparseTensor`, size `[ndims]` Vector. +// b_indices: 2-D. The `indices` of the second `SparseTensor`, size `[nnz, ndims]` Matrix. +// b_values: 1-D. The `values` of the second `SparseTensor`, size `[nnz]` Vector. +// b_shape: 1-D. The `shape` of the second `SparseTensor`, size `[ndims]` Vector. +// thresh: 0-D. The magnitude threshold that determines if an output value/index +// pair takes space. +func SparseAdd(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b_indices tf.Output, b_values tf.Output, b_shape tf.Output, thresh tf.Output) (sum_indices tf.Output, sum_values tf.Output, sum_shape tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } opspec := tf.OpSpec{ - Type: "SampleDistortedBoundingBox", + Type: "SparseAdd", Input: []tf.Input{ - image_size, bounding_boxes, + a_indices, a_values, a_shape, b_indices, b_values, b_shape, thresh, }, - Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0), op.Output(1), op.Output(2) } -// LRNAttr is an optional argument to LRN. -type LRNAttr func(optionalAttr) +// OrderedMapPeekAttr is an optional argument to OrderedMapPeek. +type OrderedMapPeekAttr func(optionalAttr) -// LRNDepthRadius sets the optional depth_radius attribute to value. +// OrderedMapPeekCapacity sets the optional capacity attribute to value. +// If not specified, defaults to 0 // -// value: 0-D. Half-width of the 1-D normalization window. -// If not specified, defaults to 5 -func LRNDepthRadius(value int64) LRNAttr { +// REQUIRES: value >= 0 +func OrderedMapPeekCapacity(value int64) OrderedMapPeekAttr { return func(m optionalAttr) { - m["depth_radius"] = value + m["capacity"] = value } } -// LRNBias sets the optional bias attribute to value. +// OrderedMapPeekMemoryLimit sets the optional memory_limit attribute to value. +// If not specified, defaults to 0 // -// value: An offset (usually positive to avoid dividing by 0). -// If not specified, defaults to 1 -func LRNBias(value float32) LRNAttr { +// REQUIRES: value >= 0 +func OrderedMapPeekMemoryLimit(value int64) OrderedMapPeekAttr { return func(m optionalAttr) { - m["bias"] = value + m["memory_limit"] = value } } -// LRNAlpha sets the optional alpha attribute to value. -// -// value: A scale factor, usually positive. -// If not specified, defaults to 1 -func LRNAlpha(value float32) LRNAttr { +// OrderedMapPeekContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func OrderedMapPeekContainer(value string) OrderedMapPeekAttr { return func(m optionalAttr) { - m["alpha"] = value + m["container"] = value } } -// LRNBeta sets the optional beta attribute to value. -// -// value: An exponent. -// If not specified, defaults to 0.5 -func LRNBeta(value float32) LRNAttr { +// OrderedMapPeekSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func OrderedMapPeekSharedName(value string) OrderedMapPeekAttr { return func(m optionalAttr) { - m["beta"] = value + m["shared_name"] = value } } -// Local Response Normalization. -// -// The 4-D `input` tensor is treated as a 3-D array of 1-D vectors (along the last -// dimension), and each vector is normalized independently. Within a given vector, -// each component is divided by the weighted, squared sum of inputs within -// `depth_radius`. In detail, -// -// sqr_sum[a, b, c, d] = -// sum(input[a, b, c, d - depth_radius : d + depth_radius + 1] ** 2) -// output = input / (bias + alpha * sqr_sum) ** beta -// -// For details, see [Krizhevsky et al., ImageNet classification with deep -// convolutional neural networks (NIPS 2012)](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks). +// Op peeks at the values at the specified key. If the // -// Arguments: -// input: 4-D. -func LRN(scope *Scope, input tf.Output, optional ...LRNAttr) (output tf.Output) { +// underlying container does not contain this key +// this op will block until it does. This Op is optimized for +// performance. +func OrderedMapPeek(scope *Scope, key tf.Output, indices tf.Output, dtypes []tf.DataType, optional ...OrderedMapPeekAttr) (values []tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} + attrs := map[string]interface{}{"dtypes": dtypes} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "LRN", + Type: "OrderedMapPeek", Input: []tf.Input{ - input, + key, indices, }, Attrs: attrs, } - op := scope.AddOperation(opspec) - return op.Output(0) + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if values, idx, err = makeOutputList(op, idx, "values"); err != nil { + scope.UpdateErr("OrderedMapPeek", err) + return + } + return values } -// Creates a dataset that zips together `input_datasets`. -func ZipDataset(scope *Scope, input_datasets []tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { +// Inverse fast Fourier transform. +// +// Computes the inverse 1-dimensional discrete Fourier transform over the +// inner-most dimension of `input`. +// +// Arguments: +// input: A complex64 tensor. +// +// Returns A complex64 tensor of the same shape as `input`. The inner-most +// dimension of `input` is replaced with its inverse 1D Fourier transform. +// +// @compatibility(numpy) +// Equivalent to np.fft.ifft +// @end_compatibility +func IFFT(scope *Scope, input tf.Output) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} opspec := tf.OpSpec{ - Type: "ZipDataset", + Type: "IFFT", Input: []tf.Input{ - tf.OutputList(input_datasets), + input, }, - Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// ResourceSparseApplyAdagradAttr is an optional argument to ResourceSparseApplyAdagrad. -type ResourceSparseApplyAdagradAttr func(optionalAttr) +// ResourceSparseApplyRMSPropAttr is an optional argument to ResourceSparseApplyRMSProp. +type ResourceSparseApplyRMSPropAttr func(optionalAttr) -// ResourceSparseApplyAdagradUseLocking sets the optional use_locking attribute to value. +// ResourceSparseApplyRMSPropUseLocking sets the optional use_locking attribute to value. // -// value: If `True`, updating of the var and accum tensors will be protected +// value: If `True`, updating of the var, ms, and mom tensors is protected // by a lock; otherwise the behavior is undefined, but may exhibit less // contention. // If not specified, defaults to false -func ResourceSparseApplyAdagradUseLocking(value bool) ResourceSparseApplyAdagradAttr { +func ResourceSparseApplyRMSPropUseLocking(value bool) ResourceSparseApplyRMSPropAttr { return func(m optionalAttr) { m["use_locking"] = value } } -// ResourceSparseApplyAdagradUpdateSlots sets the optional update_slots attribute to value. -// If not specified, defaults to true -func ResourceSparseApplyAdagradUpdateSlots(value bool) ResourceSparseApplyAdagradAttr { - return func(m optionalAttr) { - m["update_slots"] = value - } -} - -// Update relevant entries in '*var' and '*accum' according to the adagrad scheme. +// Update '*var' according to the RMSProp algorithm. // -// That is for rows we have grad for, we update var and accum as follows: -// accum += grad * grad -// var -= lr * grad * (1 / sqrt(accum)) +// Note that in dense implementation of this algorithm, ms and mom will +// update even if the grad is zero, but in this sparse implementation, ms +// and mom will not update in iterations during which the grad is zero. +// +// mean_square = decay * mean_square + (1-decay) * gradient ** 2 +// Delta = learning_rate * gradient / sqrt(mean_square + epsilon) +// +// ms <- rho * ms_{t-1} + (1-rho) * grad * grad +// mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon) +// var <- var - mom // // Arguments: // var_: Should be from a Variable(). -// accum: Should be from a Variable(). -// lr: Learning rate. Must be a scalar. +// ms: Should be from a Variable(). +// mom: Should be from a Variable(). +// lr: Scaling factor. Must be a scalar. +// rho: Decay rate. Must be a scalar. +// +// epsilon: Ridge term. Must be a scalar. // grad: The gradient. -// indices: A vector of indices into the first dimension of var and accum. +// indices: A vector of indices into the first dimension of var, ms and mom. // // Returns the created operation. -func ResourceSparseApplyAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyAdagradAttr) (o *tf.Operation) { +func ResourceSparseApplyRMSProp(scope *Scope, var_ tf.Output, ms tf.Output, mom tf.Output, lr tf.Output, rho tf.Output, momentum tf.Output, epsilon tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyRMSPropAttr) (o *tf.Operation) { if scope.Err() != nil { return } @@ -11197,235 +11250,168 @@ func ResourceSparseApplyAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, l a(attrs) } opspec := tf.OpSpec{ - Type: "ResourceSparseApplyAdagrad", + Type: "ResourceSparseApplyRMSProp", Input: []tf.Input{ - var_, accum, lr, grad, indices, + var_, ms, mom, lr, rho, momentum, epsilon, grad, indices, }, Attrs: attrs, } return scope.AddOperation(opspec) } -// StatelessRandomUniformAttr is an optional argument to StatelessRandomUniform. -type StatelessRandomUniformAttr func(optionalAttr) - -// StatelessRandomUniformDtype sets the optional dtype attribute to value. -// -// value: The type of the output. -// If not specified, defaults to DT_FLOAT -func StatelessRandomUniformDtype(value tf.DataType) StatelessRandomUniformAttr { - return func(m optionalAttr) { - m["dtype"] = value - } -} - -// Outputs deterministic pseudorandom random values from a uniform distribution. -// -// The generated values follow a uniform distribution in the range `[0, 1)`. The -// lower bound 0 is included in the range, while the upper bound 1 is excluded. -// -// The outputs are a deterministic function of `shape` and `seed`. -// -// Arguments: -// shape: The shape of the output tensor. -// seed: 2 seeds (shape [2]). +// Returns the truth value of (x > y) element-wise. // -// Returns Random values with specified shape. -func StatelessRandomUniform(scope *Scope, shape tf.Output, seed tf.Output, optional ...StatelessRandomUniformAttr) (output tf.Output) { +// *NOTE*: `Greater` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func Greater(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } opspec := tf.OpSpec{ - Type: "StatelessRandomUniform", + Type: "Greater", Input: []tf.Input{ - shape, seed, + x, y, }, - Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// Makes its input available to the next iteration. -// -// Arguments: -// data: The tensor to be made available to the next iteration. +// SampleDistortedBoundingBoxAttr is an optional argument to SampleDistortedBoundingBox. +type SampleDistortedBoundingBoxAttr func(optionalAttr) + +// SampleDistortedBoundingBoxSeed sets the optional seed attribute to value. // -// Returns The same tensor as `data`. -func NextIteration(scope *Scope, data tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "NextIteration", - Input: []tf.Input{ - data, - }, +// value: If either `seed` or `seed2` are set to non-zero, the random number +// generator is seeded by the given `seed`. Otherwise, it is seeded by a random +// seed. +// If not specified, defaults to 0 +func SampleDistortedBoundingBoxSeed(value int64) SampleDistortedBoundingBoxAttr { + return func(m optionalAttr) { + m["seed"] = value } - op := scope.AddOperation(opspec) - return op.Output(0) } -// Output a fact about factorials. -func Fact(scope *Scope) (fact tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Fact", +// SampleDistortedBoundingBoxSeed2 sets the optional seed2 attribute to value. +// +// value: A second seed to avoid seed collision. +// If not specified, defaults to 0 +func SampleDistortedBoundingBoxSeed2(value int64) SampleDistortedBoundingBoxAttr { + return func(m optionalAttr) { + m["seed2"] = value } - op := scope.AddOperation(opspec) - return op.Output(0) } -// Elementwise computes the bitwise XOR of `x` and `y`. +// SampleDistortedBoundingBoxMinObjectCovered sets the optional min_object_covered attribute to value. // -// The result will have those bits set, that are different in `x` and `y`. The -// computation is performed on the underlying representations of `x` and `y`. -func BitwiseXor(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "BitwiseXor", - Input: []tf.Input{ - x, y, - }, +// value: The cropped area of the image must contain at least this +// fraction of any bounding box supplied. The value of this parameter should be +// non-negative. In the case of 0, the cropped area does not need to overlap +// any of the bounding boxes supplied. +// If not specified, defaults to 0.1 +func SampleDistortedBoundingBoxMinObjectCovered(value float32) SampleDistortedBoundingBoxAttr { + return func(m optionalAttr) { + m["min_object_covered"] = value } - op := scope.AddOperation(opspec) - return op.Output(0) } -// Deserialize `SparseTensor` objects. -// -// The input `serialized_sparse` must have the shape `[?, ?, ..., ?, 3]` where -// the last dimension stores serialized `SparseTensor` objects and the other N -// dimensions (N >= 0) correspond to a batch. The ranks of the original -// `SparseTensor` objects must all match. When the final `SparseTensor` is -// created, its rank is the rank of the incoming `SparseTensor` objects plus N; -// the sparse tensors have been concatenated along new dimensions, one for each -// batch. -// -// The output `SparseTensor` object's shape values for the original dimensions -// are the max across the input `SparseTensor` objects' shape values for the -// corresponding dimensions. The new dimensions match the size of the batch. -// -// The input `SparseTensor` objects' indices are assumed ordered in -// standard lexicographic order. If this is not the case, after this -// step run `SparseReorder` to restore index ordering. -// -// For example, if the serialized input is a `[2 x 3]` matrix representing two -// original `SparseTensor` objects: -// -// index = [ 0] -// [10] -// [20] -// values = [1, 2, 3] -// shape = [50] -// -// and -// -// index = [ 2] -// [10] -// values = [4, 5] -// shape = [30] -// -// then the final deserialized `SparseTensor` will be: -// -// index = [0 0] -// [0 10] -// [0 20] -// [1 2] -// [1 10] -// values = [1, 2, 3, 4, 5] -// shape = [2 50] +// SampleDistortedBoundingBoxAspectRatioRange sets the optional aspect_ratio_range attribute to value. // -// Arguments: -// serialized_sparse: The serialized `SparseTensor` objects. The last dimension -// must have 3 columns. -// dtype: The `dtype` of the serialized `SparseTensor` objects. -func DeserializeSparse(scope *Scope, serialized_sparse tf.Output, dtype tf.DataType) (sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output) { - if scope.Err() != nil { - return +// value: The cropped area of the image must have an aspect ratio = +// width / height within this range. +// If not specified, defaults to +func SampleDistortedBoundingBoxAspectRatioRange(value []float32) SampleDistortedBoundingBoxAttr { + return func(m optionalAttr) { + m["aspect_ratio_range"] = value } - attrs := map[string]interface{}{"dtype": dtype} - opspec := tf.OpSpec{ - Type: "DeserializeSparse", - Input: []tf.Input{ - serialized_sparse, - }, - Attrs: attrs, +} + +// SampleDistortedBoundingBoxAreaRange sets the optional area_range attribute to value. +// +// value: The cropped area of the image must contain a fraction of the +// supplied image within in this range. +// If not specified, defaults to +func SampleDistortedBoundingBoxAreaRange(value []float32) SampleDistortedBoundingBoxAttr { + return func(m optionalAttr) { + m["area_range"] = value } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) } -// ResourceScatterNdUpdateAttr is an optional argument to ResourceScatterNdUpdate. -type ResourceScatterNdUpdateAttr func(optionalAttr) - -// ResourceScatterNdUpdateUseLocking sets the optional use_locking attribute to value. +// SampleDistortedBoundingBoxMaxAttempts sets the optional max_attempts attribute to value. // -// value: An optional bool. Defaults to True. If True, the assignment will -// be protected by a lock; otherwise the behavior is undefined, -// but may exhibit less contention. -// If not specified, defaults to true -func ResourceScatterNdUpdateUseLocking(value bool) ResourceScatterNdUpdateAttr { +// value: Number of attempts at generating a cropped region of the image +// of the specified constraints. After `max_attempts` failures, return the entire +// image. +// If not specified, defaults to 100 +func SampleDistortedBoundingBoxMaxAttempts(value int64) SampleDistortedBoundingBoxAttr { return func(m optionalAttr) { - m["use_locking"] = value + m["max_attempts"] = value } } -// Applies sparse `updates` to individual values or slices within a given -// -// variable according to `indices`. -// -// `ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`. +// SampleDistortedBoundingBoxUseImageIfNoBoundingBoxes sets the optional use_image_if_no_bounding_boxes attribute to value. // -// `indices` must be integer tensor, containing indices into `ref`. -// It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`. +// value: Controls behavior if no bounding boxes supplied. +// If true, assume an implicit bounding box covering the whole input. If false, +// raise an error. +// If not specified, defaults to false +func SampleDistortedBoundingBoxUseImageIfNoBoundingBoxes(value bool) SampleDistortedBoundingBoxAttr { + return func(m optionalAttr) { + m["use_image_if_no_bounding_boxes"] = value + } +} + +// Generate a single randomly distorted bounding box for an image. // -// The innermost dimension of `indices` (with length `K`) corresponds to -// indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th -// dimension of `ref`. +// Bounding box annotations are often supplied in addition to ground-truth labels +// in image recognition or object localization tasks. A common technique for +// training such a system is to randomly distort an image while preserving +// its content, i.e. *data augmentation*. This Op outputs a randomly distorted +// localization of an object, i.e. bounding box, given an `image_size`, +// `bounding_boxes` and a series of constraints. // -// `updates` is `Tensor` of rank `Q-1+P-K` with shape: +// The output of this Op is a single bounding box that may be used to crop the +// original image. The output is returned as 3 tensors: `begin`, `size` and +// `bboxes`. The first 2 tensors can be fed directly into `tf.slice` to crop the +// image. The latter may be supplied to `tf.image.draw_bounding_boxes` to visualize +// what the bounding box looks like. // -// ``` -// [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]]. -// ``` +// Bounding boxes are supplied and returned as `[y_min, x_min, y_max, x_max]`. The +// bounding box coordinates are floats in `[0.0, 1.0]` relative to the width and +// height of the underlying image. // -// For example, say we want to update 4 scattered elements to a rank-1 tensor to -// 8 elements. In Python, that update would look like this: +// For example, // // ```python -// ref = tfe.Variable([1, 2, 3, 4, 5, 6, 7, 8]) -// indices = tf.constant([[4], [3], [1] ,[7]]) -// updates = tf.constant([9, 10, 11, 12]) -// update = tf.scatter_nd_update(ref, indices, updates) -// with tf.Session() as sess: -// print sess.run(update) -// ``` +// # Generate a single distorted bounding box. +// begin, size, bbox_for_draw = tf.image.sample_distorted_bounding_box( +// tf.shape(image), +// bounding_boxes=bounding_boxes) // -// The resulting update to ref would look like this: +// # Draw the bounding box in an image summary. +// image_with_box = tf.image.draw_bounding_boxes(tf.expand_dims(image, 0), +// bbox_for_draw) +// tf.summary.image('images_with_box', image_with_box) // -// [1, 11, 3, 10, 9, 6, 7, 12] +// # Employ the bounding box to distort the image. +// distorted_image = tf.slice(image, begin, size) +// ``` // -// See @{tf.scatter_nd} for more details about how to make updates to -// slices. +// Note that if no bounding box information is available, setting +// `use_image_if_no_bounding_boxes = true` will assume there is a single implicit +// bounding box covering the whole image. If `use_image_if_no_bounding_boxes` is +// false and no bounding boxes are supplied, an error is raised. // // Arguments: -// ref: A resource handle. Must be from a VarHandleOp. -// indices: A Tensor. Must be one of the following types: int32, int64. -// A tensor of indices into ref. -// updates: A Tensor. Must have the same type as ref. A tensor of updated -// values to add to ref. +// image_size: 1-D, containing `[height, width, channels]`. +// bounding_boxes: 3-D with shape `[batch, N, 4]` describing the N bounding boxes +// associated with the image. // -// Returns the created operation. -func ResourceScatterNdUpdate(scope *Scope, ref tf.Output, indices tf.Output, updates tf.Output, optional ...ResourceScatterNdUpdateAttr) (o *tf.Operation) { +// Returns 1-D, containing `[offset_height, offset_width, 0]`. Provide as input to +// `tf.slice`.1-D, containing `[target_height, target_width, -1]`. Provide as input to +// `tf.slice`.3-D with shape `[1, 1, 4]` containing the distorted bounding box. +// Provide as input to `tf.image.draw_bounding_boxes`. +func SampleDistortedBoundingBox(scope *Scope, image_size tf.Output, bounding_boxes tf.Output, optional ...SampleDistortedBoundingBoxAttr) (begin tf.Output, size tf.Output, bboxes tf.Output) { if scope.Err() != nil { return } @@ -11434,59 +11420,76 @@ func ResourceScatterNdUpdate(scope *Scope, ref tf.Output, indices tf.Output, upd a(attrs) } opspec := tf.OpSpec{ - Type: "ResourceScatterNdUpdate", + Type: "SampleDistortedBoundingBox", Input: []tf.Input{ - ref, indices, updates, + image_size, bounding_boxes, }, Attrs: attrs, } - return scope.AddOperation(opspec) + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) } -// SqueezeAttr is an optional argument to Squeeze. -type SqueezeAttr func(optionalAttr) +// LRNAttr is an optional argument to LRN. +type LRNAttr func(optionalAttr) -// SqueezeAxis sets the optional axis attribute to value. +// LRNDepthRadius sets the optional depth_radius attribute to value. // -// value: If specified, only squeezes the dimensions listed. The dimension -// index starts at 0. It is an error to squeeze a dimension that is not 1. Must -// be in the range `[-rank(input), rank(input))`. -// If not specified, defaults to <> +// value: 0-D. Half-width of the 1-D normalization window. +// If not specified, defaults to 5 +func LRNDepthRadius(value int64) LRNAttr { + return func(m optionalAttr) { + m["depth_radius"] = value + } +} + +// LRNBias sets the optional bias attribute to value. // -// REQUIRES: len(value) >= 0 -func SqueezeAxis(value []int64) SqueezeAttr { +// value: An offset (usually positive to avoid dividing by 0). +// If not specified, defaults to 1 +func LRNBias(value float32) LRNAttr { return func(m optionalAttr) { - m["squeeze_dims"] = value + m["bias"] = value } } -// Removes dimensions of size 1 from the shape of a tensor. +// LRNAlpha sets the optional alpha attribute to value. // -// Given a tensor `input`, this operation returns a tensor of the same type with -// all dimensions of size 1 removed. If you don't want to remove all size 1 -// dimensions, you can remove specific size 1 dimensions by specifying -// `axis`. +// value: A scale factor, usually positive. +// If not specified, defaults to 1 +func LRNAlpha(value float32) LRNAttr { + return func(m optionalAttr) { + m["alpha"] = value + } +} + +// LRNBeta sets the optional beta attribute to value. // -// For example: +// value: An exponent. +// If not specified, defaults to 0.5 +func LRNBeta(value float32) LRNAttr { + return func(m optionalAttr) { + m["beta"] = value + } +} + +// Local Response Normalization. // -// ``` -// # 't' is a tensor of shape [1, 2, 1, 3, 1, 1] -// shape(squeeze(t)) ==> [2, 3] -// ``` +// The 4-D `input` tensor is treated as a 3-D array of 1-D vectors (along the last +// dimension), and each vector is normalized independently. Within a given vector, +// each component is divided by the weighted, squared sum of inputs within +// `depth_radius`. In detail, // -// Or, to remove specific size 1 dimensions: +// sqr_sum[a, b, c, d] = +// sum(input[a, b, c, d - depth_radius : d + depth_radius + 1] ** 2) +// output = input / (bias + alpha * sqr_sum) ** beta // -// ``` -// # 't' is a tensor of shape [1, 2, 1, 3, 1, 1] -// shape(squeeze(t, [2, 4])) ==> [1, 2, 3, 1] -// ``` +// For details, see [Krizhevsky et al., ImageNet classification with deep +// convolutional neural networks (NIPS 2012)](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks). // // Arguments: -// input: The `input` to squeeze. -// -// Returns Contains the same data as `input`, but has one or more dimensions of -// size 1 removed. -func Squeeze(scope *Scope, input tf.Output, optional ...SqueezeAttr) (output tf.Output) { +// input: 4-D. +func LRN(scope *Scope, input tf.Output, optional ...LRNAttr) (output tf.Output) { if scope.Err() != nil { return } @@ -11495,7 +11498,7 @@ func Squeeze(scope *Scope, input tf.Output, optional ...SqueezeAttr) (output tf. a(attrs) } opspec := tf.OpSpec{ - Type: "Squeeze", + Type: "LRN", Input: []tf.Input{ input, }, @@ -11505,38 +11508,61 @@ func Squeeze(scope *Scope, input tf.Output, optional ...SqueezeAttr) (output tf. return op.Output(0) } -// ResourceApplyAdadeltaAttr is an optional argument to ResourceApplyAdadelta. -type ResourceApplyAdadeltaAttr func(optionalAttr) +// Creates a dataset that zips together `input_datasets`. +func ZipDataset(scope *Scope, input_datasets []tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "ZipDataset", + Input: []tf.Input{ + tf.OutputList(input_datasets), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} -// ResourceApplyAdadeltaUseLocking sets the optional use_locking attribute to value. +// ResourceSparseApplyAdagradAttr is an optional argument to ResourceSparseApplyAdagrad. +type ResourceSparseApplyAdagradAttr func(optionalAttr) + +// ResourceSparseApplyAdagradUseLocking sets the optional use_locking attribute to value. // -// value: If True, updating of the var, accum and update_accum tensors will be protected by -// a lock; otherwise the behavior is undefined, but may exhibit less contention. +// value: If `True`, updating of the var and accum tensors will be protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. // If not specified, defaults to false -func ResourceApplyAdadeltaUseLocking(value bool) ResourceApplyAdadeltaAttr { +func ResourceSparseApplyAdagradUseLocking(value bool) ResourceSparseApplyAdagradAttr { return func(m optionalAttr) { m["use_locking"] = value } } -// Update '*var' according to the adadelta scheme. +// ResourceSparseApplyAdagradUpdateSlots sets the optional update_slots attribute to value. +// If not specified, defaults to true +func ResourceSparseApplyAdagradUpdateSlots(value bool) ResourceSparseApplyAdagradAttr { + return func(m optionalAttr) { + m["update_slots"] = value + } +} + +// Update relevant entries in '*var' and '*accum' according to the adagrad scheme. // -// accum = rho() * accum + (1 - rho()) * grad.square(); -// update = (update_accum + epsilon).sqrt() * (accum + epsilon()).rsqrt() * grad; -// update_accum = rho() * update_accum + (1 - rho()) * update.square(); -// var -= update; +// That is for rows we have grad for, we update var and accum as follows: +// accum += grad * grad +// var -= lr * grad * (1 / sqrt(accum)) // // Arguments: // var_: Should be from a Variable(). // accum: Should be from a Variable(). -// accum_update: Should be from a Variable(). -// lr: Scaling factor. Must be a scalar. -// rho: Decay factor. Must be a scalar. -// epsilon: Constant factor. Must be a scalar. +// lr: Learning rate. Must be a scalar. // grad: The gradient. +// indices: A vector of indices into the first dimension of var and accum. // // Returns the created operation. -func ResourceApplyAdadelta(scope *Scope, var_ tf.Output, accum tf.Output, accum_update tf.Output, lr tf.Output, rho tf.Output, epsilon tf.Output, grad tf.Output, optional ...ResourceApplyAdadeltaAttr) (o *tf.Operation) { +func ResourceSparseApplyAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyAdagradAttr) (o *tf.Operation) { if scope.Err() != nil { return } @@ -11545,58 +11571,41 @@ func ResourceApplyAdadelta(scope *Scope, var_ tf.Output, accum tf.Output, accum_ a(attrs) } opspec := tf.OpSpec{ - Type: "ResourceApplyAdadelta", + Type: "ResourceSparseApplyAdagrad", Input: []tf.Input{ - var_, accum, accum_update, lr, rho, epsilon, grad, + var_, accum, lr, grad, indices, }, Attrs: attrs, } return scope.AddOperation(opspec) } -// NonMaxSuppressionAttr is an optional argument to NonMaxSuppression. -type NonMaxSuppressionAttr func(optionalAttr) +// StatelessRandomUniformAttr is an optional argument to StatelessRandomUniform. +type StatelessRandomUniformAttr func(optionalAttr) -// NonMaxSuppressionIouThreshold sets the optional iou_threshold attribute to value. +// StatelessRandomUniformDtype sets the optional dtype attribute to value. // -// value: A float representing the threshold for deciding whether boxes -// overlap too much with respect to IOU. -// If not specified, defaults to 0.5 -func NonMaxSuppressionIouThreshold(value float32) NonMaxSuppressionAttr { +// value: The type of the output. +// If not specified, defaults to DT_FLOAT +func StatelessRandomUniformDtype(value tf.DataType) StatelessRandomUniformAttr { return func(m optionalAttr) { - m["iou_threshold"] = value + m["dtype"] = value } } -// Greedily selects a subset of bounding boxes in descending order of score, +// Outputs deterministic pseudorandom random values from a uniform distribution. // -// pruning away boxes that have high intersection-over-union (IOU) overlap -// with previously selected boxes. Bounding boxes are supplied as -// [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any -// diagonal pair of box corners and the coordinates can be provided as normalized -// (i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm -// is agnostic to where the origin is in the coordinate system. Note that this -// algorithm is invariant to orthogonal transformations and translations -// of the coordinate system; thus translating or reflections of the coordinate -// system result in the same boxes being selected by the algorithm. -// The output of this operation is a set of integers indexing into the input -// collection of bounding boxes representing the selected boxes. The bounding -// box coordinates corresponding to the selected indices can then be obtained -// using the `tf.gather operation`. For example: -// selected_indices = tf.image.non_max_suppression( -// boxes, scores, max_output_size, iou_threshold) -// selected_boxes = tf.gather(boxes, selected_indices) +// The generated values follow a uniform distribution in the range `[0, 1)`. The +// lower bound 0 is included in the range, while the upper bound 1 is excluded. +// +// The outputs are a deterministic function of `shape` and `seed`. // // Arguments: -// boxes: A 2-D float tensor of shape `[num_boxes, 4]`. -// scores: A 1-D float tensor of shape `[num_boxes]` representing a single -// score corresponding to each box (each row of boxes). -// max_output_size: A scalar integer tensor representing the maximum number of -// boxes to be selected by non max suppression. +// shape: The shape of the output tensor. +// seed: 2 seeds (shape [2]). // -// Returns A 1-D integer tensor of shape `[M]` representing the selected -// indices from the boxes tensor, where `M <= max_output_size`. -func NonMaxSuppression(scope *Scope, boxes tf.Output, scores tf.Output, max_output_size tf.Output, optional ...NonMaxSuppressionAttr) (selected_indices tf.Output) { +// Returns Random values with specified shape. +func StatelessRandomUniform(scope *Scope, shape tf.Output, seed tf.Output, optional ...StatelessRandomUniformAttr) (output tf.Output) { if scope.Err() != nil { return } @@ -11605,9 +11614,9 @@ func NonMaxSuppression(scope *Scope, boxes tf.Output, scores tf.Output, max_outp a(attrs) } opspec := tf.OpSpec{ - Type: "NonMaxSuppression", + Type: "StatelessRandomUniform", Input: []tf.Input{ - boxes, scores, max_output_size, + shape, seed, }, Attrs: attrs, } @@ -11615,222 +11624,254 @@ func NonMaxSuppression(scope *Scope, boxes tf.Output, scores tf.Output, max_outp return op.Output(0) } -// Creates a dataset that emits `components` as a tuple of tensors once. -func TensorDataset(scope *Scope, components []tf.Output, output_shapes []tf.Shape) (handle tf.Output) { +// Makes its input available to the next iteration. +// +// Arguments: +// data: The tensor to be made available to the next iteration. +// +// Returns The same tensor as `data`. +func NextIteration(scope *Scope, data tf.Output) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"output_shapes": output_shapes} opspec := tf.OpSpec{ - Type: "TensorDataset", + Type: "NextIteration", Input: []tf.Input{ - tf.OutputList(components), + data, }, - Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// Component-wise multiplies a SparseTensor by a dense Tensor. -// -// The output locations corresponding to the implicitly zero elements in the sparse -// tensor will be zero (i.e., will not take up storage space), regardless of the -// contents of the dense tensor (even if it's +/-INF and that INF*0 == NaN). -// -// *Limitation*: this Op only broadcasts the dense side to the sparse side, but not -// the other direction. -// -// Arguments: -// sp_indices: 2-D. `N x R` matrix with the indices of non-empty values in a -// SparseTensor, possibly not in canonical ordering. -// sp_values: 1-D. `N` non-empty values corresponding to `sp_indices`. -// sp_shape: 1-D. Shape of the input SparseTensor. -// dense: `R`-D. The dense Tensor operand. -// -// Returns 1-D. The `N` values that are operated on. -func SparseDenseCwiseMul(scope *Scope, sp_indices tf.Output, sp_values tf.Output, sp_shape tf.Output, dense tf.Output) (output tf.Output) { +// Output a fact about factorials. +func Fact(scope *Scope) (fact tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "SparseDenseCwiseMul", - Input: []tf.Input{ - sp_indices, sp_values, sp_shape, dense, - }, + Type: "Fact", } op := scope.AddOperation(opspec) return op.Output(0) } -// 2D real-valued fast Fourier transform. -// -// Computes the 2-dimensional discrete Fourier transform of a real-valued signal -// over the inner-most 2 dimensions of `input`. -// -// Since the DFT of a real signal is Hermitian-symmetric, `RFFT2D` only returns the -// `fft_length / 2 + 1` unique components of the FFT for the inner-most dimension -// of `output`: the zero-frequency term, followed by the `fft_length / 2` -// positive-frequency terms. -// -// Along each axis `RFFT2D` is computed on, if `fft_length` is smaller than the -// corresponding dimension of `input`, the dimension is cropped. If it is larger, -// the dimension is padded with zeros. -// -// Arguments: -// input: A float32 tensor. -// fft_length: An int32 tensor of shape [2]. The FFT length for each dimension. -// -// Returns A complex64 tensor of the same rank as `input`. The inner-most 2 -// dimensions of `input` are replaced with their 2D Fourier transform. The -// inner-most dimension contains `fft_length / 2 + 1` unique frequency -// components. +// Elementwise computes the bitwise XOR of `x` and `y`. // -// @compatibility(numpy) -// Equivalent to np.fft.rfft2 -// @end_compatibility -func RFFT2D(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) { +// The result will have those bits set, that are different in `x` and `y`. The +// computation is performed on the underlying representations of `x` and `y`. +func BitwiseXor(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "RFFT2D", + Type: "BitwiseXor", Input: []tf.Input{ - input, fft_length, + x, y, }, } op := scope.AddOperation(opspec) return op.Output(0) } -// Pads a tensor with zeros. +// Deserialize `SparseTensor` objects. // -// This operation pads a `input` with zeros according to the `paddings` you -// specify. `paddings` is an integer tensor with shape `[Dn, 2]`, where n is the -// rank of `input`. For each dimension D of `input`, `paddings[D, 0]` indicates -// how many zeros to add before the contents of `input` in that dimension, and -// `paddings[D, 1]` indicates how many zeros to add after the contents of `input` -// in that dimension. +// The input `serialized_sparse` must have the shape `[?, ?, ..., ?, 3]` where +// the last dimension stores serialized `SparseTensor` objects and the other N +// dimensions (N >= 0) correspond to a batch. The ranks of the original +// `SparseTensor` objects must all match. When the final `SparseTensor` is +// created, its rank is the rank of the incoming `SparseTensor` objects plus N; +// the sparse tensors have been concatenated along new dimensions, one for each +// batch. // -// The padded size of each dimension D of the output is: +// The output `SparseTensor` object's shape values for the original dimensions +// are the max across the input `SparseTensor` objects' shape values for the +// corresponding dimensions. The new dimensions match the size of the batch. // -// `paddings(D, 0) + input.dim_size(D) + paddings(D, 1)` +// The input `SparseTensor` objects' indices are assumed ordered in +// standard lexicographic order. If this is not the case, after this +// step run `SparseReorder` to restore index ordering. // -// For example: +// For example, if the serialized input is a `[2 x 3]` matrix representing two +// original `SparseTensor` objects: // -// ``` -// # 't' is [[1, 1], [2, 2]] -// # 'paddings' is [[1, 1], [2, 2]] -// # rank of 't' is 2 -// pad(t, paddings) ==> [[0, 0, 0, 0, 0, 0] -// [0, 0, 1, 1, 0, 0] -// [0, 0, 2, 2, 0, 0] -// [0, 0, 0, 0, 0, 0]] -// ``` -func Pad(scope *Scope, input tf.Output, paddings tf.Output) (output tf.Output) { +// index = [ 0] +// [10] +// [20] +// values = [1, 2, 3] +// shape = [50] +// +// and +// +// index = [ 2] +// [10] +// values = [4, 5] +// shape = [30] +// +// then the final deserialized `SparseTensor` will be: +// +// index = [0 0] +// [0 10] +// [0 20] +// [1 2] +// [1 10] +// values = [1, 2, 3, 4, 5] +// shape = [2 50] +// +// Arguments: +// serialized_sparse: The serialized `SparseTensor` objects. The last dimension +// must have 3 columns. +// dtype: The `dtype` of the serialized `SparseTensor` objects. +func DeserializeSparse(scope *Scope, serialized_sparse tf.Output, dtype tf.DataType) (sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output) { if scope.Err() != nil { return } + attrs := map[string]interface{}{"dtype": dtype} opspec := tf.OpSpec{ - Type: "Pad", + Type: "DeserializeSparse", Input: []tf.Input{ - input, paddings, + serialized_sparse, }, + Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0) + return op.Output(0), op.Output(1), op.Output(2) } -// Checks whether a resource handle-based variable has been initialized. -// -// Arguments: -// resource: the input resource handle. +// ResourceScatterNdUpdateAttr is an optional argument to ResourceScatterNdUpdate. +type ResourceScatterNdUpdateAttr func(optionalAttr) + +// ResourceScatterNdUpdateUseLocking sets the optional use_locking attribute to value. // -// Returns a scalar boolean which is true if the variable has been -// initialized. -func VarIsInitializedOp(scope *Scope, resource tf.Output) (is_initialized tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "VarIsInitializedOp", - Input: []tf.Input{ - resource, - }, +// value: An optional bool. Defaults to True. If True, the assignment will +// be protected by a lock; otherwise the behavior is undefined, +// but may exhibit less contention. +// If not specified, defaults to true +func ResourceScatterNdUpdateUseLocking(value bool) ResourceScatterNdUpdateAttr { + return func(m optionalAttr) { + m["use_locking"] = value } - op := scope.AddOperation(opspec) - return op.Output(0) } -// Converts each string in the input Tensor to its hash mod by a number of buckets. +// Applies sparse `updates` to individual values or slices within a given +// +// variable according to `indices`. +// +// `ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`. +// +// `indices` must be integer tensor, containing indices into `ref`. +// It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`. +// +// The innermost dimension of `indices` (with length `K`) corresponds to +// indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th +// dimension of `ref`. +// +// `updates` is `Tensor` of rank `Q-1+P-K` with shape: +// +// ``` +// [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]]. +// ``` +// +// For example, say we want to update 4 scattered elements to a rank-1 tensor to +// 8 elements. In Python, that update would look like this: +// +// ```python +// ref = tfe.Variable([1, 2, 3, 4, 5, 6, 7, 8]) +// indices = tf.constant([[4], [3], [1] ,[7]]) +// updates = tf.constant([9, 10, 11, 12]) +// update = tf.scatter_nd_update(ref, indices, updates) +// with tf.Session() as sess: +// print sess.run(update) +// ``` +// +// The resulting update to ref would look like this: +// +// [1, 11, 3, 10, 9, 6, 7, 12] // -// The hash function is deterministic on the content of the string within the -// process and will never change. However, it is not suitable for cryptography. -// This function may be used when CPU time is scarce and inputs are trusted or -// unimportant. There is a risk of adversaries constructing inputs that all hash -// to the same bucket. To prevent this problem, use a strong hash function with -// `tf.string_to_hash_bucket_strong`. +// See @{tf.scatter_nd} for more details about how to make updates to +// slices. // // Arguments: -// input: The strings to assign a hash bucket. -// num_buckets: The number of buckets. +// ref: A resource handle. Must be from a VarHandleOp. +// indices: A Tensor. Must be one of the following types: int32, int64. +// A tensor of indices into ref. +// updates: A Tensor. Must have the same type as ref. A tensor of updated +// values to add to ref. // -// Returns A Tensor of the same shape as the input `string_tensor`. -func StringToHashBucketFast(scope *Scope, input tf.Output, num_buckets int64) (output tf.Output) { +// Returns the created operation. +func ResourceScatterNdUpdate(scope *Scope, ref tf.Output, indices tf.Output, updates tf.Output, optional ...ResourceScatterNdUpdateAttr) (o *tf.Operation) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"num_buckets": num_buckets} + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "StringToHashBucketFast", + Type: "ResourceScatterNdUpdate", Input: []tf.Input{ - input, + ref, indices, updates, }, Attrs: attrs, } - op := scope.AddOperation(opspec) - return op.Output(0) + return scope.AddOperation(opspec) } -// TensorArrayGatherV3Attr is an optional argument to TensorArrayGatherV3. -type TensorArrayGatherV3Attr func(optionalAttr) +// SqueezeAttr is an optional argument to Squeeze. +type SqueezeAttr func(optionalAttr) -// TensorArrayGatherV3ElementShape sets the optional element_shape attribute to value. +// SqueezeAxis sets the optional axis attribute to value. // -// value: The expected shape of an element, if known. Used to -// validate the shapes of TensorArray elements. If this shape is not -// fully specified, gathering zero-size TensorArrays is an error. -// If not specified, defaults to -func TensorArrayGatherV3ElementShape(value tf.Shape) TensorArrayGatherV3Attr { +// value: If specified, only squeezes the dimensions listed. The dimension +// index starts at 0. It is an error to squeeze a dimension that is not 1. Must +// be in the range `[-rank(input), rank(input))`. +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func SqueezeAxis(value []int64) SqueezeAttr { return func(m optionalAttr) { - m["element_shape"] = value + m["squeeze_dims"] = value } } -// Gather specific elements from the TensorArray into output `value`. +// Removes dimensions of size 1 from the shape of a tensor. // -// All elements selected by `indices` must have the same shape. +// Given a tensor `input`, this operation returns a tensor of the same type with +// all dimensions of size 1 removed. If you don't want to remove all size 1 +// dimensions, you can remove specific size 1 dimensions by specifying +// `axis`. +// +// For example: +// +// ``` +// # 't' is a tensor of shape [1, 2, 1, 3, 1, 1] +// shape(squeeze(t)) ==> [2, 3] +// ``` +// +// Or, to remove specific size 1 dimensions: +// +// ``` +// # 't' is a tensor of shape [1, 2, 1, 3, 1, 1] +// shape(squeeze(t, [2, 4])) ==> [1, 2, 3, 1] +// ``` // // Arguments: -// handle: The handle to a TensorArray. -// indices: The locations in the TensorArray from which to read tensor elements. -// flow_in: A float scalar that enforces proper chaining of operations. -// dtype: The type of the elem that is returned. +// input: The `input` to squeeze. // -// Returns All of the elements in the TensorArray, concatenated along a new -// axis (the new dimension 0). -func TensorArrayGatherV3(scope *Scope, handle tf.Output, indices tf.Output, flow_in tf.Output, dtype tf.DataType, optional ...TensorArrayGatherV3Attr) (value tf.Output) { +// Returns Contains the same data as `input`, but has one or more dimensions of +// size 1 removed. +func Squeeze(scope *Scope, input tf.Output, optional ...SqueezeAttr) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"dtype": dtype} + attrs := map[string]interface{}{} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "TensorArrayGatherV3", + Type: "Squeeze", Input: []tf.Input{ - handle, indices, flow_in, + input, }, Attrs: attrs, } @@ -11838,256 +11879,286 @@ func TensorArrayGatherV3(scope *Scope, handle tf.Output, indices tf.Output, flow return op.Output(0) } -// This op consumes a lock created by `MutexLock`. +// ResourceApplyAdadeltaAttr is an optional argument to ResourceApplyAdadelta. +type ResourceApplyAdadeltaAttr func(optionalAttr) + +// ResourceApplyAdadeltaUseLocking sets the optional use_locking attribute to value. // -// This op exists to consume a tensor created by `MutexLock` (other than -// direct control dependencies). It should be the only that consumes the tensor, -// and will raise an error if it is not. Its only purpose is to keep the -// mutex lock tensor alive until it is consumed by this op. +// value: If True, updating of the var, accum and update_accum tensors will be protected by +// a lock; otherwise the behavior is undefined, but may exhibit less contention. +// If not specified, defaults to false +func ResourceApplyAdadeltaUseLocking(value bool) ResourceApplyAdadeltaAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Update '*var' according to the adadelta scheme. // -// **NOTE**: This operation must run on the same device as its input. This may -// be enforced via the `colocate_with` mechanism. +// accum = rho() * accum + (1 - rho()) * grad.square(); +// update = (update_accum + epsilon).sqrt() * (accum + epsilon()).rsqrt() * grad; +// update_accum = rho() * update_accum + (1 - rho()) * update.square(); +// var -= update; // // Arguments: -// mutex_lock: A tensor returned by `MutexLock`. +// var_: Should be from a Variable(). +// accum: Should be from a Variable(). +// accum_update: Should be from a Variable(). +// lr: Scaling factor. Must be a scalar. +// rho: Decay factor. Must be a scalar. +// epsilon: Constant factor. Must be a scalar. +// grad: The gradient. // // Returns the created operation. -func ConsumeMutexLock(scope *Scope, mutex_lock tf.Output) (o *tf.Operation) { +func ResourceApplyAdadelta(scope *Scope, var_ tf.Output, accum tf.Output, accum_update tf.Output, lr tf.Output, rho tf.Output, epsilon tf.Output, grad tf.Output, optional ...ResourceApplyAdadeltaAttr) (o *tf.Operation) { if scope.Err() != nil { return } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "ConsumeMutexLock", + Type: "ResourceApplyAdadelta", Input: []tf.Input{ - mutex_lock, + var_, accum, accum_update, lr, rho, epsilon, grad, }, + Attrs: attrs, } return scope.AddOperation(opspec) } -// Returns x / y element-wise for integer types. +// NonMaxSuppressionAttr is an optional argument to NonMaxSuppression. +type NonMaxSuppressionAttr func(optionalAttr) + +// NonMaxSuppressionIouThreshold sets the optional iou_threshold attribute to value. // -// Truncation designates that negative numbers will round fractional quantities -// toward zero. I.e. -7 / 5 = -1. This matches C semantics but it is different -// than Python semantics. See `FloorDiv` for a division function that matches -// Python Semantics. +// value: A float representing the threshold for deciding whether boxes +// overlap too much with respect to IOU. +// If not specified, defaults to 0.5 +func NonMaxSuppressionIouThreshold(value float32) NonMaxSuppressionAttr { + return func(m optionalAttr) { + m["iou_threshold"] = value + } +} + +// Greedily selects a subset of bounding boxes in descending order of score, // -// *NOTE*: `TruncateDiv` supports broadcasting. More about broadcasting -// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) -func TruncateDiv(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { +// pruning away boxes that have high intersection-over-union (IOU) overlap +// with previously selected boxes. Bounding boxes are supplied as +// [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any +// diagonal pair of box corners and the coordinates can be provided as normalized +// (i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm +// is agnostic to where the origin is in the coordinate system. Note that this +// algorithm is invariant to orthogonal transformations and translations +// of the coordinate system; thus translating or reflections of the coordinate +// system result in the same boxes being selected by the algorithm. +// The output of this operation is a set of integers indexing into the input +// collection of bounding boxes representing the selected boxes. The bounding +// box coordinates corresponding to the selected indices can then be obtained +// using the `tf.gather operation`. For example: +// selected_indices = tf.image.non_max_suppression( +// boxes, scores, max_output_size, iou_threshold) +// selected_boxes = tf.gather(boxes, selected_indices) +// +// Arguments: +// boxes: A 2-D float tensor of shape `[num_boxes, 4]`. +// scores: A 1-D float tensor of shape `[num_boxes]` representing a single +// score corresponding to each box (each row of boxes). +// max_output_size: A scalar integer tensor representing the maximum number of +// boxes to be selected by non max suppression. +// +// Returns A 1-D integer tensor of shape `[M]` representing the selected +// indices from the boxes tensor, where `M <= max_output_size`. +func NonMaxSuppression(scope *Scope, boxes tf.Output, scores tf.Output, max_output_size tf.Output, optional ...NonMaxSuppressionAttr) (selected_indices tf.Output) { if scope.Err() != nil { return } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "TruncateDiv", + Type: "NonMaxSuppression", Input: []tf.Input{ - x, y, + boxes, scores, max_output_size, }, + Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// Restores tensors from a V2 checkpoint. -// -// For backward compatibility with the V1 format, this Op currently allows -// restoring from a V1 checkpoint as well: -// - This Op first attempts to find the V2 index file pointed to by "prefix", and -// if found proceed to read it as a V2 checkpoint; -// - Otherwise the V1 read path is invoked. -// Relying on this behavior is not recommended, as the ability to fall back to read -// V1 might be deprecated and eventually removed. -// -// By default, restores the named tensors in full. If the caller wishes to restore -// specific slices of stored tensors, "shape_and_slices" should be non-empty -// strings and correspondingly well-formed. -// -// Callers must ensure all the named tensors are indeed stored in the checkpoint. -// -// Arguments: -// prefix: Must have a single element. The prefix of a V2 checkpoint. -// tensor_names: shape {N}. The names of the tensors to be restored. -// shape_and_slices: shape {N}. The slice specs of the tensors to be restored. -// Empty strings indicate that they are non-partitioned tensors. -// dtypes: shape {N}. The list of expected dtype for the tensors. Must match -// those stored in the checkpoint. -// -// Returns shape {N}. The restored tensors, whose shapes are read from the -// checkpoint directly. -func RestoreV2(scope *Scope, prefix tf.Output, tensor_names tf.Output, shape_and_slices tf.Output, dtypes []tf.DataType) (tensors []tf.Output) { +// Creates a dataset that emits `components` as a tuple of tensors once. +func TensorDataset(scope *Scope, components []tf.Output, output_shapes []tf.Shape) (handle tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"dtypes": dtypes} + attrs := map[string]interface{}{"output_shapes": output_shapes} opspec := tf.OpSpec{ - Type: "RestoreV2", + Type: "TensorDataset", Input: []tf.Input{ - prefix, tensor_names, shape_and_slices, + tf.OutputList(components), }, Attrs: attrs, } op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - if tensors, idx, err = makeOutputList(op, idx, "tensors"); err != nil { - scope.UpdateErr("RestoreV2", err) - return - } - return tensors + return op.Output(0) } -// Receives a tensor value broadcast from another device. -func CollectiveBcastRecv(scope *Scope, T tf.DataType, group_size int64, group_key int64, instance_key int64, shape tf.Shape) (data tf.Output) { +// Component-wise multiplies a SparseTensor by a dense Tensor. +// +// The output locations corresponding to the implicitly zero elements in the sparse +// tensor will be zero (i.e., will not take up storage space), regardless of the +// contents of the dense tensor (even if it's +/-INF and that INF*0 == NaN). +// +// *Limitation*: this Op only broadcasts the dense side to the sparse side, but not +// the other direction. +// +// Arguments: +// sp_indices: 2-D. `N x R` matrix with the indices of non-empty values in a +// SparseTensor, possibly not in canonical ordering. +// sp_values: 1-D. `N` non-empty values corresponding to `sp_indices`. +// sp_shape: 1-D. Shape of the input SparseTensor. +// dense: `R`-D. The dense Tensor operand. +// +// Returns 1-D. The `N` values that are operated on. +func SparseDenseCwiseMul(scope *Scope, sp_indices tf.Output, sp_values tf.Output, sp_shape tf.Output, dense tf.Output) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"T": T, "group_size": group_size, "group_key": group_key, "instance_key": instance_key, "shape": shape} opspec := tf.OpSpec{ - Type: "CollectiveBcastRecv", - - Attrs: attrs, + Type: "SparseDenseCwiseMul", + Input: []tf.Input{ + sp_indices, sp_values, sp_shape, dense, + }, } op := scope.AddOperation(opspec) return op.Output(0) } -// Decode web-safe base64-encoded strings. +// 2D real-valued fast Fourier transform. // -// Input may or may not have padding at the end. See EncodeBase64 for padding. -// Web-safe means that input must use - and _ instead of + and /. +// Computes the 2-dimensional discrete Fourier transform of a real-valued signal +// over the inner-most 2 dimensions of `input`. +// +// Since the DFT of a real signal is Hermitian-symmetric, `RFFT2D` only returns the +// `fft_length / 2 + 1` unique components of the FFT for the inner-most dimension +// of `output`: the zero-frequency term, followed by the `fft_length / 2` +// positive-frequency terms. +// +// Along each axis `RFFT2D` is computed on, if `fft_length` is smaller than the +// corresponding dimension of `input`, the dimension is cropped. If it is larger, +// the dimension is padded with zeros. // // Arguments: -// input: Base64 strings to decode. +// input: A float32 tensor. +// fft_length: An int32 tensor of shape [2]. The FFT length for each dimension. // -// Returns Decoded strings. -func DecodeBase64(scope *Scope, input tf.Output) (output tf.Output) { +// Returns A complex64 tensor of the same rank as `input`. The inner-most 2 +// dimensions of `input` are replaced with their 2D Fourier transform. The +// inner-most dimension contains `fft_length / 2 + 1` unique frequency +// components. +// +// @compatibility(numpy) +// Equivalent to np.fft.rfft2 +// @end_compatibility +func RFFT2D(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "DecodeBase64", + Type: "RFFT2D", Input: []tf.Input{ - input, + input, fft_length, }, } op := scope.AddOperation(opspec) return op.Output(0) } -// Store the input tensor in the state of the current session. +// Pads a tensor with zeros. // -// Arguments: -// value: The tensor to be stored. +// This operation pads a `input` with zeros according to the `paddings` you +// specify. `paddings` is an integer tensor with shape `[Dn, 2]`, where n is the +// rank of `input`. For each dimension D of `input`, `paddings[D, 0]` indicates +// how many zeros to add before the contents of `input` in that dimension, and +// `paddings[D, 1]` indicates how many zeros to add after the contents of `input` +// in that dimension. // -// Returns The handle for the tensor stored in the session state, represented -// as a string. -func GetSessionHandle(scope *Scope, value tf.Output) (handle tf.Output) { +// The padded size of each dimension D of the output is: +// +// `paddings(D, 0) + input.dim_size(D) + paddings(D, 1)` +// +// For example: +// +// ``` +// # 't' is [[1, 1], [2, 2]] +// # 'paddings' is [[1, 1], [2, 2]] +// # rank of 't' is 2 +// pad(t, paddings) ==> [[0, 0, 0, 0, 0, 0] +// [0, 0, 1, 1, 0, 0] +// [0, 0, 2, 2, 0, 0] +// [0, 0, 0, 0, 0, 0]] +// ``` +func Pad(scope *Scope, input tf.Output, paddings tf.Output) (output tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "GetSessionHandle", + Type: "Pad", Input: []tf.Input{ - value, + input, paddings, }, } op := scope.AddOperation(opspec) return op.Output(0) } -// ResourceSparseApplyProximalAdagradAttr is an optional argument to ResourceSparseApplyProximalAdagrad. -type ResourceSparseApplyProximalAdagradAttr func(optionalAttr) - -// ResourceSparseApplyProximalAdagradUseLocking sets the optional use_locking attribute to value. -// -// value: If True, updating of the var and accum tensors will be protected by -// a lock; otherwise the behavior is undefined, but may exhibit less contention. -// If not specified, defaults to false -func ResourceSparseApplyProximalAdagradUseLocking(value bool) ResourceSparseApplyProximalAdagradAttr { - return func(m optionalAttr) { - m["use_locking"] = value - } -} - -// Sparse update entries in '*var' and '*accum' according to FOBOS algorithm. -// -// That is for rows we have grad for, we update var and accum as follows: -// accum += grad * grad -// prox_v = var -// prox_v -= lr * grad * (1 / sqrt(accum)) -// var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0} +// Checks whether a resource handle-based variable has been initialized. // // Arguments: -// var_: Should be from a Variable(). -// accum: Should be from a Variable(). -// lr: Learning rate. Must be a scalar. -// l1: L1 regularization. Must be a scalar. -// l2: L2 regularization. Must be a scalar. -// grad: The gradient. -// indices: A vector of indices into the first dimension of var and accum. +// resource: the input resource handle. // -// Returns the created operation. -func ResourceSparseApplyProximalAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyProximalAdagradAttr) (o *tf.Operation) { +// Returns a scalar boolean which is true if the variable has been +// initialized. +func VarIsInitializedOp(scope *Scope, resource tf.Output) (is_initialized tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } opspec := tf.OpSpec{ - Type: "ResourceSparseApplyProximalAdagrad", + Type: "VarIsInitializedOp", Input: []tf.Input{ - var_, accum, lr, l1, l2, grad, indices, + resource, }, - Attrs: attrs, } - return scope.AddOperation(opspec) + op := scope.AddOperation(opspec) + return op.Output(0) } -// MaxPool3DGradAttr is an optional argument to MaxPool3DGrad. -type MaxPool3DGradAttr func(optionalAttr) - -// MaxPool3DGradDataFormat sets the optional data_format attribute to value. +// Converts each string in the input Tensor to its hash mod by a number of buckets. // -// value: The data format of the input and output data. With the -// default format "NDHWC", the data is stored in the order of: -// [batch, in_depth, in_height, in_width, in_channels]. -// Alternatively, the format could be "NCDHW", the data storage order is: -// [batch, in_channels, in_depth, in_height, in_width]. -// If not specified, defaults to "NDHWC" -func MaxPool3DGradDataFormat(value string) MaxPool3DGradAttr { - return func(m optionalAttr) { - m["data_format"] = value - } -} - -// Computes gradients of max pooling function. +// The hash function is deterministic on the content of the string within the +// process and will never change. However, it is not suitable for cryptography. +// This function may be used when CPU time is scarce and inputs are trusted or +// unimportant. There is a risk of adversaries constructing inputs that all hash +// to the same bucket. To prevent this problem, use a strong hash function with +// `tf.string_to_hash_bucket_strong`. // // Arguments: -// orig_input: The original input tensor. -// orig_output: The original output tensor. -// grad: Output backprop of shape `[batch, depth, rows, cols, channels]`. -// ksize: 1-D tensor of length 5. The size of the window for each dimension of -// the input tensor. Must have `ksize[0] = ksize[4] = 1`. -// strides: 1-D tensor of length 5. The stride of the sliding window for each -// dimension of `input`. Must have `strides[0] = strides[4] = 1`. -// padding: The type of padding algorithm to use. -func MaxPool3DGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPool3DGradAttr) (output tf.Output) { +// input: The strings to assign a hash bucket. +// num_buckets: The number of buckets. +// +// Returns A Tensor of the same shape as the input `string_tensor`. +func StringToHashBucketFast(scope *Scope, input tf.Output, num_buckets int64) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} - for _, a := range optional { - a(attrs) - } + attrs := map[string]interface{}{"num_buckets": num_buckets} opspec := tf.OpSpec{ - Type: "MaxPool3DGrad", + Type: "StringToHashBucketFast", Input: []tf.Input{ - orig_input, orig_output, grad, + input, }, Attrs: attrs, } @@ -12095,54 +12166,45 @@ func MaxPool3DGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, gr return op.Output(0) } -// SparseReduceSumAttr is an optional argument to SparseReduceSum. -type SparseReduceSumAttr func(optionalAttr) +// TensorArrayGatherV3Attr is an optional argument to TensorArrayGatherV3. +type TensorArrayGatherV3Attr func(optionalAttr) -// SparseReduceSumKeepDims sets the optional keep_dims attribute to value. +// TensorArrayGatherV3ElementShape sets the optional element_shape attribute to value. // -// value: If true, retain reduced dimensions with length 1. -// If not specified, defaults to false -func SparseReduceSumKeepDims(value bool) SparseReduceSumAttr { +// value: The expected shape of an element, if known. Used to +// validate the shapes of TensorArray elements. If this shape is not +// fully specified, gathering zero-size TensorArrays is an error. +// If not specified, defaults to +func TensorArrayGatherV3ElementShape(value tf.Shape) TensorArrayGatherV3Attr { return func(m optionalAttr) { - m["keep_dims"] = value + m["element_shape"] = value } } -// Computes the sum of elements across dimensions of a SparseTensor. -// -// This Op takes a SparseTensor and is the sparse counterpart to -// `tf.reduce_sum()`. In particular, this Op also returns a dense `Tensor` -// instead of a sparse one. -// -// Reduces `sp_input` along the dimensions given in `reduction_axes`. Unless -// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in -// `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained -// with length 1. +// Gather specific elements from the TensorArray into output `value`. // -// If `reduction_axes` has no entries, all dimensions are reduced, and a tensor -// with a single element is returned. Additionally, the axes can be negative, -// which are interpreted according to the indexing rules in Python. +// All elements selected by `indices` must have the same shape. // // Arguments: -// input_indices: 2-D. `N x R` matrix with the indices of non-empty values in a -// SparseTensor, possibly not in canonical ordering. -// input_values: 1-D. `N` non-empty values corresponding to `input_indices`. -// input_shape: 1-D. Shape of the input SparseTensor. -// reduction_axes: 1-D. Length-`K` vector containing the reduction axes. +// handle: The handle to a TensorArray. +// indices: The locations in the TensorArray from which to read tensor elements. +// flow_in: A float scalar that enforces proper chaining of operations. +// dtype: The type of the elem that is returned. // -// Returns `R-K`-D. The reduced Tensor. -func SparseReduceSum(scope *Scope, input_indices tf.Output, input_values tf.Output, input_shape tf.Output, reduction_axes tf.Output, optional ...SparseReduceSumAttr) (output tf.Output) { +// Returns All of the elements in the TensorArray, concatenated along a new +// axis (the new dimension 0). +func TensorArrayGatherV3(scope *Scope, handle tf.Output, indices tf.Output, flow_in tf.Output, dtype tf.DataType, optional ...TensorArrayGatherV3Attr) (value tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} + attrs := map[string]interface{}{"dtype": dtype} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "SparseReduceSum", + Type: "TensorArrayGatherV3", Input: []tf.Input{ - input_indices, input_values, input_shape, reduction_axes, + handle, indices, flow_in, }, Attrs: attrs, } @@ -12150,234 +12212,245 @@ func SparseReduceSum(scope *Scope, input_indices tf.Output, input_values tf.Outp return op.Output(0) } -// VariableShapeAttr is an optional argument to VariableShape. -type VariableShapeAttr func(optionalAttr) - -// VariableShapeOutType sets the optional out_type attribute to value. -// If not specified, defaults to DT_INT32 -func VariableShapeOutType(value tf.DataType) VariableShapeAttr { - return func(m optionalAttr) { - m["out_type"] = value +// This op consumes a lock created by `MutexLock`. +// +// This op exists to consume a tensor created by `MutexLock` (other than +// direct control dependencies). It should be the only that consumes the tensor, +// and will raise an error if it is not. Its only purpose is to keep the +// mutex lock tensor alive until it is consumed by this op. +// +// **NOTE**: This operation must run on the same device as its input. This may +// be enforced via the `colocate_with` mechanism. +// +// Arguments: +// mutex_lock: A tensor returned by `MutexLock`. +// +// Returns the created operation. +func ConsumeMutexLock(scope *Scope, mutex_lock tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ConsumeMutexLock", + Input: []tf.Input{ + mutex_lock, + }, } + return scope.AddOperation(opspec) } -// Returns the shape of the variable pointed to by `resource`. -// -// This operation returns a 1-D integer tensor representing the shape of `input`. +// Returns x / y element-wise for integer types. // -// For example: +// Truncation designates that negative numbers will round fractional quantities +// toward zero. I.e. -7 / 5 = -1. This matches C semantics but it is different +// than Python semantics. See `FloorDiv` for a division function that matches +// Python Semantics. // -// ``` -// # 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]] -// shape(t) ==> [2, 2, 3] -// ``` -func VariableShape(scope *Scope, input tf.Output, optional ...VariableShapeAttr) (output tf.Output) { +// *NOTE*: `TruncateDiv` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func TruncateDiv(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } opspec := tf.OpSpec{ - Type: "VariableShape", + Type: "TruncateDiv", Input: []tf.Input{ - input, + x, y, }, - Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// SparseToSparseSetOperationAttr is an optional argument to SparseToSparseSetOperation. -type SparseToSparseSetOperationAttr func(optionalAttr) - -// SparseToSparseSetOperationValidateIndices sets the optional validate_indices attribute to value. -// If not specified, defaults to true -func SparseToSparseSetOperationValidateIndices(value bool) SparseToSparseSetOperationAttr { - return func(m optionalAttr) { - m["validate_indices"] = value - } -} - -// Applies set operation along last dimension of 2 `SparseTensor` inputs. -// -// See SetOperationOp::SetOperationFromContext for values of `set_operation`. -// -// If `validate_indices` is `True`, `SparseToSparseSetOperation` validates the -// order and range of `set1` and `set2` indices. -// -// Input `set1` is a `SparseTensor` represented by `set1_indices`, `set1_values`, -// and `set1_shape`. For `set1` ranked `n`, 1st `n-1` dimensions must be the same -// as `set2`. Dimension `n` contains values in a set, duplicates are allowed but -// ignored. +// Restores tensors from a V2 checkpoint. // -// Input `set2` is a `SparseTensor` represented by `set2_indices`, `set2_values`, -// and `set2_shape`. For `set2` ranked `n`, 1st `n-1` dimensions must be the same -// as `set1`. Dimension `n` contains values in a set, duplicates are allowed but -// ignored. +// For backward compatibility with the V1 format, this Op currently allows +// restoring from a V1 checkpoint as well: +// - This Op first attempts to find the V2 index file pointed to by "prefix", and +// if found proceed to read it as a V2 checkpoint; +// - Otherwise the V1 read path is invoked. +// Relying on this behavior is not recommended, as the ability to fall back to read +// V1 might be deprecated and eventually removed. // -// If `validate_indices` is `True`, this op validates the order and range of `set1` -// and `set2` indices. +// By default, restores the named tensors in full. If the caller wishes to restore +// specific slices of stored tensors, "shape_and_slices" should be non-empty +// strings and correspondingly well-formed. // -// Output `result` is a `SparseTensor` represented by `result_indices`, -// `result_values`, and `result_shape`. For `set1` and `set2` ranked `n`, this -// has rank `n` and the same 1st `n-1` dimensions as `set1` and `set2`. The `nth` -// dimension contains the result of `set_operation` applied to the corresponding -// `[0...n-1]` dimension of `set`. +// Callers must ensure all the named tensors are indeed stored in the checkpoint. // // Arguments: -// set1_indices: 2D `Tensor`, indices of a `SparseTensor`. Must be in row-major -// order. -// set1_values: 1D `Tensor`, values of a `SparseTensor`. Must be in row-major -// order. -// set1_shape: 1D `Tensor`, shape of a `SparseTensor`. `set1_shape[0...n-1]` must -// be the same as `set2_shape[0...n-1]`, `set1_shape[n]` is the -// max set size across `0...n-1` dimensions. -// set2_indices: 2D `Tensor`, indices of a `SparseTensor`. Must be in row-major -// order. -// set2_values: 1D `Tensor`, values of a `SparseTensor`. Must be in row-major -// order. -// set2_shape: 1D `Tensor`, shape of a `SparseTensor`. `set2_shape[0...n-1]` must -// be the same as `set1_shape[0...n-1]`, `set2_shape[n]` is the -// max set size across `0...n-1` dimensions. -// +// prefix: Must have a single element. The prefix of a V2 checkpoint. +// tensor_names: shape {N}. The names of the tensors to be restored. +// shape_and_slices: shape {N}. The slice specs of the tensors to be restored. +// Empty strings indicate that they are non-partitioned tensors. +// dtypes: shape {N}. The list of expected dtype for the tensors. Must match +// those stored in the checkpoint. // -// Returns 2D indices of a `SparseTensor`.1D values of a `SparseTensor`.1D `Tensor` shape of a `SparseTensor`. `result_shape[0...n-1]` is -// the same as the 1st `n-1` dimensions of `set1` and `set2`, `result_shape[n]` -// is the max result set size across all `0...n-1` dimensions. -func SparseToSparseSetOperation(scope *Scope, set1_indices tf.Output, set1_values tf.Output, set1_shape tf.Output, set2_indices tf.Output, set2_values tf.Output, set2_shape tf.Output, set_operation string, optional ...SparseToSparseSetOperationAttr) (result_indices tf.Output, result_values tf.Output, result_shape tf.Output) { +// Returns shape {N}. The restored tensors, whose shapes are read from the +// checkpoint directly. +func RestoreV2(scope *Scope, prefix tf.Output, tensor_names tf.Output, shape_and_slices tf.Output, dtypes []tf.DataType) (tensors []tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"set_operation": set_operation} - for _, a := range optional { - a(attrs) - } + attrs := map[string]interface{}{"dtypes": dtypes} opspec := tf.OpSpec{ - Type: "SparseToSparseSetOperation", + Type: "RestoreV2", Input: []tf.Input{ - set1_indices, set1_values, set1_shape, set2_indices, set2_values, set2_shape, + prefix, tensor_names, shape_and_slices, }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) + if scope.Err() != nil { + return + } + var idx int + var err error + if tensors, idx, err = makeOutputList(op, idx, "tensors"); err != nil { + scope.UpdateErr("RestoreV2", err) + return + } + return tensors } -// Computes softmax cross entropy cost and gradients to backpropagate. -// -// Unlike `SoftmaxCrossEntropyWithLogits`, this operation does not accept -// a matrix of label probabilities, but rather a single label per row -// of features. This label is considered to have probability 1.0 for the -// given row. +// Receives a tensor value broadcast from another device. +func CollectiveBcastRecv(scope *Scope, T tf.DataType, group_size int64, group_key int64, instance_key int64, shape tf.Shape) (data tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"T": T, "group_size": group_size, "group_key": group_key, "instance_key": instance_key, "shape": shape} + opspec := tf.OpSpec{ + Type: "CollectiveBcastRecv", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Decode web-safe base64-encoded strings. // -// Inputs are the logits, not probabilities. +// Input may or may not have padding at the end. See EncodeBase64 for padding. +// Web-safe means that input must use - and _ instead of + and /. // // Arguments: -// features: batch_size x num_classes matrix -// labels: batch_size vector with values in [0, num_classes). -// This is the label for the given minibatch entry. +// input: Base64 strings to decode. // -// Returns Per example loss (batch_size vector).backpropagated gradients (batch_size x num_classes matrix). -func SparseSoftmaxCrossEntropyWithLogits(scope *Scope, features tf.Output, labels tf.Output) (loss tf.Output, backprop tf.Output) { +// Returns Decoded strings. +func DecodeBase64(scope *Scope, input tf.Output) (output tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "SparseSoftmaxCrossEntropyWithLogits", + Type: "DecodeBase64", Input: []tf.Input{ - features, labels, + input, }, } op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) + return op.Output(0) } -// Fast Fourier transform. -// -// Computes the 1-dimensional discrete Fourier transform over the inner-most -// dimension of `input`. +// Store the input tensor in the state of the current session. // // Arguments: -// input: A complex64 tensor. -// -// Returns A complex64 tensor of the same shape as `input`. The inner-most -// dimension of `input` is replaced with its 1D Fourier transform. +// value: The tensor to be stored. // -// @compatibility(numpy) -// Equivalent to np.fft.fft -// @end_compatibility -func FFT(scope *Scope, input tf.Output) (output tf.Output) { +// Returns The handle for the tensor stored in the session state, represented +// as a string. +func GetSessionHandle(scope *Scope, value tf.Output) (handle tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "FFT", + Type: "GetSessionHandle", Input: []tf.Input{ - input, + value, }, } op := scope.AddOperation(opspec) return op.Output(0) } -// Transforms a serialized tensorflow.TensorProto proto into a Tensor. +// ResourceSparseApplyProximalAdagradAttr is an optional argument to ResourceSparseApplyProximalAdagrad. +type ResourceSparseApplyProximalAdagradAttr func(optionalAttr) + +// ResourceSparseApplyProximalAdagradUseLocking sets the optional use_locking attribute to value. +// +// value: If True, updating of the var and accum tensors will be protected by +// a lock; otherwise the behavior is undefined, but may exhibit less contention. +// If not specified, defaults to false +func ResourceSparseApplyProximalAdagradUseLocking(value bool) ResourceSparseApplyProximalAdagradAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Sparse update entries in '*var' and '*accum' according to FOBOS algorithm. +// +// That is for rows we have grad for, we update var and accum as follows: +// accum += grad * grad +// prox_v = var +// prox_v -= lr * grad * (1 / sqrt(accum)) +// var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0} // // Arguments: -// serialized: A scalar string containing a serialized TensorProto proto. -// out_type: The type of the serialized tensor. The provided type must match the -// type of the serialized tensor and no implicit conversion will take place. +// var_: Should be from a Variable(). +// accum: Should be from a Variable(). +// lr: Learning rate. Must be a scalar. +// l1: L1 regularization. Must be a scalar. +// l2: L2 regularization. Must be a scalar. +// grad: The gradient. +// indices: A vector of indices into the first dimension of var and accum. // -// Returns A Tensor of type `out_type`. -func ParseTensor(scope *Scope, serialized tf.Output, out_type tf.DataType) (output tf.Output) { +// Returns the created operation. +func ResourceSparseApplyProximalAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyProximalAdagradAttr) (o *tf.Operation) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"out_type": out_type} + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "ParseTensor", + Type: "ResourceSparseApplyProximalAdagrad", Input: []tf.Input{ - serialized, + var_, accum, lr, l1, l2, grad, indices, }, Attrs: attrs, } - op := scope.AddOperation(opspec) - return op.Output(0) + return scope.AddOperation(opspec) } -// MaxPoolWithArgmaxAttr is an optional argument to MaxPoolWithArgmax. -type MaxPoolWithArgmaxAttr func(optionalAttr) +// MaxPool3DGradAttr is an optional argument to MaxPool3DGrad. +type MaxPool3DGradAttr func(optionalAttr) -// MaxPoolWithArgmaxTargmax sets the optional Targmax attribute to value. -// If not specified, defaults to DT_INT64 -func MaxPoolWithArgmaxTargmax(value tf.DataType) MaxPoolWithArgmaxAttr { +// MaxPool3DGradDataFormat sets the optional data_format attribute to value. +// +// value: The data format of the input and output data. With the +// default format "NDHWC", the data is stored in the order of: +// [batch, in_depth, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCDHW", the data storage order is: +// [batch, in_channels, in_depth, in_height, in_width]. +// If not specified, defaults to "NDHWC" +func MaxPool3DGradDataFormat(value string) MaxPool3DGradAttr { return func(m optionalAttr) { - m["Targmax"] = value + m["data_format"] = value } } -// Performs max pooling on the input and outputs both max values and indices. -// -// The indices in `argmax` are flattened, so that a maximum value at position -// `[b, y, x, c]` becomes flattened index -// `((b * height + y) * width + x) * channels + c`. -// -// The indices returned are always in `[0, height) x [0, width)` before flattening, -// even if padding is involved and the mathematically correct answer is outside -// (either negative or too large). This is a bug, but fixing it is difficult to do -// in a safe backwards compatible way, especially due to flattening. +// Computes gradients of max pooling function. // // Arguments: -// input: 4-D with shape `[batch, height, width, channels]`. Input to pool over. -// ksize: The size of the window for each dimension of the input tensor. -// strides: The stride of the sliding window for each dimension of the -// input tensor. +// orig_input: The original input tensor. +// orig_output: The original output tensor. +// grad: Output backprop of shape `[batch, depth, rows, cols, channels]`. +// ksize: 1-D tensor of length 5. The size of the window for each dimension of +// the input tensor. Must have `ksize[0] = ksize[4] = 1`. +// strides: 1-D tensor of length 5. The stride of the sliding window for each +// dimension of `input`. Must have `strides[0] = strides[4] = 1`. // padding: The type of padding algorithm to use. -// -// Returns The max pooled output tensor.4-D. The flattened indices of the max values chosen for each output. -func MaxPoolWithArgmax(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolWithArgmaxAttr) (output tf.Output, argmax tf.Output) { +func MaxPool3DGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPool3DGradAttr) (output tf.Output) { if scope.Err() != nil { return } @@ -12386,45 +12459,53 @@ func MaxPoolWithArgmax(scope *Scope, input tf.Output, ksize []int64, strides []i a(attrs) } opspec := tf.OpSpec{ - Type: "MaxPoolWithArgmax", + Type: "MaxPool3DGrad", Input: []tf.Input{ - input, + orig_input, orig_output, grad, }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) + return op.Output(0) } -// ResourceSparseApplyAdagradDAAttr is an optional argument to ResourceSparseApplyAdagradDA. -type ResourceSparseApplyAdagradDAAttr func(optionalAttr) +// SparseReduceSumAttr is an optional argument to SparseReduceSum. +type SparseReduceSumAttr func(optionalAttr) -// ResourceSparseApplyAdagradDAUseLocking sets the optional use_locking attribute to value. +// SparseReduceSumKeepDims sets the optional keep_dims attribute to value. // -// value: If True, updating of the var and accum tensors will be protected by -// a lock; otherwise the behavior is undefined, but may exhibit less contention. +// value: If true, retain reduced dimensions with length 1. // If not specified, defaults to false -func ResourceSparseApplyAdagradDAUseLocking(value bool) ResourceSparseApplyAdagradDAAttr { +func SparseReduceSumKeepDims(value bool) SparseReduceSumAttr { return func(m optionalAttr) { - m["use_locking"] = value + m["keep_dims"] = value } } -// Update entries in '*var' and '*accum' according to the proximal adagrad scheme. +// Computes the sum of elements across dimensions of a SparseTensor. +// +// This Op takes a SparseTensor and is the sparse counterpart to +// `tf.reduce_sum()`. In particular, this Op also returns a dense `Tensor` +// instead of a sparse one. +// +// Reduces `sp_input` along the dimensions given in `reduction_axes`. Unless +// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in +// `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained +// with length 1. +// +// If `reduction_axes` has no entries, all dimensions are reduced, and a tensor +// with a single element is returned. Additionally, the axes can be negative, +// which are interpreted according to the indexing rules in Python. // // Arguments: -// var_: Should be from a Variable(). -// gradient_accumulator: Should be from a Variable(). -// gradient_squared_accumulator: Should be from a Variable(). -// grad: The gradient. -// indices: A vector of indices into the first dimension of var and accum. -// lr: Learning rate. Must be a scalar. -// l1: L1 regularization. Must be a scalar. -// l2: L2 regularization. Must be a scalar. -// global_step: Training step number. Must be a scalar. +// input_indices: 2-D. `N x R` matrix with the indices of non-empty values in a +// SparseTensor, possibly not in canonical ordering. +// input_values: 1-D. `N` non-empty values corresponding to `input_indices`. +// input_shape: 1-D. Shape of the input SparseTensor. +// reduction_axes: 1-D. Length-`K` vector containing the reduction axes. // -// Returns the created operation. -func ResourceSparseApplyAdagradDA(scope *Scope, var_ tf.Output, gradient_accumulator tf.Output, gradient_squared_accumulator tf.Output, grad tf.Output, indices tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, global_step tf.Output, optional ...ResourceSparseApplyAdagradDAAttr) (o *tf.Operation) { +// Returns `R-K`-D. The reduced Tensor. +func SparseReduceSum(scope *Scope, input_indices tf.Output, input_values tf.Output, input_shape tf.Output, reduction_axes tf.Output, optional ...SparseReduceSumAttr) (output tf.Output) { if scope.Err() != nil { return } @@ -12433,209 +12514,260 @@ func ResourceSparseApplyAdagradDA(scope *Scope, var_ tf.Output, gradient_accumul a(attrs) } opspec := tf.OpSpec{ - Type: "ResourceSparseApplyAdagradDA", + Type: "SparseReduceSum", Input: []tf.Input{ - var_, gradient_accumulator, gradient_squared_accumulator, grad, indices, lr, l1, l2, global_step, + input_indices, input_values, input_shape, reduction_axes, }, Attrs: attrs, } - return scope.AddOperation(opspec) + op := scope.AddOperation(opspec) + return op.Output(0) } -// EncodeJpegAttr is an optional argument to EncodeJpeg. -type EncodeJpegAttr func(optionalAttr) +// VariableShapeAttr is an optional argument to VariableShape. +type VariableShapeAttr func(optionalAttr) -// EncodeJpegFormat sets the optional format attribute to value. -// -// value: Per pixel image format. -// If not specified, defaults to "" -func EncodeJpegFormat(value string) EncodeJpegAttr { +// VariableShapeOutType sets the optional out_type attribute to value. +// If not specified, defaults to DT_INT32 +func VariableShapeOutType(value tf.DataType) VariableShapeAttr { return func(m optionalAttr) { - m["format"] = value + m["out_type"] = value } } -// EncodeJpegQuality sets the optional quality attribute to value. +// Returns the shape of the variable pointed to by `resource`. // -// value: Quality of the compression from 0 to 100 (higher is better and slower). -// If not specified, defaults to 95 -func EncodeJpegQuality(value int64) EncodeJpegAttr { - return func(m optionalAttr) { - m["quality"] = value - } -} - -// EncodeJpegProgressive sets the optional progressive attribute to value. +// This operation returns a 1-D integer tensor representing the shape of `input`. // -// value: If True, create a JPEG that loads progressively (coarse to fine). -// If not specified, defaults to false -func EncodeJpegProgressive(value bool) EncodeJpegAttr { - return func(m optionalAttr) { - m["progressive"] = value - } -} - -// EncodeJpegOptimizeSize sets the optional optimize_size attribute to value. +// For example: // -// value: If True, spend CPU/RAM to reduce size with no quality change. -// If not specified, defaults to false -func EncodeJpegOptimizeSize(value bool) EncodeJpegAttr { - return func(m optionalAttr) { - m["optimize_size"] = value +// ``` +// # 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]] +// shape(t) ==> [2, 2, 3] +// ``` +func VariableShape(scope *Scope, input tf.Output, optional ...VariableShapeAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "VariableShape", + Input: []tf.Input{ + input, + }, + Attrs: attrs, } + op := scope.AddOperation(opspec) + return op.Output(0) } -// EncodeJpegChromaDownsampling sets the optional chroma_downsampling attribute to value. -// -// value: See http://en.wikipedia.org/wiki/Chroma_subsampling. +// SparseToSparseSetOperationAttr is an optional argument to SparseToSparseSetOperation. +type SparseToSparseSetOperationAttr func(optionalAttr) + +// SparseToSparseSetOperationValidateIndices sets the optional validate_indices attribute to value. // If not specified, defaults to true -func EncodeJpegChromaDownsampling(value bool) EncodeJpegAttr { +func SparseToSparseSetOperationValidateIndices(value bool) SparseToSparseSetOperationAttr { return func(m optionalAttr) { - m["chroma_downsampling"] = value + m["validate_indices"] = value } } -// EncodeJpegDensityUnit sets the optional density_unit attribute to value. +// Applies set operation along last dimension of 2 `SparseTensor` inputs. // -// value: Unit used to specify `x_density` and `y_density`: -// pixels per inch (`'in'`) or centimeter (`'cm'`). -// If not specified, defaults to "in" -func EncodeJpegDensityUnit(value string) EncodeJpegAttr { - return func(m optionalAttr) { - m["density_unit"] = value - } -} - -// EncodeJpegXDensity sets the optional x_density attribute to value. +// See SetOperationOp::SetOperationFromContext for values of `set_operation`. // -// value: Horizontal pixels per density unit. -// If not specified, defaults to 300 -func EncodeJpegXDensity(value int64) EncodeJpegAttr { - return func(m optionalAttr) { - m["x_density"] = value - } -} - -// EncodeJpegYDensity sets the optional y_density attribute to value. +// If `validate_indices` is `True`, `SparseToSparseSetOperation` validates the +// order and range of `set1` and `set2` indices. // -// value: Vertical pixels per density unit. -// If not specified, defaults to 300 -func EncodeJpegYDensity(value int64) EncodeJpegAttr { - return func(m optionalAttr) { - m["y_density"] = value - } -} - -// EncodeJpegXmpMetadata sets the optional xmp_metadata attribute to value. +// Input `set1` is a `SparseTensor` represented by `set1_indices`, `set1_values`, +// and `set1_shape`. For `set1` ranked `n`, 1st `n-1` dimensions must be the same +// as `set2`. Dimension `n` contains values in a set, duplicates are allowed but +// ignored. // -// value: If not empty, embed this XMP metadata in the image header. -// If not specified, defaults to "" -func EncodeJpegXmpMetadata(value string) EncodeJpegAttr { - return func(m optionalAttr) { - m["xmp_metadata"] = value +// Input `set2` is a `SparseTensor` represented by `set2_indices`, `set2_values`, +// and `set2_shape`. For `set2` ranked `n`, 1st `n-1` dimensions must be the same +// as `set1`. Dimension `n` contains values in a set, duplicates are allowed but +// ignored. +// +// If `validate_indices` is `True`, this op validates the order and range of `set1` +// and `set2` indices. +// +// Output `result` is a `SparseTensor` represented by `result_indices`, +// `result_values`, and `result_shape`. For `set1` and `set2` ranked `n`, this +// has rank `n` and the same 1st `n-1` dimensions as `set1` and `set2`. The `nth` +// dimension contains the result of `set_operation` applied to the corresponding +// `[0...n-1]` dimension of `set`. +// +// Arguments: +// set1_indices: 2D `Tensor`, indices of a `SparseTensor`. Must be in row-major +// order. +// set1_values: 1D `Tensor`, values of a `SparseTensor`. Must be in row-major +// order. +// set1_shape: 1D `Tensor`, shape of a `SparseTensor`. `set1_shape[0...n-1]` must +// be the same as `set2_shape[0...n-1]`, `set1_shape[n]` is the +// max set size across `0...n-1` dimensions. +// set2_indices: 2D `Tensor`, indices of a `SparseTensor`. Must be in row-major +// order. +// set2_values: 1D `Tensor`, values of a `SparseTensor`. Must be in row-major +// order. +// set2_shape: 1D `Tensor`, shape of a `SparseTensor`. `set2_shape[0...n-1]` must +// be the same as `set1_shape[0...n-1]`, `set2_shape[n]` is the +// max set size across `0...n-1` dimensions. +// +// +// Returns 2D indices of a `SparseTensor`.1D values of a `SparseTensor`.1D `Tensor` shape of a `SparseTensor`. `result_shape[0...n-1]` is +// the same as the 1st `n-1` dimensions of `set1` and `set2`, `result_shape[n]` +// is the max result set size across all `0...n-1` dimensions. +func SparseToSparseSetOperation(scope *Scope, set1_indices tf.Output, set1_values tf.Output, set1_shape tf.Output, set2_indices tf.Output, set2_values tf.Output, set2_shape tf.Output, set_operation string, optional ...SparseToSparseSetOperationAttr) (result_indices tf.Output, result_values tf.Output, result_shape tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"set_operation": set_operation} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SparseToSparseSetOperation", + Input: []tf.Input{ + set1_indices, set1_values, set1_shape, set2_indices, set2_values, set2_shape, + }, + Attrs: attrs, } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) } -// JPEG-encode an image. +// Computes softmax cross entropy cost and gradients to backpropagate. // -// `image` is a 3-D uint8 Tensor of shape `[height, width, channels]`. +// Unlike `SoftmaxCrossEntropyWithLogits`, this operation does not accept +// a matrix of label probabilities, but rather a single label per row +// of features. This label is considered to have probability 1.0 for the +// given row. // -// The attr `format` can be used to override the color format of the encoded -// output. Values can be: +// Inputs are the logits, not probabilities. // -// * `''`: Use a default format based on the number of channels in the image. -// * `grayscale`: Output a grayscale JPEG image. The `channels` dimension -// of `image` must be 1. -// * `rgb`: Output an RGB JPEG image. The `channels` dimension -// of `image` must be 3. +// Arguments: +// features: batch_size x num_classes matrix +// labels: batch_size vector with values in [0, num_classes). +// This is the label for the given minibatch entry. // -// If `format` is not specified or is the empty string, a default format is picked -// in function of the number of channels in `image`: +// Returns Per example loss (batch_size vector).backpropagated gradients (batch_size x num_classes matrix). +func SparseSoftmaxCrossEntropyWithLogits(scope *Scope, features tf.Output, labels tf.Output) (loss tf.Output, backprop tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseSoftmaxCrossEntropyWithLogits", + Input: []tf.Input{ + features, labels, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// Fast Fourier transform. // -// * 1: Output a grayscale image. -// * 3: Output an RGB image. +// Computes the 1-dimensional discrete Fourier transform over the inner-most +// dimension of `input`. // // Arguments: -// image: 3-D with shape `[height, width, channels]`. +// input: A complex64 tensor. // -// Returns 0-D. JPEG-encoded image. -func EncodeJpeg(scope *Scope, image tf.Output, optional ...EncodeJpegAttr) (contents tf.Output) { +// Returns A complex64 tensor of the same shape as `input`. The inner-most +// dimension of `input` is replaced with its 1D Fourier transform. +// +// @compatibility(numpy) +// Equivalent to np.fft.fft +// @end_compatibility +func FFT(scope *Scope, input tf.Output) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } opspec := tf.OpSpec{ - Type: "EncodeJpeg", + Type: "FFT", Input: []tf.Input{ - image, + input, }, - Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// MultinomialAttr is an optional argument to Multinomial. -type MultinomialAttr func(optionalAttr) - -// MultinomialSeed sets the optional seed attribute to value. +// Transforms a serialized tensorflow.TensorProto proto into a Tensor. // -// value: If either seed or seed2 is set to be non-zero, the internal random number -// generator is seeded by the given seed. Otherwise, a random seed is used. -// If not specified, defaults to 0 -func MultinomialSeed(value int64) MultinomialAttr { - return func(m optionalAttr) { - m["seed"] = value - } -} - -// MultinomialSeed2 sets the optional seed2 attribute to value. +// Arguments: +// serialized: A scalar string containing a serialized TensorProto proto. +// out_type: The type of the serialized tensor. The provided type must match the +// type of the serialized tensor and no implicit conversion will take place. // -// value: A second seed to avoid seed collision. -// If not specified, defaults to 0 -func MultinomialSeed2(value int64) MultinomialAttr { - return func(m optionalAttr) { - m["seed2"] = value +// Returns A Tensor of type `out_type`. +func ParseTensor(scope *Scope, serialized tf.Output, out_type tf.DataType) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"out_type": out_type} + opspec := tf.OpSpec{ + Type: "ParseTensor", + Input: []tf.Input{ + serialized, + }, + Attrs: attrs, } + op := scope.AddOperation(opspec) + return op.Output(0) } -// MultinomialOutputDtype sets the optional output_dtype attribute to value. +// MaxPoolWithArgmaxAttr is an optional argument to MaxPoolWithArgmax. +type MaxPoolWithArgmaxAttr func(optionalAttr) + +// MaxPoolWithArgmaxTargmax sets the optional Targmax attribute to value. // If not specified, defaults to DT_INT64 -func MultinomialOutputDtype(value tf.DataType) MultinomialAttr { +func MaxPoolWithArgmaxTargmax(value tf.DataType) MaxPoolWithArgmaxAttr { return func(m optionalAttr) { - m["output_dtype"] = value + m["Targmax"] = value } } -// Draws samples from a multinomial distribution. +// Performs max pooling on the input and outputs both max values and indices. +// +// The indices in `argmax` are flattened, so that a maximum value at position +// `[b, y, x, c]` becomes flattened index +// `((b * height + y) * width + x) * channels + c`. +// +// The indices returned are always in `[0, height) x [0, width)` before flattening, +// even if padding is involved and the mathematically correct answer is outside +// (either negative or too large). This is a bug, but fixing it is difficult to do +// in a safe backwards compatible way, especially due to flattening. // // Arguments: -// logits: 2-D Tensor with shape `[batch_size, num_classes]`. Each slice `[i, :]` -// represents the unnormalized log probabilities for all classes. -// num_samples: 0-D. Number of independent samples to draw for each row slice. +// input: 4-D with shape `[batch, height, width, channels]`. Input to pool over. +// ksize: The size of the window for each dimension of the input tensor. +// strides: The stride of the sliding window for each dimension of the +// input tensor. +// padding: The type of padding algorithm to use. // -// Returns 2-D Tensor with shape `[batch_size, num_samples]`. Each slice `[i, :]` -// contains the drawn class labels with range `[0, num_classes)`. -func Multinomial(scope *Scope, logits tf.Output, num_samples tf.Output, optional ...MultinomialAttr) (output tf.Output) { +// Returns The max pooled output tensor.4-D. The flattened indices of the max values chosen for each output. +func MaxPoolWithArgmax(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolWithArgmaxAttr) (output tf.Output, argmax tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "Multinomial", + Type: "MaxPoolWithArgmax", Input: []tf.Input{ - logits, num_samples, + input, }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0) + return op.Output(0), op.Output(1) } // Returns the truth value of NOT x element-wise. @@ -13157,62 +13289,6 @@ func ResourceScatterSub(scope *Scope, resource tf.Output, indices tf.Output, upd return scope.AddOperation(opspec) } -// Inverse 2D fast Fourier transform. -// -// Computes the inverse 2-dimensional discrete Fourier transform over the -// inner-most 2 dimensions of `input`. -// -// Arguments: -// input: A complex64 tensor. -// -// Returns A complex64 tensor of the same shape as `input`. The inner-most 2 -// dimensions of `input` are replaced with their inverse 2D Fourier transform. -// -// @compatibility(numpy) -// Equivalent to np.fft.ifft2 -// @end_compatibility -func IFFT2D(scope *Scope, input tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "IFFT2D", - Input: []tf.Input{ - input, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// 2D fast Fourier transform. -// -// Computes the 2-dimensional discrete Fourier transform over the inner-most -// 2 dimensions of `input`. -// -// Arguments: -// input: A complex64 tensor. -// -// Returns A complex64 tensor of the same shape as `input`. The inner-most 2 -// dimensions of `input` are replaced with their 2D Fourier transform. -// -// @compatibility(numpy) -// Equivalent to np.fft.fft2 -// @end_compatibility -func FFT2D(scope *Scope, input tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "FFT2D", - Input: []tf.Input{ - input, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // ResourceApplyProximalGradientDescentAttr is an optional argument to ResourceApplyProximalGradientDescent. type ResourceApplyProximalGradientDescentAttr func(optionalAttr) @@ -15324,31 +15400,6 @@ func BoostedTreesEnsembleResourceHandleOp(scope *Scope, optional ...BoostedTrees return op.Output(0) } -// Concatenates tensors along one dimension. -// -// Arguments: -// concat_dim: 0-D. The dimension along which to concatenate. Must be in the -// range [0, rank(values)). -// values: The `N` Tensors to concatenate. Their ranks and types must match, -// and their sizes must match in all dimensions except `concat_dim`. -// -// Returns A `Tensor` with the concatenation of values stacked along the -// `concat_dim` dimension. This tensor's shape matches that of `values` except -// in `concat_dim` where it has the sum of the sizes. -func Concat(scope *Scope, concat_dim tf.Output, values []tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Concat", - Input: []tf.Input{ - concat_dim, tf.OutputList(values), - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // ResourceApplyMomentumAttr is an optional argument to ResourceApplyMomentum. type ResourceApplyMomentumAttr func(optionalAttr) @@ -16259,9 +16310,65 @@ func MutableDenseHashTableV2(scope *Scope, empty_key tf.Output, value_dtype tf.D opspec := tf.OpSpec{ Type: "MutableDenseHashTableV2", Input: []tf.Input{ - empty_key, + empty_key, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// 2D fast Fourier transform. +// +// Computes the 2-dimensional discrete Fourier transform over the inner-most +// 2 dimensions of `input`. +// +// Arguments: +// input: A complex64 tensor. +// +// Returns A complex64 tensor of the same shape as `input`. The inner-most 2 +// dimensions of `input` are replaced with their 2D Fourier transform. +// +// @compatibility(numpy) +// Equivalent to np.fft.fft2 +// @end_compatibility +func FFT2D(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "FFT2D", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Inverse 2D fast Fourier transform. +// +// Computes the inverse 2-dimensional discrete Fourier transform over the +// inner-most 2 dimensions of `input`. +// +// Arguments: +// input: A complex64 tensor. +// +// Returns A complex64 tensor of the same shape as `input`. The inner-most 2 +// dimensions of `input` are replaced with their inverse 2D Fourier transform. +// +// @compatibility(numpy) +// Equivalent to np.fft.ifft2 +// @end_compatibility +func IFFT2D(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "IFFT2D", + Input: []tf.Input{ + input, }, - Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) @@ -17777,77 +17884,6 @@ func SparseCross(scope *Scope, indices []tf.Output, values []tf.Output, shapes [ return op.Output(0), op.Output(1), op.Output(2) } -// Concatenates quantized tensors along one dimension. -// -// Arguments: -// concat_dim: 0-D. The dimension along which to concatenate. Must be in the -// range [0, rank(values)). -// values: The `N` Tensors to concatenate. Their ranks and types must match, -// and their sizes must match in all dimensions except `concat_dim`. -// input_mins: The minimum scalar values for each of the input tensors. -// input_maxes: The maximum scalar values for each of the input tensors. -// -// Returns A `Tensor` with the concatenation of values stacked along the -// `concat_dim` dimension. This tensor's shape matches that of `values` except -// in `concat_dim` where it has the sum of the sizes.The float value that the minimum quantized output value represents.The float value that the maximum quantized output value represents. -func QuantizedConcat(scope *Scope, concat_dim tf.Output, values []tf.Output, input_mins []tf.Output, input_maxes []tf.Output) (output tf.Output, output_min tf.Output, output_max tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "QuantizedConcat", - Input: []tf.Input{ - concat_dim, tf.OutputList(values), tf.OutputList(input_mins), tf.OutputList(input_maxes), - }, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// Slice a `SparseTensor` based on the `start` and `size`. -// -// For example, if the input is -// -// input_tensor = shape = [2, 7] -// [ a d e ] -// [b c ] -// -// Graphically the output tensors are: -// -// sparse_slice([0, 0], [2, 4]) = shape = [2, 4] -// [ a ] -// [b c ] -// -// sparse_slice([0, 4], [2, 3]) = shape = [2, 3] -// [ d e ] -// [ ] -// -// Arguments: -// indices: 2-D tensor represents the indices of the sparse tensor. -// values: 1-D tensor represents the values of the sparse tensor. -// shape: 1-D. tensor represents the shape of the sparse tensor. -// start: 1-D. tensor represents the start of the slice. -// size: 1-D. tensor represents the size of the slice. -// output indices: A list of 1-D tensors represents the indices of the output -// sparse tensors. -// -// Returns A list of 1-D tensors represents the values of the output sparse -// tensors.A list of 1-D tensors represents the shape of the output sparse -// tensors. -func SparseSlice(scope *Scope, indices tf.Output, values tf.Output, shape tf.Output, start tf.Output, size tf.Output) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SparseSlice", - Input: []tf.Input{ - indices, values, shape, start, size, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - // Returns the element-wise min of two SparseTensors. // // Assumes the two SparseTensors have the same shape, i.e., no broadcasting. @@ -17978,52 +18014,6 @@ func TakeManySparseFromTensorsMap(scope *Scope, sparse_handles tf.Output, dtype return op.Output(0), op.Output(1), op.Output(2) } -// MaxPoolAttr is an optional argument to MaxPool. -type MaxPoolAttr func(optionalAttr) - -// MaxPoolDataFormat sets the optional data_format attribute to value. -// -// value: Specify the data format of the input and output data. With the -// default format "NHWC", the data is stored in the order of: -// [batch, in_height, in_width, in_channels]. -// Alternatively, the format could be "NCHW", the data storage order of: -// [batch, in_channels, in_height, in_width]. -// If not specified, defaults to "NHWC" -func MaxPoolDataFormat(value string) MaxPoolAttr { - return func(m optionalAttr) { - m["data_format"] = value - } -} - -// Performs max pooling on the input. -// -// Arguments: -// input: 4-D input to pool over. -// ksize: The size of the window for each dimension of the input tensor. -// strides: The stride of the sliding window for each dimension of the -// input tensor. -// padding: The type of padding algorithm to use. -// -// Returns The max pooled output tensor. -func MaxPool(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "MaxPool", - Input: []tf.Input{ - input, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // Assigns a new value to a variable. // // Any ReadVariableOp with a control dependency on this op is guaranteed to return @@ -18605,69 +18595,6 @@ func SdcaOptimizer(scope *Scope, sparse_example_indices []tf.Output, sparse_feat return out_example_state_data, out_delta_sparse_weights, out_delta_dense_weights } -// SparseMatMulAttr is an optional argument to SparseMatMul. -type SparseMatMulAttr func(optionalAttr) - -// SparseMatMulTransposeA sets the optional transpose_a attribute to value. -// If not specified, defaults to false -func SparseMatMulTransposeA(value bool) SparseMatMulAttr { - return func(m optionalAttr) { - m["transpose_a"] = value - } -} - -// SparseMatMulTransposeB sets the optional transpose_b attribute to value. -// If not specified, defaults to false -func SparseMatMulTransposeB(value bool) SparseMatMulAttr { - return func(m optionalAttr) { - m["transpose_b"] = value - } -} - -// SparseMatMulAIsSparse sets the optional a_is_sparse attribute to value. -// If not specified, defaults to false -func SparseMatMulAIsSparse(value bool) SparseMatMulAttr { - return func(m optionalAttr) { - m["a_is_sparse"] = value - } -} - -// SparseMatMulBIsSparse sets the optional b_is_sparse attribute to value. -// If not specified, defaults to false -func SparseMatMulBIsSparse(value bool) SparseMatMulAttr { - return func(m optionalAttr) { - m["b_is_sparse"] = value - } -} - -// Multiply matrix "a" by matrix "b". -// -// The inputs must be two-dimensional matrices and the inner dimension of "a" must -// match the outer dimension of "b". This op is optimized for the case where at -// least one of "a" or "b" is sparse. The breakeven for using this versus a dense -// matrix multiply on one platform was 30% zero values in the sparse matrix. -// -// The gradient computation of this operation will only take advantage of sparsity -// in the input gradient when that gradient comes from a Relu. -func SparseMatMul(scope *Scope, a tf.Output, b tf.Output, optional ...SparseMatMulAttr) (product tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "SparseMatMul", - Input: []tf.Input{ - a, b, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // ShapeAttr is an optional argument to Shape. type ShapeAttr func(optionalAttr) @@ -19513,6 +19440,79 @@ func OrderedMapIncompleteSize(scope *Scope, dtypes []tf.DataType, optional ...Or return op.Output(0) } +// DestroyResourceOpAttr is an optional argument to DestroyResourceOp. +type DestroyResourceOpAttr func(optionalAttr) + +// DestroyResourceOpIgnoreLookupError sets the optional ignore_lookup_error attribute to value. +// +// value: whether to ignore the error when the resource +// doesn't exist. +// If not specified, defaults to true +func DestroyResourceOpIgnoreLookupError(value bool) DestroyResourceOpAttr { + return func(m optionalAttr) { + m["ignore_lookup_error"] = value + } +} + +// Deletes the resource specified by the handle. +// +// All subsequent operations using the resource will result in a NotFound +// error status. +// +// Arguments: +// resource: handle to the resource to delete. +// +// Returns the created operation. +func DestroyResourceOp(scope *Scope, resource tf.Output, optional ...DestroyResourceOpAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DestroyResourceOp", + Input: []tf.Input{ + resource, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Generates values in an interval. +// +// A sequence of `num` evenly-spaced values are generated beginning at `start`. +// If `num > 1`, the values in the sequence increase by `stop - start / num - 1`, +// so that the last one is exactly `stop`. +// +// For example: +// +// ``` +// tf.linspace(10.0, 12.0, 3, name="linspace") => [ 10.0 11.0 12.0] +// ``` +// +// Arguments: +// start: First entry in the range. +// stop: Last entry in the range. +// num: Number of values to generate. +// +// Returns 1-D. The generated values. +func LinSpace(scope *Scope, start tf.Output, stop tf.Output, num tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "LinSpace", + Input: []tf.Input{ + start, stop, num, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // ComplexAttr is an optional argument to Complex. type ComplexAttr func(optionalAttr) -- GitLab From c9b142cec6e5340709279f8f373fcc139509168b Mon Sep 17 00:00:00 2001 From: Gunhan Gulsoy Date: Mon, 18 Jun 2018 22:50:57 -0700 Subject: [PATCH 648/816] Automated g4 rollback of changelist 200988382 PiperOrigin-RevId: 201119398 --- .../tools/ci_build/windows/cpu/pip/build_tf_windows.sh | 7 +------ tensorflow/tools/ci_build/windows/libtensorflow_cpu.sh | 2 +- 2 files changed, 2 insertions(+), 7 deletions(-) diff --git a/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh b/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh index 4aa270ea86..0b13b97209 100644 --- a/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh +++ b/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh @@ -77,12 +77,7 @@ fi # to distinct them. This helps avoid building the same targets twice. echo "build --distinct_host_configuration=false" >> "${TMP_BAZELRC}" -# Enable short object file path to avoid long path issue on Windows. -echo "build --output_user_root=${TMPDIR}" >> "${TMP_BAZELRC}" - -if ! grep -q "import %workspace%/${TMP_BAZELRC}" .bazelrc; then - echo "import %workspace%/${TMP_BAZELRC}" >> .bazelrc -fi +echo "import %workspace%/${TMP_BAZELRC}" >> .bazelrc run_configure_for_cpu_build diff --git a/tensorflow/tools/ci_build/windows/libtensorflow_cpu.sh b/tensorflow/tools/ci_build/windows/libtensorflow_cpu.sh index 022f120dbd..583d1d5f09 100755 --- a/tensorflow/tools/ci_build/windows/libtensorflow_cpu.sh +++ b/tensorflow/tools/ci_build/windows/libtensorflow_cpu.sh @@ -41,7 +41,7 @@ run_configure_for_cpu_build # build_libtensorflow_tarball in ../builds/libtensorflow.sh # cannot be used on Windows since it relies on pkg_tar rules. # So we do something special here -bazel build -c opt --copt=/arch:AVX --output_user_root=${TMPDIR} \ +bazel build -c opt --copt=/arch:AVX \ tensorflow:libtensorflow.so \ tensorflow/tools/lib_package:clicenses_generate \ tensorflow/java:libtensorflow_jni.so \ -- GitLab From 70d76387d941f493fd25b5da1a93c1da6d744bff Mon Sep 17 00:00:00 2001 From: Ilya Biryukov Date: Tue, 19 Jun 2018 00:28:31 -0700 Subject: [PATCH 649/816] Update downloadable clang to r334100. PiperOrigin-RevId: 201127564 --- third_party/clang_toolchain/download_clang.bzl | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/third_party/clang_toolchain/download_clang.bzl b/third_party/clang_toolchain/download_clang.bzl index a203245005..b61e901037 100644 --- a/third_party/clang_toolchain/download_clang.bzl +++ b/third_party/clang_toolchain/download_clang.bzl @@ -35,18 +35,18 @@ def download_clang(repo_ctx, out_folder): # Latest CLANG_REVISION and CLANG_SUB_REVISION of the Chromiums's release # can be found in https://chromium.googlesource.com/chromium/src/tools/clang/+/master/scripts/update.py - CLANG_REVISION = '332838' + CLANG_REVISION = '334100' CLANG_SUB_REVISION = 1 package_version = '%s-%s' % (CLANG_REVISION, CLANG_SUB_REVISION) checksums = { 'Linux_x64': - 'b9ef55de7500778f366039dbe62d1632074a3ef3673022eabf4e59d405730968', + '3c57420b591601cd14b5babd74b58fcaefa877112938d70cca6f0a1b0b293ab4', 'Mac': - '30d808512763c98cecf15f7bb654d845de3e8d065a95f5c5b6b3459254cc98d6', + '97d313996fb97a6138635f963d7ef4efa9f028a8168bb7917cc428b9eab05ebb', 'Win': - '277e799a190b22727c26b09986c0cedbd667a189f425318f421addf6a21ca4bd', + '52c1d6d20a0733276597f4ced59d18b545769dbf8beb8c6bdc26a7a862da7fc9', } platform_folder = _get_platform_folder(repo_ctx.os.name) -- GitLab From d091290a22aba19cf43a697c6194bb4da98ebae6 Mon Sep 17 00:00:00 2001 From: Adrian Kuegel Date: Tue, 19 Jun 2018 01:42:23 -0700 Subject: [PATCH 650/816] Mark Gather as fusile. There is an elementwise implementation for Gather. PiperOrigin-RevId: 201136554 --- tensorflow/compiler/xla/service/gpu/instruction_fusion.cc | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/compiler/xla/service/gpu/instruction_fusion.cc b/tensorflow/compiler/xla/service/gpu/instruction_fusion.cc index 6c4519185b..64ed3d748f 100644 --- a/tensorflow/compiler/xla/service/gpu/instruction_fusion.cc +++ b/tensorflow/compiler/xla/service/gpu/instruction_fusion.cc @@ -40,6 +40,7 @@ bool IsFusile(const HloInstruction& hlo) { hlo.opcode() == HloOpcode::kDynamicSlice || hlo.opcode() == HloOpcode::kDynamicUpdateSlice || hlo.opcode() == HloOpcode::kFusion || + hlo.opcode() == HloOpcode::kGather || hlo.opcode() == HloOpcode::kPad || hlo.opcode() == HloOpcode::kReduce || hlo.opcode() == HloOpcode::kReduceWindow || -- GitLab From a89726dea8d9005a5f9ca73ad14f28c32cd87e56 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 19 Jun 2018 02:55:11 -0700 Subject: [PATCH 651/816] Derivative of tf.igamma(a, x) and tf.igammac(a, x) with respect to a. Previously, both functions only supported the derivative with respect to x. We add the derivative with respect to the other argument. It is computed using the Eigen function igamma_der_a that performs forward-mode differentiation of the code for igamma. This function is not exposed in the public TensorFlow API. PiperOrigin-RevId: 201145398 --- .../base_api/api_def_IgammaGradA.pbtxt | 5 ++++ .../core/kernels/cwise_op_gpu_igammas.cu.cc | 2 ++ tensorflow/core/kernels/cwise_op_igammas.cc | 3 +++ tensorflow/core/kernels/cwise_ops_gradients.h | 3 +++ tensorflow/core/ops/math_ops.cc | 7 ++++++ .../python/kernel_tests/cwise_ops_test.py | 7 +++--- tensorflow/python/ops/math_grad.py | 25 ++++++++++--------- tensorflow/workspace.bzl | 8 +++--- 8 files changed, 40 insertions(+), 20 deletions(-) create mode 100644 tensorflow/core/api_def/base_api/api_def_IgammaGradA.pbtxt diff --git a/tensorflow/core/api_def/base_api/api_def_IgammaGradA.pbtxt b/tensorflow/core/api_def/base_api/api_def_IgammaGradA.pbtxt new file mode 100644 index 0000000000..747a8badfd --- /dev/null +++ b/tensorflow/core/api_def/base_api/api_def_IgammaGradA.pbtxt @@ -0,0 +1,5 @@ +op { + graph_op_name: "IgammaGradA" + visibility: HIDDEN + summary: "Computes the gradient of `igamma(a, x)` wrt `a`." +} diff --git a/tensorflow/core/kernels/cwise_op_gpu_igammas.cu.cc b/tensorflow/core/kernels/cwise_op_gpu_igammas.cu.cc index 5a529bd8ca..508a47deda 100644 --- a/tensorflow/core/kernels/cwise_op_gpu_igammas.cu.cc +++ b/tensorflow/core/kernels/cwise_op_gpu_igammas.cu.cc @@ -16,10 +16,12 @@ limitations under the License. #if GOOGLE_CUDA #include "tensorflow/core/kernels/cwise_ops_gpu_common.cu.h" +#include "tensorflow/core/kernels/cwise_ops_gpu_gradients.cu.h" namespace tensorflow { namespace functor { DEFINE_BINARY2(igamma, float, double); +DEFINE_BINARY2(igamma_grad_a, float, double); DEFINE_BINARY2(igammac, float, double); } // namespace functor } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_igammas.cc b/tensorflow/core/kernels/cwise_op_igammas.cc index 4b5f888bc1..cadda3b723 100644 --- a/tensorflow/core/kernels/cwise_op_igammas.cc +++ b/tensorflow/core/kernels/cwise_op_igammas.cc @@ -14,12 +14,15 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/core/kernels/cwise_ops_common.h" +#include "tensorflow/core/kernels/cwise_ops_gradients.h" namespace tensorflow { REGISTER2(BinaryOp, CPU, "Igamma", functor::igamma, float, double); +REGISTER2(BinaryOp, CPU, "IgammaGradA", functor::igamma_grad_a, float, double); REGISTER2(BinaryOp, CPU, "Igammac", functor::igammac, float, double); #if GOOGLE_CUDA REGISTER2(BinaryOp, GPU, "Igamma", functor::igamma, float, double); +REGISTER2(BinaryOp, GPU, "IgammaGradA", functor::igamma_grad_a, float, double); REGISTER2(BinaryOp, GPU, "Igammac", functor::igammac, float, double); #endif } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_ops_gradients.h b/tensorflow/core/kernels/cwise_ops_gradients.h index 82cdae9a34..7a6f14babc 100644 --- a/tensorflow/core/kernels/cwise_ops_gradients.h +++ b/tensorflow/core/kernels/cwise_ops_gradients.h @@ -202,6 +202,9 @@ struct sqrt_grad : base> {}; template struct rsqrt_grad : base> {}; +template +struct igamma_grad_a : base> {}; + } // end namespace functor } // end namespace tensorflow diff --git a/tensorflow/core/ops/math_ops.cc b/tensorflow/core/ops/math_ops.cc index b3487122e2..1681d63930 100644 --- a/tensorflow/core/ops/math_ops.cc +++ b/tensorflow/core/ops/math_ops.cc @@ -489,6 +489,13 @@ REGISTER_OP("Igamma") .Attr("T: {float, double}") .SetShapeFn(shape_inference::BroadcastBinaryOpShapeFn); +REGISTER_OP("IgammaGradA") + .Input("a: T") + .Input("x: T") + .Output("z: T") + .Attr("T: {float, double}") + .SetShapeFn(shape_inference::BroadcastBinaryOpShapeFn); + REGISTER_OP("Zeta") .Input("x: T") .Input("q: T") diff --git a/tensorflow/python/kernel_tests/cwise_ops_test.py b/tensorflow/python/kernel_tests/cwise_ops_test.py index 8a3e64b174..ccd05a8820 100644 --- a/tensorflow/python/kernel_tests/cwise_ops_test.py +++ b/tensorflow/python/kernel_tests/cwise_ops_test.py @@ -668,12 +668,11 @@ class BinaryOpTest(test.TestCase): self._compareCpu(x, y, np_func, tf_func, also_compare_variables) if x.dtype in (np.float16, np.float32, np.float64, np.complex64, np.complex128): - if tf_func not in (_FLOORDIV, math_ops.floordiv, math_ops.igamma, - math_ops.igammac, math_ops.zeta, math_ops.polygamma): + if tf_func not in (_FLOORDIV, math_ops.floordiv, math_ops.zeta, + math_ops.polygamma): self._compareGradientX(x, y, np_func, tf_func) self._compareGradientY(x, y, np_func, tf_func) - if tf_func in (math_ops.igamma, math_ops.igammac, math_ops.zeta, - math_ops.polygamma): + if tf_func in (math_ops.zeta, math_ops.polygamma): # These methods only support gradients in the second parameter self._compareGradientY(x, y, np_func, tf_func) self._compareGpu(x, y, np_func, tf_func) diff --git a/tensorflow/python/ops/math_grad.py b/tensorflow/python/ops/math_grad.py index a48b3c9395..f0c6bd532f 100644 --- a/tensorflow/python/ops/math_grad.py +++ b/tensorflow/python/ops/math_grad.py @@ -651,27 +651,28 @@ def _BesselI1eGrad(op, grad): @ops.RegisterGradient("Igamma") def _IgammaGrad(op, grad): - """Returns gradient of igamma(a, x) with respect to x.""" - # TODO(ebrevdo): Perhaps add the derivative w.r.t. a + """Returns gradient of igamma(a, x) with respect to a and x.""" a = op.inputs[0] x = op.inputs[1] sa = array_ops.shape(a) sx = array_ops.shape(x) - unused_ra, rx = gen_array_ops.broadcast_gradient_args(sa, sx) + ra, rx = gen_array_ops.broadcast_gradient_args(sa, sx) - # Perform operations in log space before summing, because Gamma(a) - # and Gamma'(a) can grow large. - partial_x = math_ops.exp(-x + (a - 1) * math_ops.log(x) - math_ops.lgamma(a)) - # TODO(b/36815900): Mark None return values as NotImplemented - return (None, array_ops.reshape( - math_ops.reduce_sum(partial_x * grad, rx), sx)) + with ops.control_dependencies([grad]): + partial_a = gen_math_ops.igamma_grad_a(a, x) + # Perform operations in log space before summing, because Gamma(a) + # and Gamma'(a) can grow large. + partial_x = math_ops.exp(-x + (a - 1) * math_ops.log(x) + - math_ops.lgamma(a)) + return (array_ops.reshape(math_ops.reduce_sum(partial_a * grad, ra), sa), + array_ops.reshape(math_ops.reduce_sum(partial_x * grad, rx), sx)) @ops.RegisterGradient("Igammac") def _IgammacGrad(op, grad): - """Returns gradient of igammac(a, x) = 1 - igamma(a, x) w.r.t. x.""" - _, igamma_grad_x = _IgammaGrad(op, grad) - return None, -igamma_grad_x + """Returns gradient of igammac(a, x) = 1 - igamma(a, x) w.r.t. a and x.""" + igamma_grad_a, igamma_grad_x = _IgammaGrad(op, grad) + return (-igamma_grad_a, -igamma_grad_x) @ops.RegisterGradient("Betainc") diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index b4fbbd6c23..12e7a242fd 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -107,11 +107,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "eigen_archive", urls = [ - "https://mirror.bazel.build/bitbucket.org/eigen/eigen/get/267806ed9b4f.tar.gz", - "https://bitbucket.org/eigen/eigen/get/267806ed9b4f.tar.gz", + "https://mirror.bazel.build/bitbucket.org/eigen/eigen/get/7a835107faf8.tar.gz", + "https://bitbucket.org/eigen/eigen/get/7a835107faf8.tar.gz", ], - sha256 = "ade57357093463cab9e4e51cd5749c81483a75451b1471a3ebc73f9c1d14043b", - strip_prefix = "eigen-eigen-267806ed9b4f", + sha256 = "1c65c3d9b4eb8d95ea3a4f9d3968eaf567be22fe8c445db173665d2a25d47263", + strip_prefix = "eigen-eigen-7a835107faf8", build_file = clean_dep("//third_party:eigen.BUILD"), ) -- GitLab From 27ad1f3b3c6ac7d6c192e6a2190fb33667e4bf3b Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 19 Jun 2018 03:18:04 -0700 Subject: [PATCH 652/816] Update ops-related pbtxt files. PiperOrigin-RevId: 201147873 --- .../core/ops/compat/ops_history.v1.pbtxt | 25 +++++++++++++++++++ tensorflow/core/ops/ops.pbtxt | 25 +++++++++++++++++++ 2 files changed, 50 insertions(+) diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt index 5e260b87c1..62b37ce33d 100644 --- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt +++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt @@ -25639,6 +25639,31 @@ op { } } } +op { + name: "IgammaGradA" + input_arg { + name: "a" + type_attr: "T" + } + input_arg { + name: "x" + type_attr: "T" + } + output_arg { + name: "z" + type_attr: "T" + } + attr { + name: "T" + type: "type" + allowed_values { + list { + type: DT_FLOAT + type: DT_DOUBLE + } + } + } +} op { name: "Igammac" input_arg { diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt index 94a373e990..80e8df9206 100644 --- a/tensorflow/core/ops/ops.pbtxt +++ b/tensorflow/core/ops/ops.pbtxt @@ -12446,6 +12446,31 @@ op { } } } +op { + name: "IgammaGradA" + input_arg { + name: "a" + type_attr: "T" + } + input_arg { + name: "x" + type_attr: "T" + } + output_arg { + name: "z" + type_attr: "T" + } + attr { + name: "T" + type: "type" + allowed_values { + list { + type: DT_FLOAT + type: DT_DOUBLE + } + } + } +} op { name: "Igammac" input_arg { -- GitLab From fc6ff59c0c12bedbd1ca32000a24ae9e64c0b661 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 19 Jun 2018 03:45:58 -0700 Subject: [PATCH 653/816] Go: Update generated wrapper functions for TensorFlow ops. PiperOrigin-RevId: 201150427 --- tensorflow/go/op/wrappers.go | 222 +++++++++++++++++------------------ 1 file changed, 111 insertions(+), 111 deletions(-) diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go index a443879df2..bff2264c29 100644 --- a/tensorflow/go/op/wrappers.go +++ b/tensorflow/go/op/wrappers.go @@ -3015,40 +3015,6 @@ func Concat(scope *Scope, concat_dim tf.Output, values []tf.Output) (output tf.O return op.Output(0) } -// Creates a sequence of numbers. -// -// This operation creates a sequence of numbers that begins at `start` and -// extends by increments of `delta` up to but not including `limit`. -// -// For example: -// -// ``` -// # 'start' is 3 -// # 'limit' is 18 -// # 'delta' is 3 -// tf.range(start, limit, delta) ==> [3, 6, 9, 12, 15] -// ``` -// -// Arguments: -// start: 0-D (scalar). First entry in the sequence. -// limit: 0-D (scalar). Upper limit of sequence, exclusive. -// delta: 0-D (scalar). Optional. Default is 1. Number that increments `start`. -// -// Returns 1-D. -func Range(scope *Scope, start tf.Output, limit tf.Output, delta tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Range", - Input: []tf.Input{ - start, limit, delta, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // Computes gradients for SparseSegmentSqrtN. // // Returns tensor "output" with same shape as grad, except for dimension 0 whose @@ -8309,6 +8275,83 @@ func DataFormatVecPermute(scope *Scope, x tf.Output, optional ...DataFormatVecPe return op.Output(0) } +// Converts each string in the input Tensor to its hash mod by a number of buckets. +// +// The hash function is deterministic on the content of the string within the +// process. +// +// Note that the hash function may change from time to time. +// This functionality will be deprecated and it's recommended to use +// `tf.string_to_hash_bucket_fast()` or `tf.string_to_hash_bucket_strong()`. +// +// Arguments: +// +// num_buckets: The number of buckets. +// +// Returns A Tensor of the same shape as the input `string_tensor`. +func StringToHashBucket(scope *Scope, string_tensor tf.Output, num_buckets int64) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_buckets": num_buckets} + opspec := tf.OpSpec{ + Type: "StringToHashBucket", + Input: []tf.Input{ + string_tensor, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes gradients for the exponential linear (Elu) operation. +// +// Arguments: +// gradients: The backpropagated gradients to the corresponding Elu operation. +// outputs: The outputs of the corresponding Elu operation. +// +// Returns The gradients: `gradients * (outputs + 1)` if outputs < 0, +// `gradients` otherwise. +func EluGrad(scope *Scope, gradients tf.Output, outputs tf.Output) (backprops tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "EluGrad", + Input: []tf.Input{ + gradients, outputs, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a dataset that contains `count` elements from the `input_dataset`. +// +// Arguments: +// +// count: A scalar representing the number of elements from the `input_dataset` +// that should be taken. A value of `-1` indicates that all of `input_dataset` +// is taken. +// +// +func TakeDataset(scope *Scope, input_dataset tf.Output, count tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "TakeDataset", + Input: []tf.Input{ + input_dataset, count, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Reads the value of a variable. // // The tensor returned by this operation is immutable. @@ -9918,83 +9961,6 @@ func FixedLengthRecordReaderV2(scope *Scope, record_bytes int64, optional ...Fix return op.Output(0) } -// Converts each string in the input Tensor to its hash mod by a number of buckets. -// -// The hash function is deterministic on the content of the string within the -// process. -// -// Note that the hash function may change from time to time. -// This functionality will be deprecated and it's recommended to use -// `tf.string_to_hash_bucket_fast()` or `tf.string_to_hash_bucket_strong()`. -// -// Arguments: -// -// num_buckets: The number of buckets. -// -// Returns A Tensor of the same shape as the input `string_tensor`. -func StringToHashBucket(scope *Scope, string_tensor tf.Output, num_buckets int64) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_buckets": num_buckets} - opspec := tf.OpSpec{ - Type: "StringToHashBucket", - Input: []tf.Input{ - string_tensor, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Computes gradients for the exponential linear (Elu) operation. -// -// Arguments: -// gradients: The backpropagated gradients to the corresponding Elu operation. -// outputs: The outputs of the corresponding Elu operation. -// -// Returns The gradients: `gradients * (outputs + 1)` if outputs < 0, -// `gradients` otherwise. -func EluGrad(scope *Scope, gradients tf.Output, outputs tf.Output) (backprops tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "EluGrad", - Input: []tf.Input{ - gradients, outputs, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Creates a dataset that contains `count` elements from the `input_dataset`. -// -// Arguments: -// -// count: A scalar representing the number of elements from the `input_dataset` -// that should be taken. A value of `-1` indicates that all of `input_dataset` -// is taken. -// -// -func TakeDataset(scope *Scope, input_dataset tf.Output, count tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} - opspec := tf.OpSpec{ - Type: "TakeDataset", - Input: []tf.Input{ - input_dataset, count, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // The gradient operator for the SparseAdd op. // // The SparseAdd op calculates A + B, where A, B, and the sum are all represented @@ -19440,6 +19406,40 @@ func OrderedMapIncompleteSize(scope *Scope, dtypes []tf.DataType, optional ...Or return op.Output(0) } +// Creates a sequence of numbers. +// +// This operation creates a sequence of numbers that begins at `start` and +// extends by increments of `delta` up to but not including `limit`. +// +// For example: +// +// ``` +// # 'start' is 3 +// # 'limit' is 18 +// # 'delta' is 3 +// tf.range(start, limit, delta) ==> [3, 6, 9, 12, 15] +// ``` +// +// Arguments: +// start: 0-D (scalar). First entry in the sequence. +// limit: 0-D (scalar). Upper limit of sequence, exclusive. +// delta: 0-D (scalar). Optional. Default is 1. Number that increments `start`. +// +// Returns 1-D. +func Range(scope *Scope, start tf.Output, limit tf.Output, delta tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Range", + Input: []tf.Input{ + start, limit, delta, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // DestroyResourceOpAttr is an optional argument to DestroyResourceOp. type DestroyResourceOpAttr func(optionalAttr) -- GitLab From 707ac111cfed90f35c37417d8c79ab7cbcba152a Mon Sep 17 00:00:00 2001 From: James Qin Date: Tue, 19 Jun 2018 04:15:27 -0700 Subject: [PATCH 654/816] Update a few documentation for layer-input-casting feature. PiperOrigin-RevId: 201152785 --- tensorflow/python/keras/engine/base_layer.py | 38 ++++++++++------- .../python/keras/engine/topology_test.py | 42 +++++++++++-------- tensorflow/python/layers/base_test.py | 16 +++---- 3 files changed, 57 insertions(+), 39 deletions(-) diff --git a/tensorflow/python/keras/engine/base_layer.py b/tensorflow/python/keras/engine/base_layer.py index 751cc5a8d5..b05bc96e28 100644 --- a/tensorflow/python/keras/engine/base_layer.py +++ b/tensorflow/python/keras/engine/base_layer.py @@ -89,11 +89,19 @@ class Layer(checkpointable.CheckpointableBase): once. Should actually perform the logic of applying the layer to the input tensors (which should be passed in as the first argument). - By default, layers will cast all their inputs and arguments to the layer's - dtype, if set. This is useful for creating a model with multiple dtypes, as - the user does not need to explicitly cast tensors. If a `Layer` descendant - wants only a subset of inputs/arguments to be casted, or none of them, - `_cast_inputs_and_args()` should be overridden. + A note on a layer's `dtype` property: + A layer's dtype can be specified via the constructor `dtype` argument, and + defaults to the dtype of the first input when the layer is called. The dtype + cannot be changed once set. + + All floating point tensor inputs and arguments are casted to the layer's + dtype, before the body of the layer computation happens. For models with + layers of different dtypes, this helps getting rid of the explicit casts + between layers. + + The casting behavior can be customized in subclasses by overridding + `_cast_inputs_and_args()` function, which is useful if certain or all inputs + should not be casted. Arguments: trainable: Boolean, whether the layer's variables should be trainable. @@ -675,10 +683,9 @@ class Layer(checkpointable.CheckpointableBase): kwargs['mask'] = previous_mask input_shapes = None - # We only cast inputs if self.dtype was previous set, which occurs when - # a dtype was passed to the constructor, or when this layer has previously - # been called. We cast floating point inputs to self.dtype to ensure the - # layer runs with the correct dtype. + # Inputs are only casted if a dtype is pased in the constructor, or if a + # layer's __call__() has been previously invoked. At present, only floating + # point tensor inputs are affected. # TODO(b/77478433): Perhaps we should only cast inputs if a dtype was passed # to the constructor, not when the layer has previously been called. inputs_should_be_cast = (self.dtype is not None) @@ -810,10 +817,13 @@ class Layer(checkpointable.CheckpointableBase): def _cast_inputs_and_args(self, inputs, *args, **kwargs): """Casts the inputs, args, and kwargs of a layer to the layer's dtype. - This is intended to be potentially overridden by layer subclasses. By - default, inputs, args, and kwargs are automatically casted to the layer's - dtype. Overriding this method allows only some of the inputs, args, and - kwargs (or none of them) to be casted. + This is intended to be potentially overridden by subclasses. By default, + inputs, args, and kwargs are automatically casted to the layer's dtype. + Overriding this method allows only some of the parameters to be treated + differently. + + Currently, this only casts floating point tensors to floating point dtypes, + but more types may be casted in the future. Does not modify inputs, args, or kwargs. @@ -823,7 +833,7 @@ class Layer(checkpointable.CheckpointableBase): **kwargs: The kwargs to self.__call__. Returns: - The tuple (new_inputs, new_args, new_kwargs), where tensors in inputs, + A tuple (new_inputs, new_args, new_kwargs), where tensors in inputs, args, and kwargs have been casted to self.dtype. """ new_inputs = nest.map_structure(self._cast_fn, inputs) diff --git a/tensorflow/python/keras/engine/topology_test.py b/tensorflow/python/keras/engine/topology_test.py index 7fbe6b80ad..d28c30cb7d 100644 --- a/tensorflow/python/keras/engine/topology_test.py +++ b/tensorflow/python/keras/engine/topology_test.py @@ -1057,24 +1057,30 @@ class TopologyConstructionTest(test.TestCase): def compute_output_shape(self, input_shapes): return input_shapes[0] - x = keras.layers.Input((32,), dtype='float64') - layer1 = SingleInputLayer() - layer2 = SingleInputLayer(dtype='float32') - layer3 = MultiInputLayer(dtype='float16') - i1 = layer1(x) - i2 = layer2(i1) - y = layer3((i1, i2)) - network = keras.engine.Network(x, y) - x2 = array_ops.ones((32,), dtype='float16') - y2 = network(x2) - self.assertEqual(layer1.dtype, dtypes.float64) - self.assertEqual(layer1.a.dtype, dtypes.float64) - self.assertEqual(layer2.dtype, dtypes.float32) - self.assertEqual(layer2.a.dtype, dtypes.float32) - self.assertEqual(layer3.dtype, dtypes.float16) - self.assertEqual(layer3.a.dtype, dtypes.float16) - self.assertEqual(layer3.b.dtype, dtypes.float16) - self.assertEqual(y2.dtype, dtypes.float16) + default_layer = SingleInputLayer() + fp32_layer = SingleInputLayer(dtype='float32') + fp16_layer = MultiInputLayer(dtype='float16') + + input_t = keras.layers.Input((32,), dtype='float64') + o1 = default_layer(input_t) + o2 = fp32_layer(o1) + # fp16_layer has inputs of different dtypes. + output_t = fp16_layer((o1, o2)) + network = keras.engine.Network(input_t, output_t) + + x = array_ops.ones((32,), dtype='float16') + y = network(x) + self.assertEqual(default_layer.dtype, dtypes.float64) + self.assertEqual(default_layer.a.dtype, dtypes.float64) + + self.assertEqual(fp32_layer.dtype, dtypes.float32) + self.assertEqual(fp32_layer.a.dtype, dtypes.float32) + + self.assertEqual(fp16_layer.dtype, dtypes.float16) + self.assertEqual(fp16_layer.a.dtype, dtypes.float16) + self.assertEqual(fp16_layer.b.dtype, dtypes.float16) + + self.assertEqual(y.dtype, dtypes.float16) class DeferredModeTest(test.TestCase): diff --git a/tensorflow/python/layers/base_test.py b/tensorflow/python/layers/base_test.py index 15448c6be8..ad44328aab 100644 --- a/tensorflow/python/layers/base_test.py +++ b/tensorflow/python/layers/base_test.py @@ -593,7 +593,8 @@ class BaseLayerTest(test.TestCase): @test_util.run_in_graph_and_eager_modes() def testOnlyCastInputsWhenDtypeSpecified(self): - class MyLayerBase(keras_base_layer.Layer): + + class MyKerasLayer(keras_base_layer.Layer): def call(self, inputs): self.x = inputs[0] @@ -603,13 +604,13 @@ class BaseLayerTest(test.TestCase): # Inherit from both the Keras Layer and base_layers.Layer to ensure we # still get the base_layers.Layer behavior when directly inheriting from # the Keras Layer. - class MyLayer(MyLayerBase, base_layers.Layer): + class MyTFLayer(MyKerasLayer, base_layers.Layer): pass # Test inputs are casted. input1 = array_ops.constant(1.0, dtype=dtypes.float64) input2 = array_ops.constant(1.0, dtype=dtypes.float32) - layer = MyLayer(dtype=dtypes.float16) + layer = MyTFLayer(dtype=dtypes.float16) output1, output2 = layer([input1, input2]) self.assertEqual(output1.dtype, dtypes.float16) self.assertEqual(output2.dtype, dtypes.float16) @@ -617,14 +618,15 @@ class BaseLayerTest(test.TestCase): # Test inputs are not casted. input1 = array_ops.constant(1.0, dtype=dtypes.float64) input2 = array_ops.constant(1.0, dtype=dtypes.float32) - layer = MyLayer() + layer = MyTFLayer() output1, output2 = layer([input1, input2]) self.assertEqual(output1.dtype, dtypes.float64) self.assertEqual(output2.dtype, dtypes.float32) @test_util.run_in_graph_and_eager_modes() def testVariablesDefaultToFloat32(self): - class MyLayerBase(keras_base_layer.Layer): + + class MyKerasLayer(keras_base_layer.Layer): def build(self, input_shape): self.x = self.add_weight('x', ()) @@ -635,14 +637,14 @@ class BaseLayerTest(test.TestCase): # Inherit from both the Keras Layer and base_layers.Layer to ensure we # still get the base_layers.Layer behavior when directly inheriting from # the Keras Layer. - class MyLayer(MyLayerBase, base_layers.Layer): + class MyTFLayer(MyKerasLayer, base_layers.Layer): pass try: # The behavior of Keras Layers is to default to floatx. Ensure that this # behavior is overridden to instead default to float32. backend.set_floatx('float16') - layer = MyLayer() + layer = MyTFLayer() layer.build(()) self.assertEqual(layer.dtype, None) self.assertEqual(layer.x.dtype.base_dtype, dtypes.float32) -- GitLab From bae4a271c036e6ede7cab6f4328b0a7966ef9fd4 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 19 Jun 2018 06:01:43 -0700 Subject: [PATCH 655/816] Internal change PiperOrigin-RevId: 201161803 --- tensorflow/compiler/jit/xla_device_context.cc | 8 +- .../compiler/xla/client/local_client.cc | 20 +- .../xla/service/cpu/cpu_transfer_manager.cc | 5 +- tensorflow/compiler/xla/service/executable.h | 3 +- .../xla/service/generic_transfer_manager.cc | 45 ++- .../xla/service/generic_transfer_manager.h | 16 +- tensorflow/compiler/xla/service/hlo_runner.cc | 14 +- .../xla/service/interpreter/executable.cc | 8 +- .../xla/service/interpreter/executor.cc | 2 + tensorflow/compiler/xla/service/service.cc | 42 +-- .../compiler/xla/service/transfer_manager.cc | 139 +++++++--- .../compiler/xla/service/transfer_manager.h | 71 +++-- tensorflow/compiler/xla/shape_util.cc | 8 +- tensorflow/compiler/xla/shape_util.h | 3 + tensorflow/compiler/xla/tests/BUILD | 1 + .../compiler/xla/tests/dynamic_ops_test.cc | 4 +- .../xla/tests/local_client_execute_test.cc | 100 ++++--- .../xla/tests/transfer_manager_test.cc | 258 ++++++++++++++---- .../xla/tests/xla_hlo_profile_test.cc | 10 +- .../xla/tests/xla_internal_test_main.cc | 1 + 20 files changed, 520 insertions(+), 238 deletions(-) diff --git a/tensorflow/compiler/jit/xla_device_context.cc b/tensorflow/compiler/jit/xla_device_context.cc index 71e63b110b..37005479dc 100644 --- a/tensorflow/compiler/jit/xla_device_context.cc +++ b/tensorflow/compiler/jit/xla_device_context.cc @@ -74,7 +74,7 @@ Status XlaTransferManager::TransferLiteralToDevice( XlaTensor::FromTensor(device_tensor)->shaped_buffer(); VLOG(1) << "Transfer to device as literal: " << literal.ToString() << " " << shaped_buffer.ToString(); - return transfer_manager_->TransferLiteralToDevice(stream_->parent(), literal, + return transfer_manager_->TransferLiteralToDevice(stream_, literal, shaped_buffer); } @@ -83,9 +83,9 @@ Status XlaTransferManager::TransferLiteralFromDevice( const xla::ShapedBuffer& shaped_buffer = XlaTensor::FromTensor(&device_tensor)->shaped_buffer(); - TF_ASSIGN_OR_RETURN(std::unique_ptr literal, - transfer_manager_->TransferLiteralFromDevice( - stream_->parent(), shaped_buffer)); + TF_ASSIGN_OR_RETURN( + std::unique_ptr literal, + transfer_manager_->TransferLiteralFromDevice(stream_, shaped_buffer)); VLOG(1) << "Transfer from device as literal: " << literal->ToString() << " " << shaped_buffer.ToString(); Tensor tensor; diff --git a/tensorflow/compiler/xla/client/local_client.cc b/tensorflow/compiler/xla/client/local_client.cc index ae0308020d..cf07910c4a 100644 --- a/tensorflow/compiler/xla/client/local_client.cc +++ b/tensorflow/compiler/xla/client/local_client.cc @@ -230,10 +230,9 @@ Status LocalExecutable::RecordResult(const ShapedBuffer* result, StatusOr> LocalExecutable::LiteralFromShapedBuffer( const ShapedBuffer& shaped_buffer) { - TF_ASSIGN_OR_RETURN( - se::StreamExecutor * executor, - backend_->stream_executor(shaped_buffer.device_ordinal())); - return backend_->transfer_manager()->TransferLiteralFromDevice(executor, + TF_ASSIGN_OR_RETURN(auto stream, + backend_->BorrowStream(shaped_buffer.device_ordinal())); + return backend_->transfer_manager()->TransferLiteralFromDevice(stream.get(), shaped_buffer); } @@ -288,19 +287,18 @@ StatusOr LocalClient::LiteralToShapedBuffer( TF_ASSIGN_OR_RETURN(auto scoped_buffer, backend().transfer_manager()->AllocateScopedShapedBuffer( literal.shape(), allocator, device_ordinal)); - TF_ASSIGN_OR_RETURN(se::StreamExecutor * executor, - backend().stream_executor(device_ordinal)); + TF_ASSIGN_OR_RETURN(auto stream, + mutable_backend()->BorrowStream(device_ordinal)); TF_RETURN_IF_ERROR(backend().transfer_manager()->TransferLiteralToDevice( - executor, literal, scoped_buffer)); + stream.get(), literal, scoped_buffer)); return std::move(scoped_buffer); } StatusOr> LocalClient::ShapedBufferToLiteral( const ShapedBuffer& shaped_buffer) { - TF_ASSIGN_OR_RETURN( - se::StreamExecutor * executor, - backend().stream_executor(shaped_buffer.device_ordinal())); - return backend().transfer_manager()->TransferLiteralFromDevice(executor, + TF_ASSIGN_OR_RETURN(auto stream, mutable_backend()->BorrowStream( + shaped_buffer.device_ordinal())); + return backend().transfer_manager()->TransferLiteralFromDevice(stream.get(), shaped_buffer); } diff --git a/tensorflow/compiler/xla/service/cpu/cpu_transfer_manager.cc b/tensorflow/compiler/xla/service/cpu/cpu_transfer_manager.cc index d97802ee45..b877b29581 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_transfer_manager.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_transfer_manager.cc @@ -160,9 +160,8 @@ CpuTransferManager::TransferBufferToInfeedInternal(se::StreamExecutor* executor, int32 size_32 = static_cast(size); CpuInfeedBuffer* queued_buffer = new CpuInfeedBuffer(size_32); - Status s = - TransferBufferToDevice(executor, /*size=*/size, - /*source=*/source, queued_buffer->device_memory()); + Status s = executor->SynchronousMemcpyH2D( + /*host_src=*/source, /*size=*/size, queued_buffer->device_memory()); if (!s.ok()) { queued_buffer->Done(s); diff --git a/tensorflow/compiler/xla/service/executable.h b/tensorflow/compiler/xla/service/executable.h index dc1f26ea65..1a91aca9d1 100644 --- a/tensorflow/compiler/xla/service/executable.h +++ b/tensorflow/compiler/xla/service/executable.h @@ -88,8 +88,7 @@ class Executable { // called explicitly for other (async, for example) variants after the stream // has completed. virtual Status PopulateExecutionProfile( - HloExecutionProfile* hlo_execution_profile, - se::StreamExecutor* executor) { + HloExecutionProfile* hlo_execution_profile, se::Stream* stream) { return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/generic_transfer_manager.cc b/tensorflow/compiler/xla/service/generic_transfer_manager.cc index d9f62c21c4..85e28a0dfe 100644 --- a/tensorflow/compiler/xla/service/generic_transfer_manager.cc +++ b/tensorflow/compiler/xla/service/generic_transfer_manager.cc @@ -43,7 +43,7 @@ se::Platform::Id GenericTransferManager::PlatformId() const { } Status GenericTransferManager::WriteSingleTupleIndexTable( - se::StreamExecutor* executor, + se::Stream* stream, tensorflow::gtl::ArraySlice elements, const Shape& shape, se::DeviceMemoryBase* region) { TF_RET_CHECK(elements.size() == ShapeUtil::TupleElementCount(shape)); @@ -52,12 +52,24 @@ Status GenericTransferManager::WriteSingleTupleIndexTable( for (const se::DeviceMemoryBase& element : elements) { element_pointers.push_back(element.opaque()); } - return TransferBufferToDevice(executor, GetByteSizeRequirement(shape), - element_pointers.data(), region); + TF_RETURN_IF_ERROR(TransferBufferToDevice( + stream, GetByteSizeRequirement(shape), element_pointers.data(), region)); + // Ensure the buffer is transferred before we destroy element_pointers. + return stream->BlockHostUntilDone(); +} + +void GenericTransferManager::TransferLiteralFromDevice( + se::Stream* stream, const ShapedBuffer& device_buffer, + std::function>)> done) { + Status status = stream->BlockHostUntilDone(); + if (!status.ok()) { + return done(status); + } + done(TransferLiteralFromDeviceInternal(stream->parent(), device_buffer)); } StatusOr> -GenericTransferManager::TransferLiteralFromDevice( +GenericTransferManager::TransferLiteralFromDeviceInternal( se::StreamExecutor* executor, const ShapedBuffer& device_buffer) { VLOG(2) << "transferring literal from device ordinal " << executor->device_ordinal() << "; device buffer: " << device_buffer; @@ -75,8 +87,7 @@ GenericTransferManager::TransferLiteralFromDevice( device_buffer.on_host_shape(), [&](const Shape& subshape, const ShapeIndex& index) -> Status { if (ShapeUtil::IsArray(subshape)) { - TF_RETURN_IF_ERROR(TransferBufferFromDevice( - executor, + TF_RETURN_IF_ERROR(executor->SynchronousMemcpyD2H( /*source=*/device_buffer.buffer(index), /*size=*/GetByteSizeRequirement(subshape), /*destination=*/ @@ -88,8 +99,8 @@ GenericTransferManager::TransferLiteralFromDevice( return std::move(literal); } -Status GenericTransferManager::TransferLiteralToDevice( - se::StreamExecutor* executor, const LiteralSlice& literal, +Status GenericTransferManager::TransferLiteralToDeviceAsync( + se::Stream* stream, const LiteralSlice& literal, const ShapedBuffer& device_buffer) { const Shape& shape = literal.shape(); VLOG(2) << "transferring literal shape to device: " @@ -103,9 +114,10 @@ Status GenericTransferManager::TransferLiteralToDevice( TF_RET_CHECK( ShapeUtil::Compatible(literal.shape(), device_buffer.on_host_shape())); - TF_RET_CHECK(executor->device_ordinal() == device_buffer.device_ordinal()); + TF_RET_CHECK(stream->parent()->device_ordinal() == + device_buffer.device_ordinal()); - TF_RETURN_IF_ERROR(WriteTupleIndexTables(executor, device_buffer)); + TF_RETURN_IF_ERROR(WriteTupleIndexTables(stream, device_buffer)); return ShapeUtil::ForEachSubshapeWithStatus( device_buffer.on_host_shape(), @@ -121,16 +133,21 @@ Status GenericTransferManager::TransferLiteralToDevice( if (LayoutUtil::Equal(device_subshape.layout(), subliteral.shape().layout())) { source = subliteral.untyped_data(); + return TransferBufferToDevice( + stream, + /*size=*/GetByteSizeRequirement(device_subshape), source, + &device_memory); } else { // Relayout data before transferring. relayed_out_literal = subliteral.Relayout(device_subshape.layout(), /*shape_index=*/{}); source = relayed_out_literal->untyped_data(); + TF_RETURN_IF_ERROR(TransferBufferToDevice( + stream, + /*size=*/GetByteSizeRequirement(device_subshape), source, + &device_memory)); + return stream->BlockHostUntilDone(); } - return TransferBufferToDevice( - executor, - /*size=*/GetByteSizeRequirement(device_subshape), source, - &device_memory); } return Status::OK(); }); diff --git a/tensorflow/compiler/xla/service/generic_transfer_manager.h b/tensorflow/compiler/xla/service/generic_transfer_manager.h index 3da9570ef7..d216fe7d29 100644 --- a/tensorflow/compiler/xla/service/generic_transfer_manager.h +++ b/tensorflow/compiler/xla/service/generic_transfer_manager.h @@ -41,12 +41,13 @@ class GenericTransferManager : public TransferManager { se::Platform::Id PlatformId() const override; - StatusOr> TransferLiteralFromDevice( - se::StreamExecutor* executor, const ShapedBuffer& device_buffer) override; + void TransferLiteralFromDevice( + se::Stream* stream, const ShapedBuffer& device_buffer, + std::function>)> done) override; - Status TransferLiteralToDevice(se::StreamExecutor* executor, - const LiteralSlice& literal, - const ShapedBuffer& device_buffer) override; + Status TransferLiteralToDeviceAsync( + se::Stream* stream, const LiteralSlice& literal, + const ShapedBuffer& device_buffer) override; Status TransferLiteralToInfeed(se::StreamExecutor* executor, const LiteralSlice& literal) override; @@ -64,11 +65,14 @@ class GenericTransferManager : public TransferManager { const void* source) override; Status WriteSingleTupleIndexTable( - se::StreamExecutor* executor, + se::Stream* stream, tensorflow::gtl::ArraySlice elements, const Shape& shape, se::DeviceMemoryBase* region) override; private: + StatusOr> TransferLiteralFromDeviceInternal( + se::StreamExecutor* executor, const ShapedBuffer& device_buffer); + // The platform this transfer manager targets. const se::Platform::Id platform_id_; diff --git a/tensorflow/compiler/xla/service/hlo_runner.cc b/tensorflow/compiler/xla/service/hlo_runner.cc index e1f9d8efd4..4f0569f405 100644 --- a/tensorflow/compiler/xla/service/hlo_runner.cc +++ b/tensorflow/compiler/xla/service/hlo_runner.cc @@ -98,8 +98,10 @@ StatusOr HloRunner::TransferLiteralToDevice( backend().transfer_manager()->AllocateScopedShapedBuffer( literal.shape(), backend().memory_allocator(), backend().default_device_ordinal())); + TF_ASSIGN_OR_RETURN( + auto stream, backend().BorrowStream(backend().default_stream_executor())); TF_RETURN_IF_ERROR(backend().transfer_manager()->TransferLiteralToDevice( - backend().default_stream_executor(), literal, buffer)); + stream.get(), literal, buffer)); return std::move(buffer); } @@ -127,8 +129,10 @@ StatusOr> HloRunner::TransferLiteralsToDevice( StatusOr> HloRunner::TransferLiteralFromDevice( const ShapedBuffer& buffer) { - return backend().transfer_manager()->TransferLiteralFromDevice( - backend().default_stream_executor(), buffer); + TF_ASSIGN_OR_RETURN( + auto stream, backend().BorrowStream(backend().default_stream_executor())); + return backend().transfer_manager()->TransferLiteralFromDevice(stream.get(), + buffer); } StatusOr> HloRunner::Execute( @@ -237,7 +241,7 @@ StatusOr>> HloRunner::ExecuteReplicated( backend().transfer_manager()->AllocateScopedShapedBuffer( argument->shape(), backend().memory_allocator(), device)); TF_RETURN_IF_ERROR(backend().transfer_manager()->TransferLiteralToDevice( - executor, *argument, argument_buffer)); + streams.back().get(), *argument, argument_buffer)); argument_buffers.push_back(std::move(argument_buffer)); argument_buffer_ptrs[index++] = &argument_buffers.back(); } @@ -307,7 +311,7 @@ StatusOr>> HloRunner::ExecuteReplicated( for (int64 i = 0; i < options.num_replicas; ++i) { TF_ASSIGN_OR_RETURN(std::unique_ptr literal, backend().transfer_manager()->TransferLiteralFromDevice( - streams[i]->parent(), results[i])); + streams[i].get(), results[i])); exec_results.push_back(std::move(literal)); } return std::move(exec_results); diff --git a/tensorflow/compiler/xla/service/interpreter/executable.cc b/tensorflow/compiler/xla/service/interpreter/executable.cc index 029e71058a..9816acf650 100644 --- a/tensorflow/compiler/xla/service/interpreter/executable.cc +++ b/tensorflow/compiler/xla/service/interpreter/executable.cc @@ -75,9 +75,9 @@ StatusOr InterpreterExecutable::ExecuteOnStream( // consumes. std::vector> arg_literals; for (int64 p = 0; p < computation->num_parameters(); ++p) { - TF_ASSIGN_OR_RETURN( - std::unique_ptr arg_literal, - transfer_manager->TransferLiteralFromDevice(executor, *arguments[p])); + TF_ASSIGN_OR_RETURN(std::unique_ptr arg_literal, + transfer_manager->TransferLiteralFromDevice( + run_options->stream(), *arguments[p])); arg_literals.push_back(std::move(arg_literal)); } @@ -96,7 +96,7 @@ StatusOr InterpreterExecutable::ExecuteOnStream( result_literal->shape(), run_options->allocator(), executor->device_ordinal())); TF_RETURN_IF_ERROR(transfer_manager->TransferLiteralToDevice( - executor, *result_literal, result)); + run_options->stream(), *result_literal, result)); uint64 end_micros = tensorflow::Env::Default()->NowMicros(); diff --git a/tensorflow/compiler/xla/service/interpreter/executor.cc b/tensorflow/compiler/xla/service/interpreter/executor.cc index 97e9fa2c8e..4fb67bd0b7 100644 --- a/tensorflow/compiler/xla/service/interpreter/executor.cc +++ b/tensorflow/compiler/xla/service/interpreter/executor.cc @@ -53,6 +53,7 @@ bool XlaInterpreterExecutor::Memcpy(Stream *stream, void *host_dst, AsExecutorStream(stream)->EnqueueTask([this, host_dst, dev_src, size]() { port::Status ok = SynchronousMemcpy(host_dst, dev_src, size); }); + AsExecutorStream(stream)->BlockUntilDone(); return true; } @@ -61,6 +62,7 @@ bool XlaInterpreterExecutor::Memcpy(Stream *stream, DeviceMemoryBase *dev_dst, AsExecutorStream(stream)->EnqueueTask([this, dev_dst, host_src, size]() { port::Status ok = SynchronousMemcpy(dev_dst, host_src, size); }); + AsExecutorStream(stream)->BlockUntilDone(); return true; } diff --git a/tensorflow/compiler/xla/service/service.cc b/tensorflow/compiler/xla/service/service.cc index ff68d65fbc..7ab39e01f2 100644 --- a/tensorflow/compiler/xla/service/service.cc +++ b/tensorflow/compiler/xla/service/service.cc @@ -64,25 +64,25 @@ namespace { // Records the arguments used to invoke a computation in an HloSnapshot proto. Status RecordArguments( const tensorflow::gtl::ArraySlice arguments, - se::StreamExecutor* executor, TransferManager* transfer_manager, + se::Stream* stream, TransferManager* transfer_manager, HloSnapshot* module) { module->clear_arguments(); for (const ShapedBuffer* argument : arguments) { TF_ASSIGN_OR_RETURN( std::unique_ptr literal, - transfer_manager->TransferLiteralFromDevice(executor, *argument)); + transfer_manager->TransferLiteralFromDevice(stream, *argument)); *module->add_arguments() = literal->ToProto(); } return Status::OK(); } // Records the result of a computation in a HloSnapshot proto. -Status RecordResult(const ShapedBuffer& result, se::StreamExecutor* executor, +Status RecordResult(const ShapedBuffer& result, se::Stream* stream, TransferManager* transfer_manager, HloSnapshot* module) { module->clear_result(); TF_ASSIGN_OR_RETURN( std::unique_ptr literal, - transfer_manager->TransferLiteralFromDevice(executor, result)); + transfer_manager->TransferLiteralFromDevice(stream, result)); *module->mutable_result() = literal->ToProto(); return Status::OK(); } @@ -496,7 +496,7 @@ Service::ExecuteParallelAndRegisterResult( HloExecutionProfile hlo_profile(&executable->hlo_profile_printer_data(), &executable->hlo_profile_index_map()); TF_RETURN_IF_ERROR( - executable->PopulateExecutionProfile(&hlo_profile, stream->parent())); + executable->PopulateExecutionProfile(&hlo_profile, stream)); XLA_LOG_LINES( tensorflow::INFO, hlo_profile.ToString(streams[0]->parent()->GetDeviceDescription())); @@ -721,8 +721,10 @@ Status Service::ExecuteGraphParallel(const ExecuteGraphParallelRequest* arg, for (int i = 0; i < executable_ptrs.size(); i++) { if (executable_ptrs[i]->dumping_snapshot()) { - TF_RETURN_IF_ERROR(RecordArguments(all_arguments[i].front(), - all_executors[i][0], + TF_ASSIGN_OR_RETURN(auto stream, + execute_backend_->BorrowStream( + all_executors[i][0]->device_ordinal())); + TF_RETURN_IF_ERROR(RecordArguments(all_arguments[i].front(), stream.get(), execute_backend_->transfer_manager(), executable_ptrs[i]->hlo_snapshot())); } @@ -747,7 +749,9 @@ Status Service::ExecuteGraphParallel(const ExecuteGraphParallelRequest* arg, if (executable_ptrs[i]->dumping_snapshot()) { TF_ASSIGN_OR_RETURN(const ShapedBuffer* result_buffer, allocation_tracker_.ResolveForReplica(outputs[i], 0)); - TF_RETURN_IF_ERROR(RecordResult(*result_buffer, all_executors[i][0], + TF_ASSIGN_OR_RETURN(auto stream, + execute_backend_->BorrowStream(all_executors[i][0])); + TF_RETURN_IF_ERROR(RecordResult(*result_buffer, stream.get(), execute_backend_->transfer_manager(), executable_ptrs[i]->hlo_snapshot())); // Dump out the ith snapshot. @@ -895,12 +899,14 @@ Status Service::ExecuteGraph(const ExecuteGraphRequest* arg, execute_backend_->default_stream_executor(), /*device_allocator=*/nullptr)); + TF_ASSIGN_OR_RETURN(auto stream, + execute_backend_->BorrowStream( + execute_backend_->default_stream_executor())); if (executable->dumping_snapshot()) { executable->hlo_snapshot()->set_execution_platform( execute_backend_->platform()->Name()); TF_RETURN_IF_ERROR(RecordArguments( - replicated_arguments.front(), - execute_backend_->default_stream_executor(), + replicated_arguments.front(), stream.get(), execute_backend_->transfer_manager(), executable->hlo_snapshot())); } @@ -914,9 +920,9 @@ Status Service::ExecuteGraph(const ExecuteGraphRequest* arg, TF_ASSIGN_OR_RETURN( const ShapedBuffer* result_buffer, allocation_tracker_.ResolveForReplica(result->output(), 0)); - TF_RETURN_IF_ERROR(RecordResult( - *result_buffer, execute_backend_->default_stream_executor(), - execute_backend_->transfer_manager(), executable->hlo_snapshot())); + TF_RETURN_IF_ERROR(RecordResult(*result_buffer, stream.get(), + execute_backend_->transfer_manager(), + executable->hlo_snapshot())); TF_RETURN_IF_ERROR(executable->DumpHloSnapshot()); } @@ -954,14 +960,13 @@ Status Service::TransferToClient(const TransferToClientRequest* arg, return_shape = &shaped_buffer->on_host_shape(); } - TF_ASSIGN_OR_RETURN( - se::StreamExecutor * executor, - execute_backend_->stream_executor(shaped_buffer->device_ordinal())); + TF_ASSIGN_OR_RETURN(auto stream, execute_backend_->BorrowStream( + shaped_buffer->device_ordinal())); TF_ASSIGN_OR_RETURN( std::unique_ptr result_literal, execute_backend_->transfer_manager()->TransferLiteralFromDevice( - executor, *shaped_buffer)); + stream.get(), *shaped_buffer)); if (LayoutUtil::LayoutsInShapesEqual(*return_shape, result_literal->shape())) { @@ -1011,9 +1016,10 @@ Status Service::TransferToServer(const TransferToServerRequest* arg, execute_backend_->transfer_manager()->AllocateScopedShapedBuffer( shape, execute_backend_->memory_allocator(), executor->device_ordinal())); + TF_ASSIGN_OR_RETURN(auto stream, execute_backend_->BorrowStream(executor)); TF_RETURN_IF_ERROR( execute_backend_->transfer_manager()->TransferLiteralToDevice( - executor, *literal, shaped_buffer)); + stream.get(), *literal, shaped_buffer)); replicated_buffers.emplace_back(std::move(shaped_buffer)); } TF_ASSIGN_OR_RETURN(*result->mutable_data(), diff --git a/tensorflow/compiler/xla/service/transfer_manager.cc b/tensorflow/compiler/xla/service/transfer_manager.cc index c4d01562c4..4c5038a009 100644 --- a/tensorflow/compiler/xla/service/transfer_manager.cc +++ b/tensorflow/compiler/xla/service/transfer_manager.cc @@ -22,8 +22,12 @@ limitations under the License. #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/util.h" +#include "tensorflow/core/lib/gtl/cleanup.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/macros.h" +#include "tensorflow/core/platform/notification.h" + +using ::tensorflow::strings::StrCat; namespace xla { /* static */ tensorflow::mutex @@ -36,8 +40,73 @@ TransferManager::GetPlatformTransferManagers() { return r; } +StatusOr> TransferManager::TransferLiteralFromDevice( + se::Stream* stream, const ShapedBuffer& device_buffer) { + StatusOr> ret; + se::Stream* substream = stream->GetOrCreateSubStream(); + auto cleanup = tensorflow::gtl::MakeCleanup( + [&]() { stream->ReturnSubStream(substream); }); + + tensorflow::Notification n; + TransferLiteralFromDevice(substream, device_buffer, + [&](StatusOr> arg) { + ret = std::move(arg); + n.Notify(); + }); + n.WaitForNotification(); + return ret; +} + +Status TransferManager::TransferLiteralToDevice( + se::Stream* stream, const LiteralSlice& literal, + const ShapedBuffer& device_buffer) { + // Implement the synchronous version by waiting on the asynchronous version. + // Use a substream so that if we are called from a HostCallback we don't + // deadlock. + se::Stream* substream = stream->GetOrCreateSubStream(); + auto cleanup = tensorflow::gtl::MakeCleanup( + [&]() { stream->ReturnSubStream(substream); }); + TF_RETURN_IF_ERROR( + TransferLiteralToDeviceAsync(substream, literal, device_buffer)); + return substream->BlockHostUntilDone(); +} + +StatusOr> TransferManager::TransferArrayFromDevice( + se::Stream* stream, const Shape& shape, + const se::DeviceMemoryBase& source) { + // Implement the synchronous version by waiting on the asynchronous version. + // Use a substream so that if we are called from a HostCallback we don't + // deadlock. + StatusOr> ret; + se::Stream* substream = stream->GetOrCreateSubStream(); + auto cleanup = tensorflow::gtl::MakeCleanup( + [&]() { stream->ReturnSubStream(substream); }); + + tensorflow::Notification n; + TransferArrayFromDevice(substream, shape, source, + [&](StatusOr> arg) { + ret = std::move(arg); + n.Notify(); + }); + n.WaitForNotification(); + return ret; +} + Status TransferManager::TransferArrayToDevice( - se::StreamExecutor* executor, const LiteralSlice& literal, + se::Stream* stream, const LiteralSlice& literal, + const se::DeviceMemoryBase& dest) { + // Implement the synchronous version by waiting on the asynchronous version. + // Use a substream so that if we are called from a HostCallback we don't + // deadlock. + se::Stream* substream = stream->GetOrCreateSubStream(); + auto cleanup = tensorflow::gtl::MakeCleanup( + [&]() { stream->ReturnSubStream(substream); }); + TF_RETURN_IF_ERROR(TransferArrayToDeviceAsync(substream, literal, dest)); + return substream->BlockHostUntilDone(); +} + +Status TransferManager::TransferArrayToDeviceAsync( + se::Stream* stream, const LiteralSlice& literal, const se::DeviceMemoryBase& dest) { const Shape on_device_shape = HostShapeToDeviceShape(literal.shape()); TF_RET_CHECK(ShapeUtil::IsArray(on_device_shape)) @@ -51,28 +120,32 @@ Status TransferManager::TransferArrayToDevice( dest.size(), GetByteSizeRequirement(on_device_shape)); } ShapedBuffer shaped_buffer(/*on_host_shape=*/literal.shape(), on_device_shape, - executor->platform(), executor->device_ordinal()); + stream->parent()->platform(), + stream->parent()->device_ordinal()); shaped_buffer.set_buffer(dest, /*index=*/{}); - return TransferLiteralToDevice(executor, literal, shaped_buffer); + return TransferLiteralToDevice(stream, literal, shaped_buffer); } -StatusOr> TransferManager::TransferArrayFromDevice( - se::StreamExecutor* executor, const Shape& shape, - const se::DeviceMemoryBase& source) { - TF_RET_CHECK(ShapeUtil::Equal(HostShapeToDeviceShape(shape), shape)) - << "Shape " << ShapeUtil::HumanString(shape) - << " has a differently shaped representation on-device: " - << ShapeUtil::HumanString(HostShapeToDeviceShape(shape)); +void TransferManager::TransferArrayFromDevice( + se::Stream* stream, const Shape& shape, const se::DeviceMemoryBase& source, + std::function>)> done) { + if (!ShapeUtil::Equal(HostShapeToDeviceShape(shape), shape)) { + auto error = StrCat("Shape ", ShapeUtil::HumanString(shape), + " has a differently shaped representation on-device: ", + ShapeUtil::HumanString(HostShapeToDeviceShape(shape))); + return done(FailedPrecondition("%s", error.c_str())); + } if (source.size() < GetByteSizeRequirement(shape)) { - return FailedPrecondition( - "Allocation on device not large enough for array: " - "%lld < %lld", - source.size(), GetByteSizeRequirement(shape)); + return done( + FailedPrecondition("Allocation on device not large enough for array: " + "%lld < %lld", + source.size(), GetByteSizeRequirement(shape))); } ShapedBuffer shaped_buffer(/*on_host_shape=*/shape, shape, - executor->platform(), executor->device_ordinal()); + stream->parent()->platform(), + stream->parent()->device_ordinal()); shaped_buffer.set_buffer(source, /*index=*/{}); - return TransferLiteralFromDevice(executor, shaped_buffer); + return TransferLiteralFromDevice(stream, shaped_buffer, std::move(done)); } /* static */ void TransferManager::RegisterTransferManager( @@ -108,10 +181,14 @@ StatusOr> TransferManager::TransferArrayFromDevice( } Status TransferManager::WriteTupleIndexTables( - se::StreamExecutor* executor, const ShapedBuffer& device_buffer) { - VLOG(2) << "Writing tuple index tables for " << device_buffer; + se::Stream* stream, const ShapedBuffer& device_buffer) { + TF_RETURN_IF_ERROR(WriteTupleIndexTablesAsync(stream, device_buffer)); + return stream->BlockHostUntilDone(); +} - TF_RET_CHECK(executor->device_ordinal() == device_buffer.device_ordinal()); +Status TransferManager::WriteTupleIndexTablesAsync( + se::Stream* stream, const ShapedBuffer& device_buffer) { + VLOG(2) << "Writing tuple index tables for " << device_buffer; return ShapeUtil::ForEachSubshapeWithStatus( device_buffer.on_device_shape(), @@ -129,7 +206,7 @@ Status TransferManager::WriteTupleIndexTables( elements.push_back(device_buffer.buffer(element_index)); element_index.pop_back(); } - return WriteSingleTupleIndexTable(executor, elements, device_subshape, + return WriteSingleTupleIndexTable(stream, elements, device_subshape, &device_memory); } @@ -138,26 +215,20 @@ Status TransferManager::WriteTupleIndexTables( } Status TransferManager::TransferBufferFromDevice( - se::StreamExecutor* executor, const se::DeviceMemoryBase& source, - int64 size, void* destination) { + se::Stream* stream, const se::DeviceMemoryBase& source, int64 size, + void* destination) { if (source.size() < size) { return FailedPrecondition( "Source allocation on device not large enough for data tranfer: " "%lld < %lld", source.size(), size); } - auto copy_status = executor->SynchronousMemcpyD2H(source, size, destination); - if (!copy_status.ok()) { - return AddStatus( - Status(static_cast(copy_status.code()), - copy_status.error_message()), - "failed transfer from device to buffer"); - } + stream->ThenMemcpy(destination, source, size); return Status::OK(); } Status TransferManager::TransferBufferToDevice( - se::StreamExecutor* executor, int64 size, const void* source, + se::Stream* stream, int64 size, const void* source, se::DeviceMemoryBase* destination) { if (destination->size() < size) { return FailedPrecondition( @@ -165,13 +236,7 @@ Status TransferManager::TransferBufferToDevice( "%lld < %lld", destination->size(), size); } - auto copy_status = executor->SynchronousMemcpyH2D(source, size, destination); - if (!copy_status.ok()) { - return AddStatus( - Status(static_cast(copy_status.code()), - copy_status.error_message()), - "failed transfer of buffer to device"); - } + stream->ThenMemcpy(destination, source, size); return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/transfer_manager.h b/tensorflow/compiler/xla/service/transfer_manager.h index 43a8092b06..e384359642 100644 --- a/tensorflow/compiler/xla/service/transfer_manager.h +++ b/tensorflow/compiler/xla/service/transfer_manager.h @@ -52,30 +52,65 @@ class TransferManager { return host_shape; } - // Returns a literal containing the data held in the given ShapedBuffer. - // using the provided executor. The optional literal_shape will be the shape - // for the literal. The shape of the ShapedBuffer and - // DeviceShape(literal_shape) must be compatible, but need not have the same - // layout. + // Returns a literal containing the data held in the given ShapedBuffer + // using the provided executor. This operation is performed synchronously + // without waiting for any other operation on a stream to complete. + // + // This function should be avoided in favor of the asynchronous version below. virtual StatusOr> TransferLiteralFromDevice( - se::StreamExecutor* executor, const ShapedBuffer& device_buffer) = 0; + se::Stream* stream, const ShapedBuffer& device_buffer); + + // Begins transferring a literal containing the data held in the given + // ShapedBuffer using the provided executor. + // + // This operation is performed asynchronously on the given stream. It returns + // once the transfer is enqueued. 'done' is invoked with the result when + // complete. + // + // device_buffer is copied by reference and must live at least until done() is + // invoked. + virtual void TransferLiteralFromDevice( + se::Stream* stream, const ShapedBuffer& device_buffer, + std::function>)> done) = 0; // Transfers the given literal into the previously allocated device memory // represented by the given ShapedBuffer using the given executor. The shape // of the ShapedBuffer and DeviceShape(literal.shape()) must be compatible, - // but need not have the same layout - virtual Status TransferLiteralToDevice(se::StreamExecutor* executor, + // but need not have the same layout. + // + // This operation is performed synchronously without waiting for any other + // operation on a stream to complete. This function should be avoided in favor + // of the asynchronous version below. + virtual Status TransferLiteralToDevice(se::Stream* stream, const LiteralSlice& literal, - const ShapedBuffer& device_buffer) = 0; + const ShapedBuffer& device_buffer); + + // Transfers the given literal into the previously allocated device memory + // represented by the given ShapedBuffer using the given executor. The shape + // of the ShapedBuffer and DeviceShape(literal.shape()) must be compatible, + // but need not have the same layout. + // + // This operation is performed asynchronously on the given stream. It returns + // once the transfer is enqueued. + virtual Status TransferLiteralToDeviceAsync( + se::Stream* stream, const LiteralSlice& literal, + const ShapedBuffer& device_buffer) = 0; // Convenience methods for transferring an array to or from the device at a // known address. This avoids having to construct a ShapedBuffer just to // transfer an array at a known address. - Status TransferArrayToDevice(se::StreamExecutor* executor, - const LiteralSlice& literal, + Status TransferArrayToDevice(se::Stream* stream, const LiteralSlice& literal, const se::DeviceMemoryBase& dest); + void TransferArrayFromDevice( + se::Stream* stream, const Shape& shape, + const se::DeviceMemoryBase& source, + std::function>)> done); + + Status TransferArrayToDeviceAsync(se::Stream* stream, + const LiteralSlice& literal, + const se::DeviceMemoryBase& dest); StatusOr> TransferArrayFromDevice( - se::StreamExecutor* executor, const Shape& shape, + se::Stream* stream, const Shape& shape, const se::DeviceMemoryBase& source); // Transfers the given literal into the Infeed interface of the device, @@ -96,8 +131,10 @@ class TransferManager { // Given an allocated ShapedBuffer, constructs the tuple index table(s) in // each buffer of the given ShapedBuffer corresponding to tuple shapes. If the // ShapedBuffer is array-shaped this method does nothing. - Status WriteTupleIndexTables(se::StreamExecutor* executor, + Status WriteTupleIndexTables(se::Stream* stream, const ShapedBuffer& device_buffer); + Status WriteTupleIndexTablesAsync(se::Stream* stream, + const ShapedBuffer& device_buffer); // Determines the byte size requirement for the given shape on the underlying // architecture. This will be used to allocate an appropriately sized memory @@ -144,7 +181,7 @@ class TransferManager { // 'destination' buffer. // // size is the size to transfer to destination in bytes. - virtual Status TransferBufferFromDevice(se::StreamExecutor* executor, + virtual Status TransferBufferFromDevice(se::Stream* stream, const se::DeviceMemoryBase& source, int64 size, void* destination); @@ -152,15 +189,15 @@ class TransferManager { // destination of the device. // // size is the size to transfer from source in bytes. - virtual Status TransferBufferToDevice(se::StreamExecutor* executor, - int64 size, const void* source, + virtual Status TransferBufferToDevice(se::Stream* stream, int64 size, + const void* source, se::DeviceMemoryBase* destination); // Writes the given device-memory pointers in 'elements' to the given region // to construct a tuple index table in the platform-specific tuple // representation. virtual Status WriteSingleTupleIndexTable( - se::StreamExecutor* executor, + se::Stream* stream, tensorflow::gtl::ArraySlice elements, const Shape& shape, se::DeviceMemoryBase* region) = 0; diff --git a/tensorflow/compiler/xla/shape_util.cc b/tensorflow/compiler/xla/shape_util.cc index 51d45b2be6..e9d7178e3d 100644 --- a/tensorflow/compiler/xla/shape_util.cc +++ b/tensorflow/compiler/xla/shape_util.cc @@ -380,6 +380,13 @@ ShapeUtil::MakeShapeWithDescendingLayoutAndSamePhysicalLayout( return shape.tuple_shapes(index); } +/* static */ int64 ShapeUtil::SubshapeCount(const Shape& shape) { + int64 n = 0; + ForEachSubshape(shape, [&](const Shape& literal_subshape, + const ShapeIndex& index) { ++n; }); + return n; +} + /* static */ Shape ShapeUtil::SliceTuple(const Shape& tuple, int64 start, int64 limit) { TF_DCHECK_OK(ValidateShapeWithOptionalLayout(tuple)); @@ -422,7 +429,6 @@ ShapeUtil::MakeShapeWithDescendingLayoutAndSamePhysicalLayout( return shape.element_type() == F32 && Rank(shape) == 0; } - namespace { // Class to memoize the computation of diff --git a/tensorflow/compiler/xla/shape_util.h b/tensorflow/compiler/xla/shape_util.h index 25ed70316b..b7543c2026 100644 --- a/tensorflow/compiler/xla/shape_util.h +++ b/tensorflow/compiler/xla/shape_util.h @@ -457,6 +457,9 @@ class ShapeUtil { // Precondition: IsTuple(shape) && TupleElementCount(shape) > index static const Shape& GetTupleElementShape(const Shape& shape, int64 index); + // Returns the number of elements, recursively, in the given shape. + static int64 SubshapeCount(const Shape& shape); + // Slices tuple elements in the range [start, limit) and returns a new tuple // shape. E.g. a tuple like (f32, s32, u32) would slice via 1,3 to (s32, u32). static Shape SliceTuple(const Shape& tuple, int64 start, int64 limit); diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD index e7e0a19db0..b76830f666 100644 --- a/tensorflow/compiler/xla/tests/BUILD +++ b/tensorflow/compiler/xla/tests/BUILD @@ -1986,6 +1986,7 @@ xla_test( "//tensorflow/compiler/xla/service:shaped_buffer", "//tensorflow/core:lib", "//tensorflow/core:stream_executor_no_cuda", + "//tensorflow/core:test", ], ) diff --git a/tensorflow/compiler/xla/tests/dynamic_ops_test.cc b/tensorflow/compiler/xla/tests/dynamic_ops_test.cc index 49f3a10d22..a918c91f07 100644 --- a/tensorflow/compiler/xla/tests/dynamic_ops_test.cc +++ b/tensorflow/compiler/xla/tests/dynamic_ops_test.cc @@ -716,8 +716,10 @@ void BM_DynamicSlice(int num_iters) { .ConsumeValueOrDie(); auto start_indices_literal = Literal::CreateR1({0, 1, 2, 3}); + auto stream = + client->mutable_backend()->BorrowStream(device_ordinal).ValueOrDie(); ASSERT_IS_OK(transfer_manager->TransferLiteralToDevice( - executors[device_ordinal], *start_indices_literal, buffer)); + stream.get(), *start_indices_literal, buffer)); std::unique_ptr executable = client diff --git a/tensorflow/compiler/xla/tests/local_client_execute_test.cc b/tensorflow/compiler/xla/tests/local_client_execute_test.cc index 96858c00d6..5a70c2a9ae 100644 --- a/tensorflow/compiler/xla/tests/local_client_execute_test.cc +++ b/tensorflow/compiler/xla/tests/local_client_execute_test.cc @@ -209,13 +209,12 @@ XLA_TEST_F(LocalClientExecuteTest, TupleResult) { EXPECT_EQ(3, ShapeUtil::TupleElementCount(result.on_host_shape())); std::unique_ptr result_literal = ShapedBufferToLiteral(result); - LiteralTestUtil::ExpectR2Equal( - {{1.0f, 2.0f}, {3.0f, 4.0f}}, LiteralSlice(*result_literal, {0})); - LiteralTestUtil::ExpectR2Equal( - {{10.0f, 20.0f}, {30.0f, 40.0f}}, - LiteralSlice(*result_literal, {1})); - LiteralTestUtil::ExpectR2Equal( - {{1.0f, 2.0f}, {3.0f, 4.0f}}, LiteralSlice(*result_literal, {2})); + LiteralTestUtil::ExpectR2Equal({{1.0f, 2.0f}, {3.0f, 4.0f}}, + LiteralSlice(*result_literal, {0})); + LiteralTestUtil::ExpectR2Equal({{10.0f, 20.0f}, {30.0f, 40.0f}}, + LiteralSlice(*result_literal, {1})); + LiteralTestUtil::ExpectR2Equal({{1.0f, 2.0f}, {3.0f, 4.0f}}, + LiteralSlice(*result_literal, {2})); } XLA_TEST_F(LocalClientExecuteTest, NestedTupleResult) { @@ -238,17 +237,14 @@ XLA_TEST_F(LocalClientExecuteTest, NestedTupleResult) { EXPECT_EQ(2, ShapeUtil::TupleElementCount(result.on_host_shape())); std::unique_ptr result_literal = ShapedBufferToLiteral(result); - LiteralTestUtil::ExpectR2Equal( - {{1.0f, 2.0f}, {3.0f, 4.0f}}, LiteralSlice(*result_literal, {1})); - LiteralTestUtil::ExpectR2Equal( - {{1.0f, 2.0f}, {3.0f, 4.0f}}, - LiteralSlice(*result_literal, {0, 0})); - LiteralTestUtil::ExpectR2Equal( - {{10.0f, 20.0f}, {30.0f, 40.0f}}, - LiteralSlice(*result_literal, {0, 1})); - LiteralTestUtil::ExpectR2Equal( - {{1.0f, 2.0f}, {3.0f, 4.0f}}, - LiteralSlice(*result_literal, {0, 2})); + LiteralTestUtil::ExpectR2Equal({{1.0f, 2.0f}, {3.0f, 4.0f}}, + LiteralSlice(*result_literal, {1})); + LiteralTestUtil::ExpectR2Equal({{1.0f, 2.0f}, {3.0f, 4.0f}}, + LiteralSlice(*result_literal, {0, 0})); + LiteralTestUtil::ExpectR2Equal({{10.0f, 20.0f}, {30.0f, 40.0f}}, + LiteralSlice(*result_literal, {0, 1})); + LiteralTestUtil::ExpectR2Equal({{1.0f, 2.0f}, {3.0f, 4.0f}}, + LiteralSlice(*result_literal, {0, 2})); } XLA_TEST_F(LocalClientExecuteTest, TupleResultWithLayout) { @@ -273,10 +269,10 @@ XLA_TEST_F(LocalClientExecuteTest, TupleResultWithLayout) { options, DefaultExecutableRunOptions()); std::unique_ptr result_literal = ShapedBufferToLiteral(result); - LiteralTestUtil::ExpectR2Equal( - {{1.0f, 2.0f}, {3.0f, 4.0f}}, LiteralSlice(*result_literal, {0})); - LiteralTestUtil::ExpectR2Equal( - {{1.0f, 2.0f}, {3.0f, 4.0f}}, LiteralSlice(*result_literal, {1})); + LiteralTestUtil::ExpectR2Equal({{1.0f, 2.0f}, {3.0f, 4.0f}}, + LiteralSlice(*result_literal, {0})); + LiteralTestUtil::ExpectR2Equal({{1.0f, 2.0f}, {3.0f, 4.0f}}, + LiteralSlice(*result_literal, {1})); } XLA_TEST_F(LocalClientExecuteTest, TupleArguments) { @@ -319,11 +315,10 @@ XLA_TEST_F(LocalClientExecuteTest, TupleArguments) { EXPECT_EQ(2, ShapeUtil::TupleElementCount(result.on_host_shape())); std::unique_ptr result_literal = ShapedBufferToLiteral(result); - LiteralTestUtil::ExpectR2Equal( - {{56.0f, 46.0f}, {36.0f, 26.0f}}, - LiteralSlice(*result_literal, {0})); - LiteralTestUtil::ExpectR1Equal( - {40.0f, 71.0f, 117.0f}, LiteralSlice(*result_literal, {1})); + LiteralTestUtil::ExpectR2Equal({{56.0f, 46.0f}, {36.0f, 26.0f}}, + LiteralSlice(*result_literal, {0})); + LiteralTestUtil::ExpectR1Equal({40.0f, 71.0f, 117.0f}, + LiteralSlice(*result_literal, {1})); } XLA_TEST_F(LocalClientExecuteTest, NestedTupleArgument) { @@ -360,10 +355,10 @@ XLA_TEST_F(LocalClientExecuteTest, NestedTupleArgument) { ScopedShapedBuffer result = ExecuteLocallyOrDie(computation, {&arg_buffer}); std::unique_ptr result_literal = ShapedBufferToLiteral(result); - LiteralTestUtil::ExpectR2Equal( - {{-1.0, -2.0}, {-3.0, -4}}, LiteralSlice(*result_literal, {0})); - LiteralTestUtil::ExpectR1Equal( - {264.0, 73.0, 133.0}, LiteralSlice(*result_literal, {1})); + LiteralTestUtil::ExpectR2Equal({{-1.0, -2.0}, {-3.0, -4}}, + LiteralSlice(*result_literal, {0})); + LiteralTestUtil::ExpectR1Equal({264.0, 73.0, 133.0}, + LiteralSlice(*result_literal, {1})); } XLA_TEST_F(LocalClientExecuteTest, PassingTupleResultBackIntoComputation) { @@ -389,18 +384,17 @@ XLA_TEST_F(LocalClientExecuteTest, PassingTupleResultBackIntoComputation) { ScopedShapedBuffer result_0 = ExecuteLocallyOrDie(computation, {&arg_buffer}); std::unique_ptr result_0_literal = ShapedBufferToLiteral(result_0); - LiteralTestUtil::ExpectR2Equal( - {{-1.0, -2.0}, {-3.0, -4.0}}, - LiteralSlice(*result_0_literal, {0})); - LiteralTestUtil::ExpectR2Equal( - {{22.0, 6.0}, {8.0, 10}}, LiteralSlice(*result_0_literal, {1})); + LiteralTestUtil::ExpectR2Equal({{-1.0, -2.0}, {-3.0, -4.0}}, + LiteralSlice(*result_0_literal, {0})); + LiteralTestUtil::ExpectR2Equal({{22.0, 6.0}, {8.0, 10}}, + LiteralSlice(*result_0_literal, {1})); ScopedShapedBuffer result_1 = ExecuteLocallyOrDie(computation, {&result_0}); std::unique_ptr result_1_literal = ShapedBufferToLiteral(result_1); - LiteralTestUtil::ExpectR2Equal( - {{1.0, 2.0}, {3.0, 4.0}}, LiteralSlice(*result_1_literal, {0})); - LiteralTestUtil::ExpectR2Equal( - {{44.0, 12.0}, {16.0, 20}}, LiteralSlice(*result_1_literal, {1})); + LiteralTestUtil::ExpectR2Equal({{1.0, 2.0}, {3.0, 4.0}}, + LiteralSlice(*result_1_literal, {0})); + LiteralTestUtil::ExpectR2Equal({{44.0, 12.0}, {16.0, 20}}, + LiteralSlice(*result_1_literal, {1})); } XLA_TEST_F(LocalClientExecuteTest, LargeTuple) { @@ -447,8 +441,7 @@ XLA_TEST_F(LocalClientExecuteTest, LargeTuple) { for (int i = 0; i < kElementCount; ++i) { LiteralTestUtil::ExpectR1Near( - {2.0f * i, 0.0f}, LiteralSlice(*result_literal, {i}), - error_spec_); + {2.0f * i, 0.0f}, LiteralSlice(*result_literal, {i}), error_spec_); } } @@ -547,8 +540,8 @@ XLA_TEST_F(LocalClientExecuteTest, DeepTuple) { for (int i = 0; i < kTupleDepth; ++i) { index.push_back(0); } - LiteralTestUtil::ExpectR0Equal( - 165.0, LiteralSlice(*result_literal, index)); + LiteralTestUtil::ExpectR0Equal(165.0, + LiteralSlice(*result_literal, index)); } XLA_TEST_F(LocalClientExecuteTest, InvalidNumberOfArguments) { @@ -753,10 +746,10 @@ XLA_TEST_F(LocalClientExecuteTest, SelectBetweenTuples) { ScopedShapedBuffer result = ExecuteLocallyOrDie(builder.Build().ValueOrDie(), {}); std::unique_ptr tuple_literal = ShapedBufferToLiteral(result); - LiteralTestUtil::ExpectR1Equal( - {2.0f, 4.0f, 6.0f}, LiteralSlice(*tuple_literal, {0})); - LiteralTestUtil::ExpectR1Equal( - {1.0f, 2.0f, 3.0f}, LiteralSlice(*tuple_literal, {1})); + LiteralTestUtil::ExpectR1Equal({2.0f, 4.0f, 6.0f}, + LiteralSlice(*tuple_literal, {0})); + LiteralTestUtil::ExpectR1Equal({1.0f, 2.0f, 3.0f}, + LiteralSlice(*tuple_literal, {1})); } XLA_TEST_F(LocalClientExecuteTest, CompileExecutable) { @@ -900,8 +893,10 @@ void BM_LocalClientOverhead(int num_iters) { ->AllocateScopedShapedBuffer(shape, &allocator, /*device_ordinal=*/0) .ConsumeValueOrDie(); auto literal = Literal::CreateR2({{0, 0, 0}, {0, 0, 0}}); - ASSERT_IS_OK(transfer_manager->TransferLiteralToDevice( - executors[device_ordinal], *literal, buffer)); + auto stream = + client->mutable_backend()->BorrowStream(device_ordinal).ValueOrDie(); + ASSERT_IS_OK(transfer_manager->TransferLiteralToDevice(stream.get(), *literal, + buffer)); const int kWarmups = 2; @@ -911,11 +906,8 @@ void BM_LocalClientOverhead(int num_iters) { std::unique_ptr executable = executable_status.ConsumeValueOrDie(); - se::Stream stream(executors[client->default_device_ordinal()]); - stream.Init(); - ExecutableRunOptions run_options; - run_options.set_allocator(&allocator).set_stream(&stream); + run_options.set_allocator(&allocator).set_stream(stream.get()); for (int i = 0; i < kWarmups; ++i) { auto result = executable->Run({&buffer}, run_options); diff --git a/tensorflow/compiler/xla/tests/transfer_manager_test.cc b/tensorflow/compiler/xla/tests/transfer_manager_test.cc index 0063e7ad41..85799d4cfb 100644 --- a/tensorflow/compiler/xla/tests/transfer_manager_test.cc +++ b/tensorflow/compiler/xla/tests/transfer_manager_test.cc @@ -31,6 +31,7 @@ limitations under the License. #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/stream_executor_no_cuda.h" +#include "tensorflow/core/platform/test_benchmark.h" #include "tensorflow/core/platform/types.h" namespace xla { @@ -41,7 +42,12 @@ class TransferManagerTest : public LocalClientTestBase { TransferManagerTest() : shape_size_fn_([this](const Shape& shape) { return transfer_manager_->GetByteSizeRequirement(shape); - }) {} + }) { + stream_ptr_ = local_client_->mutable_backend() + ->BorrowStream(stream_executor_) + .ValueOrDie(); + stream_ = stream_ptr_.get(); + } ~TransferManagerTest() override = default; @@ -53,6 +59,10 @@ class TransferManagerTest : public LocalClientTestBase { .ValueOrDie(); } + protected: + Backend::StreamPtr stream_ptr_; + se::Stream* stream_; + private: std::function shape_size_fn_; }; @@ -63,11 +73,11 @@ XLA_TEST_F(TransferManagerTest, TransferR0U32) { auto device_buffer = AllocateDeviceBuffer(shape); // Round trip literal through device. - ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice( - stream_executor_, *literal, device_buffer)); - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr result, - transfer_manager_->TransferLiteralFromDevice( - stream_executor_, device_buffer)); + ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice(stream_, *literal, + device_buffer)); + TF_ASSERT_OK_AND_ASSIGN( + std::unique_ptr result, + transfer_manager_->TransferLiteralFromDevice(stream_, device_buffer)); LiteralTestUtil::ExpectR0Equal(42, *result); } @@ -79,11 +89,11 @@ XLA_TEST_F(TransferManagerTest, TransferR1F32) { auto device_buffer = AllocateDeviceBuffer(shape); // Round trip literal through device. - ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice( - stream_executor_, *literal, device_buffer)); - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr result, - transfer_manager_->TransferLiteralFromDevice( - stream_executor_, device_buffer)); + ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice(stream_, *literal, + device_buffer)); + TF_ASSERT_OK_AND_ASSIGN( + std::unique_ptr result, + transfer_manager_->TransferLiteralFromDevice(stream_, device_buffer)); LiteralTestUtil::ExpectR1Equal({1.25f, 2.5f, -17.0f, -20.125f}, *result); @@ -97,11 +107,11 @@ XLA_TEST_F(TransferManagerTest, TransferR1LargeF32) { auto device_buffer = AllocateDeviceBuffer(shape); // Round trip literal through device. - ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice( - stream_executor_, *literal, device_buffer)); - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr result, - transfer_manager_->TransferLiteralFromDevice( - stream_executor_, device_buffer)); + ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice(stream_, *literal, + device_buffer)); + TF_ASSERT_OK_AND_ASSIGN( + std::unique_ptr result, + transfer_manager_->TransferLiteralFromDevice(stream_, device_buffer)); LiteralTestUtil::ExpectR1Equal(test_vector, *result); } @@ -113,11 +123,11 @@ XLA_TEST_F(TransferManagerTest, TransferR1U8) { auto device_buffer = AllocateDeviceBuffer(shape); // Round trip literal through device. - ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice( - stream_executor_, *literal, device_buffer)); - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr result, - transfer_manager_->TransferLiteralFromDevice( - stream_executor_, device_buffer)); + ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice(stream_, *literal, + device_buffer)); + TF_ASSERT_OK_AND_ASSIGN( + std::unique_ptr result, + transfer_manager_->TransferLiteralFromDevice(stream_, device_buffer)); EXPECT_EQ(result->GetR1U8AsString(), test_string); } @@ -129,11 +139,11 @@ XLA_TEST_F(TransferManagerTest, TransferR2F32) { auto device_buffer = AllocateDeviceBuffer(shape); // Round trip literal through device. - ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice( - stream_executor_, *literal, device_buffer)); - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr result, - transfer_manager_->TransferLiteralFromDevice( - stream_executor_, device_buffer)); + ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice(stream_, *literal, + device_buffer)); + TF_ASSERT_OK_AND_ASSIGN( + std::unique_ptr result, + transfer_manager_->TransferLiteralFromDevice(stream_, device_buffer)); LiteralTestUtil::ExpectR2Equal( {{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}}, *result); @@ -149,11 +159,11 @@ XLA_TEST_F(TransferManagerTest, // Round trip literal through device. Set the on-device layout to something // different than the literal layout. - ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice( - stream_executor_, *literal, device_buffer)); - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr result, - transfer_manager_->TransferLiteralFromDevice( - stream_executor_, device_buffer)); + ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice(stream_, *literal, + device_buffer)); + TF_ASSERT_OK_AND_ASSIGN( + std::unique_ptr result, + transfer_manager_->TransferLiteralFromDevice(stream_, device_buffer)); EXPECT_FALSE( LayoutUtil::Equal(result->shape().layout(), literal->shape().layout())); @@ -169,11 +179,11 @@ XLA_TEST_F(TransferManagerTest, TransferTuple) { auto device_buffer = AllocateDeviceBuffer(literal->shape()); // Round trip literal through device. - ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice( - stream_executor_, *literal, device_buffer)); - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr result, - transfer_manager_->TransferLiteralFromDevice( - stream_executor_, device_buffer)); + ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice(stream_, *literal, + device_buffer)); + TF_ASSERT_OK_AND_ASSIGN( + std::unique_ptr result, + transfer_manager_->TransferLiteralFromDevice(stream_, device_buffer)); EXPECT_TRUE(LiteralTestUtil::Equal(*literal, *result)); } @@ -183,11 +193,11 @@ XLA_TEST_F(TransferManagerTest, TransferEmptyTuple) { auto device_buffer = AllocateDeviceBuffer(literal->shape()); // Round trip literal through device. - ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice( - stream_executor_, *literal, device_buffer)); - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr result, - transfer_manager_->TransferLiteralFromDevice( - stream_executor_, device_buffer)); + ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice(stream_, *literal, + device_buffer)); + TF_ASSERT_OK_AND_ASSIGN( + std::unique_ptr result, + transfer_manager_->TransferLiteralFromDevice(stream_, device_buffer)); EXPECT_TRUE(LiteralTestUtil::Equal(*literal, *result)); } @@ -203,11 +213,11 @@ XLA_TEST_F(TransferManagerTest, TransferNestedTuple) { auto device_buffer = AllocateDeviceBuffer(literal->shape()); // Round trip literal through device. - ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice( - stream_executor_, *literal, device_buffer)); - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr result, - transfer_manager_->TransferLiteralFromDevice( - stream_executor_, device_buffer)); + ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice(stream_, *literal, + device_buffer)); + TF_ASSERT_OK_AND_ASSIGN( + std::unique_ptr result, + transfer_manager_->TransferLiteralFromDevice(stream_, device_buffer)); EXPECT_TRUE(LiteralTestUtil::Equal(*literal, *result)); } @@ -218,11 +228,11 @@ XLA_TEST_F(TransferManagerTest, TransferComplexValue) { auto device_buffer = AllocateDeviceBuffer(literal->shape()); // Round trip literal through device. - ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice( - stream_executor_, *literal, device_buffer)); - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr result, - transfer_manager_->TransferLiteralFromDevice( - stream_executor_, device_buffer)); + ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice(stream_, *literal, + device_buffer)); + TF_ASSERT_OK_AND_ASSIGN( + std::unique_ptr result, + transfer_manager_->TransferLiteralFromDevice(stream_, device_buffer)); EXPECT_TRUE(LiteralTestUtil::Equal(*literal, *result)); } @@ -237,14 +247,150 @@ XLA_TEST_F(TransferManagerTest, TransferComplexValueInTuple) { auto device_buffer = AllocateDeviceBuffer(literal->shape()); // Round trip literal through device. - ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice( - stream_executor_, *literal, device_buffer)); - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr result, - transfer_manager_->TransferLiteralFromDevice( - stream_executor_, device_buffer)); + ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice(stream_, *literal, + device_buffer)); + TF_ASSERT_OK_AND_ASSIGN( + std::unique_ptr result, + transfer_manager_->TransferLiteralFromDevice(stream_, device_buffer)); EXPECT_TRUE(LiteralTestUtil::Equal(*literal, *result)); } +XLA_TEST_F(TransferManagerTest, MultiStreamRoundTripSoak) { + const int64 kIterationCount = 5000; + std::unique_ptr literal1 = Literal::MakeTuple( + {Literal::CreateR0(123.0f).get(), + Literal::MakeTuple( + {Literal::CreateR2({{1.0f, 2.0f}, {4.0f, 5.0f}}).get(), + Literal::CreateR1({44.0f, -10.0f, 3333333.3f}).get()}) + .get(), + Literal::CreateR1({-10.0f, 123.0f}).get()}); + std::unique_ptr literal2 = Literal::MakeTuple( + {Literal::CreateR0(456.0f).get(), + Literal::MakeTuple( + {Literal::CreateR2({{5.0f, 7.0f}, {9.0f, 4.0f}}).get(), + Literal::CreateR1({44.0f, -11.0f, 3333333.3f}).get()}) + .get(), + Literal::CreateR1({-98.0f, 153.0f}).get()}); + + auto device_buffer1 = AllocateDeviceBuffer(literal1->shape()); + auto device_buffer2 = AllocateDeviceBuffer(literal2->shape()); + + auto stream1 = stream_; + auto stream2 = stream_->GetOrCreateSubStream(); + + std::unique_ptr result1, result2; + + // Round trip literals through device in multiple streams asynchronously. + for (int i = 0; i < kIterationCount; ++i) { + ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice(stream1, *literal1, + device_buffer1)); + ASSERT_IS_OK(transfer_manager_->TransferLiteralToDevice(stream2, *literal2, + device_buffer2)); + TF_ASSERT_OK_AND_ASSIGN( + std::unique_ptr this_result1, + transfer_manager_->TransferLiteralFromDevice(stream1, device_buffer1)); + TF_ASSERT_OK_AND_ASSIGN( + std::unique_ptr this_result2, + transfer_manager_->TransferLiteralFromDevice(stream2, device_buffer2)); + result1 = std::move(this_result1); + result2 = std::move(this_result2); + } + + EXPECT_TRUE(LiteralTestUtil::Equal(*literal1, *result1)); + EXPECT_TRUE(LiteralTestUtil::Equal(*literal2, *result2)); +} + +class TransferDeviceToHostBenchmark : public TransferManagerTest { + public: + using TransferManagerTest::TransferManagerTest; + ~TransferDeviceToHostBenchmark() override {} + + void Run(int iters, int num_tuple_elements, int array_size) { + tensorflow::testing::StopTiming(); + SetUp(); + + std::vector> tuple_elements; + for (int i = 0; i < num_tuple_elements; ++i) { + tuple_elements.push_back( + Literal::CreateR2F32Linspace(0.0f, 1.0f, array_size, array_size)); + } + std::unique_ptr literal = + Literal::MakeTupleOwned(std::move(tuple_elements)); + auto device_buffer = AllocateDeviceBuffer(literal->shape()); + TF_CHECK_OK(transfer_manager_->TransferLiteralToDevice(stream_, *literal, + device_buffer)); + tensorflow::testing::StartTiming(); + for (int i = 0; i < iters; ++i) { + TF_ASSERT_OK_AND_ASSIGN( + std::unique_ptr result, + transfer_manager_->TransferLiteralFromDevice(stream_, device_buffer)); + } + tensorflow::testing::StopTiming(); + TearDown(); + } + + void TestBody() override {} +}; + +class TransferHostToDeviceBenchmark : public TransferManagerTest { + public: + using TransferManagerTest::TransferManagerTest; + ~TransferHostToDeviceBenchmark() override {} + + void Run(int iters, int num_tuple_elements, int array_size) { + tensorflow::testing::StopTiming(); + SetUp(); + + std::vector> tuple_elements; + for (int i = 0; i < num_tuple_elements; ++i) { + tuple_elements.push_back( + Literal::CreateR2F32Linspace(0.0f, 1.0f, array_size, array_size)); + } + std::unique_ptr literal = + Literal::MakeTupleOwned(std::move(tuple_elements)); + auto device_buffer = AllocateDeviceBuffer(literal->shape()); + tensorflow::testing::StartTiming(); + for (int i = 0; i < iters; ++i) { + TF_CHECK_OK(transfer_manager_->TransferLiteralToDevice(stream_, *literal, + device_buffer)); + } + tensorflow::testing::StopTiming(); + TearDown(); + } + + void TestBody() override {} +}; + +void BM_TransferDeviceToHost(int iters, int num_tuple_elements, + int array_size) { + TransferDeviceToHostBenchmark bm; + bm.Run(iters, num_tuple_elements, array_size); +} + +void BM_TransferHostToDevice(int iters, int num_tuple_elements, + int array_size) { + TransferHostToDeviceBenchmark bm; + bm.Run(iters, num_tuple_elements, array_size); +} + +BENCHMARK(BM_TransferHostToDevice) + ->ArgPair(1, 256) + ->ArgPair(1, 257) + ->ArgPair(100, 256) + ->ArgPair(100, 257); + +BENCHMARK(BM_TransferDeviceToHost) + ->ArgPair(1, 256) + ->ArgPair(1, 257) + ->ArgPair(100, 256) + ->ArgPair(100, 257); + +int main(int argc, char** argv) { + ::testing::InitGoogleTest(&argc, argv); + tensorflow::testing::RunBenchmarks(); + return RUN_ALL_TESTS(); +} + } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc b/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc index 3c9a01653c..0be950cacb 100644 --- a/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc +++ b/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc @@ -128,20 +128,23 @@ void ExecuteAndFetchProfile(string* profile_output, LocalClient* client, se::StreamExecutor* executor = backend->default_stream_executor(); DeviceMemoryAllocator* allocator = backend->memory_allocator(); auto* transfer_manager = backend->transfer_manager(); + TF_ASSERT_OK_AND_ASSIGN( + Backend::StreamPtr stream_ptr, + backend->BorrowStream(backend->default_device_ordinal())); TF_ASSERT_OK_AND_ASSIGN( ScopedShapedBuffer lhs_arg, transfer_manager->AllocateScopedShapedBuffer( lhs_arg_shape, allocator, backend->default_device_ordinal())); TF_ASSERT_OK(transfer_manager->TransferLiteralToDevice( - executor, *Literal::CreateFromShape(lhs_arg_shape), lhs_arg)); + stream_ptr.get(), *Literal::CreateFromShape(lhs_arg_shape), lhs_arg)); TF_ASSERT_OK_AND_ASSIGN( ScopedShapedBuffer rhs_arg, transfer_manager->AllocateScopedShapedBuffer( rhs_arg_shape, allocator, backend->default_device_ordinal())); TF_ASSERT_OK(transfer_manager->TransferLiteralToDevice( - executor, *Literal::CreateFromShape(rhs_arg_shape), rhs_arg)); + stream_ptr.get(), *Literal::CreateFromShape(rhs_arg_shape), rhs_arg)); TF_ASSERT_OK_AND_ASSIGN( std::unique_ptr local_executable, @@ -153,9 +156,6 @@ void ExecuteAndFetchProfile(string* profile_output, LocalClient* client, &executable->hlo_profile_printer_data(), &executable->hlo_profile_index_map()); - TF_ASSERT_OK_AND_ASSIGN( - Backend::StreamPtr stream_ptr, - backend->BorrowStream(backend->default_device_ordinal())); ExecutableRunOptions exec_run_options; exec_run_options.set_stream(stream_ptr.get()); exec_run_options.set_allocator(backend->memory_allocator()); diff --git a/tensorflow/compiler/xla/tests/xla_internal_test_main.cc b/tensorflow/compiler/xla/tests/xla_internal_test_main.cc index a9f2915b45..a075195618 100644 --- a/tensorflow/compiler/xla/tests/xla_internal_test_main.cc +++ b/tensorflow/compiler/xla/tests/xla_internal_test_main.cc @@ -49,6 +49,7 @@ GTEST_API_ int main(int argc, char** argv) { } // Unfortunately Google's internal benchmark infrastructure has a // different API than Tensorflow's. + testing::InitGoogleTest(&argc, argv); #if defined(PLATFORM_GOOGLE) base::SetFlag(&FLAGS_benchmarks, pattern); RunSpecifiedBenchmarks(); -- GitLab From 1e3caf55ba86cd6ea36b8b9dfe5e7670ace29c05 Mon Sep 17 00:00:00 2001 From: Dan Moldovan Date: Tue, 19 Jun 2018 06:20:59 -0700 Subject: [PATCH 656/816] Disable test on windows. PiperOrigin-RevId: 201163760 --- tensorflow/contrib/autograph/converters/BUILD | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/contrib/autograph/converters/BUILD b/tensorflow/contrib/autograph/converters/BUILD index 94e465066f..931ff62064 100644 --- a/tensorflow/contrib/autograph/converters/BUILD +++ b/tensorflow/contrib/autograph/converters/BUILD @@ -120,6 +120,7 @@ py_test( name = "decorators_test", srcs = ["decorators_test.py"], srcs_version = "PY2AND3", + tags = ["no_windows"], deps = [ ":converters", "//tensorflow/contrib/autograph/core:test_lib", -- GitLab From 124fadcf1cc6a4b95f91c69e67b5fb592556e363 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 19 Jun 2018 07:34:09 -0700 Subject: [PATCH 657/816] Performance microtweaks: Pass by reference rather than by value; pre-reserve capacity when total vectoroid size is known. PiperOrigin-RevId: 201172723 --- tensorflow/compiler/xla/service/gpu/ir_emitter_nested.cc | 6 ++++-- tensorflow/compiler/xla/service/hlo_query.cc | 4 ++-- tensorflow/compiler/xla/service/hlo_query.h | 4 ++-- tensorflow/compiler/xla/service/shape_inference.cc | 2 ++ tensorflow/compiler/xla/shape_util.cc | 1 + 5 files changed, 11 insertions(+), 6 deletions(-) diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_nested.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter_nested.cc index bb47a42805..c9574c87a3 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter_nested.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_nested.cc @@ -120,9 +120,10 @@ Status IrEmitterNested::EmitTargetElementLoop( // For MOF we give the loop emitter an array for every output it should // generate. if (hlo.IsMultiOutputFusion()) { + const int64 num_elems = ShapeUtil::TupleElementCount(hlo.shape()); std::vector target_arrays; - for (int64 i = 0, e = ShapeUtil::TupleElementCount(hlo.shape()); i != e; - ++i) { + target_arrays.reserve(num_elems); + for (int64 i = 0; i != num_elems; ++i) { target_arrays.push_back(GetIrArray(hlo, hlo, {i})); } TF_RETURN_IF_ERROR( @@ -130,6 +131,7 @@ Status IrEmitterNested::EmitTargetElementLoop( .EmitLoop()); std::vector tuple_operand_ptrs; + tuple_operand_ptrs.reserve(num_elems); for (const llvm_ir::IrArray& array : target_arrays) { tuple_operand_ptrs.push_back(array.GetBasePointer()); } diff --git a/tensorflow/compiler/xla/service/hlo_query.cc b/tensorflow/compiler/xla/service/hlo_query.cc index d45038f1f4..2418c19f3d 100644 --- a/tensorflow/compiler/xla/service/hlo_query.cc +++ b/tensorflow/compiler/xla/service/hlo_query.cc @@ -61,7 +61,7 @@ bool AllOperandsAreConstants(const HloInstruction& instruction) { } HloInstruction* GetMatchingOperand( - std::function matcher, + const std::function& matcher, HloInstruction* instruction) { for (HloInstruction* op : instruction->operands()) { if (matcher(op)) { @@ -72,7 +72,7 @@ HloInstruction* GetMatchingOperand( } bool MatchBinaryInstructionOperand( - std::function matcher, + const std::function& matcher, HloInstruction* instruction, HloInstruction** matching_operand, HloInstruction** other_operand) { CHECK_EQ(instruction->operand_count(), 2); diff --git a/tensorflow/compiler/xla/service/hlo_query.h b/tensorflow/compiler/xla/service/hlo_query.h index c79347bbf9..c0826a6aee 100644 --- a/tensorflow/compiler/xla/service/hlo_query.h +++ b/tensorflow/compiler/xla/service/hlo_query.h @@ -45,7 +45,7 @@ bool IsScalarConstant(const HloInstruction* instruction); // multiple matching operands, then the first matching operand is returned. If // there are no matching operands then nullptr is returned. HloInstruction* GetMatchingOperand( - std::function matcher, + const std::function& matcher, HloInstruction* instruction); // Returns whether a binary instruction has a matching operand. Sets @@ -53,7 +53,7 @@ HloInstruction* GetMatchingOperand( // other_operand. Note: in the case where both operands match, the first operand // of the instruction is returned. bool MatchBinaryInstructionOperand( - std::function matcher, + const std::function& matcher, HloInstruction* instruction, HloInstruction** matching_operand, HloInstruction** other_operand); diff --git a/tensorflow/compiler/xla/service/shape_inference.cc b/tensorflow/compiler/xla/service/shape_inference.cc index e25f5e67c7..4606d8f202 100644 --- a/tensorflow/compiler/xla/service/shape_inference.cc +++ b/tensorflow/compiler/xla/service/shape_inference.cc @@ -939,6 +939,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, HloOpcode opcode, tensorflow::gtl::ArraySlice operands) { std::vector operand_shapes; + operand_shapes.reserve(operands.size()); for (const HloInstruction* operand : operands) { operand_shapes.push_back(&operand->shape()); } @@ -954,6 +955,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, switch (opcode) { case HloOpcode::kTuple: { Shape result = ShapeUtil::MakeTupleShape({}); + result.mutable_tuple_shapes()->Reserve(operand_shapes.size()); for (const Shape* shape : operand_shapes) { ShapeUtil::AppendShapeToTuple(*shape, &result); } diff --git a/tensorflow/compiler/xla/shape_util.cc b/tensorflow/compiler/xla/shape_util.cc index e9d7178e3d..ba09b63859 100644 --- a/tensorflow/compiler/xla/shape_util.cc +++ b/tensorflow/compiler/xla/shape_util.cc @@ -264,6 +264,7 @@ ShapeUtil::MakeShapeWithDescendingLayoutAndSamePhysicalLayout( tensorflow::gtl::ArraySlice shapes) { Shape result; result.set_element_type(TUPLE); + result.mutable_tuple_shapes()->Reserve(shapes.size()); for (const auto& shape : shapes) { AppendShapeToTuple(shape, &result); } -- GitLab From 2f7c783d9ff5bc059fb58b875c9b9dae2fc96392 Mon Sep 17 00:00:00 2001 From: Derek Murray Date: Tue, 19 Jun 2018 08:26:37 -0700 Subject: [PATCH 658/816] [tf.data] Fix a performance-related finding from clang-tidy. * the parameter 'done' is copied for each invocation but only used as a const reference; consider making it a const reference PiperOrigin-RevId: 201179686 --- tensorflow/core/kernels/data/iterator_ops.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/core/kernels/data/iterator_ops.cc b/tensorflow/core/kernels/data/iterator_ops.cc index f33e9cec29..b476a452a5 100644 --- a/tensorflow/core/kernels/data/iterator_ops.cc +++ b/tensorflow/core/kernels/data/iterator_ops.cc @@ -779,7 +779,7 @@ class OneShotIteratorOp : public AsyncOpKernel { } private: - void Init(OpKernelContext* ctx, DoneCallback done) { + void Init(OpKernelContext* ctx, const DoneCallback& done) { IteratorResource* iterator = nullptr; ContainerInfo cinfo; Status s = TryInit(ctx, &iterator, &cinfo); -- GitLab From 316fee40d4978db2f6abbb5ff35cf8d979bee93e Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 19 Jun 2018 08:57:04 -0700 Subject: [PATCH 659/816] Update TFLite "minimal" example PiperOrigin-RevId: 201183828 --- .../contrib/lite/examples/minimal/BUILD | 27 +++++++++++++++++++ .../contrib/lite/examples/minimal/minimal.cc | 24 ++++++++++------- .../contrib/lite/optional_debug_tools.cc | 13 ++++----- .../contrib/lite/optional_debug_tools.h | 3 --- 4 files changed, 46 insertions(+), 21 deletions(-) create mode 100644 tensorflow/contrib/lite/examples/minimal/BUILD diff --git a/tensorflow/contrib/lite/examples/minimal/BUILD b/tensorflow/contrib/lite/examples/minimal/BUILD new file mode 100644 index 0000000000..b403628d6c --- /dev/null +++ b/tensorflow/contrib/lite/examples/minimal/BUILD @@ -0,0 +1,27 @@ +# Description: +# TensorFlow Lite minimal example. + +package(default_visibility = ["//visibility:public"]) + +licenses(["notice"]) # Apache 2.0 + +load("//tensorflow:tensorflow.bzl", "tf_cc_binary") +load("//tensorflow/contrib/lite:build_def.bzl", "tflite_linkopts") + +tf_cc_binary( + name = "minimal", + srcs = [ + "minimal.cc", + ], + linkopts = tflite_linkopts() + select({ + "//tensorflow:android": [ + "-pie", # Android 5.0 and later supports only PIE + "-lm", # some builtin ops, e.g., tanh, need -lm + ], + "//conditions:default": [], + }), + deps = [ + "//tensorflow/contrib/lite:framework", + "//tensorflow/contrib/lite/kernels:builtin_ops", + ], +) diff --git a/tensorflow/contrib/lite/examples/minimal/minimal.cc b/tensorflow/contrib/lite/examples/minimal/minimal.cc index 8b0ace96cc..8b65cde7b7 100644 --- a/tensorflow/contrib/lite/examples/minimal/minimal.cc +++ b/tensorflow/contrib/lite/examples/minimal/minimal.cc @@ -12,10 +12,11 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/contrib/lite/model.h" +#include #include "tensorflow/contrib/lite/interpreter.h" #include "tensorflow/contrib/lite/kernels/register.h" -#include +#include "tensorflow/contrib/lite/model.h" +#include "tensorflow/contrib/lite/optional_debug_tools.h" // This is an example that is minimal to read a model // from disk and perform inference. There is no data being loaded @@ -29,14 +30,13 @@ limitations under the License. using namespace tflite; -#define TFLITE_MINIMAL_CHECK(x) \ - if(!(x)) { \ - fprintf(stderr, "Error at %s:%d\n", __FILE__, __LINE__); \ - exit(1); \ +#define TFLITE_MINIMAL_CHECK(x) \ + if (!(x)) { \ + fprintf(stderr, "Error at %s:%d\n", __FILE__, __LINE__); \ + exit(1); \ } - -int main(int argc, char *argv[]) { +int main(int argc, char* argv[]) { if(argc != 2) { fprintf(stderr, "minimal \n"); return 1; @@ -44,8 +44,8 @@ int main(int argc, char *argv[]) { const char* filename = argv[1]; // Load model - std::unique_ptr model - = tflite::FlatBufferModel::BuildFromFile(filename); + std::unique_ptr model = + tflite::FlatBufferModel::BuildFromFile(filename); TFLITE_MINIMAL_CHECK(model != nullptr); // Build the interpreter @@ -57,12 +57,16 @@ int main(int argc, char *argv[]) { // Allocate tensor buffers. TFLITE_MINIMAL_CHECK(interpreter->AllocateTensors() == kTfLiteOk); + printf("=== Pre-invoke Interpreter State ===\n"); + tflite::PrintInterpreterState(interpreter.get()); // Fill input buffers // TODO(user): Insert code to fill input tensors // Run inference TFLITE_MINIMAL_CHECK(interpreter->Invoke() == kTfLiteOk); + printf("\n\n=== Post-invoke Interpreter State ===\n"); + tflite::PrintInterpreterState(interpreter.get()); // Read output buffers // TODO(user): Insert getting data out code. diff --git a/tensorflow/contrib/lite/optional_debug_tools.cc b/tensorflow/contrib/lite/optional_debug_tools.cc index 3af809a2a1..99c35b9caf 100644 --- a/tensorflow/contrib/lite/optional_debug_tools.cc +++ b/tensorflow/contrib/lite/optional_debug_tools.cc @@ -84,13 +84,13 @@ void PrintInterpreterState(Interpreter* interpreter) { for (int tensor_index = 0; tensor_index < interpreter->tensors_size(); tensor_index++) { TfLiteTensor* tensor = interpreter->tensor(tensor_index); - printf("Tensor %3d %10s %15s %10zu bytes (%4.1f MB) ", tensor_index, - TensorTypeName(tensor->type), AllocTypeName(tensor->allocation_type), - tensor->bytes, float(tensor->bytes) / float(1 << 20)); + printf("Tensor %3d %-20s %10s %15s %10zu bytes (%4.1f MB) ", tensor_index, + tensor->name, TensorTypeName(tensor->type), + AllocTypeName(tensor->allocation_type), tensor->bytes, + (static_cast(tensor->bytes) / (1 << 20))); PrintTfLiteIntVector(tensor->dims); - printf("\n"); } - + printf("\n"); for (int node_index = 0; node_index < interpreter->nodes_size(); node_index++) { const std::pair* node_and_reg = @@ -106,7 +106,4 @@ void PrintInterpreterState(Interpreter* interpreter) { } } -// Prints a dump of what tensors and what nodes are in the interpreter. -TfLiteStatus ValidateInterpreterState(const Interpreter* interpreter); - } // namespace tflite diff --git a/tensorflow/contrib/lite/optional_debug_tools.h b/tensorflow/contrib/lite/optional_debug_tools.h index 1b6998cda3..7fb4b8d8b7 100644 --- a/tensorflow/contrib/lite/optional_debug_tools.h +++ b/tensorflow/contrib/lite/optional_debug_tools.h @@ -24,9 +24,6 @@ namespace tflite { // Prints a dump of what tensors and what nodes are in the interpreter. void PrintInterpreterState(Interpreter* interpreter); -// Prints a dump of what tensors and what nodes are in the interpreter. -TfLiteStatus ValidateInterpreterState(const Interpreter* interpreter); - } // namespace tflite #endif // TENSORFLOW_CONTRIB_LITE_DEBUG_TOOLS_H_ -- GitLab From a14de341d069387ff8c8a98ff73bf1e5782a5cae Mon Sep 17 00:00:00 2001 From: Jingyue Wu Date: Tue, 19 Jun 2018 09:42:05 -0700 Subject: [PATCH 660/816] Automated g4 rollback of changelist 201069367 PiperOrigin-RevId: 201190626 --- tensorflow/core/grappler/op_types.cc | 3 +- .../optimizers/arithmetic_optimizer.cc | 45 ++++++++++--------- .../optimizers/arithmetic_optimizer_test.cc | 26 ++++++++--- 3 files changed, 44 insertions(+), 30 deletions(-) diff --git a/tensorflow/core/grappler/op_types.cc b/tensorflow/core/grappler/op_types.cc index bdeb5c66fc..b4ddd61c29 100644 --- a/tensorflow/core/grappler/op_types.cc +++ b/tensorflow/core/grappler/op_types.cc @@ -629,8 +629,7 @@ bool HasOpDef(const NodeDef& node) { } bool IsIdempotent(const NodeDef& node) { - return IsValueAndOrderAndShapePreserving(node) && IsFreeOfSideEffect(node) && - !ModifiesFrameInfo(node); + return IsValueAndOrderAndShapePreserving(node) && IsFreeOfSideEffect(node); } } // namespace grappler diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc index 0d69e0dde3..d518685216 100644 --- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc +++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc @@ -1083,6 +1083,14 @@ class RemoveIdentityTranspose : public ArithmeticOptimizerStage { Status TrySimplify(NodeDef* node, string* simplified_node_name) override { TF_RETURN_IF_ERROR(EnsureNodeIsSupported(node)); + NodeDef* tail = node; + // TODO(rmlarsen): Enable after debugging breakage in Bayesflow. + if (ctx().opt_level == RewriterConfig::AGGRESSIVE) { + tail = GetTailOfIdempotentChain(*tail, *ctx().node_map, + *ctx().nodes_to_preserve); + } + NodeDef* first_transpose; + TF_RETURN_IF_ERROR(GetInputNode(tail->input(0), &first_transpose)); NodeDef* node_perm; TF_RETURN_IF_ERROR(GetInputNode(node->input(1), &node_perm)); @@ -1091,21 +1099,7 @@ class RemoveIdentityTranspose : public ArithmeticOptimizerStage { } std::vector node_perm_values; TF_RETURN_IF_ERROR(GetPermutation(*node_perm, &node_perm_values)); - - // Remove simple identity transposes. - if (IsIdentityPermutation(node_perm_values)) { - *simplified_node_name = node->input(0); - return Status::OK(); - } - - NodeDef* tail = node; - tail = GetTailOfIdempotentChain(*tail, *ctx().node_map, - *ctx().nodes_to_preserve); - NodeDef* first_transpose; - TF_RETURN_IF_ERROR(GetInputNode(tail->input(0), &first_transpose)); - - if (first_transpose->op() == node->op() && - NumNonControlOutputs(*first_transpose, *ctx().node_map) == 1) { + if (first_transpose->op() == node->op()) { // Remove pairs of transposes that cancel each other. NodeDef* first_transpose_perm; TF_RETURN_IF_ERROR( @@ -1130,6 +1124,11 @@ class RemoveIdentityTranspose : public ArithmeticOptimizerStage { *simplified_node_name = node->input(0); } } + } else { + // Remove simple identity transposes. + if (IsIdentityPermutation(node_perm_values)) { + *simplified_node_name = node->input(0); + } } return Status::OK(); } @@ -1723,15 +1722,19 @@ class RemoveIdempotentStage : public ArithmeticOptimizerStage { ~RemoveIdempotentStage() override = default; bool IsSupported(const NodeDef* node) const override { - return node->input_size() == 1 && IsIdempotent(*node) && - !IsInPreserveSet(*node); + return IsIdempotent(*node) && !IsInPreserveSet(*node); } Status TrySimplify(NodeDef* node, string* simplified_node_name) override { NodeDef* input; TF_RETURN_IF_ERROR(GetInputNode(node->input(0), &input)); - if (input->op() == node->op() && input->device() == node->device()) { - *simplified_node_name = node->input(0); + auto root_scope_and_name = ParseNodeScopeAndName(node->name()); + const string new_name = OptimizedNodeName(root_scope_and_name); + if (input->op() == node->op() && input->device() == node->device() && + IsIdempotent(*input) && !ctx().node_map->NodeExists(new_name)) { + NodeDef* new_input_node = AddCopyNode(new_name, input); + ForwardControlDependencies(new_input_node, {node}); + *simplified_node_name = new_input_node->name(); } return Status::OK(); } @@ -2898,7 +2901,7 @@ Status ArithmeticOptimizer::SimplifyArithmeticOps(bool can_use_shapes) { pipeline.AddStage(ctx, ctx_ext); if (options_.minimize_broadcasts && can_use_shapes) pipeline.AddStage(ctx, ctx_ext); - if (options_.remove_identity_transpose) + if (options_.remove_identity_transpose && can_use_shapes) pipeline.AddStage(ctx, ctx_ext); if (options_.remove_involution) pipeline.AddStage(ctx, ctx_ext); @@ -2906,7 +2909,7 @@ Status ArithmeticOptimizer::SimplifyArithmeticOps(bool can_use_shapes) { pipeline.AddStage(ctx, ctx_ext); if (options_.remove_redundant_cast) pipeline.AddStage(ctx, ctx_ext); - if (options_.remove_redundant_reshape && can_use_shapes) + if (options_.remove_redundant_reshape) pipeline.AddStage(ctx, ctx_ext); if (options_.remove_negation) pipeline.AddStage(ctx, ctx_ext); diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc b/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc index d0e6b04679..e1d55cdf5f 100644 --- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc +++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc @@ -2976,8 +2976,12 @@ TEST_F(ArithmeticOptimizerTest, HoistCWiseUnaryIntoSplit) { TEST_F(ArithmeticOptimizerTest, RemoveIdempotent) { tensorflow::Scope s = tensorflow::Scope::NewRootScope(); Output a = ops::Const(s.WithOpName("a"), 3.14f, {32}); - Output sn1 = ops::Snapshot(s.WithOpName("sn1"), a); - Output sn2 = ops::Snapshot(s.WithOpName("sn2"), sn1); + Output ctrl1 = ops::Const(s.WithOpName("ctrl1"), 1, {}); + Output ctrl2 = ops::Const(s.WithOpName("ctrl2"), 2, {}); + Output sn1 = + ops::Snapshot(s.WithOpName("sn1").WithControlDependencies(ctrl1), a); + Output sn2 = + ops::Snapshot(s.WithOpName("sn2").WithControlDependencies(ctrl2), sn1); Output out1 = ops::Identity(s.WithOpName("out1"), sn2); Output id1 = ops::Identity(s.WithOpName("id1"), a); Output id2 = ops::Identity(s.WithOpName("id2"), id1); @@ -2993,24 +2997,32 @@ TEST_F(ArithmeticOptimizerTest, RemoveIdempotent) { EnableOnlyRemoveIdempotent(&optimizer); OptimizeTwice(&optimizer, &item, &output); - EXPECT_EQ(7, output.node_size()); + EXPECT_EQ(11, output.node_size()); int found = 0; for (const NodeDef& node : output.node()) { if (node.name() == "out1") { EXPECT_EQ(1, node.input_size()); - EXPECT_EQ("sn1", node.input(0)); + EXPECT_EQ("ArithmeticOptimizer/RemoveIdempotent_sn2", node.input(0)); + found++; + } else if (node.name() == "ArithmeticOptimizer/RemoveIdempotent_sn2") { + EXPECT_EQ(3, node.input_size()); + EXPECT_EQ("Snapshot", node.op()); + EXPECT_EQ("a", node.input(0)); + EXPECT_EQ("^ctrl1", node.input(1)); + EXPECT_EQ("^ctrl2", node.input(2)); found++; } else if (node.name() == "out2") { EXPECT_EQ(1, node.input_size()); - EXPECT_EQ("id1", node.input(0)); + EXPECT_EQ("ArithmeticOptimizer/RemoveIdempotent_id2", node.input(0)); found++; - } else if (node.name() == "sn1") { + } else if (node.name() == "ArithmeticOptimizer/RemoveIdempotent_id2") { + EXPECT_EQ("Identity", node.op()); EXPECT_EQ(1, node.input_size()); EXPECT_EQ("a", node.input(0)); found++; } } - EXPECT_EQ(3, found); + EXPECT_EQ(4, found); auto tensors = EvaluateNodes(output, item.fetch); EXPECT_EQ(tensors.size(), tensors_expected.size()); -- GitLab From c532c3f319c72074e6fb8cb10c6d05a3839bcc0a Mon Sep 17 00:00:00 2001 From: Peter Hawkins Date: Tue, 19 Jun 2018 09:47:13 -0700 Subject: [PATCH 661/816] [TF:XLA] Add a global mutex around XlaCompileOnDemandOp's call to Executable::Run() to work around a concurrency problem in XLA. PiperOrigin-RevId: 201191495 --- .../compiler/jit/xla_compile_on_demand_op.cc | 14 ++++++++++++-- 1 file changed, 12 insertions(+), 2 deletions(-) diff --git a/tensorflow/compiler/jit/xla_compile_on_demand_op.cc b/tensorflow/compiler/jit/xla_compile_on_demand_op.cc index b1943d3e1a..9beeb3517e 100644 --- a/tensorflow/compiler/jit/xla_compile_on_demand_op.cc +++ b/tensorflow/compiler/jit/xla_compile_on_demand_op.cc @@ -61,14 +61,24 @@ Status XlaCompileOnDemandOp::Run(OpKernelContext* ctx, ctx->op_device_context() ? ctx->op_device_context()->stream() : nullptr; TF_RET_CHECK(stream); - VLOG(2) << "Executing computation."; + VLOG(2) << "Executing computation: " << name(); + for (const xla::ShapedBuffer* arg : launch_context.arguments()) { + VLOG(2) << name() << ": " << *arg; + } xla::ExecutableRunOptions run_options; run_options.set_stream(stream); run_options.set_allocator(client->backend().memory_allocator()); run_options.set_intra_op_thread_pool(&ctx->eigen_cpu_device()); run_options.set_rng_seed(ctx->step_id()); - auto run_result = executable->Run(launch_context.arguments(), run_options); + xla::StatusOr run_result; + { + // TODO(b/110383871): fix concurrency problems and remove this mutex. + static mutex* mu = new mutex; + mutex_lock lock(*mu); + + run_result = executable->Run(launch_context.arguments(), run_options); + } TF_RETURN_IF_ERROR(run_result.status()); launch_context.PopulateOutputs(ctx, result, run_result.ConsumeValueOrDie()); -- GitLab From 5fc2bdd2d5f624a6bad9e83b992029e3799ab64e Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 19 Jun 2018 09:49:17 -0700 Subject: [PATCH 662/816] Implement TFLite sqrt/rsqrt unary operators PiperOrigin-RevId: 201191877 --- tensorflow/contrib/lite/build_def.bzl | 2 ++ tensorflow/contrib/lite/builtin_ops.h | 2 ++ .../lite/g3doc/tf_ops_compatibility.md | 22 +++++++++++++++++++ .../contrib/lite/kernels/elementwise.cc | 20 +++++++++++++++++ .../contrib/lite/kernels/elementwise_test.cc | 18 +++++++++++++++ tensorflow/contrib/lite/kernels/register.cc | 4 ++++ tensorflow/contrib/lite/model.cc | 2 ++ tensorflow/contrib/lite/nnapi_delegate.cc | 2 ++ tensorflow/contrib/lite/schema/schema.fbs | 2 ++ .../contrib/lite/schema/schema_generated.h | 12 +++++++--- .../contrib/lite/testing/generate_examples.py | 12 +++++++++- .../graph_transformations/identify_l2_pool.cc | 7 ++++++ .../contrib/lite/toco/tflite/operator.cc | 7 ++++-- .../contrib/lite/toco/tflite/operator_test.cc | 4 ++++ 14 files changed, 110 insertions(+), 6 deletions(-) diff --git a/tensorflow/contrib/lite/build_def.bzl b/tensorflow/contrib/lite/build_def.bzl index 62e35b90ee..828a516235 100644 --- a/tensorflow/contrib/lite/build_def.bzl +++ b/tensorflow/contrib/lite/build_def.bzl @@ -238,6 +238,7 @@ def generated_test_models(): "relu6", "reshape", "resize_bilinear", + "rsqrt", "sigmoid", "sin", "slice", @@ -246,6 +247,7 @@ def generated_test_models(): "space_to_depth", "sparse_to_dense", "split", + "sqrt", "squeeze", "strided_slice", "strided_slice_1d_exhaustive", diff --git a/tensorflow/contrib/lite/builtin_ops.h b/tensorflow/contrib/lite/builtin_ops.h index 4fedd871bd..3474df7812 100644 --- a/tensorflow/contrib/lite/builtin_ops.h +++ b/tensorflow/contrib/lite/builtin_ops.h @@ -100,6 +100,8 @@ typedef enum { kTfLiteBuiltinNotEqual = 72, kTfLiteBuiltinLog = 73, kTfLiteBuiltinSum = 74, + kTfLiteBuiltinSqrt = 75, + kTfLiteBuiltinRsqrt = 76, } TfLiteBuiltinOperator; #ifdef __cplusplus diff --git a/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md b/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md index 965273f0f0..cf672d2f0d 100644 --- a/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md +++ b/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md @@ -584,6 +584,17 @@ Options { } ``` +**RSQRT** + +``` +Inputs { + 0: a tensor +} +Outputs { + 0: result of computing element-wise reciprocal square root of the input tensor +} +``` + **SLICE** ``` @@ -670,6 +681,17 @@ Options { } ``` +**SQRT** + +``` +Inputs { + 0: a tensor +} +Outputs { + 0: result of computing element-wise square root of the input tensor +} +``` + **SQUEEZE** ``` diff --git a/tensorflow/contrib/lite/kernels/elementwise.cc b/tensorflow/contrib/lite/kernels/elementwise.cc index 98c21ce9d3..59bab3c4ec 100644 --- a/tensorflow/contrib/lite/kernels/elementwise.cc +++ b/tensorflow/contrib/lite/kernels/elementwise.cc @@ -64,6 +64,14 @@ TfLiteStatus LogEval(TfLiteContext* context, TfLiteNode* node) { return Eval(context, node, std::log); } +TfLiteStatus SqrtEval(TfLiteContext* context, TfLiteNode* node) { + return Eval(context, node, std::sqrt); +} + +TfLiteStatus RsqrtEval(TfLiteContext* context, TfLiteNode* node) { + return Eval(context, node, [](float f) { return 1.f / std::sqrt(f); }); +} + } // namespace elementwise TfLiteRegistration* Register_SIN() { @@ -78,6 +86,18 @@ TfLiteRegistration* Register_LOG() { return &r; } +TfLiteRegistration* Register_SQRT() { + static TfLiteRegistration r = {nullptr, nullptr, elementwise::GenericPrepare, + elementwise::SqrtEval}; + return &r; +} + +TfLiteRegistration* Register_RSQRT() { + static TfLiteRegistration r = {nullptr, nullptr, elementwise::GenericPrepare, + elementwise::RsqrtEval}; + return &r; +} + } // namespace builtin } // namespace ops } // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/elementwise_test.cc b/tensorflow/contrib/lite/kernels/elementwise_test.cc index 10e88d5a31..ce4c602ee5 100644 --- a/tensorflow/contrib/lite/kernels/elementwise_test.cc +++ b/tensorflow/contrib/lite/kernels/elementwise_test.cc @@ -60,6 +60,24 @@ TEST(ElementWise, Log) { EXPECT_THAT(m.GetTensorShape(m.output()), ElementsAreArray({1, 1, 4, 1})); } +TEST(ElementWise, Sqrt) { + ElementWiseOpModel m(BuiltinOperator_SQRT, {1, 1, 4, 1}); + m.PopulateTensor(m.input(), {0, 1, 2, 4}); + m.Invoke(); + EXPECT_THAT(m.ExtractVector(m.output()), + ElementsAreArray(ArrayFloatNear({0, 1, 1.41421, 2}))); + EXPECT_THAT(m.GetTensorShape(m.output()), ElementsAreArray({1, 1, 4, 1})); +} + +TEST(ElementWise, Rsqrt) { + ElementWiseOpModel m(BuiltinOperator_RSQRT, {1, 1, 4, 1}); + m.PopulateTensor(m.input(), {1, 2, 4, 9}); + m.Invoke(); + EXPECT_THAT(m.ExtractVector(m.output()), + ElementsAreArray(ArrayFloatNear({1, 0.7071, 0.5, 0.33333}))); + EXPECT_THAT(m.GetTensorShape(m.output()), ElementsAreArray({1, 1, 4, 1})); +} + } // namespace } // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/register.cc b/tensorflow/contrib/lite/kernels/register.cc index b893e40fe3..07a7ee9115 100644 --- a/tensorflow/contrib/lite/kernels/register.cc +++ b/tensorflow/contrib/lite/kernels/register.cc @@ -98,6 +98,8 @@ TfLiteRegistration* Register_EXPAND_DIMS(); TfLiteRegistration* Register_SPARSE_TO_DENSE(); TfLiteRegistration* Register_EQUAL(); TfLiteRegistration* Register_NOT_EQUAL(); +TfLiteRegistration* Register_SQRT(); +TfLiteRegistration* Register_RSQRT(); BuiltinOpResolver::BuiltinOpResolver() { AddBuiltin(BuiltinOperator_RELU, Register_RELU()); @@ -177,6 +179,8 @@ BuiltinOpResolver::BuiltinOpResolver() { AddBuiltin(BuiltinOperator_SPARSE_TO_DENSE, Register_SPARSE_TO_DENSE()); AddBuiltin(BuiltinOperator_EQUAL, Register_EQUAL()); AddBuiltin(BuiltinOperator_NOT_EQUAL, Register_NOT_EQUAL()); + AddBuiltin(BuiltinOperator_SQRT, Register_SQRT()); + AddBuiltin(BuiltinOperator_RSQRT, Register_RSQRT()); // TODO(andrewharp, ahentz): Move these somewhere more appropriate so that // custom ops aren't always included by default. diff --git a/tensorflow/contrib/lite/model.cc b/tensorflow/contrib/lite/model.cc index b9d100b7c9..1f8e796bc7 100644 --- a/tensorflow/contrib/lite/model.cc +++ b/tensorflow/contrib/lite/model.cc @@ -704,10 +704,12 @@ TfLiteStatus ParseOpData(const Operator* op, BuiltinOperator op_type, case BuiltinOperator_RELU: case BuiltinOperator_RELU6: case BuiltinOperator_RELU_N1_TO_1: + case BuiltinOperator_RSQRT: case BuiltinOperator_SELECT: case BuiltinOperator_SIN: case BuiltinOperator_SLICE: case BuiltinOperator_SPACE_TO_BATCH_ND: + case BuiltinOperator_SQRT: case BuiltinOperator_TANH: case BuiltinOperator_TILE: case BuiltinOperator_TOPK_V2: diff --git a/tensorflow/contrib/lite/nnapi_delegate.cc b/tensorflow/contrib/lite/nnapi_delegate.cc index 8d506f562f..1e012c89ae 100644 --- a/tensorflow/contrib/lite/nnapi_delegate.cc +++ b/tensorflow/contrib/lite/nnapi_delegate.cc @@ -501,6 +501,8 @@ void AddOpsAndParams(tflite::Interpreter* interpreter, case tflite::BuiltinOperator_EQUAL: case tflite::BuiltinOperator_NOT_EQUAL: case tflite::BuiltinOperator_SUM: + case tflite::BuiltinOperator_SQRT: + case tflite::BuiltinOperator_RSQRT: FATAL("Op code %d is currently not delegated to NNAPI", builtin); nn_op_type = -1; // set to invalid break; diff --git a/tensorflow/contrib/lite/schema/schema.fbs b/tensorflow/contrib/lite/schema/schema.fbs index 18cb7b9509..0b127e1c14 100644 --- a/tensorflow/contrib/lite/schema/schema.fbs +++ b/tensorflow/contrib/lite/schema/schema.fbs @@ -155,6 +155,8 @@ enum BuiltinOperator : byte { NOT_EQUAL = 72, LOG = 73, SUM=74, + SQRT = 75, + RSQRT = 76, } // Options for the builtin operators. diff --git a/tensorflow/contrib/lite/schema/schema_generated.h b/tensorflow/contrib/lite/schema/schema_generated.h index c6fa94e38f..2558625e2d 100755 --- a/tensorflow/contrib/lite/schema/schema_generated.h +++ b/tensorflow/contrib/lite/schema/schema_generated.h @@ -330,11 +330,13 @@ enum BuiltinOperator { BuiltinOperator_NOT_EQUAL = 72, BuiltinOperator_LOG = 73, BuiltinOperator_SUM = 74, + BuiltinOperator_SQRT = 75, + BuiltinOperator_RSQRT = 76, BuiltinOperator_MIN = BuiltinOperator_ADD, - BuiltinOperator_MAX = BuiltinOperator_SUM + BuiltinOperator_MAX = BuiltinOperator_RSQRT }; -inline BuiltinOperator (&EnumValuesBuiltinOperator())[74] { +inline BuiltinOperator (&EnumValuesBuiltinOperator())[76] { static BuiltinOperator values[] = { BuiltinOperator_ADD, BuiltinOperator_AVERAGE_POOL_2D, @@ -409,7 +411,9 @@ inline BuiltinOperator (&EnumValuesBuiltinOperator())[74] { BuiltinOperator_EQUAL, BuiltinOperator_NOT_EQUAL, BuiltinOperator_LOG, - BuiltinOperator_SUM + BuiltinOperator_SUM, + BuiltinOperator_SQRT, + BuiltinOperator_RSQRT }; return values; } @@ -491,6 +495,8 @@ inline const char **EnumNamesBuiltinOperator() { "NOT_EQUAL", "LOG", "SUM", + "SQRT", + "RSQRT", nullptr }; return names; diff --git a/tensorflow/contrib/lite/testing/generate_examples.py b/tensorflow/contrib/lite/testing/generate_examples.py index 92589686c8..53f1fce346 100644 --- a/tensorflow/contrib/lite/testing/generate_examples.py +++ b/tensorflow/contrib/lite/testing/generate_examples.py @@ -2437,7 +2437,7 @@ def _make_elementwise_tests(op): }] def build_graph(parameters): - """Build the sin op testing graph.""" + """Build the unary op testing graph.""" input_value = tf.placeholder( dtype=parameters["input_dtype"], name="input1", @@ -2466,6 +2466,16 @@ def make_log_tests(zip_path): return _make_elementwise_tests(tf.log)(zip_path) +def make_sqrt_tests(zip_path): + """Make a set of tests to do sqrt.""" + return _make_elementwise_tests(tf.sqrt)(zip_path) + + +def make_rsqrt_tests(zip_path): + """Make a set of tests to do 1/sqrt.""" + return _make_elementwise_tests(tf.rsqrt)(zip_path) + + def make_where_tests(zip_path): """Make a set of tests to do where.""" diff --git a/tensorflow/contrib/lite/toco/graph_transformations/identify_l2_pool.cc b/tensorflow/contrib/lite/toco/graph_transformations/identify_l2_pool.cc index e4d52476c6..f69400b82f 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/identify_l2_pool.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/identify_l2_pool.cc @@ -52,6 +52,13 @@ bool IdentifyL2Pool::Run(Model* model, std::size_t op_index) { const Operator* square_op; Operator* prev_to_sqrt_op = GetOpWithOutput(*model, sqrt_op->inputs[0]); + if (prev_to_sqrt_op == nullptr) { + AddMessageF( + "Giving up trying to identify L2Pool subgraph: " + "expected AveragePool op, but Sqrt op has no preceding op"); + return false; + } + if (prev_to_sqrt_op->type != OperatorType::kAveragePool) { AddMessageF( "Giving up trying to identify L2Pool subgraph: " diff --git a/tensorflow/contrib/lite/toco/tflite/operator.cc b/tensorflow/contrib/lite/toco/tflite/operator.cc index c5eafa2281..669fb9fa08 100644 --- a/tensorflow/contrib/lite/toco/tflite/operator.cc +++ b/tensorflow/contrib/lite/toco/tflite/operator.cc @@ -1117,8 +1117,7 @@ std::vector> BuildOperatorList() { // attributes. ops.emplace_back( new SimpleOperator("ADDN", OperatorType::kAddN)); - ops.emplace_back(new SimpleOperator( - "RSQRT", OperatorType::kTensorFlowRsqrt)); + // Simple Operators. ops.emplace_back(new SimpleOperator( "DEQUANTIZE", OperatorType::kDequantize)); @@ -1163,6 +1162,10 @@ std::vector> BuildOperatorList() { // Element-wise operator ops.emplace_back(new SimpleOperator("SIN", OperatorType::kSin)); ops.emplace_back(new SimpleOperator("LOG", OperatorType::kLog)); + ops.emplace_back(new SimpleOperator( + "SQRT", OperatorType::kTensorFlowSqrt)); + ops.emplace_back(new SimpleOperator( + "RSQRT", OperatorType::kTensorFlowRsqrt)); return ops; } diff --git a/tensorflow/contrib/lite/toco/tflite/operator_test.cc b/tensorflow/contrib/lite/toco/tflite/operator_test.cc index 03bb20b320..a7136af2e2 100644 --- a/tensorflow/contrib/lite/toco/tflite/operator_test.cc +++ b/tensorflow/contrib/lite/toco/tflite/operator_test.cc @@ -126,6 +126,10 @@ TEST_F(OperatorTest, SimpleOperators) { CheckSimpleOperator( "NOT_EQUAL", OperatorType::kTensorFlowNotEqual); CheckSimpleOperator("LOG", OperatorType::kLog); + CheckSimpleOperator("SQRT", + OperatorType::kTensorFlowSqrt); + CheckSimpleOperator("RSQRT", + OperatorType::kTensorFlowRsqrt); } TEST_F(OperatorTest, BuiltinAdd) { -- GitLab From 7f449920f8910561a4e57cc35b96fb7faf08ef98 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 19 Jun 2018 10:02:11 -0700 Subject: [PATCH 663/816] Refresh allocations in the presence of dynamic tensors PiperOrigin-RevId: 201193941 --- tensorflow/contrib/lite/BUILD | 1 + tensorflow/contrib/lite/interpreter.cc | 10 ++++ tensorflow/contrib/lite/interpreter.h | 5 ++ tensorflow/contrib/lite/interpreter_test.cc | 59 +++++++++++++++++++++ 4 files changed, 75 insertions(+) diff --git a/tensorflow/contrib/lite/BUILD b/tensorflow/contrib/lite/BUILD index 9c804d2785..8c17c65fcc 100644 --- a/tensorflow/contrib/lite/BUILD +++ b/tensorflow/contrib/lite/BUILD @@ -184,6 +184,7 @@ cc_test( deps = [ ":framework", ":string_util", + "//tensorflow/contrib/lite/kernels:builtin_ops", "//tensorflow/contrib/lite/kernels:kernel_util", "//tensorflow/contrib/lite/kernels/internal:tensor_utils", "//tensorflow/contrib/lite/schema:schema_fbs", diff --git a/tensorflow/contrib/lite/interpreter.cc b/tensorflow/contrib/lite/interpreter.cc index 3287f9c4fd..57b2c0f32b 100644 --- a/tensorflow/contrib/lite/interpreter.cc +++ b/tensorflow/contrib/lite/interpreter.cc @@ -605,9 +605,17 @@ TfLiteStatus Interpreter::Invoke() { } EnsureTensorsVectorCapacity(); + tensor_resized_since_op_invoke_ = false; if (OpInvoke(registration, &node) == kTfLiteError) { status = kTfLiteError; } + + // Force execution prep for downstream ops if the latest op triggered the + // resize of a dynamic tensor. + if (tensor_resized_since_op_invoke_ && + HasDynamicTensor(context_, node.outputs)) { + next_execution_plan_index_to_prepare_ = execution_plan_index + 1; + } } if (!allow_buffer_handle_output_) { @@ -783,6 +791,8 @@ TfLiteStatus Interpreter::ResizeTensorImpl(TfLiteTensor* tensor, if (tensor->allocation_type == kTfLiteArenaRw || tensor->allocation_type == kTfLiteDynamic || tensor->allocation_type == kTfLiteArenaRwPersistent) { + tensor_resized_since_op_invoke_ |= + TfLiteIntArrayEqual(tensor->dims, new_size) == 0; if (tensor->type != kTfLiteString) { size_t bytesRequired; TfLiteStatus status = BytesRequired(tensor->type, new_size->data, diff --git a/tensorflow/contrib/lite/interpreter.h b/tensorflow/contrib/lite/interpreter.h index 37961cd1dc..436c1007af 100644 --- a/tensorflow/contrib/lite/interpreter.h +++ b/tensorflow/contrib/lite/interpreter.h @@ -589,6 +589,11 @@ class Interpreter { bool allow_buffer_handle_output_ = false; + // Tracking bit for whether a tensor was resized in the course of an op + // invocation. This is a useful hint to ensure that dynamic tensor outputs + // trigger downstream reallocation after op invocation. + bool tensor_resized_since_op_invoke_ = false; + // Profiler for this interpreter instance. profiling::Profiler* profiler_; }; diff --git a/tensorflow/contrib/lite/interpreter_test.cc b/tensorflow/contrib/lite/interpreter_test.cc index b977cb089c..21cdf87d1e 100644 --- a/tensorflow/contrib/lite/interpreter_test.cc +++ b/tensorflow/contrib/lite/interpreter_test.cc @@ -23,6 +23,12 @@ limitations under the License. #include "tensorflow/contrib/lite/testing/util.h" namespace tflite { +namespace ops { +namespace builtin { +TfLiteRegistration* Register_PADV2(); +TfLiteRegistration* Register_NEG(); +} // namespace builtin +} // namespace ops namespace { // Make an interpreter that has no tensors and no nodes @@ -615,6 +621,59 @@ TEST(BasicInterpreter, TestUnsupportedDelegateFunctions) { EXPECT_EQ(interpreter.AllocateTensors(), kTfLiteError); } +TEST(BasicInterpreter, DynamicTensorsResizeDescendants) { + // Assemble a graph with a node that has dynamically sized output (via the + // pad op), followed by a node with a standard element-wise op (negate). + Interpreter interpreter; + interpreter.AddTensors(4); + interpreter.SetInputs({0, 1}); + interpreter.SetOutputs({3}); + TfLiteQuantizationParams quant; + interpreter.SetTensorParametersReadWrite(0, kTfLiteFloat32, "", {2, 2, 1, 1}, + quant); + interpreter.SetTensorParametersReadWrite(1, kTfLiteInt32, "", {4, 2}, quant); + interpreter.SetTensorParametersReadWrite(2, kTfLiteFloat32, "", {}, quant); + interpreter.SetTensorParametersReadWrite(3, kTfLiteFloat32, "", {}, quant); + + TfLiteRegistration* pad_op = tflite::ops::builtin::Register_PADV2(); + TfLiteRegistration* neg_op = tflite::ops::builtin::Register_NEG(); + interpreter.AddNodeWithParameters({0, 1}, {2}, nullptr, 0, nullptr, pad_op); + interpreter.AddNodeWithParameters({2}, {3}, nullptr, 0, nullptr, neg_op); + ASSERT_EQ(interpreter.AllocateTensors(), kTfLiteOk); + + // Configure [[2,2],[4,4]] padding and execute the graph. + interpreter.typed_tensor(1)[0] = 2; + interpreter.typed_tensor(1)[1] = 2; + interpreter.typed_tensor(1)[2] = 2; + interpreter.typed_tensor(1)[3] = 2; + interpreter.typed_tensor(1)[4] = 0; + interpreter.typed_tensor(1)[5] = 0; + interpreter.typed_tensor(1)[6] = 0; + interpreter.typed_tensor(1)[7] = 0; + ASSERT_EQ(interpreter.Invoke(), kTfLiteOk); + + // Both the output and intermediate tensor sizes should reflect the output + // from the dynamic pad operation. + ASSERT_EQ(interpreter.tensor(2)->bytes, sizeof(float) * 6 * 6); + ASSERT_EQ(interpreter.tensor(3)->bytes, sizeof(float) * 6 * 6); + + // Now configure [[4,4],[6,6]] padding and execute the graph. + interpreter.typed_tensor(1)[0] = 4; + interpreter.typed_tensor(1)[1] = 4; + interpreter.typed_tensor(1)[2] = 6; + interpreter.typed_tensor(1)[3] = 6; + interpreter.typed_tensor(1)[4] = 0; + interpreter.typed_tensor(1)[5] = 0; + interpreter.typed_tensor(1)[6] = 0; + interpreter.typed_tensor(1)[7] = 0; + ASSERT_EQ(interpreter.Invoke(), kTfLiteOk); + + // Again, the output and intermediate tensor sizes should reflect the *new* + // resize from the latest pad operation. + ASSERT_EQ(interpreter.tensor(2)->bytes, sizeof(float) * 10 * 14); + ASSERT_EQ(interpreter.tensor(3)->bytes, sizeof(float) * 10 * 14); +} + TEST(InterpreterTensorsCapacityTest, TestWithinHeadroom) { Interpreter interpreter; ASSERT_EQ(interpreter.AddTensors(Interpreter::kTensorsReservedCapacity), -- GitLab From f1a08078db57de510f266d0d381220071aee2065 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 19 Jun 2018 10:04:40 -0700 Subject: [PATCH 664/816] Apply runtime shapes to pooling and activation kernels. PiperOrigin-RevId: 201194552 --- .../contrib/lite/kernels/activations.cc | 24 +- .../internal/logsoftmax_quantized_test.cc | 64 +-- .../internal/optimized/legacy_optimized_ops.h | 282 ++++++++++++- .../internal/optimized/optimized_ops.h | 390 +++++++----------- .../internal/reference/legacy_reference_ops.h | 290 ++++++++++++- .../internal/reference/reference_ops.h | 354 ++++++---------- .../internal/softmax_quantized_test.cc | 62 +-- .../contrib/lite/kernels/internal/types.h | 48 ++- .../contrib/lite/kernels/log_softmax_test.cc | 7 +- tensorflow/contrib/lite/kernels/pooling.cc | 57 +-- .../contrib/lite/kernels/softmax_test.cc | 14 +- 11 files changed, 1001 insertions(+), 591 deletions(-) diff --git a/tensorflow/contrib/lite/kernels/activations.cc b/tensorflow/contrib/lite/kernels/activations.cc index add36b46c0..d03fa42c92 100644 --- a/tensorflow/contrib/lite/kernels/activations.cc +++ b/tensorflow/contrib/lite/kernels/activations.cc @@ -251,11 +251,11 @@ TfLiteStatus TanhEval(TfLiteContext* context, TfLiteNode* node) { return kTfLiteOk; } break; case kTfLiteUInt8: { - optimized_ops::Tanh(GetTensorData(input), GetTensorDims(input), + optimized_ops::Tanh(GetTensorData(input), GetTensorShape(input), input->params.zero_point, data->input_range_radius, data->input_multiplier, data->input_left_shift, GetTensorData(output), - GetTensorDims(output)); + GetTensorShape(output)); return kTfLiteOk; } break; default: @@ -282,10 +282,10 @@ TfLiteStatus SigmoidEval(TfLiteContext* context, TfLiteNode* node) { } case kTfLiteUInt8: { optimized_ops::Logistic( - GetTensorData(input), GetTensorDims(input), + GetTensorData(input), GetTensorShape(input), input->params.zero_point, data->input_range_radius, data->input_multiplier, data->input_left_shift, - GetTensorData(output), GetTensorDims(output)); + GetTensorData(output), GetTensorShape(output)); break; } default: @@ -341,26 +341,26 @@ void Softmax2DQuantized(const TfLiteTensor* input, TfLiteTensor* output, const int batch_size = input->dims->data[0]; const int input_size = input->dims->data[1]; optimized_ops::Softmax(GetTensorData(input), - GetTensorDims({batch_size, 1, 1, input_size}), + GetTensorShape({batch_size, 1, 1, input_size}), data->input_multiplier, data->input_left_shift, data->diff_min, GetTensorData(output), - GetTensorDims({batch_size, 1, 1, input_size})); + GetTensorShape({batch_size, 1, 1, input_size})); } // Takes a 4D tensor and perform softmax along the forth dimension. void Softmax4DFloat(const TfLiteTensor* input, TfLiteTensor* output, TfLiteSoftmaxParams* params) { - optimized_ops::Softmax(GetTensorData(input), GetTensorDims(input), + optimized_ops::Softmax(GetTensorData(input), GetTensorShape(input), params->beta, GetTensorData(output), - GetTensorDims(output)); + GetTensorShape(output)); } void Softmax4DQuantized(const TfLiteTensor* input, TfLiteTensor* output, TfLiteSoftmaxParams* params, OpData* data) { - optimized_ops::Softmax(GetTensorData(input), GetTensorDims(input), + optimized_ops::Softmax(GetTensorData(input), GetTensorShape(input), data->input_multiplier, data->input_left_shift, data->diff_min, GetTensorData(output), - GetTensorDims(output)); + GetTensorShape(output)); } TfLiteStatus SoftmaxEval(TfLiteContext* context, TfLiteNode* node) { @@ -415,8 +415,8 @@ TfLiteStatus LogSoftmaxEval(TfLiteContext* context, TfLiteNode* node) { switch (input->type) { case kTfLiteFloat32: optimized_ops::LogSoftmax( - GetTensorData(input), GetTensorDims(input), - GetTensorData(output), GetTensorDims(output)); + GetTensorData(input), GetTensorShape(input), + GetTensorData(output), GetTensorShape(output)); return kTfLiteOk; default: context->ReportError(context, "Only float32 supported currently., got %d", diff --git a/tensorflow/contrib/lite/kernels/internal/logsoftmax_quantized_test.cc b/tensorflow/contrib/lite/kernels/internal/logsoftmax_quantized_test.cc index e786f785ab..d2f1103e14 100644 --- a/tensorflow/contrib/lite/kernels/internal/logsoftmax_quantized_test.cc +++ b/tensorflow/contrib/lite/kernels/internal/logsoftmax_quantized_test.cc @@ -32,19 +32,21 @@ namespace tflite { namespace { void RunLogSoftmaxFloatReference(const uint8* input_data, - const Dims<4>& dims_common, int32 input_offset, - const double input_scale, int stride, - float beta, uint8* reference_output_data) { - const int ref_buffer_size = RequiredBufferSizeForDims(dims_common); + const RuntimeShape& shape_common, + int32 input_offset, const double input_scale, + int stride, float beta, + uint8* reference_output_data) { + const int ref_buffer_size = shape_common.FlatSize(); std::vector reference_dequant_data(ref_buffer_size); std::vector reference_output_float_data(ref_buffer_size); // Reference data generated via Dequant of input into float, and then applying // float LogSoftmax. - reference_ops::Dequantize(input_data, dims_common, input_offset, input_scale, - reference_dequant_data.data(), dims_common); - optimized_ops::LogSoftmax(reference_dequant_data.data(), dims_common, - reference_output_float_data.data(), dims_common); + reference_ops::Dequantize( + input_data, ToRuntimeDims(shape_common), input_offset, input_scale, + reference_dequant_data.data(), ToRuntimeDims(shape_common)); + optimized_ops::LogSoftmax(reference_dequant_data.data(), shape_common, + reference_output_float_data.data(), shape_common); // Work with quantized scaling for LogSoftmax, under which 255 represents 0, // and -16 gets nudged up to 0. for (int i = 0; i < ref_buffer_size; i++) { @@ -55,9 +57,9 @@ void RunLogSoftmaxFloatReference(const uint8* input_data, } void CheckOutputData(const uint8* test_output, const uint8* reference_output, - const Dims<4>& dims_common, const string& check_label, - bool be_exacting) { - const int buffer_size = RequiredBufferSizeForDims(dims_common); + const RuntimeShape& shape_common, + const string& check_label, bool be_exacting) { + const int buffer_size = shape_common.FlatSize(); // While calculating some metrics in floating point, we work with quantized // scaling. std::vector diff(buffer_size); @@ -99,15 +101,15 @@ void CheckOutputData(const uint8* test_output, const uint8* reference_output, // Runs the LogSoftmax and compares against the float reference implementation // and the quantized reference implementation. -void RunOneLogSoftmaxTest(const uint8* input_data, const Dims<4>& dims_common, - int32 input_offset, const double input_scale, - int stride, float beta) { - const int buffer_size = RequiredBufferSizeForDims(dims_common); +void RunOneLogSoftmaxTest(const uint8* input_data, + const RuntimeShape& shape_common, int32 input_offset, + const double input_scale, int stride, float beta) { + const int buffer_size = shape_common.FlatSize(); std::vector optimized_logsoftmax_output(buffer_size); std::vector reference_float_logsoftmax_output(buffer_size); std::vector reference_quant_logsoftmax_output(buffer_size); - RunLogSoftmaxFloatReference(input_data, dims_common, input_offset, + RunLogSoftmaxFloatReference(input_data, shape_common, input_offset, input_scale, stride, beta, reference_float_logsoftmax_output.data()); @@ -126,23 +128,23 @@ void RunOneLogSoftmaxTest(const uint8* input_data, const Dims<4>& dims_common, const int diff_min = -tflite::CalculateInputRadius(kScaledDiffIntegerBits, input_beta_left_shift); - optimized_ops::LogSoftmax(input_data, dims_common, input_beta_multiplier, + optimized_ops::LogSoftmax(input_data, shape_common, input_beta_multiplier, input_beta_left_shift, reverse_scaling_divisor, reverse_scaling_right_shift, diff_min, - optimized_logsoftmax_output.data(), dims_common); + optimized_logsoftmax_output.data(), shape_common); reference_ops::LogSoftmax( - input_data, dims_common, input_beta_multiplier, input_beta_left_shift, + input_data, shape_common, input_beta_multiplier, input_beta_left_shift, reverse_scaling_divisor, reverse_scaling_right_shift, diff_min, - reference_quant_logsoftmax_output.data(), dims_common); + reference_quant_logsoftmax_output.data(), shape_common); CheckOutputData(optimized_logsoftmax_output.data(), - reference_float_logsoftmax_output.data(), dims_common, + reference_float_logsoftmax_output.data(), shape_common, "Optimized vs float reference", false); CheckOutputData(optimized_logsoftmax_output.data(), - reference_quant_logsoftmax_output.data(), dims_common, + reference_quant_logsoftmax_output.data(), shape_common, "Optimized vs quant reference", true); CheckOutputData(reference_quant_logsoftmax_output.data(), - reference_float_logsoftmax_output.data(), dims_common, + reference_float_logsoftmax_output.data(), shape_common, "Quant reference vs float reference", false); } @@ -165,13 +167,13 @@ bool TryOneUniformLogSoftmax() { const int32 input_offset = UniformRandomInt(-256, 0); static constexpr float beta = 1.0f; - Dims<4> dims_common = - MakeDimsForInference(input_depth, input_width, input_height, batch); - const int buffer_size = RequiredBufferSizeForDims(dims_common); + auto shape_common = + RuntimeShape({batch, input_height, input_width, input_depth}); + const int buffer_size = shape_common.FlatSize(); std::vector input_data(buffer_size); FillRandom(&input_data); - RunOneLogSoftmaxTest(input_data.data(), dims_common, input_offset, + RunOneLogSoftmaxTest(input_data.data(), shape_common, input_offset, input_scale, stride, beta); return true; } @@ -203,14 +205,14 @@ bool TryOneSkyscraperLogSoftmax(bool small_depth) { const int middle_min = UniformRandomInt(0, 255); const int sides_max = UniformRandomInt(0, middle_min); - Dims<4> dims_common = - MakeDimsForInference(input_depth, input_width, input_height, batch); - const int buffer_size = RequiredBufferSizeForDims(dims_common); + auto shape_common = + RuntimeShape({batch, input_height, input_width, input_depth}); + const int buffer_size = shape_common.FlatSize(); std::vector input_data(buffer_size); FillRandomSkyscraper(&input_data, input_depth, middle_proportion, middle_min, sides_max); - RunOneLogSoftmaxTest(input_data.data(), dims_common, input_offset, + RunOneLogSoftmaxTest(input_data.data(), shape_common, input_offset, input_scale, stride, beta); return true; } diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/legacy_optimized_ops.h b/tensorflow/contrib/lite/kernels/internal/optimized/legacy_optimized_ops.h index c0dda4acf1..7816752132 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/legacy_optimized_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/legacy_optimized_ops.h @@ -26,6 +26,10 @@ limitations under the License. namespace tflite { namespace optimized_ops { +// Unoptimized reference ops: +using reference_ops::Relu1; +using reference_ops::Relu6; + inline RuntimeShape DimsToShape(const tflite::Dims<4>& dims) { return RuntimeShape( {dims.sizes[3], dims.sizes[2], dims.sizes[1], dims.sizes[0]}); @@ -34,15 +38,285 @@ inline RuntimeShape DimsToShape(const tflite::Dims<4>& dims) { template void L2Normalization(const float* input_data, const Dims<4>& input_dims, float* output_data, const Dims<4>& output_dims) { - return L2Normalization(input_data, DimsToShape(input_dims), output_data, - DimsToShape(output_dims)); + L2Normalization(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); } inline void L2Normalization(const uint8* input_data, const Dims<4>& input_dims, int32 input_zero_point, uint8* output_data, const Dims<4>& output_dims) { - return L2Normalization(input_data, DimsToShape(input_dims), input_zero_point, - output_data, DimsToShape(output_dims)); + L2Normalization(input_data, DimsToShape(input_dims), input_zero_point, + output_data, DimsToShape(output_dims)); +} + +inline void Relu(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { + Relu(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); +} + +inline void AveragePool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int kwidth, int kheight, + float output_activation_min, + float output_activation_max, float* output_data, + const Dims<4>& output_dims) { + AveragePool(input_data, DimsToShape(input_dims), stride_width, stride_height, + pad_width, pad_height, kwidth, kheight, output_activation_min, + output_activation_max, output_data, DimsToShape(output_dims)); +} + +// legacy, for compatibility with old checked-in code +template +void AveragePool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int kwidth, int kheight, float* output_data, + const Dims<4>& output_dims) { + float output_activation_min, output_activation_max; + GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); + + AveragePool(input_data, input_dims, stride_width, stride_height, pad_width, + pad_height, kwidth, kheight, output_activation_min, + output_activation_max, output_data, output_dims); +} + +// legacy, for compatibility with old checked-in code +template +void AveragePool(const float* input_data, const Dims<4>& input_dims, int stride, + int pad_width, int pad_height, int filter_width, + int filter_height, float* output_data, + const Dims<4>& output_dims) { + AveragePool(input_data, input_dims, stride, stride, pad_width, pad_height, + filter_width, filter_height, output_data, output_dims); +} + +inline void AveragePool(const uint8* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int filter_width, int filter_height, + int32 output_activation_min, + int32 output_activation_max, uint8* output_data, + const Dims<4>& output_dims) { + AveragePool(input_data, DimsToShape(input_dims), stride_width, stride_height, + pad_width, pad_height, filter_width, filter_height, + output_activation_min, output_activation_max, output_data, + DimsToShape(output_dims)); +} + +// legacy, for compatibility with old checked-in code +template +void AveragePool(const uint8* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int filter_width, int filter_height, + int32 output_activation_min, int32 output_activation_max, + uint8* output_data, const Dims<4>& output_dims) { + static_assert(Ac == FusedActivationFunctionType::kNone || + Ac == FusedActivationFunctionType::kRelu || + Ac == FusedActivationFunctionType::kRelu6 || + Ac == FusedActivationFunctionType::kRelu1, + ""); + if (Ac == FusedActivationFunctionType::kNone) { + TFLITE_DCHECK_EQ(output_activation_min, 0); + TFLITE_DCHECK_EQ(output_activation_max, 255); + } + AveragePool(input_data, input_dims, stride_width, stride_height, pad_width, + pad_height, filter_width, filter_height, output_activation_min, + output_activation_max, output_data, output_dims); +} + +// legacy, for compatibility with old checked-in code +template +void AveragePool(const uint8* input_data, const Dims<4>& input_dims, int stride, + int pad_width, int pad_height, int filter_width, + int filter_height, int32 output_activation_min, + int32 output_activation_max, uint8* output_data, + const Dims<4>& output_dims) { + AveragePool(input_data, input_dims, stride, stride, pad_width, pad_height, + filter_width, filter_height, output_activation_min, + output_activation_max, output_data, output_dims); +} + +inline void MaxPool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int kwidth, int kheight, + float output_activation_min, float output_activation_max, + float* output_data, const Dims<4>& output_dims) { + MaxPool(input_data, DimsToShape(input_dims), stride_width, stride_height, + pad_width, pad_height, kwidth, kheight, output_activation_min, + output_activation_max, output_data, DimsToShape(output_dims)); +} + +// legacy, for compatibility with old checked-in code +template +void MaxPool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, int pad_height, + int kwidth, int kheight, float* output_data, + const Dims<4>& output_dims) { + float output_activation_min, output_activation_max; + GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); + MaxPool(input_data, input_dims, stride_width, stride_height, pad_width, + pad_height, kwidth, kheight, output_activation_min, + output_activation_max, output_data, output_dims); +} + +// legacy, for compatibility with old checked-in code +template +void MaxPool(const float* input_data, const Dims<4>& input_dims, int stride, + int pad_width, int pad_height, int filter_width, int filter_height, + float* output_data, const Dims<4>& output_dims) { + MaxPool(input_data, input_dims, stride, stride, pad_width, pad_height, + filter_width, filter_height, output_data, output_dims); +} + +inline void MaxPool(const uint8* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int filter_width, int filter_height, + int32 output_activation_min, int32 output_activation_max, + uint8* output_data, const Dims<4>& output_dims) { + MaxPool(input_data, DimsToShape(input_dims), stride_width, stride_height, + pad_width, pad_height, filter_width, filter_height, + output_activation_min, output_activation_max, output_data, + DimsToShape(output_dims)); +} + +// legacy, for compatibility with old checked-in code +template +void MaxPool(const uint8* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, int pad_height, + int filter_width, int filter_height, int32 output_activation_min, + int32 output_activation_max, uint8* output_data, + const Dims<4>& output_dims) { + static_assert(Ac == FusedActivationFunctionType::kNone || + Ac == FusedActivationFunctionType::kRelu || + Ac == FusedActivationFunctionType::kRelu6 || + Ac == FusedActivationFunctionType::kRelu1, + ""); + if (Ac == FusedActivationFunctionType::kNone) { + TFLITE_DCHECK_EQ(output_activation_min, 0); + TFLITE_DCHECK_EQ(output_activation_max, 255); + } + MaxPool(input_data, input_dims, stride_width, stride_height, pad_width, + pad_height, filter_width, filter_height, output_activation_min, + output_activation_max, output_data, output_dims); +} + +// legacy, for compatibility with old checked-in code +template +void MaxPool(const uint8* input_data, const Dims<4>& input_dims, int stride, + int pad_width, int pad_height, int filter_width, int filter_height, + int32 output_activation_min, int32 output_activation_max, + uint8* output_data, const Dims<4>& output_dims) { + MaxPool(input_data, input_dims, stride, stride, pad_width, pad_height, + filter_width, filter_height, output_activation_min, + output_activation_max, output_data, output_dims); +} + +inline void L2Pool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int filter_width, int filter_height, + float output_activation_min, float output_activation_max, + float* output_data, const Dims<4>& output_dims) { + L2Pool(input_data, DimsToShape(input_dims), stride_width, stride_height, + pad_width, pad_height, filter_width, filter_height, + output_activation_min, output_activation_max, output_data, + DimsToShape(output_dims)); +} + +// legacy, for compatibility with old checked-in code +template +void L2Pool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, int pad_height, + int filter_width, int filter_height, float* output_data, + const Dims<4>& output_dims) { + float output_activation_min, output_activation_max; + GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); + L2Pool(input_data, input_dims, stride_width, stride_height, pad_width, + pad_height, filter_width, filter_height, output_activation_min, + output_activation_max, output_data, output_dims); +} + +// legacy, for compatibility with old checked-in code +template +void L2Pool(const float* input_data, const Dims<4>& input_dims, int stride, + int pad_width, int pad_height, int filter_width, int filter_height, + float* output_data, const Dims<4>& output_dims) { + L2Pool(input_data, input_dims, stride, stride, pad_width, pad_height, + filter_width, filter_height, output_data, output_dims); +} + +inline void Softmax(const float* input_data, const Dims<4>& input_dims, + float beta, float* output_data, + const Dims<4>& output_dims) { + Softmax(input_data, DimsToShape(input_dims), beta, output_data, + DimsToShape(output_dims)); +} + +inline void Softmax(const uint8* input_data, const Dims<4>& input_dims, + int32 input_beta_multiplier, int32 input_beta_left_shift, + int diff_min, uint8* output_data, + const Dims<4>& output_dims) { + Softmax(input_data, DimsToShape(input_dims), input_beta_multiplier, + input_beta_left_shift, diff_min, output_data, + DimsToShape(output_dims)); +} + +inline void LogSoftmax(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { + LogSoftmax(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); +} + +inline void LogSoftmax(const uint8* input_data, const Dims<4>& input_dims, + int32 input_multiplier, int32 input_left_shift, + int32 reverse_scaling_divisor, + int32 reverse_scaling_right_shift, int diff_min, + uint8* output_data, const Dims<4>& output_dims) { + LogSoftmax(input_data, DimsToShape(input_dims), input_multiplier, + input_left_shift, reverse_scaling_divisor, + reverse_scaling_right_shift, diff_min, output_data, + DimsToShape(output_dims)); +} + +inline void Logistic(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { + Logistic(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); +} + +inline void Logistic(const uint8* input_data, const Dims<4>& input_dims, + int32 input_zero_point, int32 input_range_radius, + int32 input_multiplier, int input_left_shift, + uint8* output_data, const Dims<4>& output_dims) { + Logistic(input_data, DimsToShape(input_dims), input_zero_point, + input_range_radius, input_multiplier, input_left_shift, output_data, + DimsToShape(output_dims)); +} + +inline void Logistic(const int16* input_data, const Dims<4>& input_dims, + int16* output_data, const Dims<4>& output_dims) { + Logistic(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); +} + +inline void Tanh(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { + Tanh(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); +} + +inline void Tanh(const uint8* input_data, const Dims<4>& input_dims, + int32 input_zero_point, int32 input_range_radius, + int32 input_multiplier, int input_left_shift, + uint8* output_data, const Dims<4>& output_dims) { + Tanh(input_data, DimsToShape(input_dims), input_zero_point, + input_range_radius, input_multiplier, input_left_shift, output_data, + DimsToShape(output_dims)); +} + +inline void Tanh(const int16* input_data, const Dims<4>& input_dims, + int input_left_shift, int16* output_data, + const Dims<4>& output_dims) { + Tanh(input_data, DimsToShape(input_dims), input_left_shift, output_data, + DimsToShape(output_dims)); } } // namespace optimized_ops diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h index cf989ce51d..930e26107e 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h @@ -85,6 +85,12 @@ using VectorMap = typename std::conditional< Eigen::Dynamic, 1>>, Eigen::Map>>::type; +template +VectorMap MapAsVector(Scalar* data, const RuntimeShape& shape) { + const int size = shape.FlatSize(); + return VectorMap(data, size, 1); +} + template VectorMap MapAsVector(Scalar* data, const Dims& dims) { const int size = FlatSize(dims); @@ -101,6 +107,23 @@ using MatrixMap = typename std::conditional< Eigen::Dynamic, Eigen::Dynamic>>, Eigen::Map>>::type; +template +MatrixMap MapAsMatrixWithLastDimAsRows(Scalar* data, + const RuntimeShape& shape) { + const int dims_count = shape.DimensionsCount(); + const int rows = shape.Dims(dims_count - 1); + const int cols = FlatSizeSkipDim(shape, dims_count - 1); + return MatrixMap(data, rows, cols); +} + +template +MatrixMap MapAsMatrixWithFirstDimAsCols(Scalar* data, + const RuntimeShape& shape) { + const int cols = shape.Dims(0); + const int rows = FlatSizeSkipDim(shape, 0); + return MatrixMap(data, rows, cols); +} + template MatrixMap MapAsMatrixWithFirstDimAsRows(Scalar* data, const Dims& dims) { @@ -2343,12 +2366,12 @@ void GlobalBatchNormalization(const float* input_data, } } -inline void Relu(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { +inline void Relu(const float* input_data, const RuntimeShape& input_shape, + float* output_data, const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("Relu (not fused)"); - const auto input = MapAsVector(input_data, input_dims); - auto output = MapAsVector(output_data, output_dims); + const auto input = MapAsVector(input_data, input_shape); + auto output = MapAsVector(output_data, output_shape); output = input.cwiseMax(0.0f); } @@ -3729,23 +3752,25 @@ inline int NodeOffset(int b, int h, int w, int height, int width) { return (b * height + h) * width + w; } -inline void AveragePool(const float* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, - int pad_height, int kwidth, int kheight, - float output_activation_min, +inline void AveragePool(const float* input_data, + const RuntimeShape& input_shape, int stride_width, + int stride_height, int pad_width, int pad_height, + int kwidth, int kheight, float output_activation_min, float output_activation_max, float* output_data, - const Dims<4>& output_dims) { + const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("AveragePool"); - const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); - const int input_height = ArraySize(input_dims, 2); - const int input_width = ArraySize(input_dims, 1); - const int output_height = ArraySize(output_dims, 2); - const int output_width = ArraySize(output_dims, 1); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int depth = MatchingDim(input_shape, 3, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); // TODO(benoitjacob) make this a proper reference impl without Eigen! - const auto in_mat = MapAsMatrixWithFirstDimAsRows(input_data, input_dims); - auto out_mat = MapAsMatrixWithFirstDimAsRows(output_data, output_dims); + const auto in_mat = MapAsMatrixWithLastDimAsRows(input_data, input_shape); + auto out_mat = MapAsMatrixWithLastDimAsRows(output_data, output_shape); // TODO(benoitjacob) get rid of the dynamic memory allocation here! Eigen::VectorXf out_count(out_mat.cols()); out_count.setZero(); @@ -3783,9 +3808,9 @@ inline void AveragePool(const float* input_data, const Dims<4>& input_dims, for (int y = 0; y < output_height; ++y) { for (int x = 0; x < output_width; ++x) { for (int c = 0; c < depth; ++c) { - output_data[Offset(output_dims, c, x, y, b)] = + output_data[Offset(output_shape, b, y, x, c)] = ActivationFunctionWithMinMax( - output_data[Offset(output_dims, c, x, y, b)], + output_data[Offset(output_shape, b, y, x, c)], output_activation_min, output_activation_max); } } @@ -3793,44 +3818,23 @@ inline void AveragePool(const float* input_data, const Dims<4>& input_dims, } } -// legacy, for compatibility with old checked-in code -template -void AveragePool(const float* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, - int pad_height, int kwidth, int kheight, float* output_data, - const Dims<4>& output_dims) { - float output_activation_min, output_activation_max; - GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); - - AveragePool(input_data, input_dims, stride_width, stride_height, pad_width, - pad_height, kwidth, kheight, output_activation_min, - output_activation_max, output_data, output_dims); -} - -// legacy, for compatibility with old checked-in code -template -void AveragePool(const float* input_data, const Dims<4>& input_dims, int stride, - int pad_width, int pad_height, int filter_width, - int filter_height, float* output_data, - const Dims<4>& output_dims) { - AveragePool(input_data, input_dims, stride, stride, pad_width, pad_height, - filter_width, filter_height, output_data, output_dims); -} - -inline void AveragePool(const uint8* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, - int pad_height, int filter_width, int filter_height, +inline void AveragePool(const uint8* input_data, + const RuntimeShape& input_shape, int stride_width, + int stride_height, int pad_width, int pad_height, + int filter_width, int filter_height, int32 output_activation_min, int32 output_activation_max, uint8* output_data, - const Dims<4>& output_dims) { + const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("AveragePool/8bit"); TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); - const int input_height = ArraySize(input_dims, 2); - const int input_width = ArraySize(input_dims, 1); - const int output_height = ArraySize(output_dims, 2); - const int output_width = ArraySize(output_dims, 1); + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int depth = MatchingDim(input_shape, 3, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); for (int batch = 0; batch < batches; ++batch) { for (int out_y = 0; out_y < output_height; ++out_y) { for (int out_x = 0; out_x < output_width; ++out_x) { @@ -3850,11 +3854,12 @@ inline void AveragePool(const uint8* input_data, const Dims<4>& input_dims, uint16 acc[kAccBufferMaxSize]; memset(acc, 0, depth * sizeof(acc[0])); const uint8* input_ptr = - input_data + input_dims.strides[1] * in_x_origin + - input_dims.strides[2] * in_y_origin + input_dims.strides[3] * batch; + input_data + + depth * (in_x_origin + + input_width * (in_y_origin + input_height * batch)); for (int fy = filter_y_start; fy < filter_y_end; fy++) { - const uint8* input_row_ptr = input_ptr + fy * input_dims.strides[2] + - filter_x_start * input_dims.strides[1]; + const uint8* input_row_ptr = + input_ptr + depth * (fy * input_width + filter_x_start); for (int fx = filter_x_start; fx < filter_x_end; fx++) { int channel = 0; #ifdef USE_NEON @@ -3885,7 +3890,7 @@ inline void AveragePool(const uint8* input_data, const Dims<4>& input_dims, } } uint8* output_ptr = - output_data + Offset(output_dims, 0, out_x, out_y, batch); + output_data + Offset(output_shape, batch, out_y, out_x, 0); int channel = 0; #ifdef USE_NEON #define AVGPOOL_DIVIDING_BY(FILTER_COUNT) \ @@ -3926,54 +3931,23 @@ inline void AveragePool(const uint8* input_data, const Dims<4>& input_dims, } } -// legacy, for compatibility with old checked-in code -template -void AveragePool(const uint8* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, - int pad_height, int filter_width, int filter_height, - int32 output_activation_min, int32 output_activation_max, - uint8* output_data, const Dims<4>& output_dims) { - static_assert(Ac == FusedActivationFunctionType::kNone || - Ac == FusedActivationFunctionType::kRelu || - Ac == FusedActivationFunctionType::kRelu6 || - Ac == FusedActivationFunctionType::kRelu1, - ""); - if (Ac == FusedActivationFunctionType::kNone) { - TFLITE_DCHECK_EQ(output_activation_min, 0); - TFLITE_DCHECK_EQ(output_activation_max, 255); - } - AveragePool(input_data, input_dims, stride_width, stride_height, pad_width, - pad_height, filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - -// legacy, for compatibility with old checked-in code -template -void AveragePool(const uint8* input_data, const Dims<4>& input_dims, int stride, - int pad_width, int pad_height, int filter_width, - int filter_height, int32 output_activation_min, - int32 output_activation_max, uint8* output_data, - const Dims<4>& output_dims) { - AveragePool(input_data, input_dims, stride, stride, pad_width, pad_height, - filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - -inline void MaxPool(const float* input_data, const Dims<4>& input_dims, +inline void MaxPool(const float* input_data, const RuntimeShape& input_shape, int stride_width, int stride_height, int pad_width, int pad_height, int kwidth, int kheight, float output_activation_min, float output_activation_max, - float* output_data, const Dims<4>& output_dims) { + float* output_data, const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("MaxPool"); - const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); - const int input_height = ArraySize(input_dims, 2); - const int input_width = ArraySize(input_dims, 1); - const int output_height = ArraySize(output_dims, 2); - const int output_width = ArraySize(output_dims, 1); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); - - const auto in_mat = MapAsMatrixWithFirstDimAsRows(input_data, input_dims); - auto out_mat = MapAsMatrixWithFirstDimAsRows(output_data, output_dims); + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int depth = MatchingDim(input_shape, 3, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); + + const auto in_mat = MapAsMatrixWithLastDimAsRows(input_data, input_shape); + auto out_mat = MapAsMatrixWithLastDimAsRows(output_data, output_shape); // Prefill the output to minimum representable float value out_mat.setConstant(std::numeric_limits::lowest()); for (int b = 0; b < batches; ++b) { @@ -4006,9 +3980,9 @@ inline void MaxPool(const float* input_data, const Dims<4>& input_dims, for (int y = 0; y < output_height; ++y) { for (int x = 0; x < output_width; ++x) { for (int c = 0; c < depth; ++c) { - output_data[Offset(output_dims, c, x, y, b)] = + output_data[Offset(output_shape, b, y, x, c)] = ActivationFunctionWithMinMax( - output_data[Offset(output_dims, c, x, y, b)], + output_data[Offset(output_shape, b, y, x, c)], output_activation_min, output_activation_max); } } @@ -4016,41 +3990,21 @@ inline void MaxPool(const float* input_data, const Dims<4>& input_dims, } } -// legacy, for compatibility with old checked-in code -template -void MaxPool(const float* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, int pad_height, - int kwidth, int kheight, float* output_data, - const Dims<4>& output_dims) { - float output_activation_min, output_activation_max; - GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); - MaxPool(input_data, input_dims, stride_width, stride_height, pad_width, - pad_height, kwidth, kheight, output_activation_min, - output_activation_max, output_data, output_dims); -} - -// legacy, for compatibility with old checked-in code -template -void MaxPool(const float* input_data, const Dims<4>& input_dims, int stride, - int pad_width, int pad_height, int filter_width, int filter_height, - float* output_data, const Dims<4>& output_dims) { - MaxPool(input_data, input_dims, stride, stride, pad_width, pad_height, - filter_width, filter_height, output_data, output_dims); -} - -inline void MaxPool(const uint8* input_data, const Dims<4>& input_dims, +inline void MaxPool(const uint8* input_data, const RuntimeShape& input_shape, int stride_width, int stride_height, int pad_width, int pad_height, int filter_width, int filter_height, int32 output_activation_min, int32 output_activation_max, - uint8* output_data, const Dims<4>& output_dims) { + uint8* output_data, const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("MaxPool/8bit"); TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); - const int input_height = ArraySize(input_dims, 2); - const int input_width = ArraySize(input_dims, 1); - const int output_height = ArraySize(output_dims, 2); - const int output_width = ArraySize(output_dims, 1); + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int depth = MatchingDim(input_shape, 3, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); for (int batch = 0; batch < batches; ++batch) { for (int out_y = 0; out_y < output_height; ++out_y) { for (int out_x = 0; out_x < output_width; ++out_x) { @@ -4068,11 +4022,12 @@ inline void MaxPool(const uint8* input_data, const Dims<4>& input_dims, uint8 acc[kAccBufferMaxSize]; memset(acc, 0, depth * sizeof(acc[0])); const uint8* input_ptr = - input_data + input_dims.strides[1] * in_x_origin + - input_dims.strides[2] * in_y_origin + input_dims.strides[3] * batch; + input_data + + depth * (in_x_origin + + input_width * (in_y_origin + input_height * batch)); for (int fy = filter_y_start; fy < filter_y_end; fy++) { - const uint8* input_row_ptr = input_ptr + fy * input_dims.strides[2] + - filter_x_start * input_dims.strides[1]; + const uint8* input_row_ptr = + input_ptr + depth * (fy * input_width + filter_x_start); for (int fx = filter_x_start; fx < filter_x_end; fx++) { int channel = 0; #ifdef USE_NEON @@ -4098,7 +4053,7 @@ inline void MaxPool(const uint8* input_data, const Dims<4>& input_dims, } } uint8* output_ptr = - output_data + Offset(output_dims, 0, out_x, out_y, batch); + output_data + Offset(output_shape, batch, out_y, out_x, 0); int channel = 0; #ifdef USE_NEON for (; channel <= depth - 16; channel += 16) { @@ -4125,53 +4080,23 @@ inline void MaxPool(const uint8* input_data, const Dims<4>& input_dims, } } -// legacy, for compatibility with old checked-in code -template -void MaxPool(const uint8* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, int pad_height, - int filter_width, int filter_height, int32 output_activation_min, - int32 output_activation_max, uint8* output_data, - const Dims<4>& output_dims) { - static_assert(Ac == FusedActivationFunctionType::kNone || - Ac == FusedActivationFunctionType::kRelu || - Ac == FusedActivationFunctionType::kRelu6 || - Ac == FusedActivationFunctionType::kRelu1, - ""); - if (Ac == FusedActivationFunctionType::kNone) { - TFLITE_DCHECK_EQ(output_activation_min, 0); - TFLITE_DCHECK_EQ(output_activation_max, 255); - } - MaxPool(input_data, input_dims, stride_width, stride_height, pad_width, - pad_height, filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - -// legacy, for compatibility with old checked-in code -template -void MaxPool(const uint8* input_data, const Dims<4>& input_dims, int stride, - int pad_width, int pad_height, int filter_width, int filter_height, - int32 output_activation_min, int32 output_activation_max, - uint8* output_data, const Dims<4>& output_dims) { - MaxPool(input_data, input_dims, stride, stride, pad_width, pad_height, - filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - -inline void L2Pool(const float* input_data, const Dims<4>& input_dims, +inline void L2Pool(const float* input_data, const RuntimeShape& input_shape, int stride_width, int stride_height, int pad_width, int pad_height, int filter_width, int filter_height, float output_activation_min, float output_activation_max, - float* output_data, const Dims<4>& output_dims) { + float* output_data, const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("L2Pool"); - const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); - const int input_height = ArraySize(input_dims, 2); - const int input_width = ArraySize(input_dims, 1); - const int output_height = ArraySize(output_dims, 2); - const int output_width = ArraySize(output_dims, 1); + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); // Actually carry out L2 Pool. Code is written in forward mode: we go through // the input values once, and write to all the pooled regions that it maps to. - const auto in_mat = MapAsMatrixWithFirstDimAsRows(input_data, input_dims); - auto out_mat = MapAsMatrixWithFirstDimAsRows(output_data, output_dims); + const auto in_mat = MapAsMatrixWithLastDimAsRows(input_data, input_shape); + auto out_mat = MapAsMatrixWithLastDimAsRows(output_data, output_shape); Eigen::VectorXf in_square(in_mat.rows()); Eigen::VectorXf out_count(out_mat.cols()); out_count.setZero(); @@ -4213,28 +4138,6 @@ inline void L2Pool(const float* input_data, const Dims<4>& input_dims, (out_mat.array().rowwise() * out_count.transpose().array()).cwiseSqrt(); } -// legacy, for compatibility with old checked-in code -template -void L2Pool(const float* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, int pad_height, - int filter_width, int filter_height, float* output_data, - const Dims<4>& output_dims) { - float output_activation_min, output_activation_max; - GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); - L2Pool(input_data, input_dims, stride_width, stride_height, pad_width, - pad_height, filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - -// legacy, for compatibility with old checked-in code -template -void L2Pool(const float* input_data, const Dims<4>& input_dims, int stride, - int pad_width, int pad_height, int filter_width, int filter_height, - float* output_data, const Dims<4>& output_dims) { - L2Pool(input_data, input_dims, stride, stride, pad_width, pad_height, - filter_width, filter_height, output_data, output_dims); -} - inline void LocalResponseNormalization(const float* input_data, const Dims<4>& input_dims, int range, float bias, float alpha, float beta, @@ -4280,14 +4183,14 @@ inline void LocalResponseNormalization(const float* input_data, } } -inline void Softmax(const float* input_data, const Dims<4>& input_dims, +inline void Softmax(const float* input_data, const RuntimeShape& input_shape, float beta, float* output_data, - const Dims<4>& output_dims) { + const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("Softmax"); - MatchingFlatSize(input_dims, output_dims); + MatchingFlatSize(input_shape, output_shape); - const auto in_mat = MapAsMatrixWithFirstDimAsRows(input_data, input_dims); - auto out_mat = MapAsMatrixWithFirstDimAsRows(output_data, output_dims); + const auto in_mat = MapAsMatrixWithLastDimAsRows(input_data, input_shape); + auto out_mat = MapAsMatrixWithLastDimAsRows(output_data, output_shape); // Compute the exponential first, removing the max coefficient for numerical // stability. out_mat = (in_mat.rowwise() - in_mat.colwise().maxCoeff()).array() * beta; @@ -4299,10 +4202,10 @@ inline void Softmax(const float* input_data, const Dims<4>& input_dims, out_mat.array().rowwise() *= scale; } -inline void Softmax(const uint8* input_data, const Dims<4>& input_dims, +inline void Softmax(const uint8* input_data, const RuntimeShape& input_shape, int32 input_beta_multiplier, int32 input_beta_left_shift, int diff_min, uint8* output_data, - const Dims<4>& output_dims) { + const RuntimeShape& output_shape) { // The representation chosen for the input to the exp() function is Q5.26. // We need to leave extra space since values that we skip might be as large as // -32 before multiplying by input_beta_multiplier, and therefore as large as @@ -4316,8 +4219,11 @@ inline void Softmax(const uint8* input_data, const Dims<4>& input_dims, using FixedPoint0 = gemmlowp::FixedPoint; gemmlowp::ScopedProfilingLabel label("Softmax/8bit"); - const int outer_size = MatchingFlatSizeSkipDim(input_dims, 0, output_dims); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); + const int trailing_dim = input_shape.DimensionsCount() - 1; + const int outer_size = + MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); + const int depth = + MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); for (int b = 0; b < outer_size; ++b) { const uint8* input_data_ptr = input_data + b * depth; @@ -4507,11 +4413,14 @@ inline void Softmax(const uint8* input_data, const Dims<4>& input_dims, // TODO(myenik): This is the same as the reference implementation, not actually // optimized yet. -inline void LogSoftmax(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { +inline void LogSoftmax(const float* input_data, const RuntimeShape& input_shape, + float* output_data, const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("LogSoftmax"); - const int outer_size = MatchingFlatSizeSkipDim(input_dims, 0, output_dims); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); + const int trailing_dim = input_shape.DimensionsCount() - 1; + const int outer_size = + MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); + const int depth = + MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); for (int i = 0; i < outer_size; ++i) { const float* block_input_data = input_data + i * depth; @@ -4652,11 +4561,11 @@ log_x_for_x_greater_than_or_equal_to_1( } // Currently just a copy of the reference code. -inline void LogSoftmax(const uint8* input_data, const Dims<4>& input_dims, +inline void LogSoftmax(const uint8* input_data, const RuntimeShape& input_shape, int32 input_multiplier, int32 input_left_shift, int32 reverse_scaling_divisor, int32 reverse_scaling_right_shift, int diff_min, - uint8* output_data, const Dims<4>& output_dims) { + uint8* output_data, const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("LogSoftmax/Uint8"); // The representation chosen for the input to the exp() function is Q5.26. // We need to leave extra space since values that we skip might be as large as @@ -4671,8 +4580,11 @@ inline void LogSoftmax(const uint8* input_data, const Dims<4>& input_dims, using FixedPointAccum = gemmlowp::FixedPoint; using FixedPoint0 = gemmlowp::FixedPoint; - const int outer_size = MatchingFlatSizeSkipDim(input_dims, 0, output_dims); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); + const int trailing_dim = input_shape.DimensionsCount() - 1; + const int outer_size = + MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); + const int depth = + MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); for (int i = 0; i < outer_size; ++i) { const uint8* block_input_data = input_data + i * depth; @@ -4736,21 +4648,21 @@ inline void LogSoftmax(const uint8* input_data, const Dims<4>& input_dims, } } -inline void Logistic(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { +inline void Logistic(const float* input_data, const RuntimeShape& input_shape, + float* output_data, const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("Logistic"); - auto input_map = MapAsVector(input_data, input_dims); - auto output_map = MapAsVector(output_data, output_dims); + auto input_map = MapAsVector(input_data, input_shape); + auto output_map = MapAsVector(output_data, output_shape); output_map.array() = input_map.array().unaryExpr(Eigen::internal::scalar_sigmoid_op()); } -inline void Logistic(const uint8* input_data, const Dims<4>& input_dims, +inline void Logistic(const uint8* input_data, const RuntimeShape& input_shape, int32 input_zero_point, int32 input_range_radius, int32 input_multiplier, int input_left_shift, - uint8* output_data, const Dims<4>& output_dims) { + uint8* output_data, const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("Logistic/Uint8"); - const int size = MatchingFlatSize(input_dims, output_dims); + const int size = MatchingFlatSize(input_shape, output_shape); int c = 0; #ifdef USE_NEON @@ -4882,10 +4794,10 @@ inline void Logistic(const uint8* input_data, const Dims<4>& input_dims, } } -inline void Logistic(const int16* input_data, const Dims<4>& input_dims, - int16* output_data, const Dims<4>& output_dims) { +inline void Logistic(const int16* input_data, const RuntimeShape& input_shape, + int16* output_data, const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("Logistic/Int16"); - const int flat_size = MatchingFlatSize(output_dims, input_dims); + const int flat_size = MatchingFlatSize(input_shape, output_shape); for (int i = 0; i < flat_size; i++) { } @@ -4942,21 +4854,21 @@ inline void Logistic(const int16* input_data, const Dims<4>& input_dims, } } -inline void Tanh(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { +inline void Tanh(const float* input_data, const RuntimeShape& input_shape, + float* output_data, const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("Tanh"); - auto input_map = MapAsVector(input_data, input_dims); - auto output_map = MapAsVector(output_data, output_dims); + auto input_map = MapAsVector(input_data, input_shape); + auto output_map = MapAsVector(output_data, output_shape); output_map.array() = input_map.array().tanh(); } -inline void Tanh(const uint8* input_data, const Dims<4>& input_dims, +inline void Tanh(const uint8* input_data, const RuntimeShape& input_shape, int32 input_zero_point, int32 input_range_radius, int32 input_multiplier, int input_left_shift, - uint8* output_data, const Dims<4>& output_dims) { + uint8* output_data, const RuntimeShape& output_shape) { // Note that this is almost the exact same code as in Logistic(). gemmlowp::ScopedProfilingLabel label("Tanh"); - const int size = MatchingFlatSize(input_dims, output_dims); + const int size = MatchingFlatSize(input_shape, output_shape); int c = 0; int32_t output_zero_point = 128; @@ -5097,16 +5009,16 @@ inline void Tanh(const uint8* input_data, const Dims<4>& input_dims, } } -inline void Tanh(const int16* input_data, const Dims<4>& input_dims, +inline void Tanh(const int16* input_data, const RuntimeShape& input_shape, int input_left_shift, int16* output_data, - const Dims<4>& output_dims) { + const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("Tanh/Int16"); // Support for shifts is limited until we have a parameterized version of // SaturatingRoundingMultiplyByPOT(). TFLITE_DCHECK_GE(input_left_shift, 0); TFLITE_DCHECK_LE(input_left_shift, 1); - const int flat_size = MatchingFlatSize(output_dims, input_dims); + const int flat_size = MatchingFlatSize(input_shape, output_shape); int c = 0; const int16* input_data_ptr = input_data; diff --git a/tensorflow/contrib/lite/kernels/internal/reference/legacy_reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/legacy_reference_ops.h index 6f5f6a3e6f..878b2441b4 100644 --- a/tensorflow/contrib/lite/kernels/internal/reference/legacy_reference_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/reference/legacy_reference_ops.h @@ -34,15 +34,297 @@ inline RuntimeShape DimsToShape(const tflite::Dims<4>& dims) { template void L2Normalization(const float* input_data, const Dims<4>& input_dims, float* output_data, const Dims<4>& output_dims) { - return L2Normalization(input_data, DimsToShape(input_dims), output_data, - DimsToShape(output_dims)); + L2Normalization(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); } inline void L2Normalization(const uint8* input_data, const Dims<4>& input_dims, int32 input_zero_point, uint8* output_data, const Dims<4>& output_dims) { - return L2Normalization(input_data, DimsToShape(input_dims), input_zero_point, - output_data, DimsToShape(output_dims)); + L2Normalization(input_data, DimsToShape(input_dims), input_zero_point, + output_data, DimsToShape(output_dims)); +} + +inline void Relu(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { + Relu(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); +} + +inline void Relu1(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { + Relu1(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); +} + +inline void Relu6(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { + Relu6(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); +} + +inline void AveragePool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int kwidth, int kheight, + float output_activation_min, + float output_activation_max, float* output_data, + const Dims<4>& output_dims) { + AveragePool(input_data, DimsToShape(input_dims), stride_width, stride_height, + pad_width, pad_height, kwidth, kheight, output_activation_min, + output_activation_max, output_data, DimsToShape(output_dims)); +} + +// legacy, for compatibility with old checked-in code +template +void AveragePool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int kwidth, int kheight, float* output_data, + const Dims<4>& output_dims) { + float output_activation_min, output_activation_max; + GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); + + AveragePool(input_data, input_dims, stride_width, stride_height, pad_width, + pad_height, kwidth, kheight, output_activation_min, + output_activation_max, output_data, output_dims); +} + +// legacy, for compatibility with old checked-in code +template +void AveragePool(const float* input_data, const Dims<4>& input_dims, int stride, + int pad_width, int pad_height, int filter_width, + int filter_height, float* output_data, + const Dims<4>& output_dims) { + AveragePool(input_data, input_dims, stride, stride, pad_width, pad_height, + filter_width, filter_height, output_data, output_dims); +} + +inline void AveragePool(const uint8* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int filter_width, int filter_height, + int32 output_activation_min, + int32 output_activation_max, uint8* output_data, + const Dims<4>& output_dims) { + AveragePool(input_data, DimsToShape(input_dims), stride_width, stride_height, + pad_width, pad_height, filter_width, filter_height, + output_activation_min, output_activation_max, output_data, + DimsToShape(output_dims)); +} + +// legacy, for compatibility with old checked-in code +template +void AveragePool(const uint8* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int filter_width, int filter_height, + int32 output_activation_min, int32 output_activation_max, + uint8* output_data, const Dims<4>& output_dims) { + static_assert(Ac == FusedActivationFunctionType::kNone || + Ac == FusedActivationFunctionType::kRelu || + Ac == FusedActivationFunctionType::kRelu6 || + Ac == FusedActivationFunctionType::kRelu1, + ""); + if (Ac == FusedActivationFunctionType::kNone) { + TFLITE_DCHECK_EQ(output_activation_min, 0); + TFLITE_DCHECK_EQ(output_activation_max, 255); + } + AveragePool(input_data, input_dims, stride_width, stride_height, pad_width, + pad_height, filter_width, filter_height, output_activation_min, + output_activation_max, output_data, output_dims); +} + +// legacy, for compatibility with old checked-in code +template +void AveragePool(const uint8* input_data, const Dims<4>& input_dims, int stride, + int pad_width, int pad_height, int filter_width, + int filter_height, int32 output_activation_min, + int32 output_activation_max, uint8* output_data, + const Dims<4>& output_dims) { + AveragePool(input_data, input_dims, stride, stride, pad_width, pad_height, + filter_width, filter_height, output_activation_min, + output_activation_max, output_data, output_dims); +} + +inline void MaxPool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int kwidth, int kheight, + float output_activation_min, float output_activation_max, + float* output_data, const Dims<4>& output_dims) { + MaxPool(input_data, DimsToShape(input_dims), stride_width, stride_height, + pad_width, pad_height, kwidth, kheight, output_activation_min, + output_activation_max, output_data, DimsToShape(output_dims)); +} + +// legacy, for compatibility with old checked-in code +template +void MaxPool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, int pad_height, + int kwidth, int kheight, float* output_data, + const Dims<4>& output_dims) { + float output_activation_min, output_activation_max; + GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); + MaxPool(input_data, input_dims, stride_width, stride_height, pad_width, + pad_height, kwidth, kheight, output_activation_min, + output_activation_max, output_data, output_dims); +} + +// legacy, for compatibility with old checked-in code +template +void MaxPool(const float* input_data, const Dims<4>& input_dims, int stride, + int pad_width, int pad_height, int filter_width, int filter_height, + float* output_data, const Dims<4>& output_dims) { + MaxPool(input_data, input_dims, stride, stride, pad_width, pad_height, + filter_width, filter_height, output_data, output_dims); +} + +inline void MaxPool(const uint8* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int filter_width, int filter_height, + int32 output_activation_min, int32 output_activation_max, + uint8* output_data, const Dims<4>& output_dims) { + MaxPool(input_data, DimsToShape(input_dims), stride_width, stride_height, + pad_width, pad_height, filter_width, filter_height, + output_activation_min, output_activation_max, output_data, + DimsToShape(output_dims)); +} + +// legacy, for compatibility with old checked-in code +template +void MaxPool(const uint8* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, int pad_height, + int filter_width, int filter_height, int32 output_activation_min, + int32 output_activation_max, uint8* output_data, + const Dims<4>& output_dims) { + static_assert(Ac == FusedActivationFunctionType::kNone || + Ac == FusedActivationFunctionType::kRelu || + Ac == FusedActivationFunctionType::kRelu6 || + Ac == FusedActivationFunctionType::kRelu1, + ""); + if (Ac == FusedActivationFunctionType::kNone) { + TFLITE_DCHECK_EQ(output_activation_min, 0); + TFLITE_DCHECK_EQ(output_activation_max, 255); + } + MaxPool(input_data, input_dims, stride_width, stride_height, pad_width, + pad_height, filter_width, filter_height, output_activation_min, + output_activation_max, output_data, output_dims); +} + +// legacy, for compatibility with old checked-in code +template +void MaxPool(const uint8* input_data, const Dims<4>& input_dims, int stride, + int pad_width, int pad_height, int filter_width, int filter_height, + int32 output_activation_min, int32 output_activation_max, + uint8* output_data, const Dims<4>& output_dims) { + MaxPool(input_data, input_dims, stride, stride, pad_width, pad_height, + filter_width, filter_height, output_activation_min, + output_activation_max, output_data, output_dims); +} + +inline void L2Pool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int filter_width, int filter_height, + float output_activation_min, float output_activation_max, + float* output_data, const Dims<4>& output_dims) { + L2Pool(input_data, DimsToShape(input_dims), stride_width, stride_height, + pad_width, pad_height, filter_width, filter_height, + output_activation_min, output_activation_max, output_data, + DimsToShape(output_dims)); +} + +// legacy, for compatibility with old checked-in code +template +void L2Pool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, int pad_height, + int filter_width, int filter_height, float* output_data, + const Dims<4>& output_dims) { + float output_activation_min, output_activation_max; + GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); + L2Pool(input_data, input_dims, stride_width, stride_height, pad_width, + pad_height, filter_width, filter_height, output_activation_min, + output_activation_max, output_data, output_dims); +} + +// legacy, for compatibility with old checked-in code +template +void L2Pool(const float* input_data, const Dims<4>& input_dims, int stride, + int pad_width, int pad_height, int filter_width, int filter_height, + float* output_data, const Dims<4>& output_dims) { + L2Pool(input_data, input_dims, stride, stride, pad_width, pad_height, + filter_width, filter_height, output_data, output_dims); +} + +inline void Softmax(const float* input_data, const Dims<4>& input_dims, + float beta, float* output_data, + const Dims<4>& output_dims) { + Softmax(input_data, DimsToShape(input_dims), beta, output_data, + DimsToShape(output_dims)); +} + +inline void Softmax(const uint8* input_data, const Dims<4>& input_dims, + int32 input_beta_multiplier, int32 input_beta_left_shift, + int diff_min, uint8* output_data, + const Dims<4>& output_dims) { + Softmax(input_data, DimsToShape(input_dims), input_beta_multiplier, + input_beta_left_shift, diff_min, output_data, + DimsToShape(output_dims)); +} + +inline void LogSoftmax(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { + LogSoftmax(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); +} + +inline void LogSoftmax(const uint8* input_data, const Dims<4>& input_dims, + int32 input_multiplier, int32 input_left_shift, + int32 reverse_scaling_divisor, + int32 reverse_scaling_right_shift, int diff_min, + uint8* output_data, const Dims<4>& output_dims) { + LogSoftmax(input_data, DimsToShape(input_dims), input_multiplier, + input_left_shift, reverse_scaling_divisor, + reverse_scaling_right_shift, diff_min, output_data, + DimsToShape(output_dims)); +} + +inline void Logistic(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { + Logistic(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); +} + +inline void Logistic(const uint8* input_data, const Dims<4>& input_dims, + int32 input_zero_point, int32 input_range_radius, + int32 input_multiplier, int input_left_shift, + uint8* output_data, const Dims<4>& output_dims) { + Logistic(input_data, DimsToShape(input_dims), input_zero_point, + input_range_radius, input_multiplier, input_left_shift, output_data, + DimsToShape(output_dims)); +} + +inline void Logistic(const int16* input_data, const Dims<4>& input_dims, + int16* output_data, const Dims<4>& output_dims) { + Logistic(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); +} + +inline void Tanh(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { + Tanh(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); +} + +inline void Tanh(const uint8* input_data, const Dims<4>& input_dims, + int32 input_zero_point, int32 input_range_radius, + int32 input_multiplier, int input_left_shift, + uint8* output_data, const Dims<4>& output_dims) { + Tanh(input_data, DimsToShape(input_dims), input_zero_point, + input_range_radius, input_multiplier, input_left_shift, output_data, + DimsToShape(output_dims)); +} + +inline void Tanh(const int16* input_data, const Dims<4>& input_dims, + int input_left_shift, int16* output_data, + const Dims<4>& output_dims) { + Tanh(input_data, DimsToShape(input_dims), input_left_shift, output_data, + DimsToShape(output_dims)); } } // namespace reference_ops diff --git a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h index 1908f7fa6c..1ac010dd7e 100644 --- a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h @@ -914,9 +914,9 @@ void GlobalBatchNormalization(const float* input_data, } } -inline void Relu(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { - const int flat_size = MatchingFlatSize(input_dims, output_dims); +inline void Relu(const float* input_data, const RuntimeShape& input_shape, + float* output_data, const RuntimeShape& output_shape) { + const int flat_size = MatchingFlatSize(input_shape, output_shape); for (int i = 0; i < flat_size; ++i) { const float val = input_data[i]; const float lower = 0; @@ -925,9 +925,10 @@ inline void Relu(const float* input_data, const Dims<4>& input_dims, } } -inline void Relu1(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { - const int flat_size = MatchingFlatSize(input_dims, output_dims); +inline void Relu1(const float* input_data, const RuntimeShape& input_shape, + float* output_data, const RuntimeShape& output_shape) { + gemmlowp::ScopedProfilingLabel label("Relu1 (not fused)"); + const int flat_size = MatchingFlatSize(input_shape, output_shape); for (int i = 0; i < flat_size; ++i) { const float val = input_data[i]; const float upper = 1; @@ -937,9 +938,10 @@ inline void Relu1(const float* input_data, const Dims<4>& input_dims, } } -inline void Relu6(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { - const int flat_size = MatchingFlatSize(input_dims, output_dims); +inline void Relu6(const float* input_data, const RuntimeShape& input_shape, + float* output_data, const RuntimeShape& output_shape) { + gemmlowp::ScopedProfilingLabel label("Relu6 (not fused)"); + const int flat_size = MatchingFlatSize(input_shape, output_shape); for (int i = 0; i < flat_size; ++i) { const float val = input_data[i]; const float upper = 6; @@ -2245,18 +2247,21 @@ inline int NodeOffset(int b, int h, int w, int height, int width) { return (b * height + h) * width + w; } -inline void AveragePool(const float* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, - int pad_height, int filter_width, int filter_height, +inline void AveragePool(const float* input_data, + const RuntimeShape& input_shape, int stride_width, + int stride_height, int pad_width, int pad_height, + int filter_width, int filter_height, float output_activation_min, float output_activation_max, float* output_data, - const Dims<4>& output_dims) { - const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); - const int input_height = ArraySize(input_dims, 2); - const int input_width = ArraySize(input_dims, 1); - const int output_height = ArraySize(output_dims, 2); - const int output_width = ArraySize(output_dims, 1); + const RuntimeShape& output_shape) { + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int depth = MatchingDim(input_shape, 3, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); for (int batch = 0; batch < batches; ++batch) { for (int out_y = 0; out_y < output_height; ++out_y) { for (int out_x = 0; out_x < output_width; ++out_x) { @@ -2280,12 +2285,12 @@ inline void AveragePool(const float* input_data, const Dims<4>& input_dims, const int in_x = in_x_origin + filter_x; const int in_y = in_y_origin + filter_y; total += - input_data[Offset(input_dims, channel, in_x, in_y, batch)]; + input_data[Offset(input_shape, batch, in_y, in_x, channel)]; filter_count++; } } const float average = total / filter_count; - output_data[Offset(output_dims, channel, out_x, out_y, batch)] = + output_data[Offset(output_shape, batch, out_y, out_x, channel)] = ActivationFunctionWithMinMax(average, output_activation_min, output_activation_max); } @@ -2294,42 +2299,22 @@ inline void AveragePool(const float* input_data, const Dims<4>& input_dims, } } -// legacy, for compatibility with old checked-in code -template -void AveragePool(const float* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, - int pad_height, int filter_width, int filter_height, - float* output_data, const Dims<4>& output_dims) { - float output_activation_min, output_activation_max; - GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); - AveragePool(input_data, input_dims, stride_width, stride_height, pad_width, - pad_height, filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - -// legacy, for compatibility with old checked-in code -template -void AveragePool(const float* input_data, const Dims<4>& input_dims, int stride, - int pad_width, int pad_height, int filter_width, - int filter_height, float* output_data, - const Dims<4>& output_dims) { - AveragePool(input_data, input_dims, stride, stride, pad_width, pad_height, - filter_width, filter_height, output_data, output_dims); -} - -inline void AveragePool(const uint8* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, - int pad_height, int filter_width, int filter_height, +inline void AveragePool(const uint8* input_data, + const RuntimeShape& input_shape, int stride_width, + int stride_height, int pad_width, int pad_height, + int filter_width, int filter_height, int32 output_activation_min, int32 output_activation_max, uint8* output_data, - const Dims<4>& output_dims) { + const RuntimeShape& output_shape) { TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); - const int input_height = ArraySize(input_dims, 2); - const int input_width = ArraySize(input_dims, 1); - const int output_height = ArraySize(output_dims, 2); - const int output_width = ArraySize(output_dims, 1); + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int depth = MatchingDim(input_shape, 3, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); for (int batch = 0; batch < batches; ++batch) { for (int out_y = 0; out_y < output_height; ++out_y) { for (int out_x = 0; out_x < output_width; ++out_x) { @@ -2352,14 +2337,15 @@ inline void AveragePool(const uint8* input_data, const Dims<4>& input_dims, ++filter_x) { const int in_x = in_x_origin + filter_x; const int in_y = in_y_origin + filter_y; - acc += input_data[Offset(input_dims, channel, in_x, in_y, batch)]; + acc += + input_data[Offset(input_shape, batch, in_y, in_x, channel)]; filter_count++; } } acc = (acc + filter_count / 2) / filter_count; acc = std::max(acc, output_activation_min); acc = std::min(acc, output_activation_max); - output_data[Offset(output_dims, channel, out_x, out_y, batch)] = + output_data[Offset(output_shape, batch, out_y, out_x, channel)] = static_cast(acc); } } @@ -2367,50 +2353,19 @@ inline void AveragePool(const uint8* input_data, const Dims<4>& input_dims, } } -// legacy, for compatibility with old checked-in code -template -void AveragePool(const uint8* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, - int pad_height, int filter_width, int filter_height, - int32 output_activation_min, int32 output_activation_max, - uint8* output_data, const Dims<4>& output_dims) { - static_assert(Ac == FusedActivationFunctionType::kNone || - Ac == FusedActivationFunctionType::kRelu || - Ac == FusedActivationFunctionType::kRelu6 || - Ac == FusedActivationFunctionType::kRelu1, - ""); - if (Ac == FusedActivationFunctionType::kNone) { - TFLITE_DCHECK_EQ(output_activation_min, 0); - TFLITE_DCHECK_EQ(output_activation_max, 255); - } - AveragePool(input_data, input_dims, stride_width, stride_height, pad_width, - pad_height, filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - -// legacy, for compatibility with old checked-in code -template -void AveragePool(const uint8* input_data, const Dims<4>& input_dims, int stride, - int pad_width, int pad_height, int filter_width, - int filter_height, int32 output_activation_min, - int32 output_activation_max, uint8* output_data, - const Dims<4>& output_dims) { - AveragePool(input_data, input_dims, stride, stride, pad_width, pad_height, - filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - -inline void L2Pool(const float* input_data, const Dims<4>& input_dims, +inline void L2Pool(const float* input_data, const RuntimeShape& input_shape, int stride_width, int stride_height, int pad_width, int pad_height, int filter_width, int filter_height, float output_activation_min, float output_activation_max, - float* output_data, const Dims<4>& output_dims) { - const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); - const int input_height = ArraySize(input_dims, 2); - const int input_width = ArraySize(input_dims, 1); - const int output_height = ArraySize(output_dims, 2); - const int output_width = ArraySize(output_dims, 1); + float* output_data, const RuntimeShape& output_shape) { + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int depth = MatchingDim(input_shape, 3, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); for (int batch = 0; batch < batches; ++batch) { for (int out_y = 0; out_y < output_height; ++out_y) { for (int out_x = 0; out_x < output_width; ++out_x) { @@ -2434,13 +2389,13 @@ inline void L2Pool(const float* input_data, const Dims<4>& input_dims, const int in_x = in_x_origin + filter_x; const int in_y = in_y_origin + filter_y; const float val = - input_data[Offset(input_dims, channel, in_x, in_y, batch)]; + input_data[Offset(input_shape, batch, in_y, in_x, channel)]; sum_squares += val * val; filter_count++; } } const float l2pool_result = std::sqrt(sum_squares / filter_count); - output_data[Offset(output_dims, channel, out_x, out_y, batch)] = + output_data[Offset(output_shape, batch, out_y, out_x, channel)] = ActivationFunctionWithMinMax(l2pool_result, output_activation_min, output_activation_max); } @@ -2449,40 +2404,19 @@ inline void L2Pool(const float* input_data, const Dims<4>& input_dims, } } -// legacy, for compatibility with old checked-in code -template -void L2Pool(const float* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, int pad_height, - int filter_width, int filter_height, float* output_data, - const Dims<4>& output_dims) { - float output_activation_min, output_activation_max; - GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); - - L2Pool(input_data, input_dims, stride_width, stride_height, pad_width, - pad_height, filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - -// legacy, for compatibility with old checked-in code -template -void L2Pool(const float* input_data, const Dims<4>& input_dims, int stride, - int pad_width, int pad_height, int filter_width, int filter_height, - float* output_data, const Dims<4>& output_dims) { - L2Pool(input_data, input_dims, stride, stride, pad_width, pad_height, - filter_width, filter_height, output_data, output_dims); -} - -inline void MaxPool(const float* input_data, const Dims<4>& input_dims, +inline void MaxPool(const float* input_data, const RuntimeShape& input_shape, int stride_width, int stride_height, int pad_width, int pad_height, int filter_width, int filter_height, float output_activation_min, float output_activation_max, - float* output_data, const Dims<4>& output_dims) { - const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); - const int input_height = ArraySize(input_dims, 2); - const int input_width = ArraySize(input_dims, 1); - const int output_height = ArraySize(output_dims, 2); - const int output_width = ArraySize(output_dims, 1); + float* output_data, const RuntimeShape& output_shape) { + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int depth = MatchingDim(input_shape, 3, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); for (int batch = 0; batch < batches; ++batch) { for (int out_y = 0; out_y < output_height; ++out_y) { for (int out_x = 0; out_x < output_width; ++out_x) { @@ -2506,10 +2440,10 @@ inline void MaxPool(const float* input_data, const Dims<4>& input_dims, const int in_y = in_y_origin + filter_y; max = std::max( max, - input_data[Offset(input_dims, channel, in_x, in_y, batch)]); + input_data[Offset(input_shape, batch, in_y, in_x, channel)]); } } - output_data[Offset(output_dims, channel, out_x, out_y, batch)] = + output_data[Offset(output_shape, batch, out_y, out_x, channel)] = ActivationFunctionWithMinMax(max, output_activation_min, output_activation_max); } @@ -2518,42 +2452,22 @@ inline void MaxPool(const float* input_data, const Dims<4>& input_dims, } } -// legacy, for compatibility with old checked-in code -template -void MaxPool(const float* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, int pad_height, - int filter_width, int filter_height, float* output_data, - const Dims<4>& output_dims) { - float output_activation_min, output_activation_max; - GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); - MaxPool(input_data, input_dims, stride_width, stride_height, pad_width, - pad_height, filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - -// legacy, for compatibility with old checked-in code -template -void MaxPool(const float* input_data, const Dims<4>& input_dims, int stride, - int pad_width, int pad_height, int filter_width, int filter_height, - float* output_data, const Dims<4>& output_dims) { - MaxPool(input_data, input_dims, stride, stride, pad_width, pad_height, - filter_width, filter_height, output_data, output_dims); -} - -inline void MaxPool(const uint8* input_data, const Dims<4>& input_dims, +inline void MaxPool(const uint8* input_data, const RuntimeShape& input_shape, int stride_width, int stride_height, int pad_width, int pad_height, int filter_width, int filter_height, int32 output_activation_min, int32 output_activation_max, - uint8* output_data, const Dims<4>& output_dims) { + uint8* output_data, const RuntimeShape& output_shape) { TFLITE_DCHECK_LE(output_activation_min, output_activation_max); TFLITE_DCHECK_GE(output_activation_min, 0); TFLITE_DCHECK_LE(output_activation_max, 255); - const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); - const int input_height = ArraySize(input_dims, 2); - const int input_width = ArraySize(input_dims, 1); - const int output_height = ArraySize(output_dims, 2); - const int output_width = ArraySize(output_dims, 1); + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int depth = MatchingDim(input_shape, 3, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); for (int batch = 0; batch < batches; ++batch) { for (int out_y = 0; out_y < output_height; ++out_y) { for (int out_x = 0; out_x < output_width; ++out_x) { @@ -2577,12 +2491,12 @@ inline void MaxPool(const uint8* input_data, const Dims<4>& input_dims, const int in_y = in_y_origin + filter_y; max = std::max( max, - input_data[Offset(input_dims, channel, in_x, in_y, batch)]); + input_data[Offset(input_shape, batch, in_y, in_x, channel)]); } } max = std::max(max, output_activation_min); max = std::min(max, output_activation_max); - output_data[Offset(output_dims, channel, out_x, out_y, batch)] = + output_data[Offset(output_shape, batch, out_y, out_x, channel)] = static_cast(max); } } @@ -2590,38 +2504,6 @@ inline void MaxPool(const uint8* input_data, const Dims<4>& input_dims, } } -// legacy, for compatibility with old checked-in code -template -void MaxPool(const uint8* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, int pad_height, - int filter_width, int filter_height, int32 output_activation_min, - int32 output_activation_max, uint8* output_data, - const Dims<4>& output_dims) { - static_assert(Ac == FusedActivationFunctionType::kNone || - Ac == FusedActivationFunctionType::kRelu || - Ac == FusedActivationFunctionType::kRelu6 || - Ac == FusedActivationFunctionType::kRelu1, - ""); - if (Ac == FusedActivationFunctionType::kNone) { - TFLITE_DCHECK_EQ(output_activation_min, 0); - TFLITE_DCHECK_EQ(output_activation_max, 255); - } - MaxPool(input_data, input_dims, stride_width, stride_height, pad_width, - pad_height, filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - -// legacy, for compatibility with old checked-in code -template -void MaxPool(const uint8* input_data, const Dims<4>& input_dims, int stride, - int pad_width, int pad_height, int filter_width, int filter_height, - int32 output_activation_min, int32 output_activation_max, - uint8* output_data, const Dims<4>& output_dims) { - MaxPool(input_data, input_dims, stride, stride, pad_width, pad_height, - filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - inline void LocalResponseNormalization(const float* input_data, const Dims<4>& input_dims, int range, float bias, float alpha, float beta, @@ -2645,11 +2527,14 @@ inline void LocalResponseNormalization(const float* input_data, } } -inline void Softmax(const float* input_data, const Dims<4>& input_dims, +inline void Softmax(const float* input_data, const RuntimeShape& input_shape, float beta, float* output_data, - const Dims<4>& output_dims) { - const int outer_size = MatchingFlatSizeSkipDim(input_dims, 0, output_dims); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); + const RuntimeShape& output_shape) { + const int trailing_dim = input_shape.DimensionsCount() - 1; + const int outer_size = + MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); + const int depth = + MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); for (int i = 0; i < outer_size; ++i) { // Find max element value which we'll use to ensure numerical stability @@ -2674,10 +2559,10 @@ inline void Softmax(const float* input_data, const Dims<4>& input_dims, } } -inline void Softmax(const uint8* input_data, const Dims<4>& input_dims, +inline void Softmax(const uint8* input_data, const RuntimeShape& input_shape, int32 input_beta_multiplier, int32 input_beta_left_shift, int diff_min, uint8* output_data, - const Dims<4>& output_dims) { + const RuntimeShape& output_shape) { // The representation chosen for the input to the exp() function is Q5.26. // We need to leave extra space since values that we skip might be as large as // -32 before multiplying by input_beta_multiplier, and therefore as large as @@ -2690,8 +2575,11 @@ inline void Softmax(const uint8* input_data, const Dims<4>& input_dims, using FixedPointAccum = gemmlowp::FixedPoint; using FixedPoint0 = gemmlowp::FixedPoint; - const int outer_size = MatchingFlatSizeSkipDim(input_dims, 0, output_dims); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); + const int trailing_dim = input_shape.DimensionsCount() - 1; + const int outer_size = + MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); + const int depth = + MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); for (int i = 0; i < outer_size; ++i) { uint8 max_in_row = 0; @@ -2752,10 +2640,13 @@ inline void Softmax(const uint8* input_data, const Dims<4>& input_dims, } } -inline void LogSoftmax(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { - const int outer_size = MatchingFlatSizeSkipDim(input_dims, 0, output_dims); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); +inline void LogSoftmax(const float* input_data, const RuntimeShape& input_shape, + float* output_data, const RuntimeShape& output_shape) { + const int trailing_dim = input_shape.DimensionsCount() - 1; + const int outer_size = + MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); + const int depth = + MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); for (int i = 0; i < outer_size; ++i) { // Find max element value which we'll use to ensure numerical stability @@ -2895,11 +2786,11 @@ log_x_for_x_greater_than_or_equal_to_1( input_val); } -inline void LogSoftmax(const uint8* input_data, const Dims<4>& input_dims, +inline void LogSoftmax(const uint8* input_data, const RuntimeShape& input_shape, int32 input_multiplier, int32 input_left_shift, int32 reverse_scaling_divisor, int32 reverse_scaling_right_shift, int diff_min, - uint8* output_data, const Dims<4>& output_dims) { + uint8* output_data, const RuntimeShape& output_shape) { // The representation chosen for the input to the exp() function is Q5.26. // We need to leave extra space since values that we skip might be as large as // -32 before multiplying by input_beta_multiplier, and therefore as large as @@ -2913,8 +2804,11 @@ inline void LogSoftmax(const uint8* input_data, const Dims<4>& input_dims, using FixedPointAccum = gemmlowp::FixedPoint; using FixedPoint0 = gemmlowp::FixedPoint; - const int outer_size = MatchingFlatSizeSkipDim(input_dims, 0, output_dims); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); + const int trailing_dim = input_shape.DimensionsCount() - 1; + const int outer_size = + MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); + const int depth = + MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); for (int i = 0; i < outer_size; ++i) { uint8 max_in_row = 0; @@ -2978,9 +2872,9 @@ inline void LogSoftmax(const uint8* input_data, const Dims<4>& input_dims, } } -inline void Logistic(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { - const int flat_size = MatchingFlatSize(output_dims, input_dims); +inline void Logistic(const float* input_data, const RuntimeShape& input_shape, + float* output_data, const RuntimeShape& output_shape) { + const int flat_size = MatchingFlatSize(input_shape, output_shape); for (int i = 0; i < flat_size; i++) { float val = input_data[i]; @@ -2989,11 +2883,11 @@ inline void Logistic(const float* input_data, const Dims<4>& input_dims, } } -inline void Logistic(const uint8* input_data, const Dims<4>& input_dims, +inline void Logistic(const uint8* input_data, const RuntimeShape& input_shape, int32 input_zero_point, int32 input_range_radius, int32 input_multiplier, int input_left_shift, - uint8* output_data, const Dims<4>& output_dims) { - const int flat_size = MatchingFlatSize(output_dims, input_dims); + uint8* output_data, const RuntimeShape& output_shape) { + const int flat_size = MatchingFlatSize(input_shape, output_shape); for (int i = 0; i < flat_size; i++) { const uint8 input_val_u8 = input_data[i]; @@ -3027,9 +2921,9 @@ inline void Logistic(const uint8* input_data, const Dims<4>& input_dims, } } -inline void Logistic(const int16* input_data, const Dims<4>& input_dims, - int16* output_data, const Dims<4>& output_dims) { - const int flat_size = MatchingFlatSize(output_dims, input_dims); +inline void Logistic(const int16* input_data, const RuntimeShape& input_shape, + int16* output_data, const RuntimeShape& output_shape) { + const int flat_size = MatchingFlatSize(input_shape, output_shape); for (int i = 0; i < flat_size; i++) { // F0 uses 0 integer bits, range [-1, 1]. @@ -3045,9 +2939,9 @@ inline void Logistic(const int16* input_data, const Dims<4>& input_dims, } } -inline void Tanh(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { - const int flat_size = MatchingFlatSize(output_dims, input_dims); +inline void Tanh(const float* input_data, const RuntimeShape& input_shape, + float* output_data, const RuntimeShape& output_shape) { + const int flat_size = MatchingFlatSize(input_shape, output_shape); for (int i = 0; i < flat_size; i++) { float val = input_data[i]; @@ -3056,12 +2950,12 @@ inline void Tanh(const float* input_data, const Dims<4>& input_dims, } } -inline void Tanh(const uint8* input_data, const Dims<4>& input_dims, +inline void Tanh(const uint8* input_data, const RuntimeShape& input_shape, int32 input_zero_point, int32 input_range_radius, int32 input_multiplier, int input_left_shift, - uint8* output_data, const Dims<4>& output_dims) { + uint8* output_data, const RuntimeShape& output_shape) { const int32 output_zero_point = 128; - const int flat_size = MatchingFlatSize(output_dims, input_dims); + const int flat_size = MatchingFlatSize(input_shape, output_shape); for (int i = 0; i < flat_size; i++) { const uint8 input_val_u8 = input_data[i]; @@ -3096,15 +2990,15 @@ inline void Tanh(const uint8* input_data, const Dims<4>& input_dims, } } -inline void Tanh(const int16* input_data, const Dims<4>& input_dims, +inline void Tanh(const int16* input_data, const RuntimeShape& input_shape, int input_left_shift, int16* output_data, - const Dims<4>& output_dims) { + const RuntimeShape& output_shape) { // Support for shifts is limited until we have a parameterized version of // SaturatingRoundingMultiplyByPOT(). TFLITE_DCHECK_GE(input_left_shift, 0); TFLITE_DCHECK_LE(input_left_shift, 1); - const int flat_size = MatchingFlatSize(output_dims, input_dims); + const int flat_size = MatchingFlatSize(input_shape, output_shape); // F0 uses 0 integer bits, range [-1, 1]. // This is the return type of math functions such as tanh, logistic, diff --git a/tensorflow/contrib/lite/kernels/internal/softmax_quantized_test.cc b/tensorflow/contrib/lite/kernels/internal/softmax_quantized_test.cc index d781a7b642..a7dad3c14e 100644 --- a/tensorflow/contrib/lite/kernels/internal/softmax_quantized_test.cc +++ b/tensorflow/contrib/lite/kernels/internal/softmax_quantized_test.cc @@ -32,19 +32,21 @@ namespace tflite { namespace { void RunSoftmaxFloatReference(const uint8* input_data, - const Dims<4>& dims_common, int32 input_offset, - const double input_scale, int stride, float beta, + const RuntimeShape& shape_common, + int32 input_offset, const double input_scale, + int stride, float beta, uint8* reference_output_data) { - const int ref_buffer_size = RequiredBufferSizeForDims(dims_common); + const int ref_buffer_size = shape_common.FlatSize(); std::vector reference_dequant_data(ref_buffer_size); std::vector reference_output_float_data(ref_buffer_size); // Reference data generated via Dequant of input into float, and then applying // float Softmax. - reference_ops::Dequantize(input_data, dims_common, input_offset, input_scale, - reference_dequant_data.data(), dims_common); - optimized_ops::Softmax(reference_dequant_data.data(), dims_common, beta, - reference_output_float_data.data(), dims_common); + reference_ops::Dequantize( + input_data, ToRuntimeDims(shape_common), input_offset, input_scale, + reference_dequant_data.data(), ToRuntimeDims(shape_common)); + optimized_ops::Softmax(reference_dequant_data.data(), shape_common, beta, + reference_output_float_data.data(), shape_common); // Work with quantized scaling for Softmax, under which 256 represents 1, but // we limit this to 255. for (int i = 0; i < ref_buffer_size; i++) { @@ -55,9 +57,9 @@ void RunSoftmaxFloatReference(const uint8* input_data, } void CheckOutputData(const uint8* test_output, const uint8* reference_output, - const Dims<4>& dims_common, const string& check_label, - bool be_exacting) { - const int buffer_size = RequiredBufferSizeForDims(dims_common); + const RuntimeShape& shape_common, + const string& check_label, bool be_exacting) { + const int buffer_size = shape_common.FlatSize(); // While calculating some metrics in floating point, we work with quantized // scaling. std::vector diff(buffer_size); @@ -91,15 +93,15 @@ void CheckOutputData(const uint8* test_output, const uint8* reference_output, // Runs the Softmax and compares against the float reference implementation and // the quantized reference implementation. -void RunOneSoftmaxTest(const uint8* input_data, const Dims<4>& dims_common, - int32 input_offset, const double input_scale, int stride, - float beta) { - const int buffer_size = RequiredBufferSizeForDims(dims_common); +void RunOneSoftmaxTest(const uint8* input_data, + const RuntimeShape& shape_common, int32 input_offset, + const double input_scale, int stride, float beta) { + const int buffer_size = shape_common.FlatSize(); std::vector optimized_softmax_output(buffer_size); std::vector reference_float_softmax_output(buffer_size); std::vector reference_quant_softmax_output(buffer_size); - RunSoftmaxFloatReference(input_data, dims_common, input_offset, input_scale, + RunSoftmaxFloatReference(input_data, shape_common, input_offset, input_scale, stride, beta, reference_float_softmax_output.data()); int32 input_beta_multiplier; @@ -113,21 +115,21 @@ void RunOneSoftmaxTest(const uint8* input_data, const Dims<4>& dims_common, const int diff_min = -tflite::CalculateInputRadius(kScaledDiffIntegerBits, input_beta_left_shift); - optimized_ops::Softmax(input_data, dims_common, input_beta_multiplier, + optimized_ops::Softmax(input_data, shape_common, input_beta_multiplier, input_beta_left_shift, diff_min, - optimized_softmax_output.data(), dims_common); - reference_ops::Softmax(input_data, dims_common, input_beta_multiplier, + optimized_softmax_output.data(), shape_common); + reference_ops::Softmax(input_data, shape_common, input_beta_multiplier, input_beta_left_shift, diff_min, - reference_quant_softmax_output.data(), dims_common); + reference_quant_softmax_output.data(), shape_common); CheckOutputData(optimized_softmax_output.data(), - reference_float_softmax_output.data(), dims_common, + reference_float_softmax_output.data(), shape_common, "Optimized vs float reference", false); CheckOutputData(optimized_softmax_output.data(), - reference_quant_softmax_output.data(), dims_common, + reference_quant_softmax_output.data(), shape_common, "Optimized vs quant reference", true); CheckOutputData(reference_quant_softmax_output.data(), - reference_float_softmax_output.data(), dims_common, + reference_float_softmax_output.data(), shape_common, "Quant reference vs float reference", false); } @@ -150,13 +152,13 @@ bool TryOneUniformSoftmax() { const int32 input_offset = UniformRandomInt(-256, 0); const float beta = 1.0f + ExponentialRandomPositiveFloat(0.9f, 2, 10); - Dims<4> dims_common = - MakeDimsForInference(input_depth, input_width, input_height, batch); - const int buffer_size = RequiredBufferSizeForDims(dims_common); + auto shape_common = + RuntimeShape({batch, input_height, input_width, input_depth}); + const int buffer_size = shape_common.FlatSize(); std::vector input_data(buffer_size); FillRandom(&input_data); - RunOneSoftmaxTest(input_data.data(), dims_common, input_offset, input_scale, + RunOneSoftmaxTest(input_data.data(), shape_common, input_offset, input_scale, stride, beta); return true; } @@ -188,14 +190,14 @@ bool TryOneSkyscraperSoftmax(bool small_depth) { const int middle_min = UniformRandomInt(0, 255); const int sides_max = UniformRandomInt(0, middle_min); - Dims<4> dims_common = - MakeDimsForInference(input_depth, input_width, input_height, batch); - const int buffer_size = RequiredBufferSizeForDims(dims_common); + auto shape_common = + RuntimeShape({batch, input_height, input_width, input_depth}); + const int buffer_size = shape_common.FlatSize(); std::vector input_data(buffer_size); FillRandomSkyscraper(&input_data, input_depth, middle_proportion, middle_min, sides_max); - RunOneSoftmaxTest(input_data.data(), dims_common, input_offset, input_scale, + RunOneSoftmaxTest(input_data.data(), shape_common, input_offset, input_scale, stride, beta); return true; } diff --git a/tensorflow/contrib/lite/kernels/internal/types.h b/tensorflow/contrib/lite/kernels/internal/types.h index 64f4881a46..707d2d261a 100644 --- a/tensorflow/contrib/lite/kernels/internal/types.h +++ b/tensorflow/contrib/lite/kernels/internal/types.h @@ -294,6 +294,50 @@ inline int RequiredBufferSizeForDims(const Dims<4>& dims) { return FlatSize(dims); } +// Flat size calculation, checking that dimensions match with one or more other +// arrays. +inline int MatchingFlatSize(const RuntimeShape& shape, + const RuntimeShape& check_shape_0) { + const int dims_count = shape.DimensionsCount(); + for (int i = 0; i < dims_count; ++i) { + TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); + } + return shape.FlatSize(); +} + +inline int MatchingFlatSize(const RuntimeShape& shape, + const RuntimeShape& check_shape_0, + const RuntimeShape& check_shape_1) { + const int dims_count = shape.DimensionsCount(); + for (int i = 0; i < dims_count; ++i) { + TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); + } + return MatchingFlatSize(shape, check_shape_1); +} + +inline int MatchingFlatSize(const RuntimeShape& shape, + const RuntimeShape& check_shape_0, + const RuntimeShape& check_shape_1, + const RuntimeShape& check_shape_2) { + const int dims_count = shape.DimensionsCount(); + for (int i = 0; i < dims_count; ++i) { + TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); + } + return MatchingFlatSize(shape, check_shape_1, check_shape_2); +} + +inline int MatchingFlatSize(const RuntimeShape& shape, + const RuntimeShape& check_shape_0, + const RuntimeShape& check_shape_1, + const RuntimeShape& check_shape_2, + const RuntimeShape& check_shape_3) { + const int dims_count = shape.DimensionsCount(); + for (int i = 0; i < dims_count; ++i) { + TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); + } + return MatchingFlatSize(shape, check_shape_1, check_shape_2, check_shape_3); +} + // Flat size calculation, checking that dimensions match with one or more other // arrays. template @@ -320,7 +364,7 @@ inline int MatchingFlatSize(const Dims& dims, const Dims& check_dims_0, for (int i = 0; i < N; ++i) { TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); } - return FlatSize(dims, check_dims_1, check_dims_2); + return MatchingFlatSize(dims, check_dims_1, check_dims_2); } template @@ -331,7 +375,7 @@ inline int MatchingFlatSize(const Dims& dims, const Dims& check_dims_0, for (int i = 0; i < N; ++i) { TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); } - return FlatSize(dims, check_dims_1, check_dims_2, check_dims_3); + return MatchingFlatSize(dims, check_dims_1, check_dims_2, check_dims_3); } // Data is required to be contiguous, and so many operators can use either the diff --git a/tensorflow/contrib/lite/kernels/log_softmax_test.cc b/tensorflow/contrib/lite/kernels/log_softmax_test.cc index 62820a2f51..9a8d35e82c 100644 --- a/tensorflow/contrib/lite/kernels/log_softmax_test.cc +++ b/tensorflow/contrib/lite/kernels/log_softmax_test.cc @@ -90,10 +90,9 @@ TEST(LogSoftmaxOpTest, CompareWithTFmini) { m.Invoke(); std::unique_ptr output_buffer(new float[input_size * batch_size]); - static tflite::Dims<4> input_dims = {{input_size, 1, 1, batch_size}, - {1, 0, 0, input_size}}; - tflite::reference_ops::LogSoftmax(input_buffer, input_dims, - output_buffer.get(), input_dims); + auto input_shape = RuntimeShape({batch_size, 1, 1, input_size}); + tflite::reference_ops::LogSoftmax(input_buffer, input_shape, + output_buffer.get(), input_shape); std::vector expected; expected.insert(expected.end(), output_buffer.get(), diff --git a/tensorflow/contrib/lite/kernels/pooling.cc b/tensorflow/contrib/lite/kernels/pooling.cc index 311e9b8399..41771e60bc 100644 --- a/tensorflow/contrib/lite/kernels/pooling.cc +++ b/tensorflow/contrib/lite/kernels/pooling.cc @@ -126,12 +126,13 @@ void AverageEvalFloat(TfLiteContext* context, TfLiteNode* node, float activation_min, activation_max; CalculateActivationRangeFloat(params->activation, &activation_min, &activation_max); -#define TF_LITE_AVERAGE_POOL(type) \ - type::AveragePool( \ - GetTensorData(input), GetTensorDims(input), params->stride_width, \ - params->stride_height, data->padding.width, data->padding.height, \ - params->filter_width, params->filter_height, activation_min, \ - activation_max, GetTensorData(output), GetTensorDims(output)) +#define TF_LITE_AVERAGE_POOL(type) \ + type::AveragePool(GetTensorData(input), GetTensorShape(input), \ + params->stride_width, params->stride_height, \ + data->padding.width, data->padding.height, \ + params->filter_width, params->filter_height, \ + activation_min, activation_max, \ + GetTensorData(output), GetTensorShape(output)) if (kernel_type == kReference) { TF_LITE_AVERAGE_POOL(reference_ops); } else { @@ -148,13 +149,13 @@ void AverageEvalQuantized(TfLiteContext* context, TfLiteNode* node, int32_t activation_max; CalculateActivationRangeUint8(params->activation, output, &activation_min, &activation_max); -#define TF_LITE_AVERAGE_POOL(type) \ - type::AveragePool(GetTensorData(input), GetTensorDims(input), \ - params->stride_width, params->stride_height, \ - data->padding.width, data->padding.height, \ - params->filter_width, params->filter_height, \ - activation_min, activation_max, \ - GetTensorData(output), GetTensorDims(output)) +#define TF_LITE_AVERAGE_POOL(type) \ + type::AveragePool(GetTensorData(input), GetTensorShape(input), \ + params->stride_width, params->stride_height, \ + data->padding.width, data->padding.height, \ + params->filter_width, params->filter_height, \ + activation_min, activation_max, \ + GetTensorData(output), GetTensorShape(output)) if (kernel_type == kReference) { TF_LITE_AVERAGE_POOL(reference_ops); } else { @@ -170,12 +171,13 @@ void MaxEvalFloat(TfLiteContext* context, TfLiteNode* node, float activation_min, activation_max; CalculateActivationRangeFloat(params->activation, &activation_min, &activation_max); -#define TF_LITE_MAX_POOL(type) \ - type::MaxPool( \ - GetTensorData(input), GetTensorDims(input), params->stride_width, \ - params->stride_height, data->padding.width, data->padding.height, \ - params->filter_width, params->filter_height, activation_min, \ - activation_max, GetTensorData(output), GetTensorDims(output)) +#define TF_LITE_MAX_POOL(type) \ + type::MaxPool(GetTensorData(input), GetTensorShape(input), \ + params->stride_width, params->stride_height, \ + data->padding.width, data->padding.height, \ + params->filter_width, params->filter_height, activation_min, \ + activation_max, GetTensorData(output), \ + GetTensorShape(output)) if (kernel_type == kReference) { TF_LITE_MAX_POOL(reference_ops); } else { @@ -193,12 +195,12 @@ void MaxEvalQuantized(TfLiteContext* context, TfLiteNode* node, CalculateActivationRangeUint8(params->activation, output, &activation_min, &activation_max); #define TF_LITE_MAX_POOL(type) \ - type::MaxPool(GetTensorData(input), GetTensorDims(input), \ + type::MaxPool(GetTensorData(input), GetTensorShape(input), \ params->stride_width, params->stride_height, \ data->padding.width, data->padding.height, \ params->filter_width, params->filter_height, activation_min, \ activation_max, GetTensorData(output), \ - GetTensorDims(output)) + GetTensorShape(output)) if (kernel_type == kReference) { TF_LITE_MAX_POOL(reference_ops); } else { @@ -214,12 +216,13 @@ void L2EvalFloat(TfLiteContext* context, TfLiteNode* node, float activation_min, activation_max; CalculateActivationRangeFloat(params->activation, &activation_min, &activation_max); -#define TF_LITE_L2_POOL(type) \ - type::L2Pool( \ - GetTensorData(input), GetTensorDims(input), params->stride_width, \ - params->stride_height, data->padding.width, data->padding.height, \ - params->filter_width, params->filter_height, activation_min, \ - activation_max, GetTensorData(output), GetTensorDims(output)) +#define TF_LITE_L2_POOL(type) \ + type::L2Pool(GetTensorData(input), GetTensorShape(input), \ + params->stride_width, params->stride_height, \ + data->padding.width, data->padding.height, \ + params->filter_width, params->filter_height, activation_min, \ + activation_max, GetTensorData(output), \ + GetTensorShape(output)) if (kernel_type == kReference) { TF_LITE_L2_POOL(reference_ops); } else { diff --git a/tensorflow/contrib/lite/kernels/softmax_test.cc b/tensorflow/contrib/lite/kernels/softmax_test.cc index 6c5338ff0f..727822f6be 100644 --- a/tensorflow/contrib/lite/kernels/softmax_test.cc +++ b/tensorflow/contrib/lite/kernels/softmax_test.cc @@ -92,10 +92,9 @@ TEST(SoftmaxOpTest, CompareWithTFminiBetaEq1) { m.Invoke(); std::unique_ptr output_buffer(new float[input_size * batch_size]); - static tflite::Dims<4> input_dims = {{input_size, 1, 1, batch_size}, - {1, 0, 0, input_size}}; - tflite::reference_ops::Softmax(input_buffer, input_dims, beta, - output_buffer.get(), input_dims); + auto input_shape = RuntimeShape({batch_size, 1, 1, input_size}); + tflite::reference_ops::Softmax(input_buffer, input_shape, beta, + output_buffer.get(), input_shape); std::vector expected; expected.insert(expected.end(), output_buffer.get(), @@ -120,10 +119,9 @@ TEST(SoftmaxOpTest, CompareWithTFminiBetaNotEq1) { m.Invoke(); std::unique_ptr output_buffer(new float[input_size * batch_size]); - static tflite::Dims<4> input_dims = {{input_size, 1, 1, batch_size}, - {1, 0, 0, input_size}}; - tflite::reference_ops::Softmax(input_buffer, input_dims, beta, - output_buffer.get(), input_dims); + auto input_shape = RuntimeShape({batch_size, 1, 1, input_size}); + tflite::reference_ops::Softmax(input_buffer, input_shape, beta, + output_buffer.get(), input_shape); std::vector expected; expected.insert(expected.end(), output_buffer.get(), -- GitLab From f3075bda64bd03423859f7b4da61a73fec77ff9f Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Tue, 19 Jun 2018 17:20:59 +0000 Subject: [PATCH 665/816] Remove duplicate imports in dynamic_stitch_op_test.py There is a duplicate `from tensorflow.python.framework import dtypes` in dynamic_stitch_op_test.py (See line 24 above). Signed-off-by: Yong Tang --- tensorflow/python/kernel_tests/dynamic_stitch_op_test.py | 1 - 1 file changed, 1 deletion(-) diff --git a/tensorflow/python/kernel_tests/dynamic_stitch_op_test.py b/tensorflow/python/kernel_tests/dynamic_stitch_op_test.py index 159cba5fa3..c4d4ce780b 100644 --- a/tensorflow/python/kernel_tests/dynamic_stitch_op_test.py +++ b/tensorflow/python/kernel_tests/dynamic_stitch_op_test.py @@ -27,7 +27,6 @@ from tensorflow.python.ops import data_flow_ops from tensorflow.python.ops import gradients_impl import tensorflow.python.ops.data_flow_grad # pylint: disable=unused-import from tensorflow.python.platform import test -from tensorflow.python.framework import dtypes class DynamicStitchTestBase(object): -- GitLab From ccaf2ca02739792a8a8e50a95246f2db1197aa97 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 19 Jun 2018 10:22:02 -0700 Subject: [PATCH 666/816] Use --output_user_root to specify a short output base for Windows build (Prepare for upgrading Bazel to 0.14.1 on Windows) PiperOrigin-RevId: 201197774 --- .../tools/ci_build/windows/cpu/pip/build_tf_windows.sh | 7 ++++++- tensorflow/tools/ci_build/windows/libtensorflow_cpu.sh | 2 +- 2 files changed, 7 insertions(+), 2 deletions(-) diff --git a/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh b/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh index 0b13b97209..5c305f7512 100644 --- a/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh +++ b/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh @@ -77,7 +77,12 @@ fi # to distinct them. This helps avoid building the same targets twice. echo "build --distinct_host_configuration=false" >> "${TMP_BAZELRC}" -echo "import %workspace%/${TMP_BAZELRC}" >> .bazelrc +# Enable short object file path to avoid long path issue on Windows. +echo "startup --output_user_root=${TMPDIR}" >> "${TMP_BAZELRC}" + +if ! grep -q "import %workspace%/${TMP_BAZELRC}" .bazelrc; then + echo "import %workspace%/${TMP_BAZELRC}" >> .bazelrc +fi run_configure_for_cpu_build diff --git a/tensorflow/tools/ci_build/windows/libtensorflow_cpu.sh b/tensorflow/tools/ci_build/windows/libtensorflow_cpu.sh index 583d1d5f09..fdbd1120b2 100755 --- a/tensorflow/tools/ci_build/windows/libtensorflow_cpu.sh +++ b/tensorflow/tools/ci_build/windows/libtensorflow_cpu.sh @@ -41,7 +41,7 @@ run_configure_for_cpu_build # build_libtensorflow_tarball in ../builds/libtensorflow.sh # cannot be used on Windows since it relies on pkg_tar rules. # So we do something special here -bazel build -c opt --copt=/arch:AVX \ +bazel --output_user_root=${TMPDIR} build -c opt --copt=/arch:AVX \ tensorflow:libtensorflow.so \ tensorflow/tools/lib_package:clicenses_generate \ tensorflow/java:libtensorflow_jni.so \ -- GitLab From c740b345e8c17cde0dd4691c7e240a065cb8c88c Mon Sep 17 00:00:00 2001 From: Akshay Modi Date: Tue, 19 Jun 2018 10:25:10 -0700 Subject: [PATCH 667/816] Allow setting server def on the eager context, and add the eager service to the grpc_tensorflow_server. PiperOrigin-RevId: 201198350 --- tensorflow/c/eager/BUILD | 5 +- tensorflow/c/eager/c_api.cc | 48 ++++++--- tensorflow/c/eager/c_api_internal.h | 4 +- tensorflow/c/eager/c_api_test.cc | 18 ++-- tensorflow/core/common_runtime/eager/BUILD | 2 +- .../core/common_runtime/eager/context.cc | 4 +- .../core/common_runtime/eager/context.h | 10 +- tensorflow/core/distributed_runtime/rpc/BUILD | 2 + .../core/distributed_runtime/rpc/eager/BUILD | 17 +--- .../rpc/eager/eager_grpc_server_lib.h | 97 ------------------- .../rpc/eager/grpc_eager_service_impl.cc | 11 +-- .../rpc/eager/grpc_eager_service_impl.h | 10 +- .../rpc/grpc_server_lib.cc | 30 +++++- .../distributed_runtime/rpc/grpc_server_lib.h | 17 +++- tensorflow/python/eager/context.py | 10 +- tensorflow/python/framework/ops.py | 31 +++++- tensorflow/python/pywrap_tfe.i | 1 + 17 files changed, 144 insertions(+), 173 deletions(-) delete mode 100644 tensorflow/core/distributed_runtime/rpc/eager/eager_grpc_server_lib.h diff --git a/tensorflow/c/eager/BUILD b/tensorflow/c/eager/BUILD index f265da2c2c..93d07135e1 100644 --- a/tensorflow/c/eager/BUILD +++ b/tensorflow/c/eager/BUILD @@ -54,7 +54,6 @@ tf_cuda_library( "//tensorflow/core/distributed_runtime/eager:eager_client", "//tensorflow/core/distributed_runtime/rpc/eager:grpc_eager_client", "//tensorflow/core/distributed_runtime/rpc:grpc_channel", - "//tensorflow/core/distributed_runtime/rpc/eager:eager_grpc_server_lib", "//tensorflow/core/distributed_runtime/rpc:grpc_server_lib", "//tensorflow/core/distributed_runtime/rpc:grpc_worker_cache", "//tensorflow/core/distributed_runtime/rpc:grpc_worker_service", @@ -93,10 +92,10 @@ tf_cuda_library( "//tensorflow/core/distributed_runtime/eager:eager_client", "//tensorflow/core/distributed_runtime/eager:remote_tensor_handle", "//tensorflow/core/distributed_runtime/rpc:grpc_channel", + "//tensorflow/core/distributed_runtime/rpc:grpc_server_lib", "//tensorflow/core/distributed_runtime/rpc:grpc_worker_cache", "//tensorflow/core/distributed_runtime/rpc:grpc_worker_service", "//tensorflow/core/distributed_runtime/rpc:rpc_rendezvous_mgr", - "//tensorflow/core/distributed_runtime/rpc/eager:eager_grpc_server_lib", "//tensorflow/core/distributed_runtime/rpc/eager:grpc_eager_client", ], ) @@ -139,7 +138,7 @@ tf_cuda_cc_test( "//tensorflow/core:protos_all_cc", "//tensorflow/core:test", "//tensorflow/core:test_main", - "//tensorflow/core/distributed_runtime/rpc/eager:eager_grpc_server_lib", + "//tensorflow/core/distributed_runtime/rpc:grpc_server_lib", ], ) diff --git a/tensorflow/c/eager/c_api.cc b/tensorflow/c/eager/c_api.cc index 81221c4078..55d9c26b0d 100644 --- a/tensorflow/c/eager/c_api.cc +++ b/tensorflow/c/eager/c_api.cc @@ -36,9 +36,9 @@ limitations under the License. #include "tensorflow/core/common_runtime/eager/execute.h" #include "tensorflow/core/common_runtime/function.h" #include "tensorflow/core/common_runtime/rendezvous_mgr.h" -#include "tensorflow/core/distributed_runtime/rpc/eager/eager_grpc_server_lib.h" #include "tensorflow/core/distributed_runtime/rpc/eager/grpc_eager_client.h" #include "tensorflow/core/distributed_runtime/rpc/grpc_channel.h" +#include "tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h" #include "tensorflow/core/distributed_runtime/server_lib.h" #include "tensorflow/core/distributed_runtime/worker_env.h" #include "tensorflow/core/framework/node_def_util.h" @@ -147,46 +147,66 @@ tensorflow::Status CreateRemoteContexts( tensorflow::Status NewRemoteAwareTFE_Context(const TFE_ContextOptions* opts, TFE_Context** ctx) { + // We don't use the TF_RETURN_IF_ERROR macro directly since that destroys the + // server object (which currently CHECK-fails) and we miss the error, instead, + // we log the error, and then return to allow the user to see the error + // message. +#define LOG_AND_RETURN_IF_ERROR(...) \ + do { \ + const ::tensorflow::Status _status = (__VA_ARGS__); \ + LOG(ERROR) << _status.error_message(); \ + if (TF_PREDICT_FALSE(!_status.ok())) return _status; \ + } while (0) + string worker_name = tensorflow::strings::StrCat( "/job:", opts->server_def.job_name(), "/replica:0/task:", opts->server_def.task_index()); - std::unique_ptr server; - TF_RETURN_IF_ERROR( - tensorflow::eager::EagerGrpcServer::Create(opts->server_def, &server)); - TF_RETURN_IF_ERROR(server->Start()); + std::unique_ptr server; + LOG_AND_RETURN_IF_ERROR(tensorflow::NewServer(opts->server_def, &server)); + + tensorflow::GrpcServer* grpc_server = + dynamic_cast(server.get()); + if (grpc_server == nullptr) { + LOG_AND_RETURN_IF_ERROR(tensorflow::errors::Internal( + "Currently, TFE_NewContext only supports tensorflow::GrpcServer.")); + } + + LOG_AND_RETURN_IF_ERROR(grpc_server->Start()); std::vector remote_workers; - server->master_env()->worker_cache->ListWorkers(&remote_workers); + grpc_server->master_env()->worker_cache->ListWorkers(&remote_workers); remote_workers.erase( std::remove(remote_workers.begin(), remote_workers.end(), worker_name), remote_workers.end()); std::unique_ptr remote_device_mgr; - TF_RETURN_IF_ERROR(GetAllRemoteDevices( - remote_workers, server->master_env()->worker_cache, &remote_device_mgr)); + LOG_AND_RETURN_IF_ERROR(GetAllRemoteDevices( + remote_workers, grpc_server->master_env()->worker_cache, + &remote_device_mgr)); std::shared_ptr channel_cache = - server->channel_cache(); + grpc_server->channel_cache(); std::unique_ptr remote_eager_workers( tensorflow::eager::NewGrpcEagerClientCache(channel_cache)); // Initialize remote eager workers. tensorflow::gtl::FlatMap remote_contexts; - TF_RETURN_IF_ERROR(CreateRemoteContexts(remote_workers, - remote_eager_workers.get(), - opts->async, &remote_contexts)); + LOG_AND_RETURN_IF_ERROR(CreateRemoteContexts(remote_workers, + remote_eager_workers.get(), + opts->async, &remote_contexts)); tensorflow::RemoteRendezvous* r = - server->worker_env()->rendezvous_mgr->Find(0); + grpc_server->worker_env()->rendezvous_mgr->Find(0); - auto* device_mgr = server->worker_env()->device_mgr; + auto* device_mgr = grpc_server->worker_env()->device_mgr; *ctx = new TFE_Context(opts->session_options.options, opts->policy, opts->async, device_mgr, r, std::move(server), std::move(remote_eager_workers), std::move(remote_device_mgr), remote_contexts); return tensorflow::Status::OK(); +#undef LOG_AND_RETURN_IF_ERROR } } // namespace diff --git a/tensorflow/c/eager/c_api_internal.h b/tensorflow/c/eager/c_api_internal.h index 04a6efc47c..4c5077023d 100644 --- a/tensorflow/c/eager/c_api_internal.h +++ b/tensorflow/c/eager/c_api_internal.h @@ -39,7 +39,7 @@ limitations under the License. #include "tensorflow/core/common_runtime/rendezvous_mgr.h" #include "tensorflow/core/distributed_runtime/eager/eager_client.h" #include "tensorflow/core/distributed_runtime/remote_device.h" -#include "tensorflow/core/distributed_runtime/rpc/eager/eager_grpc_server_lib.h" +#include "tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h" #include "tensorflow/core/distributed_runtime/rpc/grpc_worker_cache.h" #include "tensorflow/core/distributed_runtime/rpc/grpc_worker_service.h" #include "tensorflow/core/distributed_runtime/rpc/rpc_rendezvous_mgr.h" @@ -78,7 +78,7 @@ struct TFE_Context { TFE_ContextDevicePlacementPolicy default_policy, bool async, tensorflow::DeviceMgr* local_device_mgr, tensorflow::Rendezvous* rendezvous, - std::unique_ptr server, + std::unique_ptr server, std::unique_ptr remote_eager_workers, std::unique_ptr remote_device_mgr, const tensorflow::gtl::FlatMap& diff --git a/tensorflow/c/eager/c_api_test.cc b/tensorflow/c/eager/c_api_test.cc index 992d1afd5f..1d71a78b75 100644 --- a/tensorflow/c/eager/c_api_test.cc +++ b/tensorflow/c/eager/c_api_test.cc @@ -17,7 +17,7 @@ limitations under the License. #include #include "tensorflow/c/eager/c_api_test_util.h" -#include "tensorflow/core/distributed_runtime/rpc/eager/eager_grpc_server_lib.h" +#include "tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h" #include "tensorflow/core/framework/function.pb.h" #include "tensorflow/core/lib/strings/strcat.h" #include "tensorflow/core/platform/logging.h" @@ -132,10 +132,10 @@ void TestRemoteExecute(bool async) { server_def.set_task_index(1); - std::unique_ptr worker_server; - ASSERT_TRUE( - tensorflow::eager::EagerGrpcServer::Create(server_def, &worker_server) - .ok()); + std::unique_ptr worker_server; + ASSERT_TRUE(tensorflow::GrpcServer::Create( + server_def, tensorflow::Env::Default(), &worker_server) + .ok()); ASSERT_TRUE(worker_server->Start().ok()); TF_Status* status = TF_NewStatus(); @@ -215,10 +215,10 @@ void TestRemoteExecuteSilentCopies(bool async) { server_def.set_task_index(1); - std::unique_ptr worker_server; - ASSERT_TRUE( - tensorflow::eager::EagerGrpcServer::Create(server_def, &worker_server) - .ok()); + std::unique_ptr worker_server; + ASSERT_TRUE(tensorflow::GrpcServer::Create( + server_def, tensorflow::Env::Default(), &worker_server) + .ok()); ASSERT_TRUE(worker_server->Start().ok()); TF_Status* status = TF_NewStatus(); diff --git a/tensorflow/core/common_runtime/eager/BUILD b/tensorflow/core/common_runtime/eager/BUILD index b5120f2872..671cd142fb 100644 --- a/tensorflow/core/common_runtime/eager/BUILD +++ b/tensorflow/core/common_runtime/eager/BUILD @@ -51,9 +51,9 @@ tf_cuda_library( "//tensorflow/core:lib_internal", "//tensorflow/core:protos_all_cc", "//tensorflow/core:session_options", + "//tensorflow/core/distributed_runtime:server_lib", "//tensorflow/core/distributed_runtime:worker_session", "//tensorflow/core/distributed_runtime/eager:eager_client", - "//tensorflow/core/distributed_runtime/rpc:grpc_server_lib", ], ) diff --git a/tensorflow/core/common_runtime/eager/context.cc b/tensorflow/core/common_runtime/eager/context.cc index 8381cb58d2..cb9ee668cf 100644 --- a/tensorflow/core/common_runtime/eager/context.cc +++ b/tensorflow/core/common_runtime/eager/context.cc @@ -41,7 +41,7 @@ EagerContext::EagerContext(const SessionOptions& opts, EagerContext::EagerContext( const SessionOptions& opts, ContextDevicePlacementPolicy default_policy, bool async, DeviceMgr* local_device_mgr, Rendezvous* rendezvous, - std::unique_ptr server, + std::unique_ptr server, std::unique_ptr remote_eager_workers, std::unique_ptr remote_device_manager, const gtl::FlatMap& remote_contexts) @@ -128,7 +128,7 @@ EagerContext::~EagerContext() { if (server_) { // TODO(nareshmodi): Fix this. LOG(WARNING) << "Unable to destroy server_ object, so releasing instead. " - "GrpcServer doesn't support clean shutdown."; + "Servers don't support clean shutdown."; server_.release(); } diff --git a/tensorflow/core/common_runtime/eager/context.h b/tensorflow/core/common_runtime/eager/context.h index 096ed3112e..3766299826 100644 --- a/tensorflow/core/common_runtime/eager/context.h +++ b/tensorflow/core/common_runtime/eager/context.h @@ -30,7 +30,7 @@ limitations under the License. #include "tensorflow/core/common_runtime/function.h" #include "tensorflow/core/common_runtime/rendezvous_mgr.h" #include "tensorflow/core/distributed_runtime/eager/eager_client.h" -#include "tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h" +#include "tensorflow/core/distributed_runtime/server_lib.h" #include "tensorflow/core/framework/rendezvous.h" #include "tensorflow/core/lib/core/stringpiece.h" #include "tensorflow/core/lib/core/threadpool.h" @@ -75,8 +75,8 @@ class EagerContext { // workers. // // Additional remote-specific args are: - // - server: A GrpcServer that exports the tensorflow.WorkerService. Note - // that this class expects the server to already have been started. + // - server: A ServerInterface that exports the tensorflow.WorkerService. + // Note that this class expects the server to already have been started. // - remote_eager_workers: A cache from which we can get "EagerClient"s to // communicate with remote eager services. // - remote_device_mgr: A DeviceMgr* which contains all remote devices @@ -85,7 +85,7 @@ class EagerContext { explicit EagerContext( const SessionOptions& opts, ContextDevicePlacementPolicy default_policy, bool async, DeviceMgr* local_device_mgr, Rendezvous* rendezvous, - std::unique_ptr server, + std::unique_ptr server, std::unique_ptr remote_eager_workers, std::unique_ptr remote_device_manager, const gtl::FlatMap& remote_contexts); @@ -231,7 +231,7 @@ class EagerContext { // The server_ is not const since we release it when the context is destroyed. // Therefore the server_ object is not marked as const (even though it should // be). - std::unique_ptr server_; + std::unique_ptr server_; const std::unique_ptr remote_eager_workers_; const std::unique_ptr remote_device_manager_; diff --git a/tensorflow/core/distributed_runtime/rpc/BUILD b/tensorflow/core/distributed_runtime/rpc/BUILD index 882271e3f5..7b19427e4b 100644 --- a/tensorflow/core/distributed_runtime/rpc/BUILD +++ b/tensorflow/core/distributed_runtime/rpc/BUILD @@ -284,7 +284,9 @@ cc_library( "//tensorflow/core/distributed_runtime:rpc_collective_executor_mgr", "//tensorflow/core/distributed_runtime:server_lib", "//tensorflow/core/distributed_runtime:session_mgr", + "//tensorflow/core/distributed_runtime:worker_cache_wrapper", "//tensorflow/core/distributed_runtime:worker_env", + "//tensorflow/core/distributed_runtime/rpc/eager:grpc_eager_service_impl", "@grpc", "@grpc//:grpc++", ], diff --git a/tensorflow/core/distributed_runtime/rpc/eager/BUILD b/tensorflow/core/distributed_runtime/rpc/eager/BUILD index a5472159cc..8cec497361 100644 --- a/tensorflow/core/distributed_runtime/rpc/eager/BUILD +++ b/tensorflow/core/distributed_runtime/rpc/eager/BUILD @@ -42,26 +42,11 @@ cc_library( "//tensorflow/core:framework", "//tensorflow/core:ptr_util", "//tensorflow/core/distributed_runtime/eager:eager_service_impl", + "//tensorflow/core/distributed_runtime/rpc:async_service_interface", "//tensorflow/core/distributed_runtime/rpc:grpc_call", "//tensorflow/core/distributed_runtime/rpc:grpc_channel", - "//tensorflow/core/distributed_runtime/rpc:grpc_server_lib", "//tensorflow/core/distributed_runtime/rpc:grpc_util", "//tensorflow/core/distributed_runtime/rpc:grpc_worker_cache", - "//tensorflow/core/distributed_runtime/rpc:grpc_worker_service", "@grpc//:grpc++", ], ) - -cc_library( - name = "eager_grpc_server_lib", - hdrs = ["eager_grpc_server_lib.h"], - deps = [ - ":grpc_eager_service_impl", - "//tensorflow/core:core_cpu", - "//tensorflow/core/distributed_runtime:rendezvous_mgr_interface", - "//tensorflow/core/distributed_runtime:worker_cache_wrapper", - "//tensorflow/core/distributed_runtime/eager:eager_service_impl", - "//tensorflow/core/distributed_runtime/rpc:grpc_server_lib", - "//tensorflow/core/distributed_runtime/rpc:grpc_worker_service", - ], -) diff --git a/tensorflow/core/distributed_runtime/rpc/eager/eager_grpc_server_lib.h b/tensorflow/core/distributed_runtime/rpc/eager/eager_grpc_server_lib.h deleted file mode 100644 index 9b863ccee5..0000000000 --- a/tensorflow/core/distributed_runtime/rpc/eager/eager_grpc_server_lib.h +++ /dev/null @@ -1,97 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_CORE_DISTRIBUTED_RUNTIME_RPC_EAGER_EAGER_GRPC_SERVER_LIB_H_ -#define TENSORFLOW_CORE_DISTRIBUTED_RUNTIME_RPC_EAGER_EAGER_GRPC_SERVER_LIB_H_ - -#include "tensorflow/core/common_runtime/device_factory.h" -#include "tensorflow/core/distributed_runtime/eager/eager_service_impl.h" -#include "tensorflow/core/distributed_runtime/rendezvous_mgr_interface.h" -#include "tensorflow/core/distributed_runtime/rpc/eager/grpc_eager_service_impl.h" -#include "tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h" -#include "tensorflow/core/distributed_runtime/rpc/grpc_worker_service.h" -#include "tensorflow/core/distributed_runtime/worker_cache_wrapper.h" - -namespace tensorflow { -namespace eager { - -class EagerGrpcServer : public GrpcServer { - public: - static Status Create(const ServerDef& server_def, - std::unique_ptr* server) { - std::unique_ptr ret(new EagerGrpcServer(server_def)); - - TF_RETURN_IF_ERROR(ret->InitEager()); - - *server = std::move(ret); - - return Status::OK(); - } - - Status Start() override { - TF_RETURN_IF_ERROR(GrpcServer::Start()); - - eager_service_->Start(); - - return Status::OK(); - } - - Status Stop() override { - TF_RETURN_IF_ERROR(GrpcServer::Stop()); - - eager_service_->Stop(); - - return Status::OK(); - } - - using GrpcServer::channel_cache; - using GrpcServer::master_env; - using GrpcServer::worker_env; - - private: - EagerGrpcServer(const ServerDef& server_def) - : GrpcServer(server_def, Env::Default()), - worker_name_( - strings::StrCat("/job:", server_def.job_name(), - "/replica:0/task:", server_def.task_index())) {} - - Status InitEager() { - TF_RETURN_IF_ERROR(this->Init( - [this](const WorkerEnv* worker_env, - ::grpc::ServerBuilder* server_builder) { - this->eager_service_.reset( - new eager::GrpcEagerServiceImpl(worker_env, server_builder)); - }, - nullptr, nullptr)); - - worker_session_ = WorkerSession::CreateWithBorrowedDeviceMgr( - "", worker_name_, - std::unique_ptr( - new WorkerCacheWrapper(master_env()->worker_cache)), - worker_env()->device_mgr, {}); - - auto* r = worker_env()->rendezvous_mgr->Find(0); - return r->Initialize(worker_session_.get()); - } - - std::unique_ptr eager_service_; - std::shared_ptr worker_session_; - const string worker_name_; -}; // namespace eager - -} // namespace eager -} // namespace tensorflow - -#endif // TENSORFLOW_CORE_DISTRIBUTED_RUNTIME_RPC_EAGER_EAGER_GRPC_SERVER_LIB_H_ diff --git a/tensorflow/core/distributed_runtime/rpc/eager/grpc_eager_service_impl.cc b/tensorflow/core/distributed_runtime/rpc/eager/grpc_eager_service_impl.cc index b36c6dce86..52e06c263d 100644 --- a/tensorflow/core/distributed_runtime/rpc/eager/grpc_eager_service_impl.cc +++ b/tensorflow/core/distributed_runtime/rpc/eager/grpc_eager_service_impl.cc @@ -18,10 +18,8 @@ limitations under the License. #include "tensorflow/core/distributed_runtime/rpc/eager/grpc_eager_service.h" #include "tensorflow/core/distributed_runtime/rpc/grpc_call.h" #include "tensorflow/core/distributed_runtime/rpc/grpc_channel.h" -#include "tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h" #include "tensorflow/core/distributed_runtime/rpc/grpc_util.h" #include "tensorflow/core/distributed_runtime/rpc/grpc_worker_cache.h" -#include "tensorflow/core/distributed_runtime/rpc/grpc_worker_service.h" #include "tensorflow/core/util/ptr_util.h" namespace tensorflow { @@ -36,7 +34,7 @@ GrpcEagerServiceImpl::GrpcEagerServiceImpl( cq_ = server_builder->AddCompletionQueue(); } -void GrpcEagerServiceImpl::DriveCQ() { +void GrpcEagerServiceImpl::HandleRPCsLoop() { #define ENQUEUE_REQUEST(method) \ do { \ CallSchedule([this]() { DriveCQ(); }); -} - -void GrpcEagerServiceImpl::Stop() { +void GrpcEagerServiceImpl::Shutdown() { // This enqueues a special event (with a null tag) // that causes the completion queue to be shut down on the // polling thread. diff --git a/tensorflow/core/distributed_runtime/rpc/eager/grpc_eager_service_impl.h b/tensorflow/core/distributed_runtime/rpc/eager/grpc_eager_service_impl.h index e94aedf535..9a94026342 100644 --- a/tensorflow/core/distributed_runtime/rpc/eager/grpc_eager_service_impl.h +++ b/tensorflow/core/distributed_runtime/rpc/eager/grpc_eager_service_impl.h @@ -20,16 +20,16 @@ limitations under the License. #include "grpcpp/completion_queue.h" #include "grpcpp/server_builder.h" #include "tensorflow/core/distributed_runtime/eager/eager_service_impl.h" +#include "tensorflow/core/distributed_runtime/rpc/async_service_interface.h" #include "tensorflow/core/distributed_runtime/rpc/eager/grpc_eager_service.h" #include "tensorflow/core/distributed_runtime/rpc/grpc_call.h" -#include "tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h" #include "tensorflow/core/distributed_runtime/rpc/grpc_util.h" namespace tensorflow { namespace eager { // This class is a wrapper that handles communication for gRPC. -class GrpcEagerServiceImpl { +class GrpcEagerServiceImpl : public AsyncServiceInterface { public: template using EagerCall = Call shutdown_alarm_; std::unique_ptr<::grpc::ServerCompletionQueue> cq_; diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc b/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc index 43dbe20836..2dd3e8678b 100644 --- a/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc +++ b/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc @@ -35,6 +35,7 @@ limitations under the License. #include "tensorflow/core/distributed_runtime/master_env.h" #include "tensorflow/core/distributed_runtime/master_session.h" #include "tensorflow/core/distributed_runtime/rpc/async_service_interface.h" +#include "tensorflow/core/distributed_runtime/rpc/eager/grpc_eager_service_impl.h" #include "tensorflow/core/distributed_runtime/rpc/grpc_channel.h" #include "tensorflow/core/distributed_runtime/rpc/grpc_master_service.h" #include "tensorflow/core/distributed_runtime/rpc/grpc_worker_cache.h" @@ -42,6 +43,7 @@ limitations under the License. #include "tensorflow/core/distributed_runtime/rpc/rpc_rendezvous_mgr.h" #include "tensorflow/core/distributed_runtime/rpc_collective_executor_mgr.h" #include "tensorflow/core/distributed_runtime/server_lib.h" +#include "tensorflow/core/distributed_runtime/worker_cache_wrapper.h" #include "tensorflow/core/distributed_runtime/worker_env.h" #include "tensorflow/core/framework/op.h" #include "tensorflow/core/lib/strings/strcat.h" @@ -81,6 +83,7 @@ GrpcServer::~GrpcServer() { delete master_service_; delete worker_service_; + delete eager_service_; // TODO(mrry): Refactor the *Env classes so that it is less fiddly // to destroy them. @@ -192,6 +195,8 @@ Status GrpcServer::Init( worker_func ? worker_func(&worker_env_) : NewGrpcWorker(&worker_env_); worker_service_ = NewGrpcWorkerService(worker_impl_.get(), &builder).release(); + eager_service_ = new eager::GrpcEagerServiceImpl(&worker_env_, &builder); + // extra service: if (service_func != nullptr) { service_func(&worker_env_, &builder); @@ -264,7 +269,15 @@ Status GrpcServer::Init( LocalMaster::Register(target(), master_impl_.get(), config.operation_timeout_in_ms()); - return Status::OK(); + // Generate a dummy worker session that is used to register the + // Rendezvous for eager (we use Step 0 for eager). + worker_session_ = WorkerSession::CreateWithBorrowedDeviceMgr( + "", name_prefix, + std::unique_ptr( + new WorkerCacheWrapper(master_env_.worker_cache)), + worker_env_.device_mgr, {}); + auto* r = worker_env()->rendezvous_mgr->Find(0); + return r->Initialize(worker_session_.get()); } Status GrpcServer::Init( @@ -357,6 +370,9 @@ Status GrpcServer::Start() { worker_thread_.reset( env_->StartThread(ThreadOptions(), "TF_worker_service", [this] { worker_service_->HandleRPCsLoop(); })); + eager_thread_.reset( + env_->StartThread(ThreadOptions(), "TF_eager_service", + [this] { eager_service_->HandleRPCsLoop(); })); state_ = STARTED; LOG(INFO) << "Started server with target: " << target(); return Status::OK(); @@ -399,6 +415,7 @@ Status GrpcServer::Join() { case STOPPED: master_thread_.reset(); worker_thread_.reset(); + eager_thread_.reset(); return Status::OK(); default: LOG(FATAL); @@ -435,6 +452,17 @@ Status GrpcServer::Create(const ServerDef& server_def, Env* env, return Status::OK(); } +/* static */ +Status GrpcServer::Create(const ServerDef& server_def, Env* env, + std::unique_ptr* out_server) { + std::unique_ptr ret( + new GrpcServer(server_def, env == nullptr ? Env::Default() : env)); + ServiceInitFunction service_func = nullptr; + TF_RETURN_IF_ERROR(ret->Init(service_func, NewRpcRendezvousMgr, nullptr)); + *out_server = std::move(ret); + return Status::OK(); +} + namespace { class GrpcServerFactory : public ServerFactory { diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h b/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h index ca9946cafc..c674da9490 100644 --- a/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h +++ b/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h @@ -63,6 +63,8 @@ class GrpcServer : public ServerInterface { public: static Status Create(const ServerDef& server_def, Env* env, std::unique_ptr* out_server); + static Status Create(const ServerDef& server_def, Env* env, + std::unique_ptr* out_server); // Destruction is only supported in the factory method. Clean // shutdown is not currently implemented for this server type. @@ -74,6 +76,11 @@ class GrpcServer : public ServerInterface { Status Join() override; const string target() const override; + WorkerEnv* worker_env() { return &worker_env_; } + MasterEnv* master_env() { return &master_env_; } + + std::shared_ptr channel_cache() { return channel_cache_; } + protected: Status Init(ServiceInitFunction service_func, const RendezvousMgrCreationFunction& rendezvous_mgr_func, @@ -112,11 +119,6 @@ class GrpcServer : public ServerInterface { // This method may only be called after `this->Init()` returns successfully. int bound_port() const { return bound_port_; } - WorkerEnv* worker_env() { return &worker_env_; } - MasterEnv* master_env() { return &master_env_; } - - std::shared_ptr channel_cache() { return channel_cache_; } - const ServerDef& server_def() const { return server_def_; } private: @@ -155,6 +157,11 @@ class GrpcServer : public ServerInterface { AsyncServiceInterface* worker_service_ = nullptr; std::unique_ptr worker_thread_ GUARDED_BY(mu_); + // TensorFlow Eager implementation, and RPC polling thread. + AsyncServiceInterface* eager_service_ = nullptr; + std::unique_ptr eager_thread_ GUARDED_BY(mu_); + std::shared_ptr worker_session_; + std::unique_ptr<::grpc::Server> server_ GUARDED_BY(mu_); }; diff --git a/tensorflow/python/eager/context.py b/tensorflow/python/eager/context.py index 9e146f021e..85b9491903 100644 --- a/tensorflow/python/eager/context.py +++ b/tensorflow/python/eager/context.py @@ -143,7 +143,11 @@ class Context(object): # TODO(agarwal): create and link in some documentation for `execution_mode`. # pylint: disable=redefined-outer-name - def __init__(self, config=None, device_policy=None, execution_mode=None): + def __init__(self, + config=None, + device_policy=None, + execution_mode=None, + server_def=None): """Creates a new Context. Args: @@ -192,6 +196,7 @@ class Context(object): if execution_mode is None: execution_mode = SYNC self._execution_mode = execution_mode + self._server_def = server_def # pylint: enable=redefined-outer-name @@ -231,6 +236,9 @@ class Context(object): opts, self._device_policy) if self._execution_mode == ASYNC: pywrap_tensorflow.TFE_ContextOptionsSetAsync(opts, True) + if self._server_def is not None: + server_def_str = self._server_def.SerializeToString() + pywrap_tensorflow.TFE_ContextOptionsSetServerDef(opts, server_def_str) self._context_handle = pywrap_tensorflow.TFE_NewContext(opts) finally: pywrap_tensorflow.TFE_DeleteContextOptions(opts) diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py index ec3c829840..0d2f8a3acc 100644 --- a/tensorflow/python/framework/ops.py +++ b/tensorflow/python/framework/ops.py @@ -5147,7 +5147,8 @@ def init_scope(): @tf_export("enable_eager_execution") -def enable_eager_execution(config=None, device_policy=None, +def enable_eager_execution(config=None, + device_policy=None, execution_mode=None): """Enables eager execution for the lifetime of this program. @@ -5207,6 +5208,31 @@ def enable_eager_execution(config=None, device_policy=None, TensorFlow graph, or if options provided conflict with a previous call to this function. """ + return enable_eager_execution_internal( + config, device_policy, execution_mode, None) + + +def enable_eager_execution_internal(config=None, + device_policy=None, + execution_mode=None, + server_def=None): + """Enables eager execution for the lifetime of this program. + + Most of the doc string for enable_eager_execution is relevant here as well. + Args: + config: See enable_eager_execution doc string + device_policy: See enable_eager_execution doc string + execution_mode: See enable_eager_execution doc string + server_def: (Optional.) A tensorflow::ServerDef proto. + Enables execution on remote devices. GrpcServers need to be started by + creating an identical server_def to this, and setting the appropriate + task_indexes, so that the servers can communicate. It will then be + possible to execute operations on remote devices. + + Raises: + ValueError + + """ if config is not None and not isinstance(config, config_pb2.ConfigProto): raise TypeError( "config must be a tf.ConfigProto, but got %s" % type(config)) @@ -5234,7 +5260,8 @@ def enable_eager_execution(config=None, device_policy=None, context._context = context.Context( config=config, device_policy=device_policy, - execution_mode=execution_mode) + execution_mode=execution_mode, + server_def=server_def) elif ((config is not None and config is not context._context._config) or (device_policy is not None and device_policy is not context._context._device_policy) or diff --git a/tensorflow/python/pywrap_tfe.i b/tensorflow/python/pywrap_tfe.i index 500dc30cc3..5d7535cf34 100644 --- a/tensorflow/python/pywrap_tfe.i +++ b/tensorflow/python/pywrap_tfe.i @@ -59,6 +59,7 @@ limitations under the License. %rename("%s") TFE_ContextOptionsSetConfig; %rename("%s") TFE_ContextOptionsSetDevicePlacementPolicy; %rename("%s") TFE_ContextOptionsSetAsync; +%rename("%s") TFE_ContextOptionsSetServerDef; %rename("%s") TFE_DeleteContextOptions; %rename("%s") TFE_Py_TensorShapeSlice; %rename("%s") TFE_Py_TensorShapeOnDevice; -- GitLab From 941dd4d4ae6d4cfa9b70cd061aa207e04e7730ae Mon Sep 17 00:00:00 2001 From: Sami Kama Date: Tue, 19 Jun 2018 10:40:33 -0700 Subject: [PATCH 668/816] Fix line too long error on method doc --- tensorflow/contrib/tensorrt/python/trt_convert.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/tensorrt/python/trt_convert.py b/tensorflow/contrib/tensorrt/python/trt_convert.py index 0478df9585..490c74a701 100644 --- a/tensorflow/contrib/tensorrt/python/trt_convert.py +++ b/tensorflow/contrib/tensorrt/python/trt_convert.py @@ -158,7 +158,7 @@ def calib_graph_to_infer_graph(calibration_graph_def, is_dynamic_op=False): Args: calibration_graph_def: the calibration GraphDef object with calibration data - is_dynamic_op : whether to create dynamic engines or static engines from calibration + is_dynamic_op: whether to create dynamic static engines from calibration Returns: New GraphDef with TRTEngineOps placed in graph replacing calibration nodes. Raises: -- GitLab From afd1c2c558bfeb2e82c30717cee23bcf2d28b78d Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 19 Jun 2018 10:49:43 -0700 Subject: [PATCH 669/816] Automated g4 rollback of changelist 201190626 PiperOrigin-RevId: 201202998 --- tensorflow/core/grappler/op_types.cc | 3 ++- .../optimizers/arithmetic_optimizer.cc | 12 +++------ .../optimizers/arithmetic_optimizer_test.cc | 26 +++++-------------- 3 files changed, 13 insertions(+), 28 deletions(-) diff --git a/tensorflow/core/grappler/op_types.cc b/tensorflow/core/grappler/op_types.cc index b4ddd61c29..bdeb5c66fc 100644 --- a/tensorflow/core/grappler/op_types.cc +++ b/tensorflow/core/grappler/op_types.cc @@ -629,7 +629,8 @@ bool HasOpDef(const NodeDef& node) { } bool IsIdempotent(const NodeDef& node) { - return IsValueAndOrderAndShapePreserving(node) && IsFreeOfSideEffect(node); + return IsValueAndOrderAndShapePreserving(node) && IsFreeOfSideEffect(node) && + !ModifiesFrameInfo(node); } } // namespace grappler diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc index d518685216..90be051764 100644 --- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc +++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc @@ -1722,19 +1722,15 @@ class RemoveIdempotentStage : public ArithmeticOptimizerStage { ~RemoveIdempotentStage() override = default; bool IsSupported(const NodeDef* node) const override { - return IsIdempotent(*node) && !IsInPreserveSet(*node); + return node->input_size() == 1 && IsIdempotent(*node) && + !IsInPreserveSet(*node); } Status TrySimplify(NodeDef* node, string* simplified_node_name) override { NodeDef* input; TF_RETURN_IF_ERROR(GetInputNode(node->input(0), &input)); - auto root_scope_and_name = ParseNodeScopeAndName(node->name()); - const string new_name = OptimizedNodeName(root_scope_and_name); - if (input->op() == node->op() && input->device() == node->device() && - IsIdempotent(*input) && !ctx().node_map->NodeExists(new_name)) { - NodeDef* new_input_node = AddCopyNode(new_name, input); - ForwardControlDependencies(new_input_node, {node}); - *simplified_node_name = new_input_node->name(); + if (input->op() == node->op() && input->device() == node->device()) { + *simplified_node_name = node->input(0); } return Status::OK(); } diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc b/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc index e1d55cdf5f..d0e6b04679 100644 --- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc +++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer_test.cc @@ -2976,12 +2976,8 @@ TEST_F(ArithmeticOptimizerTest, HoistCWiseUnaryIntoSplit) { TEST_F(ArithmeticOptimizerTest, RemoveIdempotent) { tensorflow::Scope s = tensorflow::Scope::NewRootScope(); Output a = ops::Const(s.WithOpName("a"), 3.14f, {32}); - Output ctrl1 = ops::Const(s.WithOpName("ctrl1"), 1, {}); - Output ctrl2 = ops::Const(s.WithOpName("ctrl2"), 2, {}); - Output sn1 = - ops::Snapshot(s.WithOpName("sn1").WithControlDependencies(ctrl1), a); - Output sn2 = - ops::Snapshot(s.WithOpName("sn2").WithControlDependencies(ctrl2), sn1); + Output sn1 = ops::Snapshot(s.WithOpName("sn1"), a); + Output sn2 = ops::Snapshot(s.WithOpName("sn2"), sn1); Output out1 = ops::Identity(s.WithOpName("out1"), sn2); Output id1 = ops::Identity(s.WithOpName("id1"), a); Output id2 = ops::Identity(s.WithOpName("id2"), id1); @@ -2997,32 +2993,24 @@ TEST_F(ArithmeticOptimizerTest, RemoveIdempotent) { EnableOnlyRemoveIdempotent(&optimizer); OptimizeTwice(&optimizer, &item, &output); - EXPECT_EQ(11, output.node_size()); + EXPECT_EQ(7, output.node_size()); int found = 0; for (const NodeDef& node : output.node()) { if (node.name() == "out1") { EXPECT_EQ(1, node.input_size()); - EXPECT_EQ("ArithmeticOptimizer/RemoveIdempotent_sn2", node.input(0)); - found++; - } else if (node.name() == "ArithmeticOptimizer/RemoveIdempotent_sn2") { - EXPECT_EQ(3, node.input_size()); - EXPECT_EQ("Snapshot", node.op()); - EXPECT_EQ("a", node.input(0)); - EXPECT_EQ("^ctrl1", node.input(1)); - EXPECT_EQ("^ctrl2", node.input(2)); + EXPECT_EQ("sn1", node.input(0)); found++; } else if (node.name() == "out2") { EXPECT_EQ(1, node.input_size()); - EXPECT_EQ("ArithmeticOptimizer/RemoveIdempotent_id2", node.input(0)); + EXPECT_EQ("id1", node.input(0)); found++; - } else if (node.name() == "ArithmeticOptimizer/RemoveIdempotent_id2") { - EXPECT_EQ("Identity", node.op()); + } else if (node.name() == "sn1") { EXPECT_EQ(1, node.input_size()); EXPECT_EQ("a", node.input(0)); found++; } } - EXPECT_EQ(4, found); + EXPECT_EQ(3, found); auto tensors = EvaluateNodes(output, item.fetch); EXPECT_EQ(tensors.size(), tensors_expected.size()); -- GitLab From bed3fcdc02409a823e498fcac88d8bf7a3789657 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 19 Jun 2018 10:52:15 -0700 Subject: [PATCH 670/816] Adding reference to the following classes: ConvolutionDeltaOrthogonal ConvolutionOrthogonal1D ConvolutionOrthogonal2D ConvolutionOrthogonal3D PiperOrigin-RevId: 201203440 --- tensorflow/python/ops/init_ops.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/tensorflow/python/ops/init_ops.py b/tensorflow/python/ops/init_ops.py index 724fcc39cd..c41e952167 100644 --- a/tensorflow/python/ops/init_ops.py +++ b/tensorflow/python/ops/init_ops.py @@ -551,7 +551,9 @@ class ConvolutionDeltaOrthogonal(Initializer): The shape of the tensor must have length 3, 4 or 5. The number of input filters must not exceed the number of output filters. The center pixels of the - tensor form an orthogonal matrix. Other pixels are set to be zero. + tensor form an orthogonal matrix. Other pixels are set to be zero. See + algorithm 2 in [Xiao et al., 2018]: https://arxiv.org/abs/1806.05393 + Args: gain: Multiplicative factor to apply to the orthogonal matrix. Default is 1. @@ -672,6 +674,7 @@ class ConvolutionOrthogonal2D(ConvolutionOrthogonal): filters must not exceed the number of output filters. The orthogonality(==isometry) is exact when the inputs are circular padded. There are finite-width effects with non-circular padding (e.g. zero padding). + See algorithm 1 in [Xiao et al., 2018]: https://arxiv.org/abs/1806.05393 Args: gain: Multiplicative factor to apply to the orthogonal matrix. Default is 1. @@ -807,6 +810,7 @@ class ConvolutionOrthogonal1D(ConvolutionOrthogonal): filters must not exceed the number of output filters. The orthogonality(==isometry) is exact when the inputs are circular padded. There are finite-width effects with non-circular padding (e.g. zero padding). + See algorithm 1 in [Xiao et al., 2018]: https://arxiv.org/abs/1806.05393 Args: gain: Multiplicative factor to apply to the orthogonal matrix. Default is 1. @@ -923,6 +927,7 @@ class ConvolutionOrthogonal3D(ConvolutionOrthogonal): filters must not exceed the number of output filters. The orthogonality(==isometry) is exact when the inputs are circular padded. There are finite-width effects with non-circular padding (e.g. zero padding). + See algorithm 1 [Xiao et al., 2018] in: https://arxiv.org/abs/1806.05393 Args: gain: Multiplicative factor to apply to the orthogonal matrix. Default is 1. -- GitLab From d2385b23b96741d34cb14f2e5e092a5d5a754d1f Mon Sep 17 00:00:00 2001 From: Reed Wanderman-Milne Date: Tue, 19 Jun 2018 10:57:50 -0700 Subject: [PATCH 671/816] Automated g4 rollback of changelist 200783477 PiperOrigin-RevId: 201204573 --- tensorflow/python/keras/engine/base_layer.py | 69 +------ tensorflow/python/keras/engine/network.py | 20 +- .../python/keras/engine/topology_test.py | 172 ------------------ tensorflow/python/layers/base.py | 10 +- tensorflow/python/layers/base_test.py | 62 ------- 5 files changed, 11 insertions(+), 322 deletions(-) diff --git a/tensorflow/python/keras/engine/base_layer.py b/tensorflow/python/keras/engine/base_layer.py index b05bc96e28..e8cdda30a2 100644 --- a/tensorflow/python/keras/engine/base_layer.py +++ b/tensorflow/python/keras/engine/base_layer.py @@ -41,7 +41,6 @@ from tensorflow.python.keras.utils.generic_utils import to_snake_case # pylint: from tensorflow.python.keras.utils.tf_utils import is_tensor_or_tensor_list # pylint: disable=unused-import from tensorflow.python.ops import array_ops from tensorflow.python.ops import init_ops -from tensorflow.python.ops import math_ops from tensorflow.python.ops import variable_scope as vs from tensorflow.python.ops import variables as tf_variables from tensorflow.python.training.checkpointable import base as checkpointable @@ -89,11 +88,6 @@ class Layer(checkpointable.CheckpointableBase): once. Should actually perform the logic of applying the layer to the input tensors (which should be passed in as the first argument). - A note on a layer's `dtype` property: - A layer's dtype can be specified via the constructor `dtype` argument, and - defaults to the dtype of the first input when the layer is called. The dtype - cannot be changed once set. - All floating point tensor inputs and arguments are casted to the layer's dtype, before the body of the layer computation happens. For models with layers of different dtypes, this helps getting rid of the explicit casts @@ -106,15 +100,13 @@ class Layer(checkpointable.CheckpointableBase): Arguments: trainable: Boolean, whether the layer's variables should be trainable. name: String name of the layer. - dtype: Default dtype of the layer's weights and computations (default of - `None` means use the type of the first input). If not None, inputs will be - casted to this dtype. + dtype: Default dtype of the layer's weights (default of `None` means use the + type of the first input). Read-only properties: name: The name of the layer (string). - dtype: Default dtype of the layer's weights and computations. (default of - `None` means use the type of the first input). If not None, inputs will be - casted to this dtype. + dtype: Default dtype of the layer's weights (default of `None` means use the + type of the first input). trainable_variables: List of trainable variables. non_trainable_variables: List of non-trainable variables. variables: List of all variables of this layer, trainable and @@ -683,12 +675,6 @@ class Layer(checkpointable.CheckpointableBase): kwargs['mask'] = previous_mask input_shapes = None - # Inputs are only casted if a dtype is pased in the constructor, or if a - # layer's __call__() has been previously invoked. At present, only floating - # point tensor inputs are affected. - # TODO(b/77478433): Perhaps we should only cast inputs if a dtype was passed - # to the constructor, not when the layer has previously been called. - inputs_should_be_cast = (self.dtype is not None) with ops.name_scope(self._name_scope()): if not self.built: @@ -723,12 +709,7 @@ class Layer(checkpointable.CheckpointableBase): self._assert_input_compatibility(inputs) if not in_deferred_mode: - if inputs_should_be_cast: - cast_inputs, cast_args, cast_kwargs = self._cast_inputs_and_args( - inputs, *args, **kwargs) - else: - cast_inputs, cast_args, cast_kwargs = inputs, args, kwargs - outputs = self.call(cast_inputs, *cast_args, **cast_kwargs) + outputs = self.call(inputs, *args, **kwargs) if outputs is None: raise ValueError('A layer\'s `call` method should return a Tensor ' 'or a list of Tensors, not None (layer: ' + @@ -743,9 +724,6 @@ class Layer(checkpointable.CheckpointableBase): output_shapes = nest.flatten(output_shapes) outputs = [ # TODO(fchollet): name the deferred tensors? - # TODO(b/77478433): Compute the proper dtype here, by adding a - # compute_output_dtype method. Currently keras Models do not - # properly compute the output dtype. DeferredTensor(shape=shape, dtype=self._dtype) for shape in output_shapes ] @@ -804,43 +782,6 @@ class Layer(checkpointable.CheckpointableBase): """ return self.__call__(inputs, *args, **kwargs) - def _cast_fn(self, x): - """If x is a tensor, casts to this layer's dtype.""" - # TODO(b/77478433): Cast tensor-like things like SparseTensors, Variables, - # ResourceVariables, etc. - if (isinstance(x, ops.Tensor) and x.dtype.is_floating and - dtypes.as_dtype(self.dtype).is_floating): - return math_ops.cast(x, self.dtype) - else: - return x - - def _cast_inputs_and_args(self, inputs, *args, **kwargs): - """Casts the inputs, args, and kwargs of a layer to the layer's dtype. - - This is intended to be potentially overridden by subclasses. By default, - inputs, args, and kwargs are automatically casted to the layer's dtype. - Overriding this method allows only some of the parameters to be treated - differently. - - Currently, this only casts floating point tensors to floating point dtypes, - but more types may be casted in the future. - - Does not modify inputs, args, or kwargs. - - Args: - inputs: The inputs to self.__call__. - *args: The args to self.__call__. - **kwargs: The kwargs to self.__call__. - - Returns: - A tuple (new_inputs, new_args, new_kwargs), where tensors in inputs, - args, and kwargs have been casted to self.dtype. - """ - new_inputs = nest.map_structure(self._cast_fn, inputs) - new_args = nest.map_structure(self._cast_fn, args) - new_kwargs = nest.map_structure(self._cast_fn, kwargs) - return new_inputs, new_args, new_kwargs - def _set_learning_phase_metadata(self, inputs, outputs): # Update learning phase info. To work with subclassed models, # this should be done even if Keras metadata is absent. diff --git a/tensorflow/python/keras/engine/network.py b/tensorflow/python/keras/engine/network.py index 1c9135982e..427efaaf11 100644 --- a/tensorflow/python/keras/engine/network.py +++ b/tensorflow/python/keras/engine/network.py @@ -887,16 +887,8 @@ class Network(base_layer.Layer): if 'training' in tf_inspect.getargspec(layer.call).args: kwargs.setdefault('training', training) - if layer.dtype is not None: - cast_computed_tensors, cast_args, cast_kwargs = ( - layer._cast_inputs_and_args(computed_tensor, **kwargs)) - else: - cast_computed_tensors = [computed_tensor] - cast_args = () - cast_kwargs = kwargs - output_tensors = nest.flatten( - layer.call(cast_computed_tensors, *cast_args, **cast_kwargs)) + layer.call(computed_tensor, **kwargs)) if hasattr(layer, 'compute_mask'): output_masks = layer.compute_mask(computed_tensor, computed_mask) @@ -916,16 +908,8 @@ class Network(base_layer.Layer): if 'training' in tf_inspect.getargspec(layer.call).args: kwargs.setdefault('training', training) - if layer.dtype is not None: - cast_computed_tensors, cast_args, cast_kwargs = ( - layer._cast_inputs_and_args(computed_tensors, **kwargs)) - else: - cast_computed_tensors = computed_tensors - cast_args = () - cast_kwargs = kwargs - output_tensors = nest.flatten( - layer.call(cast_computed_tensors, *cast_args, **cast_kwargs)) + layer.call(computed_tensors, **kwargs)) if hasattr(layer, 'compute_mask'): output_masks = layer.compute_mask(computed_tensors, diff --git a/tensorflow/python/keras/engine/topology_test.py b/tensorflow/python/keras/engine/topology_test.py index d28c30cb7d..183e26e8bf 100644 --- a/tensorflow/python/keras/engine/topology_test.py +++ b/tensorflow/python/keras/engine/topology_test.py @@ -18,8 +18,6 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import collections - import numpy as np from tensorflow.python import keras @@ -912,176 +910,6 @@ class TopologyConstructionTest(test.TestCase): assert out.shape == (4, 3, 2, 1) self.assertAllClose(out, x * 0.2 + x * 0.3, atol=1e-4) - @test_util.run_in_graph_and_eager_modes() - def test_casting_args(self): - # args of type B will be casted, as we cast elements of namedtuples - B = collections.namedtuple('B', ['x', 'y', 'z']) # pylint: disable=invalid-name - - # args of type C will not be casted, as we do not look at object - # attributes for tensors to cast - class C(object): - - def __init__(self, w): - self.w = w - - inp = array_ops.ones((1,), name='input', dtype='float64') - a = array_ops.ones((1,), name='a', dtype='float64') - b = B(array_ops.ones((1,), name='a', dtype='float64'), None, - np.ones((1,), 'float64')) # Numpy tensors should not be casted - c = C(array_ops.ones((1,), name='a', dtype='float64')) - - # Test inputs are automatically casted. - class MyLayer(keras.layers.Layer): - - def call(self, inputs, a, b, c): - self.a = a - self.b = b - self.c = c - return inputs - - def compute_output_shape(self, input_shape): - return input_shape - - layer = MyLayer(dtype='float16') - out = layer(inp, a=a, b=b, c=c) - self.assertEqual(out.dtype, dtypes.float16) - self.assertEqual(layer.a.dtype, dtypes.float16) - self.assertEqual(layer.b.x.dtype, dtypes.float16) - self.assertEqual(layer.b.y, None) - self.assertEqual(layer.b.z.dtype, np.float64) - self.assertEqual(layer.c.w.dtype, dtypes.float64) - - # Test overriding _cast_inputs_and_args - class MyLayerOverrideCastInputs(MyLayer): - - def _cast_inputs_and_args(self, inputs, a, b, c): - new_inputs = self._cast_fn(inputs) - new_a = a - new_b = b - new_c = C(self._cast_fn(c.w)) - return new_inputs, (new_a, new_b, new_c), {} - - layer = MyLayerOverrideCastInputs(dtype='float16') - out = layer(inp, a=a, b=b, c=c) - self.assertEqual(out.dtype, dtypes.float16) - self.assertEqual(layer.a.dtype, dtypes.float64) - self.assertEqual(layer.b.x.dtype, dtypes.float64) - self.assertEqual(layer.b.y, None) - self.assertEqual(layer.b.z.dtype, np.float64) - self.assertEqual(layer.c.w.dtype, dtypes.float16) - - @test_util.run_in_graph_and_eager_modes() - def test_do_not_cast_ints(self): - class MyLayer(keras.layers.Layer): - - def build(self, input_shape): - self.v = self.add_variable('v', (), 'int32') - super(MyLayer, self).build(input_shape) - - def call(self, inputs): - return inputs + self.v - - def compute_output_shape(self, input_shape): - return input_shape - - a = array_ops.ones((10, 32), dtype='int32') - layer = MyLayer(dtype='float32') - b = layer(a) - self.assertEqual(layer.v.dtype.base_dtype, dtypes.int32) - self.assertEqual(b.dtype, dtypes.int32) - - @test_util.run_in_graph_and_eager_modes() - def test_casting_when_dtype_not_passed_to_constructor(self): - class MyLayer(keras.layers.Layer): - - def call(self, a): - self.a = a - return a - - def compute_output_shape(self, input_shape): - return input_shape - - # Do not cast inputs for the first __call__ if a dtype is not passed to the - # constructor. - a = array_ops.ones((10, 32), dtype='float64') - layer = MyLayer() - self.assertEqual(layer.dtype, None) - b = layer(a) - self.assertEqual(layer.dtype, 'float64') - self.assertEqual(layer.a.dtype, dtypes.float64) - self.assertEqual(b.dtype, dtypes.float64) - - # For a subsequent __call__, the layer's dtype has been set so inputs should - # be casted to the dtype of the input to the first __call__. - a = array_ops.ones((10, 32), dtype='float32') - b = layer(a) - self.assertEqual(layer.dtype, 'float64') - self.assertEqual(layer.a.dtype, dtypes.float64) - self.assertEqual(b.dtype, dtypes.float64) - - @test_util.run_in_graph_and_eager_modes() - def test_casting_with_build_before_call(self): - a = keras.Input(shape=(32,), name='input_a', dtype='float32') - dense_layer = keras.layers.Dense(16, dtype='float16') - dense_layer.build((32,)) - b = dense_layer(a) - - self.assertEqual(dense_layer.dtype, 'float16') - self.assertEqual(dense_layer.input, a) - self.assertEqual(dense_layer.output, b) - self.assertEqual(a.dtype, dtypes.float32) - self.assertEqual(dense_layer.kernel.dtype.base_dtype, dtypes.float16) - self.assertEqual(dense_layer.bias.dtype.base_dtype, dtypes.float16) - self.assertEqual(b.dtype, dtypes.float16) - - @test_util.run_in_graph_and_eager_modes() - def test_casting_in_network(self): - - class SingleInputLayer(keras.layers.Layer): - - def call(self, a): - self.a = a - return a - - def compute_output_shape(self, input_shape): - return input_shape - - class MultiInputLayer(keras.layers.Layer): - - def call(self, inputs): - a, b = inputs - self.a = a - self.b = b - return a + b - - def compute_output_shape(self, input_shapes): - return input_shapes[0] - - default_layer = SingleInputLayer() - fp32_layer = SingleInputLayer(dtype='float32') - fp16_layer = MultiInputLayer(dtype='float16') - - input_t = keras.layers.Input((32,), dtype='float64') - o1 = default_layer(input_t) - o2 = fp32_layer(o1) - # fp16_layer has inputs of different dtypes. - output_t = fp16_layer((o1, o2)) - network = keras.engine.Network(input_t, output_t) - - x = array_ops.ones((32,), dtype='float16') - y = network(x) - self.assertEqual(default_layer.dtype, dtypes.float64) - self.assertEqual(default_layer.a.dtype, dtypes.float64) - - self.assertEqual(fp32_layer.dtype, dtypes.float32) - self.assertEqual(fp32_layer.a.dtype, dtypes.float32) - - self.assertEqual(fp16_layer.dtype, dtypes.float16) - self.assertEqual(fp16_layer.a.dtype, dtypes.float16) - self.assertEqual(fp16_layer.b.dtype, dtypes.float16) - - self.assertEqual(y.dtype, dtypes.float16) - class DeferredModeTest(test.TestCase): diff --git a/tensorflow/python/layers/base.py b/tensorflow/python/layers/base.py index abbe9d0c56..b8969a41ab 100644 --- a/tensorflow/python/layers/base.py +++ b/tensorflow/python/layers/base.py @@ -43,15 +43,13 @@ class Layer(base_layer.Layer): Arguments: trainable: Boolean, whether the layer's variables should be trainable. name: String name of the layer. - dtype: Default dtype of the layer's weights and computations (default of - `None` means use the type of the first input). If not None, inputs will be - casted to this dtype. + dtype: Default dtype of the layer's weights (default of `None` means use the + type of the first input). Read-only properties: name: The name of the layer (string). - dtype: Default dtype of the layer's weights and computations. (default of - `None` means use the type of the first input). If not None, inputs will be - casted to this dtype. + dtype: Default dtype of the layer's weights (default of `None` means use the + type of the first input). trainable_variables: List of trainable variables. non_trainable_variables: List of non-trainable variables. variables: List of all variables of this layer, trainable and diff --git a/tensorflow/python/layers/base_test.py b/tensorflow/python/layers/base_test.py index ad44328aab..fcacc8d603 100644 --- a/tensorflow/python/layers/base_test.py +++ b/tensorflow/python/layers/base_test.py @@ -25,8 +25,6 @@ from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import test_util -from tensorflow.python.keras import backend -from tensorflow.python.keras.engine import base_layer as keras_base_layer from tensorflow.python.layers import base as base_layers from tensorflow.python.layers import core as core_layers from tensorflow.python.ops import array_ops @@ -591,65 +589,5 @@ class BaseLayerTest(test.TestCase): ValueError, 'Input graph and Layer graph are not the same'): layer.apply(constant_op.constant([[1.]])) - @test_util.run_in_graph_and_eager_modes() - def testOnlyCastInputsWhenDtypeSpecified(self): - - class MyKerasLayer(keras_base_layer.Layer): - - def call(self, inputs): - self.x = inputs[0] - self.y = inputs[1] - return self.x + 1, self.y + 2 - - # Inherit from both the Keras Layer and base_layers.Layer to ensure we - # still get the base_layers.Layer behavior when directly inheriting from - # the Keras Layer. - class MyTFLayer(MyKerasLayer, base_layers.Layer): - pass - - # Test inputs are casted. - input1 = array_ops.constant(1.0, dtype=dtypes.float64) - input2 = array_ops.constant(1.0, dtype=dtypes.float32) - layer = MyTFLayer(dtype=dtypes.float16) - output1, output2 = layer([input1, input2]) - self.assertEqual(output1.dtype, dtypes.float16) - self.assertEqual(output2.dtype, dtypes.float16) - - # Test inputs are not casted. - input1 = array_ops.constant(1.0, dtype=dtypes.float64) - input2 = array_ops.constant(1.0, dtype=dtypes.float32) - layer = MyTFLayer() - output1, output2 = layer([input1, input2]) - self.assertEqual(output1.dtype, dtypes.float64) - self.assertEqual(output2.dtype, dtypes.float32) - - @test_util.run_in_graph_and_eager_modes() - def testVariablesDefaultToFloat32(self): - - class MyKerasLayer(keras_base_layer.Layer): - - def build(self, input_shape): - self.x = self.add_weight('x', ()) - - def call(self, inputs): - return inputs + self.x - - # Inherit from both the Keras Layer and base_layers.Layer to ensure we - # still get the base_layers.Layer behavior when directly inheriting from - # the Keras Layer. - class MyTFLayer(MyKerasLayer, base_layers.Layer): - pass - - try: - # The behavior of Keras Layers is to default to floatx. Ensure that this - # behavior is overridden to instead default to float32. - backend.set_floatx('float16') - layer = MyTFLayer() - layer.build(()) - self.assertEqual(layer.dtype, None) - self.assertEqual(layer.x.dtype.base_dtype, dtypes.float32) - finally: - backend.set_floatx('float32') - if __name__ == '__main__': test.main() -- GitLab From a1043d41758bbabf0f441e1cd84ebd8cb41974b8 Mon Sep 17 00:00:00 2001 From: Akshay Agrawal Date: Tue, 19 Jun 2018 11:03:42 -0700 Subject: [PATCH 672/816] Correctly compute real and side outputs when constructing backprop function. Prior to this change, we assumed that the number of real outputs of the TF function was equal to the number of outputs of the Python function. This assumption was incorrect, as the Python function might return non-Tensor objects whereas the TF function exclusively returns Tensors. PiperOrigin-RevId: 201205657 --- tensorflow/python/eager/function.py | 41 +++++++++++++----------- tensorflow/python/eager/function_test.py | 20 +++++++++--- 2 files changed, 39 insertions(+), 22 deletions(-) diff --git a/tensorflow/python/eager/function.py b/tensorflow/python/eager/function.py index 2f6318bb92..aa621d7f5a 100644 --- a/tensorflow/python/eager/function.py +++ b/tensorflow/python/eager/function.py @@ -313,7 +313,7 @@ class GraphModeFunction(object): graph, operations, outputs, - func_outputs, + python_func_outputs, output_shapes, variables=None, attrs=None): @@ -332,9 +332,10 @@ class GraphModeFunction(object): definition. outputs: a flat list of the Tensors in the graph used as outputs to the function - func_outputs: a possibly nested python object which will be returned by - this function. The Tensors in this structure will be replaced by their - corresponding values in outputs. + python_func_outputs: a possibly nested python object which will be + returned by this function. The Tensors in this structure will be + replaced by their corresponding values in outputs. Note that this + structure might contain Python `None`s. output_shapes: List of shapes of all tensors in outputs variables: (optional) List of variables to watch during function execution. @@ -356,9 +357,10 @@ class GraphModeFunction(object): self._function_def = defined_function self._num_outputs = len(defined_function.signature.output_arg) self._ops = operations - self._func_outputs = func_outputs - self._returns = [func_outputs] if isinstance( - func_outputs, (ops.Tensor, type(None))) else _flatten(func_outputs) + self._python_func_outputs = python_func_outputs + self._python_returns = [python_func_outputs] if isinstance( + python_func_outputs, + (ops.Tensor, type(None))) else _flatten(python_func_outputs) self._output_shapes = output_shapes self._variables = variables if variables is not None else [] @@ -373,7 +375,7 @@ class GraphModeFunction(object): c_captured_tensors = set() existing_op_len = len(self._graph.get_operations()) - filtered_outputs = [x for x in self._returns if x is not None] + filtered_outputs = [x for x in self._python_returns if x is not None] self._out_grad_placeholders = [ graph_placeholder(x.dtype, x.shape) for x in filtered_outputs] in_gradients = gradients_impl.gradients( @@ -454,8 +456,11 @@ class GraphModeFunction(object): for i, shape in enumerate(shapes): outputs[i].set_shape(shape) - real_outputs = outputs[:len(self._returns)] - side_outputs = outputs[len(self._returns):] + # `real_outputs` are the actual outputs of the inference graph function; + # `side_outputs` are the intermediate Tensors that were added as outputs to + # the forward graph function so that we can compute its gradient. + real_outputs = outputs[:self._num_outputs] + side_outputs = outputs[self._num_outputs:] def backward_function(*args): return self._backward_function(*(list(args) + side_outputs)) # pylint: disable=not-callable @@ -472,8 +477,8 @@ class GraphModeFunction(object): def output_shapes(self): """The function's output shapes.""" # TODO(ebrevdo): Should we only keep the output shapes associated - # with len(self._returns) outputs? - outputs_list = nest.flatten(self._func_outputs) + # with len(self._python_returns) outputs? + outputs_list = nest.flatten(self._python_func_outputs) j = 0 for i, o in enumerate(outputs_list): if o is not None: @@ -487,12 +492,12 @@ class GraphModeFunction(object): else: outputs_list[i] = self._output_shapes[j] j += 1 - return nest.pack_sequence_as(self._func_outputs, outputs_list) + return nest.pack_sequence_as(self._python_func_outputs, outputs_list) @property def output_dtypes(self): return nest.map_structure( - lambda x: x.dtype if x is not None else None, self._func_outputs) + lambda x: x.dtype if x is not None else None, self._python_func_outputs) @property def captured_inputs(self): @@ -561,11 +566,11 @@ class GraphModeFunction(object): Returns: The actual call output. """ - if self._func_outputs is None: + if self._python_func_outputs is None: return None # Use `nest.flatten` instead of `_flatten` in order to preserve any - # IndexedSlices in `self._func_outputs`. - outputs_list = nest.flatten(self._func_outputs) + # IndexedSlices in `self._python_func_outputs`. + outputs_list = nest.flatten(self._python_func_outputs) j = 0 for i, o in enumerate(outputs_list): if o is not None: @@ -585,7 +590,7 @@ class GraphModeFunction(object): else: outputs_list[i] = result[j] j += 1 - ret = nest.pack_sequence_as(self._func_outputs, outputs_list) + ret = nest.pack_sequence_as(self._python_func_outputs, outputs_list) return ret diff --git a/tensorflow/python/eager/function_test.py b/tensorflow/python/eager/function_test.py index 393279b313..85c1bbc393 100644 --- a/tensorflow/python/eager/function_test.py +++ b/tensorflow/python/eager/function_test.py @@ -512,6 +512,20 @@ class FunctionTest(test.TestCase): g = backprop.gradients_function(wrapper, [0])(constant_op.constant(0.0)) self.assertAllEqual(g[0], 1.) + @function.defun + def foo(a): + return None, a * a + + x = constant_op.constant(5.0) + with backprop.GradientTape() as tp: + tp.watch(x) + none, r = foo(x) + g = tp.gradient(r, x) + + self.assertIs(none, None) + self.assertAllEqual(r, 25.0) + self.assertAllEqual(g, 2 * 5.0) + def testNestedDifferentiableFunction(self): @function.defun def foo(a, b): @@ -542,16 +556,14 @@ class FunctionTest(test.TestCase): with backprop.GradientTape(persistent=True) as tp: tp.watch(x) none1, r1, none2, r2 = bar(x) - g1 = tp.gradient(r1, x) # pylint: disable=unused-variable + g1 = tp.gradient(r1, x) g2 = tp.gradient(r2, x) self.assertAllEqual(r1, 30.0) self.assertAllEqual(r2, 10.0) self.assertIs(none1, None) self.assertIs(none2, None) - # TODO(b/110213087) Differentiating nested tfe.defuns returning some - # Nones does not work. The following returns 1 instead of correct 11. - # self.assertAllEqual(g1, 2 * 5.0 + 1.0) + self.assertAllEqual(g1, 2 * 5.0 + 1.0) self.assertAllEqual(g2, 2.0) def testNoneOutput(self): -- GitLab From f7372b83b0f82b0e1a963ba01f3c29b08a4ddfda Mon Sep 17 00:00:00 2001 From: Michael Case Date: Tue, 19 Jun 2018 11:19:05 -0700 Subject: [PATCH 673/816] Internal Change. PiperOrigin-RevId: 201208955 --- tensorflow/python/estimator/BUILD | 32 +++++++++++++++++++++++++++++++ 1 file changed, 32 insertions(+) diff --git a/tensorflow/python/estimator/BUILD b/tensorflow/python/estimator/BUILD index 20522098b0..326019ff2a 100644 --- a/tensorflow/python/estimator/BUILD +++ b/tensorflow/python/estimator/BUILD @@ -999,3 +999,35 @@ py_test( "//third_party/py/numpy", ], ) + +py_library( + name = "expect_numpy_installed", + # This is a dummy rule used as a numpy dependency in open-source. + # We expect numpy to already be installed on the system, e.g. via + # `pip install numpy` + visibility = ["//visibility:public"], +) + +py_library( + name = "expect_pandas_installed", + # This is a dummy rule used as a numpy dependency in open-source. + # We expect pandas to already be installed on the system, e.g. via + # `pip install pandas` + visibility = ["//visibility:public"], +) + +py_library( + name = "expect_six_installed", + # This is a dummy rule used as a numpy dependency in open-source. + # We expect six to already be installed on the system, e.g. via + # `pip install six` + visibility = ["//visibility:public"], +) + +py_library( + name = "expect_tensorflow_installed", + # This is a dummy rule used as a numpy dependency in open-source. + # We expect tensorflow to already be installed on the system, e.g. via + # `pip install tensorflow` or `pip install tensorflow_gpu` + visibility = ["//visibility:public"], +) -- GitLab From a8e7bc8d131d75b76ed8f449db581ea6eaf0300c Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 19 Jun 2018 11:27:56 -0700 Subject: [PATCH 674/816] Reconcile enum types. PiperOrigin-RevId: 201210730 --- .../lite/toco/graph_transformations/resolve_constant_stack.cc | 2 +- tensorflow/contrib/lite/toco/model.h | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_stack.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_stack.cc index 69db1942cd..a4d5f1923a 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_stack.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_stack.cc @@ -41,7 +41,7 @@ void Stack(Model* model, StackOperator const& op) { const auto& input_array = model->GetArray(op.inputs[i]); int input_size = RequiredBufferSizeForShape(input_array.shape()); memcpy(&output_data[dst_offset], &input_array.GetBuffer().data[0], - input_size * sizeof(Type)); + input_size * ElementSize(Type)); dst_offset += input_size; } CHECK_EQ(dst_offset, output_data.size()); diff --git a/tensorflow/contrib/lite/toco/model.h b/tensorflow/contrib/lite/toco/model.h index 619fc9fd42..0faadedf3b 100644 --- a/tensorflow/contrib/lite/toco/model.h +++ b/tensorflow/contrib/lite/toco/model.h @@ -32,7 +32,7 @@ namespace toco { using tflite::QuantizationParams; -enum class OperatorType { +enum class OperatorType : uint8 { kNone, // General-purpose neural network operators. kAdd, @@ -174,7 +174,7 @@ enum class AxesOrder { // because we'll be dropping the array anyway (e.g. some exotic array types // may be involved only in debug-only subgraphs that we may not be interested // in actually supporting). -enum class ArrayDataType { +enum class ArrayDataType : uint8 { kNone, // 0 kBool, kFloat, -- GitLab From 8170ca09a86e63c93eae4db1a929956be81c786d Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Tue, 19 Jun 2018 11:43:31 -0700 Subject: [PATCH 675/816] [TF:XLA] Bump open source llvm revision to r335024 PiperOrigin-RevId: 201213520 --- tensorflow/workspace.bzl | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index 12e7a242fd..3b7a333c46 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -451,11 +451,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "llvm", urls = [ - "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/45a02a4f8474b4b8c5cc106b5cecb06cf6e1b3c6.tar.gz", - "https://github.com/llvm-mirror/llvm/archive/45a02a4f8474b4b8c5cc106b5cecb06cf6e1b3c6.tar.gz", + "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/21cf43199f6e79fcc345d177c8740d392f0b898e.tar.gz", + "https://github.com/llvm-mirror/llvm/archive/21cf43199f6e79fcc345d177c8740d392f0b898e.tar.gz", ], - sha256 = "056f7316a354d1f95e013176bd9b8be74e8f4d47fb0d908e0e742613187dbd59", - strip_prefix = "llvm-45a02a4f8474b4b8c5cc106b5cecb06cf6e1b3c6", + sha256 = "c8ceb180ce51e00e047061dac48f014e5430ac33ea2447029065f922119b122c", + strip_prefix = "llvm-21cf43199f6e79fcc345d177c8740d392f0b898e", build_file = clean_dep("//third_party/llvm:llvm.BUILD"), ) -- GitLab From 0fb21f608c334dfcaadab7b918c06b88afa8c592 Mon Sep 17 00:00:00 2001 From: Sami Kama Date: Tue, 19 Jun 2018 11:51:52 -0700 Subject: [PATCH 676/816] Another linter fix --- tensorflow/contrib/tensorrt/test/test_tftrt.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/tensorrt/test/test_tftrt.py b/tensorflow/contrib/tensorrt/test/test_tftrt.py index 12e84f7d3c..9a031ddf4e 100644 --- a/tensorflow/contrib/tensorrt/test/test_tftrt.py +++ b/tensorflow/contrib/tensorrt/test/test_tftrt.py @@ -236,7 +236,7 @@ def auto(multi_engine): orig_graph = get_simple_graph_def() # use a frozen graph for inference dummy_input = np.random.random_sample(inp_dims) opt_config = rwpb2.RewriterConfig() - opt_config.meta_optimizer_iterations=opt_config.ONE + opt_config.meta_optimizer_iterations = opt_config.ONE opt_config.optimizers.extend(["constfold", "layout"]) custom_op = opt_config.custom_optimizers.add() custom_op.name = "TensorRTOptimizer" -- GitLab From f75a7e2b1f1129cb4b763c9391823f8550438f5c Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 19 Jun 2018 11:50:45 -0700 Subject: [PATCH 677/816] Rollback of changelist 200200356. We might want to support GPUs on MacOS again in the future. Users are interested to make it work and we don't want to be in the way. PiperOrigin-RevId: 201214857 --- .../stream_executor/cuda/cuda_diagnostics.cc | 98 ++++++++++++++++++- .../stream_executor/cuda/cuda_gpu_executor.cc | 16 ++- 2 files changed, 109 insertions(+), 5 deletions(-) diff --git a/tensorflow/stream_executor/cuda/cuda_diagnostics.cc b/tensorflow/stream_executor/cuda/cuda_diagnostics.cc index 10f6d21d54..124d5905b9 100644 --- a/tensorflow/stream_executor/cuda/cuda_diagnostics.cc +++ b/tensorflow/stream_executor/cuda/cuda_diagnostics.cc @@ -24,12 +24,17 @@ limitations under the License. #include #include #include +#ifdef __APPLE__ +#include +#include +#else #if !defined(PLATFORM_WINDOWS) #include #include #include #endif #include +#endif #include #include #include @@ -49,7 +54,9 @@ limitations under the License. namespace stream_executor { namespace cuda { -#if !defined(PLATFORM_WINDOWS) +#ifdef __APPLE__ +static const CFStringRef kDriverKextIdentifier = CFSTR("com.nvidia.CUDA"); +#elif !defined(PLATFORM_WINDOWS) static const char *kDriverVersionPath = "/proc/driver/nvidia/version"; #endif @@ -114,7 +121,31 @@ string Diagnostician::GetDevNodePath(int dev_node_ordinal) { } void Diagnostician::LogDiagnosticInformation() { -#if !defined(PLATFORM_WINDOWS) +#ifdef __APPLE__ + CFStringRef kext_ids[1]; + kext_ids[0] = kDriverKextIdentifier; + CFArrayRef kext_id_query = CFArrayCreate(nullptr, (const void **)kext_ids, 1, + &kCFTypeArrayCallBacks); + CFDictionaryRef kext_infos = + KextManagerCopyLoadedKextInfo(kext_id_query, nullptr); + CFRelease(kext_id_query); + + CFDictionaryRef cuda_driver_info = nullptr; + if (CFDictionaryGetValueIfPresent(kext_infos, kDriverKextIdentifier, + (const void **)&cuda_driver_info)) { + bool started = CFBooleanGetValue((CFBooleanRef)CFDictionaryGetValue( + cuda_driver_info, CFSTR("OSBundleStarted"))); + if (!started) { + LOG(INFO) << "kernel driver is installed, but does not appear to be " + "running on this host " + << "(" << port::Hostname() << ")"; + } + } else { + LOG(INFO) << "kernel driver does not appear to be installed on this host " + << "(" << port::Hostname() << ")"; + } + CFRelease(kext_infos); +#elif !defined(PLATFORM_WINDOWS) if (access(kDriverVersionPath, F_OK) != 0) { LOG(INFO) << "kernel driver does not appear to be running on this host " << "(" << port::Hostname() << "): " @@ -168,7 +199,8 @@ void Diagnostician::LogDiagnosticInformation() { << DriverVersionStatusToString(kernel_version); #endif -#if !defined(PLATFORM_WINDOWS) + // OS X kernel driver does not report version accurately +#if !defined(__APPLE__) && !defined(PLATFORM_WINDOWS) if (kernel_version.ok() && dso_version.ok()) { WarnOnDsoKernelMismatch(dso_version, kernel_version); } @@ -182,6 +214,29 @@ port::StatusOr Diagnostician::FindDsoVersion() { port::error::NOT_FOUND, "was unable to find libcuda.so DSO loaded into this program")); +#if defined(__APPLE__) + // OSX CUDA libraries have names like: libcuda_310.41.15_mercury.dylib + const string prefix("libcuda_"); + const string suffix("_mercury.dylib"); + for (uint32_t image_index = 0; image_index < _dyld_image_count(); + ++image_index) { + const string path(_dyld_get_image_name(image_index)); + const size_t suffix_pos = path.rfind(suffix); + const size_t prefix_pos = path.rfind(prefix, suffix_pos); + if (prefix_pos == string::npos || suffix_pos == string::npos) { + // no match + continue; + } + const size_t start = prefix_pos + prefix.size(); + if (start >= suffix_pos) { + // version not included + continue; + } + const size_t length = suffix_pos - start; + const string version = path.substr(start, length); + result = StringToDriverVersion(version); + } +#else #if !defined(PLATFORM_WINDOWS) && !defined(ANDROID_TEGRA) // Callback used when iterating through DSOs. Looks for the driver-interfacing // DSO and yields its version number into the callback data, when found. @@ -214,6 +269,7 @@ port::StatusOr Diagnostician::FindDsoVersion() { }; dl_iterate_phdr(iterate_phdr, &result); +#endif #endif return result; @@ -259,7 +315,41 @@ void Diagnostician::WarnOnDsoKernelMismatch( port::StatusOr Diagnostician::FindKernelDriverVersion() { -#if defined(PLATFORM_WINDOWS) +#if defined(__APPLE__) + CFStringRef kext_ids[1]; + kext_ids[0] = kDriverKextIdentifier; + CFArrayRef kext_id_query = CFArrayCreate(nullptr, (const void **)kext_ids, 1, + &kCFTypeArrayCallBacks); + CFDictionaryRef kext_infos = + KextManagerCopyLoadedKextInfo(kext_id_query, nullptr); + CFRelease(kext_id_query); + + CFDictionaryRef cuda_driver_info = nullptr; + if (CFDictionaryGetValueIfPresent(kext_infos, kDriverKextIdentifier, + (const void **)&cuda_driver_info)) { + // NOTE: OSX CUDA driver does not currently store the same driver version + // in kCFBundleVersionKey as is returned by cuDriverGetVersion + CFRelease(kext_infos); + const CFStringRef str = (CFStringRef)CFDictionaryGetValue( + cuda_driver_info, kCFBundleVersionKey); + const char *version = CFStringGetCStringPtr(str, kCFStringEncodingUTF8); + + // version can be NULL in which case treat it as empty string + // see + // https://developer.apple.com/library/mac/documentation/CoreFoundation/Conceptual/CFStrings/Articles/AccessingContents.html#//apple_ref/doc/uid/20001184-100980-TPXREF112 + if (version == NULL) { + return StringToDriverVersion(""); + } + return StringToDriverVersion(version); + } + CFRelease(kext_infos); + auto status = port::Status( + port::error::INTERNAL, + port::StrCat( + "failed to read driver bundle version: ", + CFStringGetCStringPtr(kDriverKextIdentifier, kCFStringEncodingUTF8))); + return status; +#elif defined(PLATFORM_WINDOWS) auto status = port::Status(port::error::UNIMPLEMENTED, "kernel reported driver version not implemented on Windows"); diff --git a/tensorflow/stream_executor/cuda/cuda_gpu_executor.cc b/tensorflow/stream_executor/cuda/cuda_gpu_executor.cc index edf217875f..f11022ef1d 100644 --- a/tensorflow/stream_executor/cuda/cuda_gpu_executor.cc +++ b/tensorflow/stream_executor/cuda/cuda_gpu_executor.cc @@ -15,6 +15,9 @@ limitations under the License. #include "tensorflow/stream_executor/cuda/cuda_gpu_executor.h" +#if defined(__APPLE__) +#include +#endif #if defined(PLATFORM_WINDOWS) #include #define PATH_MAX MAX_PATH @@ -176,11 +179,19 @@ bool CUDAExecutor::FindOnDiskForComputeCapability( // would return /usr/bin. static string GetBinaryDir(bool strip_exe) { char exe_path[PATH_MAX] = {0}; +#if defined(__APPLE__) + uint32_t buffer_size = 0U; + _NSGetExecutablePath(nullptr, &buffer_size); + char unresolved_path[buffer_size]; + _NSGetExecutablePath(unresolved_path, &buffer_size); + CHECK_ERR(realpath(unresolved_path, exe_path) ? 1 : -1); +#else #if defined(PLATFORM_WINDOWS) HMODULE hModule = GetModuleHandle(NULL); GetModuleFileName(hModule, exe_path, MAX_PATH); #else CHECK_ERR(readlink("/proc/self/exe", exe_path, sizeof(exe_path) - 1)); +#endif #endif // Make sure it's null-terminated: exe_path[sizeof(exe_path) - 1] = 0; @@ -843,7 +854,10 @@ CudaContext* CUDAExecutor::cuda_context() { return context_; } // For anything more complicated/prod-focused than this, you'll likely want to // turn to gsys' topology modeling. static int TryToReadNumaNode(const string &pci_bus_id, int device_ordinal) { -#if defined(PLATFORM_WINDOWS) +#if defined(__APPLE__) + LOG(INFO) << "OS X does not support NUMA - returning NUMA node zero"; + return 0; +#elif defined(PLATFORM_WINDOWS) // Windows support for NUMA is not currently implemented. Return node 0. return 0; #elif defined(__aarch64__) -- GitLab From ebe34a138382a873063e7472fc33ee33a2d6ae36 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 19 Jun 2018 12:06:49 -0700 Subject: [PATCH 678/816] fix a bug about converting Log1p - we are checking the x tensor (not the constant tensor) to be 1. PiperOrigin-RevId: 201217989 --- .../optimizers/arithmetic_optimizer.cc | 24 +++++++++---------- 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc index 90be051764..d49c087071 100644 --- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc +++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc @@ -2519,14 +2519,14 @@ class ConvertLog1pStage : public ArithmeticOptimizerStage { bool* modified) { const auto& t = ctx().graph_properties->GetInputProperties(input->name())[i]; - for (int k = 0; k < t.shape().dim_size(); ++k) { - // Skip if t shape is not fully determined. - if (t.shape().dim(k).size() < 0) { + const auto& c = + ctx().graph_properties->GetInputProperties(input->name())[j]; + for (int k = 0; k < c.shape().dim_size(); ++k) { + // Skip if c shape is not fully determined. + if (c.shape().dim(k).size() < 0) { return Status::OK(); } } - const auto& c = - ctx().graph_properties->GetInputProperties(input->name())[j]; TensorShapeProto broadcast_shape; if (!ShapeAfterBroadcast(t.shape(), c.shape(), &broadcast_shape)) { return errors::InvalidArgument("Cannot get broadcast shape for: ", @@ -2537,15 +2537,15 @@ class ConvertLog1pStage : public ArithmeticOptimizerStage { // broadcast. return Status::OK(); } - if (TensorShape::IsValid(t.shape()) && t.has_value()) { - Tensor tensor(t.dtype(), t.shape()); - if (!tensor.FromProto(t.value())) { + if (TensorShape::IsValid(c.shape()) && c.has_value()) { + Tensor constant(c.dtype(), c.shape()); + if (!constant.FromProto(c.value())) { return errors::InvalidArgument("Cannot parse tensor from proto: ", t.value().DebugString()); } complex128 element; - for (int k = 0; k < tensor.NumElements(); ++k) { - if (!GetElement(tensor, k, &element)) { + for (int k = 0; k < constant.NumElements(); ++k) { + if (!GetElement(constant, k, &element)) { // input data type is not supported by log1p. Skip. return Status::OK(); } @@ -2558,8 +2558,8 @@ class ConvertLog1pStage : public ArithmeticOptimizerStage { TF_RETURN_IF_ERROR(GetInputNode(input->input(i), &x)); TF_RETURN_IF_ERROR(GetInputNode(input->input(j), &y)); node->set_op("Log1p"); - node->set_input(0, y->name()); - node->add_input(AsControlDependency(x->name())); + node->set_input(0, x->name()); + node->add_input(AsControlDependency(y->name())); ForwardControlDependencies(node, {input}); AddToOptimizationQueue(node); -- GitLab From 8f19772410ec20010e9930f9765dbd3aaeb06111 Mon Sep 17 00:00:00 2001 From: Reed Wanderman-Milne Date: Tue, 19 Jun 2018 12:08:24 -0700 Subject: [PATCH 679/816] Rollback documentation that I forgot to rollback last time. PiperOrigin-RevId: 201218249 --- tensorflow/python/keras/engine/base_layer.py | 9 --------- 1 file changed, 9 deletions(-) diff --git a/tensorflow/python/keras/engine/base_layer.py b/tensorflow/python/keras/engine/base_layer.py index e8cdda30a2..4814275fd5 100644 --- a/tensorflow/python/keras/engine/base_layer.py +++ b/tensorflow/python/keras/engine/base_layer.py @@ -88,15 +88,6 @@ class Layer(checkpointable.CheckpointableBase): once. Should actually perform the logic of applying the layer to the input tensors (which should be passed in as the first argument). - All floating point tensor inputs and arguments are casted to the layer's - dtype, before the body of the layer computation happens. For models with - layers of different dtypes, this helps getting rid of the explicit casts - between layers. - - The casting behavior can be customized in subclasses by overridding - `_cast_inputs_and_args()` function, which is useful if certain or all inputs - should not be casted. - Arguments: trainable: Boolean, whether the layer's variables should be trainable. name: String name of the layer. -- GitLab From b5a8d9ea0ec49b1e3fee5441a78a3fb33cd4d470 Mon Sep 17 00:00:00 2001 From: gracehoney <31743510+aaroey@users.noreply.github.com> Date: Tue, 19 Jun 2018 12:14:10 -0700 Subject: [PATCH 680/816] Multiple changes: 1. use unique_ptr instead of shared_ptr, and fix a bug in destructor of TrtEngineOp where it did't reset the shared_ptr but a copy of it 2. fix the include order 3. shorten the reference to tensorflow::tensorrt::xxx 4. remove some code that sets something which will be overwritten later 5. fix format, including: function signature, variable names, const reference, etc 6. remove some deadcode 7. add a lot of comments and TODOs 8. in TrtEngineOp, replace the map of allocators with a single unique_ptr 9. in TrtEngineOp, remove parameter ignore_dim_change from GetEngine(), since it always uses member fixed_input_size_ --- .../contrib/tensorrt/convert/convert_graph.cc | 272 ++++++++-------- .../contrib/tensorrt/convert/convert_graph.h | 8 +- .../contrib/tensorrt/convert/convert_nodes.cc | 214 ++++++------ .../contrib/tensorrt/convert/convert_nodes.h | 61 +++- .../contrib/tensorrt/kernels/trt_engine_op.cc | 306 +++++++++--------- .../contrib/tensorrt/kernels/trt_engine_op.h | 33 +- .../tensorrt/resources/trt_int8_calibrator.h | 32 +- .../tensorrt/resources/trt_resources.h | 37 +-- tensorflow/contrib/tensorrt/segment/segment.h | 7 +- 9 files changed, 514 insertions(+), 456 deletions(-) diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.cc b/tensorflow/contrib/tensorrt/convert/convert_graph.cc index c17ef5fdab..bd6ed2d593 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_graph.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_graph.cc @@ -14,7 +14,6 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/contrib/tensorrt/convert/convert_graph.h" -#include "tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h" #include #include @@ -25,6 +24,8 @@ limitations under the License. #include #include "tensorflow/contrib/tensorrt/convert/convert_nodes.h" +#include "tensorflow/contrib/tensorrt/convert/utils.h" +#include "tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h" #include "tensorflow/contrib/tensorrt/resources/trt_resource_manager.h" #include "tensorflow/contrib/tensorrt/resources/trt_resources.h" #include "tensorflow/contrib/tensorrt/segment/segment.h" @@ -76,6 +77,7 @@ std::vector GetLoadedTensorRTVersion() { int ver_patch = ver - ver_minor * 100; return {ver_major, ver_minor, ver_patch}; } + namespace { bool IsTensorRTCandidate(const tensorflow::Node* node) { @@ -121,13 +123,14 @@ tensorflow::Status BuildNodeMap( } } // namespace + // Function to get calibration from ResourceMgr and put them into nodedef. tensorflow::Status ConvertCalibGraphToInferGraph( const tensorflow::GraphDef& graph_def, tensorflow::GraphDef* infer_graph, bool is_dyn_op) { VLOG(0) << "Starting Calib Conversion"; infer_graph->CopyFrom(graph_def); - auto trt_rm = tensorflow::tensorrt::TRTResourceManager::instance(); + auto trt_rm = TRTResourceManager::instance(); auto calib_rm = trt_rm->getManager("TRTCalibration"); int num_nodes = infer_graph->node_size(); if (!is_dyn_op) { @@ -139,7 +142,7 @@ tensorflow::Status ConvertCalibGraphToInferGraph( if (n->op() == "TRTEngineOp") { VLOG(1) << "Processing " << n->name(); string container_name = n->attr().at("segment_funcdef_name").s(); - tensorflow::tensorrt::TRTCalibrationResource* cres = nullptr; + TRTCalibrationResource* cres = nullptr; auto status = calib_rm->Lookup(container_name, "Calibrator", &cres); if (!status.ok()) { LOG(ERROR) << "Could not get Calibration information. Did you run with " @@ -240,14 +243,16 @@ EngineInfo GetEngineInfo( const tensorflow::grappler::GraphProperties& graph_properties, const std::set& segment_nodes, const std::unordered_map& node_map, - const std::vector& topological_order) { + const std::vector& reverse_topo_order) { std::vector subgraph_node_ids; EngineInfo info; std::set segment_devices; int input_port = 0; int output_port = 0; + // TODO(aaroey): consider using node id and port instead. Also, here we assume + // that input edge set and output edge set have no intersection, is this true? std::unordered_map created_edges; - for (auto it = topological_order.rbegin(); it != topological_order.rend(); + for (auto it = reverse_topo_order.rbegin(); it != reverse_topo_order.rend(); ++it) { auto node_name = (*it)->name(); @@ -287,9 +292,11 @@ EngineInfo GetEngineInfo( created_edges.insert({s, port}); input_port++; } - EngineConnections ec(input_node->name(), input_node->id(), + EngineConnection ec(input_node->name(), input_node->id(), edge->src_output(), node_name, node_id, edge->dst_input(), true, port); + // TODO(aaroey): this will be rewritten in + // ConvertSegmentToSubGraphDef, fix it. ec.connection_type = input_node->output_type(edge->src_output()); info.connections.emplace_back(std::move(ec)); @@ -317,10 +324,9 @@ EngineInfo GetEngineInfo( } } - ConvertSegmentToGraphDef(g, graph_properties, subgraph_node_ids, - &info.connections, &info.segment_graph_def, - &info.engine_name); - info.engine_type = EngineInfo::EngineType::TRTStatic; + ConvertSegmentToSubGraphDef(g, graph_properties, subgraph_node_ids, + &info.connections, &info.segment_graph_def, + &info.engine_name); // TODO(sami): This should not happen once segmenter is updated. if (segment_devices.size() == 1) { info.device = *segment_devices.begin(); @@ -336,23 +342,27 @@ EngineInfo GetEngineInfo( } // Function to insert a TRT node into the graph. +// 'alloc' is only used for creating static engine. tensorflow::Status CreateTRTNode(tensorflow::Graph* graph, const std::vector& infos, int pos, - tensorflow::NodeDef* trt_node, nvinfer1::IGpuAllocator* alloc, int max_batch_size) { - auto& info = infos.at(pos); + const auto& info = infos.at(pos); std::vector out_shapes; std::vector input_shapes; std::vector shapes; std::vector inputs; std::vector out_types; VLOG(1) << "Processing " << info.engine_name; - for (const auto conn : info.connections) { - if (!conn.is_input_edge) { // output edge + + // Update the shape and data types of input/output nodes, and find all unique + // inputs. + for (const auto& conn : info.connections) { + if (!conn.is_input_edge) { + // Set the shapes and data types of output edge. tensorflow::TensorShapeProto out_shape; - conn.inside_shape.AsProto( - &out_shape); // shape of the output node inside segment + // shape of the output node inside segment + conn.inside_shape.AsProto(&out_shape); if (out_shapes.size() <= conn.port_number) { out_shapes.resize(conn.port_number + 1); out_types.resize(conn.port_number + 1); @@ -360,10 +370,11 @@ tensorflow::Status CreateTRTNode(tensorflow::Graph* graph, out_shapes.at(conn.port_number) = out_shape; out_types.at(conn.port_number) = conn.connection_type; continue; - } // input edge + } + + // Set the shapes and data types of input edge. tensorflow::TensorShapeProto in_shape; conn.outside_shape.AsProto(&in_shape); - if (input_shapes.size() <= conn.port_number) { input_shapes.resize(conn.port_number + 1); shapes.resize(conn.port_number + 1); @@ -373,18 +384,13 @@ tensorflow::Status CreateTRTNode(tensorflow::Graph* graph, string input_node = conn.outside_node_name; int input_port = conn.outside_port; - auto dtype = conn.connection_type; bool found_engine = false; // Rewire the inputs to other engines if they contain original input node for (size_t t = 0; t < infos.size(); ++t) { - if (t == pos) { - continue; - } + if (t == pos) continue; auto& engine_info = infos.at(t); for (const auto& eng_conn : engine_info.connections) { - if (eng_conn.is_input_edge) { - continue; - } + if (eng_conn.is_input_edge) continue; if (eng_conn.inside_node_name == input_node) { input_node = engine_info.engine_name; if (eng_conn.inside_port == input_port) { @@ -398,6 +404,7 @@ tensorflow::Status CreateTRTNode(tensorflow::Graph* graph, } VLOG(1) << "Engine Input " << input_node << ":" << input_port << " -> " << info.engine_name << ":" << inputs.size(); + // Skip duplicate inputs. bool new_input = true; for (const auto& inp : inputs) { if (inp.node == input_node && inp.index == input_port) { @@ -406,78 +413,63 @@ tensorflow::Status CreateTRTNode(tensorflow::Graph* graph, } } if (new_input) { - inputs.emplace_back(input_node, input_port, dtype); + inputs.emplace_back(input_node, input_port, conn.connection_type); } } + + // Build the engine and get its serialized representation. string segment_string; if (info.engine_type == EngineInfo::EngineType::TRTStatic || info.precision_mode == INT8MODE) { // Create static engine and for int8 test validity of the engine. - tensorflow::tensorrt::Logger trt_logger; - auto builder = std::shared_ptr( - nvinfer1::createInferBuilder(trt_logger), [](nvinfer1::IBuilder* p) { - if (p) p->destroy(); - }); + Logger trt_logger; + auto builder = std::unique_ptr< + nvinfer1::IBuilder, std::function>( + nvinfer1::createInferBuilder(trt_logger), + [](nvinfer1::IBuilder* p) { if (p) p->destroy(); }); builder->setMaxBatchSize(max_batch_size); - if (info.precision_mode == tensorflow::tensorrt::convert::FP16MODE) { - builder->setHalf2Mode(true); - } + if (info.precision_mode == FP16MODE) builder->setHalf2Mode(true); builder->setMaxWorkspaceSize(info.max_workspace_size_bytes); #if NV_TENSORRT_MAJOR > 3 builder->setGpuAllocator(alloc); #endif - nvinfer1::ICudaEngine* engine = nullptr; + TrtUniquePtrType engine; // TODO(sami): What happens if 1st dim is not batch? - auto status = ConvertSubgraphToEngine(info.segment_graph_def, builder.get(), - shapes, &engine, info.precision_mode); - if (!status.ok()) { - if (engine) engine->destroy(); - return status; - } - if (engine) { - auto engine_data = std::shared_ptr( - engine->serialize(), [](nvinfer1::IHostMemory* p) { - if (p) p->destroy(); - }); - segment_string = - string((const char*)engine_data->data(), engine_data->size()); - engine->destroy(); - } + TF_RETURN_IF_ERROR(ConvertSubGraphDefToEngine( + info.segment_graph_def, info.precision_mode, shapes, builder.get(), + &engine, /*convert_successfully=*/nullptr)); + TrtUniquePtrType engine_data(engine->serialize()); + segment_string = + string((const char*)engine_data->data(), engine_data->size()); if (info.precision_mode == INT8MODE) { + // TODO(aaroey): why not put this inside the 'else' branch? segment_string = info.segment_graph_def.SerializeAsString(); } } else { segment_string = info.segment_graph_def.SerializeAsString(); } + + // TODO(aaroey): use enum instead, and add a helper method to do the + // conversion. string prec_string; switch (info.precision_mode) { - case FP32MODE: { + case FP32MODE: prec_string = "FP32"; break; - } - case FP16MODE: { + case FP16MODE: prec_string = "FP16"; break; - } - case INT8MODE: { + case INT8MODE: prec_string = "INT8"; - auto trt_rm = tensorflow::tensorrt::TRTResourceManager::instance(); - auto calib_rm = trt_rm->getManager("TRTCalibration"); - if (!calib_rm) { + if (!TRTResourceManager::instance()->getManager("TRTCalibration")) { LOG(ERROR) << "Failed to construct calibration storage"; } break; - } - default: { + default: return tensorflow::errors::OutOfRange("Unknown precision mode"); - } } - tensorflow::Status status; - tensorflow::Node* engine_node = nullptr; tensorflow::NodeDefBuilder node_builder(info.engine_name, "TRTEngineOp"); - if (!info.device.empty()) { - node_builder.Device(info.device); - } + if (!info.device.empty()) node_builder.Device(info.device); if (VLOG_IS_ON(1)) { string ins=StrCat(info.engine_name," inputs= "); for (const auto& ii : inputs) { @@ -486,50 +478,53 @@ tensorflow::Status CreateTRTNode(tensorflow::Graph* graph, VLOG(1) << ins; } node_builder.Input(inputs); - if (info.engine_type == EngineInfo::EngineType::TRTStatic) { - if (info.cached_engine_batches.size()) { - LOG(WARNING) << "Cached engine batches are ignored for static engines"; - } + if (info.engine_type == EngineInfo::EngineType::TRTStatic && + info.cached_engine_batches.size()) { + LOG(WARNING) << "Cached engine batches are ignored for static engines"; } - status = node_builder.Attr("input_shapes", input_shapes) - .Attr("output_shapes", out_shapes) - .Attr("static_engine", - info.engine_type == EngineInfo::EngineType::TRTStatic) - .Attr("segment_funcdef_name", - StrCat(info.engine_name, "_native_segment")) - .Attr("serialized_segment", segment_string) - .Attr("calibration_data", "") - .Attr("max_cached_engines_count", info.maximum_cached_engines) - .Attr("cached_engine_batches", {max_batch_size}) - .Attr("workspace_size_bytes", info.max_workspace_size_bytes) - .Attr("precision_mode", prec_string) - .Attr("OutT", out_types) - .Finalize(trt_node); + tensorflow::NodeDef trt_node; + tensorflow::Status status = + node_builder.Attr("input_shapes", input_shapes) + .Attr("output_shapes", out_shapes) + .Attr("static_engine", + info.engine_type == EngineInfo::EngineType::TRTStatic) + .Attr("segment_funcdef_name", + StrCat(info.engine_name, "_native_segment")) + .Attr("serialized_segment", segment_string) + .Attr("calibration_data", "") + .Attr("max_cached_engines_count", info.maximum_cached_engines) + .Attr("cached_engine_batches", {max_batch_size}) + .Attr("workspace_size_bytes", info.max_workspace_size_bytes) + .Attr("precision_mode", prec_string) + .Attr("OutT", out_types) + .Finalize(&trt_node); if (!status.ok()) { LOG(ERROR) << "Node construction failed with" << status; return status; } VLOG(1) << "Adding TRTEngine " << info.engine_name << " to graph"; - engine_node = graph->AddNode(*trt_node, &status); + tensorflow::Node* engine_node = graph->AddNode(trt_node, &status); if (!status.ok()) { LOG(ERROR) << "Adding node failed " << status; return status; } - + // Updates the inputs of output edges destination nodes, and point them to the + // engine node. for (auto& conn : info.connections) { if (conn.is_input_edge) continue; VLOG(1) << " Updating DBG " << engine_node->name() << " out_port " << conn.port_number << " out_id " << conn.outside_id << " name=" << conn.outside_node_name; auto dst_node = graph->FindNodeId(conn.outside_id); - if (!dst_node) { // node removed skip. - continue; - } + // TODO(aaroey): node could be removed during construction of other TRT + // nodes, but then in that case who is going to update their input nodes? + if (!dst_node) continue; VLOG(1) << "Updating " << engine_node->name() << ":" << conn.port_number << " to " << dst_node->name() << ":" << conn.outside_port; status = graph->UpdateEdge(engine_node, conn.port_number, dst_node, conn.outside_port); if (!status.ok()) { + // TODO(aaroey): should we return the status? LOG(ERROR) << "Edge update failed " << engine_node->name() << ":" << conn.port_number << " -> " << dst_node->name() << ":" << conn.outside_port << " status= " << status; @@ -631,9 +626,7 @@ tensorflow::Status RegisterSegmentFunctionToFunctionLibrary( std::pair GetDeviceAndAllocator( ConversionParams& params, EngineInfo& engine) { int cuda_device_id = -1; - // we need to us PM here since in python path there is no way to get - // to allocators - auto CheckDeviceID = [](int tfid) -> int { + auto check_device_id = [](int tfid) -> int { tensorflow::TfGpuId tf_gpu_id(tfid); CudaGpuId cuda_gpu_id; Status s = GpuIdManager::TfToCudaGpuId(tf_gpu_id, &cuda_gpu_id); @@ -646,6 +639,9 @@ std::pair GetDeviceAndAllocator( return -1; }; tensorflow::Allocator* dev_allocator = nullptr; + // we need to us PM here since in python path there is no way to get + // to allocators + // TODO(aaroey): fix this. auto pm = tensorflow::ProcessState::singleton(); if (params.cluster) { // get allocator const tensorflow::Device* device = nullptr; @@ -653,15 +649,15 @@ std::pair GetDeviceAndAllocator( device = params.cluster->GetDeviceSet()->FindDeviceByName(engine.device); } if (device) { - cuda_device_id = CheckDeviceID(device->parsed_name().id); + cuda_device_id = check_device_id(device->parsed_name().id); if (cuda_device_id < 0) { - LOG(ERROR) << "Cuda device identification failed, using device " - "0."; + LOG(ERROR) << "Cuda device identification failed, using device 0."; cuda_device_id = 0; } tensorflow::GPUOptions gpuoptions; // this should be instantiated by now tensorflow::TfGpuId tf_gpu_id(device->parsed_name().id); + // TODO(aaroey): why not using device->GetAllocator()? dev_allocator = pm->GetGPUAllocator(gpuoptions, tf_gpu_id, 1); VLOG(1) << "Got an allocator for device tf_device=" << tf_gpu_id.value() << " cuda device= " << cuda_device_id << " at " << dev_allocator; @@ -676,19 +672,16 @@ std::pair GetDeviceAndAllocator( // if device is set, try to find the device. Might be a problem for multi // host case but TensorRT do not support multi host setups yet. if (!engine.device.empty()) { - tensorflow::DeviceNameUtils::ParsedName parsed_name; - if (tensorflow::DeviceNameUtils::ParseFullName(engine.device, - &parsed_name)) { + DeviceNameUtils::ParsedName parsed_name; + if (DeviceNameUtils::ParseFullName(engine.device, &parsed_name)) { cuda_device_id = parsed_name.has_id ? parsed_name.id : -1; } try_gpu_ids = !parsed_name.has_id; } if (try_gpu_ids) { while (found_device < 100) { - cuda_device_id = CheckDeviceID(found_device); - if (cuda_device_id >= 0) { - break; - } + cuda_device_id = check_device_id(found_device); + if (cuda_device_id >= 0) break; found_device++; } } @@ -698,31 +691,32 @@ std::pair GetDeviceAndAllocator( return std::make_pair(cuda_device_id, dev_allocator); } LOG(WARNING) - << "Can't determine the device constructing an allocator at device " + << "Can't determine the device, constructing an allocator at device " << found_device; tensorflow::GPUOptions gpuoptions; - gpuoptions.set_allow_growth( - true); // this will be a noop if device is already initialized + // this will be a noop if device is already initialized + gpuoptions.set_allow_growth(true); tensorflow::TfGpuId tf_gpu_id(found_device); dev_allocator = pm->GetGPUAllocator(gpuoptions, tf_gpu_id, 1); } return std::make_pair(cuda_device_id, dev_allocator); } + // Entry function from optimization pass. tensorflow::Status ConvertAfterShapes(ConversionParams& params) { - // Segment the graph into subgraphs that can be converted to TensorRT - tensorflow::tensorrt::segment::SegmentOptions segment_options; + // Convert graphdef to graph. tensorflow::FunctionLibraryDefinition flib(tensorflow::OpRegistry::Global(), params.input_graph_def->library()); tensorflow::Graph graph(flib); TF_RETURN_IF_ERROR(tensorflow::ConvertGraphDefToGraph( tensorflow::GraphConstructorOptions(), *params.input_graph_def, &graph)); + // Segment the graph into subgraphs that can be converted to TensorRT + tensorflow::tensorrt::segment::SegmentOptions segment_options; // TODO(ben,jie,sami): exclude output nodes (DISCUSS IT) for (auto node : *(params.output_names)) { segment_options.exclude_node_list.insert(node); } - segment_options.minimum_segment_size = params.minimum_segment_size; tensorflow::tensorrt::segment::SegmentNodesVector segments; TF_RETURN_IF_ERROR(tensorrt::segment::SegmentGraph( @@ -730,34 +724,38 @@ tensorflow::Status ConvertAfterShapes(ConversionParams& params) { if (segments.size() > 1) { VLOG(0) << "MULTIPLE tensorrt candidate conversion: " << segments.size(); } + + // Get the EngineInfo for each segment. std::unordered_map node_map; TF_RETURN_IF_ERROR(BuildNodeMap(graph, &node_map)); - std::unordered_map> output_edge_map; float total_num_nodes_in_segments = 0.; std::vector engine_segments; engine_segments.reserve(segments.size()); - std::vector topo_order; - tensorflow::GetPostOrder(graph, &topo_order); - size_t total_engine_size = 0; - std::vector engine_sizes; + std::vector reverse_topo_order; + tensorflow::GetPostOrder(graph, &reverse_topo_order); + size_t total_engine_bytes_size = 0; + std::vector engine_bytes_size; for (size_t t = 0; t < segments.size(); t++) { auto& s = segments.at(t); - engine_segments.emplace_back(GetEngineInfo(&graph, *params.graph_properties, - s.first, node_map, topo_order)); + engine_segments.emplace_back(GetEngineInfo( + &graph, *params.graph_properties, s.first, node_map, + reverse_topo_order)); auto& curr_engine = engine_segments.back(); curr_engine.precision_mode = params.precision_mode; - engine_sizes.push_back(curr_engine.segment_graph_def.ByteSizeLong()); curr_engine.engine_type = (params.is_dyn_op || params.precision_mode == INT8MODE ? EngineInfo::EngineType::TRTDynamic : EngineInfo::EngineType::TRTStatic); curr_engine.cached_engine_batches = params.cached_engine_batches; curr_engine.maximum_cached_engines = params.max_cached_engines; - total_engine_size += engine_sizes.back(); - total_num_nodes_in_segments += s.first.size(); StrAppend(&curr_engine.engine_name, "my_trt_op_", t); RegisterSegmentFunctionToFunctionLibrary( &graph, curr_engine.segment_graph_def, curr_engine.engine_name); + + engine_bytes_size.push_back(curr_engine.segment_graph_def.ByteSizeLong()); + total_engine_bytes_size += engine_bytes_size.back(); + total_num_nodes_in_segments += s.first.size(); + if (VLOG_IS_ON(8)) { string fname = curr_engine.engine_name; StrAppend(&fname, ".pb"); @@ -767,54 +765,54 @@ tensorflow::Status ConvertAfterShapes(ConversionParams& params) { f.close(); } } - std::vector trt_nodes; - trt_nodes.reserve(engine_segments.size()); + + // Create a TRT node for each segment using its EngineInfo. int old_cuda_device = 0; auto err = cudaGetDevice(&old_cuda_device); if (err != cudaSuccess) { - LOG(ERROR) << "Couldn't get current device error is " - << cudaGetErrorString(err); + LOG(ERROR) << "Couldn't get current device: " << cudaGetErrorString(err); } VLOG(1) << "Current cuda device is " << old_cuda_device; for (int i = 0; i < engine_segments.size(); ++i) { - auto trt_node = new tensorflow::NodeDef; - trt_nodes.push_back(trt_node); auto& engine = engine_segments.at(i); // Partition the workspace size by the average of node ratio and segment // graphdef size engine.max_workspace_size_bytes = params.max_workspace_size_bytes * - (engine_sizes.at(i) / total_engine_size + + (engine_bytes_size.at(i) / total_engine_bytes_size + segments.at(i).first.size() / total_num_nodes_in_segments) / 2.0; - std::shared_ptr alloc; + // The allocator is used to build the engine. The build and the built engine + // will be destroyed after we get the serialized engine string, so it's fine + // to use unique_ptr here. + std::unique_ptr alloc; auto device_alloc = GetDeviceAndAllocator(params, engine); int cuda_device_id = 0; if (device_alloc.first >= 0) { cuda_device_id = device_alloc.first; alloc.reset(new TRTDeviceAllocator(device_alloc.second)); - } else { // Setting allocator as nullptr should get revert to the - // cudamalloc + } else { + // Setting allocator as nullptr should get revert to the cudamalloc LOG(WARNING) << "Can't identify the cuda device. Running on device 0 "; } cudaSetDevice(cuda_device_id); - auto status = CreateTRTNode(&graph, engine_segments, i, trt_node, - alloc.get(), params.max_batch_size); + auto status = CreateTRTNode( + &graph, engine_segments, i, alloc.get(), params.max_batch_size); if (status.ok()) { - const auto& internal_nodes = segments.at(i).first; - for (auto node_id : internal_nodes) { - graph.RemoveNode(node_map.at(node_id)); + for (auto node_name : segments.at(i).first) { + graph.RemoveNode(node_map.at(node_name)); } } else { + // TODO(aaroey): in this case, the graph is already modified, we should + // return the status? LOG(WARNING) << "Engine creation for segment " << i << ", composed of " - << segments.at(i).first.size() << " nodes failed. Skipping"; - VLOG(1) << "Failure reason " << status; + << segments.at(i).first.size() << " nodes failed: " + << status << ". Skipping..."; } } cudaSetDevice(old_cuda_device); graph.ToGraphDef(params.output_graph_def); - for (auto tn : trt_nodes) delete tn; - VLOG(1)<<"Returning from conversion"; + VLOG(1) << "Returning from conversion"; return tensorflow::Status::OK(); } diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.h b/tensorflow/contrib/tensorrt/convert/convert_graph.h index e2f4c1c83f..9d986e4890 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_graph.h +++ b/tensorflow/contrib/tensorrt/convert/convert_graph.h @@ -64,10 +64,10 @@ tensorflow::Status ConvertCalibGraphToInferGraph( const tensorflow::GraphDef& graph_def, tensorflow::GraphDef* new_graph_def, bool is_dyn_op); -// max_batch_size: maximum batch size which can be used for inference for -// optimization targets inference run with max batch size. -// max_workspace_size_bytes: The upper bound of memory allowance for -// engine building. +// - max_batch_size: maximum batch size which can be used for inference for +// optimization targets inference run with max batch size. +// - max_workspace_size_bytes: The upper bound of memory allowance for engine +// building. tensorflow::Status ConvertGraphDefToTensorRT( const tensorflow::GraphDef& graph_def, const std::vector& output_names, size_t max_batch_size, diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index 6ad2d7e68f..a252ea67df 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -14,7 +14,6 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/contrib/tensorrt/convert/convert_nodes.h" -#include "tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h" #include #include @@ -25,7 +24,9 @@ limitations under the License. #include #include +#include "tensorflow/contrib/tensorrt/convert/utils.h" #include "tensorflow/contrib/tensorrt/log/trt_logger.h" +#include "tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h" #include "tensorflow/contrib/tensorrt/resources/trt_resource_manager.h" #include "tensorflow/contrib/tensorrt/resources/trt_resources.h" #include "tensorflow/core/framework/node_def.pb.h" // NOLINT @@ -125,12 +126,10 @@ static std::vector> CreateSamePadding( string GetCommonNameScope(const string& op_name_a, const string& op_name_b) { size_t last_scope_separator = 0; - for (size_t i = 0; i < std::min(op_name_a.size(), op_name_b.size()); ++i) { - if (op_name_a[i] != op_name_b[i]) { - break; - } else if (op_name_a[i] == '/') { - last_scope_separator = i + 1; - } + const size_t min_size = std::min(op_name_a.size(), op_name_b.size()); + for (size_t i = 0; i < min_size; ++i) { + if (op_name_a[i] != op_name_b[i]) break; + if (op_name_a[i] == '/') last_scope_separator = i + 1; } return op_name_a.substr(0, last_scope_separator); } @@ -2144,10 +2143,14 @@ void Converter::register_op_converters() { } // namespace -tensorflow::Status ConvertSubgraphToEngine( - const tensorflow::GraphDef& gdef, nvinfer1::IBuilder* builder, +tensorflow::Status ConvertSubGraphDefToEngine( + const tensorflow::GraphDef& gdef, int precision_mode, const std::vector& input_shapes, - nvinfer1::ICudaEngine** engine, int precision_mode) { + nvinfer1::IBuilder* builder, + TrtUniquePtrType* engine, + bool* convert_successfully) { + engine->reset(); + if (convert_successfully) *convert_successfully = false; auto trt_network = infer_object(builder->createNetwork()); if (!trt_network) { return tensorflow::errors::Internal( @@ -2159,7 +2162,7 @@ tensorflow::Status ConvertSubgraphToEngine( VLOG(1) << "Starting engine conversion "; Converter converter(trt_network.get(), ws.get(), precision_mode == FP16MODE); std::vector> output_tensors; - // graph nodes are already topologically sorted during construction + // Graph nodes are already topologically sorted during construction for (const auto& node_def : gdef.node()) { string node_name = node_def.name(); VLOG(1) << "Converting op name=" << node_name << ", op=" << node_def.op(); @@ -2215,7 +2218,7 @@ tensorflow::Status ConvertSubgraphToEngine( } } else if (tensorflow::str_util::StartsWith(node_name, kOutputPHName) && (node_def.op() == "Identity")) { - tensorflow::int32 slot_number = -1; + int32 slot_number = -1; if (!tensorflow::strings::safe_strto32(node_name.c_str() + 9, &slot_number)) { LOG(ERROR) << "Failed to parse slot number from " << node_name @@ -2248,122 +2251,130 @@ tensorflow::Status ConvertSubgraphToEngine( converter.network()->markOutput(*tensor); } + if (convert_successfully) *convert_successfully = true; + + // Build the engine. VLOG(1) << "Starting engine creation"; - *engine = builder->buildCudaEngine(*converter.network()); + engine->reset(builder->buildCudaEngine(*converter.network())); + if (engine->get() == nullptr) { + return tensorflow::errors::Internal("Failed to build TensorRT engine"); + } VLOG(1) << "Finished conversion"; return tensorflow::Status::OK(); } -tensorflow::Status ConvertSegmentToGraphDef( +tensorflow::Status ConvertSegmentToSubGraphDef( const tensorflow::Graph* graph, const tensorflow::grappler::GraphProperties& graph_properties, - const std::vector& subgraph_node_ids, - std::vector* connections, + const std::vector& subgraph_node_ids, // In topological order + std::vector* connections, tensorflow::GraphDef* segment_def, string* common_scope) { std::set marker_nodes; + // Update connection shapes/data types and add corresponding input/output + // nodes in the segment graphdef. for (size_t i = 0; i < connections->size(); ++i) { auto& connection = connections->at(i); auto outside_node = graph->FindNodeId(connection.outside_id); - if (outside_node) { - tensorflow::DataType input_type = tensorflow::DT_FLOAT; - tensorflow::PartialTensorShape partial_shape; - if (connection.is_input_edge) { - if (graph_properties.HasOutputProperties( - connection.outside_node_name)) { - auto output_params = graph_properties.GetOutputProperties( - connection.outside_node_name); - auto out_shape = output_params.at(connection.outside_port); - input_type = out_shape.dtype(); - std::vector dims; - partial_shape = out_shape.shape(); - connection.outside_shape = partial_shape; - } else { - VLOG(0) << "Unknown output shape" << outside_node->name(); - input_type = graph->FindNodeId(connection.outside_id) - ->output_type(connection.outside_port); - } - connection.connection_type = input_type; - - } else { // output edge - if (graph_properties.HasInputProperties(connection.outside_node_name)) { - auto input_params = - graph_properties.GetInputProperties(connection.outside_node_name); - auto in_shape = input_params.at(connection.outside_port); - input_type = in_shape.dtype(); - partial_shape = in_shape.shape(); - connection.inside_shape = partial_shape; - } else { - input_type = graph->FindNodeId(connection.inside_id) - ->output_type(connection.outside_port); - } - connection.connection_type = input_type; + if (!outside_node) { + // TODO(aaroey): this should never happen, so make it a CHECK? + return tensorflow::errors::NotFound( + "Cannot find node with id ", connection.outside_id, " in the graph."); + } + // Updates the shape and data types of input/output connections. + tensorflow::DataType input_type = tensorflow::DT_FLOAT; + tensorflow::PartialTensorShape partial_shape; + if (connection.is_input_edge) { + if (graph_properties.HasOutputProperties(connection.outside_node_name)) { + auto output_params = graph_properties.GetOutputProperties( + connection.outside_node_name); + auto out_shape = output_params.at(connection.outside_port); + input_type = out_shape.dtype(); + std::vector dims; + partial_shape = out_shape.shape(); + connection.outside_shape = partial_shape; + } else { + VLOG(0) << "Unknown output shape" << outside_node->name(); + input_type = graph->FindNodeId(connection.outside_id) + ->output_type(connection.outside_port); } + connection.connection_type = input_type; + + } else { // output edge + if (graph_properties.HasInputProperties(connection.outside_node_name)) { + auto input_params = + graph_properties.GetInputProperties(connection.outside_node_name); + auto in_shape = input_params.at(connection.outside_port); + input_type = in_shape.dtype(); + partial_shape = in_shape.shape(); + connection.inside_shape = partial_shape; + } else { + input_type = graph->FindNodeId(connection.inside_id) + ->output_type(connection.outside_port); + } + connection.connection_type = input_type; + } - tensorflow::NodeDef dummy_placeholder; - string node_name; - if (connection.is_input_edge) { - StrAppend(&node_name, kInputPHName, connection.port_number); - if (marker_nodes.count(node_name)) { - VLOG(1) << "Reusing input " << node_name << " for the edge " - << connection.outside_node_name << ":" - << connection.outside_port << " -> " - << connection.inside_node_name << ":" - << connection.inside_port; - continue; - } - marker_nodes.insert(node_name); - auto seg_node = segment_def->add_node(); - tensorflow::NodeDefBuilder dph_builder(node_name, "Placeholder"); - auto status = dph_builder.Attr("shape", partial_shape) - .Attr("dtype", input_type) - .Finalize(seg_node); - VLOG(1) << "Constructing input " << node_name << " for the edge " + // Add dummy input/output nodes to the segment graphdef. + if (connection.is_input_edge) { + const string node_name = StrCat(kInputPHName, connection.port_number); + if (marker_nodes.count(node_name)) { + VLOG(1) << "Reusing input " << node_name << " for the edge " << connection.outside_node_name << ":" << connection.outside_port << " -> " - << connection.inside_node_name << ":" << connection.inside_port; - } else { - StrAppend(&node_name, kOutputPHName, connection.port_number); - if (marker_nodes.count(node_name)) { - VLOG(1) << "Reusing output " << node_name << " for the edge " - << connection.inside_node_name << ":" - << connection.inside_port << " -> " - << connection.outside_node_name << ":" - << connection.outside_port; - continue; - } - marker_nodes.insert(node_name); - auto seg_node = segment_def->add_node(); - tensorflow::NodeDefBuilder dph_builder(node_name, "Identity"); - auto status = - dph_builder.Input(connection.inside_node_name, 0, input_type) - .Finalize(seg_node); - VLOG(1) << "Constructing output " << node_name << " for the edge " - << connection.inside_node_name << ":" << connection.inside_port - << " -> " << connection.outside_node_name << ":" + << connection.inside_node_name << ":" + << connection.inside_port; + continue; + } + marker_nodes.insert(node_name); + auto seg_node = segment_def->add_node(); + tensorflow::NodeDefBuilder builder(node_name, "Placeholder"); + auto status = builder.Attr("shape", partial_shape) + .Attr("dtype", input_type).Finalize(seg_node); + VLOG(1) << "Constructing input " << node_name << " for the edge " + << connection.outside_node_name << ":" + << connection.outside_port << " -> " + << connection.inside_node_name << ":" << connection.inside_port; + } else { + const string node_name = StrCat(kOutputPHName, connection.port_number); + if (marker_nodes.count(node_name)) { + VLOG(1) << "Reusing output " << node_name << " for the edge " + << connection.inside_node_name << ":" + << connection.inside_port << " -> " + << connection.outside_node_name << ":" << connection.outside_port; + continue; } + marker_nodes.insert(node_name); + auto seg_node = segment_def->add_node(); + tensorflow::NodeDefBuilder builder(node_name, "Identity"); + auto status = builder.Input(connection.inside_node_name, 0, input_type) + .Finalize(seg_node); + VLOG(1) << "Constructing output " << node_name << " for the edge " + << connection.inside_node_name << ":" << connection.inside_port + << " -> " << connection.outside_node_name << ":" + << connection.outside_port; } - } - std::unordered_map newIdMap; - // Copy nodes to new graphdef + } // for each connection. + + std::unordered_map old_to_new_id_map; + // Copy internal nodes to new graphdef string local_scope = graph->FindNodeId(*subgraph_node_ids.begin())->name(); for (const auto node_id : subgraph_node_ids) { const auto node = graph->FindNodeId(node_id); local_scope = GetCommonNameScope(local_scope, node->name()); - if (node) { - newIdMap[node_id] = segment_def->node_size(); - auto snode = segment_def->add_node(); - snode->CopyFrom(node->def()); - VLOG(1) << "Copying " << snode->name() << " to subgraph"; - } + old_to_new_id_map[node_id] = segment_def->node_size(); + auto snode = segment_def->add_node(); + snode->CopyFrom(node->def()); + VLOG(1) << "Copying " << snode->name() << " to subgraph"; } - // update the inputs of the new nodes to point to dummy inputs + // Update the inputs of the new input nodes to point to placeholder nodes. for (int i = 0; i < connections->size(); ++i) { auto& connection = connections->at(i); if (!connection.is_input_edge) continue; - auto snode = segment_def->mutable_node(newIdMap[connection.inside_id]); - string placeholder_name(kInputPHName); - StrAppend(&placeholder_name, connection.port_number); + auto snode = segment_def->mutable_node( + old_to_new_id_map[connection.inside_id]); + const string placeholder_name = + StrCat(kInputPHName, connection.port_number); VLOG(1) << "Updating " << snode->name() << ":" << connection.inside_port << " from " << snode->input(connection.inside_port) << " to " << placeholder_name; @@ -2373,6 +2384,7 @@ tensorflow::Status ConvertSegmentToGraphDef( VLOG(0) << "Segment @scope '" << local_scope << "', converted to graph"; return tensorflow::Status::OK(); } + } // namespace convert } // namespace tensorrt } // namespace tensorflow diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.h b/tensorflow/contrib/tensorrt/convert/convert_nodes.h index 971322d07c..b8d6012df2 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.h +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.h @@ -22,11 +22,13 @@ limitations under the License. #include #include +#include "tensorflow/contrib/tensorrt/convert/utils.h" #include "tensorflow/contrib/tensorrt/resources/trt_allocator.h" #include "tensorflow/core/framework/graph.pb.h" #include "tensorflow/core/graph/graph.h" #include "tensorflow/core/grappler/costs/graph_properties.h" #include "tensorflow/core/lib/core/status.h" + #if GOOGLE_CUDA #if GOOGLE_TENSORRT @@ -36,11 +38,13 @@ static const char* kInputPHName = "InputPH_"; static const char* kOutputPHName = "OutputPH_"; namespace convert { +// TODO(aaroey): use an enum instead. const int FP32MODE = 0; const int FP16MODE = 1; const int INT8MODE = 2; -struct EngineConnections { - EngineConnections(const string& outside, int out_id, int out_port, + +struct EngineConnection { + EngineConnection(const string& outside, int out_id, int out_port, const string& inside, int in_id, int in_port, bool input_edge, int port) : outside_node_name(outside), @@ -51,16 +55,21 @@ struct EngineConnections { inside_port(in_port), is_input_edge(input_edge), port_number(port) {} + const string outside_node_name; const int outside_id; const int outside_port; tensorflow::PartialTensorShape outside_shape; - tensorflow::DataType connection_type; + const string inside_node_name; const int inside_id; const int inside_port; tensorflow::PartialTensorShape inside_shape; + + tensorflow::DataType connection_type; bool is_input_edge; + + // The port number of the TRT node connecting to this edge. int port_number; }; @@ -68,36 +77,54 @@ struct EngineInfo { EngineInfo() : engine_type(EngineType::TRTStatic), max_workspace_size_bytes(0), - precision_mode(FP32MODE){}; + precision_mode(FP32MODE) {}; + string engine_name; string device; tensorflow::GraphDef segment_graph_def; - std::vector connections; // order matters! + + // The segment nodes that are on one side of the edges are topological sorted. + std::vector connections; + enum class EngineType { TRTStatic = 0, TRTDynamic = 1 }; EngineType engine_type; - tensorflow::int64 max_workspace_size_bytes; + int64 max_workspace_size_bytes; int maximum_cached_engines; std::vector cached_engine_batches; int precision_mode; }; -; -// Constructs a graphdef from the segment in the given graph. Adds placeholder -// nodes for input edges (InputPH_*) and identity nodes for output edges -// (OutputPH_*). This function needs to be called before TensorRT nodes -// inserted in order to correctly get sizes from the original graph. -tensorflow::Status ConvertSegmentToGraphDef( +// Constructs a graphdef from the segment in the given graph. Adds placeholder +// nodes for input edges (InputPH_*) and identity nodes for output edges +// (OutputPH_*). This function needs to be called before TensorRT nodes +// inserted in order to correctly get sizes from the original graph. +// +// - subgraph_node_ids: the node ids of the subgraph, must be sorted in +// topological order. +// - segment_def: the output GraphDef, whose non-input/output nodedefs will be +// sorted in topological order. +tensorflow::Status ConvertSegmentToSubGraphDef( const tensorflow::Graph* graph, const tensorflow::grappler::GraphProperties& graph_properties, const std::vector& subgraph_node_ids, - std::vector* connections, + std::vector* connections, tensorflow::GraphDef* segment_def, string* common_scope); -// Converts given subgraph to a TRT engine. -tensorflow::Status ConvertSubgraphToEngine( - const tensorflow::GraphDef& gdef, nvinfer1::IBuilder* builder, +// Converts given subgraph to a TRT engine saved in 'engine'. Returns ok iff +// 'builder' successfully build the engine. If the result is not ok, 'engine' +// will be set to nullptr +// Once returned, 'builder' is not needed any more and can be safely detroyed. +// +// - convert_successfully: indicates whether the converson to TensorRT network +// is successful. This is different than successfully building the engine: +// building can still fail afterwards. +tensorflow::Status ConvertSubGraphDefToEngine( + const tensorflow::GraphDef& gdef, int precision_mode, const std::vector& input_shapes, - nvinfer1::ICudaEngine** engine, int precision_mode); + nvinfer1::IBuilder* builder, + TrtUniquePtrType* engine, + bool* convert_successfully); + } // namespace convert } // namespace tensorrt } // namespace tensorflow diff --git a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc index 2dddc4541c..0d1d7e3b0e 100644 --- a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc +++ b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc @@ -15,6 +15,7 @@ limitations under the License. #include "tensorflow/contrib/tensorrt/kernels/trt_engine_op.h" #include +#include "tensorflow/contrib/tensorrt/convert/utils.h" #include "tensorflow/contrib/tensorrt/convert/convert_nodes.h" #include "tensorflow/contrib/tensorrt/log/trt_logger.h" #include "tensorflow/contrib/tensorrt/resources/trt_resource_manager.h" @@ -32,14 +33,14 @@ limitations under the License. #include "cuda/include/cuda_runtime_api.h" namespace tensorflow { -static ::tensorflow::tensorrt::Logger logger; -using IRuntime = nvinfer1::IRuntime; -using Dims = nvinfer1::Dims; - namespace tensorrt { -using tensorflow::strings::StrAppend; -using tensorflow::strings::StrCat; -// A helper class to call done() for asynchronous execution. +static Logger logger; +using ::nvinfer1::IRuntime; +using ::nvinfer1::Dims; +using ::tensorflow::strings::StrAppend; +using ::tensorflow::strings::StrCat; + +// A helper class to call done() when destructed for asynchronous execution. // Helps simultaneous execution of native and TRT engines. class AsyncHelper : public tensorflow::core::RefCounted { public: @@ -78,8 +79,8 @@ tensorflow::Status TRTEngineOp::ConstructFunctionHandle(OpKernelContext* ctx) { auto fdef = lib->GetFunctionLibraryDefinition()->Find(funcdef_name_); if (fdef == nullptr) { return tensorflow::errors::Internal( - StrCat("Native FunctionDef ", funcdef_name_, - " can't be found in function library")); + "Native FunctionDef ", funcdef_name_, + " can't be found in function library"); } tensorflow::FunctionLibraryRuntime::InstantiateOptions inst_ops; inst_ops.overlay_lib = nullptr; @@ -122,15 +123,14 @@ TRTEngineOp::TRTEngineOp(OpKernelConstruction* context) OP_REQUIRES_OK(context, context->GetAttr("segment_funcdef_name", &funcdef_name_)); if (precision_string == "FP32") { - precision_mode_ = tensorflow::tensorrt::convert::FP32MODE; + precision_mode_ = convert::FP32MODE; } else if (precision_string == "FP16") { - precision_mode_ = tensorflow::tensorrt::convert::FP16MODE; + precision_mode_ = convert::FP16MODE; } else if (precision_string == "INT8") { - precision_mode_ = tensorflow::tensorrt::convert::INT8MODE; + precision_mode_ = convert::INT8MODE; } - calibration_mode_ = - precision_mode_ == tensorflow::tensorrt::convert::INT8MODE && - calibration_data.size() == 0; + calibration_mode_ = (precision_mode_ == convert::INT8MODE && + calibration_data.size() == 0); if (calibration_data.size()) { calibrator_.reset(new TRTInt8Calibrator(calibration_data)); calibration_data.resize(0); @@ -190,21 +190,20 @@ void TRTEngineOp::ExecuteNativeSegment(tensorflow::OpKernelContext* ctx, ctx->set_output(t, outputs->at(t)); } delete outputs; - return; }); - return; } void TRTEngineOp::ExecuteCalibration(tensorflow::OpKernelContext* ctx, AsyncHelper* helper) { + helper->Ref(); tensorflow::core::ScopedUnref sc(helper); - auto trt_rm = tensorflow::tensorrt::TRTResourceManager::instance(); + // TODO(aaroey): remove the ResourceMgr singleton. + auto trt_rm = TRTResourceManager::instance(); auto res_mgr = trt_rm->getManager("TRTCalibration"); - tensorflow::tensorrt::TRTCalibrationResource* calib_res = nullptr; + TRTCalibrationResource* calib_res = nullptr; auto status = res_mgr->LookupOrCreate( funcdef_name_, "Calibrator", &calib_res, - {[ctx, this](tensorflow::tensorrt::TRTCalibrationResource** cr) - -> tensorflow::Status { + {[ctx, this](TRTCalibrationResource** cr) -> tensorflow::Status { return this->AllocateCalibrationResources(ctx, cr); }}); if (!status.ok()) { @@ -219,7 +218,7 @@ void TRTEngineOp::ExecuteCalibration(tensorflow::OpKernelContext* ctx, void* data_address = GetTensorAddress(&t); if (data_address == nullptr) { ctx->SetStatus(tensorflow::errors::InvalidArgument( - StrCat("Unsupported data type encountered in input ", i))); + "Unsupported data type encountered in input ", i)); return; } // Check the allocated buffer is sufficient for input @@ -237,7 +236,6 @@ void TRTEngineOp::ExecuteCalibration(tensorflow::OpKernelContext* ctx, calib_res->calibrator_->setBatch(input_data, *stream); VLOG(2) << "Passed calibration data"; ExecuteNativeSegment(ctx, helper); - return; } int TRTEngineOp::GetEngineBatch(tensorflow::OpKernelContext* ctx) { @@ -274,27 +272,28 @@ void TRTEngineOp::ComputeAsync(tensorflow::OpKernelContext* ctx, auto helper = new AsyncHelper(done); tensorflow::core::ScopedUnref sc(helper); if (calibration_mode_) { - helper->Ref(); ExecuteCalibration(ctx, helper); return; } - int num_binding = ctx->num_inputs() + ctx->num_outputs(); - std::vector buffers(num_binding); - int smallest_engine = GetEngineBatch(ctx); - if (smallest_engine < 0) return; - int num_batch = ctx->input(0).shape().dim_size(0); - size_t binding_index; - auto engine_ctx_pair = GetEngine(smallest_engine, ctx, fixed_input_size_); - auto trt_engine_ptr = engine_ctx_pair.first; + const int smallest_engine = GetEngineBatch(ctx); + if (smallest_engine < 0) return; // GetEngineBatch already set the status. + + const int num_batch = ctx->input(0).shape().dim_size(0); + auto& engine_ctx_pair = GetEngine(smallest_engine, ctx); + auto& trt_engine_ptr = engine_ctx_pair.first; if (!trt_engine_ptr) { LOG(WARNING) << "Engine retrieval for batch size " << num_batch << " failed Running native segment"; ExecuteNativeSegment(ctx, helper); return; } + + const int num_binding = ctx->num_inputs() + ctx->num_outputs(); + std::vector buffers(num_binding); for (int i = 0; i < ctx->num_inputs(); i++) { - string inp_name = StrCat(kInputPHName, i); - binding_index = trt_engine_ptr->getBindingIndex(inp_name.c_str()); + const string inp_name = StrCat(kInputPHName, i); + const size_t binding_index = trt_engine_ptr->getBindingIndex( + inp_name.c_str()); const Tensor& input_tensor = ctx->input(i); const TensorShape& input_shape = input_tensor.shape(); @@ -322,17 +321,16 @@ void TRTEngineOp::ComputeAsync(tensorflow::OpKernelContext* ctx, default: LOG(ERROR) << "Unknown TRT data type: " << int(dtype); ctx->SetStatus(tensorflow::errors::InvalidArgument( - "Unknown ouput TRT data type! " + int(dtype))); + "Unknown ouput TRT data type! ", int(dtype))); return; } } for (int i = 0; i < ctx->num_outputs(); i++) { - // This is bad that we have to reallocate output buffer every run. // Create an output tensor - - auto output_name = StrCat(kOutputPHName, i); - binding_index = trt_engine_ptr->getBindingIndex(output_name.c_str()); + const string output_name = StrCat(kOutputPHName, i); + const size_t binding_index = trt_engine_ptr->getBindingIndex( + output_name.c_str()); Tensor* output_tensor = nullptr; TensorShape output_shape; @@ -346,8 +344,8 @@ void TRTEngineOp::ComputeAsync(tensorflow::OpKernelContext* ctx, &output_shape)); } else { LOG(ERROR) << "output node not found, at " << output_name; - ctx->SetStatus(tensorflow::errors::Internal("output " + output_name + - " but couldn't be found!")); + ctx->SetStatus(tensorflow::errors::Internal( + "output ", output_name, " couldn't be found!")); return; } auto status = ctx->allocate_output(i, output_shape, &output_tensor); @@ -375,7 +373,7 @@ void TRTEngineOp::ComputeAsync(tensorflow::OpKernelContext* ctx, default: LOG(ERROR) << "Unknown TRT data type: " << int(dtype); ctx->SetStatus(tensorflow::errors::InvalidArgument( - "Unsupported output data type! " + int(dtype))); + "Unsupported output data type! ", int(dtype))); return; } } @@ -387,46 +385,47 @@ void TRTEngineOp::ComputeAsync(tensorflow::OpKernelContext* ctx, ->CudaStreamMemberHack())); // TODO(jie): trt enqueue does not return error - auto trt_execution_context_ptr = engine_ctx_pair.second; + auto& trt_execution_context_ptr = engine_ctx_pair.second; auto ret = trt_execution_context_ptr->enqueue(num_batch, &buffers[0], *stream, nullptr); if (!ret) { - LOG(ERROR) << "Enqueueing of TRT execution failed!"; + LOG(ERROR) << "Failed to enqueue batch for TRT engine: " << name(); + ctx->SetStatus(tensorflow::errors::Internal( + "Failed to enqueue batch for TRT engine: ", name())); } // sync should be done by TF. } TRTEngineOp::~TRTEngineOp() { - // Order matters! - for (auto eng : engine_map_) { + // We need to manually destroy the engine and execution context before + // the allocator is destructed. + for (auto& eng : engine_map_) { eng.second.first.reset(); eng.second.second.reset(); } - for (auto alloc : allocators_) alloc.second.reset(); + allocator_.reset(); } nvinfer1::IGpuAllocator* TRTEngineOp::GetAllocator(OpKernelContext* ctx) { + if (allocator_) return allocator_.get(); auto device = ctx->device(); - const auto& device_name = device->name(); - if (allocators_.count(device_name)) { - return allocators_.at(device_name).get(); - } - auto dev_allocator = device->GetAllocator(tensorflow::AllocatorAttributes()); - if (!dev_allocator) { + auto alloc = device->GetAllocator(tensorflow::AllocatorAttributes()); + if (!alloc) { LOG(ERROR) << "Can't find device allocator for gpu device " << device->name(); ctx->SetStatus(tensorflow::errors::Internal( - StrCat("Can't get device allocator for device ", device_name))); + "Can't get device allocator for device ", device->name())); return nullptr; } - auto allocator = std::make_shared(dev_allocator); - allocators_.insert({device_name, allocator}); - return allocator.get(); + allocator_.reset(new TRTDeviceAllocator(alloc)); + return allocator_.get(); } -TRTEngineOp::EngineCtxPair TRTEngineOp::GetEngine(int batch_size, - OpKernelContext* ctx, - bool ignore_dim_change) { +TRTEngineOp::EngineCtxPair& TRTEngineOp::GetEngine(int batch_size, + OpKernelContext* ctx) { + static EngineCtxPair null_pair = { + TrtUniquePtrType(nullptr), + TrtUniquePtrType(nullptr)}; // TODO(sami): This method needs to be re-written to use resource manager and // with LRU mechanism option. tensorflow::mutex_lock lock(engine_mutex_); @@ -435,113 +434,106 @@ TRTEngineOp::EngineCtxPair TRTEngineOp::GetEngine(int batch_size, if (engine_map_.size()) { if (engine_map_.begin()->first >= batch_size) { return engine_map_.begin()->second; - } else { - return {nullptr, nullptr}; } - } else { - std::shared_ptr infer(nvinfer1::createInferRuntime(logger), - [](IRuntime* p) { - if (p) p->destroy(); - }); + return null_pair; + } + TrtUniquePtrType infer(nvinfer1::createInferRuntime(logger)); #if NV_TENSORRT_MAJOR > 3 - auto allocator = GetAllocator(ctx); - if (allocator == nullptr) { - return {nullptr, nullptr}; - }; - infer->setGpuAllocator(allocator); + auto allocator = GetAllocator(ctx); + if (allocator == nullptr) { + return null_pair; + }; + infer->setGpuAllocator(allocator); #endif - std::shared_ptr static_engine( - infer->deserializeCudaEngine(serialized_segment_.c_str(), - serialized_segment_.size(), nullptr), - Destroyer()); - engine_map_.insert({static_engine->getMaxBatchSize(), - {static_engine, - {static_engine->createExecutionContext(), - Destroyer()}}}); - // Runtime is safe to delete after engine creation - serialized_segment_.clear(); - if (static_engine->getMaxBatchSize() < batch_size) { - return {nullptr, nullptr}; - } - return engine_map_.at(static_engine->getMaxBatchSize()); - } - } else { - auto engine_it = engine_map_.find(batch_size); - if (engine_it == engine_map_.end() && - engine_map_.size() < (size_t)max_cached_engines_) { - auto builder = std::shared_ptr( - nvinfer1::createInferBuilder(logger), - Destroyer()); // reset the builder to ensure - // device is correct + TrtUniquePtrType static_engine( + infer->deserializeCudaEngine(serialized_segment_.c_str(), + serialized_segment_.size(), nullptr)); + auto raw_static_engine = static_engine.get(); + const auto max_batch_size = raw_static_engine->getMaxBatchSize(); + engine_map_[max_batch_size] = { + std::move(static_engine), + TrtUniquePtrType( + raw_static_engine->createExecutionContext())}; + // Runtime is safe to delete after engine creation + serialized_segment_.clear(); + if (max_batch_size < batch_size) return null_pair; + return engine_map_.at(max_batch_size); + } // static_engine_ + + // Handle the dynamic engine case. + auto engine_it = engine_map_.find(batch_size); + if (engine_it == engine_map_.end() && + engine_map_.size() < (size_t)max_cached_engines_) { + TrtUniquePtrType builder( + nvinfer1::createInferBuilder(logger)); #if NV_TENSORRT_MAJOR > 3 - auto allocator = GetAllocator(ctx); - if (allocator == nullptr) { - return {nullptr, nullptr}; - } - builder->setGpuAllocator(allocator); + auto allocator = GetAllocator(ctx); + if (allocator == nullptr) { + // GetAllocator already set the Status. + return null_pair; + } + builder->setGpuAllocator(allocator); #endif - VLOG(0) << name() << " Constructing a new engine with batch size " - << batch_size; - builder->setMaxBatchSize(batch_size); - if (precision_mode_ == tensorflow::tensorrt::convert::FP16MODE) { - builder->setHalf2Mode(true); - } else if (precision_mode_ == tensorflow::tensorrt::convert::INT8MODE) { - builder->setInt8Mode(true); - builder->setInt8Calibrator(calibrator_.get()); - } - builder->setMaxWorkspaceSize(workspace_size_); - nvinfer1::ICudaEngine* engine = nullptr; - std::vector shapes; - for (int i = 0; i < ctx->num_inputs(); ++i) { - shapes.emplace_back(ctx->input(i).shape()); - } - VLOG(1) << "Calling conversion for " << batch_size << " " << name(); - auto status = tensorflow::tensorrt::convert::ConvertSubgraphToEngine( - segment_graph_, builder.get(), shapes, &engine, precision_mode_); - VLOG(1) << "Conversion is done"; - if (engine) { - engine_map_[batch_size] = { - std::shared_ptr( - engine, Destroyer()), - std::shared_ptr( - engine->createExecutionContext(), - Destroyer())}; - } else { - LOG(ERROR) << "Engine creation for batch size " << batch_size - << " failed"; - ctx->SetStatus(tensorflow::errors::Internal("Engine creation failed!")); + VLOG(0) << name() << " Constructing a new engine with batch size " + << batch_size; + builder->setMaxBatchSize(batch_size); + if (precision_mode_ == convert::FP16MODE) { + builder->setHalf2Mode(true); + } else if (precision_mode_ == convert::INT8MODE) { + builder->setInt8Mode(true); + // TODO(aaroey): what if it's empty? I.e. when calibration data is empty? + builder->setInt8Calibrator(calibrator_.get()); + } + // TODO(aaroey): use the allocator to allocate the TRT workspace. + builder->setMaxWorkspaceSize(workspace_size_); + std::vector shapes; + for (int i = 0; i < ctx->num_inputs(); ++i) { + shapes.emplace_back(ctx->input(i).shape()); + } + TrtUniquePtrType engine; + bool convert_successfully = false; + VLOG(1) << "Calling conversion for " << batch_size << " " << name(); + auto status = convert::ConvertSubGraphDefToEngine( + segment_graph_, precision_mode_, shapes, builder.get(), &engine, + &convert_successfully); + if (!status.ok()) { + if (convert_successfully) { + // This means it fail to build the engine even when the network is built + // successfully, probably due to internal issues. In this case we don't + // retry in the future. engine_map_[batch_size] = {nullptr, nullptr}; - return {nullptr, nullptr}; } + LOG(ERROR) << "Engine creation for batch size " << batch_size + << " failed " << status; + ctx->SetStatus(tensorflow::errors::Internal("Engine creation failed!")); + return null_pair; } - return engine_map_.at(batch_size); + VLOG(1) << "Conversion is done"; + TrtUniquePtrType exec_context( + engine->createExecutionContext()); + engine_map_[batch_size] = {std::move(engine), std::move(exec_context)}; } + return engine_map_.at(batch_size); } tensorflow::Status TRTEngineOp::AllocateCalibrationResources( tensorflow::OpKernelContext* ctx, - tensorflow::tensorrt::TRTCalibrationResource** cr) { + TRTCalibrationResource** cr) { auto cres = new TRTCalibrationResource(); *cr = cres; - cres->logger_ = new tensorflow::tensorrt::Logger(); + cres->logger_ = new Logger(); #if NV_TENSORRT_MAJOR > 3 - auto dev = ctx->device(); - auto dev_allocator = dev->GetAllocator(tensorflow::AllocatorAttributes()); - if (!dev_allocator) { + auto alloc = ctx->device()->GetAllocator(tensorflow::AllocatorAttributes()); + if (!alloc) { LOG(WARNING) << "Can't get device allocator will not be able to " "allocate memory from TensorFlow memory pool"; - cres->allocator_ = - std::make_shared(); + cres->allocator_.reset(new TRTCudaAllocator); } else { - cres->allocator_ = - std::make_shared( - dev_allocator); + cres->allocator_.reset(new TRTDeviceAllocator(alloc)); } - #endif int batch_size = ctx->input(0).dim_size(0); - cres->engine_ = nullptr; std::vector shapes; int num_inputs = ctx->num_inputs(); // first run instantiate calibrator @@ -558,7 +550,7 @@ tensorflow::Status TRTEngineOp::AllocateCalibrationResources( void* device_address = GetTensorAddress(device_tensor); if (device_address == nullptr) { return tensorflow::errors::InvalidArgument( - StrCat("Unsupported data type encountered in input ", i)); + "Unsupported data type encountered in input ", i); } device_buffers_.emplace( StrCat(kInputPHName, i), @@ -579,26 +571,29 @@ tensorflow::Status TRTEngineOp::AllocateCalibrationResources( batch_size, workspace_size]() { VLOG(0) << "Starting calibration thread on device " << cuda_device << ", Calibration Resource @ " << cres; - // ConvertSubgraphToEngine() will try to build the engine and this thread - // will be consuming the calibration data that is set by the TF op, driving - // the builder until calibrator returns false; Engine is discarded after - // calibration table is generated auto err = cudaSetDevice(cuda_device); if (err != cudaSuccess) { VLOG(0) << "Couldn't set cuda device to " << cuda_device << " in calibration thread"; } // initialize builder here - cres->builder_ = nvinfer1::createInferBuilder(*(cres->logger_)); - cres->builder_->setGpuAllocator(cres->allocator_.get()); + cres->builder_.reset(nvinfer1::createInferBuilder(*(cres->logger_))); + // TODO(aaroey): maybe setting the max batch size using the python + // calibration wrapper class. cres->builder_->setMaxBatchSize(batch_size); +#if NV_TENSORRT_MAJOR > 3 + cres->builder_->setGpuAllocator(cres->allocator_.get()); +#endif cres->builder_->setInt8Mode(true); cres->builder_->setMaxWorkspaceSize(workspace_size); cres->builder_->setInt8Calibrator(cres->calibrator_); - auto s = tensorflow::tensorrt::convert::ConvertSubgraphToEngine( - *segment_graph, cres->builder_, shapes, &cres->engine_, - tensorflow::tensorrt::convert::INT8MODE); // calibrator will loop until - // we terminate calibration + // ConvertSubGraphDefToEngine() will try to build the engine. This thread + // will loop inside buildCudaEngine() consuming the calibration data + // that is set by the TF op, and drive the builder until calibrator returns + // false. Engine is discarded after calibration table is generated + auto s = convert::ConvertSubGraphDefToEngine( + *segment_graph, convert::INT8MODE, shapes, cres->builder_.get(), + &cres->engine_, /*convert_successfully=*/nullptr); if (!s.ok()) { LOG(ERROR) << "Calibration failed. Engine will not be calibrated! Error is" << s; @@ -609,6 +604,7 @@ tensorflow::Status TRTEngineOp::AllocateCalibrationResources( VLOG(1) << "initialized calibrator resource"; return tensorflow::Status::OK(); } + REGISTER_KERNEL_BUILDER(Name("TRTEngineOp").Device(DEVICE_GPU), TRTEngineOp); } // namespace tensorrt diff --git a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h index 6faef09b62..cb43403130 100644 --- a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h +++ b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h @@ -19,6 +19,7 @@ limitations under the License. #include #include +#include "tensorflow/contrib/tensorrt/convert/utils.h" #include "tensorflow/contrib/tensorrt/resources/trt_allocator.h" #include "tensorflow/core/framework/function.h" #include "tensorflow/core/framework/graph.pb.h" @@ -33,7 +34,6 @@ limitations under the License. namespace tensorflow { namespace tensorrt { -class Logger; class TRTInt8Calibrator; class TRTCalibrationResource; class AsyncHelper; @@ -50,13 +50,6 @@ class TRTEngineOp : public AsyncOpKernel { ~TRTEngineOp(); private: - template - struct Destroyer { - void operator()(T* d) { - if (d) d->destroy(); - } - }; - // Execute calibration void ExecuteCalibration(tensorflow::OpKernelContext* ctx, AsyncHelper* helper); @@ -74,11 +67,10 @@ class TRTEngineOp : public AsyncOpKernel { tensorflow::tensorrt::TRTCalibrationResource** cr); // TODO(samikama): context should go to a resource manager! - typedef std::pair, - std::shared_ptr> + typedef std::pair, + TrtUniquePtrType> EngineCtxPair; - EngineCtxPair GetEngine(int batch_size, OpKernelContext* ctx, - bool ignore_dim_change = true); + EngineCtxPair& GetEngine(int batch_size, OpKernelContext* ctx); // Return engine batch closest to input batch. int GetEngineBatch(OpKernelContext* ctx); @@ -89,32 +81,45 @@ class TRTEngineOp : public AsyncOpKernel { std::unordered_map engine_map_; std::vector input_nodes_; std::vector output_nodes_; + // keep device allocator for TRT. - std::unordered_map> allocators_; + std::unique_ptr allocator_; + // serialized protobuf segment or trt engine depending on static_engine_ flag. string serialized_segment_; + // Name of the function for TF native execution of the segment. string funcdef_name_; + // GraphDef representation of the segment. tensorflow::GraphDef segment_graph_; + // Lookup table for temporary staging areas of input tensors for calibration. std::unordered_map> device_buffers_; + // Temporary staging areas for calibration inputs. std::vector dev_tensors_; + // Engine Precision mode. int precision_mode_; + // Whether engine is constructed during the conversion or needs to be // constructed from protobuf segment. bool static_engine_; + // Whether to calibrate INT8 engine. bool calibration_mode_; + // Whether non-batch ranks of the inputs are assumed to be fixed or not for - // engine construction + // engine construction. bool fixed_input_size_; + // Batches of the cached engines std::vector cached_engine_batches_; + // Maximum number of cached engines int max_cached_engines_; + tensorflow::int64 workspace_size_; tensorflow::mutex engine_mutex_; tensorflow::FunctionLibraryRuntime::Handle native_func_; diff --git a/tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.h b/tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.h index 894e9d6e85..994312d7c3 100644 --- a/tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.h +++ b/tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.h @@ -39,30 +39,46 @@ struct TRTInt8Calibrator : public nvinfer1::IInt8EntropyCalibrator { TRTInt8Calibrator( const std::unordered_map>& dev_buffers, int batch_size, string engine_name); + TRTInt8Calibrator(const string& calibration_data); + + ~TRTInt8Calibrator(); + int getBatchSize() const override; + bool getBatch(void* bindings[], const char* names[], int num_bindings) override; + bool setBatch(const std::unordered_map& data, const cudaStream_t stream); + void setDone(); + + // If not null, calibration is skipped. const void* readCalibrationCache(std::size_t& length) override; + void writeCalibrationCache(const void* ptr, std::size_t length) override; + const string& getCalibrationTableAsString() { return calibration_table_; } - ~TRTInt8Calibrator(); private: const int batch_size_; - tensorflow::mutex cond_mtx_; // mutex for condition_variable - tensorflow::condition_variable cond_; // condition variable to implement - // producer-consumer queue for - // calibration + + // mutex for condition_variable + tensorflow::mutex cond_mtx_; + + // condition variable to implement producer-consumer queue for calibration + tensorflow::condition_variable cond_; + + // Is calibration finished? bool done_; - const std::unordered_map> - dev_buffers_; // map to keep tensorrt input buffers and sizes keyed with - // buffer names + + // Map to keep tensorrt input buffers and sizes keyed with buffer names + const std::unordered_map> dev_buffers_; + bool calib_running_; bool batch_is_set_; + string engine_name_; string calibration_table_; }; diff --git a/tensorflow/contrib/tensorrt/resources/trt_resources.h b/tensorflow/contrib/tensorrt/resources/trt_resources.h index 022639dc01..43734bbdd8 100644 --- a/tensorflow/contrib/tensorrt/resources/trt_resources.h +++ b/tensorflow/contrib/tensorrt/resources/trt_resources.h @@ -22,6 +22,7 @@ limitations under the License. #include #include +#include "tensorflow/contrib/tensorrt/convert/utils.h" #include "tensorflow/contrib/tensorrt/log/trt_logger.h" #include "tensorflow/contrib/tensorrt/resources/trt_allocator.h" #include "tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.h" @@ -34,21 +35,21 @@ limitations under the License. namespace tensorflow { namespace tensorrt { + class TRTCalibrationResource : public tensorflow::ResourceBase { public: TRTCalibrationResource() : calibrator_(nullptr), - builder_(nullptr), - network_(nullptr), - engine_(nullptr), logger_(nullptr), thr_(nullptr) {} ~TRTCalibrationResource() { VLOG(0) << "Destroying Calibration Resource " << std::endl << DebugString(); - builder_->destroy(); - network_->destroy(); - engine_->destroy(); + builder_.reset(); + engine_.reset(); + // We need to manually destroy the builder and engine before the allocator + // is destroyed. + allocator_.reset(); delete thr_; delete logger_; delete calibrator_; @@ -56,22 +57,22 @@ class TRTCalibrationResource : public tensorflow::ResourceBase { string DebugString() override { std::stringstream oss; - oss << " Calibrator = " << std::hex << calibrator_ << std::dec << std::endl - << " Builder = " << std::hex << builder_ << std::dec << std::endl - << " Network = " << std::hex << network_ << std::dec << std::endl - << " Engine = " << std::hex << engine_ << std::dec << std::endl - << " Logger = " << std::hex << logger_ << std::dec << std::endl - << " Allocator = " << std::hex << allocator_.get() << std::dec - << std::endl - << " Thread = " << std::hex << thr_ << std::dec << std::endl; + using std::hex; + using std::dec; + using std::endl; + oss << " Calibrator = " << hex << calibrator_ << dec << endl + << " Builder = " << hex << builder_.get() << dec << endl + << " Engine = " << hex << engine_.get() << dec << endl + << " Logger = " << hex << logger_ << dec << endl + << " Allocator = " << hex << allocator_.get() << dec << endl + << " Thread = " << hex << thr_ << dec << endl; return oss.str(); } TRTInt8Calibrator* calibrator_; - nvinfer1::IBuilder* builder_; - nvinfer1::INetworkDefinition* network_; - nvinfer1::ICudaEngine* engine_; - std::shared_ptr allocator_; + TrtUniquePtrType builder_; + TrtUniquePtrType engine_; + std::unique_ptr allocator_; tensorflow::tensorrt::Logger* logger_; // TODO(sami): Use threadpool threads! std::thread* thr_; diff --git a/tensorflow/contrib/tensorrt/segment/segment.h b/tensorflow/contrib/tensorrt/segment/segment.h index 1568dd9153..81b4bfe49f 100644 --- a/tensorflow/contrib/tensorrt/segment/segment.h +++ b/tensorflow/contrib/tensorrt/segment/segment.h @@ -29,8 +29,9 @@ namespace tensorflow { namespace tensorrt { namespace segment { -// vector of segments, each entry contains a device name and a set of nodes in -// segment +// Vector of segments, each entry contains a set of node names and a device name +// in the segment. +// TODO(aaroey): use node pointer instead of node name. using SegmentNodesVector = std::vector, string>>; struct SegmentOptions { @@ -48,6 +49,8 @@ struct SegmentOptions { // in the vector describes a subgraph by giving a set of the names of // all the NodeDefs in that subgraph. // @return the status. +// +// TODO(aaroey): remove this method. tensorflow::Status SegmentGraph( const tensorflow::GraphDef& gdef, const std::function& candidate_fn, -- GitLab From f3f6ef4c74982f867bf0d1e96f79097598f55eb3 Mon Sep 17 00:00:00 2001 From: gracehoney <31743510+aaroey@users.noreply.github.com> Date: Tue, 19 Jun 2018 12:18:18 -0700 Subject: [PATCH 681/816] Add missing utils.h --- tensorflow/contrib/tensorrt/convert/utils.h | 37 +++++++++++++++++++++ 1 file changed, 37 insertions(+) create mode 100644 tensorflow/contrib/tensorrt/convert/utils.h diff --git a/tensorflow/contrib/tensorrt/convert/utils.h b/tensorflow/contrib/tensorrt/convert/utils.h new file mode 100644 index 0000000000..021fdaf8c5 --- /dev/null +++ b/tensorflow/contrib/tensorrt/convert/utils.h @@ -0,0 +1,37 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_CONTRIB_TENSORRT_CONVERT_UTILS_H_ +#define TENSORFLOW_CONTRIB_TENSORRT_CONVERT_UTILS_H_ + +#include + +namespace tensorflow { +namespace tensorrt { + +template +struct TrtDestroyer { + void operator()(T* t) { + if (t) t->destroy(); + } +}; + +template +using TrtUniquePtrType = std::unique_ptr>; + +} // namespace convert +} // namespace tensorrt + +#endif // TENSORFLOW_CONTRIB_TENSORRT_CONVERT_UTILS_H_ -- GitLab From 5fab6df2788937bee1cce3a4e8f5b9d1db7497ec Mon Sep 17 00:00:00 2001 From: Yu-Cheng Ling Date: Tue, 19 Jun 2018 12:35:44 -0700 Subject: [PATCH 682/816] Support Variable Tensor API in LSTM Full kernel. TFLite LSTM now supports 5 inputs, 18 inputs and 20 inputs. PiperOrigin-RevId: 201222516 --- tensorflow/contrib/lite/kernels/lstm.cc | 161 ++++++++++++------ tensorflow/contrib/lite/kernels/lstm_test.cc | 8 + .../lite/kernels/optional_tensor_test.cc | 8 + tensorflow/contrib/lite/kernels/test_util.cc | 5 +- tensorflow/contrib/lite/kernels/test_util.h | 11 +- .../contrib/lite/testing/tflite_driver.cc | 6 +- .../identify_lstm_split_inputs.cc | 10 +- .../toco/graph_transformations/lstm_utils.h | 6 +- tensorflow/contrib/lite/toco/tflite/BUILD | 1 + .../contrib/lite/toco/tflite/operator.cc | 17 +- 10 files changed, 158 insertions(+), 75 deletions(-) diff --git a/tensorflow/contrib/lite/kernels/lstm.cc b/tensorflow/contrib/lite/kernels/lstm.cc index eb26a02455..1dda97c101 100644 --- a/tensorflow/contrib/lite/kernels/lstm.cc +++ b/tensorflow/contrib/lite/kernels/lstm.cc @@ -37,14 +37,17 @@ namespace builtin { namespace lstm { struct OpData { - // Which kernel type to use. Full kernel (18-inputs) or basic kernel - // (5-inputs). + // Which kernel type to use. Full kernel (18 or 20 inputs) or basic kernel + // (5 inputs). TfLiteLSTMKernelType kernel_type; - // Only used by full kernel. + + // These fields are only used by full kernel. + int activation_state_tensor_index; + int cell_state_tensor_index; int scratch_tensor_index; }; -// For full inputs kernel (18-inputs). +// For full inputs kernel (18 or 20 inputs). namespace full { // Input Tensors of size {n_batch, n_input} @@ -78,7 +81,16 @@ constexpr int kProjectionWeightsTensor = 16; // Optional // Projection bias tensor of size {n_output} constexpr int kProjectionBiasTensor = 17; // Optional +// If the node has 20 inputs, the following 2 tensors are used as state tensors. +// These are defined as variable tensors, and will be modified by this op. +constexpr int kInputActivationStateTensor = 18; +constexpr int kInputCellStateTensor = 19; + // Output tensors. +// * If the node has 18 inputs, these 2 tensors are used as state tensors. +// * If the node has 20 inputs, these 2 tensors are ignored. +// TODO(ycling): Make the 2 output state tensors optional, and propagate the +// state to output tensors when the 2 tensors present. constexpr int kOutputStateTensor = 0; constexpr int kCellStateTensor = 1; constexpr int kOutputTensor = 2; @@ -246,10 +258,31 @@ TfLiteStatus CheckInputTensorDimensions(TfLiteContext* context, TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { OpData* op_data = reinterpret_cast(node->user_data); - // Check we have all the inputs and outputs we need. - TF_LITE_ENSURE_EQ(context, node->inputs->size, 18); TF_LITE_ENSURE_EQ(context, node->outputs->size, 3); + // True if the node is using input variable state tensors. It means: + // * The state tensors are defined as inputs. In this case it would be the + // 19th and 20th input tensors. + // * Otherwise, the output tensors are used to store states. + bool use_input_variable_states; + if (node->inputs->size == 20) { + use_input_variable_states = true; + op_data->activation_state_tensor_index = + node->inputs->data[kInputActivationStateTensor]; + op_data->cell_state_tensor_index = + node->inputs->data[kInputCellStateTensor]; + } else if (node->inputs->size == 18) { + use_input_variable_states = false; + op_data->activation_state_tensor_index = + node->outputs->data[kOutputStateTensor]; + op_data->cell_state_tensor_index = node->outputs->data[kCellStateTensor]; + } else { + context->ReportError( + context, "The LSTM Full kernel expects 18 or 20 inputs. Got %d inputs", + node->inputs->size); + return kTfLiteError; + } + // Inferring batch size, number of outputs and number of cells from the // input tensors. const TfLiteTensor* input = GetInput(context, node, kInputTensor); @@ -274,34 +307,47 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { // Check that input tensor dimensions matches with each other. CheckInputTensorDimensions(context, node, n_input, n_output, n_cell); - // Get the pointer to output, output_state and cell_state tensors. + // Get the pointer to output, activation_state and cell_state tensors. TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - TfLiteTensor* output_state = GetOutput(context, node, kOutputStateTensor); - TfLiteTensor* cell_state = GetOutput(context, node, kCellStateTensor); - // Resize the output, output_state and cell_state tensors. + TfLiteTensor* activation_state = + &context->tensors[op_data->activation_state_tensor_index]; + TfLiteTensor* cell_state = + &context->tensors[op_data->cell_state_tensor_index]; + + if (use_input_variable_states) { + // Check the shape of input state tensors. + // These tensor may be 1D or 2D. It's fine as long as the total size is + // correct. + TF_LITE_ENSURE_EQ(context, NumElements(activation_state), + n_batch * n_output); + TF_LITE_ENSURE_EQ(context, NumElements(cell_state), n_batch * n_cell); + } else { + // If the state tensors are outputs, this function takes the + // responsibility to resize the state tensors. + TfLiteIntArray* activation_state_size = TfLiteIntArrayCreate(2); + activation_state_size->data[0] = n_batch; + activation_state_size->data[1] = n_output; + TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, activation_state, + activation_state_size)); + + TfLiteIntArray* cell_size = TfLiteIntArrayCreate(2); + cell_size->data[0] = n_batch; + cell_size->data[1] = n_cell; + TF_LITE_ENSURE_OK(context, + context->ResizeTensor(context, cell_state, cell_size)); + // Mark state tensors as persistent tensors. + activation_state->allocation_type = kTfLiteArenaRwPersistent; + cell_state->allocation_type = kTfLiteArenaRwPersistent; + } + + // Resize the output tensors. TfLiteIntArray* output_size = TfLiteIntArrayCreate(2); output_size->data[0] = n_batch; output_size->data[1] = n_output; TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, output, output_size)); - TfLiteIntArray* output_state_size = TfLiteIntArrayCreate(2); - output_state_size->data[0] = n_batch; - output_state_size->data[1] = n_output; - TF_LITE_ENSURE_OK( - context, context->ResizeTensor(context, output_state, output_state_size)); - - TfLiteIntArray* cell_size = TfLiteIntArrayCreate(2); - cell_size->data[0] = n_batch; - cell_size->data[1] = n_cell; - TF_LITE_ENSURE_OK(context, - context->ResizeTensor(context, cell_state, cell_size)); - - // Mark state tensors as persistent tensors. - output_state->allocation_type = kTfLiteArenaRwPersistent; - cell_state->allocation_type = kTfLiteArenaRwPersistent; - // The weights are of consistent type, so it suffices to check one. // TODO(mirkov): create a utility/macro for this check, so all Ops can use it. const bool is_hybrid_op = (input_to_output_weights->type == kTfLiteUInt8 && @@ -337,7 +383,7 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { if (is_hybrid_op) { // Allocate temporary tensors to store quantized values of input, - // output_state and cell_state tensors. + // activation_state and cell_state tensors. node->temporaries->data[1] = op_data->scratch_tensor_index + 1; TfLiteTensor* input_quantized = GetTemporary(context, node, /*index=*/1); input_quantized->type = kTfLiteUInt8; @@ -348,17 +394,17 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { input_quantized_size)); } node->temporaries->data[2] = op_data->scratch_tensor_index + 2; - TfLiteTensor* output_state_quantized = + TfLiteTensor* activation_state_quantized = GetTemporary(context, node, /*index=*/2); - output_state_quantized->type = kTfLiteUInt8; - output_state_quantized->allocation_type = kTfLiteArenaRw; - if (!TfLiteIntArrayEqual(output_state_quantized->dims, - output_state->dims)) { - TfLiteIntArray* output_state_quantized_size = - TfLiteIntArrayCopy(output_state->dims); - TF_LITE_ENSURE_OK(context, - context->ResizeTensor(context, output_state_quantized, - output_state_quantized_size)); + activation_state_quantized->type = kTfLiteUInt8; + activation_state_quantized->allocation_type = kTfLiteArenaRw; + if (!TfLiteIntArrayEqual(activation_state_quantized->dims, + activation_state->dims)) { + TfLiteIntArray* activation_state_quantized_size = + TfLiteIntArrayCopy(activation_state->dims); + TF_LITE_ENSURE_OK( + context, context->ResizeTensor(context, activation_state_quantized, + activation_state_quantized_size)); } node->temporaries->data[3] = op_data->scratch_tensor_index + 3; TfLiteTensor* cell_state_quantized = @@ -438,7 +484,7 @@ TfLiteStatus EvalFloat( const TfLiteTensor* cell_bias, const TfLiteTensor* output_gate_bias, const TfLiteTensor* projection_weights, const TfLiteTensor* projection_bias, const TfLiteLSTMParams* params, TfLiteTensor* scratch_buffer, - TfLiteTensor* output_state, TfLiteTensor* cell_state, + TfLiteTensor* activation_state, TfLiteTensor* cell_state, TfLiteTensor* output) { const int n_batch = input->dims->data[0]; const int n_input = input->dims->data[1]; @@ -499,7 +545,7 @@ TfLiteStatus EvalFloat( const float* cell_bias_ptr = cell_bias->data.f; const float* output_gate_bias_ptr = output_gate_bias->data.f; - float* output_state_ptr = output_state->data.f; + float* activation_state_ptr = activation_state->data.f; float* cell_state_ptr = cell_state->data.f; float* output_ptr_batch = output->data.f; @@ -512,8 +558,8 @@ TfLiteStatus EvalFloat( cell_to_output_weights_ptr, input_gate_bias_ptr, forget_gate_bias_ptr, cell_bias_ptr, output_gate_bias_ptr, projection_weights_ptr, projection_bias_ptr, params, n_batch, n_cell, n_input, n_output, - output_state_ptr, cell_state_ptr, input_gate_scratch, forget_gate_scratch, - cell_scratch, output_gate_scratch, output_ptr_batch); + activation_state_ptr, cell_state_ptr, input_gate_scratch, + forget_gate_scratch, cell_scratch, output_gate_scratch, output_ptr_batch); return kTfLiteOk; } @@ -536,9 +582,9 @@ TfLiteStatus EvalHybrid( const TfLiteLSTMParams* params, TfLiteTensor* scratch_buffer, TfLiteTensor* scaling_factors, TfLiteTensor* prod_scaling_factors, TfLiteTensor* recovered_cell_weights, TfLiteTensor* input_quantized, - TfLiteTensor* output_state_quantized, TfLiteTensor* cell_state_quantized, - TfLiteTensor* output_state, TfLiteTensor* cell_state, - TfLiteTensor* output) { + TfLiteTensor* activation_state_quantized, + TfLiteTensor* cell_state_quantized, TfLiteTensor* activation_state, + TfLiteTensor* cell_state, TfLiteTensor* output) { const int n_batch = input->dims->data[0]; const int n_input = input->dims->data[1]; // n_cell and n_output will be the same size when there is no projection. @@ -639,15 +685,15 @@ TfLiteStatus EvalHybrid( const float* cell_bias_ptr = cell_bias->data.f; const float* output_gate_bias_ptr = output_gate_bias->data.f; - float* output_state_ptr = output_state->data.f; + float* activation_state_ptr = activation_state->data.f; float* cell_state_ptr = cell_state->data.f; float* output_ptr_batch = output->data.f; // Temporary storage for quantized values and scaling factors. int8_t* quantized_input_ptr = reinterpret_cast(input_quantized->data.uint8); - int8_t* quantized_output_state_ptr = - reinterpret_cast(output_state_quantized->data.uint8); + int8_t* quantized_activation_state_ptr = + reinterpret_cast(activation_state_quantized->data.uint8); int8_t* quantized_cell_state_ptr = reinterpret_cast(cell_state_quantized->data.uint8); float* scaling_factors_ptr = scaling_factors->data.f; @@ -672,14 +718,16 @@ TfLiteStatus EvalHybrid( input_gate_scratch, forget_gate_scratch, cell_scratch, output_gate_scratch, scaling_factors_ptr, prod_scaling_factors_ptr, recovered_cell_weights_ptr, quantized_input_ptr, - quantized_output_state_ptr, quantized_cell_state_ptr, output_state_ptr, - cell_state_ptr, output_ptr_batch); + quantized_activation_state_ptr, quantized_cell_state_ptr, + activation_state_ptr, cell_state_ptr, output_ptr_batch); return kTfLiteOk; } TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { const auto* params = reinterpret_cast(node->builtin_data); + OpData* op_data = reinterpret_cast(node->user_data); + const TfLiteTensor* input = GetInput(context, node, kInputTensor); const TfLiteTensor* input_to_input_weights = @@ -723,8 +771,11 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { // Index the scratch buffers pointers to the global scratch buffer. TfLiteTensor* scratch_buffer = GetTemporary(context, node, /*index=*/0); - TfLiteTensor* output_state = GetOutput(context, node, kOutputStateTensor); - TfLiteTensor* cell_state = GetOutput(context, node, kCellStateTensor); + TfLiteTensor* activation_state = + &context->tensors[op_data->activation_state_tensor_index]; + TfLiteTensor* cell_state = + &context->tensors[op_data->cell_state_tensor_index]; + TfLiteTensor* output = GetOutput(context, node, kOutputTensor); // TODO(mirkov): add a check that weights are all uint8s or all floats. @@ -738,11 +789,11 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_bias, output_gate_bias, projection_weights, projection_bias, params, - scratch_buffer, output_state, cell_state, output); + scratch_buffer, activation_state, cell_state, output); } case kTfLiteUInt8: { TfLiteTensor* input_quantized = GetTemporary(context, node, /*index=*/1); - TfLiteTensor* output_state_quantized = + TfLiteTensor* activation_state_quantized = GetTemporary(context, node, /*index=*/2); TfLiteTensor* cell_state_quantized = GetTemporary(context, node, /*index=*/3); @@ -760,8 +811,8 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { input_gate_bias, forget_gate_bias, cell_bias, output_gate_bias, projection_weights, projection_bias, params, scratch_buffer, scaling_factors, prod_scaling_factors, recovered_cell_weights, - input_quantized, output_state_quantized, cell_state_quantized, - output_state, cell_state, output); + input_quantized, activation_state_quantized, cell_state_quantized, + activation_state, cell_state, output); } default: context->ReportError(context, "Type %d is not currently supported.", diff --git a/tensorflow/contrib/lite/kernels/lstm_test.cc b/tensorflow/contrib/lite/kernels/lstm_test.cc index 6da29a4a92..3f5c44a63e 100644 --- a/tensorflow/contrib/lite/kernels/lstm_test.cc +++ b/tensorflow/contrib/lite/kernels/lstm_test.cc @@ -97,6 +97,12 @@ class LSTMOpModel : public SingleOpModel { projection_bias_ = AddNullInput(); } + // Adding the 2 input state tensors. + input_activation_state_ = + AddInput(TensorData{TensorType_FLOAT32, {n_output_ * n_batch_}}, true); + input_cell_state_ = + AddInput(TensorData{TensorType_FLOAT32, {n_cell_ * n_batch_}}, true); + output_state_ = AddOutput(TensorType_FLOAT32); cell_state_ = AddOutput(TensorType_FLOAT32); output_ = AddOutput(TensorType_FLOAT32); @@ -227,6 +233,8 @@ class LSTMOpModel : public SingleOpModel { int projection_weights_; int projection_bias_; + int input_activation_state_; + int input_cell_state_; int output_; int output_state_; diff --git a/tensorflow/contrib/lite/kernels/optional_tensor_test.cc b/tensorflow/contrib/lite/kernels/optional_tensor_test.cc index bcad58406a..1c728a4733 100644 --- a/tensorflow/contrib/lite/kernels/optional_tensor_test.cc +++ b/tensorflow/contrib/lite/kernels/optional_tensor_test.cc @@ -95,6 +95,12 @@ class LSTMOpModel : public SingleOpModel { projection_bias_ = AddNullInput(); } + // Adding the 2 input state tensors. + input_activation_state_ = + AddInput(TensorData{TensorType_FLOAT32, {n_output_ * n_batch_}}, true); + input_cell_state_ = + AddInput(TensorData{TensorType_FLOAT32, {n_cell_ * n_batch_}}, true); + output_state_ = AddOutput(TensorType_FLOAT32); cell_state_ = AddOutput(TensorType_FLOAT32); output_ = AddOutput(TensorType_FLOAT32); @@ -228,6 +234,8 @@ class LSTMOpModel : public SingleOpModel { int projection_weights_; int projection_bias_; + int input_activation_state_; + int input_cell_state_; int output_; int output_state_; diff --git a/tensorflow/contrib/lite/kernels/test_util.cc b/tensorflow/contrib/lite/kernels/test_util.cc index d23ec201b4..9156917140 100644 --- a/tensorflow/contrib/lite/kernels/test_util.cc +++ b/tensorflow/contrib/lite/kernels/test_util.cc @@ -32,8 +32,8 @@ std::vector> ArrayFloatNear(const std::vector& values, return matchers; } -int SingleOpModel::AddInput(const TensorData& t) { - int id = AddTensor(t, {}); +int SingleOpModel::AddInput(const TensorData& t, bool is_variable) { + int id = AddTensor(t, {}, is_variable); inputs_.push_back(id); return id; } @@ -120,6 +120,7 @@ void SingleOpModel::BuildInterpreter( CHECK(interpreter_->AllocateTensors() == kTfLiteOk) << "Cannot allocate tensors"; + interpreter_->ResetVariableTensorsToZero(); } void SingleOpModel::Invoke() { CHECK(interpreter_->Invoke() == kTfLiteOk); } diff --git a/tensorflow/contrib/lite/kernels/test_util.h b/tensorflow/contrib/lite/kernels/test_util.h index db80c0082c..6dcece4af6 100644 --- a/tensorflow/contrib/lite/kernels/test_util.h +++ b/tensorflow/contrib/lite/kernels/test_util.h @@ -126,8 +126,10 @@ class SingleOpModel { SingleOpModel& operator=(const SingleOpModel&) = delete; // Add a TensorType input tensor and return its index. - int AddInput(TensorType type) { return AddInput(TensorData{type}); } - int AddInput(const TensorData& t); + int AddInput(TensorType type, bool is_variable = false) { + return AddInput(TensorData{type}, is_variable); + } + int AddInput(const TensorData& t, bool is_variable = false); // Templated version of AddConstInput(). template @@ -260,7 +262,8 @@ class SingleOpModel { } template - int AddTensor(TensorData t, std::initializer_list data) { + int AddTensor(TensorData t, std::initializer_list data, + bool is_variable = false) { int id = tensors_.size(); // This is slightly different depending on whether we are adding a @@ -309,7 +312,7 @@ class SingleOpModel { tensors_.push_back(CreateTensor(builder_, builder_.CreateVector(t.shape), t.type, /*buffer=*/buffer_id, - /*name=*/0, q_params)); + /*name=*/0, q_params, is_variable)); tensor_data_[id] = t; diff --git a/tensorflow/contrib/lite/testing/tflite_driver.cc b/tensorflow/contrib/lite/testing/tflite_driver.cc index 54edfdfb1d..4d08fb5458 100644 --- a/tensorflow/contrib/lite/testing/tflite_driver.cc +++ b/tensorflow/contrib/lite/testing/tflite_driver.cc @@ -288,8 +288,8 @@ void TfLiteDriver::ResetLSTMStateTensors() { interpreter_->ResetVariableTensorsToZero(); // Below is a workaround for initializing state tensors for LSTM. - // TODO(ycling): Refactoring and find a better way to initialize state - // tensors. Maybe write the reset instructions into the test data. + // TODO(ycling): Remove the code below after nobody is using the 18-inputs + // definition. for (auto node_index : interpreter_->execution_plan()) { const auto& node_and_reg = interpreter_->node_and_registration(node_index); const auto& node = node_and_reg->first; @@ -299,7 +299,7 @@ void TfLiteDriver::ResetLSTMStateTensors() { const auto* params = reinterpret_cast(node.builtin_data); if (params->kernel_type == kTfLiteLSTMFullKernel && - node.outputs->size >= 2) { + node.inputs->size == 18 && node.outputs->size >= 2) { // The first 2 outputs of LSTM are state tensors. for (int i = 0; i < 2; ++i) { int node_index = node.outputs->data[i]; diff --git a/tensorflow/contrib/lite/toco/graph_transformations/identify_lstm_split_inputs.cc b/tensorflow/contrib/lite/toco/graph_transformations/identify_lstm_split_inputs.cc index e6e3dfa1de..46d1fce50e 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/identify_lstm_split_inputs.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/identify_lstm_split_inputs.cc @@ -74,6 +74,12 @@ bool SplitLstmCellInputs::Run(Model* model, std::size_t op_index) { lstm_cell_op->inputs[kInputTensor] = curr_op->inputs[LstmCellOperator::ACTIV_OUTPUT]; + // Previous states. + lstm_cell_op->inputs[kInputActivationStateTensor] = + curr_op->inputs[LstmCellOperator::PREV_ACTIV_INPUT]; + lstm_cell_op->inputs[kInputCellStateTensor] = + curr_op->inputs[LstmCellOperator::PREV_STATE_INPUT]; + // Get original weight tensor and decompose 1 tensor to 8 sub tensors. Array& kernel = model->GetArray(curr_op->inputs[LstmCellOperator::WEIGHTS_INPUT]); @@ -160,10 +166,6 @@ bool SplitLstmCellInputs::Run(Model* model, std::size_t op_index) { // Erase curr lstm op being replaced. DeleteArrayIfUnused(curr_op->inputs[LstmCellOperator::WEIGHTS_INPUT], model); DeleteArrayIfUnused(curr_op->inputs[LstmCellOperator::BIASES_INPUT], model); - DeleteArrayIfUnused(curr_op->inputs[LstmCellOperator::PREV_ACTIV_INPUT], - model); - DeleteArrayIfUnused(curr_op->inputs[LstmCellOperator::PREV_STATE_INPUT], - model); model->operators.erase(FindOp(*model, curr_op)); return true; diff --git a/tensorflow/contrib/lite/toco/graph_transformations/lstm_utils.h b/tensorflow/contrib/lite/toco/graph_transformations/lstm_utils.h index 1c32a78169..6d8603a113 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/lstm_utils.h +++ b/tensorflow/contrib/lite/toco/graph_transformations/lstm_utils.h @@ -47,10 +47,14 @@ enum ExtendedLstmCellInputs { kOutputGateBiasTensor = 15, kProjectionWeightsTensor = 16, // Optional kProjectionBiasTensor = 17, // Optional - kExtendedLstmInputCount = 18 + kInputActivationStateTensor = 18, + // The op can handle 18 inputs or 20 inputs. + kInputCellStateTensor = 19, + kExtendedLstmInputCount = 20, }; enum ExtendedLstmCellOutputs { + // TODO(ycling): Make the 2 output state tensors optional. kOutputStateTensor = 0, kCellStateTensor = 1, kOutputTensor = 2, diff --git a/tensorflow/contrib/lite/toco/tflite/BUILD b/tensorflow/contrib/lite/toco/tflite/BUILD index e1025c6664..a02f90988b 100644 --- a/tensorflow/contrib/lite/toco/tflite/BUILD +++ b/tensorflow/contrib/lite/toco/tflite/BUILD @@ -24,6 +24,7 @@ cc_library( deps = [ ":types", "//tensorflow/contrib/lite/schema:schema_fbs", + "//tensorflow/contrib/lite/toco:graph_transformations", "//tensorflow/contrib/lite/toco:model", "//tensorflow/core:protos_all_cc", "@com_google_absl//absl/memory", diff --git a/tensorflow/contrib/lite/toco/tflite/operator.cc b/tensorflow/contrib/lite/toco/tflite/operator.cc index 669fb9fa08..c93c0a6b90 100644 --- a/tensorflow/contrib/lite/toco/tflite/operator.cc +++ b/tensorflow/contrib/lite/toco/tflite/operator.cc @@ -14,6 +14,9 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/contrib/lite/toco/tflite/operator.h" +// TODO(ycling): Consider refactoring to extract the LSTM definition out of +// graph_transformation module. +#include "tensorflow/contrib/lite/toco/graph_transformations/lstm_utils.h" #include "tensorflow/contrib/lite/toco/tflite/builtin_operator.h" #include "tensorflow/contrib/lite/toco/tflite/custom_operator.h" #include "tensorflow/contrib/lite/toco/tflite/simple_operator.h" @@ -673,18 +676,20 @@ class Lstm : public BuiltinOperator(op); + std::vector mutating_input_variables(op.inputs.size(), false); switch (lstm_op.kernel_type) { - case LstmCellOperator::KERNEL_FULL: - // TODO(ycling): Change the full kernel to use the new variable tensor - // design. This requires moving the state tensors from output to input. - return std::vector(); + case LstmCellOperator::KERNEL_FULL: { + mutating_input_variables[kInputActivationStateTensor] = true; + mutating_input_variables[kInputCellStateTensor] = true; + break; + } case LstmCellOperator::KERNEL_BASIC: { - std::vector mutating_input_variables(op.inputs.size(), false); mutating_input_variables[LstmCellOperator::PREV_ACTIV_INPUT] = true; mutating_input_variables[LstmCellOperator::PREV_STATE_INPUT] = true; - return mutating_input_variables; + break; } } + return mutating_input_variables; } }; -- GitLab From 520384df634f64cb6d803884f5f0c9462a6ef9fd Mon Sep 17 00:00:00 2001 From: gracehoney <31743510+aaroey@users.noreply.github.com> Date: Tue, 19 Jun 2018 12:39:24 -0700 Subject: [PATCH 683/816] Use TrtUniquePtrType for all builder/network/engine construction; add build rules for utils.h; add more TODOs --- tensorflow/contrib/tensorrt/BUILD | 9 ++++++++- .../contrib/tensorrt/convert/convert_graph.cc | 6 ++---- .../contrib/tensorrt/convert/convert_nodes.cc | 18 +++--------------- 3 files changed, 13 insertions(+), 20 deletions(-) diff --git a/tensorflow/contrib/tensorrt/BUILD b/tensorflow/contrib/tensorrt/BUILD index fd0f97f3af..e7b3fe38e5 100644 --- a/tensorflow/contrib/tensorrt/BUILD +++ b/tensorflow/contrib/tensorrt/BUILD @@ -87,6 +87,7 @@ cc_library( ":trt_plugins", ":trt_resources", ":trt_conversion", + ":utils", "//tensorflow/core:gpu_headers_lib", "//tensorflow/core:lib_proto_parsing", "//tensorflow/core:stream_executor_headers_lib", @@ -94,7 +95,7 @@ cc_library( ] + if_tensorrt([ "@local_config_tensorrt//:nv_infer", ]) + tf_custom_op_library_additional_deps(), - # TODO(laigd) + # TODO(laigd): fix this by merging header file in cc file. alwayslink = 1, # buildozer: disable=alwayslink-with-hdrs ) @@ -232,6 +233,7 @@ tf_cuda_library( ":trt_plugins", ":trt_logging", ":trt_resources", + ":utils", "//tensorflow/core/grappler/clusters:cluster", "//tensorflow/core/grappler/optimizers:custom_graph_optimizer", "//tensorflow/core/grappler/optimizers:custom_graph_optimizer_registry", @@ -337,3 +339,8 @@ py_test( "//tensorflow/python:framework_test_lib", ], ) + +cc_library( + name = "utils", + hdrs = ["convert/utils.h"], +) diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.cc b/tensorflow/contrib/tensorrt/convert/convert_graph.cc index bd6ed2d593..9f0b3ef5dd 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_graph.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_graph.cc @@ -423,10 +423,8 @@ tensorflow::Status CreateTRTNode(tensorflow::Graph* graph, info.precision_mode == INT8MODE) { // Create static engine and for int8 test validity of the engine. Logger trt_logger; - auto builder = std::unique_ptr< - nvinfer1::IBuilder, std::function>( - nvinfer1::createInferBuilder(trt_logger), - [](nvinfer1::IBuilder* p) { if (p) p->destroy(); }); + TrtUniquePtrType builder( + nvinfer1::createInferBuilder(trt_logger)); builder->setMaxBatchSize(max_batch_size); if (info.precision_mode == FP16MODE) builder->setHalf2Mode(true); builder->setMaxWorkspaceSize(info.max_workspace_size_bytes); diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index a252ea67df..69d7b765fa 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -420,20 +420,6 @@ void ReorderRSCKToKCRS(const TRT_ShapedWeights& iweights, } } -struct InferDeleter { - template - void operator()(T* obj) const { - if (obj) { - obj->destroy(); - } - } -}; - -template -inline std::shared_ptr infer_object(T* obj) { - return std::shared_ptr(obj, InferDeleter()); -} - class Converter; using OpConverter = @@ -2151,7 +2137,8 @@ tensorflow::Status ConvertSubGraphDefToEngine( bool* convert_successfully) { engine->reset(); if (convert_successfully) *convert_successfully = false; - auto trt_network = infer_object(builder->createNetwork()); + auto trt_network = + TrtUniquePtrType(builder->createNetwork()); if (!trt_network) { return tensorflow::errors::Internal( "Failed to create TensorRT network object"); @@ -2207,6 +2194,7 @@ tensorflow::Status ConvertSubGraphDefToEngine( nvinfer1::ITensor* input_tensor = converter.network()->addInput( node_name.c_str(), dtype, input_dim_pseudo_chw); if (!input_tensor) { + // TODO(aaroey): remove StrCat when constructing errors. return tensorflow::errors::InvalidArgument( StrCat("Failed to create Input layer tensor ", node_name, " rank=", shape.dims() - 1)); -- GitLab From 878e6673791debdad7a6aa449c49b424ae3f1b33 Mon Sep 17 00:00:00 2001 From: Jiri Simsa Date: Tue, 19 Jun 2018 12:53:58 -0700 Subject: [PATCH 684/816] Changing test size to "medium" to prevent test timeouts. PiperOrigin-RevId: 201225326 --- tensorflow/contrib/data/python/kernel_tests/serialization/BUILD | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/BUILD b/tensorflow/contrib/data/python/kernel_tests/serialization/BUILD index e9bc18ac2e..686788522a 100644 --- a/tensorflow/contrib/data/python/kernel_tests/serialization/BUILD +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/BUILD @@ -88,7 +88,7 @@ py_test( py_test( name = "filter_dataset_serialization_test", - size = "small", + size = "medium", srcs = ["filter_dataset_serialization_test.py"], srcs_version = "PY2AND3", tags = ["no_pip"], -- GitLab From ca226664780bf980848ffe3552d215568139ed6d Mon Sep 17 00:00:00 2001 From: Rohan Jain Date: Tue, 19 Jun 2018 13:25:32 -0700 Subject: [PATCH 685/816] Moving SharedEmbeddingColumns state management back to graph collections. Erroring out SharedEmbeddingColumn usage in Eager mode since collections aren't supported in eager. PiperOrigin-RevId: 201230316 --- .../python/feature_column/feature_column.py | 123 +++++++++--------- .../feature_column/feature_column_test.py | 10 +- 2 files changed, 68 insertions(+), 65 deletions(-) diff --git a/tensorflow/python/feature_column/feature_column.py b/tensorflow/python/feature_column/feature_column.py index 670c933d56..5ae60028f4 100644 --- a/tensorflow/python/feature_column/feature_column.py +++ b/tensorflow/python/feature_column/feature_column.py @@ -466,13 +466,25 @@ def linear_model(features, def _add_to_collections(var, weight_collections): - # TODO(rohanj): Explore adding a _get_variable_list method on `Variable` - # so that we don't have to do this check. - if isinstance(var, variables.PartitionedVariable): - for constituent_var in list(var): - ops.add_to_collections(weight_collections, constituent_var) - else: - ops.add_to_collections(weight_collections, var) + """Adds a var to the list of weight_collections provided. + + Handles the case for partitioned and non-partitioned variables. + + Args: + var: A variable or Partitioned Variable. + weight_collections: List of collections to add variable to. + """ + for weight_collection in weight_collections: + # The layer self.add_variable call already adds it to GLOBAL_VARIABLES. + if weight_collection == ops.GraphKeys.GLOBAL_VARIABLES: + continue + # TODO(rohanj): Explore adding a _get_variable_list method on `Variable` + # so that we don't have to do this check. + if isinstance(var, variables.PartitionedVariable): + for constituent_var in list(var): + ops.add_to_collection(weight_collection, constituent_var) + else: + ops.add_to_collection(weight_collection, var) class _FCLinearWrapper(base.Layer): @@ -583,6 +595,8 @@ class _LinearModel(training.Model): self._feature_columns = _normalize_feature_columns( feature_columns) self._weight_collections = list(weight_collections or []) + if ops.GraphKeys.GLOBAL_VARIABLES not in self._weight_collections: + self._weight_collections.append(ops.GraphKeys.GLOBAL_VARIABLES) if ops.GraphKeys.MODEL_VARIABLES not in self._weight_collections: self._weight_collections.append(ops.GraphKeys.MODEL_VARIABLES) @@ -971,7 +985,12 @@ def shared_embedding_columns( ValueError: if exactly one of `ckpt_to_load_from` and `tensor_name_in_ckpt` is specified. ValueError: if `initializer` is specified and is not callable. + RuntimeError: if eager execution is enabled. """ + if context.executing_eagerly(): + raise RuntimeError('shared_embedding_columns are not supported when eager ' + 'execution is enabled.') + if (dimension is None) or (dimension < 1): raise ValueError('Invalid dimension {}.'.format(dimension)) if (ckpt_to_load_from is None) != (tensor_name_in_ckpt is None): @@ -1016,16 +1035,6 @@ def shared_embedding_columns( shared_embedding_collection_name = '_'.join(c.name for c in sorted_columns) shared_embedding_collection_name += '_shared_embedding' - # Create the state (_SharedEmbeddingColumnLayer) here. - embedding_shape = num_buckets, dimension - - shared_embedding_column_layer = _EmbeddingColumnLayer( - embedding_shape=embedding_shape, - initializer=initializer, - weight_collections=[], - trainable=trainable, - name=shared_embedding_collection_name) - result = [] for column in categorical_columns: result.append( @@ -1034,16 +1043,12 @@ def shared_embedding_columns( initializer=initializer, dimension=dimension, combiner=combiner, - var_scope_name=shared_embedding_collection_name, + shared_embedding_collection_name=shared_embedding_collection_name, ckpt_to_load_from=ckpt_to_load_from, tensor_name_in_ckpt=tensor_name_in_ckpt, max_norm=max_norm, trainable=trainable)) - for single_result in result: - single_result._set_layer(shared_embedding_column_layer) # pylint: disable=protected-access - single_result._set_all_columns(result) # pylint: disable=protected-access - return result @@ -1863,11 +1868,8 @@ class _EmbeddingColumnLayer(base.Layer): dtype=dtypes.float32, initializer=self._initializer, trainable=self.trainable) - # self.add_variable already appends to GLOBAL_VARIABLES collection. if self._weight_collections and not context.executing_eagerly(): - for weight_collection in self._weight_collections: - if weight_collection != ops.GraphKeys.GLOBAL_VARIABLES: - _add_to_collections(self._embedding_weight_var, [weight_collection]) + _add_to_collections(self._embedding_weight_var, self._weight_collections) self.built = True def call(self, _): @@ -2649,8 +2651,8 @@ class _SharedEmbeddingColumn( collections.namedtuple( '_SharedEmbeddingColumn', ('categorical_column', 'dimension', 'combiner', 'initializer', - 'var_scope_name', 'ckpt_to_load_from', 'tensor_name_in_ckpt', - 'max_norm', 'trainable'))): + 'shared_embedding_collection_name', 'ckpt_to_load_from', + 'tensor_name_in_ckpt', 'max_norm', 'trainable'))): """See `embedding_column`.""" @property @@ -2661,7 +2663,7 @@ class _SharedEmbeddingColumn( @property def _var_scope_name(self): - return self.var_scope_name + return self.shared_embedding_collection_name @property def _parse_example_spec(self): @@ -2670,22 +2672,6 @@ class _SharedEmbeddingColumn( def _transform_feature(self, inputs): return inputs.get(self.categorical_column) - def _set_layer(self, layer): - self._layer = layer - - def _set_all_columns(self, all_columns): - self._all_columns = all_columns - - def _reset_config(self): - config = self._layer.get_config() - config['embedding_shape'] = ( - self.categorical_column._num_buckets, # pylint: disable=protected-access - self.dimension) - config['initializer'] = self.initializer - self._layer = self._layer.__class__.from_config(config) - for column in self._all_columns: - column._set_layer(self._layer) # pylint: disable=protected-access - @property def _variable_shape(self): if not hasattr(self, '_shape'): @@ -2707,19 +2693,38 @@ class _SharedEmbeddingColumn( sparse_ids = sparse_tensors.id_tensor sparse_weights = sparse_tensors.weight_tensor - self._layer.set_weight_collections(weight_collections) - embedding_weights = self._layer( - None, scope=variable_scope.get_variable_scope()) - # If we're in graph mode and this is called with a different graph, - # then we should reset. - if not context.executing_eagerly() and ( - ops.get_default_graph() != - _get_graph_for_variable(embedding_weights)): - self._reset_config() - self._layer.set_weight_collections(weight_collections) - embedding_weights = self._layer( - None, scope=variable_scope.get_variable_scope()) - + embedding_shape = (self.categorical_column._num_buckets, self.dimension) # pylint: disable=protected-access + shared_embedding_collection = ops.get_collection( + self.shared_embedding_collection_name) + if shared_embedding_collection: + if len(shared_embedding_collection) > 1: + raise ValueError( + 'Collection {} can only contain one variable. ' + 'Suggested fix A: Choose a unique name for this collection. ' + 'Suggested fix B: Do not add any variables to this collection. ' + 'The feature_column library already adds a variable under the ' + 'hood.'.format(shared_embedding_collection)) + embedding_weights = shared_embedding_collection[0] + if embedding_weights.get_shape() != embedding_shape: + raise ValueError( + 'Shared embedding collection {} contains variable {} of ' + 'unexpected shape {}. Expected shape is {}. ' + 'Suggested fix A: Choose a unique name for this collection. ' + 'Suggested fix B: Do not add any variables to this collection. ' + 'The feature_column library already adds a variable under the ' + 'hood.'.format(self.shared_embedding_collection_name, + embedding_weights.name, + embedding_weights.get_shape(), embedding_shape)) + else: + embedding_weights = variable_scope.get_variable( + name='embedding_weights', + shape=embedding_shape, + dtype=dtypes.float32, + initializer=self.initializer, + trainable=self.trainable and trainable, + collections=weight_collections) + ops.add_to_collection(self.shared_embedding_collection_name, + embedding_weights) if self.ckpt_to_load_from is not None: to_restore = embedding_weights if isinstance(to_restore, variables.PartitionedVariable): @@ -3579,5 +3584,3 @@ class _SequenceCategoricalColumn( weight_tensor, shape=array_ops.concat([weight_tensor.dense_shape, [1]], axis=0)) return _CategoricalColumn.IdWeightPair(id_tensor, weight_tensor) - - diff --git a/tensorflow/python/feature_column/feature_column_test.py b/tensorflow/python/feature_column/feature_column_test.py index 627430d6bc..c80c1d1866 100644 --- a/tensorflow/python/feature_column/feature_column_test.py +++ b/tensorflow/python/feature_column/feature_column_test.py @@ -5329,9 +5329,9 @@ class SharedEmbeddingColumnTest(test.TestCase): self.assertIsNone(embedding_column_a.ckpt_to_load_from) self.assertIsNone(embedding_column_b.ckpt_to_load_from) self.assertEqual('aaa_bbb_shared_embedding', - embedding_column_a.var_scope_name) + embedding_column_a.shared_embedding_collection_name) self.assertEqual('aaa_bbb_shared_embedding', - embedding_column_b.var_scope_name) + embedding_column_b.shared_embedding_collection_name) self.assertIsNone(embedding_column_a.tensor_name_in_ckpt) self.assertIsNone(embedding_column_b.tensor_name_in_ckpt) self.assertIsNone(embedding_column_a.max_norm) @@ -5378,9 +5378,9 @@ class SharedEmbeddingColumnTest(test.TestCase): self.assertEqual('my_combiner', embedding_column_a.combiner) self.assertEqual('my_combiner', embedding_column_b.combiner) self.assertEqual('shared_embedding_collection_name', - embedding_column_a.var_scope_name) + embedding_column_a.shared_embedding_collection_name) self.assertEqual('shared_embedding_collection_name', - embedding_column_b.var_scope_name) + embedding_column_b.shared_embedding_collection_name) self.assertEqual('my_ckpt', embedding_column_a.ckpt_to_load_from) self.assertEqual('my_ckpt', embedding_column_b.ckpt_to_load_from) self.assertEqual('my_ckpt_tensor', embedding_column_a.tensor_name_in_ckpt) @@ -5431,7 +5431,7 @@ class SharedEmbeddingColumnTest(test.TestCase): self.assertEqual(embedding_dimension, embedding_column_a.dimension) self.assertEqual('my_combiner', embedding_column_a.combiner) self.assertEqual('shared_embedding_collection_name', - embedding_column_a.var_scope_name) + embedding_column_a.shared_embedding_collection_name) self.assertEqual('my_ckpt', embedding_column_a.ckpt_to_load_from) self.assertEqual('my_ckpt_tensor', embedding_column_a.tensor_name_in_ckpt) self.assertEqual(42., embedding_column_a.max_norm) -- GitLab From f9af1e1f742210615a9eed4866cf6744419fde24 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 19 Jun 2018 13:39:50 -0700 Subject: [PATCH 686/816] Disable caching_device for mirrored variables. PiperOrigin-RevId: 201232817 --- tensorflow/contrib/distribute/python/mirrored_strategy.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/tensorflow/contrib/distribute/python/mirrored_strategy.py b/tensorflow/contrib/distribute/python/mirrored_strategy.py index 900aa10e93..c1b4b870a5 100644 --- a/tensorflow/contrib/distribute/python/mirrored_strategy.py +++ b/tensorflow/contrib/distribute/python/mirrored_strategy.py @@ -109,6 +109,9 @@ class MirroredStrategy(distribute_lib.DistributionStrategy): if tower_local is not None: kwargs["trainable"] = False + # Ignore user-specified caching device, not needed for mirrored variables. + kwargs.pop("caching_device", None) + # TODO(josh11b,apassos): It would be better if variable initialization # was never recorded on the tape instead of having to do this manually # here. -- GitLab From 765f6d50ab9c51523eddf4c2ef8100eda2f1b23a Mon Sep 17 00:00:00 2001 From: Xuechen Li Date: Tue, 19 Jun 2018 13:59:27 -0700 Subject: [PATCH 687/816] Automated g4 rollback of changelist 201101839 PiperOrigin-RevId: 201236075 --- .../python/training/learning_rate_decay.py | 385 +++++++++----- .../training/learning_rate_decay_test.py | 499 +++++++++--------- 2 files changed, 499 insertions(+), 385 deletions(-) diff --git a/tensorflow/python/training/learning_rate_decay.py b/tensorflow/python/training/learning_rate_decay.py index bae3e51494..51190264e8 100644 --- a/tensorflow/python/training/learning_rate_decay.py +++ b/tensorflow/python/training/learning_rate_decay.py @@ -19,6 +19,7 @@ from __future__ import print_function import math +from tensorflow.python.eager import context from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops @@ -87,6 +88,12 @@ def exponential_decay(learning_rate, Raises: ValueError: if `global_step` is not supplied. + + @compatibility(eager) + When eager execution is enabled, this function returns a function which in + turn returns the decayed learning rate Tensor. This can be useful for changing + the learning rate value across different invocations of optimizer functions. + @end_compatibility """ if global_step is None: raise ValueError("global_step is required for exponential_decay.") @@ -95,14 +102,22 @@ def exponential_decay(learning_rate, [learning_rate, global_step, decay_steps, decay_rate]) as name: learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") dtype = learning_rate.dtype - global_step = math_ops.cast(global_step, dtype) decay_steps = math_ops.cast(decay_steps, dtype) decay_rate = math_ops.cast(decay_rate, dtype) - p = global_step / decay_steps - if staircase: - p = math_ops.floor(p) - return math_ops.multiply( - learning_rate, math_ops.pow(decay_rate, p), name=name) + + def decayed_lr(): + """Helper to recompute learning rate; most helpful in eager-mode.""" + global_step_recomp = math_ops.cast(global_step, dtype) + p = global_step_recomp / decay_steps + if staircase: + p = math_ops.floor(p) + return math_ops.multiply( + learning_rate, math_ops.pow(decay_rate, p), name=name) + + if not context.executing_eagerly(): + decayed_lr = decayed_lr() + + return decayed_lr @tf_export("train.piecewise_constant") @@ -141,48 +156,62 @@ def piecewise_constant(x, boundaries, values, name=None): ValueError: if types of `x` and `boundaries` do not match, or types of all `values` do not match or the number of elements in the lists does not match. + + @compatibility(eager) + When eager execution is enabled, this function returns a function which in + turn returns the decayed learning rate Tensor. This can be useful for changing + the learning rate value across different invocations of optimizer functions. + @end_compatibility """ if len(boundaries) != len(values) - 1: raise ValueError( "The length of boundaries should be 1 less than the length of values") with ops.name_scope(name, "PiecewiseConstant", [x, boundaries, values, name]) as name: - x = ops.convert_to_tensor(x) - # Avoid explicit conversion to x's dtype. This could result in faulty - # comparisons, for example if floats are converted to integers. boundaries = ops.convert_n_to_tensor(boundaries) - for i, b in enumerate(boundaries): - if b.dtype.base_dtype != x.dtype.base_dtype: - # We can promote int32 boundaries to int64 without loss of precision. - # This covers the most common case where the user passes in boundaries - # as an array of Python integers. - if (b.dtype.base_dtype == dtypes.int32 and - x.dtype.base_dtype == dtypes.int64): - b = math_ops.cast(b, x.dtype.base_dtype) - boundaries[i] = b - else: - raise ValueError( - "Boundaries (%s) must have the same dtype as x (%s)." % - (b.dtype.base_dtype, x.dtype.base_dtype)) - # TODO(rdipietro): Ensure that boundaries' elements are strictly increasing. values = ops.convert_n_to_tensor(values) - for v in values[1:]: - if v.dtype.base_dtype != values[0].dtype.base_dtype: - raise ValueError( - "Values must have elements all with the same dtype (%s vs %s)." % - (values[0].dtype.base_dtype, v.dtype.base_dtype)) - pred_fn_pairs = [] - pred_fn_pairs.append((x <= boundaries[0], lambda: values[0])) - pred_fn_pairs.append((x > boundaries[-1], lambda: values[-1])) - for low, high, v in zip(boundaries[:-1], boundaries[1:], values[1:-1]): - # Need to bind v here; can do this with lambda v=v: ... - pred = (x > low) & (x <= high) - pred_fn_pairs.append((pred, lambda v=v: v)) - - # The default isn't needed here because our conditions are mutually - # exclusive and exhaustive, but tf.case requires it. - default = lambda: values[0] - return control_flow_ops.case(pred_fn_pairs, default, exclusive=True) + + def decayed_lr(): + """Helper to recompute learning rate; most helpful in eager-mode.""" + x_recomp = ops.convert_to_tensor(x) + # Avoid explicit conversion to x's dtype. This could result in faulty + # comparisons, for example if floats are converted to integers. + for i, b in enumerate(boundaries): + if b.dtype.base_dtype != x_recomp.dtype.base_dtype: + # We can promote int32 boundaries to int64 without loss of precision. + # This covers the most common case where the user passes in boundaries + # as an array of Python integers. + if (b.dtype.base_dtype == dtypes.int32 and + x_recomp.dtype.base_dtype == dtypes.int64): + b = math_ops.cast(b, x_recomp.dtype.base_dtype) + boundaries[i] = b + else: + raise ValueError( + "Boundaries (%s) must have the same dtype as x (%s)." % + (b.dtype.base_dtype, x_recomp.dtype.base_dtype)) + # TODO(rdipietro): Ensure that boundaries' elements strictly increases. + for v in values[1:]: + if v.dtype.base_dtype != values[0].dtype.base_dtype: + raise ValueError( + "Values must have elements all with the same dtype (%s vs %s)." % + (values[0].dtype.base_dtype, v.dtype.base_dtype)) + pred_fn_pairs = [] + pred_fn_pairs.append((x_recomp <= boundaries[0], lambda: values[0])) + pred_fn_pairs.append((x_recomp > boundaries[-1], lambda: values[-1])) + for low, high, v in zip(boundaries[:-1], boundaries[1:], values[1:-1]): + # Need to bind v here; can do this with lambda v=v: ... + pred = (x_recomp > low) & (x_recomp <= high) + pred_fn_pairs.append((pred, lambda v=v: v)) + + # The default isn't needed here because our conditions are mutually + # exclusive and exhaustive, but tf.case requires it. + default = lambda: values[0] + return control_flow_ops.case(pred_fn_pairs, default, exclusive=True) + + if not context.executing_eagerly(): + decayed_lr = decayed_lr() + + return decayed_lr @tf_export("train.polynomial_decay") @@ -263,6 +292,12 @@ def polynomial_decay(learning_rate, Raises: ValueError: if `global_step` is not supplied. + + @compatibility(eager) + When eager execution is enabled, this function returns a function which in + turn returns the decayed learning rate Tensor. This can be useful for changing + the learning rate value across different invocations of optimizer functions. + @end_compatibility """ if global_step is None: raise ValueError("global_step is required for polynomial_decay.") @@ -272,27 +307,35 @@ def polynomial_decay(learning_rate, ]) as name: learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") dtype = learning_rate.dtype - global_step = math_ops.cast(global_step, dtype) - decay_steps = math_ops.cast(decay_steps, dtype) end_learning_rate = math_ops.cast(end_learning_rate, dtype) power = math_ops.cast(power, dtype) - if cycle: - # Find the first multiple of decay_steps that is bigger than global_step. - # If global_step is zero set the multiplier to 1 - multiplier = control_flow_ops.cond( - math_ops.equal(global_step, 0), lambda: 1.0, - lambda: math_ops.ceil(global_step / decay_steps)) - decay_steps = math_ops.multiply(decay_steps, multiplier) - else: - # Make sure that the global_step used is not bigger than decay_steps. - global_step = math_ops.minimum(global_step, decay_steps) - - p = math_ops.div(global_step, decay_steps) - return math_ops.add( - math_ops.multiply(learning_rate - end_learning_rate, - math_ops.pow(1 - p, power)), - end_learning_rate, - name=name) + + def decayed_lr(): + """Helper to recompute learning rate; most helpful in eager-mode.""" + global_step_recomp = math_ops.cast(global_step, dtype) + decay_steps_recomp = math_ops.cast(decay_steps, dtype) + if cycle: + # Find the first multiple of decay_steps that is bigger than + # global_step. If global_step is zero set the multiplier to 1 + multiplier = control_flow_ops.cond( + math_ops.equal(global_step_recomp, 0), lambda: 1.0, + lambda: math_ops.ceil(global_step_recomp / decay_steps)) + decay_steps_recomp = math_ops.multiply(decay_steps_recomp, multiplier) + else: + # Make sure that the global_step used is not bigger than decay_steps. + global_step_recomp = math_ops.minimum(global_step_recomp, decay_steps) + + p = math_ops.div(global_step_recomp, decay_steps_recomp) + return math_ops.add( + math_ops.multiply(learning_rate - end_learning_rate, + math_ops.pow(1 - p, power)), + end_learning_rate, + name=name) + + if not context.executing_eagerly(): + decayed_lr = decayed_lr() + + return decayed_lr @tf_export("train.natural_exp_decay") @@ -350,6 +393,12 @@ def natural_exp_decay(learning_rate, Raises: ValueError: if `global_step` is not supplied. + + @compatibility(eager) + When eager execution is enabled, this function returns a function which in + turn returns the decayed learning rate Tensor. This can be useful for changing + the learning rate value across different invocations of optimizer functions. + @end_compatibility """ if global_step is None: raise ValueError("global_step is required for natural_exp_decay.") @@ -357,14 +406,23 @@ def natural_exp_decay(learning_rate, [learning_rate, global_step, decay_rate]) as name: learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") dtype = learning_rate.dtype - global_step = math_ops.cast(global_step, dtype) decay_steps = math_ops.cast(decay_steps, dtype) decay_rate = math_ops.cast(decay_rate, dtype) - p = global_step / decay_steps - if staircase: - p = math_ops.floor(p) - exponent = math_ops.exp(math_ops.multiply(math_ops.negative(decay_rate), p)) - return math_ops.multiply(learning_rate, exponent, name=name) + + def decayed_lr(): + """Helper to recompute learning rate; most helpful in eager-mode.""" + global_step_recomp = math_ops.cast(global_step, dtype) + p = global_step_recomp / decay_steps + if staircase: + p = math_ops.floor(p) + exponent = math_ops.exp( + math_ops.multiply(math_ops.negative(decay_rate), p)) + return math_ops.multiply(learning_rate, exponent, name=name) + + if not context.executing_eagerly(): + decayed_lr = decayed_lr() + + return decayed_lr @tf_export("train.inverse_time_decay") @@ -432,6 +490,12 @@ def inverse_time_decay(learning_rate, Raises: ValueError: if `global_step` is not supplied. + + @compatibility(eager) + When eager execution is enabled, this function returns a function which in + turn returns the decayed learning rate Tensor. This can be useful for changing + the learning rate value across different invocations of optimizer functions. + @end_compatibility """ if global_step is None: raise ValueError("global_step is required for inverse_time_decay.") @@ -439,15 +503,23 @@ def inverse_time_decay(learning_rate, [learning_rate, global_step, decay_rate]) as name: learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") dtype = learning_rate.dtype - global_step = math_ops.cast(global_step, dtype) decay_steps = math_ops.cast(decay_steps, dtype) decay_rate = math_ops.cast(decay_rate, dtype) - p = global_step / decay_steps - if staircase: - p = math_ops.floor(p) - const = math_ops.cast(constant_op.constant(1), learning_rate.dtype) - denom = math_ops.add(const, math_ops.multiply(decay_rate, p)) - return math_ops.div(learning_rate, denom, name=name) + + def decayed_lr(): + """Helper to recompute learning rate; most helpful in eager-mode.""" + global_step_recomp = math_ops.cast(global_step, dtype) + p = global_step_recomp / decay_steps + if staircase: + p = math_ops.floor(p) + const = math_ops.cast(constant_op.constant(1), dtype) + denom = math_ops.add(const, math_ops.multiply(decay_rate, p)) + return math_ops.div(learning_rate, denom, name=name) + + if not context.executing_eagerly(): + decayed_lr = decayed_lr() + + return decayed_lr @tf_export("train.cosine_decay") @@ -492,6 +564,12 @@ def cosine_decay(learning_rate, global_step, decay_steps, alpha=0.0, name=None): learning rate. Raises: ValueError: if `global_step` is not supplied. + + @compatibility(eager) + When eager execution is enabled, this function returns a function which in + turn returns the decayed learning rate Tensor. This can be useful for changing + the learning rate value across different invocations of optimizer functions. + @end_compatibility """ if global_step is None: raise ValueError("cosine decay requires global_step") @@ -499,15 +577,23 @@ def cosine_decay(learning_rate, global_step, decay_steps, alpha=0.0, name=None): [learning_rate, global_step]) as name: learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") dtype = learning_rate.dtype - global_step = math_ops.cast(global_step, dtype) decay_steps = math_ops.cast(decay_steps, dtype) - global_step = math_ops.minimum(global_step, decay_steps) - completed_fraction = global_step / decay_steps - cosine_decayed = 0.5 * ( - 1.0 + math_ops.cos(constant_op.constant(math.pi) * completed_fraction)) - decayed = (1 - alpha) * cosine_decayed + alpha - return math_ops.multiply(learning_rate, decayed) + def decayed_lr(): + """Helper to recompute learning rate; most helpful in eager-mode.""" + global_step_recomp = math_ops.cast(global_step, dtype) + global_step_recomp = math_ops.minimum(global_step_recomp, decay_steps) + completed_fraction = global_step_recomp / decay_steps + cosine_decayed = 0.5 * (1.0 + math_ops.cos( + constant_op.constant(math.pi) * completed_fraction)) + + decayed = (1 - alpha) * cosine_decayed + alpha + return math_ops.multiply(learning_rate, decayed) + + if not context.executing_eagerly(): + decayed_lr = decayed_lr() + + return decayed_lr @tf_export("train.cosine_decay_restarts") @@ -561,6 +647,12 @@ def cosine_decay_restarts(learning_rate, learning rate. Raises: ValueError: if `global_step` is not supplied. + + @compatibility(eager) + When eager execution is enabled, this function returns a function which in + turn returns the decayed learning rate Tensor. This can be useful for changing + the learning rate value across different invocations of optimizer functions. + @end_compatibility """ if global_step is None: raise ValueError("cosine decay restarts requires global_step") @@ -568,41 +660,48 @@ def cosine_decay_restarts(learning_rate, learning_rate = ops.convert_to_tensor( learning_rate, name="initial_learning_rate") dtype = learning_rate.dtype - global_step = math_ops.cast(global_step, dtype) first_decay_steps = math_ops.cast(first_decay_steps, dtype) alpha = math_ops.cast(alpha, dtype) t_mul = math_ops.cast(t_mul, dtype) m_mul = math_ops.cast(m_mul, dtype) - completed_fraction = global_step / first_decay_steps + def decayed_lr(): + """Helper to recompute learning rate; most helpful in eager-mode.""" + global_step_recomp = math_ops.cast(global_step, dtype) + completed_fraction = global_step_recomp / first_decay_steps - def compute_step(completed_fraction, geometric=False): - """Compute restart step and completed fraction.""" - if geometric: - i_restart = math_ops.floor( - math_ops.log(1.0 - completed_fraction * (1.0 - t_mul)) / - math_ops.log(t_mul)) + def compute_step(completed_fraction, geometric=False): + """Helper for `cond` operation.""" + if geometric: + i_restart = math_ops.floor( + math_ops.log(1.0 - completed_fraction * (1.0 - t_mul)) / + math_ops.log(t_mul)) - sum_r = (1.0 - t_mul**i_restart) / (1.0 - t_mul) - completed_fraction = (completed_fraction - sum_r) / t_mul**i_restart + sum_r = (1.0 - t_mul**i_restart) / (1.0 - t_mul) + completed_fraction = (completed_fraction - sum_r) / t_mul**i_restart - else: - i_restart = math_ops.floor(completed_fraction) - completed_fraction -= i_restart + else: + i_restart = math_ops.floor(completed_fraction) + completed_fraction -= i_restart + + return i_restart, completed_fraction - return i_restart, completed_fraction + i_restart, completed_fraction = control_flow_ops.cond( + math_ops.equal(t_mul, 1.0), + lambda: compute_step(completed_fraction, geometric=False), + lambda: compute_step(completed_fraction, geometric=True)) - i_restart, completed_fraction = control_flow_ops.cond( - math_ops.equal(t_mul, 1.0), - lambda: compute_step(completed_fraction, geometric=False), - lambda: compute_step(completed_fraction, geometric=True)) + m_fac = m_mul**i_restart + cosine_decayed = 0.5 * m_fac * (1.0 + math_ops.cos( + constant_op.constant(math.pi) * completed_fraction)) + decayed = (1 - alpha) * cosine_decayed + alpha - m_fac = m_mul**i_restart - cosine_decayed = 0.5 * m_fac * ( - 1.0 + math_ops.cos(constant_op.constant(math.pi) * completed_fraction)) - decayed = (1 - alpha) * cosine_decayed + alpha + return math_ops.multiply(learning_rate, decayed, name=name) - return math_ops.multiply(learning_rate, decayed, name=name) + if not context.executing_eagerly(): + decayed_lr = decayed_lr() + + return decayed_lr @tf_export("train.linear_cosine_decay") @@ -665,6 +764,12 @@ def linear_cosine_decay(learning_rate, learning rate. Raises: ValueError: if `global_step` is not supplied. + + @compatibility(eager) + When eager execution is enabled, this function returns a function which in + turn returns the decayed learning rate Tensor. This can be useful for changing + the learning rate value across different invocations of optimizer functions. + @end_compatibility """ if global_step is None: raise ValueError("linear cosine decay requires global_step") @@ -672,21 +777,28 @@ def linear_cosine_decay(learning_rate, [learning_rate, global_step]) as name: learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") dtype = learning_rate.dtype - global_step = math_ops.cast(global_step, dtype) decay_steps = math_ops.cast(decay_steps, dtype) num_periods = math_ops.cast(num_periods, dtype) - global_step = math_ops.minimum(global_step, decay_steps) alpha = math_ops.cast(alpha, dtype) beta = math_ops.cast(beta, dtype) - linear_decayed = (decay_steps - global_step) / decay_steps - completed_fraction = global_step / decay_steps - fraction = 2.0 * num_periods * completed_fraction - cosine_decayed = 0.5 * ( - 1.0 + math_ops.cos(constant_op.constant(math.pi) * fraction)) + def decayed_lr(): + """Helper to recompute learning rate; most helpful in eager-mode.""" + global_step_recomp = math_ops.cast(global_step, dtype) + global_step_recomp = math_ops.minimum(global_step_recomp, decay_steps) + linear_decayed = (decay_steps - global_step_recomp) / decay_steps + completed_fraction = global_step_recomp / decay_steps + fraction = 2.0 * num_periods * completed_fraction + cosine_decayed = 0.5 * ( + 1.0 + math_ops.cos(constant_op.constant(math.pi) * fraction)) + + linear_cosine_decayed = (alpha + linear_decayed) * cosine_decayed + beta + return math_ops.multiply(learning_rate, linear_cosine_decayed, name=name) - linear_cosine_decayed = (alpha + linear_decayed) * cosine_decayed + beta - return math_ops.multiply(learning_rate, linear_cosine_decayed, name=name) + if not context.executing_eagerly(): + decayed_lr = decayed_lr() + + return decayed_lr @tf_export("train.noisy_linear_cosine_decay") @@ -757,6 +869,12 @@ def noisy_linear_cosine_decay(learning_rate, learning rate. Raises: ValueError: if `global_step` is not supplied. + + @compatibility(eager) + When eager execution is enabled, this function returns a function which in + turn returns the decayed learning rate Tensor. This can be useful for changing + the learning rate value across different invocations of optimizer functions. + @end_compatibility """ if global_step is None: raise ValueError("noisy linear cosine decay requires global_step") @@ -764,29 +882,36 @@ def noisy_linear_cosine_decay(learning_rate, [learning_rate, global_step]) as name: learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") dtype = learning_rate.dtype - global_step = math_ops.cast(global_step, dtype) decay_steps = math_ops.cast(decay_steps, dtype) - global_step = math_ops.minimum(global_step, decay_steps) initial_variance = math_ops.cast(initial_variance, dtype) variance_decay = math_ops.cast(variance_decay, dtype) num_periods = math_ops.cast(num_periods, dtype) alpha = math_ops.cast(alpha, dtype) beta = math_ops.cast(beta, dtype) - linear_decayed = (decay_steps - global_step) / decay_steps - variance = initial_variance / ( - math_ops.pow(1.0 + global_step, variance_decay)) - std = math_ops.sqrt(variance) - noisy_linear_decayed = ( - linear_decayed + - random_ops.random_normal(linear_decayed.shape, stddev=std)) - - completed_fraction = global_step / decay_steps - fraction = 2.0 * num_periods * completed_fraction - cosine_decayed = 0.5 * ( - 1.0 + math_ops.cos(constant_op.constant(math.pi) * fraction)) - noisy_linear_cosine_decayed = ( - (alpha + noisy_linear_decayed) * cosine_decayed + beta) - - return math_ops.multiply( - learning_rate, noisy_linear_cosine_decayed, name=name) + def decayed_lr(): + """Helper to recompute learning rate; most helpful in eager-mode.""" + global_step_recomp = math_ops.cast(global_step, dtype) + global_step_recomp = math_ops.minimum(global_step_recomp, decay_steps) + linear_decayed = (decay_steps - global_step_recomp) / decay_steps + variance = initial_variance / ( + math_ops.pow(1.0 + global_step_recomp, variance_decay)) + std = math_ops.sqrt(variance) + noisy_linear_decayed = ( + linear_decayed + random_ops.random_normal( + linear_decayed.shape, stddev=std)) + + completed_fraction = global_step_recomp / decay_steps + fraction = 2.0 * num_periods * completed_fraction + cosine_decayed = 0.5 * ( + 1.0 + math_ops.cos(constant_op.constant(math.pi) * fraction)) + noisy_linear_cosine_decayed = ( + (alpha + noisy_linear_decayed) * cosine_decayed + beta) + + return math_ops.multiply( + learning_rate, noisy_linear_cosine_decayed, name=name) + + if not context.executing_eagerly(): + decayed_lr = decayed_lr() + + return decayed_lr diff --git a/tensorflow/python/training/learning_rate_decay_test.py b/tensorflow/python/training/learning_rate_decay_test.py index f56f4bb442..efcf47edda 100644 --- a/tensorflow/python/training/learning_rate_decay_test.py +++ b/tensorflow/python/training/learning_rate_decay_test.py @@ -21,12 +21,9 @@ from __future__ import print_function import math from tensorflow.python.eager import context -from tensorflow.python.framework import dtypes from tensorflow.python.framework import test_util -from tensorflow.python.ops import gen_state_ops # Import resource_variable_ops for the variables-to-tensor implicit conversion. from tensorflow.python.ops import resource_variable_ops # pylint: disable=unused-import -from tensorflow.python.ops import state_ops from tensorflow.python.ops import variables from tensorflow.python.platform import googletest from tensorflow.python.training import learning_rate_decay @@ -34,31 +31,35 @@ from tensorflow.python.training import learning_rate_decay class LRDecayTest(test_util.TensorFlowTestCase): + @test_util.run_in_graph_and_eager_modes() def testContinuous(self): - with self.test_session(): - step = 5 - decayed_lr = learning_rate_decay.exponential_decay(0.05, step, 10, 0.96) - expected = .05 * 0.96 ** (5.0 / 10.0) - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + self.evaluate(variables.global_variables_initializer()) + step = 5 + decayed_lr = learning_rate_decay.exponential_decay(0.05, step, 10, 0.96) + expected = .05 * 0.96**(5.0 / 10.0) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + @test_util.run_in_graph_and_eager_modes() def testStaircase(self): - with self.test_session(): - step = gen_state_ops.variable(shape=[], dtype=dtypes.int32, - name="step", container="", shared_name="") - assign_100 = state_ops.assign(step, 100) - assign_1 = state_ops.assign(step, 1) - assign_2 = state_ops.assign(step, 2) - decayed_lr = learning_rate_decay.exponential_decay(.1, step, 3, 0.96, - staircase=True) - # No change to learning rate - assign_1.op.run() - self.assertAllClose(decayed_lr.eval(), .1, 1e-6) - assign_2.op.run() - self.assertAllClose(decayed_lr.eval(), .1, 1e-6) + if context.executing_eagerly(): + step = resource_variable_ops.ResourceVariable(0) + self.evaluate(variables.global_variables_initializer()) + decayed_lr = learning_rate_decay.exponential_decay( + .1, step, 3, 0.96, staircase=True) + + # No change to learning rate due to staircase + expected = .1 + self.evaluate(step.assign(1)) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + + expected = .1 + self.evaluate(step.assign(2)) + self.assertAllClose(self.evaluate(decayed_lr), .1, 1e-6) + # Decayed learning rate - assign_100.op.run() expected = .1 * 0.96 ** (100 // 3) - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + self.evaluate(step.assign(100)) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) def testVariables(self): with self.test_session(): @@ -82,23 +83,22 @@ class LRDecayTest(test_util.TensorFlowTestCase): @test_util.run_in_graph_and_eager_modes() def testPiecewiseConstant(self): x = resource_variable_ops.ResourceVariable(-999) - def pc(): - return learning_rate_decay.piecewise_constant(x, [100, 110, 120], - [1.0, 0.1, 0.01, 0.001]) + decayed_lr = learning_rate_decay.piecewise_constant( + x, [100, 110, 120], [1.0, 0.1, 0.01, 0.001]) self.evaluate(variables.global_variables_initializer()) - self.assertAllClose(self.evaluate(pc()), 1.0, 1e-6) + self.assertAllClose(self.evaluate(decayed_lr), 1.0, 1e-6) self.evaluate(x.assign(100)) - self.assertAllClose(self.evaluate(pc()), 1.0, 1e-6) + self.assertAllClose(self.evaluate(decayed_lr), 1.0, 1e-6) self.evaluate(x.assign(105)) - self.assertAllClose(self.evaluate(pc()), 0.1, 1e-6) + self.assertAllClose(self.evaluate(decayed_lr), 0.1, 1e-6) self.evaluate(x.assign(110)) - self.assertAllClose(self.evaluate(pc()), 0.1, 1e-6) + self.assertAllClose(self.evaluate(decayed_lr), 0.1, 1e-6) self.evaluate(x.assign(120)) - self.assertAllClose(self.evaluate(pc()), 0.01, 1e-6) + self.assertAllClose(self.evaluate(decayed_lr), 0.01, 1e-6) self.evaluate(x.assign(999)) - self.assertAllClose(self.evaluate(pc()), 0.001, 1e-6) + self.assertAllClose(self.evaluate(decayed_lr), 0.001, 1e-6) @test_util.run_in_graph_and_eager_modes() def testPiecewiseConstantEdgeCases(self): @@ -106,11 +106,18 @@ class LRDecayTest(test_util.TensorFlowTestCase): 0, dtype=variables.dtypes.int32) boundaries, values = [-1.0, 1.0], [1, 2, 3] with self.assertRaises(ValueError): - learning_rate_decay.piecewise_constant(x_int, boundaries, values) + decayed_lr = learning_rate_decay.piecewise_constant( + x_int, boundaries, values) + if context.executing_eagerly(): + decayed_lr() + x = resource_variable_ops.ResourceVariable(0.0) boundaries, values = [-1.0, 1.0], [1.0, 2, 3] with self.assertRaises(ValueError): - learning_rate_decay.piecewise_constant(x, boundaries, values) + decayed_lr = learning_rate_decay.piecewise_constant( + x, boundaries, values) + if context.executing_eagerly(): + decayed_lr() # Test that ref types are valid. if not context.executing_eagerly(): @@ -123,221 +130,205 @@ class LRDecayTest(test_util.TensorFlowTestCase): x_int64 = resource_variable_ops.ResourceVariable( 0, dtype=variables.dtypes.int64) boundaries, values = [1, 2, 3], [0.4, 0.5, 0.6, 0.7] - def pc(): - return learning_rate_decay.piecewise_constant(x_int64, boundaries, values) + decayed_lr = learning_rate_decay.piecewise_constant( + x_int64, boundaries, values) self.evaluate(variables.global_variables_initializer()) - self.assertAllClose(self.evaluate(pc()), 0.4, 1e-6) + self.assertAllClose(self.evaluate(decayed_lr), 0.4, 1e-6) self.evaluate(x_int64.assign(1)) - self.assertAllClose(self.evaluate(pc()), 0.4, 1e-6) + self.assertAllClose(self.evaluate(decayed_lr), 0.4, 1e-6) self.evaluate(x_int64.assign(2)) - self.assertAllClose(self.evaluate(pc()), 0.5, 1e-6) + self.assertAllClose(self.evaluate(decayed_lr), 0.5, 1e-6) self.evaluate(x_int64.assign(3)) - self.assertAllClose(self.evaluate(pc()), 0.6, 1e-6) + self.assertAllClose(self.evaluate(decayed_lr), 0.6, 1e-6) self.evaluate(x_int64.assign(4)) - self.assertAllClose(self.evaluate(pc()), 0.7, 1e-6) + self.assertAllClose(self.evaluate(decayed_lr), 0.7, 1e-6) class LinearDecayTest(test_util.TensorFlowTestCase): + @test_util.run_in_graph_and_eager_modes() def testHalfWay(self): - with self.test_session(): - step = 5 - lr = 0.05 - end_lr = 0.0 - decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr) - expected = lr * 0.5 - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + step = 5 + lr = 0.05 + end_lr = 0.0 + decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr) + expected = lr * 0.5 + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + @test_util.run_in_graph_and_eager_modes() def testEnd(self): - with self.test_session(): - step = 10 - lr = 0.05 - end_lr = 0.001 - decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr) - expected = end_lr - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + step = 10 + lr = 0.05 + end_lr = 0.001 + decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr) + expected = end_lr + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + @test_util.run_in_graph_and_eager_modes() def testHalfWayWithEnd(self): - with self.test_session(): - step = 5 - lr = 0.05 - end_lr = 0.001 - decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr) - expected = (lr + end_lr) * 0.5 - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + step = 5 + lr = 0.05 + end_lr = 0.001 + decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr) + expected = (lr + end_lr) * 0.5 + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + @test_util.run_in_graph_and_eager_modes() def testBeyondEnd(self): - with self.test_session(): - step = 15 - lr = 0.05 - end_lr = 0.001 - decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr) - expected = end_lr - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + step = 15 + lr = 0.05 + end_lr = 0.001 + decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr) + expected = end_lr + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + @test_util.run_in_graph_and_eager_modes() def testBeyondEndWithCycle(self): - with self.test_session(): - step = 15 - lr = 0.05 - end_lr = 0.001 - decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr, - cycle=True) - expected = (lr - end_lr) * 0.25 + end_lr - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + step = 15 + lr = 0.05 + end_lr = 0.001 + decayed_lr = learning_rate_decay.polynomial_decay( + lr, step, 10, end_lr, cycle=True) + expected = (lr - end_lr) * 0.25 + end_lr + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) class SqrtDecayTest(test_util.TensorFlowTestCase): + @test_util.run_in_graph_and_eager_modes() def testHalfWay(self): - with self.test_session(): - step = 5 - lr = 0.05 - end_lr = 0.0 - power = 0.5 - decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr, - power=power) - expected = lr * 0.5 ** power - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + step = 5 + lr = 0.05 + end_lr = 0.0 + power = 0.5 + decayed_lr = learning_rate_decay.polynomial_decay( + lr, step, 10, end_lr, power=power) + expected = lr * 0.5**power + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + @test_util.run_in_graph_and_eager_modes() def testEnd(self): - with self.test_session(): - step = 10 - lr = 0.05 - end_lr = 0.001 - power = 0.5 - decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr, - power=power) - expected = end_lr - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + step = 10 + lr = 0.05 + end_lr = 0.001 + power = 0.5 + decayed_lr = learning_rate_decay.polynomial_decay( + lr, step, 10, end_lr, power=power) + expected = end_lr + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + @test_util.run_in_graph_and_eager_modes() def testHalfWayWithEnd(self): - with self.test_session(): - step = 5 - lr = 0.05 - end_lr = 0.001 - power = 0.5 - decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr, - power=power) - expected = (lr - end_lr) * 0.5 ** power + end_lr - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + step = 5 + lr = 0.05 + end_lr = 0.001 + power = 0.5 + decayed_lr = learning_rate_decay.polynomial_decay( + lr, step, 10, end_lr, power=power) + expected = (lr - end_lr) * 0.5**power + end_lr + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + @test_util.run_in_graph_and_eager_modes() def testBeyondEnd(self): - with self.test_session(): - step = 15 - lr = 0.05 - end_lr = 0.001 - power = 0.5 - decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr, - power=power) - expected = end_lr - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + step = 15 + lr = 0.05 + end_lr = 0.001 + power = 0.5 + decayed_lr = learning_rate_decay.polynomial_decay( + lr, step, 10, end_lr, power=power) + expected = end_lr + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + @test_util.run_in_graph_and_eager_modes() def testBeyondEndWithCycle(self): - with self.test_session(): - step = 15 - lr = 0.05 - end_lr = 0.001 - power = 0.5 - decayed_lr = learning_rate_decay.polynomial_decay(lr, step, 10, end_lr, - power=power, cycle=True) - expected = (lr - end_lr) * 0.25 ** power + end_lr - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + step = 15 + lr = 0.05 + end_lr = 0.001 + power = 0.5 + decayed_lr = learning_rate_decay.polynomial_decay( + lr, step, 10, end_lr, power=power, cycle=True) + expected = (lr - end_lr) * 0.25**power + end_lr + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) class PolynomialDecayTest(test_util.TensorFlowTestCase): + @test_util.run_in_graph_and_eager_modes() def testBeginWithCycle(self): - with self.test_session(): - lr = 0.001 - decay_steps = 10 - step = 0 - decayed_lr = learning_rate_decay.polynomial_decay(lr, step, - decay_steps, cycle=True) - expected = lr - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + lr = 0.001 + decay_steps = 10 + step = 0 + decayed_lr = learning_rate_decay.polynomial_decay( + lr, step, decay_steps, cycle=True) + expected = lr + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) class ExponentialDecayTest(test_util.TensorFlowTestCase): + @test_util.run_in_graph_and_eager_modes() def testDecay(self): initial_lr = 0.1 k = 10 decay_rate = 0.96 - step = gen_state_ops.variable( - shape=[], dtype=dtypes.int32, name="step", container="", shared_name="") - assign_step = state_ops.assign(step, 0) - increment_step = state_ops.assign_add(step, 1) - decayed_lr = learning_rate_decay.natural_exp_decay(initial_lr, step, - k, decay_rate) - with self.test_session(): - assign_step.op.run() - for i in range(k+1): - expected = initial_lr * math.exp(-i / k * decay_rate) - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) - increment_step.op.run() + step = resource_variable_ops.ResourceVariable(0) + decayed_lr = learning_rate_decay.natural_exp_decay(initial_lr, step, k, + decay_rate) + self.evaluate(variables.global_variables_initializer()) + for i in range(k + 1): + expected = initial_lr * math.exp(-i / k * decay_rate) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + self.evaluate(step.assign_add(1)) + + @test_util.run_in_graph_and_eager_modes() def testStaircase(self): initial_lr = 0.1 k = 10 decay_rate = 0.96 - step = gen_state_ops.variable( - shape=[], dtype=dtypes.int32, name="step", container="", shared_name="") - assign_step = state_ops.assign(step, 0) - increment_step = state_ops.assign_add(step, 1) - decayed_lr = learning_rate_decay.natural_exp_decay(initial_lr, - step, - k, - decay_rate, - staircase=True) - with self.test_session(): - assign_step.op.run() - for i in range(k+1): - expected = initial_lr * math.exp(-decay_rate * (i // k)) - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) - increment_step.op.run() + step = resource_variable_ops.ResourceVariable(0) + decayed_lr = learning_rate_decay.natural_exp_decay( + initial_lr, step, k, decay_rate, staircase=True) + + self.evaluate(variables.global_variables_initializer()) + for i in range(k + 1): + expected = initial_lr * math.exp(-decay_rate * (i // k)) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + self.evaluate(step.assign_add(1)) class InverseDecayTest(test_util.TensorFlowTestCase): + @test_util.run_in_graph_and_eager_modes() def testDecay(self): initial_lr = 0.1 k = 10 decay_rate = 0.96 - step = gen_state_ops.variable( - shape=[], dtype=dtypes.int32, name="step", container="", shared_name="") - assign_step = state_ops.assign(step, 0) - increment_step = state_ops.assign_add(step, 1) - decayed_lr = learning_rate_decay.inverse_time_decay(initial_lr, - step, - k, + step = resource_variable_ops.ResourceVariable(0) + decayed_lr = learning_rate_decay.inverse_time_decay(initial_lr, step, k, decay_rate) - with self.test_session(): - assign_step.op.run() - for i in range(k+1): - expected = initial_lr / (1 + i / k * decay_rate) - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) - increment_step.op.run() + self.evaluate(variables.global_variables_initializer()) + for i in range(k + 1): + expected = initial_lr / (1 + i / k * decay_rate) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + self.evaluate(step.assign_add(1)) + + @test_util.run_in_graph_and_eager_modes() def testStaircase(self): initial_lr = 0.1 k = 10 decay_rate = 0.96 - step = gen_state_ops.variable( - shape=[], dtype=dtypes.int32, name="step", container="", shared_name="") - assign_step = state_ops.assign(step, 0) - increment_step = state_ops.assign_add(step, 1) - decayed_lr = learning_rate_decay.inverse_time_decay(initial_lr, - step, - k, - decay_rate, - staircase=True) - with self.test_session(): - assign_step.op.run() - for i in range(k+1): - expected = initial_lr / (1 + decay_rate * (i // k)) - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) - increment_step.op.run() + step = resource_variable_ops.ResourceVariable(0) + decayed_lr = learning_rate_decay.inverse_time_decay( + initial_lr, step, k, decay_rate, staircase=True) + + self.evaluate(variables.global_variables_initializer()) + for i in range(k + 1): + expected = initial_lr / (1 + decay_rate * (i // k)) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + self.evaluate(step.assign_add(1)) class CosineDecayTest(test_util.TensorFlowTestCase): @@ -348,26 +339,26 @@ class CosineDecayTest(test_util.TensorFlowTestCase): decay = 0.5 * (1.0 + math.cos(math.pi * completed_fraction)) return (1.0 - alpha) * decay + alpha + @test_util.run_in_graph_and_eager_modes() def testDecay(self): num_training_steps = 1000 initial_lr = 1.0 for step in range(0, 1500, 250): - with self.test_session(): - decayed_lr = learning_rate_decay.cosine_decay( - initial_lr, step, num_training_steps) - expected = self.np_cosine_decay(step, num_training_steps) - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + decayed_lr = learning_rate_decay.cosine_decay(initial_lr, step, + num_training_steps) + expected = self.np_cosine_decay(step, num_training_steps) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + @test_util.run_in_graph_and_eager_modes() def testAlpha(self): num_training_steps = 1000 initial_lr = 1.0 alpha = 0.1 for step in range(0, 1500, 250): - with self.test_session(): - decayed_lr = learning_rate_decay.cosine_decay( - initial_lr, step, num_training_steps, alpha) - expected = self.np_cosine_decay(step, num_training_steps, alpha) - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + decayed_lr = learning_rate_decay.cosine_decay(initial_lr, step, + num_training_steps, alpha) + expected = self.np_cosine_decay(step, num_training_steps, alpha) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) class CosineDecayRestartsTest(test_util.TensorFlowTestCase): @@ -384,51 +375,51 @@ class CosineDecayRestartsTest(test_util.TensorFlowTestCase): decay = fac * 0.5 * (1.0 + math.cos(math.pi * completed_fraction)) return (1.0 - alpha) * decay + alpha + @test_util.run_in_graph_and_eager_modes() def testDecay(self): num_training_steps = 1000 initial_lr = 1.0 for step in range(0, 1500, 250): - with self.test_session(): - decayed_lr = learning_rate_decay.cosine_decay_restarts( - initial_lr, step, num_training_steps) - expected = self.np_cosine_decay_restarts(step, num_training_steps) - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + decayed_lr = learning_rate_decay.cosine_decay_restarts( + initial_lr, step, num_training_steps) + expected = self.np_cosine_decay_restarts(step, num_training_steps) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + @test_util.run_in_graph_and_eager_modes() def testAlpha(self): num_training_steps = 1000 initial_lr = 1.0 alpha = 0.1 for step in range(0, 1500, 250): - with self.test_session(): - decayed_lr = learning_rate_decay.cosine_decay_restarts( - initial_lr, step, num_training_steps, alpha=alpha) - expected = self.np_cosine_decay_restarts(step, num_training_steps, - alpha=alpha) - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + decayed_lr = learning_rate_decay.cosine_decay_restarts( + initial_lr, step, num_training_steps, alpha=alpha) + expected = self.np_cosine_decay_restarts( + step, num_training_steps, alpha=alpha) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + @test_util.run_in_graph_and_eager_modes() def testMMul(self): num_training_steps = 1000 initial_lr = 1.0 m_mul = 0.9 for step in range(0, 1500, 250): - with self.test_session(): - decayed_lr = learning_rate_decay.cosine_decay_restarts( - initial_lr, step, num_training_steps, m_mul=m_mul) - expected = self.np_cosine_decay_restarts(step, num_training_steps, - m_mul=m_mul) - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + decayed_lr = learning_rate_decay.cosine_decay_restarts( + initial_lr, step, num_training_steps, m_mul=m_mul) + expected = self.np_cosine_decay_restarts( + step, num_training_steps, m_mul=m_mul) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + @test_util.run_in_graph_and_eager_modes() def testTMul(self): num_training_steps = 1000 initial_lr = 1.0 t_mul = 1.0 for step in range(0, 1500, 250): - with self.test_session(): - decayed_lr = learning_rate_decay.cosine_decay_restarts( - initial_lr, step, num_training_steps, t_mul=t_mul) - expected = self.np_cosine_decay_restarts(step, num_training_steps, - t_mul=t_mul) - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + decayed_lr = learning_rate_decay.cosine_decay_restarts( + initial_lr, step, num_training_steps, t_mul=t_mul) + expected = self.np_cosine_decay_restarts( + step, num_training_steps, t_mul=t_mul) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) class LinearCosineDecayTest(test_util.TensorFlowTestCase): @@ -445,65 +436,63 @@ class LinearCosineDecayTest(test_util.TensorFlowTestCase): cosine_decayed = 0.5 * (1.0 + math.cos(math.pi * fraction)) return (alpha + linear_decayed) * cosine_decayed + beta + @test_util.run_in_graph_and_eager_modes() def testDefaultDecay(self): num_training_steps = 1000 initial_lr = 1.0 for step in range(0, 1500, 250): - with self.test_session(): - decayed_lr = learning_rate_decay.linear_cosine_decay( - initial_lr, step, num_training_steps) - expected = self.np_linear_cosine_decay(step, num_training_steps) - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + decayed_lr = learning_rate_decay.linear_cosine_decay( + initial_lr, step, num_training_steps) + expected = self.np_linear_cosine_decay(step, num_training_steps) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) + @test_util.run_in_graph_and_eager_modes() def testNonDefaultDecay(self): num_training_steps = 1000 initial_lr = 1.0 for step in range(0, 1500, 250): - with self.test_session(): - decayed_lr = learning_rate_decay.linear_cosine_decay( - initial_lr, - step, - num_training_steps, - alpha=0.1, - beta=1e-4, - num_periods=5) - expected = self.np_linear_cosine_decay( - step, - num_training_steps, - alpha=0.1, - beta=1e-4, - num_periods=5) - self.assertAllClose(decayed_lr.eval(), expected, 1e-6) + decayed_lr = learning_rate_decay.linear_cosine_decay( + initial_lr, + step, + num_training_steps, + alpha=0.1, + beta=1e-4, + num_periods=5) + expected = self.np_linear_cosine_decay( + step, num_training_steps, alpha=0.1, beta=1e-4, num_periods=5) + self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) class NoisyLinearCosineDecayTest(test_util.TensorFlowTestCase): + @test_util.run_in_graph_and_eager_modes() def testDefaultNoisyLinearCosine(self): num_training_steps = 1000 initial_lr = 1.0 for step in range(0, 1500, 250): - with self.test_session(): - # No numerical check because of noise - decayed_lr = learning_rate_decay.noisy_linear_cosine_decay( - initial_lr, step, num_training_steps) - decayed_lr.eval() + # No numerical check because of noise + decayed_lr = learning_rate_decay.noisy_linear_cosine_decay( + initial_lr, step, num_training_steps) + # Cannot be deterministically tested + self.evaluate(decayed_lr) + @test_util.run_in_graph_and_eager_modes() def testNonDefaultNoisyLinearCosine(self): num_training_steps = 1000 initial_lr = 1.0 for step in range(0, 1500, 250): - with self.test_session(): - # No numerical check because of noise - decayed_lr = learning_rate_decay.noisy_linear_cosine_decay( - initial_lr, - step, - num_training_steps, - initial_variance=0.5, - variance_decay=0.1, - alpha=0.1, - beta=1e-4, - num_periods=5) - decayed_lr.eval() + # No numerical check because of noise + decayed_lr = learning_rate_decay.noisy_linear_cosine_decay( + initial_lr, + step, + num_training_steps, + initial_variance=0.5, + variance_decay=0.1, + alpha=0.1, + beta=1e-4, + num_periods=5) + # Cannot be deterministically tested + self.evaluate(decayed_lr) if __name__ == "__main__": -- GitLab From 92a55c7abd5a99771315724f162fea711ee3d9fb Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 19 Jun 2018 14:02:10 -0700 Subject: [PATCH 688/816] Refactor the impl of Shard() so that the caller can use a Runner. PiperOrigin-RevId: 201236564 --- tensorflow/core/util/work_sharder.cc | 9 ++++++++- tensorflow/core/util/work_sharder.h | 14 ++++++++++++++ 2 files changed, 22 insertions(+), 1 deletion(-) diff --git a/tensorflow/core/util/work_sharder.cc b/tensorflow/core/util/work_sharder.cc index b443bcfa79..f4bd2950e9 100644 --- a/tensorflow/core/util/work_sharder.cc +++ b/tensorflow/core/util/work_sharder.cc @@ -45,6 +45,13 @@ void Shard(int max_parallelism, thread::ThreadPool* workers, int64 total, workers->ParallelFor(total, cost_per_unit, work); return; } + Sharder::Do(total, cost_per_unit, work, + [&workers](Sharder::Closure c) { workers->Schedule(c); }, + max_parallelism); +} + +void Sharder::Do(int64 total, int64 cost_per_unit, const Work& work, + const Runner& runner, int max_parallelism) { cost_per_unit = std::max(int64{1}, cost_per_unit); // We shard [0, total) into "num_shards" shards. // 1 <= num_shards <= num worker threads @@ -73,7 +80,7 @@ void Shard(int max_parallelism, thread::ThreadPool* workers, int64 total, BlockingCounter counter(num_shards_used - 1); for (int64 start = block_size; start < total; start += block_size) { auto limit = std::min(start + block_size, total); - workers->Schedule([&work, &counter, start, limit]() { + runner([&work, &counter, start, limit]() { work(start, limit); // Compute the shard. counter.DecrementCount(); // The shard is done. }); diff --git a/tensorflow/core/util/work_sharder.h b/tensorflow/core/util/work_sharder.h index cb3708fec8..72ce493c1b 100644 --- a/tensorflow/core/util/work_sharder.h +++ b/tensorflow/core/util/work_sharder.h @@ -79,6 +79,20 @@ class ScopedPerThreadMaxParallelism { int previous_ = -1; }; +// Implementation details for Shard(). +class Sharder { + public: + typedef std::function Closure; + typedef std::function Runner; + typedef std::function Work; + + // Refers to Shard()'s comment for the meaning of total, + // cost_per_unit, work, max_parallelism. runner is an interface to + // schedule a closure. Shard() uses thread::ThreadPool instead. + static void Do(int64 total, int64 cost_per_unit, const Work& work, + const Runner& runner, int max_parallelism); +}; + } // end namespace tensorflow #endif // TENSORFLOW_UTIL_WORK_SHARDER_H_ -- GitLab From 445f16740007f209f426149fcf9b3c6ef4344532 Mon Sep 17 00:00:00 2001 From: Priya Gupta Date: Tue, 19 Jun 2018 14:13:08 -0700 Subject: [PATCH 689/816] Create hyper parameter tensors in optimizer v2 outside any control flow contexts. Also, use lambdas for creating the non slot variables in adam v2. These changes are needed to allow optimizer.minimize to run inside a while loop, which will be done in distribution strategies shortly. PiperOrigin-RevId: 201238566 --- tensorflow/contrib/optimizer_v2/adam.py | 4 ++-- tensorflow/contrib/optimizer_v2/optimizer_v2.py | 5 +++-- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/tensorflow/contrib/optimizer_v2/adam.py b/tensorflow/contrib/optimizer_v2/adam.py index d538ad0fb0..631d4f44df 100644 --- a/tensorflow/contrib/optimizer_v2/adam.py +++ b/tensorflow/contrib/optimizer_v2/adam.py @@ -103,9 +103,9 @@ class AdamOptimizer(optimizer_v2.OptimizerV2): def _create_vars(self, var_list, state): # Non-slot variables end up on the same device(s). - state.create_non_slot(initial_value=state.get_hyper("beta1"), + state.create_non_slot(initial_value=lambda: state.get_hyper("beta1"), name="beta1_power") - state.create_non_slot(initial_value=state.get_hyper("beta2"), + state.create_non_slot(initial_value=lambda: state.get_hyper("beta2"), name="beta2_power") # Create slots for the first and second moments. diff --git a/tensorflow/contrib/optimizer_v2/optimizer_v2.py b/tensorflow/contrib/optimizer_v2/optimizer_v2.py index f537318b32..a44f29fa37 100644 --- a/tensorflow/contrib/optimizer_v2/optimizer_v2.py +++ b/tensorflow/contrib/optimizer_v2/optimizer_v2.py @@ -211,8 +211,9 @@ class _OptimizerV2State(object): # This dict starts with a single item with key "None" with the hyper # parameter value converted to a Tensor. Other items have dtype keys # with that Tensor cast to that dtype. - self._hyper = {name: {None: ops.convert_to_tensor(value, name=name)} - for name, (dynamic, value) in hyper.items() if not dynamic} + with ops.init_scope(): + self._hyper = {name: {None: ops.convert_to_tensor(value, name=name)} + for name, (dynamic, value) in hyper.items() if not dynamic} self._slots = {} self._non_slot_dict = {} # Extra state to help Optimizers implement Checkpointable. Holds information -- GitLab From 27c27c58e1f8b4ac86f85eb201f0d9d667fa83a1 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 19 Jun 2018 14:17:54 -0700 Subject: [PATCH 690/816] Improve filter for cuBLAS bug. PiperOrigin-RevId: 201239428 --- tensorflow/stream_executor/cuda/cuda_blas.cc | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/stream_executor/cuda/cuda_blas.cc b/tensorflow/stream_executor/cuda/cuda_blas.cc index 31e407f199..874bf0e8cb 100644 --- a/tensorflow/stream_executor/cuda/cuda_blas.cc +++ b/tensorflow/stream_executor/cuda/cuda_blas.cc @@ -2183,8 +2183,8 @@ bool CUDABlas::DoBlasGemmWithAlgorithmImpl( // Return false if we might be hitting a cuBLAS bug that produces the wrong // result. See nvbugs/2156201, b/79126339. -#if (CUDA_VERSION >= 9000) - if (CUDA_VERSION < 9020 && algorithm != CUBLAS_GEMM_ALGO12 && +#if CUDA_VERSION >= 9000 && CUDA_VERSION < 9020 + if ((algorithm == CUBLAS_GEMM_DEFAULT || algorithm >= CUBLAS_GEMM_ALGO13) && std::max({m, n, k}) >= 2097153 && cc_major < 7) { return false; } -- GitLab From 48832eff2833c34294a46d49af5a78c9318ca528 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 19 Jun 2018 14:27:43 -0700 Subject: [PATCH 691/816] Automated g4 rollback of changelist 201194552 PiperOrigin-RevId: 201241214 --- .../contrib/lite/kernels/activations.cc | 24 +- .../internal/logsoftmax_quantized_test.cc | 64 ++- .../internal/optimized/legacy_optimized_ops.h | 282 +------------ .../internal/optimized/optimized_ops.h | 390 +++++++++++------- .../internal/reference/legacy_reference_ops.h | 290 +------------ .../internal/reference/reference_ops.h | 354 ++++++++++------ .../internal/softmax_quantized_test.cc | 62 ++- .../contrib/lite/kernels/internal/types.h | 48 +-- .../contrib/lite/kernels/log_softmax_test.cc | 7 +- tensorflow/contrib/lite/kernels/pooling.cc | 57 ++- .../contrib/lite/kernels/softmax_test.cc | 14 +- 11 files changed, 591 insertions(+), 1001 deletions(-) diff --git a/tensorflow/contrib/lite/kernels/activations.cc b/tensorflow/contrib/lite/kernels/activations.cc index d03fa42c92..add36b46c0 100644 --- a/tensorflow/contrib/lite/kernels/activations.cc +++ b/tensorflow/contrib/lite/kernels/activations.cc @@ -251,11 +251,11 @@ TfLiteStatus TanhEval(TfLiteContext* context, TfLiteNode* node) { return kTfLiteOk; } break; case kTfLiteUInt8: { - optimized_ops::Tanh(GetTensorData(input), GetTensorShape(input), + optimized_ops::Tanh(GetTensorData(input), GetTensorDims(input), input->params.zero_point, data->input_range_radius, data->input_multiplier, data->input_left_shift, GetTensorData(output), - GetTensorShape(output)); + GetTensorDims(output)); return kTfLiteOk; } break; default: @@ -282,10 +282,10 @@ TfLiteStatus SigmoidEval(TfLiteContext* context, TfLiteNode* node) { } case kTfLiteUInt8: { optimized_ops::Logistic( - GetTensorData(input), GetTensorShape(input), + GetTensorData(input), GetTensorDims(input), input->params.zero_point, data->input_range_radius, data->input_multiplier, data->input_left_shift, - GetTensorData(output), GetTensorShape(output)); + GetTensorData(output), GetTensorDims(output)); break; } default: @@ -341,26 +341,26 @@ void Softmax2DQuantized(const TfLiteTensor* input, TfLiteTensor* output, const int batch_size = input->dims->data[0]; const int input_size = input->dims->data[1]; optimized_ops::Softmax(GetTensorData(input), - GetTensorShape({batch_size, 1, 1, input_size}), + GetTensorDims({batch_size, 1, 1, input_size}), data->input_multiplier, data->input_left_shift, data->diff_min, GetTensorData(output), - GetTensorShape({batch_size, 1, 1, input_size})); + GetTensorDims({batch_size, 1, 1, input_size})); } // Takes a 4D tensor and perform softmax along the forth dimension. void Softmax4DFloat(const TfLiteTensor* input, TfLiteTensor* output, TfLiteSoftmaxParams* params) { - optimized_ops::Softmax(GetTensorData(input), GetTensorShape(input), + optimized_ops::Softmax(GetTensorData(input), GetTensorDims(input), params->beta, GetTensorData(output), - GetTensorShape(output)); + GetTensorDims(output)); } void Softmax4DQuantized(const TfLiteTensor* input, TfLiteTensor* output, TfLiteSoftmaxParams* params, OpData* data) { - optimized_ops::Softmax(GetTensorData(input), GetTensorShape(input), + optimized_ops::Softmax(GetTensorData(input), GetTensorDims(input), data->input_multiplier, data->input_left_shift, data->diff_min, GetTensorData(output), - GetTensorShape(output)); + GetTensorDims(output)); } TfLiteStatus SoftmaxEval(TfLiteContext* context, TfLiteNode* node) { @@ -415,8 +415,8 @@ TfLiteStatus LogSoftmaxEval(TfLiteContext* context, TfLiteNode* node) { switch (input->type) { case kTfLiteFloat32: optimized_ops::LogSoftmax( - GetTensorData(input), GetTensorShape(input), - GetTensorData(output), GetTensorShape(output)); + GetTensorData(input), GetTensorDims(input), + GetTensorData(output), GetTensorDims(output)); return kTfLiteOk; default: context->ReportError(context, "Only float32 supported currently., got %d", diff --git a/tensorflow/contrib/lite/kernels/internal/logsoftmax_quantized_test.cc b/tensorflow/contrib/lite/kernels/internal/logsoftmax_quantized_test.cc index d2f1103e14..e786f785ab 100644 --- a/tensorflow/contrib/lite/kernels/internal/logsoftmax_quantized_test.cc +++ b/tensorflow/contrib/lite/kernels/internal/logsoftmax_quantized_test.cc @@ -32,21 +32,19 @@ namespace tflite { namespace { void RunLogSoftmaxFloatReference(const uint8* input_data, - const RuntimeShape& shape_common, - int32 input_offset, const double input_scale, - int stride, float beta, - uint8* reference_output_data) { - const int ref_buffer_size = shape_common.FlatSize(); + const Dims<4>& dims_common, int32 input_offset, + const double input_scale, int stride, + float beta, uint8* reference_output_data) { + const int ref_buffer_size = RequiredBufferSizeForDims(dims_common); std::vector reference_dequant_data(ref_buffer_size); std::vector reference_output_float_data(ref_buffer_size); // Reference data generated via Dequant of input into float, and then applying // float LogSoftmax. - reference_ops::Dequantize( - input_data, ToRuntimeDims(shape_common), input_offset, input_scale, - reference_dequant_data.data(), ToRuntimeDims(shape_common)); - optimized_ops::LogSoftmax(reference_dequant_data.data(), shape_common, - reference_output_float_data.data(), shape_common); + reference_ops::Dequantize(input_data, dims_common, input_offset, input_scale, + reference_dequant_data.data(), dims_common); + optimized_ops::LogSoftmax(reference_dequant_data.data(), dims_common, + reference_output_float_data.data(), dims_common); // Work with quantized scaling for LogSoftmax, under which 255 represents 0, // and -16 gets nudged up to 0. for (int i = 0; i < ref_buffer_size; i++) { @@ -57,9 +55,9 @@ void RunLogSoftmaxFloatReference(const uint8* input_data, } void CheckOutputData(const uint8* test_output, const uint8* reference_output, - const RuntimeShape& shape_common, - const string& check_label, bool be_exacting) { - const int buffer_size = shape_common.FlatSize(); + const Dims<4>& dims_common, const string& check_label, + bool be_exacting) { + const int buffer_size = RequiredBufferSizeForDims(dims_common); // While calculating some metrics in floating point, we work with quantized // scaling. std::vector diff(buffer_size); @@ -101,15 +99,15 @@ void CheckOutputData(const uint8* test_output, const uint8* reference_output, // Runs the LogSoftmax and compares against the float reference implementation // and the quantized reference implementation. -void RunOneLogSoftmaxTest(const uint8* input_data, - const RuntimeShape& shape_common, int32 input_offset, - const double input_scale, int stride, float beta) { - const int buffer_size = shape_common.FlatSize(); +void RunOneLogSoftmaxTest(const uint8* input_data, const Dims<4>& dims_common, + int32 input_offset, const double input_scale, + int stride, float beta) { + const int buffer_size = RequiredBufferSizeForDims(dims_common); std::vector optimized_logsoftmax_output(buffer_size); std::vector reference_float_logsoftmax_output(buffer_size); std::vector reference_quant_logsoftmax_output(buffer_size); - RunLogSoftmaxFloatReference(input_data, shape_common, input_offset, + RunLogSoftmaxFloatReference(input_data, dims_common, input_offset, input_scale, stride, beta, reference_float_logsoftmax_output.data()); @@ -128,23 +126,23 @@ void RunOneLogSoftmaxTest(const uint8* input_data, const int diff_min = -tflite::CalculateInputRadius(kScaledDiffIntegerBits, input_beta_left_shift); - optimized_ops::LogSoftmax(input_data, shape_common, input_beta_multiplier, + optimized_ops::LogSoftmax(input_data, dims_common, input_beta_multiplier, input_beta_left_shift, reverse_scaling_divisor, reverse_scaling_right_shift, diff_min, - optimized_logsoftmax_output.data(), shape_common); + optimized_logsoftmax_output.data(), dims_common); reference_ops::LogSoftmax( - input_data, shape_common, input_beta_multiplier, input_beta_left_shift, + input_data, dims_common, input_beta_multiplier, input_beta_left_shift, reverse_scaling_divisor, reverse_scaling_right_shift, diff_min, - reference_quant_logsoftmax_output.data(), shape_common); + reference_quant_logsoftmax_output.data(), dims_common); CheckOutputData(optimized_logsoftmax_output.data(), - reference_float_logsoftmax_output.data(), shape_common, + reference_float_logsoftmax_output.data(), dims_common, "Optimized vs float reference", false); CheckOutputData(optimized_logsoftmax_output.data(), - reference_quant_logsoftmax_output.data(), shape_common, + reference_quant_logsoftmax_output.data(), dims_common, "Optimized vs quant reference", true); CheckOutputData(reference_quant_logsoftmax_output.data(), - reference_float_logsoftmax_output.data(), shape_common, + reference_float_logsoftmax_output.data(), dims_common, "Quant reference vs float reference", false); } @@ -167,13 +165,13 @@ bool TryOneUniformLogSoftmax() { const int32 input_offset = UniformRandomInt(-256, 0); static constexpr float beta = 1.0f; - auto shape_common = - RuntimeShape({batch, input_height, input_width, input_depth}); - const int buffer_size = shape_common.FlatSize(); + Dims<4> dims_common = + MakeDimsForInference(input_depth, input_width, input_height, batch); + const int buffer_size = RequiredBufferSizeForDims(dims_common); std::vector input_data(buffer_size); FillRandom(&input_data); - RunOneLogSoftmaxTest(input_data.data(), shape_common, input_offset, + RunOneLogSoftmaxTest(input_data.data(), dims_common, input_offset, input_scale, stride, beta); return true; } @@ -205,14 +203,14 @@ bool TryOneSkyscraperLogSoftmax(bool small_depth) { const int middle_min = UniformRandomInt(0, 255); const int sides_max = UniformRandomInt(0, middle_min); - auto shape_common = - RuntimeShape({batch, input_height, input_width, input_depth}); - const int buffer_size = shape_common.FlatSize(); + Dims<4> dims_common = + MakeDimsForInference(input_depth, input_width, input_height, batch); + const int buffer_size = RequiredBufferSizeForDims(dims_common); std::vector input_data(buffer_size); FillRandomSkyscraper(&input_data, input_depth, middle_proportion, middle_min, sides_max); - RunOneLogSoftmaxTest(input_data.data(), shape_common, input_offset, + RunOneLogSoftmaxTest(input_data.data(), dims_common, input_offset, input_scale, stride, beta); return true; } diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/legacy_optimized_ops.h b/tensorflow/contrib/lite/kernels/internal/optimized/legacy_optimized_ops.h index 7816752132..c0dda4acf1 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/legacy_optimized_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/legacy_optimized_ops.h @@ -26,10 +26,6 @@ limitations under the License. namespace tflite { namespace optimized_ops { -// Unoptimized reference ops: -using reference_ops::Relu1; -using reference_ops::Relu6; - inline RuntimeShape DimsToShape(const tflite::Dims<4>& dims) { return RuntimeShape( {dims.sizes[3], dims.sizes[2], dims.sizes[1], dims.sizes[0]}); @@ -38,285 +34,15 @@ inline RuntimeShape DimsToShape(const tflite::Dims<4>& dims) { template void L2Normalization(const float* input_data, const Dims<4>& input_dims, float* output_data, const Dims<4>& output_dims) { - L2Normalization(input_data, DimsToShape(input_dims), output_data, - DimsToShape(output_dims)); + return L2Normalization(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); } inline void L2Normalization(const uint8* input_data, const Dims<4>& input_dims, int32 input_zero_point, uint8* output_data, const Dims<4>& output_dims) { - L2Normalization(input_data, DimsToShape(input_dims), input_zero_point, - output_data, DimsToShape(output_dims)); -} - -inline void Relu(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { - Relu(input_data, DimsToShape(input_dims), output_data, - DimsToShape(output_dims)); -} - -inline void AveragePool(const float* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, - int pad_height, int kwidth, int kheight, - float output_activation_min, - float output_activation_max, float* output_data, - const Dims<4>& output_dims) { - AveragePool(input_data, DimsToShape(input_dims), stride_width, stride_height, - pad_width, pad_height, kwidth, kheight, output_activation_min, - output_activation_max, output_data, DimsToShape(output_dims)); -} - -// legacy, for compatibility with old checked-in code -template -void AveragePool(const float* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, - int pad_height, int kwidth, int kheight, float* output_data, - const Dims<4>& output_dims) { - float output_activation_min, output_activation_max; - GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); - - AveragePool(input_data, input_dims, stride_width, stride_height, pad_width, - pad_height, kwidth, kheight, output_activation_min, - output_activation_max, output_data, output_dims); -} - -// legacy, for compatibility with old checked-in code -template -void AveragePool(const float* input_data, const Dims<4>& input_dims, int stride, - int pad_width, int pad_height, int filter_width, - int filter_height, float* output_data, - const Dims<4>& output_dims) { - AveragePool(input_data, input_dims, stride, stride, pad_width, pad_height, - filter_width, filter_height, output_data, output_dims); -} - -inline void AveragePool(const uint8* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, - int pad_height, int filter_width, int filter_height, - int32 output_activation_min, - int32 output_activation_max, uint8* output_data, - const Dims<4>& output_dims) { - AveragePool(input_data, DimsToShape(input_dims), stride_width, stride_height, - pad_width, pad_height, filter_width, filter_height, - output_activation_min, output_activation_max, output_data, - DimsToShape(output_dims)); -} - -// legacy, for compatibility with old checked-in code -template -void AveragePool(const uint8* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, - int pad_height, int filter_width, int filter_height, - int32 output_activation_min, int32 output_activation_max, - uint8* output_data, const Dims<4>& output_dims) { - static_assert(Ac == FusedActivationFunctionType::kNone || - Ac == FusedActivationFunctionType::kRelu || - Ac == FusedActivationFunctionType::kRelu6 || - Ac == FusedActivationFunctionType::kRelu1, - ""); - if (Ac == FusedActivationFunctionType::kNone) { - TFLITE_DCHECK_EQ(output_activation_min, 0); - TFLITE_DCHECK_EQ(output_activation_max, 255); - } - AveragePool(input_data, input_dims, stride_width, stride_height, pad_width, - pad_height, filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - -// legacy, for compatibility with old checked-in code -template -void AveragePool(const uint8* input_data, const Dims<4>& input_dims, int stride, - int pad_width, int pad_height, int filter_width, - int filter_height, int32 output_activation_min, - int32 output_activation_max, uint8* output_data, - const Dims<4>& output_dims) { - AveragePool(input_data, input_dims, stride, stride, pad_width, pad_height, - filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - -inline void MaxPool(const float* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, - int pad_height, int kwidth, int kheight, - float output_activation_min, float output_activation_max, - float* output_data, const Dims<4>& output_dims) { - MaxPool(input_data, DimsToShape(input_dims), stride_width, stride_height, - pad_width, pad_height, kwidth, kheight, output_activation_min, - output_activation_max, output_data, DimsToShape(output_dims)); -} - -// legacy, for compatibility with old checked-in code -template -void MaxPool(const float* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, int pad_height, - int kwidth, int kheight, float* output_data, - const Dims<4>& output_dims) { - float output_activation_min, output_activation_max; - GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); - MaxPool(input_data, input_dims, stride_width, stride_height, pad_width, - pad_height, kwidth, kheight, output_activation_min, - output_activation_max, output_data, output_dims); -} - -// legacy, for compatibility with old checked-in code -template -void MaxPool(const float* input_data, const Dims<4>& input_dims, int stride, - int pad_width, int pad_height, int filter_width, int filter_height, - float* output_data, const Dims<4>& output_dims) { - MaxPool(input_data, input_dims, stride, stride, pad_width, pad_height, - filter_width, filter_height, output_data, output_dims); -} - -inline void MaxPool(const uint8* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, - int pad_height, int filter_width, int filter_height, - int32 output_activation_min, int32 output_activation_max, - uint8* output_data, const Dims<4>& output_dims) { - MaxPool(input_data, DimsToShape(input_dims), stride_width, stride_height, - pad_width, pad_height, filter_width, filter_height, - output_activation_min, output_activation_max, output_data, - DimsToShape(output_dims)); -} - -// legacy, for compatibility with old checked-in code -template -void MaxPool(const uint8* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, int pad_height, - int filter_width, int filter_height, int32 output_activation_min, - int32 output_activation_max, uint8* output_data, - const Dims<4>& output_dims) { - static_assert(Ac == FusedActivationFunctionType::kNone || - Ac == FusedActivationFunctionType::kRelu || - Ac == FusedActivationFunctionType::kRelu6 || - Ac == FusedActivationFunctionType::kRelu1, - ""); - if (Ac == FusedActivationFunctionType::kNone) { - TFLITE_DCHECK_EQ(output_activation_min, 0); - TFLITE_DCHECK_EQ(output_activation_max, 255); - } - MaxPool(input_data, input_dims, stride_width, stride_height, pad_width, - pad_height, filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - -// legacy, for compatibility with old checked-in code -template -void MaxPool(const uint8* input_data, const Dims<4>& input_dims, int stride, - int pad_width, int pad_height, int filter_width, int filter_height, - int32 output_activation_min, int32 output_activation_max, - uint8* output_data, const Dims<4>& output_dims) { - MaxPool(input_data, input_dims, stride, stride, pad_width, pad_height, - filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - -inline void L2Pool(const float* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, - int pad_height, int filter_width, int filter_height, - float output_activation_min, float output_activation_max, - float* output_data, const Dims<4>& output_dims) { - L2Pool(input_data, DimsToShape(input_dims), stride_width, stride_height, - pad_width, pad_height, filter_width, filter_height, - output_activation_min, output_activation_max, output_data, - DimsToShape(output_dims)); -} - -// legacy, for compatibility with old checked-in code -template -void L2Pool(const float* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, int pad_height, - int filter_width, int filter_height, float* output_data, - const Dims<4>& output_dims) { - float output_activation_min, output_activation_max; - GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); - L2Pool(input_data, input_dims, stride_width, stride_height, pad_width, - pad_height, filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - -// legacy, for compatibility with old checked-in code -template -void L2Pool(const float* input_data, const Dims<4>& input_dims, int stride, - int pad_width, int pad_height, int filter_width, int filter_height, - float* output_data, const Dims<4>& output_dims) { - L2Pool(input_data, input_dims, stride, stride, pad_width, pad_height, - filter_width, filter_height, output_data, output_dims); -} - -inline void Softmax(const float* input_data, const Dims<4>& input_dims, - float beta, float* output_data, - const Dims<4>& output_dims) { - Softmax(input_data, DimsToShape(input_dims), beta, output_data, - DimsToShape(output_dims)); -} - -inline void Softmax(const uint8* input_data, const Dims<4>& input_dims, - int32 input_beta_multiplier, int32 input_beta_left_shift, - int diff_min, uint8* output_data, - const Dims<4>& output_dims) { - Softmax(input_data, DimsToShape(input_dims), input_beta_multiplier, - input_beta_left_shift, diff_min, output_data, - DimsToShape(output_dims)); -} - -inline void LogSoftmax(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { - LogSoftmax(input_data, DimsToShape(input_dims), output_data, - DimsToShape(output_dims)); -} - -inline void LogSoftmax(const uint8* input_data, const Dims<4>& input_dims, - int32 input_multiplier, int32 input_left_shift, - int32 reverse_scaling_divisor, - int32 reverse_scaling_right_shift, int diff_min, - uint8* output_data, const Dims<4>& output_dims) { - LogSoftmax(input_data, DimsToShape(input_dims), input_multiplier, - input_left_shift, reverse_scaling_divisor, - reverse_scaling_right_shift, diff_min, output_data, - DimsToShape(output_dims)); -} - -inline void Logistic(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { - Logistic(input_data, DimsToShape(input_dims), output_data, - DimsToShape(output_dims)); -} - -inline void Logistic(const uint8* input_data, const Dims<4>& input_dims, - int32 input_zero_point, int32 input_range_radius, - int32 input_multiplier, int input_left_shift, - uint8* output_data, const Dims<4>& output_dims) { - Logistic(input_data, DimsToShape(input_dims), input_zero_point, - input_range_radius, input_multiplier, input_left_shift, output_data, - DimsToShape(output_dims)); -} - -inline void Logistic(const int16* input_data, const Dims<4>& input_dims, - int16* output_data, const Dims<4>& output_dims) { - Logistic(input_data, DimsToShape(input_dims), output_data, - DimsToShape(output_dims)); -} - -inline void Tanh(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { - Tanh(input_data, DimsToShape(input_dims), output_data, - DimsToShape(output_dims)); -} - -inline void Tanh(const uint8* input_data, const Dims<4>& input_dims, - int32 input_zero_point, int32 input_range_radius, - int32 input_multiplier, int input_left_shift, - uint8* output_data, const Dims<4>& output_dims) { - Tanh(input_data, DimsToShape(input_dims), input_zero_point, - input_range_radius, input_multiplier, input_left_shift, output_data, - DimsToShape(output_dims)); -} - -inline void Tanh(const int16* input_data, const Dims<4>& input_dims, - int input_left_shift, int16* output_data, - const Dims<4>& output_dims) { - Tanh(input_data, DimsToShape(input_dims), input_left_shift, output_data, - DimsToShape(output_dims)); + return L2Normalization(input_data, DimsToShape(input_dims), input_zero_point, + output_data, DimsToShape(output_dims)); } } // namespace optimized_ops diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h index 930e26107e..cf989ce51d 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h @@ -85,12 +85,6 @@ using VectorMap = typename std::conditional< Eigen::Dynamic, 1>>, Eigen::Map>>::type; -template -VectorMap MapAsVector(Scalar* data, const RuntimeShape& shape) { - const int size = shape.FlatSize(); - return VectorMap(data, size, 1); -} - template VectorMap MapAsVector(Scalar* data, const Dims& dims) { const int size = FlatSize(dims); @@ -107,23 +101,6 @@ using MatrixMap = typename std::conditional< Eigen::Dynamic, Eigen::Dynamic>>, Eigen::Map>>::type; -template -MatrixMap MapAsMatrixWithLastDimAsRows(Scalar* data, - const RuntimeShape& shape) { - const int dims_count = shape.DimensionsCount(); - const int rows = shape.Dims(dims_count - 1); - const int cols = FlatSizeSkipDim(shape, dims_count - 1); - return MatrixMap(data, rows, cols); -} - -template -MatrixMap MapAsMatrixWithFirstDimAsCols(Scalar* data, - const RuntimeShape& shape) { - const int cols = shape.Dims(0); - const int rows = FlatSizeSkipDim(shape, 0); - return MatrixMap(data, rows, cols); -} - template MatrixMap MapAsMatrixWithFirstDimAsRows(Scalar* data, const Dims& dims) { @@ -2366,12 +2343,12 @@ void GlobalBatchNormalization(const float* input_data, } } -inline void Relu(const float* input_data, const RuntimeShape& input_shape, - float* output_data, const RuntimeShape& output_shape) { +inline void Relu(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { gemmlowp::ScopedProfilingLabel label("Relu (not fused)"); - const auto input = MapAsVector(input_data, input_shape); - auto output = MapAsVector(output_data, output_shape); + const auto input = MapAsVector(input_data, input_dims); + auto output = MapAsVector(output_data, output_dims); output = input.cwiseMax(0.0f); } @@ -3752,25 +3729,23 @@ inline int NodeOffset(int b, int h, int w, int height, int width) { return (b * height + h) * width + w; } -inline void AveragePool(const float* input_data, - const RuntimeShape& input_shape, int stride_width, - int stride_height, int pad_width, int pad_height, - int kwidth, int kheight, float output_activation_min, +inline void AveragePool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int kwidth, int kheight, + float output_activation_min, float output_activation_max, float* output_data, - const RuntimeShape& output_shape) { + const Dims<4>& output_dims) { gemmlowp::ScopedProfilingLabel label("AveragePool"); - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int depth = MatchingDim(input_shape, 3, output_shape, 3); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); + const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); + const int input_height = ArraySize(input_dims, 2); + const int input_width = ArraySize(input_dims, 1); + const int output_height = ArraySize(output_dims, 2); + const int output_width = ArraySize(output_dims, 1); + const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); // TODO(benoitjacob) make this a proper reference impl without Eigen! - const auto in_mat = MapAsMatrixWithLastDimAsRows(input_data, input_shape); - auto out_mat = MapAsMatrixWithLastDimAsRows(output_data, output_shape); + const auto in_mat = MapAsMatrixWithFirstDimAsRows(input_data, input_dims); + auto out_mat = MapAsMatrixWithFirstDimAsRows(output_data, output_dims); // TODO(benoitjacob) get rid of the dynamic memory allocation here! Eigen::VectorXf out_count(out_mat.cols()); out_count.setZero(); @@ -3808,9 +3783,9 @@ inline void AveragePool(const float* input_data, for (int y = 0; y < output_height; ++y) { for (int x = 0; x < output_width; ++x) { for (int c = 0; c < depth; ++c) { - output_data[Offset(output_shape, b, y, x, c)] = + output_data[Offset(output_dims, c, x, y, b)] = ActivationFunctionWithMinMax( - output_data[Offset(output_shape, b, y, x, c)], + output_data[Offset(output_dims, c, x, y, b)], output_activation_min, output_activation_max); } } @@ -3818,23 +3793,44 @@ inline void AveragePool(const float* input_data, } } -inline void AveragePool(const uint8* input_data, - const RuntimeShape& input_shape, int stride_width, - int stride_height, int pad_width, int pad_height, - int filter_width, int filter_height, +// legacy, for compatibility with old checked-in code +template +void AveragePool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int kwidth, int kheight, float* output_data, + const Dims<4>& output_dims) { + float output_activation_min, output_activation_max; + GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); + + AveragePool(input_data, input_dims, stride_width, stride_height, pad_width, + pad_height, kwidth, kheight, output_activation_min, + output_activation_max, output_data, output_dims); +} + +// legacy, for compatibility with old checked-in code +template +void AveragePool(const float* input_data, const Dims<4>& input_dims, int stride, + int pad_width, int pad_height, int filter_width, + int filter_height, float* output_data, + const Dims<4>& output_dims) { + AveragePool(input_data, input_dims, stride, stride, pad_width, pad_height, + filter_width, filter_height, output_data, output_dims); +} + +inline void AveragePool(const uint8* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int filter_width, int filter_height, int32 output_activation_min, int32 output_activation_max, uint8* output_data, - const RuntimeShape& output_shape) { + const Dims<4>& output_dims) { gemmlowp::ScopedProfilingLabel label("AveragePool/8bit"); TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int depth = MatchingDim(input_shape, 3, output_shape, 3); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); + const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); + const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); + const int input_height = ArraySize(input_dims, 2); + const int input_width = ArraySize(input_dims, 1); + const int output_height = ArraySize(output_dims, 2); + const int output_width = ArraySize(output_dims, 1); for (int batch = 0; batch < batches; ++batch) { for (int out_y = 0; out_y < output_height; ++out_y) { for (int out_x = 0; out_x < output_width; ++out_x) { @@ -3854,12 +3850,11 @@ inline void AveragePool(const uint8* input_data, uint16 acc[kAccBufferMaxSize]; memset(acc, 0, depth * sizeof(acc[0])); const uint8* input_ptr = - input_data + - depth * (in_x_origin + - input_width * (in_y_origin + input_height * batch)); + input_data + input_dims.strides[1] * in_x_origin + + input_dims.strides[2] * in_y_origin + input_dims.strides[3] * batch; for (int fy = filter_y_start; fy < filter_y_end; fy++) { - const uint8* input_row_ptr = - input_ptr + depth * (fy * input_width + filter_x_start); + const uint8* input_row_ptr = input_ptr + fy * input_dims.strides[2] + + filter_x_start * input_dims.strides[1]; for (int fx = filter_x_start; fx < filter_x_end; fx++) { int channel = 0; #ifdef USE_NEON @@ -3890,7 +3885,7 @@ inline void AveragePool(const uint8* input_data, } } uint8* output_ptr = - output_data + Offset(output_shape, batch, out_y, out_x, 0); + output_data + Offset(output_dims, 0, out_x, out_y, batch); int channel = 0; #ifdef USE_NEON #define AVGPOOL_DIVIDING_BY(FILTER_COUNT) \ @@ -3931,23 +3926,54 @@ inline void AveragePool(const uint8* input_data, } } -inline void MaxPool(const float* input_data, const RuntimeShape& input_shape, +// legacy, for compatibility with old checked-in code +template +void AveragePool(const uint8* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int filter_width, int filter_height, + int32 output_activation_min, int32 output_activation_max, + uint8* output_data, const Dims<4>& output_dims) { + static_assert(Ac == FusedActivationFunctionType::kNone || + Ac == FusedActivationFunctionType::kRelu || + Ac == FusedActivationFunctionType::kRelu6 || + Ac == FusedActivationFunctionType::kRelu1, + ""); + if (Ac == FusedActivationFunctionType::kNone) { + TFLITE_DCHECK_EQ(output_activation_min, 0); + TFLITE_DCHECK_EQ(output_activation_max, 255); + } + AveragePool(input_data, input_dims, stride_width, stride_height, pad_width, + pad_height, filter_width, filter_height, output_activation_min, + output_activation_max, output_data, output_dims); +} + +// legacy, for compatibility with old checked-in code +template +void AveragePool(const uint8* input_data, const Dims<4>& input_dims, int stride, + int pad_width, int pad_height, int filter_width, + int filter_height, int32 output_activation_min, + int32 output_activation_max, uint8* output_data, + const Dims<4>& output_dims) { + AveragePool(input_data, input_dims, stride, stride, pad_width, pad_height, + filter_width, filter_height, output_activation_min, + output_activation_max, output_data, output_dims); +} + +inline void MaxPool(const float* input_data, const Dims<4>& input_dims, int stride_width, int stride_height, int pad_width, int pad_height, int kwidth, int kheight, float output_activation_min, float output_activation_max, - float* output_data, const RuntimeShape& output_shape) { + float* output_data, const Dims<4>& output_dims) { gemmlowp::ScopedProfilingLabel label("MaxPool"); - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int depth = MatchingDim(input_shape, 3, output_shape, 3); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - - const auto in_mat = MapAsMatrixWithLastDimAsRows(input_data, input_shape); - auto out_mat = MapAsMatrixWithLastDimAsRows(output_data, output_shape); + const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); + const int input_height = ArraySize(input_dims, 2); + const int input_width = ArraySize(input_dims, 1); + const int output_height = ArraySize(output_dims, 2); + const int output_width = ArraySize(output_dims, 1); + const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); + + const auto in_mat = MapAsMatrixWithFirstDimAsRows(input_data, input_dims); + auto out_mat = MapAsMatrixWithFirstDimAsRows(output_data, output_dims); // Prefill the output to minimum representable float value out_mat.setConstant(std::numeric_limits::lowest()); for (int b = 0; b < batches; ++b) { @@ -3980,9 +4006,9 @@ inline void MaxPool(const float* input_data, const RuntimeShape& input_shape, for (int y = 0; y < output_height; ++y) { for (int x = 0; x < output_width; ++x) { for (int c = 0; c < depth; ++c) { - output_data[Offset(output_shape, b, y, x, c)] = + output_data[Offset(output_dims, c, x, y, b)] = ActivationFunctionWithMinMax( - output_data[Offset(output_shape, b, y, x, c)], + output_data[Offset(output_dims, c, x, y, b)], output_activation_min, output_activation_max); } } @@ -3990,21 +4016,41 @@ inline void MaxPool(const float* input_data, const RuntimeShape& input_shape, } } -inline void MaxPool(const uint8* input_data, const RuntimeShape& input_shape, +// legacy, for compatibility with old checked-in code +template +void MaxPool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, int pad_height, + int kwidth, int kheight, float* output_data, + const Dims<4>& output_dims) { + float output_activation_min, output_activation_max; + GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); + MaxPool(input_data, input_dims, stride_width, stride_height, pad_width, + pad_height, kwidth, kheight, output_activation_min, + output_activation_max, output_data, output_dims); +} + +// legacy, for compatibility with old checked-in code +template +void MaxPool(const float* input_data, const Dims<4>& input_dims, int stride, + int pad_width, int pad_height, int filter_width, int filter_height, + float* output_data, const Dims<4>& output_dims) { + MaxPool(input_data, input_dims, stride, stride, pad_width, pad_height, + filter_width, filter_height, output_data, output_dims); +} + +inline void MaxPool(const uint8* input_data, const Dims<4>& input_dims, int stride_width, int stride_height, int pad_width, int pad_height, int filter_width, int filter_height, int32 output_activation_min, int32 output_activation_max, - uint8* output_data, const RuntimeShape& output_shape) { + uint8* output_data, const Dims<4>& output_dims) { gemmlowp::ScopedProfilingLabel label("MaxPool/8bit"); TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int depth = MatchingDim(input_shape, 3, output_shape, 3); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); + const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); + const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); + const int input_height = ArraySize(input_dims, 2); + const int input_width = ArraySize(input_dims, 1); + const int output_height = ArraySize(output_dims, 2); + const int output_width = ArraySize(output_dims, 1); for (int batch = 0; batch < batches; ++batch) { for (int out_y = 0; out_y < output_height; ++out_y) { for (int out_x = 0; out_x < output_width; ++out_x) { @@ -4022,12 +4068,11 @@ inline void MaxPool(const uint8* input_data, const RuntimeShape& input_shape, uint8 acc[kAccBufferMaxSize]; memset(acc, 0, depth * sizeof(acc[0])); const uint8* input_ptr = - input_data + - depth * (in_x_origin + - input_width * (in_y_origin + input_height * batch)); + input_data + input_dims.strides[1] * in_x_origin + + input_dims.strides[2] * in_y_origin + input_dims.strides[3] * batch; for (int fy = filter_y_start; fy < filter_y_end; fy++) { - const uint8* input_row_ptr = - input_ptr + depth * (fy * input_width + filter_x_start); + const uint8* input_row_ptr = input_ptr + fy * input_dims.strides[2] + + filter_x_start * input_dims.strides[1]; for (int fx = filter_x_start; fx < filter_x_end; fx++) { int channel = 0; #ifdef USE_NEON @@ -4053,7 +4098,7 @@ inline void MaxPool(const uint8* input_data, const RuntimeShape& input_shape, } } uint8* output_ptr = - output_data + Offset(output_shape, batch, out_y, out_x, 0); + output_data + Offset(output_dims, 0, out_x, out_y, batch); int channel = 0; #ifdef USE_NEON for (; channel <= depth - 16; channel += 16) { @@ -4080,23 +4125,53 @@ inline void MaxPool(const uint8* input_data, const RuntimeShape& input_shape, } } -inline void L2Pool(const float* input_data, const RuntimeShape& input_shape, +// legacy, for compatibility with old checked-in code +template +void MaxPool(const uint8* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, int pad_height, + int filter_width, int filter_height, int32 output_activation_min, + int32 output_activation_max, uint8* output_data, + const Dims<4>& output_dims) { + static_assert(Ac == FusedActivationFunctionType::kNone || + Ac == FusedActivationFunctionType::kRelu || + Ac == FusedActivationFunctionType::kRelu6 || + Ac == FusedActivationFunctionType::kRelu1, + ""); + if (Ac == FusedActivationFunctionType::kNone) { + TFLITE_DCHECK_EQ(output_activation_min, 0); + TFLITE_DCHECK_EQ(output_activation_max, 255); + } + MaxPool(input_data, input_dims, stride_width, stride_height, pad_width, + pad_height, filter_width, filter_height, output_activation_min, + output_activation_max, output_data, output_dims); +} + +// legacy, for compatibility with old checked-in code +template +void MaxPool(const uint8* input_data, const Dims<4>& input_dims, int stride, + int pad_width, int pad_height, int filter_width, int filter_height, + int32 output_activation_min, int32 output_activation_max, + uint8* output_data, const Dims<4>& output_dims) { + MaxPool(input_data, input_dims, stride, stride, pad_width, pad_height, + filter_width, filter_height, output_activation_min, + output_activation_max, output_data, output_dims); +} + +inline void L2Pool(const float* input_data, const Dims<4>& input_dims, int stride_width, int stride_height, int pad_width, int pad_height, int filter_width, int filter_height, float output_activation_min, float output_activation_max, - float* output_data, const RuntimeShape& output_shape) { + float* output_data, const Dims<4>& output_dims) { gemmlowp::ScopedProfilingLabel label("L2Pool"); - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); + const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); + const int input_height = ArraySize(input_dims, 2); + const int input_width = ArraySize(input_dims, 1); + const int output_height = ArraySize(output_dims, 2); + const int output_width = ArraySize(output_dims, 1); // Actually carry out L2 Pool. Code is written in forward mode: we go through // the input values once, and write to all the pooled regions that it maps to. - const auto in_mat = MapAsMatrixWithLastDimAsRows(input_data, input_shape); - auto out_mat = MapAsMatrixWithLastDimAsRows(output_data, output_shape); + const auto in_mat = MapAsMatrixWithFirstDimAsRows(input_data, input_dims); + auto out_mat = MapAsMatrixWithFirstDimAsRows(output_data, output_dims); Eigen::VectorXf in_square(in_mat.rows()); Eigen::VectorXf out_count(out_mat.cols()); out_count.setZero(); @@ -4138,6 +4213,28 @@ inline void L2Pool(const float* input_data, const RuntimeShape& input_shape, (out_mat.array().rowwise() * out_count.transpose().array()).cwiseSqrt(); } +// legacy, for compatibility with old checked-in code +template +void L2Pool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, int pad_height, + int filter_width, int filter_height, float* output_data, + const Dims<4>& output_dims) { + float output_activation_min, output_activation_max; + GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); + L2Pool(input_data, input_dims, stride_width, stride_height, pad_width, + pad_height, filter_width, filter_height, output_activation_min, + output_activation_max, output_data, output_dims); +} + +// legacy, for compatibility with old checked-in code +template +void L2Pool(const float* input_data, const Dims<4>& input_dims, int stride, + int pad_width, int pad_height, int filter_width, int filter_height, + float* output_data, const Dims<4>& output_dims) { + L2Pool(input_data, input_dims, stride, stride, pad_width, pad_height, + filter_width, filter_height, output_data, output_dims); +} + inline void LocalResponseNormalization(const float* input_data, const Dims<4>& input_dims, int range, float bias, float alpha, float beta, @@ -4183,14 +4280,14 @@ inline void LocalResponseNormalization(const float* input_data, } } -inline void Softmax(const float* input_data, const RuntimeShape& input_shape, +inline void Softmax(const float* input_data, const Dims<4>& input_dims, float beta, float* output_data, - const RuntimeShape& output_shape) { + const Dims<4>& output_dims) { gemmlowp::ScopedProfilingLabel label("Softmax"); - MatchingFlatSize(input_shape, output_shape); + MatchingFlatSize(input_dims, output_dims); - const auto in_mat = MapAsMatrixWithLastDimAsRows(input_data, input_shape); - auto out_mat = MapAsMatrixWithLastDimAsRows(output_data, output_shape); + const auto in_mat = MapAsMatrixWithFirstDimAsRows(input_data, input_dims); + auto out_mat = MapAsMatrixWithFirstDimAsRows(output_data, output_dims); // Compute the exponential first, removing the max coefficient for numerical // stability. out_mat = (in_mat.rowwise() - in_mat.colwise().maxCoeff()).array() * beta; @@ -4202,10 +4299,10 @@ inline void Softmax(const float* input_data, const RuntimeShape& input_shape, out_mat.array().rowwise() *= scale; } -inline void Softmax(const uint8* input_data, const RuntimeShape& input_shape, +inline void Softmax(const uint8* input_data, const Dims<4>& input_dims, int32 input_beta_multiplier, int32 input_beta_left_shift, int diff_min, uint8* output_data, - const RuntimeShape& output_shape) { + const Dims<4>& output_dims) { // The representation chosen for the input to the exp() function is Q5.26. // We need to leave extra space since values that we skip might be as large as // -32 before multiplying by input_beta_multiplier, and therefore as large as @@ -4219,11 +4316,8 @@ inline void Softmax(const uint8* input_data, const RuntimeShape& input_shape, using FixedPoint0 = gemmlowp::FixedPoint; gemmlowp::ScopedProfilingLabel label("Softmax/8bit"); - const int trailing_dim = input_shape.DimensionsCount() - 1; - const int outer_size = - MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); - const int depth = - MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); + const int outer_size = MatchingFlatSizeSkipDim(input_dims, 0, output_dims); + const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); for (int b = 0; b < outer_size; ++b) { const uint8* input_data_ptr = input_data + b * depth; @@ -4413,14 +4507,11 @@ inline void Softmax(const uint8* input_data, const RuntimeShape& input_shape, // TODO(myenik): This is the same as the reference implementation, not actually // optimized yet. -inline void LogSoftmax(const float* input_data, const RuntimeShape& input_shape, - float* output_data, const RuntimeShape& output_shape) { +inline void LogSoftmax(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { gemmlowp::ScopedProfilingLabel label("LogSoftmax"); - const int trailing_dim = input_shape.DimensionsCount() - 1; - const int outer_size = - MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); - const int depth = - MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); + const int outer_size = MatchingFlatSizeSkipDim(input_dims, 0, output_dims); + const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); for (int i = 0; i < outer_size; ++i) { const float* block_input_data = input_data + i * depth; @@ -4561,11 +4652,11 @@ log_x_for_x_greater_than_or_equal_to_1( } // Currently just a copy of the reference code. -inline void LogSoftmax(const uint8* input_data, const RuntimeShape& input_shape, +inline void LogSoftmax(const uint8* input_data, const Dims<4>& input_dims, int32 input_multiplier, int32 input_left_shift, int32 reverse_scaling_divisor, int32 reverse_scaling_right_shift, int diff_min, - uint8* output_data, const RuntimeShape& output_shape) { + uint8* output_data, const Dims<4>& output_dims) { gemmlowp::ScopedProfilingLabel label("LogSoftmax/Uint8"); // The representation chosen for the input to the exp() function is Q5.26. // We need to leave extra space since values that we skip might be as large as @@ -4580,11 +4671,8 @@ inline void LogSoftmax(const uint8* input_data, const RuntimeShape& input_shape, using FixedPointAccum = gemmlowp::FixedPoint; using FixedPoint0 = gemmlowp::FixedPoint; - const int trailing_dim = input_shape.DimensionsCount() - 1; - const int outer_size = - MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); - const int depth = - MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); + const int outer_size = MatchingFlatSizeSkipDim(input_dims, 0, output_dims); + const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); for (int i = 0; i < outer_size; ++i) { const uint8* block_input_data = input_data + i * depth; @@ -4648,21 +4736,21 @@ inline void LogSoftmax(const uint8* input_data, const RuntimeShape& input_shape, } } -inline void Logistic(const float* input_data, const RuntimeShape& input_shape, - float* output_data, const RuntimeShape& output_shape) { +inline void Logistic(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { gemmlowp::ScopedProfilingLabel label("Logistic"); - auto input_map = MapAsVector(input_data, input_shape); - auto output_map = MapAsVector(output_data, output_shape); + auto input_map = MapAsVector(input_data, input_dims); + auto output_map = MapAsVector(output_data, output_dims); output_map.array() = input_map.array().unaryExpr(Eigen::internal::scalar_sigmoid_op()); } -inline void Logistic(const uint8* input_data, const RuntimeShape& input_shape, +inline void Logistic(const uint8* input_data, const Dims<4>& input_dims, int32 input_zero_point, int32 input_range_radius, int32 input_multiplier, int input_left_shift, - uint8* output_data, const RuntimeShape& output_shape) { + uint8* output_data, const Dims<4>& output_dims) { gemmlowp::ScopedProfilingLabel label("Logistic/Uint8"); - const int size = MatchingFlatSize(input_shape, output_shape); + const int size = MatchingFlatSize(input_dims, output_dims); int c = 0; #ifdef USE_NEON @@ -4794,10 +4882,10 @@ inline void Logistic(const uint8* input_data, const RuntimeShape& input_shape, } } -inline void Logistic(const int16* input_data, const RuntimeShape& input_shape, - int16* output_data, const RuntimeShape& output_shape) { +inline void Logistic(const int16* input_data, const Dims<4>& input_dims, + int16* output_data, const Dims<4>& output_dims) { gemmlowp::ScopedProfilingLabel label("Logistic/Int16"); - const int flat_size = MatchingFlatSize(input_shape, output_shape); + const int flat_size = MatchingFlatSize(output_dims, input_dims); for (int i = 0; i < flat_size; i++) { } @@ -4854,21 +4942,21 @@ inline void Logistic(const int16* input_data, const RuntimeShape& input_shape, } } -inline void Tanh(const float* input_data, const RuntimeShape& input_shape, - float* output_data, const RuntimeShape& output_shape) { +inline void Tanh(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { gemmlowp::ScopedProfilingLabel label("Tanh"); - auto input_map = MapAsVector(input_data, input_shape); - auto output_map = MapAsVector(output_data, output_shape); + auto input_map = MapAsVector(input_data, input_dims); + auto output_map = MapAsVector(output_data, output_dims); output_map.array() = input_map.array().tanh(); } -inline void Tanh(const uint8* input_data, const RuntimeShape& input_shape, +inline void Tanh(const uint8* input_data, const Dims<4>& input_dims, int32 input_zero_point, int32 input_range_radius, int32 input_multiplier, int input_left_shift, - uint8* output_data, const RuntimeShape& output_shape) { + uint8* output_data, const Dims<4>& output_dims) { // Note that this is almost the exact same code as in Logistic(). gemmlowp::ScopedProfilingLabel label("Tanh"); - const int size = MatchingFlatSize(input_shape, output_shape); + const int size = MatchingFlatSize(input_dims, output_dims); int c = 0; int32_t output_zero_point = 128; @@ -5009,16 +5097,16 @@ inline void Tanh(const uint8* input_data, const RuntimeShape& input_shape, } } -inline void Tanh(const int16* input_data, const RuntimeShape& input_shape, +inline void Tanh(const int16* input_data, const Dims<4>& input_dims, int input_left_shift, int16* output_data, - const RuntimeShape& output_shape) { + const Dims<4>& output_dims) { gemmlowp::ScopedProfilingLabel label("Tanh/Int16"); // Support for shifts is limited until we have a parameterized version of // SaturatingRoundingMultiplyByPOT(). TFLITE_DCHECK_GE(input_left_shift, 0); TFLITE_DCHECK_LE(input_left_shift, 1); - const int flat_size = MatchingFlatSize(input_shape, output_shape); + const int flat_size = MatchingFlatSize(output_dims, input_dims); int c = 0; const int16* input_data_ptr = input_data; diff --git a/tensorflow/contrib/lite/kernels/internal/reference/legacy_reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/legacy_reference_ops.h index 878b2441b4..6f5f6a3e6f 100644 --- a/tensorflow/contrib/lite/kernels/internal/reference/legacy_reference_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/reference/legacy_reference_ops.h @@ -34,297 +34,15 @@ inline RuntimeShape DimsToShape(const tflite::Dims<4>& dims) { template void L2Normalization(const float* input_data, const Dims<4>& input_dims, float* output_data, const Dims<4>& output_dims) { - L2Normalization(input_data, DimsToShape(input_dims), output_data, - DimsToShape(output_dims)); + return L2Normalization(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); } inline void L2Normalization(const uint8* input_data, const Dims<4>& input_dims, int32 input_zero_point, uint8* output_data, const Dims<4>& output_dims) { - L2Normalization(input_data, DimsToShape(input_dims), input_zero_point, - output_data, DimsToShape(output_dims)); -} - -inline void Relu(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { - Relu(input_data, DimsToShape(input_dims), output_data, - DimsToShape(output_dims)); -} - -inline void Relu1(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { - Relu1(input_data, DimsToShape(input_dims), output_data, - DimsToShape(output_dims)); -} - -inline void Relu6(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { - Relu6(input_data, DimsToShape(input_dims), output_data, - DimsToShape(output_dims)); -} - -inline void AveragePool(const float* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, - int pad_height, int kwidth, int kheight, - float output_activation_min, - float output_activation_max, float* output_data, - const Dims<4>& output_dims) { - AveragePool(input_data, DimsToShape(input_dims), stride_width, stride_height, - pad_width, pad_height, kwidth, kheight, output_activation_min, - output_activation_max, output_data, DimsToShape(output_dims)); -} - -// legacy, for compatibility with old checked-in code -template -void AveragePool(const float* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, - int pad_height, int kwidth, int kheight, float* output_data, - const Dims<4>& output_dims) { - float output_activation_min, output_activation_max; - GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); - - AveragePool(input_data, input_dims, stride_width, stride_height, pad_width, - pad_height, kwidth, kheight, output_activation_min, - output_activation_max, output_data, output_dims); -} - -// legacy, for compatibility with old checked-in code -template -void AveragePool(const float* input_data, const Dims<4>& input_dims, int stride, - int pad_width, int pad_height, int filter_width, - int filter_height, float* output_data, - const Dims<4>& output_dims) { - AveragePool(input_data, input_dims, stride, stride, pad_width, pad_height, - filter_width, filter_height, output_data, output_dims); -} - -inline void AveragePool(const uint8* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, - int pad_height, int filter_width, int filter_height, - int32 output_activation_min, - int32 output_activation_max, uint8* output_data, - const Dims<4>& output_dims) { - AveragePool(input_data, DimsToShape(input_dims), stride_width, stride_height, - pad_width, pad_height, filter_width, filter_height, - output_activation_min, output_activation_max, output_data, - DimsToShape(output_dims)); -} - -// legacy, for compatibility with old checked-in code -template -void AveragePool(const uint8* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, - int pad_height, int filter_width, int filter_height, - int32 output_activation_min, int32 output_activation_max, - uint8* output_data, const Dims<4>& output_dims) { - static_assert(Ac == FusedActivationFunctionType::kNone || - Ac == FusedActivationFunctionType::kRelu || - Ac == FusedActivationFunctionType::kRelu6 || - Ac == FusedActivationFunctionType::kRelu1, - ""); - if (Ac == FusedActivationFunctionType::kNone) { - TFLITE_DCHECK_EQ(output_activation_min, 0); - TFLITE_DCHECK_EQ(output_activation_max, 255); - } - AveragePool(input_data, input_dims, stride_width, stride_height, pad_width, - pad_height, filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - -// legacy, for compatibility with old checked-in code -template -void AveragePool(const uint8* input_data, const Dims<4>& input_dims, int stride, - int pad_width, int pad_height, int filter_width, - int filter_height, int32 output_activation_min, - int32 output_activation_max, uint8* output_data, - const Dims<4>& output_dims) { - AveragePool(input_data, input_dims, stride, stride, pad_width, pad_height, - filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - -inline void MaxPool(const float* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, - int pad_height, int kwidth, int kheight, - float output_activation_min, float output_activation_max, - float* output_data, const Dims<4>& output_dims) { - MaxPool(input_data, DimsToShape(input_dims), stride_width, stride_height, - pad_width, pad_height, kwidth, kheight, output_activation_min, - output_activation_max, output_data, DimsToShape(output_dims)); -} - -// legacy, for compatibility with old checked-in code -template -void MaxPool(const float* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, int pad_height, - int kwidth, int kheight, float* output_data, - const Dims<4>& output_dims) { - float output_activation_min, output_activation_max; - GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); - MaxPool(input_data, input_dims, stride_width, stride_height, pad_width, - pad_height, kwidth, kheight, output_activation_min, - output_activation_max, output_data, output_dims); -} - -// legacy, for compatibility with old checked-in code -template -void MaxPool(const float* input_data, const Dims<4>& input_dims, int stride, - int pad_width, int pad_height, int filter_width, int filter_height, - float* output_data, const Dims<4>& output_dims) { - MaxPool(input_data, input_dims, stride, stride, pad_width, pad_height, - filter_width, filter_height, output_data, output_dims); -} - -inline void MaxPool(const uint8* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, - int pad_height, int filter_width, int filter_height, - int32 output_activation_min, int32 output_activation_max, - uint8* output_data, const Dims<4>& output_dims) { - MaxPool(input_data, DimsToShape(input_dims), stride_width, stride_height, - pad_width, pad_height, filter_width, filter_height, - output_activation_min, output_activation_max, output_data, - DimsToShape(output_dims)); -} - -// legacy, for compatibility with old checked-in code -template -void MaxPool(const uint8* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, int pad_height, - int filter_width, int filter_height, int32 output_activation_min, - int32 output_activation_max, uint8* output_data, - const Dims<4>& output_dims) { - static_assert(Ac == FusedActivationFunctionType::kNone || - Ac == FusedActivationFunctionType::kRelu || - Ac == FusedActivationFunctionType::kRelu6 || - Ac == FusedActivationFunctionType::kRelu1, - ""); - if (Ac == FusedActivationFunctionType::kNone) { - TFLITE_DCHECK_EQ(output_activation_min, 0); - TFLITE_DCHECK_EQ(output_activation_max, 255); - } - MaxPool(input_data, input_dims, stride_width, stride_height, pad_width, - pad_height, filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - -// legacy, for compatibility with old checked-in code -template -void MaxPool(const uint8* input_data, const Dims<4>& input_dims, int stride, - int pad_width, int pad_height, int filter_width, int filter_height, - int32 output_activation_min, int32 output_activation_max, - uint8* output_data, const Dims<4>& output_dims) { - MaxPool(input_data, input_dims, stride, stride, pad_width, pad_height, - filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - -inline void L2Pool(const float* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, - int pad_height, int filter_width, int filter_height, - float output_activation_min, float output_activation_max, - float* output_data, const Dims<4>& output_dims) { - L2Pool(input_data, DimsToShape(input_dims), stride_width, stride_height, - pad_width, pad_height, filter_width, filter_height, - output_activation_min, output_activation_max, output_data, - DimsToShape(output_dims)); -} - -// legacy, for compatibility with old checked-in code -template -void L2Pool(const float* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, int pad_height, - int filter_width, int filter_height, float* output_data, - const Dims<4>& output_dims) { - float output_activation_min, output_activation_max; - GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); - L2Pool(input_data, input_dims, stride_width, stride_height, pad_width, - pad_height, filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - -// legacy, for compatibility with old checked-in code -template -void L2Pool(const float* input_data, const Dims<4>& input_dims, int stride, - int pad_width, int pad_height, int filter_width, int filter_height, - float* output_data, const Dims<4>& output_dims) { - L2Pool(input_data, input_dims, stride, stride, pad_width, pad_height, - filter_width, filter_height, output_data, output_dims); -} - -inline void Softmax(const float* input_data, const Dims<4>& input_dims, - float beta, float* output_data, - const Dims<4>& output_dims) { - Softmax(input_data, DimsToShape(input_dims), beta, output_data, - DimsToShape(output_dims)); -} - -inline void Softmax(const uint8* input_data, const Dims<4>& input_dims, - int32 input_beta_multiplier, int32 input_beta_left_shift, - int diff_min, uint8* output_data, - const Dims<4>& output_dims) { - Softmax(input_data, DimsToShape(input_dims), input_beta_multiplier, - input_beta_left_shift, diff_min, output_data, - DimsToShape(output_dims)); -} - -inline void LogSoftmax(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { - LogSoftmax(input_data, DimsToShape(input_dims), output_data, - DimsToShape(output_dims)); -} - -inline void LogSoftmax(const uint8* input_data, const Dims<4>& input_dims, - int32 input_multiplier, int32 input_left_shift, - int32 reverse_scaling_divisor, - int32 reverse_scaling_right_shift, int diff_min, - uint8* output_data, const Dims<4>& output_dims) { - LogSoftmax(input_data, DimsToShape(input_dims), input_multiplier, - input_left_shift, reverse_scaling_divisor, - reverse_scaling_right_shift, diff_min, output_data, - DimsToShape(output_dims)); -} - -inline void Logistic(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { - Logistic(input_data, DimsToShape(input_dims), output_data, - DimsToShape(output_dims)); -} - -inline void Logistic(const uint8* input_data, const Dims<4>& input_dims, - int32 input_zero_point, int32 input_range_radius, - int32 input_multiplier, int input_left_shift, - uint8* output_data, const Dims<4>& output_dims) { - Logistic(input_data, DimsToShape(input_dims), input_zero_point, - input_range_radius, input_multiplier, input_left_shift, output_data, - DimsToShape(output_dims)); -} - -inline void Logistic(const int16* input_data, const Dims<4>& input_dims, - int16* output_data, const Dims<4>& output_dims) { - Logistic(input_data, DimsToShape(input_dims), output_data, - DimsToShape(output_dims)); -} - -inline void Tanh(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { - Tanh(input_data, DimsToShape(input_dims), output_data, - DimsToShape(output_dims)); -} - -inline void Tanh(const uint8* input_data, const Dims<4>& input_dims, - int32 input_zero_point, int32 input_range_radius, - int32 input_multiplier, int input_left_shift, - uint8* output_data, const Dims<4>& output_dims) { - Tanh(input_data, DimsToShape(input_dims), input_zero_point, - input_range_radius, input_multiplier, input_left_shift, output_data, - DimsToShape(output_dims)); -} - -inline void Tanh(const int16* input_data, const Dims<4>& input_dims, - int input_left_shift, int16* output_data, - const Dims<4>& output_dims) { - Tanh(input_data, DimsToShape(input_dims), input_left_shift, output_data, - DimsToShape(output_dims)); + return L2Normalization(input_data, DimsToShape(input_dims), input_zero_point, + output_data, DimsToShape(output_dims)); } } // namespace reference_ops diff --git a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h index 1ac010dd7e..1908f7fa6c 100644 --- a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h @@ -914,9 +914,9 @@ void GlobalBatchNormalization(const float* input_data, } } -inline void Relu(const float* input_data, const RuntimeShape& input_shape, - float* output_data, const RuntimeShape& output_shape) { - const int flat_size = MatchingFlatSize(input_shape, output_shape); +inline void Relu(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { + const int flat_size = MatchingFlatSize(input_dims, output_dims); for (int i = 0; i < flat_size; ++i) { const float val = input_data[i]; const float lower = 0; @@ -925,10 +925,9 @@ inline void Relu(const float* input_data, const RuntimeShape& input_shape, } } -inline void Relu1(const float* input_data, const RuntimeShape& input_shape, - float* output_data, const RuntimeShape& output_shape) { - gemmlowp::ScopedProfilingLabel label("Relu1 (not fused)"); - const int flat_size = MatchingFlatSize(input_shape, output_shape); +inline void Relu1(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { + const int flat_size = MatchingFlatSize(input_dims, output_dims); for (int i = 0; i < flat_size; ++i) { const float val = input_data[i]; const float upper = 1; @@ -938,10 +937,9 @@ inline void Relu1(const float* input_data, const RuntimeShape& input_shape, } } -inline void Relu6(const float* input_data, const RuntimeShape& input_shape, - float* output_data, const RuntimeShape& output_shape) { - gemmlowp::ScopedProfilingLabel label("Relu6 (not fused)"); - const int flat_size = MatchingFlatSize(input_shape, output_shape); +inline void Relu6(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { + const int flat_size = MatchingFlatSize(input_dims, output_dims); for (int i = 0; i < flat_size; ++i) { const float val = input_data[i]; const float upper = 6; @@ -2247,21 +2245,18 @@ inline int NodeOffset(int b, int h, int w, int height, int width) { return (b * height + h) * width + w; } -inline void AveragePool(const float* input_data, - const RuntimeShape& input_shape, int stride_width, - int stride_height, int pad_width, int pad_height, - int filter_width, int filter_height, +inline void AveragePool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int filter_width, int filter_height, float output_activation_min, float output_activation_max, float* output_data, - const RuntimeShape& output_shape) { - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int depth = MatchingDim(input_shape, 3, output_shape, 3); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); + const Dims<4>& output_dims) { + const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); + const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); + const int input_height = ArraySize(input_dims, 2); + const int input_width = ArraySize(input_dims, 1); + const int output_height = ArraySize(output_dims, 2); + const int output_width = ArraySize(output_dims, 1); for (int batch = 0; batch < batches; ++batch) { for (int out_y = 0; out_y < output_height; ++out_y) { for (int out_x = 0; out_x < output_width; ++out_x) { @@ -2285,12 +2280,12 @@ inline void AveragePool(const float* input_data, const int in_x = in_x_origin + filter_x; const int in_y = in_y_origin + filter_y; total += - input_data[Offset(input_shape, batch, in_y, in_x, channel)]; + input_data[Offset(input_dims, channel, in_x, in_y, batch)]; filter_count++; } } const float average = total / filter_count; - output_data[Offset(output_shape, batch, out_y, out_x, channel)] = + output_data[Offset(output_dims, channel, out_x, out_y, batch)] = ActivationFunctionWithMinMax(average, output_activation_min, output_activation_max); } @@ -2299,22 +2294,42 @@ inline void AveragePool(const float* input_data, } } -inline void AveragePool(const uint8* input_data, - const RuntimeShape& input_shape, int stride_width, - int stride_height, int pad_width, int pad_height, - int filter_width, int filter_height, +// legacy, for compatibility with old checked-in code +template +void AveragePool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int filter_width, int filter_height, + float* output_data, const Dims<4>& output_dims) { + float output_activation_min, output_activation_max; + GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); + AveragePool(input_data, input_dims, stride_width, stride_height, pad_width, + pad_height, filter_width, filter_height, output_activation_min, + output_activation_max, output_data, output_dims); +} + +// legacy, for compatibility with old checked-in code +template +void AveragePool(const float* input_data, const Dims<4>& input_dims, int stride, + int pad_width, int pad_height, int filter_width, + int filter_height, float* output_data, + const Dims<4>& output_dims) { + AveragePool(input_data, input_dims, stride, stride, pad_width, pad_height, + filter_width, filter_height, output_data, output_dims); +} + +inline void AveragePool(const uint8* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int filter_width, int filter_height, int32 output_activation_min, int32 output_activation_max, uint8* output_data, - const RuntimeShape& output_shape) { + const Dims<4>& output_dims) { TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int depth = MatchingDim(input_shape, 3, output_shape, 3); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); + const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); + const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); + const int input_height = ArraySize(input_dims, 2); + const int input_width = ArraySize(input_dims, 1); + const int output_height = ArraySize(output_dims, 2); + const int output_width = ArraySize(output_dims, 1); for (int batch = 0; batch < batches; ++batch) { for (int out_y = 0; out_y < output_height; ++out_y) { for (int out_x = 0; out_x < output_width; ++out_x) { @@ -2337,15 +2352,14 @@ inline void AveragePool(const uint8* input_data, ++filter_x) { const int in_x = in_x_origin + filter_x; const int in_y = in_y_origin + filter_y; - acc += - input_data[Offset(input_shape, batch, in_y, in_x, channel)]; + acc += input_data[Offset(input_dims, channel, in_x, in_y, batch)]; filter_count++; } } acc = (acc + filter_count / 2) / filter_count; acc = std::max(acc, output_activation_min); acc = std::min(acc, output_activation_max); - output_data[Offset(output_shape, batch, out_y, out_x, channel)] = + output_data[Offset(output_dims, channel, out_x, out_y, batch)] = static_cast(acc); } } @@ -2353,19 +2367,50 @@ inline void AveragePool(const uint8* input_data, } } -inline void L2Pool(const float* input_data, const RuntimeShape& input_shape, +// legacy, for compatibility with old checked-in code +template +void AveragePool(const uint8* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int filter_width, int filter_height, + int32 output_activation_min, int32 output_activation_max, + uint8* output_data, const Dims<4>& output_dims) { + static_assert(Ac == FusedActivationFunctionType::kNone || + Ac == FusedActivationFunctionType::kRelu || + Ac == FusedActivationFunctionType::kRelu6 || + Ac == FusedActivationFunctionType::kRelu1, + ""); + if (Ac == FusedActivationFunctionType::kNone) { + TFLITE_DCHECK_EQ(output_activation_min, 0); + TFLITE_DCHECK_EQ(output_activation_max, 255); + } + AveragePool(input_data, input_dims, stride_width, stride_height, pad_width, + pad_height, filter_width, filter_height, output_activation_min, + output_activation_max, output_data, output_dims); +} + +// legacy, for compatibility with old checked-in code +template +void AveragePool(const uint8* input_data, const Dims<4>& input_dims, int stride, + int pad_width, int pad_height, int filter_width, + int filter_height, int32 output_activation_min, + int32 output_activation_max, uint8* output_data, + const Dims<4>& output_dims) { + AveragePool(input_data, input_dims, stride, stride, pad_width, pad_height, + filter_width, filter_height, output_activation_min, + output_activation_max, output_data, output_dims); +} + +inline void L2Pool(const float* input_data, const Dims<4>& input_dims, int stride_width, int stride_height, int pad_width, int pad_height, int filter_width, int filter_height, float output_activation_min, float output_activation_max, - float* output_data, const RuntimeShape& output_shape) { - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int depth = MatchingDim(input_shape, 3, output_shape, 3); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); + float* output_data, const Dims<4>& output_dims) { + const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); + const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); + const int input_height = ArraySize(input_dims, 2); + const int input_width = ArraySize(input_dims, 1); + const int output_height = ArraySize(output_dims, 2); + const int output_width = ArraySize(output_dims, 1); for (int batch = 0; batch < batches; ++batch) { for (int out_y = 0; out_y < output_height; ++out_y) { for (int out_x = 0; out_x < output_width; ++out_x) { @@ -2389,13 +2434,13 @@ inline void L2Pool(const float* input_data, const RuntimeShape& input_shape, const int in_x = in_x_origin + filter_x; const int in_y = in_y_origin + filter_y; const float val = - input_data[Offset(input_shape, batch, in_y, in_x, channel)]; + input_data[Offset(input_dims, channel, in_x, in_y, batch)]; sum_squares += val * val; filter_count++; } } const float l2pool_result = std::sqrt(sum_squares / filter_count); - output_data[Offset(output_shape, batch, out_y, out_x, channel)] = + output_data[Offset(output_dims, channel, out_x, out_y, batch)] = ActivationFunctionWithMinMax(l2pool_result, output_activation_min, output_activation_max); } @@ -2404,19 +2449,40 @@ inline void L2Pool(const float* input_data, const RuntimeShape& input_shape, } } -inline void MaxPool(const float* input_data, const RuntimeShape& input_shape, +// legacy, for compatibility with old checked-in code +template +void L2Pool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, int pad_height, + int filter_width, int filter_height, float* output_data, + const Dims<4>& output_dims) { + float output_activation_min, output_activation_max; + GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); + + L2Pool(input_data, input_dims, stride_width, stride_height, pad_width, + pad_height, filter_width, filter_height, output_activation_min, + output_activation_max, output_data, output_dims); +} + +// legacy, for compatibility with old checked-in code +template +void L2Pool(const float* input_data, const Dims<4>& input_dims, int stride, + int pad_width, int pad_height, int filter_width, int filter_height, + float* output_data, const Dims<4>& output_dims) { + L2Pool(input_data, input_dims, stride, stride, pad_width, pad_height, + filter_width, filter_height, output_data, output_dims); +} + +inline void MaxPool(const float* input_data, const Dims<4>& input_dims, int stride_width, int stride_height, int pad_width, int pad_height, int filter_width, int filter_height, float output_activation_min, float output_activation_max, - float* output_data, const RuntimeShape& output_shape) { - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int depth = MatchingDim(input_shape, 3, output_shape, 3); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); + float* output_data, const Dims<4>& output_dims) { + const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); + const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); + const int input_height = ArraySize(input_dims, 2); + const int input_width = ArraySize(input_dims, 1); + const int output_height = ArraySize(output_dims, 2); + const int output_width = ArraySize(output_dims, 1); for (int batch = 0; batch < batches; ++batch) { for (int out_y = 0; out_y < output_height; ++out_y) { for (int out_x = 0; out_x < output_width; ++out_x) { @@ -2440,10 +2506,10 @@ inline void MaxPool(const float* input_data, const RuntimeShape& input_shape, const int in_y = in_y_origin + filter_y; max = std::max( max, - input_data[Offset(input_shape, batch, in_y, in_x, channel)]); + input_data[Offset(input_dims, channel, in_x, in_y, batch)]); } } - output_data[Offset(output_shape, batch, out_y, out_x, channel)] = + output_data[Offset(output_dims, channel, out_x, out_y, batch)] = ActivationFunctionWithMinMax(max, output_activation_min, output_activation_max); } @@ -2452,22 +2518,42 @@ inline void MaxPool(const float* input_data, const RuntimeShape& input_shape, } } -inline void MaxPool(const uint8* input_data, const RuntimeShape& input_shape, +// legacy, for compatibility with old checked-in code +template +void MaxPool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, int pad_height, + int filter_width, int filter_height, float* output_data, + const Dims<4>& output_dims) { + float output_activation_min, output_activation_max; + GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); + MaxPool(input_data, input_dims, stride_width, stride_height, pad_width, + pad_height, filter_width, filter_height, output_activation_min, + output_activation_max, output_data, output_dims); +} + +// legacy, for compatibility with old checked-in code +template +void MaxPool(const float* input_data, const Dims<4>& input_dims, int stride, + int pad_width, int pad_height, int filter_width, int filter_height, + float* output_data, const Dims<4>& output_dims) { + MaxPool(input_data, input_dims, stride, stride, pad_width, pad_height, + filter_width, filter_height, output_data, output_dims); +} + +inline void MaxPool(const uint8* input_data, const Dims<4>& input_dims, int stride_width, int stride_height, int pad_width, int pad_height, int filter_width, int filter_height, int32 output_activation_min, int32 output_activation_max, - uint8* output_data, const RuntimeShape& output_shape) { + uint8* output_data, const Dims<4>& output_dims) { TFLITE_DCHECK_LE(output_activation_min, output_activation_max); TFLITE_DCHECK_GE(output_activation_min, 0); TFLITE_DCHECK_LE(output_activation_max, 255); - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int depth = MatchingDim(input_shape, 3, output_shape, 3); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); + const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); + const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); + const int input_height = ArraySize(input_dims, 2); + const int input_width = ArraySize(input_dims, 1); + const int output_height = ArraySize(output_dims, 2); + const int output_width = ArraySize(output_dims, 1); for (int batch = 0; batch < batches; ++batch) { for (int out_y = 0; out_y < output_height; ++out_y) { for (int out_x = 0; out_x < output_width; ++out_x) { @@ -2491,12 +2577,12 @@ inline void MaxPool(const uint8* input_data, const RuntimeShape& input_shape, const int in_y = in_y_origin + filter_y; max = std::max( max, - input_data[Offset(input_shape, batch, in_y, in_x, channel)]); + input_data[Offset(input_dims, channel, in_x, in_y, batch)]); } } max = std::max(max, output_activation_min); max = std::min(max, output_activation_max); - output_data[Offset(output_shape, batch, out_y, out_x, channel)] = + output_data[Offset(output_dims, channel, out_x, out_y, batch)] = static_cast(max); } } @@ -2504,6 +2590,38 @@ inline void MaxPool(const uint8* input_data, const RuntimeShape& input_shape, } } +// legacy, for compatibility with old checked-in code +template +void MaxPool(const uint8* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, int pad_height, + int filter_width, int filter_height, int32 output_activation_min, + int32 output_activation_max, uint8* output_data, + const Dims<4>& output_dims) { + static_assert(Ac == FusedActivationFunctionType::kNone || + Ac == FusedActivationFunctionType::kRelu || + Ac == FusedActivationFunctionType::kRelu6 || + Ac == FusedActivationFunctionType::kRelu1, + ""); + if (Ac == FusedActivationFunctionType::kNone) { + TFLITE_DCHECK_EQ(output_activation_min, 0); + TFLITE_DCHECK_EQ(output_activation_max, 255); + } + MaxPool(input_data, input_dims, stride_width, stride_height, pad_width, + pad_height, filter_width, filter_height, output_activation_min, + output_activation_max, output_data, output_dims); +} + +// legacy, for compatibility with old checked-in code +template +void MaxPool(const uint8* input_data, const Dims<4>& input_dims, int stride, + int pad_width, int pad_height, int filter_width, int filter_height, + int32 output_activation_min, int32 output_activation_max, + uint8* output_data, const Dims<4>& output_dims) { + MaxPool(input_data, input_dims, stride, stride, pad_width, pad_height, + filter_width, filter_height, output_activation_min, + output_activation_max, output_data, output_dims); +} + inline void LocalResponseNormalization(const float* input_data, const Dims<4>& input_dims, int range, float bias, float alpha, float beta, @@ -2527,14 +2645,11 @@ inline void LocalResponseNormalization(const float* input_data, } } -inline void Softmax(const float* input_data, const RuntimeShape& input_shape, +inline void Softmax(const float* input_data, const Dims<4>& input_dims, float beta, float* output_data, - const RuntimeShape& output_shape) { - const int trailing_dim = input_shape.DimensionsCount() - 1; - const int outer_size = - MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); - const int depth = - MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); + const Dims<4>& output_dims) { + const int outer_size = MatchingFlatSizeSkipDim(input_dims, 0, output_dims); + const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); for (int i = 0; i < outer_size; ++i) { // Find max element value which we'll use to ensure numerical stability @@ -2559,10 +2674,10 @@ inline void Softmax(const float* input_data, const RuntimeShape& input_shape, } } -inline void Softmax(const uint8* input_data, const RuntimeShape& input_shape, +inline void Softmax(const uint8* input_data, const Dims<4>& input_dims, int32 input_beta_multiplier, int32 input_beta_left_shift, int diff_min, uint8* output_data, - const RuntimeShape& output_shape) { + const Dims<4>& output_dims) { // The representation chosen for the input to the exp() function is Q5.26. // We need to leave extra space since values that we skip might be as large as // -32 before multiplying by input_beta_multiplier, and therefore as large as @@ -2575,11 +2690,8 @@ inline void Softmax(const uint8* input_data, const RuntimeShape& input_shape, using FixedPointAccum = gemmlowp::FixedPoint; using FixedPoint0 = gemmlowp::FixedPoint; - const int trailing_dim = input_shape.DimensionsCount() - 1; - const int outer_size = - MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); - const int depth = - MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); + const int outer_size = MatchingFlatSizeSkipDim(input_dims, 0, output_dims); + const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); for (int i = 0; i < outer_size; ++i) { uint8 max_in_row = 0; @@ -2640,13 +2752,10 @@ inline void Softmax(const uint8* input_data, const RuntimeShape& input_shape, } } -inline void LogSoftmax(const float* input_data, const RuntimeShape& input_shape, - float* output_data, const RuntimeShape& output_shape) { - const int trailing_dim = input_shape.DimensionsCount() - 1; - const int outer_size = - MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); - const int depth = - MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); +inline void LogSoftmax(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { + const int outer_size = MatchingFlatSizeSkipDim(input_dims, 0, output_dims); + const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); for (int i = 0; i < outer_size; ++i) { // Find max element value which we'll use to ensure numerical stability @@ -2786,11 +2895,11 @@ log_x_for_x_greater_than_or_equal_to_1( input_val); } -inline void LogSoftmax(const uint8* input_data, const RuntimeShape& input_shape, +inline void LogSoftmax(const uint8* input_data, const Dims<4>& input_dims, int32 input_multiplier, int32 input_left_shift, int32 reverse_scaling_divisor, int32 reverse_scaling_right_shift, int diff_min, - uint8* output_data, const RuntimeShape& output_shape) { + uint8* output_data, const Dims<4>& output_dims) { // The representation chosen for the input to the exp() function is Q5.26. // We need to leave extra space since values that we skip might be as large as // -32 before multiplying by input_beta_multiplier, and therefore as large as @@ -2804,11 +2913,8 @@ inline void LogSoftmax(const uint8* input_data, const RuntimeShape& input_shape, using FixedPointAccum = gemmlowp::FixedPoint; using FixedPoint0 = gemmlowp::FixedPoint; - const int trailing_dim = input_shape.DimensionsCount() - 1; - const int outer_size = - MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); - const int depth = - MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); + const int outer_size = MatchingFlatSizeSkipDim(input_dims, 0, output_dims); + const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); for (int i = 0; i < outer_size; ++i) { uint8 max_in_row = 0; @@ -2872,9 +2978,9 @@ inline void LogSoftmax(const uint8* input_data, const RuntimeShape& input_shape, } } -inline void Logistic(const float* input_data, const RuntimeShape& input_shape, - float* output_data, const RuntimeShape& output_shape) { - const int flat_size = MatchingFlatSize(input_shape, output_shape); +inline void Logistic(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { + const int flat_size = MatchingFlatSize(output_dims, input_dims); for (int i = 0; i < flat_size; i++) { float val = input_data[i]; @@ -2883,11 +2989,11 @@ inline void Logistic(const float* input_data, const RuntimeShape& input_shape, } } -inline void Logistic(const uint8* input_data, const RuntimeShape& input_shape, +inline void Logistic(const uint8* input_data, const Dims<4>& input_dims, int32 input_zero_point, int32 input_range_radius, int32 input_multiplier, int input_left_shift, - uint8* output_data, const RuntimeShape& output_shape) { - const int flat_size = MatchingFlatSize(input_shape, output_shape); + uint8* output_data, const Dims<4>& output_dims) { + const int flat_size = MatchingFlatSize(output_dims, input_dims); for (int i = 0; i < flat_size; i++) { const uint8 input_val_u8 = input_data[i]; @@ -2921,9 +3027,9 @@ inline void Logistic(const uint8* input_data, const RuntimeShape& input_shape, } } -inline void Logistic(const int16* input_data, const RuntimeShape& input_shape, - int16* output_data, const RuntimeShape& output_shape) { - const int flat_size = MatchingFlatSize(input_shape, output_shape); +inline void Logistic(const int16* input_data, const Dims<4>& input_dims, + int16* output_data, const Dims<4>& output_dims) { + const int flat_size = MatchingFlatSize(output_dims, input_dims); for (int i = 0; i < flat_size; i++) { // F0 uses 0 integer bits, range [-1, 1]. @@ -2939,9 +3045,9 @@ inline void Logistic(const int16* input_data, const RuntimeShape& input_shape, } } -inline void Tanh(const float* input_data, const RuntimeShape& input_shape, - float* output_data, const RuntimeShape& output_shape) { - const int flat_size = MatchingFlatSize(input_shape, output_shape); +inline void Tanh(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { + const int flat_size = MatchingFlatSize(output_dims, input_dims); for (int i = 0; i < flat_size; i++) { float val = input_data[i]; @@ -2950,12 +3056,12 @@ inline void Tanh(const float* input_data, const RuntimeShape& input_shape, } } -inline void Tanh(const uint8* input_data, const RuntimeShape& input_shape, +inline void Tanh(const uint8* input_data, const Dims<4>& input_dims, int32 input_zero_point, int32 input_range_radius, int32 input_multiplier, int input_left_shift, - uint8* output_data, const RuntimeShape& output_shape) { + uint8* output_data, const Dims<4>& output_dims) { const int32 output_zero_point = 128; - const int flat_size = MatchingFlatSize(input_shape, output_shape); + const int flat_size = MatchingFlatSize(output_dims, input_dims); for (int i = 0; i < flat_size; i++) { const uint8 input_val_u8 = input_data[i]; @@ -2990,15 +3096,15 @@ inline void Tanh(const uint8* input_data, const RuntimeShape& input_shape, } } -inline void Tanh(const int16* input_data, const RuntimeShape& input_shape, +inline void Tanh(const int16* input_data, const Dims<4>& input_dims, int input_left_shift, int16* output_data, - const RuntimeShape& output_shape) { + const Dims<4>& output_dims) { // Support for shifts is limited until we have a parameterized version of // SaturatingRoundingMultiplyByPOT(). TFLITE_DCHECK_GE(input_left_shift, 0); TFLITE_DCHECK_LE(input_left_shift, 1); - const int flat_size = MatchingFlatSize(input_shape, output_shape); + const int flat_size = MatchingFlatSize(output_dims, input_dims); // F0 uses 0 integer bits, range [-1, 1]. // This is the return type of math functions such as tanh, logistic, diff --git a/tensorflow/contrib/lite/kernels/internal/softmax_quantized_test.cc b/tensorflow/contrib/lite/kernels/internal/softmax_quantized_test.cc index a7dad3c14e..d781a7b642 100644 --- a/tensorflow/contrib/lite/kernels/internal/softmax_quantized_test.cc +++ b/tensorflow/contrib/lite/kernels/internal/softmax_quantized_test.cc @@ -32,21 +32,19 @@ namespace tflite { namespace { void RunSoftmaxFloatReference(const uint8* input_data, - const RuntimeShape& shape_common, - int32 input_offset, const double input_scale, - int stride, float beta, + const Dims<4>& dims_common, int32 input_offset, + const double input_scale, int stride, float beta, uint8* reference_output_data) { - const int ref_buffer_size = shape_common.FlatSize(); + const int ref_buffer_size = RequiredBufferSizeForDims(dims_common); std::vector reference_dequant_data(ref_buffer_size); std::vector reference_output_float_data(ref_buffer_size); // Reference data generated via Dequant of input into float, and then applying // float Softmax. - reference_ops::Dequantize( - input_data, ToRuntimeDims(shape_common), input_offset, input_scale, - reference_dequant_data.data(), ToRuntimeDims(shape_common)); - optimized_ops::Softmax(reference_dequant_data.data(), shape_common, beta, - reference_output_float_data.data(), shape_common); + reference_ops::Dequantize(input_data, dims_common, input_offset, input_scale, + reference_dequant_data.data(), dims_common); + optimized_ops::Softmax(reference_dequant_data.data(), dims_common, beta, + reference_output_float_data.data(), dims_common); // Work with quantized scaling for Softmax, under which 256 represents 1, but // we limit this to 255. for (int i = 0; i < ref_buffer_size; i++) { @@ -57,9 +55,9 @@ void RunSoftmaxFloatReference(const uint8* input_data, } void CheckOutputData(const uint8* test_output, const uint8* reference_output, - const RuntimeShape& shape_common, - const string& check_label, bool be_exacting) { - const int buffer_size = shape_common.FlatSize(); + const Dims<4>& dims_common, const string& check_label, + bool be_exacting) { + const int buffer_size = RequiredBufferSizeForDims(dims_common); // While calculating some metrics in floating point, we work with quantized // scaling. std::vector diff(buffer_size); @@ -93,15 +91,15 @@ void CheckOutputData(const uint8* test_output, const uint8* reference_output, // Runs the Softmax and compares against the float reference implementation and // the quantized reference implementation. -void RunOneSoftmaxTest(const uint8* input_data, - const RuntimeShape& shape_common, int32 input_offset, - const double input_scale, int stride, float beta) { - const int buffer_size = shape_common.FlatSize(); +void RunOneSoftmaxTest(const uint8* input_data, const Dims<4>& dims_common, + int32 input_offset, const double input_scale, int stride, + float beta) { + const int buffer_size = RequiredBufferSizeForDims(dims_common); std::vector optimized_softmax_output(buffer_size); std::vector reference_float_softmax_output(buffer_size); std::vector reference_quant_softmax_output(buffer_size); - RunSoftmaxFloatReference(input_data, shape_common, input_offset, input_scale, + RunSoftmaxFloatReference(input_data, dims_common, input_offset, input_scale, stride, beta, reference_float_softmax_output.data()); int32 input_beta_multiplier; @@ -115,21 +113,21 @@ void RunOneSoftmaxTest(const uint8* input_data, const int diff_min = -tflite::CalculateInputRadius(kScaledDiffIntegerBits, input_beta_left_shift); - optimized_ops::Softmax(input_data, shape_common, input_beta_multiplier, + optimized_ops::Softmax(input_data, dims_common, input_beta_multiplier, input_beta_left_shift, diff_min, - optimized_softmax_output.data(), shape_common); - reference_ops::Softmax(input_data, shape_common, input_beta_multiplier, + optimized_softmax_output.data(), dims_common); + reference_ops::Softmax(input_data, dims_common, input_beta_multiplier, input_beta_left_shift, diff_min, - reference_quant_softmax_output.data(), shape_common); + reference_quant_softmax_output.data(), dims_common); CheckOutputData(optimized_softmax_output.data(), - reference_float_softmax_output.data(), shape_common, + reference_float_softmax_output.data(), dims_common, "Optimized vs float reference", false); CheckOutputData(optimized_softmax_output.data(), - reference_quant_softmax_output.data(), shape_common, + reference_quant_softmax_output.data(), dims_common, "Optimized vs quant reference", true); CheckOutputData(reference_quant_softmax_output.data(), - reference_float_softmax_output.data(), shape_common, + reference_float_softmax_output.data(), dims_common, "Quant reference vs float reference", false); } @@ -152,13 +150,13 @@ bool TryOneUniformSoftmax() { const int32 input_offset = UniformRandomInt(-256, 0); const float beta = 1.0f + ExponentialRandomPositiveFloat(0.9f, 2, 10); - auto shape_common = - RuntimeShape({batch, input_height, input_width, input_depth}); - const int buffer_size = shape_common.FlatSize(); + Dims<4> dims_common = + MakeDimsForInference(input_depth, input_width, input_height, batch); + const int buffer_size = RequiredBufferSizeForDims(dims_common); std::vector input_data(buffer_size); FillRandom(&input_data); - RunOneSoftmaxTest(input_data.data(), shape_common, input_offset, input_scale, + RunOneSoftmaxTest(input_data.data(), dims_common, input_offset, input_scale, stride, beta); return true; } @@ -190,14 +188,14 @@ bool TryOneSkyscraperSoftmax(bool small_depth) { const int middle_min = UniformRandomInt(0, 255); const int sides_max = UniformRandomInt(0, middle_min); - auto shape_common = - RuntimeShape({batch, input_height, input_width, input_depth}); - const int buffer_size = shape_common.FlatSize(); + Dims<4> dims_common = + MakeDimsForInference(input_depth, input_width, input_height, batch); + const int buffer_size = RequiredBufferSizeForDims(dims_common); std::vector input_data(buffer_size); FillRandomSkyscraper(&input_data, input_depth, middle_proportion, middle_min, sides_max); - RunOneSoftmaxTest(input_data.data(), shape_common, input_offset, input_scale, + RunOneSoftmaxTest(input_data.data(), dims_common, input_offset, input_scale, stride, beta); return true; } diff --git a/tensorflow/contrib/lite/kernels/internal/types.h b/tensorflow/contrib/lite/kernels/internal/types.h index 707d2d261a..64f4881a46 100644 --- a/tensorflow/contrib/lite/kernels/internal/types.h +++ b/tensorflow/contrib/lite/kernels/internal/types.h @@ -294,50 +294,6 @@ inline int RequiredBufferSizeForDims(const Dims<4>& dims) { return FlatSize(dims); } -// Flat size calculation, checking that dimensions match with one or more other -// arrays. -inline int MatchingFlatSize(const RuntimeShape& shape, - const RuntimeShape& check_shape_0) { - const int dims_count = shape.DimensionsCount(); - for (int i = 0; i < dims_count; ++i) { - TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); - } - return shape.FlatSize(); -} - -inline int MatchingFlatSize(const RuntimeShape& shape, - const RuntimeShape& check_shape_0, - const RuntimeShape& check_shape_1) { - const int dims_count = shape.DimensionsCount(); - for (int i = 0; i < dims_count; ++i) { - TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); - } - return MatchingFlatSize(shape, check_shape_1); -} - -inline int MatchingFlatSize(const RuntimeShape& shape, - const RuntimeShape& check_shape_0, - const RuntimeShape& check_shape_1, - const RuntimeShape& check_shape_2) { - const int dims_count = shape.DimensionsCount(); - for (int i = 0; i < dims_count; ++i) { - TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); - } - return MatchingFlatSize(shape, check_shape_1, check_shape_2); -} - -inline int MatchingFlatSize(const RuntimeShape& shape, - const RuntimeShape& check_shape_0, - const RuntimeShape& check_shape_1, - const RuntimeShape& check_shape_2, - const RuntimeShape& check_shape_3) { - const int dims_count = shape.DimensionsCount(); - for (int i = 0; i < dims_count; ++i) { - TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); - } - return MatchingFlatSize(shape, check_shape_1, check_shape_2, check_shape_3); -} - // Flat size calculation, checking that dimensions match with one or more other // arrays. template @@ -364,7 +320,7 @@ inline int MatchingFlatSize(const Dims& dims, const Dims& check_dims_0, for (int i = 0; i < N; ++i) { TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); } - return MatchingFlatSize(dims, check_dims_1, check_dims_2); + return FlatSize(dims, check_dims_1, check_dims_2); } template @@ -375,7 +331,7 @@ inline int MatchingFlatSize(const Dims& dims, const Dims& check_dims_0, for (int i = 0; i < N; ++i) { TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); } - return MatchingFlatSize(dims, check_dims_1, check_dims_2, check_dims_3); + return FlatSize(dims, check_dims_1, check_dims_2, check_dims_3); } // Data is required to be contiguous, and so many operators can use either the diff --git a/tensorflow/contrib/lite/kernels/log_softmax_test.cc b/tensorflow/contrib/lite/kernels/log_softmax_test.cc index 9a8d35e82c..62820a2f51 100644 --- a/tensorflow/contrib/lite/kernels/log_softmax_test.cc +++ b/tensorflow/contrib/lite/kernels/log_softmax_test.cc @@ -90,9 +90,10 @@ TEST(LogSoftmaxOpTest, CompareWithTFmini) { m.Invoke(); std::unique_ptr output_buffer(new float[input_size * batch_size]); - auto input_shape = RuntimeShape({batch_size, 1, 1, input_size}); - tflite::reference_ops::LogSoftmax(input_buffer, input_shape, - output_buffer.get(), input_shape); + static tflite::Dims<4> input_dims = {{input_size, 1, 1, batch_size}, + {1, 0, 0, input_size}}; + tflite::reference_ops::LogSoftmax(input_buffer, input_dims, + output_buffer.get(), input_dims); std::vector expected; expected.insert(expected.end(), output_buffer.get(), diff --git a/tensorflow/contrib/lite/kernels/pooling.cc b/tensorflow/contrib/lite/kernels/pooling.cc index 41771e60bc..311e9b8399 100644 --- a/tensorflow/contrib/lite/kernels/pooling.cc +++ b/tensorflow/contrib/lite/kernels/pooling.cc @@ -126,13 +126,12 @@ void AverageEvalFloat(TfLiteContext* context, TfLiteNode* node, float activation_min, activation_max; CalculateActivationRangeFloat(params->activation, &activation_min, &activation_max); -#define TF_LITE_AVERAGE_POOL(type) \ - type::AveragePool(GetTensorData(input), GetTensorShape(input), \ - params->stride_width, params->stride_height, \ - data->padding.width, data->padding.height, \ - params->filter_width, params->filter_height, \ - activation_min, activation_max, \ - GetTensorData(output), GetTensorShape(output)) +#define TF_LITE_AVERAGE_POOL(type) \ + type::AveragePool( \ + GetTensorData(input), GetTensorDims(input), params->stride_width, \ + params->stride_height, data->padding.width, data->padding.height, \ + params->filter_width, params->filter_height, activation_min, \ + activation_max, GetTensorData(output), GetTensorDims(output)) if (kernel_type == kReference) { TF_LITE_AVERAGE_POOL(reference_ops); } else { @@ -149,13 +148,13 @@ void AverageEvalQuantized(TfLiteContext* context, TfLiteNode* node, int32_t activation_max; CalculateActivationRangeUint8(params->activation, output, &activation_min, &activation_max); -#define TF_LITE_AVERAGE_POOL(type) \ - type::AveragePool(GetTensorData(input), GetTensorShape(input), \ - params->stride_width, params->stride_height, \ - data->padding.width, data->padding.height, \ - params->filter_width, params->filter_height, \ - activation_min, activation_max, \ - GetTensorData(output), GetTensorShape(output)) +#define TF_LITE_AVERAGE_POOL(type) \ + type::AveragePool(GetTensorData(input), GetTensorDims(input), \ + params->stride_width, params->stride_height, \ + data->padding.width, data->padding.height, \ + params->filter_width, params->filter_height, \ + activation_min, activation_max, \ + GetTensorData(output), GetTensorDims(output)) if (kernel_type == kReference) { TF_LITE_AVERAGE_POOL(reference_ops); } else { @@ -171,13 +170,12 @@ void MaxEvalFloat(TfLiteContext* context, TfLiteNode* node, float activation_min, activation_max; CalculateActivationRangeFloat(params->activation, &activation_min, &activation_max); -#define TF_LITE_MAX_POOL(type) \ - type::MaxPool(GetTensorData(input), GetTensorShape(input), \ - params->stride_width, params->stride_height, \ - data->padding.width, data->padding.height, \ - params->filter_width, params->filter_height, activation_min, \ - activation_max, GetTensorData(output), \ - GetTensorShape(output)) +#define TF_LITE_MAX_POOL(type) \ + type::MaxPool( \ + GetTensorData(input), GetTensorDims(input), params->stride_width, \ + params->stride_height, data->padding.width, data->padding.height, \ + params->filter_width, params->filter_height, activation_min, \ + activation_max, GetTensorData(output), GetTensorDims(output)) if (kernel_type == kReference) { TF_LITE_MAX_POOL(reference_ops); } else { @@ -195,12 +193,12 @@ void MaxEvalQuantized(TfLiteContext* context, TfLiteNode* node, CalculateActivationRangeUint8(params->activation, output, &activation_min, &activation_max); #define TF_LITE_MAX_POOL(type) \ - type::MaxPool(GetTensorData(input), GetTensorShape(input), \ + type::MaxPool(GetTensorData(input), GetTensorDims(input), \ params->stride_width, params->stride_height, \ data->padding.width, data->padding.height, \ params->filter_width, params->filter_height, activation_min, \ activation_max, GetTensorData(output), \ - GetTensorShape(output)) + GetTensorDims(output)) if (kernel_type == kReference) { TF_LITE_MAX_POOL(reference_ops); } else { @@ -216,13 +214,12 @@ void L2EvalFloat(TfLiteContext* context, TfLiteNode* node, float activation_min, activation_max; CalculateActivationRangeFloat(params->activation, &activation_min, &activation_max); -#define TF_LITE_L2_POOL(type) \ - type::L2Pool(GetTensorData(input), GetTensorShape(input), \ - params->stride_width, params->stride_height, \ - data->padding.width, data->padding.height, \ - params->filter_width, params->filter_height, activation_min, \ - activation_max, GetTensorData(output), \ - GetTensorShape(output)) +#define TF_LITE_L2_POOL(type) \ + type::L2Pool( \ + GetTensorData(input), GetTensorDims(input), params->stride_width, \ + params->stride_height, data->padding.width, data->padding.height, \ + params->filter_width, params->filter_height, activation_min, \ + activation_max, GetTensorData(output), GetTensorDims(output)) if (kernel_type == kReference) { TF_LITE_L2_POOL(reference_ops); } else { diff --git a/tensorflow/contrib/lite/kernels/softmax_test.cc b/tensorflow/contrib/lite/kernels/softmax_test.cc index 727822f6be..6c5338ff0f 100644 --- a/tensorflow/contrib/lite/kernels/softmax_test.cc +++ b/tensorflow/contrib/lite/kernels/softmax_test.cc @@ -92,9 +92,10 @@ TEST(SoftmaxOpTest, CompareWithTFminiBetaEq1) { m.Invoke(); std::unique_ptr output_buffer(new float[input_size * batch_size]); - auto input_shape = RuntimeShape({batch_size, 1, 1, input_size}); - tflite::reference_ops::Softmax(input_buffer, input_shape, beta, - output_buffer.get(), input_shape); + static tflite::Dims<4> input_dims = {{input_size, 1, 1, batch_size}, + {1, 0, 0, input_size}}; + tflite::reference_ops::Softmax(input_buffer, input_dims, beta, + output_buffer.get(), input_dims); std::vector expected; expected.insert(expected.end(), output_buffer.get(), @@ -119,9 +120,10 @@ TEST(SoftmaxOpTest, CompareWithTFminiBetaNotEq1) { m.Invoke(); std::unique_ptr output_buffer(new float[input_size * batch_size]); - auto input_shape = RuntimeShape({batch_size, 1, 1, input_size}); - tflite::reference_ops::Softmax(input_buffer, input_shape, beta, - output_buffer.get(), input_shape); + static tflite::Dims<4> input_dims = {{input_size, 1, 1, batch_size}, + {1, 0, 0, input_size}}; + tflite::reference_ops::Softmax(input_buffer, input_dims, beta, + output_buffer.get(), input_dims); std::vector expected; expected.insert(expected.end(), output_buffer.get(), -- GitLab From b22f57b8e8ebcd47c1b18638f23ea9dcdcc4921d Mon Sep 17 00:00:00 2001 From: David Norman Date: Tue, 19 Jun 2018 22:33:28 +0100 Subject: [PATCH 692/816] Fix kCall comparison --- tensorflow/compiler/xla/service/hlo_instruction.cc | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc index 0b4dd6412f..a1af8939e7 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction.cc +++ b/tensorflow/compiler/xla/service/hlo_instruction.cc @@ -1493,6 +1493,7 @@ bool HloInstruction::IdenticalSlowPath( return protobuf_util::ProtobufEquals(padding_config(), other.padding_config()); case HloOpcode::kCall: + return eq_computations(to_apply(), other.to_apply()); case HloOpcode::kCrossReplicaSum: return replica_group_ids() == other.replica_group_ids() && cross_replica_sum_barrier() == other.cross_replica_sum_barrier() && -- GitLab From 577b256460dfca4e7c429437dded48e76715fee7 Mon Sep 17 00:00:00 2001 From: Tristan Rice Date: Mon, 18 Jun 2018 12:43:51 -0700 Subject: [PATCH 693/816] tensorflow/go: add tests for zero length arrays passed to C --- tensorflow/go/attrs.go | 36 ++++++- tensorflow/go/attrs_test.go | 172 +++++++++++++++++++++++++++++--- tensorflow/go/operation.go | 3 + tensorflow/go/operation_test.go | 4 + 4 files changed, 198 insertions(+), 17 deletions(-) diff --git a/tensorflow/go/attrs.go b/tensorflow/go/attrs.go index bfa60d2aa8..f86c5737bc 100644 --- a/tensorflow/go/attrs.go +++ b/tensorflow/go/attrs.go @@ -33,7 +33,8 @@ func makeCShape(shape []C.int64_t) Shape { return s } -// Attr returns the value of an attribute on op. +// Attr returns the value of an attribute on op. It returns an error if the +// attribute does not exist. func (op *Operation) Attr(name string) (interface{}, error) { cname := C.CString(name) defer C.free(unsafe.Pointer(cname)) @@ -55,9 +56,13 @@ func listAttribute(op *Operation, cname *C.char, meta C.TF_AttrMetadata) (interf switch meta._type { case C.TF_ATTR_STRING: + if meta.list_size == 0 { + return []string(nil), nil + } values := make([]unsafe.Pointer, meta.list_size) lengths := make([]C.size_t, meta.list_size) - storage := make([]C.char, meta.total_size) + // Add one element in case total_size is zero. + storage := make([]C.char, meta.total_size+1) C.TF_OperationGetAttrStringList(op.c, cname, &values[0], &lengths[0], C.int(meta.list_size), unsafe.Pointer(&storage[0]), C.size_t(meta.total_size), status.c) if err := status.Err(); err != nil { return nil, err @@ -70,6 +75,9 @@ func listAttribute(op *Operation, cname *C.char, meta C.TF_AttrMetadata) (interf return list, nil case C.TF_ATTR_INT: + if meta.list_size == 0 { + return []int64(nil), nil + } list := make([]C.int64_t, meta.list_size) C.TF_OperationGetAttrIntList(op.c, cname, &list[0], C.int(meta.list_size), status.c) if err := status.Err(); err != nil { @@ -82,6 +90,9 @@ func listAttribute(op *Operation, cname *C.char, meta C.TF_AttrMetadata) (interf return vals, nil case C.TF_ATTR_FLOAT: + if meta.list_size == 0 { + return []float32(nil), nil + } list := make([]C.float, meta.list_size) C.TF_OperationGetAttrFloatList(op.c, cname, &list[0], C.int(meta.list_size), status.c) if err := status.Err(); err != nil { @@ -94,6 +105,9 @@ func listAttribute(op *Operation, cname *C.char, meta C.TF_AttrMetadata) (interf return vals, nil case C.TF_ATTR_BOOL: + if meta.list_size == 0 { + return []bool(nil), nil + } list := make([]C.uchar, meta.list_size) C.TF_OperationGetAttrBoolList(op.c, cname, &list[0], C.int(meta.list_size), status.c) if err := status.Err(); err != nil { @@ -106,6 +120,9 @@ func listAttribute(op *Operation, cname *C.char, meta C.TF_AttrMetadata) (interf return vals, nil case C.TF_ATTR_TYPE: + if meta.list_size == 0 { + return []DataType(nil), nil + } list := make([]C.TF_DataType, meta.list_size) C.TF_OperationGetAttrTypeList(op.c, cname, &list[0], C.int(meta.list_size), status.c) if err := status.Err(); err != nil { @@ -118,6 +135,9 @@ func listAttribute(op *Operation, cname *C.char, meta C.TF_AttrMetadata) (interf return vals, nil case C.TF_ATTR_TENSOR: + if meta.list_size == 0 { + return []*Tensor(nil), nil + } list := make([]*C.TF_Tensor, meta.list_size) C.TF_OperationGetAttrTensorList(op.c, cname, &list[0], C.int(meta.list_size), status.c) if err := status.Err(); err != nil { @@ -130,9 +150,13 @@ func listAttribute(op *Operation, cname *C.char, meta C.TF_AttrMetadata) (interf return vals, nil case C.TF_ATTR_SHAPE: + if meta.list_size == 0 { + return []Shape(nil), nil + } dims := make([]*C.int64_t, meta.list_size) numDims := make([]C.int, meta.list_size) - storage := make([]C.int64_t, meta.total_size) + // Add one element in case total_size is zero. + storage := make([]C.int64_t, meta.total_size+1) C.TF_OperationGetAttrShapeList(op.c, cname, &dims[0], &numDims[0], C.int(meta.list_size), &storage[0], C.int(meta.total_size), status.c) if err := status.Err(); err != nil { return nil, err @@ -161,6 +185,9 @@ func scalarAttribute(op *Operation, cname *C.char, meta C.TF_AttrMetadata) (inte switch meta._type { case C.TF_ATTR_STRING: + if meta.total_size == 0 { + return "", nil + } v := make([]C.char, meta.total_size) C.TF_OperationGetAttrString(op.c, cname, unsafe.Pointer(&v[0]), C.size_t(meta.total_size), status.c) if err := status.Err(); err != nil { @@ -202,6 +229,9 @@ func scalarAttribute(op *Operation, cname *C.char, meta C.TF_AttrMetadata) (inte if numDims < 0 { return Shape{}, nil } + if numDims == 0 { + return ScalarShape(), nil + } dims := make([]C.int64_t, numDims) C.TF_OperationGetAttrShape(op.c, cname, (*C.int64_t)(unsafe.Pointer(&dims[0])), C.int(numDims), status.c) if err := status.Err(); err != nil { diff --git a/tensorflow/go/attrs_test.go b/tensorflow/go/attrs_test.go index 18fc0de90a..35b0cb352e 100644 --- a/tensorflow/go/attrs_test.go +++ b/tensorflow/go/attrs_test.go @@ -17,31 +17,175 @@ limitations under the License. package tensorflow import ( + "fmt" "reflect" "testing" ) func TestOperationAttrs(t *testing.T) { - attrs := map[string]interface{}{ - "dtype": Float, + g := NewGraph() + + i := 0 + makeConst := func(v interface{}) Output { + op, err := Const(g, fmt.Sprintf("const/%d/%+v", i, v), v) + i += 1 + if err != nil { + t.Fatal(err) + } + return op } - g := NewGraph() - op, err := g.AddOperation(OpSpec{ - Type: "Placeholder", - Name: "placeholder", - Attrs: attrs, - }) - if err != nil { - t.Fatal(err) + makeTensor := func(v interface{}) *Tensor { + tensor, err := NewTensor(v) + if err != nil { + t.Fatal(err) + } + return tensor } - for key, want := range attrs { - out, err := op.Attr(key) + + cases := []OpSpec{ + { + Name: "type", + Type: "Placeholder", + Attrs: map[string]interface{}{ + "dtype": Float, + }, + }, + { + Name: "list(float)", + Type: "Bucketize", + Input: []Input{ + makeConst([]float32{1, 2, 3, 4}), + }, + Attrs: map[string]interface{}{ + "boundaries": []float32{0, 1, 2, 3, 4, 5}, + }, + }, + { + Name: "list(float) empty", + Type: "Bucketize", + Input: []Input{ + makeConst([]float32{}), + }, + Attrs: map[string]interface{}{ + "boundaries": []float32(nil), + }, + }, + { + Name: "list(type),list(shape)", + Type: "InfeedEnqueueTuple", + Input: []Input{ + OutputList([]Output{ + makeConst(float32(1)), + makeConst([][]int32{{2}}), + }), + }, + Attrs: map[string]interface{}{ + "dtypes": []DataType{Float, Int32}, + "shapes": []Shape{ScalarShape(), MakeShape(1, 1)}, + }, + }, + { + Name: "list(type),list(shape) empty", + Type: "InfeedEnqueueTuple", + Input: []Input{ + OutputList([]Output{ + makeConst([][]int32{{2}}), + }), + }, + Attrs: map[string]interface{}{ + "dtypes": []DataType{Int32}, + "shapes": []Shape(nil), + }, + }, + { + Name: "list(type) empty,string empty,int", + Type: "_XlaSendFromHost", + Input: []Input{ + OutputList([]Output{}), + makeConst(""), + }, + Attrs: map[string]interface{}{ + "Tinputs": []DataType(nil), + "key": "", + "device_ordinal": int64(0), + }, + }, + { + Name: "list(int),int", + Type: "StringToHashBucketStrong", + Input: []Input{ + makeConst(""), + }, + Attrs: map[string]interface{}{ + "num_buckets": int64(2), + "key": []int64{1, 2}, + }, + }, + { + Name: "list(int) empty,int", + Type: "StringToHashBucketStrong", + Input: []Input{ + makeConst(""), + }, + Attrs: map[string]interface{}{ + "num_buckets": int64(2), + "key": ([]int64)(nil), + }, + }, + { + Name: "list(string),type", + Type: "TensorSummary", + Input: []Input{ + makeConst(""), + }, + Attrs: map[string]interface{}{ + "T": String, + "labels": []string{"foo", "bar"}, + }, + }, + { + Name: "list(string) empty,type", + Type: "TensorSummary", + Input: []Input{ + makeConst(""), + }, + Attrs: map[string]interface{}{ + "T": String, + "labels": ([]string)(nil), + }, + }, + { + Name: "tensor", + Type: "Const", + Attrs: map[string]interface{}{ + "dtype": String, + "value": makeTensor("foo"), + }, + }, + } + + for i, spec := range cases { + op, err := g.AddOperation(spec) if err != nil { t.Fatal(err) } - if !reflect.DeepEqual(out, want) { - t.Fatalf("%q: Got %+v, wanted %+v", key, out, want) + for key, want := range spec.Attrs { + out, err := op.Attr(key) + if err != nil { + t.Fatal(err) + } + if !reflect.DeepEqual(out, want) { + t.Fatalf("%d. %q: Got %#v, wanted %#v", i, key, out, want) + } + wantT, ok := want.(*Tensor) + if ok { + wantVal := wantT.Value() + outVal := out.(*Tensor).Value() + if !reflect.DeepEqual(outVal, wantVal) { + t.Fatalf("%d. %q: Got %#v, wanted %#v", i, key, outVal, wantVal) + } + } } } } diff --git a/tensorflow/go/operation.go b/tensorflow/go/operation.go index baaac41f4e..25ec718703 100644 --- a/tensorflow/go/operation.go +++ b/tensorflow/go/operation.go @@ -131,6 +131,9 @@ func (p Output) canBeAnInput() {} // Consumers returns the inputs that consume this output. func (p Output) Consumers() []Consumer { max := int(C.TF_OperationOutputNumConsumers(p.c())) + if max == 0 { + return nil + } inputs := make([]C.TF_Input, max) n := C.TF_OperationOutputConsumers(p.c(), (*C.TF_Input)(unsafe.Pointer(&inputs[0])), C.int(max)) inputs = inputs[:int(n)] diff --git a/tensorflow/go/operation_test.go b/tensorflow/go/operation_test.go index 0672e8ecc7..06b65bdfb7 100644 --- a/tensorflow/go/operation_test.go +++ b/tensorflow/go/operation_test.go @@ -222,6 +222,10 @@ func TestOperationConsumers(t *testing.T) { t.Fatalf("%d. Got op name %q, wanted %q", i, got, want) } } + + if len(b.Consumers()) != 0 { + t.Fatalf("expected %+v to have no consumers", b) + } } func forceGC() { -- GitLab From 10091aa9a90c6733ac9b9800e0a54584e7acde2f Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Tue, 19 Jun 2018 15:05:15 -0700 Subject: [PATCH 694/816] Rename llvm.BUILD to llvm.autogenerated.BUILD In practice folks tend to miss the "# This BUILD file is auto-generated; do not edit!" admonition. PiperOrigin-RevId: 201248010 --- tensorflow/workspace.bzl | 2 +- third_party/llvm/{llvm.BUILD => llvm.autogenerated.BUILD} | 0 2 files changed, 1 insertion(+), 1 deletion(-) rename third_party/llvm/{llvm.BUILD => llvm.autogenerated.BUILD} (100%) diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index 3b7a333c46..019f446b15 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -456,7 +456,7 @@ def tf_workspace(path_prefix="", tf_repo_name=""): ], sha256 = "c8ceb180ce51e00e047061dac48f014e5430ac33ea2447029065f922119b122c", strip_prefix = "llvm-21cf43199f6e79fcc345d177c8740d392f0b898e", - build_file = clean_dep("//third_party/llvm:llvm.BUILD"), + build_file = clean_dep("//third_party/llvm:llvm.autogenerated.BUILD"), ) tf_http_archive( diff --git a/third_party/llvm/llvm.BUILD b/third_party/llvm/llvm.autogenerated.BUILD similarity index 100% rename from third_party/llvm/llvm.BUILD rename to third_party/llvm/llvm.autogenerated.BUILD -- GitLab From b299731449fc0086bb87611663423386e72e34bc Mon Sep 17 00:00:00 2001 From: David Norman Date: Tue, 19 Jun 2018 23:16:12 +0100 Subject: [PATCH 695/816] Add test for verifying that the kCall change doesn't break --- .../xla/service/hlo_instruction_test.cc | 34 +++++++++++++++++++ 1 file changed, 34 insertions(+) diff --git a/tensorflow/compiler/xla/service/hlo_instruction_test.cc b/tensorflow/compiler/xla/service/hlo_instruction_test.cc index 5d6f8b931f..8ee24f9d92 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction_test.cc +++ b/tensorflow/compiler/xla/service/hlo_instruction_test.cc @@ -923,6 +923,40 @@ TEST_F(HloInstructionTest, IdenticalInstructions) { *HloInstruction::CreateBinary(shape, HloOpcode::kDivide, op1, op2))); } +TEST_F(HloInstructionTest, IdenticalCallInstructions) { + const char* const hlo_string = R"( +HloModule Module + +subcomp1 (x: f32[]) -> f32[] { + x = f32[] parameter(0) + ROOT n = f32[] sine(x) +} + +subcomp2 (x: f32[]) -> f32[] { + x = f32[] parameter(0) + ROOT n = f32[] cosine(x) +} + +ENTRY entry (param: f32[]) -> (f32[], f32[], f32[]) { + p = f32[] parameter(0) + t1 = f32[] call(p), to_apply=subcomp1 + t2 = f32[] call(p), to_apply=subcomp1 + t3 = f32[] call(p), to_apply=subcomp2 + ROOT t = (f32[], f32[], f32[]) tuple(t1, t2, t3) + } +)"; + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module, + ParseHloString(hlo_string)); + + auto* root = module->entry_computation()->root_instruction(); + auto* t1 = root->operand(0); + auto* t2 = root->operand(1); + auto* t3 = root->operand(2); + + EXPECT_TRUE(StructuralEqual(*t1, *t2)); + EXPECT_FALSE(StructuralEqual(*t1, *t3)); +} + TEST_F(HloInstructionTest, FunctionVisitor) { // Verify the function visitor HloInstruction::Accept visits all instructions // from a root properly given the following graph: -- GitLab From bbba4e06e9351bc34707bc2698b6c446acb4614c Mon Sep 17 00:00:00 2001 From: Tony Wang Date: Tue, 19 Jun 2018 15:29:38 -0700 Subject: [PATCH 696/816] Allow default TF/XLA op registration with specific backend overrides. PiperOrigin-RevId: 201252399 --- tensorflow/compiler/tf2xla/BUILD | 10 + tensorflow/compiler/tf2xla/xla_op_registry.cc | 232 +++++++++++------- tensorflow/compiler/tf2xla/xla_op_registry.h | 2 +- .../compiler/tf2xla/xla_op_registry_test.cc | 86 +++++++ 4 files changed, 234 insertions(+), 96 deletions(-) create mode 100644 tensorflow/compiler/tf2xla/xla_op_registry_test.cc diff --git a/tensorflow/compiler/tf2xla/BUILD b/tensorflow/compiler/tf2xla/BUILD index 6b73cee2a8..49c57a9f51 100644 --- a/tensorflow/compiler/tf2xla/BUILD +++ b/tensorflow/compiler/tf2xla/BUILD @@ -489,3 +489,13 @@ cc_library( "//tensorflow/core:protos_all_cc", ], ) + +tf_cc_test( + name = "xla_op_registry_test", + srcs = ["xla_op_registry_test.cc"], + deps = [ + ":xla_compiler", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + ], +) diff --git a/tensorflow/compiler/tf2xla/xla_op_registry.cc b/tensorflow/compiler/tf2xla/xla_op_registry.cc index 4692038b61..ee6da6a67a 100644 --- a/tensorflow/compiler/tf2xla/xla_op_registry.cc +++ b/tensorflow/compiler/tf2xla/xla_op_registry.cc @@ -71,16 +71,18 @@ XlaOpRegistry::~XlaOpRegistry() = default; << " have incompatible allow_resource_types settings."; return false; } - if (!x.has_device_whitelist || !y.has_device_whitelist) { - LOG(WARNING) << "Registrations of " << x.name - << " do not both have device whitelists."; + if (!x.has_device_whitelist && !y.has_device_whitelist) { + LOG(WARNING) << "Duplicate registrations of " << x.name + << "with no device whitelists."; return false; } - for (const auto& device : x.device_whitelist) { - if (y.device_whitelist.count(device) != 0) { - LOG(WARNING) << "Multiple registrations of " << x.name << " on device " - << device; - return false; + if (x.has_device_whitelist && y.has_device_whitelist) { + for (const auto& device : x.device_whitelist) { + if (y.device_whitelist.count(device) != 0) { + LOG(WARNING) << "Multiple registrations of " << x.name << " on device " + << device; + return false; + } } } if (x.compile_time_constant_inputs != y.compile_time_constant_inputs) { @@ -157,97 +159,135 @@ void XlaOpRegistry::RegisterCompilationKernels() { registry.jit_kernels_registered_ = true; OpRegistryInterface* op_registry = OpRegistry::Global(); - for (const auto& op : registry.ops_) { - const string& op_name = op.first; - const std::unique_ptr& op_registration = op.second; - const OpDef* op_def; - Status lookup_status = op_registry->LookUpOpDef(op_name, &op_def); - if (!lookup_status.ok()) { - LOG(ERROR) << lookup_status.error_message(); - XLA_LOG_LINES( - ERROR, "Ops registered: \n" + - dynamic_cast(op_registry)->DebugString(true)); + // Order of op registration: + // The goal is to allow the co-existence of backend-specific kernels and + // generic kernels. To achieve this, we enforce the following order of + // registrations for one op: + // 1. Process op registration with device whitelists: + // this pass registers backend-specific kernels for this op. + // 2. Process op registration without device whitelists: + // this pass registers the kernels for all the other supported backends. + for (auto& ops : registry.ops_) { + const string& op_name = ops.first; + std::vector>& op_registrations = ops.second; + // Partition the op registration so that the ones with device whitelists + // precede the one without device whitelist. + std::partition(op_registrations.begin(), op_registrations.end(), + [](const std::unique_ptr& op_reg) { + return op_reg->has_device_whitelist; + }); + + // Collect a set of backend registered by ops with device whitelists. + // The op registration without whitelists will register a generic kernel + // for all other backends not in this set. + std::unordered_set whitelisted_backend; + for (auto& op_registration : op_registrations) { + if (op_registration->has_device_whitelist) { + whitelisted_backend.insert(op_registration->device_whitelist.begin(), + op_registration->device_whitelist.end()); + } } - TF_CHECK_OK(lookup_status); - std::unordered_set type_attrs; - for (const OpDef::AttrDef& attr_def : op_def->attr()) { - if (attr_def.type() == "type" || attr_def.type() == "list(type)") { - type_attrs.insert(attr_def.name()); + for (auto& op_registration : op_registrations) { + const OpDef* op_def; + Status lookup_status = op_registry->LookUpOpDef(op_name, &op_def); + if (!lookup_status.ok()) { + LOG(ERROR) << lookup_status.error_message(); + XLA_LOG_LINES( + ERROR, + "Ops registered: \n" + + dynamic_cast(op_registry)->DebugString(true)); } - } + TF_CHECK_OK(lookup_status); - // Checks there are no type constraints referring to unknown attributes. - for (const auto& constraint : op_registration->type_constraints) { - if (type_attrs.find(constraint.first) == type_attrs.end()) { - LOG(FATAL) << "Unknown type attribute " << constraint.first - << " in XLA op registration for " << op_name; + std::unordered_set type_attrs; + for (const OpDef::AttrDef& attr_def : op_def->attr()) { + if (attr_def.type() == "type" || attr_def.type() == "list(type)") { + type_attrs.insert(attr_def.name()); + } } - } - for (auto& backend : registry.backends_) { - // If the operator has a device whitelist, only register on whitelisted - // devices. - if (op_registration->has_device_whitelist && - op_registration->device_whitelist.find(backend.first) == - op_registration->device_whitelist.end()) { - continue; + // Checks there are no type constraints referring to unknown attributes. + for (const auto& constraint : op_registration->type_constraints) { + if (type_attrs.find(constraint.first) == type_attrs.end()) { + LOG(FATAL) << "Unknown type attribute " << constraint.first + << " in XLA op registration for " << op_name; + } } - std::unique_ptr kdef(new KernelDef); - kdef->set_op(op_registration->name); - kdef->set_device_type(backend.first); - - // Constrain each type attribute to the intersection of: - // a) the types supported by the backend, and - // b) the types allowed by the OpDef, and - // c) the type constraints. - for (const string& type_attr : type_attrs) { - KernelDef::AttrConstraint* attr_constraint = kdef->add_constraint(); - attr_constraint->set_name(type_attr); - auto* allowed_values = - attr_constraint->mutable_allowed_values()->mutable_list(); - - const OpDef::AttrDef& op_def_attr = *FindAttr(type_attr, *op_def); - const auto* op_def_allowed_types = - op_def_attr.has_allowed_values() - ? &op_def_attr.allowed_values().list().type() - : nullptr; - auto constraint_it = op_registration->type_constraints.find(type_attr); - const std::set* type_constraints = - constraint_it != op_registration->type_constraints.end() - ? &constraint_it->second - : nullptr; - for (DataType dtype : backend.second.supported_types) { - // Filter out types that aren't allowed by the OpDef. - if (op_def_allowed_types != nullptr && - std::find(op_def_allowed_types->begin(), - op_def_allowed_types->end(), - dtype) == op_def_allowed_types->end()) { - continue; + for (auto& backend : registry.backends_) { + // If the operator has a device whitelist, only register on whitelisted + // devices. + if (op_registration->has_device_whitelist && + op_registration->device_whitelist.find(backend.first) == + op_registration->device_whitelist.end()) { + continue; + } + + // If the operator does NOT has a device whitelist, skip all devices + // that has already been registered. + if (!op_registration->has_device_whitelist && + whitelisted_backend.find(backend.first) != + whitelisted_backend.end()) { + continue; + } + + std::unique_ptr kdef(new KernelDef); + kdef->set_op(op_registration->name); + kdef->set_device_type(backend.first); + + // Constrain each type attribute to the intersection of: + // a) the types supported by the backend, and + // b) the types allowed by the OpDef, and + // c) the type constraints. + for (const string& type_attr : type_attrs) { + KernelDef::AttrConstraint* attr_constraint = kdef->add_constraint(); + attr_constraint->set_name(type_attr); + auto* allowed_values = + attr_constraint->mutable_allowed_values()->mutable_list(); + + const OpDef::AttrDef& op_def_attr = *FindAttr(type_attr, *op_def); + const auto* op_def_allowed_types = + op_def_attr.has_allowed_values() + ? &op_def_attr.allowed_values().list().type() + : nullptr; + auto constraint_it = + op_registration->type_constraints.find(type_attr); + const std::set* type_constraints = + constraint_it != op_registration->type_constraints.end() + ? &constraint_it->second + : nullptr; + for (DataType dtype : backend.second.supported_types) { + // Filter out types that aren't allowed by the OpDef. + if (op_def_allowed_types != nullptr && + std::find(op_def_allowed_types->begin(), + op_def_allowed_types->end(), + dtype) == op_def_allowed_types->end()) { + continue; + } + // Filter out types based on the type constraints. + if (type_constraints != nullptr && + type_constraints->find(dtype) == type_constraints->end()) { + continue; + } + // Passed all the filters, this type is allowed. + allowed_values->add_type(dtype); } - // Filter out types based on the type constraints. - if (type_constraints != nullptr && - type_constraints->find(dtype) == type_constraints->end()) { - continue; + if (op_registration->allow_resource_types) { + allowed_values->add_type(DT_RESOURCE); } - // Passed all the filters, this type is allowed. - allowed_values->add_type(dtype); } - if (op_registration->allow_resource_types) { - allowed_values->add_type(DT_RESOURCE); + if (backend.second.op_filter != nullptr && + !backend.second.op_filter(kdef.get())) { + continue; } + VLOG(2) << "XLA op registration: device: " << backend.first + << " op: " << op_name; + registry.kernel_registrars_.emplace_back( + new kernel_factory::OpKernelRegistrar( + new KernelDef(*kdef), "XlaJitOp", op_registration->factory)); + backend.second.kernel_defs.push_back(std::move(kdef)); } - if (backend.second.op_filter != nullptr && - !backend.second.op_filter(kdef.get())) { - continue; - } - VLOG(2) << "XLA op registration: device: " << backend.first - << " op: " << op_name; - registry.kernel_registrars_.emplace_back( - new kernel_factory::OpKernelRegistrar( - new KernelDef(*kdef), "XlaJitOp", op_registration->factory)); - backend.second.kernel_defs.push_back(std::move(kdef)); } } } @@ -265,12 +305,12 @@ std::vector XlaOpRegistry::DeviceKernels( << "Unknown backend " << compilation_device_name; for (const std::unique_ptr& k : it->second.kernel_defs) { auto op_iter = registry.ops_.find(k->op()); - CHECK(op_iter != registry.ops_.end()); + CHECK(op_iter != registry.ops_.end() && !op_iter->second.empty()); // The test in IsCompatible ensures that if there are multiple matching // registrations for this op name, they all have the same value of // compilation_only, so only the first match needs to be tested. if (include_compilation_only_kernels || - !op_iter->second->compilation_only) { + !op_iter->second.front()->compilation_only) { kernels.push_back(k.get()); } } @@ -282,10 +322,13 @@ XlaOpRegistry::CompileTimeConstantInputs(const string& op) { XlaOpRegistry& registry = Instance(); mutex_lock lock(registry.mutex_); auto it = registry.ops_.find(op); - if (it == registry.ops_.end()) { + if (it == registry.ops_.end() || it->second.empty()) { return nullptr; } - return &it->second->compile_time_constant_inputs; + // The test in IsCompatible ensures that if there are multiple matching + // registrations for this op name, they all have the same value of + // compile_time_constant_inputs, so only the first match is returned. + return &it->second.front()->compile_time_constant_inputs; } std::vector XlaOpRegistry::BackendNames() { @@ -378,16 +421,15 @@ XlaOpRegistrar::XlaOpRegistrar( std::unique_ptr registration) { XlaOpRegistry& registry = XlaOpRegistry::Instance(); mutex_lock lock(registry.mutex_); - auto existing_ops = registry.ops_.equal_range(registration->name); - for (auto existing = existing_ops.first; existing != existing_ops.second; - ++existing) { - if (!XlaOpRegistry::IsCompatible(*existing->second, *registration)) { + auto& existing_ops = registry.ops_[registration->name]; + for (auto& existing : existing_ops) { + if (!XlaOpRegistry::IsCompatible(*existing, *registration)) { LOG(FATAL) << "XLA op registration " << registration->name << " is incompatible with existing registration of the same name."; } } - registry.ops_.emplace(registration->name, std::move(registration)); + existing_ops.emplace_back(std::move(registration)); } XlaBackendRegistrar::XlaBackendRegistrar( diff --git a/tensorflow/compiler/tf2xla/xla_op_registry.h b/tensorflow/compiler/tf2xla/xla_op_registry.h index e255b01dd7..2d4593ea49 100644 --- a/tensorflow/compiler/tf2xla/xla_op_registry.h +++ b/tensorflow/compiler/tf2xla/xla_op_registry.h @@ -203,7 +203,7 @@ class XlaOpRegistry { // Map from operator name to OpRegistrations, populated by REGISTER_XLA_OP. // Registrations present under the same key must satisfy IsCompatible above, // and this is checked during registration. - std::unordered_multimap> ops_ + std::unordered_map>> ops_ GUARDED_BY(mutex_); // Have we already registered the JIT kernels on the JIT devices? diff --git a/tensorflow/compiler/tf2xla/xla_op_registry_test.cc b/tensorflow/compiler/tf2xla/xla_op_registry_test.cc new file mode 100644 index 0000000000..a2ec8dc730 --- /dev/null +++ b/tensorflow/compiler/tf2xla/xla_op_registry_test.cc @@ -0,0 +1,86 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/tf2xla/xla_op_kernel.h" +#include "tensorflow/core/platform/test.h" + +namespace tensorflow { +namespace { + +// This test is to verify the correctness of XLA op registration with specific +// backend overrides. + +// A dummy backend-specific OpKernel for CPU. +class DummyCPUOp : public XlaOpKernel { + public: + explicit DummyCPUOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {} + void Compile(XlaOpKernelContext* ctx) override { + ctx->SetOutput(0, ctx->Input(0)); + } +}; + +// A dummy generic OpKernel for all backends. +class DummyGenericOp : public XlaOpKernel { + public: + explicit DummyGenericOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {} + void Compile(XlaOpKernelContext* ctx) override { + ctx->SetOutput(0, ctx->Input(0)); + } +}; + +REGISTER_OP("DummyDuplicateOp") + .Attr("T: {float, int32}") + .Input("input: int32") + .Output("output: int32") + .Doc(R"doc( +A dummy Op. + +input: dummy input. +output: dummy output. +)doc"); + +// Register the DummyCPUOp kernel for CPU with type INT32. +REGISTER_XLA_OP(Name("DummyDuplicateOp") + .Device(DEVICE_CPU_XLA_JIT) + .TypeConstraint("T", DT_INT32), + DummyCPUOp); +// Register the DummyGeneric kernel for all registered device (except CPU since +// it is already registered), with type FLOAT. +REGISTER_XLA_OP(Name("DummyDuplicateOp").TypeConstraint("T", DT_FLOAT), + DummyGenericOp); + +// Test the correctness of registered kernels. The kernel registered for CPU +// should have type INT32 while all other kernels should have type FLOAT. +TEST(XlaOpRegistryTest, XlaOpRegistrationWithOverride) { + XlaOpRegistry::RegisterCompilationKernels(); + auto registered_kernels = GetAllRegisteredKernels(); + for (const auto& kernels : registered_kernels) { + if (kernels.op() == "DummyDuplicateOp") { + EXPECT_EQ(kernels.constraint_size(), 1); + EXPECT_EQ(kernels.constraint(0).name(), "T"); + if (kernels.device_type() == "XLA_CPU_JIT") { + EXPECT_EQ(kernels.constraint(0).allowed_values().list().type(0), + DT_INT32); + } else { + EXPECT_EQ(kernels.constraint(0).allowed_values().list().type(0), + DT_FLOAT); + } + } + } +} + +} // namespace +} // namespace tensorflow -- GitLab From 3f46969e8609584a940ccdc8626247ffa7e45d0c Mon Sep 17 00:00:00 2001 From: Kay Zhu Date: Tue, 19 Jun 2018 15:30:06 -0700 Subject: [PATCH 697/816] Automated g4 rollback of changelist 200777514 PiperOrigin-RevId: 201252470 --- .../compiler/tf2xla/kernels/mirror_pad_op.cc | 2 +- tensorflow/compiler/tf2xla/kernels/pad_op.cc | 4 +- .../tf2xla/kernels/reduction_ops_common.cc | 6 +-- .../compiler/tf2xla/kernels/sequence_ops.cc | 15 +++---- .../compiler/tf2xla/kernels/split_op.cc | 4 +- tensorflow/compiler/tf2xla/literal_util.cc | 18 --------- tensorflow/compiler/tf2xla/literal_util.h | 4 -- tensorflow/compiler/tf2xla/xla_context.cc | 2 +- tensorflow/compiler/tf2xla/xla_context.h | 2 +- tensorflow/compiler/tf2xla/xla_helpers.cc | 2 +- tensorflow/compiler/tf2xla/xla_op_kernel.cc | 39 +++++++++++++++---- tensorflow/compiler/xla/literal_util.cc | 1 - 12 files changed, 51 insertions(+), 48 deletions(-) diff --git a/tensorflow/compiler/tf2xla/kernels/mirror_pad_op.cc b/tensorflow/compiler/tf2xla/kernels/mirror_pad_op.cc index 7e9de3ef9b..c3326b4d11 100644 --- a/tensorflow/compiler/tf2xla/kernels/mirror_pad_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/mirror_pad_op.cc @@ -27,7 +27,7 @@ class MirrorPadOp : public XlaOpKernel { xla::StatusOr DoMirrorPad(const xla::XlaOp& t, const xla::Shape& original_shape, - const xla::Literal& pad_literal, + const xla::LiteralSlice& pad_literal, xla::XlaBuilder* b) { xla::XlaOp accum = t; for (int64 dimno = xla::ShapeUtil::Rank(original_shape) - 1; dimno >= 0; diff --git a/tensorflow/compiler/tf2xla/kernels/pad_op.cc b/tensorflow/compiler/tf2xla/kernels/pad_op.cc index 7c95475e7b..17b85338f7 100644 --- a/tensorflow/compiler/tf2xla/kernels/pad_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/pad_op.cc @@ -63,8 +63,8 @@ class PadOp : public XlaOpKernel { int before = pad_literal.Get({i, 0}); int after = pad_literal.Get({i, 1}); OP_REQUIRES(ctx, before >= 0 && after >= 0, - errors::InvalidArgument("Paddings must be non-negative: ", - before, " ", after)); + errors::InvalidArgument( + "Paddings must be non-negative: ", before, " ", after)); dim->set_edge_padding_low(before); dim->set_edge_padding_high(after); } diff --git a/tensorflow/compiler/tf2xla/kernels/reduction_ops_common.cc b/tensorflow/compiler/tf2xla/kernels/reduction_ops_common.cc index 4fd5bfd039..44510c731e 100644 --- a/tensorflow/compiler/tf2xla/kernels/reduction_ops_common.cc +++ b/tensorflow/compiler/tf2xla/kernels/reduction_ops_common.cc @@ -56,9 +56,9 @@ void XlaReductionOp::Compile(XlaOpKernelContext* ctx) { // Evaluate the constant, reshaping to a 1-vector if it is a scalar. xla::Literal axes_literal; - OP_REQUIRES_OK(ctx, - ctx->ConstantInputReshaped( - 1, {axes_tensor_shape.num_elements()}, &axes_literal)); + OP_REQUIRES_OK( + ctx, ctx->ConstantInputReshaped(1, {axes_tensor_shape.num_elements()}, + &axes_literal)); VLOG(1) << "data shape: " << data_shape.DebugString(); VLOG(1) << "axes : " << axes_literal.ToString(); diff --git a/tensorflow/compiler/tf2xla/kernels/sequence_ops.cc b/tensorflow/compiler/tf2xla/kernels/sequence_ops.cc index 2c31f8d908..bc3d0bf5df 100644 --- a/tensorflow/compiler/tf2xla/kernels/sequence_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/sequence_ops.cc @@ -55,9 +55,10 @@ Status GetIntValue(int index, XlaOpKernelContext* ctx, int64* value) { // The type-specific part of the implementation of Range. template -Status CreateRangeTensor(const xla::Literal& start_literal, - const xla::Literal& limit_literal, - const xla::Literal& delta_literal, Tensor* output) { +Status CreateRangeTensor(const xla::LiteralSlice& start_literal, + const xla::LiteralSlice& limit_literal, + const xla::LiteralSlice& delta_literal, + Tensor* output) { T start = start_literal.Get({}); T limit = limit_literal.Get({}); T delta = delta_literal.Get({}); @@ -67,13 +68,13 @@ Status CreateRangeTensor(const xla::Literal& start_literal, } if (delta > 0) { if (start > limit) { - return errors::InvalidArgument("Requires start <= limit when delta > 0: ", - start, "/", limit); + return errors::InvalidArgument( + "Requires start <= limit when delta > 0: ", start, "/", limit); } } else { if (start < limit) { - return errors::InvalidArgument("Requires start >= limit when delta < 0: ", - start, "/", limit); + return errors::InvalidArgument( + "Requires start >= limit when delta < 0: ", start, "/", limit); } } int64 size = diff --git a/tensorflow/compiler/tf2xla/kernels/split_op.cc b/tensorflow/compiler/tf2xla/kernels/split_op.cc index 8958b2e770..9b54058541 100644 --- a/tensorflow/compiler/tf2xla/kernels/split_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/split_op.cc @@ -134,7 +134,7 @@ class SplitVOp : public XlaOpKernel { errors::InvalidArgument( "Number of ways to split should be > 0, but got ", num_split)); - // check that sizes are correct + // Check that sizes are correct. int total_split_size = 0; int neg_one_dim = -1; std::vector split_sizes_vec(num_split, -1); @@ -148,7 +148,7 @@ class SplitVOp : public XlaOpKernel { " number of elements as the output. Got ", split_size_shape.dims(), "-D and ", split_size_shape.num_elements(), " elements")); - // get the dimension of this split + // Get the dimension of this split. xla::Literal split_size_literal; OP_REQUIRES_OK(ctx, ctx->ConstantInput(1, &split_size_literal)); diff --git a/tensorflow/compiler/tf2xla/literal_util.cc b/tensorflow/compiler/tf2xla/literal_util.cc index db56b12837..b43405a1a4 100644 --- a/tensorflow/compiler/tf2xla/literal_util.cc +++ b/tensorflow/compiler/tf2xla/literal_util.cc @@ -22,24 +22,6 @@ limitations under the License. namespace tensorflow { -Status HostTensorToLiteral(const Tensor& host_tensor, xla::Literal* literal) { - xla::Shape literal_shape; - TF_RETURN_IF_ERROR(TensorShapeToXLAShape( - host_tensor.dtype(), host_tensor.shape(), &literal_shape)); - - *literal = xla::Literal(literal_shape); - - // memcpy over the payload ... - // TODO(phawkins): handle string types. - size_t total_bytes = host_tensor.TotalBytes(); - if (total_bytes > 0) { - void* dst_ptr = literal->untyped_data(); - const void* src_ptr = DMAHelper::base(&host_tensor); - memcpy(dst_ptr, src_ptr, total_bytes); - } - return Status::OK(); -} - Status HostTensorToBorrowingLiteral(const Tensor& host_tensor, xla::BorrowingLiteral* literal) { xla::Shape xla_shape; diff --git a/tensorflow/compiler/tf2xla/literal_util.h b/tensorflow/compiler/tf2xla/literal_util.h index 74685025c1..ab7e861f33 100644 --- a/tensorflow/compiler/tf2xla/literal_util.h +++ b/tensorflow/compiler/tf2xla/literal_util.h @@ -26,10 +26,6 @@ limitations under the License. namespace tensorflow { -// Copies 'host_tensor' to an XLA Literal. Fails if host_tensor is of an -// unsupported type. -Status HostTensorToLiteral(const Tensor& host_tensor, xla::Literal* literal); - // Returns a BorrowingLiteral that utilizes the same underlying buffer owned by // 'host_tensor'. Status HostTensorToBorrowingLiteral(const Tensor& host_tensor, diff --git a/tensorflow/compiler/tf2xla/xla_context.cc b/tensorflow/compiler/tf2xla/xla_context.cc index 098072d33c..67174b251d 100644 --- a/tensorflow/compiler/tf2xla/xla_context.cc +++ b/tensorflow/compiler/tf2xla/xla_context.cc @@ -92,7 +92,7 @@ void XlaContext::AddRetval(int retval_index, DataType type, } Status XlaContext::AddConstRetval(int retval_index, DataType dtype, - const xla::Literal& literal) { + const xla::LiteralSlice& literal) { VLOG(1) << "Adding retval index " << retval_index << " with non-data-dependent tensor to XLA computation"; if (retvals_.size() <= retval_index) { diff --git a/tensorflow/compiler/tf2xla/xla_context.h b/tensorflow/compiler/tf2xla/xla_context.h index 341bf6ff1f..5960daaefd 100644 --- a/tensorflow/compiler/tf2xla/xla_context.h +++ b/tensorflow/compiler/tf2xla/xla_context.h @@ -83,7 +83,7 @@ class XlaContext : public ResourceBase { // As for Retval, but for return values that are compile-time constants. Status AddConstRetval(int retval_index, DataType dtype, - const xla::Literal& literal); + const xla::LiteralSlice& literal); // Creates a resource with resource `kind` and initial value `handle`. `name` // is a descriptive name for use in error messages. See the `XlaResource` diff --git a/tensorflow/compiler/tf2xla/xla_helpers.cc b/tensorflow/compiler/tf2xla/xla_helpers.cc index a1da176fe3..93cd340485 100644 --- a/tensorflow/compiler/tf2xla/xla_helpers.cc +++ b/tensorflow/compiler/tf2xla/xla_helpers.cc @@ -25,7 +25,6 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" #include "tensorflow/compiler/xla/types.h" -#include "tensorflow/core/common_runtime/dma_helper.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/lib/gtl/array_slice.h" @@ -248,6 +247,7 @@ Status XlaHelpers::OneHot(xla::XlaBuilder* builder, int64 depth, int axis, return errors::InvalidArgument("Invalid argument type ", DataTypeString(index_type)); } + xla::BorrowingLiteral linspace_literal; TF_RETURN_IF_ERROR(HostTensorToBorrowingLiteral(linspace, &linspace_literal)); diff --git a/tensorflow/compiler/tf2xla/xla_op_kernel.cc b/tensorflow/compiler/tf2xla/xla_op_kernel.cc index 76c68d81af..c6ddbcc6e1 100644 --- a/tensorflow/compiler/tf2xla/xla_op_kernel.cc +++ b/tensorflow/compiler/tf2xla/xla_op_kernel.cc @@ -20,6 +20,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/literal_util.h" #include "tensorflow/compiler/tf2xla/shape_util.h" #include "tensorflow/compiler/tf2xla/xla_context.h" +#include "tensorflow/core/common_runtime/dma_helper.h" namespace tensorflow { @@ -87,6 +88,25 @@ Status XlaOpKernelContext::ConstantInputReshaped( } const XlaExpression* expression = CastExpressionFromTensor(tensor); + auto copy_tensor_to_literal = [](const Tensor& tensor, + xla::Literal* literal) { + xla::Shape literal_shape; + TF_RETURN_IF_ERROR( + TensorShapeToXLAShape(tensor.dtype(), tensor.shape(), &literal_shape)); + + *literal = xla::Literal(literal_shape); + + // memcpy over the payload ... + // TODO(phawkins): handle string types. + size_t total_bytes = tensor.TotalBytes(); + if (total_bytes > 0) { + void* dst_ptr = literal->untyped_data(); + const void* src_ptr = DMAHelper::base(&tensor); + memcpy(dst_ptr, src_ptr, total_bytes); + } + return Status::OK(); + }; + // If the tensor has a known constant value, there is no need to invoke XLA. if (expression->has_constant_value()) { Tensor temp(tensor.dtype()); @@ -95,13 +115,15 @@ Status XlaOpKernelContext::ConstantInputReshaped( // with the enclosing Tensor. return errors::Internal("Incompatible shapes in ConstantInputReshaped."); } - return HostTensorToLiteral(temp, constant_literal); + + return copy_tensor_to_literal(temp, constant_literal); } // Make sure we treat zero-element tensors as constant. if (new_shape.num_elements() == 0) { Tensor temp(tensor.dtype(), new_shape); - return HostTensorToLiteral(temp, constant_literal); + + return copy_tensor_to_literal(temp, constant_literal); } xla::XlaOp handle = expression->handle(); @@ -162,7 +184,8 @@ Status XlaOpKernelContext::ConstantInputReshaped( } // Converts an int32 or int64 scalar literal to an int64. -static Status LiteralToInt64Scalar(const xla::Literal& literal, int64* out) { +static Status LiteralToInt64Scalar(const xla::LiteralSlice& literal, + int64* out) { if (xla::ShapeUtil::Rank(literal.shape()) != 0) { return errors::InvalidArgument("value is not a scalar"); } @@ -177,7 +200,8 @@ static Status LiteralToInt64Scalar(const xla::Literal& literal, int64* out) { } // Converts an float32 or float64 scalar literal to a float64. -static Status LiteralToFloat64Scalar(const xla::Literal& literal, double* out) { +static Status LiteralToFloat64Scalar(const xla::LiteralSlice& literal, + double* out) { if (xla::ShapeUtil::Rank(literal.shape()) != 0) { return errors::InvalidArgument("value is not a scalar"); } @@ -204,7 +228,7 @@ Status XlaOpKernelContext::ConstantInputAsFloatScalar(int index, double* out) { } // Converts an int32 or int64 1D literal to an int64 vector. -static Status LiteralToInt64Vector(const xla::Literal& literal, +static Status LiteralToInt64Vector(const xla::LiteralSlice& literal, std::vector* out) { if (xla::ShapeUtil::Rank(literal.shape()) != 1) { return errors::InvalidArgument("value is not 1D"); @@ -368,8 +392,9 @@ void XlaOpKernelContext::SetOutput(int index, const xla::XlaOp& handle) { void XlaOpKernelContext::SetConstantOutput(int index, const Tensor& constant) { const TensorShape& shape = constant.shape(); - xla::Literal literal; - OP_REQUIRES_OK(context_, HostTensorToLiteral(constant, &literal)); + xla::BorrowingLiteral literal; + OP_REQUIRES_OK(context_, HostTensorToBorrowingLiteral(constant, &literal)); + xla::XlaOp handle = builder()->ConstantLiteral(literal); CHECK_NE(handle.builder(), nullptr); diff --git a/tensorflow/compiler/xla/literal_util.cc b/tensorflow/compiler/xla/literal_util.cc index 19e6d288c0..7c6a181b0a 100644 --- a/tensorflow/compiler/xla/literal_util.cc +++ b/tensorflow/compiler/xla/literal_util.cc @@ -2355,7 +2355,6 @@ LiteralSlice::LiteralSlice(const LiteralBase& literal, BorrowingLiteral::BorrowingLiteral(const char* src_buf_ptr, const Shape& shape) : LiteralBase(), shape_(MakeUnique(shape)) { CHECK(ShapeUtil::IsArray(*shape_)); - CHECK_NE(src_buf_ptr, nullptr); CHECK(LayoutUtil::HasLayout(*shape_)); root_piece_ = Piece(); -- GitLab From e4c2f5234dbb193cd7b137227cf7eca490fc3acd Mon Sep 17 00:00:00 2001 From: Billy Lamberta Date: Tue, 19 Jun 2018 15:51:11 -0700 Subject: [PATCH 698/816] Lowercase filename --- .../{NMT_with_Attention.ipynb => nmt_with_attention.ipynb} | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) rename tensorflow/contrib/eager/python/examples/nmt_with_attention/{NMT_with_Attention.ipynb => nmt_with_attention.ipynb} (99%) diff --git a/tensorflow/contrib/eager/python/examples/nmt_with_attention/NMT_with_Attention.ipynb b/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb similarity index 99% rename from tensorflow/contrib/eager/python/examples/nmt_with_attention/NMT_with_Attention.ipynb rename to tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb index d40dbfe63b..1e7f2f060f 100644 --- a/tensorflow/contrib/eager/python/examples/nmt_with_attention/NMT_with_Attention.ipynb +++ b/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb @@ -3,7 +3,7 @@ "nbformat_minor": 0, "metadata": { "colab": { - "name": "NMT_with_Attention.ipynb", + "name": "nmt_with_attention.ipynb", "version": "0.3.2", "views": {}, "default_view": {}, @@ -42,10 +42,10 @@ "# Neural Machine Translation with Attention\n", "\n", "
\n", - "\n", + "\n", " Run in Google Colab \n", "\n", - "View source on Github
" + "View source on Github" ] }, { -- GitLab From b5a75b274434a75c1782a878fe4b32fa7f5ba01b Mon Sep 17 00:00:00 2001 From: Billy Lamberta Date: Tue, 19 Jun 2018 15:53:15 -0700 Subject: [PATCH 699/816] Cleanup NMT notebook, fix image links --- .../nmt_with_attention.ipynb | 252 +++++------------- 1 file changed, 73 insertions(+), 179 deletions(-) diff --git a/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb b/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb index 1e7f2f060f..c17afe5b6d 100644 --- a/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb +++ b/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb @@ -55,18 +55,15 @@ }, "cell_type": "markdown", "source": [ - "This notebook trains a sequence to sequence (seq2seq) model for Spanish to English translation using [tf.keras](https://www.tensorflow.org/programmers_guide/keras) and [eager execution](https://www.tensorflow.org/programmers_guide/eager). This is an advanced example for readers with prior background in sequence to sequence models.\n", + "This notebook trains a sequence to sequence (seq2seq) model for Spanish to English translation using [tf.keras](https://www.tensorflow.org/programmers_guide/keras) and [eager execution](https://www.tensorflow.org/programmers_guide/eager). This is an advanced example that assumes some knowledge of sequence to sequence models.\n", "\n", - "Here's an example output you'll see after running this notebook. After training the model, we'll translate the Spanish sentence \"¿todavia estan en casa?\", and we'll see the output \"are you still at home ?\". \n", + "After training the model in this notebook, you will be able to input a Spanish sentence, such as *\"¿todavia estan en casa?\"*, and return the English translation: *\"are you still at home?\"*\n", "\n", - "The translation quality is reasonable for a toy example, but what's even cooler is the attention plot that will be generated:\n", + "The translation quality is reasonable for a toy example, but the generated attention plot is perhaps more interesting. This shows which parts of the input sentence has the model's attention while translating:\n", "\n", - "This shows which parts of the input sentence the model is attending to while translating. \n", + "\"spanish-english\n", "\n", - "![alt text](https://tensorflow.org/images/spanish-english.png)\n", - "\n", - "\n", - "Ballpark, this example will take approximately 10 mintues to run on a single P100 GPU.\n", + "Note: This example takes approximately 10 mintues to run on a single P100 GPU.\n", "\n", "This notebook requires Tensorflow version >= 1.9" ] @@ -84,16 +81,15 @@ }, "cell_type": "code", "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", "# Import TensorFlow and enable eager execution\n", "import tensorflow as tf\n", "import tensorflow.contrib.eager as tfe\n", + "\n", "tf.enable_eager_execution()\n", "\n", - "# We'll generate plots of attention in order to see which parts of a sentence\n", - "# our model focuses on during translation\n", "import matplotlib.pyplot as plt\n", - "\n", - "# Scikit-learn includes many handy utilities\n", "from sklearn.model_selection import train_test_split\n", "\n", "import unicodedata\n", @@ -114,22 +110,18 @@ "source": [ "## Download and prepare the dataset\n", "\n", - "We'll use a dataset helpfully provided by http://www.manythings.org/anki/. This contains language translation pairs, in this format:\n", + "We'll use a language dataset provided by http://www.manythings.org/anki/. This dataset contains language translation pairs in the format:\n", "\n", "```\n", "May I borrow this book?\t¿Puedo tomar prestado este libro?\n", "```\n", "\n", - "There are a variety of such datasets you can explore. This notebook will download and use the English-Spanish dataset. \n", - "\n", - "We've hosted a copy on Google Cloud for convenience. Alternatively, you can download and use a similar dataset (like English -> German) from http://www.manythings.org/anki/ and use it instead without changing any other code.\n", + "There are a variety of languages available, but we'll use the English-Spanish dataset. For convenience, we've hosted a copy of this dataset on Google Cloud, but you can also download your own copy. After downloading the dataset, here are the steps we'll take to prepare the data:\n", "\n", - "After we've downloaded it, here are the steps we'll use to prepare the data:\n", - "\n", - "* Add a start and end token to each sentence\n", - "* Clean the sentences by removing special characters\n", - "* Create a word index and reverse word index (dictionaries mapping from word -> id and id -> word)\n", - "* Pad each sentence to a maximum length" + "1. Add a *start* and *end* token to each sentence.\n", + "2. Clean the sentences by removing special characters.\n", + "3. Create a word index and reverse word index (dictionaries mapping from word → id and id → word).\n", + "4. Pad each sentence to a maximum length." ] }, { @@ -157,7 +149,7 @@ }, { "metadata": { - "id": "DzIS_cRu3jEb", + "id": "rd0jw-eC3jEh", "colab_type": "code", "colab": { "autoexec": { @@ -171,24 +163,9 @@ "# Converts the unicode file to ascii\n", "def unicode_to_ascii(s):\n", " return ''.join(c for c in unicodedata.normalize('NFD', s)\n", - " if unicodedata.category(c) != 'Mn')" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "id": "rd0jw-eC3jEh", - "colab_type": "code", - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - } - }, - "cell_type": "code", - "source": [ + " if unicodedata.category(c) != 'Mn')\n", + "\n", + "\n", "def preprocess_sentence(w):\n", " w = unicode_to_ascii(w.lower().strip())\n", " \n", @@ -224,9 +201,9 @@ }, "cell_type": "code", "source": [ - "# first we remove the pronunciations\n", - "# second we clean the sentences\n", - "# and third we return word pairs in [ENGLISH, SPANISH] format\n", + "# 1. Remove the pronunciations\n", + "# 2. Clean the sentences\n", + "# 3. Return word pairs in the format: [ENGLISH, SPANISH]\n", "def create_dataset(path, num_examples):\n", " lines = open(path, encoding='UTF-8').read().strip().split('\\n')\n", " \n", @@ -277,25 +254,6 @@ "execution_count": 0, "outputs": [] }, - { - "metadata": { - "id": "lU4fj_gG3jE6", - "colab_type": "code", - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - } - }, - "cell_type": "code", - "source": [ - "def max_length(tensor):\n", - " return max(len(t) for t in tensor)" - ], - "execution_count": 0, - "outputs": [] - }, { "metadata": { "id": "eAY9k49G3jE_", @@ -309,6 +267,10 @@ }, "cell_type": "code", "source": [ + "def max_length(tensor):\n", + " return max(len(t) for t in tensor)\n", + "\n", + "\n", "def load_dataset(path, num_examples):\n", " # creating cleaned input, output pairs\n", " pairs = create_dataset(path, num_examples)\n", @@ -350,9 +312,9 @@ }, "cell_type": "markdown", "source": [ - "## Limit the size of the dataset to experiment faster (optional)\n", + "### Limit the size of the dataset to experiment faster (optional)\n", "\n", - "Training on the complete dataset of >100,000 sentences will take some time. Below, we'll limit the size of the dataset to 30,000 sentences, in order to experiment faster (of course, translation quality will improve with more data)." + "Training on the complete dataset of >100,000 sentences will take a long time. To train faster, we can limit the size of the dataset to 30,000 sentences (of course, translation quality degrades will less data):" ] }, { @@ -390,6 +352,8 @@ "source": [ "# Creating training and validation sets using an 80-20 split\n", "input_tensor_train, input_tensor_val, target_tensor_train, target_tensor_val = train_test_split(input_tensor, target_tensor, test_size=0.2)\n", + "\n", + "# Show length\n", "len(input_tensor_train), len(target_tensor_train), len(input_tensor_val), len(target_tensor_val)" ], "execution_count": 0, @@ -402,7 +366,7 @@ }, "cell_type": "markdown", "source": [ - "## Create a tf.data dataset" + "### Create a tf.data dataset" ] }, { @@ -423,24 +387,8 @@ "embedding_dim = 256\n", "units = 1024\n", "vocab_inp_size = len(inp_lang.word2idx)\n", - "vocab_tar_size = len(targ_lang.word2idx)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "id": "fYLzjawH3jFW", - "colab_type": "code", - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - } - }, - "cell_type": "code", - "source": [ + "vocab_tar_size = len(targ_lang.word2idx)\n", + "\n", "dataset = tf.data.Dataset.from_tensor_slices((input_tensor_train, target_tensor_train)).shuffle(BUFFER_SIZE)\n", "dataset = dataset.apply(tf.contrib.data.batch_and_drop_remainder(BATCH_SIZE))" ], @@ -454,39 +402,36 @@ }, "cell_type": "markdown", "source": [ - "## Write the encoder and decoder model with attention\n", - "Here, we'll implement an encoder-deocder model. For background on how these work, you can read more about them in this previous [tutorial](https://www.tensorflow.org/tutorials/seq2seq). In this example, we'll use a more recent (and much easier) set of APIs.\n", + "## Write the encoder and decoder model\n", "\n", - "![alt text](https://storage.googleapis.com/yashkatariya/attention_picture.png)\n", + "Here, we'll implement an encoder-decoder model with attention which you can read about in the TensorFlow [Neural Machine Translation (seq2seq) tutorial](https://www.tensorflow.org/tutorials/seq2seq). This example uses a more recent set of APIs. This notebook implements the [attention equations](https://www.tensorflow.org/tutorials/seq2seq#background_on_the_attention_mechanism) from the seq2seq tutorial. The following diagram shows that each input words is assigned a weight by the attention mechanism which is then used by the decoder to predict the next word in the sentence.\n", "\n", - "The code below implements the attention [equations](https://www.tensorflow.org/tutorials/seq2seq#background_on_the_attention_mechanism) from the previous tutorial. In the above diagram, each of the input words is assigned a weight by the attention mechanism which is then used by the decoder to predict the next word in the sentence.\n", + "\"attention\n", "\n", "The input is put through an encoder model which gives us the encoder output of shape *(batch_size, max_length, hidden_size)* and the encoder hidden state of shape *(batch_size, hidden_size)*. \n", "\n", - "Here are the equations we'll implement below:\n", + "Here are the equations that are implemented:\n", "\n", - "![alt text](https://storage.googleapis.com/yashkatariya/attention_eq1.png)\n", - "![alt text](https://storage.googleapis.com/yashkatariya/attention_eq2.png)\n", + "\"attention\n", + "\"attention\n", "\n", - "We'll use *Bahdanau attention*. Lets decide on some notations before we write the simplified form:\n", + "We're using *Bahdanau attention*. Lets decide on notation before writing the simplified form:\n", "\n", "* FC = Fully connected (dense) layer\n", "* EO = Encoder output\n", "* H = hidden state\n", "* X = input to the decoder\n", "\n", - "Pseudo-code:\n", + "And the pseudo-code:\n", "\n", - " * score = FC(tanh(FC(EO) + FC(H)))*\n", - " * attention weights = softmax(score, axis = 1)*. Softmax by default is applied on the last axis but here we want to apply it on the *1st axis*, since the shape of score is *(batch_size, max_length, hidden_size)*. Max_length is the length of our input. Since we are trying to assign a weight to each input, softmax should be applied on that axis.\n", - " * context vector = sum(attention weights * EO, axis = 1)*. Same reason as above for choosing axis as 1.\n", - " * embedding output = The input to the decoder X is passed through an embedding layer.*\n", - " * merged vector = concat(embedding output, context vector)*\n", - " * This merged vector is then given to the GRU*\n", + "* `score = FC(tanh(FC(EO) + FC(H)))`\n", + "* `attention weights = softmax(score, axis = 1)`. Softmax by default is applied on the last axis but here we want to apply it on the *1st axis*, since the shape of score is *(batch_size, max_length, hidden_size)*. `Max_length` is the length of our input. Since we are trying to assign a weight to each input, softmax should be applied on that axis.\n", + "* `context vector = sum(attention weights * EO, axis = 1)`. Same reason as above for choosing axis as 1.\n", + "* `embedding output` = The input to the decoder X is passed through an embedding layer.\n", + "* `merged vector = concat(embedding output, context vector)`\n", + "* This merged vector is then given to the GRU\n", " \n", - "The shapes of all the vectors at each step have been specified in the comments in the code.\n", - " \n", - " " + "The shapes of all the vectors at each step have been specified in the comments in the code:" ] }, { @@ -647,7 +592,7 @@ }, "cell_type": "markdown", "source": [ - "## Define the optimizers and the loss function" + "## Define the optimizer and the loss function" ] }, { @@ -663,24 +608,9 @@ }, "cell_type": "code", "source": [ - "optimizer = tf.train.AdamOptimizer()" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "id": "rdLCjYff3jFv", - "colab_type": "code", - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - } - }, - "cell_type": "code", - "source": [ + "optimizer = tf.train.AdamOptimizer()\n", + "\n", + "\n", "def loss_function(real, pred):\n", " mask = 1 - np.equal(real, 0)\n", " loss_ = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=real, logits=pred) * mask\n", @@ -698,13 +628,13 @@ "source": [ "## Training\n", "\n", - "* Here we pass the input through the encoder which return *encoder output* and the *encoder hidden state*.\n", - "* The encoder output, encoder hidden state and the decoder input (which is the \"start\" token) is passed to the decoder.\n", - "* The decoder returns the *predictions* and the *decoder hidden state*.\n", - "* The decoder hidden state is then passed back into the model and the predictions are used to calculate the loss.\n", - "* To decide the next input to the decoder we use *teacher forcing*.\n", - "* *Teacher forcing* is the technique in which we pass the *target word as the next input* to the decoder.\n", - "* The final step is to calculate the gradients and apply it to the optimizer and backpropagate." + "1. Pass the *input* through the *encoder* which return *encoder output* and the *encoder hidden state*.\n", + "2. The encoder output, encoder hidden state and the decoder input (which is the *start token*) is passed to the decoder.\n", + "3. The decoder returns the *predictions* and the *decoder hidden state*.\n", + "4. The decoder hidden state is then passed back into the model and the predictions are used to calculate the loss.\n", + "5. Use *teacher forcing* to decide the next input to the decoder.\n", + "6. *Teacher forcing* is the technique where the *target word* is passed as the *next input* to the decoder.\n", + "7. The final step is to calculate the gradients and apply it to the optimizer and backpropagate." ] }, { @@ -757,29 +687,13 @@ " optimizer.apply_gradients(zip(gradients, variables), tf.train.get_or_create_global_step())\n", "\n", " if batch % 100 == 0:\n", - " print ('Epoch {} Batch {} Loss {:.4f}'.format(epoch + 1, batch, loss.numpy() / int(targ.shape[1])))\n", + " print('Epoch {} Batch {} Loss {:.4f}'.format(epoch + 1,\n", + " batch,\n", + " loss.numpy() / int(targ.shape[1])))\n", " \n", - " print ('Epoch {} Loss {:.4f}'.format(epoch + 1, total_loss/len(input_tensor)))\n", - " print ('Time taken for 1 epoch', time.time() - start, 'sec')\n", - " print ()" - ], - "execution_count": 0, - "outputs": [] - }, - { - "metadata": { - "id": "K5bWEZM53jF3", - "colab_type": "code", - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - } - }, - "cell_type": "code", - "source": [ - "" + " print('Epoch {} Loss {:.4f}'.format(epoch + 1,\n", + " total_loss/len(input_tensor)))\n", + " print('Time taken for 1 epoch {} sec\\n'.format(time.time() - start))" ], "execution_count": 0, "outputs": [] @@ -793,11 +707,11 @@ "source": [ "## Translate\n", "\n", - "* The evaluate function is similar to the training loop. The only change is that we don't use teacher forcing here. The input to the decoder at each time step is its previous predictions along with the hidden state and the encoder output.\n", - "* We stop predicting when the model predicts the *'end' token*.\n", - "* We also store the *attention weights for every time step*.\n", + "* The evaluate function is similar to the training loop, except we don't use *teacher forcing* here. The input to the decoder at each time step is its previous predictions along with the hidden state and the encoder output.\n", + "* Stop predicting when the model predicts the *end token*.\n", + "* And store the *attention weights for every time step*.\n", "\n", - "NOTE: The encoder output is calculated only once for one input." + "Note: The encoder output is calculated only once for one input." ] }, { @@ -897,8 +811,8 @@ "def translate(sentence, encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ):\n", " result, sentence, attention_plot = evaluate(sentence, encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ)\n", " \n", - " print ('Input:', sentence)\n", - " print ('Predicted translation:', result)\n", + " print('Input: {}'.format(sentence))\n", + " print('Predicted translation: {}'.format(result))\n", " \n", " attention_plot = attention_plot[:len(result.split(' ')), :len(sentence.split(' '))]\n", " plot_attention(attention_plot, sentence.split(' '), result.split(' '))" @@ -986,31 +900,11 @@ }, "cell_type": "markdown", "source": [ - "Next steps\n", + "## Next steps\n", "\n", - "* If you like, you can experiment with a different dataset (say, for Englsh to German, or English to French) translation by downloading one from http://www.manythings.org/anki/\n", - "* Experiment with training with a larger dataset, or for more epochs\n", - "\n", - "Thanks for reading, we hope you enjoyed and find this code useful. If you find anything we can improve in this notebook, please open a pull request. \n" + "* [Download a different dataset](http://www.manythings.org/anki/) to experiment with translations, for example, English to German, or English to French.\n", + "* Experiment with training on a larger dataset, or using more epochs\n" ] - }, - { - "metadata": { - "id": "yMUwCtOizvxg", - "colab_type": "code", - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - } - }, - "cell_type": "code", - "source": [ - "" - ], - "execution_count": 0, - "outputs": [] } ] } \ No newline at end of file -- GitLab From 94c6e1b3e13b1456e4578eaa50e2066b1d26b40a Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 19 Jun 2018 15:56:44 -0700 Subject: [PATCH 700/816] ConfigureGcsHooks: Fixed a couple of typos. - _configure_op was spelled with a trailing 's' - _block_cache_op was only conditionally set but unconditionally read. Added a fake test that triggered the bugs before and passes after. PiperOrigin-RevId: 201256874 --- tensorflow/contrib/cloud/python/ops/gcs_config_ops.py | 7 ++++++- .../contrib/cloud/python/ops/gcs_config_ops_test.py | 10 ++++++++++ 2 files changed, 16 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/cloud/python/ops/gcs_config_ops.py b/tensorflow/contrib/cloud/python/ops/gcs_config_ops.py index 8c8c5acb31..95e7e744d3 100644 --- a/tensorflow/contrib/cloud/python/ops/gcs_config_ops.py +++ b/tensorflow/contrib/cloud/python/ops/gcs_config_ops.py @@ -120,13 +120,18 @@ class ConfigureGcsHook(training.SessionRunHook): def begin(self): if self._credentials: self._credentials_placeholder = array_ops.placeholder(dtypes.string) - self._credentials_ops = gen_gcs_config_ops.gcs_configure_credentials( + self._credentials_op = gen_gcs_config_ops.gcs_configure_credentials( self._credentials_placeholder) + else: + self._credentials_op = None + if self._block_cache: self._block_cache_op = gen_gcs_config_ops.gcs_configure_block_cache( max_cache_size=self._block_cache.max_bytes, block_size=self._block_cache.block_size, max_staleness=self._block_cache.max_staleness) + else: + self._block_cache_op = None def after_create_session(self, session, coord): del coord diff --git a/tensorflow/contrib/cloud/python/ops/gcs_config_ops_test.py b/tensorflow/contrib/cloud/python/ops/gcs_config_ops_test.py index fc0c994812..9b6c056d6c 100644 --- a/tensorflow/contrib/cloud/python/ops/gcs_config_ops_test.py +++ b/tensorflow/contrib/cloud/python/ops/gcs_config_ops_test.py @@ -29,6 +29,16 @@ class GcsConfigOpsTest(test.TestCase): with self.test_session() as sess: gcs_config_ops.configure_gcs(sess, block_cache=cfg) + def testConfigureGcsHook(self): + creds = {'client_id': 'fake_client', + 'refresh_token': 'fake_token', + 'client_secret': 'fake_secret', + 'type': 'authorized_user'} + hook = gcs_config_ops.ConfigureGcsHook(credentials=creds) + hook.begin() + with self.test_session() as sess: + sess.run = lambda _, feed_dict=None, options=None, run_metadata=None: None + hook.after_create_session(sess, None) if __name__ == '__main__': test.main() -- GitLab From aec5a0191e21ce022f47d743a4954e13f710cd8f Mon Sep 17 00:00:00 2001 From: Dimitris Vardoulakis Date: Tue, 19 Jun 2018 16:00:53 -0700 Subject: [PATCH 701/816] [TF:XLA] Prevent overflow in hlo_scheduling, when compiling AutoML models. PiperOrigin-RevId: 201257475 --- tensorflow/compiler/xla/service/hlo_scheduling.cc | 15 ++++++++++----- 1 file changed, 10 insertions(+), 5 deletions(-) diff --git a/tensorflow/compiler/xla/service/hlo_scheduling.cc b/tensorflow/compiler/xla/service/hlo_scheduling.cc index 641b9ecec9..c6d3909af6 100644 --- a/tensorflow/compiler/xla/service/hlo_scheduling.cc +++ b/tensorflow/compiler/xla/service/hlo_scheduling.cc @@ -399,12 +399,9 @@ StatusOr> DFSMemoryScheduler( const LogicalBuffer::SizeFunction& size_function, const tensorflow::gtl::FlatMap& memory_by_computation) { - // This ordering is based on DFS post-order, with a heuristic to decide which - // operand to visit first. The heuristic is based on 'extra_users', which is - // simply users-1 for each instruction. By subtracting 1, we're saying that - // instructions with no users or a single user don't count; instructions with - // lots of fan-out will be visited earlier. + // These variables are a hack to prevent overflows. int64 cumulative_total_size = 0; + int64 total_hlos = computation.parent()->NumUniqueInstructionIds(); tensorflow::gtl::FlatMap extra_users; tensorflow::gtl::FlatMap total_sizes; for (const HloInstruction* hlo : computation.MakeInstructionPostOrder()) { @@ -413,6 +410,11 @@ StatusOr> DFSMemoryScheduler( total_sizes[hlo] = 0; continue; } + // This ordering is based on DFS post-order, with a heuristic to decide + // which operand to visit first. The heuristic is based on 'extra_users', + // which is simply users-1 for each instruction. By subtracting 1, we're + // saying that instructions with no users or a single user don't count; + // instructions with lots of fan-out will be visited earlier. extra_users[hlo] = hlo->users().empty() ? 0 : hlo->users().size() - 1; int64 logical_buffer_size = SumLogicalBufferSizes( points_to_analysis.GetBuffersDefinedByInstruction(hlo), size_function); @@ -428,10 +430,13 @@ StatusOr> DFSMemoryScheduler( // lead to it. But computation is a DAG, so we are double-counting nodes, // which can lead to overflows for large programs. // cumulative_total_size caps the size to prevent overflows. + // Same for total_hlos: it prevents overflows on very large and branchy + // models, where the number of paths is exponential to the number of nodes. // NOTE(dimvar): this is quite ugly and should be changed. It's unclear // why we care about transitive sizes; when scheduling a node, its input // and output buffers should be all that matters, not its "history". total_sizes[hlo] = std::min(total_sizes[hlo], cumulative_total_size); + extra_users[hlo] = std::min(extra_users[hlo], total_hlos); } CHECK_EQ(extra_users.size(), computation.instruction_count()); CHECK_EQ(total_sizes.size(), computation.instruction_count()); -- GitLab From 5bc928f1f52e512a53f9e3297f6421cd9462dfc3 Mon Sep 17 00:00:00 2001 From: Zhenyu Tan Date: Tue, 19 Jun 2018 16:01:46 -0700 Subject: [PATCH 702/816] Add an advanced activation layer for ReLU PiperOrigin-RevId: 201257601 --- tensorflow/python/keras/layers/__init__.py | 1 + .../keras/layers/advanced_activations.py | 37 ++++ .../keras/layers/advanced_activations_test.py | 14 ++ .../tensorflow.keras.layers.-re-l-u.pbtxt | 175 ++++++++++++++++++ .../api/golden/tensorflow.keras.layers.pbtxt | 4 + 5 files changed, 231 insertions(+) create mode 100644 tensorflow/tools/api/golden/tensorflow.keras.layers.-re-l-u.pbtxt diff --git a/tensorflow/python/keras/layers/__init__.py b/tensorflow/python/keras/layers/__init__.py index 8fb663a17e..647bda1fa2 100644 --- a/tensorflow/python/keras/layers/__init__.py +++ b/tensorflow/python/keras/layers/__init__.py @@ -29,6 +29,7 @@ from tensorflow.python.keras.engine import Layer from tensorflow.python.keras.layers.advanced_activations import LeakyReLU from tensorflow.python.keras.layers.advanced_activations import PReLU from tensorflow.python.keras.layers.advanced_activations import ELU +from tensorflow.python.keras.layers.advanced_activations import ReLU from tensorflow.python.keras.layers.advanced_activations import ThresholdedReLU from tensorflow.python.keras.layers.advanced_activations import Softmax diff --git a/tensorflow/python/keras/layers/advanced_activations.py b/tensorflow/python/keras/layers/advanced_activations.py index 8ade3c3174..bb52ed5ad0 100644 --- a/tensorflow/python/keras/layers/advanced_activations.py +++ b/tensorflow/python/keras/layers/advanced_activations.py @@ -278,3 +278,40 @@ class Softmax(Layer): @tf_utils.shape_type_conversion def compute_output_shape(self, input_shape): return input_shape + + +@tf_export('keras.layers.ReLU') +class ReLU(Layer): + """Rectified Linear Unit activation function. + + Input shape: + Arbitrary. Use the keyword argument `input_shape` + (tuple of integers, does not include the samples axis) + when using this layer as the first layer in a model. + + Output shape: + Same shape as the input. + + Arguments: + max_value: float >= 0. Maximum activation value. + """ + + def __init__(self, max_value=None, **kwargs): + super(ReLU, self).__init__(**kwargs) + self.support_masking = True + self.max_value = K.cast_to_floatx(max_value) + if self.max_value < 0.: + raise ValueError('max_value of Relu layer ' + 'cannot be negative value: ' + str(max_value)) + + def call(self, inputs): + return activations.relu(inputs, max_value=self.max_value) + + def get_config(self): + config = {'max_value': self.max_value} + base_config = super(ReLU, self).get_config() + return dict(list(base_config.items()) + list(config.items())) + + @tf_utils.shape_type_conversion + def compute_output_shape(self, input_shape): + return input_shape diff --git a/tensorflow/python/keras/layers/advanced_activations_test.py b/tensorflow/python/keras/layers/advanced_activations_test.py index 81c76db14c..9e1f15b1bc 100644 --- a/tensorflow/python/keras/layers/advanced_activations_test.py +++ b/tensorflow/python/keras/layers/advanced_activations_test.py @@ -62,6 +62,20 @@ class AdvancedActivationsTest(test.TestCase): kwargs={'axis': 1}, input_shape=(2, 3, 4)) + def test_relu(self): + with self.test_session(): + testing_utils.layer_test(keras.layers.ReLU, + kwargs={'max_value': 10}, + input_shape=(2, 3, 4)) + + def test_relu_with_invalid_arg(self): + with self.assertRaisesRegexp( + ValueError, 'max_value of Relu layer cannot be negative value: -10'): + with self.test_session(): + testing_utils.layer_test(keras.layers.ReLU, + kwargs={'max_value': -10}, + input_shape=(2, 3, 4)) + if __name__ == '__main__': test.main() diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-re-l-u.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-re-l-u.pbtxt new file mode 100644 index 0000000000..f3a96ab895 --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-re-l-u.pbtxt @@ -0,0 +1,175 @@ +path: "tensorflow.keras.layers.ReLU" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "non_trainable_variables" + mtype: "" + } + member { + name: "non_trainable_weights" + mtype: "" + } + member { + name: "outbound_nodes" + mtype: "" + } + member { + name: "output" + mtype: "" + } + member { + name: "output_mask" + mtype: "" + } + member { + name: "output_shape" + mtype: "" + } + member { + name: "trainable_variables" + mtype: "" + } + member { + name: "trainable_weights" + mtype: "" + } + member { + name: "updates" + mtype: "" + } + member { + name: "variables" + mtype: "" + } + member { + name: "weights" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'max_value\'], varargs=None, keywords=kwargs, defaults=[\'None\'], " + } + member_method { + name: "add_loss" + argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_update" + argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_variable" + argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "add_weight" + argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "apply" + argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "build" + argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "call" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "compute_mask" + argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "compute_output_shape" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "count_params" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_mask_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_shape_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_losses_for" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_mask_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_shape_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_updates_for" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_weights" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_weights" + argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.pbtxt index 709eb5be55..0df5a1b91e 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.pbtxt @@ -296,6 +296,10 @@ tf_module { name: "RNN" mtype: "" } + member { + name: "ReLU" + mtype: "" + } member { name: "RepeatVector" mtype: "" -- GitLab From a455319208888e72af34fc3021122803a53a047d Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 19 Jun 2018 16:02:35 -0700 Subject: [PATCH 703/816] Automated g4 rollback of changelist 201217989 PiperOrigin-RevId: 201257755 --- .../optimizers/arithmetic_optimizer.cc | 24 +++++++++---------- 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc index d49c087071..90be051764 100644 --- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc +++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc @@ -2519,14 +2519,14 @@ class ConvertLog1pStage : public ArithmeticOptimizerStage { bool* modified) { const auto& t = ctx().graph_properties->GetInputProperties(input->name())[i]; - const auto& c = - ctx().graph_properties->GetInputProperties(input->name())[j]; - for (int k = 0; k < c.shape().dim_size(); ++k) { - // Skip if c shape is not fully determined. - if (c.shape().dim(k).size() < 0) { + for (int k = 0; k < t.shape().dim_size(); ++k) { + // Skip if t shape is not fully determined. + if (t.shape().dim(k).size() < 0) { return Status::OK(); } } + const auto& c = + ctx().graph_properties->GetInputProperties(input->name())[j]; TensorShapeProto broadcast_shape; if (!ShapeAfterBroadcast(t.shape(), c.shape(), &broadcast_shape)) { return errors::InvalidArgument("Cannot get broadcast shape for: ", @@ -2537,15 +2537,15 @@ class ConvertLog1pStage : public ArithmeticOptimizerStage { // broadcast. return Status::OK(); } - if (TensorShape::IsValid(c.shape()) && c.has_value()) { - Tensor constant(c.dtype(), c.shape()); - if (!constant.FromProto(c.value())) { + if (TensorShape::IsValid(t.shape()) && t.has_value()) { + Tensor tensor(t.dtype(), t.shape()); + if (!tensor.FromProto(t.value())) { return errors::InvalidArgument("Cannot parse tensor from proto: ", t.value().DebugString()); } complex128 element; - for (int k = 0; k < constant.NumElements(); ++k) { - if (!GetElement(constant, k, &element)) { + for (int k = 0; k < tensor.NumElements(); ++k) { + if (!GetElement(tensor, k, &element)) { // input data type is not supported by log1p. Skip. return Status::OK(); } @@ -2558,8 +2558,8 @@ class ConvertLog1pStage : public ArithmeticOptimizerStage { TF_RETURN_IF_ERROR(GetInputNode(input->input(i), &x)); TF_RETURN_IF_ERROR(GetInputNode(input->input(j), &y)); node->set_op("Log1p"); - node->set_input(0, x->name()); - node->add_input(AsControlDependency(y->name())); + node->set_input(0, y->name()); + node->add_input(AsControlDependency(x->name())); ForwardControlDependencies(node, {input}); AddToOptimizationQueue(node); -- GitLab From 5d93b995160fe7fbf92fa05a427be6a43fa73764 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 19 Jun 2018 16:07:14 -0700 Subject: [PATCH 704/816] Derivative of tf.random_gamma with respect to the alpha parameter. Previously, tf.random_gamma(shape, alpha, beta) was differentiable only w.r.t. beta. This commit adds the derivative w.r.t. alpha. The implementation is based on Eigen's gamma_sample_der_alpha function, which computes the "implicit reparameterization" derivative. This function is not directly exposed in the public TensorFlow API. PiperOrigin-RevId: 201258617 --- tensorflow/core/BUILD | 1 + .../base_api/api_def_RandomGammaGrad.pbtxt | 5 + .../kernels/cwise_op_gpu_random_grad.cu.cc | 26 ++ .../core/kernels/cwise_op_random_grad.cc | 25 ++ tensorflow/core/kernels/cwise_ops.h | 4 + tensorflow/core/ops/random_ops.cc | 7 + tensorflow/python/BUILD | 14 + tensorflow/python/kernel_tests/random/BUILD | 17 ++ .../kernel_tests/random/random_grad_test.py | 240 ++++++++++++++++++ tensorflow/python/ops/random_grad.py | 65 +++++ tensorflow/python/ops/random_ops.py | 48 ++-- tensorflow/python/ops/standard_ops.py | 1 + 12 files changed, 436 insertions(+), 17 deletions(-) create mode 100644 tensorflow/core/api_def/base_api/api_def_RandomGammaGrad.pbtxt create mode 100644 tensorflow/core/kernels/cwise_op_gpu_random_grad.cu.cc create mode 100644 tensorflow/core/kernels/cwise_op_random_grad.cc create mode 100644 tensorflow/python/kernel_tests/random/random_grad_test.py create mode 100644 tensorflow/python/ops/random_grad.py diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index a0cf59852b..b37198310e 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -998,6 +998,7 @@ tf_gen_op_libs( "nn_ops", "no_op", "parsing_ops", + "random_grad", "random_ops", "remote_fused_graph_ops", "resource_variable_ops", diff --git a/tensorflow/core/api_def/base_api/api_def_RandomGammaGrad.pbtxt b/tensorflow/core/api_def/base_api/api_def_RandomGammaGrad.pbtxt new file mode 100644 index 0000000000..d2bd76f8b9 --- /dev/null +++ b/tensorflow/core/api_def/base_api/api_def_RandomGammaGrad.pbtxt @@ -0,0 +1,5 @@ +op { + graph_op_name: "RandomGammaGrad" + visibility: HIDDEN + summary: "Computes the derivative of a Gamma random sample w.r.t. `alpha`." +} diff --git a/tensorflow/core/kernels/cwise_op_gpu_random_grad.cu.cc b/tensorflow/core/kernels/cwise_op_gpu_random_grad.cu.cc new file mode 100644 index 0000000000..fd0a95ecc5 --- /dev/null +++ b/tensorflow/core/kernels/cwise_op_gpu_random_grad.cu.cc @@ -0,0 +1,26 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#if GOOGLE_CUDA + +#include "tensorflow/core/kernels/cwise_ops_gpu_common.cu.h" + +namespace tensorflow { +namespace functor { +DEFINE_BINARY2(random_gamma_grad, float, double); +} // namespace functor +} // namespace tensorflow + +#endif // GOOGLE_CUDA diff --git a/tensorflow/core/kernels/cwise_op_random_grad.cc b/tensorflow/core/kernels/cwise_op_random_grad.cc new file mode 100644 index 0000000000..8e388ead9e --- /dev/null +++ b/tensorflow/core/kernels/cwise_op_random_grad.cc @@ -0,0 +1,25 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/core/kernels/cwise_ops_common.h" + +namespace tensorflow { +REGISTER2(BinaryOp, CPU, "RandomGammaGrad", functor::random_gamma_grad, float, + double); +#if GOOGLE_CUDA +REGISTER2(BinaryOp, GPU, "RandomGammaGrad", functor::random_gamma_grad, float, + double); +#endif +} // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_ops.h b/tensorflow/core/kernels/cwise_ops.h index 8b015df4e1..1b1a704d42 100644 --- a/tensorflow/core/kernels/cwise_ops.h +++ b/tensorflow/core/kernels/cwise_ops.h @@ -770,6 +770,10 @@ struct minimum : base> {}; template struct igamma : base> {}; +template +struct random_gamma_grad + : base> {}; + template struct igammac : base> {}; diff --git a/tensorflow/core/ops/random_ops.cc b/tensorflow/core/ops/random_ops.cc index 80ffae5796..a76248e05f 100644 --- a/tensorflow/core/ops/random_ops.cc +++ b/tensorflow/core/ops/random_ops.cc @@ -138,6 +138,13 @@ REGISTER_OP("RandomGamma") return Status::OK(); }); +REGISTER_OP("RandomGammaGrad") + .Input("alpha: T") + .Input("sample: T") + .Output("output: T") + .Attr("T: {float, double}") + .SetShapeFn(shape_inference::BroadcastBinaryOpShapeFn); + REGISTER_OP("RandomPoisson") .SetIsStateful() .Input("shape: S") diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD index cf4eac5328..3fc25772f6 100644 --- a/tensorflow/python/BUILD +++ b/tensorflow/python/BUILD @@ -1990,6 +1990,7 @@ py_library( ":math_grad", ":math_ops", ":platform", + ":random_grad", ":resource_variable_ops", ":spectral_grad", ":util", @@ -2368,6 +2369,19 @@ py_library( ], ) +py_library( + name = "random_grad", + srcs = ["ops/random_grad.py"], + srcs_version = "PY2AND3", + deps = [ + ":array_ops", + ":dtypes", + ":framework_ops", + ":math_ops", + ":random_ops_gen", + ], +) + py_library( name = "random_ops", srcs = ["ops/random_ops.py"], diff --git a/tensorflow/python/kernel_tests/random/BUILD b/tensorflow/python/kernel_tests/random/BUILD index 4855e1c564..a9bd68971e 100644 --- a/tensorflow/python/kernel_tests/random/BUILD +++ b/tensorflow/python/kernel_tests/random/BUILD @@ -111,6 +111,23 @@ cuda_py_test( tags = ["nozapfhahn"], ) +cuda_py_test( + name = "random_grad_test", + size = "small", + srcs = ["random_grad_test.py"], + additional_deps = [ + "//third_party/py/numpy", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework", + "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:math_ops", + "//tensorflow/python:platform", + "//tensorflow/python:random_grad", + "//tensorflow/python:random_ops", + ], +) + cuda_py_test( name = "random_poisson_test", size = "medium", diff --git a/tensorflow/python/kernel_tests/random/random_grad_test.py b/tensorflow/python/kernel_tests/random/random_grad_test.py new file mode 100644 index 0000000000..c1d455b785 --- /dev/null +++ b/tensorflow/python/kernel_tests/random/random_grad_test.py @@ -0,0 +1,240 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for tensorflow.ops.random_grad.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import gradients_impl +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import random_grad +from tensorflow.python.ops import random_ops +from tensorflow.python.platform import test +from tensorflow.python.platform import tf_logging + + +class AddLeadingUnitDimensionsTest(test.TestCase): + + def testBasic(self): + ret = random_grad.add_leading_unit_dimensions(array_ops.ones([3, 2, 1]), 3) + self.assertAllEqual(ret.shape, [1, 1, 1, 3, 2, 1]) + + def testZeroExtraDimensions(self): + ret = random_grad.add_leading_unit_dimensions(array_ops.ones([3, 2, 1]), 0) + self.assertAllEqual(ret.shape, [3, 2, 1]) + + def testScalarInput(self): + ret = random_grad.add_leading_unit_dimensions(1.0, 2) + self.assertAllEqual(ret.shape, [1, 1]) + + def testUnknownShape(self): + x = array_ops.placeholder(dtypes.float32) + num_dimensions = array_ops.placeholder(dtypes.int32) + ret = random_grad.add_leading_unit_dimensions(x, num_dimensions) + with self.test_session() as sess: + ret_val = sess.run(ret, {x: np.ones([2, 2]), num_dimensions: 2}) + self.assertAllEqual(ret_val.shape, [1, 1, 2, 2]) + + +class RandomGammaGradTest(test.TestCase): + """Tests for derivative of a sample ~ Gamma(alpha, beta) wrt alpha and beta. + + The sample is an "implicit" function of alpha, beta and the independent random + noise u. The derivatives we are looking for are + d sample(alpha, beta, u) / dalpha (and dbeta). + + The derivative w.r.t. beta is computed by the standard automatic + differentiation, so we trust that it is computed correctly. + + The derivative w.r.t. alpha is computed by Eigen function, so we test it in + several ways. Unfortunately, the standard derivative checking by perturbing + the parameter is impossible here, because we cannot fix the value of u + in the random sampler. Instead, we compare the derivative for the given pair + of (sample, alpha) to the values computed in various ways, and also check + some statistical properties of the derivative. + """ + + def testGradientsShape(self): + shape = [2, 3] + alpha = array_ops.ones([2, 2]) + beta = array_ops.ones([1, 2]) + sample = random_ops.random_gamma(shape, alpha, beta) + grads_alpha, grads_beta = gradients_impl.gradients(sample, [alpha, beta]) + self.assertAllEqual(grads_alpha.shape, alpha.shape) + self.assertAllEqual(grads_beta.shape, beta.shape) + + def testGradientsShapeWithOneSamplePerParameter(self): + shape = [] + alpha = array_ops.ones([2, 2]) + beta = array_ops.ones([1, 2]) + sample = random_ops.random_gamma(shape, alpha, beta) + grads_alpha, grads_beta = gradients_impl.gradients(sample, [alpha, beta]) + self.assertAllEqual(grads_alpha.shape, alpha.shape) + self.assertAllEqual(grads_beta.shape, beta.shape) + + def testGradientsUnknownShape(self): + shape = array_ops.placeholder(dtypes.int32) + alpha = array_ops.placeholder(dtypes.float32) + beta = array_ops.placeholder(dtypes.float32) + sample = random_ops.random_gamma(shape, alpha, beta) + grads_alpha, grads_beta = gradients_impl.gradients(sample, [alpha, beta]) + + alpha_val = np.ones([1, 2]) + beta_val = np.ones([2, 1]) + with self.test_session() as sess: + grads_alpha_val, grads_beta_val = sess.run( + [grads_alpha, grads_beta], + {alpha: alpha_val, beta: beta_val, shape: [2, 1]}) + self.assertAllEqual(grads_alpha_val.shape, alpha_val.shape) + self.assertAllEqual(grads_beta_val.shape, beta_val.shape) + + def _testCompareToExplicitDerivative(self, dtype): + """Compare to the explicit reparameterization derivative. + + Verifies that the computed derivative satisfies + dsample / dalpha = d igammainv(alpha, u) / dalpha, + where u = igamma(alpha, sample). + + Args: + dtype: TensorFlow dtype to perform the computations in. + """ + delta = 1e-3 + np_dtype = dtype.as_numpy_dtype + try: + from scipy import misc # pylint: disable=g-import-not-at-top + from scipy import special # pylint: disable=g-import-not-at-top + + alpha_val = np.logspace(-2, 3, dtype=np_dtype) + alpha = constant_op.constant(alpha_val) + sample = random_ops.random_gamma([], alpha, np_dtype(1.0), dtype=dtype) + actual = gradients_impl.gradients(sample, alpha)[0] + + (sample_val, actual_val) = self.evaluate((sample, actual)) + + u = special.gammainc(alpha_val, sample_val) + expected_val = misc.derivative( + lambda alpha_prime: special.gammaincinv(alpha_prime, u), + alpha_val, dx=delta * alpha_val) + + self.assertAllClose(actual_val, expected_val, rtol=1e-3, atol=1e-3) + except ImportError as e: + tf_logging.warn("Cannot use special functions in a test: %s" % str(e)) + + def testCompareToExplicitDerivativeFloat(self): + self._testCompareToExplicitDerivative(dtypes.float32) + + def testCompareToExplicitDerivativeDouble(self): + self._testCompareToExplicitDerivative(dtypes.float64) + + def _testCompareToImplicitDerivative(self, dtype): + """Compare to the implicit reparameterization derivative. + + Let's derive the formula we compare to. + + Start from the fact that CDF maps a random variable to the Uniform + random variable: + igamma(alpha, sample) = u, where u ~ Uniform(0, 1). + + Apply d / dalpha to both sides: + d igamma(alpha, sample) / dalpha + + d igamma(alpha, sample) / dsample * dsample/dalpha = 0 + d igamma(alpha, sample) / dalpha + + d igamma(alpha, sample) / dsample * dsample / dalpha = 0 + dsample/dalpha = - (d igamma(alpha, sample) / dalpha) + / d igamma(alpha, sample) / dsample + + This is the equation (8) of https://arxiv.org/abs/1805.08498 + + Args: + dtype: TensorFlow dtype to perform the computations in. + """ + np_dtype = dtype.as_numpy_dtype + alpha = constant_op.constant(np.logspace(-2, 3, dtype=np_dtype)) + sample = random_ops.random_gamma([], alpha, np_dtype(1.0), dtype=dtype) + actual = gradients_impl.gradients(sample, alpha)[0] + + sample_sg = array_ops.stop_gradient(sample) + cdf = math_ops.igamma(alpha, sample_sg) + dcdf_dalpha, dcdf_dsample = gradients_impl.gradients( + cdf, [alpha, sample_sg]) + # Numerically unstable due to division, do not try at home. + expected = -dcdf_dalpha / dcdf_dsample + + (actual_val, expected_val) = self.evaluate((actual, expected)) + + self.assertAllClose(actual_val, expected_val, rtol=1e-3, atol=1e-3) + + def testCompareToImplicitDerivativeFloat(self): + self._testCompareToImplicitDerivative(dtypes.float32) + + def testCompareToImplicitDerivativeDouble(self): + self._testCompareToImplicitDerivative(dtypes.float64) + + def testAverageAlphaGradient(self): + """Statistical test for the gradient. + + Using the equation (5) of https://arxiv.org/abs/1805.08498, we have + 1 = d/dalpha E_{sample ~ Gamma(alpha, 1)} sample + = E_{sample ~ Gamma(alpha, 1)} dsample/dalpha. + Here we verify that the rhs is fairly close to one. + The convergence speed is not great, so we use many samples and loose bounds. + """ + num_samples = 1000 + alpha = constant_op.constant([0.8, 1e1, 1e3], dtype=dtypes.float32) + sample = random_ops.random_gamma([num_samples], alpha) + # We need to average the gradients, which is equivalent to averaging the + # samples and then doing backprop. + mean_sample = math_ops.reduce_mean(sample, axis=0) + dsample_dalpha = gradients_impl.gradients(mean_sample, alpha)[0] + dsample_dalpha_val = self.evaluate(dsample_dalpha) + self.assertAllClose(dsample_dalpha_val, [1.0] * 3, atol=1e-1, rtol=1e-1) + + def testQuadraticLoss(self): + """Statistical test for the gradient. + + The equation (5) of https://arxiv.org/abs/1805.08498 says + d/dalpha E_{sample ~ Gamma(alpha, 1)} f(sample) + = E_{sample ~ Gamma(alpha, 1)} df(sample)/dalpha. + + Choose a quadratic loss function f(sample) = (sample - t)^2. + Then, the lhs can be computed analytically: + d/dalpha E_{sample ~ Gamma(alpha, 1)} f(sample) + = d/dalpha [ (alpha + alpha^2) - 2 * t * alpha + t^2 ] + = 1 + 2 * alpha - 2 * t. + + We compare the Monte-Carlo estimate of the expectation with the + true gradient. + """ + num_samples = 1000 + t = 0.3 + alpha = 0.5 + expected = 1 + 2 * alpha - 2 * t + + alpha = constant_op.constant(alpha) + sample = random_ops.random_gamma([num_samples], alpha, 1.0) + loss = math_ops.reduce_mean(math_ops.square(sample - t)) + dloss_dalpha = gradients_impl.gradients(loss, alpha)[0] + dloss_dalpha_val = self.evaluate(dloss_dalpha) + self.assertAllClose(expected, dloss_dalpha_val, atol=1e-1, rtol=1e-1) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/python/ops/random_grad.py b/tensorflow/python/ops/random_grad.py new file mode 100644 index 0000000000..baa8e2e2cd --- /dev/null +++ b/tensorflow/python/ops/random_grad.py @@ -0,0 +1,65 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Gradients for operators defined in random_ops.py.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import gen_random_ops +from tensorflow.python.ops import math_ops + + +def add_leading_unit_dimensions(x, num_dimensions): + new_shape = array_ops.concat( + [array_ops.ones([num_dimensions], dtype=dtypes.int32), + array_ops.shape(x)], axis=0) + return array_ops.reshape(x, new_shape) + + +@ops.RegisterGradient("RandomGamma") +def _RandomGammaGrad(op, grad): # pylint: disable=invalid-name + """Returns the gradient of a Gamma sample w.r.t. alpha. + + The gradient is computed using implicit differentiation, see + "Implicit Reparameterization Gradients" (https://arxiv.org/abs/1805.08498). + + Args: + op: A `RandomGamma` operation. We assume that the inputs to the operation + are `shape` and `alpha` tensors, and the output is the `sample` tensor. + grad: The incoming gradient `dloss / dsample` of the same shape as + `op.outputs[0]`. + + Returns: + A `Tensor` with derivatives `dloss / dalpha` + """ + shape = op.inputs[0] + alpha = op.inputs[1] + sample = op.outputs[0] + + with ops.control_dependencies([grad]): + # Make the parameters alpha broadcastable with samples by appending + # unit dimensions. + num_sample_dimensions = array_ops.shape(shape)[0] + alpha_broadcastable = add_leading_unit_dimensions( + alpha, num_sample_dimensions) + partial_a = gen_random_ops.random_gamma_grad(alpha_broadcastable, sample) + + # The first input is shape; the second input is alpha. + return (None, math_ops.reduce_sum( + grad * partial_a, axis=math_ops.range(num_sample_dimensions))) diff --git a/tensorflow/python/ops/random_ops.py b/tensorflow/python/ops/random_ops.py index 6a2dd3f1cd..ad154d204e 100644 --- a/tensorflow/python/ops/random_ops.py +++ b/tensorflow/python/ops/random_ops.py @@ -368,25 +368,41 @@ def random_gamma(shape, `alpha` is the shape parameter describing the distribution(s), and `beta` is the inverse scale parameter(s). - Example: + Note: Because internal calculations are done using `float64` and casting has + `floor` semantics, we must manually map zero outcomes to the smallest + possible positive floating-point value, i.e., `np.finfo(dtype).tiny`. This + means that `np.finfo(dtype).tiny` occurs more frequently than it otherwise + should. This bias can only happen for small values of `alpha`, i.e., + `alpha << 1` or large values of `beta`, i.e., `beta >> 1`. - samples = tf.random_gamma([10], [0.5, 1.5]) - # samples has shape [10, 2], where each slice [:, 0] and [:, 1] represents - # the samples drawn from each distribution + The samples are differentiable w.r.t. alpha and beta. + The derivatives are computed using the approach described in the paper - samples = tf.random_gamma([7, 5], [0.5, 1.5]) - # samples has shape [7, 5, 2], where each slice [:, :, 0] and [:, :, 1] - # represents the 7x5 samples drawn from each of the two distributions + [Michael Figurnov, Shakir Mohamed, Andriy Mnih. + Implicit Reparameterization Gradients, 2018](https://arxiv.org/abs/1805.08498) - samples = tf.random_gamma([30], [[1.],[3.],[5.]], beta=[[3., 4.]]) - # samples has shape [30, 3, 2], with 30 samples each of 3x2 distributions. + Example: - Note: Because internal calculations are done using `float64` and casting has - `floor` semantics, we must manually map zero outcomes to the smallest - possible positive floating-point value, i.e., `np.finfo(dtype).tiny`. This - means that `np.finfo(dtype).tiny` occurs more frequently than it otherwise - should. This bias can only happen for small values of `alpha`, i.e., - `alpha << 1` or large values of `beta`, i.e., `beta >> 1`. + ```python + samples = tf.random_gamma([10], [0.5, 1.5]) + # samples has shape [10, 2], where each slice [:, 0] and [:, 1] represents + # the samples drawn from each distribution + + samples = tf.random_gamma([7, 5], [0.5, 1.5]) + # samples has shape [7, 5, 2], where each slice [:, :, 0] and [:, :, 1] + # represents the 7x5 samples drawn from each of the two distributions + + alpha = tf.constant([[1.],[3.],[5.]]) + beta = tf.constant([[3., 4.]]) + samples = tf.random_gamma([30], alpha=alpha, beta=beta) + # samples has shape [30, 3, 2], with 30 samples each of 3x2 distributions. + + loss = tf.reduce_mean(tf.square(samples)) + dloss_dalpha, dloss_dbeta = tf.gradients(loss, [alpha, beta]) + # unbiased stochastic derivatives of the loss function + alpha.shape == dloss_dalpha.shape # True + beta.shape == dloss_dbeta.shape # True + ``` Args: shape: A 1-D integer Tensor or Python array. The shape of the output samples @@ -421,8 +437,6 @@ def random_gamma(shape, gen_random_ops.random_gamma( shape, alpha_broadcast, seed=seed1, seed2=seed2) / beta) -ops.NotDifferentiable("RandomGamma") - @tf_export("random_poisson") def random_poisson(lam, shape, dtype=dtypes.float32, seed=None, name=None): diff --git a/tensorflow/python/ops/standard_ops.py b/tensorflow/python/ops/standard_ops.py index a2d24711e2..d0e5f70025 100644 --- a/tensorflow/python/ops/standard_ops.py +++ b/tensorflow/python/ops/standard_ops.py @@ -29,6 +29,7 @@ from tensorflow.python.ops import cudnn_rnn_grad from tensorflow.python.ops import data_flow_grad from tensorflow.python.ops import manip_grad from tensorflow.python.ops import math_grad +from tensorflow.python.ops import random_grad from tensorflow.python.ops import sparse_grad from tensorflow.python.ops import spectral_grad from tensorflow.python.ops import state_grad -- GitLab From e0e566e3a16d417d823ef83cfce5dfcc81762a6d Mon Sep 17 00:00:00 2001 From: Billy Lamberta Date: Tue, 19 Jun 2018 16:21:29 -0700 Subject: [PATCH 705/816] typo --- .../python/examples/nmt_with_attention/nmt_with_attention.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb b/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb index c17afe5b6d..cacb7c1872 100644 --- a/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb +++ b/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb @@ -61,7 +61,7 @@ "\n", "The translation quality is reasonable for a toy example, but the generated attention plot is perhaps more interesting. This shows which parts of the input sentence has the model's attention while translating:\n", "\n", - "\"spanish-english\n", + "\"spanish-english\n", "\n", "Note: This example takes approximately 10 mintues to run on a single P100 GPU.\n", "\n", -- GitLab From d8d7cd6c6c70446be60d4eea653c043bb4324206 Mon Sep 17 00:00:00 2001 From: Billy Lamberta Date: Tue, 19 Jun 2018 16:36:57 -0700 Subject: [PATCH 706/816] minor fixes --- .../nmt_with_attention/nmt_with_attention.ipynb | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb b/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb index cacb7c1872..ada101828b 100644 --- a/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb +++ b/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb @@ -63,9 +63,7 @@ "\n", "\"spanish-english\n", "\n", - "Note: This example takes approximately 10 mintues to run on a single P100 GPU.\n", - "\n", - "This notebook requires Tensorflow version >= 1.9" + "Note: This example takes approximately 10 mintues to run on a single P100 GPU." ] }, { @@ -83,7 +81,7 @@ "source": [ "from __future__ import absolute_import, division, print_function\n", "\n", - "# Import TensorFlow and enable eager execution\n", + "# Import TensorFlow >= 1.9 and enable eager execution\n", "import tensorflow as tf\n", "import tensorflow.contrib.eager as tfe\n", "\n", @@ -96,7 +94,9 @@ "import re\n", "import numpy as np\n", "import os\n", - "import time" + "import time\n", + "\n", + "print(tf.__version__)" ], "execution_count": 0, "outputs": [] @@ -314,7 +314,7 @@ "source": [ "### Limit the size of the dataset to experiment faster (optional)\n", "\n", - "Training on the complete dataset of >100,000 sentences will take a long time. To train faster, we can limit the size of the dataset to 30,000 sentences (of course, translation quality degrades will less data):" + "Training on the complete dataset of >100,000 sentences will take a long time. To train faster, we can limit the size of the dataset to 30,000 sentences (of course, translation quality degrades with less data):" ] }, { -- GitLab From e1a7a2ded90fbbdfc3a41954a332a04c73dd62c6 Mon Sep 17 00:00:00 2001 From: Xuechen Li Date: Tue, 19 Jun 2018 16:35:36 -0700 Subject: [PATCH 707/816] Add scripts to write to tfrecords, read from tfrecords and training. PiperOrigin-RevId: 201263223 --- .../eager/python/examples/revnet/BUILD | 32 ++++ .../eager/python/examples/revnet/blocks.py | 16 +- .../python/examples/revnet/cifar_input.py | 105 +++++++++++++ .../python/examples/revnet/cifar_tfrecords.py | 123 +++++++++++++++ .../eager/python/examples/revnet/config.py | 20 ++- .../eager/python/examples/revnet/main.py | 147 ++++++++++++++++++ .../eager/python/examples/revnet/revnet.py | 39 ++--- .../python/examples/revnet/revnet_test.py | 47 ++---- 8 files changed, 456 insertions(+), 73 deletions(-) create mode 100644 tensorflow/contrib/eager/python/examples/revnet/cifar_input.py create mode 100644 tensorflow/contrib/eager/python/examples/revnet/cifar_tfrecords.py create mode 100644 tensorflow/contrib/eager/python/examples/revnet/main.py diff --git a/tensorflow/contrib/eager/python/examples/revnet/BUILD b/tensorflow/contrib/eager/python/examples/revnet/BUILD index a2bdd9f8a6..432bb546f8 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/BUILD +++ b/tensorflow/contrib/eager/python/examples/revnet/BUILD @@ -80,3 +80,35 @@ cuda_py_test( "optonly", ], ) + +# Training +py_library( + name = "cifar_input", + srcs = ["cifar_input.py"], + srcs_version = "PY2AND3", + deps = [ + ":revnet", + "//tensorflow:tensorflow_py", + ], +) + +py_binary( + name = "cifar_tfrecords", + srcs = ["cifar_tfrecords.py"], + srcs_version = "PY2AND3", + deps = [ + "//tensorflow:tensorflow_py", + ], +) + +py_binary( + name = "main", + srcs = ["main.py"], + srcs_version = "PY2AND3", + deps = [ + ":cifar_input", + ":config", + ":revnet", + "//tensorflow:tensorflow_py", + ], +) diff --git a/tensorflow/contrib/eager/python/examples/revnet/blocks.py b/tensorflow/contrib/eager/python/examples/revnet/blocks.py index 8751651fed..af41f64286 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/blocks.py +++ b/tensorflow/contrib/eager/python/examples/revnet/blocks.py @@ -200,19 +200,19 @@ class _Residual(tf.keras.Model): x2, self.filters // 2, self.strides, axis=self.axis) grads_combined = tape.gradient( - y2, [y1] + self.g.variables, output_gradients=[dy2]) + y2, [y1] + self.g.trainable_variables, output_gradients=[dy2]) dy2_y1, dg = grads_combined[0], grads_combined[1:] dy1_plus = dy2_y1 + dy1 grads_combined = tape.gradient( - y1, [x1, x2] + self.f.variables, output_gradients=[dy1_plus]) + y1, [x1, x2] + self.f.trainable_variables, output_gradients=[dy1_plus]) dx1, dx2, df = grads_combined[0], grads_combined[1], grads_combined[2:] dx2 += tape.gradient(x2_down, [x2], output_gradients=[dy2])[0] del tape grads = df + dg - vars_ = self.f.variables + self.g.variables + vars_ = self.f.trainable_variables + self.g.trainable_variables return tf.concat([dx1, dx2], axis=self.axis), grads, vars_ @@ -246,7 +246,7 @@ def _BottleneckResidualInner(filters, model.add( tf.keras.layers.BatchNormalization( axis=axis, input_shape=input_shape, fused=fused)) - model.add(tf.keras.layers.LeakyReLU(alpha=0.)) + model.add(tf.keras.layers.Activation("relu")) model.add( tf.keras.layers.Conv2D( filters=filters // 4, @@ -258,7 +258,7 @@ def _BottleneckResidualInner(filters, padding="SAME")) model.add(tf.keras.layers.BatchNormalization(axis=axis, fused=fused)) - model.add(tf.keras.layers.LeakyReLU(alpha=0.)) + model.add(tf.keras.layers.Activation("relu")) model.add( tf.keras.layers.Conv2D( filters=filters // 4, @@ -269,7 +269,7 @@ def _BottleneckResidualInner(filters, padding="SAME")) model.add(tf.keras.layers.BatchNormalization(axis=axis, fused=fused)) - model.add(tf.keras.layers.LeakyReLU(alpha=0.)) + model.add(tf.keras.layers.Activation("relu")) model.add( tf.keras.layers.Conv2D( filters=filters, @@ -310,7 +310,7 @@ def _ResidualInner(filters, model.add( tf.keras.layers.BatchNormalization( axis=axis, input_shape=input_shape, fused=fused)) - model.add(tf.keras.layers.LeakyReLU(alpha=0.)) + model.add(tf.keras.layers.Activation("relu")) model.add( tf.keras.layers.Conv2D( filters=filters, @@ -322,7 +322,7 @@ def _ResidualInner(filters, padding="SAME")) model.add(tf.keras.layers.BatchNormalization(axis=axis, fused=fused)) - model.add(tf.keras.layers.LeakyReLU(alpha=0.)) + model.add(tf.keras.layers.Activation("relu")) model.add( tf.keras.layers.Conv2D( filters=filters, diff --git a/tensorflow/contrib/eager/python/examples/revnet/cifar_input.py b/tensorflow/contrib/eager/python/examples/revnet/cifar_input.py new file mode 100644 index 0000000000..3bc69da5ad --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/revnet/cifar_input.py @@ -0,0 +1,105 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Script for reading and loading CIFAR-10.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os + +import tensorflow as tf + +# Global constants describing the CIFAR data set. +IMAGE_HEIGHT = 32 +IMAGE_WIDTH = 32 +NUM_CHANNEL = 3 +NUM_TRAIN_IMG = 50000 +NUM_TEST_IMG = 10000 + + +def get_ds_from_tfrecords(data_dir, + split, + data_aug=True, + batch_size=100, + epochs=None, + shuffle=True, + data_format="channels_first", + num_parallel_calls=4, + prefetch=True, + div255=True, + dtype=tf.float32): + """Returns a tf.train.Dataset object from reading tfrecords. + + Args: + data_dir: Directory of tfrecords + split: "train", "validation", or "test" + data_aug: Apply data augmentation if True + batch_size: Batch size of dataset object + epochs: Number of epochs to repeat the dataset + shuffle: Shuffle the dataset if True + data_format: `channels_first` or `channels_last` + num_parallel_calls: Number of threads for dataset preprocess + prefetch: Apply prefetch for the dataset if True + div255: Divide the images by 255 if True + dtype: Data type of images + Returns: + A tf.train.Dataset object + + Raises: + ValueError: Unknown split + """ + + if split not in ["train", "validation", "test"]: + raise ValueError("Unknown split {}".format(split)) + + def _parser(serialized_example): + """Parses a single tf.Example into image and label tensors.""" + features = tf.parse_single_example( + serialized_example, + features={ + "image": tf.FixedLenFeature([], tf.string), + "label": tf.FixedLenFeature([], tf.int64), + }) + image = tf.decode_raw(features["image"], tf.uint8) + image = tf.reshape(image, [IMAGE_HEIGHT, IMAGE_WIDTH, NUM_CHANNEL]) + image = tf.cast(image, dtype) + label = tf.cast(features["label"], tf.int32) + + if data_aug: + image = tf.image.resize_image_with_crop_or_pad(image, IMAGE_HEIGHT + 4, + IMAGE_WIDTH + 4) + image = tf.random_crop(image, [IMAGE_HEIGHT, IMAGE_WIDTH, NUM_CHANNEL]) + image = tf.image.random_flip_left_right(image) + + if data_format == "channels_first": + image = tf.transpose(image, [2, 0, 1]) + + if div255: + image /= 255. + + return image, label + + filename = os.path.join(data_dir, split + ".tfrecords") + dataset = tf.data.TFRecordDataset(filename).repeat(epochs) + dataset = dataset.map(_parser, num_parallel_calls=num_parallel_calls) + + if prefetch: + dataset = dataset.prefetch(batch_size) + if shuffle: + dataset = dataset.shuffle(NUM_TRAIN_IMG) + dataset = dataset.batch(batch_size) + + return dataset diff --git a/tensorflow/contrib/eager/python/examples/revnet/cifar_tfrecords.py b/tensorflow/contrib/eager/python/examples/revnet/cifar_tfrecords.py new file mode 100644 index 0000000000..f79428b2a9 --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/revnet/cifar_tfrecords.py @@ -0,0 +1,123 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Read CIFAR-10 data from pickled numpy arrays and writes TFRecords. + +Generates tf.train.Example protos and writes them to TFRecord files from the +python version of the CIFAR-10 dataset downloaded from +https://www.cs.toronto.edu/~kriz/cifar.html. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import sys +import tarfile + +from absl import flags +from six.moves import cPickle as pickle +from six.moves import urllib +import tensorflow as tf + +CIFAR_FILENAME = 'cifar-10-python.tar.gz' +CIFAR_DOWNLOAD_URL = 'https://www.cs.toronto.edu/~kriz/' + CIFAR_FILENAME +CIFAR_LOCAL_FOLDER = 'cifar-10-batches-py' + + +def download_and_extract(data_dir): + """Download CIFAR-10 if not already downloaded.""" + filepath = os.path.join(data_dir, CIFAR_FILENAME) + if tf.gfile.Exists(filepath): + return filepath + if not tf.gfile.Exists(data_dir): + tf.gfile.MakeDirs(data_dir) + + urllib.request.urlretrieve(CIFAR_DOWNLOAD_URL, filepath) + tarfile.open(os.path.join(filepath), 'r:gz').extractall(data_dir) + return filepath + + +def _int64_feature(value): + return tf.train.Feature(int64_list=tf.train.Int64List(value=[value])) + + +def _bytes_feature(value): + return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value])) + + +def _get_file_names(): + """Returns the file names expected to exist in the input_dir.""" + file_names = {} + file_names['train'] = ['data_batch_%d' % i for i in range(1, 5)] + file_names['validation'] = ['data_batch_5'] + file_names['test'] = ['test_batch'] + return file_names + + +def read_pickle_from_file(filename): + with tf.gfile.Open(filename, 'rb') as f: + if sys.version_info >= (3, 0): + data_dict = pickle.load(f, encoding='bytes') + else: + data_dict = pickle.load(f) + return data_dict + + +def convert_to_tfrecord(input_files, output_file): + """Converts files with pickled data to TFRecords.""" + print('Generating %s' % output_file) + with tf.python_io.TFRecordWriter(output_file) as record_writer: + for input_file in input_files: + data_dict = read_pickle_from_file(input_file) + data = data_dict[b'data'] + labels = data_dict[b'labels'] + num_entries_in_batch = len(labels) + + for i in range(num_entries_in_batch): + example = tf.train.Example( + features=tf.train.Features( + feature={ + 'image': _bytes_feature(data[i].tobytes()), + 'label': _int64_feature(labels[i]) + })) + record_writer.write(example.SerializeToString()) + + +def main(_): + print('Download from {} and extract.'.format(CIFAR_DOWNLOAD_URL)) + download_and_extract(FLAGS.data_dir) + file_names = _get_file_names() + input_dir = os.path.join(FLAGS.data_dir, CIFAR_LOCAL_FOLDER) + + for mode, files in file_names.items(): + input_files = [os.path.join(input_dir, f) for f in files] + output_file = os.path.join(FLAGS.data_dir, mode + '.tfrecords') + try: + os.remove(output_file) + except OSError: + pass + convert_to_tfrecord(input_files, output_file) + print('Done!') + + +if __name__ == '__main__': + FLAGS = flags.FLAGS + flags.DEFINE_string( + 'data_dir', + default=None, + help='Directory to download and extract CIFAR-10 to.') + + tf.app.run(main) diff --git a/tensorflow/contrib/eager/python/examples/revnet/config.py b/tensorflow/contrib/eager/python/examples/revnet/config.py index 495a78d550..263a65dc76 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/config.py +++ b/tensorflow/contrib/eager/python/examples/revnet/config.py @@ -27,6 +27,7 @@ from __future__ import division from __future__ import print_function import tensorflow as tf +tfe = tf.contrib.eager def get_hparams_cifar_38(): @@ -41,11 +42,11 @@ def get_hparams_cifar_38(): config.add_hparam("n_res", [3, 3, 3]) config.add_hparam("filters", [32, 64, 112]) config.add_hparam("strides", [1, 2, 2]) - config.add_hparam("batch_size", 10) + config.add_hparam("batch_size", 100) config.add_hparam("bottleneck", False) config.add_hparam("fused", True) config.add_hparam("init_max_pool", False) - if tf.test.is_gpu_available(): + if tfe.num_gpus() > 0: config.add_hparam("input_shape", (3, 32, 32)) config.add_hparam("data_format", "channels_first") else: @@ -61,12 +62,13 @@ def get_hparams_cifar_38(): config.add_hparam("seed", 1234) config.add_hparam("shuffle", True) config.add_hparam("prefetch", True) - config.add_hparam("print_every", 50) + config.add_hparam("log_every", 50) + config.add_hparam("save_every", 50) config.add_hparam("dtype", tf.float32) config.add_hparam("eval_batch_size", 500) config.add_hparam("div255", True) - # For tf.data.Dataset - config.add_hparam("epochs", config.max_train_iter // config.batch_size) + config.add_hparam("iters_per_epoch", 50000 // config.batch_size) + config.add_hparam("epochs", config.max_train_iter // config.iters_per_epoch) return config @@ -103,12 +105,14 @@ def get_hparams_imagenet_56(): config.add_hparam("seed", 1234) config.add_hparam("shuffle", True) config.add_hparam("prefetch", True) - config.add_hparam("print_every", 50) + config.add_hparam("log_every", 50) + config.add_hparam("save_every", 50) config.add_hparam("dtype", tf.float32) config.add_hparam("eval_batch_size", 500) config.add_hparam("div255", True) - # For tf.data.Dataset - config.add_hparam("epochs", config.max_train_iter // config.batch_size) + # TODO(lxuechen): Update this according to ImageNet data + config.add_hparam("iters_per_epoch", 50000 // config.batch_size) + config.add_hparam("epochs", config.max_train_iter // config.iters_per_epoch) if config.bottleneck: filters = [f * 4 for f in config.filters] diff --git a/tensorflow/contrib/eager/python/examples/revnet/main.py b/tensorflow/contrib/eager/python/examples/revnet/main.py new file mode 100644 index 0000000000..9ef11f8e9b --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/revnet/main.py @@ -0,0 +1,147 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Eager execution workflow with RevNet train on CIFAR-10.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os + +from absl import flags +import tensorflow as tf +from tensorflow.contrib.eager.python.examples.revnet import cifar_input +from tensorflow.contrib.eager.python.examples.revnet import config as config_ +from tensorflow.contrib.eager.python.examples.revnet import revnet +tfe = tf.contrib.eager + + +def main(_): + """Eager execution workflow with RevNet trained on CIFAR-10.""" + if FLAGS.data_dir is None: + raise ValueError("No supplied data directory") + + if not os.path.exists(FLAGS.data_dir): + raise ValueError("Data directory {} does not exist".format(FLAGS.data_dir)) + + tf.enable_eager_execution() + config = config_.get_hparams_cifar_38() + model = revnet.RevNet(config=config) + + ds_train = cifar_input.get_ds_from_tfrecords( + data_dir=FLAGS.data_dir, + split="train", + data_aug=True, + batch_size=config.batch_size, + epochs=config.epochs, + shuffle=config.shuffle, + data_format=config.data_format, + dtype=config.dtype, + prefetch=config.prefetch) + + ds_validation = cifar_input.get_ds_from_tfrecords( + data_dir=FLAGS.data_dir, + split="validation", + data_aug=False, + batch_size=config.eval_batch_size, + epochs=1, + data_format=config.data_format, + dtype=config.dtype, + prefetch=config.prefetch) + + ds_test = cifar_input.get_ds_from_tfrecords( + data_dir=FLAGS.data_dir, + split="test", + data_aug=False, + batch_size=config.eval_batch_size, + epochs=1, + data_format=config.data_format, + dtype=config.dtype, + prefetch=config.prefetch) + + global_step = tfe.Variable(1, trainable=False) + + def learning_rate(): # TODO(lxuechen): Remove once cl/201089859 is in place + return tf.train.piecewise_constant(global_step, config.lr_decay_steps, + config.lr_list) + + optimizer = tf.train.MomentumOptimizer(learning_rate, momentum=0.9) + checkpoint = tf.train.Checkpoint( + optimizer=optimizer, model=model, optimizer_step=global_step) + + if FLAGS.train_dir: + summary_writer = tf.contrib.summary.create_file_writer(FLAGS.train_dir) + if FLAGS.restore: + latest_path = tf.train.latest_checkpoint(FLAGS.train_dir) + checkpoint.restore(latest_path) + + for x, y in ds_train: + loss = train_one_iter(model, x, y, optimizer, global_step=global_step) + + if global_step % config.log_every == 0: + it_validation = ds_validation.make_one_shot_iterator() + it_test = ds_test.make_one_shot_iterator() + acc_validation = evaluate(model, it_validation) + acc_test = evaluate(model, it_test) + print("Iter {}, " + "train loss {}, " + "validation accuracy {}, " + "test accuracy {}".format(global_step.numpy(), loss, acc_validation, + acc_test)) + + if FLAGS.train_dir: + with summary_writer.as_default(): + with tf.contrib.summary.always_record_summaries(): + tf.contrib.summary.scalar("Validation accuracy", acc_validation) + tf.contrib.summary.scalar("Test accuracy", acc_test) + tf.contrib.summary.scalar("Training loss", loss) + + if global_step.numpy() % config.save_every == 0 and FLAGS.train_dir: + checkpoint.save(file_prefix=FLAGS.train_dir + "ckpt") + + +def train_one_iter(model, inputs, labels, optimizer, global_step=None): + """Train for one iteration.""" + grads, vars_, loss = model.compute_gradients(inputs, labels, training=True) + optimizer.apply_gradients(zip(grads, vars_), global_step=global_step) + + return loss.numpy() + + +def evaluate(model, iterator): + """Compute accuracy with the given dataset iterator.""" + accuracy = tfe.metrics.Accuracy() + for x, y in iterator: + logits, _ = model(x, training=False) + accuracy( + labels=tf.cast(y, tf.int64), + predictions=tf.argmax(logits, axis=1, output_type=tf.int64)) + + return accuracy.result().numpy() + + +if __name__ == "__main__": + flags.DEFINE_string( + "train_dir", + default=None, + help="[Optional] Directory to store the training information") + flags.DEFINE_string( + "data_dir", default=None, help="Directory to load tfrecords.") + flags.DEFINE_boolean( + "restore", + default=True, + help="[Optional] Restore the latest checkpoint from `train_dir` if True") + FLAGS = flags.FLAGS + tf.app.run(main) diff --git a/tensorflow/contrib/eager/python/examples/revnet/revnet.py b/tensorflow/contrib/eager/python/examples/revnet/revnet.py index 1e17bf1eab..b3b8c262b1 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/revnet.py +++ b/tensorflow/contrib/eager/python/examples/revnet/revnet.py @@ -61,7 +61,7 @@ class RevNet(tf.keras.Model): input_shape=self.config.input_shape), tf.keras.layers.BatchNormalization( axis=self.axis, fused=self.config.fused), - tf.keras.layers.LeakyReLU(alpha=0.) + tf.keras.layers.Activation("relu"), ], name="init") if self.config.init_max_pool: @@ -96,7 +96,7 @@ class RevNet(tf.keras.Model): axis=self.axis, input_shape=input_shape, fused=self.config.fused), - tf.keras.layers.LeakyReLU(alpha=0.), # Vanilla ReLU + tf.keras.layers.Activation("relu"), tf.keras.layers.GlobalAveragePooling2D( data_format=self.config.data_format), tf.keras.layers.Dense(self.config.n_classes) @@ -202,12 +202,13 @@ class RevNet(tf.keras.Model): x = tf.identity(x) # TODO(lxuechen): Remove after b/110264016 is fixed tape.watch(x) logits = self._final_block(x, training=training) - cost = self.compute_loss(logits, labels) + loss = self.compute_loss(logits, labels) - grads_combined = tape.gradient(cost, [x] + self._final_block.variables) + grads_combined = tape.gradient(loss, + [x] + self._final_block.trainable_variables) dy, grads_ = grads_combined[0], grads_combined[1:] grads_all += grads_ - vars_all += self._final_block.variables + vars_all += self._final_block.trainable_variables # Manually backprop through intermediate blocks for block in reversed(self._block_list): @@ -224,27 +225,17 @@ class RevNet(tf.keras.Model): assert not saved_hidden # Cleared after backprop with tf.GradientTape() as tape: - y = self._init_block(x, training=training) # Recomputing + x = tf.identity(x) # TODO(lxuechen): Remove after b/110264016 is fixed + y = self._init_block(x, training=training) grads_all += tape.gradient( - y, self._init_block.variables, output_gradients=[dy]) - vars_all += self._init_block.variables - - return grads_all, vars_all + y, self._init_block.trainable_variables, output_gradients=[dy]) + vars_all += self._init_block.trainable_variables - def train_step(self, - inputs, - labels, - optimizer, - global_step=None, - report=False): - """Train for one iteration.""" + grads_all = self._apply_weight_decay(grads_all, vars_all) - grads_all, vars_all = self.compute_gradients(inputs, labels, training=True) - optimizer.apply_gradients(zip(grads_all, vars_all), global_step=global_step) - - if report: - logits, _ = self.call(inputs, training=True) - loss = self.compute_loss(logits, labels) + return grads_all, vars_all, loss - return loss + def _apply_weight_decay(self, grads, vars_): + """Update gradients to reflect weight decay.""" + return [g + self.config.weight_decay * v for g, v in zip(grads, vars_)] diff --git a/tensorflow/contrib/eager/python/examples/revnet/revnet_test.py b/tensorflow/contrib/eager/python/examples/revnet/revnet_test.py index d2d2f65bbd..c712e61858 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/revnet_test.py +++ b/tensorflow/contrib/eager/python/examples/revnet/revnet_test.py @@ -28,6 +28,14 @@ from tensorflow.python.client import device_lib tfe = tf.contrib.eager +def train_one_iter(model, inputs, labels, optimizer, global_step=None): + """Train for one iteration.""" + grads, vars_, loss = model.compute_gradients(inputs, labels, training=True) + optimizer.apply_gradients(zip(grads, vars_), global_step=global_step) + + return loss + + class RevnetTest(tf.test.TestCase): def setUp(self): @@ -59,7 +67,7 @@ class RevnetTest(tf.test.TestCase): def test_compute_gradients(self): """Test `compute_gradients` function.""" - grads, vars_ = self.model.compute_gradients(inputs=self.x, labels=self.t) + grads, vars_, _ = self.model.compute_gradients(inputs=self.x, labels=self.t) self.assertTrue(isinstance(grads, list)) self.assertTrue(isinstance(vars_, list)) self.assertEqual(len(grads), len(vars_)) @@ -67,19 +75,6 @@ class RevnetTest(tf.test.TestCase): if grad is not None: self.assertEqual(grad.shape, var.shape) - def test_train_step(self): - """Test `train_step` function.""" - - logits, _ = self.model(self.x, training=True) - loss = self.model.compute_loss(logits=logits, labels=self.t) - optimizer = tf.train.AdamOptimizer(learning_rate=1e-3) - - # Loss should be decreasing after each optimization step - for _ in range(1): - loss_ = self.model.train_step(self.x, self.t, optimizer, report=True) - self.assertTrue(loss_.numpy() <= loss.numpy()) - loss = loss_ - def test_call_defun(self): """Test `call` function with defun.""" @@ -89,7 +84,7 @@ class RevnetTest(tf.test.TestCase): def test_compute_gradients_defun(self): """Test `compute_gradients` function with defun.""" compute_gradients = tfe.defun(self.model.compute_gradients) - grads, vars_ = compute_gradients(self.x, self.t) + grads, vars_, _ = compute_gradients(self.x, self.t) self.assertTrue(isinstance(grads, list)) self.assertTrue(isinstance(vars_, list)) self.assertEqual(len(grads), len(vars_)) @@ -97,21 +92,6 @@ class RevnetTest(tf.test.TestCase): if grad is not None: self.assertEqual(grad.shape, var.shape) - def test_train_step_defun(self): - """Test `train_step` function with defun.""" - self.model.call = tfe.defun(self.model.call) - logits, _ = self.model(self.x, training=True) - loss = self.model.compute_loss(logits=logits, labels=self.t) - optimizer = tf.train.AdamOptimizer(learning_rate=1e-3) - - for _ in range(1): - loss_ = self.model.train_step(self.x, self.t, optimizer, report=True) - self.assertTrue(loss_.numpy() <= loss.numpy()) - loss = loss_ - - # Initialize new model, so that other tests are not affected - self.model = revnet.RevNet(config=self.config) - def test_training_graph(self): """Test model training in graph mode.""" @@ -125,8 +105,9 @@ class RevnetTest(tf.test.TestCase): dtype=tf.int32) global_step = tfe.Variable(0., trainable=False) model = revnet.RevNet(config=self.config) - grads_all, vars_all = model.compute_gradients(x, t, training=True) + grads_all, vars_all, _ = model.compute_gradients(x, t, training=True) optimizer = tf.train.AdamOptimizer(learning_rate=1e-3) + # TODO(lxuechen): This doesn't work due to b/110145168 with tf.control_dependencies(model.updates): train_op = optimizer.apply_gradients( zip(grads_all, vars_all), global_step=global_step) @@ -263,7 +244,7 @@ class RevnetBenchmark(tf.test.Benchmark): iterator = make_iterator((images, labels)) for _ in range(num_burn): (images, labels) = iterator.next() - model.train_step(images, labels, optimizer) + train_one_iter(model, images, labels, optimizer) if execution_mode: tfe.async_wait() self._force_device_sync() @@ -272,7 +253,7 @@ class RevnetBenchmark(tf.test.Benchmark): start = time.time() for _ in range(num_iters): (images, labels) = iterator.next() - model.train_step(images, labels, optimizer) + train_one_iter(model, images, labels, optimizer) if execution_mode: tfe.async_wait() self._force_device_sync() -- GitLab From 0b5fa51214ca681aaca7db4a17526d4a95de5fdc Mon Sep 17 00:00:00 2001 From: Billy Lamberta Date: Tue, 19 Jun 2018 16:57:32 -0700 Subject: [PATCH 708/816] accents --- .../python/examples/nmt_with_attention/nmt_with_attention.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb b/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb index ada101828b..3d162d186b 100644 --- a/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb +++ b/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb @@ -201,7 +201,7 @@ }, "cell_type": "code", "source": [ - "# 1. Remove the pronunciations\n", + "# 1. Remove the accents\n", "# 2. Clean the sentences\n", "# 3. Return word pairs in the format: [ENGLISH, SPANISH]\n", "def create_dataset(path, num_examples):\n", -- GitLab From da8dfdb3c1014c03598fddcdb889c9eee4b489b5 Mon Sep 17 00:00:00 2001 From: Sami Kama Date: Tue, 19 Jun 2018 17:12:43 -0700 Subject: [PATCH 709/816] Address some comments --- .../contrib/tensorrt/convert/convert_graph.cc | 101 +++++++++--------- .../contrib/tensorrt/convert/convert_nodes.cc | 32 +++--- .../contrib/tensorrt/convert/convert_nodes.h | 2 +- .../contrib/tensorrt/test/test_tftrt.py | 5 +- 4 files changed, 68 insertions(+), 72 deletions(-) diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.cc b/tensorflow/contrib/tensorrt/convert/convert_graph.cc index 9f0b3ef5dd..eac46f679e 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_graph.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_graph.cc @@ -249,8 +249,9 @@ EngineInfo GetEngineInfo( std::set segment_devices; int input_port = 0; int output_port = 0; - // TODO(aaroey): consider using node id and port instead. Also, here we assume - // that input edge set and output edge set have no intersection, is this true? + // Each input can have only one incoming edge, outputs can have multiple edges + // though since we are keeping outside name, this can only fail in case of 2 + // op loops in the graph. std::unordered_map created_edges; for (auto it = reverse_topo_order.rbegin(); it != reverse_topo_order.rend(); ++it) { @@ -292,14 +293,9 @@ EngineInfo GetEngineInfo( created_edges.insert({s, port}); input_port++; } - EngineConnection ec(input_node->name(), input_node->id(), - edge->src_output(), node_name, node_id, - edge->dst_input(), true, port); - // TODO(aaroey): this will be rewritten in - // ConvertSegmentToSubGraphDef, fix it. - ec.connection_type = input_node->output_type(edge->src_output()); - - info.connections.emplace_back(std::move(ec)); + info.connections.emplace_back(input_node->name(), input_node->id(), + edge->src_output(), node_name, node_id, + edge->dst_input(), true, port); } } } @@ -324,9 +320,9 @@ EngineInfo GetEngineInfo( } } - ConvertSegmentToSubGraphDef(g, graph_properties, subgraph_node_ids, - &info.connections, &info.segment_graph_def, - &info.engine_name); + ConvertSegmentToGraphDef(g, graph_properties, subgraph_node_ids, + &info.connections, &info.segment_graph_def, + &info.engine_name); // TODO(sami): This should not happen once segmenter is updated. if (segment_devices.size() == 1) { info.device = *segment_devices.begin(); @@ -421,7 +417,11 @@ tensorflow::Status CreateTRTNode(tensorflow::Graph* graph, string segment_string; if (info.engine_type == EngineInfo::EngineType::TRTStatic || info.precision_mode == INT8MODE) { - // Create static engine and for int8 test validity of the engine. + // Create static engine and for int8 test validity of the engine. We can not + // allow engine to fail at the calibration time. So we are constructing a + // FP32 engine here to check its validity. If it is a valid engine then we + // put the serialized graphdef to the op. Otherwise we skip node creation + // for this engine. Logger trt_logger; TrtUniquePtrType builder( nvinfer1::createInferBuilder(trt_logger)); @@ -440,7 +440,6 @@ tensorflow::Status CreateTRTNode(tensorflow::Graph* graph, segment_string = string((const char*)engine_data->data(), engine_data->size()); if (info.precision_mode == INT8MODE) { - // TODO(aaroey): why not put this inside the 'else' branch? segment_string = info.segment_graph_def.SerializeAsString(); } } else { @@ -469,7 +468,7 @@ tensorflow::Status CreateTRTNode(tensorflow::Graph* graph, tensorflow::NodeDefBuilder node_builder(info.engine_name, "TRTEngineOp"); if (!info.device.empty()) node_builder.Device(info.device); if (VLOG_IS_ON(1)) { - string ins=StrCat(info.engine_name," inputs= "); + string ins = StrCat(info.engine_name, " inputs= "); for (const auto& ii : inputs) { StrAppend(&ins, ii.node, ":", ii.index, " "); } @@ -501,6 +500,9 @@ tensorflow::Status CreateTRTNode(tensorflow::Graph* graph, return status; } VLOG(1) << "Adding TRTEngine " << info.engine_name << " to graph"; + + // up until this point, graph is not modified. If we return !status.ok() from + // here, this segment will be skipped tensorflow::Node* engine_node = graph->AddNode(trt_node, &status); if (!status.ok()) { LOG(ERROR) << "Adding node failed " << status; @@ -514,18 +516,21 @@ tensorflow::Status CreateTRTNode(tensorflow::Graph* graph, << conn.port_number << " out_id " << conn.outside_id << " name=" << conn.outside_node_name; auto dst_node = graph->FindNodeId(conn.outside_id); - // TODO(aaroey): node could be removed during construction of other TRT - // nodes, but then in that case who is going to update their input nodes? + // dst_node can only be removed if it is an input node of another engine. + // In this case, other engines input edge is updated in nodedef to point to + // this engine. Even though edge doesn't exists in the graph, when it is + // deserialized again, correct edges will be constructed. This is a problem + // of graph. if (!dst_node) continue; VLOG(1) << "Updating " << engine_node->name() << ":" << conn.port_number << " to " << dst_node->name() << ":" << conn.outside_port; - status = graph->UpdateEdge(engine_node, conn.port_number, dst_node, - conn.outside_port); - if (!status.ok()) { - // TODO(aaroey): should we return the status? - LOG(ERROR) << "Edge update failed " << engine_node->name() << ":" - << conn.port_number << " -> " << dst_node->name() << ":" - << conn.outside_port << " status= " << status; + auto new_edge = graph->AddEdge(engine_node, conn.port_number, dst_node, + conn.outside_port); + // this should never happen! + if (!new_edge) { + LOG(WARNING) << "Adding a new edge failed " << engine_node->name() << ":" + << conn.port_number << " -> " << dst_node->name() << ":" + << conn.outside_port; } } return status; @@ -616,7 +621,7 @@ tensorflow::Status RegisterSegmentFunctionToFunctionLibrary( VLOG(7) << name << " Function_Def "; VLOG(7) << native_segment->DebugString(); } - VLOG(1)<<"Adding funcdef to graphlib"; + VLOG(1) << "Adding funcdef to graphlib"; TF_RETURN_IF_ERROR(graph->AddFunctionLibrary(fdeflib)); return tensorflow::Status::OK(); } @@ -638,30 +643,22 @@ std::pair GetDeviceAndAllocator( }; tensorflow::Allocator* dev_allocator = nullptr; // we need to us PM here since in python path there is no way to get - // to allocators - // TODO(aaroey): fix this. + // to allocators. + // TODO(sami): when grappler devices become available else path will not be + // necessary auto pm = tensorflow::ProcessState::singleton(); if (params.cluster) { // get allocator - const tensorflow::Device* device = nullptr; + tensorflow::Device* device = nullptr; if (params.cluster->GetDeviceSet()) { device = params.cluster->GetDeviceSet()->FindDeviceByName(engine.device); } if (device) { - cuda_device_id = check_device_id(device->parsed_name().id); - if (cuda_device_id < 0) { - LOG(ERROR) << "Cuda device identification failed, using device 0."; - cuda_device_id = 0; - } - tensorflow::GPUOptions gpuoptions; - // this should be instantiated by now - tensorflow::TfGpuId tf_gpu_id(device->parsed_name().id); - // TODO(aaroey): why not using device->GetAllocator()? - dev_allocator = pm->GetGPUAllocator(gpuoptions, tf_gpu_id, 1); - VLOG(1) << "Got an allocator for device tf_device=" << tf_gpu_id.value() - << " cuda device= " << cuda_device_id << " at " << dev_allocator; + tensorflow::AllocatorAttributes alloc_attr; + dev_allocator = device->GetAllocator(alloc_attr); + VLOG(1) << "Using allocator " << dev_allocator->Name(); } else { - LOG(WARNING) << "Cluster is set but device " << engine.device - << " is not found in the cluster"; + LOG(WARNING) << "Cluster is set but device '" << engine.device + << "' is not found in the cluster"; } } else { // cluster not found, possibly a python call VLOG(1) << "Cluster is not set, probably called from python"; @@ -735,9 +732,9 @@ tensorflow::Status ConvertAfterShapes(ConversionParams& params) { std::vector engine_bytes_size; for (size_t t = 0; t < segments.size(); t++) { auto& s = segments.at(t); - engine_segments.emplace_back(GetEngineInfo( - &graph, *params.graph_properties, s.first, node_map, - reverse_topo_order)); + engine_segments.emplace_back(GetEngineInfo(&graph, *params.graph_properties, + s.first, node_map, + reverse_topo_order)); auto& curr_engine = engine_segments.back(); curr_engine.precision_mode = params.precision_mode; curr_engine.engine_type = @@ -794,18 +791,18 @@ tensorflow::Status ConvertAfterShapes(ConversionParams& params) { LOG(WARNING) << "Can't identify the cuda device. Running on device 0 "; } cudaSetDevice(cuda_device_id); - auto status = CreateTRTNode( - &graph, engine_segments, i, alloc.get(), params.max_batch_size); + auto status = CreateTRTNode(&graph, engine_segments, i, alloc.get(), + params.max_batch_size); + // If status is ok, we successfuly added the node to the graph and can + // remove segment ops. Otherwise graph is not modified. if (status.ok()) { for (auto node_name : segments.at(i).first) { graph.RemoveNode(node_map.at(node_name)); } } else { - // TODO(aaroey): in this case, the graph is already modified, we should - // return the status? LOG(WARNING) << "Engine creation for segment " << i << ", composed of " - << segments.at(i).first.size() << " nodes failed: " - << status << ". Skipping..."; + << segments.at(i).first.size() << " nodes failed: " << status + << ". Skipping..."; } } cudaSetDevice(old_cuda_device); diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index 69d7b765fa..03afbae113 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -2194,10 +2194,9 @@ tensorflow::Status ConvertSubGraphDefToEngine( nvinfer1::ITensor* input_tensor = converter.network()->addInput( node_name.c_str(), dtype, input_dim_pseudo_chw); if (!input_tensor) { - // TODO(aaroey): remove StrCat when constructing errors. return tensorflow::errors::InvalidArgument( - StrCat("Failed to create Input layer tensor ", node_name, - " rank=", shape.dims() - 1)); + "Failed to create Input layer tensor ", node_name, + " rank=", shape.dims() - 1); } VLOG(1) << "Input tensor name :" << node_name; if (!converter.insert_input_tensor(node_name, input_tensor)) { @@ -2251,7 +2250,7 @@ tensorflow::Status ConvertSubGraphDefToEngine( return tensorflow::Status::OK(); } -tensorflow::Status ConvertSegmentToSubGraphDef( +tensorflow::Status ConvertSegmentToGraphDef( const tensorflow::Graph* graph, const tensorflow::grappler::GraphProperties& graph_properties, const std::vector& subgraph_node_ids, // In topological order @@ -2273,8 +2272,8 @@ tensorflow::Status ConvertSegmentToSubGraphDef( tensorflow::PartialTensorShape partial_shape; if (connection.is_input_edge) { if (graph_properties.HasOutputProperties(connection.outside_node_name)) { - auto output_params = graph_properties.GetOutputProperties( - connection.outside_node_name); + auto output_params = + graph_properties.GetOutputProperties(connection.outside_node_name); auto out_shape = output_params.at(connection.outside_port); input_type = out_shape.dtype(); std::vector dims; @@ -2309,26 +2308,25 @@ tensorflow::Status ConvertSegmentToSubGraphDef( VLOG(1) << "Reusing input " << node_name << " for the edge " << connection.outside_node_name << ":" << connection.outside_port << " -> " - << connection.inside_node_name << ":" - << connection.inside_port; + << connection.inside_node_name << ":" << connection.inside_port; continue; } marker_nodes.insert(node_name); auto seg_node = segment_def->add_node(); tensorflow::NodeDefBuilder builder(node_name, "Placeholder"); auto status = builder.Attr("shape", partial_shape) - .Attr("dtype", input_type).Finalize(seg_node); + .Attr("dtype", input_type) + .Finalize(seg_node); VLOG(1) << "Constructing input " << node_name << " for the edge " - << connection.outside_node_name << ":" - << connection.outside_port << " -> " - << connection.inside_node_name << ":" << connection.inside_port; + << connection.outside_node_name << ":" << connection.outside_port + << " -> " << connection.inside_node_name << ":" + << connection.inside_port; } else { const string node_name = StrCat(kOutputPHName, connection.port_number); if (marker_nodes.count(node_name)) { VLOG(1) << "Reusing output " << node_name << " for the edge " - << connection.inside_node_name << ":" - << connection.inside_port << " -> " - << connection.outside_node_name << ":" + << connection.inside_node_name << ":" << connection.inside_port + << " -> " << connection.outside_node_name << ":" << connection.outside_port; continue; } @@ -2359,8 +2357,8 @@ tensorflow::Status ConvertSegmentToSubGraphDef( for (int i = 0; i < connections->size(); ++i) { auto& connection = connections->at(i); if (!connection.is_input_edge) continue; - auto snode = segment_def->mutable_node( - old_to_new_id_map[connection.inside_id]); + auto snode = + segment_def->mutable_node(old_to_new_id_map[connection.inside_id]); const string placeholder_name = StrCat(kInputPHName, connection.port_number); VLOG(1) << "Updating " << snode->name() << ":" << connection.inside_port diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.h b/tensorflow/contrib/tensorrt/convert/convert_nodes.h index b8d6012df2..220e5145cf 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.h +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.h @@ -103,7 +103,7 @@ struct EngineInfo { // topological order. // - segment_def: the output GraphDef, whose non-input/output nodedefs will be // sorted in topological order. -tensorflow::Status ConvertSegmentToSubGraphDef( +tensorflow::Status ConvertSegmentToGraphDef( const tensorflow::Graph* graph, const tensorflow::grappler::GraphProperties& graph_properties, const std::vector& subgraph_node_ids, diff --git a/tensorflow/contrib/tensorrt/test/test_tftrt.py b/tensorflow/contrib/tensorrt/test/test_tftrt.py index 9a031ddf4e..631438fed4 100644 --- a/tensorflow/contrib/tensorrt/test/test_tftrt.py +++ b/tensorflow/contrib/tensorrt/test/test_tftrt.py @@ -36,6 +36,7 @@ from tensorflow.python.framework import dtypes as dtypes from tensorflow.python.framework import importer as importer from tensorflow.python.framework import ops as ops from tensorflow.python.ops import array_ops as aops +from tensorflow.python.ops import math_ops as mops from tensorflow.python.ops import nn as nn from tensorflow.python.ops import nn_ops as nn_ops @@ -221,8 +222,8 @@ def user(multi_engine, _ = run_calibration(int8_calib_gdef, dummy_input) int8_graph = trt.calib_graph_to_infer_graph(int8_calib_gdef) o5 = run_graph(int8_graph, dummy_input) - assert np.allclose(o1, o4) - assert np.allclose(o1, o5) + print("Is FP32 == FP16? %s (False is possible)"%np.allclose(o1, o4)) + print("Is FP32 == INT8? %s (False is possible)"%np.allclose(o1, o5)) print("Pass") -- GitLab From da861da63df724339e0148ff43192de05770a3c8 Mon Sep 17 00:00:00 2001 From: Katherine Wu Date: Tue, 19 Jun 2018 17:10:22 -0700 Subject: [PATCH 710/816] Refactor loader.load function into a class that splits the graph loading and variable restoration steps. PiperOrigin-RevId: 201268712 --- tensorflow/python/saved_model/BUILD | 24 ++ tensorflow/python/saved_model/loader_impl.py | 176 ++++++++++++--- tensorflow/python/saved_model/loader_test.py | 217 +++++++++++++++++++ 3 files changed, 386 insertions(+), 31 deletions(-) create mode 100644 tensorflow/python/saved_model/loader_test.py diff --git a/tensorflow/python/saved_model/BUILD b/tensorflow/python/saved_model/BUILD index 81786fbf43..076f2d8760 100644 --- a/tensorflow/python/saved_model/BUILD +++ b/tensorflow/python/saved_model/BUILD @@ -87,6 +87,30 @@ py_library( "//tensorflow/python:platform", "//tensorflow/python:training", "//tensorflow/python:util", + "//tensorflow/python:variables", + ], +) + +py_test( + name = "loader_test", + size = "small", + srcs = ["loader_test.py"], + srcs_version = "PY2AND3", + visibility = ["//visibility:private"], + deps = [ + ":builder", + ":loader", + ":signature_def_utils", + ":utils", + "//tensorflow/python:client", + "//tensorflow/python:client_testlib", + "//tensorflow/python:control_flow_ops", + "//tensorflow/python:errors", + "//tensorflow/python:framework_ops", + "//tensorflow/python:lib", + "//tensorflow/python:state_ops", + "//tensorflow/python:training", + "//tensorflow/python:variables", ], ) diff --git a/tensorflow/python/saved_model/loader_impl.py b/tensorflow/python/saved_model/loader_impl.py index d1bd8d47ae..e5f649fdab 100644 --- a/tensorflow/python/saved_model/loader_impl.py +++ b/tensorflow/python/saved_model/loader_impl.py @@ -28,6 +28,7 @@ from tensorflow.core.protobuf import meta_graph_pb2 from tensorflow.core.protobuf import saved_model_pb2 from tensorflow.python.framework import ops from tensorflow.python.lib.io import file_io +from tensorflow.python.ops import variables from tensorflow.python.platform import tf_logging from tensorflow.python.saved_model import constants from tensorflow.python.training import saver as tf_saver @@ -207,11 +208,56 @@ def load(sess, tags, export_dir, import_scope=None, **saver_kwargs): Raises: RuntimeError: MetaGraphDef associated with the tags cannot be found. """ - with sess.graph.as_default(): - # Build the SavedModel protocol buffer and find requested meta graph def. - saved_model = _parse_saved_model(export_dir) + loader = SavedModelLoader(export_dir) + return loader.load(sess, tags, import_scope, **saver_kwargs) + + +class SavedModelLoader(object): + """Load graphs and restore variable values from a `SavedModel`.""" + + def __init__(self, export_dir): + """Creates a `SavedModelLoader`. + + Args: + export_dir: Directory in which the SavedModel protocol buffer and + variables to be loaded are located. + """ + self._export_dir = export_dir + self._variables_path = os.path.join( + compat.as_bytes(export_dir), + compat.as_bytes(constants.VARIABLES_DIRECTORY), + compat.as_bytes(constants.VARIABLES_FILENAME)) + self._saved_model = _parse_saved_model(export_dir) + + @property + def export_dir(self): + """Directory containing the SavedModel.""" + return self._export_dir + + @property + def variables_path(self): + """Path to variable checkpoint files.""" + return self._variables_path + + @property + def saved_model(self): + """SavedModel object parsed from the export directory.""" + return self._saved_model + + def get_meta_graph_def_from_tags(self, tags): + """Return MetaGraphDef with the exact specified tags. + + Args: + tags: A list or set of string tags that identify the MetaGraphDef. + + Returns: + MetaGraphDef with the same tags. + + Raises: + RuntimeError: if no metagraphs were found with the associated tags. + """ found_match = False - for meta_graph_def in saved_model.meta_graphs: + for meta_graph_def in self._saved_model.meta_graphs: if set(meta_graph_def.meta_info_def.tags) == set(tags): meta_graph_def_to_load = meta_graph_def found_match = True @@ -223,32 +269,100 @@ def load(sess, tags, export_dir, import_scope=None, **saver_kwargs): " could not be found in SavedModel. To inspect available tag-sets in" " the SavedModel, please use the SavedModel CLI: `saved_model_cli`" ) + return meta_graph_def_to_load - # Build a saver by importing the meta graph def to load. - saver = tf_saver.import_meta_graph( - meta_graph_def_to_load, import_scope=import_scope, **saver_kwargs) - - if saver: - # Build the checkpoint path where the variables are located. - variables_path = os.path.join( - compat.as_bytes(export_dir), - compat.as_bytes(constants.VARIABLES_DIRECTORY), - compat.as_bytes(constants.VARIABLES_FILENAME)) - - # Restore the variables using the built saver in the provided session. - saver.restore(sess, variables_path) - else: - tf_logging.info("The specified SavedModel has no variables; no " - "checkpoints were restored.") - - # Get asset tensors, if any. - asset_tensors_dictionary = _get_asset_tensors( - export_dir, meta_graph_def_to_load, import_scope=import_scope) - - main_op_tensor = ( - _get_main_op_tensor(meta_graph_def_to_load) or - (_get_legacy_init_op_tensor(meta_graph_def_to_load))) - if main_op_tensor is not None: - sess.run(fetches=[main_op_tensor], feed_dict=asset_tensors_dictionary) + def load_graph(self, graph, tags, import_scope=None, **saver_kwargs): + """Load ops and nodes from SavedModel MetaGraph into graph. - return meta_graph_def_to_load + Args: + graph: tf.Graph object. + tags: a set of string tags identifying a MetaGraphDef. + import_scope: Optional `string` -- if specified, prepend this string + followed by '/' to all loaded tensor names. This scope is applied to + tensor instances loaded into the passed session, but it is *not* written + through to the static `MetaGraphDef` protocol buffer that is returned. + **saver_kwargs: keyword arguments to pass to tf.train.import_meta_graph. + + Returns: + Saver defined by the MetaGraph, which can be used to restore the variable + values. + """ + meta_graph_def = self.get_meta_graph_def_from_tags(tags) + with graph.as_default(): + return tf_saver.import_meta_graph( + meta_graph_def, import_scope=import_scope, **saver_kwargs) + + def restore_variables(self, sess, saver, import_scope=None): + """Restore SavedModel variable values into the session. + + Args: + sess: tf.Session to restore variable values. + saver: a tf.train.Saver object. Can be None if there are no variables in + graph. This may be the saver returned by the load_graph() function, or a + default `tf.train.Saver()`. + import_scope: Optional `string` -- if specified, prepend this string + followed by '/' to all loaded tensor names. This scope is applied to + tensor instances loaded into the passed session, but it is *not* written + through to the static `MetaGraphDef` protocol buffer that is returned. + + Raises: + ValueError: if no saver was passed to the saver argument, and there are + variables in the graph. + """ + with sess.graph.as_default(): + if (saver is None and + not variables._all_saveable_objects(scope=import_scope)): # pylint: disable=protected-access + tf_logging.info("The specified SavedModel has no variables; no " + "checkpoints were restored.") + elif isinstance(saver, tf_saver.Saver): + saver.restore(sess, self._variables_path) + else: + raise ValueError( + "No tf.train.Saver object was passed to the function " + "SavedModelLoader.restore_variables. Since there are variables in " + "the graph, a saver is required.") + + def run_init_ops(self, sess, tags, import_scope=None): + """Run initialization ops defined in the `MetaGraphDef`. + + Args: + sess: tf.Session to restore variable values. + tags: a set of string tags identifying a MetaGraphDef. + import_scope: Optional `string` -- if specified, prepend this string + followed by '/' to all loaded tensor names. This scope is applied to + tensor instances loaded into the passed session, but it is *not* written + through to the static `MetaGraphDef` protocol buffer that is returned. + """ + meta_graph_def = self.get_meta_graph_def_from_tags(tags) + with sess.graph.as_default(): + # Get asset tensors, if any. + asset_tensors_dictionary = _get_asset_tensors( + self._export_dir, meta_graph_def, import_scope=import_scope) + + main_op_tensor = ( + _get_main_op_tensor(meta_graph_def) or + (_get_legacy_init_op_tensor(meta_graph_def))) + if main_op_tensor is not None: + sess.run(fetches=[main_op_tensor], feed_dict=asset_tensors_dictionary) + + def load(self, sess, tags, import_scope=None, **saver_kwargs): + """Load the MetaGraphDef graph and restore variable values into the session. + + Args: + sess: tf.Session to restore variable values. + tags: a set of string tags identifying a MetaGraphDef. + import_scope: Optional `string` -- if specified, prepend this string + followed by '/' to all loaded tensor names. This scope is applied to + tensor instances loaded into the passed session, but it is *not* written + through to the static `MetaGraphDef` protocol buffer that is returned. + **saver_kwargs: keyword arguments to pass to tf.train.import_meta_graph. + + Returns: + `MetagraphDef` proto of the graph that was loaded. + """ + with sess.graph.as_default(): + saver = self.load_graph(sess.graph, tags, import_scope, + **saver_kwargs) + self.restore_variables(sess, saver, import_scope) + self.run_init_ops(sess, tags, import_scope) + return self.get_meta_graph_def_from_tags(tags) diff --git a/tensorflow/python/saved_model/loader_test.py b/tensorflow/python/saved_model/loader_test.py new file mode 100644 index 0000000000..ce18859f6b --- /dev/null +++ b/tensorflow/python/saved_model/loader_test.py @@ -0,0 +1,217 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for SavedModelLoader class.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os + +from tensorflow.python.client import session +from tensorflow.python.framework import errors +from tensorflow.python.framework import ops +from tensorflow.python.lib.io import file_io +from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import state_ops +from tensorflow.python.ops import variables +from tensorflow.python.platform import test +from tensorflow.python.saved_model import builder as saved_model_builder +from tensorflow.python.saved_model import loader_impl +from tensorflow.python.saved_model import signature_def_utils +from tensorflow.python.saved_model import utils +from tensorflow.python.training import saver as tf_saver + + +def _get_export_dir(label): + return os.path.join(test.get_temp_dir(), label) + +SIMPLE_ADD_SAVED_MODEL = _get_export_dir("simple_add_saved_model") +SAVED_MODEL_WITH_MAIN_OP = _get_export_dir("saved_model_with_main_op") + + +class SavedModelLoaderTest(test.TestCase): + + def setUp(self): + """Write test SavedModels to a temp directory.""" + with session.Session(graph=ops.Graph()) as sess: + x = variables.Variable(5, name="x") + y = variables.Variable(11, name="y") + z = x + y + sess.run(variables.global_variables_initializer()) + + foo_sig_def = signature_def_utils.build_signature_def( + {"foo_input": utils.build_tensor_info(x)}, + {"foo_output": utils.build_tensor_info(z)}) + bar_sig_def = signature_def_utils.build_signature_def( + {"bar_x": utils.build_tensor_info(x), + "bar_y": utils.build_tensor_info(y)}, + {"bar_z": utils.build_tensor_info(z)}) + + builder = saved_model_builder.SavedModelBuilder(SIMPLE_ADD_SAVED_MODEL) + builder.add_meta_graph_and_variables( + sess, ["foo_graph"], {"foo": foo_sig_def, "bar": bar_sig_def}) + builder.save() + + # Write SavedModel with a main_op + assign_op = control_flow_ops.group(state_ops.assign(y, 7)) + + builder = saved_model_builder.SavedModelBuilder(SAVED_MODEL_WITH_MAIN_OP) + builder.add_meta_graph_and_variables( + sess, ["foo_graph"], {"foo": foo_sig_def, "bar": bar_sig_def}, + main_op=assign_op) + builder.save() + + def tearDown(self): + file_io.delete_recursively(test.get_temp_dir()) + + def test_load_function(self): + loader = loader_impl.SavedModelLoader(SIMPLE_ADD_SAVED_MODEL) + with self.test_session(graph=ops.Graph()) as sess: + loader.load(sess, ["foo_graph"]) + self.assertEqual(5, sess.graph.get_tensor_by_name("x:0").eval()) + self.assertEqual(11, sess.graph.get_tensor_by_name("y:0").eval()) + + loader2 = loader_impl.SavedModelLoader(SAVED_MODEL_WITH_MAIN_OP) + with self.test_session(graph=ops.Graph()) as sess: + loader2.load(sess, ["foo_graph"]) + self.assertEqual(5, sess.graph.get_tensor_by_name("x:0").eval()) + self.assertEqual(7, sess.graph.get_tensor_by_name("y:0").eval()) + + def test_load_graph(self): + loader = loader_impl.SavedModelLoader(SIMPLE_ADD_SAVED_MODEL) + graph = ops.Graph() + loader.load_graph(graph, ["foo_graph"]) + + x = graph.get_tensor_by_name("x:0") + y = graph.get_tensor_by_name("y:0") + + with self.assertRaises(KeyError): + graph.get_tensor_by_name("z:0") + + with self.test_session(graph=graph) as sess: + # Check that x and y are not initialized + with self.assertRaises(errors.FailedPreconditionError): + sess.run(x) + with self.assertRaises(errors.FailedPreconditionError): + sess.run(y) + + def test_load_with_import_scope(self): + loader = loader_impl.SavedModelLoader(SAVED_MODEL_WITH_MAIN_OP) + with self.test_session(graph=ops.Graph()) as sess: + saver = loader.load_graph(sess.graph, ["foo_graph"], import_scope="baz") + + # The default saver should not work when the import scope is set. + with self.assertRaises(errors.NotFoundError): + loader.restore_variables(sess, tf_saver.Saver()) + + loader.restore_variables(sess, saver) + loader.run_init_ops(sess, ["foo_graph"]) + + self.assertEqual(5, sess.graph.get_tensor_by_name("baz/x:0").eval()) + self.assertEqual(7, sess.graph.get_tensor_by_name("baz/y:0").eval()) + + # Test combined load function. + loader = loader_impl.SavedModelLoader(SAVED_MODEL_WITH_MAIN_OP) + with self.test_session(graph=ops.Graph()) as sess: + loader.load(sess, ["foo_graph"], import_scope="baa") + self.assertEqual(5, sess.graph.get_tensor_by_name("baa/x:0").eval()) + self.assertEqual(7, sess.graph.get_tensor_by_name("baa/y:0").eval()) + + def test_restore_variables(self): + loader = loader_impl.SavedModelLoader(SAVED_MODEL_WITH_MAIN_OP) + with self.test_session(graph=ops.Graph()) as sess: + x = variables.Variable(0, name="x") + y = variables.Variable(0, name="y") + z = x * y + + sess.run(variables.global_variables_initializer()) + + # There are variables to restore, so a saver must be created. + with self.assertRaises(ValueError): + loader.restore_variables(sess, None) + + loader.restore_variables(sess, tf_saver.Saver()) + self.assertEqual(55, z.eval()) + + def test_run_init_op(self): + loader = loader_impl.SavedModelLoader(SAVED_MODEL_WITH_MAIN_OP) + graph = ops.Graph() + saver = loader.load_graph(graph, ["foo_graph"]) + with self.test_session(graph=graph) as sess: + loader.restore_variables(sess, saver) + self.assertEqual(5, sess.graph.get_tensor_by_name("x:0").eval()) + self.assertEqual(11, sess.graph.get_tensor_by_name("y:0").eval()) + + loader.run_init_ops(sess, ["foo_graph"]) + self.assertEqual(5, sess.graph.get_tensor_by_name("x:0").eval()) + self.assertEqual(7, sess.graph.get_tensor_by_name("y:0").eval()) + + def test_parse_saved_model(self): + loader = loader_impl.SavedModelLoader(SIMPLE_ADD_SAVED_MODEL) + meta_graph = loader.get_meta_graph_def_from_tags(["foo_graph"]) + self.assertIsNotNone(meta_graph) + self.assertIn("foo", meta_graph.signature_def) + self.assertIn("bar", meta_graph.signature_def) + + def test_load_invalid_meta_graph(self): + loader = loader_impl.SavedModelLoader(SIMPLE_ADD_SAVED_MODEL) + with self.assertRaises(RuntimeError): + loader.get_meta_graph_def_from_tags([]) + with self.assertRaises(RuntimeError): + loader.get_meta_graph_def_from_tags([""]) + with self.assertRaises(RuntimeError): + loader.get_meta_graph_def_from_tags(["not_a_graph"]) + + def test_load_saved_model_with_no_variables(self): + """Test that SavedModel runs saver when there appear to be no variables. + + When no variables are detected, this may mean that the variables were saved + to different collections, or the collections weren't saved to the + SavedModel. If the SavedModel MetaGraphDef contains a saver, it should still + run in either of these cases. + """ + path = _get_export_dir("no_variable_saved_model") + with session.Session(graph=ops.Graph()) as sess: + x = variables.Variable(5, name="x", collections=["not_global_variable"]) + y = variables.Variable(11, name="y", collections=["not_global_variable"]) + self.assertFalse(variables._all_saveable_objects()) + z = x + y + sess.run(variables.variables_initializer([x, y])) + + foo_sig_def = signature_def_utils.build_signature_def( + {"foo_input": utils.build_tensor_info(x)}, + {"foo_output": utils.build_tensor_info(z)}) + + builder = saved_model_builder.SavedModelBuilder(path) + builder.add_meta_graph_and_variables( + sess, ["foo_graph"], {"foo": foo_sig_def}, + saver=tf_saver.Saver([x, y])) + builder.save() + + loader = loader_impl.SavedModelLoader(path) + with self.test_session(graph=ops.Graph()) as sess: + saver = loader.load_graph(sess.graph, ["foo_graph"]) + self.assertFalse(variables._all_saveable_objects()) + self.assertIsNotNone(saver) + + with self.test_session(graph=ops.Graph()) as sess: + loader.load(sess, ["foo_graph"]) + self.assertEqual(5, sess.graph.get_tensor_by_name("x:0").eval()) + self.assertEqual(11, sess.graph.get_tensor_by_name("y:0").eval()) + + +if __name__ == "__main__": + test.main() -- GitLab From 841031362630230c5e3bcb6915a842087619ec12 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 19 Jun 2018 17:18:15 -0700 Subject: [PATCH 711/816] Update ops-related pbtxt files. PiperOrigin-RevId: 201269772 --- .../core/ops/compat/ops_history.v1.pbtxt | 25 +++++++++++++++++++ tensorflow/core/ops/ops.pbtxt | 25 +++++++++++++++++++ 2 files changed, 50 insertions(+) diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt index 62b37ce33d..11ed50d30e 100644 --- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt +++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt @@ -41340,6 +41340,31 @@ op { } is_stateful: true } +op { + name: "RandomGammaGrad" + input_arg { + name: "alpha" + type_attr: "T" + } + input_arg { + name: "sample" + type_attr: "T" + } + output_arg { + name: "output" + type_attr: "T" + } + attr { + name: "T" + type: "type" + allowed_values { + list { + type: DT_FLOAT + type: DT_DOUBLE + } + } + } +} op { name: "RandomPoisson" input_arg { diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt index 80e8df9206..c7f74c205a 100644 --- a/tensorflow/core/ops/ops.pbtxt +++ b/tensorflow/core/ops/ops.pbtxt @@ -20732,6 +20732,31 @@ op { } is_stateful: true } +op { + name: "RandomGammaGrad" + input_arg { + name: "alpha" + type_attr: "T" + } + input_arg { + name: "sample" + type_attr: "T" + } + output_arg { + name: "output" + type_attr: "T" + } + attr { + name: "T" + type: "type" + allowed_values { + list { + type: DT_FLOAT + type: DT_DOUBLE + } + } + } +} op { name: "RandomPoisson" input_arg { -- GitLab From 1f48db29a4a0cf7e0017ad6aa3bb1f8f7ee8ff92 Mon Sep 17 00:00:00 2001 From: Rohan Jain Date: Tue, 19 Jun 2018 17:26:04 -0700 Subject: [PATCH 712/816] Fixing a bug in linear_model where the name for the model is always set to 'linear_model'. This causes issues when we create multiple linear models in the same graph. PiperOrigin-RevId: 201270816 --- .../python/feature_column/feature_column.py | 4 ++- .../feature_column/feature_column_test.py | 29 ++++++++++++++++--- 2 files changed, 28 insertions(+), 5 deletions(-) diff --git a/tensorflow/python/feature_column/feature_column.py b/tensorflow/python/feature_column/feature_column.py index 5ae60028f4..40219e4b34 100644 --- a/tensorflow/python/feature_column/feature_column.py +++ b/tensorflow/python/feature_column/feature_column.py @@ -452,13 +452,15 @@ def linear_model(features, ValueError: if an item in `feature_columns` is neither a `_DenseColumn` nor `_CategoricalColumn`. """ + with variable_scope.variable_scope(None, 'linear_model') as vs: + model_name = _strip_leading_slashes(vs.name) linear_model_layer = _LinearModel( feature_columns=feature_columns, units=units, sparse_combiner=sparse_combiner, weight_collections=weight_collections, trainable=trainable, - name='linear_model') + name=model_name) retval = linear_model_layer(features) # pylint: disable=not-callable if cols_to_vars is not None: cols_to_vars.update(linear_model_layer.cols_to_vars()) diff --git a/tensorflow/python/feature_column/feature_column_test.py b/tensorflow/python/feature_column/feature_column_test.py index c80c1d1866..dc3dde6710 100644 --- a/tensorflow/python/feature_column/feature_column_test.py +++ b/tensorflow/python/feature_column/feature_column_test.py @@ -1257,14 +1257,14 @@ class CrossedColumnTest(test.TestCase): }, (crossed,)) -def get_linear_model_bias(): - with variable_scope.variable_scope('linear_model', reuse=True): +def get_linear_model_bias(name='linear_model'): + with variable_scope.variable_scope(name, reuse=True): return variable_scope.get_variable('bias_weights') -def get_linear_model_column_var(column): +def get_linear_model_column_var(column, name='linear_model'): return ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES, - 'linear_model/' + column.name)[0] + name + '/' + column.name)[0] def get_keras_linear_model_predictions(features, @@ -1928,6 +1928,27 @@ class LinearModelTest(test.TestCase): with self.assertRaisesOpError('Feature .* cannot have rank 0'): sess.run(net, feed_dict={features['price']: np.array(1)}) + def test_multiple_linear_models(self): + price = fc.numeric_column('price') + with ops.Graph().as_default(): + features1 = {'price': [[1.], [5.]]} + features2 = {'price': [[2.], [10.]]} + predictions1 = fc.linear_model(features1, [price]) + predictions2 = fc.linear_model(features2, [price]) + bias1 = get_linear_model_bias(name='linear_model') + bias2 = get_linear_model_bias(name='linear_model_1') + price_var1 = get_linear_model_column_var(price, name='linear_model') + price_var2 = get_linear_model_column_var(price, name='linear_model_1') + with _initialized_session() as sess: + self.assertAllClose([0.], bias1.eval()) + sess.run(price_var1.assign([[10.]])) + sess.run(bias1.assign([5.])) + self.assertAllClose([[15.], [55.]], predictions1.eval()) + self.assertAllClose([0.], bias2.eval()) + sess.run(price_var2.assign([[10.]])) + sess.run(bias2.assign([5.])) + self.assertAllClose([[25.], [105.]], predictions2.eval()) + class _LinearModelTest(test.TestCase): -- GitLab From b10bf00750720269aacc31ef08021fb722b5e8c5 Mon Sep 17 00:00:00 2001 From: Bjarke Hammersholt Roune Date: Tue, 19 Jun 2018 17:28:24 -0700 Subject: [PATCH 713/816] Add interface in Compiler for computing the default backend configuration of an op. Add interface in Executable for computing the size of the executable. PiperOrigin-RevId: 201271132 --- tensorflow/compiler/xla/service/compiler.cc | 7 +++++++ tensorflow/compiler/xla/service/compiler.h | 10 ++++++++++ tensorflow/compiler/xla/service/executable.cc | 7 +++++++ tensorflow/compiler/xla/service/executable.h | 4 ++++ tensorflow/compiler/xla/shape_util.cc | 12 ++++++++++++ tensorflow/compiler/xla/shape_util.h | 3 +++ tensorflow/compiler/xla/xla_data.proto | 3 +++ 7 files changed, 46 insertions(+) diff --git a/tensorflow/compiler/xla/service/compiler.cc b/tensorflow/compiler/xla/service/compiler.cc index 0dceed853d..6b3b9820f0 100644 --- a/tensorflow/compiler/xla/service/compiler.cc +++ b/tensorflow/compiler/xla/service/compiler.cc @@ -35,6 +35,13 @@ Compiler::ComputeBackendConfigs(const HloInstruction& hlo, return {}; } +std::unique_ptr +Compiler::ComputeDefaultBackendConfig(const HloInstruction& hlo, + se::StreamExecutor* executor) const { + CHECK(executor != nullptr); + return nullptr; +} + // Define a default version where metadata is not used. StatusOr>> Compiler::CompileAheadOfTime( diff --git a/tensorflow/compiler/xla/service/compiler.h b/tensorflow/compiler/xla/service/compiler.h index d1144f97bb..99abb9bae3 100644 --- a/tensorflow/compiler/xla/service/compiler.h +++ b/tensorflow/compiler/xla/service/compiler.h @@ -179,6 +179,16 @@ class Compiler { ComputeBackendConfigs(const HloInstruction& hlo, se::StreamExecutor* executor) const; + // Returns the backend configuration that the backend chooses by default for + // the given HLO. Returns no configuration if the backend does not support + // configurations for the given HLO. + // + // The stream executor is passed in to provide information about the hardware + // that the backend configurations would be targeting. + virtual std::unique_ptr + ComputeDefaultBackendConfig(const HloInstruction& hlo, + se::StreamExecutor* executor) const; + // Compiles the HLO module for ahead-of-time execution. This is intended for // use in static compilation. virtual StatusOr>> diff --git a/tensorflow/compiler/xla/service/executable.cc b/tensorflow/compiler/xla/service/executable.cc index 6df172db8e..7cf2746947 100644 --- a/tensorflow/compiler/xla/service/executable.cc +++ b/tensorflow/compiler/xla/service/executable.cc @@ -116,6 +116,11 @@ StatusOr Executable::ExecuteOnStreamWrapper( if (profile->compute_time_ns() == 0) { profile->set_compute_time_ns(profile->compute_and_transfer_time_ns()); } + + const int64 executable_size_in_bytes = SizeInBytes(); + if (executable_size_in_bytes != 0) { + profile->set_executable_size_in_bytes(executable_size_in_bytes); + } } if (profile_ptr != nullptr) { @@ -129,6 +134,8 @@ StatusOr Executable::ExecuteOnStreamWrapper( return return_value; } +int64 Executable::SizeInBytes() { return -1; } + Status Executable::DumpHloSnapshot() { TF_RET_CHECK(dumping_snapshot()); TF_RET_CHECK(hlo_snapshot_->has_hlo() && diff --git a/tensorflow/compiler/xla/service/executable.h b/tensorflow/compiler/xla/service/executable.h index 1a91aca9d1..bd92bfa50f 100644 --- a/tensorflow/compiler/xla/service/executable.h +++ b/tensorflow/compiler/xla/service/executable.h @@ -135,6 +135,10 @@ class Executable { return hlo_module_->config().host_entry_computation_layout().result_shape(); } + // Returns the size of the executable in bytes. Returns -1 by default if the + // method is not overridden to support this kind of query. + virtual int64 SizeInBytes(); + // Dumping helpers. void set_hlo_snapshot(std::unique_ptr hlo_snapshot) { hlo_snapshot_ = std::move(hlo_snapshot); diff --git a/tensorflow/compiler/xla/shape_util.cc b/tensorflow/compiler/xla/shape_util.cc index ba09b63859..98c3095499 100644 --- a/tensorflow/compiler/xla/shape_util.cc +++ b/tensorflow/compiler/xla/shape_util.cc @@ -422,6 +422,18 @@ ShapeUtil::MakeShapeWithDescendingLayoutAndSamePhysicalLayout( std::multiplies()); } +/* static */ int64 ShapeUtil::ElementsInRecursive(const Shape& shape) { + CHECK(IsArray(shape) || IsTuple(shape)); + if (IsArray(shape)) { + return ElementsIn(shape); + } + int64 count = 0; + for (const Shape& element_shape : shape.tuple_shapes()) { + count += ElementsInRecursive(element_shape); + } + return count; +} + /* static */ bool ShapeUtil::IsZeroElementArray(const Shape& shape) { return ShapeUtil::IsArray(shape) && ElementsIn(shape) == 0; } diff --git a/tensorflow/compiler/xla/shape_util.h b/tensorflow/compiler/xla/shape_util.h index b7543c2026..02e4f41505 100644 --- a/tensorflow/compiler/xla/shape_util.h +++ b/tensorflow/compiler/xla/shape_util.h @@ -175,6 +175,9 @@ class ShapeUtil { // Precondition: IsArray(shape) static int64 ElementsIn(const Shape& shape); + // As ElementsIn(), but recurses through tuples. + static int64 ElementsInRecursive(const Shape& shape); + // Returns true if 'shape' is an array with zero elements. static bool IsZeroElementArray(const Shape& shape); diff --git a/tensorflow/compiler/xla/xla_data.proto b/tensorflow/compiler/xla/xla_data.proto index 0af73e8a93..c7472173a7 100644 --- a/tensorflow/compiler/xla/xla_data.proto +++ b/tensorflow/compiler/xla/xla_data.proto @@ -274,6 +274,9 @@ message ExecutionProfile { // for the input data transfer since the memory is initialized with the proper // values before the execution. int64 compute_and_transfer_time_ns = 5; + + // The size of the binary code in the executable. + int64 executable_size_in_bytes = 6; } // Handle given to a user that represents an execution that the user launched -- GitLab From eb7005d54dcf9330dedec28b917692d6dfc2391c Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 19 Jun 2018 17:46:04 -0700 Subject: [PATCH 714/816] Go: Update generated wrapper functions for TensorFlow ops. PiperOrigin-RevId: 201273382 --- tensorflow/go/op/wrappers.go | 326 +++++++++++++++++------------------ 1 file changed, 163 insertions(+), 163 deletions(-) diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go index bff2264c29..b2dbdafc5f 100644 --- a/tensorflow/go/op/wrappers.go +++ b/tensorflow/go/op/wrappers.go @@ -3015,6 +3015,36 @@ func Concat(scope *Scope, concat_dim tf.Output, values []tf.Output) (output tf.O return op.Output(0) } +// Converts a flat index or array of flat indices into a tuple of +// +// coordinate arrays. +// +// @compatibility(numpy) +// Equivalent to np.unravel_index +// @end_compatibility +// +// Arguments: +// indices: An 0-D or 1-D `int` Tensor whose elements are indices into the +// flattened version of an array of dimensions dims. +// dims: An 1-D `int` Tensor. The shape of the array to use for unraveling +// indices. +// +// Returns An 2-D (or 1-D if indices is 0-D) tensor where each row has the +// same shape as the indices array. +func UnravelIndex(scope *Scope, indices tf.Output, dims tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "UnravelIndex", + Input: []tf.Input{ + indices, dims, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Computes gradients for SparseSegmentSqrtN. // // Returns tensor "output" with same shape as grad, except for dimension 0 whose @@ -3914,24 +3944,6 @@ func AddV2(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { return op.Output(0) } -// Returns x + y element-wise. -// -// *NOTE*: `Add` supports broadcasting. `AddN` does not. More about broadcasting -// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) -func Add(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Add", - Input: []tf.Input{ - x, y, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // NthElementAttr is an optional argument to NthElement. type NthElementAttr func(optionalAttr) @@ -4675,6 +4687,24 @@ func MatrixInverse(scope *Scope, input tf.Output, optional ...MatrixInverseAttr) return op.Output(0) } +// Returns x + y element-wise. +// +// *NOTE*: `Add` supports broadcasting. `AddN` does not. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func Add(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Add", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Computes square of x element-wise. // // I.e., \\(y = x * x = x^2\\). @@ -7780,121 +7810,6 @@ func StringJoin(scope *Scope, inputs []tf.Output, optional ...StringJoinAttr) (o return op.Output(0) } -// LRNGradAttr is an optional argument to LRNGrad. -type LRNGradAttr func(optionalAttr) - -// LRNGradDepthRadius sets the optional depth_radius attribute to value. -// -// value: A depth radius. -// If not specified, defaults to 5 -func LRNGradDepthRadius(value int64) LRNGradAttr { - return func(m optionalAttr) { - m["depth_radius"] = value - } -} - -// LRNGradBias sets the optional bias attribute to value. -// -// value: An offset (usually > 0 to avoid dividing by 0). -// If not specified, defaults to 1 -func LRNGradBias(value float32) LRNGradAttr { - return func(m optionalAttr) { - m["bias"] = value - } -} - -// LRNGradAlpha sets the optional alpha attribute to value. -// -// value: A scale factor, usually positive. -// If not specified, defaults to 1 -func LRNGradAlpha(value float32) LRNGradAttr { - return func(m optionalAttr) { - m["alpha"] = value - } -} - -// LRNGradBeta sets the optional beta attribute to value. -// -// value: An exponent. -// If not specified, defaults to 0.5 -func LRNGradBeta(value float32) LRNGradAttr { - return func(m optionalAttr) { - m["beta"] = value - } -} - -// Gradients for Local Response Normalization. -// -// Arguments: -// input_grads: 4-D with shape `[batch, height, width, channels]`. -// input_image: 4-D with shape `[batch, height, width, channels]`. -// output_image: 4-D with shape `[batch, height, width, channels]`. -// -// Returns The gradients for LRN. -func LRNGrad(scope *Scope, input_grads tf.Output, input_image tf.Output, output_image tf.Output, optional ...LRNGradAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "LRNGrad", - Input: []tf.Input{ - input_grads, input_image, output_image, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// AnyAttr is an optional argument to Any. -type AnyAttr func(optionalAttr) - -// AnyKeepDims sets the optional keep_dims attribute to value. -// -// value: If true, retain reduced dimensions with length 1. -// If not specified, defaults to false -func AnyKeepDims(value bool) AnyAttr { - return func(m optionalAttr) { - m["keep_dims"] = value - } -} - -// Computes the "logical or" of elements across dimensions of a tensor. -// -// Reduces `input` along the dimensions given in `axis`. Unless -// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in -// `axis`. If `keep_dims` is true, the reduced dimensions are -// retained with length 1. -// -// Arguments: -// input: The tensor to reduce. -// axis: The dimensions to reduce. Must be in the range -// `[-rank(input), rank(input))`. -// -// Returns The reduced tensor. -func Any(scope *Scope, input tf.Output, axis tf.Output, optional ...AnyAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "Any", - Input: []tf.Input{ - input, axis, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // ResourceApplyFtrlAttr is an optional argument to ResourceApplyFtrl. type ResourceApplyFtrlAttr func(optionalAttr) @@ -19406,6 +19321,121 @@ func OrderedMapIncompleteSize(scope *Scope, dtypes []tf.DataType, optional ...Or return op.Output(0) } +// LRNGradAttr is an optional argument to LRNGrad. +type LRNGradAttr func(optionalAttr) + +// LRNGradDepthRadius sets the optional depth_radius attribute to value. +// +// value: A depth radius. +// If not specified, defaults to 5 +func LRNGradDepthRadius(value int64) LRNGradAttr { + return func(m optionalAttr) { + m["depth_radius"] = value + } +} + +// LRNGradBias sets the optional bias attribute to value. +// +// value: An offset (usually > 0 to avoid dividing by 0). +// If not specified, defaults to 1 +func LRNGradBias(value float32) LRNGradAttr { + return func(m optionalAttr) { + m["bias"] = value + } +} + +// LRNGradAlpha sets the optional alpha attribute to value. +// +// value: A scale factor, usually positive. +// If not specified, defaults to 1 +func LRNGradAlpha(value float32) LRNGradAttr { + return func(m optionalAttr) { + m["alpha"] = value + } +} + +// LRNGradBeta sets the optional beta attribute to value. +// +// value: An exponent. +// If not specified, defaults to 0.5 +func LRNGradBeta(value float32) LRNGradAttr { + return func(m optionalAttr) { + m["beta"] = value + } +} + +// Gradients for Local Response Normalization. +// +// Arguments: +// input_grads: 4-D with shape `[batch, height, width, channels]`. +// input_image: 4-D with shape `[batch, height, width, channels]`. +// output_image: 4-D with shape `[batch, height, width, channels]`. +// +// Returns The gradients for LRN. +func LRNGrad(scope *Scope, input_grads tf.Output, input_image tf.Output, output_image tf.Output, optional ...LRNGradAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "LRNGrad", + Input: []tf.Input{ + input_grads, input_image, output_image, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// AnyAttr is an optional argument to Any. +type AnyAttr func(optionalAttr) + +// AnyKeepDims sets the optional keep_dims attribute to value. +// +// value: If true, retain reduced dimensions with length 1. +// If not specified, defaults to false +func AnyKeepDims(value bool) AnyAttr { + return func(m optionalAttr) { + m["keep_dims"] = value + } +} + +// Computes the "logical or" of elements across dimensions of a tensor. +// +// Reduces `input` along the dimensions given in `axis`. Unless +// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in +// `axis`. If `keep_dims` is true, the reduced dimensions are +// retained with length 1. +// +// Arguments: +// input: The tensor to reduce. +// axis: The dimensions to reduce. Must be in the range +// `[-rank(input), rank(input))`. +// +// Returns The reduced tensor. +func Any(scope *Scope, input tf.Output, axis tf.Output, optional ...AnyAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Any", + Input: []tf.Input{ + input, axis, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Creates a sequence of numbers. // // This operation creates a sequence of numbers that begins at `start` and @@ -30680,33 +30710,3 @@ func InplaceSub(scope *Scope, x tf.Output, i tf.Output, v tf.Output) (y tf.Outpu op := scope.AddOperation(opspec) return op.Output(0) } - -// Converts a flat index or array of flat indices into a tuple of -// -// coordinate arrays. -// -// @compatibility(numpy) -// Equivalent to np.unravel_index -// @end_compatibility -// -// Arguments: -// indices: An 0-D or 1-D `int` Tensor whose elements are indices into the -// flattened version of an array of dimensions dims. -// dims: An 1-D `int` Tensor. The shape of the array to use for unraveling -// indices. -// -// Returns An 2-D (or 1-D if indices is 0-D) tensor where each row has the -// same shape as the indices array. -func UnravelIndex(scope *Scope, indices tf.Output, dims tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "UnravelIndex", - Input: []tf.Input{ - indices, dims, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} -- GitLab From 9751540a91a31499aa1530d542f4cff9e81b682a Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 19 Jun 2018 17:52:40 -0700 Subject: [PATCH 715/816] [TF:XLA] Fix for HLO instruction post-order DFS and multioutput fusion. Cycles were not handled correctly when computing the postorder of an HLO computation. Add methods to multioutput fusion that allows subclasses to recompute and query the current reachability map. PiperOrigin-RevId: 201274181 --- .../compiler/xla/service/hlo_computation.cc | 56 ++++++++++--------- .../xla/service/hlo_computation_test.cc | 3 + .../xla/service/multi_output_fusion.cc | 13 +++-- .../xla/service/multi_output_fusion.h | 11 +++- 4 files changed, 50 insertions(+), 33 deletions(-) diff --git a/tensorflow/compiler/xla/service/hlo_computation.cc b/tensorflow/compiler/xla/service/hlo_computation.cc index 74173a1685..c057be8201 100644 --- a/tensorflow/compiler/xla/service/hlo_computation.cc +++ b/tensorflow/compiler/xla/service/hlo_computation.cc @@ -279,37 +279,42 @@ void ComputeComputationPostOrder( } } +enum State { kVisiting, kVisited }; + void ComputeInstructionPostOrder( std::vector* post_order, HloInstruction* root, - tensorflow::gtl::FlatSet* visited) { - std::vector> dfs_stack; - dfs_stack.emplace_back(root, false); + tensorflow::gtl::FlatMap* visited) { + std::vector dfs_stack; + dfs_stack.push_back(root); while (!dfs_stack.empty()) { const auto current = dfs_stack.back(); - if (current.second) { - dfs_stack.pop_back(); - if (!visited->insert(current.first).second) { - continue; - } - post_order->push_back(current.first); - } else { - if (visited->count(current.first)) { + auto it = visited->find(current); + if (it != visited->end()) { + if (it->second == kVisited) { + // Already visited. dfs_stack.pop_back(); continue; } - dfs_stack.back().second = true; - - // Add the operands to the stack in reverse order so the first operand is - // processed first. This will produce a more natural ordering and a nicer - // result for thigns like HLO stringification. - const auto& operands = current.first->operands(); - for (int64 i = operands.size() - 1; i >= 0; --i) { - dfs_stack.emplace_back(operands[i], false); - } + // Visit this node. + CHECK_EQ(kVisiting, it->second); + dfs_stack.pop_back(); + post_order->push_back(current); + it->second = kVisited; + continue; + } - for (HloInstruction* op : current.first->control_predecessors()) { - dfs_stack.emplace_back(op, false); - } + visited->insert({current, kVisiting}); + + // Add the operands to the stack in reverse order so the first operand is + // processed first. This will produce a more natural ordering and a nicer + // result for thigns like HLO stringification. + const auto& operands = current->operands(); + for (int64 i = operands.size() - 1; i >= 0; --i) { + dfs_stack.emplace_back(operands[i]); + } + + for (HloInstruction* op : current->control_predecessors()) { + dfs_stack.emplace_back(op); } } } @@ -320,7 +325,7 @@ std::vector HloComputation::MakeInstructionPostOrder() const { std::vector post_order; post_order.reserve(instruction_count()); std::vector trace_instructions; - tensorflow::gtl::FlatSet added_instructions; + tensorflow::gtl::FlatMap visited; for (auto& instruction : instructions_) { if (instruction->opcode() == HloOpcode::kTrace) { // Trace instructions aren't handled by the DFS visitor. Add trace @@ -328,8 +333,7 @@ std::vector HloComputation::MakeInstructionPostOrder() const { // users). trace_instructions.push_back(instruction.get()); } else if (instruction->users().empty()) { - ComputeInstructionPostOrder(&post_order, instruction.get(), - &added_instructions); + ComputeInstructionPostOrder(&post_order, instruction.get(), &visited); } } post_order.insert(post_order.end(), trace_instructions.begin(), diff --git a/tensorflow/compiler/xla/service/hlo_computation_test.cc b/tensorflow/compiler/xla/service/hlo_computation_test.cc index 3f59d31bb9..c504fc51d2 100644 --- a/tensorflow/compiler/xla/service/hlo_computation_test.cc +++ b/tensorflow/compiler/xla/service/hlo_computation_test.cc @@ -417,6 +417,9 @@ TEST_F(HloComputationTest, CycleDetection) { // Add a control dependency to create a cycle. ASSERT_IS_OK(add->AddControlDependencyTo(negate)); + auto instructions = computation->MakeInstructionPostOrder(); + EXPECT_EQ(3, instructions.size()); + const auto visitor = [](HloInstruction* instruction) { return Status::OK(); }; auto visit_status = computation->Accept(visitor); ASSERT_FALSE(visit_status.ok()); diff --git a/tensorflow/compiler/xla/service/multi_output_fusion.cc b/tensorflow/compiler/xla/service/multi_output_fusion.cc index f9f9c7dcf7..79b5a442aa 100644 --- a/tensorflow/compiler/xla/service/multi_output_fusion.cc +++ b/tensorflow/compiler/xla/service/multi_output_fusion.cc @@ -28,7 +28,7 @@ StatusOr MultiOutputFusion::Run(HloModule* module) { for (auto* computation : module->MakeNonfusionComputations()) { computation_ = computation; - reachability_ = computation_->ComputeReachability(); + RecomputeReachability(); candidates_.clear(); candidates_index_.clear(); all_fusion_candidates_.clear(); @@ -277,6 +277,10 @@ bool MultiOutputFusion::LegalToFuse(HloInstruction* instr1, return true; } +void MultiOutputFusion::RecomputeReachability() { + reachability_ = computation_->ComputeReachability(); +} + void MultiOutputFusion::UpdateReachability( HloInstruction* instr1, HloInstruction* instr2, tensorflow::gtl::ArraySlice instrs_to_update, @@ -345,14 +349,11 @@ bool MultiOutputFusion::Perform() { --fuel_; } } - if (DoProducerConsumerMultiOutputFusion(computation_)) { + if (DoProducerConsumerMultiOutputFusion()) { changed = true; } return changed; } -bool MultiOutputFusion::DoProducerConsumerMultiOutputFusion( - HloComputation* /*computation*/) { - return false; -} +bool MultiOutputFusion::DoProducerConsumerMultiOutputFusion() { return false; } } // namespace xla diff --git a/tensorflow/compiler/xla/service/multi_output_fusion.h b/tensorflow/compiler/xla/service/multi_output_fusion.h index d9c36fa284..d23822e33e 100644 --- a/tensorflow/compiler/xla/service/multi_output_fusion.h +++ b/tensorflow/compiler/xla/service/multi_output_fusion.h @@ -78,6 +78,15 @@ class MultiOutputFusion : public HloPassInterface { // Test if it's legal to fuse instr1 and instr2 into one fusion instruction. virtual bool LegalToFuse(HloInstruction* instr1, HloInstruction* instr2); + // Recompute reachability for the current computation. + void RecomputeReachability(); + + // Returns the reachability map for the current computation. + HloReachabilityMap* reachability() const { return reachability_.get(); } + + // Returns the computation for the pass. + HloComputation* computation() const { return computation_; } + // Update the reachability map after fusing instr1 and instr2. void UpdateReachability( HloInstruction* instr1, HloInstruction* instr2, @@ -89,7 +98,7 @@ class MultiOutputFusion : public HloPassInterface { // // TODO(b/80420762): Perform producer-consumer multi-output fusion in // InstructionFusion instead. - virtual bool DoProducerConsumerMultiOutputFusion(HloComputation* computation); + virtual bool DoProducerConsumerMultiOutputFusion(); private: // Fuse HloInstrctuion instr1 and instr2 and return the fused instruction. -- GitLab From c04396e3fd7a449429212d37899703bc3cf507e9 Mon Sep 17 00:00:00 2001 From: Priya Gupta Date: Tue, 19 Jun 2018 18:40:23 -0700 Subject: [PATCH 716/816] Implement new API for TPUStrategy to run multiple steps, and move most of the TPU specific logic into this method from `call_for_each_tower`. Disable TPU tests temporarily, will enable again in subsequent code changes. PiperOrigin-RevId: 201279470 --- .../contrib/distribute/python/combinations.py | 4 - .../distribute/python/minimize_loss_test.py | 20 +++- .../contrib/distribute/python/tpu_strategy.py | 104 +++++++++--------- 3 files changed, 67 insertions(+), 61 deletions(-) diff --git a/tensorflow/contrib/distribute/python/combinations.py b/tensorflow/contrib/distribute/python/combinations.py index ba03b14deb..9a8ea4aa48 100644 --- a/tensorflow/contrib/distribute/python/combinations.py +++ b/tensorflow/contrib/distribute/python/combinations.py @@ -321,10 +321,6 @@ default_strategy = NamedDistribution( one_device_strategy = NamedDistribution( "OneDeviceCPU", lambda: one_device_lib.OneDeviceStrategy("/cpu:0"), required_gpus=None) -tpu_strategy_single_iteration = NamedDistribution( - "TPUSingleIteration", - lambda: tpu_lib.TPUStrategy(iterations_per_step=1), - required_tpu=True) tpu_strategy = NamedDistribution("TPU", tpu_lib.TPUStrategy, required_tpu=True) # Note that we disable prefetching for testing since prefetching makes # the input non-deterministic. diff --git a/tensorflow/contrib/distribute/python/minimize_loss_test.py b/tensorflow/contrib/distribute/python/minimize_loss_test.py index 5c056a7c73..c11a05f227 100644 --- a/tensorflow/contrib/distribute/python/minimize_loss_test.py +++ b/tensorflow/contrib/distribute/python/minimize_loss_test.py @@ -56,6 +56,10 @@ class MinimizeLossStepTest(test.TestCase, parameterized.TestCase): is_tpu=[True])) def testTrainNetwork(self, distribution, optimizer_fn, use_callable_loss, is_tpu): + # TODO(priyag): Remove this once the step TPU Strategy is stable. + if is_tpu: + self.skipTest("TPU tests are WIP.") + with distribution.scope(): model_fn, dataset_fn, layer = minimize_loss_example( optimizer_fn, use_bias=True, use_callable_loss=use_callable_loss) @@ -111,6 +115,10 @@ class MinimizeLossStepTest(test.TestCase, parameterized.TestCase): is_tpu=[True])) def testOptimizerInsideModelFn(self, distribution, optimizer_fn, is_tpu): + # TODO(priyag): Remove this once the step TPU Strategy is stable. + if is_tpu: + self.skipTest("TPU tests are WIP.") + created_variables = [] trainable_variables = [] @@ -186,7 +194,7 @@ class MinimizeLossStepTest(test.TestCase, parameterized.TestCase): # towers will re-execute UPDATE_OPS of previous towers. update_ops_in_cross_tower_mode=[True])) + combinations.combine( - distribution=[combinations.tpu_strategy_single_iteration], + distribution=[combinations.tpu_strategy], optimizer_fn=[ combinations.gradient_descent_optimizer_v1_fn, combinations.gradient_descent_optimizer_v2_fn @@ -198,6 +206,10 @@ class MinimizeLossStepTest(test.TestCase, parameterized.TestCase): renorm, is_tpu, update_ops_in_cross_tower_mode): """Verifies that moving mean updates are reduced across towers.""" + # TODO(priyag): Remove this once the step TPU Strategy is stable. + if is_tpu: + self.skipTest("TPU tests are WIP.") + with distribution.scope(): num_towers = len(distribution.worker_devices) model_fn, dataset_fn, batchnorm = batchnorm_example( @@ -279,12 +291,16 @@ class MinimizeLossStepTest(test.TestCase, parameterized.TestCase): mode=["graph"], use_callable_loss=[True, False]) + combinations.combine(mode=["eager"], use_callable_loss=[True])) + combinations.combine( - distribution=[combinations.tpu_strategy_single_iteration], + distribution=[combinations.tpu_strategy], is_tpu=[True], mode=["graph"], use_callable_loss=[True, False]))) def testMeanVsSum(self, distribution, optimizer_fn, loss_reduction, use_callable_loss, is_tpu): + # TODO(priyag): Remove this once the step TPU Strategy is stable. + if is_tpu: + self.skipTest("TPU tests are WIP.") + with distribution.scope(): all_vars = [] diff --git a/tensorflow/contrib/distribute/python/tpu_strategy.py b/tensorflow/contrib/distribute/python/tpu_strategy.py index 75441786a6..b177e09adb 100644 --- a/tensorflow/contrib/distribute/python/tpu_strategy.py +++ b/tensorflow/contrib/distribute/python/tpu_strategy.py @@ -21,11 +21,8 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import itertools - from tensorflow.contrib import tpu from tensorflow.contrib.distribute.python import one_device_strategy -from tensorflow.contrib.distribute.python import values from tensorflow.contrib.tpu.python.ops import tpu_ops from tensorflow.python.framework import constant_op from tensorflow.python.framework import ops @@ -36,86 +33,83 @@ from tensorflow.python.util import nest class TPUStrategy(one_device_strategy.OneDeviceStrategy): """Experimental TPU distribution strategy implementation.""" - def __init__(self, - num_cores_per_host=2, - iterations_per_step=2): + def __init__(self, num_cores_per_host=2): # TODO(isaprykin): Generalize the defaults. They are currently tailored for # the unit test. super(TPUStrategy, self).__init__('/cpu:0') # TODO(isaprykin): Auto-detect number of cores and hosts. self._num_cores_per_host = num_cores_per_host - # TODO(isaprykin): This might have to be per-call. - self._iterations_per_step = iterations_per_step + # TODO(priyag): This should not be hardcoded here. + self._host = '/task:0/device:CPU:0' def distribute_dataset(self, dataset_fn): - return values.PerIterationDataset( - self._call_dataset_fn(dataset_fn), self._iterations_per_step, - self._num_cores_per_host) - - def _call_for_each_tower(self, fn, *args, **kwargs): - kwargs.pop('run_concurrently', None) - - inputs = {'args': args, 'kwargs': kwargs} - flat_inputs = nest.flatten(inputs) - - feed_mask = [isinstance(f, values.PerIteration) for f in flat_inputs] + # TODO(priyag): Perhaps distribute across cores here. + return self._call_dataset_fn(dataset_fn) - feeds = lambda: itertools.compress(flat_inputs, feed_mask) - shapes = [f.get_shape() for f in feeds()] + # TODO(priyag): Deal with OutOfRange errors. + def run_steps_on_dataset(self, fn, iterator, iterations): + # Enqueue ops + shapes = nest.flatten(iterator.output_shapes) if any([not s.is_fully_defined() for s in shapes]): raise ValueError( 'TPU currently requires fully defined shapes. Either use ' 'set_shape() on the input tensors or use ' 'dataset.apply(map_and_batch(..., drop_remainder=True)).') - types = [f.get_dtype() for f in feeds()] - - def infeed_input(i): - """Get input, split it and then enqueue.""" - iteration_inputs = [f.get(i) for f in feeds()] - infeed_inputs = [[inputs_per_core[core_id] - for inputs_per_core in iteration_inputs] - for core_id in range(self._num_cores_per_host)] - - infeed_ops = [] - for core_id, infeed_input in enumerate(infeed_inputs): - infeed_ops.append( + types = nest.flatten(iterator.output_types) + + def enqueue_ops_fn(): + """Enqueue ops for one iteration.""" + control_deps = [] + sharded_inputs = [] + with ops.device(self._host): + for _ in range(self._num_cores_per_host): + # Use control dependencies to ensure a deterministic ordering. + with ops.control_dependencies(control_deps): + inputs = nest.flatten(iterator.get_next()) + control_deps.extend(inputs) + sharded_inputs.append(inputs) + + enqueue_ops = [] + for core_id, shard_input in enumerate(sharded_inputs): + enqueue_ops.append( tpu_ops.infeed_enqueue_tuple( - inputs=infeed_input, shapes=shapes, device_ordinal=core_id)) + inputs=shard_input, shapes=shapes, device_ordinal=core_id)) + return enqueue_ops - with ops.control_dependencies(infeed_ops): + def enqueue_ops_loop_body(i): + with ops.control_dependencies(enqueue_ops_fn()): return i + 1 - with ops.device('/task:0/device:CPU:0'): + with ops.device(self._host): enqueue_ops = control_flow_ops.while_loop( - lambda i: i < self._iterations_per_step, - infeed_input, [constant_op.constant(0)], + lambda i: i < iterations, + enqueue_ops_loop_body, + [constant_op.constant(0)], parallel_iterations=1) - def dequeueing_fn(*args, **kwargs): - """Dequeue input arguments and supply them to `fn`.""" - del args, kwargs + # Dequeue ops + def dequeue_fn(): dequeued = tpu.infeed_dequeue_tuple(dtypes=types, shapes=shapes) - dequeued = iter(dequeued) + return nest.pack_sequence_as(iterator.output_shapes, dequeued) - fn_inputs = [] - for inp, is_feed in zip(flat_inputs, feed_mask): - if is_feed: - fn_inputs.append(next(dequeued)) - else: - fn_inputs.append(inp) - - fn_inputs = nest.pack_sequence_as(inputs, fn_inputs) - return fn(*fn_inputs['args'], **fn_inputs['kwargs']) + # Wrap `fn` for repeat. + run_fn = lambda: fn(dequeue_fn()) + # Repeat def iterate_on_tpu(): - return tpu.repeat(self._iterations_per_step, dequeueing_fn, []) + return tpu.repeat(iterations, run_fn, []) - with one_device_strategy._OneDeviceTowerContext(self): # pylint: disable=protected-access - tpu_result = tpu.batch_parallel( - iterate_on_tpu, [], num_shards=self._num_cores_per_host) + # Re-write and distribute computation. + tpu_result = tpu.batch_parallel( + iterate_on_tpu, [], num_shards=self._num_cores_per_host) return control_flow_ops.group(tpu_result, enqueue_ops) + def _call_for_each_tower(self, fn, *args, **kwargs): + kwargs.pop('run_concurrently', None) + with one_device_strategy._OneDeviceTowerContext(self): # pylint: disable=protected-access + return fn(*args, **kwargs) + def _reduce(self, method_string, value, destinations): del destinations # TPU is graph mode only. Rely on implicit Send/Recv. if method_string == 'mean': -- GitLab From 9ab04addfb80cbf9334bb330acee5fca09353d23 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 19 Jun 2018 19:40:00 -0700 Subject: [PATCH 717/816] Remove the ambiguity of device/host computation layouts within the HloModuleConfig. PiperOrigin-RevId: 201284741 --- .../compiler/xla/client/local_client.cc | 33 +++---------- .../compiler/xla/service/cpu/cpu_compiler.cc | 3 +- .../xla/service/cpu/cpu_executable.cc | 4 +- tensorflow/compiler/xla/service/executable.h | 4 +- .../compiler/xla/service/gpu/gpu_compiler.cc | 2 +- tensorflow/compiler/xla/service/hlo_module.cc | 18 +++---- tensorflow/compiler/xla/service/hlo_module.h | 19 ++++--- .../compiler/xla/service/hlo_module_config.cc | 23 +++------ .../compiler/xla/service/hlo_module_config.h | 49 +++++++------------ tensorflow/compiler/xla/service/hlo_parser.cc | 11 +---- .../compiler/xla/service/hlo_parser_test.cc | 2 +- .../xla/service/interpreter/compiler.cc | 2 +- .../compiler/xla/service/local_service.cc | 6 +-- tensorflow/compiler/xla/service/service.cc | 48 +++--------------- tensorflow/compiler/xla/service/service.h | 3 -- tensorflow/compiler/xla/tests/hlo_test_base.h | 20 ++------ 16 files changed, 70 insertions(+), 177 deletions(-) diff --git a/tensorflow/compiler/xla/client/local_client.cc b/tensorflow/compiler/xla/client/local_client.cc index cf07910c4a..5f9710914b 100644 --- a/tensorflow/compiler/xla/client/local_client.cc +++ b/tensorflow/compiler/xla/client/local_client.cc @@ -51,24 +51,17 @@ LocalExecutable::LocalExecutable(std::unique_ptr executable, Status LocalExecutable::ValidateExecutionOptions( const tensorflow::gtl::ArraySlice arguments, const ExecutableRunOptions& run_options, const Backend& backend) { - const ComputationLayout& host_computation_layout = - executable_->module_config().host_entry_computation_layout(); - const ComputationLayout& device_computation_layout = - executable_->module_config().device_entry_computation_layout(); + const ComputationLayout& computation_layout = + executable_->module_config().entry_computation_layout(); // Check argument number, shapes, and layouts. - if (arguments.size() != host_computation_layout.parameter_count()) { + if (arguments.size() != computation_layout.parameter_count()) { return InvalidArgument( "invalid number of arguments for computation: expected %d, got %zu", - host_computation_layout.parameter_count(), arguments.size()); - } - if (arguments.size() != device_computation_layout.parameter_count()) { - return InvalidArgument( - "invalid number of arguments for computation: expected %d, got %zu", - device_computation_layout.parameter_count(), arguments.size()); + computation_layout.parameter_count(), arguments.size()); } for (int i = 0; i < arguments.size(); ++i) { - if (!host_computation_layout.parameter_layout(i).MatchesLayoutInShape( + if (!computation_layout.parameter_layout(i).MatchesLayoutInShape( arguments[i]->on_host_shape())) { return InvalidParameterArgument( executable_.get(), i, @@ -76,24 +69,10 @@ Status LocalExecutable::ValidateExecutionOptions( "parameter " "%d: want %s, got %s", i, - ShapeUtil::HumanString( - host_computation_layout.parameter_layout(i).shape()) + ShapeUtil::HumanString(computation_layout.parameter_layout(i).shape()) .c_str(), ShapeUtil::HumanString(arguments[i]->on_host_shape()).c_str()); } - if (!device_computation_layout.parameter_layout(i).MatchesLayoutInShape( - arguments[i]->on_device_shape())) { - return InvalidParameterArgument( - executable_.get(), i, - "Argument does not match device shape or layout of computation " - "parameter " - "%d: want %s, got %s", - i, - ShapeUtil::HumanString( - device_computation_layout.parameter_layout(i).shape()) - .c_str(), - ShapeUtil::HumanString(arguments[i]->on_device_shape()).c_str()); - } } if (run_options.stream() != nullptr) { diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc index d039132535..52da9d6eac 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc @@ -303,8 +303,7 @@ Status CpuCompiler::RunHloPasses(HloModule* module, bool is_aot_compile, ReducePrecisionInsertion::PassTiming::AFTER_FUSION); pipeline.AddPass( - module->mutable_device_entry_computation_layout(), - &target_machine_features); + module->mutable_entry_computation_layout(), &target_machine_features); // The LayoutAssignment pass may leave behind kCopy instructions which are // duplicate or NOPs, so remove them with algebraic simplification and CSE. pipeline.AddPass>( diff --git a/tensorflow/compiler/xla/service/cpu/cpu_executable.cc b/tensorflow/compiler/xla/service/cpu/cpu_executable.cc index cf43b74c69..1093559892 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_executable.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_executable.cc @@ -206,8 +206,8 @@ StatusOr CpuExecutable::CreateResultShapedBuffer( tensorflow::gtl::MutableArraySlice buffers) { se::Stream* stream = run_options->stream(); ScopedShapedBuffer result_buffer( - /*on_host_shape=*/host_result_shape(), - /*on_device_shape=*/host_result_shape(), run_options->allocator(), + /*on_host_shape=*/result_shape(), + /*on_device_shape=*/result_shape(), run_options->allocator(), stream->parent()->device_ordinal()); // Move OwningDeviceMemory values which contain the array(s) of the result diff --git a/tensorflow/compiler/xla/service/executable.h b/tensorflow/compiler/xla/service/executable.h index bd92bfa50f..98eaeee30a 100644 --- a/tensorflow/compiler/xla/service/executable.h +++ b/tensorflow/compiler/xla/service/executable.h @@ -131,8 +131,8 @@ class Executable { // The shape (including layout) that results from this execution. This is the // shape of the DeviceMemoryBase result value in ExecuteOnStream above. - const Shape& host_result_shape() const { - return hlo_module_->config().host_entry_computation_layout().result_shape(); + const Shape& result_shape() const { + return hlo_module_->config().entry_computation_layout().result_shape(); } // Returns the size of the executable in bytes. Returns -1 by default if the diff --git a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc index a040e6b681..decfc40daf 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc +++ b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc @@ -205,7 +205,7 @@ Status OptimizeHloModule(HloModule* hlo_module, se::StreamExecutor* stream_exec, { HloPassPipeline pipeline("layout_assignment"); pipeline.AddPass( - hlo_module->mutable_device_entry_computation_layout(), stream_exec); + hlo_module->mutable_entry_computation_layout(), stream_exec); // The LayoutAssignment pass may leave behind kCopy instructions which are // duplicate or NOPs, so remove them with algebraic simplification and CSE. diff --git a/tensorflow/compiler/xla/service/hlo_module.cc b/tensorflow/compiler/xla/service/hlo_module.cc index 11384c1456..39bc25ba42 100644 --- a/tensorflow/compiler/xla/service/hlo_module.cc +++ b/tensorflow/compiler/xla/service/hlo_module.cc @@ -58,7 +58,7 @@ HloComputation* HloModule::AddComputationInternal( // If the module configuration has no entry layout computation set, create a // default one based on the program shape. - if (!config_.has_host_entry_computation_layout()) { + if (!config_.has_entry_computation_layout()) { config_.SetDefaultComputationLayout( entry_computation_->ComputeProgramShape()); } @@ -231,14 +231,11 @@ StatusOr> HloModule::CreateFromProto( TF_RET_CHECK(proto.has_program_shape()) << "No program shape found in the proto"; const auto& expected_program_shape = proto.program_shape(); - TF_RET_CHECK( - expected_program_shape.parameters_size() == - module_config.device_entry_computation_layout().parameter_count()); + TF_RET_CHECK(expected_program_shape.parameters_size() == + module_config.entry_computation_layout().parameter_count()); for (int i = 0; i < expected_program_shape.parameters_size(); ++i) { const Shape& parameter_shape = - module_config.device_entry_computation_layout() - .parameter_layout(i) - .shape(); + module_config.entry_computation_layout().parameter_layout(i).shape(); TF_RET_CHECK(ShapeUtil::Compatible(expected_program_shape.parameters(i), parameter_shape)) << "HloModuleConfig has different shape for parameter " << i @@ -248,7 +245,7 @@ StatusOr> HloModule::CreateFromProto( << ", actual: " << ShapeUtil::HumanStringWithLayout(parameter_shape); } const Shape& result_shape = - module_config.device_entry_computation_layout().result_layout().shape(); + module_config.entry_computation_layout().result_layout().shape(); TF_RET_CHECK( ShapeUtil::Compatible(expected_program_shape.result(), result_shape)) << "HloModuleConfig has different result shape than the HLO module. " @@ -327,7 +324,7 @@ StatusOr HloModule::CreateModuleConfigFromProto( // The module config is constructed with default layouts regardless of what is // passed in via the ProgramShape. Set the layouts to the appropriate values. ComputationLayout* entry_layout = - module_config.mutable_host_entry_computation_layout(); + module_config.mutable_entry_computation_layout(); for (int64 i = 0; i < entry_layout->parameter_count(); ++i) { TF_RETURN_IF_ERROR( entry_layout->mutable_parameter_layout(i)->CopyLayoutFromShape( @@ -335,9 +332,6 @@ StatusOr HloModule::CreateModuleConfigFromProto( } TF_RETURN_IF_ERROR(entry_layout->mutable_result_layout()->CopyLayoutFromShape( program_shape.result())); - *module_config.mutable_device_entry_computation_layout() = - module_config.host_entry_computation_layout(); - return module_config; } diff --git a/tensorflow/compiler/xla/service/hlo_module.h b/tensorflow/compiler/xla/service/hlo_module.h index 5dc94e78e3..d2e726a0db 100644 --- a/tensorflow/compiler/xla/service/hlo_module.h +++ b/tensorflow/compiler/xla/service/hlo_module.h @@ -105,20 +105,19 @@ class HloModule { return entry_computation_; } - ComputationLayout* mutable_host_entry_computation_layout() { - return config_.mutable_host_entry_computation_layout(); + // Creates the ComputationLayout which describes the current status of the HLO + // module entry computation. + ComputationLayout compute_computation_layout() const { + return ComputationLayout(entry_computation()->ComputeProgramShape(), + /*ignore_layouts=*/false); } - const ComputationLayout& host_entry_computation_layout() const { - return config_.host_entry_computation_layout(); + ComputationLayout* mutable_entry_computation_layout() { + return config_.mutable_entry_computation_layout(); } - ComputationLayout* mutable_device_entry_computation_layout() { - return config_.mutable_device_entry_computation_layout(); - } - - const ComputationLayout& device_entry_computation_layout() const { - return config_.device_entry_computation_layout(); + const ComputationLayout& entry_computation_layout() const { + return config_.entry_computation_layout(); } // Gets the computations in this module. diff --git a/tensorflow/compiler/xla/service/hlo_module_config.cc b/tensorflow/compiler/xla/service/hlo_module_config.cc index dae5578a31..07a8c798db 100644 --- a/tensorflow/compiler/xla/service/hlo_module_config.cc +++ b/tensorflow/compiler/xla/service/hlo_module_config.cc @@ -28,16 +28,14 @@ namespace xla { using tensorflow::strings::StrAppend; -HloModuleConfig::HloModuleConfig() {} - -HloModuleConfig::HloModuleConfig(const ProgramShape& program_shape) - : host_entry_computation_layout_(program_shape), - device_entry_computation_layout_(program_shape) {} +HloModuleConfig::HloModuleConfig(const ProgramShape& program_shape, + bool ignore_layouts) + : entry_computation_layout_( + ComputationLayout(program_shape, ignore_layouts)) {} void HloModuleConfig::SetDefaultComputationLayout( const ProgramShape& program_shape) { - host_entry_computation_layout_ = ComputationLayout(program_shape); - device_entry_computation_layout_ = ComputationLayout(program_shape); + entry_computation_layout_ = ComputationLayout(program_shape); } string HloModuleConfig::compilation_cache_key() const { @@ -46,18 +44,11 @@ string HloModuleConfig::compilation_cache_key() const { StrAppend(&key, "::("); std::vector params; for (const ShapeLayout& param_layout : - host_entry_computation_layout_->parameter_layouts()) { + entry_computation_layout_->parameter_layouts()) { params.push_back(param_layout.shape().DebugString()); } StrAppend(&key, tensorflow::str_util::Join(params, ", "), ") => ", - host_entry_computation_layout_->result_shape().SerializeAsString()); - for (const ShapeLayout& param_layout : - device_entry_computation_layout_->parameter_layouts()) { - params.push_back(param_layout.shape().DebugString()); - } - StrAppend( - &key, tensorflow::str_util::Join(params, ", "), ") => ", - device_entry_computation_layout_->result_shape().SerializeAsString()); + entry_computation_layout_->result_shape().SerializeAsString()); if (seed() != 0) { // TODO(b/32083678): force recompilation to reset global state. static std::atomic counter{0}; diff --git a/tensorflow/compiler/xla/service/hlo_module_config.h b/tensorflow/compiler/xla/service/hlo_module_config.h index cdb0b29a23..074e9c9070 100644 --- a/tensorflow/compiler/xla/service/hlo_module_config.h +++ b/tensorflow/compiler/xla/service/hlo_module_config.h @@ -37,48 +37,34 @@ class HloModuleConfig { // ComputationLayout. The default ctor creates it without -- in this case // accessing entry_computation_layout will CHECK-fail. The ctor accepting a // ProgramShape creates a computation layout using this shape. - HloModuleConfig(); - explicit HloModuleConfig(const ProgramShape& program_shape); + // The layouts in the ProgramShape will be reset to default unless + // ignore_layouts is set to false. + HloModuleConfig() = default; - // Checks if this config has an entry computation layout already. - bool has_host_entry_computation_layout() const { - return host_entry_computation_layout_.has_value(); - } + explicit HloModuleConfig(const ProgramShape& program_shape, + bool ignore_layouts = true); - bool has_device_entry_computation_layout() const { - return device_entry_computation_layout_.has_value(); + // Checks if this config has an entry computation layout already. + bool has_entry_computation_layout() const { + return entry_computation_layout_.has_value(); } // Sets the entry computation layout for this config. If the entry computation // layout already exists, it is silently replaced. void SetDefaultComputationLayout(const ProgramShape& program_shape); - // Returns a constant reference to the on-host layout of the entry - // computation. Assumes the layout was set. - const ComputationLayout& host_entry_computation_layout() const { - CHECK(host_entry_computation_layout_.has_value()); - return *host_entry_computation_layout_; - } - - // Returns a mutable pointer to the layout of the on-host entry computation. + // Returns a constant reference to the layout of the entry computation. // Assumes the layout was set. - ComputationLayout* mutable_host_entry_computation_layout() { - CHECK(host_entry_computation_layout_.has_value()); - return &(*host_entry_computation_layout_); - } - - // Returns a constant reference to the on-device layout of the entry - // computation. Assumes the layout was set. - const ComputationLayout& device_entry_computation_layout() const { - CHECK(device_entry_computation_layout_.has_value()); - return *device_entry_computation_layout_; + const ComputationLayout& entry_computation_layout() const { + CHECK(entry_computation_layout_.has_value()); + return *entry_computation_layout_; } - // Returns a mutable pointer to the layout of the on-device entry computation. + // Returns a mutable pointer to the layout of the entry computation. // Assumes the layout was set. - ComputationLayout* mutable_device_entry_computation_layout() { - CHECK(device_entry_computation_layout_.has_value()); - return &(*device_entry_computation_layout_); + ComputationLayout* mutable_entry_computation_layout() { + CHECK(entry_computation_layout_.has_value()); + return &(*entry_computation_layout_); } // Returns whether to enable HLO-level profiling. @@ -127,8 +113,7 @@ class HloModuleConfig { private: // If you add new members, be sure to update compilation_cache_key. - tensorflow::gtl::optional host_entry_computation_layout_; - tensorflow::gtl::optional device_entry_computation_layout_; + tensorflow::gtl::optional entry_computation_layout_; // Whether this is a 'host module'. bool is_host_module_ = false; diff --git a/tensorflow/compiler/xla/service/hlo_parser.cc b/tensorflow/compiler/xla/service/hlo_parser.cc index daa3bc4232..2cee74c314 100644 --- a/tensorflow/compiler/xla/service/hlo_parser.cc +++ b/tensorflow/compiler/xla/service/hlo_parser.cc @@ -327,22 +327,15 @@ bool HloParser::ParseComputations() { // set the layouts to what the hlo text says. for (int p = 0; p < computation->num_parameters(); p++) { const Shape& param_shape = computation->parameter_instruction(p)->shape(); - TF_CHECK_OK(module_->mutable_host_entry_computation_layout() - ->mutable_parameter_layout(p) - ->CopyLayoutFromShape(param_shape)); - TF_CHECK_OK(module_->mutable_device_entry_computation_layout() + TF_CHECK_OK(module_->mutable_entry_computation_layout() ->mutable_parameter_layout(p) ->CopyLayoutFromShape(param_shape)); } const Shape& result_shape = computation->root_instruction()->shape(); - TF_CHECK_OK(module_->mutable_host_entry_computation_layout() - ->mutable_result_layout() - ->CopyLayoutFromShape(result_shape)); - TF_CHECK_OK(module_->mutable_device_entry_computation_layout() + TF_CHECK_OK(module_->mutable_entry_computation_layout() ->mutable_result_layout() ->CopyLayoutFromShape(result_shape)); } - return true; } diff --git a/tensorflow/compiler/xla/service/hlo_parser_test.cc b/tensorflow/compiler/xla/service/hlo_parser_test.cc index d551400d1e..d481e07f60 100644 --- a/tensorflow/compiler/xla/service/hlo_parser_test.cc +++ b/tensorflow/compiler/xla/service/hlo_parser_test.cc @@ -1302,7 +1302,7 @@ ENTRY %Reduce (input: f32[8,16,256]) -> f32[8,16] { auto module = ParseHloString(original); TF_ASSERT_OK(module.status()); - auto program_layout = module.ValueOrDie()->host_entry_computation_layout(); + auto program_layout = module.ValueOrDie()->entry_computation_layout(); ASSERT_EQ(program_layout.parameter_count(), 1); auto param_layout = program_layout.parameter_layout(0).layout(); auto result_layout = program_layout.result_layout().layout(); diff --git a/tensorflow/compiler/xla/service/interpreter/compiler.cc b/tensorflow/compiler/xla/service/interpreter/compiler.cc index c166653068..9f8f4bda87 100644 --- a/tensorflow/compiler/xla/service/interpreter/compiler.cc +++ b/tensorflow/compiler/xla/service/interpreter/compiler.cc @@ -44,7 +44,7 @@ Status InterpreterCompiler::RunHloOptimization(HloModule* hlo_module) { HloPassPipeline pipeline("Interpreter"); pipeline.AddPass( - hlo_module->mutable_device_entry_computation_layout()); + hlo_module->mutable_entry_computation_layout()); return pipeline.Run(hlo_module).status(); } diff --git a/tensorflow/compiler/xla/service/local_service.cc b/tensorflow/compiler/xla/service/local_service.cc index a6aa8bf82c..53efc30c36 100644 --- a/tensorflow/compiler/xla/service/local_service.cc +++ b/tensorflow/compiler/xla/service/local_service.cc @@ -190,10 +190,8 @@ StatusOr> LocalService::CompileExecutable( std::unique_ptr module_config, CreateModuleConfig(program_shape, argument_layouts, &execution_options)); - VLOG(3) << "Host Computation Layout: " - << module_config->host_entry_computation_layout().ToString(); - VLOG(3) << "Device Computation Layout: " - << module_config->device_entry_computation_layout().ToString(); + VLOG(3) << "Computation Layout: " + << module_config->entry_computation_layout().ToString(); TF_ASSIGN_OR_RETURN( se::StreamExecutor * executor, diff --git a/tensorflow/compiler/xla/service/service.cc b/tensorflow/compiler/xla/service/service.cc index 7ab39e01f2..da3b622bfa 100644 --- a/tensorflow/compiler/xla/service/service.cc +++ b/tensorflow/compiler/xla/service/service.cc @@ -244,10 +244,8 @@ StatusOr> Service::CreateModuleConfig( tensorflow::gtl::ArraySlice argument_shapes, const ExecutionOptions* execution_options) { auto config = MakeUnique(program_shape); - ComputationLayout* host_computation_layout = - config->mutable_host_entry_computation_layout(); - ComputationLayout* device_computation_layout = - config->mutable_device_entry_computation_layout(); + ComputationLayout* computation_layout = + config->mutable_entry_computation_layout(); if (program_shape.parameters_size() != argument_shapes.size()) { return InvalidArgument("computation takes %d parameters, but %zu given", program_shape.parameters_size(), @@ -264,10 +262,9 @@ StatusOr> Service::CreateModuleConfig( i, ShapeUtil::HumanString(program_shape.parameters(i)).c_str(), ShapeUtil::HumanString(*argument_shapes[i]).c_str()); } - TF_RETURN_IF_ERROR(host_computation_layout->mutable_parameter_layout(i) - ->CopyLayoutFromShape(*argument_shapes[i])); - TF_RETURN_IF_ERROR(device_computation_layout->mutable_parameter_layout(i) - ->CopyLayoutFromShape(*argument_shapes[i])); + TF_RETURN_IF_ERROR( + computation_layout->mutable_parameter_layout(i)->CopyLayoutFromShape( + *argument_shapes[i])); } if (execution_options != nullptr && execution_options->has_shape_with_output_layout()) { @@ -276,20 +273,11 @@ StatusOr> Service::CreateModuleConfig( TF_RETURN_IF_ERROR( ValidateResultShape(shape_with_output_layout, program_shape.result())); TF_RETURN_IF_ERROR( - host_computation_layout->mutable_result_layout()->CopyLayoutFromShape( - shape_with_output_layout)); - TF_RETURN_IF_ERROR( - device_computation_layout->mutable_result_layout()->CopyLayoutFromShape( + computation_layout->mutable_result_layout()->CopyLayoutFromShape( shape_with_output_layout)); } else { // If the result layout is not set, then choose the default. - // TODO(b/29118294): Allow the compiler to choose a better layout in this - // case. - // TODO(b/78356948): We are forcing the default layout here. We should fix - // clients which expect a default layout, to be explicit about it, by - // passing the proper ExecutionOptions with shape_with_output_layout set. - host_computation_layout->mutable_result_layout()->SetToDefaultLayout(); - device_computation_layout->mutable_result_layout()->SetToDefaultLayout(); + computation_layout->mutable_result_layout()->SetToDefaultLayout(); } config->set_replica_count(options_.number_of_replicas()); @@ -377,24 +365,6 @@ StatusOr>> Service::BuildExecutables( return std::move(executables); } -Status Service::ValidateEntryComputationLayout(HloModule* module) { - const ComputationLayout& on_host = module->host_entry_computation_layout(); - const ComputationLayout& on_device = - module->device_entry_computation_layout(); - for (int64 i = 0; i < on_device.parameter_count(); ++i) { - TF_RET_CHECK(ShapeUtil::Compatible(on_device.parameter_shape(i), - on_host.parameter_shape(i))) - << ShapeUtil::HumanStringWithLayout(on_device.parameter_shape(i)) - << " vs " - << ShapeUtil::HumanStringWithLayout(on_host.parameter_shape(i)); - } - TF_RET_CHECK( - ShapeUtil::Compatible(on_device.result_shape(), on_host.result_shape())) - << ShapeUtil::HumanStringWithLayout(on_device.result_shape()) << " vs " - << ShapeUtil::HumanStringWithLayout(on_host.result_shape()); - return Status::OK(); -} - StatusOr> Service::ExecuteParallelAndRegisterResult( tensorflow::gtl::ArraySlice executables, @@ -690,7 +660,7 @@ Status Service::ExecuteGraphParallel(const ExecuteGraphParallelRequest* arg, request.execution_options())); VLOG(3) << "ExecuteGraphParallel created HloModuleConfig computation layout: " - << module_config->host_entry_computation_layout().ToString(); + << module_config->entry_computation_layout().ToString(); // Adds to the vectors to build and execute the computations after the loop. all_arguments.push_back(replicated_arguments); @@ -851,8 +821,6 @@ StatusOr> Service::BuildExecutable( TF_ASSIGN_OR_RETURN( module, backend->compiler()->RunHloPasses(std::move(module), executor, device_allocator)); - // Check that on-host and on-device shapes are consistent. - TF_RETURN_IF_ERROR(ValidateEntryComputationLayout(module.get())); TF_ASSIGN_OR_RETURN(std::unique_ptr executable, backend->compiler()->RunBackend( diff --git a/tensorflow/compiler/xla/service/service.h b/tensorflow/compiler/xla/service/service.h index 7960429084..47d196fb2a 100644 --- a/tensorflow/compiler/xla/service/service.h +++ b/tensorflow/compiler/xla/service/service.h @@ -193,9 +193,6 @@ class Service : public ServiceInterface { const ExecutionOptions& execution_options, tensorflow::gtl::ArraySlice arguments); - // Assert that host- and device-shapes are in a consistent state. - Status ValidateEntryComputationLayout(HloModule* module); - protected: friend class LocalExecutable; diff --git a/tensorflow/compiler/xla/tests/hlo_test_base.h b/tensorflow/compiler/xla/tests/hlo_test_base.h index 249da87f48..9009d67cea 100644 --- a/tensorflow/compiler/xla/tests/hlo_test_base.h +++ b/tensorflow/compiler/xla/tests/hlo_test_base.h @@ -185,13 +185,9 @@ class HloTestBase : public ::testing::Test { // 'layout'. void ForceParameterLayout(HloModule* module, int64 param_no, const Layout& layout) { - ASSERT_LT( - param_no, - module->mutable_host_entry_computation_layout()->parameter_count()); - module->mutable_host_entry_computation_layout() - ->mutable_parameter_layout(param_no) - ->ResetLayout(layout); - module->mutable_device_entry_computation_layout() + ASSERT_LT(param_no, + module->mutable_entry_computation_layout()->parameter_count()); + module->mutable_entry_computation_layout() ->mutable_parameter_layout(param_no) ->ResetLayout(layout); } @@ -199,10 +195,7 @@ class HloTestBase : public ::testing::Test { // Convenience method to force the layout of the computation result in a // module. The result layout of 'module' is set to 'layout'. void ForceResultLayout(HloModule* module, const Layout& layout) { - module->mutable_host_entry_computation_layout() - ->mutable_result_layout() - ->ResetLayout(layout); - module->mutable_device_entry_computation_layout() + module->mutable_entry_computation_layout() ->mutable_result_layout() ->ResetLayout(layout); } @@ -210,10 +203,7 @@ class HloTestBase : public ::testing::Test { // Convenience method to clear the layout of the computation result in // 'module'. void ForceClearResultLayout(HloModule* module) { - module->mutable_host_entry_computation_layout() - ->mutable_result_layout() - ->Clear(); - module->mutable_device_entry_computation_layout() + module->mutable_entry_computation_layout() ->mutable_result_layout() ->Clear(); } -- GitLab From 081f30a7bc2a11e2556629a14cdab2c3c313312e Mon Sep 17 00:00:00 2001 From: David Majnemer Date: Tue, 19 Jun 2018 22:07:22 -0700 Subject: [PATCH 718/816] [TF2XLA] Optimize TruncatedNormalOp Re-sampling when encountering a rejected value can be quite slow. If we directly use the inverse CDF of the normal distribution, the probit function, we can avoid the need to resample. PiperOrigin-RevId: 201296864 --- tensorflow/compiler/tests/random_ops_test.py | 2 +- .../compiler/tf2xla/kernels/random_ops.cc | 77 +++++++++---------- .../tf2xla/kernels/stateless_random_ops.cc | 49 +----------- .../compiler/tf2xla/kernels/unary_ops.cc | 12 +-- .../compiler/xla/client/lib/arithmetic.cc | 53 ++++++++++++- .../compiler/xla/client/lib/arithmetic.h | 11 ++- 6 files changed, 101 insertions(+), 103 deletions(-) diff --git a/tensorflow/compiler/tests/random_ops_test.py b/tensorflow/compiler/tests/random_ops_test.py index 8c6366faa6..2e71b00ba6 100644 --- a/tensorflow/compiler/tests/random_ops_test.py +++ b/tensorflow/compiler/tests/random_ops_test.py @@ -124,7 +124,7 @@ class RandomOpsTest(XLATestCase): # Department of Scientific Computing website. Florida State University. expected_mean = mu + (normal_pdf(alpha) - normal_pdf(beta)) / z * sigma actual_mean = np.mean(y) - self.assertAllClose(actual_mean, expected_mean, atol=3e-4) + self.assertAllClose(actual_mean, expected_mean, atol=2e-4) expected_median = mu + probit( (normal_cdf(alpha) + normal_cdf(beta)) / 2.) * sigma diff --git a/tensorflow/compiler/tf2xla/kernels/random_ops.cc b/tensorflow/compiler/tf2xla/kernels/random_ops.cc index a08654b12b..aa4d242a11 100644 --- a/tensorflow/compiler/tf2xla/kernels/random_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/random_ops.cc @@ -17,6 +17,8 @@ limitations under the License. // TODO(misard,phawkins): handle random number generator seeds/states correctly. // TODO(misard,phawkins): add tests. +#include + #include "tensorflow/compiler/tf2xla/kernels/gather_op_helpers.h" #include "tensorflow/compiler/tf2xla/lib/util.h" #include "tensorflow/compiler/tf2xla/lib/while_loop.h" @@ -205,53 +207,44 @@ class TruncatedNormalOp : public XlaOpKernel { xla::XlaBuilder* b = ctx->builder(); - auto out_of_range_mask = [dtype](xla::XlaOp candidate, xla::XlaBuilder* b) { - xla::XlaOp two_sd = XlaHelpers::FloatLiteral(b, dtype, 2.0); - return b->Gt(b->Abs(candidate), two_sd); + auto normal_cdf = [](double x) { + return (1.0 + std::erf(x / std::sqrt(2.0))) / 2.0; }; - // The algorithm we're using is roughly: - // - // while (any(candidate < mean-2*sd || candidate > mean+2*sd)) { - // out_of_range_mask := candidate < mean-2*sd || candidate > mean+2*sd - // candidate = select(out_of_range_mask, rng_normal(), candidate) - // } - std::vector initial_values = { - // The current candidate. - b->Broadcast(XlaHelpers::Zero(b, dtype), shape.dim_sizes()), - // The to_resample mask, where 'true' identifies a location in the - // current candidate that is out of range and must be regenerated. - b->Broadcast(b->ConstantR0(true), shape.dim_sizes()), - // Is any element in the mask true? - b->ConstantR0(true)}; - auto condition = [&](gtl::ArraySlice values, - xla::XlaBuilder* b) -> xla::StatusOr { - // Continue while any element in the mask is true. - return values[2]; - }; - auto body = - [&](gtl::ArraySlice values, - xla::XlaBuilder* b) -> xla::StatusOr> { - xla::XlaOp candidate = values[0]; - xla::XlaOp to_resample = values[1]; - xla::XlaOp mean = XlaHelpers::Zero(b, dtype); - xla::XlaOp stddev = XlaHelpers::One(b, dtype); - candidate = b->Select(to_resample, b->RngNormal(mean, stddev, xla_shape), - candidate); - // Compute a new to_resample mask, and determine whether any value is - // still out of range. - to_resample = out_of_range_mask(candidate, b); - TF_ASSIGN_OR_RETURN(xla::XlaOp done, Any(to_resample, b)); - return std::vector{candidate, to_resample, done}; - }; - auto result = - XlaWhileLoop(condition, body, initial_values, "truncated_normal", b); - OP_REQUIRES_OK(ctx, result.status()); - ctx->SetOutput(0, result.ValueOrDie()[0]); + const double kA = -2.0; + const double kB = 2.0; + const double kMu = 0.0; + const double kSigma = 1.0; + const double kAlpha = (kA - kMu) / kSigma; + const double kBeta = (kB - kMu) / kSigma; + const double kAlphaNormalCdf = normal_cdf(kAlpha); + const double kBetaNormalCdf = normal_cdf(kBeta); + const double kZ = kBetaNormalCdf - kAlphaNormalCdf; + + xla::XlaOp one = XlaHelpers::FloatLiteral(b, dtype, 1.0); + xla::XlaOp two = XlaHelpers::FloatLiteral(b, dtype, 2.0); + xla::XlaOp sqrt_2 = XlaHelpers::FloatLiteral(b, dtype, std::sqrt(2.0)); + xla::XlaOp min_positive = + XlaHelpers::FloatLiteral(b, dtype, std::numeric_limits::min()); + + xla::XlaOp z = XlaHelpers::FloatLiteral(b, dtype, kZ); + xla::XlaOp alpha_normal_cdf = + XlaHelpers::FloatLiteral(b, dtype, kAlphaNormalCdf); + + auto uniform = b->RngUniform(min_positive, one, xla_shape); + // probit(p) = sqrt(2) * erfinv(2*p-1) + auto p = b->Add(alpha_normal_cdf, b->Mul(z, uniform)); + auto erfinv_input = b->Sub(b->Mul(p, two), one); + auto erfinv_or_status = ErfInv(b, erfinv_input); + OP_REQUIRES_OK(ctx, erfinv_or_status.status()); + auto probit = b->Mul(sqrt_2, erfinv_or_status.ValueOrDie()); + ctx->SetOutput(0, probit); } }; -REGISTER_XLA_OP(Name("TruncatedNormal").CompileTimeConstInput("shape"), +REGISTER_XLA_OP(Name("TruncatedNormal") + .CompileTimeConstInput("shape") + .TypeConstraint("dtype", DT_FLOAT), TruncatedNormalOp); } // anonymous namespace diff --git a/tensorflow/compiler/tf2xla/kernels/stateless_random_ops.cc b/tensorflow/compiler/tf2xla/kernels/stateless_random_ops.cc index a99d4ddc7c..58c5dc5aa9 100644 --- a/tensorflow/compiler/tf2xla/kernels/stateless_random_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/stateless_random_ops.cc @@ -163,51 +163,6 @@ xla::XlaOp RandomUniform(xla::XlaBuilder* builder, const xla::XlaOp& seed, return floats; } -// Approximation for the inverse error function from -// Giles, M., "Approximating the erfinv function". -// The approximation has the form: -// w = -log((1 - x) * (1 + x)) -// if ( w < 5 ) { -// w = w - 2.5 -// p = sum_{i=1}^n lq[i]*w^i -// } else { -// w = sqrt(w) - 3 -// p = sum_{i=1}^n gq[i]*w^i -// } -// return p*x -xla::XlaOp ErfInvF32(xla::XlaBuilder* b, const xla::XlaOp& x, - const TensorShape& shape) { - constexpr int kDegree = 9; - constexpr std::array w_less_than_5_constants = { - 2.81022636e-08f, 3.43273939e-07f, -3.5233877e-06f, - -4.39150654e-06f, 0.00021858087f, -0.00125372503f, - -0.00417768164f, 0.246640727f, 1.50140941f}; - constexpr std::array w_greater_than_5_constants = { - -0.000200214257f, 0.000100950558f, 0.00134934322f, - -0.00367342844f, 0.00573950773f, -0.0076224613f, - 0.00943887047f, 1.00167406f, 2.83297682f}; - - auto one = b->ConstantR0(1.0); - auto w = b->Neg(b->Log(b->Mul(b->Sub(one, x), b->Add(one, x)))); - - auto lt = b->Lt(w, b->ConstantR0(5.0)); - auto coefficient = [&](int i) { - return b->Select( - lt, - b->Broadcast(b->ConstantR0(w_less_than_5_constants[i]), - shape.dim_sizes()), - b->Broadcast(b->ConstantR0(w_greater_than_5_constants[i]), - shape.dim_sizes())); - }; - w = b->Select(lt, b->Sub(w, b->ConstantR0(2.5f)), - b->Sub(b->SqrtF32(w), b->ConstantR0(3.0f))); - auto p = coefficient(0); - for (int i = 1; i < kDegree; ++i) { - p = b->Add(coefficient(i), b->Mul(p, w)); - } - return b->Mul(p, x); -} - } // namespace class StatelessRandomUniformOp : public XlaOpKernel { @@ -259,8 +214,10 @@ class StatelessRandomNormalOp : public XlaOpKernel { RandomUniform(builder, seed, shape, std::nextafter(-1.0f, 0.0f), 1.0); // Convert uniform distribution to normal distribution by computing // sqrt(2) * erfinv(x) + auto erfinv_or_status = ErfInv(builder, uniform); + OP_REQUIRES_OK(ctx, erfinv_or_status.status()); auto normal = builder->Mul(builder->ConstantR0(std::sqrt(2.0)), - ErfInvF32(builder, uniform, shape)); + erfinv_or_status.ValueOrDie()); ctx->SetOutput(0, normal); } diff --git a/tensorflow/compiler/tf2xla/kernels/unary_ops.cc b/tensorflow/compiler/tf2xla/kernels/unary_ops.cc index 2521445e86..1d078de211 100644 --- a/tensorflow/compiler/tf2xla/kernels/unary_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/unary_ops.cc @@ -202,9 +202,9 @@ class ErfOp : public XlaOpKernel { OP_REQUIRES_OK(ctx, DataTypeToPrimitiveType(input_type(0), &primitive_type)); - auto y = b->Select(b->Gt(abs_x, one), - b->Sub(one, ComputeErfc(b, x, primitive_type)), - ComputeErf(b, x, primitive_type)); + auto y = + b->Select(b->Gt(abs_x, one), b->Sub(one, Erfc(b, x, primitive_type)), + Erf(b, x, primitive_type)); ctx->SetOutput(0, y); } }; @@ -223,9 +223,9 @@ class ErfcOp : public XlaOpKernel { OP_REQUIRES_OK(ctx, DataTypeToPrimitiveType(input_type(0), &primitive_type)); - auto y = b->Select(b->Lt(abs_x, one), - b->Sub(one, ComputeErf(b, x, primitive_type)), - ComputeErfc(b, x, primitive_type)); + auto y = + b->Select(b->Lt(abs_x, one), b->Sub(one, Erf(b, x, primitive_type)), + Erfc(b, x, primitive_type)); ctx->SetOutput(0, y); } }; diff --git a/tensorflow/compiler/xla/client/lib/arithmetic.cc b/tensorflow/compiler/xla/client/lib/arithmetic.cc index 639f85737f..f095ec9213 100644 --- a/tensorflow/compiler/xla/client/lib/arithmetic.cc +++ b/tensorflow/compiler/xla/client/lib/arithmetic.cc @@ -176,8 +176,8 @@ xla::XlaOp EvaluatePolynomial(xla::XlaBuilder* b, const xla::XlaOp& x, } // Compute an approximation of the error function complement (1 - erf(x)). -xla::XlaOp ComputeErfc(xla::XlaBuilder* b, const xla::XlaOp& x, - PrimitiveType data_type) { +xla::XlaOp Erfc(xla::XlaBuilder* b, const xla::XlaOp& x, + PrimitiveType data_type) { xla::XlaOp zero = FloatLiteral(b, data_type, 0.0); xla::XlaOp two = FloatLiteral(b, data_type, 2.0); xla::XlaOp eight = FloatLiteral(b, data_type, 8.0); @@ -197,12 +197,57 @@ xla::XlaOp ComputeErfc(xla::XlaBuilder* b, const xla::XlaOp& x, } // Compute a polynomial approximation of the error function. -xla::XlaOp ComputeErf(xla::XlaBuilder* b, const xla::XlaOp& x, - PrimitiveType data_type) { +xla::XlaOp Erf(xla::XlaBuilder* b, const xla::XlaOp& x, + PrimitiveType data_type) { xla::XlaOp z = b->Mul(x, x); xla::XlaOp pt = EvaluatePolynomial(b, z, kErfTCoefficient, data_type); xla::XlaOp pu = EvaluatePolynomial(b, z, kErfUCoefficient, data_type); return b->Div(b->Mul(x, pt), pu); } +// Approximation for the inverse error function from +// Giles, M., "Approximating the erfinv function". +// The approximation has the form: +// w = -log((1 - x) * (1 + x)) +// if ( w < 5 ) { +// w = w - 2.5 +// p = sum_{i=1}^n lq[i]*w^i +// } else { +// w = sqrt(w) - 3 +// p = sum_{i=1}^n gq[i]*w^i +// } +// return p*x +StatusOr ErfInv(xla::XlaBuilder* b, const xla::XlaOp& x) { + TF_ASSIGN_OR_RETURN(Shape shape, b->GetShape(x)); + constexpr int kDegree = 9; + constexpr std::array w_less_than_5_constants = { + 2.81022636e-08f, 3.43273939e-07f, -3.5233877e-06f, + -4.39150654e-06f, 0.00021858087f, -0.00125372503f, + -0.00417768164f, 0.246640727f, 1.50140941f}; + constexpr std::array w_greater_than_5_constants = { + -0.000200214257f, 0.000100950558f, 0.00134934322f, + -0.00367342844f, 0.00573950773f, -0.0076224613f, + 0.00943887047f, 1.00167406f, 2.83297682f}; + + auto one = b->ConstantR0(1.0); + auto w = b->Neg(b->Log(b->Mul(b->Sub(one, x), b->Add(one, x)))); + + auto lt = b->Lt(w, b->ConstantR0(5.0)); + auto coefficient = [&](int i) { + return b->Select( + lt, + b->Broadcast(b->ConstantR0(w_less_than_5_constants[i]), + AsInt64Slice(shape.dimensions())), + b->Broadcast(b->ConstantR0(w_greater_than_5_constants[i]), + AsInt64Slice(shape.dimensions()))); + }; + w = b->Select(lt, b->Sub(w, b->ConstantR0(2.5f)), + b->Sub(b->SqrtF32(w), b->ConstantR0(3.0f))); + auto p = coefficient(0); + for (int i = 1; i < kDegree; ++i) { + p = b->Add(coefficient(i), b->Mul(p, w)); + } + return b->Mul(p, x); +} + } // namespace xla diff --git a/tensorflow/compiler/xla/client/lib/arithmetic.h b/tensorflow/compiler/xla/client/lib/arithmetic.h index f11cc00317..efdcc7e198 100644 --- a/tensorflow/compiler/xla/client/lib/arithmetic.h +++ b/tensorflow/compiler/xla/client/lib/arithmetic.h @@ -62,12 +62,15 @@ xla::XlaOp EvaluatePolynomial(xla::XlaBuilder* b, const xla::XlaOp& x, PrimitiveType data_type); // Compute an approximation of the error function complement (1 - erf(x)). -xla::XlaOp ComputeErfc(xla::XlaBuilder* b, const xla::XlaOp& x, - PrimitiveType data_type); +xla::XlaOp Erfc(xla::XlaBuilder* b, const xla::XlaOp& x, + PrimitiveType data_type); // Compute an approximation of the error function. -xla::XlaOp ComputeErf(xla::XlaBuilder* b, const xla::XlaOp& x, - PrimitiveType data_type); +xla::XlaOp Erf(xla::XlaBuilder* b, const xla::XlaOp& x, + PrimitiveType data_type); + +// Compute an approximation of the inverse of the error function. +StatusOr ErfInv(xla::XlaBuilder* b, const xla::XlaOp& x); } // namespace xla -- GitLab From b8f0b7391e59d47175782ddbe95cd944ca4fadf3 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 19 Jun 2018 23:07:57 -0700 Subject: [PATCH 719/816] Internal change PiperOrigin-RevId: 201301504 --- tensorflow/tensorflow.bzl | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tensorflow/tensorflow.bzl b/tensorflow/tensorflow.bzl index c3bc9ccd45..6bb393a3f4 100644 --- a/tensorflow/tensorflow.bzl +++ b/tensorflow/tensorflow.bzl @@ -922,6 +922,7 @@ def tf_gpu_kernel_library(srcs, hdrs=[], **kwargs): copts = copts + _cuda_copts() + if_cuda(cuda_copts) + tf_copts() + kwargs["features"] = kwargs.get("features", []) + ["-use_header_modules"] native.cc_library( srcs=srcs, @@ -1305,6 +1306,7 @@ def tf_custom_op_library(name, srcs=[], gpu_srcs=[], deps=[], linkopts=[]): name=basename + "_gpu", srcs=gpu_srcs, copts=_cuda_copts() + if_tensorrt(["-DGOOGLE_TENSORRT=1"]), + features = if_cuda(["-use_header_modules"]), deps=deps + if_cuda(cuda_deps)) cuda_deps.extend([":" + basename + "_gpu"]) -- GitLab From 7c754a6db364443c1103bd362e826fafab8f2718 Mon Sep 17 00:00:00 2001 From: Billy Lamberta Date: Tue, 19 Jun 2018 23:11:00 -0700 Subject: [PATCH 720/816] Get started landing page. Move "Datasets Quickstart" to "Datasets for Estimators" under guide. PiperOrigin-RevId: 201301717 --- tensorflow/docs_src/get_started/_index.yaml | 255 ++++++++++++++++++ .../get_started/basic_classification.md | 3 + .../docs_src/get_started/basic_regression.md | 3 + .../get_started/basic_text_classification.md | 3 + tensorflow/docs_src/get_started/eager.md | 2 +- tensorflow/docs_src/get_started/index.md | 29 -- tensorflow/docs_src/get_started/leftnav_files | 12 +- tensorflow/docs_src/get_started/next_steps.md | 36 +++ .../get_started/overfit_and_underfit.md | 3 + .../get_started/save_and_restore_models.md | 3 + tensorflow/docs_src/install/install_linux.md | 8 +- tensorflow/docs_src/install/install_mac.md | 6 +- .../docs_src/install/install_raspbian.md | 6 +- .../docs_src/install/install_sources.md | 2 +- .../docs_src/install/install_windows.md | 7 +- .../datasets_for_estimators.md} | 2 +- .../docs_src/programmers_guide/index.md | 1 + .../docs_src/programmers_guide/leftnav_files | 1 + .../programmers_guide/premade_estimators.md | 8 +- tensorflow/docs_src/tutorials/index.md | 5 +- 20 files changed, 329 insertions(+), 66 deletions(-) create mode 100644 tensorflow/docs_src/get_started/_index.yaml create mode 100644 tensorflow/docs_src/get_started/basic_classification.md create mode 100644 tensorflow/docs_src/get_started/basic_regression.md create mode 100644 tensorflow/docs_src/get_started/basic_text_classification.md delete mode 100644 tensorflow/docs_src/get_started/index.md create mode 100644 tensorflow/docs_src/get_started/next_steps.md create mode 100644 tensorflow/docs_src/get_started/overfit_and_underfit.md create mode 100644 tensorflow/docs_src/get_started/save_and_restore_models.md rename tensorflow/docs_src/{get_started/datasets_quickstart.md => programmers_guide/datasets_for_estimators.md} (99%) diff --git a/tensorflow/docs_src/get_started/_index.yaml b/tensorflow/docs_src/get_started/_index.yaml new file mode 100644 index 0000000000..af255a482d --- /dev/null +++ b/tensorflow/docs_src/get_started/_index.yaml @@ -0,0 +1,255 @@ +project_path: /_project.yaml +book_path: /_book.yaml +description: +landing_page: + show_side_navs: True + rows: + - description: > +

Get Started with TensorFlow

+

+ TensorFlow is an open-source machine learning library for research and + production. TensorFlow offers APIs for beginners and experts to develop + for desktop, mobile, web, and cloud. See the sections below to get + started. +

+ items: + - custom_html: > + +
+ +

Learn and use ML

+ +
+

+ The high-level Keras API provides building blocks to create and + train deep learning models. Start with these beginner-friendly + notebook examples, then read the + TensorFlow Keras guide. +

+
    +
  1. Basic classification
    2. +
  2. Text classification
    3. +
  3. Regression
    4. +
  4. Overfitting and underfitting
    5. +
  5. Save and load
    6. +
+
+
+ Read the Keras guide +
+
+ - classname: tfo-landing-row-item-code-block + code_block: | + 
+        import tensorflow as tf
+        mnist = tf.keras.datasets.mnist
+
+        (x_train, y_train),(x_test, y_test) = mnist.load_data()
+        x_train, x_test = x_train / 255.0, x_test / 255.0
+
+        model = tf.keras.models.Sequential([
+          tf.keras.layers.Flatten(),
+          tf.keras.layers.Dense(512, activation=tf.nn.relu),
+          tf.keras.layers.Dropout(0.2),
+          tf.keras.layers.Dense(10, activation=tf.nn.softmax)
+        ])
+        model.compile(optimizer='adam',
+                      loss='sparse_categorical_crossentropy',
+                      metrics=['accuracy'])
+
+        model.fit(x_train, y_train, epochs=5)
+        model.evaluate(x_test, y_test)
+
+ {% dynamic if request.tld != 'cn' %} + Run in a Notebook + {% dynamic endif %} + + - items: + - custom_html: > +
+ +

Research and experimentation

+ +
+

+ Eager execution provides an imperative, define-by-run interface for advanced operations. Write custom layers, forward passes, and training loops with auto‑differentiation. Start with + these notebooks, then read the eager execution guide. +

+
    +
  1. + {% dynamic if request.tld == 'cn' %} + Eager execution basics + {% dynamic else %} + Eager execution basics + {% dynamic endif %} +
    2. +
  2. + {% dynamic if request.tld == 'cn' %} + Automatic differentiation and gradient tapes + {% dynamic else %} + Automatic differentiation and gradient tapes + {% dynamic endif %} +
    3. +
  3. + {% dynamic if request.tld == 'cn' %} + Variables, models, and training + {% dynamic else %} + Variables, models, and training + {% dynamic endif %} +
    4. +
  4. + {% dynamic if request.tld == 'cn' %} + Custom layers + {% dynamic else %} + Custom layers + {% dynamic endif %} +
    5. +
  5. Custom training walkthrough
    6. +
  6. + {% dynamic if request.tld == 'cn' %} + Example: Neural machine translation w/ attention + {% dynamic else %} + Example: Neural machine translation w/ attention + {% dynamic endif %} +
    7. +
+
+
+ Read the eager execution guide +
+
+ - custom_html: > +
+ +

ML at production scale

+ +
+

+ Estimators can train large models on multiple machines in a + production environment. Try the examples below and read the + Estimators guide. +

+
    +
  1. How to build a simple text classifier with TF-Hub
    2. +
  2. Classifying Higgs boson processes
    3. +
  3. Wide and deep learning using estimators
    4. +
+
+
+ Read the Estimators guide +
+
+ + - description: > +

Google Colab: An easy way to learn and use TensorFlow

+

+ Colaboratory + is a Google research project created to help disseminate machine learning + education and research. It's a Jupyter notebook environment that requires + no setup to use and runs entirely in the cloud. + Read the blog post. +

+ + - description: > +

Build your first ML app

+

Create and deploy TensorFlow models on web and mobile.

+ background: grey + items: + - custom_html: > +
+ +

Web developers

+ +
+ TensorFlow.js is a WebGL accelerated, JavaScript library to train and + deploy ML models in the browser and for Node.js. +
+
+ - custom_html: > +
+ +

Mobile developers

+ +
+ TensorFlow Lite is lightweight solution for mobile and embedded devices. +
+
+ + - description: > +

Videos and updates

+

+ Subscribe to the TensorFlow + YouTube channel + and blog for + the latest videos and updates. +

+ items: + - description: > +

Get started with TensorFlow's High-Level APIs

+ youtube_id: tjsHSIG8I08 + buttons: + - label: Watch the video + path: https://www.youtube.com/watch?v=tjsHSIG8I08 + - description: > +

Eager execution

+ youtube_id: T8AW0fKP0Hs + background: grey + buttons: + - label: Watch the video + path: https://www.youtube.com/watch?v=T8AW0fKP0Hs + - description: > +

tf.data: Fast, flexible, and easy-to-use input pipelines

+ youtube_id: uIcqeP7MFH0 + buttons: + - label: Watch the video + path: https://www.youtube.com/watch?v=uIcqeP7MFH0 diff --git a/tensorflow/docs_src/get_started/basic_classification.md b/tensorflow/docs_src/get_started/basic_classification.md new file mode 100644 index 0000000000..91bbd85b24 --- /dev/null +++ b/tensorflow/docs_src/get_started/basic_classification.md @@ -0,0 +1,3 @@ +# Basic Classification + +[Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/master/samples/core/get_started/basic_classification.ipynb) diff --git a/tensorflow/docs_src/get_started/basic_regression.md b/tensorflow/docs_src/get_started/basic_regression.md new file mode 100644 index 0000000000..a535f22f5a --- /dev/null +++ b/tensorflow/docs_src/get_started/basic_regression.md @@ -0,0 +1,3 @@ +# Basic Regression + +[Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/master/samples/core/get_started/basic_regression.ipynb) diff --git a/tensorflow/docs_src/get_started/basic_text_classification.md b/tensorflow/docs_src/get_started/basic_text_classification.md new file mode 100644 index 0000000000..7c5d4f7896 --- /dev/null +++ b/tensorflow/docs_src/get_started/basic_text_classification.md @@ -0,0 +1,3 @@ +# Basic Text Classification + +[Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/master/samples/core/get_started/basic_text_classification.ipynb) diff --git a/tensorflow/docs_src/get_started/eager.md b/tensorflow/docs_src/get_started/eager.md index bbb25e20c6..ddf239485a 100644 --- a/tensorflow/docs_src/get_started/eager.md +++ b/tensorflow/docs_src/get_started/eager.md @@ -1,3 +1,3 @@ -# Get Started with Eager Execution +# Custom Training Walkthrough [Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/r1.9.0/samples/core/get_started/eager.ipynb) diff --git a/tensorflow/docs_src/get_started/index.md b/tensorflow/docs_src/get_started/index.md deleted file mode 100644 index 232d2f1547..0000000000 --- a/tensorflow/docs_src/get_started/index.md +++ /dev/null @@ -1,29 +0,0 @@ -# Get Started - -If you are new to machine learning, we recommend taking the following online -course prior to diving into TensorFlow documentation: - - * [Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course/), - which introduces machine learning concepts and encourages experimentation - with existing TensorFlow code. - -TensorFlow is a tool for machine learning. While it contains a wide range of -functionality, TensorFlow is mainly designed for deep neural network models. - -The easiest way to get started with TensorFlow is by using Eager Execution. - - * @{$get_started/eager}, is for anyone new to machine learning or TensorFlow. - -TensorFlow provides many APIs. The remainder of this section focuses on the -Estimator API which provide scalable, high-performance models. See the -@{$estimators} guide. - -For more advanced users: - - * The @{$low_level_intro$Low Level Introduction} demonstrates how to use - TensorFlow outside of the Estimator framework, for debugging and - experimentation. - * The @{$programmers_guide$Programmer's Guide} details major - TensorFlow components. - * The @{$tutorials$Tutorials} provide walkthroughs of a variety of - TensorFlow models. diff --git a/tensorflow/docs_src/get_started/leftnav_files b/tensorflow/docs_src/get_started/leftnav_files index e6cc8d5658..9a60496cb5 100644 --- a/tensorflow/docs_src/get_started/leftnav_files +++ b/tensorflow/docs_src/get_started/leftnav_files @@ -1,4 +1,10 @@ -index.md +### Learn and use ML +basic_classification.md +basic_text_classification.md +basic_regression.md +overfit_and_underfit.md +save_and_restore_models.md +next_steps.md -eager.md -datasets_quickstart.md +### Research and experimentation +custom_training_walkthrough.md diff --git a/tensorflow/docs_src/get_started/next_steps.md b/tensorflow/docs_src/get_started/next_steps.md new file mode 100644 index 0000000000..79c0ef3346 --- /dev/null +++ b/tensorflow/docs_src/get_started/next_steps.md @@ -0,0 +1,36 @@ +# Next Steps + +## Learn more about TensorFlow + +* The [TensorFlow Guide](/programmers_guide) includes usage guides for the + high-level APIs, as well as advanced TensorFlow operations. +* [Premade Estimators](/programmers_guide/premade_estimators) are designed to + get results out of the box. Use TensorFlow without building your own models. +* [TensorFlow.js](https://js.tensorflow.org/) allows web developers to train and + deploy ML models in the browser and using Node.js. +* [TFLite](/mobile/tflite) allows mobile developers to do inference efficiently + on mobile devices. +* [TensorFlow Serving](/serving) is an open-source project that can put + TensorFlow models in production quickly. +* The [ecosystem](/ecosystem) contains more projects, including + [Magenta](https://magenta.tensorflow.org/), [TFX](/tfx), + [Swift for TensorFlow](https://github.com/tensorflow/swift), and more. + +## Learn more about machine learning + +Recommended resources include: + +* [Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course/), + a course from Google that introduces machine learning concepts. +* [CS 20: Tensorflow for Deep Learning Research](http://web.stanford.edu/class/cs20si/), + notes from an intro course from Stanford. +* [CS231n: Convolutional Neural Networks for Visual Recognition](http://cs231n.stanford.edu/), + a course that teaches how convolutional networks work. +* [Machine Learning Recipes](https://www.youtube.com/watch?v=cKxRvEZd3Mw&list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal), + a video series that introduces basic machine learning concepts with few prerequisites. +* [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python), + a book by Francois Chollet about the Keras API, as well as an excellent hands on intro to Deep Learning. +* [Hands-on Machine Learning with Scikit-Learn and TensorFlow](https://github.com/ageron/handson-ml), + a book by Aurélien Geron's that is a clear getting-started guide to data science and deep learning. +* [Deep Learning](https://www.deeplearningbook.org/), a book by Ian Goodfellow et al. + that provides a technical dive into learning machine learning. diff --git a/tensorflow/docs_src/get_started/overfit_and_underfit.md b/tensorflow/docs_src/get_started/overfit_and_underfit.md new file mode 100644 index 0000000000..e5b5ae7b5a --- /dev/null +++ b/tensorflow/docs_src/get_started/overfit_and_underfit.md @@ -0,0 +1,3 @@ +# Overfitting and Underfitting + +[Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/master/samples/core/get_started/overfit_and_underfit.ipynb) diff --git a/tensorflow/docs_src/get_started/save_and_restore_models.md b/tensorflow/docs_src/get_started/save_and_restore_models.md new file mode 100644 index 0000000000..44b3772945 --- /dev/null +++ b/tensorflow/docs_src/get_started/save_and_restore_models.md @@ -0,0 +1,3 @@ +# Save and restore Models + +[Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/master/samples/core/get_started/save_and_restore_models.ipynb) diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md index c8d706cf3c..c573acaf45 100644 --- a/tensorflow/docs_src/install/install_linux.md +++ b/tensorflow/docs_src/install/install_linux.md @@ -489,13 +489,7 @@ TensorFlow programs: If the system outputs an error message instead of a greeting, see [Common installation problems](#common_installation_problems). -If you are new to machine learning, we recommend the following: - -* [Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course) -* @{$get_started/eager} - -If you are experienced with machine learning but new to TensorFlow, see -@{$get_started/eager}. +To learn more, see [Get Started with TensorFlow](https://www.tensorflow.org/get_started). ## TensorFlow GPU support diff --git a/tensorflow/docs_src/install/install_mac.md b/tensorflow/docs_src/install/install_mac.md index 9d01271c5a..584f1e2e35 100644 --- a/tensorflow/docs_src/install/install_mac.md +++ b/tensorflow/docs_src/install/install_mac.md @@ -403,11 +403,7 @@ writing TensorFlow programs: If the system outputs an error message instead of a greeting, see [Common installation problems](#common_installation_problems). -If you are new to machine learning, we recommend the -[Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course). - -If you are experienced with machine learning but new to TensorFlow, see -@{$get_started/eager}. +To learn more, see [Get Started with TensorFlow](https://www.tensorflow.org/get_started). ## Common installation problems diff --git a/tensorflow/docs_src/install/install_raspbian.md b/tensorflow/docs_src/install/install_raspbian.md index 2f425162a1..0caab6d335 100644 --- a/tensorflow/docs_src/install/install_raspbian.md +++ b/tensorflow/docs_src/install/install_raspbian.md @@ -230,11 +230,7 @@ problems, despite the log message. If the system outputs an error message instead of a greeting, see [Common installation problems](#common_installation_problems). -If you are new to machine learning, we recommend the [Machine Learning Crash -Course](https://developers.google.com/machine-learning/crash-course). - -If you are experienced with machine learning but new to TensorFlow, see -@{$get_started/eager}. +To learn more, see [Get Started with TensorFlow](https://www.tensorflow.org/get_started). ## Common installation problems diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md index dc6c1e36fc..e55520ceaa 100644 --- a/tensorflow/docs_src/install/install_sources.md +++ b/tensorflow/docs_src/install/install_sources.md @@ -362,7 +362,7 @@ TensorFlow programs: 
Hello, TensorFlow!
-If you are new to TensorFlow, see @{$get_started/eager}. +To learn more, see [Get Started with TensorFlow](https://www.tensorflow.org/get_started). If the system outputs an error message instead of a greeting, see [Common installation problems](#common_installation_problems). diff --git a/tensorflow/docs_src/install/install_windows.md b/tensorflow/docs_src/install/install_windows.md index 6c4f5b85ab..7fe94f0bc3 100644 --- a/tensorflow/docs_src/install/install_windows.md +++ b/tensorflow/docs_src/install/install_windows.md @@ -157,12 +157,7 @@ TensorFlow programs: If the system outputs an error message instead of a greeting, see [Common installation problems](#common_installation_problems). -If you are new to machine learning, we recommend the -[Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course). - -If you are experienced with machine learning but new to TensorFlow, see -@{$get_started/eager}. - +To learn more, see [Get Started with TensorFlow](https://www.tensorflow.org/get_started). ## Common installation problems diff --git a/tensorflow/docs_src/get_started/datasets_quickstart.md b/tensorflow/docs_src/programmers_guide/datasets_for_estimators.md similarity index 99% rename from tensorflow/docs_src/get_started/datasets_quickstart.md rename to tensorflow/docs_src/programmers_guide/datasets_for_estimators.md index 020e40dd3b..345a31b985 100644 --- a/tensorflow/docs_src/get_started/datasets_quickstart.md +++ b/tensorflow/docs_src/programmers_guide/datasets_for_estimators.md @@ -1,4 +1,4 @@ -# Datasets Quick Start +# Datasets for Estimators The @{tf.data} module contains a collection of classes that allows you to easily load data, manipulate it, and pipe it into your model. This document diff --git a/tensorflow/docs_src/programmers_guide/index.md b/tensorflow/docs_src/programmers_guide/index.md index 0c2d4afb11..9c58a3b45e 100644 --- a/tensorflow/docs_src/programmers_guide/index.md +++ b/tensorflow/docs_src/programmers_guide/index.md @@ -22,6 +22,7 @@ works. The units are as follows: design yourself. * @{$feature_columns}, which shows how an Estimator can handle a variety of input data types without changes to the model. +* @{$datasets_for_estimators} describes using tf.data with estimators. * @{$checkpoints}, which explains how to save training progress and resume where you left off. diff --git a/tensorflow/docs_src/programmers_guide/leftnav_files b/tensorflow/docs_src/programmers_guide/leftnav_files index 3bcf864e13..357a2a1cb9 100644 --- a/tensorflow/docs_src/programmers_guide/leftnav_files +++ b/tensorflow/docs_src/programmers_guide/leftnav_files @@ -10,6 +10,7 @@ estimators.md: Introduction to Estimators premade_estimators.md custom_estimators.md feature_columns.md +datasets_for_estimators.md checkpoints.md ### Accelerators diff --git a/tensorflow/docs_src/programmers_guide/premade_estimators.md b/tensorflow/docs_src/programmers_guide/premade_estimators.md index f6dd75eaca..02e2caf64b 100644 --- a/tensorflow/docs_src/programmers_guide/premade_estimators.md +++ b/tensorflow/docs_src/programmers_guide/premade_estimators.md @@ -81,7 +81,7 @@ We strongly recommend writing TensorFlow programs with the following APIs: * @{$programmers_guide/estimators$Estimators}, which represent a complete model. The Estimator API provides methods to train the model, to judge the model's accuracy, and to generate predictions. -* @{$get_started/datasets_quickstart$Datasets}, which build a data input +* @{$programmers_guide/datasets_for_estimators}, which build a data input pipeline. The Dataset API has methods to load and manipulate data, and feed it into your model. The Dataset API meshes well with the Estimators API. @@ -424,9 +424,7 @@ Now that you've gotten started writing TensorFlow programs, consider the following material: * @{$checkpoints$Checkpoints} to learn how to save and restore models. -* @{$get_started/datasets_quickstart$Datasets} to learn more about importing - data into your - model. +* @{$programmers_guide/datasets_for_estimators} to learn more about importing + data into your model. * @{$custom_estimators$Creating Custom Estimators} to learn how to write your own Estimator, customized for a particular problem. - diff --git a/tensorflow/docs_src/tutorials/index.md b/tensorflow/docs_src/tutorials/index.md index af01d3eaa1..6bd3a3a897 100644 --- a/tensorflow/docs_src/tutorials/index.md +++ b/tensorflow/docs_src/tutorials/index.md @@ -2,9 +2,8 @@ This section contains tutorials demonstrating how to do specific tasks -in TensorFlow. If you are new to TensorFlow, we recommend reading the -documents in the "@{$get_started$Get Started}" section before reading -these tutorials. +in TensorFlow. If you are new to TensorFlow, we recommend reading +[Get Started with TensorFlow](/get_started/). ## Images -- GitLab From 4283949adca17d2fcbf49cf510fff961a572dbaf Mon Sep 17 00:00:00 2001 From: Bixia Zheng Date: Tue, 19 Jun 2018 23:35:24 -0700 Subject: [PATCH 721/816] Allow the use of 32 bit integer type for loop index and tensor element index. The GPU LLVM IR generator currently uses 64 bit integer type for arithmetic operations related to loop index and tensor element index and relies on LLVM optimization to narrow the operations to 32 bit integer type. There are situations whether LLVM optimization fail to perform such an optimization, see LLVM D46760 for more detail. This change modifies the XLA LLVM IR code generation infrastructure to support the use of 32 bit integer type for loop index and tensor element index as follows: .Extends the loop emitter interface in ParallelLoopEmitter and ForLoopNest to allow users to specify the loop index type. .Modifies the tensor access interface in IrArray::Index interface to record the llvm type for the index when an object is constructed. This index type is usually propagated from a loop index type. .Modifies kernel_support_library to retrieve the loop index type from the input llvm::Value. .Modifies elemental_ir_emitter to retrieve the data type from the input IrArray::Index and use it tensor offset expression. This change also modifies the emission of the fusion kernel, the row and scalar reduction kernel and SelectAndScatter kernel to use 32 bit integer type for index calculation when the size of the launch dimension and the size of tensors used in the kernel are within the range of 32 bit integer representation. PiperOrigin-RevId: 201303468 --- .../xla/service/cpu/dot_op_emitter.cc | 12 +- .../compiler/xla/service/cpu/ir_emitter.cc | 18 +- .../xla/service/cpu/parallel_loop_emitter.cc | 6 +- .../xla/service/cpu/parallel_loop_emitter.h | 2 +- .../xla/service/elemental_ir_emitter.cc | 104 ++++--- .../xla/service/gpu/elemental_ir_emitter.cc | 24 +- .../compiler/xla/service/gpu/ir_emitter.cc | 12 +- .../xla/service/gpu/ir_emitter_unnested.cc | 274 ++++++++++++------ .../xla/service/gpu/parallel_loop_emitter.cc | 27 +- .../xla/service/gpu/parallel_loop_emitter.h | 2 +- .../xla/service/gpu/partition_assignment.h | 1 + .../compiler/xla/service/llvm_ir/ir_array.cc | 73 +++-- .../compiler/xla/service/llvm_ir/ir_array.h | 48 ++- .../service/llvm_ir/kernel_support_library.h | 13 +- .../compiler/xla/service/llvm_ir/llvm_loop.cc | 16 +- .../compiler/xla/service/llvm_ir/llvm_loop.h | 24 +- .../xla/service/llvm_ir/loop_emitter.cc | 16 +- .../xla/service/llvm_ir/loop_emitter.h | 9 +- .../compiler/xla/service/llvm_ir/ops.cc | 4 +- 19 files changed, 460 insertions(+), 225 deletions(-) diff --git a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc index e8b205051e..58228180ca 100644 --- a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc +++ b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc @@ -1380,7 +1380,7 @@ Status DotOpEmitter::Emit() { // the rhs and lhs indexes with the reduction dimensions removed. The terms // from the rhs index are the lower dimensions in the index so we add them // first. - llvm_ir::IrArray::Index target_index; + llvm_ir::IrArray::Index target_index(lhs_index.GetType()); for (int dimension = 0; dimension < lhs_index.size(); ++dimension) { if (dimension != lhs_reduction_dimension) { target_index.push_back(lhs_index[dimension]); @@ -1404,10 +1404,13 @@ Status DotOpEmitter::Emit() { Status DotOpEmitter::EmitScalarDot() { // A scalar dot is just a scalar multiply. llvm::Value* result; + // Use the same index_type for all tensor accesses in the same kernel. + llvm::Type* index_type = ir_builder_->getInt64Ty(); + llvm_ir::IrArray::Index element_index(index_type); llvm::Value* lhs_value = - lhs_array_.EmitReadArrayElement(/*index=*/{}, ir_builder_); + lhs_array_.EmitReadArrayElement(/*index=*/element_index, ir_builder_); llvm::Value* rhs_value = - rhs_array_.EmitReadArrayElement(/*index=*/{}, ir_builder_); + rhs_array_.EmitReadArrayElement(/*index=*/element_index, ir_builder_); if (ShapeUtil::ElementIsComplex(lhs_array_.GetShape())) { #define REAL(x) ir_builder_->CreateExtractValue(x, {0}) #define IMAG(x) ir_builder_->CreateExtractValue(x, {1}) @@ -1425,7 +1428,8 @@ Status DotOpEmitter::EmitScalarDot() { } else { result = ir_builder_->CreateFMul(lhs_value, rhs_value); } - target_array_.EmitWriteArrayElement(/*index=*/{}, result, ir_builder_); + target_array_.EmitWriteArrayElement(/*index=*/element_index, result, + ir_builder_); return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc index 758b8c62b4..5c04f381f2 100644 --- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc +++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc @@ -563,7 +563,8 @@ Status IrEmitter::HandleReduceWindow(HloInstruction* reduce_window) { SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(), &ir_builder_); - llvm_ir::IrArray::Index input_index(index.size()); + llvm_ir::IrArray::Index input_index(ir_builder_.getInt64Ty(), + index.size()); llvm::Value* in_bounds_condition = nullptr; for (size_t i = 0; i < index.size(); ++i) { llvm::Value* strided_index = ir_builder_.CreateNSWMul( @@ -694,7 +695,8 @@ Status IrEmitter::HandleSelectAndScatter(HloInstruction* select_and_scatter) { // Compute the operand index to visit and evaluate the condition whether the // operand index is within the bounds. The unsigned comparison includes // checking whether the operand index >= 0. - llvm_ir::IrArray::Index operand_index(source_index.size()); + llvm_ir::IrArray::Index operand_index(ir_builder_.getInt64Ty(), + source_index.size()); llvm::Value* in_bounds_condition = ir_builder_.getTrue(); for (int64 i = 0; i < rank; ++i) { llvm::Value* strided_index = ir_builder_.CreateNSWMul( @@ -768,7 +770,7 @@ Status IrEmitter::HandleSelectAndScatter(HloInstruction* select_and_scatter) { // value and the current output value. SetToFirstInsertPoint(window_loops.GetOuterLoopExitBasicBlock(), &ir_builder_); - llvm_ir::IrArray::Index selected_index; + llvm_ir::IrArray::Index selected_index(source_index.GetType()); for (int64 i = 0; i < rank; ++i) { llvm::Value* selected_index_address_slot = ir_builder_.CreateInBoundsGEP( selected_index_address, {ir_builder_.getInt32(i)}); @@ -1110,7 +1112,7 @@ Status IrEmitter::HandleConvolution(HloInstruction* convolution) { // We are not in the padding, so carry out the computation. int num_dims = num_spatial_dims + 2; - llvm_ir::IrArray::Index input_index(num_dims); + llvm_ir::IrArray::Index input_index(ir_builder_.getInt64Ty(), num_dims); for (int i = 0; i < num_spatial_dims; ++i) { input_index[dnums.input_spatial_dimensions(i)] = input_spatial[i]; } @@ -1118,7 +1120,8 @@ Status IrEmitter::HandleConvolution(HloInstruction* convolution) { input_index[dnums.input_batch_dimension()] = batch; llvm_ir::IrArray kernel_array(GetIrArrayFor(rhs)); - llvm_ir::IrArray::Index kernel_index(num_dims); + llvm_ir::IrArray::Index kernel_index(ir_builder_.getInt64Ty(), + num_dims); for (int i = 0; i < num_spatial_dims; ++i) { kernel_index[dnums.kernel_spatial_dimensions(i)] = window.dimensions(i).window_reversal() @@ -1685,7 +1688,8 @@ StatusOr IrEmitter::EmitVectorizedReduce( // } llvm_ir::ForLoopNest loop_nest(IrName(reduce), &ir_builder_); - llvm_ir::IrArray::Index array_index(reduce->shape().dimensions_size()); + llvm_ir::IrArray::Index array_index(ir_builder_.getInt64Ty(), + reduce->shape().dimensions_size()); for (int i = LayoutUtil::MinorToMajor(reduce->shape()).size() - 1; i > 0; --i) { int64 dimension = LayoutUtil::Minor(reduce->shape().layout(), i); @@ -2069,7 +2073,7 @@ Status IrEmitter::HandlePad(HloInstruction* pad) { // Compute the output index the operand element should be assigned to. // output_index := edge_padding_low + operand_index * (interior_padding + 1) const PaddingConfig& padding_config = pad->padding_config(); - llvm_ir::IrArray::Index output_index; + llvm_ir::IrArray::Index output_index(operand_index.GetType()); for (size_t i = 0; i < operand_index.size(); ++i) { llvm::Value* offset = ir_builder_.CreateMul( operand_index[i], diff --git a/tensorflow/compiler/xla/service/cpu/parallel_loop_emitter.cc b/tensorflow/compiler/xla/service/cpu/parallel_loop_emitter.cc index 54af40506d..59ae5acd8b 100644 --- a/tensorflow/compiler/xla/service/cpu/parallel_loop_emitter.cc +++ b/tensorflow/compiler/xla/service/cpu/parallel_loop_emitter.cc @@ -31,13 +31,15 @@ ParallelLoopEmitter::ParallelLoopEmitter( std::vector ParallelLoopEmitter::EmitIndexAndSetExitBasicBlock( - tensorflow::StringPiece loop_name) { + tensorflow::StringPiece loop_name, llvm::Type* index_type) { + CHECK_NE(index_type, nullptr); + CHECK(!ShapeUtil::IsTuple(shape_)); CHECK(!ShapeUtil::IsScalar(shape_)); llvm_ir::ForLoopNest loop_nest(loop_name, ir_builder_); const int64 num_dims = shape_.dimensions_size(); - llvm_ir::IrArray::Index array_index(num_dims); + llvm_ir::IrArray::Index array_index(index_type, num_dims); // Add loops from outer-most to inner-most dimensions. for (int i = LayoutUtil::MinorToMajor(shape_).size() - 1; i >= 0; --i) { diff --git a/tensorflow/compiler/xla/service/cpu/parallel_loop_emitter.h b/tensorflow/compiler/xla/service/cpu/parallel_loop_emitter.h index 755715634a..25e182a26d 100644 --- a/tensorflow/compiler/xla/service/cpu/parallel_loop_emitter.h +++ b/tensorflow/compiler/xla/service/cpu/parallel_loop_emitter.h @@ -61,7 +61,7 @@ class ParallelLoopEmitter : public llvm_ir::LoopEmitter { ~ParallelLoopEmitter() override = default; std::vector EmitIndexAndSetExitBasicBlock( - tensorflow::StringPiece loop_name) override; + tensorflow::StringPiece loop_name, llvm::Type* index_type) override; private: const DynamicLoopBounds* dynamic_loop_bounds_; diff --git a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc index 93fea7ead7..4ccd85307d 100644 --- a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc +++ b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc @@ -1220,7 +1220,7 @@ llvm_ir::IrArray::Index ElementalIrEmitter::ElementwiseSourceIndex( const Shape& operand_shape = hlo.operand(operand_no)->shape(); // If the operand is scalar, the source index is always {}. if (ShapeUtil::IsScalar(operand_shape)) { - return llvm_ir::IrArray::Index(); + return llvm_ir::IrArray::Index(target_index.GetType()); } // If no implicit broadcast is needed for this operand, returns the target @@ -1232,13 +1232,13 @@ llvm_ir::IrArray::Index ElementalIrEmitter::ElementwiseSourceIndex( // If implicit broadcast is needed, the source dimensions that are broadcast // have index 0. CHECK_EQ(ShapeUtil::Rank(operand_shape), ShapeUtil::Rank(hlo.shape())); - llvm_ir::IrArray::Index source_index; + llvm_ir::IrArray::Index source_index(target_index.GetType()); for (int64 i = 0; i < ShapeUtil::Rank(hlo.shape()); ++i) { if (hlo.shape().dimensions(i) == operand_shape.dimensions(i)) { source_index.push_back(target_index[i]); } else { CHECK_EQ(1, operand_shape.dimensions(i)); - source_index.push_back(ir_builder_->getInt64(0)); + source_index.push_back(target_index.GetConstantWithIndexType(0)); } } return source_index; @@ -1540,9 +1540,14 @@ StatusOr ElementalIrEmitter::EmitElementalDynamicSlice( // Emit IR to read dynamic start indices from hlo->operand(1). const HloInstruction* input_hlo = hlo->operand(0); const int64 rank = ShapeUtil::Rank(input_hlo->shape()); - llvm_ir::IrArray::Index slice_start_index(rank); + // Use the same index type for all tensor accesses in the same kernel. + llvm::Type* index_type = index.GetType(); + llvm_ir::IrArray::Index slice_start_index(index_type, rank); for (int64 i = 0; i < rank; ++i) { - llvm_ir::IrArray::Index dim_index(1, ir_builder_->getInt64(i)); + auto index_typed_const = [&](uint64 c) -> llvm::Constant* { + return llvm::ConstantInt::get(index_type, c); + }; + llvm_ir::IrArray::Index dim_index(1, index_typed_const(i)); TF_ASSIGN_OR_RETURN(llvm::Value * start_index_value, operand_to_generator.at(hlo->operand(1))(dim_index)); @@ -1552,17 +1557,17 @@ StatusOr ElementalIrEmitter::EmitElementalDynamicSlice( // TODO(b/74360564): This is implementation defined behavior, but is // currently respected by all implementations. Change this if we ever decide // to oficially document different behavior. - start_index_value = ir_builder_->CreateSExtOrBitCast(start_index_value, - index[i]->getType()); - llvm::Value* operand_dim_size = llvm::ConstantInt::get( - start_index_value->getType(), input_hlo->shape().dimensions(i)); - llvm::Value* output_dim_size = llvm::ConstantInt::get( - start_index_value->getType(), hlo->shape().dimensions(i)); + start_index_value = + ir_builder_->CreateSExtOrTrunc(start_index_value, index_type); + llvm::Value* operand_dim_size = + index_typed_const(input_hlo->shape().dimensions(i)); + llvm::Value* output_dim_size = + index_typed_const(hlo->shape().dimensions(i)); start_index_value = EmitIntegralMin( ir_builder_->CreateSub(operand_dim_size, output_dim_size), - EmitIntegralMax(llvm::ConstantInt::get(start_index_value->getType(), 0), - start_index_value, /*is_signed=*/true), + EmitIntegralMax(index_typed_const(0), start_index_value, + /*is_signed=*/true), /*is_signed=*/true); start_index_value->setName( @@ -1570,7 +1575,7 @@ StatusOr ElementalIrEmitter::EmitElementalDynamicSlice( slice_start_index[i] = start_index_value; } - llvm_ir::IrArray::Index input_index(rank); + llvm_ir::IrArray::Index input_index(index_type, rank); for (int64 i = 0; i < rank; ++i) { // Emit IR which computes: // input_index = start_index + offset_index @@ -1594,17 +1599,18 @@ StatusOr ElementalIrEmitter::EmitElementalGather( const llvm_ir::ElementGenerator& indices_generator = operand_to_generator.at(hlo->operand(1)); + llvm::Type* index_type = index.GetType(); // This is the index into `operand` that holds the element we want to // generate. This index "unsafe" as in the components in here may be // out of bounds. - IrArray::Index unsafe_operand_index; + IrArray::Index unsafe_operand_index(index_type); // First copy in the window indices to unsafe_operand_index. for (int64 i = 0, e = operand_shape.dimensions_size(), unsafe_operand_index_dim = 0; i < e; i++) { if (c_binary_search(dim_numbers.elided_window_dims(), i)) { - unsafe_operand_index.push_back(ir_builder_->getInt64(0)); + unsafe_operand_index.push_back(index.GetConstantWithIndexType(0)); } else { unsafe_operand_index.push_back( index[dim_numbers.output_window_dims(unsafe_operand_index_dim++)]); @@ -1612,7 +1618,7 @@ StatusOr ElementalIrEmitter::EmitElementalGather( } // This is the index of the index vector in the gather_indices tensor. - IrArray::Index gather_index_index; + IrArray::Index gather_index_index(index_type); { std::vector gather_index_index_components; for (int64 i = 0, e = output_shape.dimensions_size(); i < e; i++) { @@ -1628,8 +1634,8 @@ StatusOr ElementalIrEmitter::EmitElementalGather( auto add_to_unsafe_operand_index = [&](llvm::Value* index_component, int64 dim) { - llvm::Value* gather_dim_component_extended = ir_builder_->CreateSExtOrTrunc( - index_component, ir_builder_->getInt64Ty()); + llvm::Value* gather_dim_component_extended = + ir_builder_->CreateSExtOrTrunc(index_component, index_type); unsafe_operand_index[dim_numbers.gather_dims_to_operand_dims(dim)] = ir_builder_->CreateAdd( unsafe_operand_index[dim_numbers.gather_dims_to_operand_dims(dim)], @@ -1645,18 +1651,18 @@ StatusOr ElementalIrEmitter::EmitElementalGather( indices_shape.dimensions(dim_numbers.index_vector_dim()); for (int64 i = 0; i < index_vector_size; i++) { gather_index_index[dim_numbers.index_vector_dim()] = - ir_builder_->getInt64(i); + index.GetConstantWithIndexType(i); TF_ASSIGN_OR_RETURN(llvm::Value * gather_dim_component, indices_generator(gather_index_index)); add_to_unsafe_operand_index(gather_dim_component, i); } } - IrArray::Index safe_operand_index; + IrArray::Index safe_operand_index(index_type); for (int64 i = 0, e = unsafe_operand_index.size(); i < e; i++) { safe_operand_index.push_back(ir_builder_->CreateURem( unsafe_operand_index[i], - ir_builder_->getInt64(operand_shape.dimensions(i)))); + index.GetConstantWithIndexType(operand_shape.dimensions(i)))); } return operand_generator(safe_operand_index); @@ -1671,14 +1677,18 @@ StatusOr ElementalIrEmitter::EmitElementalDynamicUpdateSlice( const HloInstruction* start_hlo = hlo->operand(2); // Calculate slice start/end indices. const int64 rank = ShapeUtil::Rank(input_hlo->shape()); - llvm_ir::IrArray::Index slice_start_index(rank); - llvm_ir::IrArray::Index slice_limit_index(rank); + llvm_ir::IrArray::Index slice_start_index(index.GetType(), rank); + llvm_ir::IrArray::Index slice_limit_index(index.GetType(), rank); // Slice intersection gathers (ANDs) conditions on all ranks for which // 'input' is set to 'update' llvm::Value* slice_intersection = ir_builder_->getTrue(); for (int64 i = 0; i < rank; ++i) { - llvm_ir::IrArray::Index dim_index(1, ir_builder_->getInt64(i)); + llvm::Type* index_type = index[0]->getType(); + auto index_typed_const = [&](uint64 c) -> llvm::Constant* { + return llvm::ConstantInt::get(index_type, c); + }; + llvm_ir::IrArray::Index dim_index(1, index_typed_const(i)); TF_ASSIGN_OR_RETURN(llvm::Value * start_index_value, operand_to_generator.at(start_hlo)(dim_index)); @@ -1688,18 +1698,18 @@ StatusOr ElementalIrEmitter::EmitElementalDynamicUpdateSlice( // TODO(b/74360564): This is implementation defined behavior, but is // currently respected by all implementations. Change this if we ever decide // to oficially document different behavior. - start_index_value = ir_builder_->CreateSExtOrBitCast(start_index_value, - index[i]->getType()); - llvm::Value* input_dim_size = llvm::ConstantInt::get( - index[i]->getType(), input_hlo->shape().dimensions(i)); - llvm::Value* update_dim_size = llvm::ConstantInt::get( - index[i]->getType(), update_hlo->shape().dimensions(i)); - - start_index_value = EmitIntegralMin( - ir_builder_->CreateSub(input_dim_size, update_dim_size), - EmitIntegralMax(llvm::ConstantInt::get(start_index_value->getType(), 0), - start_index_value, /*is_signed=*/true), - /*is_signed=*/true); + start_index_value = + ir_builder_->CreateSExtOrTrunc(start_index_value, index_type); + llvm::Value* input_dim_size = + index_typed_const(input_hlo->shape().dimensions(i)); + llvm::Value* update_dim_size = + index_typed_const(update_hlo->shape().dimensions(i)); + + start_index_value = + EmitIntegralMin(ir_builder_->CreateSub(input_dim_size, update_dim_size), + EmitIntegralMax(index_typed_const(0), start_index_value, + /*is_signed=*/true), + /*is_signed=*/true); start_index_value->setName( AsStringRef(IrName(hlo, StrCat("start_idx", i)))); @@ -1729,7 +1739,7 @@ StatusOr ElementalIrEmitter::EmitElementalDynamicUpdateSlice( // Handle true BB (return data from 'update') SetToFirstInsertPoint(if_data.true_block, ir_builder_); // Compute update index for intersection case. - llvm_ir::IrArray::Index update_index(rank); + llvm_ir::IrArray::Index update_index(index.GetType(), rank); for (int64 i = 0; i < rank; ++i) { update_index[i] = ir_builder_->CreateSub(index[i], slice_start_index[i]); } @@ -1797,7 +1807,8 @@ StatusOr ElementalIrEmitter::EmitElementalPad( SetToFirstInsertPoint(if_data.false_block, ir_builder_); TF_ASSIGN_OR_RETURN(llvm::Value * padding_value, - operand_to_generator.at(hlo->operand(1))({})); + operand_to_generator.at(hlo->operand(1))( + IrArray::Index(index.GetType()))); ir_builder_->CreateStore(padding_value, ret_value_addr); SetToFirstInsertPoint(if_data.after_block, ir_builder_); @@ -1824,10 +1835,15 @@ StatusOr ElementalIrEmitter::EmitElementalDot( int64 lhs_dims = hlo->operand(0)->shape().dimensions_size(); int64 rhs_dims = hlo->operand(1)->shape().dimensions_size(); - std::unique_ptr inner_loop = llvm_ir::ForLoop::EmitForLoop( - IrName(hlo, "inner"), ir_builder_->getInt64(0), - ir_builder_->getInt64(contracted_dim_size), ir_builder_->getInt64(1), - ir_builder_); + llvm::Type* index_type = dot_result_index[0]->getType(); + auto index_typed_const = [&](uint64 c) -> llvm::Constant* { + return llvm::ConstantInt::get(index_type, c); + }; + + std::unique_ptr inner_loop = + llvm_ir::ForLoop::EmitForLoop(IrName(hlo, "inner"), index_typed_const(0), + index_typed_const(contracted_dim_size), + index_typed_const(1), ir_builder_); SetToFirstInsertPoint(inner_loop->GetPreheaderBasicBlock(), ir_builder_); PrimitiveType primitive_type = hlo->shape().element_type(); @@ -1846,7 +1862,7 @@ StatusOr ElementalIrEmitter::EmitElementalDot( // Given an output index [a,b,c,d,e] in the result, we compute: // sum(lhs[a,b,c,t]*rhs[d,t,e] for t in [0, T)) - IrArray::Index lhs_index, rhs_index; + IrArray::Index lhs_index(index_type), rhs_index(index_type); for (int64 i = 0; i < lhs_dims - 1; i++) { lhs_index.push_back(dot_result_index[i]); diff --git a/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.cc b/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.cc index b812dd7d3f..27d2c3e491 100644 --- a/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.cc +++ b/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.cc @@ -376,11 +376,17 @@ llvm_ir::ElementGenerator GpuElementalIrEmitter::MakeElementGenerator( "reduce_window_accum_ptr", ir_builder_); { TF_ASSIGN_OR_RETURN(llvm::Value * init_value, - operand_to_generator.at(hlo->operand(1))({})); + operand_to_generator.at(hlo->operand(1))( + IrArray::Index(index.GetType()))); ir_builder_->CreateStore(init_value, accum_ptr); } - llvm_ir::ForLoopNest loops(IrName(hlo), ir_builder_); + llvm::Type* index_type = index.GetType(); + auto index_typed_const = [&](uint64 c) -> llvm::Constant* { + return index.GetConstantWithIndexType(c); + }; + + llvm_ir::ForLoopNest loops(IrName(hlo), ir_builder_, index_type); std::vector window_size; for (const auto& dim : window.dimensions()) { window_size.push_back(dim.size()); @@ -391,14 +397,14 @@ llvm_ir::ElementGenerator GpuElementalIrEmitter::MakeElementGenerator( SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(), ir_builder_); - IrArray::Index input_index(index.size()); + IrArray::Index input_index(index_type, index.size()); llvm::Value* in_bounds = ir_builder_->getInt1(true); for (size_t i = 0; i < index.size(); ++i) { llvm::Value* stridden_index = ir_builder_->CreateNSWMul( - index[i], ir_builder_->getInt64(window.dimensions(i).stride())); + index[i], index_typed_const(window.dimensions(i).stride())); input_index[i] = ir_builder_->CreateNSWSub( ir_builder_->CreateNSWAdd(stridden_index, window_index[i]), - ir_builder_->getInt64(window.dimensions(i).padding_low())); + index_typed_const(window.dimensions(i).padding_low())); // We must check whether 0 ≤ input_index[i] < bound, as otherwise // we are in the pad and so can skip the computation. This @@ -409,7 +415,7 @@ llvm_ir::ElementGenerator GpuElementalIrEmitter::MakeElementGenerator( in_bounds, ir_builder_->CreateICmpULT( input_index[i], - ir_builder_->getInt64(operand->shape().dimensions(i)))); + index_typed_const(operand->shape().dimensions(i)))); } llvm_ir::LlvmIfData if_data = @@ -435,11 +441,13 @@ llvm_ir::ElementGenerator GpuElementalIrEmitter::MakeElementGenerator( llvm::Value* accum_ptr = ir_builder()->CreateAlloca(llvm_ir::PrimitiveTypeToIrType( hlo->shape().element_type(), module_)); + llvm::Type* index_type = output_index.GetType(); TF_ASSIGN_OR_RETURN(llvm::Value * init_value, - operand_to_generator.at(hlo->operand(1))({})); + operand_to_generator.at(hlo->operand(1))( + IrArray::Index(index_type))); ir_builder()->CreateStore(init_value, accum_ptr); - llvm_ir::ForLoopNest loops(IrName(hlo), ir_builder_); + llvm_ir::ForLoopNest loops(IrName(hlo), ir_builder_, index_type); IrArray::Index input_index = loops.AddLoopsForShapeOnDimensions( operand->shape(), hlo->dimensions(), "reduction_dim"); if (!ShapeUtil::IsScalar(hlo->shape())) { diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter.cc index 7b7dd673a5..d38a496fea 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter.cc @@ -478,12 +478,15 @@ Status IrEmitter::HandleDot(HloInstruction* dot) { const Shape& lhs_shape = lhs_instruction->shape(); const Shape& rhs_shape = rhs_instruction->shape(); + // TODO(b/110211620): Convert to use i32 index_type when it is possible. + llvm::Type* index_type = ir_builder_.getInt64Ty(); + llvm_ir::IrArray::Index element_index(index_type); if (ShapeUtil::IsScalar(lhs_shape) && ShapeUtil::IsScalar(rhs_shape)) { // If the operands are scalar, don't emit any loops. llvm::Value* lhs_value = - lhs_array.EmitReadArrayElement(/*index=*/{}, &ir_builder_); + lhs_array.EmitReadArrayElement(/*index=*/element_index, &ir_builder_); llvm::Value* rhs_value = - rhs_array.EmitReadArrayElement(/*index=*/{}, &ir_builder_); + rhs_array.EmitReadArrayElement(/*index=*/element_index, &ir_builder_); llvm::Value* result; if (ShapeUtil::ElementIsComplex(lhs_shape)) { auto value = MultiplyComplex(lhs_value, rhs_value, &ir_builder_); @@ -493,7 +496,8 @@ Status IrEmitter::HandleDot(HloInstruction* dot) { } else { result = ir_builder_.CreateFMul(lhs_value, rhs_value); } - target_array.EmitWriteArrayElement(/*index=*/{}, result, &ir_builder_); + target_array.EmitWriteArrayElement(/*index=*/element_index, result, + &ir_builder_); return Status::OK(); } @@ -584,7 +588,7 @@ Status IrEmitter::HandleDot(HloInstruction* dot) { // address. The index into the target address is the concatenation of the rhs // and lhs indexes with the reduction dimensions removed. The terms from the // rhs index are the lower dimensions in the index so we add them first. - llvm_ir::IrArray::Index target_index; + llvm_ir::IrArray::Index target_index(index_type); for (size_t dimension = 0; dimension < lhs_index.size(); ++dimension) { if (dimension != lhs_reduction_dimension) { target_index.push_back(lhs_index[dimension]); diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc index 4a013a7f53..a94119b0e9 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc @@ -283,6 +283,69 @@ int ComputeMaxUnrollFactor(const HloInstruction* hlo) { // Cannot unroll. return 1; } + +// Returns the llvm type for the indices used in the kernel that contains the +// hlo instruction. Such indices include the index for the parallel loop and +// the indices for the tensors accessed by the kernel. The return type is i32 +// iff the following conditions are met: +// . The launch_size of the kernel is within the range of i32. +// . The sizes of all the tensors accessed within the kernel are within the +// range of i32. +// Otherwise, the return type is i64. +llvm::Type* GetIndexTypeForKernel(const HloInstruction* hlo, int64 launch_size, + llvm::IRBuilder<>* ir_builder) { + // Find the unnested hlo instructon for which the kernel is generated for. + const HloInstruction* unnested_hlo = hlo; + const HloComputation* computation = hlo->parent(); + if (computation->IsFusionComputation()) { + unnested_hlo = computation->FusionInstruction(); + } + + auto shape_in_range = [&](const Shape& s) { + bool in_range = true; + ShapeUtil::ForEachSubshape( + s, [&](const Shape& sub_shape, const ShapeIndex& /*index*/) { + if (ShapeUtil::IsArray(sub_shape) && + !IsInt32(ShapeUtil::ElementsIn(sub_shape))) { + in_range = false; + } + }); + + return in_range; + }; + + llvm::Type* i64_ty = ir_builder->getInt64Ty(); + // Check launch dimension + if (!IsInt32(launch_size)) { + return i64_ty; + } + + // Check the size of result tensors + if (!shape_in_range(unnested_hlo->shape())) { + return i64_ty; + } + + auto hlo_shape_in_range = [&](const HloInstruction* operand) -> bool { + return shape_in_range(operand->shape()); + }; + + // Check the size of input tensors + if (!c_all_of(unnested_hlo->operands(), hlo_shape_in_range)) { + return i64_ty; + } + + // Check the size of the internal result tensors + if (unnested_hlo->opcode() == HloOpcode::kFusion) { + if (!c_all_of( + unnested_hlo->fused_instructions_computation()->instructions(), + hlo_shape_in_range)) { + return i64_ty; + } + } + + return ir_builder->getInt32Ty(); +} + } // namespace Status IrEmitterUnnested::DefaultAction(HloInstruction* hlo) { @@ -1004,6 +1067,20 @@ Status IrEmitterUnnested::EmitReductionToScalar( int64 num_tiles = RoundUpToNearest(CeilOfRatio(num_elems, kTileSize), kWarpSize); + Shape tiled_input_shape = ShapeUtil::MakeShapeWithLayout( + reduce->shape().element_type(), {num_tiles}, {0}); + LaunchDimensions launch_dimensions = CalculateLaunchDimensions( + tiled_input_shape, ir_emitter_context_->device_description()); + + llvm::Type* index_ty = GetIndexTypeForKernel( + reduce, + launch_dimensions.block_count() * launch_dimensions.threads_per_block(), + &ir_builder_); + + auto index_typed_const = [&](uint64 c) -> llvm::Constant* { + return llvm::ConstantInt::get(index_ty, c); + }; + // Check whether every thread will process a full tile's worth of elements // without reading outside the bounds of the input. If this is true, we can // skip some bounds checks in the final algorithm. @@ -1052,40 +1129,42 @@ Status IrEmitterUnnested::EmitReductionToScalar( llvm::Value* partial_reduction_result_address = ir_builder_.CreateAlloca( element_ir_type, /*ArraySize=*/nullptr, "partial_reduction_result." + llvm::Twine(i)); - TF_ASSIGN_OR_RETURN(llvm::Value* const init_ir_value, - init_value_gens[i](llvm_ir::IrArray::Index({}))); + TF_ASSIGN_OR_RETURN( + llvm::Value* const init_ir_value, + init_value_gens[i](llvm_ir::IrArray::Index(index_ty))); ir_builder_.CreateStore(init_ir_value, partial_reduction_result_address); partial_reduction_result_addresses.push_back( partial_reduction_result_address); } llvm::Value* x_in_tiles = tile_index[0]; + x_in_tiles = ir_builder_.CreateZExtOrTrunc(x_in_tiles, index_ty); // Emit an inner for-loop that reduces the elements in the tile. auto emit_tile_element_loop = [=](bool tile_in_bounds) -> Status { std::unique_ptr tile_element_loop = - llvm_ir::ForLoop::EmitForLoop("element_id_in_tile", - ir_builder_.getInt64(0), - ir_builder_.getInt64(kTileSize), - ir_builder_.getInt64(1), &ir_builder_); + llvm_ir::ForLoop::EmitForLoop( + "element_id_in_tile", index_typed_const(0), + index_typed_const(kTileSize), index_typed_const(1), &ir_builder_); // Emit the body of the partial reduction loop. llvm_ir::SetToFirstInsertPoint(tile_element_loop->GetBodyBasicBlock(), &ir_builder_); llvm::Value* x = ir_builder_.CreateNSWAdd( - ir_builder_.CreateNSWMul(x_in_tiles, ir_builder_.getInt64(kTileSize)), + ir_builder_.CreateNSWMul(x_in_tiles, index_typed_const(kTileSize)), tile_element_loop->GetIndVarValue()); // Unless we know the tile is entirely in bounds, we have to emit a // x-in-bounds check before reading from the input. if (!tile_in_bounds) { llvm_ir::LlvmIfData if_data = llvm_ir::EmitIfThenElse( - ir_builder_.CreateICmpULT(x, ir_builder_.getInt64(num_elems)), + ir_builder_.CreateICmpULT(x, index_typed_const(num_elems)), "x_in_bounds", &ir_builder_); // Emit code that reads the input element and accumulates it to // the partial reduction result. llvm_ir::SetToFirstInsertPoint(if_data.true_block, &ir_builder_); } + llvm_ir::IrArray::Index input_index( /*linear=*/x, input_shape, &ir_builder_); llvm::Value* input_address = ir_builder_.CreateAlloca(element_ir_type); @@ -1104,12 +1183,12 @@ Status IrEmitterUnnested::EmitReductionToScalar( // x_end = kTileSize + x_in_tiles * kTileSize, i.e., the location that's // immediately beyond the tile. llvm::Value* x_end = ir_builder_.CreateNSWAdd( - ir_builder_.getInt64(kTileSize), - ir_builder_.CreateNSWMul(x_in_tiles, ir_builder_.getInt64(kTileSize))); + index_typed_const(kTileSize), + ir_builder_.CreateNSWMul(x_in_tiles, index_typed_const(kTileSize))); // The tile is entirely in bound if all_threads_in_bounds or // x_end <= num_elems. llvm::Value* tile_in_bounds = ir_builder_.CreateOr( - ir_builder_.CreateICmpULE(x_end, ir_builder_.getInt64(num_elems)), + ir_builder_.CreateICmpULE(x_end, index_typed_const(num_elems)), ir_builder_.getInt1(all_threads_in_bounds)); llvm_ir::LlvmIfData if_tile_in_bounds_data = llvm_ir::EmitIfThenElse(tile_in_bounds, "tile_in_bounds", &ir_builder_); @@ -1160,9 +1239,9 @@ Status IrEmitterUnnested::EmitReductionToScalar( // lane 0 (which holds the partially accumulated result for its warp) to the // output element. llvm::Value* lane_id = ir_builder_.CreateURem( - x_in_tiles, ir_builder_.getInt64(kWarpSize), "lane_id"); + x_in_tiles, index_typed_const(kWarpSize), "lane_id"); llvm_ir::LlvmIfData if_lane_id_is_zero_data = llvm_ir::EmitIfThenElse( - ir_builder_.CreateICmpEQ(lane_id, ir_builder_.getInt64(0)), + ir_builder_.CreateICmpEQ(lane_id, index_typed_const(0)), "lane_id_is_zero", &ir_builder_); llvm_ir::SetToFirstInsertPoint(if_lane_id_is_zero_data.true_block, &ir_builder_); @@ -1184,10 +1263,6 @@ Status IrEmitterUnnested::EmitReductionToScalar( }; // Emit a parallel loop that iterates through all input tiles, one per thread. - Shape tiled_input_shape = ShapeUtil::MakeShapeWithLayout( - reduce->shape().element_type(), {num_tiles}, {0}); - LaunchDimensions launch_dimensions = CalculateLaunchDimensions( - tiled_input_shape, ir_emitter_context_->device_description()); CHECK(LastThunk()->kind() == Thunk::Kind::kSequential); UpdateLaunchDimensions( launch_dimensions, @@ -1195,7 +1270,7 @@ Status IrEmitterUnnested::EmitReductionToScalar( ir_emitter_context_->llvm_module()); return ParallelLoopEmitter(loop_body_emitter, tiled_input_shape, launch_dimensions, &ir_builder_) - .EmitLoop(IrName(reduce)); + .EmitLoop(IrName(reduce), index_ty); } Status IrEmitterUnnested::EmitColumnReduction( @@ -1226,6 +1301,17 @@ Status IrEmitterUnnested::EmitColumnReduction( // If the height is not a multiple of the tile size, we pad the bottom of the // input matrix. const int64 height_in_tiles = CeilOfRatio(height, kTileSize); + Shape tiled_input_shape = ShapeUtil::MakeShapeWithLayout( + reduce->shape().element_type(), {height_in_tiles, width}, {1, 0}); + LaunchDimensions launch_dimensions = CalculateLaunchDimensions( + tiled_input_shape, ir_emitter_context_->device_description()); + + // TODO(b/110211620): Convert to use i32 index_type when it is possible. + llvm::Type* index_ty = ir_builder_.getInt64Ty(); + + auto index_typed_const = [&](uint64 c) -> llvm::Constant* { + return llvm::ConstantInt::get(index_ty, c); + }; // for (linear_index = threadIdx.x + blockIdx.x * blockDim.x; // linear_index < height_in_tiles * width; @@ -1261,8 +1347,9 @@ Status IrEmitterUnnested::EmitColumnReduction( llvm::Value* partial_reduction_result_address = ir_builder_.CreateAlloca( element_ir_type, /*ArraySize=*/nullptr, "partial_reduction_result." + llvm::Twine(i)); - TF_ASSIGN_OR_RETURN(llvm::Value* const init_ir_value, - init_value_gens[i](llvm_ir::IrArray::Index({}))); + TF_ASSIGN_OR_RETURN( + llvm::Value* const init_ir_value, + init_value_gens[i](llvm_ir::IrArray::Index(index_ty))); ir_builder_.CreateStore(init_ir_value, partial_reduction_result_address); partial_reduction_result_addresses.push_back( partial_reduction_result_address); @@ -1273,24 +1360,27 @@ Status IrEmitterUnnested::EmitColumnReduction( llvm::Value* y_in_tiles = tile_index[0]; llvm::Value* x = tile_index[1]; + y_in_tiles = ir_builder_.CreateZExtOrTrunc(y_in_tiles, index_ty); + x = ir_builder_.CreateZExtOrTrunc(x, index_ty); + auto emit_tile_element_loop = [=](bool tile_in_bounds) -> Status { std::unique_ptr tile_element_loop = - llvm_ir::ForLoop::EmitForLoop("element_id_in_tile", - ir_builder_.getInt64(0), - ir_builder_.getInt64(kTileSize), - ir_builder_.getInt64(1), &ir_builder_); + llvm_ir::ForLoop::EmitForLoop( + "element_id_in_tile", index_typed_const(0), + index_typed_const(kTileSize), index_typed_const(1), &ir_builder_); // Emit the body of the partial reduction loop. llvm_ir::SetToFirstInsertPoint(tile_element_loop->GetBodyBasicBlock(), &ir_builder_); llvm::Value* y = ir_builder_.CreateNSWAdd( - ir_builder_.CreateNSWMul(y_in_tiles, ir_builder_.getInt64(kTileSize)), + ir_builder_.CreateNSWMul(y_in_tiles, index_typed_const(kTileSize)), tile_element_loop->GetIndVarValue()); + // Unless we know the tile is entirely in bounds, we have to emit a // y-in-bounds check before reading from the input. if (!tile_in_bounds) { llvm_ir::LlvmIfData if_data = llvm_ir::EmitIfThenElse( - ir_builder_.CreateICmpULT(y, ir_builder_.getInt64(height)), + ir_builder_.CreateICmpULT(y, index_typed_const(height)), "y_in_bounds", &ir_builder_); // Emit code that reads the input element and accumulates it to @@ -1340,10 +1430,10 @@ Status IrEmitterUnnested::EmitColumnReduction( // y_end = kTileSize + y_in_tiles * kTileSize, i.e., the y location that's // immediately beyond the tile. llvm::Value* y_end = ir_builder_.CreateNSWAdd( - ir_builder_.getInt64(kTileSize), - ir_builder_.CreateNSWMul(y_in_tiles, ir_builder_.getInt64(kTileSize))); + index_typed_const(kTileSize), + ir_builder_.CreateNSWMul(y_in_tiles, index_typed_const(kTileSize))); llvm::Value* tile_in_bounds = ir_builder_.CreateOr( - ir_builder_.CreateICmpULE(y_end, ir_builder_.getInt64(height)), + ir_builder_.CreateICmpULE(y_end, index_typed_const(height)), ir_builder_.getInt1(height % kTileSize == 0)); // The tile is entirely in bound if "height" is a multiple of kTileSize or // y_end <= height. @@ -1380,10 +1470,6 @@ Status IrEmitterUnnested::EmitColumnReduction( }; // Emit a parallel loop that iterate through all input tiles. - Shape tiled_input_shape = ShapeUtil::MakeShapeWithLayout( - reduce->shape().element_type(), {height_in_tiles, width}, {1, 0}); - LaunchDimensions launch_dimensions = CalculateLaunchDimensions( - tiled_input_shape, ir_emitter_context_->device_description()); CHECK(LastThunk()->kind() == Thunk::Kind::kSequential); UpdateLaunchDimensions( launch_dimensions, @@ -1391,7 +1477,7 @@ Status IrEmitterUnnested::EmitColumnReduction( ir_emitter_context_->llvm_module()); return ParallelLoopEmitter(loop_body_emitter, tiled_input_shape, launch_dimensions, &ir_builder_) - .EmitLoop(IrName(reduce)); + .EmitLoop(IrName(reduce), index_ty); } static std::pair ComputeTilingSchemeForReduction( @@ -1533,9 +1619,21 @@ Status IrEmitterUnnested::EmitRowReduction( // the use of shfl_down is valid. const int64 width_in_tiles = RoundUpToNearest(CeilOfRatio(width, x_tile_size), kWarpSize); + Shape tiled_input_shape = ShapeUtil::MakeShapeWithLayout( + reduce->shape().element_type(), + {depth / z_tile_size, height, width_in_tiles}, {2, 1, 0}); + LaunchDimensions launch_dimensions = CalculateLaunchDimensions( + tiled_input_shape, ir_emitter_context_->device_description()); + llvm::Type* index_ty = GetIndexTypeForKernel( + reduce, + launch_dimensions.block_count() * launch_dimensions.threads_per_block(), + &ir_builder_); + + auto index_typed_const = [&](uint64 c) -> llvm::Constant* { + return llvm::ConstantInt::get(index_ty, c); + }; auto loop_body_emitter = [=](const llvm_ir::IrArray::Index& tile_index) { - // Emit the loop body that reduces one z-x-tile. const int num_reduces = reducers.size(); llvm::Type* element_ir_type = llvm_ir::PrimitiveTypeToIrType( input_shape.element_type(), ir_emitter_context_->llvm_module()); @@ -1544,8 +1642,9 @@ Status IrEmitterUnnested::EmitRowReduction( llvm::Value* partial_reduction_result_address = ir_builder_.CreateAlloca( element_ir_type, /*ArraySize=*/nullptr, "partial_reduction_result." + llvm::Twine(i)); - TF_ASSIGN_OR_RETURN(llvm::Value* const init_ir_value, - init_value_gens[i](llvm_ir::IrArray::Index({}))); + TF_ASSIGN_OR_RETURN( + llvm::Value* const init_ir_value, + init_value_gens[i](llvm_ir::IrArray::Index(index_ty))); ir_builder_.CreateStore(init_ir_value, partial_reduction_result_address); partial_reduction_result_addresses.push_back( partial_reduction_result_address); @@ -1554,20 +1653,23 @@ Status IrEmitterUnnested::EmitRowReduction( llvm::Value* z_tile = tile_index[0]; llvm::Value* y = tile_index[1]; llvm::Value* x_tile = tile_index[2]; - llvm::Value* warp_id = ir_builder_.CreateUDiv( - x_tile, ir_builder_.getInt64(kWarpSize), "warp_id"); - llvm::Value* lane_id = ir_builder_.CreateURem( - x_tile, ir_builder_.getInt64(kWarpSize), "lane_id"); + + x_tile = ir_builder_.CreateZExtOrTrunc(x_tile, index_ty); + + llvm::Value* warp_id = + ir_builder_.CreateUDiv(x_tile, index_typed_const(kWarpSize), "warp_id"); + llvm::Value* lane_id = + ir_builder_.CreateURem(x_tile, index_typed_const(kWarpSize), "lane_id"); // The x-location of the last element in this z-x-tile. // last_x = lane_id + warpSize * (x_tile_size - 1 + warp_id * x_tile_size); llvm::Value* last_x = ir_builder_.CreateNSWAdd( lane_id, ir_builder_.CreateNSWMul( - ir_builder_.getInt64(kWarpSize), + index_typed_const(kWarpSize), ir_builder_.CreateNSWAdd( - ir_builder_.getInt64(x_tile_size - 1), + index_typed_const(x_tile_size - 1), ir_builder_.CreateNSWMul( - warp_id, ir_builder_.getInt64(x_tile_size))))); + warp_id, index_typed_const(x_tile_size))))); KernelSupportLibrary ksl( &ir_builder_, @@ -1580,31 +1682,31 @@ Status IrEmitterUnnested::EmitRowReduction( int64 x_tile_loop_bound) -> Status { auto emit_z_tile_element_loop = [&](llvm::Value* z_indvar) -> Status { llvm::Value* z = ir_builder_.CreateNSWAdd( - z_indvar, ir_builder_.CreateNSWMul( - ir_builder_.getInt64(z_tile_size), z_tile)); - + z_indvar, + ir_builder_.CreateNSWMul(index_typed_const(z_tile_size), z_tile)); TF_RETURN_IF_ERROR(ksl.For( "x_tile", - /*start=*/0, /*end=*/x_tile_loop_bound, /*step=*/1, - [&](llvm::Value* x_indvar) -> Status { + /*start=*/index_typed_const(0), + /*end=*/index_typed_const(x_tile_loop_bound), + /*step=*/1, [&](llvm::Value* x_indvar) -> Status { // x = lane_id + // warpSize * (element_id_in_x_tile + warp_id * x_tile_size); llvm::Value* x = ir_builder_.CreateNSWAdd( lane_id, ir_builder_.CreateNSWMul( - ir_builder_.getInt64(kWarpSize), + index_typed_const(kWarpSize), ir_builder_.CreateNSWAdd( - x_indvar, - ir_builder_.CreateNSWMul( - warp_id, ir_builder_.getInt64(x_tile_size))))); + x_indvar, ir_builder_.CreateNSWMul( + warp_id, llvm::ConstantInt::get( + index_ty, x_tile_size))))); // Unless we know the x-tile is entirely in bounds, we have to // emit a x-in-bounds check before reading from the input. if (!x_tile_in_bounds) { llvm_ir::LlvmIfData if_x_in_bounds_data = - llvm_ir::EmitIfThenElse(ir_builder_.CreateICmpULT( - x, ir_builder_.getInt64(width)), - "x_in_bounds", &ir_builder_); + llvm_ir::EmitIfThenElse( + ir_builder_.CreateICmpULT(x, index_typed_const(width)), + "x_in_bounds", &ir_builder_); // Points ir_builder_ to the then-block. llvm_ir::SetToFirstInsertPoint(if_x_in_bounds_data.true_block, &ir_builder_); @@ -1659,13 +1761,14 @@ Status IrEmitterUnnested::EmitRowReduction( }; return ksl.For("z_tile", - /*start=*/0, /*end=*/z_tile_size, /*step=*/1, - emit_z_tile_element_loop); + /*start=*/index_typed_const(0), + /*end=*/index_typed_const(z_tile_size), + /*step=*/1, emit_z_tile_element_loop); }; llvm::Value* tile_in_bounds = ir_builder_.CreateOr( ir_builder_.getInt1(width % (x_tile_size * kWarpSize) == 0), - ir_builder_.CreateICmpULT(last_x, ir_builder_.getInt64(width))); + ir_builder_.CreateICmpULT(last_x, index_typed_const(width))); TF_RETURN_IF_ERROR( ksl.If(tile_in_bounds, @@ -1719,7 +1822,7 @@ Status IrEmitterUnnested::EmitRowReduction( // lane 0 (which holds the partially accumulated result for its warp) to the // output element. llvm_ir::LlvmIfData if_lane_id_is_zero_data = llvm_ir::EmitIfThenElse( - ir_builder_.CreateICmpEQ(lane_id, ir_builder_.getInt64(0)), + ir_builder_.CreateICmpEQ(lane_id, index_typed_const(0)), "lane_id_is_zero", &ir_builder_); llvm_ir::SetToFirstInsertPoint(if_lane_id_is_zero_data.true_block, &ir_builder_); @@ -1748,11 +1851,6 @@ Status IrEmitterUnnested::EmitRowReduction( }; // Emit a parallel loop that iterates through every input tiles. - Shape tiled_input_shape = ShapeUtil::MakeShapeWithLayout( - reduce->shape().element_type(), - {depth / z_tile_size, height, width_in_tiles}, {2, 1, 0}); - LaunchDimensions launch_dimensions = CalculateLaunchDimensions( - tiled_input_shape, ir_emitter_context_->device_description()); CHECK(LastThunk()->kind() == Thunk::Kind::kSequential); UpdateLaunchDimensions( launch_dimensions, @@ -1760,7 +1858,7 @@ Status IrEmitterUnnested::EmitRowReduction( ir_emitter_context_->llvm_module()); return ParallelLoopEmitter(loop_body_emitter, tiled_input_shape, launch_dimensions, &ir_builder_) - .EmitLoop(IrName(reduce)); + .EmitLoop(IrName(reduce), index_ty); } // Figures out whether `reduce` is a row or column reduction, and which @@ -1872,7 +1970,7 @@ Status IrEmitterUnnested::HandleReduce(HloInstruction* reduce) { HloComputation* reducer = reduce->to_apply(); // HandleReduce specializes reduction from a multi-dimensional array to a 1D // array. The specialized version requires an initializer thunk that - // initializes the output array to the initial value of the reduce. + // ingitializes the output array to the initial value of the reduce. if (IsReductionToVector(*reduce) && // NVPTX backend can't do atomic cmpxchg any narrower than 32 bits 32 <= primitive_util::BitWidth(reduce->shape().element_type())) { @@ -1960,6 +2058,14 @@ Status IrEmitterUnnested::HandleSelectAndScatter( "Dilation for SelectAndScatter not implemented on GPU."); } + LaunchDimensions launch_dimensions = CalculateLaunchDimensions( + source->shape(), ir_emitter_context_->device_description()); + llvm::Type* index_type = GetIndexTypeForKernel( + select_and_scatter, launch_dimensions.launch_bound(), &ir_builder_); + auto index_typed_const = [&](uint64 c) -> llvm::Constant* { + return llvm::ConstantInt::get(index_type, c); + }; + // kSelectAndScatter is implemented as two kernel launches: the first launch // initializes the output array to the given initial value, // and the second accumulates the "source" matrix to the @@ -1990,8 +2096,8 @@ Status IrEmitterUnnested::HandleSelectAndScatter( "selected_value_address", &ir_builder_); llvm::Value* selected_index_address = llvm_ir::EmitAllocaAtFunctionEntryWithCount( - ir_builder_.getInt64Ty(), ir_builder_.getInt32(rank), - "selected_index_address", &ir_builder_); + index_type, index_typed_const(rank), "selected_index_address", + &ir_builder_); llvm::Value* initialized_flag_address = llvm_ir::EmitAllocaAtFunctionEntry( ir_builder_.getInt1Ty(), "initialized_flag_address", &ir_builder_); ir_builder_.CreateStore(ir_builder_.getInt1(false), @@ -1999,7 +2105,7 @@ Status IrEmitterUnnested::HandleSelectAndScatter( // Create the inner loop to iterate over the window. llvm_ir::ForLoopNest window_loops(IrName(select_and_scatter, "inner"), - &ir_builder_); + &ir_builder_, index_type); std::vector window_size; for (const auto& dim : window.dimensions()) { window_size.push_back(dim.size()); @@ -2013,17 +2119,17 @@ Status IrEmitterUnnested::HandleSelectAndScatter( // Compute the operand index to visit and evaluate the condition whether the // operand index is within the bounds. The unsigned comparison includes // checking whether the operand index >= 0. - llvm_ir::IrArray::Index operand_index(source_index.size()); + llvm_ir::IrArray::Index operand_index(index_type, source_index.size()); llvm::Value* in_bounds_condition = ir_builder_.getInt1(true); for (int64 i = 0; i < rank; ++i) { llvm::Value* strided_index = ir_builder_.CreateNSWMul( - source_index[i], ir_builder_.getInt64(window.dimensions(i).stride())); + source_index[i], index_typed_const(window.dimensions(i).stride())); operand_index[i] = ir_builder_.CreateNSWSub( ir_builder_.CreateNSWAdd(strided_index, window_index[i]), - ir_builder_.getInt64(window.dimensions(i).padding_low())); + index_typed_const(window.dimensions(i).padding_low())); llvm::Value* index_condition = ir_builder_.CreateICmpULT( operand_index[i], - ir_builder_.getInt64(ShapeUtil::GetDimension(operand->shape(), i))); + index_typed_const(ShapeUtil::GetDimension(operand->shape(), i))); in_bounds_condition = ir_builder_.CreateAnd(in_bounds_condition, index_condition); } @@ -2095,7 +2201,7 @@ Status IrEmitterUnnested::HandleSelectAndScatter( // value and the current output value. llvm_ir::SetToFirstInsertPoint(window_loops.GetOuterLoopExitBasicBlock(), &ir_builder_); - llvm_ir::IrArray::Index selected_index; + llvm_ir::IrArray::Index selected_index(operand_index.GetType()); for (int64 i = 0; i < rank; ++i) { llvm::Value* selected_index_address_slot = ir_builder_.CreateInBoundsGEP( selected_index_address, {ir_builder_.getInt32(i)}); @@ -2113,8 +2219,6 @@ Status IrEmitterUnnested::HandleSelectAndScatter( source_value_address); }; - LaunchDimensions launch_dimensions = CalculateLaunchDimensions( - source->shape(), ir_emitter_context_->device_description()); UpdateLaunchDimensions( launch_dimensions, // IrEmitterUnnested implements kSelectAndScatter as a SequentialThunk @@ -2125,7 +2229,7 @@ Status IrEmitterUnnested::HandleSelectAndScatter( ir_emitter_context_->llvm_module()); return ParallelLoopEmitter(loop_body_emitter, source->shape(), launch_dimensions, &ir_builder_) - .EmitLoop(IrName(select_and_scatter)); + .EmitLoop(IrName(select_and_scatter), index_type); } Status IrEmitterUnnested::HandleWhile(HloInstruction* xla_while) { @@ -2835,7 +2939,9 @@ Status IrEmitterUnnested::EmitTargetElementLoopInThunk( if (!hlo.IsMultiOutputFusion()) { return ParallelLoopEmitter(element_generator, GetIrArray(hlo, hlo), launch_dimensions, &ir_builder_, unroll_factor) - .EmitLoop(IrName(&hlo)); + .EmitLoop(IrName(&hlo), + GetIndexTypeForKernel(&hlo, launch_dimensions.launch_bound(), + &ir_builder_)); } // For multiple outputs fusion, we need to emit each operand and the root. @@ -2843,10 +2949,12 @@ Status IrEmitterUnnested::EmitTargetElementLoopInThunk( for (int64 i = 0; i < ShapeUtil::TupleElementCount(hlo.shape()); ++i) { output_arrays.push_back(GetIrArray(hlo, hlo, {i})); } - TF_RETURN_IF_ERROR(ParallelLoopEmitter(element_generator, output_arrays, - launch_dimensions, &ir_builder_, - unroll_factor) - .EmitLoop(IrName(&hlo))); + TF_RETURN_IF_ERROR( + ParallelLoopEmitter(element_generator, output_arrays, launch_dimensions, + &ir_builder_, unroll_factor) + .EmitLoop(IrName(&hlo), + GetIndexTypeForKernel( + &hlo, launch_dimensions.launch_bound(), &ir_builder_))); std::vector tuple_operand_ptrs; for (int64 i = 0; i < output_arrays.size(); ++i) { diff --git a/tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.cc b/tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.cc index d8c07dc311..cd833ec7bd 100644 --- a/tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.cc +++ b/tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.cc @@ -58,7 +58,7 @@ ParallelLoopEmitter::ParallelLoopEmitter( std::vector ParallelLoopEmitter::EmitIndexAndSetExitBasicBlock( - tensorflow::StringPiece loop_name) { + tensorflow::StringPiece loop_name, llvm::Type* index_type) { // Emit the following code in LLVM IR: // linear_index = blockIdx.x * blockDim.x + threadIdx.x; // if (linear_index < num_elements) { @@ -71,14 +71,13 @@ ParallelLoopEmitter::EmitIndexAndSetExitBasicBlock( // // %nctaid.x is currently specified as 2147483647. VLOG(3) << "EmitIndexAndSetExitBasicBlock unroll_factor " << unroll_factor_; + CHECK_NE(index_type, nullptr); std::vector array_indices; - llvm::Value* block_id = llvm_ir::EmitCallToIntrinsic( llvm::Intrinsic::nvvm_read_ptx_sreg_ctaid_x, {}, {}, ir_builder_); llvm_ir::AddRangeMetadata(0, launch_dimensions_.block_count(), static_cast(block_id)); - block_id = - ir_builder_->CreateZExt(block_id, ir_builder_->getInt64Ty(), "block_id"); + block_id = ir_builder_->CreateZExtOrTrunc(block_id, index_type, "block_id"); // Per the PTX documentation: // "It is guaranteed that [...] 0 <= %tid.x < %ntid.x" @@ -88,13 +87,15 @@ ParallelLoopEmitter::EmitIndexAndSetExitBasicBlock( llvm::Intrinsic::nvvm_read_ptx_sreg_tid_x, {}, {}, ir_builder_); llvm_ir::AddRangeMetadata(0, launch_dimensions_.threads_per_block(), static_cast(thread_id)); - thread_id = ir_builder_->CreateZExt(thread_id, ir_builder_->getInt64Ty(), - "thread_id"); + thread_id = + ir_builder_->CreateZExtOrTrunc(thread_id, index_type, "thread_id"); llvm::Value* linear_index_base = ir_builder_->CreateAdd( ir_builder_->CreateMul( block_id, - ir_builder_->getInt64(launch_dimensions_.threads_per_block()), "", + llvm::ConstantInt::get(index_type, + launch_dimensions_.threads_per_block()), + "", /*HasNUW=*/true, /*HasNSW=*/true), thread_id, "linear_index", /*HasNUW=*/true, /*HasNSW=*/true); @@ -110,21 +111,23 @@ ParallelLoopEmitter::EmitIndexAndSetExitBasicBlock( llvm::Intrinsic::assume, {ir_builder_->CreateICmpULT( linear_index_base, - ir_builder_->getInt64(launch_dimensions_.threads_per_block() * - launch_dimensions_.block_count()), + llvm::ConstantInt::get(index_type, + launch_dimensions_.threads_per_block() * + launch_dimensions_.block_count()), "linear_index_in_range")}, {}, ir_builder_); if (unroll_factor_ > 1) { linear_index_base = ir_builder_->CreateMul( - linear_index_base, ir_builder_->getInt64(unroll_factor_), + linear_index_base, llvm::ConstantInt::get(index_type, unroll_factor_), "linear_index_base", /*HasNUW=*/true, /*HasNSW=*/true); } array_indices.emplace_back(linear_index_base, shape_, ir_builder_); for (int i = 1; i < unroll_factor_; ++i) { llvm::Value* linear_index = ir_builder_->CreateAdd( - linear_index_base, ir_builder_->getInt64(i), "linear_index", + linear_index_base, llvm::ConstantInt::get(index_type, i), + "linear_index", /*HasNUW=*/true, /*HasNSW=*/true); array_indices.emplace_back(linear_index, shape_, ir_builder_); } @@ -132,7 +135,7 @@ ParallelLoopEmitter::EmitIndexAndSetExitBasicBlock( auto if_in_bounds = llvm_ir::EmitIfThenElse( ir_builder_->CreateICmpULT( linear_index_base, - ir_builder_->getInt64(ShapeUtil::ElementsIn(shape_))), + llvm::ConstantInt::get(index_type, ShapeUtil::ElementsIn(shape_))), llvm_ir::IrName(loop_name, "in_bounds"), ir_builder_, false); // Set exit_bb_ to the exit block of the if structure. diff --git a/tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.h b/tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.h index 25318b3bed..302e1bf1bc 100644 --- a/tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.h +++ b/tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.h @@ -58,7 +58,7 @@ class ParallelLoopEmitter : public llvm_ir::LoopEmitter { ~ParallelLoopEmitter() override = default; std::vector EmitIndexAndSetExitBasicBlock( - tensorflow::StringPiece loop_name) override; + tensorflow::StringPiece loop_name, llvm::Type* index_type) override; private: // The thread and block dimension to parallelize the loop on. diff --git a/tensorflow/compiler/xla/service/gpu/partition_assignment.h b/tensorflow/compiler/xla/service/gpu/partition_assignment.h index c125474edb..02471129e0 100644 --- a/tensorflow/compiler/xla/service/gpu/partition_assignment.h +++ b/tensorflow/compiler/xla/service/gpu/partition_assignment.h @@ -47,6 +47,7 @@ class LaunchDimensions { int64 block_count() const { return block_count_; } int64 threads_per_block() const { return threads_per_block_; } + int64 launch_bound() const { return block_count() * threads_per_block(); } private: int64 block_count_; diff --git a/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc b/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc index 7323abeb20..ea10cef49a 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc @@ -29,9 +29,9 @@ limitations under the License. namespace xla { namespace llvm_ir { -static void Delinearize(std::vector* multidim, - llvm::Value* linear, const Shape& shape, - llvm::IRBuilder<>* ir_builder) { +void IrArray::Index::Delinearize(std::vector* multidim, + llvm::Value* linear, const Shape& shape, + llvm::IRBuilder<>* ir_builder) const { int64 divisor = 1; const Layout& layout = shape.layout(); for (int64 i = 0; i < layout.minor_to_major_size(); ++i) { @@ -48,10 +48,11 @@ static void Delinearize(std::vector* multidim, // useful because cuda-memcheck can't help us much in XLA: Most of our // memory lives in one big allocation, so cuda-memcheck can't detect // out-of-bounds accesses. - auto* quot = ir_builder->CreateUDiv(linear, ir_builder->getInt64(divisor)); + auto* quot = + ir_builder->CreateUDiv(linear, GetConstantWithIndexType(divisor)); if (i < layout.minor_to_major_size() - 1) { (*multidim)[dimension] = ir_builder->CreateURem( - quot, ir_builder->getInt64(size_of_current_dimension)); + quot, GetConstantWithIndexType(size_of_current_dimension)); } else { (*multidim)[dimension] = quot; } @@ -65,6 +66,8 @@ IrArray::Index::Index(llvm::Value* linear, const Shape& shape, linear_(linear), layout_(shape.layout()), dims_(shape.dimensions().begin(), shape.dimensions().end()) { + CHECK_NE(linear, nullptr); + index_type_ = linear->getType(); CHECK(LayoutUtil::HasLayout(shape)) << "Shape " << ShapeUtil::HumanStringWithLayout(shape) << " should have a layout."; @@ -77,6 +80,13 @@ IrArray::Index::Index(tensorflow::gtl::ArraySlice multidim, linear_(linear), layout_(shape.layout()), dims_(shape.dimensions().begin(), shape.dimensions().end()) { + if (size()) { + index_type_ = multidim_[0]->getType(); + } else { + CHECK_NE(linear_, nullptr); + index_type_ = linear_->getType(); + } + CHECK_NE(index_type_, nullptr); CHECK_EQ(shape.dimensions_size(), multidim.size()); CHECK(LayoutUtil::HasLayout(shape)) << "Shape " << ShapeUtil::HumanStringWithLayout(shape) @@ -88,6 +98,9 @@ IrArray::Index::Index(tensorflow::gtl::ArraySlice multidim, : multidim_(multidim.begin(), multidim.end()), layout_(shape.layout()), dims_(shape.dimensions().begin(), shape.dimensions().end()) { + CHECK_GT(multidim_.size(), 0); + index_type_ = multidim[0]->getType(); + CHECK_NE(index_type_, nullptr); CHECK_EQ(shape.dimensions_size(), multidim.size()); CHECK(LayoutUtil::HasLayout(shape)); } @@ -130,15 +143,15 @@ IrArray::Index IrArray::Index::SourceIndexOfReshape( CommonFactors(AsInt64Slice(input_shape.dimensions()), AsInt64Slice(output_shape.dimensions())); std::vector source_multidim_index( - ShapeUtil::Rank(input_shape), - llvm::UndefValue::get(builder->getInt64Ty())); + ShapeUtil::Rank(input_shape), llvm::UndefValue::get(index_type_)); // We compute the source indices in each common factor from only the target // indices in the same common factor. for (ssize_t k = common_factors.size() - 2; k >= 0; --k) { llvm::Value* logical_linear_index = Index(tensorflow::gtl::ArraySlice( multidim_, common_factors[k].second, - common_factors[k + 1].second - common_factors[k].second)) + common_factors[k + 1].second - common_factors[k].second), + index_type_) .Linearize( tensorflow::gtl::ArraySlice( AsInt64Slice(output_shape.dimensions()), @@ -150,9 +163,10 @@ IrArray::Index IrArray::Index::SourceIndexOfReshape( // linear index by each dimension size. for (int64 i = common_factors[k + 1].first - 1; i >= common_factors[k].first; --i) { - llvm::Value* divisor = builder->getInt64(input_shape.dimensions(i)); + llvm::Value* divisor = + GetConstantWithIndexType(input_shape.dimensions(i)); if (input_shape.dimensions(i) == 1) { - source_multidim_index[i] = builder->getInt64(0); + source_multidim_index[i] = GetConstantWithIndexType(0); } else if (i == common_factors[k].first) { source_multidim_index[i] = logical_linear_index; } else { @@ -168,14 +182,14 @@ IrArray::Index IrArray::Index::SourceIndexOfReshape( ShapeUtil::ReshapeIsBitcast(input_shape, output_shape)) { return Index(source_multidim_index, linear(), input_shape); } - return Index(source_multidim_index); + return Index(source_multidim_index, index_type_); } IrArray::Index IrArray::Index::SourceIndexOfSlice( const Shape& shape, tensorflow::gtl::ArraySlice starts, tensorflow::gtl::ArraySlice strides, llvm::IRBuilder<>* builder) const { - Index source_index(multidim_.size()); + Index source_index(index_type_, multidim_.size()); for (int i = 0; i < multidim_.size(); ++i) { int64 stride = strides[i]; auto type = multidim_[i]->getType(); @@ -224,11 +238,12 @@ IrArray::Index IrArray::Index::SourceIndexOfBitcast( // the physical index of the element in the buffer. This is like Linearize, // but takes the layout into account. int64 scale = 1; - llvm::Value* linear_index = builder->getInt64(0); + llvm::Value* linear_index = GetConstantWithIndexType(0); for (auto dimension : LayoutUtil::MinorToMajor(shape)) { linear_index = builder->CreateAdd( linear_index, - builder->CreateMul(multidim_[dimension], builder->getInt64(scale), "", + builder->CreateMul(multidim_[dimension], + GetConstantWithIndexType(scale), "", /*HasNUW=*/true, /*HasNSW=*/true), "", /*HasNUW=*/true, /*HasNSW=*/true); scale *= shape.dimensions(dimension); @@ -252,7 +267,7 @@ IrArray::Index IrArray::Index::SourceIndexOfBroadcast( } if (linear_ == nullptr || !LayoutUtil::HasLayout(operand_shape) || !LayoutUtil::HasLayout(shape)) { - return Index(source_index); + return Index(source_index, index_type_); } // High-level idea: we can reuse the linear index if the broadcasted // dimensions are contiguous, and this part of the operation is a bitcast. @@ -274,7 +289,7 @@ IrArray::Index IrArray::Index::SourceIndexOfBroadcast( bool contiguous_broadcast_dimensions = max_broadcasted_dimension - min_broadcasted_dimension == rank - 1; if (!contiguous_broadcast_dimensions) { - return Index(source_index); + return Index(source_index, index_type_); } // Check if the mapped dimensions are a bitcast. std::vector operand_logical_to_physical = @@ -282,7 +297,7 @@ IrArray::Index IrArray::Index::SourceIndexOfBroadcast( for (int64 i = 0; i < rank; ++i) { if (operand_logical_to_physical[i] != logical_to_physical[dimension_mapping[i]] - min_broadcasted_dimension) { - return Index(source_index); + return Index(source_index, index_type_); } } llvm::Value* linear = linear_; @@ -291,7 +306,9 @@ IrArray::Index IrArray::Index::SourceIndexOfBroadcast( divisor *= shape.dimensions(LayoutUtil::Major(shape.layout(), i)); } if (divisor > 1) { - linear = builder->CreateUDiv(linear, builder->getInt64(divisor)); + linear = builder->CreateUDiv( + linear, + IrArray::Index(linear->getType()).GetConstantWithIndexType(divisor)); } if (min_broadcasted_dimension > 0) { int64 mod = 1; @@ -299,7 +316,9 @@ IrArray::Index IrArray::Index::SourceIndexOfBroadcast( ++i) { mod *= shape.dimensions(LayoutUtil::Major(shape.layout(), i)); } - linear = builder->CreateURem(linear, builder->getInt64(mod)); + linear = builder->CreateURem( + linear, + IrArray::Index(linear->getType()).GetConstantWithIndexType(mod)); } return Index(source_index, linear, operand_shape); } @@ -309,12 +328,13 @@ llvm::Value* IrArray::Index::Linearize( llvm::IRBuilder<>* builder) const { // Each dimension is multiplied by the product of the sizes of all // earlier dimensions and added to the accumulator logical_linear_index. - llvm::Value* logical_linear_index = builder->getInt64(0); + llvm::Value* logical_linear_index = GetConstantWithIndexType(0); int64 multiplier = 1; for (ssize_t i = size() - 1; i >= 0; --i) { llvm::Value* addend = - builder->CreateMul((*this)[i], builder->getInt64(multiplier), "", + builder->CreateMul((*this)[i], GetConstantWithIndexType(multiplier), "", /*HasNUW=*/true, /*HasNSW=*/true); + addend = builder->CreateZExtOrTrunc(addend, index_type_); logical_linear_index = builder->CreateAdd(logical_linear_index, addend, "", /*HasNUW=*/true, /*HasNSW=*/true); multiplier *= dimensions[i]; @@ -349,7 +369,8 @@ llvm::Value* IrArray::EmitArrayElementAddress( // index[i] with 0. However, setting index[i] to 0 here still allows LLVM to // produce better code in some cases. auto dim = shape_->dimensions(i); - actual_index.push_back(dim == 1 ? ir_builder->getInt64(0) : index[i]); + actual_index.push_back( + dim == 1 ? llvm::ConstantInt::get(index[i]->getType(), 0) : index[i]); } // "base_ptr_" has the type of "*" @@ -357,7 +378,9 @@ llvm::Value* IrArray::EmitArrayElementAddress( // should be computed by // // getelementptr base_ptr_, 0, most major index, ..., most minor index - std::vector gep_indices(1, ir_builder->getInt64(0)); + CHECK_GT(index.size(), 0); + std::vector gep_indices( + 1, llvm::ConstantInt::get(index[0]->getType(), 0)); for (int64 i = 0; i < LayoutUtil::MinorToMajor(*shape_).size(); ++i) { int64 dimension = LayoutUtil::Major(shape_->layout(), i); gep_indices.push_back(actual_index[dimension]); @@ -410,7 +433,9 @@ IrArray IrArray::CastToShape(const Shape& new_shape, llvm::IRBuilder<>* ir_builder) { Index new_index = index; new_index[which_dimension] = ir_builder->CreateAdd( - index[which_dimension], ir_builder->getInt64(addend), "", /*HasNUW=*/true, + index[which_dimension], + llvm::ConstantInt::get(index[which_dimension]->getType(), addend), "", + /*HasNUW=*/true, /*HasNSW=*/true); return new_index; } diff --git a/tensorflow/compiler/xla/service/llvm_ir/ir_array.h b/tensorflow/compiler/xla/service/llvm_ir/ir_array.h index 4c3195c29c..4648c6d7ac 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/ir_array.h +++ b/tensorflow/compiler/xla/service/llvm_ir/ir_array.h @@ -53,18 +53,38 @@ class IrArray { // multidimensional index, which LLVM DCE can delete. class Index { public: - // Constructs an empty zero-dimensional index. - Index() {} - // Constructs an index of rank "size". Each dimension of the index is // initialized to "value". - explicit Index(size_t size, llvm::Value* value = nullptr) - : multidim_(size, value) {} + explicit Index(size_t size, llvm::Value* value) + : multidim_(size, value), index_type_(value->getType()) { + CHECK_NE(index_type_, nullptr); + } + + // Constructs an index of rank "size". Each dimension of the index is + // initialized to nullptr. + explicit Index(llvm::Type* index_ty, size_t size = 0) + : multidim_(size, nullptr), index_type_(index_ty) { + CHECK(index_ty->isIntegerTy()); + } // Constructs an index from multi-dimensional index "multidim". The linear // index is set to nullptr. - explicit Index(tensorflow::gtl::ArraySlice multidim) - : multidim_(multidim.begin(), multidim.end()) {} + explicit Index(tensorflow::gtl::ArraySlice multidim, + llvm::Type* index_ty = nullptr) + : multidim_(multidim.begin(), multidim.end()) { + if (size() == 0) { + index_type_ = index_ty; + } else { + index_type_ = (*this)[0]->getType(); + if (index_ty != nullptr) { + CHECK_EQ(index_type_, index_ty); + } + } + CHECK_NE(index_type_, nullptr); + CHECK(c_all_of(multidim, [&](llvm::Value* v) { + return index_type_ == v->getType(); + })); + } // Constructs an index from linear index "linear" and computes the // multi-dimensional index from "linear" and "shape". "ir_builder" is the IR @@ -154,6 +174,15 @@ class IrArray { llvm::Value* Linearize(tensorflow::gtl::ArraySlice dimensions, llvm::IRBuilder<>* builder) const; + llvm::Type* GetType() const { return index_type_; } + + llvm::Constant* GetConstantWithIndexType(int64 c) const { + // The LLVM function makes sure that the value can be represented by the + // specified type, see ConstantInt::ConstantInt(IntegerType *Ty, const + // APInt &V). + return llvm::ConstantInt::get(index_type_, c); + } + private: // Changing the multi-dimensional index invalidates the linear index. std::vector& multidim() { @@ -161,6 +190,9 @@ class IrArray { return multidim_; } + void Delinearize(std::vector* multidim, llvm::Value* linear, + const Shape& shape, llvm::IRBuilder<>* ir_builder) const; + std::vector multidim_; // These values are purely for efficiency; `multidim_` is enough to find the @@ -177,6 +209,8 @@ class IrArray { llvm::Value* linear_ = nullptr; Layout layout_; std::vector dims_; + + llvm::Type* index_type_; }; // Default constructor. Constructs an IrArray in a null status. diff --git a/tensorflow/compiler/xla/service/llvm_ir/kernel_support_library.h b/tensorflow/compiler/xla/service/llvm_ir/kernel_support_library.h index e17c649e52..6f7a9d94e3 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/kernel_support_library.h +++ b/tensorflow/compiler/xla/service/llvm_ir/kernel_support_library.h @@ -125,8 +125,8 @@ class KernelSupportLibrary { llvm::Value* is_first_iteration)>& for_body_generator) { return For(name, /*start=*/start, /*end=*/end, - /*step=*/ir_builder_->getInt64(step), peel_first_iteration, - for_body_generator); + /*step=*/llvm::ConstantInt::get(start->getType(), step), + peel_first_iteration, for_body_generator); } void ForReturnVoid(tensorflow::StringPiece name, llvm::Value* start, @@ -135,8 +135,8 @@ class KernelSupportLibrary { llvm::Value* is_first_iteration)>& for_body_generator) { ForReturnVoid(name, /*start=*/start, /*end=*/end, - /*step=*/ir_builder_->getInt64(step), peel_first_iteration, - for_body_generator); + /*step=*/llvm::ConstantInt::get(start->getType(), step), + peel_first_iteration, for_body_generator); } Status For( @@ -165,7 +165,7 @@ class KernelSupportLibrary { tensorflow::StringPiece name, llvm::Value* start, llvm::Value* end, int64 step, const std::function& for_body_generator) { - return For(name, start, end, ir_builder_->getInt64(step), + return For(name, start, end, llvm::ConstantInt::get(start->getType(), step), /*peel_first_iteration=*/false, [&](llvm::Value* indvar, llvm::Value*) -> Status { return for_body_generator(indvar); @@ -176,7 +176,8 @@ class KernelSupportLibrary { tensorflow::StringPiece name, llvm::Value* start, llvm::Value* end, int64 step, const std::function& for_body_generator) { - ForReturnVoid(name, start, end, ir_builder_->getInt64(step), + ForReturnVoid(name, start, end, + llvm::ConstantInt::get(start->getType(), step), for_body_generator); } diff --git a/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.cc b/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.cc index 9f867014fb..c9ae7d3afd 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.cc @@ -97,7 +97,7 @@ void ForLoop::Emit(llvm::IRBuilder<>* ir_builder) { ir_builder->SetInsertPoint(&func->getEntryBlock(), func->getEntryBlock().getFirstInsertionPt()); llvm::Value* indvar_address = - ir_builder->CreateAlloca(ir_builder->getInt64Ty(), nullptr, + ir_builder->CreateAlloca(start_index_->getType(), nullptr, AsStringRef(GetQualifiedName("invar_address"))); // Preheader basic block. @@ -185,7 +185,7 @@ std::unique_ptr ForLoopNest::AddLoop(tensorflow::StringPiece suffix, llvm::Value* end_index, UnrollMode unroll_mode, bool prevent_vectorization) { - return AddLoop(suffix, start_index, end_index, ir_builder_->getInt64(1), + return AddLoop(suffix, start_index, end_index, GetConstantWithIndexType(1), unroll_mode, prevent_vectorization); } @@ -223,8 +223,8 @@ std::unique_ptr ForLoopNest::AddLoop(int64 start_index, UnrollMode unroll_mode, bool prevent_vectorization) { CHECK_LE(start_index, end_index); - return AddLoop(suffix, ir_builder_->getInt64(start_index), - ir_builder_->getInt64(end_index), unroll_mode, + return AddLoop(suffix, GetConstantWithIndexType(start_index), + GetConstantWithIndexType(end_index), unroll_mode, prevent_vectorization); } @@ -234,9 +234,9 @@ std::unique_ptr ForLoopNest::AddLoop(int64 start_index, UnrollMode unroll_mode, bool prevent_vectorization) { CHECK_LE(start_index, end_index); - return AddLoop(suffix, ir_builder_->getInt64(start_index), - ir_builder_->getInt64(end_index), - ir_builder_->getInt64(stride), unroll_mode, + return AddLoop(suffix, GetConstantWithIndexType(start_index), + GetConstantWithIndexType(end_index), + GetConstantWithIndexType(stride), unroll_mode, prevent_vectorization); } @@ -250,7 +250,7 @@ IrArray::Index ForLoopNest::AddLoopsForShape(const Shape& shape, IrArray::Index ForLoopNest::AddLoopsForShapeOnDimensions( const Shape& shape, tensorflow::gtl::ArraySlice dimensions, tensorflow::StringPiece suffix) { - llvm_ir::IrArray::Index index(shape.dimensions_size(), nullptr); + llvm_ir::IrArray::Index index(index_type_, shape.dimensions_size()); for (int64 dimension : dimensions) { std::unique_ptr loop = AddLoop( /*start_index=*/0, diff --git a/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h b/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h index 4e403cd994..0dd5b9d3b2 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h +++ b/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h @@ -177,15 +177,21 @@ class ForLoop { // A simple class for constructing nested for-loops. class ForLoopNest { public: - explicit ForLoopNest(llvm::IRBuilder<>* ir_builder) - : ForLoopNest(/*name=*/"", ir_builder) {} + explicit ForLoopNest(llvm::IRBuilder<>* ir_builder, + llvm::Type* index_ty = nullptr) + : ForLoopNest(/*name=*/"", ir_builder) { + SetIndexType(index_ty); + } - ForLoopNest(tensorflow::StringPiece name, llvm::IRBuilder<>* ir_builder) + ForLoopNest(tensorflow::StringPiece name, llvm::IRBuilder<>* ir_builder, + llvm::Type* index_ty = nullptr) : name_(std::string(name)), outer_loop_preheader_bb_(nullptr), outer_loop_exit_bb_(nullptr), inner_loop_body_bb_(nullptr), - ir_builder_(ir_builder) {} + ir_builder_(ir_builder) { + SetIndexType(index_ty); + } // Adds a loop to the nest. If no loop has been added yet then emit a loop at // the current insert point of the given builder. If one or more loops have @@ -252,6 +258,14 @@ class ForLoopNest { llvm::BasicBlock* GetInnerLoopBodyBasicBlock() { return inner_loop_body_bb_; } private: + void SetIndexType(llvm::Type* index_ty) { + index_type_ = index_ty == nullptr ? ir_builder_->getInt64Ty() : index_ty; + } + + llvm::Constant* GetConstantWithIndexType(int64 c) const { + return llvm::ConstantInt::get(index_type_, c); + } + // Human-friendly name of the loop nest. string name_; @@ -266,6 +280,8 @@ class ForLoopNest { llvm::IRBuilder<>* ir_builder_; + llvm::Type* index_type_; + TF_DISALLOW_COPY_AND_ASSIGN(ForLoopNest); }; diff --git a/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.cc b/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.cc index dc2934a34c..e8b0605b9d 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.cc @@ -90,11 +90,12 @@ LoopEmitter::LoopEmitter(const ElementGenerator& target_element_generator, } std::vector LoopEmitter::EmitIndexAndSetExitBasicBlock( - tensorflow::StringPiece loop_name) { + tensorflow::StringPiece loop_name, llvm::Type* index_type) { + CHECK_NE(index_type, nullptr); if (ShapeUtil::IsScalar(shape_)) { // No loop needed, so set exit_bb_ to nullptr. exit_bb_ = nullptr; - return {IrArray::Index()}; + return {IrArray::Index(index_type)}; } // Create loop nest with one for-loop for each dimension of the target shape. @@ -102,7 +103,7 @@ std::vector LoopEmitter::EmitIndexAndSetExitBasicBlock( // class so emit loops in order from most-major dimension down to most-minor // dimension (of the target shape). ForLoopNest loop_nest(loop_name, ir_builder_); - IrArray::Index array_index(shape_.dimensions_size()); + IrArray::Index array_index(index_type, shape_.dimensions_size()); for (int i = 0; i < LayoutUtil::MinorToMajor(shape_).size(); ++i) { int64 dimension = LayoutUtil::Major(shape_.layout(), i); std::unique_ptr loop = loop_nest.AddLoop( @@ -125,9 +126,14 @@ std::vector LoopEmitter::EmitIndexAndSetExitBasicBlock( return {array_index}; } -Status LoopEmitter::EmitLoop(tensorflow::StringPiece loop_name) { +Status LoopEmitter::EmitLoop(tensorflow::StringPiece loop_name, + llvm::Type* index_type) { + if (index_type == nullptr) { + index_type = ir_builder_->getInt64Ty(); + } + for (const IrArray::Index& array_index : - EmitIndexAndSetExitBasicBlock(loop_name)) { + EmitIndexAndSetExitBasicBlock(loop_name, index_type)) { TF_RETURN_IF_ERROR(body_emitter_(array_index)); } diff --git a/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.h b/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.h index b70d28ecd3..6be1c2fba2 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.h +++ b/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.h @@ -65,13 +65,16 @@ class LoopEmitter { // specifies the element, will return multiple indices if the loop is // unrolled. std::vector EmitIndexAndSetExitBasicBlock() { - return EmitIndexAndSetExitBasicBlock(/*loop_name=*/""); + return EmitIndexAndSetExitBasicBlock(/*loop_name=*/"", + ir_builder_->getInt64Ty()); } + virtual std::vector EmitIndexAndSetExitBasicBlock( - tensorflow::StringPiece loop_name); + tensorflow::StringPiece loop_name, llvm::Type* index_type); // Emits a complete loop nest for every element in the given shape. - Status EmitLoop(tensorflow::StringPiece loop_name = ""); + Status EmitLoop(tensorflow::StringPiece loop_name = "", + llvm::Type* index_type = nullptr); protected: // An IR emitter that generates the loop body. diff --git a/tensorflow/compiler/xla/service/llvm_ir/ops.cc b/tensorflow/compiler/xla/service/llvm_ir/ops.cc index dacc54742c..3b298f4746 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/ops.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/ops.cc @@ -45,7 +45,7 @@ static Status EmitDynamicUpdateSliceInPlaceImpl( // Read start indices from start_indices_generator. const int64 rank = ShapeUtil::Rank(output_shape); - IrArray::Index start_index(rank); + IrArray::Index start_index(ir_builder->getInt64Ty(), rank); for (int64 i = 0; i < rank; ++i) { IrArray::Index dim_index({ir_builder->getInt64(i)}); TF_ASSIGN_OR_RETURN(start_index[i], start_indices_generator(dim_index)); @@ -79,7 +79,7 @@ static Status EmitDynamicUpdateSliceInPlaceImpl( // // output_index[dim] = start_index[dim] + update_index[dim] // - IrArray::Index output_index(rank); + IrArray::Index output_index(start_index.GetType(), rank); for (int64 i = 0; i < rank; ++i) { llvm::Value* start_index0 = ir_builder->CreateSExtOrBitCast( start_index[i], update_index[i]->getType()); -- GitLab From 89e0ce6c9162dee74df714d3b1352172faaec6bc Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 20 Jun 2018 03:28:03 -0700 Subject: [PATCH 722/816] Improvements in the documentation of tf.random_gamma, tf.random_poisson and tf.distributions. * Marked the Python code in docstrings. * Fixed the output shapes in docstrings. * Fixed a typo in the normalization constant in tf.distributions.Gamma docstring. * Updated the warning in tf.distributions.Gamma docstring. * Added warnings regarding zero samples in tf.distributions.Beta and tf.distributions.Dirichlet docstrings. PiperOrigin-RevId: 201328305 --- tensorflow/python/ops/distributions/beta.py | 5 ++++ .../python/ops/distributions/dirichlet.py | 5 ++++ tensorflow/python/ops/distributions/gamma.py | 9 +++++--- tensorflow/python/ops/random_ops.py | 23 +++++++++++-------- 4 files changed, 29 insertions(+), 13 deletions(-) diff --git a/tensorflow/python/ops/distributions/beta.py b/tensorflow/python/ops/distributions/beta.py index f28f76b6c4..0d8a75ce23 100644 --- a/tensorflow/python/ops/distributions/beta.py +++ b/tensorflow/python/ops/distributions/beta.py @@ -84,6 +84,11 @@ class Beta(distribution.Distribution): Distribution parameters are automatically broadcast in all functions; see examples for details. + Warning: The samples can be zero due to finite precision. + This happens more often when some of the concentrations are very small. + Make sure to round the samples to `np.finfo(dtype).tiny` before computing the + density. + #### Examples ```python diff --git a/tensorflow/python/ops/distributions/dirichlet.py b/tensorflow/python/ops/distributions/dirichlet.py index 2dba61d43b..d45a05063b 100644 --- a/tensorflow/python/ops/distributions/dirichlet.py +++ b/tensorflow/python/ops/distributions/dirichlet.py @@ -90,6 +90,11 @@ class Dirichlet(distribution.Distribution): Distribution parameters are automatically broadcast in all functions; see examples for details. + Warning: Some components of the samples can be zero due to finite precision. + This happens more often when some of the concentrations are very small. + Make sure to round the samples to `np.finfo(dtype).tiny` before computing the + density. + #### Examples ```python diff --git a/tensorflow/python/ops/distributions/gamma.py b/tensorflow/python/ops/distributions/gamma.py index 163a27f758..4f05b58fdb 100644 --- a/tensorflow/python/ops/distributions/gamma.py +++ b/tensorflow/python/ops/distributions/gamma.py @@ -55,7 +55,7 @@ class Gamma(distribution.Distribution): ```none pdf(x; alpha, beta, x > 0) = x**(alpha - 1) exp(-x beta) / Z - Z = Gamma(alpha) beta**alpha + Z = Gamma(alpha) beta**(-alpha) ``` where: @@ -85,8 +85,11 @@ class Gamma(distribution.Distribution): Distribution parameters are automatically broadcast in all functions; see examples for details. - WARNING: This distribution may draw 0-valued samples for small `concentration` - values. See note in `tf.random_gamma` docstring. + Warning: The samples of this distribution are always non-negative. However, + the samples that are smaller than `np.finfo(dtype).tiny` are rounded + to this value, so it appears more often than it should. + This should only be noticeable when the `concentration` is very small, or the + `rate` is very large. See note in `tf.random_gamma` docstring. #### Examples diff --git a/tensorflow/python/ops/random_ops.py b/tensorflow/python/ops/random_ops.py index ad154d204e..b8738adf66 100644 --- a/tensorflow/python/ops/random_ops.py +++ b/tensorflow/python/ops/random_ops.py @@ -422,8 +422,9 @@ def random_gamma(shape, name: Optional name for the operation. Returns: - samples: a `Tensor` of shape `tf.concat(shape, tf.shape(alpha + beta))` - with values of type `dtype`. + samples: a `Tensor` of shape + `tf.concat([shape, tf.shape(alpha + beta)], axis=0)` with values of type + `dtype`. """ with ops.name_scope(name, "random_gamma", [shape, alpha, beta]): shape = ops.convert_to_tensor(shape, name="shape", dtype=dtypes.int32) @@ -446,13 +447,15 @@ def random_poisson(lam, shape, dtype=dtypes.float32, seed=None, name=None): Example: - samples = tf.random_poisson([0.5, 1.5], [10]) - # samples has shape [10, 2], where each slice [:, 0] and [:, 1] represents - # the samples drawn from each distribution + ```python + samples = tf.random_poisson([0.5, 1.5], [10]) + # samples has shape [10, 2], where each slice [:, 0] and [:, 1] represents + # the samples drawn from each distribution - samples = tf.random_poisson([12.2, 3.3], [7, 5]) - # samples has shape [7, 5, 2], where each slice [:, :, 0] and [:, :, 1] - # represents the 7x5 samples drawn from each of the two distributions + samples = tf.random_poisson([12.2, 3.3], [7, 5]) + # samples has shape [7, 5, 2], where each slice [:, :, 0] and [:, :, 1] + # represents the 7x5 samples drawn from each of the two distributions + ``` Args: lam: A Tensor or Python value or N-D array of type `dtype`. @@ -469,8 +472,8 @@ def random_poisson(lam, shape, dtype=dtypes.float32, seed=None, name=None): name: Optional name for the operation. Returns: - samples: a `Tensor` of shape `tf.concat(shape, tf.shape(lam))` with - values of type `dtype`. + samples: a `Tensor` of shape `tf.concat([shape, tf.shape(lam)], axis=0)` + with values of type `dtype`. """ with ops.name_scope(name, "random_poisson", [lam, shape]): shape = ops.convert_to_tensor(shape, name="shape", dtype=dtypes.int32) -- GitLab From 18fd25c19c5c7111d1ba4a1c58718b87a63ad82c Mon Sep 17 00:00:00 2001 From: Adrian Kuegel Date: Wed, 20 Jun 2018 05:17:28 -0700 Subject: [PATCH 723/816] [TF:XLA] Bump open source llvm revision to r335074 PiperOrigin-RevId: 201337140 --- tensorflow/workspace.bzl | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index 019f446b15..b32d473219 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -451,11 +451,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "llvm", urls = [ - "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/21cf43199f6e79fcc345d177c8740d392f0b898e.tar.gz", - "https://github.com/llvm-mirror/llvm/archive/21cf43199f6e79fcc345d177c8740d392f0b898e.tar.gz", + "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/a587557962e93552e1a8b9270b435b021891e9cd.tar.gz", + "https://github.com/llvm-mirror/llvm/archive/a587557962e93552e1a8b9270b435b021891e9cd.tar.gz", ], - sha256 = "c8ceb180ce51e00e047061dac48f014e5430ac33ea2447029065f922119b122c", - strip_prefix = "llvm-21cf43199f6e79fcc345d177c8740d392f0b898e", + sha256 = "5cf25652e8913e88ce2fb02f1186affd25cf5c1cb2146f9754881daaf3450ddb", + strip_prefix = "llvm-a587557962e93552e1a8b9270b435b021891e9cd", build_file = clean_dep("//third_party/llvm:llvm.autogenerated.BUILD"), ) -- GitLab From 352461a3228b13a6b5cc511487580ab4878d07dc Mon Sep 17 00:00:00 2001 From: Adrian Kuegel Date: Wed, 20 Jun 2018 05:47:25 -0700 Subject: [PATCH 724/816] Simplify ConvertLiteralToIrConstant() Also use ConstantDataArray for C64 types. This allows to delete the old LiteralToDataConstant() method. PiperOrigin-RevId: 201339634 --- .../compiler/xla/service/llvm_ir/llvm_util.cc | 165 +----------------- 1 file changed, 7 insertions(+), 158 deletions(-) diff --git a/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc b/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc index d18c9dee82..e61a2fd12d 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc @@ -249,167 +249,16 @@ StatusOr DecodeSelfDescribingShapeConstant(const void* shape_ptr, return shape; } -namespace { - -// Recursively construct a multidimensional LLVM constant which represents the -// given literal. The minor-to-major dimension ordering in the constant matches -// that of the literal. For example, given a [2 x 3 x 4] Literal (dimension 0 -// has size 4, dimension 1 has size 3, etc) of primitive type F32 with a -// minor_to_major value of [2, 1, 0] (column major), a LLVM constant of type -// [4 x [3 x [2 x float]] will be returned. -// -// multi_index is a multidimensional index into the array. dimension_index is an -// index into the minor_to_major field in the literal shape. This determines -// which dimension is iterated over in this level of the recursion. Dimensions -// are iterated from most major down to most minor (highest dimension_index -// value down to zero). -llvm::Constant* LiteralToConstant(const Literal& literal, int64 dimension_index, - std::vector* multi_index, - llvm::Module* module) { - const Shape& shape = literal.shape(); - llvm::Type* ir_element_type = - llvm_ir::PrimitiveTypeToIrType(shape.element_type(), module); - if (dimension_index == -1) { - // Base case of the recursion. Index into the data field of the protobuf - // with the multi index. - llvm::Constant* value; - switch (shape.element_type()) { - case PRED: - value = llvm::ConstantInt::get(ir_element_type, - literal.Get(*multi_index)); - break; - case U8: - value = llvm::ConstantInt::get(ir_element_type, - literal.Get(*multi_index)); - break; - case S32: - value = llvm::ConstantInt::get(ir_element_type, - literal.Get(*multi_index)); - break; - case U32: - value = llvm::ConstantInt::get(ir_element_type, - literal.Get(*multi_index)); - break; - case S64: - value = llvm::ConstantInt::get(ir_element_type, - literal.Get(*multi_index)); - break; - case U64: - value = llvm::ConstantInt::get(ir_element_type, - literal.Get(*multi_index)); - break; - case F32: - value = llvm::ConstantFP::get(ir_element_type, - literal.Get(*multi_index)); - break; - case BF16: - value = llvm::ConstantInt::get( - ir_element_type, - tensorflow::bit_cast(literal.Get(*multi_index))); - break; - case F16: - value = llvm::ConstantFP::get( - ir_element_type, - static_cast(literal.Get(*multi_index))); - break; - case F64: - value = llvm::ConstantFP::get(ir_element_type, - literal.Get(*multi_index)); - break; - case C64: { - complex64 x = literal.Get(*multi_index); - value = llvm::ConstantStruct::get( - static_cast(ir_element_type), - llvm::ConstantFP::get(llvm_ir::PrimitiveTypeToIrType(F32, module), - x.real()), - llvm::ConstantFP::get(llvm_ir::PrimitiveTypeToIrType(F32, module), - x.imag())); - break; - } - default: - LOG(FATAL) << "unsupported type " << shape.element_type(); - } - return value; - } - - // The dimension index starts at the one less than the rank of the array and - // decrements with each recursive call. We want to iterate through the - // dimensions in major-to-minor order as we recurse so just index into - // minor_to_major to get the dimension number for this level of the recursion. - int64 dimension = LayoutUtil::Minor(shape.layout(), dimension_index); - - // Recursively call LiteralToConstant to construct subarrays for the - // more-minor dimensions. Gather the subarrays into a vector for bundling into - // a new (higher-dimensional) ConstantArray. - std::vector elements; - for (int64 i = 0; i < shape.dimensions(dimension); ++i) { - (*multi_index)[dimension] = i; - elements.push_back( - LiteralToConstant(literal, dimension_index - 1, multi_index, module)); - } - - llvm::Type* element_type; - if (elements.empty()) { - element_type = ir_element_type; - for (int i = 0; i < dimension_index; ++i) { - int64 index = LayoutUtil::Minor(shape.layout(), i); - element_type = - llvm::ArrayType::get(element_type, shape.dimensions(index)); - } - } else { - element_type = elements[0]->getType(); - } - llvm::ArrayType* aggregate_type = - llvm::ArrayType::get(element_type, shape.dimensions(dimension)); - return llvm::ConstantArray::get(aggregate_type, elements); -} - -template -llvm::Constant* GetConstantDataArray(const Literal& literal, - llvm::Module* module) { - const T* data = static_cast(literal.untyped_data()); - int64 num_elements = literal.size_bytes() / sizeof(T); - return llvm::ConstantDataArray::get(module->getContext(), - llvm::makeArrayRef(data, num_elements)); -} - -} // namespace - llvm::Constant* ConvertLiteralToIrConstant(const Literal& literal, llvm::Module* module) { const Shape& shape = literal.shape(); - // TODO(b/29904935): We can get rid of this switch by exposing a - // ConstantDataArray factory method that takes a llvm::Type and a StringRef. - switch (shape.element_type()) { - case U64: - return GetConstantDataArray(literal, module); - case U32: - return GetConstantDataArray(literal, module); - case U8: - return GetConstantDataArray(literal, module); - case S64: - return GetConstantDataArray(literal, module); - case S32: - return GetConstantDataArray(literal, module); - case F64: - return GetConstantDataArray(literal, module); - case F32: - return GetConstantDataArray(literal, module); - case BF16: - case F16: - return GetConstantDataArray(literal, module); - case PRED: - return GetConstantDataArray(literal, module); - // TODO(b/29904935): Also use ConstantDataArray for complex numbers. - case C64: { - int64 dimensions = ShapeUtil::Rank(shape); - std::vector multi_index(dimensions, 0); - return LiteralToConstant(literal, /*dimension_index=*/dimensions - 1, - &multi_index, module); - } - default: - LOG(FATAL) << "unsupported type " << shape.element_type(); - } + llvm::Type* type = shape.element_type() == C64 + ? llvm::Type::getFloatTy(module->getContext()) + : PrimitiveTypeToIrType(shape.element_type(), module); + const char* data = static_cast(literal.untyped_data()); + uint64 num_elements = literal.size_bytes() * 8 / GetSizeInBits(type); + return llvm::ConstantDataArray::getRaw( + llvm::StringRef(data, literal.size_bytes()), num_elements, type); } llvm::AllocaInst* EmitAllocaAtFunctionEntry(llvm::Type* type, -- GitLab From 55e70e54085c4b355376dc7d3218f2d0f75dd7e2 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 20 Jun 2018 07:58:33 -0700 Subject: [PATCH 725/816] Make common_runtime/eager libraries compile for Android, by eliding the dependency on GRPC. PiperOrigin-RevId: 201353152 --- tensorflow/core/common_runtime/eager/BUILD | 144 +++++++++++------- .../core/common_runtime/eager/context.cc | 8 +- .../core/common_runtime/eager/context.h | 13 +- .../core/common_runtime/eager/execute.cc | 28 ++-- tensorflow/core/platform/fingerprint.h | 2 +- 5 files changed, 124 insertions(+), 71 deletions(-) diff --git a/tensorflow/core/common_runtime/eager/BUILD b/tensorflow/core/common_runtime/eager/BUILD index 671cd142fb..7f28f3b793 100644 --- a/tensorflow/core/common_runtime/eager/BUILD +++ b/tensorflow/core/common_runtime/eager/BUILD @@ -22,14 +22,19 @@ tf_cuda_library( "eager_executor.h", ], visibility = ["//tensorflow:internal"], - deps = [ - "//tensorflow/core:core_cpu_lib", - "//tensorflow/core:framework", - "//tensorflow/core:framework_internal", - "//tensorflow/core:lib", - "//tensorflow/core:lib_internal", - "//tensorflow/core:protos_all_cc", - ], + deps = select({ + "//tensorflow:android": [ + "//tensorflow/core:android_tensorflow_lib_lite", + ], + "//conditions:default": [ + "//tensorflow/core:core_cpu_lib", + "//tensorflow/core:framework", + "//tensorflow/core:framework_internal", + "//tensorflow/core:lib", + "//tensorflow/core:lib_internal", + "//tensorflow/core:protos_all_cc", + ], + }), ) tf_cuda_library( @@ -44,17 +49,23 @@ tf_cuda_library( deps = [ ":eager_executor", ":kernel_and_device", - "//tensorflow/core:core_cpu_lib", - "//tensorflow/core:framework", - "//tensorflow/core:framework_internal", - "//tensorflow/core:lib", - "//tensorflow/core:lib_internal", - "//tensorflow/core:protos_all_cc", - "//tensorflow/core:session_options", - "//tensorflow/core/distributed_runtime:server_lib", - "//tensorflow/core/distributed_runtime:worker_session", - "//tensorflow/core/distributed_runtime/eager:eager_client", - ], + ] + select({ + "//tensorflow:android": [ + "//tensorflow/core:android_tensorflow_lib_lite", + ], + "//conditions:default": [ + "//tensorflow/core:core_cpu_lib", + "//tensorflow/core:framework", + "//tensorflow/core:framework_internal", + "//tensorflow/core:lib", + "//tensorflow/core:lib_internal", + "//tensorflow/core:protos_all_cc", + "//tensorflow/core:session_options", + "//tensorflow/core/distributed_runtime:server_lib", + "//tensorflow/core/distributed_runtime:worker_session", + "//tensorflow/core/distributed_runtime/eager:eager_client", + ], + }), ) tf_cuda_library( @@ -86,14 +97,20 @@ tf_cuda_library( ":context", ":eager_executor", ":kernel_and_device", - "//tensorflow/core:core_cpu_lib", - "//tensorflow/core:framework", - "//tensorflow/core:framework_internal", - "//tensorflow/core:lib", - "//tensorflow/core:lib_internal", - "//tensorflow/core:protos_all_cc", - "//tensorflow/core:session_options", - ], + ] + select({ + "//tensorflow:android": [ + "//tensorflow/core:android_tensorflow_lib_lite", + ], + "//conditions:default": [ + "//tensorflow/core:core_cpu_lib", + "//tensorflow/core:framework", + "//tensorflow/core:framework_internal", + "//tensorflow/core:lib", + "//tensorflow/core:lib_internal", + "//tensorflow/core:protos_all_cc", + "//tensorflow/core:session_options", + ], + }), ) tf_cuda_library( @@ -106,14 +123,19 @@ tf_cuda_library( ":context", ":eager_executor", ":tensor_handle", - "//tensorflow/core:core_cpu_lib", - "//tensorflow/core:framework", - "//tensorflow/core:framework_internal", - "//tensorflow/core:lib", - "//tensorflow/core:lib_internal", - "//tensorflow/core:protos_all_cc", - "//tensorflow/core:session_options", - ], + ] + select({ + "//tensorflow:android": [ + ], + "//conditions:default": [ + "//tensorflow/core:core_cpu_lib", + "//tensorflow/core:framework", + "//tensorflow/core:framework_internal", + "//tensorflow/core:lib", + "//tensorflow/core:lib_internal", + "//tensorflow/core:protos_all_cc", + "//tensorflow/core:session_options", + ], + }), ) tf_cuda_library( @@ -125,14 +147,20 @@ tf_cuda_library( "kernel_and_device.h", ], visibility = ["//tensorflow:internal"], - deps = [ - "//tensorflow/core:core_cpu_lib", - "//tensorflow/core:framework", - "//tensorflow/core:framework_internal", - "//tensorflow/core:lib", - "//tensorflow/core:lib_internal", - "//tensorflow/core:protos_all_cc", - ], + deps = select({ + "//tensorflow:android": [ + "//tensorflow/core:android_tensorflow_lib_lite", + "//util/hash:farmhash_fingerprint", + ], + "//conditions:default": [ + "//tensorflow/core:core_cpu_lib", + "//tensorflow/core:framework", + "//tensorflow/core:framework_internal", + "//tensorflow/core:lib", + "//tensorflow/core:lib_internal", + "//tensorflow/core:protos_all_cc", + ], + }), ) tf_cc_test( @@ -168,14 +196,20 @@ cc_library( ":eager_operation", ":kernel_and_device", ":tensor_handle", - "//tensorflow/core:core_cpu_lib", - "//tensorflow/core:framework", - "//tensorflow/core:lib", - "//tensorflow/core:lib_internal", - "//tensorflow/core:protos_all_cc", - "//tensorflow/core/distributed_runtime/eager:eager_client", - "//tensorflow/core/distributed_runtime/eager:remote_execute_node", - ], + ] + select({ + "//tensorflow:android": [ + "//tensorflow/core:android_tensorflow_lib_lite", + ], + "//conditions:default": [ + "//tensorflow/core:core_cpu_lib", + "//tensorflow/core:framework", + "//tensorflow/core:lib", + "//tensorflow/core:lib_internal", + "//tensorflow/core:protos_all_cc", + "//tensorflow/core/distributed_runtime/eager:eager_client", + "//tensorflow/core/distributed_runtime/eager:remote_execute_node", + ], + }), ) tf_cuda_library( @@ -183,13 +217,15 @@ tf_cuda_library( srcs = ["attr_builder.cc"], hdrs = ["attr_builder.h"], visibility = ["//tensorflow:internal"], - deps = select({ + deps = [ + ":kernel_and_device", + "//tensorflow/c:c_api", + ] + select({ "//tensorflow:android": [ "//tensorflow/core:android_tensorflow_lib_lite", + "//util/hash:farmhash_fingerprint", ], "//conditions:default": [ - ":kernel_and_device", - "//tensorflow/c:c_api", "//tensorflow/core:core_cpu", "//tensorflow/core:core_cpu_internal", "//tensorflow/core:framework", diff --git a/tensorflow/core/common_runtime/eager/context.cc b/tensorflow/core/common_runtime/eager/context.cc index cb9ee668cf..8a87ba7a19 100644 --- a/tensorflow/core/common_runtime/eager/context.cc +++ b/tensorflow/core/common_runtime/eager/context.cc @@ -38,6 +38,7 @@ EagerContext::EagerContext(const SessionOptions& opts, InitDeviceMapAndAsync(); } +#ifndef __ANDROID__ EagerContext::EagerContext( const SessionOptions& opts, ContextDevicePlacementPolicy default_policy, bool async, DeviceMgr* local_device_mgr, Rendezvous* rendezvous, @@ -55,12 +56,13 @@ EagerContext::EagerContext( &func_lib_def_, {}, thread_pool_.get())), log_device_placement_(opts.config.log_device_placement()), async_default_(async), + remote_device_manager_(std::move(remote_device_manager)), server_(std::move(server)), remote_eager_workers_(std::move(remote_eager_workers)), - remote_device_manager_(std::move(remote_device_manager)), remote_contexts_(remote_contexts) { InitDeviceMapAndAsync(); } +#endif void EagerContext::InitDeviceMapAndAsync() { if (async_default_) { @@ -125,6 +127,7 @@ ContextDevicePlacementPolicy EagerContext::GetDevicePlacementPolicy() { } EagerContext::~EagerContext() { +#ifndef __ANDROID__ if (server_) { // TODO(nareshmodi): Fix this. LOG(WARNING) << "Unable to destroy server_ object, so releasing instead. " @@ -158,6 +161,7 @@ EagerContext::~EagerContext() { } counter.Wait(); +#endif executor_.WaitForAllPendingNodes().IgnoreError(); ClearCaches(); @@ -224,6 +228,7 @@ Status GetTaskName(Device* d, string* task_name) { } } // namespace +#ifndef __ANDROID__ Status EagerContext::GetClientAndContextID(Device* device, eager::EagerClient** client, uint64* context_id) { @@ -253,5 +258,6 @@ Status EagerContext::GetClientAndContextID(Device* device, return Status::OK(); } +#endif } // namespace tensorflow diff --git a/tensorflow/core/common_runtime/eager/context.h b/tensorflow/core/common_runtime/eager/context.h index 3766299826..601b9e4545 100644 --- a/tensorflow/core/common_runtime/eager/context.h +++ b/tensorflow/core/common_runtime/eager/context.h @@ -29,8 +29,10 @@ limitations under the License. #include "tensorflow/core/common_runtime/eager/kernel_and_device.h" #include "tensorflow/core/common_runtime/function.h" #include "tensorflow/core/common_runtime/rendezvous_mgr.h" +#ifndef __ANDROID__ #include "tensorflow/core/distributed_runtime/eager/eager_client.h" #include "tensorflow/core/distributed_runtime/server_lib.h" +#endif #include "tensorflow/core/framework/rendezvous.h" #include "tensorflow/core/lib/core/stringpiece.h" #include "tensorflow/core/lib/core/threadpool.h" @@ -82,6 +84,7 @@ class EagerContext { // - remote_device_mgr: A DeviceMgr* which contains all remote devices // (should contain no local devices). // - remote_contexts: A map containing task name to remote context ID. +#ifndef __ANDROID__ explicit EagerContext( const SessionOptions& opts, ContextDevicePlacementPolicy default_policy, bool async, DeviceMgr* local_device_mgr, Rendezvous* rendezvous, @@ -89,7 +92,7 @@ class EagerContext { std::unique_ptr remote_eager_workers, std::unique_ptr remote_device_manager, const gtl::FlatMap& remote_contexts); - +#endif ~EagerContext(); // Returns the function library runtime for the given device. @@ -174,9 +177,10 @@ class EagerContext { FunctionLibraryDefinition* FuncLibDef() { return &func_lib_def_; } +#ifndef __ANDROID__ Status GetClientAndContextID(Device* device, eager::EagerClient** client, uint64* context_id); - +#endif private: void InitDeviceMapAndAsync(); @@ -228,16 +232,19 @@ class EagerContext { std::unordered_map thread_local_async_ GUARDED_BY(async_map_mu_); + const std::unique_ptr remote_device_manager_; + // The server_ is not const since we release it when the context is destroyed. // Therefore the server_ object is not marked as const (even though it should // be). +#ifndef __ANDROID__ std::unique_ptr server_; const std::unique_ptr remote_eager_workers_; - const std::unique_ptr remote_device_manager_; const gtl::FlatMap remote_contexts_; gtl::FlatMap> device_to_client_cache_; +#endif }; } // namespace tensorflow diff --git a/tensorflow/core/common_runtime/eager/execute.cc b/tensorflow/core/common_runtime/eager/execute.cc index 08abded4e4..14aa520e19 100644 --- a/tensorflow/core/common_runtime/eager/execute.cc +++ b/tensorflow/core/common_runtime/eager/execute.cc @@ -24,8 +24,10 @@ limitations under the License. #include "tensorflow/core/common_runtime/eager/execute_node.h" #include "tensorflow/core/common_runtime/eager/kernel_and_device.h" #include "tensorflow/core/common_runtime/eager/tensor_handle.h" +#ifndef __ANDROID__ #include "tensorflow/core/distributed_runtime/eager/eager_client.h" #include "tensorflow/core/distributed_runtime/eager/remote_execute_node.h" +#endif #include "tensorflow/core/framework/step_stats.pb.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/framework/types.h" @@ -573,9 +575,19 @@ Status EagerLocalExecute(EagerOperation* op, return status; } -Status EagerRemoteExecute(EagerOperation* op, eager::EagerClient* eager_client, - uint64 context_id, TensorHandle** retvals, +Status EagerRemoteExecute(EagerOperation* op, TensorHandle** retvals, int* num_retvals) { +#ifdef __ANDROID__ + return errors::Unimplemented( + "Eager's remote execution is not available on Android devices."); +#else + EagerContext* ctx = op->EagerContext(); + + eager::EagerClient* eager_client; + uint64 context_id; + TF_RETURN_IF_ERROR( + ctx->GetClientAndContextID(op->Device(), &eager_client, &context_id)); + eager::EnqueueRequest request; eager::EnqueueResponse response; @@ -636,7 +648,6 @@ Status EagerRemoteExecute(EagerOperation* op, eager::EagerClient* eager_client, } tensorflow::Device* op_device = op->Device(); - EagerContext* ctx = op->EagerContext(); const tensorflow::uint64 id = remote_op->id(); for (int i = 0; i < *num_retvals; i++) { @@ -671,6 +682,7 @@ Status EagerRemoteExecute(EagerOperation* op, eager::EagerClient* eager_client, } return Status::OK(); +#endif } } // namespace @@ -683,15 +695,7 @@ Status EagerExecute(EagerOperation* op, return EagerLocalExecute(op, retvals, num_retvals); } - auto* ctx = op->EagerContext(); - - tensorflow::eager::EagerClient* eager_client; - tensorflow::uint64 context_id; - TF_RETURN_IF_ERROR( - ctx->GetClientAndContextID(op->Device(), &eager_client, &context_id)); - - return EagerRemoteExecute(op, eager_client, context_id, retvals->data(), - num_retvals); + return EagerRemoteExecute(op, retvals->data(), num_retvals); } Status EagerExecute(EagerContext* ctx, Device* device, diff --git a/tensorflow/core/platform/fingerprint.h b/tensorflow/core/platform/fingerprint.h index b47dcdedd7..720dc4c3d6 100644 --- a/tensorflow/core/platform/fingerprint.h +++ b/tensorflow/core/platform/fingerprint.h @@ -74,7 +74,7 @@ inline uint64 FingerprintCat64(const uint64 fp1, const uint64 fp2) { } // namespace tensorflow -#if defined(PLATFORM_GOOGLE) +#if defined(PLATFORM_GOOGLE) || defined(PLATFORM_GOOGLE_ANDROID) #include "tensorflow/core/platform/google/fingerprint.h" #else #include "tensorflow/core/platform/default/fingerprint.h" -- GitLab From 33f6dabc581f02e7724597f03999b19ad5890f67 Mon Sep 17 00:00:00 2001 From: gracehoney <31743510+aaroey@users.noreply.github.com> Date: Wed, 20 Jun 2018 08:05:24 -0700 Subject: [PATCH 726/816] Add some more comments and fix some TODOs --- .../contrib/tensorrt/convert/convert_graph.cc | 35 +++++++++++-------- .../contrib/tensorrt/convert/convert_nodes.cc | 2 +- .../contrib/tensorrt/kernels/trt_engine_op.cc | 3 +- 3 files changed, 24 insertions(+), 16 deletions(-) diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.cc b/tensorflow/contrib/tensorrt/convert/convert_graph.cc index eac46f679e..3113bdc2c5 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_graph.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_graph.cc @@ -249,13 +249,16 @@ EngineInfo GetEngineInfo( std::set segment_devices; int input_port = 0; int output_port = 0; - // Each input can have only one incoming edge, outputs can have multiple edges - // though since we are keeping outside name, this can only fail in case of 2 - // op loops in the graph. + + // Map from src_node_name+port to the unique port numbers of the TRT op, where + // the src_node_name is the name of the source node of the input/output + // edge, thus there must not be any duplicates since source nodes of + // input/output edges must be in different split of the graph. + // TODO(aaroey): consider using node id and port instead. std::unordered_map created_edges; for (auto it = reverse_topo_order.rbegin(); it != reverse_topo_order.rend(); ++it) { - auto node_name = (*it)->name(); + const auto& node_name = (*it)->name(); if (segment_nodes.count(node_name) == 0) continue; auto node = node_map.at(node_name); @@ -337,7 +340,8 @@ EngineInfo GetEngineInfo( return info; } -// Function to insert a TRT node into the graph. +// Function to insert a TRT node into the graph. The graph is not modified if +// the returned status is not ok. // 'alloc' is only used for creating static engine. tensorflow::Status CreateTRTNode(tensorflow::Graph* graph, const std::vector& infos, int pos, @@ -381,7 +385,10 @@ tensorflow::Status CreateTRTNode(tensorflow::Graph* graph, string input_node = conn.outside_node_name; int input_port = conn.outside_port; bool found_engine = false; - // Rewire the inputs to other engines if they contain original input node + // Rewire the inputs to other engines if they contain original input node. + // Note that we use the information of the engine here, not the information + // of the created TRT nodes, so we're able to find all the connections to + // any other engines beforehand. for (size_t t = 0; t < infos.size(); ++t) { if (t == pos) continue; auto& engine_info = infos.at(t); @@ -440,6 +447,8 @@ tensorflow::Status CreateTRTNode(tensorflow::Graph* graph, segment_string = string((const char*)engine_data->data(), engine_data->size()); if (info.precision_mode == INT8MODE) { + // See above comment on the reason why not putting this inside the 'else' + // branch. segment_string = info.segment_graph_def.SerializeAsString(); } } else { @@ -501,7 +510,7 @@ tensorflow::Status CreateTRTNode(tensorflow::Graph* graph, } VLOG(1) << "Adding TRTEngine " << info.engine_name << " to graph"; - // up until this point, graph is not modified. If we return !status.ok() from + // Up until this point, graph is not modified. If we return !status.ok() from // here, this segment will be skipped tensorflow::Node* engine_node = graph->AddNode(trt_node, &status); if (!status.ok()) { @@ -520,18 +529,15 @@ tensorflow::Status CreateTRTNode(tensorflow::Graph* graph, // In this case, other engines input edge is updated in nodedef to point to // this engine. Even though edge doesn't exists in the graph, when it is // deserialized again, correct edges will be constructed. This is a problem - // of graph. + // of graph->AddNode(). if (!dst_node) continue; VLOG(1) << "Updating " << engine_node->name() << ":" << conn.port_number << " to " << dst_node->name() << ":" << conn.outside_port; auto new_edge = graph->AddEdge(engine_node, conn.port_number, dst_node, conn.outside_port); - // this should never happen! - if (!new_edge) { - LOG(WARNING) << "Adding a new edge failed " << engine_node->name() << ":" - << conn.port_number << " -> " << dst_node->name() << ":" - << conn.outside_port; - } + CHECK(new_edge) << "Adding a new edge failed " << engine_node->name() << ":" + << conn.port_number << " -> " << dst_node->name() << ":" + << conn.outside_port; } return status; } @@ -800,6 +806,7 @@ tensorflow::Status ConvertAfterShapes(ConversionParams& params) { graph.RemoveNode(node_map.at(node_name)); } } else { + // Graph is not modified. LOG(WARNING) << "Engine creation for segment " << i << ", composed of " << segments.at(i).first.size() << " nodes failed: " << status << ". Skipping..."; diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index 03afbae113..d4d8b7525e 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -2263,7 +2263,7 @@ tensorflow::Status ConvertSegmentToGraphDef( auto& connection = connections->at(i); auto outside_node = graph->FindNodeId(connection.outside_id); if (!outside_node) { - // TODO(aaroey): this should never happen, so make it a CHECK? + // This should never happen, unless the original graph is problematic. return tensorflow::errors::NotFound( "Cannot find node with id ", connection.outside_id, " in the graph."); } diff --git a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc index 0d1d7e3b0e..f695a93408 100644 --- a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc +++ b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc @@ -481,7 +481,8 @@ TRTEngineOp::EngineCtxPair& TRTEngineOp::GetEngine(int batch_size, builder->setHalf2Mode(true); } else if (precision_mode_ == convert::INT8MODE) { builder->setInt8Mode(true); - // TODO(aaroey): what if it's empty? I.e. when calibration data is empty? + // Up to this point, calibrator_ can never be empty, since otherwise it + // means calibration_mode_ is true and this path won't get executed. builder->setInt8Calibrator(calibrator_.get()); } // TODO(aaroey): use the allocator to allocate the TRT workspace. -- GitLab From 1bdcd6d624e4012cb9aec790a0d95076360bedb5 Mon Sep 17 00:00:00 2001 From: gracehoney <31743510+aaroey@users.noreply.github.com> Date: Wed, 20 Jun 2018 08:12:30 -0700 Subject: [PATCH 727/816] Fix name of ConvertSubGraphDefToEngine() --- tensorflow/contrib/tensorrt/convert/convert_graph.cc | 2 +- tensorflow/contrib/tensorrt/convert/convert_nodes.cc | 2 +- tensorflow/contrib/tensorrt/convert/convert_nodes.h | 2 +- tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc | 6 +++--- 4 files changed, 6 insertions(+), 6 deletions(-) diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.cc b/tensorflow/contrib/tensorrt/convert/convert_graph.cc index 3113bdc2c5..7dcd30b0b2 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_graph.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_graph.cc @@ -440,7 +440,7 @@ tensorflow::Status CreateTRTNode(tensorflow::Graph* graph, #endif TrtUniquePtrType engine; // TODO(sami): What happens if 1st dim is not batch? - TF_RETURN_IF_ERROR(ConvertSubGraphDefToEngine( + TF_RETURN_IF_ERROR(ConvertGraphDefToEngine( info.segment_graph_def, info.precision_mode, shapes, builder.get(), &engine, /*convert_successfully=*/nullptr)); TrtUniquePtrType engine_data(engine->serialize()); diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index d4d8b7525e..5608761206 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -2129,7 +2129,7 @@ void Converter::register_op_converters() { } // namespace -tensorflow::Status ConvertSubGraphDefToEngine( +tensorflow::Status ConvertGraphDefToEngine( const tensorflow::GraphDef& gdef, int precision_mode, const std::vector& input_shapes, nvinfer1::IBuilder* builder, diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.h b/tensorflow/contrib/tensorrt/convert/convert_nodes.h index 220e5145cf..b357da0d84 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.h +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.h @@ -118,7 +118,7 @@ tensorflow::Status ConvertSegmentToGraphDef( // - convert_successfully: indicates whether the converson to TensorRT network // is successful. This is different than successfully building the engine: // building can still fail afterwards. -tensorflow::Status ConvertSubGraphDefToEngine( +tensorflow::Status ConvertGraphDefToEngine( const tensorflow::GraphDef& gdef, int precision_mode, const std::vector& input_shapes, nvinfer1::IBuilder* builder, diff --git a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc index f695a93408..4b45281f51 100644 --- a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc +++ b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc @@ -494,7 +494,7 @@ TRTEngineOp::EngineCtxPair& TRTEngineOp::GetEngine(int batch_size, TrtUniquePtrType engine; bool convert_successfully = false; VLOG(1) << "Calling conversion for " << batch_size << " " << name(); - auto status = convert::ConvertSubGraphDefToEngine( + auto status = convert::ConvertGraphDefToEngine( segment_graph_, precision_mode_, shapes, builder.get(), &engine, &convert_successfully); if (!status.ok()) { @@ -588,11 +588,11 @@ tensorflow::Status TRTEngineOp::AllocateCalibrationResources( cres->builder_->setInt8Mode(true); cres->builder_->setMaxWorkspaceSize(workspace_size); cres->builder_->setInt8Calibrator(cres->calibrator_); - // ConvertSubGraphDefToEngine() will try to build the engine. This thread + // ConvertGraphDefToEngine() will try to build the engine. This thread // will loop inside buildCudaEngine() consuming the calibration data // that is set by the TF op, and drive the builder until calibrator returns // false. Engine is discarded after calibration table is generated - auto s = convert::ConvertSubGraphDefToEngine( + auto s = convert::ConvertGraphDefToEngine( *segment_graph, convert::INT8MODE, shapes, cres->builder_.get(), &cres->engine_, /*convert_successfully=*/nullptr); if (!s.ok()) { -- GitLab From a056771e1ea21d374d652aeb4583d5c60760c428 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 20 Jun 2018 08:25:32 -0700 Subject: [PATCH 728/816] Support list of integers in custom op attributes. PiperOrigin-RevId: 201356549 --- .../contrib/lite/toco/tflite/operator.cc | 22 +++++++++++++++++++ .../contrib/lite/toco/tflite/operator_test.cc | 16 ++++++++++++++ 2 files changed, 38 insertions(+) diff --git a/tensorflow/contrib/lite/toco/tflite/operator.cc b/tensorflow/contrib/lite/toco/tflite/operator.cc index c93c0a6b90..a1bd2be0a1 100644 --- a/tensorflow/contrib/lite/toco/tflite/operator.cc +++ b/tensorflow/contrib/lite/toco/tflite/operator.cc @@ -978,6 +978,20 @@ class TensorFlowUnsupported : public BaseOperator { fbb->Bool(key, attr.b()); has_valid_attr = true; break; + case tensorflow::AttrValue::kList: + if (attr.list().i_size() > 0) { + auto start = fbb->StartVector(key); + for (const int64_t v : attr.list().i()) { + fbb->Add(v); + } + fbb->EndVector(start, /*typed=*/true, /*fixed=*/false); + has_valid_attr = true; + } else { + LOG(WARNING) + << "Ignoring unsupported type in list attribute with key '" + << key << "'"; + } + break; default: LOG(WARNING) << "Ignoring unsupported attribute type with key '" << key << "'"; @@ -1014,6 +1028,14 @@ class TensorFlowUnsupported : public BaseOperator { case flexbuffers::TYPE_BOOL: (*attr)[key].set_b(value.AsBool()); break; + case flexbuffers::TYPE_VECTOR_INT: { + auto* list = (*attr)[key].mutable_list(); + const auto& vector = value.AsTypedVector(); + for (size_t i = 0; i < vector.size(); i++) { + list->add_i(vector[i].AsInt64()); + } + break; + } default: LOG(WARNING) << "Ignoring unsupported attribute type with key '" << key << "'"; diff --git a/tensorflow/contrib/lite/toco/tflite/operator_test.cc b/tensorflow/contrib/lite/toco/tflite/operator_test.cc index a7136af2e2..00e2b69f55 100644 --- a/tensorflow/contrib/lite/toco/tflite/operator_test.cc +++ b/tensorflow/contrib/lite/toco/tflite/operator_test.cc @@ -450,6 +450,13 @@ TEST_F(OperatorTest, TensorFlowUnsupported) { (*attr)["str_attr"].set_s("Hello World"); (*attr)["int_attr"].set_i(17); (*attr)["bool_attr"].set_b(true); + { + auto* list = (*attr)["list_int_attr"].mutable_list(); + list->add_i(1); + list->add_i(20); + list->add_i(1LL << 40); + list->add_i(-(1LL << 40)); + } node_def.SerializeToString(&op.tensorflow_node_def); auto output_toco_op = @@ -464,6 +471,15 @@ TEST_F(OperatorTest, TensorFlowUnsupported) { EXPECT_EQ("Hello World", output_attr.at("str_attr").s()); EXPECT_EQ(17, output_attr.at("int_attr").i()); EXPECT_EQ(true, output_attr.at("bool_attr").b()); + + { + const auto& list = output_attr.at("list_int_attr").list(); + ASSERT_EQ(4, list.i_size()); + EXPECT_EQ(1, list.i(0)); + EXPECT_EQ(20, list.i(1)); + EXPECT_EQ(1LL << 40, list.i(2)); + EXPECT_EQ(-(1LL << 40), list.i(3)); + } } TEST_F(OperatorTest, TensorFlowUnsupportedWithoutAttr) { -- GitLab From 2ff8bbd1f70e9c9cf46a07fe17d7f0033be0a967 Mon Sep 17 00:00:00 2001 From: Akshay Agrawal Date: Wed, 20 Jun 2018 09:28:40 -0700 Subject: [PATCH 729/816] Support defun-ing instance methods. This change implements the __get__ method on _PolymorphicFunction and has it forward the instance to __call__. This makes it possible to write code like class Foo(object): ... @tfe.defun def two(self, tensor): ... PiperOrigin-RevId: 201365344 --- tensorflow/python/eager/function.py | 19 +++++++++++++++++++ tensorflow/python/eager/function_test.py | 16 ++++++++++++++++ 2 files changed, 35 insertions(+) diff --git a/tensorflow/python/eager/function.py b/tensorflow/python/eager/function.py index aa621d7f5a..771e943b1e 100644 --- a/tensorflow/python/eager/function.py +++ b/tensorflow/python/eager/function.py @@ -20,6 +20,7 @@ from __future__ import division from __future__ import print_function import collections +import functools import numpy as np @@ -744,6 +745,24 @@ class _PolymorphicFunction(object): self._arguments_to_functions = {} self._variables = [] + def __get__(self, instance, owner): + """Makes it possible to defun instance methods.""" + del owner + # `instance` here is the instance that this `_PolymorphicFunction` was + # accessed through; e.g., for + # + # class Foo(object): + # + # @function.defun + # def bar(self): + # ... + # + # foo = Foo() + # foo.bar() # `foo.bar` is a `_PolymorphicFunction` instance + # + # then `instance` will be `foo` (and `owner` will be `Foo`). + return functools.partial(self.__call__, instance) + def _maybe_define_function(self, *args, **kwds): """Gets a function for these inputs, defining it if necessary. diff --git a/tensorflow/python/eager/function_test.py b/tensorflow/python/eager/function_test.py index 85c1bbc393..0b13ea6398 100644 --- a/tensorflow/python/eager/function_test.py +++ b/tensorflow/python/eager/function_test.py @@ -771,6 +771,22 @@ class FunctionTest(test.TestCase): self.assertAllEqual(f(x=constant_op.constant(1.0)), 2.0) + def testDecoratingInstanceMethod(self): + + class Foo(object): + + def one(self, tensor): + return tensor + + @function.defun + def two(self, tensor): + return self.one(tensor) + + foo = Foo() + t = constant_op.constant(1.0) + out = foo.two(t) + self.assertEqual(float(out), 1.0) + @test_util.with_c_shapes class AutomaticControlDependenciesTest(test.TestCase): -- GitLab From 62c3e3574908f535c83facb33c701d2a36142e9c Mon Sep 17 00:00:00 2001 From: Billy Lamberta Date: Wed, 20 Jun 2018 10:02:25 -0700 Subject: [PATCH 730/816] Fix eager path in get_started leftnav PiperOrigin-RevId: 201370156 --- tensorflow/docs_src/get_started/leftnav_files | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/docs_src/get_started/leftnav_files b/tensorflow/docs_src/get_started/leftnav_files index 9a60496cb5..5c400a67f0 100644 --- a/tensorflow/docs_src/get_started/leftnav_files +++ b/tensorflow/docs_src/get_started/leftnav_files @@ -7,4 +7,4 @@ save_and_restore_models.md next_steps.md ### Research and experimentation -custom_training_walkthrough.md +eager.md -- GitLab From e370e542cf76f65edbb1cc343ddc97622c4a62c2 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 20 Jun 2018 10:02:45 -0700 Subject: [PATCH 731/816] Fix a bug that would leave orphaned arrays in the graph. PiperOrigin-RevId: 201370219 --- .../resolve_constant_strided_slice.cc | 9 +-------- 1 file changed, 1 insertion(+), 8 deletions(-) diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_strided_slice.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_strided_slice.cc index 1dd52e9069..6ee231465f 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_strided_slice.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_strided_slice.cc @@ -155,14 +155,7 @@ bool ResolveConstantStridedSlice::Run(Model* model, std::size_t op_index) { break; } - // Erase input array if no longer used - if (IsDiscardableArray(*model, op->inputs[0]) && - CountOpsWithInput(*model, op->inputs[0]) == 1) { - model->EraseArray(op->inputs[0]); - } - - // Erase the operator - model->operators.erase(it); + DeleteOpAndArraysIfUnused(model, it->get()); return true; } -- GitLab From af3455aad7ebf2e70c816e642f90594625e4fd44 Mon Sep 17 00:00:00 2001 From: Derek Murray Date: Wed, 20 Jun 2018 10:10:55 -0700 Subject: [PATCH 732/816] [tf.data] Properly export `tf.contrib.data.choose_from_datasets()` PiperOrigin-RevId: 201371642 --- tensorflow/contrib/data/__init__.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/contrib/data/__init__.py b/tensorflow/contrib/data/__init__.py index 1af1ed08b5..9c6a13333e 100644 --- a/tensorflow/contrib/data/__init__.py +++ b/tensorflow/contrib/data/__init__.py @@ -72,6 +72,7 @@ from tensorflow.contrib.data.python.ops.error_ops import ignore_errors from tensorflow.contrib.data.python.ops.get_single_element import get_single_element from tensorflow.contrib.data.python.ops.grouping import bucket_by_sequence_length from tensorflow.contrib.data.python.ops.grouping import group_by_window +from tensorflow.contrib.data.python.ops.interleave_ops import choose_from_datasets from tensorflow.contrib.data.python.ops.interleave_ops import parallel_interleave from tensorflow.contrib.data.python.ops.interleave_ops import sample_from_datasets from tensorflow.contrib.data.python.ops.interleave_ops import sloppy_interleave -- GitLab From 2b0805301e4531dd7c2ed677d932f6408675460e Mon Sep 17 00:00:00 2001 From: Asim Shankar Date: Wed, 20 Jun 2018 10:14:13 -0700 Subject: [PATCH 733/816] [eager]: Support string attributes where the value contains `\0`. Apparently, some custom operations stuff non-printable characters in string valued attributes. This change also makes the eager C API consistent with the C API for graph construction (TF_SetAttrString and TF_SetAttrStringList). PiperOrigin-RevId: 201372089 --- tensorflow/c/eager/c_api.cc | 38 +++++++++++++------- tensorflow/c/eager/c_api.h | 6 ++-- tensorflow/c/eager/c_api_test.cc | 4 +-- tensorflow/python/eager/pywrap_tfe_src.cc | 42 ++++++++++++++++------- 4 files changed, 61 insertions(+), 29 deletions(-) diff --git a/tensorflow/c/eager/c_api.cc b/tensorflow/c/eager/c_api.cc index 55d9c26b0d..6e4764bcbf 100644 --- a/tensorflow/c/eager/c_api.cc +++ b/tensorflow/c/eager/c_api.cc @@ -46,6 +46,7 @@ limitations under the License. #include "tensorflow/core/framework/tensor_shape.pb.h" #include "tensorflow/core/framework/types.h" #include "tensorflow/core/lib/core/refcount.h" +#include "tensorflow/core/lib/core/stringpiece.h" #include "tensorflow/core/lib/gtl/cleanup.h" #include "tensorflow/core/lib/gtl/flatmap.h" #include "tensorflow/core/lib/gtl/map_util.h" @@ -441,8 +442,11 @@ TF_AttrType TFE_OpNameGetAttrType(TFE_Context* ctx, return ret; } -void TFE_OpSetAttrString(TFE_Op* op, const char* attr_name, const char* value) { - op->operation.MutableAttrs()->Set(attr_name, value); +void TFE_OpSetAttrString(TFE_Op* op, const char* attr_name, const void* value, + size_t length) { + op->operation.MutableAttrs()->Set( + attr_name, + tensorflow::StringPiece(static_cast(value), length)); } void TFE_OpSetAttrInt(TFE_Op* op, const char* attr_name, int64_t value) { @@ -493,16 +497,22 @@ void TFE_OpSetAttrFunction(TFE_Op* op, const char* attr_name, op->operation.MutableAttrs()->Set(attr_name, attr_value); } -#define TFE_OP_SET_ATTR_LIST(fn, type) \ - void fn(TFE_Op* op, const char* attr_name, const type* values, \ - int num_values) { \ - op->operation.MutableAttrs()->Set( \ - attr_name, \ - tensorflow::gtl::ArraySlice(values, num_values)); \ +void TFE_OpSetAttrStringList(TFE_Op* op, const char* attr_name, + const void* const* values, const size_t* lengths, + int num_values) { + std::vector v(num_values); + for (int i = 0; i < num_values; ++i) { + v[i] = tensorflow::StringPiece(static_cast(values[i]), + lengths[i]); } -TFE_OP_SET_ATTR_LIST(TFE_OpSetAttrStringList, char*) -TFE_OP_SET_ATTR_LIST(TFE_OpSetAttrFloatList, float) -#undef TFE_OP_SET_ATTR_LIST + op->operation.MutableAttrs()->Set(attr_name, v); +} + +void TFE_OpSetAttrFloatList(TFE_Op* op, const char* attr_name, + const float* values, int num_values) { + op->operation.MutableAttrs()->Set( + attr_name, tensorflow::gtl::ArraySlice(values, num_values)); +} void TFE_OpSetAttrIntList(TFE_Op* op, const char* attr_name, const int64_t* values, int num_values) { @@ -675,9 +685,11 @@ void SetOpAttrValueScalar(TFE_Context* ctx, TFE_Op* op, const tensorflow::AttrValue& default_value, const char* attr_name, TF_Status* status) { switch (default_value.value_case()) { - case tensorflow::AttrValue::kS: - TFE_OpSetAttrString(op, attr_name, default_value.s().data()); + case tensorflow::AttrValue::kS: { + const string& v = default_value.s(); + TFE_OpSetAttrString(op, attr_name, v.data(), v.size()); break; + } case tensorflow::AttrValue::kI: TFE_OpSetAttrInt(op, attr_name, static_cast(default_value.i())); break; diff --git a/tensorflow/c/eager/c_api.h b/tensorflow/c/eager/c_api.h index 1862af3ce2..fdbd5374b2 100644 --- a/tensorflow/c/eager/c_api.h +++ b/tensorflow/c/eager/c_api.h @@ -278,7 +278,8 @@ TF_CAPI_EXPORT extern TF_AttrType TFE_OpNameGetAttrType( TF_CAPI_EXPORT extern void TFE_OpSetAttrString(TFE_Op* op, const char* attr_name, - const char* value); + const void* value, + size_t length); TF_CAPI_EXPORT extern void TFE_OpSetAttrInt(TFE_Op* op, const char* attr_name, int64_t value); TF_CAPI_EXPORT extern void TFE_OpSetAttrFloat(TFE_Op* op, const char* attr_name, @@ -305,7 +306,8 @@ TF_CAPI_EXPORT extern void TFE_OpSetAttrFunction(TFE_Op* op, TF_CAPI_EXPORT extern void TFE_OpSetAttrStringList(TFE_Op* op, const char* attr_name, - const char** value, + const void* const* values, + const size_t* lengths, int num_values); TF_CAPI_EXPORT extern void TFE_OpSetAttrIntList(TFE_Op* op, const char* attr_name, diff --git a/tensorflow/c/eager/c_api_test.cc b/tensorflow/c/eager/c_api_test.cc index 1d71a78b75..cd035940ff 100644 --- a/tensorflow/c/eager/c_api_test.cc +++ b/tensorflow/c/eager/c_api_test.cc @@ -1162,8 +1162,8 @@ TFE_TensorHandle* CreateVariable(TFE_Context* ctx, float value, if (TF_GetCode(status) != TF_OK) return nullptr; TFE_OpSetAttrType(op, "dtype", TF_FLOAT); TFE_OpSetAttrShape(op, "shape", {}, 0, status); - TFE_OpSetAttrString(op, "container", ""); - TFE_OpSetAttrString(op, "shared_name", ""); + TFE_OpSetAttrString(op, "container", "", 0); + TFE_OpSetAttrString(op, "shared_name", "", 0); if (TF_GetCode(status) != TF_OK) return nullptr; TFE_TensorHandle* var_handle = nullptr; int num_retvals = 1; diff --git a/tensorflow/python/eager/pywrap_tfe_src.cc b/tensorflow/python/eager/pywrap_tfe_src.cc index 6c9481c3af..b797a3f82d 100644 --- a/tensorflow/python/eager/pywrap_tfe_src.cc +++ b/tensorflow/python/eager/pywrap_tfe_src.cc @@ -205,14 +205,20 @@ bool ParseDimensionValue(const string& key, PyObject* py_value, } bool ParseStringValue(const string& key, PyObject* py_value, TF_Status* status, - const char** value) { + tensorflow::StringPiece* value) { if (PyBytes_Check(py_value)) { - *value = PyBytes_AsString(py_value); + Py_ssize_t size = 0; + char* buf = nullptr; + if (PyBytes_AsStringAndSize(py_value, &buf, &size) < 0) return false; + *value = tensorflow::StringPiece(buf, size); return true; } #if PY_MAJOR_VERSION >= 3 if (PyUnicode_Check(py_value)) { - *value = PyUnicode_AsUTF8(py_value); + Py_ssize_t size = 0; + char* buf = PyUnicode_AsUTF8AndSize(py_value, &size); + if (buf == nullptr) return false; + *value = tensorflow::StringPiece(buf, size); return true; } #endif @@ -275,8 +281,16 @@ bool SetOpAttrList( } if (type == TF_ATTR_STRING) { - PARSE_LIST(const char*, ParseStringValue); - TFE_OpSetAttrStringList(op, key, values.get(), num_values); + std::unique_ptr values(new const void*[num_values]); + std::unique_ptr lengths(new size_t[num_values]); + for (int i = 0; i < num_values; ++i) { + tensorflow::StringPiece value; + tensorflow::Safe_PyObjectPtr py_value(PySequence_ITEM(py_list, i)); + if (!ParseStringValue(key, py_value.get(), status, &value)) return false; + values[i] = value.data(); + lengths[i] = value.size(); + } + TFE_OpSetAttrStringList(op, key, values.get(), lengths.get(), num_values); } else if (type == TF_ATTR_INT) { PARSE_LIST(int64_t, ParseInt64Value); TFE_OpSetAttrIntList(op, key, values.get(), num_values); @@ -379,12 +393,15 @@ void SetOpAttrListDefault( TF_Status* status) { if (type == TF_ATTR_STRING) { int num_values = attr.default_value().list().s_size(); - std::unique_ptr values(new const char*[num_values]); + std::unique_ptr values(new const void*[num_values]); + std::unique_ptr lengths(new size_t[num_values]); (*attr_list_sizes)[key] = num_values; for (int i = 0; i < num_values; i++) { - values[i] = attr.default_value().list().s(i).data(); + const string& v = attr.default_value().list().s(i); + values[i] = v.data(); + lengths[i] = v.size(); } - TFE_OpSetAttrStringList(op, key, values.get(), num_values); + TFE_OpSetAttrStringList(op, key, values.get(), lengths.get(), num_values); } else if (type == TF_ATTR_INT) { int num_values = attr.default_value().list().i_size(); std::unique_ptr values(new int64_t[num_values]); @@ -470,9 +487,9 @@ bool SetOpAttrScalar( tensorflow::gtl::FlatMap* attr_list_sizes, TF_Status* status) { if (type == TF_ATTR_STRING) { - const char* value; + tensorflow::StringPiece value; if (!ParseStringValue(key, py_value, status, &value)) return false; - TFE_OpSetAttrString(op, key, value); + TFE_OpSetAttrString(op, key, value.data(), value.size()); } else if (type == TF_ATTR_INT) { int64_t value; if (!ParseInt64Value(key, py_value, status, &value)) return false; @@ -533,7 +550,7 @@ bool SetOpAttrScalar( // (which is what the various "defun" or "Defun" decorators do). // And in the future also allow an object that can encapsulate // the function name and its attribute values. - const char* func_name = nullptr; + tensorflow::StringPiece func_name; if (!ParseStringValue(key, py_value, status, &func_name)) { PyObject* name_attr = PyObject_GetAttrString(py_value, "name"); if (name_attr == nullptr || @@ -549,7 +566,8 @@ bool SetOpAttrScalar( return false; } } - TFE_Op* func = TFE_NewOp(ctx, func_name, status); + TFE_Op* func = TFE_NewOp( + ctx, string(func_name.data(), func_name.size()).c_str(), status); if (TF_GetCode(status) != TF_OK) return false; TFE_OpSetAttrFunction(op, key, func); TFE_DeleteOp(func); -- GitLab From 0d85df2cffdaf284950a67510d132bbdf9f02439 Mon Sep 17 00:00:00 2001 From: Martin Patz <5219726+patzm@users.noreply.github.com> Date: Wed, 20 Jun 2018 19:17:25 +0200 Subject: [PATCH 734/816] fixed typo in docstring `init_from_checkpoint` does not accept a set as `assignment_map` --- tensorflow/python/training/checkpoint_utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/python/training/checkpoint_utils.py b/tensorflow/python/training/checkpoint_utils.py index c2f0e9d3e6..5b372e82b3 100644 --- a/tensorflow/python/training/checkpoint_utils.py +++ b/tensorflow/python/training/checkpoint_utils.py @@ -147,7 +147,7 @@ def init_from_checkpoint(ckpt_dir_or_file, assignment_map): partitioner=lambda shape, dtype: [5, 1]) # Initialize all variables in `new_scope_1` from `old_scope_1`. - init_from_checkpoint('/tmp/model.ckpt', {'old_scope_1/', 'new_scope_1'}) + init_from_checkpoint('/tmp/model.ckpt', {'old_scope_1/': 'new_scope_1'}) # Use names to specify which variables to initialize from checkpoint. init_from_checkpoint('/tmp/model.ckpt', -- GitLab From 60f965adb6c0393fe6d2ce4b990af6ffa58c0852 Mon Sep 17 00:00:00 2001 From: Asim Shankar Date: Wed, 20 Jun 2018 10:15:09 -0700 Subject: [PATCH 735/816] s/tf.contrib.eager.GradientTape/tf.GradientTape/ PiperOrigin-RevId: 201372249 --- tensorflow/python/ops/gradients_impl.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/tensorflow/python/ops/gradients_impl.py b/tensorflow/python/ops/gradients_impl.py index 169efd401c..fe464af3a4 100644 --- a/tensorflow/python/ops/gradients_impl.py +++ b/tensorflow/python/ops/gradients_impl.py @@ -548,9 +548,8 @@ def _GradientsHelper(ys, src_graph=None): """Implementation of gradients().""" if context.executing_eagerly(): - raise RuntimeError("tf.gradients not supported when eager execution " - "is enabled. Use tf.contrib.eager.GradientTape " - "instead.") + raise RuntimeError("tf.gradients is not supported when eager execution " + "is enabled. Use tf.GradientTape instead.") if src_graph is None: src_graph = ops.get_default_graph() -- GitLab From 5a8ff32bdb23b9ac4680f96b4b78493e3c4395ab Mon Sep 17 00:00:00 2001 From: gracehoney <31743510+aaroey@users.noreply.github.com> Date: Wed, 20 Jun 2018 10:20:32 -0700 Subject: [PATCH 736/816] Move the builder creation logic into ConvertGraphDefToEngine(), use unique_ptr for TRTCalibrationResource, and fix comments --- .../contrib/tensorrt/convert/convert_graph.cc | 28 +++--- .../contrib/tensorrt/convert/convert_nodes.cc | 37 ++++++-- .../contrib/tensorrt/convert/convert_nodes.h | 10 +- .../contrib/tensorrt/kernels/trt_engine_op.cc | 93 +++++++------------ .../contrib/tensorrt/kernels/trt_engine_op.h | 26 +++--- .../tensorrt/resources/trt_int8_calibrator.cc | 13 +-- .../tensorrt/resources/trt_resources.h | 26 ++---- 7 files changed, 113 insertions(+), 120 deletions(-) diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.cc b/tensorflow/contrib/tensorrt/convert/convert_graph.cc index 7dcd30b0b2..ba7d3b5f86 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_graph.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_graph.cc @@ -424,31 +424,25 @@ tensorflow::Status CreateTRTNode(tensorflow::Graph* graph, string segment_string; if (info.engine_type == EngineInfo::EngineType::TRTStatic || info.precision_mode == INT8MODE) { - // Create static engine and for int8 test validity of the engine. We can not - // allow engine to fail at the calibration time. So we are constructing a - // FP32 engine here to check its validity. If it is a valid engine then we - // put the serialized graphdef to the op. Otherwise we skip node creation - // for this engine. + // Create static engine for fp32/fp16 mode, and test validity of the engine + // for int8 mode. We don't want engine to fail at the calibration time. + // So we are constructing a FP32 engine here to check its validity, and if + // it is a valid engine then we put the serialized graphdef to the op. + // Otherwise we skip node creation for this engine. Logger trt_logger; - TrtUniquePtrType builder( - nvinfer1::createInferBuilder(trt_logger)); - builder->setMaxBatchSize(max_batch_size); - if (info.precision_mode == FP16MODE) builder->setHalf2Mode(true); - builder->setMaxWorkspaceSize(info.max_workspace_size_bytes); -#if NV_TENSORRT_MAJOR > 3 - builder->setGpuAllocator(alloc); -#endif TrtUniquePtrType engine; // TODO(sami): What happens if 1st dim is not batch? TF_RETURN_IF_ERROR(ConvertGraphDefToEngine( - info.segment_graph_def, info.precision_mode, shapes, builder.get(), - &engine, /*convert_successfully=*/nullptr)); + info.segment_graph_def, + info.precision_mode == INT8MODE ? FP32MODE : info.precision_mode, + max_batch_size, info.max_workspace_size_bytes, shapes, &trt_logger, + alloc, /*calibrator=*/nullptr, &engine, + /*convert_successfully=*/nullptr)); TrtUniquePtrType engine_data(engine->serialize()); segment_string = string((const char*)engine_data->data(), engine_data->size()); if (info.precision_mode == INT8MODE) { - // See above comment on the reason why not putting this inside the 'else' - // branch. + // See above comment about why not putting this inside the 'else' branch. segment_string = info.segment_graph_def.SerializeAsString(); } } else { diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index 5608761206..b5214b461a 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -433,7 +433,7 @@ class Converter { OpConverter plugin_converter_; nvinfer1::INetworkDefinition* trt_network_; std::list> temp_bufs_; - tensorflow::tensorrt::TRTWeightStore* weight_store_; + TRTWeightStore* weight_store_; bool fp16_; void register_op_converters(); tensorflow::Status get_inputs(const tensorflow::NodeDef& node_def, @@ -475,11 +475,11 @@ class Converter { public: explicit Converter(nvinfer1::INetworkDefinition* trt_network, - tensorflow::tensorrt::TRTWeightStore* ws, bool fp16) + TRTWeightStore* ws, bool fp16) : trt_network_(trt_network), weight_store_(ws), fp16_(fp16) { this->register_op_converters(); } - tensorflow::tensorrt::TRTWeightStore* weight_store() { return weight_store_; } + TRTWeightStore* weight_store() { return weight_store_; } TRT_ShapedWeights get_temp_weights(tensorflow::DataType type, nvinfer1::Dims shape) { TRT_ShapedWeights weights(type, nullptr, shape); @@ -2130,21 +2130,44 @@ void Converter::register_op_converters() { } // namespace tensorflow::Status ConvertGraphDefToEngine( - const tensorflow::GraphDef& gdef, int precision_mode, + const tensorflow::GraphDef& gdef, + int precision_mode, + int max_batch_size, + size_t max_workspace_size_bytes, const std::vector& input_shapes, - nvinfer1::IBuilder* builder, + Logger* logger, + nvinfer1::IGpuAllocator* allocator, + TRTInt8Calibrator* calibrator, TrtUniquePtrType* engine, bool* convert_successfully) { engine->reset(); if (convert_successfully) *convert_successfully = false; + + // Create the builder. + TrtUniquePtrType builder( + nvinfer1::createInferBuilder(*logger)); + builder->setMaxBatchSize(max_batch_size); + // TODO(aaroey): use the allocator to allocate the TRT workspace. + builder->setMaxWorkspaceSize(max_workspace_size_bytes); +#if NV_TENSORRT_MAJOR > 3 + builder->setGpuAllocator(allocator); +#endif + if (precision_mode == FP16MODE) { + builder->setHalf2Mode(true); + } else if (precision_mode == INT8MODE) { + builder->setInt8Mode(true); + builder->setInt8Calibrator(calibrator); + } + + // Create the network. auto trt_network = TrtUniquePtrType(builder->createNetwork()); if (!trt_network) { return tensorflow::errors::Internal( "Failed to create TensorRT network object"); } - auto ws = std::unique_ptr( - new TRTWeightStore()); + auto ws = std::unique_ptr(new TRTWeightStore()); + // Build the network VLOG(1) << "Starting engine conversion "; Converter converter(trt_network.get(), ws.get(), precision_mode == FP16MODE); diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.h b/tensorflow/contrib/tensorrt/convert/convert_nodes.h index b357da0d84..2da4edf7f5 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.h +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.h @@ -24,6 +24,7 @@ limitations under the License. #include "tensorflow/contrib/tensorrt/convert/utils.h" #include "tensorflow/contrib/tensorrt/resources/trt_allocator.h" +#include "tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.h" #include "tensorflow/core/framework/graph.pb.h" #include "tensorflow/core/graph/graph.h" #include "tensorflow/core/grappler/costs/graph_properties.h" @@ -119,9 +120,14 @@ tensorflow::Status ConvertSegmentToGraphDef( // is successful. This is different than successfully building the engine: // building can still fail afterwards. tensorflow::Status ConvertGraphDefToEngine( - const tensorflow::GraphDef& gdef, int precision_mode, + const tensorflow::GraphDef& gdef, + int precision_mode, + int max_batch_size, + size_t max_workspace_size_bytes, const std::vector& input_shapes, - nvinfer1::IBuilder* builder, + Logger* logger, + nvinfer1::IGpuAllocator* allocator, + TRTInt8Calibrator* calibrator, TrtUniquePtrType* engine, bool* convert_successfully); diff --git a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc index 4b45281f51..d12f738ac5 100644 --- a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc +++ b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc @@ -36,7 +36,6 @@ namespace tensorflow { namespace tensorrt { static Logger logger; using ::nvinfer1::IRuntime; -using ::nvinfer1::Dims; using ::tensorflow::strings::StrAppend; using ::tensorflow::strings::StrCat; @@ -441,6 +440,7 @@ TRTEngineOp::EngineCtxPair& TRTEngineOp::GetEngine(int batch_size, #if NV_TENSORRT_MAJOR > 3 auto allocator = GetAllocator(ctx); if (allocator == nullptr) { + // GetAllocator already set the Status. return null_pair; }; infer->setGpuAllocator(allocator); @@ -464,39 +464,27 @@ TRTEngineOp::EngineCtxPair& TRTEngineOp::GetEngine(int batch_size, auto engine_it = engine_map_.find(batch_size); if (engine_it == engine_map_.end() && engine_map_.size() < (size_t)max_cached_engines_) { - TrtUniquePtrType builder( - nvinfer1::createInferBuilder(logger)); + nvinfer1::IGpuAllocator* allocator = nullptr; #if NV_TENSORRT_MAJOR > 3 - auto allocator = GetAllocator(ctx); + allocator = GetAllocator(ctx); if (allocator == nullptr) { // GetAllocator already set the Status. return null_pair; } - builder->setGpuAllocator(allocator); #endif - VLOG(0) << name() << " Constructing a new engine with batch size " - << batch_size; - builder->setMaxBatchSize(batch_size); - if (precision_mode_ == convert::FP16MODE) { - builder->setHalf2Mode(true); - } else if (precision_mode_ == convert::INT8MODE) { - builder->setInt8Mode(true); - // Up to this point, calibrator_ can never be empty, since otherwise it - // means calibration_mode_ is true and this path won't get executed. - builder->setInt8Calibrator(calibrator_.get()); - } - // TODO(aaroey): use the allocator to allocate the TRT workspace. - builder->setMaxWorkspaceSize(workspace_size_); std::vector shapes; for (int i = 0; i < ctx->num_inputs(); ++i) { shapes.emplace_back(ctx->input(i).shape()); } TrtUniquePtrType engine; bool convert_successfully = false; - VLOG(1) << "Calling conversion for " << batch_size << " " << name(); + VLOG(0) << name() << " Constructing a new engine with batch size " + << batch_size; + // Up to this point, calibrator_ can never be empty, since otherwise it + // means calibration_mode_ is true and this path won't get executed. auto status = convert::ConvertGraphDefToEngine( - segment_graph_, precision_mode_, shapes, builder.get(), &engine, - &convert_successfully); + segment_graph_, precision_mode_, batch_size, workspace_size_, shapes, + &logger, allocator, calibrator_.get(), &engine, &convert_successfully); if (!status.ok()) { if (convert_successfully) { // This means it fail to build the engine even when the network is built @@ -522,9 +510,7 @@ tensorflow::Status TRTEngineOp::AllocateCalibrationResources( TRTCalibrationResource** cr) { auto cres = new TRTCalibrationResource(); *cr = cres; - cres->logger_ = new Logger(); - -#if NV_TENSORRT_MAJOR > 3 + // Get the allocator. auto alloc = ctx->device()->GetAllocator(tensorflow::AllocatorAttributes()); if (!alloc) { LOG(WARNING) << "Can't get device allocator will not be able to " @@ -533,11 +519,10 @@ tensorflow::Status TRTEngineOp::AllocateCalibrationResources( } else { cres->allocator_.reset(new TRTDeviceAllocator(alloc)); } -#endif - int batch_size = ctx->input(0).dim_size(0); + // Get the input shapes. + const int batch_size = ctx->input(0).dim_size(0); + const int num_inputs = ctx->num_inputs(); std::vector shapes; - int num_inputs = ctx->num_inputs(); - // first run instantiate calibrator dev_tensors_.resize(num_inputs); VLOG(1) << " Constructing calibrator"; for (int i = 0; i < num_inputs; i++) { @@ -557,51 +542,45 @@ tensorflow::Status TRTEngineOp::AllocateCalibrationResources( StrCat(kInputPHName, i), std::pair(device_address, device_tensor->TotalBytes())); } - cres->calibrator_ = - new TRTInt8Calibrator(device_buffers_, batch_size, name()); - string label(name()); + cres->calibrator_.reset( + new TRTInt8Calibrator(device_buffers_, batch_size, name())); + const string label(name()); auto segment_graph = &segment_graph_; - int cuda_device = ctx->device()->tensorflow_gpu_device_info()->gpu_id; - if (cuda_device < 0) { + const int cuda_gpu_id = ctx->device()->tensorflow_gpu_device_info()->gpu_id; + if (cuda_gpu_id < 0) { LOG(ERROR) << "Can't get gpu_device_info from context->device()"; return tensorflow::errors::InvalidArgument( "Context->device doesn't contain device info!"); } - int workspace_size = workspace_size_; - cres->thr_ = new std::thread([cres, label, segment_graph, shapes, cuda_device, - batch_size, workspace_size]() { - VLOG(0) << "Starting calibration thread on device " << cuda_device + const int64 workspace_size_bytes = workspace_size_; + cres->thr_.reset(new std::thread([cres, label, segment_graph, shapes, + cuda_gpu_id, workspace_size_bytes]() { + VLOG(0) << "Starting calibration thread on device " << cuda_gpu_id << ", Calibration Resource @ " << cres; - auto err = cudaSetDevice(cuda_device); + auto err = cudaSetDevice(cuda_gpu_id); if (err != cudaSuccess) { - VLOG(0) << "Couldn't set cuda device to " << cuda_device - << " in calibration thread"; + // TODO(aaroey): should return error here. + LOG(ERROR) << "Couldn't set cuda device to " << cuda_gpu_id + << " in calibration thread"; } - // initialize builder here - cres->builder_.reset(nvinfer1::createInferBuilder(*(cres->logger_))); - // TODO(aaroey): maybe setting the max batch size using the python - // calibration wrapper class. - cres->builder_->setMaxBatchSize(batch_size); -#if NV_TENSORRT_MAJOR > 3 - cres->builder_->setGpuAllocator(cres->allocator_.get()); -#endif - cres->builder_->setInt8Mode(true); - cres->builder_->setMaxWorkspaceSize(workspace_size); - cres->builder_->setInt8Calibrator(cres->calibrator_); // ConvertGraphDefToEngine() will try to build the engine. This thread // will loop inside buildCudaEngine() consuming the calibration data // that is set by the TF op, and drive the builder until calibrator returns // false. Engine is discarded after calibration table is generated + // + // TODO(aaroey): maybe setting the max batch size using the python + // calibration wrapper class. auto s = convert::ConvertGraphDefToEngine( - *segment_graph, convert::INT8MODE, shapes, cres->builder_.get(), - &cres->engine_, /*convert_successfully=*/nullptr); + *segment_graph, convert::INT8MODE, cres->calibrator_->getBatchSize(), + workspace_size_bytes, shapes, &cres->logger_, cres->allocator_.get(), + cres->calibrator_.get(), &cres->engine_, + /*convert_successfully=*/nullptr); if (!s.ok()) { - LOG(ERROR) - << "Calibration failed. Engine will not be calibrated! Error is" << s; - cres->calibrator_->setDone(); // ignore further pushes + LOG(ERROR) << "Calibration failed: " << s; + cres->calibrator_->setDone(); // Ignore further pushes } VLOG(1) << "Calibration loop terminated " << label; - }); + })); VLOG(1) << "initialized calibrator resource"; return tensorflow::Status::OK(); } diff --git a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h index cb43403130..0d2f9e8a9d 100644 --- a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h +++ b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h @@ -20,6 +20,7 @@ limitations under the License. #include #include "tensorflow/contrib/tensorrt/convert/utils.h" +#include "tensorflow/contrib/tensorrt/log/trt_logger.h" #include "tensorflow/contrib/tensorrt/resources/trt_allocator.h" #include "tensorflow/core/framework/function.h" #include "tensorflow/core/framework/graph.pb.h" @@ -46,25 +47,24 @@ class TRTEngineOp : public AsyncOpKernel { explicit TRTEngineOp(OpKernelConstruction* context); void ComputeAsync(OpKernelContext* context, - tensorflow::AsyncOpKernel::DoneCallback done) override; + AsyncOpKernel::DoneCallback done) override; ~TRTEngineOp(); private: // Execute calibration - void ExecuteCalibration(tensorflow::OpKernelContext* ctx, + void ExecuteCalibration(OpKernelContext* ctx, AsyncHelper* helper); // Construct a function handle for executing native funcdef graph - tensorflow::Status ConstructFunctionHandle(tensorflow::OpKernelContext* ctx); + Status ConstructFunctionHandle(OpKernelContext* ctx); // Execute replaced native segment as function Op. - void ExecuteNativeSegment(tensorflow::OpKernelContext* ctx, + void ExecuteNativeSegment(OpKernelContext* ctx, AsyncHelper* helper); // Allocate necessary resources for calibration - tensorflow::Status AllocateCalibrationResources( - tensorflow::OpKernelContext* ctx, - tensorflow::tensorrt::TRTCalibrationResource** cr); + Status AllocateCalibrationResources( + OpKernelContext* ctx, TRTCalibrationResource** cr); // TODO(samikama): context should go to a resource manager! typedef std::pair, @@ -92,13 +92,13 @@ class TRTEngineOp : public AsyncOpKernel { string funcdef_name_; // GraphDef representation of the segment. - tensorflow::GraphDef segment_graph_; + GraphDef segment_graph_; // Lookup table for temporary staging areas of input tensors for calibration. std::unordered_map> device_buffers_; // Temporary staging areas for calibration inputs. - std::vector dev_tensors_; + std::vector dev_tensors_; // Engine Precision mode. int precision_mode_; @@ -120,9 +120,11 @@ class TRTEngineOp : public AsyncOpKernel { // Maximum number of cached engines int max_cached_engines_; - tensorflow::int64 workspace_size_; - tensorflow::mutex engine_mutex_; - tensorflow::FunctionLibraryRuntime::Handle native_func_; + int64 workspace_size_; + mutex engine_mutex_; + FunctionLibraryRuntime::Handle native_func_; + + // The finalized calibrator for inference. std::unique_ptr calibrator_; }; diff --git a/tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.cc b/tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.cc index 9c1c306947..59ae860bc0 100644 --- a/tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.cc +++ b/tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.cc @@ -51,8 +51,8 @@ TRTInt8Calibrator::TRTInt8Calibrator(const string& calib_data) bool TRTInt8Calibrator::setBatch(const std::unordered_map& data, const cudaStream_t stream) { tensorflow::mutex_lock lock(cond_mtx_); - while ((calib_running_ || batch_is_set_) && - !done_) { // wait while calibration is running + // wait while calibration is running. + while ((calib_running_ || batch_is_set_) && !done_) { cond_.wait(lock); } if (done_) return false; @@ -66,8 +66,6 @@ bool TRTInt8Calibrator::setBatch(const std::unordered_map& data, } const auto& d = devptr->second; - // TODO(aaroey): we should not use sync copy on default stream. Make sure - // stream->ThenMemcpy() is used in future PRs. // TODO(sami,aaroey): Need to figure out a way to ensure synchronization // between stream, perhaps using a tensor? auto status = cudaMemcpyAsync(d.first, it.second, d.second, @@ -91,12 +89,11 @@ bool TRTInt8Calibrator::getBatch(void** bindings, const char** names, tensorflow::mutex_lock lock(cond_mtx_); calib_running_ = false; cond_.notify_all(); - while ((!batch_is_set_ && !done_)) { // wait until new batch arrives + // wait until new batch arrives + while ((!batch_is_set_ && !done_)) { cond_.wait(lock); } - if (done_) { - return false; - } + if (done_) return false; for (int i = 0; i < num_bindings; i++) { auto it = dev_buffers_.find(names[i]); diff --git a/tensorflow/contrib/tensorrt/resources/trt_resources.h b/tensorflow/contrib/tensorrt/resources/trt_resources.h index 43734bbdd8..76863503bd 100644 --- a/tensorflow/contrib/tensorrt/resources/trt_resources.h +++ b/tensorflow/contrib/tensorrt/resources/trt_resources.h @@ -38,11 +38,6 @@ namespace tensorrt { class TRTCalibrationResource : public tensorflow::ResourceBase { public: - TRTCalibrationResource() - : calibrator_(nullptr), - logger_(nullptr), - thr_(nullptr) {} - ~TRTCalibrationResource() { VLOG(0) << "Destroying Calibration Resource " << std::endl << DebugString(); builder_.reset(); @@ -50,9 +45,6 @@ class TRTCalibrationResource : public tensorflow::ResourceBase { // We need to manually destroy the builder and engine before the allocator // is destroyed. allocator_.reset(); - delete thr_; - delete logger_; - delete calibrator_; } string DebugString() override { @@ -60,22 +52,22 @@ class TRTCalibrationResource : public tensorflow::ResourceBase { using std::hex; using std::dec; using std::endl; - oss << " Calibrator = " << hex << calibrator_ << dec << endl - << " Builder = " << hex << builder_.get() << dec << endl - << " Engine = " << hex << engine_.get() << dec << endl - << " Logger = " << hex << logger_ << dec << endl - << " Allocator = " << hex << allocator_.get() << dec << endl - << " Thread = " << hex << thr_ << dec << endl; + oss << " Calibrator = " << hex << calibrator_.get() << dec << endl + << " Builder = " << hex << builder_.get() << dec << endl + << " Engine = " << hex << engine_.get() << dec << endl + << " Logger = " << hex << &logger_ << dec << endl + << " Allocator = " << hex << allocator_.get() << dec << endl + << " Thread = " << hex << thr_.get() << dec << endl; return oss.str(); } - TRTInt8Calibrator* calibrator_; + std::unique_ptr calibrator_; TrtUniquePtrType builder_; TrtUniquePtrType engine_; std::unique_ptr allocator_; - tensorflow::tensorrt::Logger* logger_; + tensorflow::tensorrt::Logger logger_; // TODO(sami): Use threadpool threads! - std::thread* thr_; + std::unique_ptr thr_; }; class TRTWeightStore { -- GitLab From 856adff285f4fb271baee5603fdb623f1e32e744 Mon Sep 17 00:00:00 2001 From: Mark Daoust Date: Wed, 20 Jun 2018 10:27:00 -0700 Subject: [PATCH 737/816] Hide py3 names we don't need to document. PiperOrigin-RevId: 201374225 --- tensorflow/tools/docs/parser.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/tensorflow/tools/docs/parser.py b/tensorflow/tools/docs/parser.py index 64e02589bb..ffb93027ed 100644 --- a/tensorflow/tools/docs/parser.py +++ b/tensorflow/tools/docs/parser.py @@ -1166,7 +1166,7 @@ class _ClassPageInfo(object): if short_name in [ '__class__', '__base__', '__weakref__', '__doc__', '__module__', '__dict__', '__abstractmethods__', '__slots__', '__getnewargs__', - '__str__', '__repr__', '__hash__' + '__str__', '__repr__', '__hash__', '__reduce__' ]: continue @@ -1370,7 +1370,8 @@ class _ModulePageInfo(object): for name in member_names: if name in ['__builtins__', '__doc__', '__file__', - '__name__', '__path__', '__package__']: + '__name__', '__path__', '__package__', + '__cached__', '__loader__', '__spec__']: continue member_full_name = self.full_name + '.' + name if self.full_name else name -- GitLab From 88625ad7257ecf9d33f36f8395bf00a427a8f4e3 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 20 Jun 2018 10:27:32 -0700 Subject: [PATCH 738/816] 16-bit quantized add support in TFLite interpreter PiperOrigin-RevId: 201374318 --- tensorflow/contrib/lite/interpreter.h | 4 + tensorflow/contrib/lite/kernels/add.cc | 193 +++++++++++++----- tensorflow/contrib/lite/kernels/add_test.cc | 38 +++- .../internal/optimized/optimized_ops.h | 34 +-- .../kernels/internal/quantization_util.cc | 13 ++ .../lite/kernels/internal/quantization_util.h | 5 + .../internal/reference/reference_ops.h | 32 ++- .../contrib/lite/kernels/kernel_util.cc | 44 +++- tensorflow/contrib/lite/kernels/kernel_util.h | 5 + tensorflow/contrib/lite/kernels/test_util.h | 3 + 10 files changed, 286 insertions(+), 85 deletions(-) diff --git a/tensorflow/contrib/lite/interpreter.h b/tensorflow/contrib/lite/interpreter.h index 436c1007af..6b36bfc11f 100644 --- a/tensorflow/contrib/lite/interpreter.h +++ b/tensorflow/contrib/lite/interpreter.h @@ -39,6 +39,10 @@ constexpr TfLiteType typeToTfLiteType() { return kTfLiteInt32; } template <> +constexpr TfLiteType typeToTfLiteType() { + return kTfLiteInt16; +} +template <> constexpr TfLiteType typeToTfLiteType() { return kTfLiteInt64; } diff --git a/tensorflow/contrib/lite/kernels/add.cc b/tensorflow/contrib/lite/kernels/add.cc index 443ce8924a..ccb957ebc5 100644 --- a/tensorflow/contrib/lite/kernels/add.cc +++ b/tensorflow/contrib/lite/kernels/add.cc @@ -39,6 +39,23 @@ constexpr int kOutputTensor = 0; struct OpData { bool requires_broadcast; + + // These fields are used in both the general 8-bit -> 8bit quantized path, + // and the special 16-bit -> 16bit quantized path + int input1_shift; + int input2_shift; + int32 output_activation_min; + int32 output_activation_max; + + // These fields are used only in the general 8-bit -> 8bit quantized path + int32 input1_multiplier; + int32 input2_multiplier; + int32 output_multiplier; + int output_shift; + int left_shift; + int32 input1_offset; + int32 input2_offset; + int32 output_offset; }; void* Init(TfLiteContext* context, const char* buffer, size_t length) { @@ -52,6 +69,7 @@ void Free(TfLiteContext* context, void* buffer) { } TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { + auto* params = reinterpret_cast(node->builtin_data); OpData* data = reinterpret_cast(node->user_data); TF_LITE_ENSURE_EQ(context, NumInputs(node), 2); @@ -74,6 +92,80 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { output_size = TfLiteIntArrayCopy(input1->dims); } + if (output->type == kTfLiteUInt8) { + // 8bit -> 8bit general quantized path, with general rescalings + data->input1_offset = -input1->params.zero_point; + data->input2_offset = -input2->params.zero_point; + data->output_offset = output->params.zero_point; + data->left_shift = 20; + const double twice_max_input_scale = + 2 * std::max(input1->params.scale, input2->params.scale); + const double real_input1_multiplier = + input1->params.scale / twice_max_input_scale; + const double real_input2_multiplier = + input2->params.scale / twice_max_input_scale; + const double real_output_multiplier = + twice_max_input_scale / + ((1 << data->left_shift) * output->params.scale); + + QuantizeMultiplierSmallerThanOneExp( + real_input1_multiplier, &data->input1_multiplier, &data->input1_shift); + data->input1_shift *= -1; + + QuantizeMultiplierSmallerThanOneExp( + real_input2_multiplier, &data->input2_multiplier, &data->input2_shift); + data->input2_shift *= -1; + + QuantizeMultiplierSmallerThanOneExp( + real_output_multiplier, &data->output_multiplier, &data->output_shift); + data->output_shift *= -1; + + CalculateActivationRangeUint8(params->activation, output, + &data->output_activation_min, + &data->output_activation_max); + + } else if (output->type == kTfLiteInt16) { + // 16bit -> 16bit special quantized path, supporting only a rather + // narrow case of quantization parameters: zero_points must all be 0 + // ("symmetric quantization") and scales must be power-of-two (which + // we abbreviate as "POT" below). The intended use case for this path + // is in LSTM cells, where, due to the constraints of implementing + // some of the math in these LSTM cells in fixed-point arithmetic, + // we need to have such symmetric, power-of-two quantization + // (Fixed-point formats are inherently symmetric, power-of-two). + TF_LITE_ENSURE_EQ(context, input1->params.zero_point, 0); + TF_LITE_ENSURE_EQ(context, input2->params.zero_point, 0); + TF_LITE_ENSURE_EQ(context, output->params.zero_point, 0); + + int input1_scale_log2_rounded; + bool input1_scale_is_pot = + CheckedLog2(input1->params.scale, &input1_scale_log2_rounded); + TF_LITE_ENSURE(context, input1_scale_is_pot); + + int input2_scale_log2_rounded; + bool input2_scale_is_pot = + CheckedLog2(input2->params.scale, &input2_scale_log2_rounded); + TF_LITE_ENSURE(context, input2_scale_is_pot); + + int output_scale_log2_rounded; + bool output_scale_is_pot = + CheckedLog2(output->params.scale, &output_scale_log2_rounded); + TF_LITE_ENSURE(context, output_scale_is_pot); + + data->input1_shift = output_scale_log2_rounded - input1_scale_log2_rounded; + data->input2_shift = output_scale_log2_rounded - input2_scale_log2_rounded; + + // Shifting of one input is supported. The graph quantization should ensure + // that the other input matches the output. + TF_LITE_ENSURE(context, data->input1_shift == 0 || data->input2_shift == 0); + TF_LITE_ENSURE(context, data->input1_shift >= 0); + TF_LITE_ENSURE(context, data->input2_shift >= 0); + + CalculateActivationRangeQuantized(context, params->activation, output, + &data->output_activation_min, + &data->output_activation_max); + } + return context->ResizeTensor(context, output, output_size); } @@ -107,59 +199,47 @@ void EvalAddFloat(TfLiteContext* context, TfLiteNode* node, } template -void EvalAddQuantized(TfLiteContext* context, TfLiteNode* node, - TfLiteAddParams* params, const OpData* data, - const TfLiteTensor* input1, const TfLiteTensor* input2, - TfLiteTensor* output) { - auto input1_offset = -input1->params.zero_point; - auto input2_offset = -input2->params.zero_point; - auto output_offset = output->params.zero_point; - const int left_shift = 20; - const double twice_max_input_scale = - 2 * std::max(input1->params.scale, input2->params.scale); - const double real_input1_multiplier = - input1->params.scale / twice_max_input_scale; - const double real_input2_multiplier = - input2->params.scale / twice_max_input_scale; - const double real_output_multiplier = - twice_max_input_scale / ((1 << left_shift) * output->params.scale); - - int32 input1_multiplier; - int input1_shift; - QuantizeMultiplierSmallerThanOneExp(real_input1_multiplier, - &input1_multiplier, &input1_shift); - input1_shift *= -1; - int32 input2_multiplier; - int input2_shift; - QuantizeMultiplierSmallerThanOneExp(real_input2_multiplier, - &input2_multiplier, &input2_shift); - input2_shift *= -1; - int32 output_multiplier; - int output_shift; - QuantizeMultiplierSmallerThanOneExp(real_output_multiplier, - &output_multiplier, &output_shift); - output_shift *= -1; - - int32 output_activation_min, output_activation_max; - CalculateActivationRangeUint8(params->activation, output, - &output_activation_min, &output_activation_max); - -#define TF_LITE_ADD(type, opname) \ - type::opname(left_shift, GetTensorData(input1), \ - GetTensorDims(input1), input1_offset, input1_multiplier, \ - input1_shift, GetTensorData(input2), \ - GetTensorDims(input2), input2_offset, input2_multiplier, \ - input2_shift, output_offset, output_multiplier, output_shift, \ - output_activation_min, output_activation_max, \ - GetTensorData(output), GetTensorDims(output)); - // The quantized version of Add doesn't support activations, so we - // always use BroadcastAdd. - if (kernel_type == kReference) { - TF_LITE_ADD(reference_ops, BroadcastAdd); - } else { - TF_LITE_ADD(optimized_ops, BroadcastAdd); - } +TfLiteStatus EvalAddQuantized(TfLiteContext* context, TfLiteNode* node, + TfLiteAddParams* params, const OpData* data, + const TfLiteTensor* input1, + const TfLiteTensor* input2, + TfLiteTensor* output) { + if (output->type == kTfLiteUInt8) { +#define TF_LITE_ADD(type, opname) \ + type::opname( \ + data->left_shift, GetTensorData(input1), GetTensorDims(input1), \ + data->input1_offset, data->input1_multiplier, data->input1_shift, \ + GetTensorData(input2), GetTensorDims(input2), \ + data->input2_offset, data->input2_multiplier, data->input2_shift, \ + data->output_offset, data->output_multiplier, data->output_shift, \ + data->output_activation_min, data->output_activation_max, \ + GetTensorData(output), GetTensorDims(output)); + // The quantized version of Add doesn't support activations, so we + // always use BroadcastAdd. + if (kernel_type == kReference) { + TF_LITE_ADD(reference_ops, BroadcastAdd); + } else { + TF_LITE_ADD(optimized_ops, BroadcastAdd); + } #undef TF_LITE_ADD + } else if (output->type == kTfLiteInt16) { +#define TF_LITE_ADD(type, opname) \ + type::opname(GetTensorData(input1), GetTensorDims(input1), \ + data->input1_shift, GetTensorData(input2), \ + GetTensorDims(input2), data->input2_shift, \ + data->output_activation_min, data->output_activation_max, \ + GetTensorData(output), GetTensorDims(output)); + // The quantized version of Add doesn't support activations, so we + // always use BroadcastAdd. + if (kernel_type == kReference) { + TF_LITE_ADD(reference_ops, Add); + } else { + TF_LITE_ADD(optimized_ops, Add); + } +#undef TF_LITE_ADD + } + + return kTfLiteOk; } template @@ -174,12 +254,13 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { if (output->type == kTfLiteFloat32) { EvalAddFloat(context, node, params, data, input1, input2, output); - } else if (output->type == kTfLiteUInt8) { - EvalAddQuantized(context, node, params, data, input1, input2, - output); + } else if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt16) { + TF_LITE_ENSURE_OK(context, + EvalAddQuantized(context, node, params, data, + input1, input2, output)); } else { context->ReportError(context, - "Inputs and outputs not all float|uint8 types."); + "Inputs and outputs not all float|uint8|int16 types."); return kTfLiteError; } diff --git a/tensorflow/contrib/lite/kernels/add_test.cc b/tensorflow/contrib/lite/kernels/add_test.cc index 956d05bed5..456a754e7e 100644 --- a/tensorflow/contrib/lite/kernels/add_test.cc +++ b/tensorflow/contrib/lite/kernels/add_test.cc @@ -60,15 +60,26 @@ class QuantizedAddOpModel : public BaseAddOpModel { return Dequantize(ExtractVector(output_), GetScale(output_), GetZeroPoint(output_)); } + + std::vector GetDequantizedOutputInt16() { + return Dequantize(ExtractVector(output_), + GetScale(output_), GetZeroPoint(output_)); + } }; // for quantized Add, the error shouldn't exceed 2*step -float GetTolerance(int min, int max) { +float GetTolerance(float min, float max) { float kQuantizedStep = (max - min) / 255.0; float kQuantizedTolerance = 2.0 * kQuantizedStep; return kQuantizedTolerance; } +float GetToleranceInt16(float min, float max) { + float kQuantizedStep = (max - min) / 32767.f; + float kQuantizedTolerance = 2.0 * kQuantizedStep; + return kQuantizedTolerance; +} + TEST(FloatAddOpModel, NoActivation) { FloatAddOpModel m({TensorType_FLOAT32, {1, 2, 2, 1}}, {TensorType_FLOAT32, {1, 2, 2, 1}}, @@ -144,6 +155,31 @@ TEST(QuantizedAddOpModel, QuantizedTestsNoActivation) { } } +TEST(QuantizedAddOpModel, QuantizedTestsNoActivationInt16) { + const float kMin = -1.f; + const float kMax = 32767.f / 32768.f; + float kQuantizedTolerance = GetToleranceInt16(kMin, kMax); + std::vector> inputs1 = { + {0.1, 0.2, 0.3, 0.4}, {-0.8, 0.2, 0.4, 0.7}, {-0.8, 0.2, 0.7, 0.3}}; + std::vector> inputs2 = { + {0.6, 0.4, 0.3, 0.1}, {0.6, 0.4, 0.5, -0.8}, {0.6, 0.4, -0.8, 0.5}}; + std::vector> results = { + {0.7, 0.6, 0.6, 0.5}, {-0.2, 0.6, 0.9, -0.1}, {-0.2, 0.6, -0.1, 0.8}}; + for (int i = 0; i < inputs1.size(); ++i) { + QuantizedAddOpModel m({TensorType_INT16, {1, 2, 2, 1}, kMin, kMax}, + {TensorType_INT16, {1, 2, 2, 1}, kMin, kMax}, + {TensorType_INT16, {}, kMin, kMax}, + ActivationFunctionType_NONE); + m.QuantizeAndPopulate(m.input1(), inputs1[i]); + m.QuantizeAndPopulate(m.input2(), inputs2[i]); + m.Invoke(); + EXPECT_THAT( + m.GetDequantizedOutputInt16(), + ElementsAreArray(ArrayFloatNear(results[i], kQuantizedTolerance))) + << "With test number " << i; + } +} + TEST(QuantizedAddOpModel, QuantizedTestsActivationRELU_N1_TO_1) { float kQuantizedTolerance = GetTolerance(-1.0, 1.0); std::vector> inputs1 = {{-0.8, 0.2, 0.9, 0.7}, diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h index cf989ce51d..107e95ea6e 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h @@ -2658,25 +2658,13 @@ inline void Add(int left_shift, const uint8* input1_data, output_activation_max, output_data); } -template inline void Add(const int16* input1_data, const Dims<4>& input1_dims, int input1_shift, const int16* input2_data, const Dims<4>& input2_dims, int input2_shift, int16 output_activation_min, int16 output_activation_max, int16* output_data, const Dims<4>& output_dims) { gemmlowp::ScopedProfilingLabel label("Add/Int16"); - // This is a copy of the reference implementation. We do not currently have a - // properly optimized version. - static_assert(Ac == FusedActivationFunctionType::kNone || - Ac == FusedActivationFunctionType::kRelu || - Ac == FusedActivationFunctionType::kRelu6 || - Ac == FusedActivationFunctionType::kRelu1, - ""); TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - if (Ac == FusedActivationFunctionType::kNone) { - TFLITE_DCHECK_EQ(output_activation_min, -32768); - TFLITE_DCHECK_EQ(output_activation_max, 32767); - } const int flat_size = MatchingFlatSize(output_dims, input1_dims, input2_dims); @@ -2702,6 +2690,28 @@ inline void Add(const int16* input1_data, const Dims<4>& input1_dims, } } +template +inline void Add(const int16* input1_data, const Dims<4>& input1_dims, + int input1_shift, const int16* input2_data, + const Dims<4>& input2_dims, int input2_shift, + int16 output_activation_min, int16 output_activation_max, + int16* output_data, const Dims<4>& output_dims) { + static_assert(Ac == FusedActivationFunctionType::kNone || + Ac == FusedActivationFunctionType::kRelu || + Ac == FusedActivationFunctionType::kRelu6 || + Ac == FusedActivationFunctionType::kRelu1, + ""); + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + if (Ac == FusedActivationFunctionType::kNone) { + TFLITE_DCHECK_EQ(output_activation_min, -32768); + TFLITE_DCHECK_EQ(output_activation_max, 32767); + } + + Add(input1_data, input1_dims, input1_shift, input2_data, input2_dims, + input2_shift, output_activation_min, output_activation_max, output_data, + output_dims); +} + template void Add(const int32* input1_data, const Dims<4>& input1_dims, const int32* input2_data, const Dims<4>& input2_dims, diff --git a/tensorflow/contrib/lite/kernels/internal/quantization_util.cc b/tensorflow/contrib/lite/kernels/internal/quantization_util.cc index 57ee859115..e224980493 100644 --- a/tensorflow/contrib/lite/kernels/internal/quantization_util.cc +++ b/tensorflow/contrib/lite/kernels/internal/quantization_util.cc @@ -12,6 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ + #include #include #include @@ -126,4 +127,16 @@ void NudgeQuantizationRange(const float min, const float max, *nudged_max = (quant_max_float - nudged_zero_point) * (*scale); } +bool CheckedLog2(const float x, int* log2_result) { + // Using TfLiteRound instead of std::round and std::log instead of + // std::log2 to work around these fuctions being missing in a toolchain + // used in some TensorFlow tests as of May 2018. + const float x_log2 = std::log(x) * (1.0f / std::log(2.0f)); + const float x_log2_rounded = TfLiteRound(x_log2); + const float x_log2_fracpart = x_log2 - x_log2_rounded; + + *log2_result = static_cast(x_log2_rounded); + return std::abs(x_log2_fracpart) < 1e-3; +} + } // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/internal/quantization_util.h b/tensorflow/contrib/lite/kernels/internal/quantization_util.h index 182ee782c7..525857a2e6 100644 --- a/tensorflow/contrib/lite/kernels/internal/quantization_util.h +++ b/tensorflow/contrib/lite/kernels/internal/quantization_util.h @@ -218,6 +218,11 @@ void NudgeQuantizationRange(const float min, const float max, const int quant_min, const int quant_max, float* nudged_min, float* nudged_max, float* scale); +// If x is approximately a power of two (with any positive or negative +// exponent), stores that exponent (i.e. log2(x)) in *log2_result, otherwise +// returns false. +bool CheckedLog2(const float x, int* log2_result); + } // namespace tflite #endif // TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_QUANTIZATION_UTIL_H_ diff --git a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h index 1908f7fa6c..483bd37ef9 100644 --- a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h @@ -1134,22 +1134,12 @@ inline void Add(int left_shift, const uint8* input1_data, } } -template inline void Add(const int16* input1_data, const Dims<4>& input1_dims, int input1_shift, const int16* input2_data, const Dims<4>& input2_dims, int input2_shift, int16 output_activation_min, int16 output_activation_max, int16* output_data, const Dims<4>& output_dims) { - static_assert(Ac == FusedActivationFunctionType::kNone || - Ac == FusedActivationFunctionType::kRelu || - Ac == FusedActivationFunctionType::kRelu6 || - Ac == FusedActivationFunctionType::kRelu1, - ""); TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - if (Ac == FusedActivationFunctionType::kNone) { - TFLITE_DCHECK_EQ(output_activation_min, -32768); - TFLITE_DCHECK_EQ(output_activation_max, 32767); - } const int flat_size = MatchingFlatSize(output_dims, input1_dims, input2_dims); @@ -1175,6 +1165,28 @@ inline void Add(const int16* input1_data, const Dims<4>& input1_dims, } } +template +inline void Add(const int16* input1_data, const Dims<4>& input1_dims, + int input1_shift, const int16* input2_data, + const Dims<4>& input2_dims, int input2_shift, + int16 output_activation_min, int16 output_activation_max, + int16* output_data, const Dims<4>& output_dims) { + static_assert(Ac == FusedActivationFunctionType::kNone || + Ac == FusedActivationFunctionType::kRelu || + Ac == FusedActivationFunctionType::kRelu6 || + Ac == FusedActivationFunctionType::kRelu1, + ""); + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + if (Ac == FusedActivationFunctionType::kNone) { + TFLITE_DCHECK_EQ(output_activation_min, -32768); + TFLITE_DCHECK_EQ(output_activation_max, 32767); + } + + Add(input1_data, input1_dims, input1_shift, input2_data, input2_dims, + input2_shift, output_activation_min, output_activation_max, output_data, + output_dims); +} + // TODO(jiawen): We can implement BroadcastAdd on buffers of arbitrary // dimensionality if the runtime code does a single loop over one dimension // that handles broadcasting as the base case. The code generator would then diff --git a/tensorflow/contrib/lite/kernels/kernel_util.cc b/tensorflow/contrib/lite/kernels/kernel_util.cc index 184028427f..fdf9856912 100644 --- a/tensorflow/contrib/lite/kernels/kernel_util.cc +++ b/tensorflow/contrib/lite/kernels/kernel_util.cc @@ -43,12 +43,11 @@ TfLiteStatus GetQuantizedConvolutionMultipler(TfLiteContext* context, return kTfLiteOk; } -void CalculateActivationRangeUint8(TfLiteFusedActivation activation, - TfLiteTensor* output, int32_t* act_min, - int32_t* act_max) { - const int32_t qmin = std::numeric_limits::min(); - const int32_t qmax = std::numeric_limits::max(); - +namespace { +void CalculateActivationRangeQuantizedImpl(TfLiteFusedActivation activation, + int32_t qmin, int32_t qmax, + TfLiteTensor* output, + int32_t* act_min, int32_t* act_max) { const auto scale = output->params.scale; const auto zero_point = output->params.zero_point; @@ -70,6 +69,39 @@ void CalculateActivationRangeUint8(TfLiteFusedActivation activation, *act_max = qmax; } } +} // namespace + +TfLiteStatus CalculateActivationRangeQuantized(TfLiteContext* context, + TfLiteFusedActivation activation, + TfLiteTensor* output, + int32_t* act_min, + int32_t* act_max) { + int32_t qmin = 0; + int32_t qmax = 0; + if (output->type == kTfLiteUInt8) { + qmin = std::numeric_limits::min(); + qmax = std::numeric_limits::max(); + } else if (output->type == kTfLiteInt16) { + qmin = std::numeric_limits::min(); + qmax = std::numeric_limits::max(); + } else { + TF_LITE_ENSURE(context, false); + } + + CalculateActivationRangeQuantizedImpl(activation, qmin, qmax, output, act_min, + act_max); + return kTfLiteOk; +} + +void CalculateActivationRangeUint8(TfLiteFusedActivation activation, + TfLiteTensor* output, int32_t* act_min, + int32_t* act_max) { + const int32_t qmin = std::numeric_limits::min(); + const int32_t qmax = std::numeric_limits::max(); + + CalculateActivationRangeQuantizedImpl(activation, qmin, qmax, output, act_min, + act_max); +} void CalculateActivationRangeFloat(TfLiteFusedActivation activation, float* activation_min, diff --git a/tensorflow/contrib/lite/kernels/kernel_util.h b/tensorflow/contrib/lite/kernels/kernel_util.h index 82cded36f2..20058a5f69 100644 --- a/tensorflow/contrib/lite/kernels/kernel_util.h +++ b/tensorflow/contrib/lite/kernels/kernel_util.h @@ -88,6 +88,11 @@ TfLiteStatus GetQuantizedConvolutionMultipler(TfLiteContext* context, // Calculates the useful range of an activation layer given its activation // tensor. +TfLiteStatus CalculateActivationRangeQuantized(TfLiteContext* context, + TfLiteFusedActivation activation, + TfLiteTensor* output, + int32_t* act_min, + int32_t* act_max); void CalculateActivationRangeUint8(TfLiteFusedActivation activation, TfLiteTensor* output, int32_t* act_min, int32_t* act_max); diff --git a/tensorflow/contrib/lite/kernels/test_util.h b/tensorflow/contrib/lite/kernels/test_util.h index 6dcece4af6..5094e1343a 100644 --- a/tensorflow/contrib/lite/kernels/test_util.h +++ b/tensorflow/contrib/lite/kernels/test_util.h @@ -280,6 +280,9 @@ class SingleOpModel { } else if (t.type == TensorType_INT32) { std::tie(t.scale, t.zero_point) = QuantizationParams(t.min, t.max); + } else if (t.type == TensorType_INT16) { + std::tie(t.scale, t.zero_point) = + QuantizationParams(t.min, t.max); } else { LOG(FATAL) << "No support for the requested quantized type"; } -- GitLab From 2b45f14362aaa00cf7fc640f375048bffba98655 Mon Sep 17 00:00:00 2001 From: Anjali Sridhar Date: Wed, 20 Jun 2018 10:54:40 -0700 Subject: [PATCH 739/816] Allow TowerLocalVars to be updated with the same value across all towers. PiperOrigin-RevId: 201379124 --- .../distribute/python/mirrored_strategy.py | 5 ++- .../python/mirrored_strategy_multigpu_test.py | 36 +++++++++++++++++++ 2 files changed, 38 insertions(+), 3 deletions(-) diff --git a/tensorflow/contrib/distribute/python/mirrored_strategy.py b/tensorflow/contrib/distribute/python/mirrored_strategy.py index c1b4b870a5..dc270ac540 100644 --- a/tensorflow/contrib/distribute/python/mirrored_strategy.py +++ b/tensorflow/contrib/distribute/python/mirrored_strategy.py @@ -323,14 +323,13 @@ class MirroredStrategy(distribute_lib.DistributionStrategy): value_destination_pairs) def _update(self, var, fn, *args, **kwargs): - # TODO(josh11b): Also support TowerLocalVariables here? If so, args and - # kwargs don't need to be mirrored. - assert isinstance(var, values.MirroredVariable) # TODO(josh11b): In eager mode, use one thread per device. + assert isinstance(var, values.DistributedVariable) updates = {} for d, v in var._index.items(): # pylint: disable=protected-access name = "update_%d" % self._device_index.get(d) with ops.device(d), distribute_lib.UpdateContext(d), ops.name_scope(name): + # If args and kwargs are not mirrored, the value is returned as is. updates[d] = fn(v, *values.select_device_mirrored(d, args), **values.select_device_mirrored(d, kwargs)) diff --git a/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py b/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py index bccd278847..7b41cfe064 100644 --- a/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py +++ b/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py @@ -530,6 +530,42 @@ class MirroredStrategyVariableCreationTest(test.TestCase): _, v1 = dist.unwrap(v) self.assertStartsWith(v1.name, "tower_1/") + @test_util.run_in_graph_and_eager_modes(config=config) + def testTowerLocalVariableUpdate(self): + with context.graph_mode(): + + def model_fn(): + tower_context = distribute_lib.get_tower_context() + with tower_context.tower_local_var_scope("sum"): + v_sum = variable_scope.variable(1.0) + self.assertTrue(isinstance(v_sum, values.TowerLocalVariable)) + return v_sum + + dist = mirrored_strategy.MirroredStrategy( + ["/device:GPU:0", "/device:GPU:1"]) + + def update(var, value): + return var.assign(value) + + with dist.scope(): + ret_v_sum = dist.call_for_each_tower(model_fn, run_concurrently=False) + update_ops = dist.unwrap(dist.update(ret_v_sum, update, 5.0)) + + # Initialize variables. + self.evaluate(variables.global_variables_initializer()) + # Assert that the aggregated value of the tower local vars is the sum of + # the individual values before running the update ops. + self.assertEquals(1.0, self.evaluate( + ret_v_sum.get(dist._devices[0]).read_value())) + self.assertEquals(2.0, self.evaluate(dist.read_var(ret_v_sum))) + # Apply updates. + self.evaluate(update_ops) + # Assert that the aggregated value of the tower local vars is the sum of + # the individual values after running the update ops. + self.assertEquals(5.0, self.evaluate( + ret_v_sum.get(dist._devices[0]).read_value())) + self.assertEquals(10.0, self.evaluate(dist.read_var(ret_v_sum))) + if __name__ == "__main__": test.main() -- GitLab From 58759659ee547a957c5d36e72f2274ab34fdb6cb Mon Sep 17 00:00:00 2001 From: Jongmin Baek Date: Wed, 20 Jun 2018 11:01:53 -0700 Subject: [PATCH 740/816] Fix OOB check for result_index in header generation --- tensorflow/compiler/aot/codegen.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/compiler/aot/codegen.cc b/tensorflow/compiler/aot/codegen.cc index 0025842aea..28070d60db 100644 --- a/tensorflow/compiler/aot/codegen.cc +++ b/tensorflow/compiler/aot/codegen.cc @@ -287,7 +287,7 @@ Status GenerateHeader(const CodegenOpts& opts, const tf2xla::Config& config, TF_RETURN_IF_ERROR(ValidateFeedFetchCppNames(config)); const int64 result_index = compile_result.aot->result_buffer_index(); const xla::BufferSizes& temp_sizes = compile_result.aot->buffer_sizes(); - if (result_index < 0 || result_index > temp_sizes.size()) { + if (result_index < 0 || result_index >= temp_sizes.size()) { return errors::InvalidArgument("result index: ", result_index, " is outside the range of temp sizes: [0,", temp_sizes.size(), ")"); -- GitLab From faba438ed136a477b0ede80d90a18d47478473e7 Mon Sep 17 00:00:00 2001 From: Dimitris Vardoulakis Date: Wed, 20 Jun 2018 11:15:23 -0700 Subject: [PATCH 741/816] [TF:XLA] Change hlo_domain_test to use HloVerifiedTestBase. PiperOrigin-RevId: 201383246 --- tensorflow/compiler/xla/service/BUILD | 1 + .../compiler/xla/service/hlo_domain_test.cc | 124 +++++++++--------- .../compiler/xla/service/hlo_sharding.h | 6 + .../xla/tests/hlo_verified_test_base.cc | 6 +- .../xla/tests/hlo_verified_test_base.h | 3 +- 5 files changed, 71 insertions(+), 69 deletions(-) diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD index 396ce13e7f..6b89db633d 100644 --- a/tensorflow/compiler/xla/service/BUILD +++ b/tensorflow/compiler/xla/service/BUILD @@ -2399,6 +2399,7 @@ tf_cc_test( "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla/legacy_flags:debug_options_flags", "//tensorflow/compiler/xla/tests:hlo_test_base", + "//tensorflow/compiler/xla/tests:hlo_verified_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:test", ], diff --git a/tensorflow/compiler/xla/service/hlo_domain_test.cc b/tensorflow/compiler/xla/service/hlo_domain_test.cc index 5553ddb153..5d8081c1ef 100644 --- a/tensorflow/compiler/xla/service/hlo_domain_test.cc +++ b/tensorflow/compiler/xla/service/hlo_domain_test.cc @@ -21,12 +21,13 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_sharding_metadata.h" #include "tensorflow/compiler/xla/test.h" #include "tensorflow/compiler/xla/tests/hlo_test_base.h" +#include "tensorflow/compiler/xla/tests/hlo_verified_test_base.h" #include "tensorflow/core/lib/core/status_test_util.h" namespace xla { namespace { -class HloDomainTest : public HloTestBase { +class HloDomainTest : public HloVerifiedTestBase { protected: bool FindUserViaDomainPath(HloInstruction* instruction, HloInstruction* operand) const { @@ -64,11 +65,11 @@ class HloDomainTest : public HloTestBase { return false; } - StatusOr> ParseModule( - tensorflow::StringPiece hlo_string) { + StatusOr ParseModule(tensorflow::StringPiece hlo_string) { HloModuleConfig config; config.set_debug_options(legacy_flags::GetDebugOptionsFromFlags()); - return ParseHloString(hlo_string, config); + ParseAndVerifyModule(hlo_string, config); + return &module(); } }; @@ -143,32 +144,31 @@ ENTRY entry { } )"; - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module, - ParseModule(hlo_string)); + TF_ASSERT_OK_AND_ASSIGN(HloModule * module, ParseModule(hlo_string)); LOG(INFO) << "Original module:\n" << module->ToString(); HloDomainIsolator isolator(CreateShardingDomain); - TF_ASSERT_OK_AND_ASSIGN(bool isolator_changed, isolator.Run(module.get())); + TF_ASSERT_OK_AND_ASSIGN(bool isolator_changed, isolator.Run(module)); EXPECT_TRUE(isolator_changed); - EXPECT_TRUE(HasDomainEdge(module.get(), "c", "a")); - EXPECT_TRUE(HasDomainEdge(module.get(), "c", "b")); - EXPECT_TRUE(HasDomainEdge(module.get(), "d", "a")); - EXPECT_TRUE(HasDomainEdge(module.get(), "d", "b")); - EXPECT_FALSE(HasDomainEdge(module.get(), "e", "c")); - EXPECT_FALSE(HasDomainEdge(module.get(), "e", "d")); + EXPECT_TRUE(HasDomainEdge(module, "c", "a")); + EXPECT_TRUE(HasDomainEdge(module, "c", "b")); + EXPECT_TRUE(HasDomainEdge(module, "d", "a")); + EXPECT_TRUE(HasDomainEdge(module, "d", "b")); + EXPECT_FALSE(HasDomainEdge(module, "e", "c")); + EXPECT_FALSE(HasDomainEdge(module, "e", "d")); HloDomainRemover remover(ShardingMetadata::KindName(), NormalizeShardingDomain); - TF_ASSERT_OK_AND_ASSIGN(bool remover_changed, remover.Run(module.get())); + TF_ASSERT_OK_AND_ASSIGN(bool remover_changed, remover.Run(module)); EXPECT_TRUE(remover_changed); - EXPECT_FALSE(HasDomainEdge(module.get(), "c", "a")); - EXPECT_FALSE(HasDomainEdge(module.get(), "c", "b")); - EXPECT_FALSE(HasDomainEdge(module.get(), "d", "a")); - EXPECT_FALSE(HasDomainEdge(module.get(), "d", "b")); - EXPECT_FALSE(HasDomainEdge(module.get(), "e", "c")); - EXPECT_FALSE(HasDomainEdge(module.get(), "e", "d")); + EXPECT_FALSE(HasDomainEdge(module, "c", "a")); + EXPECT_FALSE(HasDomainEdge(module, "c", "b")); + EXPECT_FALSE(HasDomainEdge(module, "d", "a")); + EXPECT_FALSE(HasDomainEdge(module, "d", "b")); + EXPECT_FALSE(HasDomainEdge(module, "e", "c")); + EXPECT_FALSE(HasDomainEdge(module, "e", "d")); } TEST_F(HloDomainTest, CheckNoDomainAddedIfNoSharding) { @@ -186,12 +186,11 @@ ENTRY entry { } )"; - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module, - ParseModule(hlo_string)); + TF_ASSERT_OK_AND_ASSIGN(HloModule * module, ParseModule(hlo_string)); LOG(INFO) << "Original module:\n" << module->ToString(); HloDomainIsolator isolator(CreateShardingDomain); - TF_ASSERT_OK_AND_ASSIGN(bool isolator_changed, isolator.Run(module.get())); + TF_ASSERT_OK_AND_ASSIGN(bool isolator_changed, isolator.Run(module)); EXPECT_TRUE(!isolator_changed); } @@ -212,27 +211,26 @@ ENTRY entry { } )"; - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module, - ParseModule(hlo_string)); + TF_ASSERT_OK_AND_ASSIGN(HloModule * module, ParseModule(hlo_string)); LOG(INFO) << "Original module:\n" << module->ToString(); HloDomainIsolator isolator(CreateShardingDomain); - TF_ASSERT_OK_AND_ASSIGN(bool isolator_changed, isolator.Run(module.get())); + TF_ASSERT_OK_AND_ASSIGN(bool isolator_changed, isolator.Run(module)); EXPECT_TRUE(isolator_changed); - EXPECT_TRUE(HasDomainEdge(module.get(), "b", "a")); - EXPECT_TRUE(HasDomainEdge(module.get(), "f", "e")); - EXPECT_FALSE(HasDomainEdge(module.get(), "a", "p0")); - EXPECT_FALSE(HasDomainEdge(module.get(), "c", "b")); - EXPECT_FALSE(HasDomainEdge(module.get(), "e", "d")); + EXPECT_TRUE(HasDomainEdge(module, "b", "a")); + EXPECT_TRUE(HasDomainEdge(module, "f", "e")); + EXPECT_FALSE(HasDomainEdge(module, "a", "p0")); + EXPECT_FALSE(HasDomainEdge(module, "c", "b")); + EXPECT_FALSE(HasDomainEdge(module, "e", "d")); HloDomainRemover remover(ShardingMetadata::KindName(), NormalizeShardingDomain); - TF_ASSERT_OK_AND_ASSIGN(bool remover_changed, remover.Run(module.get())); + TF_ASSERT_OK_AND_ASSIGN(bool remover_changed, remover.Run(module)); EXPECT_TRUE(remover_changed); - EXPECT_FALSE(HasDomainEdge(module.get(), "b", "a")); - EXPECT_FALSE(HasDomainEdge(module.get(), "f", "e")); + EXPECT_FALSE(HasDomainEdge(module, "b", "a")); + EXPECT_FALSE(HasDomainEdge(module, "f", "e")); } TEST_F(HloDomainTest, CheckNoDomainAddedOnPureIOComputation) { @@ -248,12 +246,11 @@ ENTRY entry { } )"; - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module, - ParseModule(hlo_string)); + TF_ASSERT_OK_AND_ASSIGN(HloModule * module, ParseModule(hlo_string)); LOG(INFO) << "Original module:\n" << module->ToString(); HloDomainIsolator isolator(CreateShardingDomain); - TF_ASSERT_OK_AND_ASSIGN(bool isolator_changed, isolator.Run(module.get())); + TF_ASSERT_OK_AND_ASSIGN(bool isolator_changed, isolator.Run(module)); EXPECT_FALSE(isolator_changed); } @@ -270,16 +267,15 @@ ENTRY entry { } )"; - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module, - ParseModule(hlo_string)); + TF_ASSERT_OK_AND_ASSIGN(HloModule * module, ParseModule(hlo_string)); LOG(INFO) << "Original module:\n" << module->ToString(); HloDomainRemover remover(ShardingMetadata::KindName(), NormalizeShardingDomain); - TF_ASSERT_OK_AND_ASSIGN(bool remover_changed, remover.Run(module.get())); + TF_ASSERT_OK_AND_ASSIGN(bool remover_changed, remover.Run(module)); EXPECT_FALSE(remover_changed); - HloInstruction* add = FindInstruction(module.get(), "c"); + HloInstruction* add = FindInstruction(module, "c"); ASSERT_NE(add, nullptr); auto device = add->sharding_unique_device(); EXPECT_TRUE(device.has_value()); @@ -302,42 +298,41 @@ ENTRY entry { } )"; - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module, - ParseModule(hlo_string)); + TF_ASSERT_OK_AND_ASSIGN(HloModule * module, ParseModule(hlo_string)); LOG(INFO) << "Original module:\n" << module->ToString(); HloDomainIsolator sharding_isolator(CreateShardingDomain); TF_ASSERT_OK_AND_ASSIGN(bool sharding_isolator_changed, - sharding_isolator.Run(module.get())); + sharding_isolator.Run(module)); EXPECT_TRUE(sharding_isolator_changed); HloDomainIsolator opname_isolator(OpNameDomainCreator); TF_ASSERT_OK_AND_ASSIGN(bool opname_isolator_changed, - opname_isolator.Run(module.get())); + opname_isolator.Run(module)); EXPECT_TRUE(opname_isolator_changed); - EXPECT_TRUE(HasDomainEdge(module.get(), "c", "a")); - EXPECT_TRUE(HasDomainEdge(module.get(), "c", "b")); - EXPECT_TRUE(HasDomainEdge(module.get(), "d", "a")); - EXPECT_TRUE(HasDomainEdge(module.get(), "d", "c")); - EXPECT_FALSE(HasDomainEdge(module.get(), "e", "d")); + EXPECT_TRUE(HasDomainEdge(module, "c", "a")); + EXPECT_TRUE(HasDomainEdge(module, "c", "b")); + EXPECT_TRUE(HasDomainEdge(module, "d", "a")); + EXPECT_TRUE(HasDomainEdge(module, "d", "c")); + EXPECT_FALSE(HasDomainEdge(module, "e", "d")); HloDomainRemover sharding_remover(ShardingMetadata::KindName(), NormalizeShardingDomain); TF_ASSERT_OK_AND_ASSIGN(bool sharding_remover_changed, - sharding_remover.Run(module.get())); + sharding_remover.Run(module)); EXPECT_TRUE(sharding_remover_changed); HloDomainRemover opname_remover(OpNameMetadata::KindName(), OpNameDomainNormalizer); TF_ASSERT_OK_AND_ASSIGN(bool opname_remover_changed, - opname_remover.Run(module.get())); + opname_remover.Run(module)); EXPECT_TRUE(opname_remover_changed); - EXPECT_FALSE(HasDomainEdge(module.get(), "c", "a")); - EXPECT_FALSE(HasDomainEdge(module.get(), "c", "b")); - EXPECT_FALSE(HasDomainEdge(module.get(), "d", "a")); - EXPECT_FALSE(HasDomainEdge(module.get(), "d", "c")); + EXPECT_FALSE(HasDomainEdge(module, "c", "a")); + EXPECT_FALSE(HasDomainEdge(module, "c", "b")); + EXPECT_FALSE(HasDomainEdge(module, "d", "a")); + EXPECT_FALSE(HasDomainEdge(module, "d", "c")); } TEST_F(HloDomainTest, CheckNormalizationOnInfeedTuple) { @@ -355,18 +350,17 @@ ENTRY entry { } )"; - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module, - ParseModule(hlo_string)); + TF_ASSERT_OK_AND_ASSIGN(HloModule * module, ParseModule(hlo_string)); LOG(INFO) << "Original module:\n" << module->ToString(); HloDomainIsolator isolator(CreateShardingDomain); - TF_ASSERT_OK_AND_ASSIGN(bool isolator_changed, isolator.Run(module.get())); + TF_ASSERT_OK_AND_ASSIGN(bool isolator_changed, isolator.Run(module)); EXPECT_TRUE(isolator_changed); - EXPECT_TRUE(HasDomainEdge(module.get(), "gte0", "infeed")); - EXPECT_TRUE(HasDomainEdge(module.get(), "gte1", "infeed")); - EXPECT_FALSE(HasDomainEdge(module.get(), "copy0", "gte0")); - EXPECT_FALSE(HasDomainEdge(module.get(), "copy1", "gte1")); + EXPECT_TRUE(HasDomainEdge(module, "gte0", "infeed")); + EXPECT_TRUE(HasDomainEdge(module, "gte1", "infeed")); + EXPECT_FALSE(HasDomainEdge(module, "copy0", "gte0")); + EXPECT_FALSE(HasDomainEdge(module, "copy1", "gte1")); // Inject unassigned tuple/gte within the infeed domain, to simulate the // HLO passes adding unexpected instructions. @@ -381,7 +375,7 @@ ENTRY entry { // TUPLE // | // DOMAIN - HloInstruction* infeed = FindInstruction(module.get(), "infeed"); + HloInstruction* infeed = FindInstruction(module, "infeed"); ASSERT_NE(infeed, nullptr); auto infeed_users = infeed->users(); HloInstruction* new_gte0 = @@ -404,7 +398,7 @@ ENTRY entry { HloDomainRemover remover(ShardingMetadata::KindName(), NormalizeShardingDomain); - TF_ASSERT_OK_AND_ASSIGN(bool remover_changed, remover.Run(module.get())); + TF_ASSERT_OK_AND_ASSIGN(bool remover_changed, remover.Run(module)); EXPECT_TRUE(remover_changed); struct Assignment { diff --git a/tensorflow/compiler/xla/service/hlo_sharding.h b/tensorflow/compiler/xla/service/hlo_sharding.h index 6a744e0247..1e843481c3 100644 --- a/tensorflow/compiler/xla/service/hlo_sharding.h +++ b/tensorflow/compiler/xla/service/hlo_sharding.h @@ -240,6 +240,12 @@ class HloSharding { tuple_(false), tile_shape_(), tile_assignment_({0}) {} + // device_id values: + // -2: magic number to mean unassigned device, used by spatial partitioning + // -1: the id of the host + // 0 or positive: the id of a device + // NOTE(dimvar): -1 is needed for outside compilation. It can be removed once + // we have fully switched to the side-effect tokens. explicit HloSharding(int64 device_id) : replicated_(false), maximal_(true), diff --git a/tensorflow/compiler/xla/tests/hlo_verified_test_base.cc b/tensorflow/compiler/xla/tests/hlo_verified_test_base.cc index 22c664d142..ad1f5b9eed 100644 --- a/tensorflow/compiler/xla/tests/hlo_verified_test_base.cc +++ b/tensorflow/compiler/xla/tests/hlo_verified_test_base.cc @@ -72,10 +72,10 @@ HloModule* HloVerifiedTestBase::CreateNewModule(const string& name) { return modules_.back().get(); } -void HloVerifiedTestBase::ParseAndVerifyModule( - tensorflow::StringPiece hlo_text) { +void HloVerifiedTestBase::ParseAndVerifyModule(tensorflow::StringPiece hlo_text, + const HloModuleConfig& config) { CHECK(!module_) << "Called ParseModule when test already has a module."; - TF_ASSERT_OK_AND_ASSIGN(module_, ParseHloString(hlo_text)); + TF_ASSERT_OK_AND_ASSIGN(module_, ParseHloString(hlo_text, config)); VerifyModule(module_.get()); } } // namespace xla diff --git a/tensorflow/compiler/xla/tests/hlo_verified_test_base.h b/tensorflow/compiler/xla/tests/hlo_verified_test_base.h index 5b59cc77f6..5b28c01c36 100644 --- a/tensorflow/compiler/xla/tests/hlo_verified_test_base.h +++ b/tensorflow/compiler/xla/tests/hlo_verified_test_base.h @@ -44,7 +44,8 @@ class HloVerifiedTestBase : public HloTestBase { // Returns the default HloModule, lazily creating it if necessary via // HloTestBase::CreateNewModule(). HloModule& module(); - void ParseAndVerifyModule(tensorflow::StringPiece hlo_text); + void ParseAndVerifyModule(tensorflow::StringPiece hlo_text, + const HloModuleConfig& config = HloModuleConfig()); // Sets the shape-size function used during hlo verification. If this isn't // called, a default ShapeVerifier is used instead. -- GitLab From 3bfd3aeb7856f414e511e20493dd1bdf952649cf Mon Sep 17 00:00:00 2001 From: Gunhan Gulsoy Date: Wed, 20 Jun 2018 11:29:27 -0700 Subject: [PATCH 742/816] Update protobuf dependency of TF to 3.6. PiperOrigin-RevId: 201386306 --- .../contrib/cmake/external/protobuf.cmake | 2 +- .../ci_build/install/install_pip_packages.sh | 4 ++-- .../tools/ci_build/install/install_proto3.sh | 2 +- .../install/install_python3.5_pip_packages.sh | 2 +- .../install/install_python3.6_pip_packages.sh | 2 +- tensorflow/tools/pip_package/setup.py | 2 +- tensorflow/workspace.bzl | 24 +++++++++---------- 7 files changed, 19 insertions(+), 19 deletions(-) diff --git a/tensorflow/contrib/cmake/external/protobuf.cmake b/tensorflow/contrib/cmake/external/protobuf.cmake index ab464bc99a..f56fb35a0f 100644 --- a/tensorflow/contrib/cmake/external/protobuf.cmake +++ b/tensorflow/contrib/cmake/external/protobuf.cmake @@ -16,7 +16,7 @@ include (ExternalProject) set(PROTOBUF_INCLUDE_DIRS ${CMAKE_CURRENT_BINARY_DIR}/protobuf/src/protobuf/src) set(PROTOBUF_URL https://github.com/google/protobuf.git) -set(PROTOBUF_TAG b04e5cba356212e4e8c66c61bbe0c3a20537c5b9) +set(PROTOBUF_TAG v3.6.0) if(WIN32) if(${CMAKE_GENERATOR} MATCHES "Visual Studio.*") diff --git a/tensorflow/tools/ci_build/install/install_pip_packages.sh b/tensorflow/tools/ci_build/install/install_pip_packages.sh index 88f1d04193..fbed4574e0 100755 --- a/tensorflow/tools/ci_build/install/install_pip_packages.sh +++ b/tensorflow/tools/ci_build/install/install_pip_packages.sh @@ -51,8 +51,8 @@ pip2 install --upgrade markdown==2.6.8 pip3 install --upgrade markdown==2.6.8 # Install protobuf. -pip2 install --upgrade protobuf==3.3.0 -pip3 install --upgrade protobuf==3.3.0 +pip2 install --upgrade protobuf==3.6.0 +pip3 install --upgrade protobuf==3.6.0 # Remove obsolete version of six, which can sometimes confuse virtualenv. rm -rf /usr/lib/python3/dist-packages/six* diff --git a/tensorflow/tools/ci_build/install/install_proto3.sh b/tensorflow/tools/ci_build/install/install_proto3.sh index 7934002b2c..821d50baff 100755 --- a/tensorflow/tools/ci_build/install/install_proto3.sh +++ b/tensorflow/tools/ci_build/install/install_proto3.sh @@ -17,7 +17,7 @@ # Install protobuf3. # Select protobuf version. -PROTOBUF_VERSION="3.3.0" +PROTOBUF_VERSION="3.6.0" protobuf_ver_flat=$(echo $PROTOBUF_VERSION | sed 's/\.//g' | sed 's/^0*//g') local_protobuf_ver=$(protoc --version) local_protobuf_ver_flat=$(echo $local_protobuf_ver | sed 's/\.//g' | sed 's/^0*//g') diff --git a/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh b/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh index acd69ef346..037fc0e2e1 100755 --- a/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh +++ b/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh @@ -48,7 +48,7 @@ pip3.5 install --upgrade absl-py pip3.5 install --upgrade six==1.10.0 # Install protobuf. -pip3.5 install --upgrade protobuf==3.3.0 +pip3.5 install --upgrade protobuf==3.6.0 # Remove obsolete version of six, which can sometimes confuse virtualenv. rm -rf /usr/lib/python3/dist-packages/six* diff --git a/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh b/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh index 323b30f48e..8fd65a3ee2 100755 --- a/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh +++ b/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh @@ -60,7 +60,7 @@ pip3 install --upgrade absl-py pip3 install --upgrade six==1.10.0 # Install protobuf. -pip3 install --upgrade protobuf==3.3.0 +pip3 install --upgrade protobuf==3.6.0 # Remove obsolete version of six, which can sometimes confuse virtualenv. rm -rf /usr/lib/python3/dist-packages/six* diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py index 97f625e7e9..253802b959 100644 --- a/tensorflow/tools/pip_package/setup.py +++ b/tensorflow/tools/pip_package/setup.py @@ -53,7 +53,7 @@ REQUIRED_PACKAGES = [ 'gast >= 0.2.0', 'numpy >= 1.13.3', 'six >= 1.10.0', - 'protobuf >= 3.4.0', + 'protobuf >= 3.6.0', 'setuptools <= 39.1.0', 'tensorboard >= 1.8.0, < 1.9.0', 'termcolor >= 1.1.0', diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index b32d473219..1f1d106bfb 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -330,11 +330,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "protobuf_archive", urls = [ - "https://mirror.bazel.build/github.com/google/protobuf/archive/396336eb961b75f03b25824fe86cf6490fb75e3a.tar.gz", - "https://github.com/google/protobuf/archive/396336eb961b75f03b25824fe86cf6490fb75e3a.tar.gz", + "https://mirror.bazel.build/github.com/google/protobuf/archive/v3.6.0.tar.gz", + "https://github.com/google/protobuf/archive/v3.6.0.tar.gz", ], - sha256 = "846d907acf472ae233ec0882ef3a2d24edbbe834b80c305e867ac65a1f2c59e3", - strip_prefix = "protobuf-396336eb961b75f03b25824fe86cf6490fb75e3a", + sha256 = "50a5753995b3142627ac55cfd496cebc418a2e575ca0236e29033c67bd5665f4", + strip_prefix = "protobuf-3.6.0", ) # We need to import the protobuf library under the names com_google_protobuf @@ -343,21 +343,21 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "com_google_protobuf", urls = [ - "https://mirror.bazel.build/github.com/google/protobuf/archive/396336eb961b75f03b25824fe86cf6490fb75e3a.tar.gz", - "https://github.com/google/protobuf/archive/396336eb961b75f03b25824fe86cf6490fb75e3a.tar.gz", + "https://mirror.bazel.build/github.com/google/protobuf/archive/v3.6.0.tar.gz", + "https://github.com/google/protobuf/archive/v3.6.0.tar.gz", ], - sha256 = "846d907acf472ae233ec0882ef3a2d24edbbe834b80c305e867ac65a1f2c59e3", - strip_prefix = "protobuf-396336eb961b75f03b25824fe86cf6490fb75e3a", + sha256 = "50a5753995b3142627ac55cfd496cebc418a2e575ca0236e29033c67bd5665f4", + strip_prefix = "protobuf-3.6.0", ) tf_http_archive( name = "com_google_protobuf_cc", urls = [ - "https://mirror.bazel.build/github.com/google/protobuf/archive/396336eb961b75f03b25824fe86cf6490fb75e3a.tar.gz", - "https://github.com/google/protobuf/archive/396336eb961b75f03b25824fe86cf6490fb75e3a.tar.gz", + "https://mirror.bazel.build/github.com/google/protobuf/archive/v3.6.0.tar.gz", + "https://github.com/google/protobuf/archive/v3.6.0.tar.gz", ], - sha256 = "846d907acf472ae233ec0882ef3a2d24edbbe834b80c305e867ac65a1f2c59e3", - strip_prefix = "protobuf-396336eb961b75f03b25824fe86cf6490fb75e3a", + sha256 = "50a5753995b3142627ac55cfd496cebc418a2e575ca0236e29033c67bd5665f4", + strip_prefix = "protobuf-3.6.0", ) tf_http_archive( -- GitLab From 6c08402e3a7d3e440d6913cb683f26d28514ad8d Mon Sep 17 00:00:00 2001 From: Jiri Simsa Date: Wed, 20 Jun 2018 11:29:49 -0700 Subject: [PATCH 743/816] [tf.data] Properly export `tf.contrib.data.group_by_reducer()` PiperOrigin-RevId: 201386380 --- tensorflow/contrib/data/__init__.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tensorflow/contrib/data/__init__.py b/tensorflow/contrib/data/__init__.py index 9c6a13333e..99699cd6d6 100644 --- a/tensorflow/contrib/data/__init__.py +++ b/tensorflow/contrib/data/__init__.py @@ -33,6 +33,7 @@ See the @{$datasets$Importing Data} Programmer's Guide for an overview. @@choose_from_datasets @@dense_to_sparse_batch @@enumerate_dataset +@@group_by_reducer @@group_by_window @@ignore_errors @@make_batched_features_dataset @@ -71,6 +72,7 @@ from tensorflow.contrib.data.python.ops.enumerate_ops import enumerate_dataset from tensorflow.contrib.data.python.ops.error_ops import ignore_errors from tensorflow.contrib.data.python.ops.get_single_element import get_single_element from tensorflow.contrib.data.python.ops.grouping import bucket_by_sequence_length +from tensorflow.contrib.data.python.ops.grouping import group_by_reducer from tensorflow.contrib.data.python.ops.grouping import group_by_window from tensorflow.contrib.data.python.ops.interleave_ops import choose_from_datasets from tensorflow.contrib.data.python.ops.interleave_ops import parallel_interleave -- GitLab From e51df5918020cdfada26022240091e5529f7da60 Mon Sep 17 00:00:00 2001 From: Dan Moldovan Date: Wed, 20 Jun 2018 11:34:22 -0700 Subject: [PATCH 744/816] Boilerplate for an ANF transformer. This is not currently related to AutoGraph, but used elsewhere. PiperOrigin-RevId: 201387308 --- .../autograph/pyct/common_transformers/BUILD | 38 +++++++++++++ .../autograph/pyct/common_transformers/anf.py | 57 +++++++++++++++++++ .../pyct/common_transformers/anf_test.py | 53 +++++++++++++++++ tensorflow/tools/pip_package/BUILD | 1 + 4 files changed, 149 insertions(+) create mode 100644 tensorflow/contrib/autograph/pyct/common_transformers/BUILD create mode 100644 tensorflow/contrib/autograph/pyct/common_transformers/anf.py create mode 100644 tensorflow/contrib/autograph/pyct/common_transformers/anf_test.py diff --git a/tensorflow/contrib/autograph/pyct/common_transformers/BUILD b/tensorflow/contrib/autograph/pyct/common_transformers/BUILD new file mode 100644 index 0000000000..ca1441cf6f --- /dev/null +++ b/tensorflow/contrib/autograph/pyct/common_transformers/BUILD @@ -0,0 +1,38 @@ +licenses(["notice"]) # Apache 2.0 + +load("//tensorflow:tensorflow.bzl", "py_test") + +filegroup( + name = "all_files", + srcs = glob( + ["**/*"], + exclude = [ + "**/METADATA", + "**/OWNERS", + ], + ), + visibility = ["//tensorflow:__subpackages__"], +) + +py_library( + name = "common_transformers", + srcs = [ + "anf.py", + ], + srcs_version = "PY2AND3", + visibility = ["//visibility:public"], + deps = [ + "//tensorflow/contrib/autograph/pyct", + "@gast_archive//:gast", + ], +) + +py_test( + name = "anf_test", + srcs = ["anf_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":common_transformers", + "//tensorflow/python:client_testlib", + ], +) diff --git a/tensorflow/contrib/autograph/pyct/common_transformers/anf.py b/tensorflow/contrib/autograph/pyct/common_transformers/anf.py new file mode 100644 index 0000000000..cc039986c2 --- /dev/null +++ b/tensorflow/contrib/autograph/pyct/common_transformers/anf.py @@ -0,0 +1,57 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Conversion to A-normal form.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.autograph.pyct import transformer + + +class DummyGensym(object): + """A dumb gensym that suffixes a stem by sequential numbers from 1000.""" + + def __init__(self, entity_info): + del entity_info + # A proper implementation needs to account for: + # * entity_info.namespace + # * all the symbols defined in the AST + # * the symbols generated so far + self._idx = 0 + + def new_name(self, stem): + self._idx += 1 + return stem + '_' + str(1000 + self._idx) + + +class AnfTransformer(transformer.Base): + """Performs the actual conversion.""" + + # TODO(mdan): Link to a reference. + # TODO(mdan): Implement. + + def __init__(self, entity_info): + """Creates a transformer. + + Args: + entity_info: transformer.EntityInfo + """ + super(AnfTransformer, self).__init__(entity_info) + self._gensym = DummyGensym(entity_info) + + +def transform(node, entity_info): + return AnfTransformer(entity_info).visit(node) diff --git a/tensorflow/contrib/autograph/pyct/common_transformers/anf_test.py b/tensorflow/contrib/autograph/pyct/common_transformers/anf_test.py new file mode 100644 index 0000000000..81983a5ecb --- /dev/null +++ b/tensorflow/contrib/autograph/pyct/common_transformers/anf_test.py @@ -0,0 +1,53 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for anf module.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.autograph.pyct import compiler +from tensorflow.contrib.autograph.pyct import parser +from tensorflow.contrib.autograph.pyct import transformer +from tensorflow.contrib.autograph.pyct.common_transformers import anf +from tensorflow.python.platform import test + + +class AnfTransformerTest(test.TestCase): + + def _simple_source_info(self): + return transformer.EntityInfo( + source_code=None, + source_file=None, + namespace=None, + arg_values=None, + arg_types=None, + owner_type=None) + + def test_basic(self): + + def test_function(): + a = 0 + return a + + node, _ = parser.parse_entity(test_function) + node = anf.transform(node, self._simple_source_info()) + result, _ = compiler.ast_to_object(node) + + self.assertEqual(test_function(), result.test_function()) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD index d149365ac1..6cfd271968 100644 --- a/tensorflow/tools/pip_package/BUILD +++ b/tensorflow/tools/pip_package/BUILD @@ -64,6 +64,7 @@ COMMON_PIP_DEPS = [ "//tensorflow/contrib/autograph/operators:operators", "//tensorflow/contrib/autograph/pyct:pyct", "//tensorflow/contrib/autograph/pyct/static_analysis:static_analysis", + "//tensorflow/contrib/autograph/pyct/common_transformers:common_transformers", "//tensorflow/contrib/boosted_trees:boosted_trees_pip", "//tensorflow/contrib/cluster_resolver:cluster_resolver_pip", "//tensorflow/contrib/constrained_optimization:constrained_optimization_pip", -- GitLab From 4efefb90391b12c95339ed3b46a02b62ea5e195d Mon Sep 17 00:00:00 2001 From: Jared Duke Date: Wed, 20 Jun 2018 11:48:15 -0700 Subject: [PATCH 745/816] Implement TFLite Shape operator PiperOrigin-RevId: 201389618 --- tensorflow/contrib/lite/build_def.bzl | 1 + tensorflow/contrib/lite/builtin_op_data.h | 4 + tensorflow/contrib/lite/builtin_ops.h | 1 + .../lite/g3doc/tf_ops_compatibility.md | 14 ++ tensorflow/contrib/lite/kernels/BUILD | 15 ++ tensorflow/contrib/lite/kernels/register.cc | 2 + tensorflow/contrib/lite/kernels/shape.cc | 93 ++++++++++++ tensorflow/contrib/lite/kernels/shape_test.cc | 95 ++++++++++++ tensorflow/contrib/lite/model.cc | 9 ++ tensorflow/contrib/lite/nnapi_delegate.cc | 1 + tensorflow/contrib/lite/schema/schema.fbs | 7 + .../contrib/lite/schema/schema_generated.h | 141 +++++++++++++++++- .../contrib/lite/testing/generate_examples.py | 28 +++- .../contrib/lite/toco/import_tensorflow.cc | 18 ++- tensorflow/contrib/lite/toco/model.h | 4 +- .../contrib/lite/toco/tflite/operator.cc | 22 +++ .../contrib/lite/toco/tflite/operator_test.cc | 8 + 17 files changed, 453 insertions(+), 10 deletions(-) create mode 100644 tensorflow/contrib/lite/kernels/shape.cc create mode 100644 tensorflow/contrib/lite/kernels/shape_test.cc diff --git a/tensorflow/contrib/lite/build_def.bzl b/tensorflow/contrib/lite/build_def.bzl index 828a516235..81883ba1fd 100644 --- a/tensorflow/contrib/lite/build_def.bzl +++ b/tensorflow/contrib/lite/build_def.bzl @@ -239,6 +239,7 @@ def generated_test_models(): "reshape", "resize_bilinear", "rsqrt", + "shape", "sigmoid", "sin", "slice", diff --git a/tensorflow/contrib/lite/builtin_op_data.h b/tensorflow/contrib/lite/builtin_op_data.h index ad547c67e6..1b1b8b2985 100644 --- a/tensorflow/contrib/lite/builtin_op_data.h +++ b/tensorflow/contrib/lite/builtin_op_data.h @@ -250,6 +250,10 @@ typedef struct { bool validate_indices; } TfLiteSparseToDenseParams; +typedef struct { + TfLiteType out_type; +} TfLiteShapeParams; + #ifdef __cplusplus } // extern "C" #endif // __cplusplus diff --git a/tensorflow/contrib/lite/builtin_ops.h b/tensorflow/contrib/lite/builtin_ops.h index 3474df7812..7a78206ebf 100644 --- a/tensorflow/contrib/lite/builtin_ops.h +++ b/tensorflow/contrib/lite/builtin_ops.h @@ -102,6 +102,7 @@ typedef enum { kTfLiteBuiltinSum = 74, kTfLiteBuiltinSqrt = 75, kTfLiteBuiltinRsqrt = 76, + kTfLiteBuiltinShape = 77, } TfLiteBuiltinOperator; #ifdef __cplusplus diff --git a/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md b/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md index cf672d2f0d..45104c1419 100644 --- a/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md +++ b/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md @@ -595,6 +595,20 @@ Outputs { } ``` +**SHAPE** + +``` +Inputs { + 0: a tensor +} +Outputs { + 0: a 1D tensor representing the shape of the input tensor +} +Options { + out_type: the output type of the op (int32 or int64). Defaults to int32. +} +``` + **SLICE** ``` diff --git a/tensorflow/contrib/lite/kernels/BUILD b/tensorflow/contrib/lite/kernels/BUILD index bb5558443b..a77897a173 100644 --- a/tensorflow/contrib/lite/kernels/BUILD +++ b/tensorflow/contrib/lite/kernels/BUILD @@ -168,6 +168,7 @@ cc_library( "reshape.cc", "resize_bilinear.cc", "select.cc", + "shape.cc", "skip_gram.cc", "slice.cc", "space_to_batch_nd.cc", @@ -994,6 +995,20 @@ tf_cc_test( ], ) +tf_cc_test( + name = "shape_test", + size = "small", + srcs = ["shape_test.cc"], + tags = ["tflite_not_portable_ios"], + deps = [ + ":builtin_ops", + "//tensorflow/contrib/lite:builtin_op_data", + "//tensorflow/contrib/lite:framework", + "//tensorflow/contrib/lite/kernels:test_util", + "@com_google_googletest//:gtest", + ], +) + filegroup( name = "all_files", srcs = glob( diff --git a/tensorflow/contrib/lite/kernels/register.cc b/tensorflow/contrib/lite/kernels/register.cc index 07a7ee9115..67f6caea67 100644 --- a/tensorflow/contrib/lite/kernels/register.cc +++ b/tensorflow/contrib/lite/kernels/register.cc @@ -100,6 +100,7 @@ TfLiteRegistration* Register_EQUAL(); TfLiteRegistration* Register_NOT_EQUAL(); TfLiteRegistration* Register_SQRT(); TfLiteRegistration* Register_RSQRT(); +TfLiteRegistration* Register_SHAPE(); BuiltinOpResolver::BuiltinOpResolver() { AddBuiltin(BuiltinOperator_RELU, Register_RELU()); @@ -181,6 +182,7 @@ BuiltinOpResolver::BuiltinOpResolver() { AddBuiltin(BuiltinOperator_NOT_EQUAL, Register_NOT_EQUAL()); AddBuiltin(BuiltinOperator_SQRT, Register_SQRT()); AddBuiltin(BuiltinOperator_RSQRT, Register_RSQRT()); + AddBuiltin(BuiltinOperator_SHAPE, Register_SHAPE()); // TODO(andrewharp, ahentz): Move these somewhere more appropriate so that // custom ops aren't always included by default. diff --git a/tensorflow/contrib/lite/kernels/shape.cc b/tensorflow/contrib/lite/kernels/shape.cc new file mode 100644 index 0000000000..dbcd2ef004 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/shape.cc @@ -0,0 +1,93 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include "tensorflow/contrib/lite/builtin_op_data.h" +#include "tensorflow/contrib/lite/context.h" +#include "tensorflow/contrib/lite/kernels/internal/tensor.h" +#include "tensorflow/contrib/lite/kernels/kernel_util.h" +#include "tensorflow/contrib/lite/kernels/op_macros.h" + +namespace tflite { +namespace ops { +namespace builtin { +namespace shape { + +constexpr int kInputTensor = 0; +constexpr int kOutputTensor = 0; + +template +void ExtractShape(const TfLiteTensor* input, OutType* output_data) { + for (int i = 0; i < NumDimensions(input); ++i) { + output_data[i] = SizeOfDimension(input, i); + } +} + +TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { + TF_LITE_ENSURE_EQ(context, NumInputs(node), 1); + TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); + + const TfLiteTensor* input = GetInput(context, node, kInputTensor); + TfLiteTensor* output = GetOutput(context, node, kOutputTensor); + + auto* params = reinterpret_cast(node->builtin_data); + switch (params->out_type) { + case kTfLiteInt32: + output->type = kTfLiteInt32; + break; + case kTfLiteInt64: + output->type = kTfLiteInt64; + break; + default: + context->ReportError(context, "Unknown shape output data type: %d", + params->out_type); + return kTfLiteError; + } + + // Shape always produces a 1-dimensional output tensor, where each output + // element is the length of the corresponding input tensor's dimension. + TfLiteIntArray* output_size = TfLiteIntArrayCreate(1); + output_size->data[0] = NumDimensions(input); + return context->ResizeTensor(context, output, output_size); +} + +TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { + const TfLiteTensor* input = GetInput(context, node, kInputTensor); + TfLiteTensor* output = GetOutput(context, node, kOutputTensor); + TFLITE_DCHECK_EQ(NumDimensions(output), 1); + TFLITE_DCHECK_EQ(SizeOfDimension(output, 0), NumDimensions(input)); + + switch (output->type) { + case kTfLiteInt32: + ExtractShape(input, GetTensorData(output)); + break; + case kTfLiteInt64: + ExtractShape(input, GetTensorData(output)); + break; + default: + return kTfLiteError; + } + + return kTfLiteOk; +} + +} // namespace shape + +TfLiteRegistration* Register_SHAPE() { + static TfLiteRegistration r = {nullptr, nullptr, shape::Prepare, shape::Eval}; + return &r; +} + +} // namespace builtin +} // namespace ops +} // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/shape_test.cc b/tensorflow/contrib/lite/kernels/shape_test.cc new file mode 100644 index 0000000000..27b48f4e99 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/shape_test.cc @@ -0,0 +1,95 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include + +#include +#include "tensorflow/contrib/lite/interpreter.h" +#include "tensorflow/contrib/lite/kernels/register.h" +#include "tensorflow/contrib/lite/kernels/test_util.h" +#include "tensorflow/contrib/lite/model.h" + +namespace tflite { +namespace { + +using ::testing::ElementsAreArray; + +template +class ShapeOpModel : public SingleOpModel { + public: + ShapeOpModel(std::initializer_list input_shape, TensorType input_type, + TensorType output_type) { + input_ = AddInput(input_type); + output_ = AddOutput(output_type); + SetBuiltinOp(BuiltinOperator_SHAPE, BuiltinOptions_ShapeOptions, + CreateShapeOptions(builder_, output_type).Union()); + BuildInterpreter({input_shape}); + } + + TfLiteStatus InvokeWithResult() { return interpreter_->Invoke(); } + + int input() { return input_; } + + int32_t GetOutputSize() { return GetTensorSize(output_); } + std::vector GetOutput() { return ExtractVector(output_); } + std::vector GetOutputShape() { return GetTensorShape(output_); } + + private: + int input_; + int output_; +}; + +TEST(ShapeOpTest, OutTypeInt) { + ShapeOpModel model({1, 3, 1, 3, 5}, TensorType_FLOAT32, + TensorType_INT32); + model.Invoke(); + + EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 3, 1, 3, 5})); + EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({5})); +} + +TEST(ShapeOpTest, OutTypeInt64) { + ShapeOpModel model({1, 3, 1, 3, 5}, TensorType_FLOAT32, + TensorType_INT64); + model.Invoke(); + + EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 3, 1, 3, 5})); + EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({5})); +} + +TEST(ShapeOpTest, ScalarTensor) { + ShapeOpModel model({}, TensorType_FLOAT32, TensorType_INT32); + model.Invoke(); + + EXPECT_EQ(model.GetOutputSize(), 0); + EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({0})); +} + +TEST(ShapeOpTest, EmptyTensor) { + ShapeOpModel model({1, 0}, TensorType_FLOAT32, TensorType_INT32); + model.Invoke(); + + EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 0})); + EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({2})); +} + +} // namespace +} // namespace tflite + +int main(int argc, char** argv) { + ::tflite::LogToStderr(); + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} diff --git a/tensorflow/contrib/lite/model.cc b/tensorflow/contrib/lite/model.cc index 1f8e796bc7..e1ec2d6d57 100644 --- a/tensorflow/contrib/lite/model.cc +++ b/tensorflow/contrib/lite/model.cc @@ -668,6 +668,15 @@ TfLiteStatus ParseOpData(const Operator* op, BuiltinOperator op_type, *builtin_data = reinterpret_cast(params); break; } + case BuiltinOperator_SHAPE: { + auto* params = MallocPOD(); + if (auto* schema_params = op->builtin_options_as_ShapeOptions()) { + ConvertTensorType(schema_params->out_type(), ¶ms->out_type, + error_reporter); + } + *builtin_data = static_cast(params); + break; + } case BuiltinOperator_DELEGATE: { // TODO(ycling): Revisit when supporting saving delegated models. error_reporter->Report("DELEGATE op shouldn't exist in model."); diff --git a/tensorflow/contrib/lite/nnapi_delegate.cc b/tensorflow/contrib/lite/nnapi_delegate.cc index 1e012c89ae..ab007993af 100644 --- a/tensorflow/contrib/lite/nnapi_delegate.cc +++ b/tensorflow/contrib/lite/nnapi_delegate.cc @@ -503,6 +503,7 @@ void AddOpsAndParams(tflite::Interpreter* interpreter, case tflite::BuiltinOperator_SUM: case tflite::BuiltinOperator_SQRT: case tflite::BuiltinOperator_RSQRT: + case tflite::BuiltinOperator_SHAPE: FATAL("Op code %d is currently not delegated to NNAPI", builtin); nn_op_type = -1; // set to invalid break; diff --git a/tensorflow/contrib/lite/schema/schema.fbs b/tensorflow/contrib/lite/schema/schema.fbs index 0b127e1c14..df43f1e5ab 100644 --- a/tensorflow/contrib/lite/schema/schema.fbs +++ b/tensorflow/contrib/lite/schema/schema.fbs @@ -157,6 +157,7 @@ enum BuiltinOperator : byte { SUM=74, SQRT = 75, RSQRT = 76, + SHAPE = 77, } // Options for the builtin operators. @@ -215,6 +216,7 @@ union BuiltinOptions { ExpandDimsOptions, EqualOptions, NotEqualOptions, + ShapeOptions, } enum Padding : byte { SAME, VALID } @@ -495,6 +497,11 @@ table EqualOptions { table NotEqualOptions { } +table ShapeOptions { + // Optional output type of the operation (int32 or int64). Defaults to int32. + out_type : TensorType; +} + // An OperatorCode can be an enum value (BuiltinOperator) if the operator is a // builtin, or a string if the operator is custom. table OperatorCode { diff --git a/tensorflow/contrib/lite/schema/schema_generated.h b/tensorflow/contrib/lite/schema/schema_generated.h index 2558625e2d..8c0660dfe2 100755 --- a/tensorflow/contrib/lite/schema/schema_generated.h +++ b/tensorflow/contrib/lite/schema/schema_generated.h @@ -193,6 +193,9 @@ struct EqualOptionsT; struct NotEqualOptions; struct NotEqualOptionsT; +struct ShapeOptions; +struct ShapeOptionsT; + struct OperatorCode; struct OperatorCodeT; @@ -332,11 +335,12 @@ enum BuiltinOperator { BuiltinOperator_SUM = 74, BuiltinOperator_SQRT = 75, BuiltinOperator_RSQRT = 76, + BuiltinOperator_SHAPE = 77, BuiltinOperator_MIN = BuiltinOperator_ADD, - BuiltinOperator_MAX = BuiltinOperator_RSQRT + BuiltinOperator_MAX = BuiltinOperator_SHAPE }; -inline BuiltinOperator (&EnumValuesBuiltinOperator())[76] { +inline BuiltinOperator (&EnumValuesBuiltinOperator())[77] { static BuiltinOperator values[] = { BuiltinOperator_ADD, BuiltinOperator_AVERAGE_POOL_2D, @@ -413,7 +417,8 @@ inline BuiltinOperator (&EnumValuesBuiltinOperator())[76] { BuiltinOperator_LOG, BuiltinOperator_SUM, BuiltinOperator_SQRT, - BuiltinOperator_RSQRT + BuiltinOperator_RSQRT, + BuiltinOperator_SHAPE }; return values; } @@ -497,6 +502,7 @@ inline const char **EnumNamesBuiltinOperator() { "SUM", "SQRT", "RSQRT", + "SHAPE", nullptr }; return names; @@ -563,11 +569,12 @@ enum BuiltinOptions { BuiltinOptions_ExpandDimsOptions = 52, BuiltinOptions_EqualOptions = 53, BuiltinOptions_NotEqualOptions = 54, + BuiltinOptions_ShapeOptions = 55, BuiltinOptions_MIN = BuiltinOptions_NONE, - BuiltinOptions_MAX = BuiltinOptions_NotEqualOptions + BuiltinOptions_MAX = BuiltinOptions_ShapeOptions }; -inline BuiltinOptions (&EnumValuesBuiltinOptions())[55] { +inline BuiltinOptions (&EnumValuesBuiltinOptions())[56] { static BuiltinOptions values[] = { BuiltinOptions_NONE, BuiltinOptions_Conv2DOptions, @@ -623,7 +630,8 @@ inline BuiltinOptions (&EnumValuesBuiltinOptions())[55] { BuiltinOptions_TileOptions, BuiltinOptions_ExpandDimsOptions, BuiltinOptions_EqualOptions, - BuiltinOptions_NotEqualOptions + BuiltinOptions_NotEqualOptions, + BuiltinOptions_ShapeOptions }; return values; } @@ -685,6 +693,7 @@ inline const char **EnumNamesBuiltinOptions() { "ExpandDimsOptions", "EqualOptions", "NotEqualOptions", + "ShapeOptions", nullptr }; return names; @@ -915,6 +924,10 @@ template<> struct BuiltinOptionsTraits { static const BuiltinOptions enum_value = BuiltinOptions_NotEqualOptions; }; +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ShapeOptions; +}; + struct BuiltinOptionsUnion { BuiltinOptions type; void *value; @@ -1378,6 +1391,14 @@ struct BuiltinOptionsUnion { return type == BuiltinOptions_NotEqualOptions ? reinterpret_cast(value) : nullptr; } + ShapeOptionsT *AsShapeOptions() { + return type == BuiltinOptions_ShapeOptions ? + reinterpret_cast(value) : nullptr; + } + const ShapeOptionsT *AsShapeOptions() const { + return type == BuiltinOptions_ShapeOptions ? + reinterpret_cast(value) : nullptr; + } }; bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *obj, BuiltinOptions type); @@ -4932,6 +4953,60 @@ inline flatbuffers::Offset CreateNotEqualOptions( flatbuffers::Offset CreateNotEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const NotEqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +struct ShapeOptionsT : public flatbuffers::NativeTable { + typedef ShapeOptions TableType; + TensorType out_type; + ShapeOptionsT() + : out_type(TensorType_FLOAT32) { + } +}; + +struct ShapeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ShapeOptionsT NativeTableType; + enum { + VT_OUT_TYPE = 4 + }; + TensorType out_type() const { + return static_cast(GetField(VT_OUT_TYPE, 0)); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_OUT_TYPE) && + verifier.EndTable(); + } + ShapeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ShapeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ShapeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ShapeOptionsBuilder { + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_out_type(TensorType out_type) { + fbb_.AddElement(ShapeOptions::VT_OUT_TYPE, static_cast(out_type), 0); + } + explicit ShapeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + ShapeOptionsBuilder &operator=(const ShapeOptionsBuilder &); + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateShapeOptions( + flatbuffers::FlatBufferBuilder &_fbb, + TensorType out_type = TensorType_FLOAT32) { + ShapeOptionsBuilder builder_(_fbb); + builder_.add_out_type(out_type); + return builder_.Finish(); +} + +flatbuffers::Offset CreateShapeOptions(flatbuffers::FlatBufferBuilder &_fbb, const ShapeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + struct OperatorCodeT : public flatbuffers::NativeTable { typedef OperatorCode TableType; BuiltinOperator builtin_code; @@ -5227,6 +5302,9 @@ struct Operator FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { const NotEqualOptions *builtin_options_as_NotEqualOptions() const { return builtin_options_type() == BuiltinOptions_NotEqualOptions ? static_cast(builtin_options()) : nullptr; } + const ShapeOptions *builtin_options_as_ShapeOptions() const { + return builtin_options_type() == BuiltinOptions_ShapeOptions ? static_cast(builtin_options()) : nullptr; + } const flatbuffers::Vector *custom_options() const { return GetPointer *>(VT_CUSTOM_OPTIONS); } @@ -5474,6 +5552,10 @@ template<> inline const NotEqualOptions *Operator::builtin_options_as inline const ShapeOptions *Operator::builtin_options_as() const { + return builtin_options_as_ShapeOptions(); +} + struct OperatorBuilder { flatbuffers::FlatBufferBuilder &fbb_; flatbuffers::uoffset_t start_; @@ -7424,6 +7506,32 @@ inline flatbuffers::Offset CreateNotEqualOptions(flatbuffers::F _fbb); } +inline ShapeOptionsT *ShapeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = new ShapeOptionsT(); + UnPackTo(_o, _resolver); + return _o; +} + +inline void ShapeOptions::UnPackTo(ShapeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = out_type(); _o->out_type = _e; }; +} + +inline flatbuffers::Offset ShapeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ShapeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateShapeOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateShapeOptions(flatbuffers::FlatBufferBuilder &_fbb, const ShapeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ShapeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _out_type = _o->out_type; + return tflite::CreateShapeOptions( + _fbb, + _out_type); +} + inline OperatorCodeT *OperatorCode::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new OperatorCodeT(); UnPackTo(_o, _resolver); @@ -7829,6 +7937,10 @@ inline bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *ob auto ptr = reinterpret_cast(obj); return verifier.VerifyTable(ptr); } + case BuiltinOptions_ShapeOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } default: return false; } } @@ -8063,6 +8175,10 @@ inline void *BuiltinOptionsUnion::UnPack(const void *obj, BuiltinOptions type, c auto ptr = reinterpret_cast(obj); return ptr->UnPack(resolver); } + case BuiltinOptions_ShapeOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } default: return nullptr; } } @@ -8285,6 +8401,10 @@ inline flatbuffers::Offset BuiltinOptionsUnion::Pack(flatbuffers::FlatBuff auto ptr = reinterpret_cast(value); return CreateNotEqualOptions(_fbb, ptr, _rehasher).Union(); } + case BuiltinOptions_ShapeOptions: { + auto ptr = reinterpret_cast(value); + return CreateShapeOptions(_fbb, ptr, _rehasher).Union(); + } default: return 0; } } @@ -8507,6 +8627,10 @@ inline BuiltinOptionsUnion::BuiltinOptionsUnion(const BuiltinOptionsUnion &u) FL value = new NotEqualOptionsT(*reinterpret_cast(u.value)); break; } + case BuiltinOptions_ShapeOptions: { + value = new ShapeOptionsT(*reinterpret_cast(u.value)); + break; + } default: break; } @@ -8784,6 +8908,11 @@ inline void BuiltinOptionsUnion::Reset() { delete ptr; break; } + case BuiltinOptions_ShapeOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } default: break; } value = nullptr; diff --git a/tensorflow/contrib/lite/testing/generate_examples.py b/tensorflow/contrib/lite/testing/generate_examples.py index 53f1fce346..c4d2d7ca52 100644 --- a/tensorflow/contrib/lite/testing/generate_examples.py +++ b/tensorflow/contrib/lite/testing/generate_examples.py @@ -137,7 +137,7 @@ def toco_options(data_types, Returns: the options in a string. """ - shape_str = ":".join([",".join(str(y) for y in x) for x in shapes]) + shape_str = ":".join([",".join(str(y) for y in x) for x in shapes if x]) inference_type = "FLOAT" # TODO(ahentz): if we get multi-input quantization to work we need this # to change @@ -1545,6 +1545,32 @@ def make_reshape_tests(zip_path): make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs) +def make_shape_tests(zip_path): + """Make a set of tests to do shape.""" + + test_parameters = [{ + "input_dtype": [tf.float32, tf.int32], + "input_shape": [[], [0], [1, 1, 1, 3], [2, 3, 4, 5], [5, 5], [10]], + "out_type": [tf.int32, tf.int64], + }] + + def build_graph(parameters): + """Build the topk op testing graph.""" + # Note that we intentionally leave out the shape from the input placeholder + # to prevent the Shape operation from being optimized out during conversion. + input_value = tf.placeholder(dtype=parameters["input_dtype"], name="input") + out = tf.shape(input_value, out_type=parameters["out_type"]) + return [input_value], [out] + + def build_inputs(parameters, sess, inputs, outputs): + input_value = create_tensor_data(parameters["input_dtype"], + parameters["input_shape"]) + return [input_value], sess.run( + outputs, feed_dict=dict(zip(inputs, [input_value]))) + + make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs) + + def make_resize_bilinear_tests(zip_path): """Make a set of tests to do resize_bilinear.""" diff --git a/tensorflow/contrib/lite/toco/import_tensorflow.cc b/tensorflow/contrib/lite/toco/import_tensorflow.cc index caca199d2e..8da33e8a22 100644 --- a/tensorflow/contrib/lite/toco/import_tensorflow.cc +++ b/tensorflow/contrib/lite/toco/import_tensorflow.cc @@ -1573,6 +1573,22 @@ tensorflow::Status ConvertOperatorSpecialCasedAsRNNBackEdge( return tensorflow::Status::OK(); } +tensorflow::Status ConvertShapeOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { + CHECK_EQ(node.op(), "Shape"); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 1)); + const auto out_type = + HasAttr(node, "out_type") ? GetDataTypeAttr(node, "out_type") : DT_INT32; + CHECK(out_type == DT_INT64 || out_type == DT_INT32); + auto op = absl::make_unique(); + op->output_data_type = ConvertDataType(out_type); + op->inputs.push_back(node.input(0)); + op->outputs.push_back(node.name()); + model->operators.push_back(std::move(op)); + return tensorflow::Status::OK(); +} + void StripCaretFromArrayNames(Model* model) { for (auto& op : model->operators) { for (auto& input : op->inputs) { @@ -1877,7 +1893,7 @@ ConverterMapType GetTensorFlowNodeConverterMap() { {"ResizeBilinear", ConvertResizeBilinearOperator}, {"Rsqrt", ConvertSimpleOperator}, {"Select", ConvertSimpleOperator}, - {"Shape", ConvertSimpleOperator}, + {"Shape", ConvertShapeOperator}, {"Sigmoid", ConvertSimpleOperator}, {"Sin", ConvertSimpleOperator}, {"Slice", ConvertSimpleOperator}, diff --git a/tensorflow/contrib/lite/toco/model.h b/tensorflow/contrib/lite/toco/model.h index 0faadedf3b..2585cff56e 100644 --- a/tensorflow/contrib/lite/toco/model.h +++ b/tensorflow/contrib/lite/toco/model.h @@ -1157,10 +1157,10 @@ struct StackOperator : Operator { // This operation outputs a 1-D integer tensor representing the shape of // the input. // -// TensorFlow equivalent: Shape. We currently assume that the output is int32 -// and not int64. The output type could be stored herein. +// TensorFlow equivalent: Shape. struct TensorFlowShapeOperator : Operator { TensorFlowShapeOperator() : Operator(OperatorType::kTensorFlowShape) {} + ArrayDataType output_data_type = ArrayDataType::kInt32; }; // Element-wise square-root (x^0.5) operator. diff --git a/tensorflow/contrib/lite/toco/tflite/operator.cc b/tensorflow/contrib/lite/toco/tflite/operator.cc index a1bd2be0a1..fd6c849889 100644 --- a/tensorflow/contrib/lite/toco/tflite/operator.cc +++ b/tensorflow/contrib/lite/toco/tflite/operator.cc @@ -918,6 +918,26 @@ class ExpandDims int GetVersion(const Operator& op) const override { return 1; } }; +class Shape + : public BuiltinOperator { + public: + using BuiltinOperator::BuiltinOperator; + flatbuffers::Offset WriteOptions( + const TocoOperator& op, + flatbuffers::FlatBufferBuilder* builder) const override { + return ::tflite::CreateShapeOptions( + *builder, DataType::Serialize(op.output_data_type)); + } + + void ReadOptions(const TfLiteOptions& options, + TocoOperator* op) const override { + op->output_data_type = DataType::Deserialize(options.out_type()); + } + + int GetVersion(const Operator& op) const override { return 1; } +}; + class TensorFlowUnsupported : public BaseOperator { public: using BaseOperator::BaseOperator; @@ -1132,6 +1152,8 @@ std::vector> BuildOperatorList() { OperatorType::kTransposeConv)); ops.emplace_back(new SparseToDense(::tflite::BuiltinOperator_SPARSE_TO_DENSE, OperatorType::kSparseToDense)); + ops.emplace_back(new Shape(::tflite::BuiltinOperator_SHAPE, + OperatorType::kTensorFlowShape)); // Custom Operators. ops.emplace_back( diff --git a/tensorflow/contrib/lite/toco/tflite/operator_test.cc b/tensorflow/contrib/lite/toco/tflite/operator_test.cc index 00e2b69f55..bd881d079e 100644 --- a/tensorflow/contrib/lite/toco/tflite/operator_test.cc +++ b/tensorflow/contrib/lite/toco/tflite/operator_test.cc @@ -431,6 +431,14 @@ TEST_F(OperatorTest, BuiltinTransposeConv) { EXPECT_EQ(op.padding.type, output_toco_op->padding.type); } +TEST_F(OperatorTest, BuiltinShape) { + TensorFlowShapeOperator op; + op.output_data_type = ArrayDataType::kInt64; + auto output_toco_op = SerializeAndDeserialize( + GetOperator("SHAPE", OperatorType::kTensorFlowShape), op); + EXPECT_EQ(op.output_data_type, output_toco_op->output_data_type); +} + TEST_F(OperatorTest, BuiltinSparseToDense) { SparseToDenseOperator op; op.validate_indices = false; -- GitLab From c40ed1d7cec07a0a8ffdfd263689e5db4fe38cc8 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 20 Jun 2018 11:49:22 -0700 Subject: [PATCH 746/816] Fix a bug: the conversion of pure Conv to DepthwiseConv did not properly check the necessary precondition that the input depth is 1. PiperOrigin-RevId: 201389819 --- .../convert_pure_conv_to_depthwise.cc | 15 ++++++++++----- .../propagate_fixed_sizes.cc | 2 +- 2 files changed, 11 insertions(+), 6 deletions(-) diff --git a/tensorflow/contrib/lite/toco/graph_transformations/convert_pure_conv_to_depthwise.cc b/tensorflow/contrib/lite/toco/graph_transformations/convert_pure_conv_to_depthwise.cc index 0fffab574d..1ea83abf8e 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/convert_pure_conv_to_depthwise.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/convert_pure_conv_to_depthwise.cc @@ -38,6 +38,16 @@ bool ConvertPureConvToDepthwise::Run(Model* model, std::size_t op_index) { // Depthwise conv does not support dilation return false; } + auto& input_array = model->GetArray(conv_op->inputs[0]); + if (!input_array.has_shape()) { + // Shapes not propagated yet + return false; + } + if (input_array.shape().dims(3) != 1) { + // Not a pure convolution: Conv does accumulation across the depth + // dimension. + return false; + } auto& weights_array = model->GetArray(conv_op->inputs[1]); if (!weights_array.buffer) { // Yield until the weights are resolved as a constant array. @@ -46,11 +56,6 @@ bool ConvertPureConvToDepthwise::Run(Model* model, std::size_t op_index) { if (weights_array.data_type != ArrayDataType::kFloat) { return false; } - if (weights_array.shape().dims(3) != 1) { - // Not a pure convolution: Conv does accumulation across the depth - // dimension. - return false; - } // At this point we know we have a pure conv. Rewrite it as DepthwiseConv. AddMessageF( "%s is purely convolutional (input/weights depth is 1), replacing it by " diff --git a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc index e7da9051d8..beda187f13 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc @@ -325,7 +325,7 @@ void ProcessDepthwiseConvOperator(Model* model, DepthwiseConvOperator* op) { if (!op->depth_multiplier) { op->depth_multiplier = output_depth / input_depth; } - QCHECK_EQ(output_depth, input_depth * op->depth_multiplier) + CHECK_EQ(output_depth, input_depth * op->depth_multiplier) << "input/output depths and depth_multiplier don't match"; const int kheight = weights_shape.dims(1); -- GitLab From 1f7d5c37b3480fae0b840aae1c316d06a3505ed3 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 20 Jun 2018 11:54:22 -0700 Subject: [PATCH 747/816] Make evaluate() work on anything that has a numpy() method in eager tests. PiperOrigin-RevId: 201390698 --- .../contrib/distribute/python/minimize_loss_test.py | 2 +- tensorflow/python/framework/test_util.py | 10 ++++------ 2 files changed, 5 insertions(+), 7 deletions(-) diff --git a/tensorflow/contrib/distribute/python/minimize_loss_test.py b/tensorflow/contrib/distribute/python/minimize_loss_test.py index c11a05f227..75754e3fe3 100644 --- a/tensorflow/contrib/distribute/python/minimize_loss_test.py +++ b/tensorflow/contrib/distribute/python/minimize_loss_test.py @@ -88,7 +88,7 @@ class MinimizeLossStepTest(test.TestCase, parameterized.TestCase): for _ in range(10): run_step() - weights.append(self.evaluate(distribution.fetch(layer.kernel))) + weights.append(self.evaluate(layer.kernel)) biases.append(self.evaluate(distribution.fetch(layer.bias))) if is_tpu: diff --git a/tensorflow/python/framework/test_util.py b/tensorflow/python/framework/test_util.py index 5582b14249..3ed5c9e6a4 100644 --- a/tensorflow/python/framework/test_util.py +++ b/tensorflow/python/framework/test_util.py @@ -61,7 +61,6 @@ from tensorflow.python.framework import random_seed from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import versions from tensorflow.python.ops import array_ops -from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variables from tensorflow.python.platform import googletest from tensorflow.python.platform import tf_logging as logging @@ -830,14 +829,13 @@ class TensorFlowTestCase(googletest.TestCase): def _eval_tensor(self, tensor): if tensor is None: return None - elif isinstance(tensor, ops.EagerTensor): - return tensor.numpy() - elif isinstance(tensor, resource_variable_ops.ResourceVariable): - return tensor.read_value().numpy() elif callable(tensor): return self._eval_helper(tensor()) else: - raise ValueError("Unsupported type %s." % type(tensor)) + try: + return tensor.numpy() + except AttributeError as e: + six.raise_from(ValueError("Unsupported type %s." % type(tensor)), e) def _eval_helper(self, tensors): if tensors is None: -- GitLab From 5988a74d16571686ae272d6ee3c740db34a2e6c8 Mon Sep 17 00:00:00 2001 From: Alexandre Passos Date: Wed, 20 Jun 2018 11:57:05 -0700 Subject: [PATCH 748/816] SymbolicGradient for some resource variables. Currently assumes variables are floats; there are TODOs to rectifiy this. PiperOrigin-RevId: 201391092 --- tensorflow/core/common_runtime/function.cc | 6 ++++++ tensorflow/core/graph/gradients.cc | 11 +++++++++-- tensorflow/core/ops/resource_variable_ops.cc | 17 +++++++++++++++++ tensorflow/python/eager/function_test.py | 14 ++++++++++++++ tensorflow/python/ops/gradients_impl.py | 6 +++++- 5 files changed, 51 insertions(+), 3 deletions(-) diff --git a/tensorflow/core/common_runtime/function.cc b/tensorflow/core/common_runtime/function.cc index 1200dcc1fe..6d8cea8297 100644 --- a/tensorflow/core/common_runtime/function.cc +++ b/tensorflow/core/common_runtime/function.cc @@ -1585,6 +1585,12 @@ FunctionBody* SymbolicGradientHelper::Compute() { g->RemoveNode(n); } gbody_->ret_types = fbody_->arg_types; + // TODO(apassos): use the right dtype for gradients of resource variables + for (int i = 0; i < gbody_->ret_types.size(); ++i) { + if (gbody_->ret_types[i] == DT_RESOURCE) { + gbody_->ret_types[i] = DT_FLOAT; + } + } gbody_->ret_nodes.clear(); // Add new return nodes to the function gradient body for each node // in 'x_grad_nodes'. diff --git a/tensorflow/core/graph/gradients.cc b/tensorflow/core/graph/gradients.cc index 6b56613470..c1a8a63784 100644 --- a/tensorflow/core/graph/gradients.cc +++ b/tensorflow/core/graph/gradients.cc @@ -106,8 +106,15 @@ static Node* AddSymGrad(Graph* g, Node* n, gtl::ArraySlice grads) { AddNodeAttr("Tin", in_types, &ndef); // The gradient node's outputs have the same types as the node 'n's - // inputs. - AddNodeAttr("Tout", n->input_types(), &ndef); + // inputs, except for resources. + DataTypeVector out_types = n->input_types(); + for (int i = 0; i < out_types.size(); ++i) { + if (out_types[i] == DT_RESOURCE) { + // TODO(apassos): figure out how to get the right dtype + out_types[i] = DT_FLOAT; + } + } + AddNodeAttr("Tout", out_types, &ndef); NameAttrList func; func.set_name(n->type_string()); for (const auto& attr : n->attrs()) { diff --git a/tensorflow/core/ops/resource_variable_ops.cc b/tensorflow/core/ops/resource_variable_ops.cc index 3d0a6c2157..26499540f1 100644 --- a/tensorflow/core/ops/resource_variable_ops.cc +++ b/tensorflow/core/ops/resource_variable_ops.cc @@ -14,6 +14,7 @@ // ============================================================================ #include "tensorflow/core/framework/common_shape_fns.h" +#include "tensorflow/core/framework/function.h" #include "tensorflow/core/framework/node_def_util.h" #include "tensorflow/core/framework/op.h" #include "tensorflow/core/framework/resource_mgr.h" @@ -84,6 +85,22 @@ REGISTER_OP("ReadVariableOp") .Attr("dtype: type") .SetShapeFn(ReadVariableShapeFn); +Status ReadGrad(const AttrSlice& attrs, FunctionDef* g) { + // clang-format off + *g = FunctionDefHelper::Define( + // Arg defs + {"x: resource", "dy: float"}, + // Ret val defs + {"dy: float"}, + // Attr defs + {}, + // Nodes + {}); + // clang-format on + return Status::OK(); +} +REGISTER_OP_GRADIENT("ReadVariableOp", ReadGrad); + REGISTER_OP("DestroyResourceOp") .Input("resource: resource") .Attr("ignore_lookup_error: bool = true") diff --git a/tensorflow/python/eager/function_test.py b/tensorflow/python/eager/function_test.py index 0b13ea6398..a5df3ef530 100644 --- a/tensorflow/python/eager/function_test.py +++ b/tensorflow/python/eager/function_test.py @@ -34,6 +34,7 @@ from tensorflow.python.layers import convolutional from tensorflow.python.ops import array_ops from tensorflow.python.ops import clip_ops from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import gradients_impl from tensorflow.python.ops import init_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops @@ -90,6 +91,19 @@ class FunctionTest(test.TestCase): self.assertAllEqual(step(), 2.0) + def testGraphGradientVariable(self): + with ops.Graph().as_default(), self.test_session(): + v = resource_variable_ops.ResourceVariable(1.0) + + @function.defun + def f(): + return 2.0 * v + + node = f() + grads, = gradients_impl.gradients(node, v) + v.initializer.run() + self.assertAllEqual(grads.eval(), 2.0) + def testBasicDefunOpGraphMode(self): matmul = function.defun(math_ops.matmul) diff --git a/tensorflow/python/ops/gradients_impl.py b/tensorflow/python/ops/gradients_impl.py index fe464af3a4..ee7a98c60b 100644 --- a/tensorflow/python/ops/gradients_impl.py +++ b/tensorflow/python/ops/gradients_impl.py @@ -379,7 +379,11 @@ def _SymGrad(op, out_grads): f.name = op.type for k in op.node_def.attr: f.attr[k].CopyFrom(op.node_def.attr[k]) - in_grads = functional_ops.symbolic_gradient(input=f_in, Tout=f_types, f=f) + # TODO(apassos) use a better dtype here + in_grads = functional_ops.symbolic_gradient( + input=f_in, + Tout=[x if x != dtypes.resource else dtypes.float32 for x in f_types], + f=f) return in_grads -- GitLab From 5d773dd3046172cb6e296840a0c8ed5eb6c1fa6f Mon Sep 17 00:00:00 2001 From: Shashi Shekhar Date: Wed, 20 Jun 2018 11:57:48 -0700 Subject: [PATCH 749/816] Fix gradle build for TFLite Android example. PiperOrigin-RevId: 201391220 --- .../contrib/lite/examples/android/BUILD | 42 ++-------- .../contrib/lite/examples/android/android.iml | 19 +++++ .../lite/examples/android/app/build.gradle | 60 ++++++++++++++ .../android/app/download-models.gradle | 73 ++++++++++++++++++ .../{ => app/src/main}/AndroidManifest.xml | 0 .../android/{ => app/src/main}/assets/BUILD | 0 .../{ => app/src/main}/assets/box_priors.txt | 0 .../src/main}/assets/coco_labels_list.txt | 0 .../src/main}/assets/conv_actions_labels.txt | 0 .../assets/labels_mobilenet_quant_v1_224.txt | 0 .../tensorflow/demo/AutoFitTextureView.java | 0 .../org/tensorflow/demo/CameraActivity.java | 0 .../demo/CameraConnectionFragment.java | 0 .../java}/org/tensorflow/demo/Classifier.java | 0 .../tensorflow/demo/ClassifierActivity.java | 0 .../org/tensorflow/demo/DetectorActivity.java | 0 .../demo/LegacyCameraConnectionFragment.java | 0 .../org/tensorflow/demo/OverlayView.java | 0 .../tensorflow/demo/RecognitionScoreView.java | 0 .../tensorflow/demo/RecognizeCommands.java | 0 .../org/tensorflow/demo/ResultsView.java | 0 .../org/tensorflow/demo/SpeechActivity.java | 0 .../demo/TFLiteImageClassifier.java | 0 .../demo/TFLiteObjectDetectionAPIModel.java | 0 .../org/tensorflow/demo/env/AssetUtils.java | 0 .../org/tensorflow/demo/env/BorderedText.java | 0 .../org/tensorflow/demo/env/ImageUtils.java | 0 .../java}/org/tensorflow/demo/env/Logger.java | 0 .../java}/org/tensorflow/demo/env/Size.java | 0 .../org/tensorflow/demo/env/SplitTimer.java | 0 .../demo/tracking/MultiBoxTracker.java | 0 .../demo/tracking/ObjectTracker.java | 0 .../main}/res/animator/color_animation.xml | 0 .../res/drawable-hdpi/ic_action_info.png | Bin .../main}/res/drawable-hdpi/ic_launcher.png | Bin .../src/main}/res/drawable-hdpi/tile.9.png | Bin .../res/drawable-mdpi/ic_action_info.png | Bin .../main}/res/drawable-mdpi/ic_launcher.png | Bin .../res/drawable-xhdpi/ic_action_info.png | Bin .../main}/res/drawable-xhdpi/ic_launcher.png | Bin .../res/drawable-xxhdpi/ic_action_info.png | Bin .../main}/res/drawable-xxhdpi/ic_launcher.png | Bin .../src/main}/res/drawable/border.xml | 0 .../src/main}/res/layout/activity_camera.xml | 0 .../src/main}/res/layout/activity_speech.xml | 0 .../res/layout/camera_connection_fragment.xml | 0 .../camera_connection_fragment_stylize.xml | 0 .../camera_connection_fragment_tracking.xml | 0 .../src/main}/res/layout/list_text_item.xml | 0 .../res/values-sw600dp/template-dimens.xml | 0 .../res/values-sw600dp/template-styles.xml | 0 .../src/main}/res/values-v11/styles.xml | 0 .../main}/res/values-v11/template-styles.xml | 0 .../src/main}/res/values-v14/styles.xml | 0 .../src/main}/res/values-v21/base-colors.xml | 0 .../res/values-v21/base-template-styles.xml | 0 .../{ => app/src/main}/res/values/attrs.xml | 0 .../src/main}/res/values/base-strings.xml | 0 .../{ => app/src/main}/res/values/colors.xml | 0 .../{ => app/src/main}/res/values/strings.xml | 0 .../{ => app/src/main}/res/values/styles.xml | 0 .../src/main}/res/values/template-dimens.xml | 0 .../src/main}/res/values/template-styles.xml | 0 .../lite/examples/android/build.gradle | 55 ++++--------- .../lite/examples/android/settings.gradle | 1 + 65 files changed, 173 insertions(+), 77 deletions(-) create mode 100644 tensorflow/contrib/lite/examples/android/android.iml create mode 100644 tensorflow/contrib/lite/examples/android/app/build.gradle create mode 100644 tensorflow/contrib/lite/examples/android/app/download-models.gradle rename tensorflow/contrib/lite/examples/android/{ => app/src/main}/AndroidManifest.xml (100%) rename tensorflow/contrib/lite/examples/android/{ => app/src/main}/assets/BUILD (100%) rename tensorflow/contrib/lite/examples/android/{ => app/src/main}/assets/box_priors.txt (100%) rename tensorflow/contrib/lite/examples/android/{ => app/src/main}/assets/coco_labels_list.txt (100%) rename tensorflow/contrib/lite/examples/android/{ => app/src/main}/assets/conv_actions_labels.txt (100%) rename tensorflow/contrib/lite/examples/android/{ => app/src/main}/assets/labels_mobilenet_quant_v1_224.txt (100%) rename tensorflow/contrib/lite/examples/android/{src => app/src/main/java}/org/tensorflow/demo/AutoFitTextureView.java (100%) rename tensorflow/contrib/lite/examples/android/{src => app/src/main/java}/org/tensorflow/demo/CameraActivity.java (100%) rename tensorflow/contrib/lite/examples/android/{src => app/src/main/java}/org/tensorflow/demo/CameraConnectionFragment.java (100%) rename tensorflow/contrib/lite/examples/android/{src => app/src/main/java}/org/tensorflow/demo/Classifier.java (100%) rename tensorflow/contrib/lite/examples/android/{src => app/src/main/java}/org/tensorflow/demo/ClassifierActivity.java (100%) rename tensorflow/contrib/lite/examples/android/{src => app/src/main/java}/org/tensorflow/demo/DetectorActivity.java (100%) rename tensorflow/contrib/lite/examples/android/{src => app/src/main/java}/org/tensorflow/demo/LegacyCameraConnectionFragment.java (100%) rename tensorflow/contrib/lite/examples/android/{src => app/src/main/java}/org/tensorflow/demo/OverlayView.java (100%) rename tensorflow/contrib/lite/examples/android/{src => app/src/main/java}/org/tensorflow/demo/RecognitionScoreView.java (100%) rename tensorflow/contrib/lite/examples/android/{src => app/src/main/java}/org/tensorflow/demo/RecognizeCommands.java (100%) rename tensorflow/contrib/lite/examples/android/{src => app/src/main/java}/org/tensorflow/demo/ResultsView.java (100%) rename tensorflow/contrib/lite/examples/android/{src => app/src/main/java}/org/tensorflow/demo/SpeechActivity.java (100%) rename tensorflow/contrib/lite/examples/android/{src => app/src/main/java}/org/tensorflow/demo/TFLiteImageClassifier.java (100%) rename tensorflow/contrib/lite/examples/android/{src => app/src/main/java}/org/tensorflow/demo/TFLiteObjectDetectionAPIModel.java (100%) rename tensorflow/contrib/lite/examples/android/{src => app/src/main/java}/org/tensorflow/demo/env/AssetUtils.java (100%) rename tensorflow/contrib/lite/examples/android/{src => app/src/main/java}/org/tensorflow/demo/env/BorderedText.java (100%) rename tensorflow/contrib/lite/examples/android/{src => app/src/main/java}/org/tensorflow/demo/env/ImageUtils.java (100%) rename tensorflow/contrib/lite/examples/android/{src => app/src/main/java}/org/tensorflow/demo/env/Logger.java (100%) rename tensorflow/contrib/lite/examples/android/{src => app/src/main/java}/org/tensorflow/demo/env/Size.java (100%) rename tensorflow/contrib/lite/examples/android/{src => app/src/main/java}/org/tensorflow/demo/env/SplitTimer.java (100%) rename tensorflow/contrib/lite/examples/android/{src => app/src/main/java}/org/tensorflow/demo/tracking/MultiBoxTracker.java (100%) rename tensorflow/contrib/lite/examples/android/{src => app/src/main/java}/org/tensorflow/demo/tracking/ObjectTracker.java (100%) rename tensorflow/contrib/lite/examples/android/{ => app/src/main}/res/animator/color_animation.xml (100%) rename tensorflow/contrib/lite/examples/android/{ => app/src/main}/res/drawable-hdpi/ic_action_info.png (100%) rename tensorflow/contrib/lite/examples/android/{ => app/src/main}/res/drawable-hdpi/ic_launcher.png (100%) rename tensorflow/contrib/lite/examples/android/{ => app/src/main}/res/drawable-hdpi/tile.9.png (100%) rename tensorflow/contrib/lite/examples/android/{ => app/src/main}/res/drawable-mdpi/ic_action_info.png (100%) rename tensorflow/contrib/lite/examples/android/{ => app/src/main}/res/drawable-mdpi/ic_launcher.png (100%) rename tensorflow/contrib/lite/examples/android/{ => app/src/main}/res/drawable-xhdpi/ic_action_info.png (100%) rename tensorflow/contrib/lite/examples/android/{ => app/src/main}/res/drawable-xhdpi/ic_launcher.png (100%) rename tensorflow/contrib/lite/examples/android/{ => app/src/main}/res/drawable-xxhdpi/ic_action_info.png (100%) rename tensorflow/contrib/lite/examples/android/{ => app/src/main}/res/drawable-xxhdpi/ic_launcher.png (100%) rename tensorflow/contrib/lite/examples/android/{ => app/src/main}/res/drawable/border.xml (100%) rename tensorflow/contrib/lite/examples/android/{ => app/src/main}/res/layout/activity_camera.xml (100%) rename tensorflow/contrib/lite/examples/android/{ => app/src/main}/res/layout/activity_speech.xml (100%) rename tensorflow/contrib/lite/examples/android/{ => app/src/main}/res/layout/camera_connection_fragment.xml (100%) rename tensorflow/contrib/lite/examples/android/{ => app/src/main}/res/layout/camera_connection_fragment_stylize.xml (100%) rename tensorflow/contrib/lite/examples/android/{ => app/src/main}/res/layout/camera_connection_fragment_tracking.xml (100%) rename tensorflow/contrib/lite/examples/android/{ => app/src/main}/res/layout/list_text_item.xml (100%) rename tensorflow/contrib/lite/examples/android/{ => app/src/main}/res/values-sw600dp/template-dimens.xml (100%) rename tensorflow/contrib/lite/examples/android/{ => app/src/main}/res/values-sw600dp/template-styles.xml (100%) rename tensorflow/contrib/lite/examples/android/{ => app/src/main}/res/values-v11/styles.xml (100%) rename tensorflow/contrib/lite/examples/android/{ => app/src/main}/res/values-v11/template-styles.xml (100%) rename tensorflow/contrib/lite/examples/android/{ => app/src/main}/res/values-v14/styles.xml (100%) rename tensorflow/contrib/lite/examples/android/{ => app/src/main}/res/values-v21/base-colors.xml (100%) rename tensorflow/contrib/lite/examples/android/{ => app/src/main}/res/values-v21/base-template-styles.xml (100%) rename tensorflow/contrib/lite/examples/android/{ => app/src/main}/res/values/attrs.xml (100%) rename tensorflow/contrib/lite/examples/android/{ => app/src/main}/res/values/base-strings.xml (100%) rename tensorflow/contrib/lite/examples/android/{ => app/src/main}/res/values/colors.xml (100%) rename tensorflow/contrib/lite/examples/android/{ => app/src/main}/res/values/strings.xml (100%) rename tensorflow/contrib/lite/examples/android/{ => app/src/main}/res/values/styles.xml (100%) rename tensorflow/contrib/lite/examples/android/{ => app/src/main}/res/values/template-dimens.xml (100%) rename tensorflow/contrib/lite/examples/android/{ => app/src/main}/res/values/template-styles.xml (100%) create mode 100644 tensorflow/contrib/lite/examples/android/settings.gradle diff --git a/tensorflow/contrib/lite/examples/android/BUILD b/tensorflow/contrib/lite/examples/android/BUILD index 3e3b4db7d3..dd2cd17324 100644 --- a/tensorflow/contrib/lite/examples/android/BUILD +++ b/tensorflow/contrib/lite/examples/android/BUILD @@ -26,28 +26,28 @@ cc_library( android_binary( name = "tflite_demo", srcs = glob([ - "src/**/*.java", + "app/src/main/java/**/*.java", ]), # Package assets from assets dir as well as all model targets. # Remove undesired models (and corresponding Activities in source) # to reduce APK size. assets = [ - "//tensorflow/contrib/lite/examples/android/assets:labels_mobilenet_quant_v1_224.txt", + "//tensorflow/contrib/lite/examples/android/app/src/main/assets:labels_mobilenet_quant_v1_224.txt", "@tflite_mobilenet//:mobilenet_quant_v1_224.tflite", "@tflite_conv_actions_frozen//:conv_actions_frozen.tflite", - "//tensorflow/contrib/lite/examples/android/assets:conv_actions_labels.txt", + "//tensorflow/contrib/lite/examples/android/app/src/main/assets:conv_actions_labels.txt", "@tflite_mobilenet_ssd//:mobilenet_ssd.tflite", - "//tensorflow/contrib/lite/examples/android/assets:box_priors.txt", - "//tensorflow/contrib/lite/examples/android/assets:coco_labels_list.txt", + "//tensorflow/contrib/lite/examples/android/app/src/main/assets:box_priors.txt", + "//tensorflow/contrib/lite/examples/android/app/src/main/assets:coco_labels_list.txt", ], assets_dir = "", custom_package = "org.tensorflow.lite.demo", inline_constants = 1, - manifest = "AndroidManifest.xml", + manifest = "app/src/main/AndroidManifest.xml", nocompress_extensions = [ ".tflite", ], - resource_files = glob(["res/**"]), + resource_files = glob(["app/src/main/res/**"]), tags = [ "manual", "notap", @@ -57,31 +57,3 @@ android_binary( "//tensorflow/contrib/lite/java:tensorflowlite", ], ) - -filegroup( - name = "all_files", - srcs = glob( - ["**/*"], - exclude = [ - "**/METADATA", - "**/OWNERS", - "bin/**", - "gen/**", - "gradleBuild/**", - "libs/**", - ], - ), - visibility = ["//tensorflow:__subpackages__"], -) - -filegroup( - name = "java_files", - srcs = glob(["src/**/*.java"]), -) - -filegroup( - name = "resource_files", - srcs = glob(["res/**"]), -) - -exports_files(["AndroidManifest.xml"]) diff --git a/tensorflow/contrib/lite/examples/android/android.iml b/tensorflow/contrib/lite/examples/android/android.iml new file mode 100644 index 0000000000..f0a5ac2bf4 --- /dev/null +++ b/tensorflow/contrib/lite/examples/android/android.iml @@ -0,0 +1,19 @@ + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/tensorflow/contrib/lite/examples/android/app/build.gradle b/tensorflow/contrib/lite/examples/android/app/build.gradle new file mode 100644 index 0000000000..8e0a98ed63 --- /dev/null +++ b/tensorflow/contrib/lite/examples/android/app/build.gradle @@ -0,0 +1,60 @@ +apply plugin: 'com.android.application' + +android { + compileSdkVersion 26 + buildToolsVersion '26.0.2' + defaultConfig { + applicationId "org.tensorflow.lite.demo" + minSdkVersion 15 + targetSdkVersion 26 + versionCode 1 + versionName "1.0" + testInstrumentationRunner "android.support.test.runner.AndroidJUnitRunner" + + // Remove this block. + jackOptions { + enabled true + } + } + lintOptions { + abortOnError false + } + buildTypes { + release { + minifyEnabled false + proguardFiles getDefaultProguardFile('proguard-android.txt'), 'proguard-rules.pro' + } + } + aaptOptions { + noCompress "tflite" + } + + compileOptions { + sourceCompatibility JavaVersion.VERSION_1_8 + targetCompatibility JavaVersion.VERSION_1_8 + } +} + +repositories { + maven { + url 'https://google.bintray.com/tensorflow' + } +} + +// import DownloadModels task +project.ext.ASSET_DIR = projectDir.toString() + '/src/main/assets' +project.ext.TMP_DIR = project.buildDir.toString() + '/downloads' + +// Download default models; if you wish to use your own models then +// place them in the "assets" directory and comment out this line. +apply from: "download-models.gradle" + +dependencies { + compile fileTree(dir: 'libs', include: ['*.jar']) + androidTestCompile('com.android.support.test.espresso:espresso-core:2.2.2', { + exclude group: 'com.android.support', module: 'support-annotations' + }) + compile 'org.tensorflow:tensorflow-lite:0.0.0-nightly' + + testCompile 'junit:junit:4.12' +} diff --git a/tensorflow/contrib/lite/examples/android/app/download-models.gradle b/tensorflow/contrib/lite/examples/android/app/download-models.gradle new file mode 100644 index 0000000000..8e65dc076f --- /dev/null +++ b/tensorflow/contrib/lite/examples/android/app/download-models.gradle @@ -0,0 +1,73 @@ +/* + * download-models.gradle + * Downloads model files from ${MODEL_URL} into application's asset folder + * Input: + * project.ext.TMP_DIR: absolute path to hold downloaded zip files + * project.ext.ASSET_DIR: absolute path to save unzipped model files + * Output: + * 3 model files will be downloaded into given folder of ext.ASSET_DIR + */ +// hard coded model files +// LINT.IfChange + +def models = ['conv_actions_tflite.zip', + 'mobilenet_ssd_tflite_v1.zip', + 'mobilenet_v1_224_android_quant_2017_11_08.zip'] +// LINT.ThenChange(//tensorflow/examples/android/BUILD) + +// Root URL for model archives +def MODEL_URL = 'https://storage.googleapis.com/download.tensorflow.org/models/tflite' + +buildscript { + repositories { + jcenter() + } + dependencies { + classpath 'de.undercouch:gradle-download-task:3.2.0' + } +} + +import de.undercouch.gradle.tasks.download.Download +task downloadFile(type: Download){ + for (f in models) { + def modelUrl = MODEL_URL + "/" + f + println "Downloading ${f} from ${modelUrl}" + src modelUrl + } + + dest new File(project.ext.TMP_DIR) + overwrite true +} + +task extractModels(type: Copy) { + for (f in models) { + def localFile = f.split("/")[-1] + from zipTree(project.ext.TMP_DIR + '/' + localFile) + } + + into file(project.ext.ASSET_DIR) + fileMode 0644 + exclude '**/LICENSE' + + def needDownload = false + for (f in models) { + def localFile = f.split("/")[-1] + if (!(new File(project.ext.TMP_DIR + '/' + localFile)).exists()) { + needDownload = true + } + } + + if (needDownload) { + dependsOn downloadFile + } +} + +tasks.whenTaskAdded { task -> + if (task.name == 'assembleDebug') { + task.dependsOn 'extractModels' + } + if (task.name == 'assembleRelease') { + task.dependsOn 'extractModels' + } +} + diff --git a/tensorflow/contrib/lite/examples/android/AndroidManifest.xml b/tensorflow/contrib/lite/examples/android/app/src/main/AndroidManifest.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/AndroidManifest.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/AndroidManifest.xml diff --git a/tensorflow/contrib/lite/examples/android/assets/BUILD b/tensorflow/contrib/lite/examples/android/app/src/main/assets/BUILD similarity index 100% rename from tensorflow/contrib/lite/examples/android/assets/BUILD rename to tensorflow/contrib/lite/examples/android/app/src/main/assets/BUILD diff --git a/tensorflow/contrib/lite/examples/android/assets/box_priors.txt b/tensorflow/contrib/lite/examples/android/app/src/main/assets/box_priors.txt similarity index 100% rename from tensorflow/contrib/lite/examples/android/assets/box_priors.txt rename to tensorflow/contrib/lite/examples/android/app/src/main/assets/box_priors.txt diff --git a/tensorflow/contrib/lite/examples/android/assets/coco_labels_list.txt b/tensorflow/contrib/lite/examples/android/app/src/main/assets/coco_labels_list.txt similarity index 100% rename from tensorflow/contrib/lite/examples/android/assets/coco_labels_list.txt rename to tensorflow/contrib/lite/examples/android/app/src/main/assets/coco_labels_list.txt diff --git a/tensorflow/contrib/lite/examples/android/assets/conv_actions_labels.txt b/tensorflow/contrib/lite/examples/android/app/src/main/assets/conv_actions_labels.txt similarity index 100% rename from tensorflow/contrib/lite/examples/android/assets/conv_actions_labels.txt rename to tensorflow/contrib/lite/examples/android/app/src/main/assets/conv_actions_labels.txt diff --git a/tensorflow/contrib/lite/examples/android/assets/labels_mobilenet_quant_v1_224.txt b/tensorflow/contrib/lite/examples/android/app/src/main/assets/labels_mobilenet_quant_v1_224.txt similarity index 100% rename from tensorflow/contrib/lite/examples/android/assets/labels_mobilenet_quant_v1_224.txt rename to tensorflow/contrib/lite/examples/android/app/src/main/assets/labels_mobilenet_quant_v1_224.txt diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/AutoFitTextureView.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/AutoFitTextureView.java similarity index 100% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/AutoFitTextureView.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/AutoFitTextureView.java diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/CameraActivity.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/CameraActivity.java similarity index 100% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/CameraActivity.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/CameraActivity.java diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/CameraConnectionFragment.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/CameraConnectionFragment.java similarity index 100% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/CameraConnectionFragment.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/CameraConnectionFragment.java diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/Classifier.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/Classifier.java similarity index 100% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/Classifier.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/Classifier.java diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/ClassifierActivity.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/ClassifierActivity.java similarity index 100% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/ClassifierActivity.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/ClassifierActivity.java diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/DetectorActivity.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/DetectorActivity.java similarity index 100% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/DetectorActivity.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/DetectorActivity.java diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/LegacyCameraConnectionFragment.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/LegacyCameraConnectionFragment.java similarity index 100% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/LegacyCameraConnectionFragment.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/LegacyCameraConnectionFragment.java diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/OverlayView.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/OverlayView.java similarity index 100% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/OverlayView.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/OverlayView.java diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/RecognitionScoreView.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/RecognitionScoreView.java similarity index 100% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/RecognitionScoreView.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/RecognitionScoreView.java diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/RecognizeCommands.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/RecognizeCommands.java similarity index 100% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/RecognizeCommands.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/RecognizeCommands.java diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/ResultsView.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/ResultsView.java similarity index 100% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/ResultsView.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/ResultsView.java diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/SpeechActivity.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/SpeechActivity.java similarity index 100% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/SpeechActivity.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/SpeechActivity.java diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/TFLiteImageClassifier.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/TFLiteImageClassifier.java similarity index 100% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/TFLiteImageClassifier.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/TFLiteImageClassifier.java diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/TFLiteObjectDetectionAPIModel.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/TFLiteObjectDetectionAPIModel.java similarity index 100% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/TFLiteObjectDetectionAPIModel.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/TFLiteObjectDetectionAPIModel.java diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/env/AssetUtils.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/env/AssetUtils.java similarity index 100% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/env/AssetUtils.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/env/AssetUtils.java diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/env/BorderedText.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/env/BorderedText.java similarity index 100% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/env/BorderedText.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/env/BorderedText.java diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/env/ImageUtils.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/env/ImageUtils.java similarity index 100% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/env/ImageUtils.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/env/ImageUtils.java diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/env/Logger.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/env/Logger.java similarity index 100% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/env/Logger.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/env/Logger.java diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/env/Size.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/env/Size.java similarity index 100% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/env/Size.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/env/Size.java diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/env/SplitTimer.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/env/SplitTimer.java similarity index 100% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/env/SplitTimer.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/env/SplitTimer.java diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/tracking/MultiBoxTracker.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/tracking/MultiBoxTracker.java similarity index 100% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/tracking/MultiBoxTracker.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/tracking/MultiBoxTracker.java diff --git a/tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/tracking/ObjectTracker.java b/tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/tracking/ObjectTracker.java similarity index 100% rename from tensorflow/contrib/lite/examples/android/src/org/tensorflow/demo/tracking/ObjectTracker.java rename to tensorflow/contrib/lite/examples/android/app/src/main/java/org/tensorflow/demo/tracking/ObjectTracker.java diff --git a/tensorflow/contrib/lite/examples/android/res/animator/color_animation.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/animator/color_animation.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/animator/color_animation.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/animator/color_animation.xml diff --git a/tensorflow/contrib/lite/examples/android/res/drawable-hdpi/ic_action_info.png b/tensorflow/contrib/lite/examples/android/app/src/main/res/drawable-hdpi/ic_action_info.png similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/drawable-hdpi/ic_action_info.png rename to tensorflow/contrib/lite/examples/android/app/src/main/res/drawable-hdpi/ic_action_info.png diff --git a/tensorflow/contrib/lite/examples/android/res/drawable-hdpi/ic_launcher.png b/tensorflow/contrib/lite/examples/android/app/src/main/res/drawable-hdpi/ic_launcher.png similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/drawable-hdpi/ic_launcher.png rename to tensorflow/contrib/lite/examples/android/app/src/main/res/drawable-hdpi/ic_launcher.png diff --git a/tensorflow/contrib/lite/examples/android/res/drawable-hdpi/tile.9.png b/tensorflow/contrib/lite/examples/android/app/src/main/res/drawable-hdpi/tile.9.png similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/drawable-hdpi/tile.9.png rename to tensorflow/contrib/lite/examples/android/app/src/main/res/drawable-hdpi/tile.9.png diff --git a/tensorflow/contrib/lite/examples/android/res/drawable-mdpi/ic_action_info.png b/tensorflow/contrib/lite/examples/android/app/src/main/res/drawable-mdpi/ic_action_info.png similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/drawable-mdpi/ic_action_info.png rename to tensorflow/contrib/lite/examples/android/app/src/main/res/drawable-mdpi/ic_action_info.png diff --git a/tensorflow/contrib/lite/examples/android/res/drawable-mdpi/ic_launcher.png b/tensorflow/contrib/lite/examples/android/app/src/main/res/drawable-mdpi/ic_launcher.png similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/drawable-mdpi/ic_launcher.png rename to tensorflow/contrib/lite/examples/android/app/src/main/res/drawable-mdpi/ic_launcher.png diff --git a/tensorflow/contrib/lite/examples/android/res/drawable-xhdpi/ic_action_info.png b/tensorflow/contrib/lite/examples/android/app/src/main/res/drawable-xhdpi/ic_action_info.png similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/drawable-xhdpi/ic_action_info.png rename to tensorflow/contrib/lite/examples/android/app/src/main/res/drawable-xhdpi/ic_action_info.png diff --git a/tensorflow/contrib/lite/examples/android/res/drawable-xhdpi/ic_launcher.png b/tensorflow/contrib/lite/examples/android/app/src/main/res/drawable-xhdpi/ic_launcher.png similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/drawable-xhdpi/ic_launcher.png rename to tensorflow/contrib/lite/examples/android/app/src/main/res/drawable-xhdpi/ic_launcher.png diff --git a/tensorflow/contrib/lite/examples/android/res/drawable-xxhdpi/ic_action_info.png b/tensorflow/contrib/lite/examples/android/app/src/main/res/drawable-xxhdpi/ic_action_info.png similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/drawable-xxhdpi/ic_action_info.png rename to tensorflow/contrib/lite/examples/android/app/src/main/res/drawable-xxhdpi/ic_action_info.png diff --git a/tensorflow/contrib/lite/examples/android/res/drawable-xxhdpi/ic_launcher.png b/tensorflow/contrib/lite/examples/android/app/src/main/res/drawable-xxhdpi/ic_launcher.png similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/drawable-xxhdpi/ic_launcher.png rename to tensorflow/contrib/lite/examples/android/app/src/main/res/drawable-xxhdpi/ic_launcher.png diff --git a/tensorflow/contrib/lite/examples/android/res/drawable/border.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/drawable/border.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/drawable/border.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/drawable/border.xml diff --git a/tensorflow/contrib/lite/examples/android/res/layout/activity_camera.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/layout/activity_camera.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/layout/activity_camera.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/layout/activity_camera.xml diff --git a/tensorflow/contrib/lite/examples/android/res/layout/activity_speech.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/layout/activity_speech.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/layout/activity_speech.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/layout/activity_speech.xml diff --git a/tensorflow/contrib/lite/examples/android/res/layout/camera_connection_fragment.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/layout/camera_connection_fragment.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/layout/camera_connection_fragment.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/layout/camera_connection_fragment.xml diff --git a/tensorflow/contrib/lite/examples/android/res/layout/camera_connection_fragment_stylize.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/layout/camera_connection_fragment_stylize.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/layout/camera_connection_fragment_stylize.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/layout/camera_connection_fragment_stylize.xml diff --git a/tensorflow/contrib/lite/examples/android/res/layout/camera_connection_fragment_tracking.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/layout/camera_connection_fragment_tracking.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/layout/camera_connection_fragment_tracking.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/layout/camera_connection_fragment_tracking.xml diff --git a/tensorflow/contrib/lite/examples/android/res/layout/list_text_item.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/layout/list_text_item.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/layout/list_text_item.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/layout/list_text_item.xml diff --git a/tensorflow/contrib/lite/examples/android/res/values-sw600dp/template-dimens.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/values-sw600dp/template-dimens.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/values-sw600dp/template-dimens.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/values-sw600dp/template-dimens.xml diff --git a/tensorflow/contrib/lite/examples/android/res/values-sw600dp/template-styles.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/values-sw600dp/template-styles.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/values-sw600dp/template-styles.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/values-sw600dp/template-styles.xml diff --git a/tensorflow/contrib/lite/examples/android/res/values-v11/styles.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/values-v11/styles.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/values-v11/styles.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/values-v11/styles.xml diff --git a/tensorflow/contrib/lite/examples/android/res/values-v11/template-styles.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/values-v11/template-styles.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/values-v11/template-styles.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/values-v11/template-styles.xml diff --git a/tensorflow/contrib/lite/examples/android/res/values-v14/styles.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/values-v14/styles.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/values-v14/styles.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/values-v14/styles.xml diff --git a/tensorflow/contrib/lite/examples/android/res/values-v21/base-colors.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/values-v21/base-colors.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/values-v21/base-colors.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/values-v21/base-colors.xml diff --git a/tensorflow/contrib/lite/examples/android/res/values-v21/base-template-styles.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/values-v21/base-template-styles.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/values-v21/base-template-styles.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/values-v21/base-template-styles.xml diff --git a/tensorflow/contrib/lite/examples/android/res/values/attrs.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/values/attrs.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/values/attrs.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/values/attrs.xml diff --git a/tensorflow/contrib/lite/examples/android/res/values/base-strings.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/values/base-strings.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/values/base-strings.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/values/base-strings.xml diff --git a/tensorflow/contrib/lite/examples/android/res/values/colors.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/values/colors.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/values/colors.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/values/colors.xml diff --git a/tensorflow/contrib/lite/examples/android/res/values/strings.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/values/strings.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/values/strings.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/values/strings.xml diff --git a/tensorflow/contrib/lite/examples/android/res/values/styles.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/values/styles.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/values/styles.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/values/styles.xml diff --git a/tensorflow/contrib/lite/examples/android/res/values/template-dimens.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/values/template-dimens.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/values/template-dimens.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/values/template-dimens.xml diff --git a/tensorflow/contrib/lite/examples/android/res/values/template-styles.xml b/tensorflow/contrib/lite/examples/android/app/src/main/res/values/template-styles.xml similarity index 100% rename from tensorflow/contrib/lite/examples/android/res/values/template-styles.xml rename to tensorflow/contrib/lite/examples/android/app/src/main/res/values/template-styles.xml diff --git a/tensorflow/contrib/lite/examples/android/build.gradle b/tensorflow/contrib/lite/examples/android/build.gradle index 0d4de35815..a47fa4bbf6 100644 --- a/tensorflow/contrib/lite/examples/android/build.gradle +++ b/tensorflow/contrib/lite/examples/android/build.gradle @@ -1,52 +1,23 @@ -apply plugin: 'com.android.application' +// Top-level build file where you can add configuration options common to all sub-projects/modules. -android { - compileSdkVersion 26 - buildToolsVersion "26.0.1" - defaultConfig { - applicationId "org.tensorflow.lite.demo" - minSdkVersion 15 - targetSdkVersion 26 - versionCode 1 - versionName "1.0" - testInstrumentationRunner "android.support.test.runner.AndroidJUnitRunner" - - // Remove this block. - jackOptions { - enabled true - } - } - lintOptions { - abortOnError false - } - buildTypes { - release { - minifyEnabled false - proguardFiles getDefaultProguardFile('proguard-android.txt'), 'proguard-rules.pro' - } - } - aaptOptions { - noCompress "tflite" +buildscript { + repositories { + jcenter() } + dependencies { + classpath 'com.android.tools.build:gradle:3.0.1' - compileOptions { - sourceCompatibility JavaVersion.VERSION_1_8 - targetCompatibility JavaVersion.VERSION_1_8 + // NOTE: Do not place your application dependencies here; they belong + // in the individual module build.gradle files } } -repositories { - maven { - url 'https://google.bintray.com/tensorflow' +allprojects { + repositories { + jcenter() } } -dependencies { - compile fileTree(dir: 'libs', include: ['*.jar']) - androidTestCompile('com.android.support.test.espresso:espresso-core:2.2.2', { - exclude group: 'com.android.support', module: 'support-annotations' - }) - compile 'org.tensorflow:tensorflow-lite:+' - - testCompile 'junit:junit:4.12' +task clean(type: Delete) { + delete rootProject.buildDir } diff --git a/tensorflow/contrib/lite/examples/android/settings.gradle b/tensorflow/contrib/lite/examples/android/settings.gradle new file mode 100644 index 0000000000..e7b4def49c --- /dev/null +++ b/tensorflow/contrib/lite/examples/android/settings.gradle @@ -0,0 +1 @@ +include ':app' -- GitLab From 4fdb7cc4f92e76a168810e9b420bf1b90eb544e9 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 20 Jun 2018 12:05:07 -0700 Subject: [PATCH 750/816] Split GradientBoostedDecisionTreeModel.train() to three steps. 1) Update stats 2) Update the number of examples visited. 3) If the number of examples reaches the target, grow the tree. PiperOrigin-RevId: 201392512 --- .../python/training/functions/gbdt_batch.py | 502 ++++++++++-------- 1 file changed, 292 insertions(+), 210 deletions(-) diff --git a/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py b/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py index 47698d45c8..28fbf07fe4 100644 --- a/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py +++ b/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py @@ -61,6 +61,17 @@ USED_HANDLERS_MASK = "used_handlers_mask" LEAF_INDEX = "leaf_index" _FEATURE_NAME_TEMPLATE = "%s_%d" +# Keys in Training state. +_NUM_LAYER_EXAMPLES = "num_layer_examples" +_NUM_LAYER_STEPS = "num_layer_steps" +_NUM_LAYERS = "num_layers" +_ACTIVE_TREE = "active_tree" +_ACTIVE_LAYER = "active_layer" +_CONTINUE_CENTERING = "continue_centering" +_BIAS_STATS_ACCUMULATOR = "bias_stats_accumulator" +_STEPS_ACCUMULATOR = "steps_accumulator" +_HANDLERS = "handlers" + def _get_column_by_index(tensor, indices): """Returns columns from a 2-D tensor by index.""" @@ -325,6 +336,19 @@ class GradientBoostedDecisionTreeModel(object): learner_config.multi_class_strategy = ( learner_pb2.LearnerConfig.DIAGONAL_HESSIAN) + if logits_dimension == 1 or learner_config.multi_class_strategy == ( + learner_pb2.LearnerConfig.TREE_PER_CLASS): + self._gradient_shape = tensor_shape.scalar() + self._hessian_shape = tensor_shape.scalar() + else: + self._gradient_shape = tensor_shape.TensorShape([logits_dimension]) + if (learner_config.multi_class_strategy == + learner_pb2.LearnerConfig.FULL_HESSIAN): + self._hessian_shape = tensor_shape.TensorShape( + ([logits_dimension, logits_dimension])) + else: + # Diagonal hessian strategy. + self._hessian_shape = tensor_shape.TensorShape(([logits_dimension])) if (learner_config.growing_mode == learner_pb2.LearnerConfig.GROWING_MODE_UNSPECIFIED): learner_config.growing_mode = learner_pb2.LearnerConfig.LAYER_BY_LAYER @@ -522,14 +546,23 @@ class GradientBoostedDecisionTreeModel(object): return self._predict_and_return_dict(self._ensemble_handle, ensemble_stamp, mode) - def train(self, loss, predictions_dict, labels): - """Grows a new tree and adds it to the ensemble. + def _get_class_id(self, predictions_dict): + # Handle different multiclass strategies. + if (self._learner_config.multi_class_strategy == + learner_pb2.LearnerConfig.TREE_PER_CLASS and + self._logits_dimension != 1): + # Choose the class for which the tree is built (one vs rest). + return math_ops.to_int32( + predictions_dict[NUM_TREES_ATTEMPTED] % self._logits_dimension) + return constant_op.constant(-1, dtype=dtypes.int32) + + def update_stats(self, loss, predictions_dict): + """Update the accumulators with stats from this batch. Args: loss: A scalar tensor representing average loss of examples. predictions_dict: Dictionary of Rank 2 `Tensor` representing information about predictions per example. - labels: Rank 2 `Tensor` representing labels per example. Returns: An op that adds a new tree to the ensemble. @@ -542,6 +575,44 @@ class GradientBoostedDecisionTreeModel(object): self._dense_floats + self._sparse_float_indices + self._sparse_int_indices) worker_device = input_deps[0].device + # Create ensemble stats variables. + num_layer_examples = variables.Variable( + initial_value=array_ops.zeros([], dtypes.int64), + name="num_layer_examples", + trainable=False) + num_layer_steps = variables.Variable( + initial_value=array_ops.zeros([], dtypes.int64), + name="num_layer_steps", + trainable=False) + num_layers = variables.Variable( + initial_value=array_ops.zeros([], dtypes.int64), + name="num_layers", + trainable=False) + active_tree = variables.Variable( + initial_value=array_ops.zeros([], dtypes.int64), + name="active_tree", + trainable=False) + active_layer = variables.Variable( + initial_value=array_ops.zeros([], dtypes.int64), + name="active_layer", + trainable=False) + # Variable that becomes false once bias centering is done. + continue_centering = variables.Variable( + initial_value=self._center_bias, + name="continue_centering", + trainable=False) + # Create bias stats accumulator. + bias_stats_accumulator = stats_accumulator_ops.StatsAccumulator( + stamp_token=0, + gradient_shape=self._gradient_shape, + hessian_shape=self._hessian_shape, + name="BiasAccumulator") + # Create steps accumulator. + steps_accumulator = stats_accumulator_ops.StatsAccumulator( + stamp_token=0, + gradient_shape=tensor_shape.scalar(), + hessian_shape=tensor_shape.scalar(), + name="StepsAccumulator") # Get tensors relevant for training and form the loss. predictions = predictions_dict[PREDICTIONS] @@ -556,13 +627,10 @@ class GradientBoostedDecisionTreeModel(object): aggregation_method=None)[0] strategy = self._learner_config.multi_class_strategy - class_id = constant_op.constant(-1, dtype=dtypes.int32) + class_id = self._get_class_id(predictions_dict) # Handle different multiclass strategies. if strategy == learner_pb2.LearnerConfig.TREE_PER_CLASS: # We build one vs rest trees. - gradient_shape = tensor_shape.scalar() - hessian_shape = tensor_shape.scalar() - if self._logits_dimension == 1: # We have only 1 score, gradients is of shape [batch, 1]. hessians = gradients_impl.gradients( @@ -579,11 +647,6 @@ class GradientBoostedDecisionTreeModel(object): hessian_list = self._diagonal_hessian(gradients, predictions) # Assemble hessian list into a tensor. hessians = array_ops.stack(hessian_list, axis=1) - - # Choose the class for which the tree is built (one vs rest). - class_id = math_ops.to_int32( - predictions_dict[NUM_TREES_ATTEMPTED] % self._logits_dimension) - # Use class id tensor to get the column with that index from gradients # and hessians. squeezed_gradients = array_ops.squeeze( @@ -592,15 +655,10 @@ class GradientBoostedDecisionTreeModel(object): _get_column_by_index(hessians, class_id)) else: # Other multiclass strategies. - gradient_shape = tensor_shape.TensorShape([self._logits_dimension]) - if strategy == learner_pb2.LearnerConfig.FULL_HESSIAN: - hessian_shape = tensor_shape.TensorShape( - ([self._logits_dimension, self._logits_dimension])) hessian_list = self._full_hessian(gradients, predictions) else: # Diagonal hessian strategy. - hessian_shape = tensor_shape.TensorShape(([self._logits_dimension])) hessian_list = self._diagonal_hessian(gradients, predictions) squeezed_gradients = gradients @@ -608,7 +666,7 @@ class GradientBoostedDecisionTreeModel(object): squeezed_hessians = hessians # Get the weights for each example for quantiles calculation, - weights = self._get_weights(hessian_shape, squeezed_hessians) + weights = self._get_weights(self._hessian_shape, squeezed_hessians) # Create all handlers ensuring resources are evenly allocated across PS. fc_name_idx = 0 @@ -640,8 +698,8 @@ class GradientBoostedDecisionTreeModel(object): num_quantiles=num_quantiles, dense_float_column=self._dense_floats[dense_float_column_idx], name=fc_name, - gradient_shape=gradient_shape, - hessian_shape=hessian_shape, + gradient_shape=self._gradient_shape, + hessian_shape=self._hessian_shape, multiclass_strategy=strategy_tensor, init_stamp_token=init_stamp_token)) fc_name_idx += 1 @@ -663,8 +721,8 @@ class GradientBoostedDecisionTreeModel(object): self._sparse_float_values[sparse_float_column_idx], self._sparse_float_shapes[sparse_float_column_idx]), name=fc_name, - gradient_shape=gradient_shape, - hessian_shape=hessian_shape, + gradient_shape=self._gradient_shape, + hessian_shape=self._hessian_shape, multiclass_strategy=strategy_tensor, init_stamp_token=init_stamp_token)) fc_name_idx += 1 @@ -684,48 +742,12 @@ class GradientBoostedDecisionTreeModel(object): self._sparse_int_values[sparse_int_column_idx], self._sparse_int_shapes[sparse_int_column_idx]), name=fc_name, - gradient_shape=gradient_shape, - hessian_shape=hessian_shape, + gradient_shape=self._gradient_shape, + hessian_shape=self._hessian_shape, multiclass_strategy=strategy_tensor, init_stamp_token=init_stamp_token)) fc_name_idx += 1 - # Create steps accumulator. - steps_accumulator = stats_accumulator_ops.StatsAccumulator( - stamp_token=0, - gradient_shape=tensor_shape.scalar(), - hessian_shape=tensor_shape.scalar(), - name="StepsAccumulator") - - # Create bias stats accumulator. - bias_stats_accumulator = stats_accumulator_ops.StatsAccumulator( - stamp_token=0, - gradient_shape=gradient_shape, - hessian_shape=hessian_shape, - name="BiasAccumulator") - - # Create ensemble stats variables. - num_layer_examples = variables.Variable( - initial_value=array_ops.zeros([], dtypes.int64), - name="num_layer_examples", - trainable=False) - num_layer_steps = variables.Variable( - initial_value=array_ops.zeros([], dtypes.int64), - name="num_layer_steps", - trainable=False) - num_layers = variables.Variable( - initial_value=array_ops.zeros([], dtypes.int64), - name="num_layers", - trainable=False) - active_tree = variables.Variable( - initial_value=array_ops.zeros([], dtypes.int64), - name="active_tree", - trainable=False) - active_layer = variables.Variable( - initial_value=array_ops.zeros([], dtypes.int64), - name="active_layer", - trainable=False) - # Create ensemble stats summaries. summary.scalar("layer_stats/num_examples", num_layer_examples) summary.scalar("layer_stats/num_steps", num_layer_steps) @@ -734,16 +756,13 @@ class GradientBoostedDecisionTreeModel(object): # Update bias stats. stats_update_ops = [] - continue_centering = variables.Variable( - initial_value=self._center_bias, - name="continue_centering", - trainable=False) + stats_update_ops.append( control_flow_ops.cond( continue_centering, - self._make_update_bias_stats_fn(ensemble_stamp, predictions, - gradients, bias_stats_accumulator), - control_flow_ops.no_op)) + self._make_update_bias_stats_fn( + ensemble_stamp, predictions, gradients, + bias_stats_accumulator), control_flow_ops.no_op)) # Update handler stats. handler_reads = collections.OrderedDict() @@ -800,8 +819,8 @@ class GradientBoostedDecisionTreeModel(object): lambda: active_handlers)) # Prepare empty gradients and hessians when handlers are not ready. - empty_hess_shape = [1] + hessian_shape.as_list() - empty_grad_shape = [1] + gradient_shape.as_list() + empty_hess_shape = [1] + self._hessian_shape.as_list() + empty_grad_shape = [1] + self._gradient_shape.as_list() empty_gradients = constant_op.constant( [], dtype=dtypes.float32, shape=empty_grad_shape) @@ -823,34 +842,66 @@ class GradientBoostedDecisionTreeModel(object): per_handler_updates, ensemble_stamp, worker_device) for update in update_results.values(): stats_update_ops += update + + training_state = { + _NUM_LAYER_EXAMPLES: num_layer_examples, + _NUM_LAYER_STEPS: num_layer_steps, + _NUM_LAYERS: num_layers, + _ACTIVE_TREE: active_tree, + _ACTIVE_LAYER: active_layer, + _CONTINUE_CENTERING: continue_centering, + _BIAS_STATS_ACCUMULATOR: bias_stats_accumulator, + _STEPS_ACCUMULATOR: steps_accumulator, + _HANDLERS: handlers + } + return stats_update_ops, training_state + + def increment_step_counter_and_maybe_update_ensemble( + self, predictions_dict, batch_size, training_state): + """Increments number of visited examples and grows the ensemble. + + If the number of visited examples reaches the target examples_per_layer, + ensemble is updated. + + Args: + predictions_dict: Dictionary of Rank 2 `Tensor` representing information + about predictions per example. + batch_size: Number of examples in the batch. + training_state: `dict` returned by update_stats. + + Returns: + An op that updates the counters and potientially grows the ensemble. + """ + ensemble_stamp = predictions_dict[ENSEMBLE_STAMP] # Accumulate a step after updating stats. - batch_size = math_ops.cast(array_ops.shape(labels)[0], dtypes.float32) - with ops.control_dependencies(stats_update_ops): - add_step_op = steps_accumulator.add(ensemble_stamp, [0], [[0, 0]], - [batch_size], [1.0]) - # Determine learning rate. - learning_rate_tuner = self._learner_config.learning_rate_tuner.WhichOneof( - "tuner") - if learning_rate_tuner == "fixed" or learning_rate_tuner == "dropout": - tuner = getattr(self._learner_config.learning_rate_tuner, - learning_rate_tuner) - learning_rate = tuner.learning_rate - else: - # TODO(nponomareva, soroush) do the line search. - raise ValueError("Line search learning rate is not yet supported.") + num_layer_examples = training_state[_NUM_LAYER_EXAMPLES] + num_layer_steps = training_state[_NUM_LAYER_STEPS] + num_layers = training_state[_NUM_LAYERS] + active_tree = training_state[_ACTIVE_TREE] + active_layer = training_state[_ACTIVE_LAYER] + continue_centering = training_state[_CONTINUE_CENTERING] + bias_stats_accumulator = training_state[_BIAS_STATS_ACCUMULATOR] + steps_accumulator = training_state[_STEPS_ACCUMULATOR] + handlers = training_state[_HANDLERS] + add_step_op = steps_accumulator.add( + ensemble_stamp, [0], [[0, 0]], [batch_size], [1.0]) # After adding the step, decide if further processing is needed. ensemble_update_ops = [add_step_op] + class_id = self._get_class_id(predictions_dict) + with ops.control_dependencies([add_step_op]): if self._is_chief: dropout_seed = predictions_dict[NUM_TREES_ATTEMPTED] # Get accumulated steps and examples for the current layer. - _, _, _, _, acc_examples, acc_steps = steps_accumulator.serialize() + _, _, _, _, acc_examples, acc_steps = ( + steps_accumulator.serialize()) acc_examples = math_ops.cast(acc_examples[0], dtypes.int64) acc_steps = math_ops.cast(acc_steps[0], dtypes.int64) - ensemble_update_ops.append(num_layer_examples.assign(acc_examples)) + ensemble_update_ops.append( + num_layer_examples.assign(acc_examples)) ensemble_update_ops.append(num_layer_steps.assign(acc_steps)) # Determine whether we need to update tree ensemble. examples_per_layer = self._examples_per_layer @@ -859,139 +910,33 @@ class GradientBoostedDecisionTreeModel(object): ensemble_update_ops.append( control_flow_ops.cond( acc_examples >= examples_per_layer, - self._make_update_ensemble_fn( - ensemble_stamp, steps_accumulator, bias_stats_accumulator, - continue_centering, learning_rate, handlers, num_layers, - active_tree, active_layer, dropout_seed, class_id), + self.make_update_ensemble_fn( + ensemble_stamp, steps_accumulator, + bias_stats_accumulator, continue_centering, + handlers, num_layers, active_tree, + active_layer, dropout_seed, class_id), control_flow_ops.no_op)) - # Calculate the loss to be reported. # Note, the loss is calculated from the prediction considering dropouts, so # that the value might look staggering over steps when the dropout ratio is # high. eval_loss might be referred instead in the aspect of convergence. return control_flow_ops.group(*ensemble_update_ops) - def _get_weights(self, hessian_shape, hessians): - """Derives weights to be used based on hessians and multiclass strategy.""" - if hessian_shape == tensor_shape.scalar(): - # This is tree per class. - weights = hessians - elif len(hessian_shape.dims) == 1: - # This is diagonal hessian. - weights = math_ops.reduce_sum(hessians, axis=1) - else: - # This is full hessian. - weights = math_ops.trace(hessians) - return weights - - def _full_hessian(self, grads, predictions): - """Prepares hessians for full-hessian multiclass strategy.""" - # Because of - # https://github.com/tensorflow/tensorflow/issues/675, we can't just - # compute the full hessian with a single call to gradients, but instead - # must compute it row-by-row. - gradients_list = array_ops.unstack( - grads, num=self._logits_dimension, axis=1) - hessian_rows = [] - - for row in range(self._logits_dimension): - # If current row is i, K is number of classes,each row returns a tensor of - # size batch_size x K representing for each example dx_i dx_1, dx_i dx_2 - # etc dx_i dx_K - hessian_row = gradients_impl.gradients( - gradients_list[row], - predictions, - name="Hessian_%d" % row, - colocate_gradients_with_ops=False, - gate_gradients=0, - aggregation_method=None) - - # hessian_row is of dimension 1, batch_size, K, => trim first dimension - # to get batch_size x K - hessian_row = array_ops.squeeze(array_ops.unstack(hessian_row), [0]) - hessian_rows.append(hessian_row) - return hessian_rows - - def _diagonal_hessian(self, grads, predictions): - """Prepares hessians for diagonal-hessian multiclass mode.""" - diag_hessian_list = [] - - gradients_list = array_ops.unstack( - grads, num=self._logits_dimension, axis=1) - - for row, row_grads in enumerate(gradients_list): - # If current row is i, K is number of classes,each row returns a tensor of - # size batch_size x K representing for each example dx_i dx_1, dx_1 dx_2 - # etc dx_i dx_K - hessian_row = gradients_impl.gradients( - row_grads, - predictions, - name="Hessian_%d" % row, - colocate_gradients_with_ops=False, - gate_gradients=0, - aggregation_method=None) - - # hessian_row is of dimension 1, batch_size, K, => trim first dimension - # to get batch_size x K - hessian_row = array_ops.squeeze(array_ops.unstack(hessian_row), [0]) - - # Get dx_i^2 for the whole batch. - elem = array_ops.transpose(hessian_row)[row] - diag_hessian_list.append(elem) - - return diag_hessian_list - - def _get_replica_device_setter(self, worker_device): - """Creates a replica device setter.""" - ps_tasks = self._num_ps_replicas - ps_ops = [ - "Variable", - "VariableV2", - "DecisionTreeEnsembleResourceHandleOp", - "StatsAccumulatorScalarResourceHandleOp", - "StatsAccumulatorTensorResourceHandleOp", - ] - ps_strategy = _OpRoundRobinStrategy(ps_ops, ps_tasks) - return device_setter.replica_device_setter( - worker_device=worker_device, - ps_tasks=ps_tasks, - merge_devices=True, - ps_ops=ps_ops, - ps_strategy=ps_strategy) - - def _make_update_bias_stats_fn(self, ensemble_stamp, predictions, gradients, - bias_stats_accumulator): - """A method to create the function which updates the bias stats.""" - - def _update_bias_stats(): - """A method to update the bias stats.""" - # Get reduced gradients and hessians. - grads_sum = math_ops.reduce_sum(gradients, 0) - hess = gradients_impl.gradients( - grads_sum, - predictions, - name="Hessians", - colocate_gradients_with_ops=False, - gate_gradients=0, - aggregation_method=None)[0] - hess_sum = math_ops.reduce_sum(hess, 0) - - # Accumulate gradients and hessians. - partition_ids = math_ops.range(self._logits_dimension) - feature_ids = array_ops.zeros( - [self._logits_dimension, 2], dtype=dtypes.int64) - - add_stats_op = bias_stats_accumulator.add( - ensemble_stamp, partition_ids, feature_ids, grads_sum, hess_sum) - return control_flow_ops.group(*[add_stats_op], name="update_bias_stats") - - return _update_bias_stats - - def _make_update_ensemble_fn(self, ensemble_stamp, steps_accumulator, - bias_stats_accumulator, continue_centering, - learning_rate, handlers, num_layers, active_tree, - active_layer, dropout_seed, class_id): + def make_update_ensemble_fn(self, ensemble_stamp, steps_accumulator, + bias_stats_accumulator, continue_centering, + handlers, num_layers, active_tree, active_layer, + dropout_seed, class_id): """A method to create the function which updates the tree ensemble.""" + # Determine learning rate. + learning_rate_tuner = self._learner_config.learning_rate_tuner.WhichOneof( + "tuner") + if learning_rate_tuner == "fixed" or learning_rate_tuner == "dropout": + tuner = getattr(self._learner_config.learning_rate_tuner, + learning_rate_tuner) + learning_rate = tuner.learning_rate + else: + # TODO(nponomareva, soroush) do the line search. + raise ValueError("Line search learning rate is not yet supported.") def _update_ensemble(): """A method to update the tree ensemble.""" @@ -1110,3 +1055,140 @@ class GradientBoostedDecisionTreeModel(object): def get_number_of_trees_tensor(self): return self._finalized_trees, self._attempted_trees + + def train(self, loss, predictions_dict, labels): + """Updates the accumalator stats and grows the ensemble. + + Args: + loss: A scalar tensor representing average loss of examples. + predictions_dict: Dictionary of Rank 2 `Tensor` representing information + about predictions per example. + labels: Rank 2 `Tensor` representing labels per example. + + Returns: + An op that adds a new tree to the ensemble. + + Raises: + ValueError: if inputs are not valid. + """ + batch_size = math_ops.cast(array_ops.shape(labels)[0], dtypes.float32) + update_op, handlers = self.update_stats(loss, predictions_dict) + with ops.control_dependencies(update_op): + return self.increment_step_counter_and_maybe_update_ensemble( + predictions_dict, batch_size, handlers) + + def _get_weights(self, hessian_shape, hessians): + """Derives weights to be used based on hessians and multiclass strategy.""" + if hessian_shape == tensor_shape.scalar(): + # This is tree per class. + weights = hessians + elif len(hessian_shape.dims) == 1: + # This is diagonal hessian. + weights = math_ops.reduce_sum(hessians, axis=1) + else: + # This is full hessian. + weights = math_ops.trace(hessians) + return weights + + def _full_hessian(self, grads, predictions): + """Prepares hessians for full-hessian multiclass strategy.""" + # Because of + # https://github.com/tensorflow/tensorflow/issues/675, we can't just + # compute the full hessian with a single call to gradients, but instead + # must compute it row-by-row. + gradients_list = array_ops.unstack( + grads, num=self._logits_dimension, axis=1) + hessian_rows = [] + + for row in range(self._logits_dimension): + # If current row is i, K is number of classes,each row returns a tensor of + # size batch_size x K representing for each example dx_i dx_1, dx_i dx_2 + # etc dx_i dx_K + hessian_row = gradients_impl.gradients( + gradients_list[row], + predictions, + name="Hessian_%d" % row, + colocate_gradients_with_ops=False, + gate_gradients=0, + aggregation_method=None) + + # hessian_row is of dimension 1, batch_size, K, => trim first dimension + # to get batch_size x K + hessian_row = array_ops.squeeze(array_ops.unstack(hessian_row), [0]) + hessian_rows.append(hessian_row) + return hessian_rows + + def _diagonal_hessian(self, grads, predictions): + """Prepares hessians for diagonal-hessian multiclass mode.""" + diag_hessian_list = [] + + gradients_list = array_ops.unstack( + grads, num=self._logits_dimension, axis=1) + + for row, row_grads in enumerate(gradients_list): + # If current row is i, K is number of classes,each row returns a tensor of + # size batch_size x K representing for each example dx_i dx_1, dx_1 dx_2 + # etc dx_i dx_K + hessian_row = gradients_impl.gradients( + row_grads, + predictions, + name="Hessian_%d" % row, + colocate_gradients_with_ops=False, + gate_gradients=0, + aggregation_method=None) + + # hessian_row is of dimension 1, batch_size, K, => trim first dimension + # to get batch_size x K + hessian_row = array_ops.squeeze(array_ops.unstack(hessian_row), [0]) + + # Get dx_i^2 for the whole batch. + elem = array_ops.transpose(hessian_row)[row] + diag_hessian_list.append(elem) + + return diag_hessian_list + + def _get_replica_device_setter(self, worker_device): + """Creates a replica device setter.""" + ps_tasks = self._num_ps_replicas + ps_ops = [ + "Variable", + "VariableV2", + "DecisionTreeEnsembleResourceHandleOp", + "StatsAccumulatorScalarResourceHandleOp", + "StatsAccumulatorTensorResourceHandleOp", + ] + ps_strategy = _OpRoundRobinStrategy(ps_ops, ps_tasks) + return device_setter.replica_device_setter( + worker_device=worker_device, + ps_tasks=ps_tasks, + merge_devices=True, + ps_ops=ps_ops, + ps_strategy=ps_strategy) + + def _make_update_bias_stats_fn(self, ensemble_stamp, predictions, gradients, + bias_stats_accumulator): + """A method to create the function which updates the bias stats.""" + + def _update_bias_stats(): + """A method to update the bias stats.""" + # Get reduced gradients and hessians. + grads_sum = math_ops.reduce_sum(gradients, 0) + hess = gradients_impl.gradients( + grads_sum, + predictions, + name="Hessians", + colocate_gradients_with_ops=False, + gate_gradients=0, + aggregation_method=None)[0] + hess_sum = math_ops.reduce_sum(hess, 0) + + # Accumulate gradients and hessians. + partition_ids = math_ops.range(self._logits_dimension) + feature_ids = array_ops.zeros( + [self._logits_dimension, 2], dtype=dtypes.int64) + + add_stats_op = bias_stats_accumulator.add( + ensemble_stamp, partition_ids, feature_ids, grads_sum, hess_sum) + return control_flow_ops.group(*[add_stats_op], name="update_bias_stats") + + return _update_bias_stats -- GitLab From cc2fae83acde7b5ddc3df122bcd5369fc4bbb24f Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Wed, 20 Jun 2018 13:14:50 -0700 Subject: [PATCH 751/816] [TF:XLA] Bump open source llvm revision to r335143 PiperOrigin-RevId: 201403339 --- tensorflow/workspace.bzl | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index 1f1d106bfb..55d505ef8e 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -451,11 +451,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "llvm", urls = [ - "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/a587557962e93552e1a8b9270b435b021891e9cd.tar.gz", - "https://github.com/llvm-mirror/llvm/archive/a587557962e93552e1a8b9270b435b021891e9cd.tar.gz", + "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/19357eaea4f9599bcb228611719e0c5b8fc65298.tar.gz", + "https://github.com/llvm-mirror/llvm/archive/19357eaea4f9599bcb228611719e0c5b8fc65298.tar.gz", ], - sha256 = "5cf25652e8913e88ce2fb02f1186affd25cf5c1cb2146f9754881daaf3450ddb", - strip_prefix = "llvm-a587557962e93552e1a8b9270b435b021891e9cd", + sha256 = "c07971d102ae5353c4a22c15e82e75f4347a16260c52060187baf4b113161216", + strip_prefix = "llvm-19357eaea4f9599bcb228611719e0c5b8fc65298", build_file = clean_dep("//third_party/llvm:llvm.autogenerated.BUILD"), ) -- GitLab From b65ae4f307abff0325bf22ef9996f054f1ae2462 Mon Sep 17 00:00:00 2001 From: Yuefeng Zhou Date: Wed, 20 Jun 2018 13:35:33 -0700 Subject: [PATCH 752/816] Make tensor_pack not a class field in cross_tower_ops PiperOrigin-RevId: 201406790 --- tensorflow/contrib/distribute/python/BUILD | 1 - tensorflow/contrib/distribute/python/cross_tower_ops.py | 8 ++++---- 2 files changed, 4 insertions(+), 5 deletions(-) diff --git a/tensorflow/contrib/distribute/python/BUILD b/tensorflow/contrib/distribute/python/BUILD index 9dfb8552f1..eba0dd0ea3 100644 --- a/tensorflow/contrib/distribute/python/BUILD +++ b/tensorflow/contrib/distribute/python/BUILD @@ -587,7 +587,6 @@ cuda_py_test( ], tags = [ "multi_and_single_gpu", - "noguitar", "notsan", ], ) diff --git a/tensorflow/contrib/distribute/python/cross_tower_ops.py b/tensorflow/contrib/distribute/python/cross_tower_ops.py index f8ae8b9712..1009c3c012 100644 --- a/tensorflow/contrib/distribute/python/cross_tower_ops.py +++ b/tensorflow/contrib/distribute/python/cross_tower_ops.py @@ -536,7 +536,7 @@ class AllReduceCrossTowerOps(CrossTowerOps): destinations = per_device_values[0].devices grouped = _group_value_by_device(per_device_values) - device_grad_packs, self._tensor_packer = _pack_tensors( + device_grad_packs, tensor_packer = _pack_tensors( grouped, self._num_packs, self._agg_small_grads_max_bytes, self._agg_small_grads_max_group) @@ -554,7 +554,7 @@ class AllReduceCrossTowerOps(CrossTowerOps): cross_tower_utils.aggregate_gradients_using_hierarchical_copy( destinations, device_grad_packs)) - reduced = _unpack_tensors(reduced, self._tensor_packer) + reduced = _unpack_tensors(reduced, tensor_packer) return _ungroup_and_make_mirrored(reduced, per_device_values[0].devices, method_string) @@ -665,13 +665,13 @@ class MultiWorkerAllReduce(AllReduceCrossTowerOps): (this_grads, remaining_grads) = cross_tower_utils.split_grads_by_size( spec_tuple.limit, remaining_grads) if this_grads: - device_grad_packs, self._tensor_packer = _pack_tensors( + device_grad_packs, tensor_packer = _pack_tensors( this_grads, self._num_packs, self._agg_small_grads_max_bytes, self._agg_small_grads_max_group) range_agg_grads = cross_tower_utils.sum_gradients_all_reduce( self._worker_devices, device_grad_packs, len(self._worker_devices), spec_tuple.alg, spec_tuple.shards, range(self._num_gpus_per_worker)) - range_agg_grads = _unpack_tensors(range_agg_grads, self._tensor_packer) + range_agg_grads = _unpack_tensors(range_agg_grads, tensor_packer) if not aggregated_grads: aggregated_grads = range_agg_grads -- GitLab From 1a517b99b6c2c1abbe5390f87f4128db5e69e142 Mon Sep 17 00:00:00 2001 From: Yuefeng Zhou Date: Wed, 20 Jun 2018 13:38:15 -0700 Subject: [PATCH 753/816] Remove a dead if block in control_flow_ops.py. PiperOrigin-RevId: 201407240 --- tensorflow/python/ops/control_flow_ops.py | 6 ------ 1 file changed, 6 deletions(-) diff --git a/tensorflow/python/ops/control_flow_ops.py b/tensorflow/python/ops/control_flow_ops.py index 9413bfa2af..837c144467 100644 --- a/tensorflow/python/ops/control_flow_ops.py +++ b/tensorflow/python/ops/control_flow_ops.py @@ -3348,12 +3348,6 @@ def group(*inputs, **kwargs): if not hasattr(inp, "device"): raise TypeError("Expected tf.group() expected Tensor arguments not " "'%s' with type '%s'" % (inp, type(inp))) - if not hasattr(inp, "device"): - if isinstance(inp, list): - raise TypeError("To call tf.group() with a list, use " - "tf.group(*[...]) not tf.group([...]).") - raise TypeError("Expected tf.group() expected Tensor arguments not " - "'%s' with type '%s'" % (inp, type(inp))) dev = inp.device if dev in ops_on_device: ops_on_device[dev].append(inp) -- GitLab From 35616039860ab25dde6f87b9a9e87f8727fa0daf Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 20 Jun 2018 13:55:35 -0700 Subject: [PATCH 754/816] Automated g4 rollback of changelist 201241214 PiperOrigin-RevId: 201410380 --- .../contrib/lite/kernels/activations.cc | 24 +- .../internal/logsoftmax_quantized_test.cc | 64 +-- .../internal/optimized/legacy_optimized_ops.h | 282 ++++++++++++- .../internal/optimized/optimized_ops.h | 390 +++++++----------- .../internal/reference/legacy_reference_ops.h | 290 ++++++++++++- .../internal/reference/reference_ops.h | 354 ++++++---------- .../internal/softmax_quantized_test.cc | 62 +-- .../contrib/lite/kernels/internal/types.h | 48 ++- .../contrib/lite/kernels/log_softmax_test.cc | 7 +- tensorflow/contrib/lite/kernels/pooling.cc | 57 +-- .../contrib/lite/kernels/softmax_test.cc | 14 +- 11 files changed, 1001 insertions(+), 591 deletions(-) diff --git a/tensorflow/contrib/lite/kernels/activations.cc b/tensorflow/contrib/lite/kernels/activations.cc index add36b46c0..d03fa42c92 100644 --- a/tensorflow/contrib/lite/kernels/activations.cc +++ b/tensorflow/contrib/lite/kernels/activations.cc @@ -251,11 +251,11 @@ TfLiteStatus TanhEval(TfLiteContext* context, TfLiteNode* node) { return kTfLiteOk; } break; case kTfLiteUInt8: { - optimized_ops::Tanh(GetTensorData(input), GetTensorDims(input), + optimized_ops::Tanh(GetTensorData(input), GetTensorShape(input), input->params.zero_point, data->input_range_radius, data->input_multiplier, data->input_left_shift, GetTensorData(output), - GetTensorDims(output)); + GetTensorShape(output)); return kTfLiteOk; } break; default: @@ -282,10 +282,10 @@ TfLiteStatus SigmoidEval(TfLiteContext* context, TfLiteNode* node) { } case kTfLiteUInt8: { optimized_ops::Logistic( - GetTensorData(input), GetTensorDims(input), + GetTensorData(input), GetTensorShape(input), input->params.zero_point, data->input_range_radius, data->input_multiplier, data->input_left_shift, - GetTensorData(output), GetTensorDims(output)); + GetTensorData(output), GetTensorShape(output)); break; } default: @@ -341,26 +341,26 @@ void Softmax2DQuantized(const TfLiteTensor* input, TfLiteTensor* output, const int batch_size = input->dims->data[0]; const int input_size = input->dims->data[1]; optimized_ops::Softmax(GetTensorData(input), - GetTensorDims({batch_size, 1, 1, input_size}), + GetTensorShape({batch_size, 1, 1, input_size}), data->input_multiplier, data->input_left_shift, data->diff_min, GetTensorData(output), - GetTensorDims({batch_size, 1, 1, input_size})); + GetTensorShape({batch_size, 1, 1, input_size})); } // Takes a 4D tensor and perform softmax along the forth dimension. void Softmax4DFloat(const TfLiteTensor* input, TfLiteTensor* output, TfLiteSoftmaxParams* params) { - optimized_ops::Softmax(GetTensorData(input), GetTensorDims(input), + optimized_ops::Softmax(GetTensorData(input), GetTensorShape(input), params->beta, GetTensorData(output), - GetTensorDims(output)); + GetTensorShape(output)); } void Softmax4DQuantized(const TfLiteTensor* input, TfLiteTensor* output, TfLiteSoftmaxParams* params, OpData* data) { - optimized_ops::Softmax(GetTensorData(input), GetTensorDims(input), + optimized_ops::Softmax(GetTensorData(input), GetTensorShape(input), data->input_multiplier, data->input_left_shift, data->diff_min, GetTensorData(output), - GetTensorDims(output)); + GetTensorShape(output)); } TfLiteStatus SoftmaxEval(TfLiteContext* context, TfLiteNode* node) { @@ -415,8 +415,8 @@ TfLiteStatus LogSoftmaxEval(TfLiteContext* context, TfLiteNode* node) { switch (input->type) { case kTfLiteFloat32: optimized_ops::LogSoftmax( - GetTensorData(input), GetTensorDims(input), - GetTensorData(output), GetTensorDims(output)); + GetTensorData(input), GetTensorShape(input), + GetTensorData(output), GetTensorShape(output)); return kTfLiteOk; default: context->ReportError(context, "Only float32 supported currently., got %d", diff --git a/tensorflow/contrib/lite/kernels/internal/logsoftmax_quantized_test.cc b/tensorflow/contrib/lite/kernels/internal/logsoftmax_quantized_test.cc index e786f785ab..d2f1103e14 100644 --- a/tensorflow/contrib/lite/kernels/internal/logsoftmax_quantized_test.cc +++ b/tensorflow/contrib/lite/kernels/internal/logsoftmax_quantized_test.cc @@ -32,19 +32,21 @@ namespace tflite { namespace { void RunLogSoftmaxFloatReference(const uint8* input_data, - const Dims<4>& dims_common, int32 input_offset, - const double input_scale, int stride, - float beta, uint8* reference_output_data) { - const int ref_buffer_size = RequiredBufferSizeForDims(dims_common); + const RuntimeShape& shape_common, + int32 input_offset, const double input_scale, + int stride, float beta, + uint8* reference_output_data) { + const int ref_buffer_size = shape_common.FlatSize(); std::vector reference_dequant_data(ref_buffer_size); std::vector reference_output_float_data(ref_buffer_size); // Reference data generated via Dequant of input into float, and then applying // float LogSoftmax. - reference_ops::Dequantize(input_data, dims_common, input_offset, input_scale, - reference_dequant_data.data(), dims_common); - optimized_ops::LogSoftmax(reference_dequant_data.data(), dims_common, - reference_output_float_data.data(), dims_common); + reference_ops::Dequantize( + input_data, ToRuntimeDims(shape_common), input_offset, input_scale, + reference_dequant_data.data(), ToRuntimeDims(shape_common)); + optimized_ops::LogSoftmax(reference_dequant_data.data(), shape_common, + reference_output_float_data.data(), shape_common); // Work with quantized scaling for LogSoftmax, under which 255 represents 0, // and -16 gets nudged up to 0. for (int i = 0; i < ref_buffer_size; i++) { @@ -55,9 +57,9 @@ void RunLogSoftmaxFloatReference(const uint8* input_data, } void CheckOutputData(const uint8* test_output, const uint8* reference_output, - const Dims<4>& dims_common, const string& check_label, - bool be_exacting) { - const int buffer_size = RequiredBufferSizeForDims(dims_common); + const RuntimeShape& shape_common, + const string& check_label, bool be_exacting) { + const int buffer_size = shape_common.FlatSize(); // While calculating some metrics in floating point, we work with quantized // scaling. std::vector diff(buffer_size); @@ -99,15 +101,15 @@ void CheckOutputData(const uint8* test_output, const uint8* reference_output, // Runs the LogSoftmax and compares against the float reference implementation // and the quantized reference implementation. -void RunOneLogSoftmaxTest(const uint8* input_data, const Dims<4>& dims_common, - int32 input_offset, const double input_scale, - int stride, float beta) { - const int buffer_size = RequiredBufferSizeForDims(dims_common); +void RunOneLogSoftmaxTest(const uint8* input_data, + const RuntimeShape& shape_common, int32 input_offset, + const double input_scale, int stride, float beta) { + const int buffer_size = shape_common.FlatSize(); std::vector optimized_logsoftmax_output(buffer_size); std::vector reference_float_logsoftmax_output(buffer_size); std::vector reference_quant_logsoftmax_output(buffer_size); - RunLogSoftmaxFloatReference(input_data, dims_common, input_offset, + RunLogSoftmaxFloatReference(input_data, shape_common, input_offset, input_scale, stride, beta, reference_float_logsoftmax_output.data()); @@ -126,23 +128,23 @@ void RunOneLogSoftmaxTest(const uint8* input_data, const Dims<4>& dims_common, const int diff_min = -tflite::CalculateInputRadius(kScaledDiffIntegerBits, input_beta_left_shift); - optimized_ops::LogSoftmax(input_data, dims_common, input_beta_multiplier, + optimized_ops::LogSoftmax(input_data, shape_common, input_beta_multiplier, input_beta_left_shift, reverse_scaling_divisor, reverse_scaling_right_shift, diff_min, - optimized_logsoftmax_output.data(), dims_common); + optimized_logsoftmax_output.data(), shape_common); reference_ops::LogSoftmax( - input_data, dims_common, input_beta_multiplier, input_beta_left_shift, + input_data, shape_common, input_beta_multiplier, input_beta_left_shift, reverse_scaling_divisor, reverse_scaling_right_shift, diff_min, - reference_quant_logsoftmax_output.data(), dims_common); + reference_quant_logsoftmax_output.data(), shape_common); CheckOutputData(optimized_logsoftmax_output.data(), - reference_float_logsoftmax_output.data(), dims_common, + reference_float_logsoftmax_output.data(), shape_common, "Optimized vs float reference", false); CheckOutputData(optimized_logsoftmax_output.data(), - reference_quant_logsoftmax_output.data(), dims_common, + reference_quant_logsoftmax_output.data(), shape_common, "Optimized vs quant reference", true); CheckOutputData(reference_quant_logsoftmax_output.data(), - reference_float_logsoftmax_output.data(), dims_common, + reference_float_logsoftmax_output.data(), shape_common, "Quant reference vs float reference", false); } @@ -165,13 +167,13 @@ bool TryOneUniformLogSoftmax() { const int32 input_offset = UniformRandomInt(-256, 0); static constexpr float beta = 1.0f; - Dims<4> dims_common = - MakeDimsForInference(input_depth, input_width, input_height, batch); - const int buffer_size = RequiredBufferSizeForDims(dims_common); + auto shape_common = + RuntimeShape({batch, input_height, input_width, input_depth}); + const int buffer_size = shape_common.FlatSize(); std::vector input_data(buffer_size); FillRandom(&input_data); - RunOneLogSoftmaxTest(input_data.data(), dims_common, input_offset, + RunOneLogSoftmaxTest(input_data.data(), shape_common, input_offset, input_scale, stride, beta); return true; } @@ -203,14 +205,14 @@ bool TryOneSkyscraperLogSoftmax(bool small_depth) { const int middle_min = UniformRandomInt(0, 255); const int sides_max = UniformRandomInt(0, middle_min); - Dims<4> dims_common = - MakeDimsForInference(input_depth, input_width, input_height, batch); - const int buffer_size = RequiredBufferSizeForDims(dims_common); + auto shape_common = + RuntimeShape({batch, input_height, input_width, input_depth}); + const int buffer_size = shape_common.FlatSize(); std::vector input_data(buffer_size); FillRandomSkyscraper(&input_data, input_depth, middle_proportion, middle_min, sides_max); - RunOneLogSoftmaxTest(input_data.data(), dims_common, input_offset, + RunOneLogSoftmaxTest(input_data.data(), shape_common, input_offset, input_scale, stride, beta); return true; } diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/legacy_optimized_ops.h b/tensorflow/contrib/lite/kernels/internal/optimized/legacy_optimized_ops.h index c0dda4acf1..7816752132 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/legacy_optimized_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/legacy_optimized_ops.h @@ -26,6 +26,10 @@ limitations under the License. namespace tflite { namespace optimized_ops { +// Unoptimized reference ops: +using reference_ops::Relu1; +using reference_ops::Relu6; + inline RuntimeShape DimsToShape(const tflite::Dims<4>& dims) { return RuntimeShape( {dims.sizes[3], dims.sizes[2], dims.sizes[1], dims.sizes[0]}); @@ -34,15 +38,285 @@ inline RuntimeShape DimsToShape(const tflite::Dims<4>& dims) { template void L2Normalization(const float* input_data, const Dims<4>& input_dims, float* output_data, const Dims<4>& output_dims) { - return L2Normalization(input_data, DimsToShape(input_dims), output_data, - DimsToShape(output_dims)); + L2Normalization(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); } inline void L2Normalization(const uint8* input_data, const Dims<4>& input_dims, int32 input_zero_point, uint8* output_data, const Dims<4>& output_dims) { - return L2Normalization(input_data, DimsToShape(input_dims), input_zero_point, - output_data, DimsToShape(output_dims)); + L2Normalization(input_data, DimsToShape(input_dims), input_zero_point, + output_data, DimsToShape(output_dims)); +} + +inline void Relu(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { + Relu(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); +} + +inline void AveragePool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int kwidth, int kheight, + float output_activation_min, + float output_activation_max, float* output_data, + const Dims<4>& output_dims) { + AveragePool(input_data, DimsToShape(input_dims), stride_width, stride_height, + pad_width, pad_height, kwidth, kheight, output_activation_min, + output_activation_max, output_data, DimsToShape(output_dims)); +} + +// legacy, for compatibility with old checked-in code +template +void AveragePool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int kwidth, int kheight, float* output_data, + const Dims<4>& output_dims) { + float output_activation_min, output_activation_max; + GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); + + AveragePool(input_data, input_dims, stride_width, stride_height, pad_width, + pad_height, kwidth, kheight, output_activation_min, + output_activation_max, output_data, output_dims); +} + +// legacy, for compatibility with old checked-in code +template +void AveragePool(const float* input_data, const Dims<4>& input_dims, int stride, + int pad_width, int pad_height, int filter_width, + int filter_height, float* output_data, + const Dims<4>& output_dims) { + AveragePool(input_data, input_dims, stride, stride, pad_width, pad_height, + filter_width, filter_height, output_data, output_dims); +} + +inline void AveragePool(const uint8* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int filter_width, int filter_height, + int32 output_activation_min, + int32 output_activation_max, uint8* output_data, + const Dims<4>& output_dims) { + AveragePool(input_data, DimsToShape(input_dims), stride_width, stride_height, + pad_width, pad_height, filter_width, filter_height, + output_activation_min, output_activation_max, output_data, + DimsToShape(output_dims)); +} + +// legacy, for compatibility with old checked-in code +template +void AveragePool(const uint8* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int filter_width, int filter_height, + int32 output_activation_min, int32 output_activation_max, + uint8* output_data, const Dims<4>& output_dims) { + static_assert(Ac == FusedActivationFunctionType::kNone || + Ac == FusedActivationFunctionType::kRelu || + Ac == FusedActivationFunctionType::kRelu6 || + Ac == FusedActivationFunctionType::kRelu1, + ""); + if (Ac == FusedActivationFunctionType::kNone) { + TFLITE_DCHECK_EQ(output_activation_min, 0); + TFLITE_DCHECK_EQ(output_activation_max, 255); + } + AveragePool(input_data, input_dims, stride_width, stride_height, pad_width, + pad_height, filter_width, filter_height, output_activation_min, + output_activation_max, output_data, output_dims); +} + +// legacy, for compatibility with old checked-in code +template +void AveragePool(const uint8* input_data, const Dims<4>& input_dims, int stride, + int pad_width, int pad_height, int filter_width, + int filter_height, int32 output_activation_min, + int32 output_activation_max, uint8* output_data, + const Dims<4>& output_dims) { + AveragePool(input_data, input_dims, stride, stride, pad_width, pad_height, + filter_width, filter_height, output_activation_min, + output_activation_max, output_data, output_dims); +} + +inline void MaxPool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int kwidth, int kheight, + float output_activation_min, float output_activation_max, + float* output_data, const Dims<4>& output_dims) { + MaxPool(input_data, DimsToShape(input_dims), stride_width, stride_height, + pad_width, pad_height, kwidth, kheight, output_activation_min, + output_activation_max, output_data, DimsToShape(output_dims)); +} + +// legacy, for compatibility with old checked-in code +template +void MaxPool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, int pad_height, + int kwidth, int kheight, float* output_data, + const Dims<4>& output_dims) { + float output_activation_min, output_activation_max; + GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); + MaxPool(input_data, input_dims, stride_width, stride_height, pad_width, + pad_height, kwidth, kheight, output_activation_min, + output_activation_max, output_data, output_dims); +} + +// legacy, for compatibility with old checked-in code +template +void MaxPool(const float* input_data, const Dims<4>& input_dims, int stride, + int pad_width, int pad_height, int filter_width, int filter_height, + float* output_data, const Dims<4>& output_dims) { + MaxPool(input_data, input_dims, stride, stride, pad_width, pad_height, + filter_width, filter_height, output_data, output_dims); +} + +inline void MaxPool(const uint8* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int filter_width, int filter_height, + int32 output_activation_min, int32 output_activation_max, + uint8* output_data, const Dims<4>& output_dims) { + MaxPool(input_data, DimsToShape(input_dims), stride_width, stride_height, + pad_width, pad_height, filter_width, filter_height, + output_activation_min, output_activation_max, output_data, + DimsToShape(output_dims)); +} + +// legacy, for compatibility with old checked-in code +template +void MaxPool(const uint8* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, int pad_height, + int filter_width, int filter_height, int32 output_activation_min, + int32 output_activation_max, uint8* output_data, + const Dims<4>& output_dims) { + static_assert(Ac == FusedActivationFunctionType::kNone || + Ac == FusedActivationFunctionType::kRelu || + Ac == FusedActivationFunctionType::kRelu6 || + Ac == FusedActivationFunctionType::kRelu1, + ""); + if (Ac == FusedActivationFunctionType::kNone) { + TFLITE_DCHECK_EQ(output_activation_min, 0); + TFLITE_DCHECK_EQ(output_activation_max, 255); + } + MaxPool(input_data, input_dims, stride_width, stride_height, pad_width, + pad_height, filter_width, filter_height, output_activation_min, + output_activation_max, output_data, output_dims); +} + +// legacy, for compatibility with old checked-in code +template +void MaxPool(const uint8* input_data, const Dims<4>& input_dims, int stride, + int pad_width, int pad_height, int filter_width, int filter_height, + int32 output_activation_min, int32 output_activation_max, + uint8* output_data, const Dims<4>& output_dims) { + MaxPool(input_data, input_dims, stride, stride, pad_width, pad_height, + filter_width, filter_height, output_activation_min, + output_activation_max, output_data, output_dims); +} + +inline void L2Pool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int filter_width, int filter_height, + float output_activation_min, float output_activation_max, + float* output_data, const Dims<4>& output_dims) { + L2Pool(input_data, DimsToShape(input_dims), stride_width, stride_height, + pad_width, pad_height, filter_width, filter_height, + output_activation_min, output_activation_max, output_data, + DimsToShape(output_dims)); +} + +// legacy, for compatibility with old checked-in code +template +void L2Pool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, int pad_height, + int filter_width, int filter_height, float* output_data, + const Dims<4>& output_dims) { + float output_activation_min, output_activation_max; + GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); + L2Pool(input_data, input_dims, stride_width, stride_height, pad_width, + pad_height, filter_width, filter_height, output_activation_min, + output_activation_max, output_data, output_dims); +} + +// legacy, for compatibility with old checked-in code +template +void L2Pool(const float* input_data, const Dims<4>& input_dims, int stride, + int pad_width, int pad_height, int filter_width, int filter_height, + float* output_data, const Dims<4>& output_dims) { + L2Pool(input_data, input_dims, stride, stride, pad_width, pad_height, + filter_width, filter_height, output_data, output_dims); +} + +inline void Softmax(const float* input_data, const Dims<4>& input_dims, + float beta, float* output_data, + const Dims<4>& output_dims) { + Softmax(input_data, DimsToShape(input_dims), beta, output_data, + DimsToShape(output_dims)); +} + +inline void Softmax(const uint8* input_data, const Dims<4>& input_dims, + int32 input_beta_multiplier, int32 input_beta_left_shift, + int diff_min, uint8* output_data, + const Dims<4>& output_dims) { + Softmax(input_data, DimsToShape(input_dims), input_beta_multiplier, + input_beta_left_shift, diff_min, output_data, + DimsToShape(output_dims)); +} + +inline void LogSoftmax(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { + LogSoftmax(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); +} + +inline void LogSoftmax(const uint8* input_data, const Dims<4>& input_dims, + int32 input_multiplier, int32 input_left_shift, + int32 reverse_scaling_divisor, + int32 reverse_scaling_right_shift, int diff_min, + uint8* output_data, const Dims<4>& output_dims) { + LogSoftmax(input_data, DimsToShape(input_dims), input_multiplier, + input_left_shift, reverse_scaling_divisor, + reverse_scaling_right_shift, diff_min, output_data, + DimsToShape(output_dims)); +} + +inline void Logistic(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { + Logistic(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); +} + +inline void Logistic(const uint8* input_data, const Dims<4>& input_dims, + int32 input_zero_point, int32 input_range_radius, + int32 input_multiplier, int input_left_shift, + uint8* output_data, const Dims<4>& output_dims) { + Logistic(input_data, DimsToShape(input_dims), input_zero_point, + input_range_radius, input_multiplier, input_left_shift, output_data, + DimsToShape(output_dims)); +} + +inline void Logistic(const int16* input_data, const Dims<4>& input_dims, + int16* output_data, const Dims<4>& output_dims) { + Logistic(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); +} + +inline void Tanh(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { + Tanh(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); +} + +inline void Tanh(const uint8* input_data, const Dims<4>& input_dims, + int32 input_zero_point, int32 input_range_radius, + int32 input_multiplier, int input_left_shift, + uint8* output_data, const Dims<4>& output_dims) { + Tanh(input_data, DimsToShape(input_dims), input_zero_point, + input_range_radius, input_multiplier, input_left_shift, output_data, + DimsToShape(output_dims)); +} + +inline void Tanh(const int16* input_data, const Dims<4>& input_dims, + int input_left_shift, int16* output_data, + const Dims<4>& output_dims) { + Tanh(input_data, DimsToShape(input_dims), input_left_shift, output_data, + DimsToShape(output_dims)); } } // namespace optimized_ops diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h index 107e95ea6e..868269477e 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h @@ -85,6 +85,12 @@ using VectorMap = typename std::conditional< Eigen::Dynamic, 1>>, Eigen::Map>>::type; +template +VectorMap MapAsVector(Scalar* data, const RuntimeShape& shape) { + const int size = shape.FlatSize(); + return VectorMap(data, size, 1); +} + template VectorMap MapAsVector(Scalar* data, const Dims& dims) { const int size = FlatSize(dims); @@ -101,6 +107,23 @@ using MatrixMap = typename std::conditional< Eigen::Dynamic, Eigen::Dynamic>>, Eigen::Map>>::type; +template +MatrixMap MapAsMatrixWithLastDimAsRows(Scalar* data, + const RuntimeShape& shape) { + const int dims_count = shape.DimensionsCount(); + const int rows = shape.Dims(dims_count - 1); + const int cols = FlatSizeSkipDim(shape, dims_count - 1); + return MatrixMap(data, rows, cols); +} + +template +MatrixMap MapAsMatrixWithFirstDimAsCols(Scalar* data, + const RuntimeShape& shape) { + const int cols = shape.Dims(0); + const int rows = FlatSizeSkipDim(shape, 0); + return MatrixMap(data, rows, cols); +} + template MatrixMap MapAsMatrixWithFirstDimAsRows(Scalar* data, const Dims& dims) { @@ -2343,12 +2366,12 @@ void GlobalBatchNormalization(const float* input_data, } } -inline void Relu(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { +inline void Relu(const float* input_data, const RuntimeShape& input_shape, + float* output_data, const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("Relu (not fused)"); - const auto input = MapAsVector(input_data, input_dims); - auto output = MapAsVector(output_data, output_dims); + const auto input = MapAsVector(input_data, input_shape); + auto output = MapAsVector(output_data, output_shape); output = input.cwiseMax(0.0f); } @@ -3739,23 +3762,25 @@ inline int NodeOffset(int b, int h, int w, int height, int width) { return (b * height + h) * width + w; } -inline void AveragePool(const float* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, - int pad_height, int kwidth, int kheight, - float output_activation_min, +inline void AveragePool(const float* input_data, + const RuntimeShape& input_shape, int stride_width, + int stride_height, int pad_width, int pad_height, + int kwidth, int kheight, float output_activation_min, float output_activation_max, float* output_data, - const Dims<4>& output_dims) { + const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("AveragePool"); - const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); - const int input_height = ArraySize(input_dims, 2); - const int input_width = ArraySize(input_dims, 1); - const int output_height = ArraySize(output_dims, 2); - const int output_width = ArraySize(output_dims, 1); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int depth = MatchingDim(input_shape, 3, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); // TODO(benoitjacob) make this a proper reference impl without Eigen! - const auto in_mat = MapAsMatrixWithFirstDimAsRows(input_data, input_dims); - auto out_mat = MapAsMatrixWithFirstDimAsRows(output_data, output_dims); + const auto in_mat = MapAsMatrixWithLastDimAsRows(input_data, input_shape); + auto out_mat = MapAsMatrixWithLastDimAsRows(output_data, output_shape); // TODO(benoitjacob) get rid of the dynamic memory allocation here! Eigen::VectorXf out_count(out_mat.cols()); out_count.setZero(); @@ -3793,9 +3818,9 @@ inline void AveragePool(const float* input_data, const Dims<4>& input_dims, for (int y = 0; y < output_height; ++y) { for (int x = 0; x < output_width; ++x) { for (int c = 0; c < depth; ++c) { - output_data[Offset(output_dims, c, x, y, b)] = + output_data[Offset(output_shape, b, y, x, c)] = ActivationFunctionWithMinMax( - output_data[Offset(output_dims, c, x, y, b)], + output_data[Offset(output_shape, b, y, x, c)], output_activation_min, output_activation_max); } } @@ -3803,44 +3828,23 @@ inline void AveragePool(const float* input_data, const Dims<4>& input_dims, } } -// legacy, for compatibility with old checked-in code -template -void AveragePool(const float* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, - int pad_height, int kwidth, int kheight, float* output_data, - const Dims<4>& output_dims) { - float output_activation_min, output_activation_max; - GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); - - AveragePool(input_data, input_dims, stride_width, stride_height, pad_width, - pad_height, kwidth, kheight, output_activation_min, - output_activation_max, output_data, output_dims); -} - -// legacy, for compatibility with old checked-in code -template -void AveragePool(const float* input_data, const Dims<4>& input_dims, int stride, - int pad_width, int pad_height, int filter_width, - int filter_height, float* output_data, - const Dims<4>& output_dims) { - AveragePool(input_data, input_dims, stride, stride, pad_width, pad_height, - filter_width, filter_height, output_data, output_dims); -} - -inline void AveragePool(const uint8* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, - int pad_height, int filter_width, int filter_height, +inline void AveragePool(const uint8* input_data, + const RuntimeShape& input_shape, int stride_width, + int stride_height, int pad_width, int pad_height, + int filter_width, int filter_height, int32 output_activation_min, int32 output_activation_max, uint8* output_data, - const Dims<4>& output_dims) { + const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("AveragePool/8bit"); TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); - const int input_height = ArraySize(input_dims, 2); - const int input_width = ArraySize(input_dims, 1); - const int output_height = ArraySize(output_dims, 2); - const int output_width = ArraySize(output_dims, 1); + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int depth = MatchingDim(input_shape, 3, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); for (int batch = 0; batch < batches; ++batch) { for (int out_y = 0; out_y < output_height; ++out_y) { for (int out_x = 0; out_x < output_width; ++out_x) { @@ -3860,11 +3864,12 @@ inline void AveragePool(const uint8* input_data, const Dims<4>& input_dims, uint16 acc[kAccBufferMaxSize]; memset(acc, 0, depth * sizeof(acc[0])); const uint8* input_ptr = - input_data + input_dims.strides[1] * in_x_origin + - input_dims.strides[2] * in_y_origin + input_dims.strides[3] * batch; + input_data + + depth * (in_x_origin + + input_width * (in_y_origin + input_height * batch)); for (int fy = filter_y_start; fy < filter_y_end; fy++) { - const uint8* input_row_ptr = input_ptr + fy * input_dims.strides[2] + - filter_x_start * input_dims.strides[1]; + const uint8* input_row_ptr = + input_ptr + depth * (fy * input_width + filter_x_start); for (int fx = filter_x_start; fx < filter_x_end; fx++) { int channel = 0; #ifdef USE_NEON @@ -3895,7 +3900,7 @@ inline void AveragePool(const uint8* input_data, const Dims<4>& input_dims, } } uint8* output_ptr = - output_data + Offset(output_dims, 0, out_x, out_y, batch); + output_data + Offset(output_shape, batch, out_y, out_x, 0); int channel = 0; #ifdef USE_NEON #define AVGPOOL_DIVIDING_BY(FILTER_COUNT) \ @@ -3936,54 +3941,23 @@ inline void AveragePool(const uint8* input_data, const Dims<4>& input_dims, } } -// legacy, for compatibility with old checked-in code -template -void AveragePool(const uint8* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, - int pad_height, int filter_width, int filter_height, - int32 output_activation_min, int32 output_activation_max, - uint8* output_data, const Dims<4>& output_dims) { - static_assert(Ac == FusedActivationFunctionType::kNone || - Ac == FusedActivationFunctionType::kRelu || - Ac == FusedActivationFunctionType::kRelu6 || - Ac == FusedActivationFunctionType::kRelu1, - ""); - if (Ac == FusedActivationFunctionType::kNone) { - TFLITE_DCHECK_EQ(output_activation_min, 0); - TFLITE_DCHECK_EQ(output_activation_max, 255); - } - AveragePool(input_data, input_dims, stride_width, stride_height, pad_width, - pad_height, filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - -// legacy, for compatibility with old checked-in code -template -void AveragePool(const uint8* input_data, const Dims<4>& input_dims, int stride, - int pad_width, int pad_height, int filter_width, - int filter_height, int32 output_activation_min, - int32 output_activation_max, uint8* output_data, - const Dims<4>& output_dims) { - AveragePool(input_data, input_dims, stride, stride, pad_width, pad_height, - filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - -inline void MaxPool(const float* input_data, const Dims<4>& input_dims, +inline void MaxPool(const float* input_data, const RuntimeShape& input_shape, int stride_width, int stride_height, int pad_width, int pad_height, int kwidth, int kheight, float output_activation_min, float output_activation_max, - float* output_data, const Dims<4>& output_dims) { + float* output_data, const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("MaxPool"); - const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); - const int input_height = ArraySize(input_dims, 2); - const int input_width = ArraySize(input_dims, 1); - const int output_height = ArraySize(output_dims, 2); - const int output_width = ArraySize(output_dims, 1); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); - - const auto in_mat = MapAsMatrixWithFirstDimAsRows(input_data, input_dims); - auto out_mat = MapAsMatrixWithFirstDimAsRows(output_data, output_dims); + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int depth = MatchingDim(input_shape, 3, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); + + const auto in_mat = MapAsMatrixWithLastDimAsRows(input_data, input_shape); + auto out_mat = MapAsMatrixWithLastDimAsRows(output_data, output_shape); // Prefill the output to minimum representable float value out_mat.setConstant(std::numeric_limits::lowest()); for (int b = 0; b < batches; ++b) { @@ -4016,9 +3990,9 @@ inline void MaxPool(const float* input_data, const Dims<4>& input_dims, for (int y = 0; y < output_height; ++y) { for (int x = 0; x < output_width; ++x) { for (int c = 0; c < depth; ++c) { - output_data[Offset(output_dims, c, x, y, b)] = + output_data[Offset(output_shape, b, y, x, c)] = ActivationFunctionWithMinMax( - output_data[Offset(output_dims, c, x, y, b)], + output_data[Offset(output_shape, b, y, x, c)], output_activation_min, output_activation_max); } } @@ -4026,41 +4000,21 @@ inline void MaxPool(const float* input_data, const Dims<4>& input_dims, } } -// legacy, for compatibility with old checked-in code -template -void MaxPool(const float* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, int pad_height, - int kwidth, int kheight, float* output_data, - const Dims<4>& output_dims) { - float output_activation_min, output_activation_max; - GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); - MaxPool(input_data, input_dims, stride_width, stride_height, pad_width, - pad_height, kwidth, kheight, output_activation_min, - output_activation_max, output_data, output_dims); -} - -// legacy, for compatibility with old checked-in code -template -void MaxPool(const float* input_data, const Dims<4>& input_dims, int stride, - int pad_width, int pad_height, int filter_width, int filter_height, - float* output_data, const Dims<4>& output_dims) { - MaxPool(input_data, input_dims, stride, stride, pad_width, pad_height, - filter_width, filter_height, output_data, output_dims); -} - -inline void MaxPool(const uint8* input_data, const Dims<4>& input_dims, +inline void MaxPool(const uint8* input_data, const RuntimeShape& input_shape, int stride_width, int stride_height, int pad_width, int pad_height, int filter_width, int filter_height, int32 output_activation_min, int32 output_activation_max, - uint8* output_data, const Dims<4>& output_dims) { + uint8* output_data, const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("MaxPool/8bit"); TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); - const int input_height = ArraySize(input_dims, 2); - const int input_width = ArraySize(input_dims, 1); - const int output_height = ArraySize(output_dims, 2); - const int output_width = ArraySize(output_dims, 1); + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int depth = MatchingDim(input_shape, 3, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); for (int batch = 0; batch < batches; ++batch) { for (int out_y = 0; out_y < output_height; ++out_y) { for (int out_x = 0; out_x < output_width; ++out_x) { @@ -4078,11 +4032,12 @@ inline void MaxPool(const uint8* input_data, const Dims<4>& input_dims, uint8 acc[kAccBufferMaxSize]; memset(acc, 0, depth * sizeof(acc[0])); const uint8* input_ptr = - input_data + input_dims.strides[1] * in_x_origin + - input_dims.strides[2] * in_y_origin + input_dims.strides[3] * batch; + input_data + + depth * (in_x_origin + + input_width * (in_y_origin + input_height * batch)); for (int fy = filter_y_start; fy < filter_y_end; fy++) { - const uint8* input_row_ptr = input_ptr + fy * input_dims.strides[2] + - filter_x_start * input_dims.strides[1]; + const uint8* input_row_ptr = + input_ptr + depth * (fy * input_width + filter_x_start); for (int fx = filter_x_start; fx < filter_x_end; fx++) { int channel = 0; #ifdef USE_NEON @@ -4108,7 +4063,7 @@ inline void MaxPool(const uint8* input_data, const Dims<4>& input_dims, } } uint8* output_ptr = - output_data + Offset(output_dims, 0, out_x, out_y, batch); + output_data + Offset(output_shape, batch, out_y, out_x, 0); int channel = 0; #ifdef USE_NEON for (; channel <= depth - 16; channel += 16) { @@ -4135,53 +4090,23 @@ inline void MaxPool(const uint8* input_data, const Dims<4>& input_dims, } } -// legacy, for compatibility with old checked-in code -template -void MaxPool(const uint8* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, int pad_height, - int filter_width, int filter_height, int32 output_activation_min, - int32 output_activation_max, uint8* output_data, - const Dims<4>& output_dims) { - static_assert(Ac == FusedActivationFunctionType::kNone || - Ac == FusedActivationFunctionType::kRelu || - Ac == FusedActivationFunctionType::kRelu6 || - Ac == FusedActivationFunctionType::kRelu1, - ""); - if (Ac == FusedActivationFunctionType::kNone) { - TFLITE_DCHECK_EQ(output_activation_min, 0); - TFLITE_DCHECK_EQ(output_activation_max, 255); - } - MaxPool(input_data, input_dims, stride_width, stride_height, pad_width, - pad_height, filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - -// legacy, for compatibility with old checked-in code -template -void MaxPool(const uint8* input_data, const Dims<4>& input_dims, int stride, - int pad_width, int pad_height, int filter_width, int filter_height, - int32 output_activation_min, int32 output_activation_max, - uint8* output_data, const Dims<4>& output_dims) { - MaxPool(input_data, input_dims, stride, stride, pad_width, pad_height, - filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - -inline void L2Pool(const float* input_data, const Dims<4>& input_dims, +inline void L2Pool(const float* input_data, const RuntimeShape& input_shape, int stride_width, int stride_height, int pad_width, int pad_height, int filter_width, int filter_height, float output_activation_min, float output_activation_max, - float* output_data, const Dims<4>& output_dims) { + float* output_data, const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("L2Pool"); - const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); - const int input_height = ArraySize(input_dims, 2); - const int input_width = ArraySize(input_dims, 1); - const int output_height = ArraySize(output_dims, 2); - const int output_width = ArraySize(output_dims, 1); + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); // Actually carry out L2 Pool. Code is written in forward mode: we go through // the input values once, and write to all the pooled regions that it maps to. - const auto in_mat = MapAsMatrixWithFirstDimAsRows(input_data, input_dims); - auto out_mat = MapAsMatrixWithFirstDimAsRows(output_data, output_dims); + const auto in_mat = MapAsMatrixWithLastDimAsRows(input_data, input_shape); + auto out_mat = MapAsMatrixWithLastDimAsRows(output_data, output_shape); Eigen::VectorXf in_square(in_mat.rows()); Eigen::VectorXf out_count(out_mat.cols()); out_count.setZero(); @@ -4223,28 +4148,6 @@ inline void L2Pool(const float* input_data, const Dims<4>& input_dims, (out_mat.array().rowwise() * out_count.transpose().array()).cwiseSqrt(); } -// legacy, for compatibility with old checked-in code -template -void L2Pool(const float* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, int pad_height, - int filter_width, int filter_height, float* output_data, - const Dims<4>& output_dims) { - float output_activation_min, output_activation_max; - GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); - L2Pool(input_data, input_dims, stride_width, stride_height, pad_width, - pad_height, filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - -// legacy, for compatibility with old checked-in code -template -void L2Pool(const float* input_data, const Dims<4>& input_dims, int stride, - int pad_width, int pad_height, int filter_width, int filter_height, - float* output_data, const Dims<4>& output_dims) { - L2Pool(input_data, input_dims, stride, stride, pad_width, pad_height, - filter_width, filter_height, output_data, output_dims); -} - inline void LocalResponseNormalization(const float* input_data, const Dims<4>& input_dims, int range, float bias, float alpha, float beta, @@ -4290,14 +4193,14 @@ inline void LocalResponseNormalization(const float* input_data, } } -inline void Softmax(const float* input_data, const Dims<4>& input_dims, +inline void Softmax(const float* input_data, const RuntimeShape& input_shape, float beta, float* output_data, - const Dims<4>& output_dims) { + const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("Softmax"); - MatchingFlatSize(input_dims, output_dims); + MatchingFlatSize(input_shape, output_shape); - const auto in_mat = MapAsMatrixWithFirstDimAsRows(input_data, input_dims); - auto out_mat = MapAsMatrixWithFirstDimAsRows(output_data, output_dims); + const auto in_mat = MapAsMatrixWithLastDimAsRows(input_data, input_shape); + auto out_mat = MapAsMatrixWithLastDimAsRows(output_data, output_shape); // Compute the exponential first, removing the max coefficient for numerical // stability. out_mat = (in_mat.rowwise() - in_mat.colwise().maxCoeff()).array() * beta; @@ -4309,10 +4212,10 @@ inline void Softmax(const float* input_data, const Dims<4>& input_dims, out_mat.array().rowwise() *= scale; } -inline void Softmax(const uint8* input_data, const Dims<4>& input_dims, +inline void Softmax(const uint8* input_data, const RuntimeShape& input_shape, int32 input_beta_multiplier, int32 input_beta_left_shift, int diff_min, uint8* output_data, - const Dims<4>& output_dims) { + const RuntimeShape& output_shape) { // The representation chosen for the input to the exp() function is Q5.26. // We need to leave extra space since values that we skip might be as large as // -32 before multiplying by input_beta_multiplier, and therefore as large as @@ -4326,8 +4229,11 @@ inline void Softmax(const uint8* input_data, const Dims<4>& input_dims, using FixedPoint0 = gemmlowp::FixedPoint; gemmlowp::ScopedProfilingLabel label("Softmax/8bit"); - const int outer_size = MatchingFlatSizeSkipDim(input_dims, 0, output_dims); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); + const int trailing_dim = input_shape.DimensionsCount() - 1; + const int outer_size = + MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); + const int depth = + MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); for (int b = 0; b < outer_size; ++b) { const uint8* input_data_ptr = input_data + b * depth; @@ -4517,11 +4423,14 @@ inline void Softmax(const uint8* input_data, const Dims<4>& input_dims, // TODO(myenik): This is the same as the reference implementation, not actually // optimized yet. -inline void LogSoftmax(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { +inline void LogSoftmax(const float* input_data, const RuntimeShape& input_shape, + float* output_data, const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("LogSoftmax"); - const int outer_size = MatchingFlatSizeSkipDim(input_dims, 0, output_dims); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); + const int trailing_dim = input_shape.DimensionsCount() - 1; + const int outer_size = + MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); + const int depth = + MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); for (int i = 0; i < outer_size; ++i) { const float* block_input_data = input_data + i * depth; @@ -4662,11 +4571,11 @@ log_x_for_x_greater_than_or_equal_to_1( } // Currently just a copy of the reference code. -inline void LogSoftmax(const uint8* input_data, const Dims<4>& input_dims, +inline void LogSoftmax(const uint8* input_data, const RuntimeShape& input_shape, int32 input_multiplier, int32 input_left_shift, int32 reverse_scaling_divisor, int32 reverse_scaling_right_shift, int diff_min, - uint8* output_data, const Dims<4>& output_dims) { + uint8* output_data, const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("LogSoftmax/Uint8"); // The representation chosen for the input to the exp() function is Q5.26. // We need to leave extra space since values that we skip might be as large as @@ -4681,8 +4590,11 @@ inline void LogSoftmax(const uint8* input_data, const Dims<4>& input_dims, using FixedPointAccum = gemmlowp::FixedPoint; using FixedPoint0 = gemmlowp::FixedPoint; - const int outer_size = MatchingFlatSizeSkipDim(input_dims, 0, output_dims); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); + const int trailing_dim = input_shape.DimensionsCount() - 1; + const int outer_size = + MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); + const int depth = + MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); for (int i = 0; i < outer_size; ++i) { const uint8* block_input_data = input_data + i * depth; @@ -4746,21 +4658,21 @@ inline void LogSoftmax(const uint8* input_data, const Dims<4>& input_dims, } } -inline void Logistic(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { +inline void Logistic(const float* input_data, const RuntimeShape& input_shape, + float* output_data, const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("Logistic"); - auto input_map = MapAsVector(input_data, input_dims); - auto output_map = MapAsVector(output_data, output_dims); + auto input_map = MapAsVector(input_data, input_shape); + auto output_map = MapAsVector(output_data, output_shape); output_map.array() = input_map.array().unaryExpr(Eigen::internal::scalar_sigmoid_op()); } -inline void Logistic(const uint8* input_data, const Dims<4>& input_dims, +inline void Logistic(const uint8* input_data, const RuntimeShape& input_shape, int32 input_zero_point, int32 input_range_radius, int32 input_multiplier, int input_left_shift, - uint8* output_data, const Dims<4>& output_dims) { + uint8* output_data, const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("Logistic/Uint8"); - const int size = MatchingFlatSize(input_dims, output_dims); + const int size = MatchingFlatSize(input_shape, output_shape); int c = 0; #ifdef USE_NEON @@ -4892,10 +4804,10 @@ inline void Logistic(const uint8* input_data, const Dims<4>& input_dims, } } -inline void Logistic(const int16* input_data, const Dims<4>& input_dims, - int16* output_data, const Dims<4>& output_dims) { +inline void Logistic(const int16* input_data, const RuntimeShape& input_shape, + int16* output_data, const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("Logistic/Int16"); - const int flat_size = MatchingFlatSize(output_dims, input_dims); + const int flat_size = MatchingFlatSize(input_shape, output_shape); for (int i = 0; i < flat_size; i++) { } @@ -4952,21 +4864,21 @@ inline void Logistic(const int16* input_data, const Dims<4>& input_dims, } } -inline void Tanh(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { +inline void Tanh(const float* input_data, const RuntimeShape& input_shape, + float* output_data, const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("Tanh"); - auto input_map = MapAsVector(input_data, input_dims); - auto output_map = MapAsVector(output_data, output_dims); + auto input_map = MapAsVector(input_data, input_shape); + auto output_map = MapAsVector(output_data, output_shape); output_map.array() = input_map.array().tanh(); } -inline void Tanh(const uint8* input_data, const Dims<4>& input_dims, +inline void Tanh(const uint8* input_data, const RuntimeShape& input_shape, int32 input_zero_point, int32 input_range_radius, int32 input_multiplier, int input_left_shift, - uint8* output_data, const Dims<4>& output_dims) { + uint8* output_data, const RuntimeShape& output_shape) { // Note that this is almost the exact same code as in Logistic(). gemmlowp::ScopedProfilingLabel label("Tanh"); - const int size = MatchingFlatSize(input_dims, output_dims); + const int size = MatchingFlatSize(input_shape, output_shape); int c = 0; int32_t output_zero_point = 128; @@ -5107,16 +5019,16 @@ inline void Tanh(const uint8* input_data, const Dims<4>& input_dims, } } -inline void Tanh(const int16* input_data, const Dims<4>& input_dims, +inline void Tanh(const int16* input_data, const RuntimeShape& input_shape, int input_left_shift, int16* output_data, - const Dims<4>& output_dims) { + const RuntimeShape& output_shape) { gemmlowp::ScopedProfilingLabel label("Tanh/Int16"); // Support for shifts is limited until we have a parameterized version of // SaturatingRoundingMultiplyByPOT(). TFLITE_DCHECK_GE(input_left_shift, 0); TFLITE_DCHECK_LE(input_left_shift, 1); - const int flat_size = MatchingFlatSize(output_dims, input_dims); + const int flat_size = MatchingFlatSize(input_shape, output_shape); int c = 0; const int16* input_data_ptr = input_data; diff --git a/tensorflow/contrib/lite/kernels/internal/reference/legacy_reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/legacy_reference_ops.h index 6f5f6a3e6f..878b2441b4 100644 --- a/tensorflow/contrib/lite/kernels/internal/reference/legacy_reference_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/reference/legacy_reference_ops.h @@ -34,15 +34,297 @@ inline RuntimeShape DimsToShape(const tflite::Dims<4>& dims) { template void L2Normalization(const float* input_data, const Dims<4>& input_dims, float* output_data, const Dims<4>& output_dims) { - return L2Normalization(input_data, DimsToShape(input_dims), output_data, - DimsToShape(output_dims)); + L2Normalization(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); } inline void L2Normalization(const uint8* input_data, const Dims<4>& input_dims, int32 input_zero_point, uint8* output_data, const Dims<4>& output_dims) { - return L2Normalization(input_data, DimsToShape(input_dims), input_zero_point, - output_data, DimsToShape(output_dims)); + L2Normalization(input_data, DimsToShape(input_dims), input_zero_point, + output_data, DimsToShape(output_dims)); +} + +inline void Relu(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { + Relu(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); +} + +inline void Relu1(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { + Relu1(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); +} + +inline void Relu6(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { + Relu6(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); +} + +inline void AveragePool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int kwidth, int kheight, + float output_activation_min, + float output_activation_max, float* output_data, + const Dims<4>& output_dims) { + AveragePool(input_data, DimsToShape(input_dims), stride_width, stride_height, + pad_width, pad_height, kwidth, kheight, output_activation_min, + output_activation_max, output_data, DimsToShape(output_dims)); +} + +// legacy, for compatibility with old checked-in code +template +void AveragePool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int kwidth, int kheight, float* output_data, + const Dims<4>& output_dims) { + float output_activation_min, output_activation_max; + GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); + + AveragePool(input_data, input_dims, stride_width, stride_height, pad_width, + pad_height, kwidth, kheight, output_activation_min, + output_activation_max, output_data, output_dims); +} + +// legacy, for compatibility with old checked-in code +template +void AveragePool(const float* input_data, const Dims<4>& input_dims, int stride, + int pad_width, int pad_height, int filter_width, + int filter_height, float* output_data, + const Dims<4>& output_dims) { + AveragePool(input_data, input_dims, stride, stride, pad_width, pad_height, + filter_width, filter_height, output_data, output_dims); +} + +inline void AveragePool(const uint8* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int filter_width, int filter_height, + int32 output_activation_min, + int32 output_activation_max, uint8* output_data, + const Dims<4>& output_dims) { + AveragePool(input_data, DimsToShape(input_dims), stride_width, stride_height, + pad_width, pad_height, filter_width, filter_height, + output_activation_min, output_activation_max, output_data, + DimsToShape(output_dims)); +} + +// legacy, for compatibility with old checked-in code +template +void AveragePool(const uint8* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int filter_width, int filter_height, + int32 output_activation_min, int32 output_activation_max, + uint8* output_data, const Dims<4>& output_dims) { + static_assert(Ac == FusedActivationFunctionType::kNone || + Ac == FusedActivationFunctionType::kRelu || + Ac == FusedActivationFunctionType::kRelu6 || + Ac == FusedActivationFunctionType::kRelu1, + ""); + if (Ac == FusedActivationFunctionType::kNone) { + TFLITE_DCHECK_EQ(output_activation_min, 0); + TFLITE_DCHECK_EQ(output_activation_max, 255); + } + AveragePool(input_data, input_dims, stride_width, stride_height, pad_width, + pad_height, filter_width, filter_height, output_activation_min, + output_activation_max, output_data, output_dims); +} + +// legacy, for compatibility with old checked-in code +template +void AveragePool(const uint8* input_data, const Dims<4>& input_dims, int stride, + int pad_width, int pad_height, int filter_width, + int filter_height, int32 output_activation_min, + int32 output_activation_max, uint8* output_data, + const Dims<4>& output_dims) { + AveragePool(input_data, input_dims, stride, stride, pad_width, pad_height, + filter_width, filter_height, output_activation_min, + output_activation_max, output_data, output_dims); +} + +inline void MaxPool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int kwidth, int kheight, + float output_activation_min, float output_activation_max, + float* output_data, const Dims<4>& output_dims) { + MaxPool(input_data, DimsToShape(input_dims), stride_width, stride_height, + pad_width, pad_height, kwidth, kheight, output_activation_min, + output_activation_max, output_data, DimsToShape(output_dims)); +} + +// legacy, for compatibility with old checked-in code +template +void MaxPool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, int pad_height, + int kwidth, int kheight, float* output_data, + const Dims<4>& output_dims) { + float output_activation_min, output_activation_max; + GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); + MaxPool(input_data, input_dims, stride_width, stride_height, pad_width, + pad_height, kwidth, kheight, output_activation_min, + output_activation_max, output_data, output_dims); +} + +// legacy, for compatibility with old checked-in code +template +void MaxPool(const float* input_data, const Dims<4>& input_dims, int stride, + int pad_width, int pad_height, int filter_width, int filter_height, + float* output_data, const Dims<4>& output_dims) { + MaxPool(input_data, input_dims, stride, stride, pad_width, pad_height, + filter_width, filter_height, output_data, output_dims); +} + +inline void MaxPool(const uint8* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int filter_width, int filter_height, + int32 output_activation_min, int32 output_activation_max, + uint8* output_data, const Dims<4>& output_dims) { + MaxPool(input_data, DimsToShape(input_dims), stride_width, stride_height, + pad_width, pad_height, filter_width, filter_height, + output_activation_min, output_activation_max, output_data, + DimsToShape(output_dims)); +} + +// legacy, for compatibility with old checked-in code +template +void MaxPool(const uint8* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, int pad_height, + int filter_width, int filter_height, int32 output_activation_min, + int32 output_activation_max, uint8* output_data, + const Dims<4>& output_dims) { + static_assert(Ac == FusedActivationFunctionType::kNone || + Ac == FusedActivationFunctionType::kRelu || + Ac == FusedActivationFunctionType::kRelu6 || + Ac == FusedActivationFunctionType::kRelu1, + ""); + if (Ac == FusedActivationFunctionType::kNone) { + TFLITE_DCHECK_EQ(output_activation_min, 0); + TFLITE_DCHECK_EQ(output_activation_max, 255); + } + MaxPool(input_data, input_dims, stride_width, stride_height, pad_width, + pad_height, filter_width, filter_height, output_activation_min, + output_activation_max, output_data, output_dims); +} + +// legacy, for compatibility with old checked-in code +template +void MaxPool(const uint8* input_data, const Dims<4>& input_dims, int stride, + int pad_width, int pad_height, int filter_width, int filter_height, + int32 output_activation_min, int32 output_activation_max, + uint8* output_data, const Dims<4>& output_dims) { + MaxPool(input_data, input_dims, stride, stride, pad_width, pad_height, + filter_width, filter_height, output_activation_min, + output_activation_max, output_data, output_dims); +} + +inline void L2Pool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, + int pad_height, int filter_width, int filter_height, + float output_activation_min, float output_activation_max, + float* output_data, const Dims<4>& output_dims) { + L2Pool(input_data, DimsToShape(input_dims), stride_width, stride_height, + pad_width, pad_height, filter_width, filter_height, + output_activation_min, output_activation_max, output_data, + DimsToShape(output_dims)); +} + +// legacy, for compatibility with old checked-in code +template +void L2Pool(const float* input_data, const Dims<4>& input_dims, + int stride_width, int stride_height, int pad_width, int pad_height, + int filter_width, int filter_height, float* output_data, + const Dims<4>& output_dims) { + float output_activation_min, output_activation_max; + GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); + L2Pool(input_data, input_dims, stride_width, stride_height, pad_width, + pad_height, filter_width, filter_height, output_activation_min, + output_activation_max, output_data, output_dims); +} + +// legacy, for compatibility with old checked-in code +template +void L2Pool(const float* input_data, const Dims<4>& input_dims, int stride, + int pad_width, int pad_height, int filter_width, int filter_height, + float* output_data, const Dims<4>& output_dims) { + L2Pool(input_data, input_dims, stride, stride, pad_width, pad_height, + filter_width, filter_height, output_data, output_dims); +} + +inline void Softmax(const float* input_data, const Dims<4>& input_dims, + float beta, float* output_data, + const Dims<4>& output_dims) { + Softmax(input_data, DimsToShape(input_dims), beta, output_data, + DimsToShape(output_dims)); +} + +inline void Softmax(const uint8* input_data, const Dims<4>& input_dims, + int32 input_beta_multiplier, int32 input_beta_left_shift, + int diff_min, uint8* output_data, + const Dims<4>& output_dims) { + Softmax(input_data, DimsToShape(input_dims), input_beta_multiplier, + input_beta_left_shift, diff_min, output_data, + DimsToShape(output_dims)); +} + +inline void LogSoftmax(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { + LogSoftmax(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); +} + +inline void LogSoftmax(const uint8* input_data, const Dims<4>& input_dims, + int32 input_multiplier, int32 input_left_shift, + int32 reverse_scaling_divisor, + int32 reverse_scaling_right_shift, int diff_min, + uint8* output_data, const Dims<4>& output_dims) { + LogSoftmax(input_data, DimsToShape(input_dims), input_multiplier, + input_left_shift, reverse_scaling_divisor, + reverse_scaling_right_shift, diff_min, output_data, + DimsToShape(output_dims)); +} + +inline void Logistic(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { + Logistic(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); +} + +inline void Logistic(const uint8* input_data, const Dims<4>& input_dims, + int32 input_zero_point, int32 input_range_radius, + int32 input_multiplier, int input_left_shift, + uint8* output_data, const Dims<4>& output_dims) { + Logistic(input_data, DimsToShape(input_dims), input_zero_point, + input_range_radius, input_multiplier, input_left_shift, output_data, + DimsToShape(output_dims)); +} + +inline void Logistic(const int16* input_data, const Dims<4>& input_dims, + int16* output_data, const Dims<4>& output_dims) { + Logistic(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); +} + +inline void Tanh(const float* input_data, const Dims<4>& input_dims, + float* output_data, const Dims<4>& output_dims) { + Tanh(input_data, DimsToShape(input_dims), output_data, + DimsToShape(output_dims)); +} + +inline void Tanh(const uint8* input_data, const Dims<4>& input_dims, + int32 input_zero_point, int32 input_range_radius, + int32 input_multiplier, int input_left_shift, + uint8* output_data, const Dims<4>& output_dims) { + Tanh(input_data, DimsToShape(input_dims), input_zero_point, + input_range_radius, input_multiplier, input_left_shift, output_data, + DimsToShape(output_dims)); +} + +inline void Tanh(const int16* input_data, const Dims<4>& input_dims, + int input_left_shift, int16* output_data, + const Dims<4>& output_dims) { + Tanh(input_data, DimsToShape(input_dims), input_left_shift, output_data, + DimsToShape(output_dims)); } } // namespace reference_ops diff --git a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h index 483bd37ef9..89ec0eb266 100644 --- a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h @@ -914,9 +914,9 @@ void GlobalBatchNormalization(const float* input_data, } } -inline void Relu(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { - const int flat_size = MatchingFlatSize(input_dims, output_dims); +inline void Relu(const float* input_data, const RuntimeShape& input_shape, + float* output_data, const RuntimeShape& output_shape) { + const int flat_size = MatchingFlatSize(input_shape, output_shape); for (int i = 0; i < flat_size; ++i) { const float val = input_data[i]; const float lower = 0; @@ -925,9 +925,10 @@ inline void Relu(const float* input_data, const Dims<4>& input_dims, } } -inline void Relu1(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { - const int flat_size = MatchingFlatSize(input_dims, output_dims); +inline void Relu1(const float* input_data, const RuntimeShape& input_shape, + float* output_data, const RuntimeShape& output_shape) { + gemmlowp::ScopedProfilingLabel label("Relu1 (not fused)"); + const int flat_size = MatchingFlatSize(input_shape, output_shape); for (int i = 0; i < flat_size; ++i) { const float val = input_data[i]; const float upper = 1; @@ -937,9 +938,10 @@ inline void Relu1(const float* input_data, const Dims<4>& input_dims, } } -inline void Relu6(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { - const int flat_size = MatchingFlatSize(input_dims, output_dims); +inline void Relu6(const float* input_data, const RuntimeShape& input_shape, + float* output_data, const RuntimeShape& output_shape) { + gemmlowp::ScopedProfilingLabel label("Relu6 (not fused)"); + const int flat_size = MatchingFlatSize(input_shape, output_shape); for (int i = 0; i < flat_size; ++i) { const float val = input_data[i]; const float upper = 6; @@ -2257,18 +2259,21 @@ inline int NodeOffset(int b, int h, int w, int height, int width) { return (b * height + h) * width + w; } -inline void AveragePool(const float* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, - int pad_height, int filter_width, int filter_height, +inline void AveragePool(const float* input_data, + const RuntimeShape& input_shape, int stride_width, + int stride_height, int pad_width, int pad_height, + int filter_width, int filter_height, float output_activation_min, float output_activation_max, float* output_data, - const Dims<4>& output_dims) { - const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); - const int input_height = ArraySize(input_dims, 2); - const int input_width = ArraySize(input_dims, 1); - const int output_height = ArraySize(output_dims, 2); - const int output_width = ArraySize(output_dims, 1); + const RuntimeShape& output_shape) { + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int depth = MatchingDim(input_shape, 3, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); for (int batch = 0; batch < batches; ++batch) { for (int out_y = 0; out_y < output_height; ++out_y) { for (int out_x = 0; out_x < output_width; ++out_x) { @@ -2292,12 +2297,12 @@ inline void AveragePool(const float* input_data, const Dims<4>& input_dims, const int in_x = in_x_origin + filter_x; const int in_y = in_y_origin + filter_y; total += - input_data[Offset(input_dims, channel, in_x, in_y, batch)]; + input_data[Offset(input_shape, batch, in_y, in_x, channel)]; filter_count++; } } const float average = total / filter_count; - output_data[Offset(output_dims, channel, out_x, out_y, batch)] = + output_data[Offset(output_shape, batch, out_y, out_x, channel)] = ActivationFunctionWithMinMax(average, output_activation_min, output_activation_max); } @@ -2306,42 +2311,22 @@ inline void AveragePool(const float* input_data, const Dims<4>& input_dims, } } -// legacy, for compatibility with old checked-in code -template -void AveragePool(const float* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, - int pad_height, int filter_width, int filter_height, - float* output_data, const Dims<4>& output_dims) { - float output_activation_min, output_activation_max; - GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); - AveragePool(input_data, input_dims, stride_width, stride_height, pad_width, - pad_height, filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - -// legacy, for compatibility with old checked-in code -template -void AveragePool(const float* input_data, const Dims<4>& input_dims, int stride, - int pad_width, int pad_height, int filter_width, - int filter_height, float* output_data, - const Dims<4>& output_dims) { - AveragePool(input_data, input_dims, stride, stride, pad_width, pad_height, - filter_width, filter_height, output_data, output_dims); -} - -inline void AveragePool(const uint8* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, - int pad_height, int filter_width, int filter_height, +inline void AveragePool(const uint8* input_data, + const RuntimeShape& input_shape, int stride_width, + int stride_height, int pad_width, int pad_height, + int filter_width, int filter_height, int32 output_activation_min, int32 output_activation_max, uint8* output_data, - const Dims<4>& output_dims) { + const RuntimeShape& output_shape) { TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); - const int input_height = ArraySize(input_dims, 2); - const int input_width = ArraySize(input_dims, 1); - const int output_height = ArraySize(output_dims, 2); - const int output_width = ArraySize(output_dims, 1); + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int depth = MatchingDim(input_shape, 3, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); for (int batch = 0; batch < batches; ++batch) { for (int out_y = 0; out_y < output_height; ++out_y) { for (int out_x = 0; out_x < output_width; ++out_x) { @@ -2364,14 +2349,15 @@ inline void AveragePool(const uint8* input_data, const Dims<4>& input_dims, ++filter_x) { const int in_x = in_x_origin + filter_x; const int in_y = in_y_origin + filter_y; - acc += input_data[Offset(input_dims, channel, in_x, in_y, batch)]; + acc += + input_data[Offset(input_shape, batch, in_y, in_x, channel)]; filter_count++; } } acc = (acc + filter_count / 2) / filter_count; acc = std::max(acc, output_activation_min); acc = std::min(acc, output_activation_max); - output_data[Offset(output_dims, channel, out_x, out_y, batch)] = + output_data[Offset(output_shape, batch, out_y, out_x, channel)] = static_cast(acc); } } @@ -2379,50 +2365,19 @@ inline void AveragePool(const uint8* input_data, const Dims<4>& input_dims, } } -// legacy, for compatibility with old checked-in code -template -void AveragePool(const uint8* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, - int pad_height, int filter_width, int filter_height, - int32 output_activation_min, int32 output_activation_max, - uint8* output_data, const Dims<4>& output_dims) { - static_assert(Ac == FusedActivationFunctionType::kNone || - Ac == FusedActivationFunctionType::kRelu || - Ac == FusedActivationFunctionType::kRelu6 || - Ac == FusedActivationFunctionType::kRelu1, - ""); - if (Ac == FusedActivationFunctionType::kNone) { - TFLITE_DCHECK_EQ(output_activation_min, 0); - TFLITE_DCHECK_EQ(output_activation_max, 255); - } - AveragePool(input_data, input_dims, stride_width, stride_height, pad_width, - pad_height, filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - -// legacy, for compatibility with old checked-in code -template -void AveragePool(const uint8* input_data, const Dims<4>& input_dims, int stride, - int pad_width, int pad_height, int filter_width, - int filter_height, int32 output_activation_min, - int32 output_activation_max, uint8* output_data, - const Dims<4>& output_dims) { - AveragePool(input_data, input_dims, stride, stride, pad_width, pad_height, - filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - -inline void L2Pool(const float* input_data, const Dims<4>& input_dims, +inline void L2Pool(const float* input_data, const RuntimeShape& input_shape, int stride_width, int stride_height, int pad_width, int pad_height, int filter_width, int filter_height, float output_activation_min, float output_activation_max, - float* output_data, const Dims<4>& output_dims) { - const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); - const int input_height = ArraySize(input_dims, 2); - const int input_width = ArraySize(input_dims, 1); - const int output_height = ArraySize(output_dims, 2); - const int output_width = ArraySize(output_dims, 1); + float* output_data, const RuntimeShape& output_shape) { + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int depth = MatchingDim(input_shape, 3, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); for (int batch = 0; batch < batches; ++batch) { for (int out_y = 0; out_y < output_height; ++out_y) { for (int out_x = 0; out_x < output_width; ++out_x) { @@ -2446,13 +2401,13 @@ inline void L2Pool(const float* input_data, const Dims<4>& input_dims, const int in_x = in_x_origin + filter_x; const int in_y = in_y_origin + filter_y; const float val = - input_data[Offset(input_dims, channel, in_x, in_y, batch)]; + input_data[Offset(input_shape, batch, in_y, in_x, channel)]; sum_squares += val * val; filter_count++; } } const float l2pool_result = std::sqrt(sum_squares / filter_count); - output_data[Offset(output_dims, channel, out_x, out_y, batch)] = + output_data[Offset(output_shape, batch, out_y, out_x, channel)] = ActivationFunctionWithMinMax(l2pool_result, output_activation_min, output_activation_max); } @@ -2461,40 +2416,19 @@ inline void L2Pool(const float* input_data, const Dims<4>& input_dims, } } -// legacy, for compatibility with old checked-in code -template -void L2Pool(const float* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, int pad_height, - int filter_width, int filter_height, float* output_data, - const Dims<4>& output_dims) { - float output_activation_min, output_activation_max; - GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); - - L2Pool(input_data, input_dims, stride_width, stride_height, pad_width, - pad_height, filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - -// legacy, for compatibility with old checked-in code -template -void L2Pool(const float* input_data, const Dims<4>& input_dims, int stride, - int pad_width, int pad_height, int filter_width, int filter_height, - float* output_data, const Dims<4>& output_dims) { - L2Pool(input_data, input_dims, stride, stride, pad_width, pad_height, - filter_width, filter_height, output_data, output_dims); -} - -inline void MaxPool(const float* input_data, const Dims<4>& input_dims, +inline void MaxPool(const float* input_data, const RuntimeShape& input_shape, int stride_width, int stride_height, int pad_width, int pad_height, int filter_width, int filter_height, float output_activation_min, float output_activation_max, - float* output_data, const Dims<4>& output_dims) { - const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); - const int input_height = ArraySize(input_dims, 2); - const int input_width = ArraySize(input_dims, 1); - const int output_height = ArraySize(output_dims, 2); - const int output_width = ArraySize(output_dims, 1); + float* output_data, const RuntimeShape& output_shape) { + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int depth = MatchingDim(input_shape, 3, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); for (int batch = 0; batch < batches; ++batch) { for (int out_y = 0; out_y < output_height; ++out_y) { for (int out_x = 0; out_x < output_width; ++out_x) { @@ -2518,10 +2452,10 @@ inline void MaxPool(const float* input_data, const Dims<4>& input_dims, const int in_y = in_y_origin + filter_y; max = std::max( max, - input_data[Offset(input_dims, channel, in_x, in_y, batch)]); + input_data[Offset(input_shape, batch, in_y, in_x, channel)]); } } - output_data[Offset(output_dims, channel, out_x, out_y, batch)] = + output_data[Offset(output_shape, batch, out_y, out_x, channel)] = ActivationFunctionWithMinMax(max, output_activation_min, output_activation_max); } @@ -2530,42 +2464,22 @@ inline void MaxPool(const float* input_data, const Dims<4>& input_dims, } } -// legacy, for compatibility with old checked-in code -template -void MaxPool(const float* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, int pad_height, - int filter_width, int filter_height, float* output_data, - const Dims<4>& output_dims) { - float output_activation_min, output_activation_max; - GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); - MaxPool(input_data, input_dims, stride_width, stride_height, pad_width, - pad_height, filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - -// legacy, for compatibility with old checked-in code -template -void MaxPool(const float* input_data, const Dims<4>& input_dims, int stride, - int pad_width, int pad_height, int filter_width, int filter_height, - float* output_data, const Dims<4>& output_dims) { - MaxPool(input_data, input_dims, stride, stride, pad_width, pad_height, - filter_width, filter_height, output_data, output_dims); -} - -inline void MaxPool(const uint8* input_data, const Dims<4>& input_dims, +inline void MaxPool(const uint8* input_data, const RuntimeShape& input_shape, int stride_width, int stride_height, int pad_width, int pad_height, int filter_width, int filter_height, int32 output_activation_min, int32 output_activation_max, - uint8* output_data, const Dims<4>& output_dims) { + uint8* output_data, const RuntimeShape& output_shape) { TFLITE_DCHECK_LE(output_activation_min, output_activation_max); TFLITE_DCHECK_GE(output_activation_min, 0); TFLITE_DCHECK_LE(output_activation_max, 255); - const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); - const int input_height = ArraySize(input_dims, 2); - const int input_width = ArraySize(input_dims, 1); - const int output_height = ArraySize(output_dims, 2); - const int output_width = ArraySize(output_dims, 1); + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int depth = MatchingDim(input_shape, 3, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); for (int batch = 0; batch < batches; ++batch) { for (int out_y = 0; out_y < output_height; ++out_y) { for (int out_x = 0; out_x < output_width; ++out_x) { @@ -2589,12 +2503,12 @@ inline void MaxPool(const uint8* input_data, const Dims<4>& input_dims, const int in_y = in_y_origin + filter_y; max = std::max( max, - input_data[Offset(input_dims, channel, in_x, in_y, batch)]); + input_data[Offset(input_shape, batch, in_y, in_x, channel)]); } } max = std::max(max, output_activation_min); max = std::min(max, output_activation_max); - output_data[Offset(output_dims, channel, out_x, out_y, batch)] = + output_data[Offset(output_shape, batch, out_y, out_x, channel)] = static_cast(max); } } @@ -2602,38 +2516,6 @@ inline void MaxPool(const uint8* input_data, const Dims<4>& input_dims, } } -// legacy, for compatibility with old checked-in code -template -void MaxPool(const uint8* input_data, const Dims<4>& input_dims, - int stride_width, int stride_height, int pad_width, int pad_height, - int filter_width, int filter_height, int32 output_activation_min, - int32 output_activation_max, uint8* output_data, - const Dims<4>& output_dims) { - static_assert(Ac == FusedActivationFunctionType::kNone || - Ac == FusedActivationFunctionType::kRelu || - Ac == FusedActivationFunctionType::kRelu6 || - Ac == FusedActivationFunctionType::kRelu1, - ""); - if (Ac == FusedActivationFunctionType::kNone) { - TFLITE_DCHECK_EQ(output_activation_min, 0); - TFLITE_DCHECK_EQ(output_activation_max, 255); - } - MaxPool(input_data, input_dims, stride_width, stride_height, pad_width, - pad_height, filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - -// legacy, for compatibility with old checked-in code -template -void MaxPool(const uint8* input_data, const Dims<4>& input_dims, int stride, - int pad_width, int pad_height, int filter_width, int filter_height, - int32 output_activation_min, int32 output_activation_max, - uint8* output_data, const Dims<4>& output_dims) { - MaxPool(input_data, input_dims, stride, stride, pad_width, pad_height, - filter_width, filter_height, output_activation_min, - output_activation_max, output_data, output_dims); -} - inline void LocalResponseNormalization(const float* input_data, const Dims<4>& input_dims, int range, float bias, float alpha, float beta, @@ -2657,11 +2539,14 @@ inline void LocalResponseNormalization(const float* input_data, } } -inline void Softmax(const float* input_data, const Dims<4>& input_dims, +inline void Softmax(const float* input_data, const RuntimeShape& input_shape, float beta, float* output_data, - const Dims<4>& output_dims) { - const int outer_size = MatchingFlatSizeSkipDim(input_dims, 0, output_dims); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); + const RuntimeShape& output_shape) { + const int trailing_dim = input_shape.DimensionsCount() - 1; + const int outer_size = + MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); + const int depth = + MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); for (int i = 0; i < outer_size; ++i) { // Find max element value which we'll use to ensure numerical stability @@ -2686,10 +2571,10 @@ inline void Softmax(const float* input_data, const Dims<4>& input_dims, } } -inline void Softmax(const uint8* input_data, const Dims<4>& input_dims, +inline void Softmax(const uint8* input_data, const RuntimeShape& input_shape, int32 input_beta_multiplier, int32 input_beta_left_shift, int diff_min, uint8* output_data, - const Dims<4>& output_dims) { + const RuntimeShape& output_shape) { // The representation chosen for the input to the exp() function is Q5.26. // We need to leave extra space since values that we skip might be as large as // -32 before multiplying by input_beta_multiplier, and therefore as large as @@ -2702,8 +2587,11 @@ inline void Softmax(const uint8* input_data, const Dims<4>& input_dims, using FixedPointAccum = gemmlowp::FixedPoint; using FixedPoint0 = gemmlowp::FixedPoint; - const int outer_size = MatchingFlatSizeSkipDim(input_dims, 0, output_dims); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); + const int trailing_dim = input_shape.DimensionsCount() - 1; + const int outer_size = + MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); + const int depth = + MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); for (int i = 0; i < outer_size; ++i) { uint8 max_in_row = 0; @@ -2764,10 +2652,13 @@ inline void Softmax(const uint8* input_data, const Dims<4>& input_dims, } } -inline void LogSoftmax(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { - const int outer_size = MatchingFlatSizeSkipDim(input_dims, 0, output_dims); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); +inline void LogSoftmax(const float* input_data, const RuntimeShape& input_shape, + float* output_data, const RuntimeShape& output_shape) { + const int trailing_dim = input_shape.DimensionsCount() - 1; + const int outer_size = + MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); + const int depth = + MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); for (int i = 0; i < outer_size; ++i) { // Find max element value which we'll use to ensure numerical stability @@ -2907,11 +2798,11 @@ log_x_for_x_greater_than_or_equal_to_1( input_val); } -inline void LogSoftmax(const uint8* input_data, const Dims<4>& input_dims, +inline void LogSoftmax(const uint8* input_data, const RuntimeShape& input_shape, int32 input_multiplier, int32 input_left_shift, int32 reverse_scaling_divisor, int32 reverse_scaling_right_shift, int diff_min, - uint8* output_data, const Dims<4>& output_dims) { + uint8* output_data, const RuntimeShape& output_shape) { // The representation chosen for the input to the exp() function is Q5.26. // We need to leave extra space since values that we skip might be as large as // -32 before multiplying by input_beta_multiplier, and therefore as large as @@ -2925,8 +2816,11 @@ inline void LogSoftmax(const uint8* input_data, const Dims<4>& input_dims, using FixedPointAccum = gemmlowp::FixedPoint; using FixedPoint0 = gemmlowp::FixedPoint; - const int outer_size = MatchingFlatSizeSkipDim(input_dims, 0, output_dims); - const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); + const int trailing_dim = input_shape.DimensionsCount() - 1; + const int outer_size = + MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); + const int depth = + MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); for (int i = 0; i < outer_size; ++i) { uint8 max_in_row = 0; @@ -2990,9 +2884,9 @@ inline void LogSoftmax(const uint8* input_data, const Dims<4>& input_dims, } } -inline void Logistic(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { - const int flat_size = MatchingFlatSize(output_dims, input_dims); +inline void Logistic(const float* input_data, const RuntimeShape& input_shape, + float* output_data, const RuntimeShape& output_shape) { + const int flat_size = MatchingFlatSize(input_shape, output_shape); for (int i = 0; i < flat_size; i++) { float val = input_data[i]; @@ -3001,11 +2895,11 @@ inline void Logistic(const float* input_data, const Dims<4>& input_dims, } } -inline void Logistic(const uint8* input_data, const Dims<4>& input_dims, +inline void Logistic(const uint8* input_data, const RuntimeShape& input_shape, int32 input_zero_point, int32 input_range_radius, int32 input_multiplier, int input_left_shift, - uint8* output_data, const Dims<4>& output_dims) { - const int flat_size = MatchingFlatSize(output_dims, input_dims); + uint8* output_data, const RuntimeShape& output_shape) { + const int flat_size = MatchingFlatSize(input_shape, output_shape); for (int i = 0; i < flat_size; i++) { const uint8 input_val_u8 = input_data[i]; @@ -3039,9 +2933,9 @@ inline void Logistic(const uint8* input_data, const Dims<4>& input_dims, } } -inline void Logistic(const int16* input_data, const Dims<4>& input_dims, - int16* output_data, const Dims<4>& output_dims) { - const int flat_size = MatchingFlatSize(output_dims, input_dims); +inline void Logistic(const int16* input_data, const RuntimeShape& input_shape, + int16* output_data, const RuntimeShape& output_shape) { + const int flat_size = MatchingFlatSize(input_shape, output_shape); for (int i = 0; i < flat_size; i++) { // F0 uses 0 integer bits, range [-1, 1]. @@ -3057,9 +2951,9 @@ inline void Logistic(const int16* input_data, const Dims<4>& input_dims, } } -inline void Tanh(const float* input_data, const Dims<4>& input_dims, - float* output_data, const Dims<4>& output_dims) { - const int flat_size = MatchingFlatSize(output_dims, input_dims); +inline void Tanh(const float* input_data, const RuntimeShape& input_shape, + float* output_data, const RuntimeShape& output_shape) { + const int flat_size = MatchingFlatSize(input_shape, output_shape); for (int i = 0; i < flat_size; i++) { float val = input_data[i]; @@ -3068,12 +2962,12 @@ inline void Tanh(const float* input_data, const Dims<4>& input_dims, } } -inline void Tanh(const uint8* input_data, const Dims<4>& input_dims, +inline void Tanh(const uint8* input_data, const RuntimeShape& input_shape, int32 input_zero_point, int32 input_range_radius, int32 input_multiplier, int input_left_shift, - uint8* output_data, const Dims<4>& output_dims) { + uint8* output_data, const RuntimeShape& output_shape) { const int32 output_zero_point = 128; - const int flat_size = MatchingFlatSize(output_dims, input_dims); + const int flat_size = MatchingFlatSize(input_shape, output_shape); for (int i = 0; i < flat_size; i++) { const uint8 input_val_u8 = input_data[i]; @@ -3108,15 +3002,15 @@ inline void Tanh(const uint8* input_data, const Dims<4>& input_dims, } } -inline void Tanh(const int16* input_data, const Dims<4>& input_dims, +inline void Tanh(const int16* input_data, const RuntimeShape& input_shape, int input_left_shift, int16* output_data, - const Dims<4>& output_dims) { + const RuntimeShape& output_shape) { // Support for shifts is limited until we have a parameterized version of // SaturatingRoundingMultiplyByPOT(). TFLITE_DCHECK_GE(input_left_shift, 0); TFLITE_DCHECK_LE(input_left_shift, 1); - const int flat_size = MatchingFlatSize(output_dims, input_dims); + const int flat_size = MatchingFlatSize(input_shape, output_shape); // F0 uses 0 integer bits, range [-1, 1]. // This is the return type of math functions such as tanh, logistic, diff --git a/tensorflow/contrib/lite/kernels/internal/softmax_quantized_test.cc b/tensorflow/contrib/lite/kernels/internal/softmax_quantized_test.cc index d781a7b642..a7dad3c14e 100644 --- a/tensorflow/contrib/lite/kernels/internal/softmax_quantized_test.cc +++ b/tensorflow/contrib/lite/kernels/internal/softmax_quantized_test.cc @@ -32,19 +32,21 @@ namespace tflite { namespace { void RunSoftmaxFloatReference(const uint8* input_data, - const Dims<4>& dims_common, int32 input_offset, - const double input_scale, int stride, float beta, + const RuntimeShape& shape_common, + int32 input_offset, const double input_scale, + int stride, float beta, uint8* reference_output_data) { - const int ref_buffer_size = RequiredBufferSizeForDims(dims_common); + const int ref_buffer_size = shape_common.FlatSize(); std::vector reference_dequant_data(ref_buffer_size); std::vector reference_output_float_data(ref_buffer_size); // Reference data generated via Dequant of input into float, and then applying // float Softmax. - reference_ops::Dequantize(input_data, dims_common, input_offset, input_scale, - reference_dequant_data.data(), dims_common); - optimized_ops::Softmax(reference_dequant_data.data(), dims_common, beta, - reference_output_float_data.data(), dims_common); + reference_ops::Dequantize( + input_data, ToRuntimeDims(shape_common), input_offset, input_scale, + reference_dequant_data.data(), ToRuntimeDims(shape_common)); + optimized_ops::Softmax(reference_dequant_data.data(), shape_common, beta, + reference_output_float_data.data(), shape_common); // Work with quantized scaling for Softmax, under which 256 represents 1, but // we limit this to 255. for (int i = 0; i < ref_buffer_size; i++) { @@ -55,9 +57,9 @@ void RunSoftmaxFloatReference(const uint8* input_data, } void CheckOutputData(const uint8* test_output, const uint8* reference_output, - const Dims<4>& dims_common, const string& check_label, - bool be_exacting) { - const int buffer_size = RequiredBufferSizeForDims(dims_common); + const RuntimeShape& shape_common, + const string& check_label, bool be_exacting) { + const int buffer_size = shape_common.FlatSize(); // While calculating some metrics in floating point, we work with quantized // scaling. std::vector diff(buffer_size); @@ -91,15 +93,15 @@ void CheckOutputData(const uint8* test_output, const uint8* reference_output, // Runs the Softmax and compares against the float reference implementation and // the quantized reference implementation. -void RunOneSoftmaxTest(const uint8* input_data, const Dims<4>& dims_common, - int32 input_offset, const double input_scale, int stride, - float beta) { - const int buffer_size = RequiredBufferSizeForDims(dims_common); +void RunOneSoftmaxTest(const uint8* input_data, + const RuntimeShape& shape_common, int32 input_offset, + const double input_scale, int stride, float beta) { + const int buffer_size = shape_common.FlatSize(); std::vector optimized_softmax_output(buffer_size); std::vector reference_float_softmax_output(buffer_size); std::vector reference_quant_softmax_output(buffer_size); - RunSoftmaxFloatReference(input_data, dims_common, input_offset, input_scale, + RunSoftmaxFloatReference(input_data, shape_common, input_offset, input_scale, stride, beta, reference_float_softmax_output.data()); int32 input_beta_multiplier; @@ -113,21 +115,21 @@ void RunOneSoftmaxTest(const uint8* input_data, const Dims<4>& dims_common, const int diff_min = -tflite::CalculateInputRadius(kScaledDiffIntegerBits, input_beta_left_shift); - optimized_ops::Softmax(input_data, dims_common, input_beta_multiplier, + optimized_ops::Softmax(input_data, shape_common, input_beta_multiplier, input_beta_left_shift, diff_min, - optimized_softmax_output.data(), dims_common); - reference_ops::Softmax(input_data, dims_common, input_beta_multiplier, + optimized_softmax_output.data(), shape_common); + reference_ops::Softmax(input_data, shape_common, input_beta_multiplier, input_beta_left_shift, diff_min, - reference_quant_softmax_output.data(), dims_common); + reference_quant_softmax_output.data(), shape_common); CheckOutputData(optimized_softmax_output.data(), - reference_float_softmax_output.data(), dims_common, + reference_float_softmax_output.data(), shape_common, "Optimized vs float reference", false); CheckOutputData(optimized_softmax_output.data(), - reference_quant_softmax_output.data(), dims_common, + reference_quant_softmax_output.data(), shape_common, "Optimized vs quant reference", true); CheckOutputData(reference_quant_softmax_output.data(), - reference_float_softmax_output.data(), dims_common, + reference_float_softmax_output.data(), shape_common, "Quant reference vs float reference", false); } @@ -150,13 +152,13 @@ bool TryOneUniformSoftmax() { const int32 input_offset = UniformRandomInt(-256, 0); const float beta = 1.0f + ExponentialRandomPositiveFloat(0.9f, 2, 10); - Dims<4> dims_common = - MakeDimsForInference(input_depth, input_width, input_height, batch); - const int buffer_size = RequiredBufferSizeForDims(dims_common); + auto shape_common = + RuntimeShape({batch, input_height, input_width, input_depth}); + const int buffer_size = shape_common.FlatSize(); std::vector input_data(buffer_size); FillRandom(&input_data); - RunOneSoftmaxTest(input_data.data(), dims_common, input_offset, input_scale, + RunOneSoftmaxTest(input_data.data(), shape_common, input_offset, input_scale, stride, beta); return true; } @@ -188,14 +190,14 @@ bool TryOneSkyscraperSoftmax(bool small_depth) { const int middle_min = UniformRandomInt(0, 255); const int sides_max = UniformRandomInt(0, middle_min); - Dims<4> dims_common = - MakeDimsForInference(input_depth, input_width, input_height, batch); - const int buffer_size = RequiredBufferSizeForDims(dims_common); + auto shape_common = + RuntimeShape({batch, input_height, input_width, input_depth}); + const int buffer_size = shape_common.FlatSize(); std::vector input_data(buffer_size); FillRandomSkyscraper(&input_data, input_depth, middle_proportion, middle_min, sides_max); - RunOneSoftmaxTest(input_data.data(), dims_common, input_offset, input_scale, + RunOneSoftmaxTest(input_data.data(), shape_common, input_offset, input_scale, stride, beta); return true; } diff --git a/tensorflow/contrib/lite/kernels/internal/types.h b/tensorflow/contrib/lite/kernels/internal/types.h index 64f4881a46..707d2d261a 100644 --- a/tensorflow/contrib/lite/kernels/internal/types.h +++ b/tensorflow/contrib/lite/kernels/internal/types.h @@ -294,6 +294,50 @@ inline int RequiredBufferSizeForDims(const Dims<4>& dims) { return FlatSize(dims); } +// Flat size calculation, checking that dimensions match with one or more other +// arrays. +inline int MatchingFlatSize(const RuntimeShape& shape, + const RuntimeShape& check_shape_0) { + const int dims_count = shape.DimensionsCount(); + for (int i = 0; i < dims_count; ++i) { + TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); + } + return shape.FlatSize(); +} + +inline int MatchingFlatSize(const RuntimeShape& shape, + const RuntimeShape& check_shape_0, + const RuntimeShape& check_shape_1) { + const int dims_count = shape.DimensionsCount(); + for (int i = 0; i < dims_count; ++i) { + TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); + } + return MatchingFlatSize(shape, check_shape_1); +} + +inline int MatchingFlatSize(const RuntimeShape& shape, + const RuntimeShape& check_shape_0, + const RuntimeShape& check_shape_1, + const RuntimeShape& check_shape_2) { + const int dims_count = shape.DimensionsCount(); + for (int i = 0; i < dims_count; ++i) { + TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); + } + return MatchingFlatSize(shape, check_shape_1, check_shape_2); +} + +inline int MatchingFlatSize(const RuntimeShape& shape, + const RuntimeShape& check_shape_0, + const RuntimeShape& check_shape_1, + const RuntimeShape& check_shape_2, + const RuntimeShape& check_shape_3) { + const int dims_count = shape.DimensionsCount(); + for (int i = 0; i < dims_count; ++i) { + TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); + } + return MatchingFlatSize(shape, check_shape_1, check_shape_2, check_shape_3); +} + // Flat size calculation, checking that dimensions match with one or more other // arrays. template @@ -320,7 +364,7 @@ inline int MatchingFlatSize(const Dims& dims, const Dims& check_dims_0, for (int i = 0; i < N; ++i) { TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); } - return FlatSize(dims, check_dims_1, check_dims_2); + return MatchingFlatSize(dims, check_dims_1, check_dims_2); } template @@ -331,7 +375,7 @@ inline int MatchingFlatSize(const Dims& dims, const Dims& check_dims_0, for (int i = 0; i < N; ++i) { TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); } - return FlatSize(dims, check_dims_1, check_dims_2, check_dims_3); + return MatchingFlatSize(dims, check_dims_1, check_dims_2, check_dims_3); } // Data is required to be contiguous, and so many operators can use either the diff --git a/tensorflow/contrib/lite/kernels/log_softmax_test.cc b/tensorflow/contrib/lite/kernels/log_softmax_test.cc index 62820a2f51..9a8d35e82c 100644 --- a/tensorflow/contrib/lite/kernels/log_softmax_test.cc +++ b/tensorflow/contrib/lite/kernels/log_softmax_test.cc @@ -90,10 +90,9 @@ TEST(LogSoftmaxOpTest, CompareWithTFmini) { m.Invoke(); std::unique_ptr output_buffer(new float[input_size * batch_size]); - static tflite::Dims<4> input_dims = {{input_size, 1, 1, batch_size}, - {1, 0, 0, input_size}}; - tflite::reference_ops::LogSoftmax(input_buffer, input_dims, - output_buffer.get(), input_dims); + auto input_shape = RuntimeShape({batch_size, 1, 1, input_size}); + tflite::reference_ops::LogSoftmax(input_buffer, input_shape, + output_buffer.get(), input_shape); std::vector expected; expected.insert(expected.end(), output_buffer.get(), diff --git a/tensorflow/contrib/lite/kernels/pooling.cc b/tensorflow/contrib/lite/kernels/pooling.cc index 311e9b8399..41771e60bc 100644 --- a/tensorflow/contrib/lite/kernels/pooling.cc +++ b/tensorflow/contrib/lite/kernels/pooling.cc @@ -126,12 +126,13 @@ void AverageEvalFloat(TfLiteContext* context, TfLiteNode* node, float activation_min, activation_max; CalculateActivationRangeFloat(params->activation, &activation_min, &activation_max); -#define TF_LITE_AVERAGE_POOL(type) \ - type::AveragePool( \ - GetTensorData(input), GetTensorDims(input), params->stride_width, \ - params->stride_height, data->padding.width, data->padding.height, \ - params->filter_width, params->filter_height, activation_min, \ - activation_max, GetTensorData(output), GetTensorDims(output)) +#define TF_LITE_AVERAGE_POOL(type) \ + type::AveragePool(GetTensorData(input), GetTensorShape(input), \ + params->stride_width, params->stride_height, \ + data->padding.width, data->padding.height, \ + params->filter_width, params->filter_height, \ + activation_min, activation_max, \ + GetTensorData(output), GetTensorShape(output)) if (kernel_type == kReference) { TF_LITE_AVERAGE_POOL(reference_ops); } else { @@ -148,13 +149,13 @@ void AverageEvalQuantized(TfLiteContext* context, TfLiteNode* node, int32_t activation_max; CalculateActivationRangeUint8(params->activation, output, &activation_min, &activation_max); -#define TF_LITE_AVERAGE_POOL(type) \ - type::AveragePool(GetTensorData(input), GetTensorDims(input), \ - params->stride_width, params->stride_height, \ - data->padding.width, data->padding.height, \ - params->filter_width, params->filter_height, \ - activation_min, activation_max, \ - GetTensorData(output), GetTensorDims(output)) +#define TF_LITE_AVERAGE_POOL(type) \ + type::AveragePool(GetTensorData(input), GetTensorShape(input), \ + params->stride_width, params->stride_height, \ + data->padding.width, data->padding.height, \ + params->filter_width, params->filter_height, \ + activation_min, activation_max, \ + GetTensorData(output), GetTensorShape(output)) if (kernel_type == kReference) { TF_LITE_AVERAGE_POOL(reference_ops); } else { @@ -170,12 +171,13 @@ void MaxEvalFloat(TfLiteContext* context, TfLiteNode* node, float activation_min, activation_max; CalculateActivationRangeFloat(params->activation, &activation_min, &activation_max); -#define TF_LITE_MAX_POOL(type) \ - type::MaxPool( \ - GetTensorData(input), GetTensorDims(input), params->stride_width, \ - params->stride_height, data->padding.width, data->padding.height, \ - params->filter_width, params->filter_height, activation_min, \ - activation_max, GetTensorData(output), GetTensorDims(output)) +#define TF_LITE_MAX_POOL(type) \ + type::MaxPool(GetTensorData(input), GetTensorShape(input), \ + params->stride_width, params->stride_height, \ + data->padding.width, data->padding.height, \ + params->filter_width, params->filter_height, activation_min, \ + activation_max, GetTensorData(output), \ + GetTensorShape(output)) if (kernel_type == kReference) { TF_LITE_MAX_POOL(reference_ops); } else { @@ -193,12 +195,12 @@ void MaxEvalQuantized(TfLiteContext* context, TfLiteNode* node, CalculateActivationRangeUint8(params->activation, output, &activation_min, &activation_max); #define TF_LITE_MAX_POOL(type) \ - type::MaxPool(GetTensorData(input), GetTensorDims(input), \ + type::MaxPool(GetTensorData(input), GetTensorShape(input), \ params->stride_width, params->stride_height, \ data->padding.width, data->padding.height, \ params->filter_width, params->filter_height, activation_min, \ activation_max, GetTensorData(output), \ - GetTensorDims(output)) + GetTensorShape(output)) if (kernel_type == kReference) { TF_LITE_MAX_POOL(reference_ops); } else { @@ -214,12 +216,13 @@ void L2EvalFloat(TfLiteContext* context, TfLiteNode* node, float activation_min, activation_max; CalculateActivationRangeFloat(params->activation, &activation_min, &activation_max); -#define TF_LITE_L2_POOL(type) \ - type::L2Pool( \ - GetTensorData(input), GetTensorDims(input), params->stride_width, \ - params->stride_height, data->padding.width, data->padding.height, \ - params->filter_width, params->filter_height, activation_min, \ - activation_max, GetTensorData(output), GetTensorDims(output)) +#define TF_LITE_L2_POOL(type) \ + type::L2Pool(GetTensorData(input), GetTensorShape(input), \ + params->stride_width, params->stride_height, \ + data->padding.width, data->padding.height, \ + params->filter_width, params->filter_height, activation_min, \ + activation_max, GetTensorData(output), \ + GetTensorShape(output)) if (kernel_type == kReference) { TF_LITE_L2_POOL(reference_ops); } else { diff --git a/tensorflow/contrib/lite/kernels/softmax_test.cc b/tensorflow/contrib/lite/kernels/softmax_test.cc index 6c5338ff0f..727822f6be 100644 --- a/tensorflow/contrib/lite/kernels/softmax_test.cc +++ b/tensorflow/contrib/lite/kernels/softmax_test.cc @@ -92,10 +92,9 @@ TEST(SoftmaxOpTest, CompareWithTFminiBetaEq1) { m.Invoke(); std::unique_ptr output_buffer(new float[input_size * batch_size]); - static tflite::Dims<4> input_dims = {{input_size, 1, 1, batch_size}, - {1, 0, 0, input_size}}; - tflite::reference_ops::Softmax(input_buffer, input_dims, beta, - output_buffer.get(), input_dims); + auto input_shape = RuntimeShape({batch_size, 1, 1, input_size}); + tflite::reference_ops::Softmax(input_buffer, input_shape, beta, + output_buffer.get(), input_shape); std::vector expected; expected.insert(expected.end(), output_buffer.get(), @@ -120,10 +119,9 @@ TEST(SoftmaxOpTest, CompareWithTFminiBetaNotEq1) { m.Invoke(); std::unique_ptr output_buffer(new float[input_size * batch_size]); - static tflite::Dims<4> input_dims = {{input_size, 1, 1, batch_size}, - {1, 0, 0, input_size}}; - tflite::reference_ops::Softmax(input_buffer, input_dims, beta, - output_buffer.get(), input_dims); + auto input_shape = RuntimeShape({batch_size, 1, 1, input_size}); + tflite::reference_ops::Softmax(input_buffer, input_shape, beta, + output_buffer.get(), input_shape); std::vector expected; expected.insert(expected.end(), output_buffer.get(), -- GitLab From c1ff1164e30186d847f7d4f9e9ce5d40936a2c1c Mon Sep 17 00:00:00 2001 From: Anna R Date: Wed, 20 Jun 2018 13:57:42 -0700 Subject: [PATCH 755/816] Exporting symbols from additional namespaces in ApiDefs. Also, setting some of the current exported endpoints to deprecated. PiperOrigin-RevId: 201410753 --- .../api_def/python_api/api_def_Acos.pbtxt | 10 + .../api_def/python_api/api_def_Acosh.pbtxt | 10 + .../core/api_def/python_api/api_def_Add.pbtxt | 10 + .../api_def/python_api/api_def_AsString.pbtxt | 10 + .../api_def/python_api/api_def_Asin.pbtxt | 10 + .../api_def/python_api/api_def_Asinh.pbtxt | 10 + .../api_def/python_api/api_def_Atan.pbtxt | 10 + .../api_def/python_api/api_def_Atan2.pbtxt | 10 + .../api_def/python_api/api_def_Atanh.pbtxt | 10 + .../python_api/api_def_BatchToSpaceND.pbtxt | 10 + .../api_def/python_api/api_def_Betainc.pbtxt | 10 + .../api_def/python_api/api_def_Ceil.pbtxt | 10 + .../python_api/api_def_CheckNumerics.pbtxt | 10 + .../api_def/python_api/api_def_Cholesky.pbtxt | 5 +- .../core/api_def/python_api/api_def_Cos.pbtxt | 10 + .../api_def/python_api/api_def_Cosh.pbtxt | 10 + .../api_def/python_api/api_def_Cross.pbtxt | 10 + .../python_api/api_def_DecodeBase64.pbtxt | 10 + .../python_api/api_def_DecodeCompressed.pbtxt | 10 + .../api_def_DecodeJSONExample.pbtxt | 10 + .../python_api/api_def_DecodeRaw.pbtxt | 10 + .../python_api/api_def_Dequantize.pbtxt | 10 + .../api_def/python_api/api_def_Diag.pbtxt | 10 + .../api_def/python_api/api_def_DiagPart.pbtxt | 10 + .../api_def/python_api/api_def_Digamma.pbtxt | 10 + .../python_api/api_def_EncodeBase64.pbtxt | 10 + .../api_def/python_api/api_def_Equal.pbtxt | 10 + .../api_def/python_api/api_def_Erfc.pbtxt | 10 + .../core/api_def/python_api/api_def_Exp.pbtxt | 10 + .../api_def/python_api/api_def_Expm1.pbtxt | 10 + .../api_def_ExtractImagePatches.pbtxt | 10 + .../core/api_def/python_api/api_def_FFT.pbtxt | 5 +- .../api_def_FakeQuantWithMinMaxArgs.pbtxt | 10 + ..._def_FakeQuantWithMinMaxArgsGradient.pbtxt | 10 + .../api_def_FakeQuantWithMinMaxVars.pbtxt | 10 + ..._def_FakeQuantWithMinMaxVarsGradient.pbtxt | 10 + ...ef_FakeQuantWithMinMaxVarsPerChannel.pbtxt | 10 + ...uantWithMinMaxVarsPerChannelGradient.pbtxt | 10 + .../api_def/python_api/api_def_Floor.pbtxt | 10 + .../api_def/python_api/api_def_GatherNd.pbtxt | 10 + .../api_def/python_api/api_def_Greater.pbtxt | 10 + .../python_api/api_def_GreaterEqual.pbtxt | 10 + .../api_def/python_api/api_def_IFFT.pbtxt | 5 +- .../api_def/python_api/api_def_Igamma.pbtxt | 10 + .../api_def/python_api/api_def_Igammac.pbtxt | 10 + .../api_def_InvertPermutation.pbtxt | 10 + .../api_def/python_api/api_def_IsFinite.pbtxt | 10 + .../api_def/python_api/api_def_IsInf.pbtxt | 10 + .../api_def/python_api/api_def_IsNan.pbtxt | 10 + .../api_def/python_api/api_def_Less.pbtxt | 10 + .../python_api/api_def_LessEqual.pbtxt | 10 + .../api_def/python_api/api_def_Lgamma.pbtxt | 10 + .../core/api_def/python_api/api_def_Log.pbtxt | 10 + .../api_def/python_api/api_def_Log1p.pbtxt | 10 + .../python_api/api_def_LogicalAnd.pbtxt | 10 + .../python_api/api_def_LogicalNot.pbtxt | 10 + .../python_api/api_def_LogicalOr.pbtxt | 10 + .../python_api/api_def_MatchingFiles.pbtxt | 10 + .../python_api/api_def_MatrixBandPart.pbtxt | 1 + .../api_def_MatrixDeterminant.pbtxt | 1 + .../python_api/api_def_MatrixDiag.pbtxt | 1 + .../python_api/api_def_MatrixDiagPart.pbtxt | 1 + .../python_api/api_def_MatrixInverse.pbtxt | 1 + .../python_api/api_def_MatrixSetDiag.pbtxt | 1 + .../python_api/api_def_MatrixSolve.pbtxt | 1 + .../api_def_MatrixTriangularSolve.pbtxt | 1 + .../api_def/python_api/api_def_Maximum.pbtxt | 10 + .../api_def/python_api/api_def_Minimum.pbtxt | 10 + .../api_def/python_api/api_def_NotEqual.pbtxt | 10 + .../python_api/api_def_ParseTensor.pbtxt | 10 + .../python_api/api_def_Polygamma.pbtxt | 10 + .../core/api_def/python_api/api_def_Qr.pbtxt | 1 + .../python_api/api_def_QuantizedConcat.pbtxt | 10 + .../api_def/python_api/api_def_ReadFile.pbtxt | 10 + .../python_api/api_def_Reciprocal.pbtxt | 10 + .../python_api/api_def_RegexReplace.pbtxt | 10 + .../api_def/python_api/api_def_Reshape.pbtxt | 10 + .../python_api/api_def_ReverseV2.pbtxt | 8 + .../api_def/python_api/api_def_Rint.pbtxt | 10 + .../api_def/python_api/api_def_Rsqrt.pbtxt | 10 + .../python_api/api_def_ScatterNd.pbtxt | 10 + .../python_api/api_def_SegmentMax.pbtxt | 10 + .../python_api/api_def_SegmentMean.pbtxt | 10 + .../python_api/api_def_SegmentMin.pbtxt | 10 + .../python_api/api_def_SegmentProd.pbtxt | 10 + .../python_api/api_def_SegmentSum.pbtxt | 10 + .../core/api_def/python_api/api_def_Sin.pbtxt | 10 + .../api_def/python_api/api_def_Sinh.pbtxt | 10 + .../api_def/python_api/api_def_Softplus.pbtxt | 3 + .../api_def/python_api/api_def_Softsign.pbtxt | 3 + .../python_api/api_def_SpaceToBatchND.pbtxt | 10 + .../api_def_SquaredDifference.pbtxt | 10 + .../python_api/api_def_StringJoin.pbtxt | 10 + .../python_api/api_def_StringStrip.pbtxt | 10 + .../api_def_StringToHashBucket.pbtxt | 10 + .../api_def_StringToHashBucketFast.pbtxt | 10 + .../api_def_StringToHashBucketStrong.pbtxt | 10 + .../python_api/api_def_StringToNumber.pbtxt | 10 + .../api_def/python_api/api_def_Substr.pbtxt | 10 + .../core/api_def/python_api/api_def_Tan.pbtxt | 10 + .../api_def/python_api/api_def_Tile.pbtxt | 10 + .../api_def_UnsortedSegmentMax.pbtxt | 10 + .../api_def_UnsortedSegmentMin.pbtxt | 10 + .../api_def_UnsortedSegmentProd.pbtxt | 10 + .../api_def_UnsortedSegmentSum.pbtxt | 10 + .../python_api/api_def_WriteFile.pbtxt | 10 + .../api_def/python_api/api_def_Zeta.pbtxt | 10 + tensorflow/python/ops/array_ops.py | 9 +- tensorflow/tools/api/generator/api_gen.bzl | 44 ++-- .../api/golden/tensorflow.debugging.pbtxt | 19 ++ .../tools/api/golden/tensorflow.dtypes.pbtxt | 7 + .../tools/api/golden/tensorflow.image.pbtxt | 4 + .../tools/api/golden/tensorflow.io.pbtxt | 39 ++++ .../tools/api/golden/tensorflow.linalg.pbtxt | 12 + .../tools/api/golden/tensorflow.manip.pbtxt | 28 +++ .../tools/api/golden/tensorflow.math.pbtxt | 216 ++++++++++++++++++ tensorflow/tools/api/golden/tensorflow.pbtxt | 16 ++ .../api/golden/tensorflow.quantization.pbtxt | 35 +++ .../tools/api/golden/tensorflow.strings.pbtxt | 32 +++ 119 files changed, 1386 insertions(+), 33 deletions(-) create mode 100644 tensorflow/core/api_def/python_api/api_def_Acos.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_Acosh.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_Add.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_AsString.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_Asin.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_Asinh.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_Atan.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_Atan2.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_Atanh.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_BatchToSpaceND.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_Betainc.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_Ceil.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_CheckNumerics.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_Cos.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_Cosh.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_Cross.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_DecodeBase64.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_DecodeCompressed.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_DecodeJSONExample.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_DecodeRaw.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_Dequantize.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_Diag.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_DiagPart.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_Digamma.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_EncodeBase64.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_Equal.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_Erfc.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_Exp.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_Expm1.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_ExtractImagePatches.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_FakeQuantWithMinMaxArgs.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_FakeQuantWithMinMaxArgsGradient.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_FakeQuantWithMinMaxVars.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_FakeQuantWithMinMaxVarsGradient.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_FakeQuantWithMinMaxVarsPerChannel.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_FakeQuantWithMinMaxVarsPerChannelGradient.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_Floor.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_GatherNd.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_Greater.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_GreaterEqual.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_Igamma.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_Igammac.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_InvertPermutation.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_IsFinite.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_IsInf.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_IsNan.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_Less.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_LessEqual.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_Lgamma.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_Log.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_Log1p.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_LogicalAnd.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_LogicalNot.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_LogicalOr.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_MatchingFiles.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_Maximum.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_Minimum.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_NotEqual.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_ParseTensor.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_Polygamma.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_QuantizedConcat.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_ReadFile.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_Reciprocal.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_RegexReplace.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_Reshape.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_Rint.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_Rsqrt.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_ScatterNd.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_SegmentMax.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_SegmentMean.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_SegmentMin.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_SegmentProd.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_SegmentSum.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_Sin.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_Sinh.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_SpaceToBatchND.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_SquaredDifference.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_StringJoin.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_StringStrip.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_StringToHashBucket.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_StringToHashBucketFast.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_StringToHashBucketStrong.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_StringToNumber.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_Substr.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_Tan.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_Tile.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_UnsortedSegmentMax.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_UnsortedSegmentMin.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_UnsortedSegmentProd.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_UnsortedSegmentSum.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_WriteFile.pbtxt create mode 100644 tensorflow/core/api_def/python_api/api_def_Zeta.pbtxt create mode 100644 tensorflow/tools/api/golden/tensorflow.debugging.pbtxt create mode 100644 tensorflow/tools/api/golden/tensorflow.dtypes.pbtxt create mode 100644 tensorflow/tools/api/golden/tensorflow.io.pbtxt create mode 100644 tensorflow/tools/api/golden/tensorflow.quantization.pbtxt diff --git a/tensorflow/core/api_def/python_api/api_def_Acos.pbtxt b/tensorflow/core/api_def/python_api/api_def_Acos.pbtxt new file mode 100644 index 0000000000..ca1ee78526 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_Acos.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "Acos" + endpoint { + name: "math.acos" + } + endpoint { + name: "acos" + deprecation_message: "tf.acos is deprecated, please use tf.math.acos instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_Acosh.pbtxt b/tensorflow/core/api_def/python_api/api_def_Acosh.pbtxt new file mode 100644 index 0000000000..7503353e41 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_Acosh.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "Acosh" + endpoint { + name: "math.acosh" + } + endpoint { + name: "acosh" + deprecation_message: "tf.acosh is deprecated, please use tf.math.acosh instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_Add.pbtxt b/tensorflow/core/api_def/python_api/api_def_Add.pbtxt new file mode 100644 index 0000000000..cc5d68b15d --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_Add.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "Add" + endpoint { + name: "math.add" + } + endpoint { + name: "add" + deprecation_message: "tf.add is deprecated, please use tf.math.add instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_AsString.pbtxt b/tensorflow/core/api_def/python_api/api_def_AsString.pbtxt new file mode 100644 index 0000000000..9306eaf373 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_AsString.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "AsString" + endpoint { + name: "dtypes.as_string" + } + endpoint { + name: "as_string" + deprecation_message: "tf.as_string is deprecated, please use tf.dtypes.as_string instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_Asin.pbtxt b/tensorflow/core/api_def/python_api/api_def_Asin.pbtxt new file mode 100644 index 0000000000..7622af7b45 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_Asin.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "Asin" + endpoint { + name: "math.asin" + } + endpoint { + name: "asin" + deprecation_message: "tf.asin is deprecated, please use tf.math.asin instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_Asinh.pbtxt b/tensorflow/core/api_def/python_api/api_def_Asinh.pbtxt new file mode 100644 index 0000000000..395275c21d --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_Asinh.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "Asinh" + endpoint { + name: "math.asinh" + } + endpoint { + name: "asinh" + deprecation_message: "tf.asinh is deprecated, please use tf.math.asinh instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_Atan.pbtxt b/tensorflow/core/api_def/python_api/api_def_Atan.pbtxt new file mode 100644 index 0000000000..dfcd632558 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_Atan.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "Atan" + endpoint { + name: "math.atan" + } + endpoint { + name: "atan" + deprecation_message: "tf.atan is deprecated, please use tf.math.atan instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_Atan2.pbtxt b/tensorflow/core/api_def/python_api/api_def_Atan2.pbtxt new file mode 100644 index 0000000000..fba79507aa --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_Atan2.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "Atan2" + endpoint { + name: "math.atan2" + } + endpoint { + name: "atan2" + deprecation_message: "tf.atan2 is deprecated, please use tf.math.atan2 instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_Atanh.pbtxt b/tensorflow/core/api_def/python_api/api_def_Atanh.pbtxt new file mode 100644 index 0000000000..f7164c33e8 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_Atanh.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "Atanh" + endpoint { + name: "math.atanh" + } + endpoint { + name: "atanh" + deprecation_message: "tf.atanh is deprecated, please use tf.math.atanh instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_BatchToSpaceND.pbtxt b/tensorflow/core/api_def/python_api/api_def_BatchToSpaceND.pbtxt new file mode 100644 index 0000000000..56e49a2221 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_BatchToSpaceND.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "BatchToSpaceND" + endpoint { + name: "manip.batch_to_space_nd" + } + endpoint { + name: "batch_to_space_nd" + deprecation_message: "tf.batch_to_space_nd is deprecated, please use tf.manip.batch_to_space_nd instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_Betainc.pbtxt b/tensorflow/core/api_def/python_api/api_def_Betainc.pbtxt new file mode 100644 index 0000000000..7c37b534c7 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_Betainc.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "Betainc" + endpoint { + name: "math.betainc" + } + endpoint { + name: "betainc" + deprecation_message: "tf.betainc is deprecated, please use tf.math.betainc instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_Ceil.pbtxt b/tensorflow/core/api_def/python_api/api_def_Ceil.pbtxt new file mode 100644 index 0000000000..0c72cf2edd --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_Ceil.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "Ceil" + endpoint { + name: "math.ceil" + } + endpoint { + name: "ceil" + deprecation_message: "tf.ceil is deprecated, please use tf.math.ceil instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_CheckNumerics.pbtxt b/tensorflow/core/api_def/python_api/api_def_CheckNumerics.pbtxt new file mode 100644 index 0000000000..7ea52d30b6 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_CheckNumerics.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "CheckNumerics" + endpoint { + name: "debugging.check_numerics" + } + endpoint { + name: "check_numerics" + deprecation_message: "tf.check_numerics is deprecated, please use tf.debugging.check_numerics instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_Cholesky.pbtxt b/tensorflow/core/api_def/python_api/api_def_Cholesky.pbtxt index 2676c92bfb..568fab4037 100644 --- a/tensorflow/core/api_def/python_api/api_def_Cholesky.pbtxt +++ b/tensorflow/core/api_def/python_api/api_def_Cholesky.pbtxt @@ -1,9 +1,10 @@ op { graph_op_name: "Cholesky" endpoint { - name: "cholesky" + name: "linalg.cholesky" } endpoint { - name: "linalg.cholesky" + name: "cholesky" + deprecation_message: "tf.cholesky is deprecated, please use tf.linalg.cholesky instead." } } diff --git a/tensorflow/core/api_def/python_api/api_def_Cos.pbtxt b/tensorflow/core/api_def/python_api/api_def_Cos.pbtxt new file mode 100644 index 0000000000..6550cd2d4e --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_Cos.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "Cos" + endpoint { + name: "math.cos" + } + endpoint { + name: "cos" + deprecation_message: "tf.cos is deprecated, please use tf.math.cos instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_Cosh.pbtxt b/tensorflow/core/api_def/python_api/api_def_Cosh.pbtxt new file mode 100644 index 0000000000..ef82a45a80 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_Cosh.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "Cosh" + endpoint { + name: "math.cosh" + } + endpoint { + name: "cosh" + deprecation_message: "tf.cosh is deprecated, please use tf.math.cosh instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_Cross.pbtxt b/tensorflow/core/api_def/python_api/api_def_Cross.pbtxt new file mode 100644 index 0000000000..33c1b8c617 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_Cross.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "Cross" + endpoint { + name: "linalg.cross" + } + endpoint { + name: "cross" + deprecation_message: "tf.cross is deprecated, please use tf.linalg.cross instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_DecodeBase64.pbtxt b/tensorflow/core/api_def/python_api/api_def_DecodeBase64.pbtxt new file mode 100644 index 0000000000..55c43ceba2 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_DecodeBase64.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "DecodeBase64" + endpoint { + name: "io.decode_base64" + } + endpoint { + name: "decode_base64" + deprecation_message: "tf.decode_base64 is deprecated, please use tf.io.decode_base64 instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_DecodeCompressed.pbtxt b/tensorflow/core/api_def/python_api/api_def_DecodeCompressed.pbtxt new file mode 100644 index 0000000000..5f6be24cc4 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_DecodeCompressed.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "DecodeCompressed" + endpoint { + name: "io.decode_compressed" + } + endpoint { + name: "decode_compressed" + deprecation_message: "tf.decode_compressed is deprecated, please use tf.io.decode_compressed instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_DecodeJSONExample.pbtxt b/tensorflow/core/api_def/python_api/api_def_DecodeJSONExample.pbtxt new file mode 100644 index 0000000000..3759047f57 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_DecodeJSONExample.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "DecodeJSONExample" + endpoint { + name: "io.decode_json_example" + } + endpoint { + name: "decode_json_example" + deprecation_message: "tf.decode_json_example is deprecated, please use tf.io.decode_json_example instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_DecodeRaw.pbtxt b/tensorflow/core/api_def/python_api/api_def_DecodeRaw.pbtxt new file mode 100644 index 0000000000..a83f702dca --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_DecodeRaw.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "DecodeRaw" + endpoint { + name: "io.decode_raw" + } + endpoint { + name: "decode_raw" + deprecation_message: "tf.decode_raw is deprecated, please use tf.io.decode_raw instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_Dequantize.pbtxt b/tensorflow/core/api_def/python_api/api_def_Dequantize.pbtxt new file mode 100644 index 0000000000..c9b4f76fab --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_Dequantize.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "Dequantize" + endpoint { + name: "quantization.dequantize" + } + endpoint { + name: "dequantize" + deprecation_message: "tf.dequantize is deprecated, please use tf.quantization.dequantize instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_Diag.pbtxt b/tensorflow/core/api_def/python_api/api_def_Diag.pbtxt new file mode 100644 index 0000000000..2043facfa9 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_Diag.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "Diag" + endpoint { + name: "linalg.tensor_diag" + } + endpoint { + name: "diag" + deprecation_message: "tf.diag is deprecated, please use tf.linalg.tensor_diag instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_DiagPart.pbtxt b/tensorflow/core/api_def/python_api/api_def_DiagPart.pbtxt new file mode 100644 index 0000000000..7fa30b2347 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_DiagPart.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "DiagPart" + endpoint { + name: "linalg.tensor_diag_part" + } + endpoint { + name: "diag_part" + deprecation_message: "tf.diag_part is deprecated, please use tf.linalg.tensor_diag_part instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_Digamma.pbtxt b/tensorflow/core/api_def/python_api/api_def_Digamma.pbtxt new file mode 100644 index 0000000000..03f57678a8 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_Digamma.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "Digamma" + endpoint { + name: "math.digamma" + } + endpoint { + name: "digamma" + deprecation_message: "tf.digamma is deprecated, please use tf.math.digamma instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_EncodeBase64.pbtxt b/tensorflow/core/api_def/python_api/api_def_EncodeBase64.pbtxt new file mode 100644 index 0000000000..47b4ab4da4 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_EncodeBase64.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "EncodeBase64" + endpoint { + name: "io.encode_base64" + } + endpoint { + name: "encode_base64" + deprecation_message: "tf.encode_base64 is deprecated, please use tf.io.encode_base64 instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_Equal.pbtxt b/tensorflow/core/api_def/python_api/api_def_Equal.pbtxt new file mode 100644 index 0000000000..2630962f7d --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_Equal.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "Equal" + endpoint { + name: "math.equal" + } + endpoint { + name: "equal" + deprecation_message: "tf.equal is deprecated, please use tf.math.equal instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_Erfc.pbtxt b/tensorflow/core/api_def/python_api/api_def_Erfc.pbtxt new file mode 100644 index 0000000000..6a511b3251 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_Erfc.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "Erfc" + endpoint { + name: "math.erfc" + } + endpoint { + name: "erfc" + deprecation_message: "tf.erfc is deprecated, please use tf.math.erfc instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_Exp.pbtxt b/tensorflow/core/api_def/python_api/api_def_Exp.pbtxt new file mode 100644 index 0000000000..e1fd718ff0 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_Exp.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "Exp" + endpoint { + name: "math.exp" + } + endpoint { + name: "exp" + deprecation_message: "tf.exp is deprecated, please use tf.math.exp instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_Expm1.pbtxt b/tensorflow/core/api_def/python_api/api_def_Expm1.pbtxt new file mode 100644 index 0000000000..ca25706407 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_Expm1.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "Expm1" + endpoint { + name: "math.expm1" + } + endpoint { + name: "expm1" + deprecation_message: "tf.expm1 is deprecated, please use tf.math.expm1 instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_ExtractImagePatches.pbtxt b/tensorflow/core/api_def/python_api/api_def_ExtractImagePatches.pbtxt new file mode 100644 index 0000000000..d302e26ad2 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_ExtractImagePatches.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "ExtractImagePatches" + endpoint { + name: "image.extract_image_patches" + } + endpoint { + name: "extract_image_patches" + deprecation_message: "tf.extract_image_patches is deprecated, please use tf.image.extract_image_patches instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_FFT.pbtxt b/tensorflow/core/api_def/python_api/api_def_FFT.pbtxt index 3bcab99415..57a00a08e3 100644 --- a/tensorflow/core/api_def/python_api/api_def_FFT.pbtxt +++ b/tensorflow/core/api_def/python_api/api_def_FFT.pbtxt @@ -1,9 +1,10 @@ op { graph_op_name: "FFT" endpoint { - name: "fft" + name: "spectral.fft" } endpoint { - name: "spectral.fft" + name: "fft" + deprecation_message: "tf.fft is deprecated, please use tf.spectral.fft instead." } } diff --git a/tensorflow/core/api_def/python_api/api_def_FakeQuantWithMinMaxArgs.pbtxt b/tensorflow/core/api_def/python_api/api_def_FakeQuantWithMinMaxArgs.pbtxt new file mode 100644 index 0000000000..cd14b13675 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_FakeQuantWithMinMaxArgs.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "FakeQuantWithMinMaxArgs" + endpoint { + name: "quantization.fake_quant_with_min_max_args" + } + endpoint { + name: "fake_quant_with_min_max_args" + deprecation_message: "tf.fake_quant_with_min_max_args is deprecated, please use tf.quantization.fake_quant_with_min_max_args instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_FakeQuantWithMinMaxArgsGradient.pbtxt b/tensorflow/core/api_def/python_api/api_def_FakeQuantWithMinMaxArgsGradient.pbtxt new file mode 100644 index 0000000000..d55cb69d1d --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_FakeQuantWithMinMaxArgsGradient.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "FakeQuantWithMinMaxArgsGradient" + endpoint { + name: "quantization.fake_quant_with_min_max_args_gradient" + } + endpoint { + name: "fake_quant_with_min_max_args_gradient" + deprecation_message: "tf.fake_quant_with_min_max_args_gradient is deprecated, please use tf.quantization.fake_quant_with_min_max_args_gradient instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_FakeQuantWithMinMaxVars.pbtxt b/tensorflow/core/api_def/python_api/api_def_FakeQuantWithMinMaxVars.pbtxt new file mode 100644 index 0000000000..6ff4f2cdb2 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_FakeQuantWithMinMaxVars.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "FakeQuantWithMinMaxVars" + endpoint { + name: "quantization.fake_quant_with_min_max_vars" + } + endpoint { + name: "fake_quant_with_min_max_vars" + deprecation_message: "tf.fake_quant_with_min_max_vars is deprecated, please use tf.quantization.fake_quant_with_min_max_vars instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_FakeQuantWithMinMaxVarsGradient.pbtxt b/tensorflow/core/api_def/python_api/api_def_FakeQuantWithMinMaxVarsGradient.pbtxt new file mode 100644 index 0000000000..817a35cc6c --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_FakeQuantWithMinMaxVarsGradient.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "FakeQuantWithMinMaxVarsGradient" + endpoint { + name: "quantization.fake_quant_with_min_max_vars_gradient" + } + endpoint { + name: "fake_quant_with_min_max_vars_gradient" + deprecation_message: "tf.fake_quant_with_min_max_vars_gradient is deprecated, please use tf.quantization.fake_quant_with_min_max_vars_gradient instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_FakeQuantWithMinMaxVarsPerChannel.pbtxt b/tensorflow/core/api_def/python_api/api_def_FakeQuantWithMinMaxVarsPerChannel.pbtxt new file mode 100644 index 0000000000..275c0d5225 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_FakeQuantWithMinMaxVarsPerChannel.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "FakeQuantWithMinMaxVarsPerChannel" + endpoint { + name: "quantization.fake_quant_with_min_max_vars_per_channel" + } + endpoint { + name: "fake_quant_with_min_max_vars_per_channel" + deprecation_message: "tf.fake_quant_with_min_max_vars_per_channel is deprecated, please use tf.quantization.fake_quant_with_min_max_vars_per_channel instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_FakeQuantWithMinMaxVarsPerChannelGradient.pbtxt b/tensorflow/core/api_def/python_api/api_def_FakeQuantWithMinMaxVarsPerChannelGradient.pbtxt new file mode 100644 index 0000000000..897312897f --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_FakeQuantWithMinMaxVarsPerChannelGradient.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "FakeQuantWithMinMaxVarsPerChannelGradient" + endpoint { + name: "quantization.fake_quant_with_min_max_vars_per_channel_gradient" + } + endpoint { + name: "fake_quant_with_min_max_vars_per_channel_gradient" + deprecation_message: "tf.fake_quant_with_min_max_vars_per_channel_gradient is deprecated, please use tf.quantization.fake_quant_with_min_max_vars_per_channel_gradient instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_Floor.pbtxt b/tensorflow/core/api_def/python_api/api_def_Floor.pbtxt new file mode 100644 index 0000000000..788d95edc1 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_Floor.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "Floor" + endpoint { + name: "math.floor" + } + endpoint { + name: "floor" + deprecation_message: "tf.floor is deprecated, please use tf.math.floor instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_GatherNd.pbtxt b/tensorflow/core/api_def/python_api/api_def_GatherNd.pbtxt new file mode 100644 index 0000000000..371dc740df --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_GatherNd.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "GatherNd" + endpoint { + name: "manip.gather_nd" + } + endpoint { + name: "gather_nd" + deprecation_message: "tf.gather_nd is deprecated, please use tf.manip.gather_nd instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_Greater.pbtxt b/tensorflow/core/api_def/python_api/api_def_Greater.pbtxt new file mode 100644 index 0000000000..c8c56515b2 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_Greater.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "Greater" + endpoint { + name: "math.greater" + } + endpoint { + name: "greater" + deprecation_message: "tf.greater is deprecated, please use tf.math.greater instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_GreaterEqual.pbtxt b/tensorflow/core/api_def/python_api/api_def_GreaterEqual.pbtxt new file mode 100644 index 0000000000..ccb390fb3e --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_GreaterEqual.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "GreaterEqual" + endpoint { + name: "math.greater_equal" + } + endpoint { + name: "greater_equal" + deprecation_message: "tf.greater_equal is deprecated, please use tf.math.greater_equal instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_IFFT.pbtxt b/tensorflow/core/api_def/python_api/api_def_IFFT.pbtxt index 6bbc4ed720..267ad8d0a0 100644 --- a/tensorflow/core/api_def/python_api/api_def_IFFT.pbtxt +++ b/tensorflow/core/api_def/python_api/api_def_IFFT.pbtxt @@ -1,9 +1,10 @@ op { graph_op_name: "IFFT" endpoint { - name: "ifft" + name: "spectral.ifft" } endpoint { - name: "spectral.ifft" + name: "ifft" + deprecation_message: "tf.ifft is deprecated, please use tf.spectral.ifft instead." } } diff --git a/tensorflow/core/api_def/python_api/api_def_Igamma.pbtxt b/tensorflow/core/api_def/python_api/api_def_Igamma.pbtxt new file mode 100644 index 0000000000..4e7e3a6e57 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_Igamma.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "Igamma" + endpoint { + name: "math.igamma" + } + endpoint { + name: "igamma" + deprecation_message: "tf.igamma is deprecated, please use tf.math.igamma instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_Igammac.pbtxt b/tensorflow/core/api_def/python_api/api_def_Igammac.pbtxt new file mode 100644 index 0000000000..ea92a0916b --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_Igammac.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "Igammac" + endpoint { + name: "math.igammac" + } + endpoint { + name: "igammac" + deprecation_message: "tf.igammac is deprecated, please use tf.math.igammac instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_InvertPermutation.pbtxt b/tensorflow/core/api_def/python_api/api_def_InvertPermutation.pbtxt new file mode 100644 index 0000000000..bce642b96a --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_InvertPermutation.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "InvertPermutation" + endpoint { + name: "math.invert_permutation" + } + endpoint { + name: "invert_permutation" + deprecation_message: "tf.invert_permutation is deprecated, please use tf.math.invert_permutation instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_IsFinite.pbtxt b/tensorflow/core/api_def/python_api/api_def_IsFinite.pbtxt new file mode 100644 index 0000000000..a2c12f2ea0 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_IsFinite.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "IsFinite" + endpoint { + name: "debugging.is_finite" + } + endpoint { + name: "is_finite" + deprecation_message: "tf.is_finite is deprecated, please use tf.debugging.is_finite instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_IsInf.pbtxt b/tensorflow/core/api_def/python_api/api_def_IsInf.pbtxt new file mode 100644 index 0000000000..7c29811fd7 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_IsInf.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "IsInf" + endpoint { + name: "debugging.is_inf" + } + endpoint { + name: "is_inf" + deprecation_message: "tf.is_inf is deprecated, please use tf.debugging.is_inf instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_IsNan.pbtxt b/tensorflow/core/api_def/python_api/api_def_IsNan.pbtxt new file mode 100644 index 0000000000..459cf3ccbd --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_IsNan.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "IsNan" + endpoint { + name: "debugging.is_nan" + } + endpoint { + name: "is_nan" + deprecation_message: "tf.is_nan is deprecated, please use tf.debugging.is_nan instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_Less.pbtxt b/tensorflow/core/api_def/python_api/api_def_Less.pbtxt new file mode 100644 index 0000000000..15cbdc6d8e --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_Less.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "Less" + endpoint { + name: "math.less" + } + endpoint { + name: "less" + deprecation_message: "tf.less is deprecated, please use tf.math.less instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_LessEqual.pbtxt b/tensorflow/core/api_def/python_api/api_def_LessEqual.pbtxt new file mode 100644 index 0000000000..35aa18698f --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_LessEqual.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "LessEqual" + endpoint { + name: "math.less_equal" + } + endpoint { + name: "less_equal" + deprecation_message: "tf.less_equal is deprecated, please use tf.math.less_equal instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_Lgamma.pbtxt b/tensorflow/core/api_def/python_api/api_def_Lgamma.pbtxt new file mode 100644 index 0000000000..89886b09d3 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_Lgamma.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "Lgamma" + endpoint { + name: "math.lgamma" + } + endpoint { + name: "lgamma" + deprecation_message: "tf.lgamma is deprecated, please use tf.math.lgamma instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_Log.pbtxt b/tensorflow/core/api_def/python_api/api_def_Log.pbtxt new file mode 100644 index 0000000000..fb82aa7e43 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_Log.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "Log" + endpoint { + name: "math.log" + } + endpoint { + name: "log" + deprecation_message: "tf.log is deprecated, please use tf.math.log instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_Log1p.pbtxt b/tensorflow/core/api_def/python_api/api_def_Log1p.pbtxt new file mode 100644 index 0000000000..6b451aa546 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_Log1p.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "Log1p" + endpoint { + name: "math.log1p" + } + endpoint { + name: "log1p" + deprecation_message: "tf.log1p is deprecated, please use tf.math.log1p instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_LogicalAnd.pbtxt b/tensorflow/core/api_def/python_api/api_def_LogicalAnd.pbtxt new file mode 100644 index 0000000000..403a8c71ff --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_LogicalAnd.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "LogicalAnd" + endpoint { + name: "math.logical_and" + } + endpoint { + name: "logical_and" + deprecation_message: "tf.logical_and is deprecated, please use tf.math.logical_and instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_LogicalNot.pbtxt b/tensorflow/core/api_def/python_api/api_def_LogicalNot.pbtxt new file mode 100644 index 0000000000..f228958c77 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_LogicalNot.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "LogicalNot" + endpoint { + name: "math.logical_not" + } + endpoint { + name: "logical_not" + deprecation_message: "tf.logical_not is deprecated, please use tf.math.logical_not instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_LogicalOr.pbtxt b/tensorflow/core/api_def/python_api/api_def_LogicalOr.pbtxt new file mode 100644 index 0000000000..ab89f236e7 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_LogicalOr.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "LogicalOr" + endpoint { + name: "math.logical_or" + } + endpoint { + name: "logical_or" + deprecation_message: "tf.logical_or is deprecated, please use tf.math.logical_or instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_MatchingFiles.pbtxt b/tensorflow/core/api_def/python_api/api_def_MatchingFiles.pbtxt new file mode 100644 index 0000000000..8930d66940 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_MatchingFiles.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "MatchingFiles" + endpoint { + name: "io.matching_files" + } + endpoint { + name: "matching_files" + deprecation_message: "tf.matching_files is deprecated, please use tf.io.matching_files instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_MatrixBandPart.pbtxt b/tensorflow/core/api_def/python_api/api_def_MatrixBandPart.pbtxt index 89b1c1f5a9..bad2f03f32 100644 --- a/tensorflow/core/api_def/python_api/api_def_MatrixBandPart.pbtxt +++ b/tensorflow/core/api_def/python_api/api_def_MatrixBandPart.pbtxt @@ -5,5 +5,6 @@ op { } endpoint { name: "matrix_band_part" + deprecation_message: "tf.matrix_band_part is deprecated, please use tf.linalg.band_part instead." } } diff --git a/tensorflow/core/api_def/python_api/api_def_MatrixDeterminant.pbtxt b/tensorflow/core/api_def/python_api/api_def_MatrixDeterminant.pbtxt index 4d289f542f..d241d4d721 100644 --- a/tensorflow/core/api_def/python_api/api_def_MatrixDeterminant.pbtxt +++ b/tensorflow/core/api_def/python_api/api_def_MatrixDeterminant.pbtxt @@ -5,5 +5,6 @@ op { } endpoint { name: "matrix_determinant" + deprecation_message: "tf.matrix_determinant is deprecated, please use tf.linalg.det instead." } } diff --git a/tensorflow/core/api_def/python_api/api_def_MatrixDiag.pbtxt b/tensorflow/core/api_def/python_api/api_def_MatrixDiag.pbtxt index fd9d34635e..208b37e297 100644 --- a/tensorflow/core/api_def/python_api/api_def_MatrixDiag.pbtxt +++ b/tensorflow/core/api_def/python_api/api_def_MatrixDiag.pbtxt @@ -5,5 +5,6 @@ op { } endpoint { name: "matrix_diag" + deprecation_message: "tf.matrix_diag is deprecated, please use tf.linalg.diag instead." } } diff --git a/tensorflow/core/api_def/python_api/api_def_MatrixDiagPart.pbtxt b/tensorflow/core/api_def/python_api/api_def_MatrixDiagPart.pbtxt index fa5d1f10af..a8a50e8a89 100644 --- a/tensorflow/core/api_def/python_api/api_def_MatrixDiagPart.pbtxt +++ b/tensorflow/core/api_def/python_api/api_def_MatrixDiagPart.pbtxt @@ -5,5 +5,6 @@ op { } endpoint { name: "matrix_diag_part" + deprecation_message: "tf.matrix_diag_part is deprecated, please use tf.linalg.diag_part instead." } } diff --git a/tensorflow/core/api_def/python_api/api_def_MatrixInverse.pbtxt b/tensorflow/core/api_def/python_api/api_def_MatrixInverse.pbtxt index c0ddd73704..944513fcd9 100644 --- a/tensorflow/core/api_def/python_api/api_def_MatrixInverse.pbtxt +++ b/tensorflow/core/api_def/python_api/api_def_MatrixInverse.pbtxt @@ -5,5 +5,6 @@ op { } endpoint { name: "matrix_inverse" + deprecation_message: "tf.matrix_inverse is deprecated, please use tf.linalg.inv instead." } } diff --git a/tensorflow/core/api_def/python_api/api_def_MatrixSetDiag.pbtxt b/tensorflow/core/api_def/python_api/api_def_MatrixSetDiag.pbtxt index 01f4f0e89d..a6080dbc2d 100644 --- a/tensorflow/core/api_def/python_api/api_def_MatrixSetDiag.pbtxt +++ b/tensorflow/core/api_def/python_api/api_def_MatrixSetDiag.pbtxt @@ -5,5 +5,6 @@ op { } endpoint { name: "matrix_set_diag" + deprecation_message: "tf.matrix_set_diag is deprecated, please use tf.linalg.set_diag instead." } } diff --git a/tensorflow/core/api_def/python_api/api_def_MatrixSolve.pbtxt b/tensorflow/core/api_def/python_api/api_def_MatrixSolve.pbtxt index cef763e4e9..caba80326b 100644 --- a/tensorflow/core/api_def/python_api/api_def_MatrixSolve.pbtxt +++ b/tensorflow/core/api_def/python_api/api_def_MatrixSolve.pbtxt @@ -5,5 +5,6 @@ op { } endpoint { name: "matrix_solve" + deprecation_message: "tf.matrix_solve is deprecated, please use tf.linalg.solve instead." } } diff --git a/tensorflow/core/api_def/python_api/api_def_MatrixTriangularSolve.pbtxt b/tensorflow/core/api_def/python_api/api_def_MatrixTriangularSolve.pbtxt index a0d576aa31..a4dfa538ed 100644 --- a/tensorflow/core/api_def/python_api/api_def_MatrixTriangularSolve.pbtxt +++ b/tensorflow/core/api_def/python_api/api_def_MatrixTriangularSolve.pbtxt @@ -5,5 +5,6 @@ op { } endpoint { name: "matrix_triangular_solve" + deprecation_message: "tf.matrix_triangular_solve is deprecated, please use tf.linalg.triangular_solve instead." } } diff --git a/tensorflow/core/api_def/python_api/api_def_Maximum.pbtxt b/tensorflow/core/api_def/python_api/api_def_Maximum.pbtxt new file mode 100644 index 0000000000..90af9e145b --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_Maximum.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "Maximum" + endpoint { + name: "math.maximum" + } + endpoint { + name: "maximum" + deprecation_message: "tf.maximum is deprecated, please use tf.math.maximum instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_Minimum.pbtxt b/tensorflow/core/api_def/python_api/api_def_Minimum.pbtxt new file mode 100644 index 0000000000..33bcd6f667 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_Minimum.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "Minimum" + endpoint { + name: "math.minimum" + } + endpoint { + name: "minimum" + deprecation_message: "tf.minimum is deprecated, please use tf.math.minimum instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_NotEqual.pbtxt b/tensorflow/core/api_def/python_api/api_def_NotEqual.pbtxt new file mode 100644 index 0000000000..385565daaf --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_NotEqual.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "NotEqual" + endpoint { + name: "math.not_equal" + } + endpoint { + name: "not_equal" + deprecation_message: "tf.not_equal is deprecated, please use tf.math.not_equal instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_ParseTensor.pbtxt b/tensorflow/core/api_def/python_api/api_def_ParseTensor.pbtxt new file mode 100644 index 0000000000..29f02ab1ac --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_ParseTensor.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "ParseTensor" + endpoint { + name: "io.parse_tensor" + } + endpoint { + name: "parse_tensor" + deprecation_message: "tf.parse_tensor is deprecated, please use tf.io.parse_tensor instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_Polygamma.pbtxt b/tensorflow/core/api_def/python_api/api_def_Polygamma.pbtxt new file mode 100644 index 0000000000..567a448642 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_Polygamma.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "Polygamma" + endpoint { + name: "math.polygamma" + } + endpoint { + name: "polygamma" + deprecation_message: "tf.polygamma is deprecated, please use tf.math.polygamma instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_Qr.pbtxt b/tensorflow/core/api_def/python_api/api_def_Qr.pbtxt index b19da0d817..a9371b5d9b 100644 --- a/tensorflow/core/api_def/python_api/api_def_Qr.pbtxt +++ b/tensorflow/core/api_def/python_api/api_def_Qr.pbtxt @@ -5,5 +5,6 @@ op { } endpoint { name: "qr" + deprecation_message: "tf.qr is deprecated, please use tf.linalg.qr instead." } } diff --git a/tensorflow/core/api_def/python_api/api_def_QuantizedConcat.pbtxt b/tensorflow/core/api_def/python_api/api_def_QuantizedConcat.pbtxt new file mode 100644 index 0000000000..44508ef079 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_QuantizedConcat.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "QuantizedConcat" + endpoint { + name: "quantization.quantized_concat" + } + endpoint { + name: "quantized_concat" + deprecation_message: "tf.quantized_concat is deprecated, please use tf.quantization.quantized_concat instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_ReadFile.pbtxt b/tensorflow/core/api_def/python_api/api_def_ReadFile.pbtxt new file mode 100644 index 0000000000..7c38fae31c --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_ReadFile.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "ReadFile" + endpoint { + name: "io.read_file" + } + endpoint { + name: "read_file" + deprecation_message: "tf.read_file is deprecated, please use tf.io.read_file instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_Reciprocal.pbtxt b/tensorflow/core/api_def/python_api/api_def_Reciprocal.pbtxt new file mode 100644 index 0000000000..0f37e99f4f --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_Reciprocal.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "Reciprocal" + endpoint { + name: "math.reciprocal" + } + endpoint { + name: "reciprocal" + deprecation_message: "tf.reciprocal is deprecated, please use tf.math.reciprocal instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_RegexReplace.pbtxt b/tensorflow/core/api_def/python_api/api_def_RegexReplace.pbtxt new file mode 100644 index 0000000000..6938e20e57 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_RegexReplace.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "RegexReplace" + endpoint { + name: "strings.regex_replace" + } + endpoint { + name: "regex_replace" + deprecation_message: "tf.regex_replace is deprecated, please use tf.strings.regex_replace instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_Reshape.pbtxt b/tensorflow/core/api_def/python_api/api_def_Reshape.pbtxt new file mode 100644 index 0000000000..907d95a6f0 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_Reshape.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "Reshape" + endpoint { + name: "manip.reshape" + } + endpoint { + name: "reshape" + deprecation_message: "tf.reshape is deprecated, please use tf.manip.reshape instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_ReverseV2.pbtxt b/tensorflow/core/api_def/python_api/api_def_ReverseV2.pbtxt index 8307a3c2dd..bbe9e97d60 100644 --- a/tensorflow/core/api_def/python_api/api_def_ReverseV2.pbtxt +++ b/tensorflow/core/api_def/python_api/api_def_ReverseV2.pbtxt @@ -1,6 +1,14 @@ op { graph_op_name: "ReverseV2" + endpoint { + name: "manip.reverse" + } + endpoint { + name: "reverse" + deprecation_message: "tf.reverse is deprecated, please use tf.manip.reverse instead." + } endpoint { name: "reverse_v2" + deprecation_message: "tf.reverse_v2 is deprecated, please use tf.manip.reverse instead." } } diff --git a/tensorflow/core/api_def/python_api/api_def_Rint.pbtxt b/tensorflow/core/api_def/python_api/api_def_Rint.pbtxt new file mode 100644 index 0000000000..4330a80d04 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_Rint.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "Rint" + endpoint { + name: "math.rint" + } + endpoint { + name: "rint" + deprecation_message: "tf.rint is deprecated, please use tf.math.rint instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_Rsqrt.pbtxt b/tensorflow/core/api_def/python_api/api_def_Rsqrt.pbtxt new file mode 100644 index 0000000000..6a45f4aff5 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_Rsqrt.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "Rsqrt" + endpoint { + name: "math.rsqrt" + } + endpoint { + name: "rsqrt" + deprecation_message: "tf.rsqrt is deprecated, please use tf.math.rsqrt instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_ScatterNd.pbtxt b/tensorflow/core/api_def/python_api/api_def_ScatterNd.pbtxt new file mode 100644 index 0000000000..cabf171cb0 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_ScatterNd.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "ScatterNd" + endpoint { + name: "manip.scatter_nd" + } + endpoint { + name: "scatter_nd" + deprecation_message: "tf.scatter_nd is deprecated, please use tf.manip.scatter_nd instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_SegmentMax.pbtxt b/tensorflow/core/api_def/python_api/api_def_SegmentMax.pbtxt new file mode 100644 index 0000000000..65e34a1fcf --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_SegmentMax.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "SegmentMax" + endpoint { + name: "math.segment_max" + } + endpoint { + name: "segment_max" + deprecation_message: "tf.segment_max is deprecated, please use tf.math.segment_max instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_SegmentMean.pbtxt b/tensorflow/core/api_def/python_api/api_def_SegmentMean.pbtxt new file mode 100644 index 0000000000..f1e19c5571 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_SegmentMean.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "SegmentMean" + endpoint { + name: "math.segment_mean" + } + endpoint { + name: "segment_mean" + deprecation_message: "tf.segment_mean is deprecated, please use tf.math.segment_mean instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_SegmentMin.pbtxt b/tensorflow/core/api_def/python_api/api_def_SegmentMin.pbtxt new file mode 100644 index 0000000000..fd9a3c380d --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_SegmentMin.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "SegmentMin" + endpoint { + name: "math.segment_min" + } + endpoint { + name: "segment_min" + deprecation_message: "tf.segment_min is deprecated, please use tf.math.segment_min instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_SegmentProd.pbtxt b/tensorflow/core/api_def/python_api/api_def_SegmentProd.pbtxt new file mode 100644 index 0000000000..f2be8baafc --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_SegmentProd.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "SegmentProd" + endpoint { + name: "math.segment_prod" + } + endpoint { + name: "segment_prod" + deprecation_message: "tf.segment_prod is deprecated, please use tf.math.segment_prod instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_SegmentSum.pbtxt b/tensorflow/core/api_def/python_api/api_def_SegmentSum.pbtxt new file mode 100644 index 0000000000..c7cc1d0c9f --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_SegmentSum.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "SegmentSum" + endpoint { + name: "math.segment_sum" + } + endpoint { + name: "segment_sum" + deprecation_message: "tf.segment_sum is deprecated, please use tf.math.segment_sum instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_Sin.pbtxt b/tensorflow/core/api_def/python_api/api_def_Sin.pbtxt new file mode 100644 index 0000000000..0794334987 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_Sin.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "Sin" + endpoint { + name: "math.sin" + } + endpoint { + name: "sin" + deprecation_message: "tf.sin is deprecated, please use tf.math.sin instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_Sinh.pbtxt b/tensorflow/core/api_def/python_api/api_def_Sinh.pbtxt new file mode 100644 index 0000000000..c42f8678c6 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_Sinh.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "Sinh" + endpoint { + name: "math.sinh" + } + endpoint { + name: "sinh" + deprecation_message: "tf.sinh is deprecated, please use tf.math.sinh instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_Softplus.pbtxt b/tensorflow/core/api_def/python_api/api_def_Softplus.pbtxt index 2de56c27be..c4da47241b 100644 --- a/tensorflow/core/api_def/python_api/api_def_Softplus.pbtxt +++ b/tensorflow/core/api_def/python_api/api_def_Softplus.pbtxt @@ -1,5 +1,8 @@ op { graph_op_name: "Softplus" + endpoint { + name: "math.softplus" + } endpoint { name: "nn.softplus" } diff --git a/tensorflow/core/api_def/python_api/api_def_Softsign.pbtxt b/tensorflow/core/api_def/python_api/api_def_Softsign.pbtxt index b47412d135..852d205024 100644 --- a/tensorflow/core/api_def/python_api/api_def_Softsign.pbtxt +++ b/tensorflow/core/api_def/python_api/api_def_Softsign.pbtxt @@ -3,4 +3,7 @@ op { endpoint { name: "nn.softsign" } + endpoint { + name: "math.softsign" + } } diff --git a/tensorflow/core/api_def/python_api/api_def_SpaceToBatchND.pbtxt b/tensorflow/core/api_def/python_api/api_def_SpaceToBatchND.pbtxt new file mode 100644 index 0000000000..63a7547e14 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_SpaceToBatchND.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "SpaceToBatchND" + endpoint { + name: "manip.space_to_batch_nd" + } + endpoint { + name: "space_to_batch_nd" + deprecation_message: "tf.space_to_batch_nd is deprecated, please use tf.manip.space_to_batch_nd instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_SquaredDifference.pbtxt b/tensorflow/core/api_def/python_api/api_def_SquaredDifference.pbtxt new file mode 100644 index 0000000000..01a33a3346 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_SquaredDifference.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "SquaredDifference" + endpoint { + name: "math.squared_difference" + } + endpoint { + name: "squared_difference" + deprecation_message: "tf.squared_difference is deprecated, please use tf.math.squared_difference instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_StringJoin.pbtxt b/tensorflow/core/api_def/python_api/api_def_StringJoin.pbtxt new file mode 100644 index 0000000000..53c1b8053d --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_StringJoin.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "StringJoin" + endpoint { + name: "strings.join" + } + endpoint { + name: "string_join" + deprecation_message: "tf.string_join is deprecated, please use tf.strings.join instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_StringStrip.pbtxt b/tensorflow/core/api_def/python_api/api_def_StringStrip.pbtxt new file mode 100644 index 0000000000..364806e1f5 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_StringStrip.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "StringStrip" + endpoint { + name: "strings.strip" + } + endpoint { + name: "string_strip" + deprecation_message: "tf.string_strip is deprecated, please use tf.strings.strip instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_StringToHashBucket.pbtxt b/tensorflow/core/api_def/python_api/api_def_StringToHashBucket.pbtxt new file mode 100644 index 0000000000..b0e93d2b22 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_StringToHashBucket.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "StringToHashBucket" + endpoint { + name: "strings.to_hash_bucket" + } + endpoint { + name: "string_to_hash_bucket" + deprecation_message: "tf.string_to_hash_bucket is deprecated, please use tf.strings.to_hash_bucket instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_StringToHashBucketFast.pbtxt b/tensorflow/core/api_def/python_api/api_def_StringToHashBucketFast.pbtxt new file mode 100644 index 0000000000..9576e1a9de --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_StringToHashBucketFast.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "StringToHashBucketFast" + endpoint { + name: "strings.to_hash_bucket_fast" + } + endpoint { + name: "string_to_hash_bucket_fast" + deprecation_message: "tf.string_to_hash_bucket_fast is deprecated, please use tf.strings.to_hash_bucket_fast instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_StringToHashBucketStrong.pbtxt b/tensorflow/core/api_def/python_api/api_def_StringToHashBucketStrong.pbtxt new file mode 100644 index 0000000000..e8c7c12608 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_StringToHashBucketStrong.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "StringToHashBucketStrong" + endpoint { + name: "strings.to_hash_bucket_strong" + } + endpoint { + name: "string_to_hash_bucket_strong" + deprecation_message: "tf.string_to_hash_bucket_strong is deprecated, please use tf.strings.to_hash_bucket_strong instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_StringToNumber.pbtxt b/tensorflow/core/api_def/python_api/api_def_StringToNumber.pbtxt new file mode 100644 index 0000000000..9de1ca0b30 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_StringToNumber.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "StringToNumber" + endpoint { + name: "strings.to_number" + } + endpoint { + name: "string_to_number" + deprecation_message: "tf.string_to_number is deprecated, please use tf.strings.to_number instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_Substr.pbtxt b/tensorflow/core/api_def/python_api/api_def_Substr.pbtxt new file mode 100644 index 0000000000..25d1bb3f51 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_Substr.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "Substr" + endpoint { + name: "strings.substr" + } + endpoint { + name: "substr" + deprecation_message: "tf.substr is deprecated, please use tf.strings.substr instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_Tan.pbtxt b/tensorflow/core/api_def/python_api/api_def_Tan.pbtxt new file mode 100644 index 0000000000..8bcf381dd4 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_Tan.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "Tan" + endpoint { + name: "math.tan" + } + endpoint { + name: "tan" + deprecation_message: "tf.tan is deprecated, please use tf.math.tan instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_Tile.pbtxt b/tensorflow/core/api_def/python_api/api_def_Tile.pbtxt new file mode 100644 index 0000000000..0b9053a529 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_Tile.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "Tile" + endpoint { + name: "manip.tile" + } + endpoint { + name: "tile" + deprecation_message: "tf.tile is deprecated, please use tf.manip.tile instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_UnsortedSegmentMax.pbtxt b/tensorflow/core/api_def/python_api/api_def_UnsortedSegmentMax.pbtxt new file mode 100644 index 0000000000..1ea59d2e63 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_UnsortedSegmentMax.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "UnsortedSegmentMax" + endpoint { + name: "math.unsorted_segment_max" + } + endpoint { + name: "unsorted_segment_max" + deprecation_message: "tf.unsorted_segment_max is deprecated, please use tf.math.unsorted_segment_max instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_UnsortedSegmentMin.pbtxt b/tensorflow/core/api_def/python_api/api_def_UnsortedSegmentMin.pbtxt new file mode 100644 index 0000000000..9857def6fe --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_UnsortedSegmentMin.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "UnsortedSegmentMin" + endpoint { + name: "math.unsorted_segment_min" + } + endpoint { + name: "unsorted_segment_min" + deprecation_message: "tf.unsorted_segment_min is deprecated, please use tf.math.unsorted_segment_min instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_UnsortedSegmentProd.pbtxt b/tensorflow/core/api_def/python_api/api_def_UnsortedSegmentProd.pbtxt new file mode 100644 index 0000000000..d9e3f7be69 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_UnsortedSegmentProd.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "UnsortedSegmentProd" + endpoint { + name: "math.unsorted_segment_prod" + } + endpoint { + name: "unsorted_segment_prod" + deprecation_message: "tf.unsorted_segment_prod is deprecated, please use tf.math.unsorted_segment_prod instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_UnsortedSegmentSum.pbtxt b/tensorflow/core/api_def/python_api/api_def_UnsortedSegmentSum.pbtxt new file mode 100644 index 0000000000..0cffd12404 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_UnsortedSegmentSum.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "UnsortedSegmentSum" + endpoint { + name: "math.unsorted_segment_sum" + } + endpoint { + name: "unsorted_segment_sum" + deprecation_message: "tf.unsorted_segment_sum is deprecated, please use tf.math.unsorted_segment_sum instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_WriteFile.pbtxt b/tensorflow/core/api_def/python_api/api_def_WriteFile.pbtxt new file mode 100644 index 0000000000..f28a9151ca --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_WriteFile.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "WriteFile" + endpoint { + name: "io.write_file" + } + endpoint { + name: "write_file" + deprecation_message: "tf.write_file is deprecated, please use tf.io.write_file instead." + } +} diff --git a/tensorflow/core/api_def/python_api/api_def_Zeta.pbtxt b/tensorflow/core/api_def/python_api/api_def_Zeta.pbtxt new file mode 100644 index 0000000000..a84ffcdf14 --- /dev/null +++ b/tensorflow/core/api_def/python_api/api_def_Zeta.pbtxt @@ -0,0 +1,10 @@ +op { + graph_op_name: "Zeta" + endpoint { + name: "math.zeta" + } + endpoint { + name: "zeta" + deprecation_message: "tf.zeta is deprecated, please use tf.math.zeta instead." + } +} diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py index fae63b1132..361667ec49 100644 --- a/tensorflow/python/ops/array_ops.py +++ b/tensorflow/python/ops/array_ops.py @@ -41,6 +41,7 @@ from tensorflow.python.ops import gen_math_ops # go/tf-wildcard-import # pylint: disable=wildcard-import from tensorflow.python.ops.gen_array_ops import * +from tensorflow.python.ops.gen_array_ops import reverse_v2 as reverse # pylint: disable=unused-import from tensorflow.python.util import deprecation from tensorflow.python.util.tf_export import tf_export # pylint: enable=wildcard-import @@ -2609,14 +2610,6 @@ def where(condition, x=None, y=None, name=None): raise ValueError("x and y must both be non-None or both be None.") -@tf_export("reverse") -def reverse(tensor, axis, name=None): - return gen_array_ops.reverse_v2(tensor, axis, name) - - -reverse.__doc__ = gen_array_ops.reverse_v2.__doc__ - - # pylint: disable=redefined-builtin @tf_export("reverse_sequence") @deprecation.deprecated_args( diff --git a/tensorflow/tools/api/generator/api_gen.bzl b/tensorflow/tools/api/generator/api_gen.bzl index 41713a94ec..b7ebcb976b 100644 --- a/tensorflow/tools/api/generator/api_gen.bzl +++ b/tensorflow/tools/api/generator/api_gen.bzl @@ -8,13 +8,16 @@ TENSORFLOW_API_INIT_FILES = [ "bitwise/__init__.py", "compat/__init__.py", "data/__init__.py", + "debugging/__init__.py", "distributions/__init__.py", "distributions/bijectors/__init__.py", + "dtypes/__init__.py", "errors/__init__.py", "feature_column/__init__.py", "gfile/__init__.py", "graph_util/__init__.py", "image/__init__.py", + "io/__init__.py", "initializers/__init__.py", "keras/__init__.py", "keras/activations/__init__.py", @@ -65,6 +68,7 @@ TENSORFLOW_API_INIT_FILES = [ "nn/rnn_cell/__init__.py", "profiler/__init__.py", "python_io/__init__.py", + "quantization/__init__.py", "resource_loader/__init__.py", "strings/__init__.py", "saved_model/__init__.py", @@ -114,22 +118,24 @@ ESTIMATOR_API_INIT_FILES = [ # template will be replaced with root imports collected by this genrule. # srcs: genrule sources. If passing root_init_template, the template file # must be included in sources. -def gen_api_init_files(name, - output_files=TENSORFLOW_API_INIT_FILES, - root_init_template=None, - srcs=[], - api_name="tensorflow", - package="tensorflow.python"): - root_init_template_flag = "" - if root_init_template: - root_init_template_flag = "--root_init_template=$(location " + root_init_template + ")" - native.genrule( - name = name, - outs = output_files, - cmd = ( - "$(location //tensorflow/tools/api/generator:create_python_api) " + - root_init_template_flag + " --apidir=$(@D) --apiname=" + api_name + " --package=" + package + " $(OUTS)"), - srcs = srcs, - tools = ["//tensorflow/tools/api/generator:create_python_api"], - visibility = ["//tensorflow:__pkg__"], - ) +def gen_api_init_files( + name, + output_files = TENSORFLOW_API_INIT_FILES, + root_init_template = None, + srcs = [], + api_name = "tensorflow", + package = "tensorflow.python"): + root_init_template_flag = "" + if root_init_template: + root_init_template_flag = "--root_init_template=$(location " + root_init_template + ")" + native.genrule( + name = name, + outs = output_files, + cmd = ( + "$(location //tensorflow/tools/api/generator:create_python_api) " + + root_init_template_flag + " --apidir=$(@D) --apiname=" + api_name + " --package=" + package + " $(OUTS)" + ), + srcs = srcs, + tools = ["//tensorflow/tools/api/generator:create_python_api"], + visibility = ["//tensorflow:__pkg__"], + ) diff --git a/tensorflow/tools/api/golden/tensorflow.debugging.pbtxt b/tensorflow/tools/api/golden/tensorflow.debugging.pbtxt new file mode 100644 index 0000000000..d9efe97821 --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.debugging.pbtxt @@ -0,0 +1,19 @@ +path: "tensorflow.debugging" +tf_module { + member_method { + name: "check_numerics" + argspec: "args=[\'tensor\', \'message\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "is_finite" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "is_inf" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "is_nan" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.dtypes.pbtxt b/tensorflow/tools/api/golden/tensorflow.dtypes.pbtxt new file mode 100644 index 0000000000..98e1feed00 --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.dtypes.pbtxt @@ -0,0 +1,7 @@ +path: "tensorflow.dtypes" +tf_module { + member_method { + name: "as_string" + argspec: "args=[\'input\', \'precision\', \'scientific\', \'shortest\', \'width\', \'fill\', \'name\'], varargs=None, keywords=None, defaults=[\'-1\', \'False\', \'False\', \'-1\', \'\', \'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.image.pbtxt b/tensorflow/tools/api/golden/tensorflow.image.pbtxt index 10171b3d60..5398d3cf28 100644 --- a/tensorflow/tools/api/golden/tensorflow.image.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.image.pbtxt @@ -84,6 +84,10 @@ tf_module { name: "extract_glimpse" argspec: "args=[\'input\', \'size\', \'offsets\', \'centered\', \'normalized\', \'uniform_noise\', \'name\'], varargs=None, keywords=None, defaults=[\'True\', \'True\', \'True\', \'None\'], " } + member_method { + name: "extract_image_patches" + argspec: "args=[\'images\', \'ksizes\', \'strides\', \'rates\', \'padding\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } member_method { name: "extract_jpeg_shape" argspec: "args=[\'contents\', \'output_type\', \'name\'], varargs=None, keywords=None, defaults=[\"\", \'None\'], " diff --git a/tensorflow/tools/api/golden/tensorflow.io.pbtxt b/tensorflow/tools/api/golden/tensorflow.io.pbtxt new file mode 100644 index 0000000000..3a36c168aa --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.io.pbtxt @@ -0,0 +1,39 @@ +path: "tensorflow.io" +tf_module { + member_method { + name: "decode_base64" + argspec: "args=[\'input\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "decode_compressed" + argspec: "args=[\'bytes\', \'compression_type\', \'name\'], varargs=None, keywords=None, defaults=[\'\', \'None\'], " + } + member_method { + name: "decode_json_example" + argspec: "args=[\'json_examples\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "decode_raw" + argspec: "args=[\'bytes\', \'out_type\', \'little_endian\', \'name\'], varargs=None, keywords=None, defaults=[\'True\', \'None\'], " + } + member_method { + name: "encode_base64" + argspec: "args=[\'input\', \'pad\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'None\'], " + } + member_method { + name: "matching_files" + argspec: "args=[\'pattern\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "parse_tensor" + argspec: "args=[\'serialized\', \'out_type\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "read_file" + argspec: "args=[\'filename\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "write_file" + argspec: "args=[\'filename\', \'contents\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.linalg.pbtxt b/tensorflow/tools/api/golden/tensorflow.linalg.pbtxt index 00b9238543..3b5845f99a 100644 --- a/tensorflow/tools/api/golden/tensorflow.linalg.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.linalg.pbtxt @@ -68,6 +68,10 @@ tf_module { name: "cholesky_solve" argspec: "args=[\'chol\', \'rhs\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } + member_method { + name: "cross" + argspec: "args=[\'a\', \'b\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } member_method { name: "det" argspec: "args=[\'input\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " @@ -140,6 +144,14 @@ tf_module { name: "svd" argspec: "args=[\'tensor\', \'full_matrices\', \'compute_uv\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'True\', \'None\'], " } + member_method { + name: "tensor_diag" + argspec: "args=[\'diagonal\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "tensor_diag_part" + argspec: "args=[\'input\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } member_method { name: "tensordot" argspec: "args=[\'a\', \'b\', \'axes\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " diff --git a/tensorflow/tools/api/golden/tensorflow.manip.pbtxt b/tensorflow/tools/api/golden/tensorflow.manip.pbtxt index 0b84165285..9add462396 100644 --- a/tensorflow/tools/api/golden/tensorflow.manip.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.manip.pbtxt @@ -1,7 +1,35 @@ path: "tensorflow.manip" tf_module { + member_method { + name: "batch_to_space_nd" + argspec: "args=[\'input\', \'block_shape\', \'crops\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "gather_nd" + argspec: "args=[\'params\', \'indices\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "reshape" + argspec: "args=[\'tensor\', \'shape\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "reverse" + argspec: "args=[\'tensor\', \'axis\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } member_method { name: "roll" argspec: "args=[\'input\', \'shift\', \'axis\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "scatter_nd" + argspec: "args=[\'indices\', \'updates\', \'shape\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "space_to_batch_nd" + argspec: "args=[\'input\', \'block_shape\', \'paddings\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "tile" + argspec: "args=[\'input\', \'multiples\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } } diff --git a/tensorflow/tools/api/golden/tensorflow.math.pbtxt b/tensorflow/tools/api/golden/tensorflow.math.pbtxt index 03fbf6266d..25573cb494 100644 --- a/tensorflow/tools/api/golden/tensorflow.math.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.math.pbtxt @@ -1,5 +1,37 @@ path: "tensorflow.math" tf_module { + member_method { + name: "acos" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "acosh" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add" + argspec: "args=[\'x\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "asin" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "asinh" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "atan" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "atan2" + argspec: "args=[\'y\', \'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "atanh" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } member_method { name: "bessel_i0" argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'bessel_i0\'], " @@ -16,8 +48,192 @@ tf_module { name: "bessel_i1e" argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } + member_method { + name: "betainc" + argspec: "args=[\'a\', \'b\', \'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "ceil" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "cos" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "cosh" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "digamma" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "equal" + argspec: "args=[\'x\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "erfc" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "exp" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "expm1" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "floor" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "greater" + argspec: "args=[\'x\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "greater_equal" + argspec: "args=[\'x\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "igamma" + argspec: "args=[\'a\', \'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "igammac" + argspec: "args=[\'a\', \'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "invert_permutation" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "less" + argspec: "args=[\'x\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "less_equal" + argspec: "args=[\'x\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "lgamma" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "log" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "log1p" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "logical_and" + argspec: "args=[\'x\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "logical_not" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "logical_or" + argspec: "args=[\'x\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "maximum" + argspec: "args=[\'x\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "minimum" + argspec: "args=[\'x\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "not_equal" + argspec: "args=[\'x\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "polygamma" + argspec: "args=[\'a\', \'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } member_method { name: "polyval" argspec: "args=[\'coeffs\', \'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } + member_method { + name: "reciprocal" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "rint" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "rsqrt" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "segment_max" + argspec: "args=[\'data\', \'segment_ids\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "segment_mean" + argspec: "args=[\'data\', \'segment_ids\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "segment_min" + argspec: "args=[\'data\', \'segment_ids\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "segment_prod" + argspec: "args=[\'data\', \'segment_ids\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "segment_sum" + argspec: "args=[\'data\', \'segment_ids\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "sin" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "sinh" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "softplus" + argspec: "args=[\'features\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "softsign" + argspec: "args=[\'features\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "squared_difference" + argspec: "args=[\'x\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "tan" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "unsorted_segment_max" + argspec: "args=[\'data\', \'segment_ids\', \'num_segments\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "unsorted_segment_min" + argspec: "args=[\'data\', \'segment_ids\', \'num_segments\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "unsorted_segment_prod" + argspec: "args=[\'data\', \'segment_ids\', \'num_segments\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "unsorted_segment_sum" + argspec: "args=[\'data\', \'segment_ids\', \'num_segments\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "zeta" + argspec: "args=[\'x\', \'q\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } } diff --git a/tensorflow/tools/api/golden/tensorflow.pbtxt b/tensorflow/tools/api/golden/tensorflow.pbtxt index 3051c4437e..329c7e003f 100644 --- a/tensorflow/tools/api/golden/tensorflow.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.pbtxt @@ -308,6 +308,10 @@ tf_module { name: "data" mtype: "" } + member { + name: "debugging" + mtype: "" + } member { name: "distributions" mtype: "" @@ -316,6 +320,10 @@ tf_module { name: "double" mtype: "" } + member { + name: "dtypes" + mtype: "" + } member { name: "errors" mtype: "" @@ -380,6 +388,10 @@ tf_module { name: "int8" mtype: "" } + member { + name: "io" + mtype: "" + } member { name: "keras" mtype: "" @@ -456,6 +468,10 @@ tf_module { name: "qint8" mtype: "" } + member { + name: "quantization" + mtype: "" + } member { name: "quint16" mtype: "" diff --git a/tensorflow/tools/api/golden/tensorflow.quantization.pbtxt b/tensorflow/tools/api/golden/tensorflow.quantization.pbtxt new file mode 100644 index 0000000000..6d865efed0 --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.quantization.pbtxt @@ -0,0 +1,35 @@ +path: "tensorflow.quantization" +tf_module { + member_method { + name: "dequantize" + argspec: "args=[\'input\', \'min_range\', \'max_range\', \'mode\', \'name\'], varargs=None, keywords=None, defaults=[\'MIN_COMBINED\', \'None\'], " + } + member_method { + name: "fake_quant_with_min_max_args" + argspec: "args=[\'inputs\', \'min\', \'max\', \'num_bits\', \'narrow_range\', \'name\'], varargs=None, keywords=None, defaults=[\'-6\', \'6\', \'8\', \'False\', \'None\'], " + } + member_method { + name: "fake_quant_with_min_max_args_gradient" + argspec: "args=[\'gradients\', \'inputs\', \'min\', \'max\', \'num_bits\', \'narrow_range\', \'name\'], varargs=None, keywords=None, defaults=[\'-6\', \'6\', \'8\', \'False\', \'None\'], " + } + member_method { + name: "fake_quant_with_min_max_vars" + argspec: "args=[\'inputs\', \'min\', \'max\', \'num_bits\', \'narrow_range\', \'name\'], varargs=None, keywords=None, defaults=[\'8\', \'False\', \'None\'], " + } + member_method { + name: "fake_quant_with_min_max_vars_gradient" + argspec: "args=[\'gradients\', \'inputs\', \'min\', \'max\', \'num_bits\', \'narrow_range\', \'name\'], varargs=None, keywords=None, defaults=[\'8\', \'False\', \'None\'], " + } + member_method { + name: "fake_quant_with_min_max_vars_per_channel" + argspec: "args=[\'inputs\', \'min\', \'max\', \'num_bits\', \'narrow_range\', \'name\'], varargs=None, keywords=None, defaults=[\'8\', \'False\', \'None\'], " + } + member_method { + name: "fake_quant_with_min_max_vars_per_channel_gradient" + argspec: "args=[\'gradients\', \'inputs\', \'min\', \'max\', \'num_bits\', \'narrow_range\', \'name\'], varargs=None, keywords=None, defaults=[\'8\', \'False\', \'None\'], " + } + member_method { + name: "quantized_concat" + argspec: "args=[\'concat_dim\', \'values\', \'input_mins\', \'input_maxes\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.strings.pbtxt b/tensorflow/tools/api/golden/tensorflow.strings.pbtxt index b641c39feb..9a831fed26 100644 --- a/tensorflow/tools/api/golden/tensorflow.strings.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.strings.pbtxt @@ -1,11 +1,43 @@ path: "tensorflow.strings" tf_module { + member_method { + name: "join" + argspec: "args=[\'inputs\', \'separator\', \'name\'], varargs=None, keywords=None, defaults=[\'\', \'None\'], " + } member_method { name: "regex_full_match" argspec: "args=[\'input\', \'pattern\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } + member_method { + name: "regex_replace" + argspec: "args=[\'input\', \'pattern\', \'rewrite\', \'replace_global\', \'name\'], varargs=None, keywords=None, defaults=[\'True\', \'None\'], " + } member_method { name: "split" argspec: "args=[\'source\', \'sep\', \'maxsplit\'], varargs=None, keywords=None, defaults=[\'None\', \'-1\'], " } + member_method { + name: "strip" + argspec: "args=[\'input\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "substr" + argspec: "args=[\'input\', \'pos\', \'len\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "to_hash_bucket" + argspec: "args=[\'string_tensor\', \'num_buckets\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "to_hash_bucket_fast" + argspec: "args=[\'input\', \'num_buckets\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "to_hash_bucket_strong" + argspec: "args=[\'input\', \'num_buckets\', \'key\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "to_number" + argspec: "args=[\'string_tensor\', \'out_type\', \'name\'], varargs=None, keywords=None, defaults=[\"\", \'None\'], " + } } -- GitLab From 7ec196c4a28352008d0c947e4a0f0bb404953f98 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 20 Jun 2018 14:09:39 -0700 Subject: [PATCH 756/816] 16-bit quantized Mul support in TFLite interpreter PiperOrigin-RevId: 201413223 --- tensorflow/contrib/lite/kernels/mul.cc | 118 +++++++++++++------- tensorflow/contrib/lite/kernels/mul_test.cc | 40 +++++++ 2 files changed, 120 insertions(+), 38 deletions(-) diff --git a/tensorflow/contrib/lite/kernels/mul.cc b/tensorflow/contrib/lite/kernels/mul.cc index b69a221447..9e01b73c49 100644 --- a/tensorflow/contrib/lite/kernels/mul.cc +++ b/tensorflow/contrib/lite/kernels/mul.cc @@ -39,6 +39,14 @@ constexpr int kOutputTensor = 0; struct OpData { bool requires_broadcast; + + // Parameters used in the quantized paths where the output is 8bit + int32 output_activation_min; + int32 output_activation_max; + + // Parameters used in all quantized paths + int32_t output_multiplier; + int output_shift; }; void* Init(TfLiteContext* context, const char* buffer, size_t length) { @@ -52,6 +60,7 @@ void Free(TfLiteContext* context, void* buffer) { } TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { + auto* params = reinterpret_cast(node->builtin_data); OpData* data = reinterpret_cast(node->user_data); TF_LITE_ENSURE_EQ(context, NumInputs(node), 2); @@ -62,7 +71,6 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { TfLiteTensor* output = GetOutput(context, node, kOutputTensor); TF_LITE_ENSURE_EQ(context, input1->type, input2->type); - output->type = input2->type; data->requires_broadcast = !HaveSameShapes(input1, input2); @@ -74,6 +82,20 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { output_size = TfLiteIntArrayCopy(input1->dims); } + if (output->type == kTfLiteUInt8) { + CalculateActivationRangeUint8(params->activation, output, + &data->output_activation_min, + &data->output_activation_max); + } + + if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt16) { + double real_multiplier = + input1->params.scale * input2->params.scale / output->params.scale; + QuantizeMultiplierSmallerThanOneExp( + real_multiplier, &data->output_multiplier, &data->output_shift); + data->output_shift *= -1; + } + return context->ResizeTensor(context, output, output_size); } @@ -107,42 +129,60 @@ void EvalFloat(TfLiteContext* context, TfLiteNode* node, } template -void EvalQuantized(TfLiteContext* context, TfLiteNode* node, - TfLiteMulParams* params, const OpData* data, - const TfLiteTensor* input1, const TfLiteTensor* input2, - TfLiteTensor* output) { - auto input1_offset = -input1->params.zero_point; - auto input2_offset = -input2->params.zero_point; - auto output_offset = output->params.zero_point; - - int32_t output_multiplier; - int output_shift; - - double real_multiplier = - input1->params.scale * input2->params.scale / output->params.scale; - QuantizeMultiplierSmallerThanOneExp(real_multiplier, &output_multiplier, - &output_shift); - output_shift *= -1; - - int32 output_activation_min, output_activation_max; - CalculateActivationRangeUint8(params->activation, output, - &output_activation_min, &output_activation_max); - -#define TF_LITE_MUL(type, opname) \ - type::opname(GetTensorData(input1), GetTensorDims(input1), \ - input1_offset, GetTensorData(input2), \ - GetTensorDims(input2), input2_offset, output_offset, \ - output_multiplier, output_shift, output_activation_min, \ - output_activation_max, GetTensorData(output), \ +TfLiteStatus EvalQuantized(TfLiteContext* context, TfLiteNode* node, + TfLiteMulParams* params, const OpData* data, + const TfLiteTensor* input1, + const TfLiteTensor* input2, TfLiteTensor* output) { + if (input1->type == kTfLiteUInt8 && input2->type == kTfLiteUInt8 && + output->type == kTfLiteUInt8) { +#define TF_LITE_MUL(type, opname) \ + type::opname(GetTensorData(input1), GetTensorDims(input1), \ + -input1->params.zero_point, GetTensorData(input2), \ + GetTensorDims(input2), -input2->params.zero_point, \ + output->params.zero_point, data->output_multiplier, \ + data->output_shift, data->output_activation_min, \ + data->output_activation_max, GetTensorData(output), \ GetTensorDims(output)); - // The quantized version of Mul doesn't support activations, so we - // always use BroadcastMul. - if (kernel_type == kReference) { - TF_LITE_MUL(reference_ops, BroadcastMul); + // The quantized version of Mul doesn't support activations, so we + // always use BroadcastMul. + if (kernel_type == kReference) { + TF_LITE_MUL(reference_ops, BroadcastMul); + } else { + TF_LITE_MUL(optimized_ops, BroadcastMul); + } +#undef TF_LITE_MUL + } else if (input1->type == kTfLiteInt16 && input2->type == kTfLiteInt16 && + output->type == kTfLiteInt16) { +#define TF_LITE_MUL(type, opname) \ + type::opname(GetTensorData(input1), GetTensorDims(input1), \ + GetTensorData(input2), GetTensorDims(input2), \ + GetTensorData(output), GetTensorDims(output)); + if (kernel_type == kReference) { + TF_LITE_MUL(reference_ops, Mul); + } else { + TF_LITE_MUL(optimized_ops, Mul); + } +#undef TF_LITE_MUL + } else if (input1->type == kTfLiteInt16 && input2->type == kTfLiteInt16 && + output->type == kTfLiteUInt8) { +#define TF_LITE_MUL(type, opname) \ + type::opname(GetTensorData(input1), GetTensorDims(input1), \ + GetTensorData(input2), GetTensorDims(input2), \ + output->params.zero_point, data->output_activation_min, \ + data->output_activation_max, GetTensorData(output), \ + GetTensorDims(output)); + if (kernel_type == kReference) { + TF_LITE_MUL(reference_ops, Mul); + } else { + TF_LITE_MUL(optimized_ops, Mul); + } +#undef TF_LITE_MUL } else { - TF_LITE_MUL(optimized_ops, BroadcastMul); + context->ReportError( + context, "Unsupported combination of input and output types in Mul."); + return kTfLiteError; } -#undef TF_LITE_MUL + return kTfLiteOk; } template @@ -156,12 +196,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { if (output->type == kTfLiteFloat32) { EvalFloat(context, node, params, data, input1, input2, output); - } else if (output->type == kTfLiteUInt8) { - EvalQuantized(context, node, params, data, input1, input2, - output); + } else if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt16) { + TF_LITE_ENSURE_OK( + context, EvalQuantized(context, node, params, data, input1, + input2, output)); } else { context->ReportError( - context, "Mul only supports FLOAT32 and quantized UINT8 now, got %d.", + context, + "Mul only supports FLOAT32 and quantized UINT8 and INT16 now, got %d.", output->type); return kTfLiteError; } diff --git a/tensorflow/contrib/lite/kernels/mul_test.cc b/tensorflow/contrib/lite/kernels/mul_test.cc index f1a30f8263..43d56e50d2 100644 --- a/tensorflow/contrib/lite/kernels/mul_test.cc +++ b/tensorflow/contrib/lite/kernels/mul_test.cc @@ -58,6 +58,9 @@ class FloatMulOpModel : public BaseMulOpModel { const float kQuantizedStep = 2.0 / 255.0; const float kQuantizedTolerance = 2.0 * kQuantizedStep + kQuantizedStep * kQuantizedStep; +const float kQuantizedStepInt16 = 2.0 / 32767.0; +const float kQuantizedToleranceInt16 = + 2.0 * kQuantizedStepInt16 + kQuantizedStepInt16 * kQuantizedStepInt16; class QuantizedMulOpModel : public BaseMulOpModel { public: @@ -67,6 +70,11 @@ class QuantizedMulOpModel : public BaseMulOpModel { return Dequantize(ExtractVector(output_), GetScale(output_), GetZeroPoint(output_)); } + + std::vector GetDequantizedOutputInt16() { + return Dequantize(ExtractVector(output_), + GetScale(output_), GetZeroPoint(output_)); + } }; TEST(FloatMulOpTest, NoActivation) { @@ -138,6 +146,38 @@ TEST(QuantizedMulOpTest, NoActivation) { kQuantizedTolerance))); } +TEST(QuantizedMulOpTest, NoActivationInt16) { + const float kMin = -1.f; + const float kMax = 32767.f / 32768.f; + QuantizedMulOpModel m({TensorType_INT16, {1, 2, 2, 1}, kMin, kMax}, + {TensorType_INT16, {1, 2, 2, 1}, kMin, kMax}, + {TensorType_INT16, {}, kMin, kMax}, + ActivationFunctionType_NONE); + m.QuantizeAndPopulate(m.input1(), {-0.8, 0.2, 0.9, 0.7}); + m.QuantizeAndPopulate(m.input2(), {0.6, 0.4, 0.9, 0.8}); + m.Invoke(); + EXPECT_THAT(m.GetDequantizedOutputInt16(), + ElementsAreArray(ArrayFloatNear({-0.48, 0.08, 0.81, 0.56}, + kQuantizedToleranceInt16))); +} + +TEST(QuantizedMulOpTest, NoActivationInt16WithUint8Output) { + const float kMinInt16 = -1.f; + const float kMaxInt16 = 32767.f / 32768.f; + const float kMinUint8 = -1.f; + const float kMaxUint8 = 127.f / 128.f; + QuantizedMulOpModel m({TensorType_INT16, {1, 2, 2, 1}, kMinInt16, kMaxInt16}, + {TensorType_INT16, {1, 2, 2, 1}, kMinInt16, kMaxInt16}, + {TensorType_UINT8, {}, kMinUint8, kMaxUint8}, + ActivationFunctionType_NONE); + m.QuantizeAndPopulate(m.input1(), {-0.8, 0.2, 0.9, 0.7}); + m.QuantizeAndPopulate(m.input2(), {0.6, 0.4, 0.9, 0.8}); + m.Invoke(); + EXPECT_THAT(m.GetDequantizedOutput(), + ElementsAreArray(ArrayFloatNear({-0.48, 0.08, 0.81, 0.56}, + kQuantizedTolerance))); +} + // for quantized Mul, the error shouldn't exceed 2*step float GetTolerance(int min, int max) { float kQuantizedStep = (max - min) / 255.0; -- GitLab From 164099ee4688432d614c754b1e01d56715811062 Mon Sep 17 00:00:00 2001 From: Nupur Garg Date: Wed, 20 Jun 2018 14:11:14 -0700 Subject: [PATCH 757/816] Add warning in TFMobile. PiperOrigin-RevId: 201413517 --- tensorflow/contrib/makefile/build_all_android.sh | 8 ++++++++ tensorflow/contrib/makefile/build_all_ios.sh | 8 ++++++++ 2 files changed, 16 insertions(+) diff --git a/tensorflow/contrib/makefile/build_all_android.sh b/tensorflow/contrib/makefile/build_all_android.sh index fc88f59e09..fb9e77ae1b 100755 --- a/tensorflow/contrib/makefile/build_all_android.sh +++ b/tensorflow/contrib/makefile/build_all_android.sh @@ -30,6 +30,14 @@ arm64-v8a armeabi armeabi-v7a mips mips64 x86 x86_64 tegra)" exit 1 } +echo "********************************************************************" +echo "TensorFlow Lite is the recommended library for mobile and embedded machine learning inference." +echo "You are currently using an older version. Please switch over to TensorFlow Lite." +echo "" +echo "Link to the code: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite" +echo "********************************************************************" +echo "" + if [[ -z "${NDK_ROOT}" ]]; then echo "NDK_ROOT should be set as an environment variable" 1>&2 exit 1 diff --git a/tensorflow/contrib/makefile/build_all_ios.sh b/tensorflow/contrib/makefile/build_all_ios.sh index 0a458a27b3..1d4677ef4b 100755 --- a/tensorflow/contrib/makefile/build_all_ios.sh +++ b/tensorflow/contrib/makefile/build_all_ios.sh @@ -31,6 +31,14 @@ usage() { exit 1 } +echo "********************************************************************" +echo "TensorFlow Lite is the recommended library for mobile and embedded machine learning inference." +echo "You are currently using an older version. Please switch over to TensorFlow Lite." +echo "" +echo "Link to the code: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite" +echo "********************************************************************" +echo "" + DEFAULT_ARCH="i386 x86_64 armv7 armv7s arm64" while getopts "a:g:T" opt_name; do case "$opt_name" in -- GitLab From 345d484c30d3fe32aefac50197c6ad41b813986f Mon Sep 17 00:00:00 2001 From: Mingxing Tan Date: Wed, 20 Jun 2018 14:20:55 -0700 Subject: [PATCH 758/816] Fix minor merging issue. --- tensorflow/tools/pip_package/BUILD | 1 - 1 file changed, 1 deletion(-) diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD index 4c86ad51d3..6cfd271968 100644 --- a/tensorflow/tools/pip_package/BUILD +++ b/tensorflow/tools/pip_package/BUILD @@ -60,7 +60,6 @@ COMMON_PIP_DEPS = [ "//tensorflow/contrib/autograph/core:core", "//tensorflow/contrib/autograph/core:test_lib", "//tensorflow/contrib/autograph/impl:impl", - "//tensorflow/contrib/autograph/operators:operators", "//tensorflow/contrib/autograph/lang:lang", "//tensorflow/contrib/autograph/operators:operators", "//tensorflow/contrib/autograph/pyct:pyct", -- GitLab From 0ee468c4bc08960a613e4d1315f9537899d3b406 Mon Sep 17 00:00:00 2001 From: Michael Case Date: Wed, 20 Jun 2018 14:08:57 -0700 Subject: [PATCH 759/816] Move external/ directory in pip package. Moving external/ directory in the pip packages (which is currently installed directly into site-packages directory). Moving the directory to tensorflow/include/external/. Also, removing all python files from external (since it should really only contain headers and license files.) --- .../tools/pip_package/build_pip_package.sh | 21 ++++++++++++------- 1 file changed, 13 insertions(+), 8 deletions(-) diff --git a/tensorflow/tools/pip_package/build_pip_package.sh b/tensorflow/tools/pip_package/build_pip_package.sh index f7e42ce536..9e41514cfa 100755 --- a/tensorflow/tools/pip_package/build_pip_package.sh +++ b/tensorflow/tools/pip_package/build_pip_package.sh @@ -24,9 +24,15 @@ function real_path() { function cp_external() { local src_dir=$1 local dest_dir=$2 - for f in `find "$src_dir" -maxdepth 1 -mindepth 1 ! -name '*local_config_cuda*' ! -name '*local_config_tensorrt*' ! -name '*org_tensorflow*'`; do - cp -R "$f" "$dest_dir" + + pushd . + cd "$src_dir" + for f in `find . ! -type d ! -name '*.py' ! -name '*local_config_cuda*' ! -name '*local_config_tensorrt*' ! -name '*org_tensorflow*'`; do + mkdir -p "${dest_dir}/$(dirname ${f})" + cp "${f}" "${dest_dir}/$(dirname ${f})/" done + popd + mkdir -p "${dest_dir}/local_config_cuda/cuda/cuda/" cp "${src_dir}/local_config_cuda/cuda/cuda/cuda_config.h" "${dest_dir}/local_config_cuda/cuda/cuda/" } @@ -49,6 +55,8 @@ function prepare_src() { TMPDIR="$1" mkdir -p "$TMPDIR" + EXTERNAL_INCLUDES="${TMPDIR}/tensorflow/include/external" + echo $(date) : "=== Preparing sources in dir: ${TMPDIR}" if [ ! -d bazel-bin/tensorflow ]; then @@ -66,10 +74,9 @@ function prepare_src() { cp -R \ bazel-bin/tensorflow/tools/pip_package/simple_console_for_window_unzip/runfiles/org_tensorflow/tensorflow \ "${TMPDIR}" - mkdir "${TMPDIR}/external" cp_external \ bazel-bin/tensorflow/tools/pip_package/simple_console_for_window_unzip/runfiles \ - "${TMPDIR}/external" + "${EXTERNAL_INCLUDES}/" RUNFILES=bazel-bin/tensorflow/tools/pip_package/simple_console_for_window_unzip/runfiles/org_tensorflow else RUNFILES=bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow @@ -78,10 +85,9 @@ function prepare_src() { cp -R \ bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow/tensorflow \ "${TMPDIR}" - mkdir "${TMPDIR}/external" cp_external \ bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow/external \ - "${TMPDIR}/external" + "${EXTERNAL_INCLUDES}" # Copy MKL libs over so they can be loaded at runtime so_lib_dir=$(ls $RUNFILES | grep solib) || true if [ -n "${so_lib_dir}" ]; then @@ -96,10 +102,9 @@ function prepare_src() { cp -R \ bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow/tensorflow \ "${TMPDIR}" - mkdir "${TMPDIR}/external" cp_external \ bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles \ - "${TMPDIR}/external" + "${EXTERNAL_INCLUDES}" # Copy MKL libs over so they can be loaded at runtime so_lib_dir=$(ls $RUNFILES | grep solib) || true if [ -n "${so_lib_dir}" ]; then -- GitLab From 2cd247d20422a41c33e0f4be265eba2df537ed3b Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 20 Jun 2018 14:56:00 -0700 Subject: [PATCH 760/816] Handle positive and negative infinity in TopKV2. TopKV2 hides iota in the low bits of the input after converting from bf16 to f32. This usually works, but for positive and negative infinity or'ing in iota produces NANs. To handle positive and negative infinity, treat bf16 as integers in sign-magnitude format. Convert to two's complement. Sort in two's complement and convert back. Add an exhaustive unit test for bfloat16 to float conversion. PiperOrigin-RevId: 201421784 --- tensorflow/compiler/tests/sort_ops_test.py | 29 +++++- tensorflow/compiler/tf2xla/kernels/topk_op.cc | 99 ++++++++++++++----- tensorflow/compiler/xla/tests/convert_test.cc | 21 ++++ 3 files changed, 121 insertions(+), 28 deletions(-) diff --git a/tensorflow/compiler/tests/sort_ops_test.py b/tensorflow/compiler/tests/sort_ops_test.py index 370085c1e2..8ae579abda 100644 --- a/tensorflow/compiler/tests/sort_ops_test.py +++ b/tensorflow/compiler/tests/sort_ops_test.py @@ -81,7 +81,7 @@ class XlaSortOpTest(xla_test.XLATestCase): def testTopKZeros(self): """Tests that positive and negative zeros sort correctly.""" - # Requires Sort HLO, which is not implemented on CPU or GPU. + # TODO(b/26783907): The Sort HLO is not implemented on CPU or GPU. if self.device in ["XLA_CPU", "XLA_GPU"]: return @@ -99,7 +99,32 @@ class XlaSortOpTest(xla_test.XLATestCase): {p: np.array([0., -0., 0., 3., -0., -4., 0., -0.], dtype=bfloat16)}) self.assertAllEqual( np.array([3., 0., 0., 0.], dtype=bfloat16), results[0]) - self.assertEqual(set([0, 2, 3, 6]), set(results[1])) + self.assertEqual(list([3, 0, 1, 2]), list(results[1])) + + def testTopKInfinities(self): + """Tests that positive and negative infinity sort correctly.""" + # TODO(b/26783907): The Sort HLO is not implemented on CPU or GPU. + if self.device in ["XLA_CPU", "XLA_GPU"]: + return + + # Only bfloat16 is implemented. + bfloat16 = dtypes.bfloat16.as_numpy_dtype + if bfloat16 not in self.numeric_types: + return + + with self.test_session() as sess: + p = array_ops.placeholder(dtypes.bfloat16) + with self.test_scope(): + topk = nn_ops.top_k(p, k=6) + results = sess.run(topk, { + p: np.array( + [1, 2, float("inf"), -float("inf"), -1, -2], dtype=bfloat16) + }) + self.assertAllEqual( + np.array( + [float("inf"), 2.0, 1.0, -1.0, -2.0, -float("inf")], + dtype=bfloat16), results[0]) + self.assertEqual(list([2, 1, 0, 4, 5, 3]), list(results[1])) if __name__ == "__main__": diff --git a/tensorflow/compiler/tf2xla/kernels/topk_op.cc b/tensorflow/compiler/tf2xla/kernels/topk_op.cc index 703e13e089..cbe3c8aaff 100644 --- a/tensorflow/compiler/tf2xla/kernels/topk_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/topk_op.cc @@ -61,42 +61,89 @@ class TopKOp : public XlaOpKernel { if (input_shape.dim_size(0) < k) { k = input_shape.dim_size(0); } - const xla::XlaOp input = context->Input(0); - xla::XlaOp iota; - OP_REQUIRES_OK(context, XlaHelpers::Iota(b, DT_INT32, n, &iota)); + const xla::XlaOp input_bf16 = context->Input(0); + xla::XlaOp iota_s32; + OP_REQUIRES_OK(context, XlaHelpers::Iota(b, DT_INT32, n, &iota_s32)); // TODO(b/73891930): add a key-value sort to HLO, rather than using // bit-packing tricks here. - // TODO(b/73891930): this implementation will convert Infs to NaNs. A - // key-value sort would avoid this; for now, it is no worse than, say, the - // CPU backend in fast-math mode. + + xla::XlaOp zero = b->ConstantR0(0); + + // max can either be 0x7FFFFFFF or 0x8000000. Neither choice is totally + // ideal. The implications of the choice are: + // + // 0x7FFFFFFF + // 1. +0.0 > -0.0 + // 2. The elements of the inputs and outputs are bitwise identical. + // 3. The sort is unstable since a later +0.0 will appear before an earlier + // -0.0. + // + // 0x8000000 + // 1. +0.0 == -0.0 + // 2. All -0.0 in the input are replaced with +0.0 in the output. + // 3. The sort is stable. + xla::XlaOp max = b->ConstantR0(0x80000000); + xla::XlaOp index_mask = b->ConstantR0(0x0000FFFF); + xla::XlaOp value_mask = b->ConstantR0(0xFFFF0000); + + // Convert to from bf16 to f32. The lower 16-bits are zero due to the + // definition of bf16. + xla::XlaOp input_f32 = b->ConvertElementType(input_bf16, xla::F32); + + // Negate the input to reverse sort it. The lower 16-bits are zero, because + // negating a float is just inverting the high-bit. + xla::XlaOp negative_input_f32 = b->Neg(input_f32); + + // Convert to a sign magnitude integer. The lower 16-bits are zero, since + // bitcast convert doesn't change any bits. + xla::XlaOp negative_input_sm32 = + b->BitcastConvertType(negative_input_f32, xla::S32); + + // Convert from sign magnitude integer to two's complement integer. The + // lower 16-bits are zero on both sides of the select. On the false side, + // the value is unchanged, and on the true side, the lower 16-bits of max + // are all zero, so the lower 16-bits of the result of the subtraction will + // also be zero. + xla::XlaOp negative_input_s32 = + b->Select(b->Lt(negative_input_sm32, zero), + b->Sub(max, negative_input_sm32), negative_input_sm32); + + // In order for the Or with iota_s32 to to work properly, the lower 16-bits + // of negative_input_32 must be zero. // Pack elements as: // * upper 16 bits are the value // * lower 16 bits are the index. - xla::XlaOp packed = b->BitcastConvertType( - b->Or(b->BitcastConvertType(b->ConvertElementType(input, xla::F32), - xla::S32), - iota), - xla::F32); + xla::XlaOp packed_s32 = b->Or(negative_input_s32, iota_s32); // TODO(phawkins): use a more efficient algorithm that does not require a // full sort. - xla::XlaOp sorted = b->Slice(b->Rev(b->Sort(packed), {0}), - /*start_indices=*/{0}, - /*limit_indices=*/{k}, - /*strides=*/{1}); - - // Unpack the value/index - xla::XlaOp x = b->BitcastConvertType(sorted, xla::S32); - xla::XlaOp indices = b->And(x, b->ConstantR0(0x0000FFFF)); - xla::XlaOp values = b->ConvertElementType( - b->BitcastConvertType(b->And(x, b->ConstantR0(0xFFFF0000)), - xla::F32), - xla::BF16); - - context->SetOutput(0, values); - context->SetOutput(1, indices); + xla::XlaOp sorted_s32 = b->Slice(b->Sort(packed_s32), + /*start_indices=*/{0}, + /*limit_indices=*/{k}, + /*strides=*/{1}); + + // Unpack the value/index. + xla::XlaOp indices_s32 = b->And(sorted_s32, index_mask); + xla::XlaOp negative_values_s32 = b->And(sorted_s32, value_mask); + + // Convert from two's complement integer to sign magnitude integer. + xla::XlaOp negative_values_sm32 = + b->Select(b->Lt(negative_values_s32, zero), + b->Sub(max, negative_values_s32), negative_values_s32); + + xla::XlaOp negative_values_f32 = + b->BitcastConvertType(negative_values_sm32, xla::F32); + + // Negate the values to get back the original inputs. + xla::XlaOp values_f32 = b->Neg(negative_values_f32); + + // Convert from f32 to bf16. + xla::XlaOp values_bf16 = b->ConvertElementType(values_f32, xla::BF16); + + context->SetOutput(0, values_bf16); + context->SetOutput(1, indices_s32); } private: diff --git a/tensorflow/compiler/xla/tests/convert_test.cc b/tensorflow/compiler/xla/tests/convert_test.cc index 722d882471..3a885b4389 100644 --- a/tensorflow/compiler/xla/tests/convert_test.cc +++ b/tensorflow/compiler/xla/tests/convert_test.cc @@ -461,5 +461,26 @@ XLA_TEST_F(ConvertTest, ConvertS64U64) { ComputeAndCompareR1(&builder, unsigned_x, {}); } +XLA_TEST_F(ConvertTest, ConvertBF16F32) { + XlaBuilder builder(TestName()); + + std::vector all_bfloats(1 << 16); + for (int i = 0; i < all_bfloats.size(); ++i) { + all_bfloats[i].value = i; + } + + std::vector expected(all_bfloats.size()); + for (int i = 0; i < expected.size(); ++i) { + expected[i] = (1U << 16) * i; + } + + // Exhaustively test all bf16 to f32 conversions. + xla::XlaOp all_bfloats_bf16 = builder.ConstantR1(all_bfloats); + xla::XlaOp all_bfloats_f32 = + builder.ConvertElementType(all_bfloats_bf16, F32); + xla::XlaOp all_bfloats_u32 = builder.BitcastConvertType(all_bfloats_f32, U32); + ComputeAndCompareR1(&builder, expected, {}); +} + } // namespace } // namespace xla -- GitLab From eacbaabf6d0983d61c99e1bb17658cd80a24f1ee Mon Sep 17 00:00:00 2001 From: Noah Eisen Date: Wed, 20 Jun 2018 14:58:02 -0700 Subject: [PATCH 761/816] Rename tensor_data_is_large to share_tensor_slice_memory PiperOrigin-RevId: 201422113 --- .../rpc/grpc_tensor_coding.cc | 24 ++++++++++++------- 1 file changed, 16 insertions(+), 8 deletions(-) diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_tensor_coding.cc b/tensorflow/core/distributed_runtime/rpc/grpc_tensor_coding.cc index d0684f1833..159435fd7d 100644 --- a/tensorflow/core/distributed_runtime/rpc/grpc_tensor_coding.cc +++ b/tensorflow/core/distributed_runtime/rpc/grpc_tensor_coding.cc @@ -26,6 +26,8 @@ limitations under the License. #include "tensorflow/core/platform/env.h" #include "tensorflow/core/protobuf/worker.pb.h" +// (Omitted internal-only flag) + namespace tensorflow { namespace grpc { @@ -168,15 +170,20 @@ void EncodeTensorToByteBuffer(bool is_dead, const Tensor& val, (header.size() + VarLengthEncodingSize(RecvTensorResponse::kTensorFieldNumber, overall_tensor_proto_bytesize)); - // If "tensor_data_is_large == false", we copy the tensor data to the - // end of the buffer we are preparing that holds the rest of the + // If "share_tensor_slice_memory == false", we copy the tensor data to + // the end of the buffer we are preparing that holds the rest of the // RecvTensorResponse protocol buffer. // - // If "tensor_data_is_large == true", we arrange to share the backing - // store of the data by creating a slice that also points to the + // If "share_tensor_slice_memory == true", we arrange to share the + // backing store of the data by creating a slice that also points to the // backing store, with appropriate reference counts to keep the // backing store alive as needed. - bool tensor_data_is_large = (tdata.size() > kLargeTensorBytes); + // + // We enable this behavior if the tensor is large. + bool share_tensor_slice_memory = (tdata.size() > kLargeTensorBytes); + + // (Omitted internal-only conditional) + size_t encoder_size = expected_size - tdata.size(); // Encode all but the actual "tdata", but including the tag and @@ -201,10 +208,11 @@ void EncodeTensorToByteBuffer(bool is_dead, const Tensor& val, ::grpc::Slice slices[2]; int num_slices = 0; { - size_t slice_len = e.size() + (tensor_data_is_large ? 0 : tdata.size()); + size_t slice_len = + e.size() + (share_tensor_slice_memory ? 0 : tdata.size()); slices[0] = ::grpc::Slice(slice_len); memcpy(const_cast(slices[0].begin()), e.data(), e.size()); - if (!tensor_data_is_large) { + if (!share_tensor_slice_memory) { // (E) memcpy(const_cast(slices[0].begin()) + e.size(), tdata.data(), tdata.size()); @@ -212,7 +220,7 @@ void EncodeTensorToByteBuffer(bool is_dead, const Tensor& val, num_slices += 1; } - if (tensor_data_is_large) { + if (share_tensor_slice_memory) { // (E) Encode tensor data, but by sharing backing store const TensorBuffer* buf = DMAHelper::buffer(&val); buf->Ref(); -- GitLab From cbbffe5f646c940723247d595d33e2e87a3c3b27 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 20 Jun 2018 15:00:44 -0700 Subject: [PATCH 762/816] Fix operator names. PiperOrigin-RevId: 201422566 --- tensorflow/contrib/lite/toco/dump_graphviz.cc | 2 +- .../contrib/lite/toco/export_tensorflow.cc | 36 +++--- .../convert_trivial_tile_to_concat.cc | 2 +- .../toco/graph_transformations/dequantize.cc | 2 +- .../graph_transformations/hardcode_min_max.cc | 4 +- .../identify_l2_normalization.cc | 12 +- .../graph_transformations/identify_l2_pool.cc | 4 +- .../graph_transformations/identify_lstm.cc | 16 +-- .../graph_transformations/identify_relu1.cc | 14 +-- .../merge_reshape_into_preceding_transpose.cc | 2 +- .../propagate_array_data_types.cc | 20 ++-- .../propagate_fake_quant_num_bits.cc | 12 +- .../propagate_fixed_sizes.cc | 60 +++++----- .../toco/graph_transformations/quantize.cc | 25 ++-- .../remove_tensorflow_assert.cc | 2 +- .../remove_tensorflow_identity.cc | 2 +- .../remove_trivial_passthrough.cc | 2 +- .../remove_trivial_quantized_min_max.cc | 8 +- .../remove_trivial_reshape.cc | 6 +- .../graph_transformations/remove_unused_op.cc | 2 +- .../reorder_elementwise_unary.cc | 6 +- .../reorder_reshape_transpose.cc | 4 +- .../resolve_constant_binary.cc | 24 ++-- .../resolve_constant_reshape.cc | 2 +- .../resolve_constant_shape_or_rank.cc | 5 +- .../resolve_constant_unary.cc | 36 +++--- .../resolve_reshape_attributes.cc | 2 +- .../resolve_squeeze_attributes.cc | 2 +- .../resolve_tensorflow_concat.cc | 6 +- .../resolve_tensorflow_matmul.cc | 4 +- .../resolve_tensorflow_merge.cc | 2 +- .../resolve_tensorflow_switch.cc | 4 +- tensorflow/contrib/lite/toco/model.h | 111 +++++++++--------- tensorflow/contrib/lite/toco/tflite/export.cc | 6 +- tensorflow/contrib/lite/toco/tflite/export.h | 2 +- .../contrib/lite/toco/tflite/export_test.cc | 4 +- tensorflow/contrib/lite/toco/tflite/import.cc | 2 +- .../contrib/lite/toco/tflite/operator.cc | 46 ++++---- .../contrib/lite/toco/tflite/operator_test.cc | 42 +++---- tensorflow/contrib/lite/toco/toco_tooling.cc | 4 +- tensorflow/contrib/lite/toco/tooling_util.cc | 60 +++++----- 41 files changed, 292 insertions(+), 315 deletions(-) diff --git a/tensorflow/contrib/lite/toco/dump_graphviz.cc b/tensorflow/contrib/lite/toco/dump_graphviz.cc index 878bda36ef..6877fb237c 100644 --- a/tensorflow/contrib/lite/toco/dump_graphviz.cc +++ b/tensorflow/contrib/lite/toco/dump_graphviz.cc @@ -227,7 +227,7 @@ NodeProperties GetPropertiesForArray(const Model& model, NodeProperties GetPropertiesForOperator(const Operator& op) { NodeProperties node_properties; - if (op.type == OperatorType::kTensorFlowUnsupported) { + if (op.type == OperatorType::kUnsupported) { node_properties.label = static_cast(op).tensorflow_op; } else { diff --git a/tensorflow/contrib/lite/toco/export_tensorflow.cc b/tensorflow/contrib/lite/toco/export_tensorflow.cc index afc6d5df20..6b78f1c05e 100644 --- a/tensorflow/contrib/lite/toco/export_tensorflow.cc +++ b/tensorflow/contrib/lite/toco/export_tensorflow.cc @@ -735,8 +735,7 @@ void ConvertSoftmaxOperator(const Model& model, const SoftmaxOperator& src_op, GraphDef* tensorflow_graph) { string softmax_input; Operator* providing_op = GetOpWithOutput(model, src_op.inputs[0]); - if (providing_op != nullptr && - providing_op->type == OperatorType::kTensorFlowReshape) { + if (providing_op != nullptr && providing_op->type == OperatorType::kReshape) { softmax_input = src_op.inputs[0]; } else { // Insert a reshape operator that reduces the dimensions down to the 2 that @@ -776,8 +775,7 @@ void ConvertLogSoftmaxOperator(const Model& model, GraphDef* tensorflow_graph) { string softmax_input; Operator* providing_op = GetOpWithOutput(model, src_op.inputs[0]); - if (providing_op != nullptr && - providing_op->type == OperatorType::kTensorFlowReshape) { + if (providing_op != nullptr && providing_op->type == OperatorType::kReshape) { softmax_input = src_op.inputs[0]; } else { // Insert a reshape operator that reduces the dimensions down to the 2 that @@ -1855,24 +1853,24 @@ void ConvertOperator(const Model& model, const Operator& src_op, ConvertConcatenationOperator( model, static_cast(src_op), tensorflow_graph); - } else if (src_op.type == OperatorType::kTensorFlowReshape) { + } else if (src_op.type == OperatorType::kReshape) { ConvertTensorFlowReshapeOperator( model, static_cast(src_op), tensorflow_graph); } else if (src_op.type == OperatorType::kL2Pool) { ConvertL2PoolOperator(static_cast(src_op), tensorflow_graph); - } else if (src_op.type == OperatorType::kTensorFlowSquare) { + } else if (src_op.type == OperatorType::kSquare) { ConvertSquareOperator(static_cast(src_op), tensorflow_graph); - } else if (src_op.type == OperatorType::kTensorFlowSqrt) { + } else if (src_op.type == OperatorType::kSqrt) { ConvertSqrtOperator(static_cast(src_op), tensorflow_graph); - } else if (src_op.type == OperatorType::kTensorFlowRsqrt) { + } else if (src_op.type == OperatorType::kRsqrt) { ConvertRsqrtOperator(model, static_cast(src_op), tensorflow_graph); - } else if (src_op.type == OperatorType::kTensorFlowSplit) { + } else if (src_op.type == OperatorType::kSplit) { ConvertSplitOperator(model, static_cast(src_op), tensorflow_graph); @@ -1916,11 +1914,11 @@ void ConvertOperator(const Model& model, const Operator& src_op, } else if (src_op.type == OperatorType::kSub) { ConvertSubOperator(model, static_cast(src_op), tensorflow_graph); - } else if (src_op.type == OperatorType::kTensorFlowMinimum) { + } else if (src_op.type == OperatorType::kMinimum) { ConvertTensorFlowMinimumOperator( model, static_cast(src_op), tensorflow_graph); - } else if (src_op.type == OperatorType::kTensorFlowMaximum) { + } else if (src_op.type == OperatorType::kMaximum) { ConvertTensorFlowMaximumOperator( model, static_cast(src_op), tensorflow_graph); @@ -1939,7 +1937,7 @@ void ConvertOperator(const Model& model, const Operator& src_op, } else if (src_op.type == OperatorType::kTranspose) { ConvertTransposeOperator( model, static_cast(src_op), tensorflow_graph); - } else if (src_op.type == OperatorType::kTensorFlowShape) { + } else if (src_op.type == OperatorType::kShape) { ConvertTensorFlowShapeOperator( model, static_cast(src_op), tensorflow_graph); @@ -1970,22 +1968,22 @@ void ConvertOperator(const Model& model, const Operator& src_op, ConvertRandomUniformOperator( model, static_cast(src_op), tensorflow_graph); - } else if (src_op.type == OperatorType::kTensorFlowEqual) { + } else if (src_op.type == OperatorType::kEqual) { ConvertComparisonOperator(model, src_op, "Equal", tensorflow_graph); - } else if (src_op.type == OperatorType::kTensorFlowNotEqual) { + } else if (src_op.type == OperatorType::kNotEqual) { ConvertComparisonOperator(model, src_op, "NotEqual", tensorflow_graph); - } else if (src_op.type == OperatorType::kTensorFlowGreater) { + } else if (src_op.type == OperatorType::kGreater) { ConvertComparisonOperator(model, src_op, "Greater", tensorflow_graph); - } else if (src_op.type == OperatorType::kTensorFlowGreaterEqual) { + } else if (src_op.type == OperatorType::kGreaterEqual) { ConvertComparisonOperator(model, src_op, "GreaterEqual", tensorflow_graph); - } else if (src_op.type == OperatorType::kTensorFlowLess) { + } else if (src_op.type == OperatorType::kLess) { ConvertComparisonOperator(model, src_op, "Less", tensorflow_graph); - } else if (src_op.type == OperatorType::kTensorFlowLessEqual) { + } else if (src_op.type == OperatorType::kLessEqual) { ConvertComparisonOperator(model, src_op, "LessEqual", tensorflow_graph); } else if (src_op.type == OperatorType::kSelect) { ConvertSelectOperator(model, static_cast(src_op), tensorflow_graph); - } else if (src_op.type == OperatorType::kTensorFlowTile) { + } else if (src_op.type == OperatorType::kTile) { ConvertTileOperator(model, static_cast(src_op), tensorflow_graph); diff --git a/tensorflow/contrib/lite/toco/graph_transformations/convert_trivial_tile_to_concat.cc b/tensorflow/contrib/lite/toco/graph_transformations/convert_trivial_tile_to_concat.cc index 5ab399206b..b689be0792 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/convert_trivial_tile_to_concat.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/convert_trivial_tile_to_concat.cc @@ -23,7 +23,7 @@ namespace toco { bool ConvertTrivialTileToConcat::Run(Model* model, std::size_t op_index) { auto tile_it = model->operators.begin() + op_index; - if (tile_it->get()->type != OperatorType::kTensorFlowTile) { + if (tile_it->get()->type != OperatorType::kTile) { return false; } auto* tile_op = static_cast(tile_it->get()); diff --git a/tensorflow/contrib/lite/toco/graph_transformations/dequantize.cc b/tensorflow/contrib/lite/toco/graph_transformations/dequantize.cc index 498c864bde..2c7ffe4884 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/dequantize.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/dequantize.cc @@ -111,7 +111,7 @@ bool DequantizeArray(const string& array_name, auto* op_outputting_array = GetOpWithOutput(*model, array_name); if (op_outputting_array) { - if (op_outputting_array->type == OperatorType::kTensorFlowReshape) { + if (op_outputting_array->type == OperatorType::kReshape) { return true; } } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/hardcode_min_max.cc b/tensorflow/contrib/lite/toco/graph_transformations/hardcode_min_max.cc index bda6dce22b..82a4308ecb 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/hardcode_min_max.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/hardcode_min_max.cc @@ -353,7 +353,7 @@ bool HardcodeMinMax::Run(Model* model, std::size_t op_index) { changed = HardcodeMinMaxForConcatenation(model, op); break; - case OperatorType::kTensorFlowSplit: + case OperatorType::kSplit: changed = HardcodeMinMaxForSplit(model, op); break; @@ -366,7 +366,7 @@ bool HardcodeMinMax::Run(Model* model, std::size_t op_index) { case OperatorType::kSlice: case OperatorType::kStridedSlice: case OperatorType::kSqueeze: - case OperatorType::kTensorFlowReshape: + case OperatorType::kReshape: case OperatorType::kPad: case OperatorType::kGather: case OperatorType::kTranspose: diff --git a/tensorflow/contrib/lite/toco/graph_transformations/identify_l2_normalization.cc b/tensorflow/contrib/lite/toco/graph_transformations/identify_l2_normalization.cc index 419a0776a6..b78efd7fc3 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/identify_l2_normalization.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/identify_l2_normalization.cc @@ -44,10 +44,9 @@ bool IdentifyL2Normalization::Run(Model* model, std::size_t op_index) { const auto* div_or_mul_op = div_it->get(); OperatorType expected_op_type_producing_div_or_mul_input; if (div_or_mul_op->type == OperatorType::kDiv) { - expected_op_type_producing_div_or_mul_input = OperatorType::kTensorFlowSqrt; + expected_op_type_producing_div_or_mul_input = OperatorType::kSqrt; } else if (div_or_mul_op->type == OperatorType::kMul) { - expected_op_type_producing_div_or_mul_input = - OperatorType::kTensorFlowRsqrt; + expected_op_type_producing_div_or_mul_input = OperatorType::kRsqrt; } else { return false; } @@ -75,8 +74,7 @@ bool IdentifyL2Normalization::Run(Model* model, std::size_t op_index) { Operator* add_op = nullptr; Operator* op_producing_add_input = nullptr; if (op_producing_sqrt_or_rsqrt_input->type == OperatorType::kAdd || - op_producing_sqrt_or_rsqrt_input->type == - OperatorType::kTensorFlowMaximum) { + op_producing_sqrt_or_rsqrt_input->type == OperatorType::kMaximum) { add_op = op_producing_sqrt_or_rsqrt_input; bool add_can_be_removed = false; CHECK_EQ(op_producing_sqrt_or_rsqrt_input->inputs.size(), 2); @@ -113,7 +111,7 @@ bool IdentifyL2Normalization::Run(Model* model, std::size_t op_index) { Operator* sum_op = add_op ? op_producing_add_input : op_producing_sqrt_or_rsqrt_input; - if (sum_op->type != OperatorType::kTensorFlowSum) { + if (sum_op->type != OperatorType::kSum) { AddMessageF( "Giving up trying to identify L2Normalization subgraph: " "expected Sum op, got %s", @@ -122,7 +120,7 @@ bool IdentifyL2Normalization::Run(Model* model, std::size_t op_index) { } Operator* square_op = GetOpWithOutput(*model, sum_op->inputs[0]); - if (square_op->type != OperatorType::kTensorFlowSquare) { + if (square_op->type != OperatorType::kSquare) { AddMessageF( "Giving up trying to identify L2Normalization subgraph: " "expected Square op, got %s", diff --git a/tensorflow/contrib/lite/toco/graph_transformations/identify_l2_pool.cc b/tensorflow/contrib/lite/toco/graph_transformations/identify_l2_pool.cc index f69400b82f..705e73779b 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/identify_l2_pool.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/identify_l2_pool.cc @@ -41,7 +41,7 @@ std::vector>::iterator FindOperator( bool IdentifyL2Pool::Run(Model* model, std::size_t op_index) { const auto sqrt_it = model->operators.begin() + op_index; const auto* sqrt_op = sqrt_it->get(); - if (sqrt_op->type != OperatorType::kTensorFlowSqrt) { + if (sqrt_op->type != OperatorType::kSqrt) { return false; } @@ -72,7 +72,7 @@ bool IdentifyL2Pool::Run(Model* model, std::size_t op_index) { square_op = GetOpWithOutput(*model, avpool_op->inputs[0]); CHECK_EQ(square_op->inputs.size(), 1); - if (square_op->type != OperatorType::kTensorFlowSquare) { + if (square_op->type != OperatorType::kSquare) { AddMessageF( "Giving up trying to identify L2Pool subgraph: " "expected Square op, got %s", diff --git a/tensorflow/contrib/lite/toco/graph_transformations/identify_lstm.cc b/tensorflow/contrib/lite/toco/graph_transformations/identify_lstm.cc index e9842524c8..910e38a6ba 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/identify_lstm.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/identify_lstm.cc @@ -266,26 +266,26 @@ bool IdentifyLstmCell::Run(Model* model, std::size_t op_index) { // State remember "information" activation function Operator* fc_output_split; - if (!MatchOperatorInputs(*state_info_tanh, *model, - OperatorType::kTensorFlowSplit, &fc_output_split)) { + if (!MatchOperatorInputs(*state_info_tanh, *model, OperatorType::kSplit, + &fc_output_split)) { return false; } // State remember gate activation function Operator* tmp; - if (!MatchOperatorInputs(*state_remember_sig, *model, - OperatorType::kTensorFlowSplit, &tmp) || + if (!MatchOperatorInputs(*state_remember_sig, *model, OperatorType::kSplit, + &tmp) || (tmp != fc_output_split)) { return false; } // State forget gate activation function - if (!MatchOperatorInputs(*state_forget_sig, *model, - OperatorType::kTensorFlowSplit, &tmp) || + if (!MatchOperatorInputs(*state_forget_sig, *model, OperatorType::kSplit, + &tmp) || (tmp != fc_output_split)) { return false; } // Fully connected output activation function - if (!MatchOperatorInputs(*fc_output_sig, *model, - OperatorType::kTensorFlowSplit, &tmp) || + if (!MatchOperatorInputs(*fc_output_sig, *model, OperatorType::kSplit, + &tmp) || (tmp != fc_output_split)) { return false; } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/identify_relu1.cc b/tensorflow/contrib/lite/toco/graph_transformations/identify_relu1.cc index bddb563206..94820a0166 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/identify_relu1.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/identify_relu1.cc @@ -60,24 +60,22 @@ bool IdentifyRelu1::Run(Model* model, std::size_t op_index) { // Follow sequences of min+max and max+min. First get the leading op. const auto op_it = model->operators.begin() + op_index; const auto* op_0 = op_it->get(); - if (op_0->type != OperatorType::kTensorFlowMinimum && - op_0->type != OperatorType::kTensorFlowMaximum) { + if (op_0->type != OperatorType::kMinimum && + op_0->type != OperatorType::kMaximum) { return false; } // Get the paired op and ensure it's the counter to the first. const auto* op_1 = GetOpWithInput(*model, op_0->outputs[0]); if (!op_1 || - (op_1->type != OperatorType::kTensorFlowMinimum && - op_1->type != OperatorType::kTensorFlowMaximum) || + (op_1->type != OperatorType::kMinimum && + op_1->type != OperatorType::kMaximum) || op_0->type == op_1->type) { return false; } - const auto* min_op = - op_0->type == OperatorType::kTensorFlowMinimum ? op_0 : op_1; - const auto* max_op = - op_0->type == OperatorType::kTensorFlowMaximum ? op_0 : op_1; + const auto* min_op = op_0->type == OperatorType::kMinimum ? op_0 : op_1; + const auto* max_op = op_0->type == OperatorType::kMaximum ? op_0 : op_1; if (min_op->inputs.size() != 2 || max_op->inputs.size() != 2) { return false; diff --git a/tensorflow/contrib/lite/toco/graph_transformations/merge_reshape_into_preceding_transpose.cc b/tensorflow/contrib/lite/toco/graph_transformations/merge_reshape_into_preceding_transpose.cc index 5065004093..95bc7f7d4b 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/merge_reshape_into_preceding_transpose.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/merge_reshape_into_preceding_transpose.cc @@ -106,7 +106,7 @@ bool MergeReshapeIntoPrecedingTranspose::Run(Model* model, std::size_t op_index) { auto it = model->operators.begin() + op_index; auto* reshape_op = ConvertOperator( - it->get(), OperatorType::kTensorFlowReshape); + it->get(), OperatorType::kReshape); if (reshape_op == nullptr) { return false; diff --git a/tensorflow/contrib/lite/toco/graph_transformations/propagate_array_data_types.cc b/tensorflow/contrib/lite/toco/graph_transformations/propagate_array_data_types.cc index 92d283ca2c..27a1049eaf 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/propagate_array_data_types.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/propagate_array_data_types.cc @@ -56,22 +56,22 @@ bool PropagateArrayDataTypes::Run(Model* model, std::size_t op_index) { // These operators unconditionally produce float outputs SetDataTypeForAllOutputs(model, op, ArrayDataType::kFloat); break; - case OperatorType::kTensorFlowLess: - case OperatorType::kTensorFlowLessEqual: - case OperatorType::kTensorFlowGreater: - case OperatorType::kTensorFlowGreaterEqual: - case OperatorType::kTensorFlowEqual: - case OperatorType::kTensorFlowNotEqual: + case OperatorType::kLess: + case OperatorType::kLessEqual: + case OperatorType::kGreater: + case OperatorType::kGreaterEqual: + case OperatorType::kEqual: + case OperatorType::kNotEqual: // These operators unconditionally produce bool outputs SetDataTypeForAllOutputs(model, op, ArrayDataType::kBool); break; case OperatorType::kRank: - case OperatorType::kTensorFlowShape: + case OperatorType::kShape: // These operators only produce int32 outputs. SetDataTypeForAllOutputs(model, op, ArrayDataType::kInt32); break; - case OperatorType::kTensorFlowSplit: - case OperatorType::kTensorFlowConcat: + case OperatorType::kSplit: + case OperatorType::kConcat: case OperatorType::kFill: { // These operators produce an output with the same type as their 2nd input CHECK_GE(op->inputs.size(), 2); @@ -135,7 +135,7 @@ bool PropagateArrayDataTypes::Run(Model* model, std::size_t op_index) { model->GetArray(op->outputs[1]).data_type = ArrayDataType ::kInt32; break; } - case OperatorType::kTensorFlowUnsupported: { + case OperatorType::kUnsupported: { auto* unsupported_op = static_cast(op); // Some output tensors from the op could be eliminated by optimization. // This can make unsupported_op->output_data_types have more elements than diff --git a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fake_quant_num_bits.cc b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fake_quant_num_bits.cc index 77c0886811..e25125b429 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fake_quant_num_bits.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fake_quant_num_bits.cc @@ -90,8 +90,8 @@ void ChangeArrayDataType(GraphTransformation* transformation, Array* array, bool DoesOpBlockBackwardPropagation(const Operator& op) { switch (op.type) { case OperatorType::kConcatenation: - case OperatorType::kTensorFlowConcat: - case OperatorType::kTensorFlowConcatV2: + case OperatorType::kConcat: + case OperatorType::kConcatV2: // Concat shouldn't block propagation, but we do expect that all inputs // have the same range. return false; @@ -100,10 +100,10 @@ bool DoesOpBlockBackwardPropagation(const Operator& op) { // FakeQuant so make sure we move across them. case OperatorType::kGather: // Gathers need their parameters changed to the appropriate data type. - case OperatorType::kTensorFlowReshape: + case OperatorType::kReshape: case OperatorType::kTranspose: case OperatorType::kSelect: - case OperatorType::kTensorFlowTile: + case OperatorType::kTile: // Reshapes and transposes don't change values. return false; default: @@ -121,11 +121,11 @@ bool DoesOpInputBlockBackwardPropagation(const Operator& op, int input_index) { // Ignore gather indices. return input_index != 0; break; - case OperatorType::kTensorFlowReshape: + case OperatorType::kReshape: case OperatorType::kTranspose: // Ignore reshape/transpose shapes/dimensions. return input_index != 0; - case OperatorType::kTensorFlowTile: + case OperatorType::kTile: // Ignore tile multiples. return input_index != 0; default: diff --git a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc index beda187f13..c61da203c6 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc @@ -572,11 +572,11 @@ void ProcessAddNOperator(Model* model, Operator* op) { bool KeepDims(const Operator& op) { switch (op.type) { - case OperatorType::kTensorFlowMin: + case OperatorType::kMin: // Reduction Min return static_cast(op).keep_dims; - case OperatorType::kTensorFlowMax: + case OperatorType::kMax: // Reduction Max return static_cast(op).keep_dims; - case OperatorType::kTensorFlowSum: + case OperatorType::kSum: return static_cast(op).keep_dims; case OperatorType::kMean: return static_cast(op).keep_dims; @@ -1577,14 +1577,14 @@ bool PropagateFixedSizes::Run(Model* model, std::size_t op_index) { case OperatorType::kLogistic: case OperatorType::kTanh: case OperatorType::kLocalResponseNormalization: - case OperatorType::kTensorFlowIdentity: + case OperatorType::kIdentity: case OperatorType::kFakeQuant: case OperatorType::kNeg: - case OperatorType::kTensorFlowRsqrt: - case OperatorType::kTensorFlowSqrt: - case OperatorType::kTensorFlowSquare: - case OperatorType::kTensorFlowAll: - case OperatorType::kTensorFlowAssert: + case OperatorType::kRsqrt: + case OperatorType::kSqrt: + case OperatorType::kSquare: + case OperatorType::kAll: + case OperatorType::kAssert: case OperatorType::kCast: case OperatorType::kFloor: case OperatorType::kExp: @@ -1603,14 +1603,14 @@ bool PropagateFixedSizes::Run(Model* model, std::size_t op_index) { case OperatorType::kDiv: case OperatorType::kFloorDiv: case OperatorType::kFloorMod: - case OperatorType::kTensorFlowLess: - case OperatorType::kTensorFlowLessEqual: - case OperatorType::kTensorFlowGreater: - case OperatorType::kTensorFlowMaximum: - case OperatorType::kTensorFlowMinimum: - case OperatorType::kTensorFlowGreaterEqual: - case OperatorType::kTensorFlowEqual: - case OperatorType::kTensorFlowNotEqual: + case OperatorType::kLess: + case OperatorType::kLessEqual: + case OperatorType::kGreater: + case OperatorType::kMaximum: // Element-wise Maximum + case OperatorType::kMinimum: // Element-wise Minimum + case OperatorType::kGreaterEqual: + case OperatorType::kEqual: + case OperatorType::kNotEqual: ProcessSimpleBinaryOperator(model, op); break; case OperatorType::kAddN: @@ -1643,7 +1643,7 @@ bool PropagateFixedSizes::Run(Model* model, std::size_t op_index) { ProcessFullyConnectedOperator(model, static_cast(op)); break; - case OperatorType::kTensorFlowReshape: + case OperatorType::kReshape: ProcessTensorFlowReshapeOperator( model, static_cast(op)); break; @@ -1656,9 +1656,9 @@ bool PropagateFixedSizes::Run(Model* model, std::size_t op_index) { case OperatorType::kL2Pool: ProcessL2PoolOperator(model, static_cast(op)); break; - case OperatorType::kTensorFlowMin: - case OperatorType::kTensorFlowMax: - case OperatorType::kTensorFlowSum: + case OperatorType::kMin: // Reduction Min + case OperatorType::kMax: // Reduction Max + case OperatorType::kSum: case OperatorType::kMean: ProcessTensorFlowReductionOperator(model, op); break; @@ -1669,26 +1669,26 @@ bool PropagateFixedSizes::Run(Model* model, std::size_t op_index) { ProcessSliceOperator(model, static_cast(op)); break; - case OperatorType::kTensorFlowSwitch: + case OperatorType::kSwitch: // We can't know the sizes of the outputs until we have resolved the // predicate, and once we have resolved the predicate, the whole // Switch node will get resolved away. // See ResolveTensorFlowSwitch. break; - case OperatorType::kTensorFlowMerge: + case OperatorType::kMerge: // No need to bother resolving TensorFlow Merge ops: other graph // transformations will remove them anyway. // See ResolveTensorFlowMerge. break; - case OperatorType::kTensorFlowSplit: + case OperatorType::kSplit: ProcessTensorFlowSplitOperator(model, static_cast(op)); break; case OperatorType::kSqueeze: ProcessSqueezeOperator(model, static_cast(op)); break; - case OperatorType::kTensorFlowConcat: - case OperatorType::kTensorFlowConcatV2: + case OperatorType::kConcat: + case OperatorType::kConcatV2: // Unimplemented, hopefully another graph transformation will // drop it or rewrite it. Concretely, either ResolveTensorFlowConcat // will resolve this node to a DepthConcatenation, or else we have @@ -1704,7 +1704,7 @@ bool PropagateFixedSizes::Run(Model* model, std::size_t op_index) { case OperatorType::kRank: ProcessRankOperator(model, static_cast(op)); break; - case OperatorType::kTensorFlowShape: + case OperatorType::kShape: ProcessShapeOperator(model, static_cast(op)); break; case OperatorType::kStack: @@ -1725,7 +1725,7 @@ bool PropagateFixedSizes::Run(Model* model, std::size_t op_index) { ProcessLstmCellOperator(model, static_cast(op)); break; case OperatorType::kBatchMatMul: - case OperatorType::kTensorFlowMatMul: + case OperatorType::kMatMul: // MatMul operators are converted to FullyConnected, after which their // shapes are propagated. break; @@ -1750,7 +1750,7 @@ bool PropagateFixedSizes::Run(Model* model, std::size_t op_index) { case OperatorType::kArgMax: ProcessArgMaxOperator(model, static_cast(op)); break; - case OperatorType::kTensorFlowUnsupported: + case OperatorType::kUnsupported: break; case OperatorType::kSvdf: ProcessSvdfOperator(model, static_cast(op)); @@ -1772,7 +1772,7 @@ bool PropagateFixedSizes::Run(Model* model, std::size_t op_index) { ProcessSparseToDenseOperator(model, static_cast(op)); break; - case OperatorType::kTensorFlowTile: + case OperatorType::kTile: ProcessTileOperator(model, static_cast(op)); break; default: diff --git a/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc b/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc index eca2c701f8..1c61b8cb36 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc @@ -33,7 +33,7 @@ namespace { bool SupportsQuantization(const Operator& op) { auto type = op.type; - if (type == OperatorType::kTensorFlowUnsupported) { + if (type == OperatorType::kUnsupported) { auto* unsupported = static_cast(&op); return unsupported->quantized; } @@ -42,15 +42,13 @@ bool SupportsQuantization(const Operator& op) { type == OperatorType::kConcatenation || type == OperatorType::kL2Normalization || type == OperatorType::kAdd || type == OperatorType::kAveragePool || type == OperatorType::kMaxPool || - type == OperatorType::kTensorFlowMinimum || - type == OperatorType::kTensorFlowMaximum || + type == OperatorType::kMinimum || type == OperatorType::kMaximum || type == OperatorType::kLogistic || type == OperatorType::kSoftmax || type == OperatorType::kLogSoftmax || type == OperatorType::kSlice || type == OperatorType::kResizeBilinear || - type == OperatorType::kTensorFlowSplit || type == OperatorType::kSub || + type == OperatorType::kSplit || type == OperatorType::kSub || type == OperatorType::kSqueeze || type == OperatorType::kPad || - type == OperatorType::kPadV2 || - type == OperatorType::kTensorFlowReshape || + type == OperatorType::kPadV2 || type == OperatorType::kReshape || type == OperatorType::kTanh || type == OperatorType::kMul || type == OperatorType::kSpaceToBatchND || type == OperatorType::kSpaceToDepth || @@ -58,11 +56,10 @@ bool SupportsQuantization(const Operator& op) { type == OperatorType::kDepthToSpace || type == OperatorType::kLstmCell || type == OperatorType::kGather || type == OperatorType::kTranspose || type == OperatorType::kMean || - type == OperatorType::kTensorFlowGreater || - type == OperatorType::kTensorFlowGreaterEqual || - type == OperatorType::kTensorFlowLess || - type == OperatorType::kTensorFlowLessEqual || - type == OperatorType::kSelect || type == OperatorType::kArgMax; + type == OperatorType::kGreater || + type == OperatorType::kGreaterEqual || type == OperatorType::kLess || + type == OperatorType::kLessEqual || type == OperatorType::kSelect || + type == OperatorType::kArgMax; } const MinMax& GetOrComputeMinMax(Model* model, const string& array_name) { @@ -330,12 +327,12 @@ bool ChooseQuantizationForOperatorOutput( } if ((op.type == OperatorType::kDepthToSpace) || (op.type == OperatorType::kSpaceToDepth) || - (op.type == OperatorType::kTensorFlowReshape) || - (op.type == OperatorType::kTensorFlowSplit) || + (op.type == OperatorType::kReshape) || + (op.type == OperatorType::kSplit) || (op.type == OperatorType::kConcatenation && model->flags.change_concat_input_ranges())) { int data_input_index = 0; - if (op.type == OperatorType::kTensorFlowSplit) { + if (op.type == OperatorType::kSplit) { data_input_index = 1; } // Copying and rearrangement ops should preserve the quantization parameters diff --git a/tensorflow/contrib/lite/toco/graph_transformations/remove_tensorflow_assert.cc b/tensorflow/contrib/lite/toco/graph_transformations/remove_tensorflow_assert.cc index 35a0c46532..73ad326299 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/remove_tensorflow_assert.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/remove_tensorflow_assert.cc @@ -26,7 +26,7 @@ namespace toco { bool RemoveTensorFlowAssert::Run(Model* model, std::size_t op_index) { const auto assert_it = model->operators.begin() + op_index; const auto* assert_op = assert_it->get(); - if (assert_op->type != OperatorType::kTensorFlowAssert) { + if (assert_op->type != OperatorType::kAssert) { return false; } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/remove_tensorflow_identity.cc b/tensorflow/contrib/lite/toco/graph_transformations/remove_tensorflow_identity.cc index 404269bbfd..7ec7752f25 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/remove_tensorflow_identity.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/remove_tensorflow_identity.cc @@ -28,7 +28,7 @@ namespace toco { bool RemoveTensorFlowIdentity::Run(Model* model, std::size_t op_index) { const auto passthru_it = model->operators.begin() + op_index; const auto* passthru_op = passthru_it->get(); - if (passthru_op->type != OperatorType::kTensorFlowIdentity) { + if (passthru_op->type != OperatorType::kIdentity) { return false; } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_passthrough.cc b/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_passthrough.cc index a950fe6442..9f5d8b9450 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_passthrough.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_passthrough.cc @@ -97,7 +97,7 @@ bool RemoveTrivialPassthroughOp(GraphTransformation* transformation, "Cannot remove %s, neither its main input nor its output may be " "discarded", LogName(*passthru_op)); - if (passthru_op->type != OperatorType::kTensorFlowReshape && + if (passthru_op->type != OperatorType::kReshape && model->GetArray(main_input_name).has_shape()) { // We can't remove either array but we can remove the op. Converting it to // a reshape gives us some hope of later on fixing that (either in the diff --git a/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_quantized_min_max.cc b/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_quantized_min_max.cc index eaee1c662b..142c876b15 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_quantized_min_max.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_quantized_min_max.cc @@ -47,11 +47,11 @@ bool IsTrivialMinMax(GraphTransformation* transformation, const Model& model, double clamp_min; double clamp_max; switch (op_type) { - case OperatorType::kTensorFlowMinimum: + case OperatorType::kMinimum: // Element-wise Minimum clamp_min = -std::numeric_limits::infinity(); clamp_max = clamp_value; break; - case OperatorType::kTensorFlowMaximum: + case OperatorType::kMaximum: // Element-wise Maximum clamp_min = clamp_value; clamp_max = std::numeric_limits::infinity(); break; @@ -72,8 +72,8 @@ bool IsTrivialMinMax(GraphTransformation* transformation, const Model& model, bool RemoveTrivialQuantizedMinMax::Run(Model* model, std::size_t op_index) { const auto it = model->operators.begin() + op_index; auto* op = it->get(); - if ((op->type != OperatorType::kTensorFlowMinimum && - op->type != OperatorType::kTensorFlowMaximum) || + if ((op->type != OperatorType::kMinimum && + op->type != OperatorType::kMaximum) || op->inputs.size() != 2) { return false; } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_reshape.cc b/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_reshape.cc index e28d8cf01e..404f27e067 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_reshape.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_reshape.cc @@ -30,7 +30,7 @@ namespace { bool IsReshapeTrivial(const Model& model, const Operator& op, RemoveTrivialReshape* transformation) { - CHECK(op.type == OperatorType::kTensorFlowReshape); + CHECK(op.type == OperatorType::kReshape); // One way in which a reshape can be trivial is if its // output shape is == its input shape @@ -58,7 +58,7 @@ bool IsReshapeTrivial(const Model& model, const Operator& op, // is only consumed by another reshape. if (CountOpsWithInput(model, op.outputs[0]) == 1) { const auto* next_op = GetOpWithInput(model, op.outputs[0]); - if (next_op->type == OperatorType::kTensorFlowReshape) { + if (next_op->type == OperatorType::kReshape) { transformation->AddMessageF( "%s is trivial because its output is only consumed by another " "Reshape op %s", @@ -75,7 +75,7 @@ bool IsReshapeTrivial(const Model& model, const Operator& op, bool RemoveTrivialReshape::Run(Model* model, std::size_t op_index) { const auto reshape_it = model->operators.begin() + op_index; auto* reshape_op = reshape_it->get(); - if (reshape_op->type != OperatorType::kTensorFlowReshape) { + if (reshape_op->type != OperatorType::kReshape) { return false; } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/remove_unused_op.cc b/tensorflow/contrib/lite/toco/graph_transformations/remove_unused_op.cc index 1956ab2d20..dde91234a8 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/remove_unused_op.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/remove_unused_op.cc @@ -48,7 +48,7 @@ bool RemoveUnusedOp::Run(Model* model, std::size_t op_index) { for (const auto& rnn_state : model->flags.rnn_states()) { if (output == rnn_state.state_array()) { CHECK(op->type == OperatorType::kFill || - op->type == OperatorType::kTensorFlowIdentity); + op->type == OperatorType::kIdentity); found_output_as_rnn_state_array = true; break; } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/reorder_elementwise_unary.cc b/tensorflow/contrib/lite/toco/graph_transformations/reorder_elementwise_unary.cc index 9f5b7920cb..550de83018 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/reorder_elementwise_unary.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/reorder_elementwise_unary.cc @@ -37,8 +37,8 @@ bool IsElementwiseOperator(OperatorType optype) { case OperatorType::kRelu1: case OperatorType::kRelu6: case OperatorType::kTanh: - case OperatorType::kTensorFlowSqrt: - case OperatorType::kTensorFlowSquare: + case OperatorType::kSqrt: + case OperatorType::kSquare: return true; default: return false; @@ -51,7 +51,7 @@ bool IsMoveOperator(OperatorType optype) { case OperatorType::kExpandDims: case OperatorType::kSpaceToDepth: case OperatorType::kSqueeze: - case OperatorType::kTensorFlowReshape: + case OperatorType::kReshape: case OperatorType::kTranspose: return true; default: diff --git a/tensorflow/contrib/lite/toco/graph_transformations/reorder_reshape_transpose.cc b/tensorflow/contrib/lite/toco/graph_transformations/reorder_reshape_transpose.cc index 9e7fe1b1cc..c907a597cb 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/reorder_reshape_transpose.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/reorder_reshape_transpose.cc @@ -123,8 +123,8 @@ bool ReorderReshapeTranspose::Run(Model* model, std::size_t op_index) { } TensorFlowReshapeOperator* reshape_op = - ConvertOperator( - reshape_it->get(), OperatorType::kTensorFlowReshape); + ConvertOperator(reshape_it->get(), + OperatorType::kReshape); if (reshape_op == nullptr) { return false; } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_binary.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_binary.cc index 6e78653fad..f7e5aa6609 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_binary.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_binary.cc @@ -145,17 +145,17 @@ void EvaluateBinaryOperatorOnConstantInputs(Model* model, outval = floor(val0 / val1); } else if (binary_op->type == OperatorType::kFloorMod) { outval = val0 - (floor(val0 / val1) * val1); - } else if (binary_op->type == OperatorType::kTensorFlowMinimum) { + } else if (binary_op->type == OperatorType::kMinimum) { outval = std::min(val0, val1); - } else if (binary_op->type == OperatorType::kTensorFlowMaximum) { + } else if (binary_op->type == OperatorType::kMaximum) { outval = std::max(val0, val1); - } else if (binary_op->type == OperatorType::kTensorFlowLess) { + } else if (binary_op->type == OperatorType::kLess) { outval = val0 < val1; - } else if (binary_op->type == OperatorType::kTensorFlowLessEqual) { + } else if (binary_op->type == OperatorType::kLessEqual) { outval = val0 <= val1; - } else if (binary_op->type == OperatorType::kTensorFlowGreater) { + } else if (binary_op->type == OperatorType::kGreater) { outval = val0 > val1; - } else if (binary_op->type == OperatorType::kTensorFlowGreaterEqual) { + } else if (binary_op->type == OperatorType::kGreaterEqual) { outval = val0 >= val1; } else { LOG(FATAL) << "should not get here"; @@ -198,12 +198,12 @@ bool ResolveConstantBinaryOperator::Run(Model* model, std::size_t op_index) { binary_op->type != OperatorType::kDiv && binary_op->type != OperatorType::kFloorDiv && binary_op->type != OperatorType::kFloorMod && - binary_op->type != OperatorType::kTensorFlowMinimum && - binary_op->type != OperatorType::kTensorFlowMaximum && - binary_op->type != OperatorType::kTensorFlowLess && - binary_op->type != OperatorType::kTensorFlowLessEqual && - binary_op->type != OperatorType::kTensorFlowGreater && - binary_op->type != OperatorType::kTensorFlowGreaterEqual) { + binary_op->type != OperatorType::kMinimum && + binary_op->type != OperatorType::kMaximum && + binary_op->type != OperatorType::kLess && + binary_op->type != OperatorType::kLessEqual && + binary_op->type != OperatorType::kGreater && + binary_op->type != OperatorType::kGreaterEqual) { return false; } CHECK_EQ(binary_op->inputs.size(), 2); diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_reshape.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_reshape.cc index 7e7ad383e7..41562ab393 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_reshape.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_reshape.cc @@ -25,7 +25,7 @@ namespace toco { bool ResolveConstantReshape::Run(Model* model, std::size_t op_index) { auto it = model->operators.begin() + op_index; const auto* base_op = it->get(); - if (base_op->type != OperatorType::kTensorFlowReshape) { + if (base_op->type != OperatorType::kReshape) { return false; } const auto* op = static_cast(base_op); diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_shape_or_rank.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_shape_or_rank.cc index 9ea01acd05..8a0e3e8995 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_shape_or_rank.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_shape_or_rank.cc @@ -22,8 +22,7 @@ namespace toco { bool ResolveConstantShapeOrRank::Run(Model* model, std::size_t op_index) { const auto it = model->operators.begin() + op_index; const auto* op = it->get(); - if (!(op->type == OperatorType::kTensorFlowShape || - op->type == OperatorType::kRank)) { + if (!(op->type == OperatorType::kShape || op->type == OperatorType::kRank)) { return false; } @@ -48,7 +47,7 @@ bool ResolveConstantShapeOrRank::Run(Model* model, std::size_t op_index) { // Compute the output CHECK(!output_array.buffer); auto& output_buffer = output_array.GetMutableBuffer(); - if (op->type == OperatorType::kTensorFlowShape) { + if (op->type == OperatorType::kShape) { // Copy the input shape into the output buffer. output_buffer.data = input_array.shape().dims(); } else if (op->type == OperatorType::kRank) { diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_unary.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_unary.cc index f6c8f79d8d..f89ef85fdb 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_unary.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_unary.cc @@ -53,13 +53,13 @@ bool ResolveConstantUnaryOperator::Run(Model* model, std::size_t op_index) { case OperatorType::kCast: case OperatorType::kLog: case OperatorType::kNeg: - case OperatorType::kTensorFlowRsqrt: - case OperatorType::kTensorFlowSqrt: - case OperatorType::kTensorFlowSquare: - case OperatorType::kTensorFlowSum: - case OperatorType::kTensorFlowMin: - case OperatorType::kTensorFlowMax: - case OperatorType::kTensorFlowReshape: + case OperatorType::kRsqrt: + case OperatorType::kSqrt: + case OperatorType::kSquare: + case OperatorType::kSum: + case OperatorType::kMin: // Reduction Min + case OperatorType::kMax: // Reduction Max + case OperatorType::kReshape: case OperatorType::kRelu6: case OperatorType::kRelu1: case OperatorType::kRelu: @@ -103,7 +103,7 @@ bool ResolveConstantUnaryOperator::Run(Model* model, std::size_t op_index) { // The min-max is only copied for ops that copy data without arithmetic. // In future trivial transpose, etc, can be handled here. - if (unary_op->type == OperatorType::kTensorFlowReshape) { + if (unary_op->type == OperatorType::kReshape) { CopyMinMaxFromFirstInput(*unary_op, model); } @@ -164,10 +164,10 @@ bool ResolveConstantUnaryOperator::Run(Model* model, std::size_t op_index) { } output_float_data[i] = outval; } - } else if (unary_op->type == OperatorType::kTensorFlowReshape) { + } else if (unary_op->type == OperatorType::kReshape) { CHECK(input_buffer_size == output_buffer_size); output_float_data = *input_float_data; - } else if (unary_op->type == OperatorType::kTensorFlowSum) { + } else if (unary_op->type == OperatorType::kSum) { CHECK_EQ(unary_op->inputs.size(), 2) << "Sum needs 2 inputs"; if (!IsConstantParameterArray(*model, unary_op->inputs[1])) { AddMessageF("Axis input is non-constant"); @@ -196,7 +196,7 @@ bool ResolveConstantUnaryOperator::Run(Model* model, std::size_t op_index) { } output_float_data[i] = sum; } - } else if (unary_op->type == OperatorType::kTensorFlowMin) { + } else if (unary_op->type == OperatorType::kMin) { // At the moment only full reduction across all dimensions is supported. // TODO(starka): Output should not be padded. for (int i = 0; i < output_dims_count; i++) { @@ -207,7 +207,7 @@ bool ResolveConstantUnaryOperator::Run(Model* model, std::size_t op_index) { min = std::min(min, (*input_float_data)[i]); } output_float_data[0] = min; - } else if (unary_op->type == OperatorType::kTensorFlowMax) { + } else if (unary_op->type == OperatorType::kMax) { // At the moment only full reduction across all dimensions is supported. // TODO(starka): Output should not be padded. for (int i = 0; i < output_dims_count; i++) { @@ -220,9 +220,9 @@ bool ResolveConstantUnaryOperator::Run(Model* model, std::size_t op_index) { output_float_data[0] = max; } else if (unary_op->type == OperatorType::kNeg || unary_op->type == OperatorType::kLog || - unary_op->type == OperatorType::kTensorFlowRsqrt || - unary_op->type == OperatorType::kTensorFlowSqrt || - unary_op->type == OperatorType::kTensorFlowSquare) { + unary_op->type == OperatorType::kRsqrt || + unary_op->type == OperatorType::kSqrt || + unary_op->type == OperatorType::kSquare) { // Element-wise ops. Should have perfectly matching sizes here. for (int i = 0; i < output_dims_count; i++) { CHECK_EQ(output_shape.dims(i), input_shape.dims(i)); @@ -235,11 +235,11 @@ bool ResolveConstantUnaryOperator::Run(Model* model, std::size_t op_index) { outval = -val; } else if (unary_op->type == OperatorType::kLog) { outval = std::log(val); - } else if (unary_op->type == OperatorType::kTensorFlowRsqrt) { + } else if (unary_op->type == OperatorType::kRsqrt) { outval = 1.0f / std::sqrt(val); - } else if (unary_op->type == OperatorType::kTensorFlowSqrt) { + } else if (unary_op->type == OperatorType::kSqrt) { outval = std::sqrt(val); - } else if (unary_op->type == OperatorType::kTensorFlowSquare) { + } else if (unary_op->type == OperatorType::kSquare) { outval = val * val; } else { LOG(FATAL) << "should not get here."; diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_reshape_attributes.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_reshape_attributes.cc index 2e063e3554..b615c9a545 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_reshape_attributes.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_reshape_attributes.cc @@ -28,7 +28,7 @@ namespace toco { bool ResolveReshapeAttributes::Run(Model* model, std::size_t op_index) { const auto reshape_it = model->operators.begin() + op_index; auto* reshape_op = reshape_it->get(); - if (reshape_op->type != OperatorType::kTensorFlowReshape) { + if (reshape_op->type != OperatorType::kReshape) { return false; } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_squeeze_attributes.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_squeeze_attributes.cc index dd3e73635a..e8bb85704e 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_squeeze_attributes.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_squeeze_attributes.cc @@ -36,7 +36,7 @@ bool ResolveSqueezeAttributes::Run(Model* model, std::size_t op_index) { // If the output is consumed by a reshape op, it's a trivial squeeze. if (CountOpsWithInput(*model, squeeze_op->outputs[0]) == 1) { const auto* next_op = GetOpWithInput(*model, squeeze_op->outputs[0]); - if (next_op->type == OperatorType::kTensorFlowReshape) { + if (next_op->type == OperatorType::kReshape) { AddMessageF( "%s is trivial because its output is only consumed by a " "Reshape op", diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_concat.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_concat.cc index 5c0c1e3478..fa5ee89933 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_concat.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_concat.cc @@ -28,8 +28,8 @@ namespace toco { bool ResolveTensorFlowConcat::Run(Model* model, std::size_t op_index) { auto concat_it = model->operators.begin() + op_index; const auto* tf_concat_op = concat_it->get(); - if (tf_concat_op->type != OperatorType::kTensorFlowConcat && - tf_concat_op->type != OperatorType::kTensorFlowConcatV2) { + if (tf_concat_op->type != OperatorType::kConcat && + tf_concat_op->type != OperatorType::kConcatV2) { return false; } @@ -38,7 +38,7 @@ bool ResolveTensorFlowConcat::Run(Model* model, std::size_t op_index) { // of inputs: in Concat,the axis is the first input, while in // ConcatV2, it is the last input. std::size_t axis_pos = 0; - if (tf_concat_op->type == OperatorType::kTensorFlowConcatV2) { + if (tf_concat_op->type == OperatorType::kConcatV2) { axis_pos = tf_concat_op->inputs.size() - 1; } const string axis_name = tf_concat_op->inputs[axis_pos]; diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_matmul.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_matmul.cc index 2a236d3f98..d496f5ae5e 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_matmul.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_matmul.cc @@ -26,7 +26,7 @@ namespace toco { bool ResolveTensorFlowMatMul::Run(Model* model, std::size_t op_index) { auto matmul_it = model->operators.begin() + op_index; - if (matmul_it->get()->type != OperatorType::kTensorFlowMatMul) { + if (matmul_it->get()->type != OperatorType::kMatMul) { return false; } const auto* matmul_op = @@ -97,7 +97,7 @@ bool ResolveTensorFlowMatMul::Run(Model* model, std::size_t op_index) { // MatMul op as a FullyConnected. However, TensorFlow skips the Reshape ops if // the input doesn't need reshaping, so we can't just match (Reshape, MatMul) // pairs. - if (previous_op && previous_op->type == OperatorType::kTensorFlowReshape) { + if (previous_op && previous_op->type == OperatorType::kReshape) { AddMessageF("Combining %s and %s into %s", LogName(*previous_op), LogName(*matmul_op), LogName(*fc_op)); const auto& previous_op_output = previous_op->outputs[0]; diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_merge.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_merge.cc index 38e0005890..4edffe3d48 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_merge.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_merge.cc @@ -27,7 +27,7 @@ namespace toco { bool ResolveTensorFlowMerge::Run(Model* model, std::size_t op_index) { const auto merge_it = model->operators.begin() + op_index; const auto* merge_op = merge_it->get(); - if (merge_op->type != OperatorType::kTensorFlowMerge) { + if (merge_op->type != OperatorType::kMerge) { return false; } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_switch.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_switch.cc index a418073441..da8e7a2d1c 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_switch.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_switch.cc @@ -27,7 +27,7 @@ namespace toco { bool ResolveTensorFlowSwitch::Run(Model* model, std::size_t op_index) { const auto switch_it = model->operators.begin() + op_index; const auto* switch_op = switch_it->get(); - if (switch_op->type != OperatorType::kTensorFlowSwitch) { + if (switch_op->type != OperatorType::kSwitch) { return false; } @@ -92,7 +92,7 @@ bool ResolveTensorFlowSwitch::Run(Model* model, std::size_t op_index) { if (*input_it == switch_op->outputs[nonselected_output_index]) { // Let us guard our assumption that only Merge nodes consume the outputs // of Switch nodes: - CHECK(other_op->type == OperatorType::kTensorFlowMerge); + CHECK(other_op->type == OperatorType::kMerge); input_it = other_op->inputs.erase(input_it); } else { ++input_it; diff --git a/tensorflow/contrib/lite/toco/model.h b/tensorflow/contrib/lite/toco/model.h index 2585cff56e..ef170b3884 100644 --- a/tensorflow/contrib/lite/toco/model.h +++ b/tensorflow/contrib/lite/toco/model.h @@ -96,38 +96,38 @@ enum class OperatorType : uint8 { // Special operators used for importing TensorFlow nodes. // The general intent is to have some graph transformation either // drop them or rewrite them as general-purpose operators. - kTensorFlowAll, - kTensorFlowAssert, - kTensorFlowConcat, - kTensorFlowConcatV2, - kTensorFlowGreater, - kTensorFlowGreaterEqual, - kTensorFlowIdentity, - kTensorFlowLess, - kTensorFlowLessEqual, - kTensorFlowMax, - kTensorFlowMaximum, - kTensorFlowMin, - kTensorFlowMinimum, - kTensorFlowMatMul, - kTensorFlowMerge, + kAll, + kAssert, + kConcat, + kConcatV2, + kGreater, + kGreaterEqual, + kIdentity, + kLess, + kLessEqual, + kMax, // Reduction Max + kMaximum, // Element-wise Maximum + kMin, // Reduction Min + kMinimum, // Element-wise Minimum + kMatMul, + kMerge, kNeg, - kTensorFlowReshape, - kTensorFlowRsqrt, - kTensorFlowShape, - kTensorFlowSplit, - kTensorFlowSqrt, - kTensorFlowSquare, - kTensorFlowSum, - kTensorFlowSwitch, - kTensorFlowTile, + kReshape, + kRsqrt, + kShape, + kSplit, + kSqrt, + kSquare, + kSum, + kSwitch, + kTile, kTranspose, kTopK_V2, kDynamicPartition, kDynamicStitch, // An unsupported TF operation. It's only needed to be able to represent TF // graph internally and is expected to be dropped by graph transformations. - kTensorFlowUnsupported, + kUnsupported, // Finally, TensorFlow uses different conventions for axes ordering, // see AxesOrder, and this cannot always be resolved at the time of importing // nodes, as TensorFlow parameters may be constant-expression subgraphs @@ -136,8 +136,8 @@ enum class OperatorType : uint8 { kReorderAxes, kSelect, kSparseToDense, - kTensorFlowEqual, - kTensorFlowNotEqual, + kEqual, + kNotEqual, }; // Helper to deal with TensorFlow arrays using a different ordering of @@ -801,7 +801,7 @@ struct DivOperator : Operator { // // TensorFlow equivalent: Identity struct TensorFlowIdentityOperator : Operator { - TensorFlowIdentityOperator() : Operator(OperatorType::kTensorFlowIdentity) {} + TensorFlowIdentityOperator() : Operator(OperatorType::kIdentity) {} }; // Batch matrix multiplication operator. This comes from the (deprecated) @@ -827,7 +827,7 @@ struct BatchMatMulOperator : Operator { // // TensorFlow equivalent: MatMul struct TensorFlowMatMulOperator : Operator { - TensorFlowMatMulOperator() : Operator(OperatorType::kTensorFlowMatMul) {} + TensorFlowMatMulOperator() : Operator(OperatorType::kMatMul) {} }; // Padding operator. Pads a tensor with zeros. @@ -961,7 +961,7 @@ struct StridedSliceOperator : Operator { // TensorFlow equivalent: Reshape --- except that we only support a special case // here, where the output shape is a matrix (2D) shape. struct TensorFlowReshapeOperator : Operator { - TensorFlowReshapeOperator() : Operator(OperatorType::kTensorFlowReshape) {} + TensorFlowReshapeOperator() : Operator(OperatorType::kReshape) {} std::vector shape; }; @@ -1131,7 +1131,7 @@ struct SelectOperator : Operator { // // TensorFlow equivalent: Rsqrt struct TensorFlowRsqrtOperator : Operator { - TensorFlowRsqrtOperator() : Operator(OperatorType::kTensorFlowRsqrt) {} + TensorFlowRsqrtOperator() : Operator(OperatorType::kRsqrt) {} }; // Stacks a list of rank-R tensors into one rank-(R+1) tensor. @@ -1159,7 +1159,7 @@ struct StackOperator : Operator { // // TensorFlow equivalent: Shape. struct TensorFlowShapeOperator : Operator { - TensorFlowShapeOperator() : Operator(OperatorType::kTensorFlowShape) {} + TensorFlowShapeOperator() : Operator(OperatorType::kShape) {} ArrayDataType output_data_type = ArrayDataType::kInt32; }; @@ -1170,7 +1170,7 @@ struct TensorFlowShapeOperator : Operator { // // TensorFlow equivalent: Sqrt struct TensorFlowSqrtOperator : Operator { - TensorFlowSqrtOperator() : Operator(OperatorType::kTensorFlowSqrt) {} + TensorFlowSqrtOperator() : Operator(OperatorType::kSqrt) {} }; // Element-wise square (x*x) operator. @@ -1180,7 +1180,7 @@ struct TensorFlowSqrtOperator : Operator { // // TensorFlow equivalent: Square struct TensorFlowSquareOperator : Operator { - TensorFlowSquareOperator() : Operator(OperatorType::kTensorFlowSquare) {} + TensorFlowSquareOperator() : Operator(OperatorType::kSquare) {} }; // Transposes a tensor. @@ -1215,7 +1215,7 @@ struct SubOperator : Operator { // // TensorFlow equivalent: Sum struct TensorFlowSumOperator : Operator { - TensorFlowSumOperator() : Operator(OperatorType::kTensorFlowSum) {} + TensorFlowSumOperator() : Operator(OperatorType::kSum) {} bool keep_dims = false; }; @@ -1225,7 +1225,7 @@ struct TensorFlowSumOperator : Operator { // inputs[0]: required: the input array // inputs[1]: required: int array with length of rank(input[0]) struct TensorFlowTileOperator : Operator { - TensorFlowTileOperator() : Operator(OperatorType::kTensorFlowTile) {} + TensorFlowTileOperator() : Operator(OperatorType::kTile) {} }; // TensorFlow Slice equivalent. Refer to TensorFlow documentation for details. @@ -1240,7 +1240,7 @@ struct SliceOperator : Operator { // Not fully supported, just a placeholder to handle TensorFlow graphs and // support graph transformations to other operator types by matching sub-graphs. struct TensorFlowSplitOperator : Operator { - TensorFlowSplitOperator() : Operator(OperatorType::kTensorFlowSplit) {} + TensorFlowSplitOperator() : Operator(OperatorType::kSplit) {} int num_split = 0; }; @@ -1251,7 +1251,7 @@ struct TensorFlowSplitOperator : Operator { // dimension then we can change this op into a DepthConcatenation op. // Otherwise, we hope for some other graph transformation to drop this node. struct TensorFlowConcatOperator : Operator { - TensorFlowConcatOperator() : Operator(OperatorType::kTensorFlowConcat) {} + TensorFlowConcatOperator() : Operator(OperatorType::kConcat) {} }; // TensorFlow ConcatV2 equivalent. Refer to TensorFlow documentation for @@ -1262,7 +1262,7 @@ struct TensorFlowConcatOperator : Operator { // dimension then we can change this op into a DepthConcatenation op. // Otherwise, we hope for some other graph transformation to drop this node. struct TensorFlowConcatV2Operator : Operator { - TensorFlowConcatV2Operator() : Operator(OperatorType::kTensorFlowConcatV2) {} + TensorFlowConcatV2Operator() : Operator(OperatorType::kConcatV2) {} }; // TensorFlow Merge equivalent. Refer to TensorFlow documentation for details. @@ -1278,7 +1278,7 @@ struct TensorFlowConcatV2Operator : Operator { // control flow that can be resolved at tooling time (independently of input // activations). struct TensorFlowMergeOperator : Operator { - TensorFlowMergeOperator() : Operator(OperatorType::kTensorFlowMerge) {} + TensorFlowMergeOperator() : Operator(OperatorType::kMerge) {} }; // TensorFlow Switch equivalent. Refer to TensorFlow documentation for details. @@ -1301,7 +1301,7 @@ struct TensorFlowMergeOperator : Operator { // control flow that can be resolved at tooling time (independently of input // activations). struct TensorFlowSwitchOperator : Operator { - TensorFlowSwitchOperator() : Operator(OperatorType::kTensorFlowSwitch) {} + TensorFlowSwitchOperator() : Operator(OperatorType::kSwitch) {} }; // TensorFlow All equivalent. Refer to TensorFlow documentation for details. @@ -1310,7 +1310,7 @@ struct TensorFlowSwitchOperator : Operator { // Typically, this is only used as an input to an Assert node, so can be // removed as an unused node as we drop Assert nodes. struct TensorFlowAllOperator : Operator { - TensorFlowAllOperator() : Operator(OperatorType::kTensorFlowAll) {} + TensorFlowAllOperator() : Operator(OperatorType::kAll) {} }; // TensorFlow Assert equivalent. Refer to TensorFlow documentation for details. @@ -1318,7 +1318,7 @@ struct TensorFlowAllOperator : Operator { // support graph transformations to other operator types by matching sub-graphs. // Typically, we just drop Assert nodes. struct TensorFlowAssertOperator : Operator { - TensorFlowAssertOperator() : Operator(OperatorType::kTensorFlowAssert) {} + TensorFlowAssertOperator() : Operator(OperatorType::kAssert) {} }; // TensorFlow Less equivalent. Refer to TensorFlow documentation for details. @@ -1327,7 +1327,7 @@ struct TensorFlowAssertOperator : Operator { // Typically, this is only used as an input to an Assert node, so can be // removed as an unused node as we drop Assert nodes. struct TensorFlowLessOperator : Operator { - TensorFlowLessOperator() : Operator(OperatorType::kTensorFlowLess) {} + TensorFlowLessOperator() : Operator(OperatorType::kLess) {} }; // TensorFlow LessEqual equivalent. Refer to TensorFlow documentation for @@ -1337,8 +1337,7 @@ struct TensorFlowLessOperator : Operator { // Typically, this is only used as an input to an Assert node, so can be // removed as an unused node as we drop Assert nodes. struct TensorFlowLessEqualOperator : Operator { - TensorFlowLessEqualOperator() - : Operator(OperatorType::kTensorFlowLessEqual) {} + TensorFlowLessEqualOperator() : Operator(OperatorType::kLessEqual) {} }; // TensorFlow Less equivalent. Refer to TensorFlow documentation for details. @@ -1347,7 +1346,7 @@ struct TensorFlowLessEqualOperator : Operator { // Typically, this is only used as an input to an Assert node, so can be // removed as an unused node as we drop Assert nodes. struct TensorFlowGreaterOperator : Operator { - TensorFlowGreaterOperator() : Operator(OperatorType::kTensorFlowGreater) {} + TensorFlowGreaterOperator() : Operator(OperatorType::kGreater) {} }; // TensorFlow GreaterEqual equivalent. Refer to TensorFlow documentation for @@ -1357,8 +1356,7 @@ struct TensorFlowGreaterOperator : Operator { // Typically, this is only used as an input to an Assert node, so can be // removed as an unused node as we drop Assert nodes. struct TensorFlowGreaterEqualOperator : Operator { - TensorFlowGreaterEqualOperator() - : Operator(OperatorType::kTensorFlowGreaterEqual) {} + TensorFlowGreaterEqualOperator() : Operator(OperatorType::kGreaterEqual) {} }; // TensorFlow Equal equivalent. Refer to TensorFlow documentation for @@ -1368,13 +1366,13 @@ struct TensorFlowGreaterEqualOperator : Operator { // Typically, this is only used as an input to an Assert node, so can be // removed as an unused node as we drop Assert nodes. struct TensorFlowEqualOperator : Operator { - TensorFlowEqualOperator() : Operator(OperatorType::kTensorFlowEqual) {} + TensorFlowEqualOperator() : Operator(OperatorType::kEqual) {} }; // TensorFlow Not Equal equivalent. Refer to TensorFlow documentation for // details. struct TensorFlowNotEqualOperator : Operator { - TensorFlowNotEqualOperator() : Operator(OperatorType::kTensorFlowNotEqual) {} + TensorFlowNotEqualOperator() : Operator(OperatorType::kNotEqual) {} }; // Global max reduction: computes the max of all of entries in the input array. @@ -1386,7 +1384,7 @@ struct TensorFlowNotEqualOperator : Operator { // TensorFlow equivalent: Max --- except that we only support the special case // of global reduction across all dimensions. struct TensorFlowMaxOperator : Operator { - TensorFlowMaxOperator() : Operator(OperatorType::kTensorFlowMax) {} + TensorFlowMaxOperator() : Operator(OperatorType::kMax) {} bool keep_dims = false; }; @@ -1399,7 +1397,7 @@ struct TensorFlowMaxOperator : Operator { // TensorFlow equivalent: Min --- except that we only support the special case // of global reduction across all dimensions. struct TensorFlowMinOperator : Operator { - TensorFlowMinOperator() : Operator(OperatorType::kTensorFlowMin) {} + TensorFlowMinOperator() : Operator(OperatorType::kMin) {} bool keep_dims = false; }; @@ -1412,7 +1410,7 @@ struct TensorFlowMinOperator : Operator { // // TensorFlow equivalent: Maximum struct TensorFlowMaximumOperator : Operator { - TensorFlowMaximumOperator() : Operator(OperatorType::kTensorFlowMaximum) {} + TensorFlowMaximumOperator() : Operator(OperatorType::kMaximum) {} }; // Element-wise minimum operator. Currently it only supports scalar as @@ -1424,14 +1422,13 @@ struct TensorFlowMaximumOperator : Operator { // // TensorFlow equivalent: Minimum struct TensorFlowMinimumOperator : Operator { - TensorFlowMinimumOperator() : Operator(OperatorType::kTensorFlowMinimum) {} + TensorFlowMinimumOperator() : Operator(OperatorType::kMinimum) {} }; // General TF operation, unsupported by tf.mini. Expected to be dropped by // graph transformations. struct TensorFlowUnsupportedOperator : Operator { - TensorFlowUnsupportedOperator() - : Operator(OperatorType::kTensorFlowUnsupported) {} + TensorFlowUnsupportedOperator() : Operator(OperatorType::kUnsupported) {} // The original TF operation type. Used for diagnostic purposes. string tensorflow_op; diff --git a/tensorflow/contrib/lite/toco/tflite/export.cc b/tensorflow/contrib/lite/toco/tflite/export.cc index 7ba2603a95..1972246807 100644 --- a/tensorflow/contrib/lite/toco/tflite/export.cc +++ b/tensorflow/contrib/lite/toco/tflite/export.cc @@ -49,7 +49,7 @@ details::OperatorKey GetOperatorKey( const ::toco::Operator& op, const std::map>& ops_by_type) { string custom_code; - if (op.type == OperatorType::kTensorFlowUnsupported) { + if (op.type == OperatorType::kUnsupported) { const TensorFlowUnsupportedOperator& unsupported_op = static_cast(op); custom_code = unsupported_op.tensorflow_op; @@ -211,7 +211,7 @@ Offset>> ExportOperatorCodes( ordered_opcodes[op_index] = CreateOperatorCode(*builder, builtin_ops[name], 0, op_version); } else { - // This could be a kTensorFlowUnsupported, in which case we should be + // This could be a kUnsupported, in which case we should be // able to retrieve the original Tensorflow name from the OperatorKey, or // this could be a proper TOCO operator that is completely unknown to TF // Lite. @@ -268,7 +268,7 @@ Offset>> ExportOperators( : tflite_op_it->second.get(); // This is a custom op unless we can find it in ops_by_type, and even then - // it could be a custom op (such as kTensorFlowUnsupported). + // it could be a custom op (such as kUnsupported). auto options = Options::Custom(0); std::vector mutating_input_variables; diff --git a/tensorflow/contrib/lite/toco/tflite/export.h b/tensorflow/contrib/lite/toco/tflite/export.h index 098d2163e6..58ea5c725c 100644 --- a/tensorflow/contrib/lite/toco/tflite/export.h +++ b/tensorflow/contrib/lite/toco/tflite/export.h @@ -45,7 +45,7 @@ namespace details { using TensorsMap = std::unordered_map; // A key to identify an operator. -// Only when `type` is `kTensorFlowUnsupported`, `custom_code` is filled to +// Only when `type` is `kUnsupported`, `custom_code` is filled to // identify which operation is used. struct OperatorKey { OperatorKey(OperatorType type, const std::string& custom_code, int version) diff --git a/tensorflow/contrib/lite/toco/tflite/export_test.cc b/tensorflow/contrib/lite/toco/tflite/export_test.cc index 409e7d72a5..d1fdbcb8e9 100644 --- a/tensorflow/contrib/lite/toco/tflite/export_test.cc +++ b/tensorflow/contrib/lite/toco/tflite/export_test.cc @@ -73,8 +73,8 @@ TEST_F(ExportTest, LoadOperatorsMap) { EXPECT_EQ(0, operators[details::OperatorKey(OperatorType::kAdd, "", 1)]); EXPECT_EQ(1, operators[details::OperatorKey(OperatorType::kConv, "", 1)]); EXPECT_EQ(2, operators[details::OperatorKey(OperatorType::kSub, "", 1)]); - EXPECT_EQ(3, operators[details::OperatorKey( - OperatorType::kTensorFlowUnsupported, "MyCrazyOp", 1)]); + EXPECT_EQ(3, operators[details::OperatorKey(OperatorType::kUnsupported, + "MyCrazyOp", 1)]); } TEST_F(ExportTest, Export) { diff --git a/tensorflow/contrib/lite/toco/tflite/import.cc b/tensorflow/contrib/lite/toco/tflite/import.cc index cb44a5e6d7..d1867bd4fa 100644 --- a/tensorflow/contrib/lite/toco/tflite/import.cc +++ b/tensorflow/contrib/lite/toco/tflite/import.cc @@ -124,7 +124,7 @@ void ImportOperators( new_op = ops_by_name.at(effective_opname) ->Deserialize(input_op->builtin_options(), input_op->custom_options()); - if (new_op->type == OperatorType::kTensorFlowUnsupported) { + if (new_op->type == OperatorType::kUnsupported) { auto* unsupported_op = static_cast(new_op.get()); unsupported_op->tensorflow_op = opname; diff --git a/tensorflow/contrib/lite/toco/tflite/operator.cc b/tensorflow/contrib/lite/toco/tflite/operator.cc index fd6c849889..290a925c1e 100644 --- a/tensorflow/contrib/lite/toco/tflite/operator.cc +++ b/tensorflow/contrib/lite/toco/tflite/operator.cc @@ -1114,8 +1114,8 @@ std::vector> BuildOperatorList() { ops.emplace_back(new Pad(::tflite::BuiltinOperator_PAD, OperatorType::kPad)); ops.emplace_back( new PadV2(::tflite::BuiltinOperator_PADV2, OperatorType::kPadV2)); - ops.emplace_back(new Reshape(::tflite::BuiltinOperator_RESHAPE, - OperatorType::kTensorFlowReshape)); + ops.emplace_back( + new Reshape(::tflite::BuiltinOperator_RESHAPE, OperatorType::kReshape)); ops.emplace_back( new Softmax(::tflite::BuiltinOperator_SOFTMAX, OperatorType::kSoftmax)); ops.emplace_back(new SpaceToDepth(::tflite::BuiltinOperator_SPACE_TO_DEPTH, @@ -1126,14 +1126,13 @@ std::vector> BuildOperatorList() { OperatorType::kTranspose)); ops.emplace_back( new Mean(::tflite::BuiltinOperator_MEAN, OperatorType::kMean)); - ops.emplace_back( - new Sum(::tflite::BuiltinOperator_SUM, OperatorType::kTensorFlowSum)); + ops.emplace_back(new Sum(::tflite::BuiltinOperator_SUM, OperatorType::kSum)); ops.emplace_back(new ResizeBilinear(::tflite::BuiltinOperator_RESIZE_BILINEAR, OperatorType::kResizeBilinear)); ops.emplace_back( new Squeeze(::tflite::BuiltinOperator_SQUEEZE, OperatorType::kSqueeze)); - ops.emplace_back(new Split(::tflite::BuiltinOperator_SPLIT, - OperatorType::kTensorFlowSplit)); + ops.emplace_back( + new Split(::tflite::BuiltinOperator_SPLIT, OperatorType::kSplit)); ops.emplace_back(new StridedSlice(::tflite::BuiltinOperator_STRIDED_SLICE, OperatorType::kStridedSlice)); ops.emplace_back( @@ -1145,28 +1144,27 @@ std::vector> BuildOperatorList() { ops.emplace_back( new ArgMax(::tflite::BuiltinOperator_ARG_MAX, OperatorType::kArgMax)); ops.emplace_back( - new Tile(::tflite::BuiltinOperator_TILE, OperatorType::kTensorFlowTile)); + new Tile(::tflite::BuiltinOperator_TILE, OperatorType::kTile)); ops.emplace_back(new ExpandDims(::tflite::BuiltinOperator_EXPAND_DIMS, OperatorType::kExpandDims)); ops.emplace_back(new TransposeConv(::tflite::BuiltinOperator_TRANSPOSE_CONV, OperatorType::kTransposeConv)); ops.emplace_back(new SparseToDense(::tflite::BuiltinOperator_SPARSE_TO_DENSE, OperatorType::kSparseToDense)); - ops.emplace_back(new Shape(::tflite::BuiltinOperator_SHAPE, - OperatorType::kTensorFlowShape)); + ops.emplace_back( + new Shape(::tflite::BuiltinOperator_SHAPE, OperatorType::kShape)); // Custom Operators. ops.emplace_back( new DepthToSpace("DEPTH_TO_SPACE", OperatorType::kDepthToSpace)); ops.emplace_back(new FakeQuant("FAKE_QUANT", OperatorType::kFakeQuant)); - ops.emplace_back(new TensorFlowUnsupported( - "TENSORFLOW_UNSUPPORTED", OperatorType::kTensorFlowUnsupported)); + ops.emplace_back(new TensorFlowUnsupported("TENSORFLOW_UNSUPPORTED", + OperatorType::kUnsupported)); // There operators are supported by Toco, but not by TF Lite, and has no // attributes. ops.emplace_back( new SimpleOperator("ADDN", OperatorType::kAddN)); - // Simple Operators. ops.emplace_back(new SimpleOperator( "DEQUANTIZE", OperatorType::kDequantize)); @@ -1188,21 +1186,21 @@ std::vector> BuildOperatorList() { ops.emplace_back(new SimpleOperator( "LOG_SOFTMAX", OperatorType::kLogSoftmax)); ops.emplace_back(new SimpleOperator( - "MAXIMUM", OperatorType::kTensorFlowMaximum)); + "MAXIMUM", OperatorType::kMaximum)); // Element-wise Maximum ops.emplace_back(new SimpleOperator( - "MINIMUM", OperatorType::kTensorFlowMinimum)); + "MINIMUM", OperatorType::kMinimum)); // Element-wise Minimum ops.emplace_back(new SimpleOperator( - "GREATER", OperatorType::kTensorFlowGreater)); + "GREATER", OperatorType::kGreater)); ops.emplace_back(new SimpleOperator( - "GREATER_EQUAL", OperatorType::kTensorFlowGreaterEqual)); - ops.emplace_back(new SimpleOperator( - "LESS", OperatorType::kTensorFlowLess)); + "GREATER_EQUAL", OperatorType::kGreaterEqual)); + ops.emplace_back( + new SimpleOperator("LESS", OperatorType::kLess)); ops.emplace_back(new SimpleOperator( - "LESS_EQUAL", OperatorType::kTensorFlowLessEqual)); + "LESS_EQUAL", OperatorType::kLessEqual)); ops.emplace_back(new SimpleOperator( - "EQUAL", OperatorType::kTensorFlowEqual)); + "EQUAL", OperatorType::kEqual)); ops.emplace_back(new SimpleOperator( - "NOT_EQUAL", OperatorType::kTensorFlowNotEqual)); + "NOT_EQUAL", OperatorType::kNotEqual)); ops.emplace_back(new SimpleOperator("NEG", OperatorType::kNeg)); ops.emplace_back( new SimpleOperator("SELECT", OperatorType::kSelect)); @@ -1211,10 +1209,10 @@ std::vector> BuildOperatorList() { // Element-wise operator ops.emplace_back(new SimpleOperator("SIN", OperatorType::kSin)); ops.emplace_back(new SimpleOperator("LOG", OperatorType::kLog)); - ops.emplace_back(new SimpleOperator( - "SQRT", OperatorType::kTensorFlowSqrt)); + ops.emplace_back( + new SimpleOperator("SQRT", OperatorType::kSqrt)); ops.emplace_back(new SimpleOperator( - "RSQRT", OperatorType::kTensorFlowRsqrt)); + "RSQRT", OperatorType::kRsqrt)); return ops; } diff --git a/tensorflow/contrib/lite/toco/tflite/operator_test.cc b/tensorflow/contrib/lite/toco/tflite/operator_test.cc index bd881d079e..79c8e5d738 100644 --- a/tensorflow/contrib/lite/toco/tflite/operator_test.cc +++ b/tensorflow/contrib/lite/toco/tflite/operator_test.cc @@ -112,24 +112,20 @@ TEST_F(OperatorTest, SimpleOperators) { CheckSimpleOperator("LOG_SOFTMAX", OperatorType::kLogSoftmax); CheckSimpleOperator( - "MAXIMUM", OperatorType::kTensorFlowMaximum); + "MAXIMUM", OperatorType::kMaximum); // Element-wise Maximum CheckSimpleOperator( - "MINIMUM", OperatorType::kTensorFlowMinimum); - CheckSimpleOperator("LESS", - OperatorType::kTensorFlowLess); + "MINIMUM", OperatorType::kMinimum); // Element-wise Minimum + CheckSimpleOperator("LESS", OperatorType::kLess); CheckSimpleOperator("NEG", OperatorType::kNeg); CheckSimpleOperator("SELECT", OperatorType::kSelect); CheckSimpleOperator("SLICE", OperatorType::kSlice); CheckSimpleOperator("SIN", OperatorType::kSin); - CheckSimpleOperator("EQUAL", - OperatorType::kTensorFlowEqual); - CheckSimpleOperator( - "NOT_EQUAL", OperatorType::kTensorFlowNotEqual); + CheckSimpleOperator("EQUAL", OperatorType::kEqual); + CheckSimpleOperator("NOT_EQUAL", + OperatorType::kNotEqual); CheckSimpleOperator("LOG", OperatorType::kLog); - CheckSimpleOperator("SQRT", - OperatorType::kTensorFlowSqrt); - CheckSimpleOperator("RSQRT", - OperatorType::kTensorFlowRsqrt); + CheckSimpleOperator("SQRT", OperatorType::kSqrt); + CheckSimpleOperator("RSQRT", OperatorType::kRsqrt); } TEST_F(OperatorTest, BuiltinAdd) { @@ -258,7 +254,7 @@ TEST_F(OperatorTest, BuiltinReshape) { TensorFlowReshapeOperator op; op.shape = {1, 2, 4, 5, 8}; auto output_toco_op = SerializeAndDeserialize( - GetOperator("RESHAPE", OperatorType::kTensorFlowReshape), op); + GetOperator("RESHAPE", OperatorType::kReshape), op); EXPECT_EQ(op.shape, output_toco_op->shape); } @@ -281,8 +277,8 @@ TEST_F(OperatorTest, BuiltinSpaceToDepth) { TEST_F(OperatorTest, CustomSplit) { TensorFlowSplitOperator op; op.num_split = 123; - auto output_toco_op = SerializeAndDeserialize( - GetOperator("SPLIT", OperatorType::kTensorFlowSplit), op); + auto output_toco_op = + SerializeAndDeserialize(GetOperator("SPLIT", OperatorType::kSplit), op); EXPECT_EQ(op.num_split, output_toco_op->num_split); } @@ -434,8 +430,8 @@ TEST_F(OperatorTest, BuiltinTransposeConv) { TEST_F(OperatorTest, BuiltinShape) { TensorFlowShapeOperator op; op.output_data_type = ArrayDataType::kInt64; - auto output_toco_op = SerializeAndDeserialize( - GetOperator("SHAPE", OperatorType::kTensorFlowShape), op); + auto output_toco_op = + SerializeAndDeserialize(GetOperator("SHAPE", OperatorType::kShape), op); EXPECT_EQ(op.output_data_type, output_toco_op->output_data_type); } @@ -467,10 +463,8 @@ TEST_F(OperatorTest, TensorFlowUnsupported) { } node_def.SerializeToString(&op.tensorflow_node_def); - auto output_toco_op = - SerializeAndDeserialize(GetOperator("TENSORFLOW_UNSUPPORTED", - OperatorType::kTensorFlowUnsupported), - op); + auto output_toco_op = SerializeAndDeserialize( + GetOperator("TENSORFLOW_UNSUPPORTED", OperatorType::kUnsupported), op); ::tensorflow::NodeDef output_node_def; output_node_def.ParseFromString(output_toco_op->tensorflow_node_def); @@ -493,10 +487,8 @@ TEST_F(OperatorTest, TensorFlowUnsupported) { TEST_F(OperatorTest, TensorFlowUnsupportedWithoutAttr) { TensorFlowUnsupportedOperator op; op.tensorflow_op = "MyCustomUnsupportedOp"; - auto output_toco_op = - SerializeAndDeserialize(GetOperator("TENSORFLOW_UNSUPPORTED", - OperatorType::kTensorFlowUnsupported), - op); + auto output_toco_op = SerializeAndDeserialize( + GetOperator("TENSORFLOW_UNSUPPORTED", OperatorType::kUnsupported), op); ::tensorflow::NodeDef output_node_def; output_node_def.ParseFromString(output_toco_op->tensorflow_node_def); diff --git a/tensorflow/contrib/lite/toco/toco_tooling.cc b/tensorflow/contrib/lite/toco/toco_tooling.cc index 3173d524b7..2534d1ef2a 100644 --- a/tensorflow/contrib/lite/toco/toco_tooling.cc +++ b/tensorflow/contrib/lite/toco/toco_tooling.cc @@ -34,11 +34,11 @@ limitations under the License. namespace toco { namespace { -// CHECK-fails if the model contains a kTensorFlowUnsupported operation. +// CHECK-fails if the model contains a kUnsupported operation. void CheckUnsupportedOperations(const Model& model) { std::set unsupported_ops; for (auto& op : model.operators) { - if (op->type == OperatorType::kTensorFlowUnsupported) { + if (op->type == OperatorType::kUnsupported) { unsupported_ops.insert( static_cast(op.get()) ->tensorflow_op); diff --git a/tensorflow/contrib/lite/toco/tooling_util.cc b/tensorflow/contrib/lite/toco/tooling_util.cc index 92bab5246c..fb2ed093a9 100644 --- a/tensorflow/contrib/lite/toco/tooling_util.cc +++ b/tensorflow/contrib/lite/toco/tooling_util.cc @@ -338,23 +338,23 @@ const char* OperatorTypeName(OperatorType type) { HANDLE_OPERATORTYPENAME_CASE(Div) HANDLE_OPERATORTYPENAME_CASE(Tanh) HANDLE_OPERATORTYPENAME_CASE(Sin) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowAll) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowAssert) + HANDLE_OPERATORTYPENAME_CASE(All) + HANDLE_OPERATORTYPENAME_CASE(Assert) HANDLE_OPERATORTYPENAME_CASE(ExpandDims) HANDLE_OPERATORTYPENAME_CASE(Fill) HANDLE_OPERATORTYPENAME_CASE(FloorMod) HANDLE_OPERATORTYPENAME_CASE(FloorDiv) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowGreater) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowGreaterEqual) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowIdentity) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowLess) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowLessEqual) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowMatMul) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowMax) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowMaximum) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowMerge) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowMin) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowMinimum) + HANDLE_OPERATORTYPENAME_CASE(Greater) + HANDLE_OPERATORTYPENAME_CASE(GreaterEqual) + HANDLE_OPERATORTYPENAME_CASE(Identity) + HANDLE_OPERATORTYPENAME_CASE(Less) + HANDLE_OPERATORTYPENAME_CASE(LessEqual) + HANDLE_OPERATORTYPENAME_CASE(MatMul) + HANDLE_OPERATORTYPENAME_CASE(Max) // Reduction Max + HANDLE_OPERATORTYPENAME_CASE(Maximum) // Element-wise Maximum + HANDLE_OPERATORTYPENAME_CASE(Merge) + HANDLE_OPERATORTYPENAME_CASE(Min) // Reduction Min + HANDLE_OPERATORTYPENAME_CASE(Minimum) // Element-wise Minimum HANDLE_OPERATORTYPENAME_CASE(Neg) HANDLE_OPERATORTYPENAME_CASE(Pad) HANDLE_OPERATORTYPENAME_CASE(PadV2) @@ -362,22 +362,22 @@ const char* OperatorTypeName(OperatorType type) { HANDLE_OPERATORTYPENAME_CASE(Stack) HANDLE_OPERATORTYPENAME_CASE(Range) HANDLE_OPERATORTYPENAME_CASE(Rank) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowReshape) + HANDLE_OPERATORTYPENAME_CASE(Reshape) HANDLE_OPERATORTYPENAME_CASE(Squeeze) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowRsqrt) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowShape) + HANDLE_OPERATORTYPENAME_CASE(Rsqrt) + HANDLE_OPERATORTYPENAME_CASE(Shape) HANDLE_OPERATORTYPENAME_CASE(Slice) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowSplit) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowSqrt) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowSquare) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowSwitch) + HANDLE_OPERATORTYPENAME_CASE(Split) + HANDLE_OPERATORTYPENAME_CASE(Sqrt) + HANDLE_OPERATORTYPENAME_CASE(Square) + HANDLE_OPERATORTYPENAME_CASE(Switch) HANDLE_OPERATORTYPENAME_CASE(Sub) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowSum) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowTile) + HANDLE_OPERATORTYPENAME_CASE(Sum) + HANDLE_OPERATORTYPENAME_CASE(Tile) HANDLE_OPERATORTYPENAME_CASE(Transpose) HANDLE_OPERATORTYPENAME_CASE(TransposeConv) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowConcat) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowConcatV2) + HANDLE_OPERATORTYPENAME_CASE(Concat) + HANDLE_OPERATORTYPENAME_CASE(ConcatV2) HANDLE_OPERATORTYPENAME_CASE(Cast) HANDLE_OPERATORTYPENAME_CASE(Floor) HANDLE_OPERATORTYPENAME_CASE(Gather) @@ -388,14 +388,14 @@ const char* OperatorTypeName(OperatorType type) { HANDLE_OPERATORTYPENAME_CASE(Svdf) HANDLE_OPERATORTYPENAME_CASE(ArgMax) HANDLE_OPERATORTYPENAME_CASE(TopK_V2) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowUnsupported) + HANDLE_OPERATORTYPENAME_CASE(Unsupported) HANDLE_OPERATORTYPENAME_CASE(Exp) HANDLE_OPERATORTYPENAME_CASE(DynamicPartition) HANDLE_OPERATORTYPENAME_CASE(DynamicStitch) HANDLE_OPERATORTYPENAME_CASE(Select) HANDLE_OPERATORTYPENAME_CASE(SparseToDense) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowEqual) - HANDLE_OPERATORTYPENAME_CASE(TensorFlowNotEqual) + HANDLE_OPERATORTYPENAME_CASE(Equal) + HANDLE_OPERATORTYPENAME_CASE(NotEqual) default: LOG(FATAL) << "Unhandled op type"; #undef HANDLE_OPERATORTYPENAME_CASE @@ -403,7 +403,7 @@ const char* OperatorTypeName(OperatorType type) { } string HelpfulOperatorTypeName(const Operator& op) { - if (op.type == OperatorType::kTensorFlowUnsupported) { + if (op.type == OperatorType::kUnsupported) { return toco::port::StringF( "(Unsupported TensorFlow op: %s)", static_cast(op).tensorflow_op); @@ -418,8 +418,8 @@ bool OperatorSupportsFusedActivation(OperatorType type) { case OperatorType::kGather: case OperatorType::kSlice: case OperatorType::kSqueeze: - case OperatorType::kTensorFlowReshape: - case OperatorType::kTensorFlowSplit: + case OperatorType::kReshape: + case OperatorType::kSplit: return false; default: return true; -- GitLab From 1f4a7264c8d374620320763148709aae43cb21ad Mon Sep 17 00:00:00 2001 From: Allen Lavoie Date: Wed, 20 Jun 2018 15:11:59 -0700 Subject: [PATCH 763/816] Fix object-based checkpoint dependencies for Keras Wrapper objects. PiperOrigin-RevId: 201424910 --- tensorflow/python/keras/layers/wrappers.py | 1 + tensorflow/python/keras/layers/wrappers_test.py | 5 +++++ 2 files changed, 6 insertions(+) diff --git a/tensorflow/python/keras/layers/wrappers.py b/tensorflow/python/keras/layers/wrappers.py index 7759561ef9..18dd35a637 100644 --- a/tensorflow/python/keras/layers/wrappers.py +++ b/tensorflow/python/keras/layers/wrappers.py @@ -46,6 +46,7 @@ class Wrapper(Layer): def __init__(self, layer, **kwargs): self.layer = layer + self._track_checkpointable(layer, name='layer') # Tracks mapping of Wrapper inputs to inner layer inputs. Useful when # the inner layer has update ops that depend on its inputs (as opposed # to the inputs to the Wrapper layer). diff --git a/tensorflow/python/keras/layers/wrappers_test.py b/tensorflow/python/keras/layers/wrappers_test.py index 5eab6aba8a..a38cd6a0f8 100644 --- a/tensorflow/python/keras/layers/wrappers_test.py +++ b/tensorflow/python/keras/layers/wrappers_test.py @@ -25,6 +25,7 @@ import numpy as np from tensorflow.python import keras from tensorflow.python.framework import test_util as tf_test_util from tensorflow.python.platform import test +from tensorflow.python.training.checkpointable import util as checkpointable_util from tensorflow.python.training.rmsprop import RMSPropOptimizer @@ -85,6 +86,10 @@ class TimeDistributedTest(test.TestCase): # test config model.get_config() + checkpointed_objects = set(checkpointable_util.list_objects(model)) + for v in model.variables: + self.assertIn(v, checkpointed_objects) + def test_timedistributed_static_batch_size(self): model = keras.models.Sequential() model.add( -- GitLab From 6caf20322cba22092a96ce961ed1cf5d7324df8a Mon Sep 17 00:00:00 2001 From: Skye Wanderman-Milne Date: Wed, 20 Jun 2018 15:26:13 -0700 Subject: [PATCH 764/816] Use PyLong_FromLongLong to convert 64-bit ints in SWIG code. On some platforms (namely Windows), a long is 32 bits, not 64. This is what was causing random_ops_test to fail on Winodws. PiperOrigin-RevId: 201427591 --- tensorflow/contrib/cmake/tf_tests.cmake | 2 -- tensorflow/python/client/tf_session.i | 6 +++--- tensorflow/python/kernel_tests/random/BUILD | 4 ---- 3 files changed, 3 insertions(+), 9 deletions(-) diff --git a/tensorflow/contrib/cmake/tf_tests.cmake b/tensorflow/contrib/cmake/tf_tests.cmake index 38573f86ef..eb9482dc25 100644 --- a/tensorflow/contrib/cmake/tf_tests.cmake +++ b/tensorflow/contrib/cmake/tf_tests.cmake @@ -229,8 +229,6 @@ if (tensorflow_BUILD_PYTHON_TESTS) "${tensorflow_source_dir}/tensorflow/python/debug/cli/profile_analyzer_cli_test.py" # Windows does not have the curses library and uses readline. "${tensorflow_source_dir}/tensorflow/python/debug/cli/curses_ui_test.py" - # Bug in shape inference (b/110283809) - "${tensorflow_source_dir}/tensorflow/python/kernel_tests/random/random_ops_test.py" # TFDBG grpc:// mode is not yet available on Windows. "${tensorflow_source_dir}/tensorflow/python/debug/lib/dist_session_debug_grpc_test.py" "${tensorflow_source_dir}/tensorflow/python/debug/lib/grpc_large_data_test.py" diff --git a/tensorflow/python/client/tf_session.i b/tensorflow/python/client/tf_session.i index def730371d..985cb90436 100644 --- a/tensorflow/python/client/tf_session.i +++ b/tensorflow/python/client/tf_session.i @@ -135,7 +135,7 @@ tensorflow::ImportNumpy(); // Convert TF_DeviceListMemoryBytes and TF_Dim int64_t output to Python integers %typemap(out) int64_t { - $result = PyInt_FromLong($1); + $result = PyLong_FromLongLong($1); } // We use TF_OperationGetControlInputs_wrapper instead of @@ -610,7 +610,7 @@ def TF_Reset(target, containers=None, config=None): } for (size_t i = 0; i < $1.size(); ++i) { - PyList_SET_ITEM($result, i, PyLong_FromLong($1[i])); + PyList_SET_ITEM($result, i, PyLong_FromLongLong($1[i])); } } @@ -673,7 +673,7 @@ def TF_Reset(target, containers=None, config=None): } for (size_t i = 0; i < $1.size(); ++i) { - PyList_SET_ITEM($result, i, PyInt_FromLong($1[i])); + PyList_SET_ITEM($result, i, PyLong_FromLongLong($1[i])); } } diff --git a/tensorflow/python/kernel_tests/random/BUILD b/tensorflow/python/kernel_tests/random/BUILD index a9bd68971e..3b3a28fc9a 100644 --- a/tensorflow/python/kernel_tests/random/BUILD +++ b/tensorflow/python/kernel_tests/random/BUILD @@ -88,10 +88,6 @@ cuda_py_test( "//tensorflow/python:framework_for_generated_wrappers", "//tensorflow/python:random_ops", ], - tags = [ - "manual", - "no_oss", - ], ) cuda_py_test( -- GitLab From 89045abeddfa4afc9089c8d93d9d22e33d7fe369 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 20 Jun 2018 15:39:41 -0700 Subject: [PATCH 765/816] Disable flaky serial_device_batch_scheduler_test PiperOrigin-RevId: 201429850 --- tensorflow/core/kernels/batching_util/BUILD | 3 +++ 1 file changed, 3 insertions(+) diff --git a/tensorflow/core/kernels/batching_util/BUILD b/tensorflow/core/kernels/batching_util/BUILD index e292ff200a..792eb74e31 100644 --- a/tensorflow/core/kernels/batching_util/BUILD +++ b/tensorflow/core/kernels/batching_util/BUILD @@ -138,6 +138,9 @@ cc_library( tf_cc_test( name = "serial_device_batch_scheduler_test", srcs = ["serial_device_batch_scheduler_test.cc"], + tags = [ + "notap", # b/110374108 + ], deps = [ ":fake_clock_env", ":serial_device_batch_scheduler", -- GitLab From 185b862db1cda8f99e719b4f287c6c1eba1c2f73 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 20 Jun 2018 15:46:24 -0700 Subject: [PATCH 766/816] Fix CholeskyOuterProduct to return scalar determinant with single matrix inputs. PiperOrigin-RevId: 201431010 --- .../bijectors/cholesky_outer_product_test.py | 22 +++++++++++++++++++ .../ops/bijectors/cholesky_outer_product.py | 15 ++++++++++++- 2 files changed, 36 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/cholesky_outer_product_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/cholesky_outer_product_test.py index e281e81bdf..d1ce273499 100644 --- a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/cholesky_outer_product_test.py +++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/cholesky_outer_product_test.py @@ -61,6 +61,28 @@ class CholeskyOuterProductBijectorTest(test.TestCase): atol=0., rtol=1e-7) + def testNoBatchStaticJacobian(self): + x = np.eye(2) + bijector = bijectors.CholeskyOuterProduct() + + # The Jacobian matrix is 2 * tf.eye(2), which has jacobian determinant 4. + self.assertAllClose( + np.log(4), + self.evaluate(bijector.forward_log_det_jacobian(x, event_ndims=2))) + + def testNoBatchDynamicJacobian(self): + x = np.eye(2) + bijector = bijectors.CholeskyOuterProduct() + x_pl = array_ops.placeholder(dtypes.float32) + + with self.test_session(): + log_det_jacobian = bijector.forward_log_det_jacobian(x_pl, event_ndims=2) + + # The Jacobian matrix is 2 * tf.eye(2), which has jacobian determinant 4. + self.assertAllClose( + np.log(4), + log_det_jacobian.eval({x_pl: x})) + def testNoBatchStatic(self): x = np.array([[1., 0], [2, 1]]) # np.linalg.cholesky(y) y = np.array([[1., 2], [2, 5]]) # np.matmul(x, x.T) diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/cholesky_outer_product.py b/tensorflow/contrib/distributions/python/ops/bijectors/cholesky_outer_product.py index 8267ee7df8..3e1e4fc829 100644 --- a/tensorflow/contrib/distributions/python/ops/bijectors/cholesky_outer_product.py +++ b/tensorflow/contrib/distributions/python/ops/bijectors/cholesky_outer_product.py @@ -182,7 +182,20 @@ class CholeskyOuterProduct(bijector.Bijector): axis=-1) fldj = p_float * np.log(2.) + sum_weighted_log_diag - return fldj + # We finally need to undo adding an extra column in non-scalar cases + # where there is a single matrix as input. + if x.get_shape().ndims is not None: + if x.get_shape().ndims == 2: + fldj = array_ops.squeeze(fldj, axis=-1) + return fldj + + shape = array_ops.shape(fldj) + maybe_squeeze_shape = array_ops.concat([ + shape[:-1], + distribution_util.pick_vector( + math_ops.equal(array_ops.rank(x), 2), + np.array([], dtype=np.int32), shape[-1:])], 0) + return array_ops.reshape(fldj, maybe_squeeze_shape) def _make_columnar(self, x): """Ensures non-scalar input has at least one column. -- GitLab From 34a12dff9812d291dff494dae9abecc13b494b8a Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 20 Jun 2018 16:14:18 -0700 Subject: [PATCH 767/816] Switch away from DistributionStrategy.fetch() (mostly just in tests) so we can delete it. Frequently we can now delete the call entirely, but in other cases we switch to read_var(). This revealed some bugs also fixed in this CL: * For MirroredStrategy: fix read_var(mean_tower_local) bug. * Support get() for Mirrored values that are not MirroredVariables, and make them DistributedDelegates so we can operate on them in cross-tower mode. * Actually iterate through the available devices in MirroredStrategy.get(). With this and already-submitted 201390698, we can pass mirrored variables and other mirrored values directly to self.evaluate() in tests. PiperOrigin-RevId: 201435436 --- .../distribute/python/minimize_loss_test.py | 6 +++--- .../distribute/python/mirrored_strategy.py | 6 ++---- .../python/mirrored_strategy_multigpu_test.py | 16 ++++++++-------- .../contrib/distribute/python/monitor_test.py | 4 ++-- .../distribute/python/optimizer_v2_test.py | 4 ++-- .../contrib/distribute/python/step_fn_test.py | 4 ++-- .../distribute/python/strategy_test_lib.py | 10 +++++----- tensorflow/contrib/distribute/python/values.py | 15 +++++++++++---- 8 files changed, 35 insertions(+), 30 deletions(-) diff --git a/tensorflow/contrib/distribute/python/minimize_loss_test.py b/tensorflow/contrib/distribute/python/minimize_loss_test.py index 75754e3fe3..aeeb9553e6 100644 --- a/tensorflow/contrib/distribute/python/minimize_loss_test.py +++ b/tensorflow/contrib/distribute/python/minimize_loss_test.py @@ -89,7 +89,7 @@ class MinimizeLossStepTest(test.TestCase, parameterized.TestCase): run_step() weights.append(self.evaluate(layer.kernel)) - biases.append(self.evaluate(distribution.fetch(layer.bias))) + biases.append(self.evaluate(layer.bias)) if is_tpu: with self.test_session() as sess: @@ -254,7 +254,7 @@ class MinimizeLossStepTest(test.TestCase, parameterized.TestCase): for _ in range(10): run_step() - moving_means = self.evaluate(distribution.fetch(batchnorm.moving_mean)) + moving_means = self.evaluate(batchnorm.moving_mean) # We make sure that the moving_mean is updated as if the sample mean is # calculated over all towers. @@ -345,7 +345,7 @@ class MinimizeLossStepTest(test.TestCase, parameterized.TestCase): v = all_vars[0] self.assertTrue(all([v is vi for vi in all_vars[1:]])) - weight = numpy.squeeze(self.evaluate(distribution.fetch(v))) + weight = numpy.squeeze(self.evaluate(v)) # Our model is: # predict = x * w # loss = (predict - y)^2 diff --git a/tensorflow/contrib/distribute/python/mirrored_strategy.py b/tensorflow/contrib/distribute/python/mirrored_strategy.py index dc270ac540..d8668b398f 100644 --- a/tensorflow/contrib/distribute/python/mirrored_strategy.py +++ b/tensorflow/contrib/distribute/python/mirrored_strategy.py @@ -31,7 +31,6 @@ from tensorflow.python.eager import tape from tensorflow.python.framework import device as tf_device from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops -from tensorflow.python.ops import math_ops from tensorflow.python.ops import variable_scope from tensorflow.python.training import coordinator from tensorflow.python.training import device_util @@ -286,8 +285,7 @@ class MirroredStrategy(distribute_lib.DistributionStrategy): def map(self, map_over, fn, *args, **kwargs): # TODO(josh11b): In eager mode, use one thread per device. index = {} - i = 0 - for m in map_over: + for i, m in enumerate(map_over): d = self._devices[i % len(self._devices)] with ops.device(d): l = index.get(d, []) @@ -349,7 +347,7 @@ class MirroredStrategy(distribute_lib.DistributionStrategy): def read_var(self, tower_local_var): """Read the aggregate value of a tower-local variable.""" if isinstance(tower_local_var, values.TowerLocalVariable): - return math_ops.add_n(self.unwrap(tower_local_var)) + return tower_local_var._get_cross_tower() # pylint: disable=protected-access assert isinstance(tower_local_var, values.Mirrored) return array_ops.identity(tower_local_var.get()) diff --git a/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py b/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py index 7b41cfe064..d0bfcc5586 100644 --- a/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py +++ b/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py @@ -385,14 +385,13 @@ class MirroredStrategyVariableCreationTest(test.TestCase): # Without get(device), should return the value you get by # applying the reduction across all towers (whether you use - # fetch(), get(), or nothing). - self.assertEqual(expected_sum, self.evaluate(dist.fetch(ret_v_sum))) - self.assertEqual(expected_mean, self.evaluate(dist.fetch(ret_v_mean))) + # read_var(), get(), or nothing). + self.assertEqual(expected_sum, self.evaluate(dist.read_var(ret_v_sum))) + self.assertEqual(expected_mean, self.evaluate(dist.read_var(ret_v_mean))) self.assertEqual(expected_sum, self.evaluate(ret_v_sum.get())) self.assertEqual(expected_mean, self.evaluate(ret_v_mean.get())) - if not context.executing_eagerly(): - self.assertEqual(expected_sum, self.evaluate(ret_v_sum)) - self.assertEqual(expected_mean, self.evaluate(ret_v_mean)) + self.assertEqual(expected_sum, self.evaluate(ret_v_sum)) + self.assertEqual(expected_mean, self.evaluate(ret_v_mean)) # NOTE(priyag): Names and name scopes are ignored in eager, hence we are not # testing this in eager mode. @@ -557,14 +556,15 @@ class MirroredStrategyVariableCreationTest(test.TestCase): # the individual values before running the update ops. self.assertEquals(1.0, self.evaluate( ret_v_sum.get(dist._devices[0]).read_value())) - self.assertEquals(2.0, self.evaluate(dist.read_var(ret_v_sum))) + self.assertEquals(2.0, self.evaluate(ret_v_sum)) + # Apply updates. self.evaluate(update_ops) # Assert that the aggregated value of the tower local vars is the sum of # the individual values after running the update ops. self.assertEquals(5.0, self.evaluate( ret_v_sum.get(dist._devices[0]).read_value())) - self.assertEquals(10.0, self.evaluate(dist.read_var(ret_v_sum))) + self.assertEquals(10.0, self.evaluate(ret_v_sum)) if __name__ == "__main__": diff --git a/tensorflow/contrib/distribute/python/monitor_test.py b/tensorflow/contrib/distribute/python/monitor_test.py index 4fdb9bf69b..2892ce4394 100644 --- a/tensorflow/contrib/distribute/python/monitor_test.py +++ b/tensorflow/contrib/distribute/python/monitor_test.py @@ -52,11 +52,11 @@ class MonitorTest(test.TestCase, parameterized.TestCase): self.assertEqual(1, len(layer.trainable_variables)) mirrored_weight_variable = layer.trainable_variables[0] - start_error = self.evaluate(distribution.fetch(mirrored_weight_variable)) + start_error = self.evaluate(mirrored_weight_variable) start_error = abs(numpy.array(start_error) - 1) monitor.run_steps(9) - end_error = self.evaluate(distribution.fetch(mirrored_weight_variable)) + end_error = self.evaluate(mirrored_weight_variable) end_error = abs(numpy.array(end_error) - 1) self.assertGreaterEqual(start_error, end_error) diff --git a/tensorflow/contrib/distribute/python/optimizer_v2_test.py b/tensorflow/contrib/distribute/python/optimizer_v2_test.py index abd3a65ac4..a2d736e422 100644 --- a/tensorflow/contrib/distribute/python/optimizer_v2_test.py +++ b/tensorflow/contrib/distribute/python/optimizer_v2_test.py @@ -59,8 +59,8 @@ class MinimizeLossOptimizerV2Test(test.TestCase, parameterized.TestCase): for _ in range(10): run_step() - weights.append(self.evaluate(distribution.fetch(layer.kernel))) - biases.append(self.evaluate(distribution.fetch(layer.bias))) + weights.append(self.evaluate(layer.kernel)) + biases.append(self.evaluate(layer.bias)) error = abs(numpy.add(numpy.squeeze(weights), numpy.squeeze(biases)) - 1) is_not_increasing = all(y <= x for x, y in zip(error, error[1:])) diff --git a/tensorflow/contrib/distribute/python/step_fn_test.py b/tensorflow/contrib/distribute/python/step_fn_test.py index 75c5ec9659..2ee94d8f70 100644 --- a/tensorflow/contrib/distribute/python/step_fn_test.py +++ b/tensorflow/contrib/distribute/python/step_fn_test.py @@ -50,8 +50,8 @@ class SingleLossStepTest(test.TestCase, parameterized.TestCase): for _ in range(10): run_step() - weights.append(self.evaluate(distribution.fetch(layer.kernel))) - biases.append(self.evaluate(distribution.fetch(layer.bias))) + weights.append(self.evaluate(layer.kernel)) + biases.append(self.evaluate(layer.bias)) error = abs(numpy.add(numpy.squeeze(weights), numpy.squeeze(biases)) - 1) is_not_increasing = all(y <= x for x, y in zip(error, error[1:])) diff --git a/tensorflow/contrib/distribute/python/strategy_test_lib.py b/tensorflow/contrib/distribute/python/strategy_test_lib.py index 2b4ad9f146..d2fe8b3b1e 100644 --- a/tensorflow/contrib/distribute/python/strategy_test_lib.py +++ b/tensorflow/contrib/distribute/python/strategy_test_lib.py @@ -106,13 +106,13 @@ class DistributionTestBase(test.TestCase): before_list = [] after_list = [] for g, v in g_v: - fetched = d.fetch(v) + fetched = d.read_var(v) before_list.append(fetched) # control_dependencies irrelevant but harmless in eager execution with ops.control_dependencies([fetched]): g = d.reduce("sum", g, destinations=v) with ops.control_dependencies(d.unwrap(d.update(v, update, g))): - after_list.append(d.fetch(v)) + after_list.append(d.read_var(v)) return before_list, after_list for i in range(10): @@ -159,12 +159,12 @@ class DistributionTestBase(test.TestCase): before_list = [] after_list = [] for g, v in g_v: - fetched = d.fetch(v) + fetched = d.read_var(v) before_list.append(fetched) with ops.control_dependencies([fetched]): g = d.reduce("sum", g, destinations=v) with ops.control_dependencies(d.unwrap(d.update(v, update, g))): - after_list.append(d.fetch(v)) + after_list.append(d.read_var(v)) return before_list, after_list before_out, after_out = step() @@ -184,7 +184,7 @@ class DistributionTestBase(test.TestCase): with d.scope(): map_in = [constant_op.constant(i) for i in range(10)] map_out = d.map(map_in, lambda x, y: x * y, 2) - observed = d.fetch(d.reduce("sum", map_out)) + observed = d.reduce("sum", map_out) expected = 90 # 2 * (0 + 1 + ... + 9) self.assertEqual(expected, observed.numpy()) diff --git a/tensorflow/contrib/distribute/python/values.py b/tensorflow/contrib/distribute/python/values.py index aca544b7e7..72def62c79 100644 --- a/tensorflow/contrib/distribute/python/values.py +++ b/tensorflow/contrib/distribute/python/values.py @@ -43,7 +43,7 @@ from tensorflow.python.util import nest # pylint: disable=line-too-long -# TODO(josh11b): Should device values be strings or DeviceSpec objects +# TODO(josh11b): Should device values be strings or DeviceSpec objects? # Not sure DeviceSpec objects are usable as a dict key. class DistributedValues(object): """Holds a map from device to values. Either PerDevice or Mirrored.""" @@ -163,9 +163,16 @@ class PerDevice(DistributedValues): pass -class Mirrored(DistributedValues): +# Note that unlike PerDevice, Mirrored values inherit from +# DistributedDelegate and so can be used directly in cross-tower mode. +class Mirrored(DistributedDelegate): """Holds a map from device to values which are kept in sync.""" - pass + + def _get_cross_tower(self): + device = device_util.canonicalize(device_util.current()) + if device in self._index: + return self._index[device] + return list(self._index.values())[0] def _assign_on_device(device, variable, tensor): @@ -353,7 +360,7 @@ class _TowerLocalSaveable(saver.BaseSaverBuilder.SaveableObject): # We use a callable so that we don't have to evaluate this expression # in the case where we are trying to restore instead of save. def tensor(): - return distribute_lib.get_distribution_strategy().fetch( + return distribute_lib.get_distribution_strategy().read_var( tower_local_variable) spec = saver.BaseSaverBuilder.SaveSpec( tensor=tensor, -- GitLab From 5bd52238dbd5ffff91a9cd85c4c841c837cf6d9e Mon Sep 17 00:00:00 2001 From: Yash Katariya Date: Wed, 20 Jun 2018 23:43:27 +0000 Subject: [PATCH 768/816] Removed tfe --- .../examples/nmt_with_attention/nmt_with_attention.ipynb | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb b/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb index 3d162d186b..54ebcad8e9 100644 --- a/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb +++ b/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb @@ -83,7 +83,6 @@ "\n", "# Import TensorFlow >= 1.9 and enable eager execution\n", "import tensorflow as tf\n", - "import tensorflow.contrib.eager as tfe\n", "\n", "tf.enable_eager_execution()\n", "\n", @@ -661,7 +660,7 @@ " for (batch, (inp, targ)) in enumerate(dataset):\n", " loss = 0\n", " \n", - " with tfe.GradientTape() as tape:\n", + " with tf.GradientTape() as tape:\n", " enc_output, enc_hidden = encoder(inp, hidden)\n", " \n", " dec_hidden = enc_hidden\n", -- GitLab From 2d6d0351a5440db144ea42b8ae19b9ee7952a7a5 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 20 Jun 2018 16:40:21 -0700 Subject: [PATCH 769/816] Propagate dominant devices to kWhile computations. PiperOrigin-RevId: 201439537 --- .../compiler/xla/service/hlo_sharding.cc | 44 +++++++++++++++++++ .../compiler/xla/service/hlo_sharding.h | 31 +++++-------- 2 files changed, 55 insertions(+), 20 deletions(-) diff --git a/tensorflow/compiler/xla/service/hlo_sharding.cc b/tensorflow/compiler/xla/service/hlo_sharding.cc index 9fb15df7c2..268b4727bc 100644 --- a/tensorflow/compiler/xla/service/hlo_sharding.cc +++ b/tensorflow/compiler/xla/service/hlo_sharding.cc @@ -100,6 +100,29 @@ bool HloSharding::UsesDevice(int64 device) const { std::find(devices.begin(), devices.end(), device) != devices.end(); } +std::map HloSharding::UsedDevices(int64* count) const { + int64 element_count = 1; + std::map device_map; + if (IsTuple()) { + for (auto& tuple_element_sharding : tuple_elements()) { + auto unique_device = tuple_element_sharding.UniqueDevice(); + if (unique_device.ok()) { + device_map[unique_device.ValueOrDie()] += 1; + } + } + element_count = tuple_elements().size(); + } else { + auto unique_device = UniqueDevice(); + if (unique_device.ok()) { + device_map[unique_device.ValueOrDie()] += 1; + } + } + if (count != nullptr) { + *count = element_count; + } + return device_map; +} + std::vector HloSharding::TileIndexForDevice(int64 device) const { CHECK(!ShapeUtil::IsTuple(tile_shape_)); CHECK(!maximal_); @@ -439,6 +462,27 @@ tensorflow::gtl::optional HloSharding::ExtractSingleSharding() return tuple_elements_.front(); } +size_t HloSharding::Hash() const { + if (!tuple_) { + size_t h = 0; + for (const auto& element : tuple_elements_) { + h = tensorflow::Hash64Combine(h, element.Hash()); + } + return h; + } + if (replicated_) { + return 0; + } + size_t h = 0; + for (uint32 v : tile_assignment_) { + h = tensorflow::Hash64Combine(h, std::hash{}(v)); + } + for (uint32 v : tile_shape_.dimensions()) { + h = tensorflow::Hash64Combine(h, std::hash{}(v)); + } + return h; +} + std::ostream& operator<<(std::ostream& out, const HloSharding& sharding) { out << sharding.ToString(); return out; diff --git a/tensorflow/compiler/xla/service/hlo_sharding.h b/tensorflow/compiler/xla/service/hlo_sharding.h index 1e843481c3..34324d2058 100644 --- a/tensorflow/compiler/xla/service/hlo_sharding.h +++ b/tensorflow/compiler/xla/service/hlo_sharding.h @@ -19,7 +19,9 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_XLA_SERVICE_HLO_SHARDING_H_ #define TENSORFLOW_COMPILER_XLA_SERVICE_HLO_SHARDING_H_ +#include #include +#include #include "tensorflow/compiler/xla/array.h" #include "tensorflow/compiler/xla/literal_util.h" @@ -118,6 +120,14 @@ class HloSharding { // Returns true if the sharding defines an operation on the given device. bool UsesDevice(int64 device) const; + // Retrieves an histogram of the devices used by the sharding. The returned + // map has the device number as key, and the occurrence count as value. + // If a sharding does not have a device, it will not be incuded in the + // histogram. The count argument, if not nullptr, will receive the total + // number of elements this sharding is made of (one for array, N leaves for + // tuples). + std::map UsedDevices(int64* count) const; + // Returns the tile that should be executed on the given device. // REQUIRES: !IsTuple() std::vector TileIndexForDevice(int64 device) const; @@ -179,26 +189,7 @@ class HloSharding { } bool operator!=(const HloSharding& other) const { return !(*this == other); } - size_t Hash() const { - if (!tuple_) { - size_t h = 0; - for (const auto& element : tuple_elements_) { - h = tensorflow::Hash64Combine(h, element.Hash()); - } - return h; - } - if (replicated_) { - return 0; - } - size_t h = 0; - for (uint32 v : tile_assignment_) { - h = tensorflow::Hash64Combine(h, std::hash{}(v)); - } - for (uint32 v : tile_shape_.dimensions()) { - h = tensorflow::Hash64Combine(h, std::hash{}(v)); - } - return h; - } + size_t Hash() const; struct Hasher { size_t operator()(const HloSharding& sharding) const { -- GitLab From d9774ba1cda55c5710fb434cadbcfdfbfcf49653 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 20 Jun 2018 16:45:46 -0700 Subject: [PATCH 770/816] Disable flaky dirichlet_multinomial_test_gpu PiperOrigin-RevId: 201440233 --- tensorflow/python/kernel_tests/distributions/BUILD | 3 +++ 1 file changed, 3 insertions(+) diff --git a/tensorflow/python/kernel_tests/distributions/BUILD b/tensorflow/python/kernel_tests/distributions/BUILD index 985922245e..bbbe70ea48 100644 --- a/tensorflow/python/kernel_tests/distributions/BUILD +++ b/tensorflow/python/kernel_tests/distributions/BUILD @@ -135,6 +135,9 @@ cuda_py_test( "//tensorflow/python:math_ops", "//tensorflow/python:platform_test", ], + tags = [ + "notap", # b/110489471 + ], ) cuda_py_test( -- GitLab From be41e845b581fd7d0c3d356173329dc0fc8e1caa Mon Sep 17 00:00:00 2001 From: Pavithra Vijay Date: Wed, 20 Jun 2018 16:50:43 -0700 Subject: [PATCH 771/816] Add check to see if Wrappers are passed a `Layer` instance. To help user identify the error as in this issue: #19292 PiperOrigin-RevId: 201440954 --- tensorflow/python/keras/layers/wrappers.py | 15 ++++++++++++++- tensorflow/python/keras/layers/wrappers_test.py | 15 +++++++++++++++ 2 files changed, 29 insertions(+), 1 deletion(-) diff --git a/tensorflow/python/keras/layers/wrappers.py b/tensorflow/python/keras/layers/wrappers.py index 18dd35a637..00d0fc67d1 100644 --- a/tensorflow/python/keras/layers/wrappers.py +++ b/tensorflow/python/keras/layers/wrappers.py @@ -45,6 +45,7 @@ class Wrapper(Layer): """ def __init__(self, layer, **kwargs): + assert isinstance(layer, Layer) self.layer = layer self._track_checkpointable(layer, name='layer') # Tracks mapping of Wrapper inputs to inner layer inputs. Useful when @@ -155,9 +156,16 @@ class TimeDistributed(Wrapper): Arguments: layer: a layer instance. + + Raises: + ValueError: If not initialized with a `Layer` instance. """ def __init__(self, layer, **kwargs): + if not isinstance(layer, Layer): + raise ValueError( + 'Please initialize `TimeDistributed` layer with a ' + '`Layer` instance. You passed: {input}'.format(input=layer)) super(TimeDistributed, self).__init__(layer, **kwargs) self.supports_masking = True @@ -250,7 +258,8 @@ class Bidirectional(Wrapper): they will be returned as a list. Raises: - ValueError: In case of invalid `merge_mode` argument. + ValueError: If not initialized with a `Layer` instance or + In case of invalid `merge_mode` argument. Examples: @@ -266,6 +275,10 @@ class Bidirectional(Wrapper): """ def __init__(self, layer, merge_mode='concat', weights=None, **kwargs): + if not isinstance(layer, Layer): + raise ValueError( + 'Please initialize `Bidirectional` layer with a ' + '`Layer` instance. You passed: {input}'.format(input=layer)) if merge_mode not in ['sum', 'mul', 'ave', 'concat', None]: raise ValueError('Invalid merge mode. ' 'Merge mode should be one of ' diff --git a/tensorflow/python/keras/layers/wrappers_test.py b/tensorflow/python/keras/layers/wrappers_test.py index a38cd6a0f8..e5f5b6f589 100644 --- a/tensorflow/python/keras/layers/wrappers_test.py +++ b/tensorflow/python/keras/layers/wrappers_test.py @@ -23,6 +23,7 @@ import copy import numpy as np from tensorflow.python import keras +from tensorflow.python.framework import constant_op from tensorflow.python.framework import test_util as tf_test_util from tensorflow.python.platform import test from tensorflow.python.training.checkpointable import util as checkpointable_util @@ -102,6 +103,13 @@ class TimeDistributedTest(test.TestCase): epochs=1, batch_size=10) + def test_timedistributed_invalid_init(self): + x = constant_op.constant(np.zeros((1, 1)).astype('float32')) + with self.assertRaisesRegexp( + ValueError, + 'Please initialize `TimeDistributed` layer with a `Layer` instance.'): + keras.layers.TimeDistributed(x) + def test_timedistributed_conv2d(self): with self.test_session(): model = keras.models.Sequential() @@ -225,6 +233,13 @@ class BidirectionalTest(test.TestCase): model = keras.models.model_from_json(model.to_json()) model.summary() + def test_bidirectional_invalid_init(self): + x = constant_op.constant(np.zeros((1, 1)).astype('float32')) + with self.assertRaisesRegexp( + ValueError, + 'Please initialize `Bidirectional` layer with a `Layer` instance.'): + keras.layers.Bidirectional(x) + def test_bidirectional_weight_loading(self): rnn = keras.layers.SimpleRNN samples = 2 -- GitLab From 740966e69e87eaee37161efc96d8ea04162e1844 Mon Sep 17 00:00:00 2001 From: Jared Duke Date: Wed, 20 Jun 2018 18:03:50 -0700 Subject: [PATCH 772/816] Make fused activation opt-in PiperOrigin-RevId: 201450857 --- tensorflow/contrib/lite/toco/tooling_util.cc | 22 +++++++++++-------- tensorflow/contrib/lite/toco/tooling_util.h | 2 ++ .../contrib/lite/toco/tooling_util_test.cc | 6 +++++ 3 files changed, 21 insertions(+), 9 deletions(-) diff --git a/tensorflow/contrib/lite/toco/tooling_util.cc b/tensorflow/contrib/lite/toco/tooling_util.cc index fb2ed093a9..a52c812ef4 100644 --- a/tensorflow/contrib/lite/toco/tooling_util.cc +++ b/tensorflow/contrib/lite/toco/tooling_util.cc @@ -413,16 +413,20 @@ string HelpfulOperatorTypeName(const Operator& op) { bool OperatorSupportsFusedActivation(OperatorType type) { switch (type) { - case OperatorType::kConcatenation: - case OperatorType::kFakeQuant: - case OperatorType::kGather: - case OperatorType::kSlice: - case OperatorType::kSqueeze: - case OperatorType::kReshape: - case OperatorType::kSplit: - return false; - default: + case OperatorType::kAdd: + case OperatorType::kAveragePool: + case OperatorType::kBatchNormalization: + case OperatorType::kConv: + case OperatorType::kDepthwiseConv: + case OperatorType::kDiv: + case OperatorType::kFullyConnected: + case OperatorType::kL2Pool: + case OperatorType::kMaxPool: + case OperatorType::kMul: + case OperatorType::kSub: return true; + default: + return false; } } diff --git a/tensorflow/contrib/lite/toco/tooling_util.h b/tensorflow/contrib/lite/toco/tooling_util.h index 7681ce9d39..791ced8d01 100644 --- a/tensorflow/contrib/lite/toco/tooling_util.h +++ b/tensorflow/contrib/lite/toco/tooling_util.h @@ -101,6 +101,8 @@ std::vector>::iterator FindOp(Model& model, const char* OperatorTypeName(OperatorType type); string HelpfulOperatorTypeName(const Operator& op); +// Whether the operator can be fused with an activation function. Note that this +// will return false by default for new operators; fusing support is opt-in. bool OperatorSupportsFusedActivation(OperatorType type); void DumpGraphvizVideoFrame(const Model& model); diff --git a/tensorflow/contrib/lite/toco/tooling_util_test.cc b/tensorflow/contrib/lite/toco/tooling_util_test.cc index a683867374..8609e5bedd 100644 --- a/tensorflow/contrib/lite/toco/tooling_util_test.cc +++ b/tensorflow/contrib/lite/toco/tooling_util_test.cc @@ -175,4 +175,10 @@ TEST(NumElementsTest, UnsignedInt64) { EXPECT_EQ(status.error_message(), kLargeTensorMessage); } +TEST(FusedActivationTest, DefaultsToUnfused) { + EXPECT_TRUE(OperatorSupportsFusedActivation(OperatorType::kAdd)); + EXPECT_FALSE(OperatorSupportsFusedActivation(OperatorType::kNone)); + EXPECT_FALSE(OperatorSupportsFusedActivation(static_cast(255))); +} + } // namespace toco -- GitLab From e8b18a6f0c02d364ff47ba5fa3dc61458d273674 Mon Sep 17 00:00:00 2001 From: Yunxing Dai Date: Wed, 20 Jun 2018 18:33:18 -0700 Subject: [PATCH 773/816] Fix a bug in test_util when generating index for dynamic slice dynamic slice's index space should be it's first operand's shape. PiperOrigin-RevId: 201454414 --- tensorflow/compiler/xla/tests/test_utils.cc | 18 +++++++++++++----- 1 file changed, 13 insertions(+), 5 deletions(-) diff --git a/tensorflow/compiler/xla/tests/test_utils.cc b/tensorflow/compiler/xla/tests/test_utils.cc index dd7c541733..000535a982 100644 --- a/tensorflow/compiler/xla/tests/test_utils.cc +++ b/tensorflow/compiler/xla/tests/test_utils.cc @@ -270,14 +270,22 @@ StatusOr> CreateLiteralForConstrainedUses( switch (use->opcode()) { case HloOpcode::kDynamicSlice: case HloOpcode::kDynamicUpdateSlice: - if (needs_index != nullptr && - !ShapeUtil::Equal(needs_index->shape(), use->shape())) { - return Unimplemented( - "Conflicting operand generation slice index constraints\n"); + if (needs_index != nullptr) { + auto needs_index_shape = needs_index->shape(); + auto use_shape = use->shape(); + if (needs_index->opcode() == HloOpcode::kDynamicSlice) { + needs_index_shape = needs_index->operand(0)->shape(); + } + if (use->opcode() == HloOpcode::kDynamicSlice) { + use_shape = use->operand(0)->shape(); + } + if (!ShapeUtil::Equal(needs_index_shape, use_shape)) { + return Unimplemented( + "Conflicting operand generation slice index constraints\n"); + } } needs_index = use; break; - case HloOpcode::kReduce: case HloOpcode::kReduceWindow: needs_constant = use; -- GitLab From 96dfcc2fdc9f3a7419d3d5c5a64489e757de624e Mon Sep 17 00:00:00 2001 From: Yao Zhang Date: Wed, 20 Jun 2018 18:36:13 -0700 Subject: [PATCH 774/816] Support filter format for FusedConv2DBiasActivation. PiperOrigin-RevId: 201454730 --- .../fused_conv2d_bias_activation_op_test.py | 20 +-- .../grappler/costs/op_level_cost_estimator.cc | 93 ++++++++------ .../grappler/costs/op_level_cost_estimator.h | 10 -- .../costs/op_level_cost_estimator_test.cc | 119 ++++++++++++++---- 4 files changed, 151 insertions(+), 91 deletions(-) diff --git a/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op_test.py b/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op_test.py index a955e21b72..4d62ac65ff 100644 --- a/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op_test.py +++ b/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op_test.py @@ -21,8 +21,6 @@ from __future__ import print_function import numpy as np from tensorflow.contrib.fused_conv.python.ops import fused_conv2d_bias_activation_op -from tensorflow.core.protobuf import config_pb2 -from tensorflow.core.protobuf import rewriter_config_pb2 from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors_impl @@ -35,13 +33,6 @@ from tensorflow.python.platform import test from tensorflow.python.platform import tf_logging -def NoMemoryOptimizationConfig(): - config = config_pb2.ConfigProto() - config.graph_options.rewrite_options.memory_optimization = ( - rewriter_config_pb2.RewriterConfig.OFF) - return config - - def GetShrunkInceptionShapes(shrink=10): """Iterator for smaller versions of convolution shapes in 2015 Inception. @@ -202,8 +193,7 @@ class FusedConv2DBiasActivationTest(test.TestCase): # This is to guarantee that there is always negative values after # bias add so that we can test whether relu works correctly. x3 = bias - # TODO(b/79323979): re-enable memory optimization after this bug is fixed. - with self.test_session(use_gpu=True, config=NoMemoryOptimizationConfig()): + with self.test_session(use_gpu=True): t1 = constant_op.constant(x1, shape=tensor_in_sizes, dtype=dtype) t2 = constant_op.constant(x2, shape=filter_in_sizes, dtype=dtype) fused_t2 = t2 @@ -251,9 +241,7 @@ class FusedConv2DBiasActivationTest(test.TestCase): x3 = np.random.rand(*[filter_in_sizes[-1]]).astype(np.float32) def _SetupVal(data_format, use_gpu): - # TODO(b/79323979): re-enable memory optimization after this bug is fixed. - with self.test_session( - use_gpu=use_gpu, config=NoMemoryOptimizationConfig()): + with self.test_session(use_gpu=use_gpu): t1 = constant_op.constant(x1, shape=tensor_in_sizes) t2 = constant_op.constant(x2, shape=filter_in_sizes) t3 = constant_op.constant(x3, shape=[filter_in_sizes[-1]]) @@ -877,9 +865,7 @@ class FusedConvInt8Tests(test.TestCase): conv_input_scale, conv_input, kernel, padding_type, strides, side_input_scale, side_input, biases) - # TODO(b/79323979): re-enable memory optimization after this bug is fixed. - with self.test_session( - use_gpu=True, config=NoMemoryOptimizationConfig()) as sess: + with self.test_session(use_gpu=True) as sess: actual_y, expected_y = sess.run([actual, expected]) tf_logging.info("actual_y = ", actual_y) tf_logging.info("expected_y = ", expected_y) diff --git a/tensorflow/core/grappler/costs/op_level_cost_estimator.cc b/tensorflow/core/grappler/costs/op_level_cost_estimator.cc index b994d26397..d34eecd009 100644 --- a/tensorflow/core/grappler/costs/op_level_cost_estimator.cc +++ b/tensorflow/core/grappler/costs/op_level_cost_estimator.cc @@ -78,6 +78,14 @@ string GetDataFormat(const OpInfo& op_features) { return data_format; } +string GetFilterFormat(const OpInfo& op_features) { + string filter_format = "HWIO"; // Default format. + if (op_features.attr().find("filter_format") != op_features.attr().end()) { + filter_format = op_features.attr().at("filter_format").s(); + } + return filter_format; +} + Padding GetPadding(const OpInfo& op_features) { if (op_features.attr().find("padding") != op_features.attr().end() && op_features.attr().at("padding").s() == "VALID") { @@ -513,29 +521,44 @@ OpLevelCostEstimator::ConvolutionDimensionsFromInputs( y_index = 3; channel_index = 1; } else { + // Use NHWC. x_index = 1; y_index = 2; channel_index = 3; } + const string& filter_format = GetFilterFormat(op_features); + int filter_x_index, filter_y_index, in_channel_index, out_channel_index; + if (filter_format == "HWIO") { + filter_x_index = 0; + filter_y_index = 1; + in_channel_index = 2; + out_channel_index = 3; + } else { + // Use OIHW + filter_x_index = 2; + filter_y_index = 3; + in_channel_index = 1; + out_channel_index = 0; + } int64 batch = image_shape.dim(0).size(); int64 ix = image_shape.dim(x_index).size(); int64 iy = image_shape.dim(y_index).size(); int64 iz = image_shape.dim(channel_index).size(); - int64 kx = filter_shape.dim(0).size(); - int64 ky = filter_shape.dim(1).size(); + int64 kx = filter_shape.dim(filter_x_index).size(); + int64 ky = filter_shape.dim(filter_y_index).size(); std::vector strides = GetStrides(op_features); const auto padding = GetPadding(op_features); int64 sx = strides[x_index]; int64 sy = strides[y_index]; int64 ox = GetOutputSize(ix, kx, sx, padding); int64 oy = GetOutputSize(iy, ky, sy, padding); - int64 oz = filter_shape.dim(3).size(); + int64 oz = filter_shape.dim(out_channel_index).size(); // Only check equality when both sizes are known (in other words, when // neither is set to a minimum dimension size of 1). - if (iz != 1 && filter_shape.dim(2).size() != 1) { - CHECK_EQ(iz, filter_shape.dim(2).size()); + if (iz != 1 && filter_shape.dim(in_channel_index).size() != 1) { + CHECK_EQ(iz, filter_shape.dim(in_channel_index).size()); } else { - iz = std::max(iz, filter_shape.dim(2).size()); + iz = std::max(iz, filter_shape.dim(in_channel_index).size()); } OpLevelCostEstimator::ConvolutionDimensions conv_dims = { batch, ix, iy, iz, kx, ky, oz, ox, oy, sx, sy, padding}; @@ -1054,6 +1077,24 @@ Costs OpLevelCostEstimator::PredictFusedConv2DBiasActivation( // // For more information, see // contrib/fused_conv/kernels/fused_conv2d_bias_activation_op.cc + + // TODO(yaozhang): Support other data formats (NCHW_VECT_C, NHWC_VECT_W) and + // filter formats (OIHW_VECT_I). + string data_format = GetDataFormat(op_context.op_info); + if (data_format != "NCHW" && data_format != "NHWC") { + LOG(WARNING) << "unsupported data format: " << data_format; + Costs cost = Costs::ZeroCosts(); + cost.inaccurate = true; + return cost; + } + string filter_format = GetFilterFormat(op_context.op_info); + if (filter_format != "HWIO" && filter_format != "OIHW") { + LOG(WARNING) << "unsupported filter format: " << filter_format; + Costs cost = Costs::ZeroCosts(); + cost.inaccurate = true; + return cost; + } + auto& conv_input = op_context.op_info.inputs(0); auto& filter = op_context.op_info.inputs(1); auto& bias = op_context.op_info.inputs(2); @@ -1069,28 +1110,12 @@ Costs OpLevelCostEstimator::PredictFusedConv2DBiasActivation( // Construct the shape of our output tensor from our convolution dimensions // and format, as it may not be available yet. - // // TODO(varomodt): should we centralize the Conv2D input/output shapes? - bool unknown_conv_format = false; OpInfo::TensorProperties output; - switch (GetConvolutionFormat(op_context)) { - case NCHW: - output = - DescribeTensor(DT_FLOAT, {dims.batch, dims.oz, dims.ox, dims.oy}); - break; - case NHWC: - output = - DescribeTensor(DT_FLOAT, {dims.batch, dims.ox, dims.oy, dims.oz}); - break; - default: - // TODO(b/77722245): support cost estimation for NCHW_VECT_C. - LOG(WARNING) << "unsupported data format: " - << GetDataFormat(op_context.op_info) - << " Defaulting to NHWC."; - output = - DescribeTensor(DT_FLOAT, {dims.batch, dims.ox, dims.oy, dims.oz}); - unknown_conv_format = true; - break; + if (data_format == "NCHW") { + output = DescribeTensor(DT_FLOAT, {dims.batch, dims.oz, dims.ox, dims.oy}); + } else if (data_format == "NHWC") { + output = DescribeTensor(DT_FLOAT, {dims.batch, dims.ox, dims.oy, dims.oz}); } // Add the operations the fused op always computes. @@ -1115,7 +1140,7 @@ Costs OpLevelCostEstimator::PredictFusedConv2DBiasActivation( // Construct component operations and run the cost computation. auto costs = PredictFusedOp(op_context_with_output, component_ops); - costs.inaccurate |= found_unknown_shapes || unknown_conv_format; + costs.inaccurate |= found_unknown_shapes; return costs; } @@ -1568,20 +1593,6 @@ Costs OpLevelCostEstimator::PredictFusedBatchNormGrad( } /* static */ -OpLevelCostEstimator::ConvolutionFormat -OpLevelCostEstimator::GetConvolutionFormat(const OpContext& op_context) { - auto data_format = GetDataFormat(op_context.op_info); - if (data_format == "NCHW") { - return NCHW; - } else if (data_format == "NHWC") { - return NHWC; - } else if (data_format == "NCHW_VECT_C") { - return NCHW_VECT_C; - } - - return UNKNOWN_CONVOLUTION_FORMAT; -} - void OpLevelCostEstimator::CombineCostsAndUpdateExecutionTime( Costs* costs) const { if (compute_memory_overlap_) { diff --git a/tensorflow/core/grappler/costs/op_level_cost_estimator.h b/tensorflow/core/grappler/costs/op_level_cost_estimator.h index d384f57279..a277dfdf65 100644 --- a/tensorflow/core/grappler/costs/op_level_cost_estimator.h +++ b/tensorflow/core/grappler/costs/op_level_cost_estimator.h @@ -84,13 +84,6 @@ class OpLevelCostEstimator { int64 sy; // Stride y. Padding padding; // SAME or VALID. }; - enum ConvolutionFormat { - UNKNOWN_CONVOLUTION_FORMAT, - NHWC, - NCHW, - NCHW_VECT_C, - NCHW_VECT_W, - }; int64 CountConv2DOperations(const OpInfo& op_features, bool* found_unknown_shapes) const; int64 CountConv2DOperations(const OpInfo& op_features, @@ -198,9 +191,6 @@ class OpLevelCostEstimator { static OpInfo::TensorProperties DescribeTensor( DataType type, const std::vector& dims); - // Returns the Conv2D format for this operation. - static ConvolutionFormat GetConvolutionFormat(const OpContext& op_context); - // This method calculates the execution time depending on whether IO can // overlap with computation. It assumes the memory and the compute times have // already been calculated. diff --git a/tensorflow/core/grappler/costs/op_level_cost_estimator_test.cc b/tensorflow/core/grappler/costs/op_level_cost_estimator_test.cc index b2c021b73a..77352f6652 100644 --- a/tensorflow/core/grappler/costs/op_level_cost_estimator_test.cc +++ b/tensorflow/core/grappler/costs/op_level_cost_estimator_test.cc @@ -155,19 +155,38 @@ OpContext DescribeDepthwiseConv2dNative(int batch, int ix, int iy, int iz1, // Note that this assumes the NHWC data format. OpContext DescribeFusedConv2DBiasActivation(int batch, int ix, int iy, int iz1, int iz2, int kx, int ky, int ox, - int oy, int oz, - bool has_side_input) { + int oy, int oz, bool has_side_input, + const string& data_format, + const string& filter_format) { OpContext op_context; SetCpuDevice(&op_context.op_info); op_context.op_info.set_op("FusedConv2DBiasActivation"); - DescribeTensor4D(batch, ix, iy, iz1, op_context.op_info.add_inputs()); - DescribeTensor4D(kx, ky, iz2, oz, op_context.op_info.add_inputs()); + auto* attr_data_format = op_context.op_info.mutable_attr(); + SetAttrValue(data_format, &(*attr_data_format)["data_format"]); + auto* attr_filter_format = op_context.op_info.mutable_attr(); + SetAttrValue(filter_format, &(*attr_filter_format)["filter_format"]); + if (data_format == "NHWC") { + DescribeTensor4D(batch, ix, iy, iz1, op_context.op_info.add_inputs()); + } else { + // Use the NCHW format. + DescribeTensor4D(batch, iz1, ix, iy, op_context.op_info.add_inputs()); + } + if (filter_format == "HWIO") { + DescribeTensor4D(kx, ky, iz2, oz, op_context.op_info.add_inputs()); + } else { + // Use the OIHW format. + DescribeTensor4D(oz, iz2, kx, ky, op_context.op_info.add_inputs()); + } DescribeTensor1D(oz, op_context.op_info.add_inputs()); // Add the side_input, if any. auto side_input = op_context.op_info.add_inputs(); if (has_side_input) { - DescribeTensor4D(batch, ox, oy, oz, side_input); + if (data_format == "NHWC") { + DescribeTensor4D(batch, ox, oy, oz, side_input); + } else { + DescribeTensor4D(batch, oz, ox, oy, side_input); + } } // Add the scaling tensors. @@ -549,25 +568,79 @@ TEST_F(OpLevelCostEstimatorTest, ExecutionTimeSumOrMax) { SetComputeMemoryOverlap(false); // Set it back to default. } -TEST_F(OpLevelCostEstimatorTest, FusedConv2DBiasActivationExecutionTime) { +TEST_F(OpLevelCostEstimatorTest, + FusedConv2DBiasActivationNCHW_HWIO_NoSideInput) { auto cost = PredictCosts(DescribeFusedConv2DBiasActivation( - 16, 19, 19, 48, 48, 5, 5, 19, 19, 256, /* has_side_input = */ true)); + 16, 19, 19, 48, 48, 5, 5, 19, 19, 256, /* has_side_input = */ false, + "NCHW", "HWIO")); + EXPECT_EQ(Costs::Duration(825345), cost.memory_time); + EXPECT_EQ(Costs::Duration(355321038), cost.compute_time); + EXPECT_EQ(Costs::Duration(356146383), cost.execution_time); + EXPECT_FALSE(cost.inaccurate); +} + +TEST_F(OpLevelCostEstimatorTest, FusedConv2DBiasActivationNCHW_HWIO) { + auto cost = PredictCosts(DescribeFusedConv2DBiasActivation( + 16, 19, 19, 48, 48, 5, 5, 19, 19, 256, /* has_side_input = */ true, + "NCHW", "HWIO")); EXPECT_EQ(Costs::Duration(1416808), cost.memory_time); EXPECT_EQ(Costs::Duration(355616770), cost.compute_time); EXPECT_EQ(Costs::Duration(357033578), cost.execution_time); EXPECT_FALSE(cost.inaccurate); } -TEST_F(OpLevelCostEstimatorTest, - FusedConv2DBiasActivationNoSideInputExecutionTime) { +TEST_F(OpLevelCostEstimatorTest, FusedConv2DBiasActivationNCHW_OIHW) { auto cost = PredictCosts(DescribeFusedConv2DBiasActivation( - 16, 19, 19, 48, 48, 5, 5, 19, 19, 256, /* has_side_input = */ false)); - EXPECT_EQ(Costs::Duration(825345), cost.memory_time); - EXPECT_EQ(Costs::Duration(355321038), cost.compute_time); - EXPECT_EQ(Costs::Duration(356146383), cost.execution_time); + 16, 19, 19, 48, 48, 5, 5, 19, 19, 256, /* has_side_input = */ true, + "NCHW", "OIHW")); + EXPECT_EQ(Costs::Duration(1416808), cost.memory_time); + EXPECT_EQ(Costs::Duration(355616770), cost.compute_time); + EXPECT_EQ(Costs::Duration(357033578), cost.execution_time); EXPECT_FALSE(cost.inaccurate); } +TEST_F(OpLevelCostEstimatorTest, FusedConv2DBiasActivationNHWC_HWIO) { + auto cost = PredictCosts(DescribeFusedConv2DBiasActivation( + 16, 19, 19, 48, 48, 5, 5, 19, 19, 256, /* has_side_input = */ true, + "NHWC", "HWIO")); + EXPECT_EQ(Costs::Duration(1416808), cost.memory_time); + EXPECT_EQ(Costs::Duration(355616770), cost.compute_time); + EXPECT_EQ(Costs::Duration(357033578), cost.execution_time); + EXPECT_FALSE(cost.inaccurate); +} + +TEST_F(OpLevelCostEstimatorTest, FusedConv2DBiasActivationNHWC_OIHW) { + auto cost = PredictCosts(DescribeFusedConv2DBiasActivation( + 16, 19, 19, 48, 48, 5, 5, 19, 19, 256, /* has_side_input = */ true, + "NHWC", "OIHW")); + EXPECT_EQ(Costs::Duration(1416808), cost.memory_time); + EXPECT_EQ(Costs::Duration(355616770), cost.compute_time); + EXPECT_EQ(Costs::Duration(357033578), cost.execution_time); + EXPECT_FALSE(cost.inaccurate); +} + +// TODO(yaozhang): Update once NCHW_VECT_C is supported. +TEST_F(OpLevelCostEstimatorTest, FusedConv2DBiasActivationNCHW_VECT_C_OIHW) { + auto cost = PredictCosts(DescribeFusedConv2DBiasActivation( + 16, 19, 19, 48, 48, 5, 5, 19, 19, 256, /* has_side_input = */ true, + "NCHW_VECT_C", "OIHW")); + EXPECT_EQ(Costs::Duration(0), cost.memory_time); + EXPECT_EQ(Costs::Duration(0), cost.compute_time); + EXPECT_EQ(Costs::Duration(0), cost.execution_time); + EXPECT_TRUE(cost.inaccurate); +} + +// TODO(yaozhang): Update once OIHW_VECT_I is supported. +TEST_F(OpLevelCostEstimatorTest, FusedConv2DBiasActivationNCHW_OIHW_VECT_I) { + auto cost = PredictCosts(DescribeFusedConv2DBiasActivation( + 16, 19, 19, 48, 48, 5, 5, 19, 19, 256, /* has_side_input = */ true, + "NCHW", "OIHW_VECT_I")); + EXPECT_EQ(Costs::Duration(0), cost.memory_time); + EXPECT_EQ(Costs::Duration(0), cost.compute_time); + EXPECT_EQ(Costs::Duration(0), cost.execution_time); + EXPECT_TRUE(cost.inaccurate); +} + TEST_F(OpLevelCostEstimatorTest, MulExecutionTime) { auto cost = PredictCosts(DescribeBinaryOp("Mul", 1000, 1)); EXPECT_EQ(Costs::Duration(2000), cost.memory_time); @@ -655,8 +728,8 @@ TEST_F(OpLevelCostEstimatorTest, GetTensorShapeProtoFromTensorProto) { TensorProto tensor_proto; TensorShapeProto tensor_shape_proto; - // Dimension larger than max value; should fail while converting to Tensor - // class. + // Dimension larger than max value; should fail while converting to + // Tensor class. tensor_proto.mutable_tensor_shape()->add_dim()->set_size(255); EXPECT_FALSE( GetTensorShapeProtoFromTensorProto(tensor_proto, &tensor_shape_proto)); @@ -676,8 +749,8 @@ TEST_F(OpLevelCostEstimatorTest, GetTensorShapeProtoFromTensorProto) { // Check GetTensorShapeProtoFromTensorProto() resturns correct values. { std::vector shape_expected = {10, 20, 30, 40}; - GetTensorProto(DT_INT32, {4}, shape_expected, /*tensor_content=*/false, - &tensor_proto); + GetTensorProto(DT_INT32, {4}, shape_expected, + /*tensor_content=*/false, &tensor_proto); EXPECT_TRUE( GetTensorShapeProtoFromTensorProto(tensor_proto, &tensor_shape_proto)); ExpectTensorShape(shape_expected, tensor_shape_proto); @@ -685,8 +758,8 @@ TEST_F(OpLevelCostEstimatorTest, GetTensorShapeProtoFromTensorProto) { { std::vector shape_expected = {40, 20, 90, 40}; - GetTensorProto(DT_INT64, {4}, shape_expected, /*tensor_content=*/false, - &tensor_proto); + GetTensorProto(DT_INT64, {4}, shape_expected, + /*tensor_content=*/false, &tensor_proto); EXPECT_TRUE( GetTensorShapeProtoFromTensorProto(tensor_proto, &tensor_shape_proto)); ExpectTensorShape(shape_expected, tensor_shape_proto); @@ -694,8 +767,8 @@ TEST_F(OpLevelCostEstimatorTest, GetTensorShapeProtoFromTensorProto) { { std::vector shape_expected = {10, 20, 30, 40}; - GetTensorProto(DT_INT32, {4}, shape_expected, /*tensor_content=*/true, - &tensor_proto); + GetTensorProto(DT_INT32, {4}, shape_expected, + /*tensor_content=*/true, &tensor_proto); EXPECT_TRUE( GetTensorShapeProtoFromTensorProto(tensor_proto, &tensor_shape_proto)); ExpectTensorShape(shape_expected, tensor_shape_proto); @@ -703,8 +776,8 @@ TEST_F(OpLevelCostEstimatorTest, GetTensorShapeProtoFromTensorProto) { { std::vector shape_expected = {40, 20, 90, 40}; - GetTensorProto(DT_INT64, {4}, shape_expected, /*tensor_content=*/true, - &tensor_proto); + GetTensorProto(DT_INT64, {4}, shape_expected, + /*tensor_content=*/true, &tensor_proto); EXPECT_TRUE( GetTensorShapeProtoFromTensorProto(tensor_proto, &tensor_shape_proto)); ExpectTensorShape(shape_expected, tensor_shape_proto); -- GitLab From f786d43494eafe5d4192e7c9f43385a2d1335595 Mon Sep 17 00:00:00 2001 From: Xuechen Li Date: Wed, 20 Jun 2018 18:46:46 -0700 Subject: [PATCH 775/816] Add self-attention GAN example with TensorFlow eager execution. PiperOrigin-RevId: 201455668 --- .../contrib/eager/python/examples/BUILD | 2 + .../contrib/eager/python/examples/sagan/BUILD | 59 +++++ .../eager/python/examples/sagan/config.py | 72 ++++++ .../eager/python/examples/sagan/ops.py | 71 ++++++ .../eager/python/examples/sagan/ops_test.py | 59 +++++ .../eager/python/examples/sagan/sagan.py | 232 ++++++++++++++++++ .../eager/python/examples/sagan/sagan_test.py | 101 ++++++++ 7 files changed, 596 insertions(+) create mode 100644 tensorflow/contrib/eager/python/examples/sagan/BUILD create mode 100644 tensorflow/contrib/eager/python/examples/sagan/config.py create mode 100644 tensorflow/contrib/eager/python/examples/sagan/ops.py create mode 100644 tensorflow/contrib/eager/python/examples/sagan/ops_test.py create mode 100644 tensorflow/contrib/eager/python/examples/sagan/sagan.py create mode 100644 tensorflow/contrib/eager/python/examples/sagan/sagan_test.py diff --git a/tensorflow/contrib/eager/python/examples/BUILD b/tensorflow/contrib/eager/python/examples/BUILD index 6f02c90368..12155a459c 100644 --- a/tensorflow/contrib/eager/python/examples/BUILD +++ b/tensorflow/contrib/eager/python/examples/BUILD @@ -15,6 +15,8 @@ py_library( "//tensorflow/contrib/eager/python/examples/revnet:config", "//tensorflow/contrib/eager/python/examples/rnn_colorbot", "//tensorflow/contrib/eager/python/examples/rnn_ptb", + "//tensorflow/contrib/eager/python/examples/sagan", + "//tensorflow/contrib/eager/python/examples/sagan:config", "//tensorflow/contrib/eager/python/examples/spinn:data", ], ) diff --git a/tensorflow/contrib/eager/python/examples/sagan/BUILD b/tensorflow/contrib/eager/python/examples/sagan/BUILD new file mode 100644 index 0000000000..b470a41d81 --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/sagan/BUILD @@ -0,0 +1,59 @@ +licenses(["notice"]) # Apache 2.0 + +package(default_visibility = ["//tensorflow:internal"]) + +load("//tensorflow:tensorflow.bzl", "cuda_py_test") + +# Model +py_library( + name = "config", + srcs = ["config.py"], + srcs_version = "PY2AND3", + deps = [ + "//tensorflow:tensorflow_py", + ], +) + +py_library( + name = "ops", + srcs = ["ops.py"], + srcs_version = "PY2AND3", + deps = [ + "//tensorflow:tensorflow_py", + ], +) + +py_library( + name = "sagan", + srcs = ["sagan.py"], + srcs_version = "PY2AND3", + deps = [ + ":ops", + "//tensorflow:tensorflow_py", + ], +) + +# Tests +cuda_py_test( + name = "ops_test", + size = "small", + srcs = ["ops_test.py"], + additional_deps = [ + ":ops", + "//tensorflow:tensorflow_py", + ], +) + +cuda_py_test( + name = "sagan_test", + size = "large", + srcs = ["sagan_test.py"], + additional_deps = [ + ":config", + ":sagan", + "//tensorflow:tensorflow_py", + ], + tags = [ + "optonly", + ], +) diff --git a/tensorflow/contrib/eager/python/examples/sagan/config.py b/tensorflow/contrib/eager/python/examples/sagan/config.py new file mode 100644 index 0000000000..1967bbd867 --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/sagan/config.py @@ -0,0 +1,72 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Self-attention generative adversarial with eager execution. + +Configuration in format of tf.contrib.training.HParams. +Supports default 128x128 ImageNet. + +Reference [Self-Attention Generative Adversarial +Networks](https://arxiv.org/pdf/1805.08318.pdf) + +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf +tfe = tf.contrib.eager + + +def get_hparams_imagenet(): + """Configurations to train SAGAN on 128x128 ImageNet dataset.""" + config = tf.contrib.training.HParams() + if tf.test.is_gpu_available(): + config.add_hparam("image_shape", (3, 128, 128)) + config.add_hparam("data_format", "channels_first") + config.add_hparam("g_init_shape", (512, 4, 4)) + else: + config.add_hparam("image_shape", (128, 128, 3)) + config.add_hparam("data_format", "channels_first") + config.add_hparam("g_init_shape", (4, 4, 512)) + + config.add_hparam("latent_dim", 128) + config.add_hparam("update_g_once_every", 1) + config.add_hparam("batch_size", 64) + config.add_hparam("d_init_filters", 32) + config.add_hparam("num_upsamples", 5) + # (512, 4, 4) -> (3, 128, 128) + return config + + +def get_hparams_mock(): + """Configurations of smaller networks for testing.""" + config = tf.contrib.training.HParams() + if tf.test.is_gpu_available(): + config.add_hparam("image_shape", (3, 16, 16)) + config.add_hparam("data_format", "channels_first") + config.add_hparam("g_init_shape", (32, 2, 2)) + else: + config.add_hparam("image_shape", (16, 16, 3)) + config.add_hparam("data_format", "channels_last") + config.add_hparam("g_init_shape", (2, 2, 32)) + + config.add_hparam("latent_dim", 16) + config.add_hparam("update_g_once_every", 1) + config.add_hparam("batch_size", 2) + config.add_hparam("d_init_filters", 4) + config.add_hparam("num_upsamples", 3) + # (32, 2, 2) -> (3, 16, 16) + return config diff --git a/tensorflow/contrib/eager/python/examples/sagan/ops.py b/tensorflow/contrib/eager/python/examples/sagan/ops.py new file mode 100644 index 0000000000..9a03cab1d1 --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/sagan/ops.py @@ -0,0 +1,71 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Self-attention generative adversarial with eager execution. + +Auxiliary operations. + +Reference [Self-Attention Generative Adversarial +Networks](https://arxiv.org/pdf/1805.08318.pdf) +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf + + +def flatten_hw(x, data_format="channels_first"): + """Flatten the input tensor across height and width dimensions.""" + if data_format == "channels_last": + x = tf.transpose(x, perm=[0, 3, 1, 2]) # Convert to `channels_first` + + old_shape = tf.shape(x) + new_shape = [old_shape[0], old_shape[2] * old_shape[3], old_shape[1]] + + return tf.reshape(x, new_shape) + + +def broaden_hw(x, h, w, c, data_format="channels_first"): + """Broaden dimension so that output has height and width.""" + if data_format == "channels_first": + shape = [-1, c, h, w] + else: + shape = [-1, h, w, c] + + return tf.reshape(x, shape) + + +class BroadenHW(tf.keras.layers.Layer): + """Wrapper class so that `broaden_hw` can be used in `tf.keras.Sequential`.""" + + def __init__(self, h, w, c, data_format="channels_first"): + super(BroadenHW, self).__init__() + self.h = h + self.w = w + self.c = c + self.data_format = data_format + + def call(self, x): + return broaden_hw( + x, h=self.h, w=self.w, c=self.c, data_format=self.data_format) + + def compute_output_shape(self, input_shape): + input_shape = tf.TensorShape(input_shape).as_list() + if self.data_format == "channels_first": + output_shape = (input_shape[0], self.c, self.h, self.w) + else: + output_shape = (input_shape[0], self.h, self.w, self.c) + + return tf.TensorShape(output_shape) diff --git a/tensorflow/contrib/eager/python/examples/sagan/ops_test.py b/tensorflow/contrib/eager/python/examples/sagan/ops_test.py new file mode 100644 index 0000000000..3454985904 --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/sagan/ops_test.py @@ -0,0 +1,59 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for auxiliary operations.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf +from tensorflow.contrib.eager.python.examples.sagan import ops + + +class OpsTest(tf.test.TestCase): + + def test_flatten_hw(self): + """Test `flatten_hw` function with mock object.""" + + batch_size = 1 + # Default NCHW format + if tf.test.is_gpu_available(): + x = tf.random_normal(shape=(batch_size, 3, 4, 4)) + y = ops.flatten_hw(x, data_format="channels_first") + self.assertEqual(y.shape, (batch_size, 4 * 4, 3)) + + # NHWC format + x = tf.random_normal(shape=(batch_size, 4, 4, 3)) + y = ops.flatten_hw(x, data_format="channels_last") + self.assertEqual(y.shape, (batch_size, 4 * 4, 3)) + + def test_broaden_hw(self): + """Test `broaden_hw` function with mock object.""" + + batch_size = 1 + # NHWC format + x = tf.random_normal(shape=[batch_size, 4 * 4 * 16]) + y = ops.broaden_hw(x, h=4, w=4, c=16, data_format="channels_last") + self.assertEqual(y.shape, (batch_size, 4, 4, 16)) + + # Default NCHW format + if tf.test.is_gpu_available(): + y = ops.broaden_hw(x, h=4, w=4, c=16, data_format="channels_first") + self.assertEqual(y.shape, (batch_size, 16, 4, 4)) + + +if __name__ == "__main__": + tf.enable_eager_execution() + tf.test.main() diff --git a/tensorflow/contrib/eager/python/examples/sagan/sagan.py b/tensorflow/contrib/eager/python/examples/sagan/sagan.py new file mode 100644 index 0000000000..561be36c91 --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/sagan/sagan.py @@ -0,0 +1,232 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Self-attention generative adversarial with eager execution. + +Code for main model. + +Reference [Self-Attention Generative Adversarial +Networks](https://arxiv.org/pdf/1805.08318.pdf) +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +import tensorflow as tf +from tensorflow.contrib.eager.python.examples.sagan import ops +tfe = tf.contrib.eager + + +class SelfAttentionModule(tf.keras.Model): + """Self-attention module composed of convolutional layers.""" + + def __init__(self, + attention_features, + original_features, + data_format="channels_first"): + """Initialize the module. + + Args: + attention_features: Number of filters for the attention computation. + original_features: Number of filters of the original Tensor. + data_format: Either 'channels_first' or 'channels_last' + """ + super(SelfAttentionModule, self).__init__() + self.data_format = data_format + # Matrix multiplication implemented as 2D Convolution + self.f = tf.keras.layers.Conv2D( + filters=attention_features, + kernel_size=1, + strides=(1, 1), + data_format=data_format) + self.g = tf.keras.layers.Conv2D( + filters=attention_features, + kernel_size=1, + strides=(1, 1), + data_format=data_format) + self.h = tf.keras.layers.Conv2D( + filters=original_features, + kernel_size=1, + strides=(1, 1), + data_format=data_format) + self.scale = tfe.Variable(0., trainable=True) + + def call(self, x): + f = self.f(x) + g = self.g(x) + h = self.h(x) + + f_flatten = ops.flatten_hw(f, data_format=self.data_format) + g_flatten = ops.flatten_hw(g, data_format=self.data_format) + h_flatten = ops.flatten_hw(h, data_format=self.data_format) + + s = tf.matmul(g_flatten, f_flatten, transpose_b=True) + b = tf.nn.softmax(s, axis=-1) + o = tf.matmul(b, h_flatten) + y = self.scale * tf.reshape(o, tf.shape(x)) + x + + return y + + def compute_output_shape(self, input_shape): + return input_shape + + +class SAGAN(tf.contrib.checkpoint.Checkpointable): + """Self-attention generative adversarial network.""" + + def __init__(self, config): + """Initialize the model. + + Args: + config: tf.contrib.training.HParams object; specifies hyperparameters + """ + super(SAGAN, self).__init__() + self.config = config + self.generator = self._construct_generator() + self.discriminator = self._construct_discriminator() + + def _construct_generator(self): + """Construct generator.""" + # TODO(lxuechen): Add spectral normalization for WGAN + axis = 1 if self.config.data_format == "channels_first" else 3 + + generator = tf.keras.Sequential() + generator.add( + tf.keras.layers.InputLayer(input_shape=(self.config.latent_dim,))) + generator.add( + tf.keras.layers.Dense( + units=np.prod(self.config.g_init_shape), activation=tf.nn.relu)) + + if self.config.data_format == "channels_first": + c, h, w = self.config.g_init_shape + else: + h, w, c = self.config.g_init_shape + + # Reshape to NHWC/NCHW + generator.add( + ops.BroadenHW(h=h, w=w, c=c, data_format=self.config.data_format)) + + filters_list = [c // 2**p for p in range(1, self.config.num_upsamples + 1)] + filters_list[-1] = 3 # Standard RGB images + + for filters in filters_list[:len(filters_list) // 2]: + generator.add( + tf.keras.layers.Conv2DTranspose( + filters=filters, + kernel_size=4, + strides=(2, 2), + use_bias=False, + padding="SAME", + data_format=self.config.data_format)) + generator.add(tf.keras.layers.BatchNormalization(axis=axis)) + generator.add(tf.keras.layers.Activation("relu")) + + # pylint: disable=undefined-loop-variable + generator.add( + SelfAttentionModule( + original_features=filters, + attention_features=filters // 8, + data_format=self.config.data_format)) + # pylint: enable=undefined-loop-variable + + for filters in filters_list[len(filters_list) // 2:]: + generator.add( + tf.keras.layers.Conv2DTranspose( + filters=filters, + kernel_size=4, + strides=(2, 2), + use_bias=False, + padding="SAME", + data_format=self.config.data_format)) + if filters == 3: + # Assume Image rescaled to [-1, 1] + generator.add(tf.keras.layers.Activation("tanh")) + else: + generator.add(tf.keras.layers.BatchNormalization(axis=axis)) + generator.add(tf.keras.layers.Activation("relu")) + + return generator + + def _construct_discriminator(self): + """Construct discriminator.""" + # TODO(lxuechen): Add spectral normalization for WGAN + discriminator = tf.keras.Sequential() + discriminator.add( + tf.keras.layers.InputLayer(input_shape=self.config.image_shape)) + + filters_list = [ + self.config.d_init_filters * 2**p + for p in range(self.config.num_upsamples) + ] + + for filters in filters_list[:(len(filters_list) + 1) // 2]: + discriminator.add( + tf.keras.layers.Conv2D( + filters=filters, + kernel_size=4, + strides=(2, 2), + padding="SAME", + data_format=self.config.data_format)) + discriminator.add(tf.keras.layers.LeakyReLU(alpha=.1)) + + # pylint: disable=undefined-loop-variable + discriminator.add( + SelfAttentionModule( + original_features=filters, + attention_features=filters // 8, + data_format=self.config.data_format)) + # pylint: enable=undefined-loop-variable + + for filters in filters_list[(len(filters_list) + 1) // 2:]: + discriminator.add( + tf.keras.layers.Conv2D( + filters=filters, + kernel_size=4, + strides=(2, 2), + padding="SAME", + data_format=self.config.data_format)) + discriminator.add(tf.keras.layers.LeakyReLU(alpha=.1)) + + discriminator.add(tf.keras.layers.Flatten()) + discriminator.add(tf.keras.layers.Dense(units=1)) + + return discriminator + + def compute_loss_and_grads(self, real_images, noise, training=True): + """Compute loss and gradients for both generator and discriminator.""" + # TODO(lxuechen): Add gradient penalty for discriminator + with tf.GradientTape() as g_tape, tf.GradientTape() as d_tape: + real_logits = self.discriminator(real_images, training=training) + + fake_images = self.generator.call(noise, training=training) + fake_logits = self.discriminator.call(fake_images) + + g_loss = self.compute_g_loss(fake_logits) + d_loss = self.compute_d_loss(fake_logits, real_logits) + + g_grads = g_tape.gradient(g_loss, self.generator.trainable_variables) + d_grads = d_tape.gradient(d_loss, self.discriminator.trainable_variables) + + return g_loss, d_loss, g_grads, d_grads + + def compute_g_loss(self, fake_logits): + return -tf.reduce_mean(fake_logits) # Hinge loss + + def compute_d_loss(self, fake_logits, real_logits): + # Hinge loss + real_loss = tf.reduce_mean(tf.nn.relu(1. - real_logits)) + fake_loss = tf.reduce_mean(tf.nn.relu(1. + fake_logits)) + return real_loss + fake_loss diff --git a/tensorflow/contrib/eager/python/examples/sagan/sagan_test.py b/tensorflow/contrib/eager/python/examples/sagan/sagan_test.py new file mode 100644 index 0000000000..1834594510 --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/sagan/sagan_test.py @@ -0,0 +1,101 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for self-attention generative adversarial network.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf +from tensorflow.contrib.eager.python.examples.sagan import config as config_ +from tensorflow.contrib.eager.python.examples.sagan import sagan +tfe = tf.contrib.eager + + +class SAGANTest(tf.test.TestCase): + + def setUp(self): + super(SAGANTest, self).setUp() + config = config_.get_hparams_mock() + self.noise_shape = (config.batch_size, config.latent_dim) + self.logits_shape = (config.batch_size, 1) + self.images_shape = (config.batch_size,) + config.image_shape + + self.model = sagan.SAGAN(config=config) + self.noise = tf.random_normal(shape=self.noise_shape) + self.real_images = tf.random_normal(shape=self.images_shape) + self.config = config + + def tearDown(self): + del self.model + del self.noise + del self.real_images + super(SAGANTest, self).tearDown() + + def test_generator_call(self): + """Test `generator.__call__` function.""" + fake_images = self.model.generator(self.noise, training=False) + self.assertEqual(fake_images.shape, self.images_shape) + + def test_generator_call_defun(self): + """Test `generator.__call__` function with defun.""" + call_ = tfe.defun(self.model.generator.__call__) + fake_images = call_(self.noise, training=False) + self.assertEqual(fake_images.shape, self.images_shape) + + def test_discriminator_call(self): + """Test `discriminator.__call__` function.""" + real_logits = self.model.discriminator(self.real_images) + self.assertEqual(real_logits.shape, self.logits_shape) + + def test_discriminator_call_defun(self): + """Test `discriminator.__call__` function with defun.""" + call_ = tfe.defun(self.model.discriminator.__call__) + real_logits = call_(self.real_images) + self.assertEqual(real_logits.shape, self.logits_shape) + + def test_compute_loss_and_grads(self): + """Test `compute_loss_and_grads` function.""" + g_loss, d_loss, g_grads, d_grads = self.model.compute_loss_and_grads( + self.real_images, self.noise, training=False) + self.assertEqual(g_loss.shape, ()) + self.assertEqual(d_loss.shape, ()) + self.assertTrue(isinstance(g_grads, list)) + self.assertTrue(isinstance(d_grads, list)) + g_vars = self.model.generator.trainable_variables + d_vars = self.model.discriminator.trainable_variables + + self.assertEqual(len(g_grads), len(g_vars)) + self.assertEqual(len(d_grads), len(d_vars)) + + def test_compute_loss_and_grads_defun(self): + """Test `compute_loss_and_grads` function with defun.""" + compute_loss_and_grads = tfe.defun(self.model.compute_loss_and_grads) + g_loss, d_loss, g_grads, d_grads = compute_loss_and_grads( + self.real_images, self.noise, training=False) + self.assertEqual(g_loss.shape, ()) + self.assertEqual(d_loss.shape, ()) + self.assertTrue(isinstance(g_grads, list)) + self.assertTrue(isinstance(d_grads, list)) + g_vars = self.model.generator.trainable_variables + d_vars = self.model.discriminator.trainable_variables + + self.assertEqual(len(g_grads), len(g_vars)) + self.assertEqual(len(d_grads), len(d_vars)) + + +if __name__ == "__main__": + tf.enable_eager_execution() + tf.test.main() -- GitLab From 23300795f32340455c06ef61f425465bbf0ed887 Mon Sep 17 00:00:00 2001 From: Mingxing Tan Date: Wed, 20 Jun 2018 20:37:41 -0700 Subject: [PATCH 776/816] Fix an XLA merging error. --- .../compiler/xla/service/hlo_instruction.cc | 19 ------------------- 1 file changed, 19 deletions(-) diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc index 00c4308cc5..2d496daab0 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction.cc +++ b/tensorflow/compiler/xla/service/hlo_instruction.cc @@ -1460,25 +1460,6 @@ bool HloInstruction::IdenticalSlowPath( // Remaining instructions with special values. case HloOpcode::kCall: return eq_computations(to_apply(), other.to_apply()); - case HloOpcode::kCrossReplicaSum: - return replica_group_ids() == other.replica_group_ids() && - cross_replica_sum_barrier() == other.cross_replica_sum_barrier() && - eq_computations(to_apply(), other.to_apply()); - case HloOpcode::kCustomCall: - if ((window_ == nullptr) != (other.window_ == nullptr) || - (window_ != nullptr && - !protobuf_util::ProtobufEquals(window(), other.window()))) { - return false; - } - if ((convolution_dimension_numbers_ == nullptr) != - (other.convolution_dimension_numbers_ == nullptr) || - (convolution_dimension_numbers_ != nullptr && - !protobuf_util::ProtobufEquals( - convolution_dimension_numbers(), - other.convolution_dimension_numbers()))) { - return false; - } - return custom_call_target_ == other.custom_call_target_; case HloOpcode::kConditional: return eq_computations(true_computation(), other.true_computation()) && eq_computations(false_computation(), other.false_computation()); -- GitLab From dde16faf1f6676c3e7d3dcf997aff8c8492b328d Mon Sep 17 00:00:00 2001 From: Vikram Date: Wed, 20 Jun 2018 22:58:04 -0700 Subject: [PATCH 777/816] Update mnist.py using `with` to be consistent with other code in datasets might also be useful in case file is not present due to corruption of something else --- tensorflow/python/keras/datasets/mnist.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tensorflow/python/keras/datasets/mnist.py b/tensorflow/python/keras/datasets/mnist.py index 03564accc7..87ccf18ea2 100644 --- a/tensorflow/python/keras/datasets/mnist.py +++ b/tensorflow/python/keras/datasets/mnist.py @@ -47,8 +47,8 @@ def load_data(path='mnist.npz'): path, origin=origin_folder + 'mnist.npz', file_hash='8a61469f7ea1b51cbae51d4f78837e45') - f = np.load(path) - x_train, y_train = f['x_train'], f['y_train'] - x_test, y_test = f['x_test'], f['y_test'] - f.close() + with np.load(path) as f: + x_train, y_train = f['x_train'], f['y_train'] + x_test, y_test = f['x_test'], f['y_test'] + return (x_train, y_train), (x_test, y_test) -- GitLab From a8c59ba450a958a1d6a1754ad1fd7476fcac3532 Mon Sep 17 00:00:00 2001 From: "Tang, Wenyi" Date: Thu, 21 Jun 2018 14:10:23 +0800 Subject: [PATCH 778/816] [CMAKE] Improve cmake build for MKL and MKL-DNN on Windows (#19715) * improve mkl compilation on Win, w/o mkl installation needed * add environment to mkl dynamic libraries * put path change into python api generation command * fix mkldnn mistakes * add path environment when executing python to generate api __init__.py * fix typo error * fix typo * add TODO comment * add TODO comment --- .gitignore | 1 + tensorflow/contrib/cmake/CMakeLists.txt | 36 ++------- tensorflow/contrib/cmake/external/mkl.cmake | 68 ++++++++++++++++ .../contrib/cmake/external/mkldnn.cmake | 12 ++- tensorflow/contrib/cmake/tf_python.cmake | 77 ++++++++++++++----- tensorflow/contrib/cmake/tf_shared_lib.cmake | 5 ++ .../core/common_runtime/mkl_cpu_allocator.cc | 7 ++ tensorflow/core/platform/windows/port.cc | 5 ++ 8 files changed, 160 insertions(+), 51 deletions(-) create mode 100644 tensorflow/contrib/cmake/external/mkl.cmake diff --git a/.gitignore b/.gitignore index 828bbe9bd3..b5306b8b79 100644 --- a/.gitignore +++ b/.gitignore @@ -16,6 +16,7 @@ __pycache__ cmake_build/ .idea/** /build/ +[Bb]uild/ /tensorflow/core/util/version_info.cc /tensorflow/python/framework/fast_tensor_util.cpp Pods diff --git a/tensorflow/contrib/cmake/CMakeLists.txt b/tensorflow/contrib/cmake/CMakeLists.txt index e524e9e743..4ca7a1b28c 100644 --- a/tensorflow/contrib/cmake/CMakeLists.txt +++ b/tensorflow/contrib/cmake/CMakeLists.txt @@ -336,40 +336,14 @@ endif() # MKL Support if (tensorflow_ENABLE_MKL_SUPPORT) add_definitions(-DINTEL_MKL -DEIGEN_USE_VML) - if (WIN32) - find_path(MKL_HOME_PLATFORM mkl - PATHS ${MKL_HOME} ${MKL_HOME}/../ ${MKL_HOME}/../../ - $ENV{MKLROOT} $ENV{MKLROOT}/../ $ENV{MKLROOT}/../../ - PATH_SUFFIXES windows) - set(MKL_INCLUDE_DIRS ${MKL_HOME_PLATFORM}/mkl/include) - set(MKL_LINK_DIRS - ${MKL_HOME_PLATFORM}/mkl/lib/intel64 - ${MKL_HOME_PLATFORM}/tbb/lib/intel64/vc_mt - ${MKL_HOME_PLATFORM}/compiler/lib/intel64 - ${MKL_HOME_PLATFORM}/mkl/tools/builder/lib) - set(MKL_REDIST_DLL_DIRS - ${MKL_HOME_PLATFORM}/redist/intel64/mkl - ${MKL_HOME_PLATFORM}/redist/intel64/tbb/vc_mt - ${MKL_HOME_PLATFORM}/redist/intel64/compiler) - list(APPEND tensorflow_EXTERNAL_LIBRARIES - mkl_intel_lp64_dll mkl_sequential_dll mkl_core_dll mkl_rt mkl_cdll_intel64) - endif() - if (UNIX) - # Fix me: complete the path on linux - find_path(MKL_HOME_PLATFORM mkl - HINTS ${MKL_HOME} ${MKL_HOME}/../ ${MKL_HOME}/../../ - $ENV{MKLROOT} $ENV{MKLROOT}/../ $ENV{MKLROOT}/../../ - PATH_SUFFIXES linux) - set(MKL_INCLUDE_DIRS ${MKL_HOME_PLATFORM}/mkl/include) - set(MKL_LINK_DIRS) # incompleted - set(MKL_REDIST_SO_DIRS) # incompleted - endif() - include_directories(${MKL_INCLUDE_DIRS}) - link_directories(${MKL_LINK_DIRS}) + include(mkl) + list(APPEND tensorflow_EXTERNAL_LIBRARIES ${mkl_STATIC_LIBRARIES}) + list(APPEND tensorflow_EXTERNAL_DEPENDENCIES mkl_copy_shared_to_destination) + include_directories(${mkl_INCLUDE_DIRS}) if (tensorflow_ENABLE_MKLDNN_SUPPORT) include(mkldnn) list(APPEND tensorflow_EXTERNAL_LIBRARIES ${mkldnn_STATIC_LIBRARIES}) - list(APPEND tensorflow_EXTERNAL_DEPENDENCIES mkldnn) + list(APPEND tensorflow_EXTERNAL_DEPENDENCIES mkldnn_copy_shared_to_destination) include_directories(${mkldnn_INCLUDE_DIRS}) else (tensorflow_ENABLE_MKLDNN_SUPPORT) add_definitions(-DINTEL_MKL_ML) diff --git a/tensorflow/contrib/cmake/external/mkl.cmake b/tensorflow/contrib/cmake/external/mkl.cmake new file mode 100644 index 0000000000..a172e3a41a --- /dev/null +++ b/tensorflow/contrib/cmake/external/mkl.cmake @@ -0,0 +1,68 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +include (ExternalProject) + +# NOTE: Different from mkldnn.cmake, this file is meant to download mkl libraries +set(mkl_INCLUDE_DIRS ${CMAKE_CURRENT_BINARY_DIR}/mkl/src/mkl/include) +set(mkl_BIN_DIRS ${CMAKE_CURRENT_BINARY_DIR}/mkl/bin) +set(mkl_WIN mklml_win_2018.0.3.20180406.zip) # match for v0.14 +set(mkl_MAC mklml_mac_2018.0.3.20180406.tgz) +set(mkl_LNX mklml_lnx_2018.0.3.20180406.tgz) +set(mkl_TAG v0.14) +set(mkl_URL https://github.com/intel/mkl-dnn/releases) + +if (WIN32) + set(mkl_DOWNLOAD_URL ${mkl_URL}/download/${mkl_TAG}/${mkl_WIN}) + list(APPEND mkl_STATIC_LIBRARIES + ${CMAKE_CURRENT_BINARY_DIR}/mkl/src/mkl/lib/mklml.lib) + list(APPEND mkl_STATIC_LIBRARIES + ${CMAKE_CURRENT_BINARY_DIR}/mkl/src/mkl/lib/libiomp5md.lib) + list(APPEND mkl_SHARED_LIBRARIES + ${CMAKE_CURRENT_BINARY_DIR}/mkl/src/mkl/lib/mklml.dll) + list(APPEND mkl_SHARED_LIBRARIES + ${CMAKE_CURRENT_BINARY_DIR}/mkl/src/mkl/lib/libiomp5md.dll) +elseif (UNIX) + set(mkl_DOWNLOAD_URL ${mkl_URL}/download/${mkl_TAG}/${mkl_LNX}) + list(APPEND mkl_SHARED_LIBRARIES + ${CMAKE_CURRENT_BINARY_DIR}/mkl/src/mkl/lib/libiomp5.so) + list(APPEND mkl_SHARED_LIBRARIES + ${CMAKE_CURRENT_BINARY_DIR}/mkl/src/mkl/lib/libmklml_gnu.so) + list(APPEND mkl_SHARED_LIBRARIES + ${CMAKE_CURRENT_BINARY_DIR}/mkl/src/mkl/lib/libmklml_intel.so) +elseif (APPLE) + set(mkl_DOWNLOAD_URL ${mkl_URL}/download/${mkl_TAG}/${mkl_MAC}) + #TODO need more information +endif () + +ExternalProject_Add(mkl + PREFIX mkl + URL ${mkl_DOWNLOAD_URL} + DOWNLOAD_DIR "${DOWNLOAD_LOCATION}" + UPDATE_COMMAND "" + CONFIGURE_COMMAND "" + BUILD_COMMAND "" + INSTALL_COMMAND "") + +# put mkl dynamic libraries in one bin directory +add_custom_target(mkl_create_destination_dir + COMMAND ${CMAKE_COMMAND} -E make_directory ${mkl_BIN_DIRS} + DEPENDS mkl) + +add_custom_target(mkl_copy_shared_to_destination DEPENDS mkl_create_destination_dir) + +foreach(dll_file ${mkl_SHARED_LIBRARIES}) + add_custom_command(TARGET mkl_copy_shared_to_destination PRE_BUILD + COMMAND ${CMAKE_COMMAND} -E copy_if_different ${dll_file} ${mkl_BIN_DIRS}) +endforeach() diff --git a/tensorflow/contrib/cmake/external/mkldnn.cmake b/tensorflow/contrib/cmake/external/mkldnn.cmake index a639fdee36..8123ee1f39 100644 --- a/tensorflow/contrib/cmake/external/mkldnn.cmake +++ b/tensorflow/contrib/cmake/external/mkldnn.cmake @@ -22,8 +22,11 @@ set(mkldnn_TAG 3063b2e4c943983f6bf5f2fb9a490d4a998cd291) if(WIN32) if(${CMAKE_GENERATOR} MATCHES "Visual Studio.*") set(mkldnn_STATIC_LIBRARIES ${CMAKE_CURRENT_BINARY_DIR}/mkldnn/src/mkldnn/src/Release/mkldnn.lib) + set(mkldnn_SHARED_LIBRARIES ${CMAKE_CURRENT_BINARY_DIR}/mkldnn/src/mkldnn/src/Release/mkldnn.dll) + set(mkldnn_BUILD ${CMAKE_CURRENT_BINARY_DIR}/mkldnn/src/mkldnn/src/Release) else() set(mkldnn_STATIC_LIBRARIES ${CMAKE_CURRENT_BINARY_DIR}/mkldnn/src/mkldnn/src/mkldnn.lib) + set(mkldnn_SHARED_LIBRARIES ${CMAKE_CURRENT_BINARY_DIR}/mkldnn/src/mkldnn/src/mkldnn.dll) endif() else() set(mkldnn_STATIC_LIBRARIES ${CMAKE_CURRENT_BINARY_DIR}/mkldnn/src/mkldnn/src/libmkldnn.a) @@ -31,6 +34,7 @@ endif() ExternalProject_Add(mkldnn PREFIX mkldnn + DEPENDS mkl GIT_REPOSITORY ${mkldnn_URL} GIT_TAG ${mkldnn_TAG} DOWNLOAD_DIR "${DOWNLOAD_LOCATION}" @@ -40,5 +44,11 @@ ExternalProject_Add(mkldnn CMAKE_CACHE_ARGS -DCMAKE_BUILD_TYPE:STRING=Release -DCMAKE_VERBOSE_MAKEFILE:BOOL=OFF - -DMKLINC:STRING=${MKL_INCLUDE_DIRS} + -DMKLINC:STRING=${mkl_INCLUDE_DIRS} ) + +# since mkldnn depends on mkl, copy the mkldnn.dll together with mklml.dll to mkl_bin_dirs +add_custom_target(mkldnn_copy_shared_to_destination DEPENDS mkldnn) + +add_custom_command(TARGET mkldnn_copy_shared_to_destination PRE_BUILD + COMMAND ${CMAKE_COMMAND} -E copy_if_different ${mkldnn_SHARED_LIBRARIES} ${mkl_BIN_DIRS}) diff --git a/tensorflow/contrib/cmake/tf_python.cmake b/tensorflow/contrib/cmake/tf_python.cmake index 9244604489..786ea05c74 100755 --- a/tensorflow/contrib/cmake/tf_python.cmake +++ b/tensorflow/contrib/cmake/tf_python.cmake @@ -743,26 +743,65 @@ set(api_init_list_file "${tensorflow_source_dir}/api_init_files_list.txt") file(WRITE "${api_init_list_file}" "${api_init_files}") # Run create_python_api.py to generate __init__.py files. -add_custom_command( - OUTPUT ${api_init_files} - DEPENDS tf_python_ops tf_python_copy_scripts_to_destination pywrap_tensorflow_internal tf_python_touchup_modules tf_extension_ops - - # tensorflow/__init__.py depends on files generated in this step. So, remove it while - # this step is running since the files aren't there yet. - COMMAND ${CMAKE_COMMAND} -E remove -f ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/__init__.py - - # Run create_python_api.py to generate API init files. - COMMAND ${CMAKE_COMMAND} -E env PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}/tf_python ${PYTHON_EXECUTABLE} - "${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/tools/api/generator/create_python_api.py" - "--root_init_template=${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/api_template.__init__.py" - "--apidir=${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow" - "--package=tensorflow.python" - "--apiname=tensorflow" - "${api_init_list_file}" - COMMENT "Generating __init__.py files for Python API." - WORKING_DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}/tf_python" -) +### TODO +# In order to download and compile MKL/MKL-DNN automatically in cmake script, mkl-built libraries should be added to system path +# to be loaded by python executor. However `add_custom_command` has an issue with `COMMAND ${CMAKE_COMMAND} -E env PATH=`, where +# arguments of multiple paths (such as D:/;D:/mkl) will be parsed in to seperate string without semicolon and that command fail to +# recongnize paths. As CUDA isn't built with MKL, the MKL built directory is the only path to this command to work around that issue. +# To not override the CUDA and system path in other circumstances, `if-else` branch used here to handle this problem, +# and should be removed if the path issue can be resolved. +### + +if (tensorflow_ENABLE_MKL_SUPPORT) + # add mkl dist dlls to system path for python + # TODO: In current cmake version, PY_RUNTIME_ENV behaves strange with multiple paths, + # so we have to specify only one path in it to work around the issue. We need this if/else + # to protect overwriting CUDA environments + set(PY_RUNTIME_ENV ${mkl_BIN_DIRS}) + add_custom_command( + OUTPUT ${api_init_files} + DEPENDS tf_python_ops tf_python_copy_scripts_to_destination pywrap_tensorflow_internal tf_python_touchup_modules tf_extension_ops + + # tensorflow/__init__.py depends on files generated in this step. So, remove it while + # this step is running since the files aren't there yet. + COMMAND ${CMAKE_COMMAND} -E remove -f ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/__init__.py + + # Run create_python_api.py to generate API init files. + COMMAND ${CMAKE_COMMAND} -E env PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}/tf_python PATH=${PY_RUNTIME_ENV} ${PYTHON_EXECUTABLE} + "${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/tools/api/generator/create_python_api.py" + "--root_init_template=${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/api_template.__init__.py" + "--apidir=${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow" + "--package=tensorflow.python" + "--apiname=tensorflow" + "${api_init_list_file}" + + COMMENT "Generating __init__.py files for Python API." + WORKING_DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}/tf_python" + VERBATIM + ) +else (tensorflow_ENABLE_MKL_SUPPORT) + add_custom_command( + OUTPUT ${api_init_files} + DEPENDS tf_python_ops tf_python_copy_scripts_to_destination pywrap_tensorflow_internal tf_python_touchup_modules tf_extension_ops + + # tensorflow/__init__.py depends on files generated in this step. So, remove it while + # this step is running since the files aren't there yet. + COMMAND ${CMAKE_COMMAND} -E remove -f ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/__init__.py + + # Run create_python_api.py to generate API init files. + COMMAND ${CMAKE_COMMAND} -E env PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}/tf_python ${PYTHON_EXECUTABLE} + "${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/tools/api/generator/create_python_api.py" + "--root_init_template=${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/api_template.__init__.py" + "--apidir=${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow" + "--package=tensorflow.python" + "--apiname=tensorflow" + "${api_init_list_file}" + + COMMENT "Generating __init__.py files for Python API." + WORKING_DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}/tf_python" + ) +endif (tensorflow_ENABLE_MKL_SUPPORT) add_custom_target(tf_python_api SOURCES ${api_init_files}) add_dependencies(tf_python_api tf_python_ops) diff --git a/tensorflow/contrib/cmake/tf_shared_lib.cmake b/tensorflow/contrib/cmake/tf_shared_lib.cmake index 38f40452b5..fdf522f1fd 100644 --- a/tensorflow/contrib/cmake/tf_shared_lib.cmake +++ b/tensorflow/contrib/cmake/tf_shared_lib.cmake @@ -145,3 +145,8 @@ install(DIRECTORY ${tensorflow_source_dir}/third_party/eigen3/ # unsupported Eigen directory install(DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/eigen/src/eigen/unsupported/Eigen/ DESTINATION include/unsupported/Eigen) +# mkl +if (tensorflow_ENABLE_MKL_SUPPORT) + install(DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/mkl/src/mkl/include/ + DESTINATION include/mkl) +endif (tensorflow_ENABLE_MKL_SUPPORT) diff --git a/tensorflow/core/common_runtime/mkl_cpu_allocator.cc b/tensorflow/core/common_runtime/mkl_cpu_allocator.cc index 43a909466e..4ec85457ad 100644 --- a/tensorflow/core/common_runtime/mkl_cpu_allocator.cc +++ b/tensorflow/core/common_runtime/mkl_cpu_allocator.cc @@ -17,6 +17,13 @@ limitations under the License. #include "tensorflow/core/common_runtime/mkl_cpu_allocator.h" +#ifdef _WIN32 +// Declare function to avoid unresolved symbol in VS +i_malloc_t i_malloc; +i_calloc_t i_calloc; +i_realloc_t i_realloc; +i_free_t i_free; +#endif namespace tensorflow { constexpr const char* MklCPUAllocator::kMaxLimitStr; diff --git a/tensorflow/core/platform/windows/port.cc b/tensorflow/core/platform/windows/port.cc index 174f41a993..f2aaf13bec 100644 --- a/tensorflow/core/platform/windows/port.cc +++ b/tensorflow/core/platform/windows/port.cc @@ -171,5 +171,10 @@ int64 AvailableRam() { return INT64_MAX; } +int NumHyperthreadsPerCore() { + static const int ht_per_core = tensorflow::port::CPUIDNumSMT(); + return (ht_per_core > 0) ? ht_per_core : 1; +} + } // namespace port } // namespace tensorflow -- GitLab From 4e071b268b8707b388e11f618d847c1f80199063 Mon Sep 17 00:00:00 2001 From: Priya Gupta Date: Wed, 20 Jun 2018 23:40:35 -0700 Subject: [PATCH 779/816] Update mnist eager example with mirrored strategy as some of the methods it was using are now deprecated. PiperOrigin-RevId: 201478331 --- tensorflow/contrib/distribute/python/values.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/tensorflow/contrib/distribute/python/values.py b/tensorflow/contrib/distribute/python/values.py index 72def62c79..389b01d3cd 100644 --- a/tensorflow/contrib/distribute/python/values.py +++ b/tensorflow/contrib/distribute/python/values.py @@ -26,7 +26,6 @@ import weakref import six -from tensorflow.contrib.data.python.ops import batching from tensorflow.contrib.distribute.python import input_ops from tensorflow.contrib.distribute.python import prefetching_ops_v2 from tensorflow.python.eager import context @@ -614,8 +613,7 @@ class PerDeviceDataset(object): # TODO(priyag): If dropping remainder is not appropriate, find another # approach to distributing the dataset when not possible to divide evenly. # Possibly not an issue when we start using PartitionedDataset. - self._dataset = dataset.apply( - batching.batch_and_drop_remainder(len(devices))) + self._dataset = dataset.batch(len(devices), drop_remainder=True) def make_one_shot_iterator(self): """Get a one time use iterator for the distributed PerDeviceDataset.""" -- GitLab From dfbdc142e6d64cebce9eb7be7e8347af16238507 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 21 Jun 2018 03:49:57 -0700 Subject: [PATCH 780/816] Add tests for the reparameterization_type of tf.distributions. Take samples from the distribution and differentiate the samples wrt the parameters. If the distribution is not reparameterized, the gradients should be None. Otherwise, they should not be None. PiperOrigin-RevId: 201502156 --- .../distributions/bernoulli_test.py | 11 +++++++++++ .../distributions/categorical_test.py | 10 ++++++++++ .../dirichlet_multinomial_test.py | 18 ++++++++++++++++++ .../distributions/exponential_test.py | 10 ++++++++++ .../kernel_tests/distributions/laplace_test.py | 13 +++++++++++++ .../distributions/multinomial_test.py | 16 ++++++++++++++++ .../kernel_tests/distributions/normal_test.py | 13 +++++++++++++ .../kernel_tests/distributions/uniform_test.py | 13 +++++++++++++ 8 files changed, 104 insertions(+) diff --git a/tensorflow/python/kernel_tests/distributions/bernoulli_test.py b/tensorflow/python/kernel_tests/distributions/bernoulli_test.py index 095d1cde15..ed5ea8b034 100644 --- a/tensorflow/python/kernel_tests/distributions/bernoulli_test.py +++ b/tensorflow/python/kernel_tests/distributions/bernoulli_test.py @@ -22,6 +22,7 @@ import importlib import numpy as np +from tensorflow.python.eager import backprop from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import test_util @@ -272,6 +273,16 @@ class BernoulliTest(test.TestCase): dist = bernoulli.Bernoulli(np.log([.2, .4])) self.assertAllEqual((1, 2), dist.sample(1, seed=42).get_shape().as_list()) + @test_util.run_in_graph_and_eager_modes() + def testNotReparameterized(self): + p = constant_op.constant([0.2, 0.6]) + with backprop.GradientTape() as tape: + tape.watch(p) + dist = bernoulli.Bernoulli(probs=p) + samples = dist.sample(100) + grad_p = tape.gradient(samples, p) + self.assertIsNone(grad_p) + def testSampleActsLikeSampleN(self): with self.test_session() as sess: p = [0.2, 0.6] diff --git a/tensorflow/python/kernel_tests/distributions/categorical_test.py b/tensorflow/python/kernel_tests/distributions/categorical_test.py index 68b4ffdb58..d8939433ce 100644 --- a/tensorflow/python/kernel_tests/distributions/categorical_test.py +++ b/tensorflow/python/kernel_tests/distributions/categorical_test.py @@ -21,6 +21,7 @@ from __future__ import print_function from absl.testing import parameterized import numpy as np +from tensorflow.python.eager import backprop from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import tensor_util @@ -376,6 +377,15 @@ class CategoricalTest(test.TestCase, parameterized.TestCase): self.assertAllClose( [0.4**2 + 0.6**2], [prob_val[:, :, :, 1].mean()], atol=1e-2) + def testNotReparameterized(self): + p = constant_op.constant([0.3, 0.3, 0.4]) + with backprop.GradientTape() as tape: + tape.watch(p) + dist = categorical.Categorical(p) + samples = dist.sample(100) + grad_p = tape.gradient(samples, p) + self.assertIsNone(grad_p) + def testLogPMFBroadcasting(self): with self.test_session(): # 1 x 2 x 2 diff --git a/tensorflow/python/kernel_tests/distributions/dirichlet_multinomial_test.py b/tensorflow/python/kernel_tests/distributions/dirichlet_multinomial_test.py index 7922fb0606..9344785b09 100644 --- a/tensorflow/python/kernel_tests/distributions/dirichlet_multinomial_test.py +++ b/tensorflow/python/kernel_tests/distributions/dirichlet_multinomial_test.py @@ -17,6 +17,9 @@ from __future__ import division from __future__ import print_function import numpy as np + +from tensorflow.python.eager import backprop +from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import array_ops @@ -475,6 +478,21 @@ class DirichletMultinomialTest(test.TestCase): self.assertAllClose( actual_covariance_, sample_covariance_, atol=0., rtol=0.15) + def testNotReparameterized(self): + total_count = constant_op.constant(5.0) + concentration = constant_op.constant([0.1, 0.1, 0.1]) + with backprop.GradientTape() as tape: + tape.watch(total_count) + tape.watch(concentration) + dist = ds.DirichletMultinomial( + total_count=total_count, + concentration=concentration) + samples = dist.sample(100) + grad_total_count, grad_concentration = tape.gradient( + samples, [total_count, concentration]) + self.assertIsNone(grad_total_count) + self.assertIsNone(grad_concentration) + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/kernel_tests/distributions/exponential_test.py b/tensorflow/python/kernel_tests/distributions/exponential_test.py index ebcd41b0e2..850da3e969 100644 --- a/tensorflow/python/kernel_tests/distributions/exponential_test.py +++ b/tensorflow/python/kernel_tests/distributions/exponential_test.py @@ -23,6 +23,7 @@ import importlib import numpy as np from tensorflow.python.client import session +from tensorflow.python.eager import backprop from tensorflow.python.framework import constant_op from tensorflow.python.framework import test_util from tensorflow.python.ops import nn_ops @@ -163,6 +164,15 @@ class ExponentialTest(test.TestCase): stats.expon(scale=1.0 / lam_v[i]).cdf)[0], 0.01) + def testFullyReparameterized(self): + lam = constant_op.constant([0.1, 1.0]) + with backprop.GradientTape() as tape: + tape.watch(lam) + exponential = exponential_lib.Exponential(rate=lam) + samples = exponential.sample(100) + grad_lam = tape.gradient(samples, lam) + self.assertIsNotNone(grad_lam) + def testExponentialWithSoftplusRate(self): with self.test_session(): lam = [-2.2, -3.4] diff --git a/tensorflow/python/kernel_tests/distributions/laplace_test.py b/tensorflow/python/kernel_tests/distributions/laplace_test.py index 918c7f63f2..24b243f647 100644 --- a/tensorflow/python/kernel_tests/distributions/laplace_test.py +++ b/tensorflow/python/kernel_tests/distributions/laplace_test.py @@ -22,6 +22,7 @@ import importlib import numpy as np from tensorflow.python.client import session +from tensorflow.python.eager import backprop from tensorflow.python.framework import constant_op from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import test_util @@ -255,6 +256,18 @@ class LaplaceTest(test.TestCase): atol=0.) self.assertTrue(self._kstest(loc_v, scale_v, sample_values)) + def testLaplaceFullyReparameterized(self): + loc = constant_op.constant(4.0) + scale = constant_op.constant(3.0) + with backprop.GradientTape() as tape: + tape.watch(loc) + tape.watch(scale) + laplace = laplace_lib.Laplace(loc=loc, scale=scale) + samples = laplace.sample(100) + grad_loc, grad_scale = tape.gradient(samples, [loc, scale]) + self.assertIsNotNone(grad_loc) + self.assertIsNotNone(grad_scale) + def testLaplaceSampleMultiDimensional(self): with session.Session(): loc_v = np.array([np.arange(1, 101, dtype=np.float32)]) # 1 x 100 diff --git a/tensorflow/python/kernel_tests/distributions/multinomial_test.py b/tensorflow/python/kernel_tests/distributions/multinomial_test.py index e24e8ade73..6d5d40123e 100644 --- a/tensorflow/python/kernel_tests/distributions/multinomial_test.py +++ b/tensorflow/python/kernel_tests/distributions/multinomial_test.py @@ -18,6 +18,8 @@ from __future__ import print_function import numpy as np +from tensorflow.python.eager import backprop +from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import array_ops @@ -343,6 +345,20 @@ class MultinomialTest(test.TestCase): self.assertAllClose( actual_covariance_, sample_covariance_, atol=0., rtol=0.10) + def testNotReparameterized(self): + total_count = constant_op.constant(5.0) + p = constant_op.constant([0.2, 0.6]) + with backprop.GradientTape() as tape: + tape.watch(total_count) + tape.watch(p) + dist = multinomial.Multinomial( + total_count=total_count, + probs=p) + samples = dist.sample(100) + grad_total_count, grad_p = tape.gradient(samples, [total_count, p]) + self.assertIsNone(grad_total_count) + self.assertIsNone(grad_p) + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/kernel_tests/distributions/normal_test.py b/tensorflow/python/kernel_tests/distributions/normal_test.py index d793e03272..c7e00ff8d8 100644 --- a/tensorflow/python/kernel_tests/distributions/normal_test.py +++ b/tensorflow/python/kernel_tests/distributions/normal_test.py @@ -23,6 +23,7 @@ import math import numpy as np +from tensorflow.python.eager import backprop from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops @@ -453,6 +454,18 @@ class NormalTest(test.TestCase): self.assertAllEqual(expected_samples_shape, samples.get_shape()) self.assertAllEqual(expected_samples_shape, sample_values.shape) + def testNormalFullyReparameterized(self): + mu = constant_op.constant(4.0) + sigma = constant_op.constant(3.0) + with backprop.GradientTape() as tape: + tape.watch(mu) + tape.watch(sigma) + normal = normal_lib.Normal(loc=mu, scale=sigma) + samples = normal.sample(100) + grad_mu, grad_sigma = tape.gradient(samples, [mu, sigma]) + self.assertIsNotNone(grad_mu) + self.assertIsNotNone(grad_sigma) + @test_util.run_in_graph_and_eager_modes() def testNormalSampleMultiDimensional(self): with self.test_session(): diff --git a/tensorflow/python/kernel_tests/distributions/uniform_test.py b/tensorflow/python/kernel_tests/distributions/uniform_test.py index e74051c901..978fff1cc1 100644 --- a/tensorflow/python/kernel_tests/distributions/uniform_test.py +++ b/tensorflow/python/kernel_tests/distributions/uniform_test.py @@ -22,6 +22,7 @@ import importlib import numpy as np +from tensorflow.python.eager import backprop from tensorflow.python.framework import constant_op from tensorflow.python.framework import errors from tensorflow.python.framework import tensor_shape @@ -299,6 +300,18 @@ class UniformTest(test.TestCase): expected_pdf = [1.0, 0.1] self.assertAllClose(expected_pdf, self.evaluate(pdf)) + def testFullyReparameterized(self): + a = constant_op.constant(0.1) + b = constant_op.constant(0.8) + with backprop.GradientTape() as tape: + tape.watch(a) + tape.watch(b) + uniform = uniform_lib.Uniform(a, b) + samples = uniform.sample(100) + grad_a, grad_b = tape.gradient(samples, [a, b]) + self.assertIsNotNone(grad_a) + self.assertIsNotNone(grad_b) + # Eager doesn't pass due to a type mismatch in one of the ops. def testUniformFloat64(self): uniform = uniform_lib.Uniform( -- GitLab From 900a3738394fc71cfa2b0626461e48767496f659 Mon Sep 17 00:00:00 2001 From: EFanZh Date: Thu, 21 Jun 2018 19:07:59 +0800 Subject: [PATCH 781/816] Fix a typo --- tensorflow/core/kernels/reduction_gpu_kernels.cu.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/core/kernels/reduction_gpu_kernels.cu.h b/tensorflow/core/kernels/reduction_gpu_kernels.cu.h index 6655084045..9af4cc23b6 100644 --- a/tensorflow/core/kernels/reduction_gpu_kernels.cu.h +++ b/tensorflow/core/kernels/reduction_gpu_kernels.cu.h @@ -295,7 +295,7 @@ __global__ void ColumnReduceMax16ColumnsKernel( // 1D array necessary due to bug in CUDA 9 compiler. // TODO(nluehr) revert to 2D array when compiler is ready. - // This is the mimic the following, but without any constructors: + // This is to mimic the following, but without any constructors: // __shared__ storage_type partial_sums[32 * 33]; __shared__ __align__( alignof(value_type)) char partial_sums_raw[32 * 33 * sizeof(value_type)]; -- GitLab From 70d8b16a452830c7399ff39133cd91cc28ab984b Mon Sep 17 00:00:00 2001 From: Benjamin Kramer Date: Thu, 21 Jun 2018 05:11:13 -0700 Subject: [PATCH 782/816] [XLA:GPU] Pick the right shape for emitting memsets for elements with size 2 Otherwise this could would be wrong for multi-output fusions of fp16 or bf16. We currently never use those for reduce-fusions on GPU. PiperOrigin-RevId: 201508558 --- tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc index a94119b0e9..f6f0a45124 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc @@ -2710,8 +2710,9 @@ StatusOr> IrEmitterUnnested::BuildInitializerThunk( // If the literal is 8 or 16 bits wide, we can emit a 32-bit memset by // repeating the literal 4 or 2 times, so long as the destination buffer is // an even multiple of 32 bits long. + const Shape& output_shape = ShapeUtil::GetSubshape(hlo->shape(), index); if ((num_bytes == 1 || num_bytes == 2) && - ShapeUtil::ByteSizeOf(hlo->shape()) % 4 == 0) { + ShapeUtil::ByteSizeOf(output_shape) % 4 == 0) { uint16 pattern16; if (num_bytes == 1) { uint8 b = literal_bytes.front(); -- GitLab From e7674c09a151cac07bae43f6fe8551e8fec6dfe0 Mon Sep 17 00:00:00 2001 From: Smit Hinsu Date: Thu, 21 Jun 2018 06:01:18 -0700 Subject: [PATCH 783/816] Avoid array index overflow in TransformFilter functor Currently it seems to be writing up to NDIMS + 1 index in an array of size NDIMS. PiperOrigin-RevId: 201512688 --- tensorflow/core/kernels/conv_2d.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/core/kernels/conv_2d.h b/tensorflow/core/kernels/conv_2d.h index 6949e5b5fd..6b7544fd4c 100644 --- a/tensorflow/core/kernels/conv_2d.h +++ b/tensorflow/core/kernels/conv_2d.h @@ -159,7 +159,7 @@ struct TransformFilter { Eigen::DSizes expanded_dims; expanded_dims[0] = in.dimension(NDIMS - 1); // output filters expanded_dims[1] = in.dimension(NDIMS - 2); // input filters - for (int i = 0; i < NDIMS; ++i) { // spatial dimensions + for (int i = 0; i < NDIMS - 2; ++i) { // spatial dimensions expanded_dims[i + 2] = in.dimension(i); } -- GitLab From d7ca38dc7e9ff7996929b4b72d5d63f02486d863 Mon Sep 17 00:00:00 2001 From: gracehoney <31743510+aaroey@users.noreply.github.com> Date: Thu, 21 Jun 2018 07:28:32 -0700 Subject: [PATCH 784/816] Fix some formatting issues --- tensorflow/contrib/tensorrt/test/test_tftrt.py | 4 ++-- tensorflow/contrib/tensorrt/trt_conversion.i | 5 ++--- 2 files changed, 4 insertions(+), 5 deletions(-) diff --git a/tensorflow/contrib/tensorrt/test/test_tftrt.py b/tensorflow/contrib/tensorrt/test/test_tftrt.py index 631438fed4..5e74f9295d 100644 --- a/tensorflow/contrib/tensorrt/test/test_tftrt.py +++ b/tensorflow/contrib/tensorrt/test/test_tftrt.py @@ -222,8 +222,8 @@ def user(multi_engine, _ = run_calibration(int8_calib_gdef, dummy_input) int8_graph = trt.calib_graph_to_infer_graph(int8_calib_gdef) o5 = run_graph(int8_graph, dummy_input) - print("Is FP32 == FP16? %s (False is possible)"%np.allclose(o1, o4)) - print("Is FP32 == INT8? %s (False is possible)"%np.allclose(o1, o5)) + print("Is FP32 == FP16? %s (False is possible)" % np.allclose(o1, o4)) + print("Is FP32 == INT8? %s (False is possible)" % np.allclose(o1, o5)) print("Pass") diff --git a/tensorflow/contrib/tensorrt/trt_conversion.i b/tensorflow/contrib/tensorrt/trt_conversion.i index 5ef0b42161..d51a0b59e2 100644 --- a/tensorflow/contrib/tensorrt/trt_conversion.i +++ b/tensorflow/contrib/tensorrt/trt_conversion.i @@ -198,9 +198,8 @@ std::pair calib_convert( graph_def_string.resize(0); tensorflow::GraphDef out_graph; tensorflow::Status conversion_status = - tensorflow::tensorrt::convert::ConvertCalibGraphToInferGraph(graph_def, - &out_graph, - is_dyn_op); + tensorflow::tensorrt::convert::ConvertCalibGraphToInferGraph( + graph_def, &out_graph, is_dyn_op); if (!conversion_status.ok()) { auto retCode = (int)conversion_status.code(); char buff[2000]; -- GitLab From 979cbf181bf207165aa8ca94c95e26b1373099b2 Mon Sep 17 00:00:00 2001 From: Thomas Joerg Date: Thu, 21 Jun 2018 07:35:04 -0700 Subject: [PATCH 785/816] [XLA:GPU] Fuse loop fusions into consuming multi-output reduce fusions. PiperOrigin-RevId: 201522121 --- .../xla/service/gpu/multi_output_fusion.cc | 119 ++++++++++++++++++ .../xla/service/gpu/multi_output_fusion.h | 3 + .../service/gpu/multi_output_fusion_test.cc | 68 ++++++++++ 3 files changed, 190 insertions(+) diff --git a/tensorflow/compiler/xla/service/gpu/multi_output_fusion.cc b/tensorflow/compiler/xla/service/gpu/multi_output_fusion.cc index d541776f00..9a4a1541ca 100644 --- a/tensorflow/compiler/xla/service/gpu/multi_output_fusion.cc +++ b/tensorflow/compiler/xla/service/gpu/multi_output_fusion.cc @@ -26,6 +26,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/service/hlo_opcode.h" #include "tensorflow/compiler/xla/shape_util.h" +#include "tensorflow/core/lib/gtl/flatset.h" #include "tensorflow/core/platform/types.h" namespace xla { @@ -69,6 +70,7 @@ bool GpuMultiOutputFusion::ShapesCompatibleForFusion(HloInstruction* instr1, // In that case, the operand of the reduce needs to have the same shape // as the other tuple operands, but also we need to compare the output // shapes of the reduces. + // TODO(tjoerg): Allow differences in fp precision. auto* element_instr_1 = get_element_instr(instr1); auto* element_instr_2 = get_element_instr(instr2); if (element_instr_1->opcode() == HloOpcode::kReduce && @@ -147,5 +149,122 @@ bool GpuMultiOutputFusion::LegalToFuse(HloInstruction* instr1, return instr1->fusion_kind() != HloInstruction::FusionKind::kLoop; } +bool GpuMultiOutputFusion::DoProducerConsumerMultiOutputFusion() { + bool changed = false; + RecomputeReachability(); + + tensorflow::gtl::FlatSet to_fuse; + // Keep a list of the instructions to fuse after making all the fusion + // decisions. We first aggressively add instructions to potential_fusion_list, + // then filter out instructions that will be no longer fusable because of + // reachability change. This avoids recalculating reachability on a large set + // of instructions. + std::vector> + potential_fusion_list; + std::vector> fusion_list; + std::vector instrs_to_update_reachability; + + // For each reduce or reduce multi-output fusion, try to fuse it with loop + // fusions operands. + for (HloInstruction* consumer : computation()->MakeInstructionPostOrder()) { + if (consumer->user_count() == 0) { + continue; + } + if (!IsReduction(consumer)) { + continue; + } + // TODO(b/110517657): Lowering multi-output reduce fusions with bfloat16 + // output element types is not supported on GPU. However, bfloat16 is used + // in shared tests. + if (consumer->shape().element_type() == PrimitiveType::BF16) { + continue; + } + + auto consumer_operands = consumer->operands(); + for (size_t i = 0; i < consumer_operands.size(); ++i) { + HloInstruction* producer = consumer_operands[i]; + if (!producer->IsFusable()) { + continue; + } + const bool is_loop_fusion = + producer->opcode() == HloOpcode::kFusion && + producer->fusion_kind() == HloInstruction::FusionKind::kLoop; + if (!is_loop_fusion) { + continue; + } + if (!ShapesCompatibleForFusion(producer, consumer)) { + continue; + } + // If we have already decided to fuse this producer, skip it. + if (ContainsKey(to_fuse, producer)) { + continue; + } + // Do not fuse a producer if the other operands of the fusion are + // reachable from the producer, this would create a cycle. + if (std::any_of(consumer_operands.begin(), consumer_operands.end(), + [&](HloInstruction* operand) { + return producer != operand && + reachability()->IsReachable(producer, operand); + })) { + continue; + } + to_fuse.insert(producer); + potential_fusion_list.emplace_back(producer, consumer); + instrs_to_update_reachability.push_back(producer); + instrs_to_update_reachability.push_back(consumer); + break; + } + } + + // Filter out pairs that will be no longer fusable because of reachability + // change. + for (auto& fusion_pair : potential_fusion_list) { + HloInstruction* producer = fusion_pair.first; + HloInstruction* consumer = fusion_pair.second; + bool fusable = true; + for (size_t i = 0; i < consumer->operand_count(); ++i) { + if (producer != consumer->operand(i) && + reachability()->IsReachable(producer, consumer->operand(i))) { + fusable = false; + break; + } + } + if (fusable) { + UpdateReachability(producer, consumer, instrs_to_update_reachability); + fusion_list.push_back(fusion_pair); + } + } + + for (auto fusions_to_create : fusion_list) { + HloInstruction* producer = fusions_to_create.first; + HloInstruction* consumer = fusions_to_create.second; + if (consumer->opcode() != HloOpcode::kFusion) { + // Fusing with a reduce (fusion) always results in an input fusion. + HloInstruction* input_fusion = + computation()->AddInstruction(HloInstruction::CreateFusion( + consumer->shape(), HloInstruction::FusionKind::kInput, consumer)); + VLOG(2) << "Fuse producer " << producer->name() << " and its consumer " + << consumer->name() << " into " << input_fusion->name(); + TF_CHECK_OK(computation()->ReplaceInstruction(consumer, input_fusion)); + if (producer->opcode() == HloOpcode::kFusion) { + input_fusion->MergeFusionInstructionIntoMultiOutput(producer); + } else { + input_fusion->FuseInstructionIntoMultiOutput(producer); + } + } else { + VLOG(2) << "Fuse producer " << producer->name() << " into its consumer " + << consumer->name(); + + if (producer->opcode() == HloOpcode::kFusion) { + consumer->MergeFusionInstructionIntoMultiOutput(producer); + } else { + consumer->FuseInstructionIntoMultiOutput(producer); + } + } + changed = true; + } + return changed; +} + } // namespace gpu } // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/multi_output_fusion.h b/tensorflow/compiler/xla/service/gpu/multi_output_fusion.h index 16db0e0f02..67ca5d49ee 100644 --- a/tensorflow/compiler/xla/service/gpu/multi_output_fusion.h +++ b/tensorflow/compiler/xla/service/gpu/multi_output_fusion.h @@ -45,6 +45,9 @@ class GpuMultiOutputFusion : public MultiOutputFusion { // Test if it's legal to fuse instr1 and instr2 into one fusion instruction. bool LegalToFuse(HloInstruction* instr1, HloInstruction* instr2) override; + + // Fuse loop fusions into reduce fusions. + bool DoProducerConsumerMultiOutputFusion() override; }; } // namespace gpu diff --git a/tensorflow/compiler/xla/service/gpu/multi_output_fusion_test.cc b/tensorflow/compiler/xla/service/gpu/multi_output_fusion_test.cc index 5e7ceb7976..bca2779464 100644 --- a/tensorflow/compiler/xla/service/gpu/multi_output_fusion_test.cc +++ b/tensorflow/compiler/xla/service/gpu/multi_output_fusion_test.cc @@ -255,5 +255,73 @@ TEST_F(InstructionFusionTest, MultiOutputFusionTwoLoops) { op::Tuple(op::Multiply(), op::Divide())); } +TEST_F(InstructionFusionTest, ProducerConsumerFusionLoopFusionAndReduce) { + auto module = ParseHloString(tensorflow::strings::StrCat(kModulePrefix, R"( + fused_add { + p0.1 = f32[2,2,2]{2,1,0} parameter(0) + p1.1 = f32[2,2,2]{2,1,0} parameter(1) + ROOT add = f32[2,2,2]{2,1,0} add(p0.1, p1.1) + } + + ENTRY reduce { + p0 = f32[2,2,2]{2,1,0} parameter(0) + p1 = f32[2,2,2]{2,1,0} parameter(1) + c0 = f32[] constant(0) + add = f32[2,2,2]{2,1,0} fusion(p0, p1), kind=kLoop, calls=fused_add + reduce = f32[2,2]{1,0} reduce(add, c0), dimensions={2}, to_apply=scalar_add_computation + ROOT root = (f32[2,2]{1,0}, f32[2,2,2]{2,1,0}) tuple(reduce, add) + })")) + .ValueOrDie(); + ASSERT_TRUE(GpuMultiOutputFusion().Run(module.get()).ValueOrDie()); + SCOPED_TRACE(module->ToString()); + const HloInstruction* root = module->entry_computation()->root_instruction(); + EXPECT_THAT(root, op::Tuple(op::GetTupleElement(), op::GetTupleElement())); + const HloInstruction* fusion = root->operand(0)->operand(0); + ASSERT_TRUE(fusion->IsMultiOutputFusion()); + EXPECT_THAT(fusion->fused_expression_root(), + op::Tuple(op::Reduce(), op::Add())); +} + +TEST_F(InstructionFusionTest, ProducerConsumerFusionLoopFusionAndReduceFusion) { + auto module = ParseHloString(tensorflow::strings::StrCat(kModulePrefix, R"( + fused_select { + p1.1 = f32[2,2,2]{2,1,0} parameter(1) + c0 = f32[] constant(0) + broadcast = f32[2,2,2]{2,1,0} broadcast(f32[] c0), dimensions={} + greater-than = pred[2,2,2]{2,1,0} greater-than(f32[2,2,2]{2,1,0} p1.1, f32[2,2,2]{2,1,0} broadcast) + p0.1 = f32[2,2,2]{2,1,0} parameter(0) + ROOT select = f32[2,2,2]{2,1,0} select(pred[2,2,2]{2,1,0} greater-than, f32[2,2,2]{2,1,0} p0.1, f32[2,2,2]{2,1,0} broadcast) + } + + fused_reduce { + p0.2 = f32[2,2,2]{2,1,0} parameter(0) + c1 = f32[] constant(0) + r1 = f32[2,2]{1,0} reduce(p0.2, c1), dimensions={2}, to_apply=scalar_add_computation + mul = f32[2,2,2]{2,1,0} multiply(p0.2, p0.2) + r2 = f32[2,2]{1,0} reduce(mul, c1), dimensions={2}, to_apply=scalar_add_computation + ROOT tuple = (f32[2,2]{1,0}, f32[2,2]{1,0}) tuple(r1, r2) + } + + ENTRY reduce { + p0 = f32[2,2,2]{2,1,0} parameter(0) + p1 = f32[2,2,2]{2,1,0} parameter(1) + select = f32[2,2,2]{2,1,0} fusion(p0, p1), kind=kLoop, calls=fused_select + fusion = (f32[2,2]{1,0}, f32[2,2]{1,0}) fusion(select), kind=kInput, calls=fused_reduce + gte0 = f32[2,2]{1,0} get-tuple-element(fusion), index=0 + gte1 = f32[2,2]{1,0} get-tuple-element(fusion), index=1 + ROOT root = (f32[2,2]{1,0}, f32[2,2]{1,0}, f32[2,2,2]{2,1,0}) tuple(gte1, gte1, select) + })")) + .ValueOrDie(); + ASSERT_TRUE(GpuMultiOutputFusion().Run(module.get()).ValueOrDie()); + SCOPED_TRACE(module->ToString()); + const HloInstruction* root = module->entry_computation()->root_instruction(); + EXPECT_THAT(root, op::Tuple(op::GetTupleElement(), op::GetTupleElement(), + op::GetTupleElement())); + const HloInstruction* fusion = root->operand(0)->operand(0); + ASSERT_TRUE(fusion->IsMultiOutputFusion()); + EXPECT_THAT(fusion->fused_expression_root(), + op::Tuple(op::Reduce(), op::Reduce(), op::Select())); +} + } // namespace gpu } // namespace xla -- GitLab From 2c4fb3633e618941c2bed6e1672052706b849189 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 21 Jun 2018 07:40:52 -0700 Subject: [PATCH 786/816] Use autograph.stack now that the list format has changed. PiperOrigin-RevId: 201522710 --- .../examples/notebooks/dev_summit_2018_demo.ipynb | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/tensorflow/contrib/autograph/examples/notebooks/dev_summit_2018_demo.ipynb b/tensorflow/contrib/autograph/examples/notebooks/dev_summit_2018_demo.ipynb index d62390494b..0702273fac 100644 --- a/tensorflow/contrib/autograph/examples/notebooks/dev_summit_2018_demo.ipynb +++ b/tensorflow/contrib/autograph/examples/notebooks/dev_summit_2018_demo.ipynb @@ -570,7 +570,7 @@ " autograph.utils.set_element_type(numbers, tf.int32)\n", " for i in range(n):\n", " numbers.append(i)\n", - " return numbers.stack() # Stack the list so that it can be used as a Tensor\n", + " return autograph.stack(numbers) # Stack the list so that it can be used as a Tensor\n", "\n", "\n", "tf_f = autograph.to_graph(f)\n", @@ -648,7 +648,7 @@ " if not is_prime:\n", " continue\n", " primes.append(i)\n", - " all_primes = primes.stack()\n", + " all_primes = autograph.stack(primes)\n", "\n", " print('The prime numbers less than', n, 'are:')\n", " print(all_primes)\n", @@ -953,8 +953,9 @@ " train_accuracies.append(step_train_accuracy)\n", " test_accuracies.append(step_test_accuracy)\n", " i += 1\n", - " return (train_losses.stack(), test_losses.stack(), train_accuracies.stack(),\n", - " test_accuracies.stack())" + " return (autograph.stack(train_losses), autograph.stack(test_losses),\n", + " autograph.stack(train_accuracies),\n", + " autograph.stack(test_accuracies))" ], "execution_count": 0, "outputs": [] @@ -1236,7 +1237,7 @@ " cell_output, (state, output) = cell.call(ch, (state, output))\n", " hidden_outputs.append(cell_output)\n", " i += 1\n", - " hidden_outputs = hidden_outputs.stack()\n", + " hidden_outputs = autograph.stack(hidden_outputs)\n", " if training:\n", " hidden_outputs = tf.nn.dropout(hidden_outputs, 0.5)\n", " return hidden_outputs\n", -- GitLab From 7a24845e237f42d3f0bc6ab031ee96e7ef896800 Mon Sep 17 00:00:00 2001 From: Vikram Tiwari Date: Thu, 21 Jun 2018 08:13:26 -0700 Subject: [PATCH 787/816] fixes file loading mechanism in datasets --- tensorflow/python/keras/datasets/boston_housing.py | 7 +++---- tensorflow/python/keras/datasets/mnist.py | 2 +- 2 files changed, 4 insertions(+), 5 deletions(-) diff --git a/tensorflow/python/keras/datasets/boston_housing.py b/tensorflow/python/keras/datasets/boston_housing.py index 4c4cab8c08..eeb7cbc44a 100644 --- a/tensorflow/python/keras/datasets/boston_housing.py +++ b/tensorflow/python/keras/datasets/boston_housing.py @@ -45,10 +45,9 @@ def load_data(path='boston_housing.npz', test_split=0.2, seed=113): origin=origin_folder + 'boston_housing.npz', file_hash= 'f553886a1f8d56431e820c5b82552d9d95cfcb96d1e678153f8839538947dff5') - f = np.load(path) - x = f['x'] - y = f['y'] - f.close() + with np.load(path) as f: + x = f['x'] + y = f['y'] np.random.seed(seed) indices = np.arange(len(x)) diff --git a/tensorflow/python/keras/datasets/mnist.py b/tensorflow/python/keras/datasets/mnist.py index 87ccf18ea2..2a1c8d5f51 100644 --- a/tensorflow/python/keras/datasets/mnist.py +++ b/tensorflow/python/keras/datasets/mnist.py @@ -51,4 +51,4 @@ def load_data(path='mnist.npz'): x_train, y_train = f['x_train'], f['y_train'] x_test, y_test = f['x_test'], f['y_test'] - return (x_train, y_train), (x_test, y_test) + return (x_train, y_train), (x_test, y_test) -- GitLab From f6df02bde672901dc25dc13f2990f5698dc5c9fd Mon Sep 17 00:00:00 2001 From: Vikram Tiwari Date: Thu, 21 Jun 2018 08:36:55 -0700 Subject: [PATCH 788/816] fixes reuters file --- tensorflow/python/keras/datasets/reuters.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/tensorflow/python/keras/datasets/reuters.py b/tensorflow/python/keras/datasets/reuters.py index 2120b4b242..cb796bb06c 100644 --- a/tensorflow/python/keras/datasets/reuters.py +++ b/tensorflow/python/keras/datasets/reuters.py @@ -130,7 +130,5 @@ def get_word_index(path='reuters_word_index.json'): path, origin=origin_folder + 'reuters_word_index.json', file_hash='4d44cc38712099c9e383dc6e5f11a921') - f = open(path) - data = json.load(f) - f.close() - return data + with open(path) as f: + return json.load(f) -- GitLab From 4ec30cf37a44b64f0d48aa78adc77c09531dd981 Mon Sep 17 00:00:00 2001 From: Benjamin Kramer Date: Thu, 21 Jun 2018 08:36:58 -0700 Subject: [PATCH 789/816] [XLA] Make ShapeTree use ShapeIndexViews Avoids creating temporary std::vectors on the consumer side. Also push ShapeIndexViews through the GPU backend a bit. PiperOrigin-RevId: 201529722 --- .../xla/service/gpu/hlo_to_ir_bindings.cc | 4 ++-- .../xla/service/gpu/hlo_to_ir_bindings.h | 6 ++--- tensorflow/compiler/xla/shape_tree.h | 22 +++++++++---------- 3 files changed, 16 insertions(+), 16 deletions(-) diff --git a/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.cc b/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.cc index e303999c63..d420863b85 100644 --- a/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.cc +++ b/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.cc @@ -137,7 +137,7 @@ llvm::Value* HloToIrBindings::EmitGetTupleElement(const HloInstruction* gte, } llvm::Value* HloToIrBindings::GetTypedIrValue(const HloInstruction& hlo, - const ShapeIndex& shape_index, + ShapeIndexView shape_index, llvm::Value* ir_value) { llvm::Type* pointee_type = llvm_ir::ShapeToIrType( ShapeUtil::GetSubshape(hlo.shape(), shape_index), module_); @@ -158,7 +158,7 @@ llvm::Value* HloToIrBindings::GetTypedIrValue(const HloInstruction& hlo, void HloToIrBindings::BindHloToIrValue(const HloInstruction& hlo, llvm::Value* ir_value, - const ShapeIndex& shape_index) { + ShapeIndexView shape_index) { VLOG(2) << "Binding " << hlo.ToString(); const Shape& hlo_shape = hlo.shape(); diff --git a/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.h b/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.h index 3d34311b43..a86e6e78c6 100644 --- a/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.h +++ b/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.h @@ -51,7 +51,7 @@ class HloToIrBindings { // Rebinds the given HLO to the LLVM IR value that represent its address. void BindHloToIrValue(const HloInstruction& hlo, llvm::Value* ir_value, - const ShapeIndex& shape_index = {}); + ShapeIndexView shape_index = {}); // Unbinds all IR values that's defined in an LLVM function, e.g., function // arguments and stack variables. Global variables will be kept in bindings_. @@ -71,7 +71,7 @@ class HloToIrBindings { // A helper method that returns the base pointer of the IrArray containing the // output of "inst".at the given ShapeIndex. llvm::Value* GetBasePointer(const HloInstruction& hlo, - const ShapeIndex& shape_index = {}) const { + ShapeIndexView shape_index = {}) const { auto it = base_ptrs_.find(&hlo); CHECK(it != base_ptrs_.end()) << hlo.ToString(); return it->second.element(shape_index); @@ -97,7 +97,7 @@ class HloToIrBindings { // Returns an llvm typed ir representation of 'ir_value' based on 'hlo' shape. llvm::Value* GetTypedIrValue(const HloInstruction& hlo, - const ShapeIndex& shape_index, + ShapeIndexView shape_index, llvm::Value* ir_value); const BufferAssignment* buffer_assignment_; diff --git a/tensorflow/compiler/xla/shape_tree.h b/tensorflow/compiler/xla/shape_tree.h index 18e54d23c2..4aacc87b78 100644 --- a/tensorflow/compiler/xla/shape_tree.h +++ b/tensorflow/compiler/xla/shape_tree.h @@ -105,8 +105,8 @@ class ShapeTree { // Returns the data element associated with the array in the shape at the // given index (see ShapeUtil::GetSubshape for how indexes are defined). - const T& element(const ShapeIndex& index) const; - T* mutable_element(const ShapeIndex& index); + const T& element(ShapeIndexView index) const; + T* mutable_element(ShapeIndexView index); // Return the shape represented with this ShapeTree. const Shape& shape() const { return *shape_; } @@ -125,7 +125,7 @@ class ShapeTree { // Returns true if the node at the given index is a leaf node (an array // shape). - bool IsLeaf(const ShapeIndex& index) const { return Lookup(index)->is_leaf; } + bool IsLeaf(ShapeIndexView index) const { return Lookup(index)->is_leaf; } ShapeTree(const ShapeTree&) = default; ShapeTree& operator=(const ShapeTree&) = default; @@ -211,12 +211,12 @@ class ShapeTree { // Returns an iterator pointing to the given ShapeIndex. // REQUIRES: index must exist in the ShapeTree. - iterator find(const ShapeIndex& index) { + iterator find(ShapeIndexView index) { Node* element = Lookup(index); return iterator(&nodes_, typename std::vector::iterator(element), /*iterate_leaves_only=*/false); } - const_iterator find(const ShapeIndex& index) const { + const_iterator find(ShapeIndexView index) const { Node* element = Lookup(index); return iterator(&nodes_, typename std::vector::const_iterator(element), @@ -285,8 +285,8 @@ class ShapeTree { static Status ForEachMutableHelper(const Fn& func, std::vector* nodes); // Return the tree node at the given index. - Node* Lookup(const ShapeIndex& index); - const Node* Lookup(const ShapeIndex& index) const; + Node* Lookup(ShapeIndexView index); + const Node* Lookup(ShapeIndexView index) const; // The nodes in this shape tree. std::vector nodes_; @@ -463,17 +463,17 @@ ShapeTree::ShapeTree(const std::shared_ptr& shape, } template -const T& ShapeTree::element(const ShapeIndex& index) const { +const T& ShapeTree::element(ShapeIndexView index) const { return Lookup(index)->data.second; } template -T* ShapeTree::mutable_element(const ShapeIndex& index) { +T* ShapeTree::mutable_element(ShapeIndexView index) { return &Lookup(index)->data.second; } template -internal::ShapeTreeNode* ShapeTree::Lookup(const ShapeIndex& index) { +internal::ShapeTreeNode* ShapeTree::Lookup(ShapeIndexView index) { Node* node = &nodes_[0]; for (const int64 i : index) { CHECK_GE(i, 0); @@ -485,7 +485,7 @@ internal::ShapeTreeNode* ShapeTree::Lookup(const ShapeIndex& index) { template const internal::ShapeTreeNode* ShapeTree::Lookup( - const ShapeIndex& index) const { + ShapeIndexView index) const { return const_cast(this)->Lookup(index); } -- GitLab From 68bb4359d4f831026888d52500b742e9f1005577 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 21 Jun 2018 09:01:39 -0700 Subject: [PATCH 790/816] External Keras sync, Fix Bidirectional Regularization item PiperOrigin-RevId: 201533115 --- .../python/keras/layers/wrappers_test.py | 36 +++++++++++++++++++ 1 file changed, 36 insertions(+) diff --git a/tensorflow/python/keras/layers/wrappers_test.py b/tensorflow/python/keras/layers/wrappers_test.py index e5f5b6f589..3b997732b5 100644 --- a/tensorflow/python/keras/layers/wrappers_test.py +++ b/tensorflow/python/keras/layers/wrappers_test.py @@ -444,6 +444,42 @@ class BidirectionalTest(test.TestCase): layer.trainable = True assert len(layer.trainable_weights) == 6 + def test_Bidirectional_updates(self): + with self.test_session(): + x = keras.layers.Input(shape=(3, 2)) + x_reachable_update = x * x + layer = keras.layers.Bidirectional(keras.layers.SimpleRNN(3)) + _ = layer(x) + assert not layer.updates + assert not layer.get_updates_for(None) + assert not layer.get_updates_for(x) + layer.forward_layer.add_update(x_reachable_update, inputs=x) + layer.forward_layer.add_update(1, inputs=None) + layer.backward_layer.add_update(x_reachable_update, inputs=x) + layer.backward_layer.add_update(1, inputs=None) + assert len(layer.updates) == 4 + assert len(layer.get_updates_for(None)) == 2 + assert len(layer.get_updates_for(x)) == 2 + + def test_Bidirectional_losses(self): + with self.test_session(): + x = keras.layers.Input(shape=(3, 2)) + x_reachable_loss = x * x + layer = keras.layers.Bidirectional( + keras.layers.SimpleRNN( + 3, kernel_regularizer='l1', bias_regularizer='l1')) + _ = layer(x) + assert len(layer.losses) == 4 + assert len(layer.get_losses_for(None)) == 4 + assert not layer.get_losses_for(x) + layer.forward_layer.add_loss(x_reachable_loss, inputs=x) + layer.forward_layer.add_loss(1, inputs=None) + layer.backward_layer.add_loss(x_reachable_loss, inputs=x) + layer.backward_layer.add_loss(1, inputs=None) + assert len(layer.losses) == 8 + assert len(layer.get_losses_for(None)) == 6 + assert len(layer.get_losses_for(x)) == 2 + def test_Bidirectional_with_constants(self): with self.test_session(): # Test basic case. -- GitLab From 51ef92ccfa042523055640261b437ebaf3060a5d Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 21 Jun 2018 09:03:52 -0700 Subject: [PATCH 791/816] Internal change for visibility to ndarray_tensor build rule PiperOrigin-RevId: 201533484 --- tensorflow/python/BUILD | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD index 3fc25772f6..d1561f5c57 100644 --- a/tensorflow/python/BUILD +++ b/tensorflow/python/BUILD @@ -4,14 +4,16 @@ # Public targets: # ":platform" - Low-level and platform-specific Python code. -package(default_visibility = [ +visibility = [ "//engedu/ml/tf_from_scratch:__pkg__", "//tensorflow:internal", "//tensorflow/contrib/lite/toco/python:__pkg__", "//tensorflow_models:__subpackages__", # TODO(aselle): to pass open source test. "//bazel_pip/tensorflow/contrib/lite/toco/python:__pkg__", -]) +] + +package(default_visibility = visibility) licenses(["notice"]) # Apache 2.0 @@ -358,6 +360,9 @@ cc_library( name = "ndarray_tensor", srcs = ["lib/core/ndarray_tensor.cc"], hdrs = ["lib/core/ndarray_tensor.h"], + visibility = visibility + [ + "//learning/deepmind/courier:__subpackages__", + ], deps = [ ":bfloat16_lib", ":ndarray_tensor_bridge", -- GitLab From 0ebf5e2a7ca265861608a6998dd860a53c015481 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 21 Jun 2018 09:07:20 -0700 Subject: [PATCH 792/816] 16-bit quantized Split support in TFLite interpreter PiperOrigin-RevId: 201534122 --- tensorflow/contrib/lite/kernels/split.cc | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/tensorflow/contrib/lite/kernels/split.cc b/tensorflow/contrib/lite/kernels/split.cc index 43387df9ce..b144486041 100644 --- a/tensorflow/contrib/lite/kernels/split.cc +++ b/tensorflow/contrib/lite/kernels/split.cc @@ -76,8 +76,9 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { TF_LITE_ENSURE_EQ(context, NumOutputs(node), op_context.params->num_splits); auto input_type = op_context.input->type; - TF_LITE_ENSURE(context, - input_type == kTfLiteFloat32 || input_type == kTfLiteUInt8); + TF_LITE_ENSURE(context, input_type == kTfLiteFloat32 || + input_type == kTfLiteUInt8 || + input_type == kTfLiteInt16); for (int i = 0; i < NumOutputs(node); ++i) { GetOutput(context, node, i)->type = input_type; } @@ -137,9 +138,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { TF_LITE_SPLIT(uint8_t); break; } + case kTfLiteInt16: { + TF_LITE_SPLIT(int16_t); + break; + } default: context->ReportError( - context, "Only float32 and uint8 are currently supported, got %d.", + context, + "Only float32, uint8 and int16 are currently supported, got %d.", op_context.input->type); return kTfLiteError; } -- GitLab From bead8aaf6caaa70ee9305c31a4a17c1f751e2a7c Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 21 Jun 2018 09:12:35 -0700 Subject: [PATCH 793/816] Disable guitar dirichlet_multinomial_test_gpu PiperOrigin-RevId: 201534842 --- tensorflow/python/kernel_tests/distributions/BUILD | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/python/kernel_tests/distributions/BUILD b/tensorflow/python/kernel_tests/distributions/BUILD index bbbe70ea48..14532965d8 100644 --- a/tensorflow/python/kernel_tests/distributions/BUILD +++ b/tensorflow/python/kernel_tests/distributions/BUILD @@ -136,6 +136,7 @@ cuda_py_test( "//tensorflow/python:platform_test", ], tags = [ + "noguitar", # b/110489471 "notap", # b/110489471 ], ) -- GitLab From 6cf61a02e15d4748e0545e1bd6b9d647b18ee6b1 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 21 Jun 2018 09:42:03 -0700 Subject: [PATCH 794/816] Run tests for tf.distributions.Gamma in both graph and eager modes. PiperOrigin-RevId: 201539026 --- .../kernel_tests/distributions/gamma_test.py | 85 ++++++++++--------- 1 file changed, 43 insertions(+), 42 deletions(-) diff --git a/tensorflow/python/kernel_tests/distributions/gamma_test.py b/tensorflow/python/kernel_tests/distributions/gamma_test.py index 5e4813ac07..154e859f3c 100644 --- a/tensorflow/python/kernel_tests/distributions/gamma_test.py +++ b/tensorflow/python/kernel_tests/distributions/gamma_test.py @@ -21,9 +21,9 @@ import importlib import numpy as np -from tensorflow.python.client import session from tensorflow.python.framework import constant_op from tensorflow.python.framework import tensor_shape +from tensorflow.python.framework import test_util from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn_ops from tensorflow.python.ops.distributions import gamma as gamma_lib @@ -45,6 +45,7 @@ special = try_import("scipy.special") stats = try_import("scipy.stats") +@test_util.run_all_in_graph_and_eager_modes class GammaTest(test.TestCase): def testGammaShape(self): @@ -53,9 +54,9 @@ class GammaTest(test.TestCase): beta = constant_op.constant(11.0) gamma = gamma_lib.Gamma(concentration=alpha, rate=beta) - self.assertEqual(gamma.batch_shape_tensor().eval(), (5,)) + self.assertEqual(self.evaluate(gamma.batch_shape_tensor()), (5,)) self.assertEqual(gamma.batch_shape, tensor_shape.TensorShape([5])) - self.assertAllEqual(gamma.event_shape_tensor().eval(), []) + self.assertAllEqual(self.evaluate(gamma.event_shape_tensor()), []) self.assertEqual(gamma.event_shape, tensor_shape.TensorShape([])) def testGammaLogPDF(self): @@ -74,8 +75,8 @@ class GammaTest(test.TestCase): if not stats: return expected_log_pdf = stats.gamma.logpdf(x, alpha_v, scale=1 / beta_v) - self.assertAllClose(log_pdf.eval(), expected_log_pdf) - self.assertAllClose(pdf.eval(), np.exp(expected_log_pdf)) + self.assertAllClose(self.evaluate(log_pdf), expected_log_pdf) + self.assertAllClose(self.evaluate(pdf), np.exp(expected_log_pdf)) def testGammaLogPDFMultidimensional(self): with self.test_session(): @@ -87,10 +88,10 @@ class GammaTest(test.TestCase): x = np.array([[2.5, 2.5, 4.0, 0.1, 1.0, 2.0]], dtype=np.float32).T gamma = gamma_lib.Gamma(concentration=alpha, rate=beta) log_pdf = gamma.log_prob(x) - log_pdf_values = log_pdf.eval() + log_pdf_values = self.evaluate(log_pdf) self.assertEqual(log_pdf.get_shape(), (6, 2)) pdf = gamma.prob(x) - pdf_values = pdf.eval() + pdf_values = self.evaluate(pdf) self.assertEqual(pdf.get_shape(), (6, 2)) if not stats: return @@ -108,10 +109,10 @@ class GammaTest(test.TestCase): x = np.array([[2.5, 2.5, 4.0, 0.1, 1.0, 2.0]], dtype=np.float32).T gamma = gamma_lib.Gamma(concentration=alpha, rate=beta) log_pdf = gamma.log_prob(x) - log_pdf_values = log_pdf.eval() + log_pdf_values = self.evaluate(log_pdf) self.assertEqual(log_pdf.get_shape(), (6, 2)) pdf = gamma.prob(x) - pdf_values = pdf.eval() + pdf_values = self.evaluate(pdf) self.assertEqual(pdf.get_shape(), (6, 2)) if not stats: @@ -135,7 +136,7 @@ class GammaTest(test.TestCase): if not stats: return expected_cdf = stats.gamma.cdf(x, alpha_v, scale=1 / beta_v) - self.assertAllClose(cdf.eval(), expected_cdf) + self.assertAllClose(self.evaluate(cdf), expected_cdf) def testGammaMean(self): with self.test_session(): @@ -146,7 +147,7 @@ class GammaTest(test.TestCase): if not stats: return expected_means = stats.gamma.mean(alpha_v, scale=1 / beta_v) - self.assertAllClose(gamma.mean().eval(), expected_means) + self.assertAllClose(self.evaluate(gamma.mean()), expected_means) def testGammaModeAllowNanStatsIsFalseWorksWhenAllBatchMembersAreDefined(self): with self.test_session(): @@ -155,7 +156,7 @@ class GammaTest(test.TestCase): gamma = gamma_lib.Gamma(concentration=alpha_v, rate=beta_v) expected_modes = (alpha_v - 1) / beta_v self.assertEqual(gamma.mode().get_shape(), (3,)) - self.assertAllClose(gamma.mode().eval(), expected_modes) + self.assertAllClose(self.evaluate(gamma.mode()), expected_modes) def testGammaModeAllowNanStatsFalseRaisesForUndefinedBatchMembers(self): with self.test_session(): @@ -166,7 +167,7 @@ class GammaTest(test.TestCase): rate=beta_v, allow_nan_stats=False) with self.assertRaisesOpError("x < y"): - gamma.mode().eval() + self.evaluate(gamma.mode()) def testGammaModeAllowNanStatsIsTrueReturnsNaNforUndefinedBatchMembers(self): with self.test_session(): @@ -179,7 +180,7 @@ class GammaTest(test.TestCase): expected_modes = (alpha_v - 1) / beta_v expected_modes[0] = np.nan self.assertEqual(gamma.mode().get_shape(), (3,)) - self.assertAllClose(gamma.mode().eval(), expected_modes) + self.assertAllClose(self.evaluate(gamma.mode()), expected_modes) def testGammaVariance(self): with self.test_session(): @@ -190,7 +191,7 @@ class GammaTest(test.TestCase): if not stats: return expected_variances = stats.gamma.var(alpha_v, scale=1 / beta_v) - self.assertAllClose(gamma.variance().eval(), expected_variances) + self.assertAllClose(self.evaluate(gamma.variance()), expected_variances) def testGammaStd(self): with self.test_session(): @@ -201,7 +202,7 @@ class GammaTest(test.TestCase): if not stats: return expected_stddev = stats.gamma.std(alpha_v, scale=1. / beta_v) - self.assertAllClose(gamma.stddev().eval(), expected_stddev) + self.assertAllClose(self.evaluate(gamma.stddev()), expected_stddev) def testGammaEntropy(self): with self.test_session(): @@ -212,10 +213,10 @@ class GammaTest(test.TestCase): if not stats: return expected_entropy = stats.gamma.entropy(alpha_v, scale=1 / beta_v) - self.assertAllClose(gamma.entropy().eval(), expected_entropy) + self.assertAllClose(self.evaluate(gamma.entropy()), expected_entropy) def testGammaSampleSmallAlpha(self): - with session.Session(): + with self.test_session(): alpha_v = 0.05 beta_v = 1.0 alpha = constant_op.constant(alpha_v) @@ -223,7 +224,7 @@ class GammaTest(test.TestCase): n = 100000 gamma = gamma_lib.Gamma(concentration=alpha, rate=beta) samples = gamma.sample(n, seed=137) - sample_values = samples.eval() + sample_values = self.evaluate(samples) self.assertEqual(samples.get_shape(), (n,)) self.assertEqual(sample_values.shape, (n,)) self.assertTrue(self._kstest(alpha_v, beta_v, sample_values)) @@ -240,7 +241,7 @@ class GammaTest(test.TestCase): atol=.15) def testGammaSample(self): - with session.Session(): + with self.test_session(): alpha_v = 4.0 beta_v = 3.0 alpha = constant_op.constant(alpha_v) @@ -248,7 +249,7 @@ class GammaTest(test.TestCase): n = 100000 gamma = gamma_lib.Gamma(concentration=alpha, rate=beta) samples = gamma.sample(n, seed=137) - sample_values = samples.eval() + sample_values = self.evaluate(samples) self.assertEqual(samples.get_shape(), (n,)) self.assertEqual(sample_values.shape, (n,)) self.assertTrue(self._kstest(alpha_v, beta_v, sample_values)) @@ -265,13 +266,13 @@ class GammaTest(test.TestCase): atol=.15) def testGammaSampleMultiDimensional(self): - with session.Session(): + with self.test_session(): alpha_v = np.array([np.arange(1, 101, dtype=np.float32)]) # 1 x 100 beta_v = np.array([np.arange(1, 11, dtype=np.float32)]).T # 10 x 1 gamma = gamma_lib.Gamma(concentration=alpha_v, rate=beta_v) n = 10000 samples = gamma.sample(n, seed=137) - sample_values = samples.eval() + sample_values = self.evaluate(samples) self.assertEqual(samples.get_shape(), (n, 10, 100)) self.assertEqual(sample_values.shape, (n, 10, 100)) zeros = np.zeros_like(alpha_v + beta_v) # 10 x 100 @@ -306,12 +307,12 @@ class GammaTest(test.TestCase): return ks < 0.02 def testGammaPdfOfSampleMultiDims(self): - with session.Session() as sess: + with self.test_session(): gamma = gamma_lib.Gamma(concentration=[7., 11.], rate=[[5.], [6.]]) num = 50000 samples = gamma.sample(num, seed=137) pdfs = gamma.prob(samples) - sample_vals, pdf_vals = sess.run([samples, pdfs]) + sample_vals, pdf_vals = self.evaluate([samples, pdfs]) self.assertEqual(samples.get_shape(), (num, 2, 2)) self.assertEqual(pdfs.get_shape(), (num, 2, 2)) self._assertIntegral(sample_vals[:, 0, 0], pdf_vals[:, 0, 0], err=0.02) @@ -345,18 +346,18 @@ class GammaTest(test.TestCase): with self.test_session(): alpha_v = constant_op.constant(0.0, name="alpha") beta_v = constant_op.constant(1.0, name="beta") - gamma = gamma_lib.Gamma(concentration=alpha_v, - rate=beta_v, - validate_args=True) - with self.assertRaisesOpError("alpha"): - gamma.mean().eval() + with self.assertRaisesOpError("x > 0"): + gamma = gamma_lib.Gamma(concentration=alpha_v, + rate=beta_v, + validate_args=True) + self.evaluate(gamma.mean()) alpha_v = constant_op.constant(1.0, name="alpha") beta_v = constant_op.constant(0.0, name="beta") - gamma = gamma_lib.Gamma(concentration=alpha_v, - rate=beta_v, - validate_args=True) - with self.assertRaisesOpError("beta"): - gamma.mean().eval() + with self.assertRaisesOpError("x > 0"): + gamma = gamma_lib.Gamma(concentration=alpha_v, + rate=beta_v, + validate_args=True) + self.evaluate(gamma.mean()) def testGammaWithSoftplusConcentrationRate(self): with self.test_session(): @@ -364,10 +365,10 @@ class GammaTest(test.TestCase): beta_v = constant_op.constant([1.0, -3.6], name="beta") gamma = gamma_lib.GammaWithSoftplusConcentrationRate( concentration=alpha_v, rate=beta_v) - self.assertAllEqual(nn_ops.softplus(alpha_v).eval(), - gamma.concentration.eval()) - self.assertAllEqual(nn_ops.softplus(beta_v).eval(), - gamma.rate.eval()) + self.assertAllEqual(self.evaluate(nn_ops.softplus(alpha_v)), + self.evaluate(gamma.concentration)) + self.assertAllEqual(self.evaluate(nn_ops.softplus(beta_v)), + self.evaluate(gamma.rate)) def testGammaGammaKL(self): alpha0 = np.array([3.]) @@ -377,15 +378,15 @@ class GammaTest(test.TestCase): beta1 = np.array([0.5, 1., 1.5, 2., 2.5, 3.]) # Build graph. - with self.test_session() as sess: + with self.test_session(): g0 = gamma_lib.Gamma(concentration=alpha0, rate=beta0) g1 = gamma_lib.Gamma(concentration=alpha1, rate=beta1) x = g0.sample(int(1e4), seed=0) kl_sample = math_ops.reduce_mean(g0.log_prob(x) - g1.log_prob(x), 0) kl_actual = kullback_leibler.kl_divergence(g0, g1) - # Execute graph. - [kl_sample_, kl_actual_] = sess.run([kl_sample, kl_actual]) + # Execute graph. + [kl_sample_, kl_actual_] = self.evaluate([kl_sample, kl_actual]) self.assertEqual(beta0.shape, kl_actual.get_shape()) -- GitLab From d9867b89fbc632836ce8309fb29a23a0f3d18606 Mon Sep 17 00:00:00 2001 From: Tom Hennigan Date: Thu, 21 Jun 2018 09:46:30 -0700 Subject: [PATCH 795/816] Expose @run_all_tests_in_graph_and_eager_modes (docstring was missing). PiperOrigin-RevId: 201539623 --- tensorflow/contrib/eager/python/tfe.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/contrib/eager/python/tfe.py b/tensorflow/contrib/eager/python/tfe.py index fee9db46fa..113aa7967c 100644 --- a/tensorflow/contrib/eager/python/tfe.py +++ b/tensorflow/contrib/eager/python/tfe.py @@ -68,6 +68,7 @@ To use, at program startup, call `tfe.enable_eager_execution()`. @@async_clear_error @@run_test_in_graph_and_eager_modes +@@run_all_tests_in_graph_and_eager_modes @@DEVICE_PLACEMENT_EXPLICIT @@DEVICE_PLACEMENT_WARN -- GitLab From 6eb9820f131448fcbb8a8cfc195a112dcb503fcc Mon Sep 17 00:00:00 2001 From: Vinu Rajashekhar Date: Thu, 21 Jun 2018 09:51:12 -0700 Subject: [PATCH 796/816] Removes some verbose debugging info left in the batch_function. PiperOrigin-RevId: 201540390 --- tensorflow/contrib/batching/python/ops/batch_ops.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/tensorflow/contrib/batching/python/ops/batch_ops.py b/tensorflow/contrib/batching/python/ops/batch_ops.py index 012a51f711..47b80bdf4a 100644 --- a/tensorflow/contrib/batching/python/ops/batch_ops.py +++ b/tensorflow/contrib/batching/python/ops/batch_ops.py @@ -119,10 +119,6 @@ def batch_function(num_batch_threads, raise ValueError("All arguments to functions decorated with " "`batch_function` are supposed to be Tensors; " "found %s" % repr(a)) - for inp in computation.captured_inputs: - print("inp: %s" % inp) - for op in inp.consumers(): - print("op: %s" % op) return gen_batch_ops.batch_function( num_batch_threads=num_batch_threads, max_batch_size=max_batch_size, -- GitLab From 293b21eddc34ee0ceda1143ec7699e54c9768a1c Mon Sep 17 00:00:00 2001 From: Jiri Simsa Date: Thu, 21 Jun 2018 10:02:46 -0700 Subject: [PATCH 797/816] [tf.data] Cleanup of tf.data.contrib, propertly exporting public API. PiperOrigin-RevId: 201542140 --- tensorflow/contrib/data/__init__.py | 13 ++++++++++-- .../contrib/data/python/kernel_tests/BUILD | 20 +++++++++++++++++++ .../directed_interleave_dataset_test.py | 4 ++-- .../iterator_ops_test.py | 0 tensorflow/contrib/data/python/ops/BUILD | 20 ------------------- .../contrib/data/python/ops/batching.py | 14 ++++++------- .../contrib/data/python/ops/error_ops.py | 6 +++--- .../contrib/data/python/ops/grouping.py | 14 ++++++------- .../contrib/data/python/ops/interleave_ops.py | 6 +++--- .../contrib/data/python/ops/optimization.py | 6 +++--- .../contrib/data/python/ops/stats_ops.py | 11 +++++++++- .../contrib/data/python/ops/threadpool.py | 4 ++++ tensorflow/contrib/data/python/ops/unique.py | 6 +++--- 13 files changed, 73 insertions(+), 51 deletions(-) rename tensorflow/contrib/data/python/{ops => kernel_tests}/iterator_ops_test.py (100%) diff --git a/tensorflow/contrib/data/__init__.py b/tensorflow/contrib/data/__init__.py index 99699cd6d6..2a4cf877f0 100644 --- a/tensorflow/contrib/data/__init__.py +++ b/tensorflow/contrib/data/__init__.py @@ -25,7 +25,10 @@ See the @{$datasets$Importing Data} Programmer's Guide for an overview. @@Counter @@CheckpointInputPipelineHook @@CsvDataset +@@RandomDataset +@@Reducer @@SqlDataset +@@TFRecordWriter @@assert_element_shape @@batch_and_drop_remainder @@ -33,12 +36,15 @@ See the @{$datasets$Importing Data} Programmer's Guide for an overview. @@choose_from_datasets @@dense_to_sparse_batch @@enumerate_dataset + +@@get_single_element @@group_by_reducer @@group_by_window @@ignore_errors @@make_batched_features_dataset @@make_csv_dataset @@make_saveable_from_iterator + @@map_and_batch @@padded_batch_and_drop_remainder @@parallel_interleave @@ -51,8 +57,7 @@ See the @{$datasets$Importing Data} Programmer's Guide for an overview. @@sliding_window_batch @@sloppy_interleave @@unbatch - -@@get_single_element +@@unique """ from __future__ import absolute_import @@ -74,6 +79,7 @@ from tensorflow.contrib.data.python.ops.get_single_element import get_single_ele from tensorflow.contrib.data.python.ops.grouping import bucket_by_sequence_length from tensorflow.contrib.data.python.ops.grouping import group_by_reducer from tensorflow.contrib.data.python.ops.grouping import group_by_window +from tensorflow.contrib.data.python.ops.grouping import Reducer from tensorflow.contrib.data.python.ops.interleave_ops import choose_from_datasets from tensorflow.contrib.data.python.ops.interleave_ops import parallel_interleave from tensorflow.contrib.data.python.ops.interleave_ops import sample_from_datasets @@ -81,6 +87,7 @@ from tensorflow.contrib.data.python.ops.interleave_ops import sloppy_interleave from tensorflow.contrib.data.python.ops.iterator_ops import CheckpointInputPipelineHook from tensorflow.contrib.data.python.ops.iterator_ops import make_saveable_from_iterator from tensorflow.contrib.data.python.ops.prefetching_ops import prefetch_to_device +from tensorflow.contrib.data.python.ops.random_ops import RandomDataset from tensorflow.contrib.data.python.ops.readers import CsvDataset from tensorflow.contrib.data.python.ops.readers import make_batched_features_dataset from tensorflow.contrib.data.python.ops.readers import make_csv_dataset @@ -90,6 +97,8 @@ from tensorflow.contrib.data.python.ops.resampling import rejection_resample from tensorflow.contrib.data.python.ops.scan_ops import scan from tensorflow.contrib.data.python.ops.shuffle_ops import shuffle_and_repeat from tensorflow.contrib.data.python.ops.sliding import sliding_window_batch +from tensorflow.contrib.data.python.ops.unique import unique +from tensorflow.contrib.data.python.ops.writers import TFRecordWriter # pylint: enable=unused-import from tensorflow.python.util.all_util import remove_undocumented diff --git a/tensorflow/contrib/data/python/kernel_tests/BUILD b/tensorflow/contrib/data/python/kernel_tests/BUILD index ed1542d03f..ef9f966fab 100644 --- a/tensorflow/contrib/data/python/kernel_tests/BUILD +++ b/tensorflow/contrib/data/python/kernel_tests/BUILD @@ -157,6 +157,26 @@ py_test( ], ) +py_test( + name = "iterator_ops_test", + size = "small", + srcs = ["iterator_ops_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + "//tensorflow/contrib/data/python/ops:iterator_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:constant_op", + "//tensorflow/python:dtypes", + "//tensorflow/python:framework_ops", + "//tensorflow/python:training", + "//tensorflow/python:variables", + "//tensorflow/python/data/ops:dataset_ops", + "//tensorflow/python/estimator", + "//tensorflow/python/estimator:model_fn", + ], +) + py_test( name = "map_dataset_op_test", size = "medium", diff --git a/tensorflow/contrib/data/python/kernel_tests/directed_interleave_dataset_test.py b/tensorflow/contrib/data/python/kernel_tests/directed_interleave_dataset_test.py index fe618cdce6..9b1857de1a 100644 --- a/tensorflow/contrib/data/python/kernel_tests/directed_interleave_dataset_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/directed_interleave_dataset_test.py @@ -33,8 +33,8 @@ class DirectedInterleaveDatasetTest(test.TestCase): input_datasets = [ dataset_ops.Dataset.from_tensors(i).repeat(100) for i in range(10) ] - dataset = interleave_ops.DirectedInterleaveDataset(selector_dataset, - input_datasets) + dataset = interleave_ops._DirectedInterleaveDataset(selector_dataset, + input_datasets) iterator = dataset.make_initializable_iterator() next_element = iterator.get_next() diff --git a/tensorflow/contrib/data/python/ops/iterator_ops_test.py b/tensorflow/contrib/data/python/kernel_tests/iterator_ops_test.py similarity index 100% rename from tensorflow/contrib/data/python/ops/iterator_ops_test.py rename to tensorflow/contrib/data/python/kernel_tests/iterator_ops_test.py diff --git a/tensorflow/contrib/data/python/ops/BUILD b/tensorflow/contrib/data/python/ops/BUILD index 33b7a75046..0240814562 100644 --- a/tensorflow/contrib/data/python/ops/BUILD +++ b/tensorflow/contrib/data/python/ops/BUILD @@ -49,26 +49,6 @@ py_library( ], ) -py_test( - name = "iterator_ops_test", - size = "small", - srcs = ["iterator_ops_test.py"], - srcs_version = "PY2AND3", - tags = ["no_pip"], - deps = [ - ":iterator_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:constant_op", - "//tensorflow/python:dtypes", - "//tensorflow/python:framework_ops", - "//tensorflow/python:training", - "//tensorflow/python:variables", - "//tensorflow/python/data/ops:dataset_ops", - "//tensorflow/python/estimator", - "//tensorflow/python/estimator:model_fn", - ], -) - py_library( name = "random_ops", srcs = [ diff --git a/tensorflow/contrib/data/python/ops/batching.py b/tensorflow/contrib/data/python/ops/batching.py index 052618e08c..5708d47c20 100644 --- a/tensorflow/contrib/data/python/ops/batching.py +++ b/tensorflow/contrib/data/python/ops/batching.py @@ -77,17 +77,17 @@ def dense_to_sparse_batch(batch_size, row_shape): """ def _apply_fn(dataset): - return DenseToSparseBatchDataset(dataset, batch_size, row_shape) + return _DenseToSparseBatchDataset(dataset, batch_size, row_shape) return _apply_fn -class UnbatchDataset(dataset_ops.Dataset): +class _UnbatchDataset(dataset_ops.Dataset): """A dataset that splits the elements of its input into multiple elements.""" def __init__(self, input_dataset): """See `unbatch()` for more details.""" - super(UnbatchDataset, self).__init__() + super(_UnbatchDataset, self).__init__() flat_shapes = nest.flatten(input_dataset.output_shapes) if any(s.ndims == 0 for s in flat_shapes): raise ValueError("Cannot unbatch an input with scalar components.") @@ -144,7 +144,7 @@ def unbatch(): def _apply_fn(dataset): """Function from `Dataset` to `Dataset` that applies the transformation.""" if not sparse.any_sparse(dataset.output_classes): - return UnbatchDataset(dataset) + return _UnbatchDataset(dataset) # NOTE(mrry): We must ensure that any SparseTensors in `dataset` # are normalized to the rank-1 dense representation, so that the @@ -170,7 +170,7 @@ def unbatch(): dataset.output_shapes, dataset.output_classes, allow_unsafe_cast=True) - return UnbatchDataset(restructured_dataset) + return _UnbatchDataset(restructured_dataset) return _apply_fn @@ -298,12 +298,12 @@ def padded_batch_and_drop_remainder(batch_size, return _apply_fn -class DenseToSparseBatchDataset(dataset_ops.Dataset): +class _DenseToSparseBatchDataset(dataset_ops.Dataset): """A `Dataset` that batches ragged dense elements into `tf.SparseTensor`s.""" def __init__(self, input_dataset, batch_size, row_shape): """See `Dataset.dense_to_sparse_batch()` for more details.""" - super(DenseToSparseBatchDataset, self).__init__() + super(_DenseToSparseBatchDataset, self).__init__() if not isinstance(input_dataset.output_types, dtypes.DType): raise TypeError("DenseToSparseDataset requires an input whose elements " "have a single component, whereas the input has %r." % diff --git a/tensorflow/contrib/data/python/ops/error_ops.py b/tensorflow/contrib/data/python/ops/error_ops.py index 5f5513849c..d46d96c461 100644 --- a/tensorflow/contrib/data/python/ops/error_ops.py +++ b/tensorflow/contrib/data/python/ops/error_ops.py @@ -46,17 +46,17 @@ def ignore_errors(): """ def _apply_fn(dataset): - return IgnoreErrorsDataset(dataset) + return _IgnoreErrorsDataset(dataset) return _apply_fn -class IgnoreErrorsDataset(dataset_ops.Dataset): +class _IgnoreErrorsDataset(dataset_ops.Dataset): """A `Dataset` that silently ignores errors when computing its input.""" def __init__(self, input_dataset): """See `Dataset.ignore_errors()` for details.""" - super(IgnoreErrorsDataset, self).__init__() + super(_IgnoreErrorsDataset, self).__init__() self._input_dataset = input_dataset def _as_variant_tensor(self): diff --git a/tensorflow/contrib/data/python/ops/grouping.py b/tensorflow/contrib/data/python/ops/grouping.py index 4068a2ffa5..348884e9fa 100644 --- a/tensorflow/contrib/data/python/ops/grouping.py +++ b/tensorflow/contrib/data/python/ops/grouping.py @@ -55,7 +55,7 @@ def group_by_reducer(key_func, reducer): def _apply_fn(dataset): """Function from `Dataset` to `Dataset` that applies the transformation.""" - return GroupByReducerDataset(dataset, key_func, reducer) + return _GroupByReducerDataset(dataset, key_func, reducer) return _apply_fn @@ -113,8 +113,8 @@ def group_by_window(key_func, def _apply_fn(dataset): """Function from `Dataset` to `Dataset` that applies the transformation.""" - return GroupByWindowDataset(dataset, key_func, reduce_func, - window_size_func) + return _GroupByWindowDataset(dataset, key_func, reduce_func, + window_size_func) return _apply_fn @@ -254,12 +254,12 @@ class _VariantDataset(dataset_ops.Dataset): return self._output_types -class GroupByReducerDataset(dataset_ops.Dataset): +class _GroupByReducerDataset(dataset_ops.Dataset): """A `Dataset` that groups its input and performs a reduction.""" def __init__(self, input_dataset, key_func, reducer): """See `group_by_reducer()` for details.""" - super(GroupByReducerDataset, self).__init__() + super(_GroupByReducerDataset, self).__init__() self._input_dataset = input_dataset @@ -388,12 +388,12 @@ class GroupByReducerDataset(dataset_ops.Dataset): **dataset_ops.flat_structure(self)) -class GroupByWindowDataset(dataset_ops.Dataset): +class _GroupByWindowDataset(dataset_ops.Dataset): """A `Dataset` that groups its input and performs a windowed reduction.""" def __init__(self, input_dataset, key_func, reduce_func, window_size_func): """See `group_by_window()` for details.""" - super(GroupByWindowDataset, self).__init__() + super(_GroupByWindowDataset, self).__init__() self._input_dataset = input_dataset diff --git a/tensorflow/contrib/data/python/ops/interleave_ops.py b/tensorflow/contrib/data/python/ops/interleave_ops.py index 70153ac575..bcc959594a 100644 --- a/tensorflow/contrib/data/python/ops/interleave_ops.py +++ b/tensorflow/contrib/data/python/ops/interleave_ops.py @@ -153,7 +153,7 @@ def sloppy_interleave(map_func, cycle_length, block_length=1): return _apply_fn -class DirectedInterleaveDataset(dataset_ops.Dataset): +class _DirectedInterleaveDataset(dataset_ops.Dataset): """A substitute for `Dataset.interleave()` on a fixed list of datasets.""" def __init__(self, selector_input, data_inputs): @@ -236,7 +236,7 @@ def sample_from_datasets(datasets, weights=None, seed=None): selector_input = dataset_ops.Dataset.zip( (logits_ds, random_ops.RandomDataset(seed).batch(2))).map(select_dataset) - return DirectedInterleaveDataset(selector_input, datasets) + return _DirectedInterleaveDataset(selector_input, datasets) def choose_from_datasets(datasets, choice_dataset): @@ -280,4 +280,4 @@ def choose_from_datasets(datasets, choice_dataset): and choice_dataset.output_classes == ops.Tensor): raise TypeError("`choice_dataset` must be a dataset of scalar " "`tf.int64` tensors.") - return DirectedInterleaveDataset(choice_dataset, datasets) + return _DirectedInterleaveDataset(choice_dataset, datasets) diff --git a/tensorflow/contrib/data/python/ops/optimization.py b/tensorflow/contrib/data/python/ops/optimization.py index 2ca3805d66..cf89657226 100644 --- a/tensorflow/contrib/data/python/ops/optimization.py +++ b/tensorflow/contrib/data/python/ops/optimization.py @@ -39,17 +39,17 @@ def optimize(optimizations=None): def _apply_fn(dataset): """Function from `Dataset` to `Dataset` that applies the transformation.""" - return OptimizeDataset(dataset, optimizations) + return _OptimizeDataset(dataset, optimizations) return _apply_fn -class OptimizeDataset(dataset_ops.Dataset): +class _OptimizeDataset(dataset_ops.Dataset): """A `Dataset` that acts as an identity, and applies optimizations.""" def __init__(self, input_dataset, optimizations): """See `optimize()` for details.""" - super(OptimizeDataset, self).__init__() + super(_OptimizeDataset, self).__init__() self._input_dataset = input_dataset if optimizations is None: optimizations = [] diff --git a/tensorflow/contrib/data/python/ops/stats_ops.py b/tensorflow/contrib/data/python/ops/stats_ops.py index 3c82a03df1..97931f75bd 100644 --- a/tensorflow/contrib/data/python/ops/stats_ops.py +++ b/tensorflow/contrib/data/python/ops/stats_ops.py @@ -23,6 +23,8 @@ from tensorflow.python.framework import ops from tensorflow.python.ops import gen_dataset_ops +# TODO(b/38416882): Properly export in the `tf.contrib.data` API when stable +# or make private / remove. class StatsAggregator(object): """A stateful resource that aggregates statistics from one or more iterators. @@ -110,7 +112,8 @@ class _SetStatsAggregatorDataset(dataset_ops.Dataset): return self._input_dataset.output_classes -# TODO(shivaniagrawal): Expose these methods in `tf.contrib.data`. +# TODO(b/38416882): Properly export in the `tf.contrib.data` API when stable +# or make private / remove. def set_stats_aggregator(stats_aggregator): """Set the given stats_aggregator for aggregating the input dataset stats. @@ -128,6 +131,8 @@ def set_stats_aggregator(stats_aggregator): return _apply_fn +# TODO(b/38416882): Properly export in the `tf.contrib.data` API when stable +# or make private / remove. def bytes_produced_stats(tag): """Records the number of bytes produced by each element of the input dataset. @@ -150,6 +155,8 @@ def bytes_produced_stats(tag): return _apply_fn +# TODO(b/38416882): Properly export in the `tf.contrib.data` API when stable +# or make private / remove. def latency_stats(tag): """Records the latency of producing each element of the input dataset. @@ -171,6 +178,8 @@ def latency_stats(tag): return _apply_fn +# TODO(b/38416882): Properly export in the `tf.contrib.data` API when stable +# or make private / remove. def feature_stats(tag): """Records the features stats from `Example` records of the input dataset. diff --git a/tensorflow/contrib/data/python/ops/threadpool.py b/tensorflow/contrib/data/python/ops/threadpool.py index bb49604d4d..f228660176 100644 --- a/tensorflow/contrib/data/python/ops/threadpool.py +++ b/tensorflow/contrib/data/python/ops/threadpool.py @@ -37,6 +37,8 @@ def _generate_shared_name(prefix): return "{}{}".format(prefix, uid) +# TODO(b/73383364): Properly export in the `tf.contrib.data` API when stable +# or make private / remove. class PrivateThreadPool(object): """A stateful resource that represents a private thread pool.""" @@ -82,6 +84,8 @@ class _ThreadPoolDataset(dataset_ops.Dataset): return self._input_dataset.output_classes +# TODO(b/73383364): Properly export in the `tf.contrib.data` API when stable +# or make private / remove. def override_threadpool(dataset, thread_pool): """Returns a new dataset that uses the given thread pool for its operations. diff --git a/tensorflow/contrib/data/python/ops/unique.py b/tensorflow/contrib/data/python/ops/unique.py index 4ce6ddede8..e0ce0a4ef1 100644 --- a/tensorflow/contrib/data/python/ops/unique.py +++ b/tensorflow/contrib/data/python/ops/unique.py @@ -42,17 +42,17 @@ def unique(): """ def _apply_fn(dataset): - return UniqueDataset(dataset) + return _UniqueDataset(dataset) return _apply_fn -class UniqueDataset(dataset_ops.Dataset): +class _UniqueDataset(dataset_ops.Dataset): """A `Dataset` contains the unique elements from its input.""" def __init__(self, input_dataset): """See `unique()` for details.""" - super(UniqueDataset, self).__init__() + super(_UniqueDataset, self).__init__() self._input_dataset = input_dataset if input_dataset.output_types not in (dtypes.int32, dtypes.int64, dtypes.string): -- GitLab From d3ab92cf907e15da2ba70bccd65e5b4ccbfad575 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 21 Jun 2018 10:06:33 -0700 Subject: [PATCH 798/816] Replace unshared convolution backend for LocallyConnected1D and LocallyConnected2D layers with a common dimension-agnostic implementation. PiperOrigin-RevId: 201542873 --- tensorflow/python/keras/BUILD | 1 + tensorflow/python/keras/backend.py | 188 ++++++++++++++---------- tensorflow/python/keras/backend_test.py | 130 ++++++++++++---- tensorflow/python/keras/layers/local.py | 20 +-- 4 files changed, 217 insertions(+), 122 deletions(-) diff --git a/tensorflow/python/keras/BUILD b/tensorflow/python/keras/BUILD index 9012f4ee38..151a26f6e6 100755 --- a/tensorflow/python/keras/BUILD +++ b/tensorflow/python/keras/BUILD @@ -866,6 +866,7 @@ py_test( "//tensorflow/python:client_testlib", "//tensorflow/python:util", "//third_party/py/numpy", + "@absl_py//absl/testing:parameterized", ], ) diff --git a/tensorflow/python/keras/backend.py b/tensorflow/python/keras/backend.py index c55a756bcc..fed779650e 100644 --- a/tensorflow/python/keras/backend.py +++ b/tensorflow/python/keras/backend.py @@ -22,6 +22,7 @@ from __future__ import division from __future__ import print_function import collections +import itertools import json import os import weakref @@ -4245,58 +4246,115 @@ def pool3d(x, return x -def local_conv1d(inputs, kernel, kernel_size, strides, data_format=None): - """Apply 1D conv with un-shared weights. - - Arguments: - inputs: 3D tensor with shape: - (batch_size, steps, input_dim) - if data_format is "channels_last" or - (batch_size, input_dim, steps) - if data_format is "channels_first". - kernel: the unshared weight for convolution, - with shape (output_length, feature_dim, filters) - kernel_size: a tuple of a single integer, - specifying the length of the 1D convolution window - strides: a tuple of a single integer, - specifying the stride length of the convolution - data_format: the data format, channels_first or channels_last - - Returns: - the tensor after 1d conv with un-shared weights, with shape (batch_size, - output_length, filters) +def local_conv(inputs, + kernel, + kernel_size, + strides, + output_shape, + data_format=None): + """Apply N-D convolution with un-shared weights. + + Arguments: + inputs: (N+2)-D tensor with shape + (batch_size, channels_in, d_in1, ..., d_inN) + if data_format='channels_first', or + (batch_size, d_in1, ..., d_inN, channels_in) + if data_format='channels_last'. + kernel: the unshared weight for N-D convolution, + with shape (output_items, feature_dim, channels_out), where + feature_dim = np.prod(kernel_size) * channels_in, + output_items = np.prod(output_shape). + kernel_size: a tuple of N integers, specifying the + spatial dimensions of the N-D convolution window. + strides: a tuple of N integers, specifying the strides + of the convolution along the spatial dimensions. + output_shape: a tuple of (d_out1, ..., d_outN) specifying the spatial + dimensionality of the output. + data_format: string, "channels_first" or "channels_last". + + Returns: + An (N+2)-D tensor with shape: + (batch_size, channels_out) + output_shape + if data_format='channels_first', or: + (batch_size,) + output_shape + (channels_out,) + if data_format='channels_last'. Raises: - ValueError: if `data_format` is neither `channels_last` or - `channels_first`. + ValueError: if `data_format` is neither + `channels_last` nor `channels_first`. """ if data_format is None: data_format = image_data_format() if data_format not in {'channels_first', 'channels_last'}: raise ValueError('Unknown data_format: ' + str(data_format)) - stride = strides[0] kernel_shape = int_shape(kernel) - output_length = kernel_shape[0] feature_dim = kernel_shape[1] + channels_out = kernel_shape[-1] + ndims = len(output_shape) + spatial_dimensions = list(range(ndims)) xs = [] - for i in range(output_length): - slice_length = slice(i * stride, i * stride + kernel_size[0]) + output_axes_ticks = [range(axis_max) for axis_max in output_shape] + for position in itertools.product(*output_axes_ticks): + slices = [slice(None)] + if data_format == 'channels_first': - xs.append(reshape(inputs[:, :, slice_length], (1, -1, feature_dim))) - else: - xs.append(reshape(inputs[:, slice_length, :], (1, -1, feature_dim))) + slices.append(slice(None)) + + slices.extend([slice(position[d] * strides[d], + position[d] * strides[d] + kernel_size[d]) + for d in spatial_dimensions]) + + if data_format == 'channels_last': + slices.append(slice(None)) + + xs.append(reshape(inputs[slices], (1, -1, feature_dim))) x_aggregate = concatenate(xs, axis=0) - # Shape: `(output_length, batch_size, filters)`. output = batch_dot(x_aggregate, kernel) + output = reshape(output, output_shape + (-1, channels_out)) if data_format == 'channels_first': - output = permute_dimensions(output, (1, 2, 0)) + permutation = [ndims, ndims + 1] + spatial_dimensions else: - output = permute_dimensions(output, (1, 0, 2)) - return output + permutation = [ndims] + spatial_dimensions + [ndims + 1] + + return permute_dimensions(output, permutation) + + +def local_conv1d(inputs, kernel, kernel_size, strides, data_format=None): + """Apply 1D conv with un-shared weights. + + Arguments: + inputs: 3D tensor with shape: + (batch_size, steps, input_dim) + if data_format is "channels_last" or + (batch_size, input_dim, steps) + if data_format is "channels_first". + kernel: the unshared weight for convolution, + with shape (output_length, feature_dim, filters). + kernel_size: a tuple of a single integer, + specifying the length of the 1D convolution window. + strides: a tuple of a single integer, + specifying the stride length of the convolution. + data_format: the data format, channels_first or channels_last. + + Returns: + A 3d tensor with shape: + (batch_size, output_length, filters) + if data_format='channels_first' + or 3D tensor with shape: + (batch_size, filters, output_length) + if data_format='channels_last'. + """ + output_shape = (kernel.shape[0],) + return local_conv(inputs, + kernel, + kernel_size, + strides, + output_shape, + data_format) def local_conv2d(inputs, @@ -4309,64 +4367,34 @@ def local_conv2d(inputs, Arguments: inputs: 4D tensor with shape: - (batch_size, filters, new_rows, new_cols) - if data_format='channels_first' - or 4D tensor with shape: - (batch_size, new_rows, new_cols, filters) - if data_format='channels_last'. + (batch_size, filters, new_rows, new_cols) + if data_format='channels_first' + or 4D tensor with shape: + (batch_size, new_rows, new_cols, filters) + if data_format='channels_last'. kernel: the unshared weight for convolution, - with shape (output_items, feature_dim, filters) + with shape (output_items, feature_dim, filters). kernel_size: a tuple of 2 integers, specifying the - width and height of the 2D convolution window. + width and height of the 2D convolution window. strides: a tuple of 2 integers, specifying the strides - of the convolution along the width and height. - output_shape: a tuple with (output_row, output_col) - data_format: the data format, channels_first or channels_last + of the convolution along the width and height. + output_shape: a tuple with (output_row, output_col). + data_format: the data format, channels_first or channels_last. Returns: - A 4d tensor with shape: + A 4D tensor with shape: (batch_size, filters, new_rows, new_cols) if data_format='channels_first' or 4D tensor with shape: (batch_size, new_rows, new_cols, filters) if data_format='channels_last'. - - Raises: - ValueError: if `data_format` is neither - `channels_last` or `channels_first`. """ - if data_format is None: - data_format = image_data_format() - if data_format not in {'channels_first', 'channels_last'}: - raise ValueError('Unknown data_format: ' + str(data_format)) - - stride_row, stride_col = strides - output_row, output_col = output_shape - kernel_shape = int_shape(kernel) - feature_dim = kernel_shape[1] - filters = kernel_shape[2] - - xs = [] - for i in range(output_row): - for j in range(output_col): - slice_row = slice(i * stride_row, i * stride_row + kernel_size[0]) - slice_col = slice(j * stride_col, j * stride_col + kernel_size[1]) - if data_format == 'channels_first': - xs.append( - reshape(inputs[:, :, slice_row, slice_col], (1, -1, feature_dim))) - else: - xs.append( - reshape(inputs[:, slice_row, slice_col, :], (1, -1, feature_dim))) - - x_aggregate = concatenate(xs, axis=0) - output = batch_dot(x_aggregate, kernel) - output = reshape(output, (output_row, output_col, -1, filters)) - - if data_format == 'channels_first': - output = permute_dimensions(output, (2, 3, 0, 1)) - else: - output = permute_dimensions(output, (2, 0, 1, 3)) - return output + return local_conv(inputs, + kernel, + kernel_size, + strides, + output_shape, + data_format) @tf_export('keras.backend.bias_add') diff --git a/tensorflow/python/keras/backend_test.py b/tensorflow/python/keras/backend_test.py index 98f36ad87f..2ba6c8ef15 100644 --- a/tensorflow/python/keras/backend_test.py +++ b/tensorflow/python/keras/backend_test.py @@ -17,6 +17,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from absl.testing import parameterized import numpy as np import scipy.sparse @@ -662,7 +663,7 @@ class BackendShapeOpsTest(test.TestCase): np_kwargs={'data_format': 'channels_first'}) -class BackendNNOpsTest(test.TestCase): +class BackendNNOpsTest(test.TestCase, parameterized.TestCase): def test_bias_add(self): with self.test_session(): @@ -811,52 +812,117 @@ class BackendNNOpsTest(test.TestCase): padding='same', data_format='channels_last') self.assertEqual(y.get_shape().as_list(), [10, 5, 5]) - def test_local_conv1d_channels_dim(self): - input_length = 5 - input_dim = 3 + def test_local_conv_channels_dim(self): + filters = 3 batch_size = 2 - inputs = np.random.normal(0, 1, (batch_size, input_dim, input_length)) - inputs_cf = keras.backend.variable(inputs) + for input_shape in [(3, 5), (2, 3, 5), (2, 5, 3, 4)]: + channels_in = input_shape[0] + input_spatial_shape = input_shape[1:] + dim = len(input_spatial_shape) - filters = 4 - for kernel_size in [(1,), (2,), (3,)]: - for strides in [(1,), (2,), (3,)]: - output_length = (input_length - kernel_size[0] - + strides[0]) // strides[0] + inputs = np.random.normal(0, 1, (batch_size,) + input_shape) + inputs_cf = keras.backend.variable(inputs) - kernel_shape = (output_length, kernel_size[0] * input_dim, filters) - kernel = np.random.normal(0, 1, (output_length, - input_dim, - kernel_size[0], - filters)) - kernel_cf = np.reshape(kernel, kernel_shape) - kernel_cf = keras.backend.variable(kernel_cf) + for kernel_size in [1, 2]: + for stride in [1, 2]: + kernel_sizes = (kernel_size,) * dim + strides = (stride,) * dim - conv_cf = keras.backend.local_conv1d(inputs_cf, + output_shape = tuple([(i - kernel_size + stride) // stride + for i in input_spatial_shape]) + + kernel_shape = (np.prod(output_shape), + np.prod(kernel_sizes) * channels_in, + filters) + + kernel = np.random.normal( + 0, + 1, + output_shape + (channels_in, np.prod(kernel_sizes), filters) + ) + + kernel_cf = np.reshape(kernel, kernel_shape) + kernel_cf = keras.backend.variable(kernel_cf) + + conv_cf = keras.backend.local_conv(inputs_cf, kernel_cf, - kernel_size, + kernel_sizes, strides, + output_shape, 'channels_first') - inputs_cl = np.transpose(inputs, (0, 2, 1)) - inputs_cl = keras.backend.variable(inputs_cl) + inputs_cl = np.transpose(inputs, [0, 2] + list(range(3, dim + 2)) + + [1]) + inputs_cl = keras.backend.variable(inputs_cl) - kernel_cl = np.reshape(np.transpose(kernel, (0, 2, 1, 3)), - kernel_shape) - kernel_cl = keras.backend.variable(kernel_cl) + kernel_cl = np.reshape( + np.transpose(kernel, list(range(dim)) + [dim + 1, dim, dim + 2]), + kernel_shape + ) + kernel_cl = keras.backend.variable(kernel_cl) - conv_cl = keras.backend.local_conv1d(inputs_cl, + conv_cl = keras.backend.local_conv(inputs_cl, kernel_cl, - kernel_size, + kernel_sizes, strides, + output_shape, 'channels_last') - with self.test_session(): - conv_cf = keras.backend.eval(conv_cf) - conv_cl = keras.backend.eval(conv_cl) + with self.test_session(): + conv_cf = keras.backend.eval(conv_cf) + conv_cl = keras.backend.eval(conv_cl) + + self.assertAllCloseAccordingToType( + conv_cf, + np.transpose(conv_cl, + [0, dim + 1] + list(range(1, dim + 1))), + atol=1e-5 + ) + + @parameterized.named_parameters( + ('local_conv1d', (5, 6), (3,), (1,), (3,)), + ('local_conv2d', (4, 5, 6), (3, 3), (1, 1), (2, 3))) + def test_local_conv_1d_and_2d(self, + input_shape, + kernel_sizes, + strides, + output_shape): + filters = 3 + batch_size = 2 + + inputs = np.random.normal(0, 1, (batch_size,) + input_shape) + inputs = keras.backend.variable(inputs) + + kernel = np.random.normal(0, 1, (np.prod(output_shape), + np.prod(kernel_sizes) * input_shape[-1], + filters)) + kernel = keras.backend.variable(kernel) + + local_conv = keras.backend.local_conv(inputs, + kernel, + kernel_sizes, + strides, + output_shape, + 'channels_last') + if len(output_shape) == 1: + local_conv_dim = keras.backend.local_conv1d(inputs, + kernel, + kernel_sizes, + strides, + 'channels_last') + else: + local_conv_dim = keras.backend.local_conv2d(inputs, + kernel, + kernel_sizes, + strides, + output_shape, + 'channels_last') + + with self.test_session(): + local_conv = keras.backend.eval(local_conv) + local_conv_dim = keras.backend.eval(local_conv_dim) - self.assertAllCloseAccordingToType(conv_cf, - np.transpose(conv_cl, (0, 2, 1))) + self.assertAllCloseAccordingToType(local_conv, local_conv_dim) def test_conv2d(self): val = np.random.random((10, 4, 10, 10)) diff --git a/tensorflow/python/keras/layers/local.py b/tensorflow/python/keras/layers/local.py index f222ea3083..0983e35e21 100644 --- a/tensorflow/python/keras/layers/local.py +++ b/tensorflow/python/keras/layers/local.py @@ -140,9 +140,9 @@ class LocallyConnected1D(Layer): if input_dim is None: raise ValueError('Axis 2 of input should be fully-defined. ' 'Found shape:', input_shape) - output_length = conv_utils.conv_output_length( + self.output_length = conv_utils.conv_output_length( input_length, self.kernel_size[0], self.padding, self.strides[0]) - self.kernel_shape = (output_length, self.kernel_size[0] * input_dim, + self.kernel_shape = (self.output_length, self.kernel_size[0] * input_dim, self.filters) self.kernel = self.add_weight( shape=self.kernel_shape, @@ -152,7 +152,7 @@ class LocallyConnected1D(Layer): constraint=self.kernel_constraint) if self.use_bias: self.bias = self.add_weight( - shape=(output_length, self.filters), + shape=(self.output_length, self.filters), initializer=self.bias_initializer, name='bias', regularizer=self.bias_regularizer, @@ -182,12 +182,13 @@ class LocallyConnected1D(Layer): return (input_shape[0], length, self.filters) def call(self, inputs): - output = K.local_conv1d(inputs, self.kernel, self.kernel_size, - self.strides, self.data_format) + output = K.local_conv(inputs, self.kernel, self.kernel_size, self.strides, + (self.output_length,), self.data_format) + if self.use_bias: output = K.bias_add(output, self.bias, data_format=self.data_format) - if self.activation is not None: - output = self.activation(output) + + output = self.activation(output) return output def get_config(self): @@ -400,9 +401,8 @@ class LocallyConnected2D(Layer): return (input_shape[0], rows, cols, self.filters) def call(self, inputs): - output = K.local_conv2d(inputs, self.kernel, self.kernel_size, self.strides, - (self.output_row, self.output_col), - self.data_format) + output = K.local_conv(inputs, self.kernel, self.kernel_size, self.strides, + (self.output_row, self.output_col), self.data_format) if self.use_bias: output = K.bias_add(output, self.bias, data_format=self.data_format) -- GitLab From 99c902cbb12f5cdd4b38c4b7be81e8a83eca14f4 Mon Sep 17 00:00:00 2001 From: Tom Hennigan Date: Thu, 21 Jun 2018 10:06:39 -0700 Subject: [PATCH 799/816] Ensure @run_test_in_graph_and_eager_modes does not support test classes. PiperOrigin-RevId: 201542892 --- tensorflow/python/framework/test_util.py | 6 ++++++ tensorflow/python/framework/test_util_test.py | 8 ++++++++ 2 files changed, 14 insertions(+) diff --git a/tensorflow/python/framework/test_util.py b/tensorflow/python/framework/test_util.py index 3ed5c9e6a4..708ab1707e 100644 --- a/tensorflow/python/framework/test_util.py +++ b/tensorflow/python/framework/test_util.py @@ -67,6 +67,7 @@ from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import server_lib from tensorflow.python.util import compat from tensorflow.python.util import nest +from tensorflow.python.util import tf_inspect from tensorflow.python.util.protobuf import compare from tensorflow.python.util.tf_export import tf_export @@ -618,6 +619,11 @@ def run_in_graph_and_eager_modes(__unused__=None, assert not __unused__, "Add () after run_in_graph_and_eager_modes." def decorator(f): + if tf_inspect.isclass(f): + raise ValueError( + "`run_test_in_graph_and_eager_modes` only supports test methods. " + "Did you mean to use `run_all_tests_in_graph_and_eager_modes`?") + def decorated(self, **kwargs): with context.graph_mode(): with self.test_session(use_gpu=use_gpu): diff --git a/tensorflow/python/framework/test_util_test.py b/tensorflow/python/framework/test_util_test.py index 0178908bcc..2a7cf88d6e 100644 --- a/tensorflow/python/framework/test_util_test.py +++ b/tensorflow/python/framework/test_util_test.py @@ -595,6 +595,14 @@ class TestUtilTest(test_util.TensorFlowTestCase): self.assertIs(test_util.get_node_def_from_graph("foo", graph_def), node_foo) self.assertIsNone(test_util.get_node_def_from_graph("bar", graph_def)) + def testRunInGraphAndEagerModesOnTestCase(self): + msg = "`run_test_in_graph_and_eager_modes` only supports test methods.*" + with self.assertRaisesRegexp(ValueError, msg): + @test_util.run_in_graph_and_eager_modes() + class Foo(object): + pass + del Foo # Make pylint unused happy. + class GarbageCollectionTest(test_util.TensorFlowTestCase): -- GitLab From 7ceb91cb8fc988bb4d30fe3be054eec5ee99ec10 Mon Sep 17 00:00:00 2001 From: Jiri Simsa Date: Thu, 21 Jun 2018 10:18:58 -0700 Subject: [PATCH 800/816] [tf.data] Updating outdated documentation for `tf.data.Dataset.batch` and `tf.data.Dataset.padded_batch`. PiperOrigin-RevId: 201544952 --- tensorflow/python/data/ops/dataset_ops.py | 37 ++++++++++++----------- 1 file changed, 19 insertions(+), 18 deletions(-) diff --git a/tensorflow/python/data/ops/dataset_ops.py b/tensorflow/python/data/ops/dataset_ops.py index 9e7af878d3..c44a6e6c84 100644 --- a/tensorflow/python/data/ops/dataset_ops.py +++ b/tensorflow/python/data/ops/dataset_ops.py @@ -809,11 +809,12 @@ class Dataset(object): def batch(self, batch_size, drop_remainder=False): """Combines consecutive elements of this dataset into batches. - NOTE: If the number of elements (`N`) in this dataset is not an exact - multiple of `batch_size`, the final batch contain smaller tensors with - shape `N % batch_size` in the batch dimension. If your program depends on - the batches having the same shape, consider using the - @{tf.contrib.data.batch_and_drop_remainder} transformation instead. + The tensors in the resulting element will have an additional outer + dimension, which will be `batch_size` (or `N % batch_size` for the last + element if `batch_size` does not divide the number of input elements `N` + evenly and `drop_remainder` is `False`). If your program depends on the + batches having the same outer dimension, you should set the `drop_remainder` + argument to `True` to prevent the smaller batch from being produced. Args: batch_size: A `tf.int64` scalar `tf.Tensor`, representing the number of @@ -836,13 +837,19 @@ class Dataset(object): """Combines consecutive elements of this dataset into padded batches. This transformation combines multiple consecutive elements of the input - dataset into a single element. Like @{tf.data.Dataset.batch}, the tensors - in the resulting element have an additional outer dimension, which will be - `batch_size` for all but the last element, and `N % batch_size` for the - last element (where `N` is the number of elements in this dataset). Unlike - @{tf.data.Dataset.batch}, the elements may have different shapes for some - of their components, and this transformation will pad each component to - the respective shape in `padding_shapes`. The `padding_shapes` argument + dataset into a single element. + + Like @{tf.data.Dataset.batch}, the tensors in the resulting element will + have an additional outer dimension, which will be `batch_size` (or + `N % batch_size` for the last element if `batch_size` does not divide the + number of input elements `N` evenly and `drop_remainder` is `False`). If + your program depends on the batches having the same outer dimension, you + should set the `drop_remainder` argument to `True` to prevent the smaller + batch from being produced. + + Unlike @{tf.data.Dataset.batch}, the input elements to be batched may have + different shapes, and this transformation will pad each component to the + respective shape in `padding_shapes`. The `padding_shapes` argument determines the resulting shape for each dimension of each component in an output element: @@ -852,12 +859,6 @@ class Dataset(object): will be padded out to the maximum length of all elements in that dimension. - NOTE: If the number of elements (`N`) in this dataset is not an exact - multiple of `batch_size`, the final batch contain smaller tensors with - shape `N % batch_size` in the batch dimension. If your program depends on - the batches having the same shape, consider using the - @{tf.contrib.data.padded_batch_and_drop_remainder} transformation instead. - See also @{tf.contrib.data.dense_to_sparse_batch}, which combines elements that may have different shapes into a @{tf.SparseTensor}. -- GitLab From f5ce4d8250ed0f87d6b6317325c8d53900c2fdfd Mon Sep 17 00:00:00 2001 From: Yifei Feng <1192265+yifeif@users.noreply.github.com> Date: Thu, 21 Jun 2018 10:30:23 -0700 Subject: [PATCH 801/816] Disable decorators_test for pip. tensorflow/contrib/autograph/converters:decorators_test uses generated code, by when private symbols have been stripped. --- tensorflow/contrib/autograph/converters/BUILD | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/autograph/converters/BUILD b/tensorflow/contrib/autograph/converters/BUILD index 931ff62064..b2e2e27673 100644 --- a/tensorflow/contrib/autograph/converters/BUILD +++ b/tensorflow/contrib/autograph/converters/BUILD @@ -120,7 +120,10 @@ py_test( name = "decorators_test", srcs = ["decorators_test.py"], srcs_version = "PY2AND3", - tags = ["no_windows"], + tags = [ + "no_pip", + "no_windows", + ], deps = [ ":converters", "//tensorflow/contrib/autograph/core:test_lib", -- GitLab From 0c73bbe4b044773e65e0be3084189316ad356bc5 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 21 Jun 2018 10:51:24 -0700 Subject: [PATCH 802/816] 16-bit quantized logistic and tanh support in TFLite interpreter PiperOrigin-RevId: 201550611 --- .../contrib/lite/kernels/activations.cc | 69 ++++++++ .../contrib/lite/kernels/activations_test.cc | 147 +++++++++++++----- 2 files changed, 175 insertions(+), 41 deletions(-) diff --git a/tensorflow/contrib/lite/kernels/activations.cc b/tensorflow/contrib/lite/kernels/activations.cc index d03fa42c92..99f81c4a8a 100644 --- a/tensorflow/contrib/lite/kernels/activations.cc +++ b/tensorflow/contrib/lite/kernels/activations.cc @@ -84,6 +84,38 @@ TfLiteStatus TanhPrepare(TfLiteContext* context, TfLiteNode* node) { &data->input_left_shift); data->input_range_radius = CalculateInputRadius(kInputIntegerBits, data->input_left_shift); + } else if (input->type == kTfLiteInt16) { + static constexpr int kInputIntegerBits = 3; + static constexpr int kOutputFractionalBits = 15; + + // These operators are implemented in fixed-point arithmetic, + // which intrinsically wants symmetric ranges (zero_point==0) + // and power-of-two scales (power-of-two is abbreviated below as POT). + // While more general support would be possible by means of rescaling, + // that would add some overhead and some loss of accuracy and wouldn't + // be used at the moment as current quantized LSTM applications are + // happy with symmetric, power-of-two-scales quantization. So we just + // implement that narrow case only for now. + + TF_LITE_ENSURE_EQ(context, input->params.zero_point, 0); + TF_LITE_ENSURE_EQ(context, output->params.zero_point, 0); + + int input_scale_log2_rounded; + TF_LITE_ENSURE(context, + CheckedLog2(input->params.scale, &input_scale_log2_rounded)); + + int output_scale_log2_rounded; + TF_LITE_ENSURE( + context, CheckedLog2(output->params.scale, &output_scale_log2_rounded)); + TF_LITE_ENSURE_EQ(context, output_scale_log2_rounded, + -kOutputFractionalBits); + + data->input_left_shift = + (15 - kInputIntegerBits) + input_scale_log2_rounded; + // Support for shifts is limited until we have a parameterized version of + // SaturatingRoundingMultiplyByPOT(). + TF_LITE_ENSURE(context, data->input_left_shift >= 0); + TF_LITE_ENSURE(context, data->input_left_shift <= 1); } return context->ResizeTensor(context, output, @@ -114,6 +146,30 @@ TfLiteStatus SigmoidPrepare(TfLiteContext* context, TfLiteNode* node) { &data->input_left_shift); data->input_range_radius = CalculateInputRadius(kInputIntegerBits, data->input_left_shift); + } else if (input->type == kTfLiteInt16) { + static constexpr int kInputIntegerBits = 3; + static constexpr int kOutputFractionalBits = 15; + + // See comments in TanhPrepare about requiring zero_point==0 + // and a power-of-two ("POT") scale. + + TF_LITE_ENSURE_EQ(context, input->params.zero_point, 0); + TF_LITE_ENSURE_EQ(context, output->params.zero_point, 0); + + int input_scale_log2_rounded; + TF_LITE_ENSURE(context, + CheckedLog2(input->params.scale, &input_scale_log2_rounded)); + + int output_scale_log2_rounded; + TF_LITE_ENSURE( + context, CheckedLog2(output->params.scale, &output_scale_log2_rounded)); + TF_LITE_ENSURE_EQ(context, output_scale_log2_rounded, + -kOutputFractionalBits); + + data->input_left_shift = + (15 - kInputIntegerBits) + input_scale_log2_rounded; + // The int16 logistic implementation does not support shifting of the input. + TF_LITE_ENSURE_EQ(context, data->input_left_shift, 0); } return context->ResizeTensor(context, output, @@ -250,6 +306,13 @@ TfLiteStatus TanhEval(TfLiteContext* context, TfLiteNode* node) { for (; in < in_end; in++, out++) *out = std::tanh(*in); return kTfLiteOk; } break; + case kTfLiteInt16: { + optimized_ops::Tanh(GetTensorData(input), GetTensorShape(input), + data->input_left_shift, + GetTensorData(output), + GetTensorShape(output)); + return kTfLiteOk; + } break; case kTfLiteUInt8: { optimized_ops::Tanh(GetTensorData(input), GetTensorShape(input), input->params.zero_point, data->input_range_radius, @@ -280,6 +343,12 @@ TfLiteStatus SigmoidEval(TfLiteContext* context, TfLiteNode* node) { for (; in < in_end; in++, out++) *out = 1.f / (1.f + std::exp(-*in)); break; } + case kTfLiteInt16: { + optimized_ops::Logistic( + GetTensorData(input), GetTensorShape(input), + GetTensorData(output), GetTensorShape(output)); + break; + } case kTfLiteUInt8: { optimized_ops::Logistic( GetTensorData(input), GetTensorShape(input), diff --git a/tensorflow/contrib/lite/kernels/activations_test.cc b/tensorflow/contrib/lite/kernels/activations_test.cc index 50a84edd47..587e1303da 100644 --- a/tensorflow/contrib/lite/kernels/activations_test.cc +++ b/tensorflow/contrib/lite/kernels/activations_test.cc @@ -75,23 +75,42 @@ class FloatActivationsOpModel : public BaseActivationsOpModel { std::vector GetOutput() { return ExtractVector(output_); } }; -// TODO(ahentz): I don't quite understand the tradeoffs in the quantized -// implementation of sigmoid and software, but a tolerance of twice the output -// scale seems reasonable. We might want to change this if we have a better -// theoretical bound. +// Our fixed-point math function implementations have roughly 12 bits of +// accuracy, when specialized to 16-bit fixed-point arithmetic. +// That is purely an implementation compromise, it would have been possible +// to get closer to 16 bits of accuracy but that would be more expensive, +// and not needed for our purposes as ultimately the output is either +// immediately down-quantized to 8 bits, or will typically be at the output +// of the surrounding LSTM cell. +// So we can require roughly 2^-12 accuracy when the output is 16-bit, and +// we can more or less expect the full 2^-8 accuracy when the output is 8-bit. +// +// However, the representable output interval is often [-1, 1] (it has to be +// for tanh, and even for logistic, when we implement it in fixed-point, we +// typically have to do so on such a symmetric interval, e.g. ARM NEON only +// has signed fixed-point arithmetic (SQRDMULH)). As the width of [-1, 1] +// is 2, our representable values are often diluted by a factor of 2, whence +// the factor of 2 below. const float kQuantizedTolerance = 2 * (1. / 256); +const float kQuantizedToleranceInt16 = 2 * (1. / 4096); class QuantizedActivationsOpModel : public BaseActivationsOpModel { public: using BaseActivationsOpModel::BaseActivationsOpModel; + template void SetInput(std::initializer_list data) { - QuantizeAndPopulate(input_, data); + QuantizeAndPopulate(input_, data); } - std::vector GetOutput() { return ExtractVector(output_); } + template + + std::vector GetOutput() { + return ExtractVector(output_); + } + template std::vector GetDequantizedOutput() { - return Dequantize(ExtractVector(output_), - GetScale(output_), GetZeroPoint(output_)); + return Dequantize(ExtractVector(output_), GetScale(output_), + GetZeroPoint(output_)); } }; @@ -152,24 +171,47 @@ TEST(FloatActivationsOpTest, Tanh) { } TEST(QuantizedActivationsOpTest, Tanh) { + const float kMin = -1; + const float kMax = 127.f / 128.f; QuantizedActivationsOpModel m( BuiltinOperator_TANH, - /*input=*/{TensorType_UINT8, {1, 2, 4, 1}, -8, 8}, - /*output=*/{TensorType_UINT8, {1, 2, 4, 1}, -1, 1}); - m.SetInput({ + /*input=*/{TensorType_UINT8, {1, 2, 4, 1}, 8 * kMin, 8 * kMax}, + /*output=*/{TensorType_UINT8, {1, 2, 4, 1}, kMin, kMax}); + m.SetInput({ 0, -6, 2, 4, // -4, -2, 8, 1, // }); m.Invoke(); - EXPECT_THAT(m.GetDequantizedOutput(), + EXPECT_THAT(m.GetDequantizedOutput(), ElementsAreArray(ArrayFloatNear( { 0.0, -0.999987, 0.964027, 0.999329, // - -0.996078, -0.96402, 0.99999, 0.76159, // + -0.999329, -0.96402, 0.99999, 0.76159, // }, - 4 * (1. / 256)))); - EXPECT_THAT(m.GetOutput(), - ElementsAreArray({128, 0, 251, 255, 0, 5, 255, 226})); + kQuantizedTolerance))); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray({128, 0, 251, 255, 0, 5, 255, 225})); +} + +TEST(QuantizedActivationsOpTest, TanhInt16) { + const float kMin = -1; + const float kMax = 32767.f / 32768.f; + QuantizedActivationsOpModel m( + BuiltinOperator_TANH, + /*input=*/{TensorType_INT16, {1, 2, 4, 1}, 8 * kMin, 8 * kMax}, + /*output=*/{TensorType_INT16, {1, 2, 4, 1}, kMin, kMax}); + m.SetInput({ + 0, -6, 2, 4, // + -4, -2, 8, 1, // + }); + m.Invoke(); + EXPECT_THAT(m.GetDequantizedOutput(), + ElementsAreArray(ArrayFloatNear( + { + 0.0, -0.999987, 0.964027, 0.999329, // + -0.999329, -0.96402, 0.99999, 0.76159, // + }, + kQuantizedToleranceInt16))); } TEST(FloatActivationsOpTest, Sigmoid) { @@ -190,22 +232,43 @@ TEST(QuantizedActivationsOpTest, Sigmoid) { QuantizedActivationsOpModel m( BuiltinOperator_LOGISTIC, /*input=*/{TensorType_UINT8, {1, 2, 4, 1}, -10, 10}); - m.SetInput({ + m.SetInput({ 0, -6, 2, 4, // 3, -2, 10, 1, // }); m.Invoke(); - EXPECT_THAT(m.GetDequantizedOutput(), + EXPECT_THAT(m.GetDequantizedOutput(), ElementsAreArray(ArrayFloatNear( { 0.5, 0.002473, 0.880797, 0.982014, // 0.952574, 0.119203, 0.999955, 0.731059, // }, kQuantizedTolerance))); - EXPECT_THAT(m.GetOutput(), + EXPECT_THAT(m.GetOutput(), ElementsAreArray({128, 1, 227, 251, 244, 32, 255, 188})); } +TEST(QuantizedActivationsOpTest, SigmoidInt16) { + const float kMin = -1; + const float kMax = 32767.f / 32768.f; + QuantizedActivationsOpModel m( + BuiltinOperator_LOGISTIC, + /*input=*/{TensorType_INT16, {1, 2, 4, 1}, 8 * kMin, 8 * kMax}, + /*output=*/{TensorType_INT16, {1, 2, 4, 1}, kMin, kMax}); + m.SetInput({ + 0, -6, 2, 4, // + 3, -2, 10, 1, // + }); + m.Invoke(); + EXPECT_THAT(m.GetDequantizedOutput(), + ElementsAreArray(ArrayFloatNear( + { + 0.5, 0.002473, 0.880797, 0.982014, // + 0.952574, 0.119203, 0.999955, 0.731059, // + }, + kQuantizedToleranceInt16))); +} + TEST(FloatActivationsOpTest, Softmax4D) { FloatActivationsOpModel m(0.1, /*input=*/{TensorType_FLOAT32, {1, 2, 1, 4}}); @@ -241,12 +304,12 @@ TEST(QuantizedActivationsOpTest, Softmax4D) { QuantizedActivationsOpModel m( 0.1, /*input=*/{TensorType_UINT8, {1, 2, 1, 4}, -10, 10}); - m.SetInput({ + m.SetInput({ 0, -6, 2, 4, // depth = 0 3, -2, 10, 1, // depth = 1 }); m.Invoke(); - EXPECT_THAT(m.GetDequantizedOutput(), + EXPECT_THAT(m.GetDequantizedOutput(), ElementsAreArray(ArrayFloatNear( { .23463, .12877, .28658, .35003, // @@ -258,21 +321,22 @@ TEST(QuantizedActivationsOpTest, Softmax4D) { QuantizedActivationsOpModel m2( 0.1, /*input=*/{TensorType_UINT8, {4, 1, 1, 2}, -10, 10}); - m2.SetInput({ + m2.SetInput({ 0, -6, // 2, 4, // 3, -2, // 10, 1, // }); m2.Invoke(); - EXPECT_THAT(m2.GetDequantizedOutput(), ElementsAreArray(ArrayFloatNear( - { - 0.645656, 0.354344, // - 0.450166, 0.549834, // - 0.622459, 0.377541, // - 0.710949, 0.28905, // - }, - kQuantizedTolerance))); + EXPECT_THAT(m2.GetDequantizedOutput(), + ElementsAreArray(ArrayFloatNear( + { + 0.645656, 0.354344, // + 0.450166, 0.549834, // + 0.622459, 0.377541, // + 0.710949, 0.28905, // + }, + kQuantizedTolerance))); } TEST(FloatActivationsOpTest, Softmax2D) { @@ -309,12 +373,12 @@ TEST(FloatActivationsOpTest, Softmax2D) { TEST(QuantizedActivationsOpTest, Softmax2D) { QuantizedActivationsOpModel m(0.1, /*input=*/{TensorType_UINT8, {2, 4}, -10, 10}); - m.SetInput({ + m.SetInput({ 0, -6, 2, 4, // 3, -2, 10, 1, // }); m.Invoke(); - EXPECT_THAT(m.GetDequantizedOutput(), + EXPECT_THAT(m.GetDequantizedOutput(), ElementsAreArray(ArrayFloatNear( { .23463, .12877, .28658, .35003, // @@ -325,21 +389,22 @@ TEST(QuantizedActivationsOpTest, Softmax2D) { // Same input, but a different shape. QuantizedActivationsOpModel m2(0.1, /*input=*/{TensorType_UINT8, {4, 2}, -10, 10}); - m2.SetInput({ + m2.SetInput({ 0, -6, // 2, 4, // 3, -2, // 10, 1, // }); m2.Invoke(); - EXPECT_THAT(m2.GetDequantizedOutput(), ElementsAreArray(ArrayFloatNear( - { - 0.645656, 0.354344, // - 0.450166, 0.549834, // - 0.622459, 0.377541, // - 0.710949, 0.28905, // - }, - kQuantizedTolerance))); + EXPECT_THAT(m2.GetDequantizedOutput(), + ElementsAreArray(ArrayFloatNear( + { + 0.645656, 0.354344, // + 0.450166, 0.549834, // + 0.622459, 0.377541, // + 0.710949, 0.28905, // + }, + kQuantizedTolerance))); } // This contains the same test values as the Softmax test, but reference answer -- GitLab From 5d38ddc691ba39f3262b261346d4eca8284f6ac4 Mon Sep 17 00:00:00 2001 From: Michael Kuperstein Date: Thu, 21 Jun 2018 11:03:23 -0700 Subject: [PATCH 803/816] [XLA] Implement Sort in the evaluator. PiperOrigin-RevId: 201552850 --- .../xla/service/hlo_evaluator_typed_visitor.h | 70 +++++++++++++++++++ 1 file changed, 70 insertions(+) diff --git a/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h index bc7340aa03..7e97eacf35 100644 --- a/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h +++ b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h @@ -1378,6 +1378,44 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { return Status::OK(); } + template ::value && + !std::is_same::value>::type* = nullptr> + Status HandleSort(HloInstruction* sort) { + TF_RET_CHECK(ShapeUtil::Rank(sort->shape()) == 1) + << "Sort is only supported for R1 shapes"; + + auto arg = sort->operand(0); + const Literal& arg_literal = parent_->GetEvaluatedLiteralFor(arg); + VLOG(3) << "HandleSort arg_literal: " << arg_literal.ToString(); + const auto& arg_data = arg_literal.data(); + + std::vector return_data(arg_data.begin(), arg_data.end()); + std::sort(return_data.begin(), return_data.end(), + [](const ReturnT& a, const ReturnT& b) { + return SafeLess(a, b); + }); + auto result_literal = MakeUnique(sort->shape()); + result_literal->PopulateR1( + tensorflow::gtl::ArraySlice(return_data)); + VLOG(3) << "HandleSort result_literal: " << result_literal->ToString(); + parent_->evaluated_[sort] = std::move(result_literal); + return Status::OK(); + } + + template ::value || + std::is_same::value>::type* = + nullptr> + Status HandleSort(HloInstruction* sort) { + return InvalidArgument("Unsupported type for Sort"); + } + + Status HandleSort(HloInstruction* sort) override { + return HandleSort(sort); + } + Status HandleReduce(HloInstruction* reduce) override { auto arg = reduce->operand(0); auto init_value = reduce->operand(1); @@ -2118,6 +2156,38 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { return rhs_unsigned >= lhs_size_unsigned; } + // It's UB to use std::sort with std::less, because of NaNs. Define + // "safe" less functions which are actually strict weak orders. + template ::value>::type* = + nullptr> + static bool SafeLess(const NativeT& a, const NativeT& b) { + return a < b; + } + + template ::value || + std::is_same::value>::type* = nullptr> + static bool SafeLess(const NativeT& a, const NativeT& b) { + if (std::isnan(b)) { + return !std::isnan(a); + } else { + return a < b; + } + } + + template ::value>::type* = nullptr> + static bool SafeLess(const NativeT& a, const NativeT& b) { + if (Eigen::half_impl::isnan(b)) { + return !Eigen::half_impl::isnan(a); + } else { + return a < b; + } + } + HloEvaluator* parent_; }; -- GitLab From 780e7714d1ddc3480e64ed484df3c0cb5b665e0d Mon Sep 17 00:00:00 2001 From: Michael Case Date: Thu, 21 Jun 2018 11:11:14 -0700 Subject: [PATCH 804/816] Internal Change. PiperOrigin-RevId: 201554374 --- tensorflow/BUILD | 18 +- tensorflow/api_template.__init__.py | 3 +- tensorflow/contrib/BUILD | 1 + tensorflow/contrib/cmake/python_modules.txt | 1 + tensorflow/python/BUILD | 3 +- tensorflow/python/__init__.py | 1 - tensorflow/python/estimator/BUILD | 410 +++--------------- tensorflow/python/estimator/__init__.py | 25 ++ tensorflow/python/estimator/api/BUILD | 1 + tensorflow/python/estimator/keras.py | 2 - tensorflow/python/keras/BUILD | 1 + tensorflow/python/keras/__init__.py | 1 + tensorflow/python/keras/estimator/__init__.py | 46 ++ tensorflow/tools/api/generator/BUILD | 30 +- tensorflow/tools/api/generator/api_gen.bzl | 32 +- tensorflow/tools/api/generator/doc_srcs.py | 2 +- 16 files changed, 187 insertions(+), 390 deletions(-) create mode 100644 tensorflow/python/keras/estimator/__init__.py diff --git a/tensorflow/BUILD b/tensorflow/BUILD index 6d134dbb80..8d0d9f14bc 100644 --- a/tensorflow/BUILD +++ b/tensorflow/BUILD @@ -398,6 +398,7 @@ config_setting( package_group( name = "internal", packages = [ + "-//third_party/tensorflow/python/estimator", "//learning/meta_rank/...", "//tensorflow/...", "//tensorflow_fold/llgtm/...", @@ -546,11 +547,20 @@ gen_api_init_files( py_library( name = "tensorflow_py", - srcs = [ - ":tensorflow_python_api_gen", - "//tensorflow/python/estimator/api:estimator_python_api_gen", + srcs = ["//tensorflow/python/estimator/api:estimator_python_api_gen"], + srcs_version = "PY2AND3", + visibility = ["//visibility:public"], + deps = [ + ":tensorflow_py_no_contrib", + "//tensorflow/contrib:contrib_py", + "//tensorflow/python/estimator:estimator_py", ], +) + +py_library( + name = "tensorflow_py_no_contrib", + srcs = [":tensorflow_python_api_gen"], srcs_version = "PY2AND3", visibility = ["//visibility:public"], - deps = ["//tensorflow/python"], + deps = ["//tensorflow/python:no_contrib"], ) diff --git a/tensorflow/api_template.__init__.py b/tensorflow/api_template.__init__.py index 9662d7b478..779f65d5b1 100644 --- a/tensorflow/api_template.__init__.py +++ b/tensorflow/api_template.__init__.py @@ -20,7 +20,6 @@ from __future__ import print_function # pylint: disable=g-bad-import-order from tensorflow.python import pywrap_tensorflow # pylint: disable=unused-import -# API IMPORTS PLACEHOLDER try: import os # pylint: disable=g-import-not-at-top @@ -37,6 +36,8 @@ try: except (ImportError, AttributeError): print('tf.estimator package not installed.') +# API IMPORTS PLACEHOLDER + from tensorflow.python.util.lazy_loader import LazyLoader # pylint: disable=g-import-not-at-top contrib = LazyLoader('contrib', globals(), 'tensorflow.contrib') del LazyLoader diff --git a/tensorflow/contrib/BUILD b/tensorflow/contrib/BUILD index 7d44a054a8..fffab5a795 100644 --- a/tensorflow/contrib/BUILD +++ b/tensorflow/contrib/BUILD @@ -114,6 +114,7 @@ py_library( "//tensorflow/contrib/training:training_py", "//tensorflow/contrib/util:util_py", "//tensorflow/python:util", + "//tensorflow/python/estimator:estimator_py", ] + if_mpi(["//tensorflow/contrib/mpi_collectives:mpi_collectives_py"]) + if_tensorrt([ "//tensorflow/contrib/tensorrt:init_py", ]) + select({ diff --git a/tensorflow/contrib/cmake/python_modules.txt b/tensorflow/contrib/cmake/python_modules.txt index 8a45858ae4..d530572e91 100644 --- a/tensorflow/contrib/cmake/python_modules.txt +++ b/tensorflow/contrib/cmake/python_modules.txt @@ -35,6 +35,7 @@ tensorflow/python/keras tensorflow/python/keras/applications tensorflow/python/keras/datasets tensorflow/python/keras/engine +tensorflow/python/keras/estimator tensorflow/python/keras/layers tensorflow/python/keras/preprocessing tensorflow/python/keras/utils diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD index d1561f5c57..c1b59e44a6 100644 --- a/tensorflow/python/BUILD +++ b/tensorflow/python/BUILD @@ -57,12 +57,12 @@ py_library( "//tensorflow/contrib/lite/toco/python:__pkg__", # TODO(b/34059704): remove when fixed "//tensorflow/python/debug:__pkg__", # TODO(b/34059704): remove when fixed "//tensorflow/python/tools:__pkg__", # TODO(b/34059704): remove when fixed - "//tensorflow/tools/api/generator:__pkg__", "//tensorflow/tools/quantization:__pkg__", # TODO(b/34059704): remove when fixed ], deps = [ ":no_contrib", "//tensorflow/contrib:contrib_py", + "//tensorflow/python/estimator:estimator_py", ], ) @@ -128,7 +128,6 @@ py_library( ":weights_broadcast_ops", "//tensorflow/core:protos_all_py", "//tensorflow/python/data", - "//tensorflow/python/estimator:estimator_py", "//tensorflow/python/feature_column:feature_column_py", "//tensorflow/python/keras", "//tensorflow/python/ops/distributions", diff --git a/tensorflow/python/__init__.py b/tensorflow/python/__init__.py index cf707fb2c7..a2ab63bb48 100644 --- a/tensorflow/python/__init__.py +++ b/tensorflow/python/__init__.py @@ -79,7 +79,6 @@ from tensorflow.python.ops import initializers_ns as initializers # Bring in subpackages. from tensorflow.python import data from tensorflow.python import keras -from tensorflow.python.estimator import estimator_lib as estimator from tensorflow.python.feature_column import feature_column_lib as feature_column from tensorflow.python.layers import layers from tensorflow.python.ops import bitwise_ops as bitwise diff --git a/tensorflow/python/estimator/BUILD b/tensorflow/python/estimator/BUILD index 326019ff2a..38e446da0c 100644 --- a/tensorflow/python/estimator/BUILD +++ b/tensorflow/python/estimator/BUILD @@ -10,7 +10,10 @@ load("//tensorflow:tensorflow.bzl", "py_test") py_library( name = "estimator_py", - srcs = ["estimator_lib.py"], + srcs = [ + "__init__.py", + "estimator_lib.py", + ], srcs_version = "PY2AND3", visibility = [ "//tensorflow:__pkg__", @@ -31,7 +34,7 @@ py_library( ":parsing_utils", ":run_config", ":training", - "//tensorflow/python:util", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -41,10 +44,7 @@ py_library( srcs_version = "PY2AND3", deps = [ ":gc", - "//tensorflow/python:errors", - "//tensorflow/python:platform", - "//tensorflow/python:summary", - "//tensorflow/python:util", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator:metric_keys", "//tensorflow/python/estimator:util", ], @@ -58,10 +58,7 @@ py_test( deps = [ ":estimator", ":exporter", - "//tensorflow/python:array_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:platform", - "//tensorflow/python:util", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -70,8 +67,7 @@ py_library( srcs = ["gc.py"], srcs_version = "PY2AND3", deps = [ - "//tensorflow/python:platform", - "//tensorflow/python:util", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -82,10 +78,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":gc", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework_test_lib", - "//tensorflow/python:platform", - "//tensorflow/python:util", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -95,12 +88,7 @@ py_library( srcs_version = "PY2AND3", deps = [ ":export_output", - "//tensorflow/python:array_ops", - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:training", - "//tensorflow/python:util", - "//tensorflow/python/saved_model:signature_constants", - "//tensorflow/python/saved_model:tag_constants", + "//tensorflow:tensorflow_py_no_contrib", "@six_archive//:six", ], ) @@ -113,12 +101,7 @@ py_test( deps = [ ":export_output", ":model_fn", - "//tensorflow/python:client_testlib", - "//tensorflow/python:control_flow_ops", - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:sparse_tensor", - "//tensorflow/python:training", - "//tensorflow/python/saved_model:signature_constants", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -130,11 +113,7 @@ py_library( ":estimator", ":exporter", ":run_config", - "//tensorflow/core:protos_all_py", - "//tensorflow/python:framework_ops", - "//tensorflow/python:platform", - "//tensorflow/python:training", - "//tensorflow/python:util", + "//tensorflow:tensorflow_py_no_contrib", "@six_archive//:six", ], ) @@ -153,13 +132,7 @@ py_test( ":inputs", ":run_config", ":training", - "//tensorflow/python:client_testlib", - "//tensorflow/python:control_flow_ops", - "//tensorflow/python:framework_ops", - "//tensorflow/python:platform", - "//tensorflow/python:training", - "//tensorflow/python:util", - "//tensorflow/python/feature_column", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -168,7 +141,7 @@ py_library( srcs = ["run_config.py"], srcs_version = "PY2AND3", deps = [ - "//tensorflow/core:protos_all_py", + "//tensorflow:tensorflow_py_no_contrib", "@six_archive//:six", ], ) @@ -180,8 +153,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":run_config", - "//tensorflow/core:protos_all_py", - "//tensorflow/python:client_testlib", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -194,14 +166,7 @@ py_library( ":head", ":model_fn", ":optimizers", - "//tensorflow/python:init_ops", - "//tensorflow/python:layers", - "//tensorflow/python:nn", - "//tensorflow/python:partitioned_variables", - "//tensorflow/python:summary", - "//tensorflow/python:training", - "//tensorflow/python:variable_scope", - "//tensorflow/python/feature_column", + "//tensorflow:tensorflow_py_no_contrib", "@six_archive//:six", ], ) @@ -225,26 +190,7 @@ py_test( ":numpy_io", ":pandas_io", ":run_config", - "//tensorflow/core:protos_all_py", - "//tensorflow/python:array_ops", - "//tensorflow/python:check_ops", - "//tensorflow/python:client", - "//tensorflow/python:client_testlib", - "//tensorflow/python:constant_op", - "//tensorflow/python:control_flow_ops", - "//tensorflow/python:data_flow_ops", - "//tensorflow/python:dtypes", - "//tensorflow/python:framework_ops", - "//tensorflow/python:math_ops", - "//tensorflow/python:parsing_ops", - "//tensorflow/python:platform", - "//tensorflow/python:sparse_tensor", - "//tensorflow/python:state_ops", - "//tensorflow/python:summary", - "//tensorflow/python:training", - "//tensorflow/python:variable_scope", - "//tensorflow/python:variables", - "//tensorflow/python/feature_column", + "//tensorflow:tensorflow_py_no_contrib", "@six_archive//:six", ], ) @@ -257,20 +203,7 @@ py_library( ":estimator", ":head", ":model_fn", - "//tensorflow/python:array_ops", - "//tensorflow/python:boosted_trees_ops", - "//tensorflow/python:data_flow_ops", - "//tensorflow/python:distribute", - "//tensorflow/python:dtypes", - "//tensorflow/python:framework_ops", - "//tensorflow/python:lookup_ops", - "//tensorflow/python:math_ops", - "//tensorflow/python:state_ops", - "//tensorflow/python:summary", - "//tensorflow/python:training", - "//tensorflow/python:variable_scope", - "//tensorflow/python/feature_column", - "//tensorflow/python/ops/losses", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -284,19 +217,8 @@ py_test( ], deps = [ ":boosted_trees", - "//tensorflow/core/kernels/boosted_trees:boosted_trees_proto_py", - "//tensorflow/python:array_ops", - "//tensorflow/python:constant_op", - "//tensorflow/python:control_flow_ops", - "//tensorflow/python:dtypes", - "//tensorflow/python:framework_test_lib", - "//tensorflow/python:math_ops", - "//tensorflow/python:platform", - "//tensorflow/python:platform_test", - "//tensorflow/python:resources", - "//tensorflow/python:training", - "//tensorflow/python/estimator:numpy_io", - "//tensorflow/python/feature_column", + ":inputs", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -309,14 +231,7 @@ py_library( ":head", ":model_fn", ":optimizers", - "//tensorflow/python:init_ops", - "//tensorflow/python:layers", - "//tensorflow/python:nn", - "//tensorflow/python:partitioned_variables", - "//tensorflow/python:summary", - "//tensorflow/python:variable_scope", - "//tensorflow/python/feature_column", - "//tensorflow/python/ops/losses", + "//tensorflow:tensorflow_py_no_contrib", "@six_archive//:six", ], ) @@ -333,22 +248,7 @@ py_library( ":model_fn", ":numpy_io", ":prediction_keys", - "//tensorflow/core:protos_all_py", - "//tensorflow/python:array_ops", - "//tensorflow/python:check_ops", - "//tensorflow/python:client", - "//tensorflow/python:client_testlib", - "//tensorflow/python:constant_op", - "//tensorflow/python:control_flow_ops", - "//tensorflow/python:distribute", - "//tensorflow/python:dtypes", - "//tensorflow/python:framework_ops", - "//tensorflow/python:math_ops", - "//tensorflow/python:state_ops", - "//tensorflow/python:summary", - "//tensorflow/python:training", - "//tensorflow/python:variables", - "//tensorflow/python/feature_column", + "//tensorflow:tensorflow_py_no_contrib", "//third_party/py/numpy", "@six_archive//:six", ], @@ -371,16 +271,7 @@ py_test( ":numpy_io", ":pandas_io", ":prediction_keys", - "//tensorflow/core:protos_all_py", - "//tensorflow/python:client_testlib", - "//tensorflow/python:data_flow_ops", - "//tensorflow/python:dtypes", - "//tensorflow/python:framework_ops", - "//tensorflow/python:parsing_ops", - "//tensorflow/python:platform", - "//tensorflow/python:summary", - "//tensorflow/python:training", - "//tensorflow/python/feature_column", + "//tensorflow:tensorflow_py_no_contrib", "@six_archive//:six", ], ) @@ -396,19 +287,7 @@ py_library( ":linear", ":model_fn", ":optimizers", - "//tensorflow/python:control_flow_ops", - "//tensorflow/python:distribute", - "//tensorflow/python:framework_ops", - "//tensorflow/python:init_ops", - "//tensorflow/python:layers", - "//tensorflow/python:nn", - "//tensorflow/python:partitioned_variables", - "//tensorflow/python:state_ops", - "//tensorflow/python:summary", - "//tensorflow/python:training", - "//tensorflow/python:variable_scope", - "//tensorflow/python/feature_column", - "//tensorflow/python/ops/losses", + "//tensorflow:tensorflow_py_no_contrib", "@six_archive//:six", ], ) @@ -431,17 +310,7 @@ py_test( ":numpy_io", ":pandas_io", ":prediction_keys", - "//tensorflow/core:protos_all_py", - "//tensorflow/python:client_testlib", - "//tensorflow/python:dtypes", - "//tensorflow/python:framework_ops", - "//tensorflow/python:nn", - "//tensorflow/python:parsing_ops", - "//tensorflow/python:platform", - "//tensorflow/python:summary", - "//tensorflow/python:training", - "//tensorflow/python:variables", - "//tensorflow/python/feature_column", + "//tensorflow:tensorflow_py_no_contrib", "@six_archive//:six", ], ) @@ -453,10 +322,7 @@ py_library( ], srcs_version = "PY2AND3", deps = [ - "//tensorflow/python:platform", - "//tensorflow/python:training", - "//tensorflow/python:util", - "//tensorflow/python/data", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -467,10 +333,7 @@ py_test( tags = ["notsan"], # b/67510291 deps = [ ":util", - "//tensorflow/python:client_testlib", - "//tensorflow/python:constant_op", - "//tensorflow/python:training", - "//tensorflow/python/data", + "//tensorflow:tensorflow_py_no_contrib", "//third_party/py/numpy", "@six_archive//:six", ], @@ -487,21 +350,7 @@ py_library( ":model_fn", ":run_config", ":util", - "//tensorflow/core:protos_all_py", - "//tensorflow/python:client", - "//tensorflow/python:control_flow_ops", - "//tensorflow/python:distribute", - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:metrics", - "//tensorflow/python:platform", - "//tensorflow/python:random_seed", - "//tensorflow/python:summary", - "//tensorflow/python:training", - "//tensorflow/python:util", - "//tensorflow/python/data", - "//tensorflow/python/saved_model:builder", - "//tensorflow/python/saved_model:constants", - "//tensorflow/python/saved_model:tag_constants", + "//tensorflow:tensorflow_py_no_contrib", "//third_party/py/numpy", "@six_archive//:six", ], @@ -520,29 +369,7 @@ py_test( ":model_fn", ":numpy_io", ":run_config", - "//tensorflow/python:array_ops", - "//tensorflow/python:check_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:control_flow_ops", - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:init_ops", - "//tensorflow/python:layers", - "//tensorflow/python:lib", - "//tensorflow/python:lookup_ops", - "//tensorflow/python:metrics", - "//tensorflow/python:parsing_ops", - "//tensorflow/python:platform", - "//tensorflow/python:saver_test_utils", - "//tensorflow/python:session", - "//tensorflow/python:state_ops", - "//tensorflow/python:summary", - "//tensorflow/python:training", - "//tensorflow/python:util", - "//tensorflow/python:variables", - "//tensorflow/python/data", - "//tensorflow/python/ops/losses", - "//tensorflow/python/saved_model:loader", - "//tensorflow/python/saved_model:tag_constants", + "//tensorflow:tensorflow_py_no_contrib", "//third_party/py/numpy", "@six_archive//:six", ], @@ -555,9 +382,7 @@ py_library( ], srcs_version = "PY2AND3", deps = [ - "//tensorflow/python:dtypes", - "//tensorflow/python:parsing_ops", - "//tensorflow/python/feature_column", + "//tensorflow:tensorflow_py_no_contrib", "@six_archive//:six", ], ) @@ -568,10 +393,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":parsing_utils", - "//tensorflow/python:client_testlib", - "//tensorflow/python:dtypes", - "//tensorflow/python:parsing_ops", - "//tensorflow/python/feature_column", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -580,9 +402,7 @@ py_library( srcs = ["export/export_output.py"], srcs_version = "PY2AND3", deps = [ - "//tensorflow/python:dtypes", - "//tensorflow/python:framework_ops", - "//tensorflow/python/saved_model:signature_def_utils", + "//tensorflow:tensorflow_py_no_contrib", "@six_archive//:six", ], ) @@ -594,13 +414,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":export_output", - "//tensorflow/core:protos_all_py", - "//tensorflow/python:array_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:constant_op", - "//tensorflow/python:dtypes", - "//tensorflow/python:sparse_tensor", - "//tensorflow/python/saved_model:signature_constants", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -613,7 +427,7 @@ py_library( deps = [ ":export_export", ":export_output", - "//tensorflow/python:util", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -625,13 +439,7 @@ py_library( srcs_version = "PY2AND3", deps = [ ":util", - "//tensorflow/python:array_ops", - "//tensorflow/python:dtypes", - "//tensorflow/python:framework_ops", - "//tensorflow/python:parsing_ops", - "//tensorflow/python:sparse_tensor", - "//tensorflow/python:tensor_shape", - "//tensorflow/python:util", + "//tensorflow:tensorflow_py_no_contrib", "@six_archive//:six", ], ) @@ -644,17 +452,8 @@ py_test( deps = [ ":export_export", ":export_output", - "//tensorflow/core:protos_all_py", - "//tensorflow/python:array_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:constant_op", - "//tensorflow/python:dtypes", - "//tensorflow/python:framework_ops", - "//tensorflow/python:framework_test_lib", - "//tensorflow/python:parsing_ops", - "//tensorflow/python:sparse_tensor", - "//tensorflow/python/saved_model:signature_constants", - "//tensorflow/python/saved_model:signature_def_utils", + ":util", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -667,24 +466,7 @@ py_library( ":metric_keys", ":model_fn", ":prediction_keys", - "//tensorflow/python:array_ops", - "//tensorflow/python:check_ops", - "//tensorflow/python:control_flow_ops", - "//tensorflow/python:dtypes", - "//tensorflow/python:framework_ops", - "//tensorflow/python:lookup_ops", - "//tensorflow/python:math_ops", - "//tensorflow/python:metrics", - "//tensorflow/python:nn", - "//tensorflow/python:sparse_tensor", - "//tensorflow/python:string_ops", - "//tensorflow/python:summary", - "//tensorflow/python:training", - "//tensorflow/python:util", - "//tensorflow/python:weights_broadcast_ops", - "//tensorflow/python/feature_column", - "//tensorflow/python/ops/losses", - "//tensorflow/python/saved_model:signature_constants", + "//tensorflow:tensorflow_py_no_contrib", "@six_archive//:six", ], ) @@ -703,23 +485,7 @@ py_test( ":model_fn", ":numpy_io", ":prediction_keys", - "//tensorflow/core:protos_all_py", - "//tensorflow/python:array_ops", - "//tensorflow/python:check_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:constant_op", - "//tensorflow/python:control_flow_ops", - "//tensorflow/python:dtypes", - "//tensorflow/python:errors", - "//tensorflow/python:framework_ops", - "//tensorflow/python:math_ops", - "//tensorflow/python:sparse_tensor", - "//tensorflow/python:string_ops", - "//tensorflow/python:training", - "//tensorflow/python:variables", - "//tensorflow/python/feature_column", - "//tensorflow/python/ops/losses", - "//tensorflow/python/saved_model:signature_constants", + "//tensorflow:tensorflow_py_no_contrib", "//third_party/py/numpy", "@six_archive//:six", ], @@ -732,7 +498,7 @@ py_library( deps = [ ":numpy_io", ":pandas_io", - "//tensorflow/python:util", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -744,11 +510,7 @@ py_library( ":estimator", ":head", ":optimizers", - "//tensorflow/python:partitioned_variables", - "//tensorflow/python:training", - "//tensorflow/python:variable_scope", - "//tensorflow/python/feature_column", - "//tensorflow/python/ops/losses", + "//tensorflow:tensorflow_py_no_contrib", "@six_archive//:six", ], ) @@ -766,25 +528,7 @@ py_library( ":numpy_io", ":pandas_io", ":run_config", - "//tensorflow/core:protos_all_py", - "//tensorflow/python:check_ops", - "//tensorflow/python:client", - "//tensorflow/python:client_testlib", - "//tensorflow/python:control_flow_ops", - "//tensorflow/python:data_flow_ops", - "//tensorflow/python:distribute", - "//tensorflow/python:dtypes", - "//tensorflow/python:framework_ops", - "//tensorflow/python:math_ops", - "//tensorflow/python:parsing_ops", - "//tensorflow/python:platform", - "//tensorflow/python:sparse_tensor", - "//tensorflow/python:state_ops", - "//tensorflow/python:summary", - "//tensorflow/python:training", - "//tensorflow/python:variable_scope", - "//tensorflow/python:variables", - "//tensorflow/python/feature_column", + "//tensorflow:tensorflow_py_no_contrib", "@six_archive//:six", ], ) @@ -802,7 +546,7 @@ py_test( deps = [ ":linear", ":linear_testing_utils", - "//tensorflow/python:client_testlib", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -831,9 +575,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":numpy_io", - "//tensorflow/python:client_testlib", - "//tensorflow/python:errors", - "//tensorflow/python:training", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -842,7 +584,7 @@ py_library( srcs = ["canned/optimizers.py"], srcs_version = "PY2AND3", deps = [ - "//tensorflow/python:training", + "//tensorflow:tensorflow_py_no_contrib", "@six_archive//:six", ], ) @@ -854,8 +596,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":optimizers", - "//tensorflow/python:client_testlib", - "//tensorflow/python:training", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -873,9 +614,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":pandas_io", - "//tensorflow/python:client_testlib", - "//tensorflow/python:errors", - "//tensorflow/python:training", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -895,15 +634,7 @@ py_library( ], srcs_version = "PY2AND3", deps = [ - "//tensorflow/python:array_ops", - "//tensorflow/python:data_flow_ops", - "//tensorflow/python:dtypes", - "//tensorflow/python:errors", - "//tensorflow/python:framework_ops", - "//tensorflow/python:math_ops", - "//tensorflow/python:platform", - "//tensorflow/python:summary", - "//tensorflow/python:training", + "//tensorflow:tensorflow_py_no_contrib", "@six_archive//:six", ], ) @@ -917,7 +648,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":inputs_queues", - "//tensorflow/python:client_testlib", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -928,10 +659,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":inputs_queues", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework_ops", - "//tensorflow/python:session", - "//tensorflow/python:training", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -944,32 +672,7 @@ py_library( ":export_export", ":model_fn", ":run_config", - "//tensorflow/python:check_ops", - "//tensorflow/python:framework_ops", - "//tensorflow/python:init_ops", - "//tensorflow/python:layers", - "//tensorflow/python:math_ops", - "//tensorflow/python:metrics", - "//tensorflow/python:nn", - "//tensorflow/python:partitioned_variables", - "//tensorflow/python:platform", - "//tensorflow/python:random_seed", - "//tensorflow/python:session", - "//tensorflow/python:sparse_tensor", - "//tensorflow/python:summary", - "//tensorflow/python:tensor_util", - "//tensorflow/python:training", - "//tensorflow/python:training_util", - "//tensorflow/python:util", - "//tensorflow/python:variable_scope", - "//tensorflow/python:variables", - "//tensorflow/python/feature_column", - "//tensorflow/python/keras:backend", - "//tensorflow/python/keras:engine", - "//tensorflow/python/keras:layers", - "//tensorflow/python/ops/losses", - "//tensorflow/python/saved_model", - "//tensorflow/python/saved_model:signature_constants", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -984,18 +687,9 @@ py_test( ], deps = [ ":keras", - "//tensorflow/core:protos_all_py", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework_ops", - "//tensorflow/python:framework_test_lib", - "//tensorflow/python:platform", - "//tensorflow/python:summary", - "//tensorflow/python:training", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator:numpy_io", "//tensorflow/python/estimator:run_config", - "//tensorflow/python/keras", - "//tensorflow/python/keras:backend", - "//tensorflow/python/keras:engine", "//third_party/py/numpy", ], ) diff --git a/tensorflow/python/estimator/__init__.py b/tensorflow/python/estimator/__init__.py index e69de29bb2..8cf8df567f 100644 --- a/tensorflow/python/estimator/__init__.py +++ b/tensorflow/python/estimator/__init__.py @@ -0,0 +1,25 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Import Estimator APIs. + +Note: This file is imported by the create_estimator_api genrule. It must +transitively import all Estimator modules/packages for their @estimator_export +annotations to generate the public Estimator python API. +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow.python.estimator.estimator_lib diff --git a/tensorflow/python/estimator/api/BUILD b/tensorflow/python/estimator/api/BUILD index cddee9b8f3..aa5a29e6dd 100644 --- a/tensorflow/python/estimator/api/BUILD +++ b/tensorflow/python/estimator/api/BUILD @@ -14,4 +14,5 @@ gen_api_init_files( api_name = "estimator", output_files = ESTIMATOR_API_INIT_FILES, package = "tensorflow.python.estimator", + package_dep = "//tensorflow/python/estimator:estimator_py", ) diff --git a/tensorflow/python/estimator/keras.py b/tensorflow/python/estimator/keras.py index 2f439f765e..312eb9a035 100644 --- a/tensorflow/python/estimator/keras.py +++ b/tensorflow/python/estimator/keras.py @@ -45,7 +45,6 @@ from tensorflow.python.saved_model import signature_constants from tensorflow.python.training import distribute as distribute_lib from tensorflow.python.training import saver as saver_lib from tensorflow.python.training import training_util -from tensorflow.python.util.tf_export import tf_export _DEFAULT_SERVING_KEY = signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY @@ -446,7 +445,6 @@ def _save_first_checkpoint(keras_model, estimator, custom_objects, saver.save(sess, os.path.join(estimator.model_dir, 'keras_model.ckpt')) -@tf_export('keras.estimator.model_to_estimator') def model_to_estimator(keras_model=None, keras_model_path=None, custom_objects=None, diff --git a/tensorflow/python/keras/BUILD b/tensorflow/python/keras/BUILD index 151a26f6e6..bc33dddc95 100755 --- a/tensorflow/python/keras/BUILD +++ b/tensorflow/python/keras/BUILD @@ -39,6 +39,7 @@ py_library( "datasets/imdb.py", "datasets/mnist.py", "datasets/reuters.py", + "estimator/__init__.py", "preprocessing/__init__.py", "preprocessing/image.py", "preprocessing/sequence.py", diff --git a/tensorflow/python/keras/__init__.py b/tensorflow/python/keras/__init__.py index 3493069a5b..198c66d9e1 100644 --- a/tensorflow/python/keras/__init__.py +++ b/tensorflow/python/keras/__init__.py @@ -27,6 +27,7 @@ from tensorflow.python.keras import backend from tensorflow.python.keras import callbacks from tensorflow.python.keras import constraints from tensorflow.python.keras import datasets +from tensorflow.python.keras import estimator from tensorflow.python.keras import initializers from tensorflow.python.keras import layers from tensorflow.python.keras import losses diff --git a/tensorflow/python/keras/estimator/__init__.py b/tensorflow/python/keras/estimator/__init__.py new file mode 100644 index 0000000000..cb86a69990 --- /dev/null +++ b/tensorflow/python/keras/estimator/__init__.py @@ -0,0 +1,46 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Keras estimator API.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.util.tf_export import tf_export + +# Keras has undeclared dependency on tensorflow/estimator:estimator_py. +# As long as you depend //third_party/py/tensorflow:tensorflow target +# everything will work as normal. + +try: + import tensorflow.python.estimator.keras as keras_lib # pylint: disable=g-import-not-at-top + model_to_estimator = tf_export('keras.estimator.model_to_estimator')( + keras_lib.model_to_estimator) +except Exception: # pylint: disable=broad-except + + # pylint: disable=unused-argument + def stub_model_to_estimator(keras_model=None, + keras_model_path=None, + custom_objects=None, + model_dir=None, + config=None): + raise NotImplementedError( + 'tf.keras.estimator.model_to_estimator function not available in your ' + 'installation.') + # pylint: enable=unused-argument + + model_to_estimator = tf_export('keras.estimator.model_to_estimator')( + stub_model_to_estimator) + diff --git a/tensorflow/tools/api/generator/BUILD b/tensorflow/tools/api/generator/BUILD index 6065c12cad..8c760e6f52 100644 --- a/tensorflow/tools/api/generator/BUILD +++ b/tensorflow/tools/api/generator/BUILD @@ -3,38 +3,37 @@ licenses(["notice"]) # Apache 2.0 -exports_files(["LICENSE"]) - load("//tensorflow/tools/api/generator:api_gen.bzl", "ESTIMATOR_API_INIT_FILES") load("//tensorflow/tools/api/generator:api_gen.bzl", "TENSORFLOW_API_INIT_FILES") -py_library( - name = "doc_srcs", - srcs = ["doc_srcs.py"], - srcs_version = "PY2AND3", - deps = [ - "//tensorflow/python:util", +exports_files( + [ + "LICENSE", + "create_python_api.py", ], ) -py_binary( - name = "create_python_api", - srcs = ["create_python_api.py"], +py_library( + name = "doc_srcs", + srcs = ["doc_srcs.py"], srcs_version = "PY2AND3", visibility = ["//visibility:public"], deps = [ - ":doc_srcs", - "//tensorflow/python:no_contrib", + "//tensorflow/python:util", ], ) py_test( name = "create_python_api_test", - srcs = ["create_python_api_test.py"], + srcs = [ + "create_python_api.py", + "create_python_api_test.py", + ], srcs_version = "PY2AND3", deps = [ - ":create_python_api", + ":doc_srcs", "//tensorflow/python:client_testlib", + "//tensorflow/python:no_contrib", ], ) @@ -67,5 +66,6 @@ py_test( ":doc_srcs", "//tensorflow/python:client_testlib", "//tensorflow/python:no_contrib", + "//tensorflow/python/estimator:estimator_py", ], ) diff --git a/tensorflow/tools/api/generator/api_gen.bzl b/tensorflow/tools/api/generator/api_gen.bzl index b7ebcb976b..d746b5d3e4 100644 --- a/tensorflow/tools/api/generator/api_gen.bzl +++ b/tensorflow/tools/api/generator/api_gen.bzl @@ -118,24 +118,44 @@ ESTIMATOR_API_INIT_FILES = [ # template will be replaced with root imports collected by this genrule. # srcs: genrule sources. If passing root_init_template, the template file # must be included in sources. +# api_name: Name of the project that you want to generate API files for +# (e.g. "tensorflow" or "estimator"). +# package: Python package containing the @tf_export decorators you want to +# process +# package_dep: Python library target containing your package. + def gen_api_init_files( name, output_files = TENSORFLOW_API_INIT_FILES, root_init_template = None, srcs = [], api_name = "tensorflow", - package = "tensorflow.python"): + package = "tensorflow.python", + package_dep = "//tensorflow/python:no_contrib"): root_init_template_flag = "" if root_init_template: - root_init_template_flag = "--root_init_template=$(location " + root_init_template + ")" + root_init_template_flag = "--root_init_template=$(location " + root_init_template + ")" + + api_gen_binary_target = "create_" + package + "_api" + native.py_binary( + name = "create_" + package + "_api", + srcs = ["//tensorflow/tools/api/generator:create_python_api.py"], + main = "//tensorflow/tools/api/generator:create_python_api.py", + srcs_version = "PY2AND3", + visibility = ["//visibility:public"], + deps = [ + package_dep, + "//tensorflow/tools/api/generator:doc_srcs", + ], + ) + native.genrule( name = name, outs = output_files, cmd = ( - "$(location //tensorflow/tools/api/generator:create_python_api) " + - root_init_template_flag + " --apidir=$(@D) --apiname=" + api_name + " --package=" + package + " $(OUTS)" - ), + "$(location :" + api_gen_binary_target + ") " + + root_init_template_flag + " --apidir=$(@D) --apiname=" + api_name + " --package=" + package + " $(OUTS)"), srcs = srcs, - tools = ["//tensorflow/tools/api/generator:create_python_api"], + tools = [":" + api_gen_binary_target ], visibility = ["//tensorflow:__pkg__"], ) diff --git a/tensorflow/tools/api/generator/doc_srcs.py b/tensorflow/tools/api/generator/doc_srcs.py index ccd5bea481..ad1988494d 100644 --- a/tensorflow/tools/api/generator/doc_srcs.py +++ b/tensorflow/tools/api/generator/doc_srcs.py @@ -43,7 +43,7 @@ _TENSORFLOW_DOC_SOURCES = { 'gfile': DocSource(docstring_module_name='platform.gfile'), 'graph_util': DocSource(docstring_module_name='framework.graph_util'), 'image': DocSource(docstring_module_name='ops.image_ops'), - 'keras.estimator': DocSource(docstring_module_name='estimator.keras'), + 'keras.estimator': DocSource(docstring_module_name='keras.estimator'), 'linalg': DocSource(docstring_module_name='ops.linalg_ops'), 'logging': DocSource(docstring_module_name='ops.logging_ops'), 'losses': DocSource(docstring_module_name='ops.losses.losses'), -- GitLab From 86fb0cdb3b1f521496ef474e215e338de3cf696d Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 21 Jun 2018 11:13:17 -0700 Subject: [PATCH 805/816] Make regroup work on tower-local variables as well. PiperOrigin-RevId: 201554738 --- .../python/mirrored_strategy_multigpu_test.py | 26 +++++++--- .../contrib/distribute/python/values.py | 50 +++++++++---------- .../contrib/optimizer_v2/optimizer_v2.py | 10 ++-- tensorflow/python/training/optimizer.py | 12 ++--- 4 files changed, 56 insertions(+), 42 deletions(-) diff --git a/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py b/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py index d0bfcc5586..cb150692de 100644 --- a/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py +++ b/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py @@ -337,6 +337,8 @@ class MirroredStrategyVariableCreationTest(test.TestCase): all_v_sum = {} all_v_mean = {} + components_sum = {} + components_mean = {} def model_fn(device_id): tower_context = distribute_lib.get_tower_context() @@ -350,21 +352,33 @@ class MirroredStrategyVariableCreationTest(test.TestCase): v_mean.assign(6.0 * device_id)] all_v_sum[device_id] = v_sum all_v_mean[device_id] = v_mean - return updates, v_sum, v_mean + c_sum = v_sum.get() + c_mean = v_mean.get() + components_sum[device_id] = c_sum + components_mean[device_id] = c_mean + self.assertIsNot(v_sum, c_sum) + self.assertIsNot(v_mean, c_mean) + return updates, v_sum, v_mean, c_sum, c_mean dist = mirrored_strategy.MirroredStrategy( ["/device:GPU:0", "/device:CPU:0"]) with dist.scope(): # Create "sum" and "mean" versions of TowerLocalVariables. - ret_ops, ret_v_sum, ret_v_mean = dist.call_for_each_tower( - model_fn, dist.worker_device_index, run_concurrently=False) + ret_ops, ret_v_sum, ret_v_mean, regrouped_sum, regrouped_mean = ( + dist.call_for_each_tower( + model_fn, dist.worker_device_index, run_concurrently=False)) # Should see the same wrapping instance in all towers. self.assertIs(all_v_sum[0], ret_v_sum) self.assertIs(all_v_mean[0], ret_v_mean) - for i in range(1, dist.num_towers): - self.assertIs(all_v_sum[0], all_v_sum[1]) - self.assertIs(all_v_mean[0], all_v_mean[1]) + self.assertIs(all_v_sum[0], all_v_sum[1]) + self.assertIs(all_v_mean[0], all_v_mean[1]) + + # Regroup should recover the same wrapper. + self.assertIs(ret_v_sum, regrouped_sum) + self.assertIs(ret_v_mean, regrouped_mean) + self.assertIsNot(components_sum[0], components_sum[1]) + self.assertIsNot(components_mean[0], components_mean[1]) # Apply updates self.evaluate(variables.global_variables_initializer()) diff --git a/tensorflow/contrib/distribute/python/values.py b/tensorflow/contrib/distribute/python/values.py index 389b01d3cd..9a48928a95 100644 --- a/tensorflow/contrib/distribute/python/values.py +++ b/tensorflow/contrib/distribute/python/values.py @@ -192,6 +192,10 @@ class DistributedVariable(DistributedDelegate): # Child class must set self._primary_var before calling # super(...).__init__(index). self._common_name = self._primary_var.name.split(":")[0] + # Use a weakref to make it easy to map from the contained values + # to the container without introducing a reference cycle. + for v in six.itervalues(index): + v._distributed_container = weakref.ref(self) # pylint: disable=protected-access super(DistributedVariable, self).__init__(index) @property @@ -287,10 +291,6 @@ class MirroredVariable(DistributedVariable, Mirrored, """Holds a map from device to variables whose values are kept in sync.""" def __init__(self, index, primary_var): - # Use a weakref to make it easy to map from the contained values - # to the container without introducing a reference cycle. - for v in six.itervalues(index): - v._mirrored_container = weakref.ref(self) # pylint: disable=protected-access self._primary_var = primary_var super(MirroredVariable, self).__init__(index) @@ -498,40 +498,40 @@ def regroup(per_device, wrap_class=PerDevice): same_id = False break # Consider three cases where same_id is true: - # * If v0 is a MirroredVariable (and same_id means it is the same - # across all devices), we want to return it. We check - # MirroredVariable specifically since it can look like it - # has a _mirrored_container member since its members do. - # * If v0 is a member of a mirrored variable, in which case - # hasattr(v0, "_mirrored_container") is true, we want to - # return the MirroredVariable that contains it using the - # _mirrored_container logic below. This case can trigger + # * If v0 is a DistributedVariable (a MirroredVariable or + # TowerLocalVariable, and same_id means it is the same across all + # devices), we want to return it. We check DistributedVariable + # specifically since it can look like it has a + # _distributed_container member since its members do. + # * If v0 is a member of a distributed variable, in which case + # hasattr(v0, "_distributed_container") is true, we want to + # return the DistributedVariable that contains it using the + # _distributed_container logic below. This case can trigger # same_id when there is only one device. # * In any other situation, same_id means we return v0. - if same_id and (isinstance(v0, MirroredVariable) or - not hasattr(v0, "_mirrored_container")): + if same_id and (isinstance(v0, DistributedVariable) or + not hasattr(v0, "_distributed_container")): return v0 # Detect the case where each device has a parallel component of the - # same MirroredVariable. In this case we want to return the - # containing MirroredVariable, after a bunch of sanity checking. - # In particular, each component should have the same container, - # and the devices of the variables should match the keys of the - # per-device dictionary. - # TODO(josh11b): Do we need similar logic for TowerLocalVariables? - if hasattr(v0, "_mirrored_container"): + # same MirroredVariable (or TowerLocalVariable). In this case we + # want to return the containing MirroredVariable, after a bunch of + # sanity checking. In particular, each component should have the + # same container, and the devices of the variables should match the + # keys of the per-device dictionary. + if hasattr(v0, "_distributed_container"): # pylint: disable=protected-access assert not isinstance(v0, MirroredVariable), ( "ids = %s, items = %s" % ([id(v[1]) for v in items], items)) assert _devices_match(v0.device, items[0][0]), ( "v0.device = %s, items = %s" % (v0.device, items)) - mirrored_container = v0._mirrored_container() - assert mirrored_container is not None + distributed_container = v0._distributed_container() + assert distributed_container is not None for d, v in items[1:]: assert _devices_match(v.device, d), ( "v.device = %s, d = %s, items = %s" % (v.device, d, items)) - assert mirrored_container is v._mirrored_container() - return mirrored_container + assert distributed_container is v._distributed_container() + return distributed_container # pylint: enable=protected-access return wrap_class(per_device) diff --git a/tensorflow/contrib/optimizer_v2/optimizer_v2.py b/tensorflow/contrib/optimizer_v2/optimizer_v2.py index a44f29fa37..c6f3bd6ee1 100644 --- a/tensorflow/contrib/optimizer_v2/optimizer_v2.py +++ b/tensorflow/contrib/optimizer_v2/optimizer_v2.py @@ -162,12 +162,12 @@ def _get_processor(v): def _var_key_v2(var): """Key for representing a primary variable, for looking up slots.""" # pylint: disable=protected-access - if hasattr(var, "_mirrored_container"): - mirrored_container = var._mirrored_container() - assert mirrored_container is not None + if hasattr(var, "_distributed_container"): + distributed_container = var._distributed_container() + assert distributed_container is not None if context.executing_eagerly(): - return mirrored_container._unique_id - return mirrored_container._shared_name + return distributed_container._unique_id + return distributed_container._shared_name if context.executing_eagerly(): return var._unique_id return var.op.name diff --git a/tensorflow/python/training/optimizer.py b/tensorflow/python/training/optimizer.py index cae29eea93..fe9ffde11c 100644 --- a/tensorflow/python/training/optimizer.py +++ b/tensorflow/python/training/optimizer.py @@ -730,15 +730,15 @@ class Optimizer( if not named_slots: return None - if hasattr(var, "_mirrored_container"): + if hasattr(var, "_distributed_container"): # NOTE: If this isn't patched, then there is no `handle` in # `_resource_apply_dense`. - mirrored_container = var._mirrored_container() - assert mirrored_container is not None + distributed_container = var._distributed_container() + assert distributed_container is not None if context.executing_eagerly(): - key = mirrored_container._unique_id + key = distributed_container._unique_id else: - key = (mirrored_container.graph, mirrored_container._shared_name) + key = (distributed_container.graph, distributed_container._shared_name) # pylint: enable=protected-access mirrored_slot = named_slots.get(key, None) if mirrored_slot is None: return None @@ -839,7 +839,7 @@ class Optimizer( def _get_non_slot_variable(self, name, graph=None): non_slot = self._non_slot_dict.get((name, graph), None) - if hasattr(non_slot, "_mirrored_container"): + if hasattr(non_slot, "_distributed_container"): # This is a mirrored non-slot. In order to enable code like `_finish` # to assign to a non-slot, return the current context replica. return non_slot.get() -- GitLab From 8fd71423ed332f56bf73d28246a28abc64a664fe Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 21 Jun 2018 11:25:56 -0700 Subject: [PATCH 806/816] Add bfloat16 support for comparison CPU ops. PiperOrigin-RevId: 201557049 --- tensorflow/core/kernels/cwise_op_equal_to_1.cc | 4 ++-- tensorflow/core/kernels/cwise_op_greater.cc | 4 ++-- tensorflow/core/kernels/cwise_op_greater_equal.cc | 4 ++-- tensorflow/core/kernels/cwise_op_less.cc | 7 +++++-- tensorflow/core/kernels/cwise_op_less_equal.cc | 7 +++++-- tensorflow/core/kernels/cwise_op_not_equal_to_1.cc | 4 ++-- tensorflow/python/kernel_tests/cwise_ops_test.py | 5 ++++- 7 files changed, 22 insertions(+), 13 deletions(-) diff --git a/tensorflow/core/kernels/cwise_op_equal_to_1.cc b/tensorflow/core/kernels/cwise_op_equal_to_1.cc index ea10ebe9a0..931f59014b 100644 --- a/tensorflow/core/kernels/cwise_op_equal_to_1.cc +++ b/tensorflow/core/kernels/cwise_op_equal_to_1.cc @@ -16,8 +16,8 @@ limitations under the License. #include "tensorflow/core/kernels/cwise_ops_common.h" namespace tensorflow { -REGISTER6(BinaryOp, CPU, "Equal", functor::equal_to, float, Eigen::half, double, - uint8, int8, int16); +REGISTER7(BinaryOp, CPU, "Equal", functor::equal_to, float, Eigen::half, double, + uint8, int8, int16, bfloat16); REGISTER_KERNEL_BUILDER( Name("ApproximateEqual").Device(DEVICE_CPU).TypeConstraint("T"), ApproximateEqualOp); diff --git a/tensorflow/core/kernels/cwise_op_greater.cc b/tensorflow/core/kernels/cwise_op_greater.cc index a4ea408836..b385e9e545 100644 --- a/tensorflow/core/kernels/cwise_op_greater.cc +++ b/tensorflow/core/kernels/cwise_op_greater.cc @@ -16,8 +16,8 @@ limitations under the License. #include "tensorflow/core/kernels/cwise_ops_common.h" namespace tensorflow { -REGISTER8(BinaryOp, CPU, "Greater", functor::greater, float, Eigen::half, - double, int32, int64, uint8, int8, int16); +REGISTER9(BinaryOp, CPU, "Greater", functor::greater, float, Eigen::half, + double, int32, int64, uint8, int8, int16, bfloat16); #if GOOGLE_CUDA REGISTER7(BinaryOp, GPU, "Greater", functor::greater, float, Eigen::half, double, int64, uint8, int8, int16); diff --git a/tensorflow/core/kernels/cwise_op_greater_equal.cc b/tensorflow/core/kernels/cwise_op_greater_equal.cc index 3f34d6269e..8bfc018052 100644 --- a/tensorflow/core/kernels/cwise_op_greater_equal.cc +++ b/tensorflow/core/kernels/cwise_op_greater_equal.cc @@ -16,8 +16,8 @@ limitations under the License. #include "tensorflow/core/kernels/cwise_ops_common.h" namespace tensorflow { -REGISTER8(BinaryOp, CPU, "GreaterEqual", functor::greater_equal, float, - Eigen::half, double, int32, int64, uint8, int8, int16); +REGISTER9(BinaryOp, CPU, "GreaterEqual", functor::greater_equal, float, + Eigen::half, double, int32, int64, uint8, int8, int16, bfloat16); #if GOOGLE_CUDA REGISTER7(BinaryOp, GPU, "GreaterEqual", functor::greater_equal, float, Eigen::half, double, int64, uint8, int8, int16); diff --git a/tensorflow/core/kernels/cwise_op_less.cc b/tensorflow/core/kernels/cwise_op_less.cc index 575968126f..e369fdcf8a 100644 --- a/tensorflow/core/kernels/cwise_op_less.cc +++ b/tensorflow/core/kernels/cwise_op_less.cc @@ -16,8 +16,11 @@ limitations under the License. #include "tensorflow/core/kernels/cwise_ops_common.h" namespace tensorflow { -REGISTER9(BinaryOp, CPU, "Less", functor::less, float, Eigen::half, double, - bfloat16, int32, int64, uint8, int8, int16); +REGISTER5(BinaryOp, CPU, "Less", functor::less, float, Eigen::half, double, + bfloat16, int32); +REGISTER5(BinaryOp, CPU, "Less", functor::less, int64, uint8, int8, int16, + bfloat16); + #if GOOGLE_CUDA REGISTER7(BinaryOp, GPU, "Less", functor::less, float, Eigen::half, double, int64, uint8, int8, int16); diff --git a/tensorflow/core/kernels/cwise_op_less_equal.cc b/tensorflow/core/kernels/cwise_op_less_equal.cc index 499200d054..3353e117cd 100644 --- a/tensorflow/core/kernels/cwise_op_less_equal.cc +++ b/tensorflow/core/kernels/cwise_op_less_equal.cc @@ -16,8 +16,11 @@ limitations under the License. #include "tensorflow/core/kernels/cwise_ops_common.h" namespace tensorflow { -REGISTER9(BinaryOp, CPU, "LessEqual", functor::less_equal, float, Eigen::half, - bfloat16, double, int32, int64, uint8, int8, int16); +REGISTER5(BinaryOp, CPU, "LessEqual", functor::less_equal, float, Eigen::half, + bfloat16, double, int32); +REGISTER5(BinaryOp, CPU, "LessEqual", functor::less_equal, int64, uint8, int8, + int16, bfloat16); + #if GOOGLE_CUDA REGISTER7(BinaryOp, GPU, "LessEqual", functor::less_equal, float, Eigen::half, double, int64, uint8, int8, int16); diff --git a/tensorflow/core/kernels/cwise_op_not_equal_to_1.cc b/tensorflow/core/kernels/cwise_op_not_equal_to_1.cc index 935619711c..9f1e575805 100644 --- a/tensorflow/core/kernels/cwise_op_not_equal_to_1.cc +++ b/tensorflow/core/kernels/cwise_op_not_equal_to_1.cc @@ -16,8 +16,8 @@ limitations under the License. #include "tensorflow/core/kernels/cwise_ops_common.h" namespace tensorflow { -REGISTER6(BinaryOp, CPU, "NotEqual", functor::not_equal_to, float, Eigen::half, - double, uint8, int8, int16); +REGISTER7(BinaryOp, CPU, "NotEqual", functor::not_equal_to, float, Eigen::half, + double, uint8, int8, int16, bfloat16); #if GOOGLE_CUDA REGISTER4(BinaryOp, GPU, "NotEqual", functor::not_equal_to, float, Eigen::half, double, uint8); diff --git a/tensorflow/python/kernel_tests/cwise_ops_test.py b/tensorflow/python/kernel_tests/cwise_ops_test.py index ccd05a8820..b61232cded 100644 --- a/tensorflow/python/kernel_tests/cwise_ops_test.py +++ b/tensorflow/python/kernel_tests/cwise_ops_test.py @@ -96,7 +96,8 @@ class UnaryOpTest(test.TestCase): np_ans = np_func(x) with self.test_session(use_gpu=False): inx = ops.convert_to_tensor(x) - if x.dtype in (np.float32, np.float64): + if x.dtype in (np.float32, np.float64, + dtypes_lib.bfloat16.as_numpy_dtype): y = 1.1 * tf_func(inx) np_ans *= 1.1 else: @@ -105,6 +106,8 @@ class UnaryOpTest(test.TestCase): self.assertShapeEqual(np_ans, y) if x.dtype == np.float16: self.assertAllClose(np_ans, tf_cpu, rtol=1e-3, atol=1e-3) + elif x.dtype == dtypes_lib.bfloat16.as_numpy_dtype: + self.assertAllClose(np_ans, tf_cpu, rtol=1e-2, atol=1e-2) else: self.assertAllClose(np_ans, tf_cpu) -- GitLab From 1c8b56c4f273eced99ffc2dff158f749c7c2d98e Mon Sep 17 00:00:00 2001 From: Shashi Shekhar Date: Thu, 21 Jun 2018 11:28:48 -0700 Subject: [PATCH 807/816] Refactor benchmarking parameters. PiperOrigin-RevId: 201557579 --- tensorflow/contrib/lite/tools/benchmark/BUILD | 11 ++ .../lite/tools/benchmark/benchmark_model.cc | 52 ++++++--- .../lite/tools/benchmark/benchmark_model.h | 22 ++-- .../lite/tools/benchmark/benchmark_params.cc | 57 ++++++++++ .../lite/tools/benchmark/benchmark_params.h | 101 ++++++++++++++++++ .../tools/benchmark/benchmark_tflite_model.cc | 54 +++++++--- .../tools/benchmark/benchmark_tflite_model.h | 11 +- .../tools/benchmark/command_line_flags.cc | 64 +++++------ .../lite/tools/benchmark/command_line_flags.h | 27 +++-- .../benchmark/command_line_flags_test.cc | 43 ++++---- 10 files changed, 335 insertions(+), 107 deletions(-) create mode 100644 tensorflow/contrib/lite/tools/benchmark/benchmark_params.cc create mode 100644 tensorflow/contrib/lite/tools/benchmark/benchmark_params.h diff --git a/tensorflow/contrib/lite/tools/benchmark/BUILD b/tensorflow/contrib/lite/tools/benchmark/BUILD index 8857062c00..183a545295 100644 --- a/tensorflow/contrib/lite/tools/benchmark/BUILD +++ b/tensorflow/contrib/lite/tools/benchmark/BUILD @@ -66,6 +66,16 @@ cc_library( ], ) +cc_library( + name = "benchmark_params", + srcs = [ + "benchmark_params.cc", + "logging.h", + ], + hdrs = ["benchmark_params.h"], + copts = common_copts, +) + cc_library( name = "benchmark_model_lib", srcs = [ @@ -75,6 +85,7 @@ cc_library( hdrs = ["benchmark_model.h"], copts = common_copts, deps = [ + ":benchmark_params", ":command_line_flags", "//tensorflow/contrib/lite:framework", "//tensorflow/contrib/lite:string_util", diff --git a/tensorflow/contrib/lite/tools/benchmark/benchmark_model.cc b/tensorflow/contrib/lite/tools/benchmark/benchmark_model.cc index a8a9a6112c..08648bcfe2 100644 --- a/tensorflow/contrib/lite/tools/benchmark/benchmark_model.cc +++ b/tensorflow/contrib/lite/tools/benchmark/benchmark_model.cc @@ -48,6 +48,19 @@ namespace tflite { namespace benchmark { using tensorflow::Stat; +BenchmarkParams BenchmarkModel::DefaultParams() { + BenchmarkParams params; + params.AddParam("num_runs", BenchmarkParam::Create(50)); + params.AddParam("run_delay", BenchmarkParam::Create(-1.0f)); + params.AddParam("num_threads", BenchmarkParam::Create(1)); + params.AddParam("benchmark_name", BenchmarkParam::Create("")); + params.AddParam("output_prefix", BenchmarkParam::Create("")); + params.AddParam("warmup_runs", BenchmarkParam::Create(1)); + return params; +} + +BenchmarkModel::BenchmarkModel() : params_(DefaultParams()) {} + void BenchmarkLoggingListener::OnBenchmarkEnd(const BenchmarkResults &results) { auto inference_us = results.inference_time_us(); auto init_us = results.startup_latency_us(); @@ -60,24 +73,29 @@ void BenchmarkLoggingListener::OnBenchmarkEnd(const BenchmarkResults &results) { std::vector BenchmarkModel::GetFlags() { return { - Flag("num_runs", ¶ms_.num_runs, "number of runs"), - Flag("run_delay", ¶ms_.run_delay, "delay between runs in seconds"), - Flag("num_threads", ¶ms_.num_threads, "number of threads"), - Flag("benchmark_name", ¶ms_.benchmark_name, "benchmark name"), - Flag("output_prefix", ¶ms_.output_prefix, "benchmark output prefix"), - Flag("warmup_runs", ¶ms_.warmup_runs, - "how many runs to initialize model"), + CreateFlag("num_runs", ¶ms_, "number of runs"), + CreateFlag("run_delay", ¶ms_, "delay between runs in seconds"), + CreateFlag("num_threads", ¶ms_, "number of threads"), + CreateFlag("benchmark_name", ¶ms_, "benchmark name"), + CreateFlag("output_prefix", ¶ms_, + "benchmark output prefix"), + CreateFlag("warmup_runs", ¶ms_, + "how many runs to initialize model"), }; } void BenchmarkModel::LogFlags() { - TFLITE_LOG(INFO) << "Num runs: [" << params_.num_runs << "]"; - TFLITE_LOG(INFO) << "Inter-run delay (seconds): [" << params_.run_delay + TFLITE_LOG(INFO) << "Num runs: [" << params_.Get("num_runs") << "]"; + TFLITE_LOG(INFO) << "Inter-run delay (seconds): [" + << params_.Get("run_delay") << "]"; + TFLITE_LOG(INFO) << "Num threads: [" << params_.Get("num_threads") + << "]"; + TFLITE_LOG(INFO) << "Benchmark name: [" + << params_.Get("benchmark_name") << "]"; + TFLITE_LOG(INFO) << "Output prefix: [" + << params_.Get("output_prefix") << "]"; + TFLITE_LOG(INFO) << "Warmup runs: [" << params_.Get("warmup_runs") << "]"; - TFLITE_LOG(INFO) << "Num threads: [" << params_.num_threads << "]"; - TFLITE_LOG(INFO) << "Benchmark name: [" << params_.benchmark_name << "]"; - TFLITE_LOG(INFO) << "Output prefix: [" << params_.output_prefix << "]"; - TFLITE_LOG(INFO) << "Warmup runs: [" << params_.warmup_runs << "]"; } Stat BenchmarkModel::Run(int num_times, RunType run_type) { @@ -91,7 +109,7 @@ Stat BenchmarkModel::Run(int num_times, RunType run_type) { listeners_.OnSingleRunEnd(); run_stats.UpdateStat(end_us - start_us); - SleepForSeconds(params_.run_delay); + SleepForSeconds(params_.Get("run_delay")); } std::stringstream stream; @@ -117,8 +135,10 @@ void BenchmarkModel::Run(int argc, char **argv) { << "ms"; uint64_t input_bytes = ComputeInputBytes(); - Stat warmup_time_us = Run(params_.warmup_runs, WARMUP); - Stat inference_time_us = Run(params_.num_runs, REGULAR); + Stat warmup_time_us = + Run(params_.Get("warmup_runs"), WARMUP); + Stat inference_time_us = + Run(params_.Get("num_runs"), REGULAR); listeners_.OnBenchmarkEnd( {startup_latency_us, input_bytes, warmup_time_us, inference_time_us}); } diff --git a/tensorflow/contrib/lite/tools/benchmark/benchmark_model.h b/tensorflow/contrib/lite/tools/benchmark/benchmark_model.h index d48f693693..942e21f67a 100644 --- a/tensorflow/contrib/lite/tools/benchmark/benchmark_model.h +++ b/tensorflow/contrib/lite/tools/benchmark/benchmark_model.h @@ -23,6 +23,7 @@ limitations under the License. #include #include +#include "tensorflow/contrib/lite/tools/benchmark/benchmark_params.h" #include "tensorflow/contrib/lite/tools/benchmark/command_line_flags.h" #include "tensorflow/core/util/stats_calculator.h" @@ -63,17 +64,6 @@ class BenchmarkResults { tensorflow::Stat inference_time_us_; }; -struct BenchmarkParams { - BenchmarkParams() - : num_runs(50), warmup_runs(1), run_delay(-1.0), num_threads(1) {} - int num_runs; - int warmup_runs; - float run_delay; - int num_threads; - std::string benchmark_name; - std::string output_prefix; -}; - class BenchmarkListener { public: virtual void OnBenchmarkStart(const BenchmarkParams& params) {} @@ -130,12 +120,22 @@ class BenchmarkLoggingListener : public BenchmarkListener { void OnBenchmarkEnd(const BenchmarkResults& results) override; }; +template +Flag CreateFlag(const char* name, BenchmarkParams* params, + const std::string& usage) { + return Flag(name, [params, name](const T& val) { params->Set(name, val); }, + params->Get(name), usage); +} + // Benchmarks a model. // // Subclasses need to implement initialization and running of the model. // The results can be collected by adding BenchmarkListener(s). class BenchmarkModel { public: + static BenchmarkParams DefaultParams(); + BenchmarkModel(); + BenchmarkModel(BenchmarkParams params) : params_(std::move(params)) {} virtual ~BenchmarkModel() {} bool ParseFlags(int argc, char** argv); virtual void Init() = 0; diff --git a/tensorflow/contrib/lite/tools/benchmark/benchmark_params.cc b/tensorflow/contrib/lite/tools/benchmark/benchmark_params.cc new file mode 100644 index 0000000000..1dcf580a9d --- /dev/null +++ b/tensorflow/contrib/lite/tools/benchmark/benchmark_params.cc @@ -0,0 +1,57 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/lite/tools/benchmark/benchmark_params.h" + +#include +#include +#include + +#include "tensorflow/contrib/lite/tools/benchmark/logging.h" + +namespace tflite { +namespace benchmark { + +void BenchmarkParam::AssertHasSameType(BenchmarkParam::ParamType a, + BenchmarkParam::ParamType b) { + TFLITE_BENCHMARK_CHECK(a == b) << "Type mismatch while accessing parameter."; +} + +template <> +BenchmarkParam::ParamType BenchmarkParam::GetValueType() { + return BenchmarkParam::ParamType::TYPE_INT32; +} + +template <> +BenchmarkParam::ParamType BenchmarkParam::GetValueType() { + return BenchmarkParam::ParamType::TYPE_BOOL; +} + +template <> +BenchmarkParam::ParamType BenchmarkParam::GetValueType() { + return BenchmarkParam::ParamType::TYPE_FLOAT; +} + +template <> +BenchmarkParam::ParamType BenchmarkParam::GetValueType() { + return BenchmarkParam::ParamType::TYPE_STRING; +} + +void BenchmarkParams::AssertParamExists(const std::string& name) const { + TFLITE_BENCHMARK_CHECK(HasParam(name)) << name << " was not found."; +} + +} // namespace benchmark +} // namespace tflite diff --git a/tensorflow/contrib/lite/tools/benchmark/benchmark_params.h b/tensorflow/contrib/lite/tools/benchmark/benchmark_params.h new file mode 100644 index 0000000000..33448dd162 --- /dev/null +++ b/tensorflow/contrib/lite/tools/benchmark/benchmark_params.h @@ -0,0 +1,101 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_CONTRIB_LITE_TOOLS_BENCHMARK_BENCHMARK_PARAMS_H_ +#define TENSORFLOW_CONTRIB_LITE_TOOLS_BENCHMARK_BENCHMARK_PARAMS_H_ +#include +#include +#include +#include + +#include "tensorflow/contrib/lite/tools/benchmark/logging.h" + +namespace tflite { +namespace benchmark { + +template +class TypedBenchmarkParam; + +class BenchmarkParam { + protected: + enum class ParamType { TYPE_INT32, TYPE_FLOAT, TYPE_BOOL, TYPE_STRING }; + + public: + template + static std::unique_ptr Create(const T& default_value) { + return std::unique_ptr( + new TypedBenchmarkParam(default_value)); + } + + template + TypedBenchmarkParam* AsTyped() { + AssertHasSameType(GetValueType(), type_); + return static_cast*>(this); + } + virtual ~BenchmarkParam() {} + BenchmarkParam(ParamType type) : type_(type) {} + + private: + static void AssertHasSameType(ParamType a, ParamType b); + template + static ParamType GetValueType(); + + const ParamType type_; +}; + +template +class TypedBenchmarkParam : public BenchmarkParam { + public: + TypedBenchmarkParam(const T& value) + : BenchmarkParam(GetValueType()), value_(value) {} + void Set(const T& value) { value_ = value; } + + T Get() { return value_; } + + private: + T value_; +}; + +class BenchmarkParams { + public: + void AddParam(const std::string& name, + std::unique_ptr value) { + params_[name] = std::move(value); + } + + bool HasParam(const std::string& name) const { + return params_.find(name) != params_.end(); + } + + template + void Set(const std::string& name, const T& value) { + AssertParamExists(name); + params_.at(name)->AsTyped()->Set(value); + } + + template + T Get(const std::string& name) const { + AssertParamExists(name); + return params_.at(name)->AsTyped()->Get(); + } + + private: + void AssertParamExists(const std::string& name) const; + std::unordered_map> params_; +}; + +} // namespace benchmark +} // namespace tflite +#endif // TENSORFLOW_CONTRIB_LITE_TOOLS_BENCHMARK_BENCHMARK_PARAMS_H_ diff --git a/tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.cc b/tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.cc index 5f803cec19..73affc26b0 100644 --- a/tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.cc +++ b/tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.cc @@ -162,15 +162,37 @@ bool PopulateInputLayerInfo( return true; } +BenchmarkParams GetDefaultParams() { + BenchmarkParams default_params = BenchmarkModel::DefaultParams(); + default_params.AddParam("graph", BenchmarkParam::Create("")); + default_params.AddParam("input_layer", + BenchmarkParam::Create("")); + default_params.AddParam("input_layer_shape", + BenchmarkParam::Create("")); + default_params.AddParam("use_nnapi", BenchmarkParam::Create(false)); + return default_params; +} + } // namespace +BenchmarkTfLiteModel::BenchmarkTfLiteModel() + : BenchmarkModel(GetDefaultParams()) { + AddListener(&profiling_listener_); +} + +BenchmarkTfLiteModel::BenchmarkTfLiteModel(BenchmarkParams params) + : BenchmarkModel(std::move(params)) { + AddListener(&profiling_listener_); +} + std::vector BenchmarkTfLiteModel::GetFlags() { std::vector flags = BenchmarkTfLiteModel::BenchmarkModel::GetFlags(); std::vector specific_flags = { - Flag("graph", &graph, "graph file name"), - Flag("input_layer", &input_layer_string, "input layer names"), - Flag("input_layer_shape", &input_layer_shape_string, "input layer shape"), - Flag("use_nnapi", &use_nnapi, "use nnapi api")}; + CreateFlag("graph", ¶ms_, "graph file name"), + CreateFlag("input_layer", ¶ms_, "input layer names"), + CreateFlag("input_layer_shape", ¶ms_, + "input layer shape"), + CreateFlag("use_nnapi", ¶ms_, "use nnapi api")}; flags.insert(flags.end(), specific_flags.begin(), specific_flags.end()); return flags; @@ -178,19 +200,22 @@ std::vector BenchmarkTfLiteModel::GetFlags() { void BenchmarkTfLiteModel::LogFlags() { BenchmarkModel::LogFlags(); - TFLITE_LOG(INFO) << "Graph: [" << graph << "]"; - TFLITE_LOG(INFO) << "Input layers: [" << input_layer_string << "]"; - TFLITE_LOG(INFO) << "Input shapes: [" << input_layer_shape_string << "]"; - TFLITE_LOG(INFO) << "Use nnapi : [" << use_nnapi << "]"; + TFLITE_LOG(INFO) << "Graph: [" << params_.Get("graph") << "]"; + TFLITE_LOG(INFO) << "Input layers: [" + << params_.Get("input_layer") << "]"; + TFLITE_LOG(INFO) << "Input shapes: [" + << params_.Get("input_layer_shape") << "]"; + TFLITE_LOG(INFO) << "Use nnapi : [" << params_.Get("use_nnapi") << "]"; } bool BenchmarkTfLiteModel::ValidateFlags() { - if (graph.empty()) { + if (params_.Get("graph").empty()) { TFLITE_LOG(ERROR) << "Please specify the name of your TF Lite input file with --graph"; return false; } - return PopulateInputLayerInfo(input_layer_string, input_layer_shape_string, + return PopulateInputLayerInfo(params_.Get("input_layer"), + params_.Get("input_layer_shape"), &inputs); } @@ -205,6 +230,7 @@ uint64_t BenchmarkTfLiteModel::ComputeInputBytes() { } void BenchmarkTfLiteModel::Init() { + std::string graph = params_.Get("graph"); model = tflite::FlatBufferModel::BuildFromFile(graph.c_str()); if (!model) { TFLITE_LOG(FATAL) << "Failed to mmap model " << graph; @@ -226,10 +252,14 @@ void BenchmarkTfLiteModel::Init() { } profiling_listener_.SetInterpreter(interpreter.get()); - if (params_.num_threads != -1) { - interpreter->SetNumThreads(params_.num_threads); + const int32_t num_threads = params_.Get("num_threads"); + + if (num_threads != -1) { + interpreter->SetNumThreads(num_threads); } + bool use_nnapi = params_.Get("use_nnapi"); + interpreter->UseNNAPI(use_nnapi); auto interpreter_inputs = interpreter->inputs(); diff --git a/tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.h b/tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.h index ffb93da964..50cc3f24b3 100644 --- a/tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.h +++ b/tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.h @@ -50,9 +50,8 @@ class ProfilingListener : public BenchmarkListener { // Benchmarks a TFLite model by running tflite interpreter. class BenchmarkTfLiteModel : public BenchmarkModel { public: - BenchmarkTfLiteModel() : use_nnapi(false) { - AddListener(&profiling_listener_); - } + BenchmarkTfLiteModel(); + BenchmarkTfLiteModel(BenchmarkParams params); std::vector GetFlags() override; void LogFlags() override; @@ -70,13 +69,7 @@ class BenchmarkTfLiteModel : public BenchmarkModel { private: std::unique_ptr model; std::unique_ptr interpreter; - std::string graph; - std::string input_layer_string; - std::string input_layer_type_string; - std::string input_layer_shape_string; - std::string input_layer_values_string; std::vector inputs; - bool use_nnapi; ProfilingListener profiling_listener_; }; diff --git a/tensorflow/contrib/lite/tools/benchmark/command_line_flags.cc b/tensorflow/contrib/lite/tools/benchmark/command_line_flags.cc index 8195fc44be..ff818b9dcb 100644 --- a/tensorflow/contrib/lite/tools/benchmark/command_line_flags.cc +++ b/tensorflow/contrib/lite/tools/benchmark/command_line_flags.cc @@ -15,6 +15,7 @@ limitations under the License. #include #include #include +#include #include namespace tflite { @@ -44,76 +45,79 @@ bool ParseFlag(const std::string& arg, const std::string& flag, } template -bool ParseFlag(const std::string& flag_value, T* value) { +bool ParseFlag(const std::string& flag_value, + const std::function& hook) { std::istringstream stream(flag_value); T read_value; stream >> read_value; if (!stream.eof() && !stream.good()) { return false; } - *value = read_value; + hook(read_value); return true; } -bool ParseBoolFlag(const std::string& flag_value, bool* value) { +bool ParseBoolFlag(const std::string& flag_value, + const std::function& hook) { if (flag_value != "true" && flag_value != "false") { return false; } - *value = (flag_value == "true"); + hook(flag_value == "true"); return true; } - -bool ParseStringFlag(const std::string& flag_value, std::string* value) { - *value = flag_value; - return true; -} - } // namespace -Flag::Flag(const char* name, int32_t* dst, const std::string& usage_text) +Flag::Flag(const char* name, const std::function& hook, + int32_t default_value, const std::string& usage_text) : name_(name), type_(TYPE_INT32), - value_hook_([dst](const std::string& flag_value) { - return ParseFlag(flag_value, dst); + value_hook_([hook](const std::string& flag_value) { + return ParseFlag(flag_value, hook); }), - default_for_display_(ToString(*dst)), + default_for_display_(ToString(default_value)), usage_text_(usage_text) {} -Flag::Flag(const char* name, int64_t* dst, const std::string& usage_text) +Flag::Flag(const char* name, const std::function& hook, + int64_t default_value, const std::string& usage_text) : name_(name), type_(TYPE_INT64), - value_hook_([dst](const std::string& flag_value) { - return ParseFlag(flag_value, dst); + value_hook_([hook](const std::string& flag_value) { + return ParseFlag(flag_value, hook); }), - default_for_display_(ToString(*dst)), + default_for_display_(ToString(default_value)), usage_text_(usage_text) {} -Flag::Flag(const char* name, float* dst, const std::string& usage_text) +Flag::Flag(const char* name, const std::function& hook, + float default_value, const std::string& usage_text) : name_(name), type_(TYPE_FLOAT), - value_hook_([dst](const std::string& flag_value) { - return ParseFlag(flag_value, dst); + value_hook_([hook](const std::string& flag_value) { + return ParseFlag(flag_value, hook); }), - default_for_display_(ToString(*dst)), + default_for_display_(ToString(default_value)), usage_text_(usage_text) {} -Flag::Flag(const char* name, bool* dst, const std::string& usage_text) +Flag::Flag(const char* name, const std::function& hook, + bool default_value, const std::string& usage_text) : name_(name), type_(TYPE_BOOL), - value_hook_([dst](const std::string& flag_value) { - return ParseBoolFlag(flag_value, dst); + value_hook_([hook](const std::string& flag_value) { + return ParseBoolFlag(flag_value, hook); }), - default_for_display_((*dst) ? "true" : "false"), + default_for_display_(default_value ? "true" : "false"), usage_text_(usage_text) {} -Flag::Flag(const char* name, std::string* dst, const std::string& usage_text) +Flag::Flag(const char* name, + const std::function& hook, + const std::string& default_value, const std::string& usage_text) : name_(name), type_(TYPE_STRING), - value_hook_([dst](const std::string& flag_value) { - return ParseStringFlag(flag_value, dst); + value_hook_([hook](const std::string& flag_value) { + hook(flag_value); + return true; }), - default_for_display_(*dst), + default_for_display_(default_value), usage_text_(usage_text) {} bool Flag::Parse(const std::string& arg, bool* value_parsing_ok) const { diff --git a/tensorflow/contrib/lite/tools/benchmark/command_line_flags.h b/tensorflow/contrib/lite/tools/benchmark/command_line_flags.h index 36f9e64767..2e514ae3ea 100644 --- a/tensorflow/contrib/lite/tools/benchmark/command_line_flags.h +++ b/tensorflow/contrib/lite/tools/benchmark/command_line_flags.h @@ -33,10 +33,11 @@ namespace tflite { // int some_int = 10; // bool some_switch = false; // std::string some_name = "something"; +// // std::vector flag_list = { -// Flag("some_int", &some_int, "an integer that affects X"), -// Flag("some_switch", &some_switch, "a bool that affects Y"), -// Flag("some_name", &some_name, "a std::string that affects Z") +// Flag::CreateFlag("some_int", &some_int, "an integer that affects X"), +// Flag::CreateFlag("some_switch", &some_switch, "a bool that affects Y"), +// Flag::CreateFlag("some_name", &some_name, "a string that affects Z") // }; // // Get usage message before ParseFlags() to capture default values. // std::string usage = Flag::Usage(argv[0], flag_list); @@ -63,11 +64,21 @@ namespace tflite { // text, and a pointer to the corresponding variable. class Flag { public: - Flag(const char* name, int32_t* dst, const std::string& usage_text); - Flag(const char* name, int64_t* dst, const std::string& usage_text); - Flag(const char* name, bool* dst, const std::string& usage_text); - Flag(const char* name, std::string* dst, const std::string& usage_text); - Flag(const char* name, float* dst, const std::string& usage_text); + template + static Flag CreateFlag(const char* name, T* val, const char* usage) { + return Flag(name, [val](const T& v) { *val = v; }, *val, usage); + } + + Flag(const char* name, const std::function& hook, + int32_t default_value, const std::string& usage_text); + Flag(const char* name, const std::function& hook, + int64_t default_value, const std::string& usage_text); + Flag(const char* name, const std::function& hook, + float default_value, const std::string& usage_text); + Flag(const char* name, const std::function& hook, + bool default_value, const std::string& usage_text); + Flag(const char* name, const std::function& hook, + const std::string& default_value, const std::string& usage_text); private: friend class Flags; diff --git a/tensorflow/contrib/lite/tools/benchmark/command_line_flags_test.cc b/tensorflow/contrib/lite/tools/benchmark/command_line_flags_test.cc index 620d61b027..03da805109 100644 --- a/tensorflow/contrib/lite/tools/benchmark/command_line_flags_test.cc +++ b/tensorflow/contrib/lite/tools/benchmark/command_line_flags_test.cc @@ -34,15 +34,15 @@ TEST(CommandLineFlagsTest, BasicUsage) { "--some_name=somethingelse", "--some_float=42.0"}; int argc = 6; - bool parsed_ok = - Flags::Parse(&argc, reinterpret_cast(argv_strings), - { - Flag("some_int32", &some_int32, "some int32"), - Flag("some_int64", &some_int64, "some int64"), - Flag("some_switch", &some_switch, "some switch"), - Flag("some_name", &some_name, "some name"), - Flag("some_float", &some_float, "some float"), - }); + bool parsed_ok = Flags::Parse( + &argc, reinterpret_cast(argv_strings), + { + Flag::CreateFlag("some_int32", &some_int32, "some int32"), + Flag::CreateFlag("some_int64", &some_int64, "some int64"), + Flag::CreateFlag("some_switch", &some_switch, "some switch"), + Flag::CreateFlag("some_name", &some_name, "some name"), + Flag::CreateFlag("some_float", &some_float, "some float"), + }); EXPECT_EQ(true, parsed_ok); EXPECT_EQ(20, some_int32); @@ -57,9 +57,9 @@ TEST(CommandLineFlagsTest, EmptyStringFlag) { int argc = 2; std::string some_string = "invalid"; const char* argv_strings[] = {"program_name", "--some_string="}; - bool parsed_ok = - Flags::Parse(&argc, reinterpret_cast(argv_strings), - {Flag("some_string", &some_string, "some string")}); + bool parsed_ok = Flags::Parse( + &argc, reinterpret_cast(argv_strings), + {Flag::CreateFlag("some_string", &some_string, "some string")}); EXPECT_EQ(true, parsed_ok); EXPECT_EQ(some_string, ""); @@ -72,7 +72,7 @@ TEST(CommandLineFlagsTest, BadIntValue) { const char* argv_strings[] = {"program_name", "--some_int=notanumber"}; bool parsed_ok = Flags::Parse(&argc, reinterpret_cast(argv_strings), - {Flag("some_int", &some_int, "some int")}); + {Flag::CreateFlag("some_int", &some_int, "some int")}); EXPECT_EQ(false, parsed_ok); EXPECT_EQ(10, some_int); @@ -83,9 +83,9 @@ TEST(CommandLineFlagsTest, BadBoolValue) { bool some_switch = false; int argc = 2; const char* argv_strings[] = {"program_name", "--some_switch=notabool"}; - bool parsed_ok = - Flags::Parse(&argc, reinterpret_cast(argv_strings), - {Flag("some_switch", &some_switch, "some switch")}); + bool parsed_ok = Flags::Parse( + &argc, reinterpret_cast(argv_strings), + {Flag::CreateFlag("some_switch", &some_switch, "some switch")}); EXPECT_EQ(false, parsed_ok); EXPECT_EQ(false, some_switch); @@ -98,7 +98,7 @@ TEST(CommandLineFlagsTest, BadFloatValue) { const char* argv_strings[] = {"program_name", "--some_float=notanumber"}; bool parsed_ok = Flags::Parse(&argc, reinterpret_cast(argv_strings), - {Flag("some_float", &some_float, "some float")}); + {Flag::CreateFlag("some_float", &some_float, "some float")}); EXPECT_EQ(false, parsed_ok); EXPECT_NEAR(-23.23f, some_float, 1e-5f); @@ -136,10 +136,11 @@ TEST(CommandLineFlagsTest, UsageString) { // match against, and we don't want a flakey test. const std::string tool_name = "some_tool_name"; std::string usage = Flags::Usage( - tool_name + " ", {Flag("some_int", &some_int, "some int"), - Flag("some_int64", &some_int64, "some int64"), - Flag("some_switch", &some_switch, "some switch"), - Flag("some_name", &some_name, "some name")}); + tool_name + " ", + {Flag::CreateFlag("some_int", &some_int, "some int"), + Flag::CreateFlag("some_int64", &some_int64, "some int64"), + Flag::CreateFlag("some_switch", &some_switch, "some switch"), + Flag::CreateFlag("some_name", &some_name, "some name")}); // Match the usage message, being sloppy about whitespace. const char* expected_usage = " usage: some_tool_name \n" -- GitLab From 9634d6c2db1cde1d6c5a1204096b07fd12b369ec Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 21 Jun 2018 11:46:34 -0700 Subject: [PATCH 808/816] Adds weights to streaming_dynamic_auc in Tensorflow contrib metrics. PiperOrigin-RevId: 201560555 --- .../contrib/metrics/python/ops/metric_ops.py | 76 ++++++++++++++----- .../metrics/python/ops/metric_ops_test.py | 38 ++++++++++ 2 files changed, 96 insertions(+), 18 deletions(-) diff --git a/tensorflow/contrib/metrics/python/ops/metric_ops.py b/tensorflow/contrib/metrics/python/ops/metric_ops.py index a6be2084aa..b14202ff9e 100644 --- a/tensorflow/contrib/metrics/python/ops/metric_ops.py +++ b/tensorflow/contrib/metrics/python/ops/metric_ops.py @@ -1064,7 +1064,7 @@ def streaming_auc(predictions, name=name) -def _compute_dynamic_auc(labels, predictions, curve='ROC'): +def _compute_dynamic_auc(labels, predictions, curve='ROC', weights=None): """Computes the apporixmate AUC by a Riemann sum with data-derived thresholds. Computes the area under the ROC or PR curve using each prediction as a @@ -1077,13 +1077,22 @@ def _compute_dynamic_auc(labels, predictions, curve='ROC'): predictions: A 1-D `Tensor` of predictions whose values are `float64`. curve: The name of the curve to be computed, 'ROC' for the Receiving Operating Characteristic or 'PR' for the Precision-Recall curve. + weights: A 1-D `Tensor` of weights whose values are `float64`. Returns: A scalar `Tensor` containing the area-under-curve value for the input. """ - # Count the total number of positive and negative labels in the input. + # Compute the total weight and the total positive weight. size = array_ops.size(predictions) - total_positive = math_ops.cast(math_ops.reduce_sum(labels), dtypes.int32) + if weights is None: + weights = array_ops.ones_like(labels, dtype=dtypes.float64) + labels, predictions, weights = metrics_impl._remove_squeezable_dimensions( + labels, predictions, weights) + total_weight = math_ops.reduce_sum(weights) + total_positive = math_ops.reduce_sum( + array_ops.where( + math_ops.greater(labels, 0), weights, + array_ops.zeros_like(labels, dtype=dtypes.float64))) def continue_computing_dynamic_auc(): """Continues dynamic auc computation, entered if labels are not all equal. @@ -1091,9 +1100,11 @@ def _compute_dynamic_auc(labels, predictions, curve='ROC'): Returns: A scalar `Tensor` containing the area-under-curve value. """ - # Sort the predictions descending, and the corresponding labels as well. + # Sort the predictions descending, keeping the same order for the + # corresponding labels and weights. ordered_predictions, indices = nn.top_k(predictions, k=size) ordered_labels = array_ops.gather(labels, indices) + ordered_weights = array_ops.gather(weights, indices) # Get the counts of the unique ordered predictions. _, _, counts = array_ops.unique_with_counts(ordered_predictions) @@ -1103,23 +1114,39 @@ def _compute_dynamic_auc(labels, predictions, curve='ROC'): array_ops.pad(math_ops.cumsum(counts), paddings=[[1, 0]]), dtypes.int32) # Count the positives to the left of the split indices. - positives = math_ops.cast( - array_ops.pad(math_ops.cumsum(ordered_labels), paddings=[[1, 0]]), - dtypes.int32) - true_positives = array_ops.gather(positives, splits) + true_positives = array_ops.gather( + array_ops.pad( + math_ops.cumsum( + array_ops.where( + math_ops.greater(ordered_labels, 0), ordered_weights, + array_ops.zeros_like(ordered_labels, + dtype=dtypes.float64))), + paddings=[[1, 0]]), splits) if curve == 'ROC': - # Count the negatives to the left of every split point and the total - # number of negatives for computing the FPR. - false_positives = math_ops.subtract(splits, true_positives) - total_negative = size - total_positive + # Compute the weight of the negatives to the left of every split point and + # the total weight of the negatives number of negatives for computing the + # FPR. + false_positives = array_ops.gather( + array_ops.pad( + math_ops.cumsum( + array_ops.where( + math_ops.less(ordered_labels, 1), ordered_weights, + array_ops.zeros_like( + ordered_labels, dtype=dtypes.float64))), + paddings=[[1, 0]]), splits) + total_negative = total_weight - total_positive x_axis_values = math_ops.truediv(false_positives, total_negative) y_axis_values = math_ops.truediv(true_positives, total_positive) elif curve == 'PR': x_axis_values = math_ops.truediv(true_positives, total_positive) # For conformance, set precision to 1 when the number of positive # classifications is 0. + positives = array_ops.gather( + array_ops.pad(math_ops.cumsum(ordered_weights), paddings=[[1, 0]]), + splits) y_axis_values = array_ops.where( - math_ops.greater(splits, 0), math_ops.truediv(true_positives, splits), + math_ops.greater(splits, 0), + math_ops.truediv(true_positives, positives), array_ops.ones_like(true_positives, dtype=dtypes.float64)) # Calculate trapezoid areas. @@ -1133,7 +1160,7 @@ def _compute_dynamic_auc(labels, predictions, curve='ROC'): return control_flow_ops.cond( math_ops.logical_or( math_ops.equal(total_positive, 0), math_ops.equal( - total_positive, size)), + total_positive, total_weight)), true_fn=lambda: array_ops.constant(0, dtypes.float64), false_fn=continue_computing_dynamic_auc) @@ -1143,7 +1170,8 @@ def streaming_dynamic_auc(labels, curve='ROC', metrics_collections=(), updates_collections=(), - name=None): + name=None, + weights=None): """Computes the apporixmate AUC by a Riemann sum with data-derived thresholds. USAGE NOTE: this approach requires storing all of the predictions and labels @@ -1168,6 +1196,8 @@ def streaming_dynamic_auc(labels, should be added to. name: An optional name for the variable_scope that contains the metric variables. + weights: A 'Tensor' of non-negative weights whose values are castable to + `float64`. Will be flattened into a 1-D `Tensor`. Returns: auc: A scalar `Tensor` containing the current area-under-curve value. @@ -1195,14 +1225,24 @@ def streaming_dynamic_auc(labels, check_ops.assert_less_equal( labels, array_ops.ones_like(labels, dtypes.int64), - message='labels must be 0 or 1, at least one is >1') + message='labels must be 0 or 1, at least one is >1'), ]): preds_accum, update_preds = streaming_concat( predictions, name='concat_preds') labels_accum, update_labels = streaming_concat( labels, name='concat_labels') - update_op = control_flow_ops.group(update_labels, update_preds) - auc = _compute_dynamic_auc(labels_accum, preds_accum, curve=curve) + if weights is not None: + weights = array_ops.reshape( + math_ops.cast(weights, dtypes.float64), [-1]) + weights_accum, update_weights = streaming_concat( + weights, name='concat_weights') + update_op = control_flow_ops.group(update_labels, update_preds, + update_weights) + else: + weights_accum = None + update_op = control_flow_ops.group(update_labels, update_preds) + auc = _compute_dynamic_auc( + labels_accum, preds_accum, curve=curve, weights=weights_accum) if updates_collections: ops.add_to_collections(updates_collections, update_op) if metrics_collections: diff --git a/tensorflow/contrib/metrics/python/ops/metric_ops_test.py b/tensorflow/contrib/metrics/python/ops/metric_ops_test.py index e720097636..a09fc4abd4 100644 --- a/tensorflow/contrib/metrics/python/ops/metric_ops_test.py +++ b/tensorflow/contrib/metrics/python/ops/metric_ops_test.py @@ -2127,6 +2127,44 @@ class StreamingDynamicAUCTest(test.TestCase): sess.run(update_op) self.assertAlmostEqual(0.90277, auc.eval(), delta=1e-5) + def testWithWeights(self): + batch_size = 10 + num_batches = 100 + labels = np.array([]) + predictions = np.array([]) + weights = np.array([]) + tf_labels = variables.Variable( + array_ops.ones(batch_size, dtypes_lib.int32), + collections=[ops.GraphKeys.LOCAL_VARIABLES], + dtype=dtypes_lib.int32) + tf_predictions = variables.Variable( + array_ops.ones(batch_size), + collections=[ops.GraphKeys.LOCAL_VARIABLES], + dtype=dtypes_lib.float32) + tf_weights = variables.Variable( + array_ops.ones(batch_size), + collections=[ops.GraphKeys.LOCAL_VARIABLES], + dtype=dtypes_lib.float32) + auc, update_op = metrics.streaming_dynamic_auc(tf_labels, + tf_predictions, + weights=tf_weights) + with self.test_session() as sess: + sess.run(variables.local_variables_initializer()) + for _ in xrange(num_batches): + new_labels = np.random.randint(0, 2, size=batch_size) + noise = np.random.uniform(-0.2, 0.2, size=batch_size) + new_predictions = 0.4 + 0.2 * new_labels + noise + new_weights = np.random.uniform(0.0, 3.0, size=batch_size) + labels = np.concatenate([labels, new_labels]) + predictions = np.concatenate([predictions, new_predictions]) + weights = np.concatenate([weights, new_weights]) + sess.run([tf_labels.assign(new_labels), + tf_predictions.assign(new_predictions), + tf_weights.assign(new_weights)]) + sess.run(update_op) + expected_auc = _np_auc(predictions, labels, weights) + self.assertAlmostEqual(expected_auc, auc.eval()) + class AucWithConfidenceIntervalsTest(test.TestCase): -- GitLab From 7b4080564c268a54a5c0b877b28e67faaadff268 Mon Sep 17 00:00:00 2001 From: Derek Murray Date: Thu, 21 Jun 2018 11:51:17 -0700 Subject: [PATCH 809/816] [tf.data] Add option for setting intra-op parallelism on a private threadpool. This changes the default behavior when using `PrivateThreadPool` with `override_threadpool()`. It now defaults to using a maximum intra-op parallelism of 1 (which tends to be the most effective setting for high-throughput pipelines that are otherwise parallelized in the `Dataset.map()` or `tf.contrib.data.map_and_batch()` transformations. PiperOrigin-RevId: 201561361 --- .../data/kernels/threadpool_dataset_op.cc | 27 +++++++-- tensorflow/contrib/data/ops/dataset_ops.cc | 3 + .../contrib/data/python/kernel_tests/BUILD | 1 + .../threadpool_dataset_ops_test.py | 59 ++++++++++--------- .../contrib/data/python/ops/threadpool.py | 8 ++- 5 files changed, 63 insertions(+), 35 deletions(-) diff --git a/tensorflow/contrib/data/kernels/threadpool_dataset_op.cc b/tensorflow/contrib/data/kernels/threadpool_dataset_op.cc index 3dfc3741c2..141706f393 100644 --- a/tensorflow/contrib/data/kernels/threadpool_dataset_op.cc +++ b/tensorflow/contrib/data/kernels/threadpool_dataset_op.cc @@ -17,6 +17,7 @@ limitations under the License. #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/resource_mgr.h" #include "tensorflow/core/lib/core/threadpool.h" +#include "tensorflow/core/util/work_sharder.h" namespace tensorflow { namespace { @@ -24,19 +25,32 @@ namespace { class ThreadPoolResource : public ResourceBase { public: ThreadPoolResource(Env* env, const ThreadOptions& thread_options, - const string& name, int num_threads, bool low_latency_hint) - : thread_pool_(env, thread_options, name, num_threads, low_latency_hint) { - } + const string& name, int num_threads, bool low_latency_hint, + int max_intra_op_parallelism) + : thread_pool_(env, thread_options, name, num_threads, low_latency_hint), + max_intra_op_parallelism_(max_intra_op_parallelism) {} // Schedules fn() for execution in the pool of threads. void Schedule(std::function fn) { - thread_pool_.Schedule(std::move(fn)); + if (max_intra_op_parallelism_ < 0) { + thread_pool_.Schedule(std::move(fn)); + } else { + thread_pool_.Schedule(std::bind( + [this](std::function bound_fn) { + // TODO(mrry): Consider moving this thread-local configuration to + // the threads themselves. + ScopedPerThreadMaxParallelism scope(max_intra_op_parallelism_); + bound_fn(); + }, + std::move(fn))); + } } string DebugString() override { return "ThreadPoolResource"; } private: thread::ThreadPool thread_pool_; + const int max_intra_op_parallelism_; }; // Creates a handle to a ThreadPool resource. Note that we don't use @@ -48,6 +62,8 @@ class ThreadPoolHandleOp : public OpKernel { explicit ThreadPoolHandleOp(OpKernelConstruction* ctx) : OpKernel(ctx) { OP_REQUIRES_OK(ctx, ctx->GetAttr("display_name", &display_name_)); OP_REQUIRES_OK(ctx, ctx->GetAttr("num_threads", &num_threads_)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("max_intra_op_parallelism", + &max_intra_op_parallelism_)); OP_REQUIRES( ctx, num_threads_ > 0, errors::InvalidArgument("`num_threads` must be greater than zero.")); @@ -78,7 +94,7 @@ class ThreadPoolHandleOp : public OpKernel { EXCLUSIVE_LOCKS_REQUIRED(mu_) { *ret = new ThreadPoolResource( ctx->env(), {}, display_name_, - num_threads_, + num_threads_, max_intra_op_parallelism_, false /* low_latency_hint */); return Status::OK(); })); @@ -95,6 +111,7 @@ class ThreadPoolHandleOp : public OpKernel { bool initialized_ GUARDED_BY(mu_) = false; string display_name_; int num_threads_; + int max_intra_op_parallelism_; }; class ThreadPoolDatasetOp : public UnaryDatasetOpKernel { diff --git a/tensorflow/contrib/data/ops/dataset_ops.cc b/tensorflow/contrib/data/ops/dataset_ops.cc index f271d269ab..f48e96509a 100644 --- a/tensorflow/contrib/data/ops/dataset_ops.cc +++ b/tensorflow/contrib/data/ops/dataset_ops.cc @@ -158,6 +158,7 @@ REGISTER_OP("ThreadPoolHandle") .Output("handle: resource") .SetShapeFn(shape_inference::ScalarShape) .Attr("num_threads: int") + .Attr("max_intra_op_parallelism: int = 1") .Attr("display_name: string") .Attr("container: string = ''") .Attr("shared_name: string = ''") @@ -166,6 +167,8 @@ Creates a custom thread pool with the given number of threads. handle: A resource that can be consumed by one or more ThreadPoolDataset ops. num_threads: The number of threads in the thread pool. +max_intra_op_parallelism: The maximum degree of parallelism to use within + operations that execute on this threadpool. display_name: A human-readable name for the threads that may be visible in some visualizations. )doc"); diff --git a/tensorflow/contrib/data/python/kernel_tests/BUILD b/tensorflow/contrib/data/python/kernel_tests/BUILD index ef9f966fab..d81654e039 100644 --- a/tensorflow/contrib/data/python/kernel_tests/BUILD +++ b/tensorflow/contrib/data/python/kernel_tests/BUILD @@ -445,6 +445,7 @@ py_test( "//tensorflow/python:script_ops", "//tensorflow/python/data/ops:dataset_ops", "//third_party/py/numpy", + "@absl_py//absl/testing:parameterized", ], ) diff --git a/tensorflow/contrib/data/python/kernel_tests/threadpool_dataset_ops_test.py b/tensorflow/contrib/data/python/kernel_tests/threadpool_dataset_ops_test.py index 9167cb3379..0486e2bce2 100644 --- a/tensorflow/contrib/data/python/kernel_tests/threadpool_dataset_ops_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/threadpool_dataset_ops_test.py @@ -19,6 +19,7 @@ from __future__ import print_function import threading +from absl.testing import parameterized import numpy as np from tensorflow.contrib.data.python.ops import threadpool @@ -30,9 +31,11 @@ from tensorflow.python.ops import script_ops from tensorflow.python.platform import test -class OverrideThreadpoolDatasetTest(test.TestCase): +class OverrideThreadpoolDatasetTest(test.TestCase, parameterized.TestCase): - def testNumThreads(self): + @parameterized.parameters((1, None), (2, None), (4, None), (8, None), + (16, None), (4, -1), (4, 0), (4, 1), (4, 4)) + def testNumThreads(self, num_threads, max_intra_op_parallelism): def get_thread_id(_): # Python creates a dummy thread object to represent the current @@ -42,35 +45,35 @@ class OverrideThreadpoolDatasetTest(test.TestCase): # identifier that maps one-to-one with the underlying OS thread. return np.array(threading.current_thread().ident).astype(np.int64) - for num_threads in [1, 2, 4, 8, 16]: + dataset = ( + dataset_ops.Dataset.range(1000).map( + lambda x: script_ops.py_func(get_thread_id, [x], dtypes.int64), + num_parallel_calls=32).apply(unique.unique())) - dataset = ( - dataset_ops.Dataset.range(1000).map( - lambda x: script_ops.py_func(get_thread_id, [x], dtypes.int64), - num_parallel_calls=32).apply(unique.unique())) + dataset = threadpool.override_threadpool( + dataset, + threadpool.PrivateThreadPool( + num_threads, + max_intra_op_parallelism=max_intra_op_parallelism, + display_name="private_thread_pool_%d" % num_threads)) - dataset = threadpool.override_threadpool( - dataset, - threadpool.PrivateThreadPool( - num_threads, display_name="private_thread_pool_%d" % num_threads)) + iterator = dataset.make_initializable_iterator() + next_element = iterator.get_next() - iterator = dataset.make_initializable_iterator() - next_element = iterator.get_next() - - with self.test_session() as sess: - sess.run(iterator.initializer) - thread_ids = [] - try: - while True: - thread_ids.append(sess.run(next_element)) - except errors.OutOfRangeError: - pass - self.assertEqual(len(thread_ids), len(set(thread_ids))) - self.assertGreater(len(thread_ids), 0) - # NOTE(mrry): We don't control the thread pool scheduling, and - # so cannot guarantee that all of the threads in the pool will - # perform work. - self.assertLessEqual(len(thread_ids), num_threads) + with self.test_session() as sess: + sess.run(iterator.initializer) + thread_ids = [] + try: + while True: + thread_ids.append(sess.run(next_element)) + except errors.OutOfRangeError: + pass + self.assertEqual(len(thread_ids), len(set(thread_ids))) + self.assertGreater(len(thread_ids), 0) + # NOTE(mrry): We don't control the thread pool scheduling, and + # so cannot guarantee that all of the threads in the pool will + # perform work. + self.assertLessEqual(len(thread_ids), num_threads) if __name__ == "__main__": diff --git a/tensorflow/contrib/data/python/ops/threadpool.py b/tensorflow/contrib/data/python/ops/threadpool.py index f228660176..9af1e784ff 100644 --- a/tensorflow/contrib/data/python/ops/threadpool.py +++ b/tensorflow/contrib/data/python/ops/threadpool.py @@ -42,19 +42,23 @@ def _generate_shared_name(prefix): class PrivateThreadPool(object): """A stateful resource that represents a private thread pool.""" - def __init__(self, num_threads, display_name=None): + def __init__(self, num_threads, display_name=None, + max_intra_op_parallelism=1): """Creates a `PrivateThreadPool` with the given number of threads.""" if context.executing_eagerly(): shared_name = _generate_shared_name("privatethreadpool") self._resource = gen_dataset_ops.thread_pool_handle( num_threads=num_threads, + max_intra_op_parallelism=max_intra_op_parallelism, display_name=display_name, shared_name=shared_name) self._resource_deleter = resource_variable_ops.EagerResourceDeleter( handle=self._resource, handle_device=context.context().device_name) else: self._resource = gen_dataset_ops.thread_pool_handle( - num_threads=num_threads, display_name=display_name) + num_threads=num_threads, + max_intra_op_parallelism=max_intra_op_parallelism, + display_name=display_name) class _ThreadPoolDataset(dataset_ops.Dataset): -- GitLab From 5dae09703ef63956071c4e753b5d29cb03b668e9 Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Thu, 21 Jun 2018 20:34:29 +0000 Subject: [PATCH 810/816] Fix doc discrepancy in tf.scatter_add This fix fixes doc discrepancy in tf.scatter_add. This fix fixes 20200 Signed-off-by: Yong Tang --- tensorflow/python/ops/state_ops.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/python/ops/state_ops.py b/tensorflow/python/ops/state_ops.py index 08b7cda73b..3af9ef3c6c 100644 --- a/tensorflow/python/ops/state_ops.py +++ b/tensorflow/python/ops/state_ops.py @@ -394,7 +394,7 @@ def scatter_add(ref, indices, updates, use_locking=False, name=None): A tensor of indices into the first dimension of `ref`. updates: A `Tensor`. Must have the same type as `ref`. A tensor of updated values to store in `ref`. - use_locking: An optional `bool`. Defaults to `True`. + use_locking: An optional `bool`. Defaults to `False`. If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). -- GitLab From 324552c05313c5c3a6a25d608277a1a1f5d06c81 Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Thu, 21 Jun 2018 20:36:38 +0000 Subject: [PATCH 811/816] Update docstring for scatter_nd_add Signed-off-by: Yong Tang --- tensorflow/python/ops/state_ops.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/python/ops/state_ops.py b/tensorflow/python/ops/state_ops.py index 3af9ef3c6c..8cb6a0537e 100644 --- a/tensorflow/python/ops/state_ops.py +++ b/tensorflow/python/ops/state_ops.py @@ -458,7 +458,7 @@ def scatter_nd_add(ref, indices, updates, use_locking=False, name=None): A tensor of indices into ref. updates: A `Tensor`. Must have the same type as `ref`. A tensor of updated values to add to ref. - use_locking: An optional `bool`. Defaults to `True`. + use_locking: An optional `bool`. Defaults to `False`. An optional bool. Defaults to True. If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. -- GitLab From 4631936e61651101932073197c08b600006530a3 Mon Sep 17 00:00:00 2001 From: gracehoney <31743510+aaroey@users.noreply.github.com> Date: Thu, 21 Jun 2018 15:23:05 -0700 Subject: [PATCH 812/816] Fix internal build errors. --- configure.py | 2 +- tensorflow/contrib/tensorrt/BUILD | 1 + .../contrib/tensorrt/convert/convert_graph.cc | 94 +++++++++++-------- .../contrib/tensorrt/convert/convert_nodes.cc | 7 +- .../contrib/tensorrt/convert/convert_nodes.h | 9 +- tensorflow/contrib/tensorrt/convert/utils.h | 2 +- .../contrib/tensorrt/kernels/trt_engine_op.cc | 28 +++--- .../contrib/tensorrt/kernels/trt_engine_op.h | 10 +- .../contrib/tensorrt/python/trt_convert.py | 12 ++- .../tensorrt/resources/trt_int8_calibrator.cc | 1 - .../tensorrt/resources/trt_resources.h | 12 +-- .../contrib/tensorrt/test/test_tftrt.py | 11 +-- 12 files changed, 101 insertions(+), 88 deletions(-) diff --git a/configure.py b/configure.py index a14d006a73..ad585fa52e 100644 --- a/configure.py +++ b/configure.py @@ -944,7 +944,7 @@ def set_tf_cudnn_version(environ_cp): def is_cuda_compatible(lib, cuda_ver, cudnn_ver): - """Check the compatibility between given library and cudnn/cudart libraries.""" + """Check compatibility between given library and cudnn/cudart libraries.""" ldd_bin = which('ldd') or '/usr/bin/ldd' ldd_out = run_shell([ldd_bin, lib], True) ldd_out = ldd_out.split(os.linesep) diff --git a/tensorflow/contrib/tensorrt/BUILD b/tensorflow/contrib/tensorrt/BUILD index e7b3fe38e5..adda0b758b 100644 --- a/tensorflow/contrib/tensorrt/BUILD +++ b/tensorflow/contrib/tensorrt/BUILD @@ -207,6 +207,7 @@ tf_cuda_library( ], deps = [ ":trt_logging", + ":utils", "//tensorflow/core:framework_headers_lib", "//tensorflow/core:framework_lite", "//tensorflow/core:lib_proto_parsing", diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.cc b/tensorflow/contrib/tensorrt/convert/convert_graph.cc index ba7d3b5f86..1c4fd4a0ce 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_graph.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_graph.cc @@ -49,13 +49,14 @@ limitations under the License. #include "tensorflow/core/lib/strings/numbers.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/types.h" -#include "tensorflow/core/protobuf/config.pb.h" +#include "tensorflow/core/protobuf/config.pb.h" // NOLINT #include "tensorflow/core/protobuf/device_properties.pb.h" // NOLINT +#include "tensorflow/core/protobuf/rewriter_config.pb.h" // NOLINT #include "tensorflow/core/util/device_name_utils.h" #if GOOGLE_CUDA #if GOOGLE_TENSORRT -#include +#include "cuda/include/cuda_runtime_api.h" #include "tensorrt/include/NvInfer.h" namespace tensorflow { namespace tensorrt { @@ -238,14 +239,14 @@ tensorflow::Status ConvertGraphDefToTensorRT( } // Function to get subsegment information structure. -EngineInfo GetEngineInfo( +tensorflow::Status GetEngineInfo( const tensorflow::Graph* g, const tensorflow::grappler::GraphProperties& graph_properties, const std::set& segment_nodes, const std::unordered_map& node_map, - const std::vector& reverse_topo_order) { + const std::vector& reverse_topo_order, + EngineInfo* info) { std::vector subgraph_node_ids; - EngineInfo info; std::set segment_devices; int input_port = 0; int output_port = 0; @@ -296,9 +297,9 @@ EngineInfo GetEngineInfo( created_edges.insert({s, port}); input_port++; } - info.connections.emplace_back(input_node->name(), input_node->id(), - edge->src_output(), node_name, node_id, - edge->dst_input(), true, port); + info->connections.emplace_back(input_node->name(), input_node->id(), + edge->src_output(), node_name, node_id, + edge->dst_input(), true, port); } } } @@ -316,28 +317,28 @@ EngineInfo GetEngineInfo( created_edges.insert({s, port}); output_port++; } - info.connections.emplace_back(output_node->name(), output_node->id(), - edge->dst_input(), node_name, node_id, - edge->src_output(), false, port); + info->connections.emplace_back(output_node->name(), output_node->id(), + edge->dst_input(), node_name, node_id, + edge->src_output(), false, port); } } } - ConvertSegmentToGraphDef(g, graph_properties, subgraph_node_ids, - &info.connections, &info.segment_graph_def, - &info.engine_name); + TF_RETURN_IF_ERROR(ConvertSegmentToGraphDef( + g, graph_properties, subgraph_node_ids, &info->connections, + &info->segment_graph_def, &info->engine_name)); // TODO(sami): This should not happen once segmenter is updated. if (segment_devices.size() == 1) { - info.device = *segment_devices.begin(); + info->device = *segment_devices.begin(); } else if (segment_devices.size() > 1) { LOG(WARNING) << "Detected multiple(" << segment_devices.size() << ") devices for the segment. Picking first one to continue " << "but this shouldn't have happened"; - info.device = *segment_devices.begin(); + info->device = *segment_devices.begin(); } else { VLOG(1) << "Segment devices size is 0"; } - return info; + return Status::OK(); } // Function to insert a TRT node into the graph. The graph is not modified if @@ -562,7 +563,9 @@ tensorflow::Status RegisterSegmentFunctionToFunctionLibrary( tensorflow::NodeDefBuilder node_builder( StrCat(name, "_Arg"), tensorflow::FunctionLibraryDefinition::kArgOp); VLOG(1) << "Adding " << StrCat(name, "_Arg"); - node_builder.Attr("T", node->output_type(0)).Attr("index", i).Finalize(&nd); + TF_RETURN_IF_ERROR(node_builder.Attr("T", node->output_type(0)) + .Attr("index", i) + .Finalize(&nd)); tensorflow::Status s; auto node_arg = sgraph.AddNode(nd, &s); if (!s.ok()) { @@ -593,7 +596,9 @@ tensorflow::Status RegisterSegmentFunctionToFunctionLibrary( VLOG(1) << " input " << nout.node << ":" << nout.index << " dtype=" << tensorflow::DataTypeString(nout.data_type); node_builder.Input({nout}); - node_builder.Attr("T", node->output_type(0)).Attr("index", i).Finalize(&nd); + TF_RETURN_IF_ERROR(node_builder.Attr("T", node->output_type(0)) + .Attr("index", i) + .Finalize(&nd)); if (VLOG_IS_ON(3)) { VLOG(3) << nd.DebugString(); } @@ -713,11 +718,12 @@ tensorflow::Status ConvertAfterShapes(ConversionParams& params) { segment_options.exclude_node_list.insert(node); } segment_options.minimum_segment_size = params.minimum_segment_size; - tensorflow::tensorrt::segment::SegmentNodesVector segments; + tensorflow::tensorrt::segment::SegmentNodesVector initial_segments; TF_RETURN_IF_ERROR(tensorrt::segment::SegmentGraph( - &graph, IsTensorRTCandidate, segment_options, &segments)); - if (segments.size() > 1) { - VLOG(0) << "MULTIPLE tensorrt candidate conversion: " << segments.size(); + &graph, IsTensorRTCandidate, segment_options, &initial_segments)); + if (initial_segments.size() > 1) { + VLOG(0) << "MULTIPLE tensorrt candidate conversion: " + << initial_segments.size(); } // Get the EngineInfo for each segment. @@ -725,17 +731,24 @@ tensorflow::Status ConvertAfterShapes(ConversionParams& params) { TF_RETURN_IF_ERROR(BuildNodeMap(graph, &node_map)); float total_num_nodes_in_segments = 0.; std::vector engine_segments; - engine_segments.reserve(segments.size()); + engine_segments.reserve(initial_segments.size()); std::vector reverse_topo_order; tensorflow::GetPostOrder(graph, &reverse_topo_order); size_t total_engine_bytes_size = 0; std::vector engine_bytes_size; - for (size_t t = 0; t < segments.size(); t++) { - auto& s = segments.at(t); - engine_segments.emplace_back(GetEngineInfo(&graph, *params.graph_properties, - s.first, node_map, - reverse_topo_order)); - auto& curr_engine = engine_segments.back(); + tensorflow::tensorrt::segment::SegmentNodesVector converted_segments; + converted_segments.reserve(initial_segments.size()); + for (size_t t = 0; t < initial_segments.size(); t++) { + auto& curr_segment = initial_segments.at(t); + EngineInfo curr_engine; + Status status = + GetEngineInfo(&graph, *params.graph_properties, curr_segment.first, + node_map, reverse_topo_order, &curr_engine); + if (!status.ok()) { + LOG(WARNING) << "Failed to get engine info for segment " << t << ": " + << status; + continue; + } curr_engine.precision_mode = params.precision_mode; curr_engine.engine_type = (params.is_dyn_op || params.precision_mode == INT8MODE @@ -744,12 +757,19 @@ tensorflow::Status ConvertAfterShapes(ConversionParams& params) { curr_engine.cached_engine_batches = params.cached_engine_batches; curr_engine.maximum_cached_engines = params.max_cached_engines; StrAppend(&curr_engine.engine_name, "my_trt_op_", t); - RegisterSegmentFunctionToFunctionLibrary( + status = RegisterSegmentFunctionToFunctionLibrary( &graph, curr_engine.segment_graph_def, curr_engine.engine_name); + if (!status.ok()) { + LOG(WARNING) << "Failed to register segment graphdef as a function " << t + << ": " << status; + continue; + } engine_bytes_size.push_back(curr_engine.segment_graph_def.ByteSizeLong()); total_engine_bytes_size += engine_bytes_size.back(); - total_num_nodes_in_segments += s.first.size(); + total_num_nodes_in_segments += curr_segment.first.size(); + engine_segments.push_back(std::move(curr_engine)); + converted_segments.push_back(std::move(curr_segment)); if (VLOG_IS_ON(8)) { string fname = curr_engine.engine_name; @@ -775,7 +795,7 @@ tensorflow::Status ConvertAfterShapes(ConversionParams& params) { engine.max_workspace_size_bytes = params.max_workspace_size_bytes * (engine_bytes_size.at(i) / total_engine_bytes_size + - segments.at(i).first.size() / total_num_nodes_in_segments) / + converted_segments.at(i).first.size() / total_num_nodes_in_segments) / 2.0; // The allocator is used to build the engine. The build and the built engine // will be destroyed after we get the serialized engine string, so it's fine @@ -793,17 +813,17 @@ tensorflow::Status ConvertAfterShapes(ConversionParams& params) { cudaSetDevice(cuda_device_id); auto status = CreateTRTNode(&graph, engine_segments, i, alloc.get(), params.max_batch_size); - // If status is ok, we successfuly added the node to the graph and can + // If status is ok, we successfully added the node to the graph and can // remove segment ops. Otherwise graph is not modified. if (status.ok()) { - for (auto node_name : segments.at(i).first) { + for (auto node_name : converted_segments.at(i).first) { graph.RemoveNode(node_map.at(node_name)); } } else { // Graph is not modified. LOG(WARNING) << "Engine creation for segment " << i << ", composed of " - << segments.at(i).first.size() << " nodes failed: " << status - << ". Skipping..."; + << converted_segments.at(i).first.size() << " nodes failed: " + << status << ". Skipping..."; } } cudaSetDevice(old_cuda_device); diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index b5214b461a..146b9c7344 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -2130,13 +2130,10 @@ void Converter::register_op_converters() { } // namespace tensorflow::Status ConvertGraphDefToEngine( - const tensorflow::GraphDef& gdef, - int precision_mode, - int max_batch_size, + const tensorflow::GraphDef& gdef, int precision_mode, int max_batch_size, size_t max_workspace_size_bytes, const std::vector& input_shapes, - Logger* logger, - nvinfer1::IGpuAllocator* allocator, + Logger* logger, nvinfer1::IGpuAllocator* allocator, TRTInt8Calibrator* calibrator, TrtUniquePtrType* engine, bool* convert_successfully) { diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.h b/tensorflow/contrib/tensorrt/convert/convert_nodes.h index 2da4edf7f5..7684d8d4a2 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.h +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.h @@ -78,7 +78,7 @@ struct EngineInfo { EngineInfo() : engine_type(EngineType::TRTStatic), max_workspace_size_bytes(0), - precision_mode(FP32MODE) {}; + precision_mode(FP32MODE) {} string engine_name; string device; @@ -120,13 +120,10 @@ tensorflow::Status ConvertSegmentToGraphDef( // is successful. This is different than successfully building the engine: // building can still fail afterwards. tensorflow::Status ConvertGraphDefToEngine( - const tensorflow::GraphDef& gdef, - int precision_mode, - int max_batch_size, + const tensorflow::GraphDef& gdef, int precision_mode, int max_batch_size, size_t max_workspace_size_bytes, const std::vector& input_shapes, - Logger* logger, - nvinfer1::IGpuAllocator* allocator, + Logger* logger, nvinfer1::IGpuAllocator* allocator, TRTInt8Calibrator* calibrator, TrtUniquePtrType* engine, bool* convert_successfully); diff --git a/tensorflow/contrib/tensorrt/convert/utils.h b/tensorflow/contrib/tensorrt/convert/utils.h index 021fdaf8c5..f601c06701 100644 --- a/tensorflow/contrib/tensorrt/convert/utils.h +++ b/tensorflow/contrib/tensorrt/convert/utils.h @@ -31,7 +31,7 @@ struct TrtDestroyer { template using TrtUniquePtrType = std::unique_ptr>; -} // namespace convert } // namespace tensorrt +} // namespace tensorflow #endif // TENSORFLOW_CONTRIB_TENSORRT_CONVERT_UTILS_H_ diff --git a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc index d12f738ac5..75e32559bb 100644 --- a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc +++ b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc @@ -15,8 +15,8 @@ limitations under the License. #include "tensorflow/contrib/tensorrt/kernels/trt_engine_op.h" #include -#include "tensorflow/contrib/tensorrt/convert/utils.h" #include "tensorflow/contrib/tensorrt/convert/convert_nodes.h" +#include "tensorflow/contrib/tensorrt/convert/utils.h" #include "tensorflow/contrib/tensorrt/log/trt_logger.h" #include "tensorflow/contrib/tensorrt/resources/trt_resource_manager.h" #include "tensorflow/contrib/tensorrt/resources/trt_resources.h" @@ -77,9 +77,8 @@ tensorflow::Status TRTEngineOp::ConstructFunctionHandle(OpKernelContext* ctx) { } auto fdef = lib->GetFunctionLibraryDefinition()->Find(funcdef_name_); if (fdef == nullptr) { - return tensorflow::errors::Internal( - "Native FunctionDef ", funcdef_name_, - " can't be found in function library"); + return tensorflow::errors::Internal("Native FunctionDef ", funcdef_name_, + " can't be found in function library"); } tensorflow::FunctionLibraryRuntime::InstantiateOptions inst_ops; inst_ops.overlay_lib = nullptr; @@ -128,8 +127,8 @@ TRTEngineOp::TRTEngineOp(OpKernelConstruction* context) } else if (precision_string == "INT8") { precision_mode_ = convert::INT8MODE; } - calibration_mode_ = (precision_mode_ == convert::INT8MODE && - calibration_data.size() == 0); + calibration_mode_ = + (precision_mode_ == convert::INT8MODE && calibration_data.size() == 0); if (calibration_data.size()) { calibrator_.reset(new TRTInt8Calibrator(calibration_data)); calibration_data.resize(0); @@ -291,8 +290,8 @@ void TRTEngineOp::ComputeAsync(tensorflow::OpKernelContext* ctx, std::vector buffers(num_binding); for (int i = 0; i < ctx->num_inputs(); i++) { const string inp_name = StrCat(kInputPHName, i); - const size_t binding_index = trt_engine_ptr->getBindingIndex( - inp_name.c_str()); + const size_t binding_index = + trt_engine_ptr->getBindingIndex(inp_name.c_str()); const Tensor& input_tensor = ctx->input(i); const TensorShape& input_shape = input_tensor.shape(); @@ -320,7 +319,7 @@ void TRTEngineOp::ComputeAsync(tensorflow::OpKernelContext* ctx, default: LOG(ERROR) << "Unknown TRT data type: " << int(dtype); ctx->SetStatus(tensorflow::errors::InvalidArgument( - "Unknown ouput TRT data type! ", int(dtype))); + "Unknown ouput TRT data type! ", static_cast(dtype))); return; } } @@ -343,8 +342,8 @@ void TRTEngineOp::ComputeAsync(tensorflow::OpKernelContext* ctx, &output_shape)); } else { LOG(ERROR) << "output node not found, at " << output_name; - ctx->SetStatus(tensorflow::errors::Internal( - "output ", output_name, " couldn't be found!")); + ctx->SetStatus(tensorflow::errors::Internal("output ", output_name, + " couldn't be found!")); return; } auto status = ctx->allocate_output(i, output_shape, &output_tensor); @@ -370,7 +369,7 @@ void TRTEngineOp::ComputeAsync(tensorflow::OpKernelContext* ctx, "INT8 outputs are not supported!")); return; default: - LOG(ERROR) << "Unknown TRT data type: " << int(dtype); + LOG(ERROR) << "Unknown TRT data type: " << static_cast(dtype); ctx->SetStatus(tensorflow::errors::InvalidArgument( "Unsupported output data type! ", int(dtype))); return; @@ -442,7 +441,7 @@ TRTEngineOp::EngineCtxPair& TRTEngineOp::GetEngine(int batch_size, if (allocator == nullptr) { // GetAllocator already set the Status. return null_pair; - }; + } infer->setGpuAllocator(allocator); #endif TrtUniquePtrType static_engine( @@ -506,8 +505,7 @@ TRTEngineOp::EngineCtxPair& TRTEngineOp::GetEngine(int batch_size, } tensorflow::Status TRTEngineOp::AllocateCalibrationResources( - tensorflow::OpKernelContext* ctx, - TRTCalibrationResource** cr) { + tensorflow::OpKernelContext* ctx, TRTCalibrationResource** cr) { auto cres = new TRTCalibrationResource(); *cr = cres; // Get the allocator. diff --git a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h index 0d2f9e8a9d..6fe318be6a 100644 --- a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h +++ b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h @@ -52,19 +52,17 @@ class TRTEngineOp : public AsyncOpKernel { private: // Execute calibration - void ExecuteCalibration(OpKernelContext* ctx, - AsyncHelper* helper); + void ExecuteCalibration(OpKernelContext* ctx, AsyncHelper* helper); // Construct a function handle for executing native funcdef graph Status ConstructFunctionHandle(OpKernelContext* ctx); // Execute replaced native segment as function Op. - void ExecuteNativeSegment(OpKernelContext* ctx, - AsyncHelper* helper); + void ExecuteNativeSegment(OpKernelContext* ctx, AsyncHelper* helper); // Allocate necessary resources for calibration - Status AllocateCalibrationResources( - OpKernelContext* ctx, TRTCalibrationResource** cr); + Status AllocateCalibrationResources(OpKernelContext* ctx, + TRTCalibrationResource** cr); // TODO(samikama): context should go to a resource manager! typedef std::pair, diff --git a/tensorflow/contrib/tensorrt/python/trt_convert.py b/tensorflow/contrib/tensorrt/python/trt_convert.py index 490c74a701..79f512dbcf 100644 --- a/tensorflow/contrib/tensorrt/python/trt_convert.py +++ b/tensorflow/contrib/tensorrt/python/trt_convert.py @@ -21,9 +21,9 @@ from __future__ import print_function # pylint: disable=unused-import,line-too-long import six as _six from tensorflow.contrib.tensorrt.wrap_conversion import calib_convert -from tensorflow.contrib.tensorrt.wrap_conversion import trt_convert -from tensorflow.contrib.tensorrt.wrap_conversion import get_loaded_tensorrt_version from tensorflow.contrib.tensorrt.wrap_conversion import get_linked_tensorrt_version +from tensorflow.contrib.tensorrt.wrap_conversion import get_loaded_tensorrt_version +from tensorflow.contrib.tensorrt.wrap_conversion import trt_convert from tensorflow.core.framework import graph_pb2 from tensorflow.core.protobuf import rewriter_config_pb2 from tensorflow.python.framework import errors @@ -58,6 +58,10 @@ def create_inference_graph(input_graph_def, precision_mode: one of 'FP32', 'FP16' and 'INT8' minimum_segment_size: the minimum number of nodes required for a subgraph to be replaced by TRTEngineOp. + is_dynamic_op: whether to generate dynamic TRT ops which will build the TRT + network and engine at run time. + maximum_cached_engines: max number of cached TRT engines in dynamic TRT ops. + cached_engine_batches: batch sizes used to pre-create cached engines. Returns: New GraphDef with TRTEngineOps placed in graph replacing subgraphs. @@ -81,7 +85,7 @@ def create_inference_graph(input_graph_def, "TensorRT %s but library loaded from environment is TensorRT %s" % (".".join([str(x) for x in compiled_version]), ".".join([str(x) for x in loaded_version])) + - ". Please make sure that correct version of TensorRT "\ + ". Please make sure that correct version of TensorRT " + "is available in the system and added to ldconfig or LD_LIBRARY_PATH" ) raise RuntimeError("Incompatible TensorRT library version") @@ -178,7 +182,7 @@ def calib_graph_to_infer_graph(calibration_graph_def, is_dynamic_op=False): is_calib_graph = False for n in calibration_graph_def.node: if n.op == "TRTEngineOp": - is_calib_graph = is_calib_graph or len(n.attr["calibration_data"].s) == 0 + is_calib_graph = is_calib_graph or not n.attr["calibration_data"].s if not is_calib_graph: tf_logging.error( "Not a calib graph. Doesn't seem to contain any calibration nodes.") diff --git a/tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.cc b/tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.cc index 59ae860bc0..32e81858b9 100644 --- a/tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.cc +++ b/tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.cc @@ -16,7 +16,6 @@ limitations under the License. #include "tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.h" #include -#include #include #include "tensorflow/core/platform/logging.h" diff --git a/tensorflow/contrib/tensorrt/resources/trt_resources.h b/tensorflow/contrib/tensorrt/resources/trt_resources.h index 76863503bd..b7d5ffd674 100644 --- a/tensorflow/contrib/tensorrt/resources/trt_resources.h +++ b/tensorflow/contrib/tensorrt/resources/trt_resources.h @@ -49,15 +49,15 @@ class TRTCalibrationResource : public tensorflow::ResourceBase { string DebugString() override { std::stringstream oss; - using std::hex; using std::dec; using std::endl; + using std::hex; oss << " Calibrator = " << hex << calibrator_.get() << dec << endl - << " Builder = " << hex << builder_.get() << dec << endl - << " Engine = " << hex << engine_.get() << dec << endl - << " Logger = " << hex << &logger_ << dec << endl - << " Allocator = " << hex << allocator_.get() << dec << endl - << " Thread = " << hex << thr_.get() << dec << endl; + << " Builder = " << hex << builder_.get() << dec << endl + << " Engine = " << hex << engine_.get() << dec << endl + << " Logger = " << hex << &logger_ << dec << endl + << " Allocator = " << hex << allocator_.get() << dec << endl + << " Thread = " << hex << thr_.get() << dec << endl; return oss.str(); } diff --git a/tensorflow/contrib/tensorrt/test/test_tftrt.py b/tensorflow/contrib/tensorrt/test/test_tftrt.py index 5e74f9295d..090aa8bdb0 100644 --- a/tensorflow/contrib/tensorrt/test/test_tftrt.py +++ b/tensorflow/contrib/tensorrt/test/test_tftrt.py @@ -76,7 +76,7 @@ def get_multi_engine_graph_def(mode="FP32"): g = ops.Graph() with g.as_default(): x = aops.placeholder(shape=[None, 3, 7, 5], name="input", dtype=dtype) - with g.name_scope("Global_scope") as scope: + with g.name_scope("Global_scope"): with g.name_scope("first_scope"): e = cop.constant( np.random.randn(3, 2, 3, 4), name="weights", dtype=dtype) @@ -92,15 +92,14 @@ def get_multi_engine_graph_def(mode="FP32"): b = cop.constant(np.random.randn(1, 4, 1, 1), name="bias2", dtype=dtype) q = conv / b - c = cop.constant(np.random.randn(1, 4, 1, 1), name="bias3", dtype=dtype) edge = mops.sin(q) edge1 = mops.cos(conv) with g.name_scope("test_scope"): de = edge + edge1 - t = t - edge1 - q = q * edge - t = t + q - t = t - de + t -= edge1 + q *= edge + t += q + t -= de k = aops.squeeze(t, name="output") print(k.dtype) return g.as_graph_def() -- GitLab From b302b73c4d0fbca4fcc015ab86040e21dd697bd4 Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Thu, 21 Jun 2018 22:05:02 -0700 Subject: [PATCH 813/816] Update curl library to curl-7.60.0 (#20181) * Update curl library to curl-7.60.0 This fix updates curl library to 7.60.0. (Previously TensorFlow links to curl 7.49.1, which was relesed in 2016) Signed-off-by: Yong Tang * Update source files in curl Signed-off-by: Yong Tang * Add missing flag for curl 7.60.0 Signed-off-by: Yong Tang * Add missing include "system.h" Signed-off-by: Yong Tang --- tensorflow/workspace.bzl | 8 ++++---- third_party/curl.BUILD | 22 ++++++++++++++++------ 2 files changed, 20 insertions(+), 10 deletions(-) diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index 696f9b08b3..5ed9d05c8b 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -416,12 +416,12 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "curl", - sha256 = "ff3e80c1ca6a068428726cd7dd19037a47cc538ce58ef61c59587191039b2ca6", + sha256 = "e9c37986337743f37fd14fe8737f246e97aec94b39d1b71e8a5973f72a9fc4f5", urls = [ - "https://mirror.bazel.build/curl.haxx.se/download/curl-7.49.1.tar.gz", - "https://curl.haxx.se/download/curl-7.49.1.tar.gz", + "https://mirror.bazel.build/curl.haxx.se/download/curl-7.60.0.tar.gz", + "https://curl.haxx.se/download/curl-7.60.0.tar.gz", ], - strip_prefix = "curl-7.49.1", + strip_prefix = "curl-7.60.0", build_file = clean_dep("//third_party:curl.BUILD"), ) diff --git a/third_party/curl.BUILD b/third_party/curl.BUILD index 4def6f9489..1638b72161 100644 --- a/third_party/curl.BUILD +++ b/third_party/curl.BUILD @@ -7,6 +7,7 @@ exports_files(["COPYING"]) CURL_WIN_COPTS = [ "/Iexternal/curl/lib", + "/DBUILDING_LIBCURL", "/DHAVE_CONFIG_H", "/DCURL_DISABLE_FTP", "/DCURL_DISABLE_NTLM", @@ -49,6 +50,8 @@ cc_library( "lib/curl_addrinfo.c", "lib/curl_addrinfo.h", "lib/curl_base64.h", + "lib/curl_ctype.c", + "lib/curl_ctype.h", "lib/curl_des.h", "lib/curl_endian.h", "lib/curl_fnmatch.c", @@ -75,6 +78,7 @@ cc_library( "lib/curl_sec.h", "lib/curl_setup.h", "lib/curl_setup_once.h", + "lib/curl_sha256.h", "lib/curl_sspi.c", "lib/curl_sspi.h", "lib/curl_threads.c", @@ -134,6 +138,8 @@ cc_library( "lib/md5.c", "lib/memdebug.c", "lib/memdebug.h", + "lib/mime.c", + "lib/mime.h", "lib/mprintf.c", "lib/multi.c", "lib/multihandle.h", @@ -153,8 +159,8 @@ cc_library( "lib/pop3.h", "lib/progress.c", "lib/progress.h", - "lib/rawstr.c", - "lib/rawstr.h", + "lib/rand.c", + "lib/rand.h", "lib/rtsp.c", "lib/rtsp.h", "lib/security.c", @@ -162,8 +168,11 @@ cc_library( "lib/select.h", "lib/sendf.c", "lib/sendf.h", + "lib/setopt.c", + "lib/setopt.h", "lib/setup-os400.h", "lib/setup-vms.h", + "lib/sha256.c", "lib/share.c", "lib/share.h", "lib/sigpipe.h", @@ -179,10 +188,10 @@ cc_library( "lib/splay.c", "lib/splay.h", "lib/ssh.h", + "lib/strcase.c", + "lib/strcase.h", "lib/strdup.c", "lib/strdup.h", - "lib/strequal.c", - "lib/strequal.h", "lib/strerror.c", "lib/strerror.h", "lib/strtok.c", @@ -241,13 +250,12 @@ cc_library( }), hdrs = [ "include/curl/curl.h", - "include/curl/curlbuild.h", - "include/curl/curlrules.h", "include/curl/curlver.h", "include/curl/easy.h", "include/curl/mprintf.h", "include/curl/multi.h", "include/curl/stdcheaders.h", + "include/curl/system.h", "include/curl/typecheck-gcc.h", ], copts = select({ @@ -256,6 +264,7 @@ cc_library( "//conditions:default": [ "-Iexternal/curl/lib", "-D_GNU_SOURCE", + "-DBUILDING_LIBCURL", "-DHAVE_CONFIG_H", "-DCURL_DISABLE_FTP", "-DCURL_DISABLE_NTLM", # turning it off in configure is not enough @@ -676,6 +685,7 @@ genrule( "# define SIZEOF_INT 4", "# define SIZEOF_LONG 8", "# define SIZEOF_OFF_T 8", + "# define SIZEOF_CURL_OFF_T 8", "# define SIZEOF_SHORT 2", "# define SIZEOF_SIZE_T 8", "# define SIZEOF_TIME_T 8", -- GitLab From d932155363d6ded97dda38ce799168d27566978b Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Thu, 21 Jun 2018 22:05:47 -0700 Subject: [PATCH 814/816] Update jsoncpp to 1.8.4 (#20182) * Update jsoncpp to 1.8.4 This fix updates the jsoncpp to 1.8.4 to address the issue raised in 20170. The jsoncpp used in tf was old and may contain security issues. This fix fixes 20170. Signed-off-by: Yong Tang * Add JSON_HAS_INT64 define to jsoncpp build Signed-off-by: Yong Tang * Fix data type conversion issue for jsoncpp. Signed-off-by: Yong Tang * Fix build by include "version.h" Signed-off-by: Yong Tang --- tensorflow/core/platform/cloud/oauth_client.cc | 4 ++-- .../core/profiler/internal/tfprof_timeline.cc | 16 ++++++++-------- tensorflow/workspace.bzl | 8 ++++---- third_party/jsoncpp.BUILD | 7 +++++-- 4 files changed, 19 insertions(+), 16 deletions(-) diff --git a/tensorflow/core/platform/cloud/oauth_client.cc b/tensorflow/core/platform/cloud/oauth_client.cc index e64653a67a..ee6ba7b041 100644 --- a/tensorflow/core/platform/cloud/oauth_client.cc +++ b/tensorflow/core/platform/cloud/oauth_client.cc @@ -137,8 +137,8 @@ Status EncodeJwtClaim(StringPiece client_email, StringPiece scope, const auto expiration_timestamp_sec = request_timestamp_sec + kRequestedTokenLifetimeSec; - root["iat"] = request_timestamp_sec; - root["exp"] = expiration_timestamp_sec; + root["iat"] = Json::Value::UInt64(request_timestamp_sec); + root["exp"] = Json::Value::UInt64(expiration_timestamp_sec); // Step 2: represent the JSON as a string. string claim = root.toStyledString(); diff --git a/tensorflow/core/profiler/internal/tfprof_timeline.cc b/tensorflow/core/profiler/internal/tfprof_timeline.cc index b0dd8ce5e0..979b437914 100644 --- a/tensorflow/core/profiler/internal/tfprof_timeline.cc +++ b/tensorflow/core/profiler/internal/tfprof_timeline.cc @@ -47,9 +47,9 @@ Json::Value ChromeTraceFormatter::CreateEvent(const string& ph, event["ph"] = Json::Value(ph); event["cat"] = Json::Value(category); event["name"] = Json::Value(name); - event["pid"] = Json::Value(pid); - event["tid"] = Json::Value(tid); - event["ts"] = Json::Value(ts); + event["pid"] = Json::Int64(pid); + event["tid"] = Json::Int64(tid); + event["ts"] = Json::Int64(ts); return event; } @@ -57,7 +57,7 @@ void ChromeTraceFormatter::EmitPID(const string& name, int64 pid) { Json::Value event(Json::objectValue); event["name"] = Json::Value("process_name"); event["ph"] = Json::Value("M"); - event["pid"] = Json::Value(pid); + event["pid"] = Json::Int64(pid); Json::Value args(Json::objectValue); args["name"] = Json::Value(name); event["args"] = args; @@ -68,7 +68,7 @@ void ChromeTraceFormatter::EmitRegion(int64 ts, int64 duration, int64 pid, int64 tid, const string& category, const string& name, Json::Value args) { Json::Value event = CreateEvent("X", category, name, pid, tid, ts); - event["dur"] = Json::Value(duration); + event["dur"] = Json::Int64(duration); event["args"] = std::move(args); metadata_.push_back(event); } @@ -76,14 +76,14 @@ void ChromeTraceFormatter::EmitRegion(int64 ts, int64 duration, int64 pid, void ChromeTraceFormatter::EmitFlowStart(const string& name, int64 ts, int64 pid, int64 tid, int64 flow_id) { Json::Value event = CreateEvent("s", "DataFlow", name, pid, tid, ts); - event["id"] = flow_id; + event["id"] = Json::Int64(flow_id); events_.push_back(event); } void ChromeTraceFormatter::EmitFlowEnd(const string& name, int64 ts, int64 pid, int64 tid, int64 flow_id) { Json::Value event = CreateEvent("t", "DataFlow", name, pid, tid, ts); - event["id"] = flow_id; + event["id"] = Json::Int64(flow_id); events_.push_back(event); } @@ -93,7 +93,7 @@ void ChromeTraceFormatter::EmitCounter( const std::map>& tensor_mem) { Json::Value event = CreateEvent("C", category, "Allocated Bytes", pid, 0, ts); Json::Value args(Json::objectValue); - args["Allocator Bytes in Use"] = Json::Value(bytes); + args["Allocator Bytes in Use"] = Json::Int64(bytes); event["args"] = args; events_.push_back(event); diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index 5ed9d05c8b..973dccc1ea 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -474,11 +474,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "jsoncpp_git", urls = [ - "https://mirror.bazel.build/github.com/open-source-parsers/jsoncpp/archive/11086dd6a7eba04289944367ca82cea71299ed70.tar.gz", - "https://github.com/open-source-parsers/jsoncpp/archive/11086dd6a7eba04289944367ca82cea71299ed70.tar.gz", + "https://mirror.bazel.build/github.com/open-source-parsers/jsoncpp/archive/1.8.4.tar.gz", + "https://github.com/open-source-parsers/jsoncpp/archive/1.8.4.tar.gz", ], - sha256 = "07d34db40593d257324ec5fb9debc4dc33f29f8fb44e33a2eeb35503e61d0fe2", - strip_prefix = "jsoncpp-11086dd6a7eba04289944367ca82cea71299ed70", + sha256 = "c49deac9e0933bcb7044f08516861a2d560988540b23de2ac1ad443b219afdb6", + strip_prefix = "jsoncpp-1.8.4", build_file = clean_dep("//third_party:jsoncpp.BUILD"), ) diff --git a/third_party/jsoncpp.BUILD b/third_party/jsoncpp.BUILD index 65f98410b2..cf3cba0555 100644 --- a/third_party/jsoncpp.BUILD +++ b/third_party/jsoncpp.BUILD @@ -6,7 +6,6 @@ cc_library( name = "jsoncpp", srcs = [ "include/json/assertions.h", - "src/lib_json/json_batchallocator.h", "src/lib_json/json_reader.cpp", "src/lib_json/json_tool.h", "src/lib_json/json_value.cpp", @@ -20,9 +19,13 @@ cc_library( "include/json/json.h", "include/json/reader.h", "include/json/value.h", + "include/json/version.h", "include/json/writer.h", ], - copts = ["-DJSON_USE_EXCEPTION=0"], + copts = [ + "-DJSON_USE_EXCEPTION=0", + "-DJSON_HAS_INT64", + ], includes = ["include"], visibility = ["//visibility:public"], deps = [":private"], -- GitLab From 0f6f9ace1eb631979339d996e2c71bd56194ebfe Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Thu, 21 Jun 2018 22:06:20 -0700 Subject: [PATCH 815/816] Update lmdb to 0.9.22 (#20184) This fix updates lmdb from 0.9.19 to 0.9.22. The old version (0.9.19) was released in 2016, which is quite old. Signed-off-by: Yong Tang --- tensorflow/workspace.bzl | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index 973dccc1ea..35d861bcc1 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -463,11 +463,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "lmdb", urls = [ - "https://mirror.bazel.build/github.com/LMDB/lmdb/archive/LMDB_0.9.19.tar.gz", - "https://github.com/LMDB/lmdb/archive/LMDB_0.9.19.tar.gz", + "https://mirror.bazel.build/github.com/LMDB/lmdb/archive/LMDB_0.9.22.tar.gz", + "https://github.com/LMDB/lmdb/archive/LMDB_0.9.22.tar.gz", ], - sha256 = "108532fb94c6f227558d45be3f3347b52539f0f58290a7bb31ec06c462d05326", - strip_prefix = "lmdb-LMDB_0.9.19/libraries/liblmdb", + sha256 = "f3927859882eb608868c8c31586bb7eb84562a40a6bf5cc3e13b6b564641ea28", + strip_prefix = "lmdb-LMDB_0.9.22/libraries/liblmdb", build_file = clean_dep("//third_party:lmdb.BUILD"), ) -- GitLab From 359f53686c87ee76e80353c32a3d22cfb1cf0989 Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Thu, 21 Jun 2018 22:09:56 -0700 Subject: [PATCH 816/816] Update flatbuffers to 1.9.0 (#20186) * Update flatbuffers to 1.9.0 This fix updates flatbuffers to 1.9.0. The previous version used (971a681) in tf was released last year, and is not a versioned release. This fix updates to the latest versioned release of 1.9.0. Signed-off-by: Yong Tang * Add missing files of java_generator.cc to fix build error. Signed-off-by: Yong Tang --- tensorflow/workspace.bzl | 8 ++++---- third_party/flatbuffers/flatbuffers.BUILD | 2 ++ 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index 35d861bcc1..857a404daf 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -695,11 +695,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "flatbuffers", - strip_prefix = "flatbuffers-971a68110e4fc1bace10fcb6deeb189e7e1a34ce", - sha256 = "874088d2ee0d9f8524191f77209556415f03dd44e156276edf19e5b90ceb5f55", + strip_prefix = "flatbuffers-1.9.0", + sha256 = "5ca5491e4260cacae30f1a5786d109230db3f3a6e5a0eb45d0d0608293d247e3", urls = [ - "https://mirror.bazel.build/github.com/google/flatbuffers/archive/971a68110e4fc1bace10fcb6deeb189e7e1a34ce.tar.gz", - "https://github.com/google/flatbuffers/archive/971a68110e4fc1bace10fcb6deeb189e7e1a34ce.tar.gz", + "https://mirror.bazel.build/github.com/google/flatbuffers/archive/v1.9.0.tar.gz", + "https://github.com/google/flatbuffers/archive/v1.9.0.tar.gz", ], build_file = clean_dep("//third_party/flatbuffers:flatbuffers.BUILD"), ) diff --git a/third_party/flatbuffers/flatbuffers.BUILD b/third_party/flatbuffers/flatbuffers.BUILD index 824c97be60..639dff2cd0 100644 --- a/third_party/flatbuffers/flatbuffers.BUILD +++ b/third_party/flatbuffers/flatbuffers.BUILD @@ -98,6 +98,8 @@ cc_binary( "grpc/src/compiler/cpp_generator.h", "grpc/src/compiler/go_generator.cc", "grpc/src/compiler/go_generator.h", + "grpc/src/compiler/java_generator.cc", + "grpc/src/compiler/java_generator.h", "grpc/src/compiler/schema_interface.h", "src/flatc_main.cpp", "src/idl_gen_cpp.cpp", -- GitLab